managin gand researching health care systems wilm quentin ... · 29-11-2017 · seminar on health...
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Improved Health (level and equity)
Managin gand Researching Health Care Systems
Wilm Quentin, Dr. med. MSc HPPF FG Management im Gesundheitswesen, Technische Universität Berlin
(WHO Collaborating Centre for Health Systems Research and Management) &
European Observatory on Health Systems and Policies
29 November 2017 Improved Health 1
WHO 2007
WHO building blocks 21 Nov
21 to 23 Nov
24 Nov
24 Nov
23 Nov
23 Nov
Week 8
27 Nov
28 Nov
22 Nov (seminar)
28 Nov
29 Nov
30 Nov
30 Nov
30 Nov/1 Dec
29 November 2017 Improved Health 2
27 Nov
Outline of the course- Week 1
Topic Date Lecturer
Introduction and Outline of the course 20.11.2017 15-17 Uhr
Wilm Quentin and Daniel Opoku
Introduction and frameworks 21.11.2017 09-12 Uhr
Reinhard Busse
Financing I: Raising Resources 13.30-17 Uhr Wilm Quentin
Seminar on health system relevant databases and information for term paper
22.11.2017 10-12 Uhr (H8173/74)
Anne Spranger
Financing II: Pooling and re-allocation 13.30-17 Uhr Reinhard Busse
Financing III: Purchasing and payment systems
23.11.2017 09-12 Uhr
Wilm Quentin
Leadership and Governance + Care Delivery
13.30-17 Uhr Reinhard Busse
Medical products 24.11.2017 9-10.30 Uhr
Reinhard Busse
Introduction to group exercise 10.30-12 Uhr Anne Spranger
Workforce 13.30-17 Uhr Claudia Maier Improved Health 3 29 November 2017
Outline of the course - Week 2
Topic Date Lecturer
Preliminary Summary of building blocks
27.11.2017 09-10.30 Uhr
Reinhard Busse
Presentation by GIZ on health system related German development cooperation
10.30-12 Uhr Ursula Bürger, Fachplanerin Kompetenz-Center Gesundheit und Soziale Sicherung, GIZ
Access and Coverage 13.30-17 Uhr Reinhard Busse
Quality and Safety 28.11.2017 09-12 Uhr
Reinhard Busse
Financial and social risk protection 13.30-17 Uhr Wilm Quentin
Improved Health 29.11.2017 13.30-17 Uhr
Wilm Quentin
Efficiency and Responsiveness 30.11.2017 09-12 Uhr
Reinhard Busse
Summary of Health System Performance Assessment
13.30-17 Uhr Reinhard Busse
Group Presentations and Wrap-up 01.12.2017 09-12 Uhr
Reinhard Busse or Wilm Quentin Improved Health 4 29 November 2017
Outline for this afternoon
• 13:30-15:30: Presentation and discussion (120 min)
• 15:30-15:45: break (15 min)
• 15:45-16:15: group work (30 min)
• 16:15-16:50: Presentation of group work and discussion
• 16:50-17:00: Wrap-up (10 min)
Financial Protection and Equity in Financing 5 28 November 2017
6 Improved Health 29 November 2017
My combined performance framework (incl. costs/ efficiency and relationship to WHO dimensions)
x =
Inputs (money and/or resources)
Efficiency (value for money, i.e.
population health and/ or responsiveness per input unit)
Population health outcomes (system-wide effectiveness,
level & distribution)
Responsiveness
(level & distribution)
Access(ability) incl. Financial protection*/
Coverage
Quality (for those who
receive services)
* Financial protection is both an enabling condition for access as well as a final outcome
Population-/ system- wide performance
dimensions
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Measuring improved health: how?
Source: Murray et al. (2002). Summary of Population Health Measures
• The defining goal for the health system is to improve the health of the population.
