integrated population based allocation (ipba) formulaintegrated population based allocation (ipba)...

Integrated Population BasedAllocation (IPBA) Formula

Prepared for the Hospital Funding Committee of theJPPC

Reference Document RD#9-8February 2001

All inquiries and questions pertaining to the methodology applied todetermine your hospital actual cost per weighted case should be sent to:

Nan Brooks, ConsultantJPPC SecretariatTel: (416) 599-5772 ext. 234Fax: (416) 599-6630Email: [email protected]

Any concerns pertaining to the DATA used in the calculation should bedirected to your Financial Representative in the Institutional Branch of theMinistry of Health and Long-Term Care.

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RD 9-8 Integrated Population Based Allocation Formula 1

TABLE OF CONTENTS

LIST OF EXHIBITS ...................................................................................... 3

INTRODUCTION ......................................................................................... 4

Historical Funding in Ontario ........................................................................ 4

Enhancements to Rate and Volume Equity Funding Methodologies ................. 5

The JPPC Volume Sub-Committee .............................................................. 5

The JPPC Rate Sub-Committee and its Working Groups ................................ 6

Overview of this Report ............................................................................... 7

RATE METHODOLOGY ............................................................................... 8

PROCESS ................................................................................................. 8

Adjustment Factor Selection Criteria and Principles ....................................... 9

Review of Adjustment Factors ....................................................................10Adjustment Factors previously analyzed by the JPPC ...................................................10Adjustment Factors that were rejected: ..............................................................................11Adjustment Factors that were deferred: .............................................................................15Adjustment Factors that were accepted: ............................................................................17

Merging the Small and Large/Community Hospital Formula. ...........................21

Complex Continuing Care ............................................................................ 2Background ...........................................................................................................................21Phase One of Work Plan .....................................................................................................22Phase Two of Work Plan .....................................................................................................24

Revenue Adjustment Factors ......................................................................26Selection Criteria and Principles ..........................................................................................26Review of Revenue Adjustment Factors ..........................................................................27

Rationale for Rate Methodology ..................................................................28

Overview of Model Application ....................................................................28

Data Sources ............................................................................................29

Data Quality Review ..................................................................................30

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Methodological Steps.................................................................................32Step 1: Calculation of Actual Cost per Equivalent Weighted Case ...............................32Step 2: Calculation of Adjustment Factors .........................................................................35Step 3: Calibration and Evaluation of Model ....................................................................36Step 4: Application of the Model for Recently Merged Facilities ....................................38

98/99 Model Application .............................................................................39

VOLUME METHODOLOGY .........................................................................40

Overview of the Recommended Volume Equity Model ..................................40Allocation of Volumes to Communities ..............................................................................40Allocation of Community-Specific Volumes (Base Year and Growth) to Hospitals .....41

Data Sources .............................................................................................42

Definition, Measurement and Allocation of Hospital Volumes ........................44Weighted Cases as the Unit of Volumes Measurement .................................................44Measurement and Allocation of Hospital Volumes ...........................................................44Step 1: Measurement of Community-Specific Weighted Cases ..................................45Step 2: Measurement and Summary of the Population Adjustment Factors ................48Factors used for Pregnancy, Childbirth and Newborns and Neonates ...........................52Step 3: Analysis and Calibration of the Population-Based Model .................................55Newborn and Neonate Case Mix ......................................................................................59Step 4: Growth Adjustment - Estimating the Impact of Demographic Growth andAging to 2000/2001 .............................................................................................................61Step 5: Hospital Allocations (Base Year and Growth) ....................................................62

98/99 Model Application .............................................................................63

CONCLUSION ...........................................................................................66

LIST OF APPENDICES ..............................................................................67

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LIST OF EXHIBITS

EXHIBIT 1: PATIENT ACTIVITY INCLUDED IN RATE MODEL BY TYPE OF HOSPITAL ... .............................29

EXHIBIT 2: REVIEW OF VALID ENTRY CODES ...............................................................................30

EXHIBIT 3: REVIEW OF ALLOCATION OF NET EXPENSES ................................................................31

EXHIBIT 4: HOSPITALS EXCLUDED FROM RATE AND VOLUME MODELS .............................................32

EXHIBIT 5: UNIT COST RATIOS CALCULATED AY TYPE OF HOSPITAL..................................................33

EXHIBIT 6: CALCULATION OF AN EMERGENCY EQUIVALENT WEIGHED CASE ......................................33

EXHIBIT 7: WEIGHTED CASE EQUIVALENCIES BY PATIENT TYPE .......................................................34

EXHIBIT 8: ADJUSTMENT FACTORS BY HOSPITAL TYPE ..................................................................36

EXHIBIT 9: ADJUSTMENT FACTOR COEFFICIENTS .........................................................................36

EXHIBIT 10: SIZE ADJUSTMENT VERSUS TOTAL EQUIVALENT WEIGHTED CASES...................................37

EXHIBIT 11: PCCF+ GEOCODING METHODOLOGY SUMMARY ...........................................................47

EXHIBIT 12: DISTRIBUTION EXCESS MORTALITY RATES (AGES 0-79), 1996-99 ...................................50

EXHIBIT 13: DISTRIBUTION OF PERCENT RURAL POPULATION (DENSITY<25 PFR SQ. KM) ...................51

EXHIBIT 14: DISTRIBUTION OF PERCENT ABORIGINAL POPULATION ...................................................52

EXHIBIT 15: ONTARIO FEMALE POPULATION FERTILITY RATES, THREE-YEAR AVERAGE 1996-1999 ........53

EXHIBIT 16: COMPARISON OF ACTUAL/EXPECTED FERTILITY RATE (AGES 10-54) DURING1995-1997....54

EXHIBIT 17: DISTRIBUTION OF FERTILITY INDEX BY COMMUNITY, 98/99 ............................................54

EXHIBIT 18: COMMUNITY VARIATION IN THE INCIDENCE OF LOW BIRTH-WEIGHT NEWBORNS ................55

EXHIBIT 19: BASE RATES PER 1000 POPULATION BY AGE AND SEX, 98/99 ......................................56

EXHIBIT 20: DISTRIBUTION OR MARI INDEX VALUES BY COMMUNITY, 98/99 ......................................57

EXHIBIT 21: PREGNANCY AND CHILDBIRTH WEIGHTED CASES PER 1000 POPULATION, 98/99 ..............58

EXHIBIT 22: COMMUNITY VARIATION IN EXPECTED NEWBORN AND NEONATE CASE MIX INDEX ...............60

EXHIBIT 23: PROVINCIAL TERTIARY PERCENTAGES BY AGE GROUP AND SEX .....................................62

EXHIBIT 24: VARIANCE RESULTS COMPARING 98/99 ACTUAL AND EXPECTED VOLUMES ......................64

EXHIBIT 25: VARIANCE RESULTS COMPARING 1998/99 ACTUAL AND 2000/01 EXPECTED VOLUMES ....65

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INTRODUCTION

Historical Funding in Ontario

Hospital funding has evolved significantly since the introduction of the governmentsponsored Hospital Insurance Plan in 1959. Hospitals have been funded through variousfunding mechanisms including line-by-line and global funding formulae. Since the early1990's, the Joint Policy and Planning Committee (JPPC) of the Ministry of Health and LongTerm Care and the Ontario Hospital Association has pursued the development of hospitalfunding formulae and policies to improve funding equity in the hospital system.

Cost equity has been evaluated by comparing each hospital's unit cost to a hospitalspecific target based on the average cost per weighted case in the province for similarfacilities. These evaluations have been used to distribute available equity funds to low unitcost hospitals, and to disproportionately reduce the funding to high unit cost hospitals. TheAdjustment Factors Committee of the JPPC Hospital Funding Committee (HFC)developed a model for setting large/community hospital expected cost per weighted casefor acute and day surgery activity, and the Small Hospital Committee developed a uniquemodel that was applicable to small hospitals. In each model, hospital specific cost perweighted case targets were set taking into consideration factors that:

• are measurable;

• are based on available data;

• have a material influence on hospital cost per weighted case; and

• are thought to be beyond management control.

The large/community acute funding model, historically called the Adjustment Factors model,was based on a weighted least squares regression mode] that predicted a hospital's costper weighted case based on adjustments for three factors, including:

• Non-neonate tertiary activity;

• Newborn and neonate tertiary activity; and,

• Teaching intensity.

The small acute funding model was based on a non-linear weighted least squaresregression model that separated direct and overhead expenditures and includedadjustments for size and isolation.

Population equity has been promoted by the development of formulae to measure theanticipated growth in hospital volumes that will result from population growth and aging.Hospitals that serve high growth communities have historically received additional funding tosupport anticipated growth pressures.

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Enhancements to Rate and Volume Equity FundingMethodologiesApplication of formulae, such as the adjustment factors and growth funding formula, hasimproved relative funding equity by rewarding providers that are low cost providers andthat have communities with substantial growth. However, there are a number ofenhancements that are needed to improve the fairness, responsiveness and scope ofrelevance of these formulae, including:

• the measurement and inclusion of all components of the hospital system (e.g.,chronic care, rehabilitation, outpatient, etc.);

• the integration of all funding formulae (e.g., small and large/community hospitalformula, acute care and chronic care funding);

• a methodology that is sensitive to both relative population needs and populationgrowth; and,

• a methodology for the evaluation of base Ministry of Health and Long Term Carefunding and the ability of hospitals to generate revenue from other sources.

The JPPC Volume Sub-Committee's recommended volume model introduces populationequity by setting hospital volumes based on the population demographics and relativeneeds of each hospital's referral population. In addition, the volume model provides ameans with which to estimate the impact on hospital volumes of population growth andaging. The JPPC rate formula and the JPPC volume formula to be discussed in this reportaddress the first three of these enhancements. The JPPC Rate Sub-Committee'srecommended rate-equity model includes chronic care activity in cost par equivalentweighted case as measured by Resource Utilization Groups, version 111 (RUG-111)weighted days. The relative weights for the RUG-111 grouper were developed by theJPPC Complex Continuing Care Funding Working Group. In addition, the small andlarge/community hospital formulas have been integrated into one formula that makesadjustments for size and isolation, along with the teaching and tertiary factors of thelarge/community hospital formula.

The model presented in this report is based on a "pie sharing" exercise. The process forapplying the model is that the Ministry of Health and Long Term Care (MOHLTC)determines the amount of money to be applied under the model (whether that be newfunding, or the total hospital allotment), and then the model will determine how that moneyshould be distributed most equitably among hospitals. The model does not determine theappropriate level of funding for the hospital system in total.

The JPPC Volume Sub-Committee"The Volume Sub-Committee's mandate was to attempt to develop a method ofestimating the expected volume of hospital activity, given the characteristics of thepopulation served by the hospital and the impact of other health service providers on thehospital activity rate. The consulting firm Geyer Szadkowski Consulting was engaged toassist the Volume and Rate Sub-Committees to conduct the initial data analysis to allowevidence-based decisions.

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The Volume Sub-Committee has divided its work plan into two phases:

Phase 1: to identify the population factors that affect hospital utilization.

Phase 2: to allocate the impact of these factors to service providers (i.e., hospitals)."1

A Discussion Paper "Predicting Hospital Volumes for Communities" was released by theVolume Sub-Committee in June of 1999 that was a culmination of phase one of their work.

The JPPC Rate Sub-Committee and its Working GroupsThe funding models have been developed to adjust the hospital's global budget for factorsthat are beyond the control of hospital management. This has created a situation wherethere could be two hospitals, each providing services below the expected unit cost, andone hospital could be in a deficit position and the other in a surplus position. This isintuitively is inequitable, and has spurred the drive to develop a new funding model.

In the past, funding models in Ontario have focused solely on the costs of providingservices. In the context of this new funding model, three committees were convened toreview and develop the Rate Model. The Rate Sub-Committee is committee with overallresponsibility for the Rate Model. The terms of reference of the Rate Sub-Committee arein Appendix 1. The members of the Rate Sub-Committee are listed in Appendix 2

The Cost Adjustment Factors Working Group was responsible for the continueddevelopment of a cost equity model that calculates a facility's expected cost per equivalentweighted case. The terms of reference of the Cost Adjustment Factors Working Group arein Appendix 3. The members of the CAFWG are listed in Appendix 4.

The goal of the Revenue Adjustment Working Group was to analyze the revenue side ofthe provision of services and recommend whether a revenue equity adjustment factorshould be included in the funding formula. In this analysis there is inherent conflict betweenthe goal of ensuring that there are incentives to make hospitals as entrepreneurial aspossible, and the goal of equalizing the external revenue opportunity inequities. It wasnoted that the goal of this working group was to develop and recommend an adjustmentfactor that adjusts an allotment, not the complete replacement of the formula. The Groupcompleted the revenue analysis, however the analysis was not accepted for inclusion in the2001/02 model.

1 Predicting Hospital Volumes for Communities, JPPC Discussion Paper #DP3-5, June 1999, pg. 5

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Overview of this ReportThis report provides the detail on the JPPC committee processes for developing themodel, the hypotheses justifying specific rate and volume adjustments, and the rationale forthe application of the models. The detailed technical methodology applied in developingboth the rate and volume methodologies is described. This document provides informationon the methods employed to derive the expected rate and volume levels calculated forhospitals.

The methodology for the rate model is discussed first. The data sources included in themethodology are described. The general rationale for the methodology is highlighted and astep-by-step description of the methodology is provided. This section also includes acomparison of hospitals' actual and expected cost per equivalent weighted case with1998/99 data.

A discussion of the volume model follows. An overview of the data sources and modeldevelopment is provided. A step-by-step methodology for predicting population modelallocations is then detailed. Hospital base-year and growth allocations are also detailed. Acomparison of hospitals' actual and expected weighted cases concludes this section.

The creation of this methodology and this report has included the dedication and experienceof over 95 hospital representative who have spent over 4,500 hours in formal meetingsand many individual hours in discussion also. The JPPC would like to thank all of thosevolunteers who have worked diligently to help us improve the equity in funding for OntarioHospitals.

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RATE METHODOLOGY

ProcessThe first step for the Cost Adjustment Factors W orking Group (CAFW G) was to develop alist of criteria and selection princip les as a reference point upon which to evaluate possib leadjustment factors. After the criteria were developed, a brain storming session was held inwhich hypotheses about the hospital care system were developed such as "small hospitalshave higher overhead costs due to the need to provide a full range of services for a smallerpopulation." These hypotheses were then tested against the selection criteria and princip les.

The CAFWG then reviewed all adjustment factors that had been considered in the past byother JPPC committees to determine if they were now valid. For existing adjustment factorsthe Working Group reviewed the hypothesis upon which the factors was based.

For those hypotheses that remained, the working group reviewed sources of data todetermine which hypotheses could be tested within the first year. As this is a multi-yearprocess, some hypotheses were deferred to future years due to lack of available data.

The CAFWG relied mainly on the following sources of data:

• Canadian Institute for Health Information Discharge Abstract Database (CIHI DAD)

• Ontario Cost Distribution Methodology (OCDM)

• Ontario Hospital Recording System (OHRS)

• Statistics Canada

• CIHI Ontario Chronic Care Patient System (CIHI OCCPS) -the provincial MinimumData Set version 2.0 (MDS 2.0) database

Due to the heavy reliance on the OCDM and Resource Intensity Weights (RIW), theCAFWG also reviewed the Ontario Case Cost Project (OCCP) and the RIW collection,analysis and verification methodologies to ensure data quality.

The JPPC Complex Continuing Care Funding Working Group was responsible for thedevelopment of the methodology used to calculate Complex Continuing Care weightedactivity, using the CIHI OCCPS database and adapting Resource Utilization Groups,version 111 (RUG-1 11) for Ontario.

The JPPC is in the middle of a multi-year project to develop case weighting methodologiesfor all hospital activity. To date work has been completed on the acute, day surgery andchronic care sectors. The working group received instructions to investigate merging theAdjustment Factors Model (large/community hospital model) and the Small HospitalsModel with the Complex Continuing Care data that had been recently compiled. Thiswould enable the integration of all areas of activity for which case weightings exist. Themethodology for merging these three models is described later. It was felt that byintegrating the three models it would increase the comprehensiveness of the funding,increase the common understanding of how hospitals are funded as well as decrease thecomplexity of funding the hospital system.

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Now that the working group had data and hypotheses, the hypotheses were tested by thedata. For existing adjustment factors, the methodology was reviewed and improvements toit were attempted. A decision was made on all adjustment factors on whether thehypothesis was supported by the data and therefore, whether to use that adjustment as afactor in the new model.

The model was then run with 1997/98 data to review for validity. The data was run with1996/97 data to ensure the robustness of the outcomes, and finally the data was run on1998199 data and a final validity check was completed.

Adjustment Factor Selection Criteria and Principles

Largely beyond management controlThe intent of this principle is to adjust for factors that are beyond management's ability tomanipulate in the short term to affect the funding of a hospital. While it is acknowledged thatail factors that a hospital manages are within management's control in the long term, thefactors that are being considered in the funding models are not likely to be modified in theshort term in response to a funding model.

Measurable, reliable and readily availableThe event being measured should have discrete values that lend themselves tomeasurement. All data that is used must be collected under accepted rules and principles.There must be perceived consistency around the data collection. At the same time, it isacknowledged that no data set is perfect.

Simple to understandThe logic behind choosing the adjustment factor should be simple to understand and easilyexplainable to a colleague in the health administration field. Although the statistical methodthat is used to develop the adjustment factor may not be simple to understand, the generalprocess and logic must be.

MaterialThere are two aspects to materiality. In order to be statistically material, a coefficient mustcontribute to the model's predictive ability. A coefficient must also be weighed according toits political materiality.

Equity between hospitals in entitlementThe historic funding in Ontario has been based on a global budget supplemented byadjustment factors. This model has not been devised through an analysis of servicesprovided by a hospital, but rather through modifications to historic funding levels, andpolitical climates during negotiation sessions. This model will provide more equitable accessamong hospitals to funds based on the volume and type of services provided.

TransparencyThe process, logic and statistical methodology must be clear and open.

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RD 9-8 Integrated Population Based Allocation Formula 1 0

TimelinessThe data and assumptions used in this methodology must be collected and decided uponin a timely manner. The data being used must be timely as there are many modifications ina hospital's organizational structure and in medical protocols each year. The working groupwill not be accepting assumptions written in the past, but will reevaluate assumptions toensure that they are valid given the current health care environment.

Discriminating power and distinctEach adjustment factor used must be discrete from other factors in its power to distinguishbetween hospitals and in its power over the model.

RobustThe model must be valid across different types of hospitals and across time.

Comprehensive yet flexibleHistory would tell us that the model is not going to be drastically changed each year. Therewill be significant changes that we would hope would be reflected in the model. The modelmust be sturdy enough that it is not open to gaming and flexible enough that it does notbreak with the tiniest of changes to the system.

Review of Adjustment Factors

Adjustment Factors previously analyzed by the JPPC

Y - The group decided to use the adjustment factor in a model

N - The group decided not to use the adjustment factor in a model

D - The group decided to defer using the adjustment factor in a model

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Adjustment Factor Replacing Peer

Groups 1995

Adjustment

Factors

Committee

1997

Volumes

Report 1999

CAFWG 1999

Technology N N

Age of Facility N N

Research Activity Y N N

Treatment Choices N N

Utilization Maybe N

Input Costs Maybe N

Mortality/PYLL Y N

Fertility/Low Birth Weight Y N

Patient Severity – Relative to Hospital

Distance

N N N

Geography: Urban/Rural/North/South N N N

Geography: Absolute Patient to Hospital

Distance

Y N N

Low Population Density Revisit N

Patient Age Y Y In RIW

Gender Y In RIW

Patient Complexity Y Y In RIW

Geography: Sole Community Provider Y Y D

Aboriginal Status IN SES Y D

Availability of Community Resources Y Y Y D

Wage Issues N D

Multi-site D

Socio-Economic Status Y Y Y D

Isolation Y

Hospital Size N Y

Teaching Activity Y Y Y

Level of Care Y Y

Adjustment Factors that were rejected:Wage IssuesHospital staff wages account for approximately 75-80% of a hospital's expenses. Thereare three main factors that determine the level of a hospital's payroll: wage scale; seniority;and professional mix. The wage scale is often beyond management's control, as theOntario Hospital Association centrally negotiates most union wages on behalf of thehospitals. Since the majority of hospital staff wages are centrally negotiated, there shouldbe relative wage parity between organizations.

Seniority (i.e., where an employee lies on the wage scale) is somewhat more withinmanagement's control, in that the management of a hospital could offer early retirementpackages and incentives. However the hospital would be effectively reducing theexperience level of their staff. (This aspect of wage funding parity probably has the lowestimpact of the three factors).

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Professional mix, such as the ratio of registered nurses to RNAs, has been changing overthe past five years. Both the professional mix and the quantity of staff (beyond a minimumrequired for patient safety) are largely within management's control.

In 1995 the Adjustment Factors Sub-Committee rejected worker wages as an adjustmentfactor, citing comparative research conducted in the U.S. and -Canada which suggested thatvariations in hospital costs are primarily due to differences in the quantity of factor inputs andoutputs rather than input prices.

TechnologyHospitals with high levels of sophisticated technology or newer hospitals with high initialtechnology cost often have higher maintenance and depreciation costs than hospitals withlower technology levels.

Investment in new medical and other technologies is within management's control forhospitals with high levels of technology. Although hospitals that make large investments intechnology within a single year will face high depreciation charges, these temporaryimbalances will even out over time. While there is a general lack of data regardinginvestment in medical and information technology in the health care field, it was felt that thiswas not a valid adjustment factor since the decision to purchase technology is withinmanagement control.

Age of FacilityBoth old and new hospitals may experience higher building costs. Hospitals housed inolder buildings experience higher costs due to increased maintenance and repairs.Hospitals housed in newer buildings could experience higher costs due to sophisticatedmechanical systems.

If an adjustment were made based on the age of a facility, each hospital would require ananalysis of square footage by age of building and type of mechanical systems. This wouldbe a very arduous task. As there are arguments, which have not been substantiated, forboth newer and older buildings both being more expensive, and since many hospitalshave a mix of type of buildings, it was decided not to use this as an adjustment factor.

Research ActivityHospitals that have teaching activity also often have research activity. It is argued that theresearch activity increases the cost of providing care due to increased tests, expensiveequipment, higher overhead, increased medical and nursing hours, and monitoring of resultsamong other things.

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The challenge is to identify valid data elements for measuring research activity. Lately inOntario there have been some efforts to gather data about research to enable comparisonsbetween Ontario and other jurisdictions. The Ontario Hospital Association, in its 1997 SixPoint Plan, announced it is developing a unified medical research strategy for all of Ontario.Although headway is being made in the improved uniformity of data collection, the data isstill in early development stages and is not adequate for a funding model. Perhaps in thenext five years data will become available that will enable an analysis of the amount ofresearch being completed by each institution, and the impact of that research on the cost ofdoing business.

There is also an inability to separate out the cost of teaching from the cost of research. It isexpected that the impact of teaching and the impact of research activity on a hospitals unitcost would be highly correlated.

The CAFWG decided to not analyze this factor.

Treatment ChoicesHospital costs will increase or decrease as physician practice patterns change. Althoughthese changes are made by physicians who are not always employees of the hospital, andtherefore technically beyond management's control, it has been observed that fiscalmanagement and information as well as clinical information provided by the management ofa hospital can have a significant impact on physician practice patterns. The ultimate controlover physician practice patterns is the appointment and cancellation of physician privilegesby hospital management.

It was felt that physicians and hospitals are constantly modifying treatment choices, and thatthis is fully within management's control.

Utilization Penalty/Bonus/IncentiveThe Adjustment Factors Sub-Committee (AFSC) of the Hospital Funding Committee ofthe JPPC considered utilization as an adjustment factor in 1997. At that time there wasdiscussion on rewarding or punishing hospitals based on how they scored in ICES' PracticeAtlas. That committee agreed that this 'factor was beyond their scope, and felt it should bepassed on to a more appropriate committee.

The CAFWG felt that the goal of the IPBA formula is to fund hospitals according to howthey perform with respect to volume and cost goals, and that once a IPBA formula isimplemented for all hospital funding, there will be no need for a penalty/bonus incentive.

Input CostsThe AFSC also discussed the "issue concerning the cost of doing business" as a potentialinitiative. The Rate Sub-Committee also discussed the possibility of including a factor toaccount for inflation in the market basket of goods that hospitals must purchase to provideservices. It has been found that wages are approximately 80% of the cost of business inOntario hospitals. The increase in cost of the other 20% would be included in an input costadjustment.

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However, as previously mentioned, one of the goals of the funding model is to adjust forfactors that affect the hospital's ability and cost of providing services that are largely beyondmanagement's control and that create inequities between hospitals. Thus, an input costadjustment would not be necessary since the increase in gross hospital input costs wouldincrease in fairly equal proportion between hospitals.

Mortality/PYLLMortality was found to be the strongest predictor of volume of weighted cases par capita inthe Volumes Report. The resource intensity weights account for the intense hospitalresource used to manage the care of those in their last weeks of living. It was felt that thesehigher RlWs would adequately account for the mortality factor in hospitals.

Potential years of life lost (PYLL) is a population index that is often used to allocateresources to preventative care. It was not seen as a factor that would increase the cost perweighted case of a hospital.

Fertility/Low Birth WeightThere does not appear to be a reason to include fertility and low birth rate in a rateadjustment formula. A low fertility and low birth rate would affect the volume of business, butnot the cost per weighted case. The case mix groups are already split into three birthweight groups to reflect the high cost of lower birth weight babies, and the tertiary factoraccounts for the inadequacy of the RIW in accounting for the cost of neonates.

Patient Age, Gender, Patient ComplexityPatient age, gender and patient complexity are factors taken into consideration in thedevelopment of the resource intensity weights. Therefore they are already accounted for inthe model.

Patient Severity Relative to Hospital DistanceSome people claim that the relative distance a patient travels to a hospital is an indication ofthe person's severity of illness. The hypothesis is that a person will travel greater distancesto obtain specialized or higher acuity care than to obtain primary care. Although there issome truth to this hypothesis when considered in relative terms, this theorem cannot beapplied to absolute distance traveled to a hospital in Ontario.

The first reason that this hypothesis cannot be true in Ontario is that the reason for a personobtaining care a great distance from home is not recorded in the data. For instance, a personmay be vacationing in northern Ontario, have a minor accident and obtain medical carethousands of kilometers from home. At the other extreme, the people living across thestreet from Toronto hospital do get critically ill and seek the highest level of acute care withina kilometer of their home.

When determining level of care, the number of patients who live within the 905 area, whoby-passed the 905 hospitals to seek care in a teaching hospital is one of three factorsconsidered. This factor could be considered a modification of patient severity relative tohospital distance. As this factor is taken into consideration in the level of care analysis, it wasdecided not to use it as a separate adjustment factor.

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GeographyThere are many hypotheses regarding the effect of geography on the cost of providingservices. One is that northern communities have higher costs due to increase costs oftransportation. Another is that the further a patient travels to seek care, the longer they mustbe kept in the hospital in case there are complications and the patient must return. It was feltby the CAFWG that these considerations would be taken into account with the investigationof an isolation factor, hospital size factor and the patient by-pass factor in the level of careanalysis.

Low Population DensityThere is no logical reason why a person who is from a low population density communityshould have a longer length of stay or a higher cost per case than a similar person whohappens to live in a high population density community. Population density does not affectthe unit cost of providing services.

Adjustment Factors that were deferred:Aboriginal Status/lmmigrant PopulationThe hypothesis for including percent aboriginal status and recent immigrant populationpercent as an adjustment factor is that in addition to the general lower socio-economic statusof aboriginal peoples, there are additional costs to providing services to these individuals.

The data on aboriginal status and recent immigrant population was not at a stage where theCAFWG felt comfortable analyzing it. Therefore, this adjustment was deferred to the nextphase of analysis.

Multi-SiteHypothesis: Running a 500-bed hospital on one site would incur less cost per weightedcase than running it on multiple sites. This could be due to duplication of some services andextended administrative travel time.

It was generally agreed that there were three models in operation in Ontario. The first isoperating multiple hospitals on multiple sites. It was suggested that this would be the mostexpensive model of hospital operations. The second model is operating one hospital onmultiple sites. It was suggested that this would be the mid-range model with respect to thecost of hospital operations per weighted case. The third model is operating one hospital onone site. It was felt that this would be the most efficient model of operating a hospital.

The multi-site hospitals hypothesized that, particularly when there is such a distancebetween sites, that sharing of services is difficult or time consuming, extra cost would beincurred over the expense of operating a hospital on a single site.

The data for investigating this perceived phenomenon was not available within the timeframe of this phase of the project. Therefore, the investigation of this possible adjustmentfactor was deferred for future analysis.

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Socio-Economic StatusHypothesis: A person with lower socio-economic status would incur more cost perweighted case than a person with higher economic status. This could be due to the fact thatpeople with poorer nutrition, lower education, and less home support tend to stay longer inthe hospital.

Analysis was completed using income as a proxy for socio-economic status, and it wasshown that the lower the income of the individual, the higher the cost of providing care. Thiswas true between hospitals with differing average incomes of their admitting population.This effect was true in a linear way. There was a difference between providing care tosomeone with an average income of $40,000 and someone with $60,000 and someonewith an $80,000 average income.

The Working Group also looked et the effect controlling for the hospital effect. In otherwords, within a hospital, patients with lower average income cost more than patients withhigher average income even with the same admission, treatment planning and dischargeplanning systems.

It was shown that the lowest average income group stayed in hospital approximately 10%longer than the highest. The Working Group expected that the lowest average incomegroup would cost approximately 10% more to treat. The effect was much larger than 10%.This result was obtained at the point that a decision had to be made about whether toinclude socio-economic status as an adjustment factor. There was no time to furtherinvestigate and understand this effect. It was decided to defer this adjustment factor foranother year until more study had been completed.

Availability of Community ResourcesHypothesis: Lack of community resources would increase length of stay et a hospital, asthere are no non-hospital alternatives to support a shorter length of stay. It was found in theVolumes Sub-Committee work that level of community resources did not impact thenumber of admissions to a hospital. It was felt that there might be an impact on the length ofstay of individuals if there were lower levels of community resources.

The availability of community resources would also include availability of physicians bothwithin and without the hospital.

It was decided to defer the investigation of this factor for future analysis.

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Adjustment Factors that were accepted:Teaching ActivityThe Adjustment Factor Sub-Committee of the JPPC established teaching activity in 1995as a cost adjustment factor for Ontario funding modeIS2. It was shown, and continues to beshown that the cost of completing medical education increases the cost of running a hospital.For academically based university affiliated hospitals the presence of teaching activity isbeyond management's control although the amount of teaching activity over the long term iswithin management's control. For some community hospitals a decision to accept a medicalteaching role is within management's control. It was felt that medical education in all spheresof medical practice is important enough that it should be supported by adjustment factors,regardless of the degree of management control.

Hospitals with teaching activity have higher costs per weighted case which may be due to:the existence and maintenance of the required teaching infrastructure and more specializedprograms; higher utilization of diagnostic testing, aggressive or innovative treatmentprocedures, ancillary services, and other resources by residents and academic physicians.Accordingly, hospitals with teaching activity incur higher cost per weighted case thanhospitals without teaching activity.

A common misunderstanding is that the teaching adjustment provided in the formula is apayment per medical student. This is not the case. In fact, teaching activity was the variablethat was being measured, not simply the number of medical students in a hospital. Throughthe JPPC's analysis it was found that the ratio of medical trainee days per average dallycensus in a teaching hospital was an appropriate proxy to reflect the "intensity" of teachingactivity in relation to the size of the institution.

Hospitals and/or affiliated medical schools annually submit information to the JPPC detailingthe number of medical trainees and their respective amount of time spent in medical trainingat each hospital. Thus, the number of medical trainees divided by a hospital's average dailycensus is the formula used to represent teaching intensity in the adjustment factors fundingformula.”3

The 1999 teaching adjustment was calculated as follows:

Teaching Activity Measure = Medical Student Days/Daily Census.

The following issues were raised with the medical student data:

• Student numbers are not consistently reported and therefore difficult to validaterecords based on text fields such as name.

• CPSO number is a mandatory field but hospitals don't consistently keep thisinformation.

2 Replacing Peer Groups with Adjustment Factors, JPPC, Discussion Paper #3-2, November 1995, pages. 15-163 Methodology Used to Calculate 1998/99 Adjustment Factors Funding Model, JPPC, Reference Document #7-4, July1998, page 3

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• University data is not a reliable means of auditing data – University data is

• usually planning data and is inconsistently managed.

• Year one and two students are not included in calculations – need to validate that

• they are not material.

• Hospitals need to respect lime lines and reporting standards.

• Data is self-reported so collection should be part of MIS reporting.

It was decided that since a process and edit checks were in place for the medical studenttrainee data, the data would be used for the 2000-2001 funding year. Follow-up on theoutstanding issues would be pursued in phase two of this project.

Level of Care"Tertiary care is defined as hospital services provided to patients requiring complextreatment. Tertiary care frequently involves a wide range of services, equipment, ortechniques that are specialized and expensive. Hospitals that provide tertiary level of careservices (I.e., tertiary centers) are believed to have higher cast per weighted case. Theextra costs incurred by tertiary centers are associated with the variable utilization ofspecialized programs and equipment; the high proportion of transfer cases; and thetreatment of more complex cases (i.e., more severe cases within the CMGs). Accordingly,hospitals that treat a higher than average proportion of tertiary level patient might incur highercost per weighted case.”4

This hypothesis has been supported by annual data analysis over the past four years, Themodel measuring the amount of impact tertiary care has on a hospital basis has beenrefined over the years. In 1999 the adult tertiary adjustment was found to be the mostpowerful predictor of cost per weighted case.

It is believed that one factor that contributes to the need for the tertiary factor is that the RIWdoes not account fully for the tertiariness of a case. The tertiary cases are in effectundervalued by the RIW system. This is due Io the fact that prior to 1999 the RIW wasbased on Maryland data of the amount charged to patients for the various cases. Thischarge data is "compressed". What happened is that the Maryland hospitals subsidize thelow volume, extremely high cost cases with low cost, high volume cases. For example, byadding a small amount on to the charge of each normal vaginal delivery, the cost of aneonatal case is reduced. In 1999 Canadian case cost data was introduced to the RIWdatabase, and it is expected that as the RIW database relies more heavily on Canadiancost data, the level of care will be more accurately reflected in the RIW, and the need for atertiary adjustment factor could be reduced.

4 Methodology Used to Calculate 1998199 Adjustment Factors Funding Model, JPPC, Reference Document #7-4,July 1998, page 2

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Non-Neonate Tertiary"In 1994, the HAY Group of Health Care consultants developed a methodology tocategorize cases into primary, secondary and tertiary levels of care. Referred Io as the HAYGroup Level of Care methodology, the measure has been adapted for use by the JIPPCin determining what constitutes a tertiary case. The measure is a OMG-based categorizationof cases based on three criteria: 1) the intensity of resources required to treat the patient(i.e.,, RIW value); 2) the number of hospitals which treat the particular case in Ontario; and 3)hospitals treating these cases do so for patients from other communities (i.e., Percent ofMetropolitan Toronto volumes provided to patients outside of the Greater Toronto Area).For a technical review of the methodology refer to: The HAY Group Level of CareMethodology - JPPC reference document 6-9."5

In reviewing the Level of Care Methodology the CAFWG decided to investigate twopossible refinements to the methodology.

The first refinement is Io the criteria number two. the number of hospitals with at least oneinpatient case in the CMG (as a measure of the current distribution of the CMG acrossCanadian hospitals). The current methodology ranks the CMGs performed in a singlehospital as a low number. For example, lung transplant would have a ranking of one.Normal newborn delivery, which is performed in many hospitals, ranked 475 on thiscriterion. The concern with this analysis is that it has the potential to mix up rare criterion withtertiary criterion. For example, if a condition is performed in only one hospital, because it is arare CMG, and not because it is tertiary, it could be labeled tertiary. It would receive aranking on criteria number two similar to lung transplant. In the initial Hay Group analysis,medical experts reviewed the tertiary list to catch rare CMGs that appeared on the tertiarylist.

ln order to attempt to decrease the number of rare diseases that are misranked as tertiary, itwas decided to analyze the possibility of using a Gini Co-efficient analysis on the numberof hospitals recording the CMG. The Gini Co-efficient looks et the cumulative number ofhospitals that record a CMG by analyzing the space under a curve on a graph with thequantity of a CIVIG recorded on the y axis, and the number of hospitals recording thatCMG on the x axis. There are two extremes in distribution of CMGs. If every hospital inOntario completed one lung transplant, there would be a perfectly equal distribution, andthe Gini Co-efficient would be 0.5. If only one hospital records completing a CMG, then theGini: Co-efficient would be close to 0 (zero).

The Gini Co-efficient analysis was performed on the data, and the outcome was comparedwith the Hay Methodology. The Gini Co-efficient caused a slight improvement to the HayMethodology. This slight improvement in the model was weighed against the increasedcomplexity of the Gini refinement. The Cost Adjustment Factors Working Group decided tonot modify the Level of Care analysis in this manner.

