managin gand researching health care systems wilm quentin ... · 29-11-2017 · seminar on health...

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


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


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


Population health measurements I

Full Health Partial Health Premature mortality

Disease (Stage and Severity)

Birth

Death among these patients

Life expectancy

Disease onset


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


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


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


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)

29 November 2017 Week 11: Improved Health 10 Improved Health


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.


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)?


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

19 29 November 2017 Improved Health

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?


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?


Inpatient AMI mortality (during hospital stay)

… and taking 30 days follow-up

into account

+0.4%

+0.9% +3.8% +3.8%

?



… 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


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


http://content.healthaffairs.org/cgi/content/full/27/1/58/DC1

Avoidable mortality: preventable and amenable deaths

Improved Health 26 29 November 2017

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


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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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


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!


... and within Germany


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.


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)


Case study I: socio-economic determinants in the US


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.”


Case study 3: Child mortality


Inequality by socio-economic status


Comparing inequality in child mortality


Ghana


Ghana

Inequalities within cities


Inequalities within cities: considering the distribution


Group work

(30 min)

4 groups


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)


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.


managin gand researching health care systems wilm quentin ... · 29-11-2017 · seminar on health...

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