• According to WHO (2000): Measuring „health“ of a population through a summary measure should reflect health throughout the life course, including both:
Mortality and
Morbidity
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Population health measurements I
Full Health Partial Health Premature mortality
Disease (Stage and Severity)
Birth
Death among these patients
Life expectancy
Disease onset
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Purposes of improved health measurements
Source: Murray et al. (2002). Summary of Population Health Measures
Comparing the health of one population to another
Comparing the health of the same population over time
Quantifying health inequalities within population
Providing attention to the effects of non-fatal health outcomes
Informing debates on health outcomes
Analysing the benefits of health interventions
A variety of aggregated population health measures were
developed as early as the 1960s, but confusion persists about their
usage and data reliability
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Population health indicators (denominator: whole population)
(denominator: patients “quality”)
Life expectancy/
overall mortality
Health-adjusted
life expectancy
YLD
Tracer: Condition-
specific mortality (e.g.
AMI, breast cancer)
YLL
Amenable/ avoidable
mortality (group of tracers)
Ambulatory-care
sensitive
hospitalisations Infant
mortality
Hospital
mortality
Health service indicators
Hospital
readmissions
Patient
safety
indicators
Condition-specific
inpatient mortality
(e.g. AMI)
Condition-
specific 5-year
survival (e.g.
breast cancer)
Condition-specific
processes
Patient-reported
outcomes (function,
quality-of-life)
Attributability to health care provider
generic
Specific (tracer)
GROUP 1
GROUP 2
DALYs
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Population health measurements I
Full Health Partial Health Premature mortality
Years of Life Lost due to premature mortality (YLL)
Years lived with Disease/Disability (YLD)
Disease (Stage and Severity)
Birth
Death among these patients
Life expectancy
= Disability-Adjusted Life Years (YLD + YLL)
Disease onset
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Population health measurements II
Assumptions Possible
measurements Usages Limitations
Life expectancy (estimated hypothetical length of life)
- Assumes that current mortality rates will continue
- At time of birth, for specific age cohorts (e.g. 40)
- Calculating health gaps, expected years of life lost
- Aggregated population data level - Time sensitivity of interventions?
Mortality - Backward looking data analysis of population
- Standardized per 100.000 or by total population
- Overall deaths or by disease groups - Infant mortality or condition-specific mortality
- Generalizability of results (e.g. varying coding for causes of death) - Reliability of Data
Accounting for quality of life-concepts (e.g. disabilities)
Disability-adjusted life years (DALYs)
- Measure the gap between ideal LE and burden of diseases and disabilities - Mortality and morbidity
- Age specific, or disease specific, overall population
- Grasps both morbidity and mortality - Burden of premature deaths, non-fatal diseases and injuries
- No co-morbidities - Individuals ability to cope with diseases („quality“)
Concept of DALYs
•Take a societal perspective
•Aim to measure the burden of disease
• Integrate both mortality and morbidity
•Years of Life of Life lost are determined in relation to the highest possible national life expectancy (Japan)
•Disability weights were originally determined by experts – but 2010 GBD study updated weights to include surveys about 220 health states
•Based on assumption that one year in full health is as good as two years in a health state with a weight of 0.5
Ökonomische Evaluation von Gesundheitstechnologien 13 15. November 2016
Health care outcome:
satisfaction, complications
etc. Structures and organisation
Patients
Process
Population health status
(need)
Health gain/
Outcome
Other sectors
Nutrition/ agriculture
Environment
QUALITY: Personnel sufficient and well qualified?
Institutions of high standards? Technologies effective?
Human resources
Technologies
Financial resources
QUALITY: Utilization responsive, appropriate, coordinated …?
Health care system
Coverage & needs-based,
equitable ACCESS?
QUALITY: Patients satisfied, services safe and of high quality?
How much? How equitable?
Are the services delivered efficiently? 29 November 2017 9 Improved Health
Population health status
(need)
Health gain/
Outcome
Health care system
What is reflected by life expectancy? In other words, does a high life expectancy reflect low need – or good outcomes?
(and similarly, a low number of DALYs)
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Life expectancy is not a good indicator for the health outcome of health systems … need to look for something more specific
Overall mortality/Deaths Life expectancy
Health care delivery
Socio-economic status/ health literacy
Lifestyle, risk factor prevalence
Environment, e.g. occupational work
Concentrate on specific conditions: the tracer concept
Kessner et al., 1973 defined six criteria to define health problems appropriate for application as tracers:
1. A definitive functional impact, i.e. require treatment, with inappropriate or absent treatment resulting in functional impairment;
2. a prevalence high enough to permit collection of adequate data;
3. a natural history which varies with utilisation and effectiveness of health care;
4. techniques of medical management which are well defined for at least one of the following prevention, diagnosis, treatment, rehabilitation; and
5. be relatively well defined and easy to diagnose, with
6. a known epidemiology.