5 Methodology Used to Calculate 1998199 Adjustment Factors Funding Model, JPPC, Reference Document #7-4.July 1998, page 2

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Neonate Tertiary“It is commonly believed that very elderly patients cost more than other patients becausethey have longer lengths of stay pet hospital visit and utilize more resources (I.e., nursingcare) than the average patient. Similarly, the very young are also perceived tee be moreexpensive as they require more intensive nursing care and ancillary resources than adultpatients. The two beliefs differ in one key aspect. For paediatrics, the belief is that the costdriver is an increased use of resources pet day; for elderly patients the cost driver is anincreased length of stay. If the Resource Intensity Weights (RIWs) are fairly valued, then allpatients should have the same cost pet weighted case. However, if the RlWs for patientsof a specific age group are undervalued, then this would explain the higher cost petweighted case of hospitals with disproportionate shares of patients in these age groups.

The results of the analysis conducted by the JPPC substantiated this hypothesis fornewborn/neonate CMGs. The JPPC's analysis revealed that for Ontario, the State ofMaryland's Resource Intensity Weights for newborns/neonates CMGs are undervaluedand thus, care in this patient group is more expansive than the weights suggest. This findingwas validated using the Ontario Case Cost Project's database. Further analysis conductedin 1997 revealed that due to changes in RIW values for newborns, there was no longer aneed for an adjustment factor for newborns. Therefore, in 1997, the Adjustment factorsmodel was modified to limit the scope of the newborn and neonate factor to tertiary neonatecases."6

Hospital SizeHypothesis: Smaller hospitals have higher percentage of indirect costs pet unit due tee thelow volume of cases

It was previously found by the JPPC Small Hospitals Sub-Committee that “very smallhospitals face a unique range of challenges by virtue of their size, typical geographicallocation and constitution of the population that they serve."7 The CAFWG decided Ioinvestigate hospital size as an adjustment factor.

IsolationIsolated hospitals must offer all essential services, despite low volume or sporadicdemand as the sole provider of health care services in a specific location. More isolatedcommunities have relatively more facilities and services per capita than othercommunities of their size. Consequently, the hospitals incur increased costs ofoperating programs due tee the low volumes involved. Increased costs result from higherclinical, staffing, administration and support costs. The increase in resources required toprovide a giver service is described as the 'basket of goods' by the Small HospitalsSub-Group."8

6Methodology Used to Calculate 199&99 Adjustment Factors Funding Modal, JPPC, ReferenceDocument #1-4, July 1 M. pages 3-4.7 An Approach for Funding Small Hospitals, JPPC, Reference Document #5-1, page 5.8An Approach for Funding Small Hospitals, JPPC, Reference Document #5-1, p- 5

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Merging the Small and Large/Community Hospital FormulaThe Small Hospital Formula was developed as a response to issues raised by the SmallHospitals Sub-Committee. These issues included:

• inability of small hospitals to allocate resources across functional centers due to thepractice of floating staff between functional centers.

• lack of computerization to keep track of costs

• diseconomies of scale – small hospitals often must provide a similar range ofservices as a community hospital, but the overhead costs are spread across fewercases

• need for adjustment factors specific to small hospitals such an adjustment for theperceived increased cost of providing services if the hospital is isolated

There have been issues related to the separation of the formulas for funding small andlarge/community hospitals. One issue is that in such a model there is a discrete point afterwhich a hospital is no longer considered small. Hospitals that that have borderline valuesand are considered community hospitals have argued that they should be able to accessthe size and isolation adjustment in the small hospitals formula. Another issue is that thesmall hospitals formula, as it accounts for a smaller portion of the total hospital budget, is runafter the work on the large/community hospital funding is completed. This creates a delaybefore small hospitals are certain of their funding, and thus increases their uncertainty inbudgeting.

The integrated formula would have one more benefit. It enables the analysis of whether ornot there should be a multi-site adjustment for any of the adjustment factors. This work onthe cast implications of providing services on multiple sites has begun with the ability toadjust the isolation factor for multi-sites, and will continue to be evaluated as workprogresses within the next fiscal years.

Complex Continuing CareBackgroundOn July 1, 1996, Ontario hospitals began the collection of the Resident AssessmentInstrument Minimum Data Set version 2.0 (MDS 2.0), mandated by the Ontario Ministry ofHealth and Long Term Care (MOHLTC) for ail patients in designated chronic beds,Subsequently, on April 8, 1998, the MOHLTC approved the use of the ResourceUtilization Groups, version 111 (RUG-111) for complex continuing care (CCC) funding. InJuly 1998, the JPPC established the Complex Continuing Care Funding Working Group(CCCFWG) with a mandate to recommend a methodology using MDS 2.0/RUG-111information to fund CCC facilities and CCC units in public hospitals for Terms of Referenceand membership see Appendices 5 and 6).

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The Resident Assessment Instrument Minimum Data Set (MDS 2.0) andResource Utilization Groups in OntarioThe Ontario Ministry of Health and Long Term Care requires ail Ontario hospitals tocomplete MDS 2.0 assessments for ail patients admitted to designated chronic care beds.Data are submitted quarterly, in electronic format, to the Canadian Institute for HealthInformation, which administrates the provincial IVIDS 2.0 database, called the OntarioChronic Care Patient System (OCCPS). This data set, designed expressly for the typesof patients in chronic care beds, captures a broad range of resource-determining patientcharacteristics including cognitive patterns, mood and behaviour, physical functioning,continence, diagnoses, and special treatments. Whereas acute inpatients are grouped intoCMGs, patients in chronic beds are classified into RUG-111 groups based on theinformation captured by the MDS 2.0. There are a total of 44 RUG111 groups, including 14rehabilitation groups. Analogous to acute inpatient RlWs, relative per diem resourceweights for each RUG-111 group have been developed and adapted using Ontario wagerates for various labour categories including RNs, RNAs, Physiotherapists, andOccupational therapists.

Phase One of Work PlanThe milestones reached during this phase were the following:

• Adaptation of RUG-111 weights for Ontario

• Calculation of cost/RUG-111 weighed unit

(For further detail, see Cost Per Case-Mix Weighted Activity For Complex Continuing Carein Ontario, JPPC Reference Documents - Summary Report #8-11 and Technical Report#8-12).

1. Adaptation of RUG-111 weights for OntarioOntario wage-weights were calculated as the ratios of licensed nursing, therapy andtherapy assistant staff wages to nursing aide wages. The wage figures used forderiving these Ontario wage weights were taken from results of fiscal year 1997/98Ontario Hospital Association salary surveys.

2. Calculation of costIRUG-11111 weighted unitCalculation of RUG-111-weighted Patient Days

In the funding system being developed for Complex Continuing Care (CCC) inOntario, the basic units of patient volume are patient days. The CCFWG assumesthat funding equity will be introduced by weighting the days of care for patients bythe resource intensity of their care relative to that of the "average" CCC patient inOntario. These RUG-111 case-mix index weighted patient days have beenlabeled as RUG-weighted patient days (RWPD).

Calculation of RUG-weighted patient days (RWPD) involved the following fourdistinct steps:

1. RUG-111 classification for each MDS assessment associated with anepisode of care.

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2. Calculation of the patient days associated with the RUG-111 classification foreach MDS assessment within an episode.

3. Weighting of patient days

4. Calculation of the facility total RWPD and a facility summary score, the FacilityCase-Mix Index.

In step 1, each MDS assessment was classified using the RUG-111 grouper in the"index maximizing" mode. This ensures that patients are classified into the groupwith the highest case-mix index for which they qualify.

In step 2, the total number of patient days associated with RUG-111 groups wascalculated as follows. The days associated with each MDS assessment weretotaled, with adjustments for assessment periods crossing the fiscal year and forassessments with missing discharge data. Patient days from episodes for which noMDS data were available were associated with a RUG group only In the followingcircumstance; The episode arose due to re-entry to a facility of a patient previouslydischarged from that facility AND discharge from the stay for which no MDSassessment was available, occurred within 90 days of an MDS assessmentperformed during the previous stay at the hospital. In such cases, the patient dayswere assigned the RUG group from the most recent MDS assessment done duringthe prior stay. All other episodes lacking MDS assessment data could not beassociated with a RUG-111 group.

In step 3, patient days were weighted in two stages. Patient days associated witheach RUG group were totaled and multiplied by the case mix index (CMI) for thatRUG group. Patient days not associated with a RUG group were assigned thefacility average CM[ (for episodes with length of stay under 14 days) or the CMI ofthe lowest RUG group (for episodes with length of stay greater than 14 days).

In step 4, the facility total RWPD was calculated by adding the RUG group-associated RWPD and the non-RUG-group-associated RWPD.

The CCFWG , upon reviewing the 1998/99 MDS/RWPD data, added a step tothe process. This step involved comparing each facility's number of reported MDSchronic patient days with the facility's number of reported MIS chronic patient days.In cases where the difference between MDS and MIS patient days was greaterthan 1 %, the CCCFWG decided that the lower of the two was to be Used tocalculate the final facility RWPD to be used for funding model development. (Forexample, if a facility with a CMI of 1.1 had 10,500 MDS-reported days comparedto 10,000 MDS-reported days, the final RWPD for that facility would be calculatedto be (1.1 x 10,000) 11,000 RWPD). This occurred for fifteen facilities; each ofthese facilities was notified of this adjustment to the final facility RWPD. This interimpolicy was applied since there was some concern that significant reportingdifferences may be due to reporting errors in the MDS such as missing dischargeinformation. Since it is anticipated that reporting practices will improve each year, theapplication of this policy will be reexamined on an annual basis.

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Linkage of RUG-111-weighted Patient Days to Facility Cost Data from theManagement Information Systems DataBased on financial data supplied by al[ hospitals to the Ontario Ministry of Health and LongTerm Care in compliance with Management Information Systems (MIS) requirements, theOntario Cost Distribution Methodology (OCDM) Is used to allocate costs to Direct Carecosts and Overhead costs. (For a detailed examination of the OCDM applied to 1998/99data, sec JPPC Reference Document #9-4). Hospitals with different levels of care (e.g.,chronic care, rehabilitation, acute care) must appropriately allocate costs to the differentlevels of care when submitting their MIS data to the Ministry of Health and Long Term Care.The MIS data submission process is audited annually9. Total Costs allocated to chroniccare were derived for each hospital with chronic care beds. The data were divided by TotalRWPD to yield facility Total Cost per RUG-weighted day (Appendix 7).

Phase Two of Work PlanThe next step for the CCCFWG was to examine the data from Phase one to determine ifany cost adjustments were needed. The committee's approach was to start by examiningthose adjustment factors that have been found to be significant cost drivers for acute careand to determine which, if any, are cost drivers for complex continuing care.

The CCCFWG also examined the MDS/RUG-111 performance with respect to specificpatient sub-populations, such as palliative care, geriatric rehabilitation, dialysis, and ventilatorpatients, and what impact, if any, this has on a facility's cost per RWPD. The intent was toidentify any potential material inequities and make recommendations accordingly.

The analysis started with an attempt Io develop working definitions for thesesubpopulations by convening focus groups to examine the MDS in the context of thesesubpopulations. Definitions were then applied to the MDS to identify the percent ofactivity, as measured by RWPD, attributable to a particular sub-population for each facility.If a non-uniform provincial distribution for a particular type of sub-population activity wasfound, regression analysis was performed to determine if there was a material andstatistically significant correlation between percent of activity of this sub-population and costper RWPD.

9The JPPC plans data quality tests through what is known as a data blitz. Annually, the JPPC along with the Ministryof Health and Long Term Care jointly organize this event, over a short period, to focus and review hospital specificMIS Trial Balances. The purpose of this review is to ensure that all data submitted by hospitals meet audit criteria forinclusion in JPPC funding formulae.

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The regression analysis found no significant relationship between cost per RUG-111-weighted patient day and percent days from any of the sub-populations explored. Oneexplanation for the results found is that inadequate sub-population definitions, due tolimitations of using only those data elements; presently captured within the MDS, wereused. Another reason may be that the relative volumes attributed to the sub-populationswere typically low for most facilities. Hence, the impact on cost due to "tertiary” chronic carewas diminished when all patient activity is taken into account. Further work is needed torefine the methodology to evidence any impact on a facility's cost per RWPD due to thepresence of these sub-populations.

The only material cost variation that was found pertains to the relatively higher unit costs forcomplex continuing care activity in stand-alone facilities as compared to the complexcontinuing care activity unit costs found in facilities that have both acute and complexcontinuing care activity. Based upon discussion with CFOs from stand-alone facilities, anumber of potential reasons/concerns for this differential have been proposed including thefollowing:(*not only a stand-alone issue)1. Lack of understanding of IPBA (need for field education)*2. Perceived need to group stand-alone3. Rising per diem costs (e.g., salaries and benefits)*4. Concerns if funding formula mirrors long term care levels of care funding*5. Data quality6. MOHLTC revenue and affect of lack of co-payment revenue from special groups

such as palliative care7. Acute care charge for specialized services (e.g., blood transfusions)8. MDSIRUG-111 performance with respect to the following patient

sub-populations*:a. Palliative rareb. Short stay (respite, behavioural neurology, concentrated care units, geriatric

assessment and treatment)c. Rehabilitation cared. Psychiatrye. Ventilator patientsf. Progressive neurological deficits such as MS, Huntington's diseaseg. Dialysis patientsh. HIV patients

9. Funding programs from global funding:a. Outreach/regional clinics (palliative, memory disorder, audiology, etc.)b. Dentistryc. Psychiatric Day Hospitald. Education (community outreach)e. Medical staff remuneration*f. Teaching and research*

10. Other allocation issues:

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a. Infrastructure costs (diagnostic, pharmacy)b. Transfer costs from inpatient care to acute care settings

11. Higher overhead costs per RWPD and its treatment under the Ontario CaseDistribution Methodology (OCDM)

Consequently, the CCCFWG recommended that for the first iteration of the rate model aninterim stand-alone flag be included to estimate the cost per activity for these facilities.However, further work still needs to be done to examine these apparent higher unit costsfor stand-alone complex continuing care facilities and obtain evidence to demonstrate thisdifferential.

Revenue Adjustment FactorsThe Hospital Funding Committee decided Io not go forward with the Revenue AdjustmentModel. The Committee congratulated the Revenue Adjustment Factors Working Group onthe excellent work that was completed. It was fait that introducing the Volume Model and theRate Model was sufficient change Io the system for one year. The thinking of the RevenueAdjustment Working Group is included in this report.

The Revenue Adjustment Factors Working Group Terms of Reference can be found inAppendix 8. The membership of the Working Group can be found in Appendix 9.

Selection Criteria and PrinciplesLargely beyond management controlThe intent of this principle is to adjust for factors that are beyond management's ability tomanipulate in the short term to affect the funding of a hospital. While it is acknowledged thatall factors that a hospital manages are within management's control in the long term, thefactors that are being considered in the funding models are not likely to be modified in theshort term in response to a funding model. For example, although a hospital managementcould theoretically change the size of the hospital to take advantage of a positive fundingfactor for small hospitals, it is unlikely that a management would modify their practice patternsto that extent,

Measurable and readily availableThe event being measured should have discrete values that lend themselves tomeasurement. All data that is used must be collected under accepted rules and principles.There must be perceived consistency around the data collection. At the same time, ill isacknowledged that no data set is perfect.

Simple to understandThe logic behind choosing the adjustment factor should be simple to understand and easilyexplainable to a colleague in the health administration field. Although the statistical methodthat is used to develop the adjustment factor may not be simple to understand, the generalprocess and logic must be.

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MaterialThere are two aspects to materiality. In order to be statistically material, a coefficient mustcontribute to the model's predictive ability. A coefficient must also be weighed according toits political materiality.

Not open to manipulationAs rules are established in a funding environment, practice follows, Il is important that themodel minimizes a hospital's ability Io game the system and that changes that occur in thedata after the model is introduced are the result of practice changes and not the movementof data.

Not decrease incentive for hospitals to be entrepreneurialHospitals should continue to be encouraged Io take advantage of each entrepreneurialopportunity that arises. This decreases the burden to the taxpayer for running the healthcare system and also increases the flexibility of the hospital's ability Io tailor their programsto their communities' distinct needs.

Increases equalization of hospital’s opportunity to generate external revenueIl is acknowledged that hospitals do not have equal opportunity tee generate funds. Forinstance, ill would intuitively make sense that a hospital that is in a large urban area may beable to generate more income from its cafeteria than a small rural hospital. Il is important thatcommunities that are unable to generate as much additional funds still have the opportunityto have excellent programs tailored tee their needs,

Review of Revenue Adjustment FactorsThe Committee discussed and analyzed two types of revenue equity. First the Committeecalculated the historical MOHLTC revenue per MOHLTC weighted case. Ail fundingassociated with the year of the activity was isolated, extracting one lime funding and othernon-recurring funding. Then a MOHLTC revenue/MOHLTC weighted case was calculated.The Committee felt that if a model was developed that had Expected Rate * ExpectedVolume = Funding, there would be no reason to have this type of revenue adjustment.However, ill was felt that there have been historical imbalances in funding that have createdextreme funding inequity. Thus, the application of this type of revenue adjustment wouldaccelerate the movement towards funding equity during the implementation phase of theEVAR model.

The second type of revenue adjustment reviewed stemmed from the hypothesis that notall hospitals have an equal opportunity to generate revenue. In other words, there are someconditions beyond management's control that enable some hospitals to generate greaternon-MOHLTC revenues than others. The types of revenues discussed and analyzed arelisted below.

CafeteriaThe ability to generate funds from a cafeteria is probably universal throughout Ontariohospitals. The amount of funds that can be generated is not equitable. Il would be logicalthat larger hospitals would have more staff and visitors tee purchase food services and thusthe hospital would have more revenue from the cafeteria.

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Other VoteThe major sources of Ministry of Heath and Long Term Care funds arc: global funding,priority funding and other vote funding. This funding is intended to provide services,including overhead, for the particular program that is being funded. However, hospitals havean ability to decide the amount of overhead including administrative costs, maintenance andother hotel costs.

ParkingIt would appear that hospitals have differing abilities to generate revenue from parking. Thelargest discrepancies in funds generated from parking would be between urban and ruralhospitals. Hospitals in downtown Toronto, for instance, have an ability to charge significantlymore par hour than hospitals in a rural setting. There is also a discrepancy between ruralhospitals. Some hospitals are located in areas where there is ample free parking forpatients and visitors to use as alternatives to paid hospital parking. For instance, a hospital ina downtown area where there is ample street and other free parking or a hospital located inresidential district with street parking would not be as successful as a hospital located in abusy downtown area where there are few alternatives, or restricted parking.

Preferred AccommodationHistorically preferred accommodation has been a significant source of external revenue formany Ontario hospitals. In the past coupe of years there has been a tightening of the rulesby insurance companies surrounding preferred accommodation. Some members of theRAFWG were predicting that preferred accommodation would no longer be a significantsource of income for Ontario hospitals within five years.

Rationale for Rate MethodologyThe rate modal allows an evaluation of cost equity by comparing each hospital's actual costpar weighted case with a unique expected cost. par weighted case that takes into accountfactors beyond management control that influence unit cost.

An expected unit cost has been calculated for large/community and small acute care facilitiesfor the last several years. For large/community acute care facilities, the rate, expressed as"cost per weighted case", has focused only on acute inpatient and day surgery activity. Aswell, there has been separate formulae for large/community and for small facilities. Theproposed modal integrates these formulae to include, where possible, other patient activitytypes into the modal. Specifically the modal:

• integrates the small and large/community hospital acute funding formulae; and,

• introduces chronic activity into hospital unit cost comparisons

Overview of Model ApplicationThe recommended rate modal includes small and large/community acute rare facilities, aswell as stand-alone chronic care facilities. The small hospital population includes any hospitalthat met the following criteria in the 1997/98 funding year:

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• less than 3,500 equivalent weighted cases (EWC) (acute inpatient and day surgeryweighted cases, emergency visit EWC, chronic patient day EWC, rehabilitationpatient day EWC);

• less than 20,000 ESI referral population; and,

• single, provincial community provider.

The small hospital formula has historically included all activity and expenses, with theexception of clinic and day/night care activity. The introduction of RUGS-111-weighteddays allows chronic care to be included in the model for small and large/community acutefacilities as well as for stand-alone chronic hospitals. With the exception for smaller facilities,all other activity and expenses (e.g., rehabilitation, outpatient) will be dealt with outside ofthis funding formula. Exhibit 1 indicates the patient types included in the model by type ofhospital.

Exhibit 1: Patient Activity Included in Rate Model by Type of HospitalSmall Acute

Care HospitalsLarge Acute Care

HospitalsStand-AloneChronic Care

Acute Inpatient and DS X X

Chronic Care X X X

Rehabilitation X

Emergency (Out-pt) X

ELDCAP X

The small hospital group includes 53 organizations In the 1998199 fiscal year.Large/community acute care facilities include both community and teaching facilities. Thereare 11 teaching organizations and 83 community organizations in 1998199. The stand-alonechronic care group includes 14 organizations that provide chronic care but do not provideacute care. It is important to note that several mergers occurred following the 1998/99 fiscalyear. Where possible, hospital mergers have been identified and the formula results areexpressed for the merged hospital in the 1998199 fiscal year.

The model calculates an overall expected cost per equivalent weighted case (ECPEWC)by hospital for the patient activity types identified above. The following sections providedetail on the model derived Io calculate hospital-specific ECPEWC.

Data SourcesData for the 1998/99 fiscal year, were utilized to generate an expected cost per weightedcase for each hospital, including:

• Ontario Cost Distribution Methodology (OCDM) data;

• MIS trial balance submissions;

• Supplementary tables;

• Canadian Institute for Health Information (CIHI) acute inpatient and day surgerydischarge abstract data grouped by complexity (Plx 98);

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• MDS/RUG-111-weighted patient days provided by the JPPC ComplexContinuing

• Care Funding Working Group;

• Medical student trainee data collected by the JPPC;

• Level of care mapping to CMG 98; and,

• Northern and rural hospital framework definitions for rural and northern hospitals.

Data Quality ReviewA data quality review was conducted to review the data elements required in the calculationof the expected cost per equivalent weighted case. This included a review of the 1998/99Ontario Cost Distribution Methodology data, specifically:

• review of valid entry codes in functional centres (e.g., if expenses greater than 0 thenrelevant statistics should be greater than 0); and,

• review of the allocation of total net expenses across patient types (e.g., netexpense should be the sum of acute, chronic, rehab, outpatient and other outpatientnet expenses).

The results of this review indicated that several facilities reported expenses but no statisticsand/or statistics but no expenses. Exclusions and/or assumptions were made case bycase to correct the data set (see Exhibit 2 below). It is important to note that no correctiveaction was necessary where expenses were incorrectly allocated from small hospitals asthe approach used for small hospitals, already corrects for this.

Exhibit 2: Review of Valid Entry Codes Net Expenses Activity

Acute Acute

Inpatient and Inpatient and Estimated

Day Surgery Day Surgery Chronic Chronic

Facility Net Chronic Net Weighted RUG – Wt'd RUG – Wt'd

Coded Facility Description Expenses Expenses Expenses Days Days Recommended Action

627 CHAPLEAU GEN 0 1331 Corrected through Small Hospital Formula

638 COCHRANE LADY MINOT 0 2328 Corrected through Small Hospital Formula

733 LOUISE MARSHALL $31,730 0 9 RUG–wt'd days estimated base on 97/98 CMI

864 MOOSONEE JAMES BAY GEN $359,892 $2,662,744 0 0 0 Exclude from overall model

898 ST. JOSEPH'S TORONTO $415,343 0 1,787 RUG–wt'd days estimated base on 97/98 CMI

631 OTTAWA ROYAL OTTAWA $20,210,529 0 Exlude Royal Ottawa – psych from overall model

679 SUDBURY ALGOMA $6,222,395 0 Exclude from overall model

766 PENETANGUISHENE GEN $355,412 0 Exclude actue & ds expenses from overall model

827 BAYCREST $2,507,479 0 Exclude actue & ds expenses from overall model

910 ST MIKES CASEY HOUSE $3,176,495 0 Exclude from overall model

In addition, the review indicated that for several facilities the sum of acute, chronic, rehab,outpatient and other outpatient net expenses did not equal the total net expensesreported. These hospitals were identified to the MOHLTC and the total net expensesreported by patient area were applied in the model. Exhibit 3 provides the list of hospitalsand functional centres affected:

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Exhibit 3: Review of Allocation of Net ExpensesFacility

Code Facility Description Un-Matched Expenses

100 Hamilton St. Joseph’s Total Direct Expenses

623 St. Catharines Shaver Net Gain or Loss on Disposal

629 Chatham St. Joseph’s Hospital Pastoral Care

675 Hamilton St. Peter’s Hospital Net Gain or Loss on Disposal

693 Kingston General Hospital Pharmacy excluding drug expenses

701 Richmond Hill York Central Hospital Pastoral Care

842 Toronto Mount Sinai Hospital Total Cost

852 Toronto St. Michael’s Hospital Total Direct Expenses

873 Well and County General Hospital Pastoral Care

907 Timmins & District General Hospital Pharmacy excluding drug expenses

932 Ottawa Hospital Of Sisters Of Charity Net Gain or Loss on Disposal

Finally, a number of facilities were excluded from both the rate and volume model becausethey are specialty facilities that don't have activity measure or private facilities that do notreport financial data. The list of al] hospitals excluded from the analysis is provided in Exhibit4 below.

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Exhibit 4, Hospitals Excluded from Rate and Volume Models

Facility Description

Facil ity

Code

GUELPH Homewood

HAMILTON St. Joseph’s Community 100

KINGSTON Inst. Of Psychotherapy

LAKEFIELD Private 702

LONDON Grace Villa 712

LONDON Parkwood 713

LONDON St. Mary’s 715

MARKHAM Shouldice 855

MOOSE FACTORY Weenebayko 720

MOOSONEE James Bay 864

OTTAWA CHEO 751

OTTAWA National Defence 730

OTTAWA Perley 750

OTTAWA Riverside 871

OTTAWA Royal (Rehab) 856

OTTAWA Royal Ottawa (Psych) 651

PENTETANGUISHENE Beechwood 765

PERTH Wiseman’s Private 770

SIOUX LOOKOUT Zone 806

SUDBURY Algoma 679

TORONTO Add & MH Serv Grp 948

TORONTO Bloorview Hug MacMillan 939

TORONTO Dewson Private 832

TORONTO Don Mills Surgical 680

TORONTO Hospital for Sick Children 837

TORONTO Inst Trauma Plastic Rest Si 838

TORONTO Lyndhurst 840

TORONTO Metfors 0

TORONTO St. Bernard’s 879

TORONTO St. John’s 880

TORONTO St. Michael’s Casey House 910

WOODSTOCK Private 891

Methodological StepsStep 1: Calculation of Actual Cost per Equivalent Weighted CaseThe MOHLTC calculates an actual cost per weighted case (ACPWC) using the OCDM.This ratio however, only pertains to acute inpatient and day surgery activity whereas theproposed model includes various types of activity. Therefore, a new measurement ofunitcost, known as the actual cost per equivalent weighted case (ACPEWC) is calculatedusing the OCDM data to include the relevant patient activity data depending upon thehospital type for which the calculation is being made. For small hospitals, acute, chronic,

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rehabilitation, ELDCAP and outpatient emergency costs and activity are included in themodel. For large/community acute facilities, acute and chronic care expenses and activity areincluded. For stand-alone chronic care facilities, only chronic care expenses and activity areincluded. The next several paragraphs detail how the various patient types areincorporated into the calculation of a new ACPEWC.

The OCDM allocates departmental hospital expenses Io patient areas, including:

• acute inpatient and day surgery (expenses, weighted cases);

• chronic care (expenses, RUG-111-weighted patient days);

• rehabilitation (expenses, patient days); and,

• emergency (expenses, visits) and other outpatient expenses

The costs and activity are summarized by patient area by hospital and, a unit cost isderived by patient area by hospital. Exhibit 5 below, details the ratios calculated for eachhospital.

Exhibit 5: Unit Cost Ratios Calculated by Type of HospitalSmall Acute

Care Hospitals

Large Acute

Care Hospitals

Stand-Alone Chronic

Care Hospitals

Acute Inpatient and DS Cost per Weighted Case Cost per Weighted Case

Chronic Care Cost per RUG-Weighted Pt Day Cost per RUG-Weighted Pt Day Cost per RUG-Weighted Pt Day

Rehabilitation Cost per Patient Day

Emergency (Out-pt) Cost per Visit

ELDCAP Cost per Patient Day

To calculate a new ACPEWC that encompasses ail of the relevant patient areas, chroniccars and other patient areas must be integrated into one cost per equivalent weighted case.This is dons by establishing a common denominator, or an equivalent weighted case(EWC). An EWC is calculated for each patient area by comparing the average cost per unitfor each patient area Io the average full cost of an acute weighted case. Exhibit 6 providesan example of the calculation using emergency data:

Exhibit 6. Calculation of an Emergency Equivalent Weighed Case

Emergency (Full cast per Emerg Visit)Equivalent = Average Full Acute

Weighted Case { Cost per Weighed Case

)

The equivalent weighted case calculation was applied to 1998/99 data. Weighted meanunit costs were calculated for all patient types and were trimmed at the 10th and 90thpercentiles. Exhibit 7 provides detail on the equivalencies by patient type. The exhibitshows that, for example, the average full cost of a mail hospital emergency department(outpatient visit) is $48 and the average full cost of an acute weighted case is $2,780.Therefore, one emergency visit is equivalent to ffli$2,780 or 0.017 equivalent weightedcases for mail acute cars facilities.

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Exhibit 7: Weighted Case Equivalencies by Patient TypeUnits of Activity Average Cost per Unit Weighted Case

Equivalency

Acute weighted cases 2780.37

Chronic patient days 271.28 0.0975

Emergency outpatient visits 47.72 0.0172

Rehabilitation patient days 394.00 0.1417

ELDCAP patient days 162.34 0.0584

The weighted case equivalencies are then applied to hospital-specific data to calculate thetotal equivalent weighted cases for each hospital. The total number of equivalent weightedcases, by hospital type, is equal to:

• small acute care hospitals

Equiv.

Weighted

Cases

= 0.0172 * Emerg.

Visits

+ 0.0975 *

Chronic

Weighted

Patient

Days

+ 0.1417 +

Rehab

Patient

Days

Equiv.

Weighted

Cases

* 0.0584 +ELDCAP

Patient

Days

+

Total

Acute

Weighted

Cases

• large/community acute care hospitals

Equivalent Total

Weighted = 0.0975 *Chronic Wt’d

+ Acute

CasesPatient Days

Wt’d Cases

• stand-alone chronic care hospitals

Equivalent

Weighted = 0.0975 *Chronic Wt’d

CasesPatient Days

It is important to note that the approach for small hospitals includes all patient activity sincesmall hospitals have more difficulty allocating their departmental costs across patient types.A typical example is that a small hospital may staff one nurse for bath a nursing unit and theemergency department. As a result, the hours and dollars associated with that nurse aremore difficult to allocate appropriately to each functional centre.

The cost allocation problem may also be true, Io a lesser extent for larger acute hospitals.However, application of an equivalent weighted case methodology for rehabilitation andemergency care has not been attempted for large/community hospitals since median costscannot be applied in areas such as emergency and rehabilitation until resource intensityweights are available. As an example, the cost of an emergency visit in a teaching facilitywith a trauma unit will be much higher than the cost of an emergency visit in a communityhospital without trauma. Applying a median emergency cost per visit across alllarge/community acute hospitals would disadvantage more tertiary emergency activity andadvantage less tertiary emergency activity.

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The calculation of an ACPEWC is therefore equal to the sum of expenses (as per theOCIDIVI) for the relevant patient areas divided by the sum of equivalent weighted casesfor a particular hospital. For small hospitals this will include all activity (not includingambulatory care). For community and teaching hospitals, the ACPEWC ratio will pertain toacute and chronic care activity. For stand-alone chronic care hospitals, the ratio will pertain tochronic care only. Appendix 10 provides detail on the breakdown of equivalent weightedcases by hospital by patient area.

Step 2: Calculation of Adjustment FactorsThe JPPC identified several factors to be included in the integrated model. The rationale forincluding these factors is discussed elsewhere in this report. These factors were defined as:

• Size adjustment;

Inverse of equivalent weighted cases

• Tertiary adjustment;

(Percent) Non-neonate tertiary factor= (Non-neonate tertiary weighted cases multiplied by 100) divided by totalequivalent weighted cases;

(Per cent) Newborn and Neonate tertiary factor= (Newborn/neonate tertiary weighted cases multiplied by 100) divided by totalequivalent weighted cases.

• Teaching adjustment;

Medical student days divided by the sum of acute patient census days, chronic(unweighted) census patient days and day surgery cases;

• Isolation adjustment; and,

Percentage of equivalent weighted cases in isolated site(s) as defined by theRural and Northern hospital framework10

• Stand-alone chronic care hospital flag

Equal to one for stand-alone chronic care facilities, otherwise equal to zero.

The above factors are calculated for each hospital and then applied and tested in theregression model. Exhibit 8 details the factors included in the regression model by hospitaltype:

10 Rural and Northern Health; Parameters and Benchmarks: Report of the Joint Committee of the Ministry of Healthand the Ontario Hospital Association, July 1998, Appendix 5. For the majority of rural and northern facilities, theisolation adjustment was 100%. However, for a multi-site organization in which one or more of its sites were definedas being isolated, the percentage applied was based upon the weighted average of activity from the isolated site(s).

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Exhibit 8., Adjustment Factors by Hospital Type

Total Equivalent

Weighted CasesTotal Costs

Percent

Tertiary

Neonate

Factor

Percent

Tertiary Non-

Neonate Factor

Teaching

Factor

Percent

Isolated

Size

Adjustment

Chronic

Care Flag

Large Acute

Facilities

Acute Inpt and DS

Chronic

Acute Inpt and DS,

ChronicYes Yes Yes Yes Yes 0

Small Acute

Facilities

Acute Inpt and DS,

Chronic, Rehab,

Emergency

Acute Inpt and DS,

Chronic, Rehab,

Emergency

No No No Yes Yes 0

Chronic and

Stand-alone

Facilities

Chronic Care Chronic Care No No Yes Yes Yes 1

Step 3: Calibration and Evaluation of ModelA weighted least squares regression model is used Io test the statistical significance ofthese factors in predicting cost per weighted case and to estimate the size of the requiredadjustment. The regression model predicts, based on the factors identified, hospital-specificexpected cost par equivalent weighted case (ECPEWC). The model is defined as:

Expected

CPEWC=

Base

Rate+

Size

Adjustment+

Neonatal and

Non-neonatal

Tertiary

Adjustment

+Teaching

Adjustment+

Isolation

Adjustment+

Stand-alone

Chronic

Flag

Three iterations of the regression model were applied to identify outlier hospitals. The firstregression modal was an un-weighted least squares regression. Observations with ateaching residual outside of three standard deviations were identified as outliers andremoved for the second regression model. The second un-weighted least squaresregression model was performed excluding the outliers identified in the first model.Observations with a teaching residual outside of three standard deviations were againidentified as outliers for the third regression. The third regression model was weighted byequivalent weighted cases and excluded outlier hospitals identified by the first two models.

The results of the model indicate that the factors: size, tertiary-ness, teaching, isolation andchronic care flag are all significant at p=0.05. Total equivalent weighted cases and percentchronic care equivalent weighted cases were not significant. The coefficients for theregression model are presented in Exhibit 9 below.

Exhibit 9: Adjustment Factor CoefficientsParameter

Base Rate 2209Size Adjustment 984,749.55Non-Neonate Tertiary Adjustment 19.69Neonate Tertiary Adjustment 45.97Teaching Adjustment 915.58Isolation Adjustment 259.28Chronic Care Flag Adjustment 431.75

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Based on the results of the regression model, the expected CPEWC for all hospitals canbe derived using the expression:Expected CPEWC = $2,209

+ $984,750 * ( 1 )EWC

+ $46.0 * (%TertiaryNeonate)+ $19.7 * (%TertiaryNon-neonate)+ $915.6 * ( MedicalStudentDays )

Daily Census

+ $259.3 * (%Isolation)+ $431.8 * (ChronicFlag).

Analysis of the factor results indicated that the teaching and tertiary factor coefficients aresimilar in magnitude Io the results of prior regression models applied in the adjustmentfactors formula, thereby providing some face validity to those factors.

The size adjustment versus total equivalent weighted cases was plotted to betterunderstand the relationship (see Exhibit 10). The exhibit below indicates that for very smallfacilities, the size adjustment is very large. For example, a hospital with one equivalentweighted case would have a size adjustment equal to $984,750 per weighted case.However, the largest facilities receive an adjustment close to zero. Based on the 1998199data, the smallest hospital in the province receives an adjustment equal to $2,741.26 perweighted case, whereas the largest hospital in the province receives an adjustment equal to$10.29 per weighted case. This adjustment clearly recognizes and adjusts fordiseconomies of scale.

Exhibit 10-- Size Adjustment versus Total Equivalent Weighted Cases

$ 0

$500

$1,000

$1,500

$2,000

$2,500

$3,000

Total Equivalent Weighted Cases

Siz

e A

dju

stm

ent

(in

$/W

t'd C

ase)

40,00010,000 20,000 30,000 50,000 60,000 70,000 80,000

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The JPPC Small Hospitals Sub-Committee and Complex Continuing Care FundingWorking Group further analyzed the isolation and stand-alone chronic care adjustments,respectively, for face validity.

Step 4: Application of the Model for Recently MergedFacilitiesDuring the 1998/99 fiscal year, several hospitals were in the process of mergers and/oramalgamations. These mergers may or may not have been finalized during the 1998199fiscal year However, they were recognized by the MOHLTC to have occurred during thatyear. Where hospitals reported separately for 1998/99, merging facilities were treated asseparate data elements in the calculation of the expected cost per equivalent weightedcase. For example, if multi-site hospitals submitted separate trial balances then site-specificactual and expected cost per equivalent weighted case were calculated. Expected andactual cost per equivalent weighted case of the merger facility was then rolled-up to themerged facility in the final presentation of the model results.

