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Safety Net Hospitalsand Minority Access to Health Care

Gloria J. Bazzoli, Ph.D.Virginia Commonwealth University

Lee R. Mobley, Ph.D.Research Triangle Institute, Inc.

This research is supported by NIH Grant # 5R01HL082707-2.

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Outline Structure of US health safety net and the role safety net

hospitals can play in reducing racial, ethnic, and socioeconomic health disparities

Overview of our study

Preliminary descriptive analysis for Florida: Patient proximity to a safety net hospital and rates of particular

admissions Location of safety net hospital closures relative to particular

patient populations

Next steps for our research

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Structure ofthe US Health Safety Net A patchwork of health providers currently provide

care for economically disadvantaged: public and private hospitals private physicians community health centers government programs for particular patient groups (e.g.,

VA, Indian Health Service) local health departments

US hospitals provide large share of this care: Holahan and Hadley (Health Affairs, 2003) estimated that

66% of indigent care in 2001 was provided by hospitals

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How Safety Net Hospitals (SNHs) Could Help Disadvantaged Populations

Offer outreach services, either independently or with other organizations: Provide information about services in diverse languages and media Establish decentralized sites of care Hire multi-lingual staff that are culturally sensitive

Provide free or reduced fee primary care services through system of local clinics

Collaborate with local agencies to assist in referral for social and behavioral services

Coordinate referral and provide access to specialty services

Provide follow-up treatment and rehabilitation post-hospitalization

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But Are SNHs Effective? SNHs are most often located in neighborhoods where

the poor and racial or ethnic minorities reside.

However, prior studies found that SNH presence in an area had: minor effects on access to care for uninsured little to no effect on health disparities

A problem with prior studies is that measures of SNH availability and capacity are crude: presence of SNH in an individual’s county number of beds or ER visits at the SNHs in a county

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Motivation for Our NIH Research Project Assess how location of uninsured patients vis-à-vis SNH affects

care patterns:

Do patients who live closer to a SNH have better access to health services than those farther away?

Is proximity to a SNH especially important to racial or ethnic minority access to care?

What happens to the uninsured when a SNH exits a community through closure or ownership conversion?

Are there regional clusters of minority individuals who are especially affected when a SNH exits?

Assess strategies that communities have taken to lessen the detrimental effects of SNH exit on disadvantaged populations

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Motivation for our NIH Research Project

In 1990, about 1,600 (32%) of 5,000 US community hospitals were SNHs, based on definition developed by Darrell Gaskin*

By 2000, 121 (7%) SNHs closed and 269 (17%) experienced an ownership conversion that could affect their mission

Although SNH changes seem small in number, these facilities may be very important locally:

o For example, the closure of Milwaukee County Hospital in Wisconsin.o The hospital had 12,568 admissions in 1990, of which 2,302 were Medicaid.o Overall, the hospital provided 9.2% of total hospital admissions and 22.7% of

all Medicaid admissions in the city of Milwaukee.

*Specifically, Gaskin and his colleagues defined SNHs as all public hospitals and thoseprivate, nonprofit hospitals with a large Medicaid patient share (specifically greater than mean plus 1 standard deviation for NFPs in the hospital’s state).

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Focus of Our Study Our study examines SNHs in 5 states:

Arizona, California, Florida, New York, Wisconsin Specifically examine hospital discharge data in AHRQ SID

Study period: Early year in 1990s (base period) and 2000 or 2003 (ending

period)

Study sample: Patients residing in zip codes within given distances to a SNH Primarily interested in those who are uninsured Within this group, distinguish non-Hispanic white, non-Hispanic

black, and Hispanic individuals

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SNH Changes in Our 5 States Over Time

Total Community Hospitals: Base Year

Total SNHs:

Base Year

SNH Closures

SNH Ownership

Conversions

Arizona 56 11 0 3

California 423 115 9 20

Florida 201 38 3 8

New York 221 53 1 3

Wisconsin 125 28 3 4

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Primary Access Measures of Interest

Examining hospital discharge data to identify specific types of hospitalizations:

Ambulatory care sensitive conditions (ACSCs) that could have been avoided if adequate primary and preventive services were present:o angina, asthma, acute diabetic events, gastroenteritis, hypertension