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5-year survival rates of breast cancer have improved but differ across countries …
But how much is attributable to health policy/ care (e.g. mammography screening)?
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How responsive is tracer mortality to health policy?
Source: Mackenbach et al., J Epidemiol Community Health 2013
Breast cancer – screening by mammography Stroke – prevention and detection of hypertension
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Another example: acute myocardial infarction in England (2002-10)
Source: Smolina et al (BMJ, 2012) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3266430/
Authors ask: how much of this decline is due to a fall in incidence and how much to declines in case fatality?
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Contribution of average annual trends in event rate and case fatality rate to average annual trend in mortality for AMI by region, 2002-10, England
From a policy maker’s perspective: why is distinguishing between these causes important?
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Inpatient AMI mortality (during hospital stay)
… and taking 30 days follow-up
into account
+0.4%
+0.9% +3.8% +3.8%
?
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… combining tracer diagnoses where health policy/ care makes a difference: “avoidable mortality”
Overall mortality/Deaths Life expectancy
Avoidable/Amenable mortality
Health care delivery
Socio-economic status/ health literacy
Lifestyle, risk factor prevalence
Environment, e.g. occupational work
Concept of Avoidable Mortality • Mortality from certain causes of death, where death is avoidable
according to current medical knowledge, practice and public health interventions in a defined age/sex group of the population, developed by Rutstein et al. 1976, Charlton 1983
• List of avoidable deaths based on expert opinion and consensus
• Used as a measure of health system performance
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List of Causes of Death Considered Amenable to Health Care (2004)
• Intestinal infections • Tuberculosis • Other infectious (Diphtheria, Tetanus,
Poliomyelitis) • Whooping cough • Septicemia • Measles • Malignant neoplasm of colon and
rectum • Malignant neoplasm of skin • Malignant neoplasm of breast • Malignant neoplasm of cervix uteri
and body of the uterus • Malignant neoplasm of testis • Hodgkin’s disease • Leukemia • Diseases of the thyroid • Diabetes mellitus • Epilepsy
Developed a set of 34 cause of death codes for persons under age 75:
Online data supplement to Nolte and McKee, Measuring the Health Of Nations. Health Affairs. Vol. 27, no. 1. (http://content.healthaffairs.org/cgi/content/full/27/1/58/DC1)
• Chronic rheumatic heart disease • Hypertensive disease • Ischemic heart disease • Cerebrovascular disease • All respiratory diseases (excl. pneumonia/influenza) • Influenza • Pneumonia • Peptic ulcer • Appendicitis • Abdominal hernia • Cholelithiasis & cholecystitis • Nephritis and nephrosis • Benign prostatic hyperplasia • Maternal deaths • Congenital cardiovascular anomalies • Perinatal deaths, all causes excluding stillbirths • Misadventures to patients during surgical and medical care
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Avoidable mortality: preventable and amenable deaths
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Deaths from certain causes could have been prevented by timely, high-quality and effective medical care e.g. hypertension, breast cancer, pneumonia, infectious diseases against which vaccines are available.
Short distinction I. Causes avoidable through primary prevention, i.e. by reducing the incidence of the disease. This category includes causes whose etiology is in part attributable to lifestyle factors (such as alcohol and/or tobacco consumption) and/or to occupational risk factors. II. Causes amenable to secondary prevention through early detection and treatment. This group includes causes of death for which “screening modalities have been established” such as cancer of breast and cervix. III. Causes amenable to improved treatment and medical care. This group includes infectious diseases, deaths from which are ‘avoidable’ largely through antibiotic treatment and immunisation as well as causes that require medical and/or surgical intervention.