The expected cost per weighted case for a merged facility that submitted separate trialbalances is therefore, calculated as follows:

• Expected CPEWC results of the regression model is calculated by site;

• Adjustment factors for the individual sites are recalculated as a merged facility;

Non-neonate tertiaryMerged = weighted average of non-neonate tertiary factors formerging facilities

Neonate tertiary factorMerged = weighted average of neonate tertiary factors formerging facilities

Teaching factorMerged = weighted average of teaching factors for merging facilities

Size adjustmentMerged = weighted average of size adjustments for merging facilities

Isolation adjustment = Only applicable where separate patient discharge abstractdata sets are provided by site (calculated as percentage of total activity isolated)

Stand-alone chronic care flag = Not applicable (stand-alone chronic status isconsistent between merging facilities)

• A new expected CPEWC is derived by taking the weighted average ECPEWCof the individual sites; and,

• A new actual CPEWC is derived by taking the weighted average ACPEWC of theindividual sites.

Appendix 11 provides detail on the calculation of the actual and expected cost perweighted case for merger facilities.

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98/99 Model ApplicationBased upon the 1998199 data, the model was applied to a total of 158 facilities includingmergers. This included 53 small facilities, 80 community facilities, 11 teaching facilities and 14stand-alone chronic care facilities. Stand-alone rehabilitation facilities. and specialty facilitieswere excluded from the analysis based on the application of the model. Appendix 12provides hospital-specific results of the model.

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VOLUME METHODOLOGYOverview of the Recommended Volume Equity ModelThe purpose of a population-based formula is to distribute volumes of health services teecommunities equitably, taking into account the factors that legitimately affect utilization ofhealth services. These factors may be population factors such as health and socioeconomicstatus, or they may be related Io the supply of alternatives Io hospital care. Themethodological goal of the JPPC Volume Sub-Committee was to develop a model topredict the volumes of services that would be used by a community with given size,demographics, population health status indicators, and the availability of alternatives. Theterms of reference of the Volume Sub-Committee are in Appendix 13. The members ofthe Volume Sub-Committee are listed in Appendix 14.

The JPPC Volume Sub-Committee developed and recommended a population-basedformula predicting the expected volume of inpatient and day surgery weighted cases for apopulation with given population characteristics at the average Ontario rate of utilization.Population characteristics used in predicting weighted cases include demographics, income,mortality, aboriginal population, fertility and the incidence of low birth-weight newborns.

Predictions based on this population-based formula can be used to equitably distributeavailable hospital resources among populations or communities. Equity amongcommunities implies that the funding for hospital services should be proportional Io eachpopulation's expected volumes given the referral population's unique characteristics. Themodel does not specify the absolute needs of populations.

The methodology allocates weighted case volumes to hospitals in two distinct steps:

First, hospital volumes are allocated to specific Ontario communities (defined variously atthe census division and subdivision level) through population-based community allocations;and

Second, community-specific volumes are allocated to individual hospital providers throughhospital allocations.

Each step is described in detail below.

Allocation of Volumes to CommunitiesThe objective of this step is to predict the volume of inpatient and day surgery weightedcases for a community with given characteristics at the average Ontario rate of utilization for1998/99.11

Three models were developed. These three models are used in combination to predictexpected weighted case volumes for each community. Each of the three models wascalibrated and tested using:

11 Note: The volumes equity formula is a "pie-sharing' exercise, seeking to equitably distribute existing Ontarioweighted case volumes to individual providers, No attempt is made to determine the absolute number of hospitalweighted cases that should exist for the province.

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• three years (1996197-1998199) of Ontario hospital utilization data: and

• an estimate of the relative needs of each community based on each community'sindividual characteristics.

The three models are described below:• Medical and surgical case mix volumes are allocated to each community based on

the Ontario experience taking into account population size, age and sex, relativemortality, income, aboriginal and rural percentages;

• Pregnancy and childbirth case mix volumes are allocated to each community takinginto account female population size and age profile, and their fertility rate relative to theprovincial average; and

• Newborn and neonate case mix volumes are allocated tee each community basedon the size and age profile of the female population, their relative fertility rate and thecommunity-specific percentage of low birth-weight newborns and neonates.

For each model, community-specific expected volumes are calculated in two steps:

Community-specific expected volumes are calculated adjusting only for a population% ageand sex distribution.

Through regression analysis, an "index" is calculated for each community, taking into accountthe other population adjustment factors (in the case of the medical and surgical case mixmodel, the other population adjustment factors are excess mortality, income, aboriginal andrural geography). The age- and sex-adjusted volumes are multiplied by the "index" factorto arrive at expected community-specific volumes.

The expected growth in weighted cases between 1998/99 and 2000/2001 is estimatedfor each community by applying the 1998199 expected per capita utilization rates for eachage group and gender cohort (e.g., males 5-9 years, females 60-64 years, etc.) tee thechange in population size. Growth in medical and surgical case mix is calculated separatelyfor tertiary and non-tertiary activity. This distinction is required to support a repatriation ofprimary and secondary medical and surgical growth volumes.

Allocation of Community-Specific Volumes (Base Year andGrowth) to HospitalsCommunity-specific expected weighted cases are allocated tee individual hospitalproviders in proportion tee each hospital's 98199 market share profile for that specificcommunity. This is done for each of the three groups of case mix (medical and surgical,pregnancy and childbirth, and newborn and neonates).

Growth is allocated to hospitals in two ways depending on the case mix:

• Growth in primary and secondary medical and surgical weighted case is allocated tohospitals in the same census division as the volume growth of the residentpopulation. This assumes that all primary and secondary medical and surgical growth

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is repatriated to local hospitals, regardless of what previous market share patternsmay have been.

• Growth in tertiary medical and surgical weighted cases, as well as growth inpregnancy and childbirth and newborn and neonate weighted cases, is allocated toindividual hospital providers in proportion to 1998/99 market share profiles for eachof these three groups of case mix. This assumes that growth in tertiary services (orpregnancy and childbirth services or newborn and neonate services) will continue toflow to those hospitals, whether local or distant, currently providing that type of care.

Overall expected volumes for each hospital are obtained by summing the base year andgrowth allocations for each of the three groups of case mix, Comparing this value tohospitals' actual acute inpatient and day surgery volume for Ontario residents provides ameasure of relative volume equity. Hospitals with actual volumes below expected areconsidered to be serving relatively under-serviced communities while hospitals withvolumes above expected are considered to be serving relatively over-servicedcommunities.

The following sections of the report describe:

• the data sources used in the development of the population-based volumes equitymodel;

• the definition of "weighted cases" as the unit of measurement in the volumes

• equity model;

• the steps taken to build the volumes equity formula;

• methodologies used in the development and application of the recommendedmodel to calculate hospital-specific expected volumes.

Data SourcesData sources used in the development of the volume equity model are outlined below:

• Population Estimates and Projections (1993-2001) for all censussub-divisions in Ontario, provided by 5-year age and gender cohorts, obtained fromStatistics Canada;

• Census (1996) data from Statistics Canada containing data on:average household income;aboriginal percentage of population; andpopulation density (land area in square kilometers and population).

• Canadian Institute for Health Information (CIHI) acute inpatient and daysurgery discharge abstract data for all Ontario hospitals grouped by complexity (Plx98) for fiscal years 1996/97 Io 1998/99. CIHI data was also used to provide

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the number of hospital-related births and deaths for all Ontario.12 Live birth data wasused to calculate community-specific fertility rates, Mortality data was used tocalculate hospital-related excess mortality rates.

Data regarding Ontario resident utilization of out-of-province hospital services were notavailable Io the JPPC during the development of the Volumes Equity Formula. As such,actual utilization is anticipated to be understated for certain border communities with asubstantial out-of-province flow rate. For example, an unknown fraction of Kenora residentsreceive hospital services in Winnipeg. In future implementations of the Volumes EquityFormula, the JPPC will work with the Ministry of Health and Long-Term Care to obtaindischarge abstracts for all Ontario residents regardless of province where service wasreceived.

12 While the model originally proposed using vital statistics data to count live births and deaths, analysis revealedthat geographic units of residence were often misrepresented in larger urban areas (e.g., residence would be listedas 'Toronto" instead of a more specific region of "East York" or 'North York"). This lad to some subdivisions. such asToronto. being attributed with a very high fertility (or death) rate and other neighbouring subdivisions, such as EastYork, with surprisingly low fertility(or death) rates. To correct for this, CIHI hospital births (and deaths) were used asa proxy for all live births and (deaths), where postal code data could more accurately pinpoint the actual censussubdivision in which a patient resided.

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Definition, Measurement and Allocation of Hospital VolumesWeighted Cases as the Unit of Volumes Measurement

Patient volumes were measured using "weighted cases" (also known as Resource IntensityWeights or RlWs). Weighted cases were obtained from the CIHI inpatient and daysurgery discharge abstract data grouped by complexity (Plx 98) for the fiscal years1995/96 through Io 1998199. CIHI RIW is the currency of hospital service volumes in allanalysis with the following exceptions:

1) The Ontario Case Weight (OCW) methodology was used to reduce the CIHI-assigned weight to outlier cases. OCWs for outlier cases are calculated by reducingthe CIHI RIW by 80% of the routine and ancillary per-diem RIW between the trimpoint and the expected length of stay (ELOS). The formula is described below:

OCW = RIW – O.8 x CMG Routine & Ancillary Per Diem RlW x (ELOS -Trim Point)

2) In addition, cases with length of stay in excess of 365 days (multi-year discharges)were trimmed to an OCW of 365 times the blended routine and ancillary weight forthe patient CMG. From 1995196 to 1998/99 this adjustment was applied to 358discharges with an average length of hospital stay of 843 days. This small number ofinpatient cases accounts for 1.02% of all acute inpatient discharge days. Thismulti-year adjustment is intended to minimize the potential weighted case impact offew multi-year cases on any single community or hospital analysis.

Measurement and Allocation of Hospital VolumesFive discrete steps were taken in the process of calculating expected weighted casevolumes by individual hospitals. They are:

1) Measurement of Community-Specific Weighted Cases. As a first step,1998199 inpatient and day surgery weighted cases are allocated to individualgeographic communities in Ontario.

2) Measurement and Summary of the Population Adjustment Factors.Analysis was undertaken to determine which demographic, socioeconomic and otherfactors legitimately influenced hospital utilization volumes. In this step, the selectedpopulation factors are described. Trend and variation results are provided at thegeographic community level.

3) Analysis and Calibration of the Community-Based Model. This step entails adiscussion of the results of regression analyses calculating the impact of eachpopulation adjustment factor on the expected volume of weighted cases forindividual geographic communities in Ontario.

4) Growth Adjustment. Because the most recent data available for analysis was1998J99 data, and it was desired to apply the model to the year 2000101, amethodology was incorporated to account for anticipated volume changes due topopulation growth and aging.

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5) Allocation of Volumes to Individual Hospital Providers. In this step,community-specific expected weighted case volumes are allocated to individualhospitals. The result is the development of hospital-specific expected weightedcase volumes that can be compared to hospital-specific actual volumes.

The schematic below depicts the steps in the order they occur. Each of these five steps isdescribed in detail.

Step 1: Measurement of Community-Specific WeightedCasesLinkage of Patient Volumes to Census Sub-Divisions

Define Level of Geography

Identify Characteristics of Regions

Calculate Regional Volumes

Allocate Volumes to Providers

To calculate community-specific volume rates, patient- 1 data must first be linked topopulation estimates based on a common unit of geography. Unfortunately, a common unitof geography does not exist among the various datasets:

• CIHI abstract data is used to gather patient-level data. This data contains twogeographic codes. a residence code (comprised of a combination of a patient'scounty and municipality codes) and patient residence postal code.

• Population estimates are obtained from the 1996 Census, Mare the geographiccodes used are census division (CD), census sub-division (CSD) and forwardsortation area (FSA or the first three digits of the postal code).

While a one-to-one link exists between the residence code contained in the patient dataand the CSD in the population data, several issues arise when attempting this linkage:

• First, the recording of the residence code on the patient's abstract la not alwaysaccurate. For example, when a patient is asked where he/she lives, the patient mayrespond with a generalized location such as Ottawa, rather than the specificmunicipality of residence, such as Nepean or Vanier.

• Second, from year-to-year, the definitions of these levels of geography can change.For example, as municipalities grow, they may split and become their own entities.

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Conversely, through amalgamation, as seen in Metropolitan Toronto, previouslydefined municipalities become coded into single large urban areas. This amalgamationresults in the inability to discriminate between significant population characteristics in thepreviously defined municipalities.

These issues suggest that the patient's postal geography should be used-to providemapping to the population data.

Although the postal code mapping is a good alternative to using the residence code, it isnot always possible to achieve a one-to-one match between postal code and censussub-division. This match is required to link patient-level abstract data with populationvolumes. Consequently, the exact census sub-division of a patient may not be known withcertainty.

Statistics Canada has developed a set of tools designed to assist in the task of linkingpostal codes Io census subdivisions. A table, called the Postal Code Conversion File(PCCF) lists the postal codes and matching census Enumeration Areas (EA). However,when roiling up to the broader CSD, many postal codes do not provide one-to-one links.As a result, a computerized linking routine (PCCF+) was developed to solve this issue.The PCCF+ is maintained by the Health Division of Statistics Canada. The routine assignsthe patient's census sub-division based on a series of criteria.

Exhibit 11 shows the results of using the PCCF+ routine on the roughly 8.2 millionabstracts used Io develop the volumes equity model

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Exhibit 11: PCCF+ Geocoding Methodology Summary

CasesPercentof Cases

Action

8,161,828 100% Total Records

144,571 1.77% No match – CSD assigned via residence with code

9,082 0.11% Post Office postal code – CSD assigned with warning

12,804 0.16% Non-residential postal code – CSD assigned with warning

7,037 0.09% Business postal code – CSD assigned with warning

53,877 0.66% Commercial postal code – CSD assigned with warning

41,861 0.51% Retired postal code – CSD assigned based on new postal code

4,279 0.05% Multiple match – CSD assigned by population weights at theenumeration area

1,328,161 16.27% Multiple match – CSD assigned by population weights

6,560,156 80.38% CSD assigned on one-to-one match

As demonstrated, over 80% of the patient data have a CSD assigned in a one-to-one link.However, the extent of bias introduced through assigning a patient to a CSD where multiplelinks exist is difficult to quantify and may differ for urban and rural geography. Analysis of theuniqueness of postal links to census subdivisions suggested that the uncertainty of a postallink is greatest in northern and rural Ontario. However, certain urban geographies were alsoshown to contain poor one-to-one links. Therefore, wherever possible, these links werereviewed for face validity. For patient postal codes that did not match exactly in thePCCF+, the first two or three characters of the postal code were used to assign partialgeographic identifiers to the extent possible. Patient data that remained unlinked to CSD,following the automated process, were assigned manually utilizing the residence code onthe abstract.

Definition of Communities for Volumes Model DevelopmentA regression model to predict patient volumes would achieve greater statistical power ifbase populations for the analysis could be made as homogeneous as possible. Forexample, if a population is heterogeneous with respect to socio-economic status, thengreater discrimination is achieved by dividing these populations into smaller sub-sets ofhomogeneous populations. However, if dividing a population into smaller sub-divisionsintroduces measurement errors, for example in assigning weighted cases to communities,then the increased inexactness of geographic allocation will cancel the advantage of havingmore homogeneous populations. In ail cases, the extent Io which populations can bedivided into smaller units is limited by the availability of geographic data in the various datasources (e.g., CIHI or Census data).

In numerous instances, municipal restructuring and differences in database communitydefinitions have made it impossible to divide census divisions into smaller units of censussubdivision.

For the purposes of the volumes equity model, the following population definitions wereused as the unit of analysis:

• For medical and surgical volumes, communities were defined as censusdivisions with the exception of the larger communities of Ottawa, Toronto, York,

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Halton, Peel, Durham, and Hamilton, where the unit of geography was defined al thecensus sub-division leVel.13

• For pregnancy and childbirth volumes and newborn and neonate volumes,communities were defined as census divisions.

Overall volumes results for each community are summarized al the census division level. Intotal, 105 communities in Ontario were defined for the volumes equity model.

Given the difficulty in obtaining accurate population estimates for Aboriginal reserves andsettlements, these communities are excluded from the analysis. Aboriginal reserve andsettlement populations and weighted cases are both excluded in the calculation ofpopulation utilization rates, births and deaths. Other population factors are calculatedexcluding the influence of these populations. Consequently, the volume model does notassess the whole of Ontario resident populations. By excluding both reserve andsettlement volumes and population from the analysis, providers serving reserves andsettlements are neither advantaged nor disadvantaged in the formula. Reserve andsettlement volumes are not included in the comparison of actual and expected weightedcases for each community. Subsequently, these volumes are not included in thecomparison of actual and expected volumes for the hospitals serving these communities.

Step 2: Measurement and Summary of the PopulationAdjustment FactorsThe most important predictors of population utilization of hospital services are age andgender. In addition to demographics, other important factors have been demonstrated Iobe important in predicting the relative utilization of hospital services. They are: mortality,socio-economic status, aboriginal population, rural geography, low income and fertility rates.

The JPPC Volumes Sub-committee developed a long-list of factors to consider in thedevelopment of the volumes equity model. This long-list was tested and refined to includeonly statistically significant factors in the model.

In this section we describe the measurement of these factors and summarize theirdistribution across Ontario communities. Where historical data are available, we alsodescribe the provincial trends. The rationale or process for the selection of these factors willbe described in a separate JPPC report

Factors used for Medical and Surgical Case MixPopulation demographics (e.g., population age and sex distributions) have been shown tobe the most important predictors of a community’s expected volume of hospital servicesutilization. Five-year age group and sex cohorts were used as the basis of regressionanalysis in the volumes equity model.

The recommended medical and surgical volumes model also predicts population volumesusing the following factors:

13 Note: for the purposes of the volume equity model, Rockcliffe Park was integrated with Ottawa.

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• Excess mortality: measured in deaths per 1,000 for individuals under the age of80. Represents the difference between actual deaths and the number of deaths thatwould be expected if the region had age and gender specific mortality rates identicalIo the Provincial average.

• Income quintiles: based on average household income grouped into five roughlyequally populated income groups. In-depth analysis revealed that only the lowestincome quintile was useful in predicting additional hospital volumes,

• Rural Geography: based on the percentage of the population living in censusdivisions and sub-divisions with population density less than 25 persons per squarekilometer; and,

• Aboriginal: percentage of the population made up of aboriginal inhabitants.

• Each of these factors is discussed in detail below.

Excess MortalityMortality rates are typically calculated by age and sex cohorts (e.g., males 15-19 years old,females 40-44 years old) to account for the natural increase in mortality rates with increasingage and the differences in age-specific mortality rates between the sexes.

Exhibit 12 below shows the distribution of community-specific actual and expectedmortality rates from 1996-1999 in the 0-79 year population. Actual mortality rates rangefrom less than two to more than 11 deaths per 1000 population while expected mortalityranges have a slightly narrower range. Greater than 60% of communities have expectedmortality rates ranging from 8-10 deaths per 1,000 population. Virtually all of thecommunities have actual mortality rates that are within five deaths per thousand of theexpected rate for that community. Community-specific mortality rates are provided inAppendix 15.

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Exhibit 12: Distribution Excess Mortality Rates (ages 0-79),1996-99

Household IncomeAverage household income, based on 1996 Statistics Canada census data, was used as ameasure of socio-economic status for each community. Analysis studying the impact ofhousehold income on hospital utilization revealed that only those populations in the lowestincome quintile (less than $46,100) demonstrated statistically higher hospital utilization rates.In other words, very low income was shown Io be an indicator of increased hospitalutilization. The regression model ultimately proposed for the volumes equity formulatherefore considers only the lowest income quintile as a criterion to increase expectedhospital volumes. Community-specific average household income and low incomedesignation are provided in Appendix 16.

Rural GeographyThe model also includes a factor for rural geography. Rural geography was defined aspopulation density less than 25 persons/sq km. This cut-off figure approximatelyrepresents the 1Oth percentile of census subdivision population density, so that roughly10% of the Ontario population is accounted for in the rural adjustment.

Exhibit 13 shows the percentage of total population considered rural by community. Widevariation exists with more than half of the communities having less than 10% ruralpopulation. A smaller number of communities, approximately 20, have more than 50% ruralpopulation. It is expected that these rural populations have less access to alternatives tohospital care and therefore, have higher utilization rates. Rural percentages for eachcommunity are provided in Appendix 17.

Distribution of Excess Mortality Rates per 1000 Population by Community,three-year average, 199601999

1

2

1

0 0

5

6

12

7

11

10 10

11

6

3

7

1

7

1

3

1

0

2

4

6

8

10

12

14

-1.1

to -1

.0

--1.0

to

0.9

-0.9

to -0

.8

-0.8

to -0

.7

-0.7

to -0

.6

-0.6

to -0

.5

-0.5

to -0

.4

-0.4

to -0

.3

-0.3

to -0

.2

-0.2

to -0

.1

-0.1

to 0

0 to

0.1

0.1

to 0

.2

0.2

to 0

.3

0.3

to 0

.4

0.4

to 0

.5

0.5

to 0

.6

0.6

to 0

.7

0.7

to 0

.8

0.8

to 0

.9

09. t

o 1

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Exhibit 13: Distribution of Percent Rural Population (density<25 per sq. km)

AboriginalData containing the percent of community population that is aboriginal was obtained from1996 Census data. Il is important to note that any aboriginal populations residing onAboriginal Reserves have been excluded from this analysis as described earlier. Exhibit14 displays the distribution of the percent of aboriginal population. Most populations havea very low percentage of aboriginals in their population. However, approximately tenpopulations have a significant proportion of aboriginals. It has been demonstrated thatthose populations with a higher percentage of aboriginals will utilize more hospital resourcesthan those populations with relatively few aboriginals. Aboriginal percentages for eachcommunity are provided in Appendix 18.

Distribution of Percent Rural Population

6 6

3 3 3 3 2 3

6

1

53 2 1 0 1 1 0 0 0

2

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

0% -

5%6%

- 10

%11

% -

15%

16%

- 20

%

21%

- 25

%26

% -

30%

31%

- 35

%36

% -

40%

41%

- 45

%46

% -

50%

51%

- 55

%56

% -

60%

61%

- 65

%66

% -

70%

71%

- 75

%76

% -

80%

81%

- 85

%86

% -

90%

91%

- 95

%96

% -

100%

% Rural Population

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Exhibit 14: Distribution of percent aboriginal population

The unavailability of aboriginal population data for reserves and settlements limits therobustness of the model. The JPPC is currently in the process of obtaining aboriginalpopulation counts for reserves and settlements from the federal government. It is expectedthat this data will become available in the near future. Once available, the data will beincorporated into the regression model to better predict hospital utilization volumes in thosepopulations serving aboriginals.

Factors used for Pregnancy, Childbirth and Newborns and NeonatesFertility RatesFertility rate is used to predict volumes for case mix relating to pregnancy and childbirth andto newborns and neonates. Fertility rates are measured by linking CIHI data on live births14by age of mother, to the female population count for each community. The number of birthsper female of childbearing age is compared to the provincial average, by five-year agegroups for female populations between 10-54 years (617). Exhibit 15 clearlydemonstrates that the age profile of the female population is an important predictor offertility: fertility rates are highest for women in the 25-29 and 30-34 age groups.

14 While the model originally proposed using vital statistics data to count live births, analysis revealed thatgeographic units of residence were often misrepresented in larger urban areas (e.g., residence would be listed as"Toronto" instead of a more specific region of "East York" or "North York"). This led to some subdivisions, such asToronto, being attributed with a very high fertility rate and other neighbouring subdivisions, such as East York, withsurprisingly low fertility rates. To correct for this, CIHI hospital births

Distribution of Percent Aboriginal Population

39

33

13

7

3

1 1 12 2

01 1

0 0 0 0 01

0

5

10

15

20

25

30

35

40

45

0% -

0.5%

0.5%

- 1.

0%

1.0%

- 1.

5%

1.5%

- 2.

0%

2.0%

- 2.

5%

2.5%

- 3.

0%

3.5%

- 4.

0%

4.0%

- 4.

5%

4.5%

- 5.

0%

5.0%

- 5.

5%

5.5%

- 6.

0%

6.0%

- 6.

5%

6.5%

- 7.

0%

7.0%

- 7.

5%

7.5%

- 8.

0%

8.0%

- 8.

5%

8.5%

- 9.

0%

9.5%

- 10

.0%

10.0

% -

10.5

%

% Aboriginal Population

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Exhibit 15: Ontario Female Population Fertility Rates, three-year average 1996-1999

Age Group Livebirths/1000 Female Population

10-14 0.1315-19 15.7520-24 46.5925-29 78.7730-34 71-8835-39 31.4940-44 5.8545-49 0.2450-54 0.01

Data Source: CIHI

Excess fertility rates (based on a comparison of actual to expected birth rates by mother'sage) were calculated for each community and five-year age group across 1993-1997 data(Exhibit 16). Community- and age group-specific excess fertility rates were calculated byaveraging community- and age group-specific excess fertility rates over the past threeyears, Estimates of fertility rates are provided in Appendix 19.

were used as a proxy for all live births, where postal code data could more accurately pinpoint the actual censussubdivision in which a female patient resided.

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Exhibit 16: Comparison of Actual/Expected Fertility Rate (ages 10-54) during1995-1997

Exhibit 17 below shows the distribution of fertility rates by community. Analysis reveals thatthe majority of communities (74 of 105, or 70%) have a fertility index between O~9 and1.1. A further 17 communities (16%) have a fertility index between 1.1 and 1.2; eightcommunities (8%) have a fertility index between 0.8 and 0.9.

Exhibit 17: Distribution of Fertility Index by Community, 98/99

Comparison of Actual/Expected Fertility Rate by Community

2 2

1

4

3

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11

4

3

13

8

3 3

8

7

2

1

8

4

1

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3

2 2 2

1

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2

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6

8

1 0

1 2

1 4

0.88 0.9 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1 1.01 1.02 1.03 1.04 1.05 1.06 1.07 1.08 1.09 1.1 1.11 1.12 1.13 1.17 1.17

Actual/Expected Fertility Rate

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8

42

32

17

3

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

4 5

0.7 to 0.8 0.8 to 0.9 0.9 to 1.0 1.0 to 1.1 1.1 to 1.2 1.2 to 1.3 1.3 to 1.4 1.4 to 1.5

Excess Fertility Index

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Incidence of Low Birth Weight NewbornsThe incidence of low birth-weight newborns is used as a factor in predicting newborn andneonate volumes for communities. A higher incidence of low birth-weight newborns andneonates is expected to result in higher weighted case volumes for the same number ofdeliveries.

Exhibit 18 shows the variation in community incidence of low birth-weight newborns from1995/96 to 1998/99. For 78 communities (74%), low birth-weight incidence fails between4.5% and 6.4%. Variation in the incidence of low birth-weight newborns has shown to bestatistically significant among communities. Individual community ratios are available inAppendix 20.

Exhibit 18: Community Variation in the Incidence of Low Birth-weight Newborns

Step 3: Analysis and Calibration of the Population-Based ModelThe population-based models for allocating volumes to communities are based onregression models that link population volumes (measured in Step 1) to the populationadjustment factors (measured in Step 2). Regression analysis tests the statisticalsignificance of the various population factors in predicting population volumes anddevelops a weighting (or coefficient) for each population factor included in the regressionequation. The regression equation calculates the expected hospital volumes (measured inweighted cases) for each community.

In this section we summarize the methodology and results of the regression analyses anddiscuss the interpretation of the coefficients.

Medical and Surgical Population Characteristics adjustmentThe medical and surgical model was calculated in two steps:

Incidence of Low Birth Weight Babies by Community, 98/99

1

4

8

43

35

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3

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

4 5

5 0

<2.5% 2.5% - 3.4% 3.5% - 4.4% 4.5% - 5.4% 5.5% - 6.4% 6.5% - 7.4% >7.4%

Low Birth Weight Rate

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• Community-specific volumes are calculated adjusting only for population age andsex.

• Through regression analysis, an index was calculated for each community, taking intoaccount the other population adjustment factors of excess mortality, income,aboriginal and rural geography. The age and sex adjusted volumes are multipliedby the index value to arrive at expected community-specific volumes.

This two-step methodology results in an easy-to-understand model that retains thestatistical power of more complex specifications.

Base Rates for Age and SexThe base expected volume rates per 1,000 population by age and sex are presented inExhibit 19. In general, weighted cases per 1000 population are higher for males from birthuntil the early teens and once beyond age 50. From teenage years to age 50, females.have higher per capita medical and surgical weighted cases.

Exhibit 19: Base Rates per 1000 Population by Age and Sex, 98/99

Calculating expected community-specific weighted case volumes adjusting only age, sexand population size accounts for greater than 98% of the variation in weighted cases. Thisunderscores the fundamental link between size and demographics and hospital utilization.

A "MARI index" was developed to account for additional variation in weighted cases. Thevariables used in the development of the index are: excess mortality, percent aboriginalpopulation, percent rural population and low income. The MARI (mortality-aboriginal-rural-income) index equation is as follows:

Age Group Female Male00 to 04 50.4 65.905 to 09 21.1 25.810 to 14 21.7 23.615 to 19 42.1 36.120 to 24 47.4 36.425 to 29 55.8 38.230 to 34 64.4 45.635 to 39 74.2 54.840 to 44 83.0 68.945 to 49 98.0 92.350 to 64 118.4 129.855 to 59 152.4 186.860 to 64 202.6 265.465 to 69 269.8 372.870 to 74 397.6 539.375 to 79 522.0 702.780 to 84 640.6 856.4

85+ 748.7 1017.3

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Communities ranged in MARI index values from 70 to 140. A detailed discussion of

MARI Index =94.9 +

21.9 * (Excess mortality/1000) +1.8 * (% aboriginal) +

0.2 * (% rural), +5 * (1 if lowest income quintile, 0 otherwise) +

where 94.9% is the base percentage applies to age and genderadjusted volumes

each of the adjustment factors follows below. The range of values is illustrated in Exhibit 20below.

Exhibit 20: Distribution of MARI Index Values by Community, 98/99

Excess Mortality Adjustment CoefficientsExcess mortality, in addition to age and sex, is statistically significant and an importantpredictor of patient volumes for age groups below age 80. A unit increase in excessdeaths per 1000 increases the MARI index by 21.9.

Income Quintile CoefficientsAll other things being equal, populations with higher socio-economic status have lowerweighted cases per 1000 population. In-depth analysis revealed that only the lowestincome quintile was a predictor of increased hospital volumes. Therefore, the volume equitymodel includes only the bottom income quintile as an upward adjustment factor to expectedhospital medical/surgical volumes. Based on the MARI index equation above, it is seenthat a low income population increases the needs index by 5.

Distribution of MARI Index Values by Community, 98/99

3

23

31

24

13

65

0

5

1 0

1 5

2 0

2 5

3 0

3 5

70 to 80 80 to 90 90 to 100 100 to 110 110 to 120 120 to 130 130 to 140

MARI Index

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Rural Percentage CoefficientAnalysis of the effect of rural geography on hospital volumes estimates that every 1% ofthe population in census sub-divisions with fewer than 25 persons per square kilometerincreases the MARI index by .2.

Aboriginal Percentage CoefficientThe effect on the MARI index of an additional unit increase in the aboriginal percentage ofthe population is an increase of 1.8. This suggests that aboriginal populations have highervolumes per 1,000 population than the provincial average. The maximum adjustmentgiven to a population is approximately 18 for a population with approximately 10%aboriginal population.

Calculation of Expected Community-Specific VolumesIn order to arrive at total expected community-specific weighted case volumes, expectedage and sex adjusted community-specific volumes are multiplied by the MARI index. TheMARI index, as described above, incorporates the effects of additional populationadjustment factors on hospital volumes utilization. In other words:

Total Expected Community-Specific Volumes =Community-Specific Volumes (age and sex adjusted only) * MARI Index

Pregnancy and Childbirth Case MixA population-weighted least squares regression model was used to test and analyze therelationship between pregnancy and childbirth weighted cases per 1000 population andfemale age and fertility using 1995/96 Io 1998/99 data. The dependent variable in thesemodels is the number of weighted cases per 1000 population for each age group, sex,community and fiscal year. Both female age and excess fertility rates were highly significantpredictors of weighted cases per 1000 population. These factors are described in detailbelow.

Community-specific predictions for the pregnancy and childbirth model are available inAppendix 21.

Female Age Base RatesExhibit 21 shows the estimated average number of pregnancy and child birth weightedcases per 1,000 females by age group for 1998/99. The 25 Io 29 and 30 to.34 yearpopulations have the highest pregnancy and childbirth weighted cases per capita.

Exhibit 21: Pregnancy and Childbirth Weighted Cases par 1000 Population, 98/99

Pregnancy and Childbirth Weighted CasesPer 1000 Population, 98/99

Age Group Weighted Case/1000 Population

10 to 14 0.13

15 to 19 15.75

20 to 24 46.59

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25 to 29 78.77

30 to 34 71.88

35 to 39 31.49

40 to 44 5.85

45 to 49 0.24

50 to 54 0.01

Data Source: CIHI

Expected pregnancy and child birth weighted cases are calculated as follows:

Age-Specific Expected Pregnancy and Childbirth Weighted Cases =

Age-Specific Expected Weighted Cases/ 1000 Pop'n * Age-Specific Pop'n

The expected pregnancy and child birth weighted cases for each age group can besummed to achieve the total expected pregnancy and childbirth weighted cases for acommunity. However, this equation does not account for differences in fertility rates acrosscommunities.

Fertility CoefficientSimilar to the MARI index developed for the medical surgical model, a "fertility index" wasdeveloped to predict the impact of higher fertility rates on hospital pregnancy and childbirthvolumes.

"Fertility" Index= 100 +4*(Excess fertility rate of 10-19 year-olds) +3 * (Excess fertility rate of 20-39 year-olds) +

5 * (Excess fertility rate of women 40 years plus)

Multiplying the community-specific "fertility" index by the expected pregnancy andchildbirth weighted cases (not accounting for differences in fertility rates) leads to thecalculation of total pregnancy and childbirth volumes expected for individual communities. Inother words:

Total Community-Specific Expected Pregnancy and Childbirth Weighted Cases ="Fertility" Index * Total Expected Pregnancy and Childbirth Weighted Cases

(age adjusted only)

Newborn and Neonate Case MixThe adjustment for low birth-weight newborns is based on a regression model relatingnewborn and neonate case mix index to the incidence of low birth-weight newborns. Theestimated equation is as follows:

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Case Mix Index = Average RIW per Case =

0.28 + 2.75 * (Incidence of Low Birth-weight)

In a hypothetical community with no low birth-weight newborns, the expected case mixindex (or average RIW per case) is approximately 0.28. On the other hand, for ahypothetical community with all low birth-weight newborns, the expected case mix index isapproximately 0.28+2.754.03. Simple community variation in the incidence of lowbirth-weight newborns is shown in Exhibit 22 below.

Exhibit 22: Community Variation In Expected Newborn and Neonate Case Mix Index

Expected community-specific newborn and neonate volume is obtained by multiplying thecase mix index with the expected number of births (obtained from the analysis of eachcommunity's female population and relative fertility rates).

Expected newborn and neonate weighted cases are available in Appendix 22.

Distribution of Expected Newborn and Neonate Case Mix by Community, 98/99

1 1

2

1

4 4

10

16

23

15

8

11

5

1 1

2

0

5

10

15

20

25

0.34 0.35 0.36 0.37 0.39 0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.51

Newborn and Neonate Case Mix Index

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Step 4: Growth Adjustment – Estimating the Impact ofDemographic Growth and Aging to 2000/2001Historically, funding tools for population growth have been used to allocate separate poolsof growth funding to communities with higher than average population growth and aging.However, these models do not evaluate the relative equity of historical populationvolumes. As a result, high growth populations with high historical volumes are compensatedat the same rate as high growth populations with low historical volumes. The use of a singlepopulation-based formula allows policy makers to evaluate the impacts of populationgrowth and aging along with the relative equity of existing population volumes.

The population-based models calibrated in Step 3 are based on the most currentlyavailable utilization and population data. It is necessary to estimate the impact of populationgrowth on hospital volumes between the time for which data was available (1998199) andthe intended year of application of the model (200012001). This section details themethods used to estimate the two-year expected growth in weighted cases for medicaland surgical, pregnancy and childbirth, and newborn and neonate weighted case volumesby community.

Growth adjustments help ensure that population-weighted case allocations are responsiveto changes in population size and demographics. No attempt has been made to forecastchanges in per capita rates, To the extent that per-capita rates for hospital services continueto decline, these estimates may overstate actual growth.

The estimated growth adjustments are based on application of the population-basedmodels from 1998199 Io the changes in community populations, by age group andgender, since that time.

Medical and surgical weighted case growth is estimated by application of the medical andsurgical volume model for 1998/99 to the estimated two-year changes in population byage group and sex. To support a repatriation of primary and secondary medical andsurgical weighted cases, it is necessary Io distinguish between tertiary and non-tertiaryweighted case growth. Non-tertiary weighted case growth is repatriated to local hospitals(i.e., within the same census division or subdivision) while tertiary weighted case growth isallocated according to existing market share patterns. (For details on the assignment ofgrowth volumes to hospital providers, please refer to Step 5).