Specialized services for which patients require hi-tech hospital services and specialty physician involvement, referred to as referral sensitive conditions (RSCs):o CABG surgery, PTCA, hip/knee joint replacement, organ/bone marrow

transplantation

Marker conditions (MCs), which require immediate urgent care, and are thus not likely to be access sensitive:o AMI, hip fracture, appendicitis

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Working Hypotheses/Expectations Presence of nearby SNH for uninsured:

reduces ACSC admissions (relative to MC) if SNH is facilitating access to primary care

increases RSC admissions (relative to MC) if SNH is providing or facilitating access to specialty services

effects should be more pronounced the closer an individual is to a SNH

potentially these effects are stronger for racial or ethnic minorities if their access to other sources of care is especially weak

Exit of a SNH through closure or ownership conversion could eliminate these beneficial effects

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Preliminary Descriptive Analysis For patients residing in low income zip codes (median family income <

250% of FPL): identified travel distance to the hospitals at which they received care based

on patient and hospital zip code centroids established near, moderate, and far travel distance thresholds for urban and

rural hospitals in each state

Next turned to SNHs in a state for its base year: identified distance to nearest SNH for each patient zip code in the state applied near, moderate, and far thresholds to these distances for a given SNH and all its associated near, moderate, and far zip codes,

selected hospital discharges of all uninsured individuals in these zip codes these discharges were not only from the SNH but also from any other

hospital at which patient received care calculated rate of ACSC to MC discharges and RSC to MC discharges for

uninsured non-Hispanic white, non-Hispanic black, and Hispanic individuals within each distance category

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ACSC Preliminary Findings for Florida: Large Metro Areas

0

2

4

6

8

10

12

White Black Hispanic

Miles to Nearest SNH:

Ratio of ACSC to MC admissions for uninsured, 1992

0-3 miles 3-8.2 miles 8.2-22 miles

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RSC Preliminary Findings for Florida: Large Metro Areas

0

0.2

0.4

0.6

0.8

1

White Black Hispanic

Miles to Nearest SNH:

Ratio of RSC to MC admissions for uninsured, 1992

0-3 miles 3-8.2 miles 8.2-22 miles

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Overall Preliminary Findings for Florida: Large Metro Areas Simple descriptive data raise many questions:

For uninsured non-Hispanic whites and Hispanic individuals, why do ACSC rates decline as they are farther from a SNH?

For uninsured non-Hispanic blacks:o what is contributing to higher ACSC rate relative to other

racial/ethnic groups for each distance category?o unlike other groups, the ACSC rate increases with distance;

although consistent with what we expected, what effect will distance have after we control for other factors?

o what is contributing to lower RSC rates relative to other racial/ethnic groups?

o why is there such a large drop in RSC rates for uninsured blacks in the 3 miles or greater categories relative to 0-3 miles?

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Planned Multivariate Analysis

Estimate a logistic model examining probability of an ACSC (or RSC) discharge relative to a MC discharge at

the person-level of observation, with the form:

)log(,

,

MCi

ACSCi

P

Piiiiiiii YTISLXSLHSX 654321 )*(

where Xi are patient characteristics (age, gender, race/ethnicity, payer);HSi are health system characteristics (SNH and FQHC capacity, availabilityof physician and non-physician personnel); TIi are indicators of travelimpedance or barriers (land use indicators, commuting patterns to work);SLi are indicators of distance to SNHs; and Yi is a year indicator.

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SNH Change Over Time in Florida As noted, we are also interested in SNH change

over study period: closures and conversions that could affect hospital mission

Some of the changes in Florida during our study period caught our attention:

As expected, SNHs in base period were primarily located in areas with high rates of poverty and minority populations

However, three SNHs closed and their local socioeconomic profiles had distinctive features.

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SNH Change Over Time in Florida: 2000 Demographics

CLOSED SNHs

% Non-Hispanic

Black

% Hispanic

% in Poverty

Total Population

Everglades Memorial Hospital, Pahokee, FL 56.1% 29.5% 32.0% 5,985

Fish Memorial Hospital, De Land, FL 19.2% 8.7% 19% 20,904

Polk General Hospital, Bartow, FL 28.4% 8.1% 13.1% 15,340

Overall FL statistics 14.6% 16.8% 12.5% 16 million

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SNH Change Over Time in Florida For Everglades Memorial Hospital:

a nearby public hospital (10 miles away) converted to for-profit 2 years after the Everglades closure

the nearest remaining SNH was 44 miles away in Palm Beach, FL.