Source: Nolte and McKee 2004
EU List of amenable and preventable mortality
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0.00 100.00 200.00 300.00 400.00 500.00
France (2000-2013)
Luxembourg (2000-2014)
Netherlands (2000-2013)
Sweden (2000-2014)
Spain (2000-2014)
Cyprus (2004-2013)
Italy (2000-2012)
Belgium (2000-2013)
Denmark (2000-2012)
Austria (2000-2014)
Ireland (2000-2013)
Malta (2000-2014)
United Kingdom (2001-2013)
Germany (2000-2014)
Finland (2000-2014)
Portugal (2000-2014)
Greece (2000-2012)
Slovenia (2000-2010)
Poland (2000-2014)
Czech Republic (2000-2014)
Croatia (2000-2014)
Slovakia (2000-2014)
Estonia (2000-2014)
Hungary (2000-2014)
Bulgaria (2000-2013)
Romania (2000-2014)
Lithuania (2000-2014)
Latvia (2000-2014)
Age-standardised rate per 100,000
Amenable mortality in the EU28, males
latest available
2000
2014: rank 14/ 28
2000: rank 12/ 28
Development of amenable mortality for
men in EU countries, 2000-2014
Source: WHO mortality files
(released September 2016);
standardised to ESP2013
29 Week 11: Improved Health 29 November 2017 Improved Health
A similar approach, listing tracer separately and combining results in a “Healthcare Access and Quality Index”
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: GB
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29 November 2017 Improved Health 30
Ambulatory-care sensitive hospitalisations
These indicators are used to point towards the strength of primary care, since COPD and Asthma can be effectively treated in settings of primary care
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Ambulatory-care sensitive hospitalisations: Hospital admission rates in Germany vs. other countries …
Hospital admissions of patients with these conditions are generally considered to be avoidable!
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... and within Germany
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Average levels health versus the distribution of health
Inequalities in health outcome related indicators are prevalent in almost every country - general socio-economic determinants - gender, - educational level, - ethnicity, - across regions - urban versus rural areas Inequalities are often overlooked (due to data constraints) but point to a persistent health policy problem, that is not yet systematically addressed.
Improved Health 34 29 November 2017
Inequalities versus inequities
• Health inequalities: differences in health status or in the distribution of health determinants between different population groups.
– differences in mobility between elderly and young
– differences in mortality rates between people from different social classes.
• Health inequities: inequalities that are attributable to environment and conditions outside the control of individuals.
– Inequalities that are unnecessary and avoidable
– (To be distinguished from those that are attributable to biological variations or free choice)
Improved Health 35 29 November 2017
Case study I: socio-economic determinants in the US
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Source: Winta et al. (2017). Racial Disparities In Surgical Mortality: The Gap Appears To Have Narrowed
Life expectancy at birth, 2014
Is this just the US?
Case Study II: socio-economic determinants in male mortality in Canada
Source: Wood, E., Sallar, A. M., Schechter, M. T., & Hogg, R. S. (1999). Social inequalities in male mortality amenable to medical intervention in British Columbia. Social science & medicine, 48(12), 1751-1758.
“For almost every cause of death examined, the rate of mortality was higher in individuals of lower social and socioeconomic classes than in individuals of the upper social and socioeconomic classes. These results were consistent regardless of the social class component, education, occupation, or income was being measured.”
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Case study 3: Child mortality
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Inequality by socio-economic status
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Comparing inequality in child mortality
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Ghana
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Ghana
Inequalities within cities
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Inequalities within cities: considering the distribution
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Group work
(30 min)
4 groups
Financial Protection and Equity in Financing 47 28 November 2017
Group task
Imagine: A new Minister of Health wants to undertake a major reform. His/her main goal is to improve health of the population. However, she is wondering about how to measure the impact of future reforms
• Select one of your countries
• The student from the country represents the MoH
• Other students form a group of consultants
• Two groups make suggestions about how to measure the impact on average health outcomes.
• The other two groups make suggestions about how to measure equity of health outcomes.
• After break: One of the consultants presents the results (5min)
Financial Protection and Equity in Financing 48 28 November 2017
Conclusions
• A range of different measures exist to assess both population health and the effectiveness of health care.
• Attributability of changes in health measures to health care is a key concern for health system performance assessments.
• Similarly, different measures are availbale to assess equity of health outcomes and developments of equity.
• The most important distinction for equity measures is between absolute differences versus relative differences and whether or not the distribution across the entire population is taken into account.
Improved Health 49 29 November 2017