Growth allocation of both tertiary and non-tertiary weighted cases is conducted on acommunity-by-community basis, and by population age and sex cohort. Exhibit 23illustrates the percent of weighted cases classified as tertiary in 1998/99 by age group andsex cohort. In general, more growth is tertiary for males than for females, and more growth istertiary for age groups 45-79 years. More than 35% of weighted cases are tertiary formales in the 55-69 year age groups. Less than 15% of weighted cases are tertiary forfemales in the 20-44 year age groups. The estimated medical and surgical weighted caseimpact of the two-year demographic growth by community is presented in Appendix 23along with tertiary and non-tertiary allocations.

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Pregnancy and childbirth and newborn and neonate growth volumes are estimated byapplying these population-based models to the change in female population ofchildbearing over this two-year period by age group and by community. Growth estimatesfor these programs are available in Appendix 24.

Exhibit 23: Provincial Tertiary Percentages by Age Group and Sex

Step 5: Hospital Allocations (Base Year and Growth)The process of allocating community-specific weighted cases in 98/99 to individual hospitalproviders is based on each providers program market share in 1998/99. For example, ifHospital X had 25% of weighted cases for Community A, then it was allocated with 25% ofthat community’s population-based expected weighted case allocation. In this way,hospitals that serve communities whose actual volumes are higher than expected areallocated fewer weighted cases than have historically been experienced in thesecommunities. On the other hand, hospitals that serve communities whose actual volumesare below expected will tend Io be allocated more weighted cases.

Pregnancy and childbirth and newborn and neonate growth is also allocated to hospitalsbased on 1998/99 market share profiles, as was tertiary medical and surgical weighted casegrowth. Non-tertiary medical and surgical growth (I.e., primary, secondary and day surgeryweighted cases) is allocated to hospitals based on the market share of the providers in thecommunity's census division, thereby allowing for repatriation.

Tertiary Percentage of Weighted Cases by Age Group and Sex

0

5

10

15

20

25

30

35

40

45

Female

Male

00to04

05to09

10to14

15to19

20to24

25to29

30to34

35to39

40to44

45to49

50to54

55to59

60to64

85+80to84

75to79

70to74

65to69

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Where mergers and amalgamations have occurred, the historical figures included all thelegacy organizations. As an example, St. Michael's Hospital includes estimated volumesfor St. Michael's Hospital, Wellesley Hospital and Central Hospital for the 1995196 to1998199 fiscal years, even though the merger was not finalized in 1995196. This allows foryear-over-year comparisons. Appendices 25, 26 and 27 provide detail on actual andexpected hospital base year and growth allocations by program and Appendix 28summarizes overall hospital specific results.

98/99 Model ApplicationThe volume model allocates volumes to individual hospital providers In two distinctsteps:

• Allocation of Ontario weighted cases volumes to specific communities.First, volumes are allocated to communities based on three case mix models thatpredict expected weighted cases for each geographic community:

• medical and surgical case mix;

• pregnancy and childbirth case mix; and

• newborn and neonate case mix.

For the medical-surgical and pregnancy childbirth components of these models,community-specific volumes are calculated adjusting only for population age and sex.The MARI or Fertility index is then applied to the base age and sex calculations toadjust for the impacts of additional population adjustment factors.

For the newborn and neonate component, a case mix index is applied to theexpected births derived from the pregnancy and childbirth component.

Expected weighted case growth from 1998199 Io 200012001 is then estimated foreach community by applying 1998199 expected per capita utilization rates to theanticipated two year change in population by age group and gender. Medical andsurgical growth is calculated separately for tertiary and non-tertiary activity taking intoaccount each community’s age profile. This distinction is required to support arepatriation of primary and secondary medical and surgical growth volumes.

• Allocation of community-specific weighted cases to individual hospitalproviders. Expected 1998199 volumes for all three groups of case mix are thenallocated to individual hospital providers in proportion to 1998199 market shareprofiles. Growth is allocated in two ways depending on the case mix (i.e., primaryand secondary medical and surgical weighted case growth methodology, tertiarymedical and surgical weighted case growth methodology).

Overall expected volumes for each hospital are obtained by summing the baseyear and growth allocations over the three groups of case mix.

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Comparing hospital-specific expected weighted case volumes to hospital-specific actualacute inpatient and day surgery volume for Ontario residents provides a measure ofrelative volume equity. Hospitals with actual volumes well below expected are consideredto be serving relatively under-serviced communities. Hospitals with volumes well aboveexpected are considered to be serving relatively over-serviced communities.

The model was applied to a total of 141 facilities including mergers. This included 53 smallfacilities, 75 community facilities, 11 teaching facilities and two specially hospitals.Stand-alone rehabilitation and chronic care facilities were excluded from the analysis basedon the application of the model. Appendix 28 provides hospital-specific results of themodel.

The results of the volume model indicate that a large degree of variation between actual andexpected weighted cases exists across the province and within each hospital type. Exhibit24 provides detail on the variance in hospital volumes. Variance was calculated as follows:

Variance = (Actual Volumes in 98/99 - Expected Volumes in 98/99)Expected Volumes in 98/99

A negative variance indicates that a hospital's actual volumes are less than expectedvolumes. Conversely, a positive variance indicates that a hospital's actual volumes arehigher than expected volumes. Exhibit 24 indicates that the overall median of variance is0.4%, suggesting that approximately 50% of hospitals are providing service volumes lessthan or equal tee their expected volumes.

The overall growth rate for the province between 98/99 and 00/01 is calculated to be a4.8% increase in expected volumes. If growth is included in the expected volume numbers(Le, expected volumes now include 98999 expected volumes plus volumes forpopulation growth and aging), a greater percentage of hospitals will exhibit negativevariance. This is a function of the variance calculation formula where actual 98/99 volumes aresubtracted from 00/01 expected volumes. Exhibit 25, depicting: overall variance oncegrowth is included in the equation, shows that the overall median variance is now negative3.9%. Once again, this is attributed to the inclusion of expected growth volumes into thevariance calculation formula,

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Exhibit 24: Variance Results Comparing 98199 Actual and Expected Volumes

Exhibit 25: Variance Results Comparing 98199 Actual and 2000/01 Expected Volumes

% Variance between 98/99 Actual and 98/99 Expected Volumes by Hospital Type

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

Percentile

% V

aria

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((A

-E)/

E)

CommunitySmallTeachingOverall

Community -6.3% -2.2% 0.2% 2.1% 6.6%

Small -4.6% -0.1% 1.1% 6.7% 7.7%

Teaching -5.9% -2.5% -1.7% 0.5% 1.9%

Overall -6.2% -1.7% 0.4% 2.5% 7.4%

10th 25th 50th 75th 90th

% Variance between 98/99 Actual and 00/01 Expected Volumes by Hospital Type

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

Percentile

% V

aria

nce

((A

-E)/

E)

CommunitySmallTeachingOverall

Community -13.2% -7.6% -4.5% -1.1% 2.4%

Small -12.0% -5.2% -2.9% 3.0% 5.3%

Teaching -8.8% -6.4% -3.9% -2.8% -1.3%

Overall -12.1% -7.2% -9.0% -1.3% 3.1%

10th 25th 50th 75th 90th

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CONCLUSIONThis document has provided a technical overview of the methodologies employed togenerate bath an expected unit cost and an expected volume.

The methodology to calculate the expected rate was based upon a regression model thatpredicts the expected cost per weighted case of acute inpatient and day surgery, as wellas chronic care activity. For small hospitals, the model also includes outpatient emergency,rehabilitation and ELDCAP activity. The model integrates the small and large/communityacute care formula as well as introduces chronic care unit costs into the methodology. Themodel estimates a hospital's cost per weighted case given the size of the facility, theamount of tertiary and teaching activity, the location of the facility (i.e., isolation) and the typeof facility (I.e., stand-alone chronic care facility). The model can be used to measure ahospital's relative cost performance or to approximate appropriate funding levels.

The volume methodology first estimates population volumes and then allocates thesevolumes to hospitals, The model predicts the number of inpatient and day surgery (medicaland surgical) weighted cases that would be used by a population with given populationcharacteristics at the average Ontario rate of utilization. Population characteristics used inpredicting weighted cases include age and sex of the population, income, mortality,aboriginal population, and percentage rural, These volumes are then allocated tee hospitalsbased on historical market share. Growth volumes are also predicted for a population andthen allocated to hospitals. Separate methodologies were derived for the allocation oftertiary and local growth. Models were also developed to predict the number of pregnancyand childbirth and newborn and neonate weighted cases that would be expected forindividual communities. The hospital predictions can be used as the basis for evaluating ahospital’s relative utilization or ill can be used to approximate funding.

This document does not discuss implementation of these models. The models canstand-alone and provide information on overall hospital cost performance and utilization orthe models can be integrated to calculate hospital-specific expected funding levels. Nextsteps will focus on evaluating the advantages and disadvantages of various implementationstrategies.

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APPENDIX 1: Rate Sub-Committee Terms of Reference

BACKGROUND

Recognizing the rapid rate of change that has been stimulated by the recommendations of the Health Services Restructuring Commission (HSRC), the JPPC Hospital Funding Committee (HFC) has recommended that the Ministry of Health determine hospital allocations for HSRC reviewed hospitals separately in order to accommodate funding requirements as hospitals implement the directives of the HSRC. This period of individual negotiation is expected to last for at least two years beginning in 1998/99.

After the HSRC has completed its work, a formula based funding methodology will again be needed. Using other provinces and jurisdictions as comparisons, the HFC initiated a 2 year work plan to develop a population needs based funding allocation methodology that the Ministry of Health could decide to use in the post-restructured system (i.e., post-HSRC). Two sub-committees of the HFC will develop this methodology. The committees are called Rate and Volume Sub-Committees of the HFC.

MANDATE

To contribute to the development of a single comprehensive hospital funding methodology. To calculate a standard rate so that it may be applied to a standard volume of patients served by hospitals.

OBJECTIVES

To work with the JPPC Volume sub-committee in the development of a population needs based funding methodology

To identify the costs associated with facility specific indicators affecting costs in hospitals.

To ensure that funding allocations resulting from the methodology developed can be reconciled

To ensure that application of the methodology promotes equal access for equal need To examine the pros and cons associated with a prospective payment mechanism to

hospitals by the Ministry of Health To ensure that the application of the funding methodology includes incentives toward

efficient practice

FREQUENCY OF MEETINGS The Rate sub-committee will meet monthly or at the discretion of the Chairperson. The Chairperson is also required to attend scheduled meetings and provide monthly updates to the Hospital Funding Committee.

REPORTING RELATIONSHIP

The Rate sub-committee will report directly to the Hospital Funding Committee

RESOURCES

The Rate sub-committee will be supported by the JPPC Secretariat. Members are entitled to have approved expenses incurred while conducting JPPC activity reimbursed. Guidelines for travel, mileage and accommodation are available from the JPPC Secretariat.

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APPENDIX 2: Rates Sub-Committee Membership List

Norman Maciver, Chair West Parry Sound Health Centre

Bert Bacchus Ministry of Health and Long-Term Care

Randy Belair Lake of the Woods District Hospital

Karen Belaire St. Joseph’s Hospital, Hamilton

Chuck Botz London Health Sciences Centre

Nan Brooks Joint Policy and Planning Committee

Mike Byrnes Ministry of Health and Long-Term Care

Dan Coghlan West Park Hospital

Chris Ferrao William Osler Health Centre, Brampton

Alexandra Flatt Geyer Szadkowski Consulting Inc.

Lee Geyer Geyer Szadkowski Consulting Inc.

Frank Markel Joint Policy and Planning Committee

Ian McKillop Wilfrid Laurier University

Kamini Milnes Ministry of Health and Long-Term Care

Patrick O’Malley St. Joseph’s Health Centre, Sarnia

Archie Outar Ministry of Health and Long-Term Care

Gary Spencer William Osler Health Centre, Brampton

Bruce Sutton Nipigon District Memorial Hospital

Mary Lou Toop Lakeridge Health Corporation - Oshawa

Adam Topp Sunnybrook & Womens Health Sciences Centre

Richard Wilson Ottawa Hospital

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APPENDIX 3: Cost Adjustment Factors Working Group Terms Of Reference

Mandate: To review the current cost adjustment factors formula and, if appropriate propose modifications to the current adjustment factors. To identify and recommend any new cost adjustment factors, as appropriate.

Objectives: 1. To review the current adjustment factors formula;

2. To propose modifications to the current adjustment factors, if appropriate;

3. To investigate new community resources factors, the relationship between direct and indirect cost factors, and new socio-economic status factors;

4. To investigate other potential cost adjustment factors;

5. To investigate and identify valid cost adjustment factors and recommend how such factors would be applied to the funding formula. To identify and where appropriate recommend implementation issues.

Deliverable:

• New, validated cost adjustment factors formula to be proposed for the fiscal 2000-2001 funding year.

Time Line: Four Months.

Meetings will be held every two weeks or as necessary.

Reporting Relationship: The Cost Adjustment Factors Working Group will report directly to the Rate Sub-Committee of the JPPC.

Membership:

Membership may include representatives from the Ministry of Health, the Ontario Hospital Association, Ontario Hospitals, the Ontario Case Cost Project (OCCP), Academia, and others, as necessary.

Affiliate members may include members from the Canadian Institute for Health Information (CIHI).

(See membership list, attached)

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APPENDIX 4: Cost Adjustment Factors Working Group Membership List

Randy Belair, Chair Lake of the Woods District Hospital

Dennis Biesiada Collingwood General & Marine Hospital


Dan Coghlan West Park Hospital

Dennis Egan Grand River Hospital Corporation

Alexandra Flatt Geyer Szadkowski Consulting Inc.



Kamini Milnes Ministry of Health and Long-Term Care

Sherry Kennedy Quinte Healthcare Corporation

Barry Lockhart Port Colbourne General Hospital

Foster Loucks Haliburton Highlands Health Centre

Jane Perry St. Thomas Elgin General Hospital

Monique Polier Halton Healthcare Services Corp.

Tim Sonnenburg Arnprior & District Memorial Hospital

Joan Sproul Mount Sinai Hospital

Vicki Truman William Osler Health Centre

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APPENDIX 5: Complex Continuing Care Funding Working Group Terms of Reference PREAMBLE On April 8, 1998 the Ministry of Health approved the use of Resource Utilization Groups III (RUG III) for the purpose of developing a funding methodology for chronic care facilities and chronic care units in public hospitals. The Ministry of Health will develop the care planning, policy planning and utilization management (i.e., outcome measurements, quality indexing) capabilities of MDS in parallel to this resource allocation component. However, the Complex Continuing Care Funding Working Group may put forth recommendations with respect to these other indexes derived from MDS. PURPOSE The Complex Continuing Care Funding Working Group will:

1. Recommend the version of RUG III to be adopted in Ontario

2. Update and recommend RUG III weights for Ontario using Ontario’s wage weights

3. Identify and isolate hospital costs related to RUG III patient activity and recommend a methodology that complements cost methodologies now in place

4. Calculate total RUG III weighted activity by hospital

5. Recommend a funding approach for determining patient and hospital-specific allocations

6. Develop and evaluate various funding models and recommend the model that most fully satisfies predetermined principles, approach and criteria

7. Assist the OHA and MoH communicate the principles, assumptions and approach that underlie the recommended funding model

TIMELINE q Adaptation of RUG III Weights for Ontario: June 1999.

q Calculation of Cost/RUG III weighted unit: June 1999.

q Development and Approval of Funding Model: December 1999.

q Implementation for Funding: April 1, 2000. DELIVERABLE Milestone updates to the Hospital Funding Committee to evaluate progress against each of the subtask deadlines.

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APPENDIX 6: Complex Continuing Care Funding Working Group Membership List Greg Fougère, Chair Perley and Rideau Veterans Health Centre Lynne Ashworth Canadian Institute for Health Information Howard Baker Joint Policy and Planning Committee Charles Botz St. Joseph’s Health Care, London Dan Coghlan West Park Healthcare Centre Debbie Dulisse Sault Area Hospitals Enza Ferro Ontario Hospital Association Jeanne Hay St. Peter’s Hospital Alan Iskiw Ministry of Health and Long-Term Care Nizar Ladak Canadian Institute for Health Information John McKinley Ministry of Health and Long-Term Care Lynn Moore Ontario Hospital Association Pat O’Malley St. Joseph’s Health Centre, Sarnia Archie Outar Ministry of Health and Long-Term Care Lou Reidel Providence Centre, Scarborough Margaret Ringland Ontario Association of Non-Profit Homes for Seniors* Pierre Soucie SCO Health Services Gary Teare Providence Centre, Scarborough Vita Vaitonis Ontario Long Term Care Association* Marian Walsh The Riverdale Hospital *Observer Status

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APPENDIX 8: Revenue Adjustment Factor Working Group Terms Of Reference

Mandate:

To date the adjustment factors model has attempted to achieve equity in hospital funding by analysis of hospital costs. This working group will broaden that cost-based focus to include analysis of funding equity issues. The working group will investigate the potential for an adjustment factor based on funding equity principles.

Objectives: 1. To review the current cost per weighted case formula with emphasis on

revenue components;

2. To identify and indicate relevant funding factors that affect the revenue of hospitals and identify data sources for those factors;

3. To propose revenue adjustment factors that achieve equity; and

4. To balance the implications of hospital incentives to maximize external revenue with the inequities of external revenue opportunities.

Deliverable:

• Provide the Rates Sub-Committee of the JPPC with evidence on which to base a decision on the value of including a revenue equity adjustment factor in the proposed fiscal 2000-2001 funding formula.

Time Line: Four Months

Meetings will be held every two weeks or as necessary.

Reporting Relationship:

The Funding Equity Working Group will report directly to the Rate Sub-Committee of the JPPC.

Membership:

Membership may include representatives from the Ministry of Health, the Ontario Hospital Association, Ontario Hospitals, the Ontario Case Cost Project (OCCP), Academia, and others, as necessary.

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APPENDIX 9: Revenue Adjustment Factors Working Group Membership List

Richard Wilson, Chair Ottawa Hospital


Mike Byrnes Ministry of Health and Long-Term Care

Doreen Clements Deep River & District Hospital

Ron Dennis Royal Victoria Hospital

Chris Ferrao William Osler Health Centre, Brampton



Mike Jackson South Bruce Grey Health Centre

Karim Mamdani University Health Network

Rami Rahal Geyer Szadkowski Consulting Inc.

Norman Rees Rouge Valley Health Centre

Lilian Scime St. Joseph’s Hospital

Joseline Sikorski St. Joseph’s Hospital

Gary Spencer William Osler Health Centre, Brampton

Mary Lou Toop Lakeridge Health Corporation - Oshawa

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Appendix 10: 1998/99 Equivalent Weighted Cases

Chronic Weighted

Patient Days

Equivalent Weighted

CasesVisits

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

Cases

591 Belleville General Hospital 12,301 11,040 1,076 - - - - - - 13,377592 Napanee Lennox And Addington C 1,815 2,476 241 28,324 487 0 0 0 0 2,544593 Newbury Four Counties General 760 2,617 255 8,911 153 0 0 0 0 1,168596 Alliston Stevenson Memorial Ho 2,783 0 0 24,243 417 0 0 0 0 3,200597 Almonte General Hospital 1,072 9,596 936 7,875 135 0 0 0 0 2,143599 Arnprior & District Memorial H 1,644 5,414 528 19,174 330 0 0 0 0 2,502600 Atikokan General Hospital 443 1,460 142 7,183 124 0 0 7,947 464 1,173602 Halton Hills Georgetown And Di 2,386 12,240 1,193 - - - - - - 3,579606 Barrie Royal Victoria Hospital 18,307 13,487 1,315 - - - - - - 19,622608 Toronto Etobicoke General Hosp 19,240 0 0 - - - - - - 19,240611 Blind River St Joseph's Health 729 3,068 299 12,464 214 0 0 3,633 212 1,455612 Clarington Bowmanville Mem Ho 5,210 9,442 921 - - - - - - 6,131613 Toronto West Park - 60,678 5,916 - - - - - - 5,916614 Bracebridge South Muskoka Memo 3,298 7,941 774 - - - - - - 4,072615 Brampton Peel Memorial 26,115 15,653 1,526 - - - - - - 27,641617 Brantford General Hospital 15,610 0 0 - - - - - - 15,610618 Brantford St Joseph's Hospital 2,113 21,480 2,094 - - - - - - 4,207619 Brockville General Hospital 5,241 6,423 626 - - - - - - 5,867620 Brockville St Vincent De Paul 1,601 5,160 503 - - - - - - 2,104623 St. Catharines Shaver - 33,590 3,275 - - - - - - 3,275624 Campbellford Memorial Hospital 1,831 2,267 221 17,143 295 0 0 0 0 2,347626 Carleton Place And District Me 988 0 0 16,902 291 0 0 0 0 1,279627 Chapleau General Hospital 265 1,331 130 2,721 47 0 0 9,325 545 986628 Chatham Public General Hospita 8,260 6,771 660 - - - - - - 8,920629 Chatham St Joseph's Hospital 3,753 0 0 - - - - - - 3,753632 Toronto North York General Hos 39,433 0 0 - - - - - - 39,433633 Clinton Public Hospital 1,239 39 4 15,892 273 0 0 0 0 1,516638 Cochrane Lady Minto Hospital 797 2,328 227 15,370 264 0 0 0 0 1,288640 Collingwood General And Marine 4,077 0 0 - - - - - - 4,077643 Cornwall General Hospital 4,872 0 0 - - - - - - 4,872644 Cornwall Hotel Dieu Hospital 6,755 17,065 1,664 - - - - - - 8,419646 Deep River And District Hospit 700 0 0 7,398 127 0 0 0 0 827647 Dryden District General Hospit 1,811 2,484 242 15,959 274 0 0 7,277 425 2,753648 Dunnville Haldimand War Memori 1,300 3,789 369 15,372 264 0 0 0 0 1,934650 Elliot Lake St Joseph's Genera 2,700 2,276 222 - - - - - - 2,922

Emergency Rehabilitation ELDCAPTotal

Equivalent Weighted

Cases

MoH Number

Facility Descirption

Acute (Inpatient and Day Surgery)

Weighted Cases

Chronic Care

Page 1

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Chronic Weighted

Patient Days

Equivalent Weighted

CasesVisits

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

Cases


Equivalent Weighted

Cases

MoH Number



Weighted Cases

Chronic Care

653 Englehart And District Hospita 573 3,086 301 0 0 0 0 0 0 874654 Espanola General Hospital 810 1,600 156 9,196 158 0 0 10,904 637 1,761655 Exeter South Huron Hospital 824 850 83 16,010 275 0 0 0 0 1,182656 Fergus Groves Memorial Communi 2,383 5,682 554 - - - - - - 2,937658 Fort Erie Douglas Memorial Hos 2,221 6,519 636 - - - - - - 2,857661 Cambridge Memorial Hospital 12,611 33,347 3,251 - - - - - - 15,862662 Geraldton District Hospital 855 2,362 230 14,242 245 0 0 6,868 401 1,731663 Goderich Alexandra Marine And 2,853 1,806 176 24,733 425 0 0 0 0 3,454664 Grimsby West Lincoln Memorial 2,663 5,736 559 - - - - - - 3,222665 Guelph General Hospital 8,964 0 0 - - - - - - 8,964666 Guelph St Joseph's Hospital 5,505 28,699 2,798 - - - - - - 8,303667 Whitby General 1,641 1,470 143 - - - - - - 1,784674 Hamilton St Joseph's Hospital 32,101 8,811 859 - - - - - - 32,960675 Hamilton St. Peter's Hospital - 92,474 9,016 - - - - - - 9,016676 Hanover And District Hospital 1,770 8,501 829 17,166 295 0 0 0 0 2,894681 Hearst Notre Dame Hospital 1,064 6,220 606 21,571 371 0 0 0 0 2,041682 Hornepayne Community Hospital 181 0 0 2,581 44 0 0 2,547 149 374684 Ingersoll Alexandra Hospital 1,725 1,324 129 22,525 387 0 0 0 0 2,242685 Iroquois Falls Anson General H 506 3,699 361 7,570 130 0 0 0 0 997686 Wawa North Algoma Health Organ 538 4,941 482 7,658 132 0 0 0 0 1,151687 Kapuskasing Sensenbrenner Hosp 1,817 3,746 365 26,135 450 0 0 0 0 2,632692 Kingston Hotel Dieu Hospital 3,191 0 0 - - - - - - 3,191693 Kingston General Hospital 31,146 0 0 - - - - - - 31,146695 Kingston St Marys-Of-The-Lake - 50,392 4,913 - - - - - - 4,913696 Kirkland And District Hospital 2,154 5,477 534 - - - - - - 2,688699 Kitchener St Mary's General Ho 13,631 0 0 - - - - - - 13,631701 Richmond Hill York Central Hos 16,440 6,442 628 - - - - - - 17,068704 Leamington District Memorial H 4,253 6,341 618 - - - - - - 4,871707 Lindsay Ross Memorial Hospital 7,727 19,583 1,909 - - - - - - 9,636709 Listowel Memorial Hospital 1,197 7,940 774 12,276 211 0 0 0 0 2,182714 London St Joseph's Hospital 26,959 103,538 10,095 - - - - - - 37,054718 Burlington Joseph Brant Memori 16,889 9,589 935 - - - - - - 17,824719 Manitouwadge General Hospital 170 0 0 3,162 54 0 0 2,309 135 359721 Marathon Wilson Memorial Gener 333 1,213 118 4,666 80 0 0 0 0 532722 Markdale Centre Grey General H 1,017 0 0 19,275 332 0 0 0 0 1,349

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Chronic Weighted

Patient Days

Equivalent Weighted

CasesVisits

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

Cases


Equivalent Weighted

Cases

MoH Number



Weighted Cases

Chronic Care

723 Matheson Bingham Memorial Hosp 267 1,450 141 4,410 76 0 0 7,259 424 908724 Mattawa General Hospital 472 622 61 7,454 128 0 0 0 0 661725 Meaford General 1,766 4,351 424 9,050 156 0 0 0 0 2,346726 Midland Huronia District Hospi 4,776 0 0 - - - - - - 4,776731 Mississauga The Credit Valley 20,835 15,214 1,483 - - - - - - 22,318732 Kemptville District Hospital 982 5,928 578 18,082 311 0 0 0 0 1,871733 Mount Forest Louise Marshall H 1,005 9 1 11,281 194 0 0 0 0 1,200734 Haldimand West Haldimand Gener 886 4,512 440 16,674 287 0 0 0 0 1,613736 Newmarket York County Hospital 19,458 7,234 705 - - - - - - 20,163737 Niagara-On-The-Lake General Ho 286 4,270 416 0 0 0 0 0 0 702738 Niagara Falls Greater Niagara 13,733 18,606 1,814 - - - - - - 15,547739 Nipigon District Memorial Hosp 578 2,072 202 6,043 104 0 0 5,380 314 1,198742 Oakville Trafalgar Memorial 16,624 10,899 1,063 - - - - - - 17,687745 Orillia Soldiers' Memorial Hos 10,134 9,890 964 - - - - - - 11,098746 Oshawa General 26,499 43,147 4,207 - - - - - - 30,706753 Ottawa Hopital Montfort 10,813 2,704 264 - - - - - - 11,077755 Ottawa Salvation Army Grace Ho 7,639 0 0 - - - - - - 7,639757 Owen Sound The Grey Bruce Regi 11,936 16,358 1,595 - - - - - - 13,531759 Palmerston And District Hospit 981 702 68 10,277 177 0 0 0 0 1,226760 Paris The Willett Hospital 228 12,180 1,188 11,152 192 0 0 0 0 1,607763 Pembroke General Hospital 7,011 8,997 877 - - - - - - 7,888766 The Penetanguishene General Ho - 12,517 1,220 - - - - - - 1,220768 Barry's Bay St Francis Memoria 644 4,920 480 8,205 141 0 0 0 0 1,265771 Peterborough Civic Hospital 23,306 20,716 2,020 - - - - - - 25,326773 Toronto Providence Centre - 79,411 7,743 - - - - - - 7,743776 Petrolia Charlotte Eleanor Eng 1,362 6,053 590 18,564 319 0 0 0 0 2,271777 Nepean Queensway-Carleton Hosp 11,945 0 0 - - - - - - 11,945778 Picton Prince Edward County Me 1,823 159 16 23,157 398 0 0 0 0 2,237781 Thunder Bay St Joseph's Genera - 43,010 4,193 - - - - - - 4,193782 Port Colborne General Hospital 2,168 8,281 807 - - - - - - 2,975784 Little Current Manitoulin Heal 1,721 0 0 21,755 374 0 0 0 0 2,095787 Toronto Scarborough Salvation 15,996 5,058 493 - - - - - - 16,489788 Renfrew Victoria Hospital 1,976 7,005 683 - - - - - - 2,659790 St Catharines Hotel Dieu Hospi 9,573 0 0 - - - - - - 9,573791 St Catharines General Hospital 14,414 12,950 1,263 - - - - - - 15,677

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Chronic Weighted

Patient Days

Equivalent Weighted

CasesVisits

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

Cases


Equivalent Weighted

Cases

MoH Number



Weighted Cases

Chronic Care

792 St Mary's Memorial Hospital 904 1,266 123 8,963 154 0 0 0 0 1,182793 St Thomas-Elgin General Hospit 9,036 26,080 2,543 - - - - - - 11,579795 Sarnia St Joseph's Health Cent 4,955 29,111 2,838 - - - - - - 7,793796 Sarnia General Hospital 10,387 0 0 - - - - - - 10,387797 Sault Ste Marie General 17,488 18,716 1,825 - - - - - - 19,313799 Toronto Scarborough General Ho 29,728 1,172 114 - - - - - - 29,842800 Hawkesbury And District Genera 3,437 6,138 598 - - - - - - 4,035801 Seaforth Community Hospital 789 1,559 152 7,762 134 0 0 0 0 1,075802 Alexandria Glengarry Memorial 933 3,849 375 20,253 348 0 0 0 0 1,657804 Simcoe Norfolk General Hospita 5,406 14,820 1,445 - - - - - - 6,851805 Sioux Lookout District Health 768 1,160 113 10,045 173 0 0 7,227 422 1,476809 Smooth Rock Falls Hospital 334 1,062 104 3,597 62 0 0 7,347 429 928810 Southampton Saugeen Memorial 807 411 40 22,664 390 0 0 0 0 1,237813 Stratford General Hospital 7,747 9,691 945 - - - - - - 8,692814 Strathroy Middlesex General Ho 3,053 11,620 1,133 - - - - - - 4,186817 Sudbury Gen Hosp Of Immac Hear 16,540 0 0 - - - - - - 16,540818 Sudbury Memorial Hospital 12,541 0 0 - - - - - - 12,541819 Terrace Bay Mc Causland Hospit 270 4,642 453 3,507 60 0 0 0 0 783824 Tillsonburg District Memorial 2,994 8,453 824 - - - - - - 3,818826 Kenora Lake-Of-The-Woods Distr 3,429 10,768 1,050 - - - - - - 4,479827 Toronto Baycrest - 89,247 8,702 - - - - - - 8,702841 Sudbury Laurentian Hospital 8,994 18,780 1,831 - - - - - - 10,825842 Toronto Mount Sinai Hospital 26,563 0 0 - - - - - - 26,563845 Toronto Orthopaedic And Arthri 6,036 0 0 - - - - - - 6,036849 Toronto Riverdale - 140,138 13,663 - - - - - - 13,663850 Toronto Runnymede - 31,972 3,117 - - - - - - 3,117852 Toronto St Michael's Hospital 52,797 0 0 - - - - - - 52,797854 Toronto Salvation Army Grace T - 37,955 3,701 - - - - - - 3,701857 Toronto Sunnybrook Health Scie 37,135 103,933 10,133 - - - - - - 47,268858 Toronto East General Orthopaed 26,852 0 0 - - - - - - 26,852862 Toronto Women's College Hospit 12,881 0 0 - - - - - - 12,881865 Trenton Memorial Hospital 3,232 6,866 669 - - - - - - 3,901870 Wallaceburg Sydenham District 1,980 1,326 129 21,838 376 0 0 0 0 2,485873 Welland County General Hospita 10,505 26,193 2,554 - - - - - - 13,059875 Wiarton Bruce Peninsula 1,029 0 0 21,392 368 0 0 0 0 1,397

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Chronic Weighted

Patient Days

Equivalent Weighted

CasesVisits

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

CasesPatient Days

Equivalent Weighted

Cases


Equivalent Weighted

Cases

MoH Number



Weighted Cases

Chronic Care

881 Sturgeon Falls West Nipissing 1,631 5,003 488 21,527 370 0 0 0 0 2,489882 Winchester District Memorial H 2,712 8,726 851 - - - - - - 3,563888 New Liskeard Temiskaming Hospi 2,869 3,583 349 - - - - - - 3,218889 Wingham And District Hospital 1,831 3,243 316 17,415 300 1,173 166 0 0 2,613890 Woodstock General Hospital 6,538 7,345 716 - - - - - - 7,254895 Thunder Bay Hogarth-Westmount - 14,446 1,408 - - - - - - 1,408896 Red Lake Marg Cochenour Mem Ho 716 1,302 127 0 0 0 0 0 0 843897 Milton District 2,631 9,708 947 - - - - - - 3,578898 Toronto St Joseph's Health Cen 24,703 1,787 174 - - - - - - 24,877900 Fort Frances Riverside Health 2,833 5,885 574 - - - - - - 3,407903 Huntsville District Memorial H 3,355 8,154 795 - - - - - - 4,150905 Markham Stouffville Hospital 12,665 11,022 1,075 - - - - - - 13,740906 North Bay General Hospital 13,594 2,246 219 - - - - - - 13,813907 Timmins & District General Hos 9,011 18,576 1,811 - - - - - - 10,822916 Orangeville Dufferin-Caledon H 6,097 7,899 770 - - - - - - 6,867927 Windsor Hotel-Dieu Grace Hospi 28,449 0 0 - - - - - - 28,449928 Smith Falls Perth And Smiths F 5,499 6,177 602 - - - - - - 6,101930 Kitchener Grand River Hospital 20,980 82,789 8,072 - - - - - - 29,052931 Parry Sound West Parry Sound H 2,794 20,841 2,032 - - - - - - 4,826932 Ottawa Hospital Of Sisters Of - 172,899 16,858 - - - - - - 16,858933 Windsor Regional Hospital 21,671 24,567 2,395 - - - - - - 24,066935 Thunder Bay Regional Hospital 26,401 0 0 - - - - - - 26,401936 London Health Sciences Centre 67,564 0 0 - - - - - - 67,564938 Haliburton Highlands Hlth Serv 567 0 0 24,799 427 0 0 0 0 994940 The Northumberland Health Care 4,230 12,176 1,187 - - - - - - 5,417941 Toronto Humber River Regional 37,787 0 0 - - - - - - 37,787942 Hamilton Health Sciences Centr 72,023 61,407 5,987 - - - - - - 78,010944 Toronto Rehabilitation Institu - 103,586 10,100 - - - - - - 10,100945 Scugog North Durham Health Ser 2,933 4,722 460 29,106 501 0 0 0 0 3,894946 Kincardine S Bruce Grey Hlth C 4,835 6,537 637 - - - - - - 5,472947 Toronto Hospital Corp 88,593 0 0 - - - - - - 88,593949 Mississauga Trillium Health Ce 39,743 74,836 7,297 - - - - - - 47,040954 Toronto Rouge Valley 33,920 32,073 3,127 - - - - - - 37,047958 The Ottawa Hospital 95,698 0 0 - - - - - - 95,698

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Appendix 12: 1998/99 Integrated Model Rate Results

Hospital Type

Merger No.