For Fish Memorial Hospital: a church-affiliated health system acquired this hospital and

a second public hospital located in De Land Fish Memorial was closed and a replacement facility was

built by the system in Orange City, FL (19 miles away) a church-affiliated hospital remains in De Land but loss of

Fish Memorial reduced SNH capacity by one-third

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% Non-Hispanic Black 2000 by Census Tract: Volusia County

Original Fish Memorial Hospital

New Fish MemorialHospital

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% Poverty 2000 by Census Tract: Volusia County

Original Fish Memorial Hospital

New Fish MemorialHospital

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Planned Multivariate Analysis Do a pre-event/post-event analysis in which we match areas

where SNH changes occurred to a similar one where SNH changes are not present.

Criteria for matching treatment and comparison hospitals (our wish list): In same state In same type of area (rural, small metro, large metro) Similar SNH hospital market structure in base year in terms of

numbers, ownership, and bed size Similar non-SNH structure ownership status in base year and

similar changes over time No overlap in patient zip codes Similar socio-demographic characteristics Similar patterns of patient travel distances

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Planned Multivariate Analysis A separate patient-level analysis for each individual matched

area:

In Florida:o Matching De Land where 1 public SNH closed and one converted

from public to NFP with Titusville, FL where one public SNH remained operational with no ownership change

o Matching Bartow, FL and Pahokee, FL to Titusville, FL in two separate analyses.

In Wisconsin:o Matching Milwaukee with its 2 SNHs that closed (Milwaukee County

Hospital and Northwest General Hospital) with Madison, WI where University of Wisconsin Hospital remained operational.

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Planned Multivariate Analysis Estimate a patient-level hospital choice model for

ACSC (RSC) where individual i’s expected utility of hospital j (Vij):

Vij = 1Zij + 2(Xi*Zij) + 3(TIi*Zij) + 4(SLi*Zij) + 5(SLi*Xi *Zij) + ij

and assume that the error is Type I extreme value to obtain the McFadden conditional logit model :

In this case, SLi will be a series of dummy variables indicating distanceto a closed or converted SNH. If no closure/conversion, they all equal 0.

setchoiceinj

Vij

VijPij

___

)exp(

)exp(

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Planned Multivariate Analysis The SLi do not enter the model directly (as a main effect) because, like

patient characteristics in Xi, they are not a characteristic of a given hospital j

The interaction of SLi with elements of Zij will reveal many things. Travel distance to each hospital option j is included in Zij, and its interaction

with SLi will reveal if patients are less deterred by travel distance to obtain hospital care when a nearby SNH exits.

We will also include the three-way interaction of SLi, travel distance, and patient race/ethnicity to assess if there are racial/ethnic differences in response to travel distance when SNHs exit.

Zij will also contain an indicator of whether a given hospital in a patient’s choice set is a SNH. Its interaction with SLi will indicate if patients are more likely to choose a SNH after the loss of a nearby SNH.

A three-way interaction of the above with patient race/ethnicity will reveal if minority individuals are more apt to seek out another SNH relative to comparable non-minority individuals.

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Planned Multivariate Analysis

We also plan to estimate a much simpler patient-level model where actual travel distance to a hospital is the dependent variable.

Specifically:

log (dij*) = 1 + 2Xi +3Zij + 4HSi + 5TIi + 6SLi + 7 (SLi*Xi )+ 8Yi + ij

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Desired Outcomes from Analysis

Our proposed analysis seeks to understand how patterns of access are affected by:

Geographic location of uninsured relative to SNHs, assuming those nearest to an SNH achieve greatest benefit

Safety net hospital contractions that occur in a market, assuming these might negate any positive benefits derived from nearness to a SNH

Differential effects based on patient race or ethnicity

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Desired Outcomes from Analysis We also plan to develop measures of accessibility using the

conditional logit analysis (namely use estimated coefficients to measure log value of denominator in the model before and after SNH closure or conversion).

We examine values of this variable across individuals to see if there are regional clusters of individuals distinguished by race or ethnicity that are especially affected by SNH exit

We will use this and other measures to identify communities with the greatest access impediments following safety net hospital closure or conversion.

We then identify a few sites for in-depth case studies of communities in which access effects were small versus those where they were larger