Merger Description

Total 98/99 Equivalent Weighted

Cases

Chronic Care Flag

Percent Isolation

Inverse of EWC

98/99 Percent of

Adult Tertiary EWC

98/99 Percent of

Non-Adult Tertiary EWC

98/99 Medical

Student Days per Census (Acute, DS & Chronic)

Actual 98/99

CPEWC

Expected 98/99

CPEWC

Over/ Under 98/99

ECPEWC

% Over/ Under 98/99

ECPEWC

Small 592 Napanee Lennox And Addington County Gen Hosp2,544 0 0.00% 0.000393 na na na $2,880 $2,596 $284 10.93%Small 593 Newbury Four Counties General Hospital ( T1,168 0 0.00% 0.000856 na na na $3,275 $3,051 $224 7.34%Small 596 Alliston Stevenson Memorial Hospital 3,200 0 0.00% 0.000313 na na na $2,096 $2,517 -$421 -16.71%Small 597 Almonte General Hospital 2,143 0 0.00% 0.000467 na na na $2,336 $2,668 -$332 -12.44%Small 599 Arnprior & District Memorial Hosp. (The) 2,502 0 0.00% 0.000400 na na na $2,661 $2,602 $59 2.25%Small 600 Atikokan General Hospital 1,173 0 100.00% 0.000853 na na na $2,927 $3,308 -$381 -11.52%Community 606 Barrie Royal Victoria Hospital 19,622 0 0.00% 0.000051 13.40% 0.34% 0.0153 $2,119 $2,552 -$433 -16.97%Small 611 Blind River St Joseph's Health Centre 1,455 0 100.00% 0.000687 na na na $3,019 $3,145 -$126 -4.00%Chronic 613 Toronto West Park 5,916 1 0.00% 0.000169 na na 0.0099 $3,396 $2,815 $582 20.66%Community 614 Bracebridge South Muskoka Memorial Hospital4,072 0 0.00% 0.000246 2.90% 0.02% 0.0000 $2,451 $2,509 -$58 -2.31%Community 617 Brantford General Hospital 15,610 0 0.00% 0.000064 8.62% 0.36% 0.0195 $2,194 $2,476 -$282 -11.38%Community 618 Brantford St Joseph's Hospital 4,207 0 0.00% 0.000238 13.05% 0.00% 0.0355 $2,464 $2,732 -$268 -9.81%Community 619 Brockville General Hospital 5,867 0 0.00% 0.000170 8.01% 0.00% 0.0003 $2,668 $2,535 $134 5.27%Community 620 Brockville St Vincent De Paul Hospital 2,104 0 0.00% 0.000475 1.00% 0.00% 0.0000 $3,518 $2,696 $822 30.48%Chronic 623 St. Catharines Shaver 3,275 1 0.00% 0.000305 na na 0.0000 $2,656 $2,934 -$278 -9.48%Small 624 Campbellford Memorial Hospital 2,347 0 0.00% 0.000426 na na na $2,814 $2,628 $186 7.07%Small 626 Carleton Place And District Mem Hospital 1,279 0 0.00% 0.000782 na na na $2,727 $2,979 -$252 -8.46%Small 627 Chapleau General Hospital 986 0 100.00% 0.001014 na na na $3,602 $3,467 $135 3.89%Community 628 Chatham Public General Hospital 8,920 0 0.00% 0.000112 3.13% 0.43% 0.0000 $2,599 $2,400 $198 8.27%Community 629 Chatham St Joseph's Hospital 3,753 0 0.00% 0.000266 14.55% 0.24% 0.0000 $3,439 $2,769 $670 24.20%Community 632 Toronto North York General Hospital 39,433 0 0.00% 0.000025 13.14% 0.92% 0.0559 $2,683 $2,586 $97 3.75%Small 633 Clinton Public Hospital 1,516 0 0.00% 0.000660 na na na $2,738 $2,858 -$121 -4.22%Small 638 Cochrane Lady Minto Hospital 1,288 0 100.00% 0.000776 na na na $3,821 $3,232 $588 18.20%Community 640 Collingwood General And Marine Hospital4,077 0 0.00% 0.000245 3.02% 0.00% 0.0520 $2,341 $2,557 -$217 -8.47%Community 643 Cornwall General Hospital 4,872 0 0.00% 0.000205 11.37% 0.00% 0.0014 $2,593 $2,636 -$43 -1.62%Community 644 Cornwall Hotel Dieu Hospital 8,419 0 0.00% 0.000119 8.53% 0.18% 0.0007 $2,616 $2,502 $114 4.55%Small 646 Deep River And District Hospital 827 0 0.00% 0.001209 na na na $3,527 $3,399 $128 3.76%Small 647 Dryden District General Hospital 2,753 0 100.00% 0.000363 na na na $2,645 $2,826 -$181 -6.40%Small 648 Dunnville Haldimand War Memorial Hospital1,934 0 0.00% 0.000517 na na na $2,771 $2,718 $53 1.95%Community 650 Elliot Lake St Joseph's General Hospital 2,922 0 100.00% 0.000342 3.63% 0.00% 0.0415 $3,115 $2,914 $200 6.86%Small 653 Englehart And District Hospital 874 0 100.00% 0.001144 na na na $2,657 $3,595 -$938 -26.09%Small 654 Espanola General Hospital 1,761 0 0.00% 0.000568 na na na $2,569 $2,768 -$199 -7.20%Small 655 Exeter South Huron Hospital 1,182 0 0.00% 0.000846 na na na $2,574 $3,041 -$466 -15.33%Community 656 Fergus Groves Memorial Community Hospital2,937 0 0.00% 0.000340 1.74% 0.00% 0.0014 $2,341 $2,580 -$238 -9.24%Community 658 Fort Erie Douglas Memorial Hospital 2,857 0 0.00% 0.000350 0.21% 0.00% 0.0000 $2,521 $2,558 -$37 -1.43%Community 661 Cambridge Memorial Hospital 15,862 0 0.00% 0.000063 6.49% 0.22% 0.0058 $2,328 $2,414 -$86 -3.57%

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Hospital Type

Merger No.

Merger Description


Cases

Chronic Care Flag

Percent Isolation

Inverse of EWC

98/99 Percent of

Adult Tertiary EWC

98/99 Percent of


98/99 Medical


Actual 98/99

CPEWC

Expected 98/99

CPEWC

Over/ Under 98/99

ECPEWC

% Over/ Under 98/99

ECPEWC

Small 662 Geraldton District Hospital 1,731 0 100.00% 0.000578 na na na $2,690 $3,037 -$347 -11.43%Small 663 Goderich Alexandra Marine And General Hospit3,454 0 0.00% 0.000289 na na na $2,875 $2,494 $381 15.28%Community 664 Grimsby West Lincoln Memorial Hospital 3,222 0 0.00% 0.000310 1.64% 0.00% 0.0621 $2,610 $2,604 $6 0.23%Community 665 Guelph General Hospital 8,964 0 0.00% 0.000112 10.35% 0.30% 0.0046 $2,346 $2,541 -$195 -7.67%Community 666 Guelph St Joseph's Hospital 8,303 0 0.00% 0.000120 12.29% 0.00% 0.0041 $2,723 $2,573 $150 5.83%Teaching 674 Hamilton St Joseph's Hospital 32,960 0 0.00% 0.000030 23.17% 1.22% 0.2160 $2,849 $2,949 -$100 -3.38%Chronic 675 Hamilton St. Peter's Hospital 9,016 1 0.00% 0.000111 na na 0.0000 $2,548 $2,750 -$202 -7.34%Small 676 Hanover And District Hospital 2,894 0 0.00% 0.000346 na na na $2,483 $2,549 -$66 -2.60%Small 681 Hearst Notre Dame Hospital 2,041 0 100.00% 0.000490 na na na $3,396 $2,950 $445 15.09%Small 682 Hornepayne Community Hospital 374 0 100.00% 0.002673 na na na $5,183 $5,100 $83 1.62%Small 684 Ingersoll Alexandra Hospital 2,242 0 0.00% 0.000446 na na na $3,200 $2,648 $552 20.85%Small 685 Iroquois Falls Anson General Hospital 997 0 0.00% 0.001003 na na na $3,481 $3,196 $285 8.91%Small 686 Wawa North Algoma Health Organization 1,151 0 100.00% 0.000868 na na na $2,711 $3,323 -$612 -18.41%Small 687 Kapuskasing Sensenbrenner Hospital (The)2,632 0 100.00% 0.000380 na na na $2,894 $2,842 $52 1.83%Teaching 692 Kingston Hotel Dieu Hospital 3,191 0 0.00% 0.000313 1.07% 0.00% 0.3150 $4,388 $2,827 $1,561 55.23%Teaching 693 Kingston General Hospital 31,146 0 0.00% 0.000032 39.27% 1.85% 0.4371 $3,793 $3,499 $294 8.40%Chronic 695 Kingston St Marys-Of-The-Lake 4,913 1 0.00% 0.000204 na na 0.0536 $3,214 $2,891 $323 11.18%Community 696 Kirkland And District Hospital 2,688 0 100.00% 0.000372 2.23% 0.00% 0.0121 $3,214 $2,889 $325 11.25%Community 699 Kitchener St Mary's General Hospital 13,631 0 0.00% 0.000073 16.36% 0.00% 0.0000 $2,473 $2,603 -$130 -4.99%Community 701 Richmond Hill York Central Hospital 17,068 0 0.00% 0.000059 12.60% 0.21% 0.0017 $2,196 $2,526 -$330 -13.07%Community 704 Leamington District Memorial Hospital 4,871 0 0.00% 0.000205 1.68% 0.00% 0.0000 $2,148 $2,444 -$296 -12.10%Community 707 Lindsay Ross Memorial Hospital 9,636 0 0.00% 0.000104 4.56% 0.00% 0.0007 $2,280 $2,401 -$122 -5.06%Small 709 Listowel Memorial Hospital 2,182 0 0.00% 0.000458 na na na $2,632 $2,660 -$28 -1.03%Teaching 714 London St Joseph's Hospital 37,054 0 0.00% 0.000027 12.22% 4.26% 0.1239 $3,088 $2,785 $303 10.86%Community 718 Burlington Joseph Brant Memorial Hospital17,824 0 0.00% 0.000056 15.50% 0.12% 0.0205 $2,477 $2,594 -$117 -4.51%Small 719 Manitouwadge General Hospital 359 0 100.00% 0.002784 na na na $5,353 $5,209 $144 2.77%Small 721 Marathon Wilson Memorial General Hospital532 0 100.00% 0.001881 na na na $4,047 $4,321 -$274 -6.34%Small 723 Matheson Bingham Memorial Hospital 908 0 0.00% 0.001101 na na na $3,386 $3,293 $93 2.81%Small 724 Mattawa General Hospital 661 0 0.00% 0.001513 na na na $3,825 $3,699 $126 3.40%Community 726 Midland Huronia District Hospital 4,776 0 0.00% 0.000209 1.59% 0.02% 0.0085 $2,468 $2,455 $13 0.53%Community 731 Mississauga The Credit Valley Hospital 22,318 0 0.00% 0.000045 11.78% 0.77% 0.0218 $2,288 $2,540 -$253 -9.94%Small 732 Kemptville District Hospital 1,871 0 0.00% 0.000534 na na na $2,641 $2,735 -$94 -3.45%Small 733 Mount Forest Louise Marshall Hospital 1,200 0 0.00% 0.000833 na na na $2,835 $3,029 -$194 -6.41%Small 734 Haldimand West Haldimand General Hospital1,613 0 0.00% 0.000620 na na na $3,402 $2,819 $583 20.67%Community 736 Newmarket York County Hospital 20,163 0 0.00% 0.000050 11.95% 0.73% 0.0026 $2,287 $2,529 -$241 -9.55%Small 737 Niagara-On-The-Lake General Hospital 702 0 0.00% 0.001424 na na na $3,252 $3,611 -$359 -9.94%

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Hospital Type

Merger No.

Merger Description


Cases

Chronic Care Flag

Percent Isolation

Inverse of EWC

98/99 Percent of

Adult Tertiary EWC

98/99 Percent of


98/99 Medical


Actual 98/99

CPEWC

Expected 98/99

CPEWC

Over/ Under 98/99

ECPEWC

% Over/ Under 98/99

ECPEWC

Community 738 Niagara Falls Greater Niagara General Hospit15,547 0 0.00% 0.000064 9.85% 0.24% 0.0014 $2,501 $2,478 $23 0.93%Small 739 Nipigon District Memorial Hospital 1,198 0 100.00% 0.000835 na na na $2,969 $3,290 -$321 -9.76%Community 745 Orillia Soldiers' Memorial Hospital 11,098 0 0.00% 0.000090 6.05% 0.73% 0.0089 $2,247 $2,458 -$212 -8.61%Community 753 Ottawa Hopital Montfort 11,077 0 0.00% 0.000090 11.65% 0.00% 0.0849 $2,333 $2,605 -$272 -10.45%Community 755 Ottawa Salvation Army Grace Hospital ( Th7,639 0 0.00% 0.000131 1.90% 0.13% 0.0057 $2,259 $2,386 -$127 -5.33%Small 759 Palmerston And District Hospital 1,226 0 0.00% 0.000816 na na na $2,861 $3,012 -$151 -5.02%Small 760 Paris The Willett Hospital 1,607 0 0.00% 0.000622 na na na $3,684 $2,821 $863 30.60%Community 763 Pembroke General Hospital 7,888 0 0.00% 0.000127 1.94% 0.01% 0.0009 $2,266 $2,373 -$107 -4.52%Chronic 766 The Penetanguishene General Hospital 1,220 1 0.00% 0.000819 na na 0.0164 $2,782 $3,438 -$656 -19.07%Small 768 Barry's Bay St Francis Memorial Hospital 1,265 0 100.00% 0.000791 na na na $3,073 $3,246 -$174 -5.36%Community 771 Peterborough Civic Hospital 25,326 0 0.00% 0.000039 13.82% 0.27% 0.0066 $2,531 $2,538 -$8 -0.30%Chronic 773 Toronto Providence Centre 7,743 1 0.00% 0.000129 na na 0.0032 $2,625 $2,770 -$145 -5.25%Small 776 Petrolia Charlotte Eleanor Englehart Hospita2,271 0 0.00% 0.000440 na na na $2,551 $2,642 -$91 -3.44%Community 777 Nepean Queensway-Carleton Hospital 11,945 0 0.00% 0.000084 12.85% 0.00% 0.0025 $2,586 $2,547 $39 1.54%Chronic 781 Thunder Bay St Joseph's General 4,193 1 0.00% 0.000238 na na 0.1006 $3,254 $2,970 $284 9.57%Community 782 Port Colborne General Hospital 2,975 0 0.00% 0.000336 4.37% 0.00% 0.0000 $2,561 $2,626 -$65 -2.48%Small 784 Little Current Manitoulin Health Centre 2,095 0 100.00% 0.000477 na na na $2,921 $2,938 -$17 -0.58%Community 787 Toronto Scarborough Salvation Army Grace Gen16,489 0 0.00% 0.000061 8.24% 0.53% 0.0103 $2,389 $2,465 -$76 -3.07%Community 788 Renfrew Victoria Hospital 2,659 0 0.00% 0.000376 2.26% 0.00% 0.0032 $2,353 $2,626 -$273 -10.39%Community 790 St Catharines Hotel Dieu Hospital 9,573 0 0.00% 0.000104 19.55% 0.00% 0.0106 $2,890 $2,706 $183 6.77%Community 791 St Catharines General Hospital 15,677 0 0.00% 0.000064 11.97% 0.24% 0.0033 $2,325 $2,522 -$197 -7.80%Small 792 St Mary's Memorial Hospital 1,182 0 0.00% 0.000846 na na na $3,395 $3,042 $352 11.59%Community 793 St Thomas-Elgin General Hospital 11,579 0 0.00% 0.000086 8.23% 0.41% 0.0210 $2,739 $2,494 $245 9.83%Community 795 Sarnia St Joseph's Health Centre Of Sarnia 7,793 0 0.00% 0.000128 6.67% 0.59% 0.0000 $2,647 $2,494 $154 6.16%Community 796 Sarnia General Hospital 10,387 0 0.00% 0.000096 10.95% 0.00% 0.0000 $3,433 $2,519 $914 36.27%Community 797 Sault Ste Marie General 19,313 0 100.00% 0.000052 11.14% 0.33% 0.0165 $2,724 $2,768 -$44 -1.60%Community 799 Toronto Scarborough General Hospital 29,842 0 0.00% 0.000034 19.73% 0.35% 0.0417 $2,417 $2,685 -$267 -9.95%Community 800 Hawkesbury And District General Hospital4,035 0 0.00% 0.000248 3.47% 0.00% 0.0000 $2,358 $2,521 -$163 -6.46%Small 801 Seaforth Community Hospital 1,075 0 0.00% 0.000931 na na na $3,050 $3,125 -$75 -2.41%Small 802 Alexandria Glengarry Memorial Hospital 1,657 0 0.00% 0.000604 na na na $2,570 $2,803 -$233 -8.31%Community 804 Simcoe Norfolk General Hospital 6,851 0 0.00% 0.000146 1.84% 0.00% 0.0043 $2,512 $2,393 $119 4.98%Small 805 Sioux Lookout District Health Centre 1,476 0 100.00% 0.000678 na na na $2,997 $3,135 -$138 -4.39%Small 809 Smooth Rock Falls Hospital 928 0 100.00% 0.001077 na na na $3,056 $3,529 -$472 -13.39%Community 813 Stratford General Hospital 8,692 0 0.00% 0.000115 13.83% 0.09% 0.0040 $2,805 $2,602 $203 7.80%Community 814 Strathroy Middlesex General Hospital 4,186 0 0.00% 0.000239 1.24% 0.24% 0.0845 $2,386 $2,557 -$171 -6.67%Small 819 Terrace Bay Mc Causland Hospital 783 0 100.00% 0.001277 na na na $3,390 $3,726 -$336 -9.02%

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Hospital Type

Merger No.

Merger Description


Cases

Chronic Care Flag

Percent Isolation

Inverse of EWC

98/99 Percent of

Adult Tertiary EWC

98/99 Percent of


98/99 Medical


Actual 98/99

CPEWC

Expected 98/99

CPEWC

Over/ Under 98/99

ECPEWC

% Over/ Under 98/99

ECPEWC

Community 824 Tillsonburg District Memorial Hospital 3,818 0 0.00% 0.000262 1.20% 0.00% 0.0000 $2,908 $2,490 $418 16.78%Community 826 Kenora Lake-Of-The-Woods District Hospital4,479 0 100.00% 0.000223 3.24% 0.02% 0.0274 $2,797 $2,778 $19 0.68%Chronic 827 Toronto Baycrest 8,702 1 0.00% 0.000115 na na 0.0416 $2,732 $2,795 -$62 -2.24%Teaching 842 Toronto Mount Sinai Hospital 26,563 0 0.00% 0.000038 24.22% 9.05% 0.5738 $3,216 $3,664 -$449 -12.24%Chronic 849 Toronto Riverdale 13,663 1 0.00% 0.000073 na na 0.0000 $2,421 $2,714 -$294 -10.82%Chronic 850 Toronto Runnymede 3,117 1 0.00% 0.000321 na na 0.0000 $3,026 $2,949 $77 2.62%Teaching 852 Toronto St Michael's Hospital 52,797 0 0.00% 0.000019 44.03% 0.06% 0.3366 $3,526 $3,405 $120 3.53%Chronic 854 Toronto Salvation Army Grace Toronto 3,701 1 0.00% 0.000270 na na 0.0000 $2,447 $2,901 -$453 -15.63%Community 858 Toronto East General Orthopaedic Hosp26,852 0 0.00% 0.000037 19.45% 0.95% 0.0945 $2,668 $2,759 -$91 -3.29%Small 870 Wallaceburg Sydenham District Hospital 2,485 0 0.00% 0.000402 na na na $3,148 $2,605 $543 20.84%Community 873 Welland County General Hospital 13,059 0 0.00% 0.000077 9.38% 0.05% 0.0016 $2,566 $2,473 $93 3.75%Small 881 Sturgeon Falls West Nipissing General Hospit2,489 0 0.00% 0.000402 na na na $3,323 $2,604 $718 27.59%Community 882 Winchester District Memorial Hospital 3,563 0 0.00% 0.000281 2.25% 0.00% 0.0108 $2,442 $2,539 -$98 -3.85%Community 888 New Liskeard Temiskaming Hospital 3,218 0 100.00% 0.000311 1.58% 0.00% 0.0040 $2,876 $2,809 $67 2.38%Small 889 Wingham And District Hospital 2,613 0 0.00% 0.000383 na na na $2,604 $2,586 $18 0.71%Community 890 Woodstock General Hospital 7,254 0 0.00% 0.000138 9.72% 0.00% 0.0021 $2,597 $2,538 $59 2.34%Chronic 895 Thunder Bay Hogarth-Westmount 1,408 0 0.00% 0.000710 na na 0.0000 $6,080 $3,318 $2,762 83.23%Small 896 Red Lake Marg Cochenour Mem Hosp 843 1 100.00% 0.001186 na na na $3,497 $3,636 -$139 -3.82%Community 898 Toronto St Joseph's Health Centre 24,877 0 0.00% 0.000040 17.75% 0.58% 0.1355 $2,668 $2,749 -$80 -2.92%Community 900 Fort Frances Riverside Health Care Fac Inc3,407 0 100.00% 0.000294 1.14% 0.00% 0.0447 $3,401 $2,821 $581 20.58%Community 903 Huntsville District Memorial Hospital 4,150 0 0.00% 0.000241 2.94% 0.00% 0.0059 $2,315 $2,509 -$194 -7.73%Community 905 Markham Stouffville Hospital 13,740 0 0.00% 0.000073 12.55% 0.44% 0.0055 $2,487 $2,553 -$66 -2.58%Community 906 North Bay General Hospital 13,813 0 0.00% 0.000072 9.93% 0.46% 0.0000 $2,634 $2,497 $137 5.49%Community 907 Timmins & District General Hospital 10,822 0 0.00% 0.000092 9.42% 0.36% 0.0098 $2,492 $2,511 -$19 -0.76%Community 916 Orangeville Dufferin-Caledon Health Care Cor6,867 0 0.00% 0.000146 2.53% 0.04% 0.0065 $2,116 $2,410 -$294 -12.19%Community 927 Windsor Hotel-Dieu Grace Hospital 28,449 0 0.00% 0.000035 21.96% 1.87% 0.0023 $2,649 $2,764 -$115 -4.15%Community 928 Smith Falls Perth And Smiths Falls District6,101 0 0.00% 0.000164 7.82% 0.00% 0.0013 $2,588 $2,525 $62 2.47%Community 930 Kitchener Grand River Hospital Corporation29,052 0 0.00% 0.000034 8.94% 0.94% 0.0083 $2,442 $2,469 -$27 -1.10%Community 931 Parry Sound West Parry Sound Health Centre4,826 0 0.00% 0.000207 1.78% 0.00% 0.0000 $2,606 $2,448 $159 6.48%Chronic 932 Ottawa Hospital Of Sisters Of Charity 16,858 1 0.00% 0.000059 na na 0.0273 $2,815 $2,726 $89 3.28%Community 933 Windsor Regional Hospital 24,066 0 0.00% 0.000042 12.05% 0.10% 0.0003 $2,674 $2,492 $182 7.30%Community 935 Thunder Bay Regional Hospital 26,401 0 0.00% 0.000038 20.72% 0.78% 0.0431 $2,637 $2,729 -$92 -3.38%Teaching 936 London Health Sciences Centre 67,564 0 0.00% 0.000015 49.04% 0.20% 0.3537 $3,561 $3,522 $39 1.11%Small 938 Haliburton Highlands Hlth Serv Corp 994 0 100.00% 0.001006 na na na $2,366 $3,459 -$1,093 -31.59%Community 940 The Northumberland Health Care Corp 5,417 0 0.00% 0.000185 1.96% 0.11% 0.0083 $2,459 $2,442 $18 0.72%Community 941 Toronto Humber River Regional 37,787 0 0.00% 0.000026 12.80% 0.35% 0.0043 $2,568 $2,507 $61 2.44%

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Hospital Type

Merger No.

Merger Description


Cases

Chronic Care Flag

Percent Isolation

Inverse of EWC

98/99 Percent of

Adult Tertiary EWC

98/99 Percent of


98/99 Medical


Actual 98/99

CPEWC

Expected 98/99

CPEWC

Over/ Under 98/99

ECPEWC

% Over/ Under 98/99

ECPEWC

Teaching 942 Hamilton Health Sciences Centre 78,010 0 0.00% 0.000013 35.59% 2.44% 0.2821 $3,488 $3,293 $196 5.94%Chronic 944 Toronto Rehabilitation Institute 10,100 1 0.00% 0.000099 na na 0.0000 $2,986 $2,739 $247 9.03%Community 946 Kincardine S Bruce Grey Hlth Ctr 5,472 0 0.00% 0.000183 1.04% 0.00% 0.0000 $2,666 $2,409 $256 10.64%Teaching 947 Toronto University Health Network 88,593 0 0.00% 0.000011 52.94% 0.38% 0.4762 $3,496 $3,716 -$219 -5.91%Community 949 Mississauga Trillium Health Centre 47,040 0 0.00% 0.000021 16.55% 0.25% 0.0017 $2,410 $2,569 -$159 -6.17%Community 950 Oakville Halton Healthcare 21,264 0 0.00% 0.000094 10.75% 0.09% 0.0066 $2,368 $2,523 -$156 -6.17%Community 951 Toronto Northwest GTA 50,461 0 0.00% 0.000059 10.88% 0.49% 0.0058 $2,466 $2,509 -$43 -1.72%Community 952 Oshawa Lakeridge 42,515 0 0.00% 0.000094 10.10% 0.34% 0.0029 $2,598 $2,519 $80 3.16%Teaching 953 Toronto Sunnybrook Women's College 66,185 0 0.00% 0.000045 36.38% 2.94% 0.2437 $3,363 $3,329 $34 1.02%Community 954 Toronto Rouge Valley 37,047 0 0.00% 0.000027 13.24% 0.60% 0.0049 $2,467 $2,528 -$61 -2.42%Community 955 Owen Sound Grey Bruce Health 19,859 0 0.00% 0.000252 9.91% 0.03% 0.0018 $2,786 $2,655 $131 4.92%Community 957 Belleville Quinte 19,516 0 2.63% 0.000154 7.67% 0.08% 0.0273 $2,897 $2,546 $350 13.75%Teaching 958 The Ottawa Hospital 95,698 0 0.00% 0.000010 35.24% 1.93% 0.2520 $3,377 $3,233 $144 4.46%Community 959 Sudbury Regional 39,906 0 0.00% 0.000075 28.74% 0.53% 0.0286 $2,850 $2,899 -$49 -1.71%

Minimum -31.6%25th Percentile -6.5%Median -1.5%75th Percentile 5.4%Maximim 83.2%

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APPENDIX 13: Volume Sub-Committee Terms Of Reference BACKGROUND Recognizing the rapid rate of change that has been stimulated by the recommendations of the Health Services Restructuring Commission (HSRC), the JPPC Hospital Funding Committee (HFC) has recommended that the Ministry of Health determine hospital allocations for HSRC reviewed hospitals separately in order to accommodate funding requirements as hospitals implement the directives of the HSRC. This period of individual negotiation is expected to last for at least two years beginning in 1998/99. After the HSRC has completed its work, a formula based funding methodology will again be needed. Using other provinces and jurisdictions as comparisons, the HFC initiated a 2 year work plan to develop a population needs-based funding methodology that the Ministry of Health could decide to use in the post -restructured system (i.e., post-HSRC). This methodology will be developed by two sub-committees of the HFC. The committees are called Rate and Volume Sub-Committees of the HFC. MANDATE By July 1, 1999, to contribute to a provincial funding methodology that, on a regional or district level, predicts the anticipated volume of hospital activity. OBJECTIVES To work with the Rates Sub-Committee to determine appropriate volumes of

service to which rates may be applied To identify the volume of activity associated with population distributions FREQUENCY OF MEETING The Volumes Sub-Committee will meet monthly or at the discretion of the Chair. MEMBERSHIP Representation from hospitals as per OHA/MoH determination. REPORTING RELATIONSHIP The Volumes Sub-Committee reports to the Hospital Funding Committee.

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APPENDIX 14: Volume Sub-Committee Membership List

Frank Lussing, Chair York Central Hospital

Antoni Basinski ICES

Catherine Cornell

Jim Cruickshank Ontario Hospital Association


Shawn Gilhuly Cambridge Memorial Hospital

Paul Huras Thames Valley District Health Council

Alan Iskiw Ministry of Health and Long-Term Care

Bill MacLeod St. Michael’s Hospital


Joe Mapa Mt. Sinai Hospital

John Marshall Kingston General Hospital

George Pink University of Toronto

Colin Preyra Joint Policy & Planning Committee

Caroline Rafferty Joint Policy & Planning Committee

Jenny Rajaballey Ministry of Health and Long-Term Care

Luc Seguin Hawkesbury & District General Hospital

Elsa van Vliet Ministry of Health and Long-Term Care

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Appendix 15: Community Mortality Rates

Community Actual Deaths Expected Deaths

Excess Deaths per 1000

populationAJAX 94.33 94.59 0.00ALGOMA DISTRICT 423.67 314.33 0.85ANCASTER 33.00 54.91 -0.90AURORA 51.67 56.90 -0.14BRAMPTON 435.33 403.96 0.11BRANT COUNTY 261.67 272.33 -0.09BROCK 32.67 31.04 0.13BRUCE COUNTY 203.67 176.25 0.41BURLINGTON 287.33 329.56 -0.29CALEDON 57.67 73.01 -0.35CAMBRIDGE 206.67 199.63 0.06CLARINGTON 110.00 113.06 -0.05COCHRANE DISTRICT 245.00 185.10 0.64CUMBERLAND 48.67 59.89 -0.21DUFFERIN COUNTY 79.33 84.59 -0.11DUNDAS 49.67 59.34 -0.40EAST GWILLIMBURY 34.67 35.35 -0.03EAST YORK 275.00 265.96 0.08ELGIN COUNTY 206.00 185.89 0.24ESSEX COUNTY 880.67 806.05 0.20ETOBICOKE 840.33 876.64 -0.10FLAMBOROUGH 59.00 70.70 -0.32FORT ERIE 96.33 77.33 0.67FRONTENAC COUNTY 302.33 327.39 -0.17GEORGINA 89.00 70.35 0.49GLANBROOK 20.00 25.84 -0.52GLOUCESTER 144.67 163.00 -0.17GOULBOURN 30.00 39.18 -0.44GREY COUNTY 248.33 247.62 0.01GRIMSBY 46.33 45.85 0.02HALDIMAND-NORFOLK REGIONAL MUNICIPALITY 262.67 249.59 0.12HALIBURTON COUNTY 44.33 59.16 -0.92HALTON HILLS 80.33 81.41 -0.02HAMILTON 914.00 824.12 0.27HASTINGS COUNTY 285.67 312.05 -0.21HURON COUNTY 144.33 168.37 -0.39KANATA 50.33 64.62 -0.27KENORA DISTRICT 115.67 106.62 0.17KENT COUNTY 322.00 264.26 0.51KING 34.00 41.34 -0.39KITCHENER 364.00 358.22 0.03LAMBTON COUNTY 383.33 321.50 0.48LANARK COUNTY 162.67 152.31 0.16LEEDS AND GRENVILLE UNITED COUNTIES 276.67 257.38 0.19LENNOX AND ADDINGTON COUNTY 90.67 96.25 -0.14

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populationLINCOLN 37.33 47.06 -0.49MANITOULIN DISTRICT 35.67 28.05 0.85MARKHAM 218.67 313.53 -0.51MIDDLESEX COUNTY 810.00 855.27 -0.11MILTON 63.00 59.81 0.10MISSISSAUGA 911.33 930.96 -0.03MUSKOKA DISTRICT MUNICIPALITY 144.33 151.73 -0.14NEPEAN 175.00 225.50 -0.42NEWMARKET 113.33 91.82 0.34NIAGARA FALLS 247.33 210.65 0.46NIAGARA-ON-THE-LAKE 39.67 44.89 -0.38NIPISSING DISTRICT 251.33 196.25 0.64NORTH DUMFRIES 15.00 15.34 -0.04NORTH YORK 1231.33 1514.33 -0.46NORTHUMBERLAND COUNTY 225.33 225.71 0.00OAKVILLE 231.00 261.15 -0.22OSGOODE 17.67 27.71 -0.59OSHAWA 340.67 280.48 0.43OTTAWA/ROCK 742.00 809.93 -0.20OXFORD COUNTY 234.00 234.01 0.00PARRY SOUND DISTRICT 122.00 124.30 -0.06PELHAM 23.00 37.37 -0.95PERTH COUNTY 132.00 172.79 -0.54PETERBOROUGH COUNTY 304.00 350.33 -0.36PICKERING 114.33 119.08 -0.05PORT COLBORNE 74.00 57.37 0.88PRESCOTT AND RUSSELL UNITED COUNTIES 150.67 146.01 0.06PRINCE EDWARD COUNTY 86.33 80.65 0.22RAINY RIVER DISTRICT 57.00 55.24 0.08RENFREW COUNTY 283.33 244.08 0.39RICHMOND HILL 151.00 187.58 -0.33RIDEAU 11.67 24.91 -1.02SCARBOROUGH 1229.33 1257.09 -0.05SCUGOG 45.00 41.41 0.18SIMCOE COUNTY 824.67 771.15 0.15ST. CATHARINES 388.33 364.36 0.18STONEY CREEK 101.00 119.15 -0.32STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES356.67 283.04 0.64SUDBURY DISTRICT 79.00 58.49 0.79SUDBURY REGIONAL MUNICIPALITY 472.67 365.67 0.63THOROLD 52.67 44.46 0.44THUNDER BAY DISTRICT 453.67 356.88 0.60TIMISKAMING DISTRICT 133.67 95.91 0.97TORONTO 1498.33 1358.39 0.20UXBRIDGE 28.67 34.98 -0.37

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populationVANIER 54.33 42.19 0.68VAUGHAN 157.00 221.00 -0.44VICTORIA COUNTY 212.67 205.07 0.11WAINFLEET 16.67 14.56 0.33WATERLOO 122.67 155.63 -0.40WELLAND 152.67 130.62 0.44WELLESLEY 10.00 14.25 -0.47WELLINGTON COUNTY 341.00 368.36 -0.15WEST CARLETON 24.33 29.60 -0.30WEST LINCOLN 20.00 21.40 -0.12WHITBY 140.33 123.06 0.21WHITCHURCH-STOUFFVILLE 36.67 41.26 -0.22WILMOT 29.67 31.54 -0.13WOOLWICH 29.33 39.13 -0.54YORK 363.67 333.34 0.20

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Appendix 16: Community Household Income and Low Income Designation

Community DescriptionAverage Household

Income ($000's)Low Income Designation*

AJAX 67.3 ALGOMA DISTRICT 44.8 YESANCASTER 84.2 AURORA 84.0 BRAMPTON 63.5 BRANT COUNTY 48.4 BROCK 46.4 BRUCE COUNTY 46.9 BURLINGTON 68.3 CALEDON 78.5 CAMBRIDGE 52.7 CLARINGTON 62.3 COCHRANE DISTRICT 48.7 CUMBERLAND 73.8 DUFFERIN COUNTY 56.6 DUNDAS 64.4 EAST GWILLIMBURY 73.4 EAST YORK 49.8 ELGIN COUNTY 48.0 ESSEX COUNTY 54.9 ETOBICOKE 56.9 FLAMBOROUGH 67.1 FORT ERIE 42.2 YESFRONTENAC COUNTY 50.3 GEORGINA 52.0 GLANBROOK 61.7 GLOUCESTER 66.8 GOULBOURN 74.8 GREY COUNTY 43.9 YESGRIMSBY 61.1 HALDIMAND-NORFOLK REGIONAL MUNICIPALITY 48.7 HALIBURTON COUNTY 38.1 YESHALTON HILLS 66.6 HAMILTON 43.0 YESHASTINGS COUNTY 43.5 YESHURON COUNTY 44.4 YESKANATA 78.4 KENORA DISTRICT 52.7 KENT COUNTY 48.0 KING 93.0 KITCHENER 48.8 LAMBTON COUNTY 52.1 LANARK COUNTY 48.5 LEEDS AND GRENVILLE UNITED COUNTIES 47.3 LENNOX AND ADDINGTON COUNTY 46.0 YESLINCOLN 55.0 MANITOULIN DISTRICT 37.6 YESMARKHAM 78.4 MIDDLESEX COUNTY 50.7 MILTON 71.3

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MISSISSAUGA 65.3 MUSKOKA DISTRICT MUNICIPALITY 43.5 YESNEPEAN 68.5 NEWMARKET 68.7 NIAGARA FALLS 44.4 YESNIAGARA-ON-THE-LAKE 57.8 NIPISSING DISTRICT 43.8 YESNORTH DUMFRIES 70.7 NORTH YORK 55.5 NORTHUMBERLAND COUNTY 48.2 OAKVILLE 83.7 OSGOODE 69.2 OSHAWA 51.8 OTTAWA 50.6 OXFORD COUNTY 50.0 PARRY SOUND DISTRICT 38.4 YESPELHAM 67.9 PERTH COUNTY 50.0 PETERBOROUGH COUNTY 45.9 YESPICKERING 73.4 PORT COLBORNE 42.4 YESPRESCOTT AND RUSSELL UNITED COUNTIES 49.3 PRINCE EDWARD COUNTY 46.1 YESRAINY RIVER DISTRICT 46.5 RENFREW COUNTY 43.8 YESRICHMOND HILL 69.6 RIDEAU 81.5 SCARBOROUGH 51.0 SCUGOG 61.6 SIMCOE COUNTY 50.6 ST. CATHARINES 46.8 STONEY CREEK 58.5 STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES 44.3 YESSUDBURY DISTRICT 44.0 YESSUDBURY REGIONAL MUNICIPALITY 49.7 THOROLD 49.2 THUNDER BAY DISTRICT 52.2 TIMISKAMING DISTRICT 41.4 YESTORONTO 56.0 UXBRIDGE 68.2 VANIER 35.1 YESVAUGHAN 74.7 VICTORIA COUNTY 44.5 YESWAINFLEET 53.7 WATERLOO 62.1 WELLAND 44.4 YESWELLESLEY 61.3 WELLINGTON COUNTY 54.8 WEST CARLETON 67.2 WEST LINCOLN 55.1

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WHITBY 67.8 WHITCHURCH-STOUFFVILLE 86.7 WILMOT 59.2 WOOLWICH 62.8 YORK 43.2 YES*Defined as average household income in the lowest income quintile (<$46,100).

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Appendix 17: Community Percentages of Rural Population

Community Description

Percentage of Population in Rural

Census Subdivisions (<= 25 persons per sq. km)

AJAX 0.0ALGOMA DISTRICT 31.5ANCASTER 0.0AURORA 0.0BRAMPTON 0.0BRANT COUNTY 6.6BROCK 0.0BRUCE COUNTY 48.7BURLINGTON 0.0CALEDON 0.0CAMBRIDGE 0.0CLARINGTON 0.0COCHRANE DISTRICT 77.3CUMBERLAND 0.0DUFFERIN COUNTY 44.6DUNDAS 0.0EAST GWILLIMBURY 0.0EAST YORK 0.0ELGIN COUNTY 29.7ESSEX COUNTY 0.6ETOBICOKE 0.0FLAMBOROUGH 0.0FORT ERIE 0.0FRONTENAC COUNTY 17.4GEORGINA 0.0GLANBROOK 0.0GLOUCESTER 0.0GOULBOURN 0.0GREY COUNTY 48.1GRIMSBY 0.0HALDIMAND-NORFOLK REGIONAL MUNICIPALITY 12.3HALIBURTON COUNTY 100.0HALTON HILLS 0.0HAMILTON 0.0HASTINGS COUNTY 22.7HURON COUNTY 57.6KANATA 0.0KENORA DISTRICT 50.2KENT COUNTY 31.1

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KING 0.0KITCHENER 0.0LAMBTON COUNTY 19.7LANARK COUNTY 51.6LEEDS AND GRENVILLE UNITED COUNTIES 45.5LENNOX AND ADDINGTON COUNTY 39.8LINCOLN 0.0MANITOULIN DISTRICT 71.3MARKHAM 0.0MIDDLESEX COUNTY 7.1MILTON 0.0MISSISSAUGA 0.0MUSKOKA DISTRICT MUNICIPALITY 100.0NEPEAN 0.0NEWMARKET 0.0NIAGARA FALLS 0.0NIAGARA-ON-THE-LAKE 0.0NIPISSING DISTRICT 23.9NORTH DUMFRIES 0.0NORTH YORK 0.0NORTHUMBERLAND COUNTY 30.4OAKVILLE 0.0OSGOODE 0.0OSHAWA 0.0OTTAWA 0.0OXFORD COUNTY 35.7PARRY SOUND DISTRICT 62.0PELHAM 0.0PERTH COUNTY 36.6PETERBOROUGH COUNTY 25.6PICKERING 0.0PORT COLBORNE 0.0PRESCOTT AND RUSSELL UNITED COUNTIES 23.1PRINCE EDWARD COUNTY 49.6RAINY RIVER DISTRICT 54.3RENFREW COUNTY 36.9RICHMOND HILL 0.0RIDEAU 0.0SCARBOROUGH 0.0

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SCUGOG 0.0SIMCOE COUNTY 9.3ST. CATHARINES 0.0STONEY CREEK 0.0STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES 39.3SUDBURY DISTRICT 59.5SUDBURY REGIONAL MUNICIPALITY 11.8THOROLD 0.0THUNDER BAY DISTRICT 19.3TIMISKAMING DISTRICT 37.1TORONTO 0.0UXBRIDGE 0.0VANIER 0.0VAUGHAN 0.0VICTORIA COUNTY 45.2WAINFLEET 0.0WATERLOO 0.0WELLAND 0.0WELLESLEY 0.0WELLINGTON COUNTY 11.7WEST CARLETON 0.0WEST LINCOLN 0.0WHITBY 0.0WHITCHURCH-STOUFFVILLE 0.0WILMOT 0.0WOOLWICH 0.0YORK 0.0

25th Percentile 0.0Median 0.075th Percentile 25.59

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Appendix 18: Community Percentages of Aboriginal Population

Community DescriptionAborignal Population

(Off

Aboriginal Percentage of

PopulationAJAX 375 0.58ALGOMA DISTRICT 5,845 4.68ANCASTER 55 0.24AURORA 90 0.26BRAMPTON 950 0.35BRANT COUNTY 2,435 2.13BROCK 50 0.43BRUCE COUNTY 650 1.00BURLINGTON 530 0.39CALEDON 70 0.18CAMBRIDGE 380 0.37CLARINGTON 460 0.76COCHRANE DISTRICT 4,820 5.32CUMBERLAND 425 0.90DUFFERIN COUNTY 145 0.32DUNDAS 105 0.45EAST GWILLIMBURY 75 0.38EAST YORK 395 0.37ELGIN COUNTY 600 0.76ESSEX COUNTY 2,685 0.77ETOBICOKE 1,015 0.31FLAMBOROUGH 160 0.47FORT ERIE 660 2.43FRONTENAC COUNTY 1,430 1.05GEORGINA 520 1.50GLANBROOK 55 0.52GLOUCESTER 1,170 1.12GOULBOURN 70 0.36GREY COUNTY 755 0.86GRIMSBY 105 0.54HALDIMAND-NORFOLK REGIONAL MUNICIPALITY 1,405 1.37HALIBURTON COUNTY 50 0.33HALTON HILLS 140 0.33HAMILTON 4,130 1.28HASTINGS COUNTY 2,270 1.91HURON COUNTY 280 0.46KANATA 275 0.57KENORA DISTRICT 4,895 10.21KENT COUNTY 955 0.87KING 80 0.44KITCHENER 1,300 0.73LAMBTON COUNTY 1,550 1.22LANARK COUNTY 485 0.81LEEDS AND GRENVILLE UNITED COUNTIES 625 0.65LENNOX AND ADDINGTON COUNTY 595 1.52LINCOLN 130 0.69

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(Off


PopulationMANITOULIN DISTRICT 450 5.21MARKHAM 185 0.11MIDDLESEX COUNTY 4,155 1.07MILTON 235 0.73MISSISSAUGA 1,425 0.26MUSKOKA DISTRICT MUNICIPALITY 745 1.48NEPEAN 900 0.78NEWMARKET 250 0.44NIAGARA FALLS 600 0.78NIAGARA-ON-THE-LAKE 35 0.26NIPISSING DISTRICT 3,370 4.05NORTH DUMFRIES 25 0.32NORTH YORK 1,335 0.23NORTHUMBERLAND COUNTY 875 1.07OAKVILLE 420 0.33OSGOODE 140 0.88OSHAWA 1,305 0.97OTTAWA 3,465 1.07OXFORD COUNTY 500 0.51PARRY SOUND DISTRICT 705 1.80PELHAM 25 0.17PERTH COUNTY 245 0.34PETERBOROUGH COUNTY 1,885 1.54PICKERING 350 0.44PORT COLBORNE 325 1.76PRESCOTT AND RUSSELL UNITED COUNTIES 645 0.87PRINCE EDWARD COUNTY 460 1.84RAINY RIVER DISTRICT 1,490 6.94RENFREW COUNTY 1,450 1.51RICHMOND HILL 200 0.20RIDEAU 35 0.28SCARBOROUGH 2,015 0.36SCUGOG 40 0.21SIMCOE COUNTY 4,565 1.39ST. CATHARINES 1,270 0.97STONEY CREEK 310 0.57STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES 1,090 0.98SUDBURY DISTRICT 1,140 4.56SUDBURY REGIONAL MUNICIPALITY 4,465 2.72THOROLD 215 1.20THUNDER BAY DISTRICT 9,580 6.17TIMISKAMING DISTRICT 760 2.01TORONTO 4,280 0.65UXBRIDGE 50 0.31VANIER 680 3.94VAUGHAN 135 0.10

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(Off


PopulationVICTORIA COUNTY 400 0.59WAINFLEET 50 0.80WATERLOO 460 0.59WELLAND 650 1.34WELLESLEY - 0.00WELLINGTON COUNTY 840 0.49WEST CARLETON 150 0.91WEST LINCOLN 50 0.43WHITBY 395 0.54WHITCHURCH-STOUFFVILLE 55 0.28WILMOT 90 0.65WOOLWICH 85 0.49YORK 860 0.59


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Appendix 19: Community Excess Fertility Rates

Community

Female Pop'n

10-54 years Age 10-19

Age 20-

39 Age 40-54 Age 10-19

Age 20-

39 Age 40-54 Age 10-19

Age 20-

39 Age 40-54

AJAX 24,112 31.0 938.3 18.0 39.3 808.4 16.3 -0.3 5.4 0.1

ALGOMA DISTRICT 40,674 141.0 1,061.3 16.3 80.4 1,283.0 26.6 1.5 -5.4 -0.3

ANCASTER 7,971 2.7 183.3 5.3 16.3 181.6 6.4 -1.7 0.2 -0.1

AURORA 13,270 9.0 520.7 11.7 20.4 398.6 9.8 -0.9 9.2 0.1

BRAMPTON 102,476 159.3 4,288.3 71.7 177.8 3,674.9 56.4 -0.2 6.0 0.1

BRANT COUNTY 37,937 125.0 1,205.7 16.3 70.9 1,219.1 24.6 1.4 -0.4 -0.2

BROCK 3,773 4.0 122.0 1.3 7.6 115.1 2.4 -0.9 1.8 -0.3

BRUCE COUNTY 20,350 35.7 539.7 8.7 45.2 565.0 14.2 -0.5 -1.2 -0.3

BURLINGTON 46,146 31.0 1,542.7 32.7 76.0 1,523.8 29.4 -1.0 0.4 0.1

CALEDON 14,775 6.7 537.7 11.3 26.7 429.5 9.6 -1.4 7.3 0.1

CAMBRIDGE 35,870 95.3 1,207.3 17.3 63.9 1,228.9 20.9 0.9 -0.6 -0.1

CLARINGTON 22,128 24.7 884.3 13.0 33.8 767.5 13.1 -0.4 5.3 0.0

COCHRANE DISTRICT 30,645 124.7 912.7 6.0 63.5 1,021.2 18.4 2.0 -3.5 -0.4

CUMBERLAND 19,436 11.3 537.0 10.7 35.7 544.5 14.2 -1.3 -0.4 -0.2

DUFFERIN COUNTY 16,351 23.3 608.0 10.3 29.1 528.6 9.7 -0.4 4.9 0.0

DUNDAS 7,706 6.7 214.0 7.3 13.2 228.5 5.7 -0.8 -1.9 0.2

EAST GWILLIMBURY 7,086 4.7 208.3 5.3 12.4 206.2 5.3 -1.1 0.3 0.0

EAST YORK 37,705 58.7 1,513.0 57.3 44.3 1,426.9 25.9 0.4 2.3 0.8

ELGIN COUNTY 25,999 78.7 908.7 12.0 51.8 837.2 15.4 1.0 2.8 -0.1

ESSEX COUNTY 119,861 279.0 4,127.7 63.0 213.6 4,183.2 70.1 0.5 -0.5 -0.1

ETOBICOKE 108,091 145.0 4,237.3 125.0 160.7 3,975.0 66.8 -0.1 2.4 0.5

FLAMBOROUGH 12,117 5.7 483.7 10.3 20.6 367.5 8.0 -1.2 9.6 0.2

FORT ERIE 8,656 24.7 248.3 5.3 15.9 274.5 5.5 1.0 -3.0 0.0

FRONTENAC COUNTY 46,425 89.3 1,287.0 19.0 75.5 1,635.2 28.5 0.3 -7.5 -0.2

GEORGINA 12,471 33.3 470.3 9.0 19.1 436.9 7.6 1.1 2.7 0.1

GLANBROOK 3,579 4.3 101.0 1.3 6.5 103.0 2.3 -0.6 -0.6 -0.3

GLOUCESTER 38,289 45.3 1,124.7 25.7 71.7 1,193.5 26.1 -0.7 -1.8 0.0

GOULBOURN 6,991 2.3 242.0 4.0 11.7 193.8 5.2 -1.3 6.9 -0.2

GREY COUNTY 27,631 72.7 744.3 17.7 55.6 809.2 18.6 0.6 -2.3 0.0

GRIMSBY 6,379 3.3 203.3 5.0 10.8 190.9 4.6 -1.2 2.0 0.1

HALDIMAND-NORFOLK REGIONAL MUNICIPALITY33,668 77.3 1,070.7 15.3 69.0 1,008.0 22.1 0.2 1.9 -0.2

HALIBURTON COUNTY 4,204 9.3 97.3 2.3 7.4 114.3 3.1 0.5 -4.0 -0.2

HALTON HILLS 15,298 12.7 564.0 11.7 23.1 517.9 9.2 -0.7 3.0 0.2

Average Actual Births based on 96/97-98/99

Expected Births using 98/99 Population

Excess Births/1000 Population*

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Community

Female Pop'n

10-54 years Age 10-19

Age 20-

39 Age 40-54 Age 10-19

Age 20-

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Age 20-

39 Age 40-54




HAMILTON 105,441 309.0 3,653.3 64.0 167.6 3,945.7 62.9 1.3 -2.8 0.0

HASTINGS COUNTY 38,125 145.0 1,132.7 13.7 72.0 1,242.9 23.8 1.9 -2.9 -0.3

HURON COUNTY 18,393 41.7 622.0 10.0 42.4 539.6 11.2 0.0 4.5 -0.1

KANATA 19,114 10.3 702.3 12.3 31.0 596.9 12.9 -1.1 5.5 0.0

KENORA DISTRICT 16,593 57.7 461.7 5.3 33.2 549.7 10.3 1.5 -5.3 -0.3

KENT COUNTY 35,430 122.3 1,120.7 11.7 72.3 1,139.0 21.5 1.4 -0.5 -0.3

KING 6,024 0.7 178.3 6.7 10.2 180.5 4.4 -1.6 -0.4 0.4

KITCHENER 62,463 156.7 2,241.7 31.3 104.9 2,292.3 36.6 0.8 -0.8 -0.1

LAMBTON COUNTY 40,596 87.3 1,218.3 15.7 83.5 1,233.5 26.6 0.1 -0.4 -0.3

LANARK COUNTY 19,947 37.7 582.7 13.7 34.2 609.0 13.6 0.2 -1.3 0.0

LEEDS AND GRENVILLE UNITED COUNTIES31,138 57.0 919.0 13.7 53.0 981.6 20.4 0.1 -2.0 -0.2

LENNOX AND ADDINGTON COUNTY12,602 30.3 353.3 5.0 24.1 388.9 8.2 0.5 -2.8 -0.3

LINCOLN 5,926 5.7 240.0 3.3 9.9 195.6 3.5 -0.7 7.5 0.0

MANITOULIN DISTRICT 2,465 15.7 84.7 1.3 5.1 74.1 1.4 4.3 4.3 0.0

MARKHAM 66,717 28.7 1,650.3 57.3 132.4 1,891.9 46.7 -1.6 -3.6 0.2

MIDDLESEX COUNTY 134,766 296.7 4,313.0 90.3 224.7 4,737.3 83.7 0.5 -3.1 0.0

MILTON 11,351 13.0 309.3 8.0 23.6 331.1 7.9 -0.9 -1.9 0.0

MISSISSAUGA 210,213 211.7 7,871.0 190.3 341.6 7,334.2 127.7 -0.6 2.6 0.3

MUSKOKA DISTRICT MUNICIPALITY15,447 30.3 442.3 5.0 29.1 460.4 10.8 0.1 -1.2 -0.4

NEPEAN 40,767 37.7 1,425.3 38.3 65.0 1,403.2 26.4 -0.7 0.5 0.3

NEWMARKET 22,511 23.3 816.7 20.3 40.0 694.8 15.3 -0.7 5.4 0.2

NIAGARA FALLS 24,816 64.0 813.0 13.3 42.2 850.6 16.1 0.9 -1.5 -0.1

NIAGARA-ON-THE-LAKE 3,693 1.7 95.0 3.7 6.6 103.3 2.6 -1.3 -2.2 0.3

NIPISSING DISTRICT 27,513 94.3 756.7 6.0 53.0 902.9 16.7 1.5 -5.3 -0.4

NORTH DUMFRIES 2,813 4.7 87.7 1.3 5.3 86.5 1.8 -0.2 0.4 -0.2

NORTH YORK 198,122 286.3 7,337.3 245.7 316.5 7,384.6 116.5 -0.2 -0.2 0.7

NORTHUMBERLAND COUNTY 25,711 61.7 696.7 11.3 49.9 774.1 16.7 0.5 -3.0 -0.2

OAKVILLE 46,465 25.0 1,492.3 45.7 75.5 1,441.1 32.3 -1.1 1.1 0.3

OSGOODE 5,769 1.7 180.7 5.0 9.8 165.9 4.1 -1.4 2.6 0.2

OSHAWA 45,809 147.7 1,586.3 21.0 79.2 1,637.8 27.6 1.5 -1.1 -0.1

OTTAWA/ROCK 109,465 194.0 3,430.3 115.7 139.6 4,350.9 65.6 0.5 -8.4 0.5

OXFORD COUNTY 31,830 82.0 1,125.3 15.0 62.1 1,026.0 19.3 0.6 3.1 -0.1

PARRY SOUND DISTRICT 11,750 38.3 296.0 6.0 22.9 340.3 7.5 1.3 -3.8 -0.1

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Community

Female Pop'n

10-54 years Age 10-19

Age 20-

39 Age 40-54 Age 10-19

Age 20-

39 Age 40-54 Age 10-19

Age 20-

39 Age 40-54




PELHAM 4,860 3.7 116.0 3.7 10.3 123.7 3.6 -1.4 -1.6 0.0

PERTH COUNTY 23,324 48.3 816.3 13.0 48.4 747.0 14.1 0.0 3.0 0.0

PETERBOROUGH COUNTY 38,835 77.3 1,075.7 21.7 72.1 1,205.8 25.4 0.1 -3.4 -0.1

PICKERING 30,661 36.3 1,033.7 23.3 53.9 936.9 21.1 -0.6 3.2 0.1

PORT COLBORNE 5,536 13.7 148.3 3.7 10.3 173.6 3.7 0.6 -4.6 0.0

PRESCOTT AND RUSSELL UNITED COUNTIES26,200 36.7 775.0 10.3 45.9 846.2 17.4 -0.4 -2.7 -0.3

PRINCE EDWARD COUNTY 7,684 18.7 174.3 3.7 14.9 223.0 5.2 0.5 -6.3 -0.2

RAINY RIVER DISTRICT 6,797 24.0 218.7 4.7 14.6 218.0 4.1 1.4 0.1 0.1

RENFREW COUNTY 30,658 78.3 996.3 13.3 58.2 1,014.2 18.8 0.7 -0.6 -0.2

RICHMOND HILL 39,250 14.3 1,238.7 43.0 65.3 1,259.9 26.4 -1.3 -0.5 0.4

RIDEAU 4,337 1.0 107.3 4.7 8.8 108.6 3.2 -1.8 -0.3 0.3

SCARBOROUGH 194,348 341.7 7,229.7 213.0 313.2 7,013.6 118.6 0.1 1.1 0.5

SCUGOG 6,577 9.3 189.3 6.0 13.5 177.5 4.9 -0.6 1.8 0.2

SIMCOE COUNTY 114,075 234.7 3,766.7 66.3 195.5 3,735.4 71.4 0.3 0.3 0.0

ST. CATHARINES 41,975 85.3 1,314.0 24.0 74.3 1,441.0 25.4 0.3 -3.0 0.0

STONEY CREEK 19,172 13.0 570.0 10.3 35.3 600.7 12.8 -1.2 -1.6 -0.1

STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES35,717 103.3 1,034.7 13.7 68.2 1,126.4 22.4 1.0 -2.6 -0.2

SUDBURY DISTRICT 8,060 28.0 227.0 2.7 16.8 250.0 5.3 1.4 -2.9 -0.3

SUDBURY REGIONAL MUNICIPALITY56,254 163.7 1,579.7 21.3 107.8 1,910.2 35.7 1.0 -5.9 -0.3

THOROLD 5,823 11.0 180.3 2.0 9.1 199.8 3.8 0.3 -3.3 -0.3

THUNDER BAY DISTRICT 51,756 148.3 1,499.7 20.7 96.3 1,754.6 33.7 1.0 -4.9 -0.3

TIMISKAMING DISTRICT 11,944 42.3 349.0 3.3 26.7 368.7 7.5 1.3 -1.7 -0.3

TORONTO 232,444 268.0 7,523.3 337.0 243.6 10,122.5 141.8 0.1 -11.2 0.8

UXBRIDGE 5,441 8.3 189.0 4.7 8.4 164.0 3.7 0.0 4.6 0.2

VANIER 5,778 27.3 178.0 3.0 6.9 242.1 3.3 3.5 -11.1 -0.1

VAUGHAN 52,119 15.7 1,818.3 42.0 95.1 1,634.6 33.0 -1.5 3.5 0.2

VICTORIA COUNTY 21,174 54.3 608.0 7.0 41.5 621.1 14.2 0.6 -0.6 -0.3

WAINFLEET 2,041 2.7 58.3 1.0 4.3 58.7 1.5 -0.8 -0.2 -0.2

WATERLOO 28,341 44.3 878.7 14.7 45.7 1,018.5 17.0 0.0 -4.9 -0.1

WELLAND 15,535 36.7 440.7 5.0 28.0 530.1 10.0 0.6 -5.8 -0.3

WELLESLEY 2,856 2.0 137.7 2.3 6.9 88.2 1.5 -1.7 17.3 0.3

WELLINGTON COUNTY 59,744 101.7 2,088.7 48.3 102.8 2,048.9 36.1 0.0 0.7 0.2

WEST CARLETON 5,861 2.3 164.3 5.0 9.9 166.6 4.4 -1.3 -0.4 0.1

WEST LINCOLN 3,823 2.3 156.0 4.3 7.8 117.9 2.3 -1.4 10.0 0.5

WHITBY 28,147 22.7 925.0 17.3 49.4 867.9 19.0 -1.0 2.0 -0.1

WHITCHURCH-STOUFFVILLE 6,962 4.3 236.7 4.3 11.1 207.9 4.7 -1.0 4.1 -0.1

WILMOT 4,603 4.7 132.7 2.7 9.6 137.4 3.1 -1.1 -1.0 -0.1

WOOLWICH 5,770 7.0 191.0 4.0 13.2 167.7 3.5 -1.1 4.0 0.1

YORK 51,378 117.3 2,145.7 60.3 68.6 2,073.8 30.4 0.9 1.4 0.6

*Excess births = (actual births - expected births) / female population

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Appendix 20: Incidence of Low Birth-Weight Newborns by Community

Community Description98/99 Incidence of Low Birth-Weight Newborns

AJAX 5.9%ALGOMA DISTRICT 5.7%ANCASTER 4.5%AURORA 5.8%BRAMPTON 6.5%BRANT COUNTY 6.1%BROCK 5.8%BRUCE COUNTY 4.5%BURLINGTON 5.8%CALEDON 5.5%CAMBRIDGE 5.8%CLARINGTON 5.5%COCHRANE DISTRICT 6.0%CUMBERLAND 6.2%DUFFERIN COUNTY 5.1%DUNDAS 7.5%EAST GWILLIMBURY 5.3%EAST YORK 6.6%ELGIN COUNTY 6.9%ESSEX COUNTY 5.3%ETOBICOKE 6.6%FLAMBOROUGH 4.5%FORT ERIE 5.1%FRONTENAC COUNTY 6.1%GEORGINA 6.6%GLANBROOK 4.0%GLOUCESTER 5.2%GOULBOURN 4.8%GREY COUNTY 5.2%GRIMSBY 3.8%HALDIMAND-NORFOLK REGIONAL MUNICIPALITY 5.4%HALIBURTON COUNTY 4.8%HALTON HILLS 4.8%HAMILTON 6.3%HASTINGS COUNTY 5.6%HURON COUNTY 5.2%KANATA 6.1%KENORA DISTRICT 2.2%KENT COUNTY 5.3%KING 4.8%KITCHENER 5.4%LAMBTON COUNTY 5.0%LANARK COUNTY 5.5%LEEDS AND GRENVILLE UNITED COUNTIES 5.4%LENNOX AND ADDINGTON COUNTY 5.8%LINCOLN 4.0%MANITOULIN DISTRICT 5.0%MARKHAM 6.2%MIDDLESEX COUNTY 5.8%MILTON 5.5%MISSISSAUGA 5.8%MUSKOKA DISTRICT MUNICIPALITY 5.7%NEPEAN 5.8%NEWMARKET 6.7%NIAGARA FALLS 5.3%NIAGARA-ON-THE-LAKE 2.7%

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Appendix 20: Incidence of Low Birth-Weight Newborns by Community

Community Description98/99 Incidence of Low Birth-Weight Newborns

NIPISSING DISTRICT 5.0%NORTH DUMFRIES 2.5%NORTH YORK 6.9%NORTHUMBERLAND COUNTY 5.8%OAKVILLE 4.6%OSGOODE 5.0%OSHAWA 5.7%OTTAWA 6.3%OXFORD COUNTY 4.9%PARRY SOUND DISTRICT 6.2%PELHAM 4.4%PERTH COUNTY 4.6%PETERBOROUGH COUNTY 5.2%PICKERING 6.4%PORT COLBORNE 6.6%PRESCOTT AND RUSSELL UNITED COUNTIES 7.0%PRINCE EDWARD COUNTY 6.4%RAINY RIVER DISTRICT 2.8%RENFREW COUNTY 4.8%RICHMOND HILL 5.1%RIDEAU 4.3%SCARBOROUGH 7.0%SCUGOG 3.2%SIMCOE COUNTY 5.2%ST. CATHARINES 5.2%STONEY CREEK 5.2%STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES 6.4%SUDBURY DISTRICT 8.3%SUDBURY REGIONAL MUNICIPALITY 5.7%THOROLD 4.5%THUNDER BAY DISTRICT 4.8%TIMISKAMING DISTRICT 4.6%TORONTO 5.6%UXBRIDGE 5.3%VANIER 8.4%VAUGHAN 5.5%VICTORIA COUNTY 5.1%WAINFLEET 4.2%WATERLOO 5.4%WELLAND 5.4%WELLESLEY 5.5%WELLINGTON COUNTY 4.6%WEST CARLETON 6.4%WEST LINCOLN 5.0%WHITBY 4.8%WHITCHURCH-STOUFFVILLE 4.9%WILMOT 3.8%WOOLWICH 4.2%YORK 6.5%

25th Percentile 4.8%Median 5.4%75th Percentile 6.0%

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Appendix 21: Community Expected Pregnancy and Childbirth Weighted Cases, 98/99

Community

Community Actual

Pregnancy and Childbirth

Weighted Cases

Community Expected P&C

Weighted Cases (adjusted

for age & sex only) Fertility Index

Expected Weighted Cases

(adjusted for age, sex and fertility

rates)Actual/

ExpectedAJAX 746 658 114 754 0.99ALGOMA DISTRICT 984 1,077 93 997 0.99ANCASTER 148 161 94 152 0.98AURORA 435 336 122 412 1.06BRAMPTON 3720 3,078 117 3,604 1.03BRANT COUNTY 974 1,019 106 1,077 0.90BROCK 106 97 101 97 1.08BRUCE COUNTY 480 485 95 461 1.04BURLINGTON 1261 1,260 99 1,246 1.01CALEDON 469 368 115 425 1.10CAMBRIDGE 1017 1,027 103 1,061 0.96CLARINGTON 795 636 114 722 1.10COCHRANE DISTRICT 889 855 99 845 1.05CUMBERLAND 425 468 94 442 0.96DUFFERIN COUNTY 517 445 113 503 1.03DUNDAS 169 195 94 183 0.92EAST GWILLIMBURY 180 172 98 169 1.07EAST YORK 1281 1,155 113 1,310 0.98ELGIN COUNTY 785 707 112 795 0.99ESSEX COUNTY 3578 3,510 102 3,596 0.99ETOBICOKE 3416 3,260 110 3,589 0.95FLAMBOROUGH 381 313 122 382 1.00FORT ERIE 217 230 98 225 0.97FRONTENAC COUNTY 1081 1,364 82 1,125 0.96GEORGINA 425 360 114 411 1.03GLANBROOK 78 88 96 84 0.93GLOUCESTER 955 995 94 935 1.02GOULBOURN 202 165 113 186 1.09GREY COUNTY 686 694 98 680 1.01GRIMSBY 181 159 102 162 1.12HALDIMAND-NORFOLK REGIONAL MUNICIPALITY 918 858 106 914 1.00HALIBURTON COUNTY 97 98 92 90 1.08HALTON HILLS 465 432 107 464 1.00HAMILTON 3146 3,240 100 3,247 0.97HASTINGS COUNTY 1000 1,040 101 1,050 0.95HURON COUNTY 613 464 113 523 1.17KANATA 602 504 111 560 1.07KENORA DISTRICT 444 459 93 425 1.04KENT COUNTY 938 958 105 1,004 0.93KING 153 150 96 144 1.07KITCHENER 1994 1,893 103 1,943 1.03LAMBTON COUNTY 997 1,045 100 1,041 0.96LANARK COUNTY 499 515 99 510 0.98LEEDS AND GRENVILLE UNITED COUNTIES 825 822 96 788 1.05LENNOX AND ADDINGTON COUNTY 335 328 95 312 1.07LINCOLN 180 164 118 193 0.93MANITOULIN DISTRICT 88 63 131 83 1.07MARKHAM 1284 1,650 86 1,427 0.90MIDDLESEX COUNTY 3746 3,918 96 3,762 1.00MILTON 242 281 93 261 0.93MISSISSAUGA 6266 6,170 107 6,611 0.95MUSKOKA DISTRICT MUNICIPALITY 411 391 97 379 1.08NEPEAN 1301 1,156 102 1,175 1.11NEWMARKET 721 596 114 677 1.07NIAGARA FALLS 716 702 101 708 1.01NIAGARA-ON-THE-LAKE 71 87 92 80 0.88NIPISSING DISTRICT 750 754 92 696 1.08NORTH DUMFRIES 65 73 101 74 0.88NORTH YORK 6283 6,077 104 6,303 1.00NORTHUMBERLAND COUNTY 586 653 95 618 0.95OAKVILLE 1226 1,216 101 1,232 0.99

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Appendix 21: Community Expected Pregnancy and Childbirth Weighted Cases, 98/99

Community

Community Actual

Pregnancy and Childbirth

Weighted Cases

Community Expected P&C

Weighted Cases (adjusted

for age & sex only) Fertility Index

Expected Weighted Cases

(adjusted for age, sex and fertility

rates)Actual/

ExpectedOSGOODE 147 138 103 142 1.04OSHAWA 1650 1,350 104 1,409 1.17OTTAWA/ROCK 3060 3,527 84 2,975 1.03OXFORD COUNTY 937 865 112 966 0.97PARRY SOUND DISTRICT 312 290 97 281 1.11PELHAM 100 108 92 99 1.00PERTH COUNTY 653 634 109 692 0.94PETERBOROUGH COUNTY 961 1,015 93 944 1.02PICKERING 843 793 108 854 0.99PORT COLBORNE 128 144 92 133 0.96PRESCOTT AND RUSSELL UNITED COUNTIES 623 706 92 648 0.96PRINCE EDWARD COUNTY 163 191 86 165 0.99RAINY RIVER DISTRICT 208 185 108 200 1.04RENFREW COUNTY 832 849 102 866 0.96RICHMOND HILL 1071 1,067 97 1,034 1.04RIDEAU 85 94 95 89 0.96SCARBOROUGH 6149 5,778 108 6,219 0.99SCUGOG 170 152 104 159 1.07SIMCOE COUNTY 3274 3,145 104 3,258 1.00ST. CATHARINES 1088 1,201 95 1,138 0.96STONEY CREEK 444 507 92 466 0.95STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES955 946 98 926 1.03SUDBURY DISTRICT 194 210 98 206 0.94SUDBURY REGIONAL MUNICIPALITY 1349 1,598 89 1,428 0.94THOROLD 170 164 93 152 1.11THUNDER BAY DISTRICT 1330 1,455 92 1,337 1.00TIMISKAMING DISTRICT 307 312 101 316 0.97TORONTO 6440 8,014 77 6,210 1.04UXBRIDGE 156 138 114 157 0.99VANIER 192 195 88 171 1.12VAUGHAN 1535 1,413 105 1,486 1.03VICTORIA COUNTY 529 531 101 535 0.99WAINFLEET 54 50 97 48 1.13WATERLOO 723 851 88 751 0.96WELLAND 392 440 87 385 1.02WELLESLEY 106 76 141 107 0.99WELLINGTON COUNTY 1725 1,716 104 1,790 0.96WEST CARLETON 138 138 96 132 1.04WEST LINCOLN 144 99 124 123 1.18WHITBY 810 737 103 756 1.07WHITCHURCH-STOUFFVILLE 188 176 108 189 0.99WILMOT 124 117 94 110 1.13WOOLWICH 162 145 108 157 1.03YORK 1868 1,678 112 1,885 0.99

25th Percentile 185 94 186 0.96 Median 596 101 618 1.00 75th Percentile 1,067 108 1,061 1.05

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Appendix 22: Community Expected Newborn and Neonate (N&N) Weighted Cases, 98/99


Actual N&N Weighted

CasesExpected

BirthsCase Mix

Index

Community Expected N&N

Weighted Cases*Actual/Expected Weighted Cases

AJAX 451 852 0.45 379 1.19ALGOMA DISTRICT 648 1,369 0.44 600 1.08ANCASTER 87 200 0.41 81 1.07AURORA 220 422 0.44 186 1.19BRAMPTON 2,183 3,857 0.46 1772 1.23BRANT COUNTY 614 1,295 0.45 583 1.05BROCK 48 123 0.44 54 0.88BRUCE COUNTY 227 614 0.41 250 0.91BURLINGTON 733 1,606 0.44 709 1.03CALEDON 251 458 0.43 198 1.27CAMBRIDGE 617 1,295 0.44 571 1.08CLARINGTON 415 803 0.43 347 1.20COCHRANE DISTRICT 436 1,088 0.45 487 0.89CUMBERLAND 246 584 0.45 264 0.93DUFFERIN COUNTY 270 559 0.42 236 1.14DUNDAS 111 243 0.49 119 0.93EAST GWILLIMBURY 84 220 0.43 94 0.89EAST YORK 746 1,478 0.47 686 1.09ELGIN COUNTY 462 891 0.47 419 1.10ESSEX COUNTY 2,129 4,405 0.43 1883 1.13ETOBICOKE 1,952 4,147 0.46 1920 1.02FLAMBOROUGH 186 390 0.41 159 1.17FORT ERIE 103 291 0.42 123 0.83FRONTENAC COUNTY 633 1,715 0.45 771 0.82GEORGINA 245 457 0.46 212 1.15GLANBROOK 36 110 0.39 43 0.85GLOUCESTER 476 1,272 0.43 541 0.88GOULBOURN 105 207 0.41 85 1.23GREY COUNTY 341 869 0.43 369 0.92GRIMSBY 83 203 0.39 78 1.05HALDIMAND-NORFOLK REGIONAL MUNICIPALITY 504 1,082 0.43 465 1.08HALIBURTON COUNTY 47 123 0.42 51 0.92HALTON HILLS 259 542 0.42 225 1.15HAMILTON 1,825 4,122 0.46 1876 0.97HASTINGS COUNTY 546 1,319 0.44 577 0.95HURON COUNTY 342 584 0.43 248 1.38KANATA 338 631 0.45 284 1.19KENORA DISTRICT 159 585 0.34 201 0.79KENT COUNTY 574 1,215 0.43 518 1.11KING 79 192 0.41 79 0.99KITCHENER 1,103 2,402 0.43 1034 1.07LAMBTON COUNTY 551 1,323 0.42 555 0.99LANARK COUNTY 266 647 0.43 281 0.95LEEDS AND GRENVILLE UNITED COUNTIES 462 1,039 0.43 446 1.03LENNOX AND ADDINGTON COUNTY 162 415 0.44 183 0.89LINCOLN 104 206 0.39 81 1.29MANITOULIN DISTRICT 42 79 0.42 33 1.25MARKHAM 773 2,037 0.45 923 0.84MIDDLESEX COUNTY 2,068 4,977 0.44 2191 0.94MILTON 139 357 0.43 154 0.90

Factors Related to Calculation of Expected N&N Weighted Cases

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Actual N&N Weighted

CasesExpected

BirthsCase Mix

Index




MISSISSAUGA 3,571 7,696 0.44 3399 1.05MUSKOKA DISTRICT MUNICIPALITY 193 492 0.44 216 0.89NEPEAN 678 1,474 0.44 652 1.04NEWMARKET 402 739 0.47 345 1.17NIAGARA FALLS 353 896 0.43 383 0.92NIAGARA-ON-THE-LAKE 33 111 0.36 39 0.85NIPISSING DISTRICT 372 958 0.42 401 0.93NORTH DUMFRIES 32 92 0.35 32 0.98NORTH YORK 3,679 7,716 0.47 3640 1.01NORTHUMBERLAND COUNTY 391 828 0.44 365 1.07OAKVILLE 654 1,525 0.41 622 1.05OSGOODE 85 177 0.42 74 1.15OSHAWA 760 1,721 0.44 754 1.01OTTAWA/ROCK 1,676 4,504 0.46 2047 0.82OXFORD COUNTY 470 1,091 0.42 455 1.03PARRY SOUND DISTRICT 157 365 0.45 165 0.95PELHAM 51 135 0.40 54 0.93PERTH COUNTY 406 797 0.41 327 1.24PETERBOROUGH COUNTY 528 1,283 0.43 546 0.97PICKERING 512 996 0.46 455 1.12PORT COLBORNE 83 185 0.46 86 0.97PRESCOTT AND RUSSELL UNITED COUNTIES 366 896 0.48 425 0.86PRINCE EDWARD COUNTY 81 239 0.46 110 0.74RAINY RIVER DISTRICT 85 233 0.36 84 1.02RENFREW COUNTY 419 1,076 0.42 446 0.94RICHMOND HILL 533 1,332 0.42 562 0.95RIDEAU 39 118 0.40 47 0.83SCARBOROUGH 3,830 7,346 0.47 3476 1.10SCUGOG 68 193 0.37 71 0.95SIMCOE COUNTY 1,664 3,944 0.43 1678 0.99ST. CATHARINES 625 1,519 0.43 646 0.97STONEY CREEK 258 639 0.43 272 0.95STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES493 1,199 0.46 550 0.90SUDBURY DISTRICT 113 268 0.51 137 0.83SUDBURY REGIONAL MUNICIPALITY 786 2,025 0.44 889 0.88THOROLD 84 210 0.41 85 0.98THUNDER BAY DISTRICT 730 1,858 0.42 771 0.95TIMISKAMING DISTRICT 145 397 0.41 162 0.90TORONTO 3,554 10,389 0.44 4526 0.79UXBRIDGE 108 173 0.43 74 1.46VANIER 102 249 0.51 128 0.80VAUGHAN 767 1,736 0.43 751 1.02VICTORIA COUNTY 281 666 0.42 281 1.00WAINFLEET 22 63 0.40 25 0.89WATERLOO 421 1,067 0.43 458 0.92WELLAND 202 560 0.43 241 0.84WELLESLEY 59 95 0.43 41 1.42WELLINGTON COUNTY 906 2,157 0.41 884 1.02WEST CARLETON 66 178 0.46 81 0.81WEST LINCOLN 73 126 0.42 53 1.37

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Actual N&N Weighted

CasesExpected

BirthsCase Mix

Index




WHITBY 399 922 0.42 382 1.04WHITCHURCH-STOUFFVILLE 120 220 0.42 92 1.31WILMOT 65 148 0.39 57 1.14WOOLWICH 92 181 0.40 72 1.27YORK 1,106 2,146 0.46 987 1.12TOTAL 59,227 134,248 0.44 59,227 1.00

25th Percentile 0.42 0.91Median 0.43 0.9975th Percentile 0.45 1.11

*Community Expected N&N Weighted Cases = Expected Births * Case Mix Index

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Appendix 23: Community Weighted Case Growth Estimated for Medical and Surgical Case Mix, 98/99

A B C D E F=C*E G=D*E H=B+F+G I=A/H


Actual 98/99 M&S Weighted

Cases

Expected 98/99 M&S Weighted

Cases

Expected Tertiary Growth

Expected Non-Tertiary

Growth MARI Index

Tertiary M&S

GrowthNon-Tertiary M&S Growth

Total Expected 00/01 M&S

Weighted CasesActual/Expected Weighted Cases

AJAX 6,713 6,346 158 444 96 151 425 6,922 0.97 ALGOMA DISTRICT 23,417 23,355 152 556 132 200 734 24,289 0.96 ANCASTER 2,544 2,392 53 160 76 40 121 2,553 1.00 AURORA 3,771 3,727 118 342 92 109 316 4,151 0.91 BRAMPTON 27,432 27,828 850 2,433 98 831 2,379 31,039 0.88 BRANT COUNTY 18,822 15,927 127 484 98 124 473 16,524 1.14 BROCK 1,848 1,796 11 47 99 11 47 1,853 1.00 BRUCE COUNTY 11,910 11,346 65 260 114 75 296 11,717 1.02 BURLINGTON 18,377 17,024 254 821 89 226 731 17,982 1.02 CALEDON 4,489 4,197 144 404 87 126 353 4,676 0.96 CAMBRIDGE 13,159 12,385 211 667 97 204 647 13,236 0.99 CLARINGTON 8,380 6,917 184 590 95 175 562 7,654 1.09 COCHRANE DISTRICT 15,837 14,729 104 332 132 138 437 15,304 1.03 CUMBERLAND 3,831 4,245 193 520 92 177 477 4,899 0.78 DUFFERIN COUNTY 6,609 5,679 124 354 101 125 358 6,162 1.07 DUNDAS 3,372 3,216 28 82 87 24 72 3,312 1.02 EAST GWILLIMBURY 2,263 2,119 56 146 95 53 139 2,312 0.98 EAST YORK 16,084 15,975 169 589 97 165 573 16,713 0.96 ELGIN COUNTY 12,101 11,904 100 371 107 107 395 12,405 0.98 ESSEX COUNTY 53,318 48,523 623 2,148 101 627 2,163 51,313 1.04 ETOBICOKE 43,855 45,867 515 2,079 93 479 1,935 48,282 0.91 FLAMBOROUGH 3,952 3,779 109 330 89 96 293 4,169 0.95 FORT ERIE 4,856 5,055 37 160 119 43 190 5,289 0.92 FRONTENAC COUNTY 17,788 18,455 199 789 96 191 757 19,403 0.92 GEORGINA 5,083 4,821 94 311 108 102 336 5,259 0.97 GLANBROOK 1,503 1,204 31 99 84 26 83 1,314 1.14 GLOUCESTER 10,527 10,252 160 338 93 149 315 10,717 0.98 GOULBOURN 2,045 2,034 64 204 86 55 176 2,265 0.90 GREY COUNTY 16,895 15,365 96 392 110 106 431 15,902 1.06 GRIMSBY 2,178 2,688 34 98 96 33 94 2,815 0.77 HALDIMAND-NORFOLK REGIONAL MUNICIPALITY 15,274 14,962 167 551 102 171 563 15,695 0.97 HALIBURTON COUNTY 2,812 2,861 21 107 98 20 105 2,986 0.94 HALTON HILLS 4,954 4,930 142 430 95 134 408 5,472 0.91 HAMILTON 51,395 50,961 335 1,483 108 362 1,600 52,923 0.97 HASTINGS COUNTY 17,041 18,230 124 544 102 127 557 18,914 0.90 HURON COUNTY 10,526 9,665 16 167 102 17 171 9,852 1.07 KANATA 3,956 4,321 186 510 90 167 459 4,947 0.80 KENORA DISTRICT 6,225 8,113 68 220 125 85 276 8,475 0.73 KENT COUNTY 17,555 17,647 89 346 113 100 391 18,138 0.97 KING 2,002 2,061 40 105 87 35 91 2,187 0.92 KITCHENER 21,550 21,800 309 965 97 299 934 23,033 0.94

Growth

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Cases


Cases



Growth MARI Index

Tertiary M&S




Growth

LAMBTON COUNTY 20,798 20,180 87 381 111 96 422 20,698 1.00 LANARK COUNTY 10,047 9,776 110 399 109 120 434 10,330 0.97 LEEDS AND GRENVILLE UNITED COUNTIES 14,475 16,039 148 539 108 160 583 16,781 0.86 LENNOX AND ADDINGTON COUNTY 4,994 6,034 54 179 106 58 190 6,282 0.79 LINCOLN 2,371 2,400 32 99 85 27 84 2,511 0.94 MANITOULIN DISTRICT 2,248 2,174 10 45 140 15 62 2,251 1.00 MARKHAM 15,727 17,355 568 1,612 84 477 1,354 19,186 0.82 MIDDLESEX COUNTY 47,371 49,800 538 1,833 96 514 1,752 52,065 0.91 MILTON 3,960 3,776 50 113 98 49 111 3,937 1.01 MISSISSAUGA 52,302 59,258 1,925 5,775 95 1,821 5,464 66,543 0.79 MUSKOKA DISTRICT MUNICIPALITY 8,454 9,639 78 330 117 91 386 10,116 0.84 NEPEAN 12,179 12,156 256 714 87 223 622 13,002 0.94 NEWMARKET 6,711 6,810 199 602 103 205 620 7,636 0.88 NIAGARA FALLS 14,236 12,970 77 349 111 85 389 13,444 1.06 NIAGARA-ON-THE-LAKE 1,942 2,049 16 76 87 14 66 2,128 0.91 NIPISSING DISTRICT 14,573 14,402 78 291 125 97 365 14,864 0.98 NORTH DUMFRIES 805 890 25 78 95 23 74 987 0.82 NORTH YORK 77,895 74,579 744 2,869 85 634 2,445 77,657 1.00 NORTHUMBERLAND COUNTY 12,896 12,820 109 456 102 111 465 13,396 0.96 OAKVILLE 14,955 14,804 373 1,092 91 338 990 16,132 0.93 OSGOODE 1,596 1,522 46 125 84 39 104 1,665 0.96 OSHAWA 19,012 18,144 221 689 106 234 729 19,107 1.00 OTTAWA/ROCK 47,864 44,614 256 1,241 92 237 1,146 45,996 1.04 OXFORD COUNTY 14,242 14,199 125 471 102 128 480 14,807 0.96 PARRY SOUND DISTRICT 7,243 7,467 58 247 113 66 278 7,811 0.93 PELHAM 1,848 1,541 35 117 74 26 87 1,654 1.12 PERTH COUNTY 9,244 9,349 48 206 90 43 185 9,578 0.97 PETERBOROUGH COUNTY 20,806 19,394 102 558 99 101 553 20,049 1.04 PICKERING 7,892 7,880 273 762 94 258 720 8,857 0.89 PORT COLBORNE 3,691 3,760 8 64 122 10 78 3,847 0.96 PRESCOTT AND RUSSELL UNITED COUNTIES 9,254 9,507 162 465 102 165 473 10,144 0.91 PRINCE EDWARD COUNTY 4,489 5,028 32 148 116 38 173 5,239 0.86 RAINY RIVER DISTRICT 3,759 3,798 9 51 118 11 61 3,869 0.97 RENFREW COUNTY 16,712 16,647 105 429 117 123 504 17,274 0.97 RICHMOND HILL 10,447 10,734 351 1,056 88 309 930 11,973 0.87 RIDEAU 1,406 1,125 32 81 73 23 59 1,207 1.17 SCARBOROUGH 67,310 70,225 1,118 3,598 94 1,056 3,399 74,679 0.90 SCUGOG 2,899 2,434 37 118 99 37 117 2,588 1.12 SIMCOE COUNTY 47,342 46,964 792 2,806 102 810 2,868 50,641 0.93 ST. CATHARINES 20,381 20,513 122 596 100 123 599 21,234 0.96 STONEY CREEK 7,035 6,376 122 388 89 109 345 6,830 1.03

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Cases


Cases



Growth MARI Index

Tertiary M&S




Growth

STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES19,043 20,147 120 443 122 147 542 20,836 0.91 SUDBURY DISTRICT 4,613 4,469 24 90 136 33 121 4,623 1.00 SUDBURY REGIONAL MUNICIPALITY 25,862 24,719 236 800 115 272 924 25,915 1.00 THOROLD 2,663 2,611 23 97 107 24 104 2,738 0.97 THUNDER BAY DISTRICT 25,922 25,631 123 518 122 151 634 26,415 0.98 TIMISKAMING DISTRICT 7,487 7,316 15 82 131 19 107 7,443 1.01 TORONTO 82,328 87,222 775 2,392 100 779 2,403 90,404 0.91 UXBRIDGE 2,502 1,881 43 132 87 38 115 2,034 1.23 VANIER 2,861 2,988 10 47 122 13 58 3,058 0.94 VAUGHAN 13,408 12,838 520 1,492 85 445 1,275 14,559 0.92 VICTORIA COUNTY 12,754 12,403 91 435 111 101 483 12,987 0.98 WAINFLEET 771 846 13 36 103 13 37 896 0.86 WATERLOO 8,305 8,442 170 561 87 148 489 9,079 0.91 WELLAND 8,829 8,205 46 220 112 51 246 8,502 1.04 WELLESLEY 671 755 9 26 84 7 22 785 0.86 WELLINGTON COUNTY 20,976 21,533 321 1,059 94 303 1,001 22,837 0.92 WEST CARLETON 1,519 1,698 45 118 90 41 106 1,845 0.82 WEST LINCOLN 1,173 1,214 22 60 93 20 56 1,290 0.91 WHITBY 7,754 8,399 239 710 100 240 713 9,352 0.83 WHITCHURCH-STOUFFVILLE 2,453 2,403 47 125 91 42 113 2,558 0.96 WILMOT 1,521 1,725 24 73 93 22 68 1,815 0.84 WOOLWICH 1,910 2,002 27 76 84 23 64 2,088 0.91 YORK 19,609 21,318 220 752 105 231 790 22,340 0.88 TOTAL 1,454,593 1,454,593 19,255 64,366 18,754 63,096 1,536,443 0.95


Notes: Columns B and C are based on age and sex adjustment only. MARI Index = Mortality (excess), Aboriginal, Rural and Income Index. Columns F and G are based on adjustments for age, sex, excess mortality, aboriginal population, rural population and low income.

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Appendix 24: Community Weighted case Growth Estimates for Pregnancy & Childbirth and Newborn & Neonates


Actual 98/99 Pregnancy &

Childbirth Weighted

Cases

Expected 98/99

Pregnancy & Childbirth Weighted

Cases

Expected Weighted

Case Growth (age+sex

adjusted only)Fertility Index

Total Expected

Pregnancy & Childbirth

Growth

Total Expected


Cases, 00/01

Actual 98/99 Newborn &

Neonate Weighted

Cases

Expected 98/99

Newborn & Neonate Weighted

CasesCase Mix Index**

New Births

Expected Growth in


Cases

Total Expected Newborn &

Neonate Weighted

Cases, 2000/01

AJAX 746 754 -43 114 -49 705 451 379 0.46 -56 -26 354 ALGOMA DISTRICT 984 997 -28 93 -26 971 648 600 0.52 -34 -18 583 ANCASTER 148 152 8 94 8 160 87 81 0.46 11 5 87 AURORA 435 412 1 122 1 413 220 186 0.40 0 0 186 BRAMPTON 3720 3604 1 117 1 3,606 2183 1772 0.48 -7 -3 1,769 BRANT COUNTY 974 1077 -23 106 -24 1,053 614 583 0.45 -29 -13 570 BROCK 106 97 0 101 0 97 48 54 0.35 -1 0 54 BRUCE COUNTY 480 461 5 95 5 465 227 250 0.36 7 2 252 BURLINGTON 1261 1246 -49 99 -49 1,198 733 709 0.46 -63 -29 680 CALEDON 469 425 10 115 12 437 251 198 0.44 12 5 203 CAMBRIDGE 1017 1061 -12 103 -13 1,048 617 571 0.47 -18 -8 563 CLARINGTON 795 722 -14 114 -16 707 415 347 0.45 -21 -9 338 COCHRANE DISTRICT 889 845 -23 99 -22 823 436 487 0.42 -28 -12 475 CUMBERLAND 425 442 16 94 15 457 246 264 0.44 20 9 273 DUFFERIN COUNTY 517 503 -2 113 -3 500 270 236 0.44 -5 -2 234 DUNDAS 169 183 -1 94 -1 182 111 119 0.48 -2 -1 118 EAST GWILLIMBURY 180 169 -4 98 -4 164 84 94 0.38 -5 -2 92 EAST YORK 1281 1310 -77 113 -88 1,223 746 686 0.46 -101 -46 640 ELGIN COUNTY 785 795 -1 112 -1 794 462 419 0.45 -2 -1 418 ESSEX COUNTY 3578 3596 0 102 0 3,596 2129 1883 0.48 -3 -2 1,881 ETOBICOKE 3416 3589 -136 110 -150 3,439 1952 1920 0.43 -178 -77 1,843 FLAMBOROUGH 381 382 5 122 6 388 186 159 0.37 6 2 161 FORT ERIE 217 225 -5 98 -5 220 103 123 0.37 -7 -3 121 FRONTENAC COUNTY 1081 1125 -3 82 -3 1,122 633 771 0.45 -4 -2 769 GEORGINA 425 411 -10 114 -12 399 245 212 0.47 -15 -7 205 GLANBROOK 78 84 2 96 2 86 36 43 0.36 3 1 44 GLOUCESTER 955 935 -34 94 -32 903 476 541 0.40 -42 -17 524 GOULBOURN 202 186 2 113 2 188 105 85 0.39 2 1 86 GREY COUNTY 686 680 11 98 10 691 341 369 0.40 13 5 374 GRIMSBY 181 162 -5 102 -5 157 83 78 0.38 -7 -3 76 HALDIMAND-NORFOLK REGIONAL MUNICIPALITY918 914 1 106 1 915 504 465 0.43 1 1 466 HALIBURTON COUNTY 97 90 2 92 2 92 47 51 0.45 3 1 52 HALTON HILLS 465 464 -4 107 -5 459 259 225 0.44 -7 -3 222 HAMILTON 3146 3247 -130 100 -130 3,117 1825 1876 0.45 -166 -75 1,801 HASTINGS COUNTY 1000 1050 -24 101 -24 1,026 546 577 0.41 -31 -13 564 HURON COUNTY 613 523 7 113 8 531 342 248 0.53 9 5 253 KANATA 602 560 -2 111 -3 558 338 284 0.47 -4 -2 282 KENORA DISTRICT 444 425 -13 93 -12 413 159 201 0.28 -17 -5 196 KENT COUNTY 938 1004 -15 105 -16 988 574 518 0.44 -19 -8 510 KING 153 144 -5 96 -5 138 79 79 0.40 -7 -3 77 KITCHENER 1994 1943 -60 103 -61 1,882 1103 1034 0.45 -78 -35 998 LAMBTON COUNTY 997 1041 -13 100 -13 1,028 551 555 0.42 -15 -6 549 LANARK COUNTY 499 510 -1 99 -1 509 266 281 0.43 -3 -1 279 LEEDS AND GRENVILLE UNITED COUNTIES825 788 -10 96 -10 778 462 446 0.45 -14 -6 440 LENNOX AND ADDINGTON COUNTY 335 312 -6 95 -6 306 162 183 0.42 -8 -4 179 LINCOLN 180 193 -3 118 -3 189 104 81 0.45 -4 -2 79 MANITOULIN DISTRICT 88 83 0 131 0 83 42 33 0.39 0 0 33 MARKHAM 1284 1427 85 86 74 1,500 773 923 0.44 110 49 972 MIDDLESEX COUNTY 3746 3762 -110 96 -105 3,656 2068 2191 0.44 -140 -62 2,129 MILTON 242 261 -5 93 -5 256 139 154 0.41 -7 -3 152

Newborns and NeonatesPregnancy and Childbirth

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Childbirth Weighted

Cases

Expected 98/99


Cases

Expected Weighted



Total Expected


Growth

Total Expected


Cases, 00/01


Neonate Weighted

Cases

Expected 98/99



New Births

Expected Growth in


Cases


Neonate Weighted

Cases, 2000/01


MISSISSAUGA 6266 6611 49 107 52 6,663 3571 3399 0.43 43 19 3,418 MUSKOKA DISTRICT MUNICIPALITY 411 379 -1 97 -1 379 193 216 0.40 -1 -1 216 NEPEAN 1301 1175 -53 102 -54 1,122 678 652 0.45 -68 -30 621 NEWMARKET 721 677 21 114 24 701 402 345 0.46 25 12 356 NIAGARA FALLS 716 708 -29 101 -29 679 353 383 0.40 -37 -15 368 NIAGARA-ON-THE-LAKE 71 80 2 92 2 82 33 39 0.33 3 1 40 NIPISSING DISTRICT 750 696 -21 92 -19 677 372 401 0.43 -26 -11 390 NORTH DUMFRIES 65 74 2 101 2 76 32 32 0.26 2 1 33 NORTH YORK 6283 6303 -221 104 -229 6,074 3679 3640 0.47 -289 -135 3,505 NORTHUMBERLAND COUNTY 586 618 -9 95 -9 610 391 365 0.52 -12 -6 359 OAKVILLE 1226 1232 3 101 3 1,236 654 622 0.41 3 1 623 OSGOODE 147 142 -4 103 -4 138 85 74 0.47 -5 -2 72 OSHAWA 1650 1409 -57 104 -60 1,349 760 754 0.43 -74 -32 722 OTTAWA/ROCK 3060 2975 -177 84 -149 2,826 1676 2047 0.45 -226 -102 1,945 OXFORD COUNTY 937 966 -3 112 -3 962 470 455 0.38 -5 -2 453 PARRY SOUND DISTRICT 312 281 1 97 1 282 157 165 0.45 1 1 166 PELHAM 100 99 5 92 4 104 51 54 0.40 6 2 57 PERTH COUNTY 653 692 -5 109 -5 687 406 327 0.47 -7 -3 323 PETERBOROUGH COUNTY 961 944 -4 93 -4 941 528 546 0.45 -4 -2 544 PICKERING 843 854 1 108 1 855 512 455 0.47 0 0 455 PORT COLBORNE 128 133 -4 92 -4 129 83 86 0.50 -5 -3 83 PRESCOTT AND RUSSELL UNITED COUNTIES623 648 -18 92 -17 631 366 425 0.44 -25 -11 414 PRINCE EDWARD COUNTY 163 165 2 86 2 166 81 110 0.39 2 1 110 RAINY RIVER DISTRICT 208 200 -1 108 -1 199 85 84 0.32 -1 0 83 RENFREW COUNTY 832 866 -13 102 -14 852 419 446 0.38 -18 -7 439 RICHMOND HILL 1071 1034 14 97 13 1,047 533 562 0.41 15 6 568 RIDEAU 85 89 2 95 2 91 39 47 0.39 3 1 48 SCARBOROUGH 6149 6219 -194 108 -208 6,011 3830 3476 0.49 -250 -123 3,353 SCUGOG 170 159 -1 104 -1 158 68 71 0.36 -1 0 71 SIMCOE COUNTY 3274 3258 9 104 9 3,267 1664 1678 0.41 3 1 1,679 ST. CATHARINES 1088 1138 -29 95 -27 1,110 625 646 0.43 -37 -16 630 STONEY CREEK 444 466 -1 92 0 466 258 272 0.43 -1 0 271 STORMONT, DUNDAS AND GLENGARRY UNITED COUNTIES955 926 -18 98 -18 908 493 550 0.42 -24 -10 540 SUDBURY DISTRICT 194 206 -6 98 -6 201 113 137 0.45 -7 -3 134 SUDBURY REGIONAL MUNICIPALITY 1349 1428 -27 89 -24 1,404 786 889 0.44 -33 -15 874 THOROLD 170 152 -11 93 -10 142 84 85 0.42 -14 -6 79 THUNDER BAY DISTRICT 1330 1337 -43 92 -39 1,298 730 771 0.43 -53 -23 748 TIMISKAMING DISTRICT 307 316 -6 101 -6 310 145 162 0.36 -8 -3 159 TORONTO 6440 6210 -759 77 -588 5,622 3554 4526 0.44 -982 -427 4,099 UXBRIDGE 156 157 0 114 0 157 108 74 0.57 -1 0 74 VANIER 192 171 -12 88 -11 160 102 128 0.49 -15 -7 120 VAUGHAN 1535 1486 72 105 76 1,562 767 751 0.40 89 36 786 VICTORIA COUNTY 529 535 5 101 5 540 281 281 0.41 5 2 283 WAINFLEET 54 48 -1 97 -1 47 22 25 0.31 -2 0 25 WATERLOO 723 751 2 88 2 753 421 458 0.45 2 1 459 WELLAND 392 385 -12 87 -10 374 202 241 0.42 -15 -6 234 WELLESLEY 106 107 0 141 0 107 59 41 0.57 0 0 41 WELLINGTON COUNTY 1725 1790 -5 104 -5 1,785 906 884 0.39 -9 -4 880 WEST CARLETON 138 132 -6 96 -6 126 66 81 0.37 -8 -3 78 WEST LINCOLN 144 123 -3 124 -4 119 73 53 0.52 -4 -2 51

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Childbirth Weighted

Cases

Expected 98/99


Cases

Expected Weighted



Total Expected


Growth

Total Expected


Cases, 00/01


Neonate Weighted

Cases

Expected 98/99



New Births

Expected Growth in


Cases


Neonate Weighted

Cases, 2000/01


WHITBY 810 756 6 103 6 762 399 382 0.41 6 2 385 WHITCHURCH-STOUFFVILLE 188 189 -5 108 -5 184 120 92 0.48 -6 -3 89 WILMOT 124 110 -3 94 -3 107 65 57 0.46 -4 -2 55 WOOLWICH 162 157 3 108 3 160 92 72 0.46 4 2 74 YORK 1868 1885 -101 112 -113 1,771 1106 987 0.48 -132 -63 924 TOTAL* 106,031 106,018 -2,380 -2,233 103,785 59,227 59,227 -3,148 -1,412 57,814

*Note: discrepancy between actual and expected 98/99 P&C weighted cases (13 weighted cases) is due to rounding.**Note: community-specific CMI values may be different from CMI values in Appendix 24. This is a result of recalibration during calculation of the growth formula.

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Appendix 25: Hospital Medical and Surgical Weighted Case Allocations to 2000/2001

MIS Facility Name Actual, 98/99

Expected 98/99 (age +

sex adjusted)

Expected 98/99 (age, sex, MARI

adjusted)

Non-Tertiary Growth to 2000/2001

Tertiary Growth to 2000/2001

M&S Expected Weighted Cases,

2000/01592 NAPANEE Lennox & Addington 1,690 1,883 1,983 190 1 2,175593 NEWBURY Four Counties 743 739 756 18 0 774 596 ALLISTON Stevenson Memorial 2,419 2,337 2,383 219 1 2,603 597 ALMONTE General 840 772 825 64 0 889 599 ARNPRIOR & District Memorial 1,635 1,480 1,651 63 1 1,716 600 ATIKOKAN General 410 350 414 9 0 423 606 BARRIE Roval Victoria 16,106 15,619 16,042 1,281 190 17,513 611 BLIND RIVER St Joseph's 698 528 696 29 1 725 614 BRACEBRIDGE S Muskoka Memorial 3,033 2,975 3,415 223 5 3,643 617 BRANTFORD General 14,048 12,331 12,074 422 47 12,544 618 BRANTFORD St Joseph's 2,068 1,846 1,811 43 20 1,874 619 BROCKVILLE General 4,914 5,001 5,423 372 21 5,816 620 BROCKVILLE St Vincent de Paul 1,670 1,696 1,836 135 1 1,972 624 CAMPBELLFORD Memorial 1,824 1,778 1,800 112 1 1,913 626 CARLETON PLACE & District Memorial 1,009 914 984 82 0 1,066 627 CHAPLEAU General 241 173 234 29 0 263 628 CHATHAM Public General 7,103 6,329 7,119 230 8 7,356 629 CHATHAM St Joseph's 3,783 3,372 3,789 108 16 3,913 632 TORONTO North York General 32,716 36,556 32,182 1,097 250 33,529 633 CLINTON Public 1,082 977 998 30 0 1,028 638 COCHRANE Lady Minto 787 557 733 30 0 763 640 COLLINGWOOD General and Marine 3,661 3,524 3,608 309 9 3,925 643 CORNWALL General 4,789 4,151 5,064 192 19 5,275 644 CORNWALL Hotel Dieu 5,622 4,872 5,945 223 24 6,192 646 DEEP RIVER and District 687 584 684 35 0 719 647 DRYDEN District General 1,587 1,645 2,061 86 3 2,151 648 DUNNVILLE Haldimand War Memorial 1,175 1,142 1,162 86 1 1,249 650 ELLIOT LAKE St Joseph's 2,469 1,855 2,455 91 4 2,550 653 ENGLEHART & District 571 427 559 13 0 571 654 ESPANOLA General 791 571 767 92 1 860 655 EXETER South Huron 824 747 770 19 0 789 656 FERGUS Groves Memorial Comm 2,141 2,306 2,185 153 3 2,342 661 CAMBRIDGE Memorial 11,264 11,101 10,716 687 85 11,487 662 GERALDTON District Hospital 635 514 628 21 0 648 663 GODERICH Alexandra Marine & General 2,708 2,430 2,502 69 1 2,571 664 GRIMSBY West Lincoln Memorial 2,165 2,571 2,368 172 3 2,543 665 GUELPH General 7,250 7,782 7,387 437 58 7,882 666 GUELPH St Joseph's Hospital 5,480 5,847 5,568 296 66 5,931 674 HAMILTON St Joseph's 27,341 26,108 26,641 878 262 27,781 676 HANOVER & District 1,644 1,368 1,508 57 1 1,567 679 SUDBURY Algoma 546 597 552 22 0 573 681 HEARST Notre Dame 850 601 791 32 0 824 682 HORNEPAYNE Community 177 135 176 7 0 184 684 INGERSOLL Alexandra 1,631 1,605 1,631 82 3 1,716 685 IROQUOIS FALLS Anson General 534 378 497 20 0 517 686 WAWA North Algoma 444 337 442 18 0 461 687 KAPUSKASING Sensenbrenner 1,696 1,201 1,579 64 1 1,644 692 KINGSTON Hotel Dieu 3,191 3,428 3,405 141 1 3,548 693 KINGSTON General 27,854 29,394 29,626 616 443 30,684 696 KIRKLAND & District 2,040 1,520 1,991 43 1 2,036 699 KITCHENER St Mary's 13,564 14,685 13,821 725 138 14,684 701 RICHMOND HILL York Central 14,060 16,123 14,237 2,557 195 16,988 704 LEAMINGTON District Memorial 3,796 3,428 3,483 195 4 3,682 707 LINDSAY Ross Memorial 7,418 6,621 7,220 483 15 7,718 709 LISTOWEL Memorial 1,105 1,197 1,103 26 0 1,130 714 LONDON St Joseph's 21,664 22,606 22,164 596 169 22,930 718 BURLINGTON Joseph Brant Memorial 14,787 15,359 13,860 689 134 14,683 719 MANITOUWADGE General 607 444 574 23 0 596 721 MARATHON Wilson Memorial 266 215 263 9 0 272 723 MATHESON Bingham Memorial 253 179 236 10 0 245

M&S Weighted Cases

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sex adjusted)


adjusted)




2000/01

M&S Weighted Cases

724 MATTAWA General 469 372 464 16 0 479 726 MIDLAND Huronia 4,106 3,998 4,088 380 5 4,473 731 MISSISSAUGA Credit Valley 16,387 18,950 17,974 2,372 312 20,658 732 KEMPTVILLE District 1,015 1,042 1,088 75 0 1,163 733 MOUNT FOREST Louise Marshall 941 965 939 55 0 995 734 HALDIMAND West Haldimand General 833 798 814 68 0 882 736 NEWMARKET Southlake Regional Health Centre17,018 16,719 16,743 1,373 248 18,364 739 NIPIGON District Memorial 533 434 527 17 0 545 745 ORILLIA Soldiers' Memorial 8,621 8,363 8,633 659 46 9,338 751 OTTAWA CHEO 6,724 6,880 6,701 218 110 7,028 753 OTTAWA Montfort 9,282 9,362 9,246 301 61 9,607 755 OTTAWA SA Grace 5,581 5,774 5,431 251 8 5,690 759 PALMERSTON & District 896 965 914 60 1 974 760 PARIS - The Willett 227 201 196 8 0 204 763 PEMBROKE General 5,961 5,068 5,938 290 5 6,233 766 PENETANGUISHENE General 205 199 204 20 0 224 768 BARRY'S BAY St Francis Memorial 639 545 638 24 0 662 771 PETERBOROUGH Civic 21,288 20,138 20,226 553 90 20,869 776 PETROLIA Charlotte Eleanor Englehart 1,331 1,167 1,293 40 0 1,334 777 NEPEAN Queensway-Carleton 11,904 12,904 11,784 699 93 12,576 784 LITTLE CURRENT Manitoulin 1,256 886 1,216 62 1 1,279 788 RENFREW Victoria 1,918 1,640 1,916 91 2 2,010 790 ST CATHARINES Hotel Dieu 10,208 10,167 10,184 377 55 10,616 792 ST MARY'S Memorial 864 949 873 21 1 894 793 ST THOMAS Elgin General 8,234 7,652 8,120 395 40 8,556 795 SARNIA St. Joseph's 3,839 3,370 3,732 102 12 3,847 796 SARNIA General 10,201 8,953 9,911 279 28 10,218 797 SAULT STE MARIE General 15,810 11,950 15,764 589 83 16,436 800 HAWKESBURY & District General 2,396 2,412 2,462 473 8 2,943 801 SEAFORTH Community 724 663 670 20 0 690 802 ALEXANDRIA Glengarry Memorial 929 809 981 41 0 1,022 804 SIMCOE Norfolk General 4,958 4,755 4,852 409 6 5,266 805 SIOUX LOOKOUT District 580 596 746 31 1 778 809 SMOOTH ROCK FALLS 410 290 381 16 0 397 813 STRATFORD General 6,821 7,370 6,822 139 29 6,989 814 STRATHROY Middlesex General 2,851 2,967 2,934 110 2 3,045 819 TERRACE BAY McCausland 257 209 254 8 0 263 824 TILLSONBURG District Memorial 2,852 2,756 2,826 105 2 2,934 826 KENORA Lake-of-the-Woods District 2,475 2,551 3,191 125 15 3,330 837 TORONTO Hospital for Sick Children 19,123 20,046 19,410 275 555 20,241 842 TORONTO Mount Sinai 19,352 20,644 19,879 465 362 20,706 852 TORONTO St Michael's 49,530 52,108 50,728 932 1,351 53,012 854 TORONTO SA Grace 3,270 3,421 3,290 89 0 3,379 858 TORONTO East General 23,363 24,711 23,953 874 228 25,055 870 WALLACEBURG Sydenham 1,670 1,485 1,673 53 1 1,726 881 STURGEON FALLS West Nipissing 1,563 1,233 1,544 51 1 1,596 882 WINCHESTER District Memorial 2,397 2,158 2,524 86 3 2,613 888 NEW LISKEARD Temiskaming 2,469 1,848 2,412 51 1 2,464 889 WINGHAM & District 1,749 1,544 1,629 34 1 1,663 890 WOODSTOCK General 6,084 5,957 6,061 293 31 6,386 896 RED LAKE Marg Cochenour Memorial 626 650 815 34 2 851 898 TORONTO St Joseph's 20,922 22,579 21,992 768 203 22,963 900 FORT FRANCES Riverside Health Care 2,364 2,024 2,397 52 1 2,449 903 HUNTSVILLE District Memorial 3,056 2,948 3,355 163 5 3,523 905 MARKHAM Stouffville 10,033 11,646 10,322 1,155 141 11,618 906 NORTH BAY General 12,001 9,699 11,923 298 45 12,266 907 TIMMINS & District General 7,835 5,559 7,317 247 43 7,607 916 ORANGEVILLE Dufferin-Caledon 5,336 4,822 4,737 358 16 5,111 927 WINDSOR Hotel Dieu Grace 25,451 23,017 23,220 1,077 299 24,596 928 SMITHS FALLS Perth & Smiths Falls 5,154 4,809 5,183 288 24 5,494

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sex adjusted)


adjusted)




2000/01

M&S Weighted Cases

930 KITCHENER Grand River 16,105 17,650 16,405 885 161 17,452 931 PARRY SOUND West Parry Sound 2,507 2,322 2,595 278 3 2,876 933 WINDSOR Regional 18,764 16,964 17,107 892 138 18,137 935 THUNDER BAY Regional 23,432 19,193 23,459 573 163 24,195 936 LONDON Health Sciences 64,395 65,152 64,871 1,027 1,266 67,165 938 MINDEN Haliburton Highlands 554 576 563 105 0 667 940 COBOURG Northumberland 3,874 3,781 3,851 352 4 4,207 941 TORONTO Humber River Regional 32,787 36,027 33,020 1,205 236 34,461 942 HAMILTON Health Sciences Centre 65,555 63,521 63,870 1,636 1,073 66,580 946 KINCARDINE S Bruce Grey Hlth Ctr 4,599 3,863 4,341 204 2 4,547 947 TORONTO University Health Network 83,100 88,241 85,379 1,284 2,818 89,481 949 MISSISSAUGA Trillium Health Centre 35,399 40,920 38,644 3,653 782 43,079 950 OAKVILLE Halton Heatlhcare Services Corporation16,669 17,928 16,567 1,212 187 17,966 951 TORONTO William Osler 39,370 42,187 40,136 3,481 527 44,144 952 OSHAWA Lakeridge Health Corporation 32,063 29,623 30,076 2,307 299 32,682 953 TORONTO Sunnybrook & Women's 48,580 51,637 49,118 895 1,391 51,404 954 TORONTO Rouge Valley Health System 28,424 30,478 28,884 2,028 354 31,266 955 OWEN SOUND Grey Bruce Health Services 15,640 13,151 14,490 465 76 15,032 957 BELLEVILLE Quinte Health Care Corporation 15,865 16,181 16,974 730 49 17,753 958 OTTAWA The Ottawa Hospital 80,201 81,378 78,426 2,053 1,403 81,882 959 SUDBURY Regional Hospital Corporation 35,241 28,452 33,986 924 451 35,361 960 TORONTO The Scarborough Hospital 39,366 43,455 40,629 1,850 438 42,917 962 ST. CATHARINES Niagara Health System 39,089 35,384 37,768 1,481 143 39,392

TOTAL 1,454,575 1,454,574 1,454,575 63,096 18,754 1,536,425

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Appendix 26: Hospital Pregnancy & Childbirth Weighted Case Allocations

MIS Facility Name

Actual Weighted

Cases, 98/99

Expected Weighted

Cases 1998/99

Pregnancy & Childbirth Growth to

2000/01

Total Expected Weighted Cases,

2000/01592 NAPANEE Lennox & Addington 91 88 -2 86593 NEWBURY Four Counties 2 2 0 2596 ALLISTON Stevenson Memorial 262 260 0 260597 ALMONTE General 175 178 -1 177599 ARNPRIOR & District Memorial 2 2 0 2600 ATIKOKAN General 15 15 0 15606 BARRIE Roval Victoria 1,438 1,430 4 1,434611 BLIND RIVER St Joseph's 2 2 0 2614 BRACEBRIDGE S Muskoka Memorial 146 135 0 135617 BRANTFORD General 928 1,013 -19 994618 BRANTFORD St Joseph's 0 0 0 0619 BROCKVILLE General 363 347 -4 343620 BROCKVILLE St Vincent de Paul 1 1 0 1624 CAMPBELLFORD Memorial 0 0 0 0626 CARLETON PLACE & District Memorial 1 1 0 1627 CHAPLEAU General 7 7 0 7628 CHATHAM Public General 736 785 -12 773629 CHATHAM St Joseph's 0 0 0 0632 TORONTO North York General 4,046 4,070 -95 3,975633 CLINTON Public 124 108 1 109638 COCHRANE Lady Minto 55 52 -1 51640 COLLINGWOOD General and Marine 262 261 1 262643 CORNWALL General 5 5 0 5644 CORNWALL Hotel Dieu 580 563 -11 552646 DEEP RIVER and District 0 0 0 0647 DRYDEN District General 134 129 -4 125648 DUNNVILLE Haldimand War Memorial 96 93 0 93650 ELLIOT LAKE St Joseph's 118 122 -3 118653 ENGLEHART & District 1 1 0 1654 ESPANOLA General 2 2 0 2655 EXETER South Huron 1 1 0 1656 FERGUS Groves Memorial Comm 183 189 0 188661 CAMBRIDGE Memorial 917 956 -10 946662 GERALDTON District Hospital 25 26 -1 25663 GODERICH Alexandra Marine & General 103 90 1 91664 GRIMSBY West Lincoln Memorial 357 344 -8 336665 GUELPH General 1,216 1,258 -4 1,254666 GUELPH St Joseph's Hospital 0 0 0 0

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MIS Facility Name

Actual Weighted

Cases, 98/99

Expected Weighted

Cases 1998/99


2000/01


2000/01674 HAMILTON St Joseph's 2,806 2,887 -85 2,801676 HANOVER & District 87 86 1 87679 SUDBURY Algoma 0 0 0 0681 HEARST Notre Dame 59 57 -1 55682 HORNEPAYNE Community 2 2 0 2684 INGERSOLL Alexandra 78 80 0 80685 IROQUOIS FALLS Anson General 2 2 0 2686 WAWA North Algoma 54 54 -1 53687 KAPUSKASING Sensenbrenner 78 74 -2 72692 KINGSTON Hotel Dieu 0 0 0 0693 KINGSTON General 1,654 1,675 -12 1,663696 KIRKLAND & District 80 82 -2 80699 KITCHENER St Mary's 0 0 0 0701 RICHMOND HILL York Central 1,543 1,503 31 1,533704 LEAMINGTON District Memorial 352 356 0 355707 LINDSAY Ross Memorial 364 366 3 369709 LISTOWEL Memorial 130 131 0 130714 LONDON St Joseph's 3,182 3,181 -62 3,119718 BURLINGTON Joseph Brant Memorial 1,209 1,211 -32 1,179719 MANITOUWADGE General 62 59 -2 58721 MARATHON Wilson Memorial 19 20 -1 19723 MATHESON Bingham Memorial 0 0 0 0724 MATTAWA General 1 1 0 1726 MIDLAND Huronia 265 264 1 264731 MISSISSAUGA Credit Valley 2,844 2,961 10 2,971732 KEMPTVILLE District 0 1 0 1733 MOUNT FOREST Louise Marshall 47 48 0 48734 HALDIMAND West Haldimand General 2 2 0 2736 NEWMARKET Southlake Regional Health Centre 1,551 1,489 3 1,492739 NIPIGON District Memorial 5 5 0 5745 ORILLIA Soldiers' Memorial 678 663 2 665751 OTTAWA CHEO 0 0 0 0753 OTTAWA Montfort 656 654 -19 635755 OTTAWA SA Grace 1,342 1,275 -40 1,235759 PALMERSTON & District 67 68 0 68760 PARIS - The Willett 0 1 0 1763 PEMBROKE General 547 568 -9 559766 PENETANGUISHENE General 0 0 0 0

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MIS Facility Name

Actual Weighted

Cases, 98/99

Expected Weighted

Cases 1998/99


2000/01


2000/01768 BARRY'S BAY St Francis Memorial 2 2 0 2771 PETERBOROUGH Civic 1,194 1,186 -6 1,180776 PETROLIA Charlotte Eleanor Englehart 36 38 0 37777 NEPEAN Queensway-Carleton 23 22 -1 21784 LITTLE CURRENT Manitoulin 40 38 0 38788 RENFREW Victoria 79 82 -1 81790 ST CATHARINES Hotel Dieu 1 2 0 1792 ST MARY'S Memorial 27 28 0 28793 ST THOMAS Elgin General 522 529 -1 528795 SARNIA St. Joseph's 728 760 -9 751796 SARNIA General 9 10 0 9797 SAULT STE MARIE General 806 817 -21 796800 HAWKESBURY & District General 149 153 -4 149801 SEAFORTH Community 49 44 0 45802 ALEXANDRIA Glengarry Memorial 0 0 0 0804 SIMCOE Norfolk General 335 335 0 335805 SIOUX LOOKOUT District 85 81 -2 79809 SMOOTH ROCK FALLS 0 0 0 0813 STRATFORD General 626 641 -3 638814 STRATHROY Middlesex General 229 232 -5 227819 TERRACE BAY McCausland 15 15 0 15824 TILLSONBURG District Memorial 105 107 0 107826 KENORA Lake-of-the-Woods District 153 147 -4 143837 TORONTO Hospital for Sick Children 1 1 0 1842 TORONTO Mount Sinai 3,351 3,331 -145 3,186852 TORONTO St Michael's 1,803 1,786 -107 1,679854 TORONTO SA Grace 0 0 0 0858 TORONTO East General 1,864 1,863 -110 1,753870 WALLACEBURG Sydenham 110 117 -2 115881 STURGEON FALLS West Nipissing 3 2 0 2882 WINCHESTER District Memorial 235 227 -4 223888 NEW LISKEARD Temiskaming 182 187 -4 184889 WINGHAM & District 56 50 1 51890 WOODSTOCK General 424 437 -2 435896 RED LAKE Marg Cochenour Memorial 43 41 -1 40898 TORONTO St Joseph's 1,947 1,959 -105 1,855900 FORT FRANCES Riverside Health Care 167 160 -1 159903 HUNTSVILLE District Memorial 203 186 0 186

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MIS Facility Name

Actual Weighted

Cases, 98/99

Expected Weighted

Cases 1998/99


2000/01


2000/01905 MARKHAM Stouffville 1,681 1,705 8 1,713906 NORTH BAY General 838 778 -18 760907 TIMMINS & District General 672 645 -17 628916 ORANGEVILLE Dufferin-Caledon 519 503 0 503927 WINDSOR Hotel Dieu Grace 1,559 1,569 -1 1,568928 SMITHS FALLS Perth & Smiths Falls 237 239 -1 238930 KITCHENER Grand River 3,093 3,070 -55 3,015931 PARRY SOUND West Parry Sound 116 105 0 106933 WINDSOR Regional 1,645 1,655 0 1,655935 THUNDER BAY Regional 1,295 1,299 -37 1,262936 LONDON Health Sciences 1,779 1,792 -40 1,751938 MINDEN Haliburton Highlands 7 6 0 6940 COBOURG Northumberland 245 258 -4 254941 TORONTO Humber River Regional 3,555 3,584 -123 3,461942 HAMILTON Health Sciences Centre 2,588 2,653 -53 2,600946 KINCARDINE S Bruce Grey Hlth Ctr 162 156 2 158947 TORONTO University Health Network 2,825 2,798 -169 2,630949 MISSISSAUGA Trillium Health Centre 2,942 3,076 0 3,076950 OAKVILLE Halton Heatlhcare Services Corporation1,609 1,634 -9 1,625951 TORONTO William Osler 5,421 5,388 -35 5,353952 OSHAWA Lakeridge Health Corporation 2,694 2,405 -63 2,343953 TORONTO Sunnybrook & Women's 3,358 3,329 -178 3,151954 TORONTO Rouge Valley Health System 3,038 3,028 -83 2,945955 OWEN SOUND Grey Bruce Health Services 652 641 8 650957 BELLEVILLE Quinte Health Care Corporation 1,196 1,247 -22 1,225958 OTTAWA The Ottawa Hospital 6,275 6,066 -201 5,865959 SUDBURY Regional Hospital Corporation 1,537 1,618 -28 1,590960 TORONTO The Scarborough Hospital 4,010 4,061 -116 3,945962 ST. CATHARINES Niagara Health System 2,717 2,750 -75 2,675

TOTAL 106,031 106,031 -2,233 103,798

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Appendix 27: Hospital Newborn & Neonate Weighted Case Allocations

MIS Facility Name

Actual Weighted

Cases, 98/99

Newborn and Neonate Expected Weighted Cases,

1998/99

Newborn and Neonate

Growth to 2000/2001

Total Expected Weighted

Cases, 2000/01592 NAPANEE Lennox & Addington 32.2 35.8 -0.7 35.1593 NEWBURY Four Counties 0.6 0.6 0.0 0.6596 ALLISTON Stevenson Memorial 72.6 71.9 0.0 71.9597 ALMONTE General 57.3 60.4 -0.5 60.0599 ARNPRIOR & District Memorial 0.3 0.3 0.0 0.3600 ATIKOKAN General 3.4 3.4 0.0 3.3606 BARRIE Roval Victoria 660.3 663.3 0.3 663.6611 BLIND RIVER St Joseph's 0.6 0.6 0.0 0.6614 BRACEBRIDGE S Muskoka Memorial 42.0 47.0 -0.1 46.9617 BRANTFORD General 478.7 453.1 -8.1 445.0618 BRANTFORD St Joseph's 0.0 0.0 0.0 0.0619 BROCKVILLE General 126.8 122.7 -1.7 120.9620 BROCKVILLE St Vincent de Paul 0.0 0.0 0.0 0.0624 CAMPBELLFORD Memorial 0.2 0.2 0.0 0.2626 CARLETON PLACE & District Memorial 0.0 0.0 0.0 0.0627 CHAPLEAU General 1.5 1.8 0.0 1.7628 CHATHAM Public General 346.4 313.1 -4.6 308.4629 CHATHAM St Joseph's 16.3 14.7 -0.2 14.5632 TORONTO North York General 1,873.9 1,882.2 -43.3 1,838.9633 CLINTON Public 32.5 24.1 0.4 24.6638 COCHRANE Lady Minto 16.3 18.3 -0.5 17.8640 COLLINGWOOD General and Marine 79.4 80.8 0.2 81.0643 CORNWALL General 0.0 0.0 0.0 0.0644 CORNWALL Hotel Dieu 197.3 219.9 -4.0 215.9646 DEEP RIVER and District 0.0 0.0 0.0 0.0647 DRYDEN District General 38.7 49.0 -1.2 47.8648 DUNNVILLE Haldimand War Memorial 24.5 23.1 -0.1 22.9650 ELLIOT LAKE St Joseph's 41.5 41.7 -1.1 40.6653 ENGLEHART & District 0.0 0.0 0.0 0.0654 ESPANOLA General 1.0 1.2 0.0 1.2655 EXETER South Huron 0.2 0.2 0.0 0.2656 FERGUS Groves Memorial Comm 55.2 53.2 -0.2 53.0661 CAMBRIDGE Memorial 421.5 392.4 -5.2 387.2662 GERALDTON District Hospital 3.9 4.0 -0.1 3.9663 GODERICH Alexandra Marine & General 28.5 22.3 0.4 22.7664 GRIMSBY West Lincoln Memorial 129.0 113.8 -2.8 111.0

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MIS Facility Name

Actual Weighted

Cases, 98/99


1998/99

Newborn and Neonate

Growth to 2000/2001


Cases, 2000/01665 GUELPH General 513.8 496.7 -2.2 494.6666 GUELPH St Joseph's Hospital 0.0 0.0 0.0 0.0674 HAMILTON St Joseph's 1,638.6 1,654.7 -47.7 1,607.0676 HANOVER & District 30.1 32.6 0.4 33.0679 SUDBURY Algoma 0.0 0.0 0.0 0.0681 HEARST Notre Dame 21.8 24.0 -0.6 23.4682 HORNEPAYNE Community 0.1 0.1 0.0 0.1684 INGERSOLL Alexandra 25.2 24.4 -0.1 24.3685 IROQUOIS FALLS Anson General 0.8 0.9 0.0 0.9686 WAWA North Algoma 17.3 16.1 -0.5 15.7687 KAPUSKASING Sensenbrenner 22.1 24.7 -0.6 24.0692 KINGSTON Hotel Dieu 0.0 0.0 0.0 0.0693 KINGSTON General 1,307.7 1,457.4 -13.2 1,444.2696 KIRKLAND & District 24.0 26.7 -0.5 26.2699 KITCHENER St Mary's 0.0 0.0 0.0 0.0701 RICHMOND HILL York Central 531.4 533.7 10.0 543.7704 LEAMINGTON District Memorial 98.9 87.7 -0.2 87.5707 LINDSAY Ross Memorial 132.6 133.5 0.9 134.4709 LISTOWEL Memorial 42.5 34.5 -0.1 34.4714 LONDON St Joseph's 2,828.3 2,798.4 -48.7 2,749.7718 BURLINGTON Joseph Brant Memorial 480.8 462.7 -13.1 449.5719 MANITOUWADGE General 17.0 18.9 -0.5 18.4721 MARATHON Wilson Memorial 6.2 6.5 -0.2 6.3723 MATHESON Bingham Memorial 0.0 0.0 0.0 0.0724 MATTAWA General 1.4 1.5 0.0 1.4726 MIDLAND Huronia 71.9 72.7 0.0 72.8731 MISSISSAUGA Credit Valley 1,430.7 1,343.7 0.7 1,344.4732 KEMPTVILLE District 0.0 0.0 0.0 0.0733 MOUNT FOREST Louise Marshall 14.0 14.1 0.1 14.2734 HALDIMAND West Haldimand General 0.0 0.0 0.0 0.0736 NEWMARKET Southlake Regional Health Centre 737.0 679.5 0.9 680.4739 NIPIGON District Memorial 1.3 1.4 0.0 1.3745 ORILLIA Soldiers' Memorial 391.3 403.6 0.1 403.7751 OTTAWA CHEO 614.6 668.1 -16.4 651.7753 OTTAWA Montfort 225.3 263.4 -8.4 255.0755 OTTAWA SA Grace 455.3 478.3 -15.9 462.3

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MIS Facility Name

Actual Weighted

Cases, 98/99


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Newborn and Neonate

Growth to 2000/2001


Cases, 2000/01759 PALMERSTON & District 18.8 17.4 0.0 17.4760 PARIS - The Willett 0.0 0.0 0.0 0.0763 PEMBROKE General 164.4 175.0 -2.8 172.2766 PENETANGUISHENE General 0.0 0.0 0.0 0.0768 BARRY'S BAY St Francis Memorial 0.8 0.9 0.0 0.9771 PETERBOROUGH Civic 620.7 634.0 -3.0 631.0776 PETROLIA Charlotte Eleanor Englehart 8.6 8.6 -0.1 8.5777 NEPEAN Queensway-Carleton 0.0 0.0 0.0 0.0784 LITTLE CURRENT Manitoulin 12.4 10.2 0.0 10.1788 RENFREW Victoria 21.2 22.6 -0.3 22.3790 ST CATHARINES Hotel Dieu 0.0 0.0 0.0 0.0792 ST MARY'S Memorial 9.8 8.2 -0.1 8.1793 ST THOMAS Elgin General 260.3 237.1 -0.6 236.5795 SARNIA St. Joseph's 348.9 350.8 -4.0 346.9796 SARNIA General 11.6 11.7 -0.1 11.6797 SAULT STE MARIE General 458.4 425.3 -12.5 412.8800 HAWKESBURY & District General 52.8 61.1 -1.5 59.6801 SEAFORTH Community 14.8 11.0 0.1 11.2802 ALEXANDRIA Glengarry Memorial 0.0 0.0 0.0 0.0804 SIMCOE Norfolk General 84.7 78.3 0.0 78.3805 SIOUX LOOKOUT District 25.8 32.5 -0.8 31.7809 SMOOTH ROCK FALLS 0.2 0.2 0.0 0.2813 STRATFORD General 309.1 251.2 -1.5 249.7814 STRATHROY Middlesex General 84.3 88.0 -2.1 85.9819 TERRACE BAY McCausland 4.8 5.1 -0.1 4.9824 TILLSONBURG District Memorial 33.1 31.3 -0.1 31.2826 KENORA Lake-of-the-Woods District 46.7 59.1 -1.4 57.7837 TORONTO Hospital for Sick Children 2,420.2 2,412.6 -75.5 2,337.1842 TORONTO Mount Sinai 3,492.9 3,500.7 -133.6 3,367.1852 TORONTO St Michael's 618.0 680.1 -44.9 635.1854 TORONTO SA Grace 0.0 0.0 0.0 0.0858 TORONTO East General 1,133.5 1,192.3 -79.5 1,112.9870 WALLACEBURG Sydenham 36.0 33.1 -0.5 32.5881 STURGEON FALLS West Nipissing 1.9 2.0 0.0 1.9882 WINCHESTER District Memorial 68.9 73.5 -1.3 72.3888 NEW LISKEARD Temiskaming 57.3 63.8 -1.1 62.6

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MIS Facility Name

Actual Weighted

Cases, 98/99


1998/99

Newborn and Neonate

Growth to 2000/2001


Cases, 2000/01889 WINGHAM & District 23.0 18.4 0.3 18.7890 WOODSTOCK General 140.5 135.9 -0.6 135.3896 RED LAKE Marg Cochenour Memorial 12.6 15.9 -0.4 15.5898 TORONTO St Joseph's 916.3 978.0 -57.5 920.5900 FORT FRANCES Riverside Health Care 49.4 48.8 -0.2 48.6903 HUNTSVILLE District Memorial 58.3 64.2 -0.1 64.1905 MARKHAM Stouffville 818.7 803.0 5.9 808.9906 NORTH BAY General 407.4 438.8 -10.3 428.4907 TIMMINS & District General 305.5 342.5 -8.3 334.1916 ORANGEVILLE Dufferin-Caledon 190.6 168.4 -0.4 168.0927 WINDSOR Hotel Dieu Grace 1,252.1 1,111.2 -2.0 1,109.2928 SMITHS FALLS Perth & Smiths Falls 79.3 82.7 -0.5 82.2930 KITCHENER Grand River 1,483.8 1,416.8 -26.7 1,390.1931 PARRY SOUND West Parry Sound 33.5 35.3 0.1 35.4933 WINDSOR Regional 635.4 562.6 -0.6 562.0935 THUNDER BAY Regional 735.8 782.3 -22.0 760.4936 LONDON Health Sciences 1,111.7 1,116.9 -22.8 1,094.1938 MINDEN Haliburton Highlands 1.4 1.6 0.0 1.6940 COBOURG Northumberland 103.1 96.5 -1.7 94.8941 TORONTO Humber River Regional 1,456.2 1,416.1 -51.6 1,364.5942 HAMILTON Health Sciences Centre 2,877.4 2,828.2 -59.5 2,768.7946 KINCARDINE S Bruce Grey Hlth Ctr 48.7 52.7 0.5 53.2947 TORONTO University Health Network 1,387.0 1,495.9 -94.7 1,401.2949 MISSISSAUGA Trillium Health Centre 1,235.6 1,173.1 -1.4 1,171.7950 OAKVILLE Halton Heatlhcare Services Corporation 661.2 633.5 -4.6 628.9951 TORONTO William Osler 2,346.1 2,047.9 -16.1 2,031.7952 OSHAWA Lakeridge Health Corporation 1,016.7 958.8 -26.9 931.9953 TORONTO Sunnybrook & Women's 3,005.8 3,089.7 -132.6 2,957.1954 TORONTO Rouge Valley Health System 1,482.4 1,361.6 -37.5 1,324.1955 OWEN SOUND Grey Bruce Health Services 263.2 286.1 3.5 289.6957 BELLEVILLE Quinte Health Care Corporation 522.5 562.1 -9.7 552.4958 OTTAWA The Ottawa Hospital 3,703.0 4,087.9 -135.2 3,952.7959 SUDBURY Regional Hospital Corporation 855.5 961.3 -16.0 945.3960 TORONTO The Scarborough Hospital 1,913.2 1,812.0 -53.0 1,759.1962 ST. CATHARINES Niagara Health System 1,207.8 1,286.9 -33.5 1,253.4

TOTAL 59,227 59,227 -1,412 57,814

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Appendix 28: Hospital Total Actual Weighted Cases, 1998/99 and Expected Weighted Cases, 2000/01

MIS Facility Name

Actual Medical/Surgical Weighted Cases

98/99

Actual Newborn &

Neonate, 98/99

Actual Pregnancy Childbirth Weighted

Cases, 98/99

Total Actual Weighted

Cases 98/99

Medical & Surgical


Newborn & Neonate

TOTAL Expected

592 NAPANEE Lennox & Addington 1,690 32 91 1,813 2,175 86 35 2,296 593 NEWBURY Four Counties 743 1 2 746 774 2 1 777 596 ALLISTON Stevenson Memorial 2,419 73 262 2,754 2,603 260 72 2,935 597 ALMONTE General 840 57 175 1,073 889 177 60 1,126 599 ARNPRIOR & District Memorial 1,635 0 2 1,637 1,716 2 0 1,719 600 ATIKOKAN General 410 3 15 429 423 15 3 441 606 BARRIE Roval Victoria 16,106 660 1,438 18,205 17,513 1,434 664 19,610 611 BLIND RIVER St Joseph's 698 1 2 701 725 2 1 728 614 BRACEBRIDGE S Muskoka Memorial 3,033 42 146 3,222 3,643 135 47 3,825 617 BRANTFORD General 14,048 479 928 15,454 12,544 994 445 13,982 618 BRANTFORD St Joseph's 2,068 0 0 2,068 1,874 - - 1,874 619 BROCKVILLE General 4,914 127 363 5,403 5,816 343 121 6,280 620 BROCKVILLE St Vincent de Paul 1,670 0 1 1,671 1,972 1 - 1,973 624 CAMPBELLFORD Memorial 1,824 0 0 1,824 1,913 - 0 1,914 626 CARLETON PLACE & District Memorial 1,009 0 1 1,010 1,066 1 - 1,067 627 CHAPLEAU General 241 1 7 249 263 7 2 271 628 CHATHAM Public General 7,103 346 736 8,186 7,356 773 308 8,438 629 CHATHAM St Joseph's 3,783 16 0 3,800 3,913 - 14 3,927 632 TORONTO North York General 32,716 1,874 4,046 38,636 33,529 3,975 1,839 39,343 633 CLINTON Public 1,082 32 124 1,239 1,028 109 25 1,162 638 COCHRANE Lady Minto 787 16 55 858 763 51 18 832 640 COLLINGWOOD General and Marine 3,661 79 262 4,002 3,925 262 81 4,268 643 CORNWALL General 4,789 0 5 4,794 5,275 5 - 5,280 644 CORNWALL Hotel Dieu 5,622 197 580 6,399 6,192 552 216 6,960 646 DEEP RIVER and District 687 0 0 687 719 - - 719 647 DRYDEN District General 1,587 39 134 1,760 2,151 125 48 2,323 648 DUNNVILLE Haldimand War Memorial 1,175 24 96 1,295 1,249 93 23 1,365 650 ELLIOT LAKE St Joseph's 2,469 42 118 2,629 2,550 118 41 2,708 653 ENGLEHART & District 571 0 1 572 571 1 - 572 654 ESPANOLA General 791 1 2 794 860 2 1 864 655 EXETER South Huron 824 0 1 825 789 1 0 790 656 FERGUS Groves Memorial Comm 2,141 55 183 2,379 2,342 188 53 2,583 661 CAMBRIDGE Memorial 11,264 421 917 12,602 11,487 946 387 12,820 662 GERALDTON District Hospital 635 4 25 664 648 25 4 677 663 GODERICH Alexandra Marine & General 2,708 29 103 2,839 2,571 91 23 2,685 664 GRIMSBY West Lincoln Memorial 2,165 129 357 2,651 2,543 336 111 2,990 665 GUELPH General 7,250 514 1,216 8,980 7,882 1,254 495 9,630 666 GUELPH St Joseph's Hospital 5,480 0 0 5,480 5,931 - - 5,931

Total Expected Weighted Cases, 2000/01Total Actual Weighted Cases, 1998/99

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MIS Facility Name


98/99

Actual Newborn &

Neonate, 98/99


Cases, 98/99


Cases 98/99

Medical & Surgical


Newborn & Neonate

TOTAL Expected


674 HAMILTON St Joseph's 27,341 1,639 2,806 31,785 27,781 2,801 1,607 32,189 676 HANOVER & District 1,644 30 87 1,761 1,567 87 33 1,687 679 SUDBURY Algoma 546 0 0 546 573 - - 573 681 HEARST Notre Dame 850 22 59 931 824 55 23 902 682 HORNEPAYNE Community 177 0 2 179 184 2 0 186 684 INGERSOLL Alexandra 1,631 25 78 1,735 1,716 80 24 1,820 685 IROQUOIS FALLS Anson General 534 1 2 536 517 2 1 520 686 WAWA North Algoma 444 17 54 515 461 53 16 529 687 KAPUSKASING Sensenbrenner 1,696 22 78 1,796 1,644 72 24 1,741 692 KINGSTON Hotel Dieu 3,191 0 0 3,191 3,548 - - 3,548 693 KINGSTON General 27,854 1,308 1,654 30,815 30,684 1,663 1,444 33,792 696 KIRKLAND & District 2,040 24 80 2,143 2,036 80 26 2,142 699 KITCHENER St Mary's 13,564 0 0 13,564 14,684 - - 14,684 701 RICHMOND HILL York Central 14,060 531 1,543 16,135 16,988 1,533 544 19,066 704 LEAMINGTON District Memorial 3,796 99 352 4,247 3,682 355 87 4,124 707 LINDSAY Ross Memorial 7,418 133 364 7,915 7,718 369 134 8,221 709 LISTOWEL Memorial 1,105 42 130 1,278 1,130 130 34 1,294 714 LONDON St Joseph's 21,664 2,828 3,182 27,674 22,930 3,119 2,750 28,799 718 BURLINGTON Joseph Brant Memorial 14,787 481 1,209 16,477 14,683 1,179 450 16,311 719 MANITOUWADGE General 607 17 62 686 596 58 18 673 721 MARATHON Wilson Memorial 266 6 19 291 272 19 6 297 723 MATHESON Bingham Memorial 253 0 0 253 245 - - 245 724 MATTAWA General 469 1 1 472 479 1 1 482 726 MIDLAND Huronia 4,106 72 265 4,444 4,473 264 73 4,810 731 MISSISSAUGA Credit Valley 16,387 1,431 2,844 20,661 20,658 2,971 1,344 24,973 732 KEMPTVILLE District 1,015 0 0 1,016 1,163 1 - 1,163 733 MOUNT FOREST Louise Marshall 941 14 47 1,002 995 48 14 1,057 734 HALDIMAND West Haldimand General 833 0 2 835 882 2 - 884 736 NEWMARKET Southlake Regional Health Centre 17,018 737 1,551 19,306 18,364 1,492 680 20,536 739 NIPIGON District Memorial 533 1 5 540 545 5 1 551 745 ORILLIA Soldiers' Memorial 8,621 391 678 9,691 9,338 665 404 10,407 751 OTTAWA CHEO 6,724 615 0 7,339 7,028 - 652 7,680 753 OTTAWA Montfort 9,282 225 656 10,163 9,607 635 255 10,497 755 OTTAWA SA Grace 5,581 455 1,342 7,378 5,690 1,235 462 7,388 759 PALMERSTON & District 896 19 67 982 974 68 17 1,059 760 PARIS - The Willett 227 0 0 228 204 1 - 204 763 PEMBROKE General 5,961 164 547 6,672 6,233 559 172 6,965 766 PENETANGUISHENE General 205 0 0 205 224 - - 224

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MIS Facility Name


98/99

Actual Newborn &

Neonate, 98/99


Cases, 98/99


Cases 98/99

Medical & Surgical


Newborn & Neonate

TOTAL Expected


768 BARRY'S BAY St Francis Memorial 639 1 2 641 662 2 1 665 771 PETERBOROUGH Civic 21,288 621 1,194 23,103 20,869 1,180 631 22,680 776 PETROLIA Charlotte Eleanor Englehart 1,331 9 36 1,376 1,334 37 9 1,380 777 NEPEAN Queensway-Carleton 11,904 0 23 11,927 12,576 21 - 12,597 784 LITTLE CURRENT Manitoulin 1,256 12 40 1,309 1,279 38 10 1,327 788 RENFREW Victoria 1,918 21 79 2,018 2,010 81 22 2,113 790 ST CATHARINES Hotel Dieu 10,208 0 1 10,209 10,616 1 - 10,617 792 ST MARY'S Memorial 864 10 27 900 894 28 8 930 793 ST THOMAS Elgin General 8,234 260 522 9,017 8,556 528 237 9,320 795 SARNIA St. Joseph's 3,839 349 728 4,916 3,847 751 347 4,945 796 SARNIA General 10,201 12 9 10,222 10,218 9 12 10,239 797 SAULT STE MARIE General 15,810 458 806 17,075 16,436 796 413 17,644 800 HAWKESBURY & District General 2,396 53 149 2,597 2,943 149 60 3,152 801 SEAFORTH Community 724 15 49 788 690 45 11 746 802 ALEXANDRIA Glengarry Memorial 929 0 0 929 1,022 - - 1,022 804 SIMCOE Norfolk General 4,958 85 335 5,378 5,266 335 78 5,680 805 SIOUX LOOKOUT District 580 26 85 690 778 79 32 888 809 SMOOTH ROCK FALLS 410 0 0 410 397 - 0 397 813 STRATFORD General 6,821 309 626 7,756 6,989 638 250 7,877 814 STRATHROY Middlesex General 2,851 84 229 3,164 3,045 227 86 3,357 819 TERRACE BAY McCausland 257 5 15 277 263 15 5 282 824 TILLSONBURG District Memorial 2,852 33 105 2,990 2,934 107 31 3,072 826 KENORA Lake-of-the-Woods District 2,475 47 153 2,675 3,330 143 58 3,531 837 TORONTO Hospital for Sick Children 19,123 2,420 1 21,545 20,241 1 2,337 22,579 842 TORONTO Mount Sinai 19,352 3,493 3,351 26,195 20,706 3,186 3,367 27,260 852 TORONTO St Michael's 49,530 618 1,803 51,951 53,012 1,679 635 55,326 854 TORONTO SA Grace 3,270 0 0 3,270 3,379 - - 3,379 858 TORONTO East General 23,363 1,134 1,864 26,360 25,055 1,753 1,113 27,921 870 WALLACEBURG Sydenham 1,670 36 110 1,816 1,726 115 33 1,874 881 STURGEON FALLS West Nipissing 1,563 2 3 1,568 1,596 2 2 1,600 882 WINCHESTER District Memorial 2,397 69 235 2,701 2,613 223 72 2,909 888 NEW LISKEARD Temiskaming 2,469 57 182 2,708 2,464 184 63 2,710 889 WINGHAM & District 1,749 23 56 1,827 1,663 51 19 1,732 890 WOODSTOCK General 6,084 140 424 6,648 6,386 435 135 6,956 896 RED LAKE Marg Cochenour Memorial 626 13 43 681 851 40 16 906 898 TORONTO St Joseph's 20,922 916 1,947 23,785 22,963 1,855 920 25,738 900 FORT FRANCES Riverside Health Care 2,364 49 167 2,580 2,449 159 49 2,657 903 HUNTSVILLE District Memorial 3,056 58 203 3,317 3,523 186 64 3,773

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MIS Facility Name


98/99

Actual Newborn &

Neonate, 98/99


Cases, 98/99


Cases 98/99

Medical & Surgical


Newborn & Neonate

TOTAL Expected


905 MARKHAM Stouffville 10,033 819 1,681 12,532 11,618 1,713 809 14,141 906 NORTH BAY General 12,001 407 838 13,247 12,266 760 428 13,454 907 TIMMINS & District General 7,835 305 672 8,812 7,607 628 334 8,569 916 ORANGEVILLE Dufferin-Caledon 5,336 191 519 6,046 5,111 503 168 5,782 927 WINDSOR Hotel Dieu Grace 25,451 1,252 1,559 28,263 24,596 1,568 1,109 27,273 928 SMITHS FALLS Perth & Smiths Falls 5,154 79 237 5,470 5,494 238 82 5,814 930 KITCHENER Grand River 16,105 1,484 3,093 20,682 17,452 3,015 1,390 21,857 931 PARRY SOUND West Parry Sound 2,507 33 116 2,656 2,876 106 35 3,017 933 WINDSOR Regional 18,764 635 1,645 21,045 18,137 1,655 562 20,353 935 THUNDER BAY Regional 23,432 736 1,295 25,463 24,195 1,262 760 26,217 936 LONDON Health Sciences 64,395 1,112 1,779 67,286 67,165 1,751 1,094 70,010 938 MINDEN Haliburton Highlands 554 1 7 562 667 6 2 675 940 COBOURG Northumberland 3,874 103 245 4,222 4,207 254 95 4,556 941 TORONTO Humber River Regional 32,787 1,456 3,555 37,798 34,461 3,461 1,364 39,286 942 HAMILTON Health Sciences Centre 65,555 2,877 2,588 71,020 66,580 2,600 2,769 71,948 946 KINCARDINE S Bruce Grey Hlth Ctr 4,599 49 162 4,810 4,547 158 53 4,758 947 TORONTO University Health Network 83,100 1,387 2,825 87,311 89,481 2,630 1,401 93,512 949 MISSISSAUGA Trillium Health Centre 35,399 1,236 2,942 39,576 43,079 3,076 1,172 47,327 950 OAKVILLE Halton Heatlhcare Services Corporation 16,669 661 1,609 18,939 17,966 1,625 629 20,220 951 BRAMPTON William Osler 39,370 2,346 5,421 47,137 44,144 5,353 2,032 51,529 952 OSHAWA Lakeridge Health Corporation 32,063 1,017 2,694 35,774 32,682 2,343 932 35,957 953 TORONTO Sunnybrook & Women's 48,580 3,006 3,358 54,943 51,404 3,151 2,957 57,512 954 TORONTO Rouge Valley Health System 28,424 1,482 3,038 32,944 31,266 2,945 1,324 35,535 955 OWEN SOUND Grey Bruce Health Services 15,640 263 652 16,556 15,032 650 290 15,971 957 BELLEVILLE Quinte Health Care Corporation 15,865 523 1,196 17,584 17,753 1,225 552 19,531 958 OTTAWA The Ottawa Hospital 80,201 3,703 6,275 90,179 81,882 5,865 3,953 91,700 959 SUDBURY Hopital Regional de Sudbury Regional Hospital 35,241 856 1,537 37,634 35,361 1,590 945 37,896 960 TORONTO The Scarborough Hospital 39,366 1,913 4,010 45,289 42,917 3,945 1,759 48,621 962 ST. CATHARINES Niagara Health System 39,089 1,208 2,717 43,013 39,392 2,675 1,253 43,320

TOTAL 1,454,575 59,227 106,031 1,619,833 1,536,425 103,798 57,814 1,698,037

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APPENDIX 29: Integration & Implementation Committee Terms Of Reference

Mandate: To date the Ontario hospital funding models have attempted to achieve equity in hospital funding by analysis of hospital costs by individual hospital programmatic areas. This Committee will investigate the possibility of integrating the various Ontario funding models (e.g. growth, priority programs, adjustment factors) into a single comprehensive formula. Objectives: 1. To review the current hospital funding models ; 2. To identify and indicate relevant funding models that could be integrated; 3. To propose other funding models that could be integrated, as data become

available; 4. To evaluate implementation options and recommend implementation strategies

to phase in new funding models and stabilize funding levels in the province; and 5. To propose strategies to provide education and information concerning the

implementation of new funding formulas Deliverables: • Provide the Funding Committee with evidence on which to base a decision on the

value of integrating all hospital funding formulas for the 2001-2002 funding year. • To provide the Funding Committee with an implementation and communication

strategy for the integration of all hospital funding formulas. Time Line: The Committee will complete the work over a three-year period. Reporting Relationship: The Integration and Implementation Committee will report directly to the Funding Committee of the JPPC. Membership: Membership will include representatives from the Ministry of Health, the Ontario Hospital Association, Ontario Hospitals, Academia, and others as necessary.

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APPENDIX 30: Integration & Implementation Committee Membership List Adam Topp Chair, September 2000 - Present

Sunnybrook & Womens Health Sciences Centre

Maureen Adamson Ministry of Health and Long-Term Care

Dan Carriere York County Hospital

Hy Eliasoph Ontario Hospital Association

Jim Elliott Toronto Rehabilitation Institute

Peter Glynn Kingston General Hospital

Michael Guerriere Chair until September 2000

Bill Hart Ministry of Health and Long-Term Care

Gordon Key Huronia District Hospital

Bill MacDonald Hotel Dieu Grace Hospital


John McKinley Ministry of Health and Long-Term Care

Robert Muir Ontario Hospital Association

George Pink University of Toronto

Barry Potter St. Joseph’s Care Group

Colin Preyra Joint Policy & Planning Committee

Natalie Rashkovan Ministry of Health and Long-Term Care

John Sutherland Huron Perth Hospitals Partnership

Vicki Truman William Osler Health Centre

Mark Vimr Cardiac Care Network

Barbara Willis London Health Science Centre

Lorne Zon Markham Stouffville Hospital

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