Approaches to Measuring Population Health Ian McDowell November, 2005

1. Approaches to Measuring Population HealthIan McDowellNovember, 2005 Mortality-based summary measures Combined disability & mortality methods Conceptual rationale for summary measures Environmental indicators Global indicators POP 8910

2. 1. Why do we need measures of population health? Governments wish to monitor health of citizens • To set priorities for health services & policies • To evaluate social and health policies • To compare health of different regions • To identify pressing health needs • To draw attention to inequalities in health • Highlight balance between length and quality of life • Numerical index desirable: a “GNP of Health”

3. Classifying Population Health Measures by their Purpose • Descriptive measures: • Current health status (e.g., health surveys) • Evaluative measures (e.g., to assess outcomes of health policies) • Analytic measures include an implicit time dimension: • Predictive methods (risk assessment; projections of disease burden) look forward; • Explanatory measures (income inequality or social cohesion) look backwards.

4. These purposes may correspond to different types of research (shown in the ellipses) Note: the figure is intended to show the typical blend of methods you might use in a particular type of study: HSR would use descriptive and analytic, for example.

5. Classifying Population Health Measures by their Focus • Aggregate measures combine data from individual people, summarized at regional or national levels. E.g., rates of smoking or lung cancer. • Environmental indicators record physical or social characteristics of the place in which people live and cover factors external to the individual, such as air or water quality, or the number of community associations that exist in a neighborhood. These can have analogues at the individual level. • Global indicators have no obvious analogue at the individual level. Examples include contextual indicators such as the existence of healthy public policy; laws restricting smoking in public places, or social equity in access to care; social cohesion, etc. Morgenstern H. Ecologic studies in epidemiology: concepts, principles, and methods. Annual Reviews of Public Health 1995; 16:61-81.

6. Linking the focus of a measure to its application • Aggregate measures are typically used in descriptive studies; they focus on the individuals within the population, i.e. idiographic. They measure health in the population • Environmental measures can be used in descriptive, analytic or explanatory studies • Global measures mainly used in analytic studies; focus on generating theory (nomothetic studies). They could measure health of the population

7. Linking the target of a population intervention to the type of measure Interventions can target people, environmental factors, or policy in general These correspond to Morgenstern’s categories of measures used to evaluate the intervention… …and to the presumed etiological sequence

8. History of changing approaches to measuring population health • Originally based on mortality rates. IMR is often used to describe level of development of a country • With declining mortality, people with chronic disease survive; morbidity & disability gain importance • Concern with quality of life, not mere survival • To compare populations at different stages of economic development, it may be desirable to combine mortality and morbidity in a single, composite index

9. Aggregate Measures:Mortality-Based Indicators Life expectancy Expected years of life lost Potential years of life lost

10. Life Expectancy • Summary of all age-specific mortality rates • Estimates hypothetical length of life of a cohort born in a particular year • This assumes that current mortality rates will continue

11. Expectancies and Gaps • From a typical survival curve, we can either consider the life expectancy (“E”), or the gap (“G”) between current life expectancy and some ideal. • Expectancies are generic; gaps can be disease-specific (e.g., life yrs lost due to cancer)

12. Classifying Health Gaps • Gaps: Compare population health to some target. = Difference between time lived in health states less than ideal health, and the specified target • The implied norm or target can be arbitrary, but must be explicit and the same for all populations being compared. The precise value does not matter

13. Gaps: Expected Years of Life Lost • Uses population life expectancy at the individual’s age of death • Problems: different countries may have different life expectancies. It’s overall mortality, so cannot identify impact of a disease. • Standard Expected Years of Life Lost • Reference is to an “ideal” life expectancy • E.g., Japan (82 years for women) • Area between survivorship curve and the chosen norm

14. Potential Years of Life Lost (PYLL) • PYLL =  ( “normal age at death” – actual age at death). Doesn’t much matter what age is chosen as reference; typically 75 • Attempts to represent impact of a disease on the population: death at a young age is a greater loss than death of an elderly person • Focuses attention on conditions that kill younger people (accidents; cancers) • All-causes or cause-specific

15. 3. Aggregate Measures that Combine Mortality & Morbidity Health expectancies Health gaps

16. Composite Measures • Aim to represent overall health of a population • Composite measures combine morbidity and mortality into a health index. (An index is a numerical summary of several indicators of health) • Mortality data typically derived from life tables; morbidity indicators from health surveys, e.g. • Self-rated health • Disability or activity limitations • A generic health index

17. Sidebar: Different Types of Morbidity Scales for Use in Composite Measures • Generic instruments cover a wide range of health topics, e.g. reflecting the WHO definition. These can be health profiles (e.g., Sickness Impact Profile, SF-36) or “health indexes” (e.g., Health Utilities Index, EuroQol) • Specific instruments • Disease-specific (e.g., Arthritis Impact Measurement Scale) • Age-specific (e.g., Child Behavior Checklist) • Gender-specific (e.g., Women’s Health Questionnaire)

18. Survivorship Functions for Health States Survivors Deaths This diagram extends the earlier one by recognizing that not all survivors are perfectly healthy. The lower area ‘H’ shows the proportion of people in good health (however defined); it shows healthy life expectancy. The top curve shows deaths; intermediate area represents levels of disability. Area ‘G’ again represents the health gap. The question arises whether the people with a disability ought to be counted with H or with G. Age

19. More details on the combined indicators • From the previous chart: • We can still read from the bottom, and talk of “health expectancies,” or from the top, and create gap indexes: years of life lost, etc. • The value of a life lived in less than perfect health is less than a healthy life-year. This is “health-adjusted life expectancy” • The indicators will fall in a descending sequence: overall life expectancy, then health-adjusted life expectancy, then healthy life expectancy.

20. A Simple Presentation:Life Expectancy and Disability-Free Life Expectancy, Canada, 1986-1991 Years Life Expectancy from birth Disability-Free Life Expectancy (‘DFLE’) M F M F 1986 1991

21. Health expectancies • Generic term: any expectation of life in various states of health. Includes other, more specific terms, such as Disability Free Life Expectancy • Two main classes: • Dichotomous rating: two health states • Health state valuations for a range of levels

22. I. Dichotomous expectancies • Here full health is rated 1, and any state of poor health (mild, moderate, severe disability) is rated 0. • This leads to Disability-free life expectancy (DFLE): weight of 1 for “no disability” and 0 for all other states. • = Expectation of life with no disability, or Healthy Life Expectancy (HLE) • Very sensitive to threshold of disability chosen

23. II. Polytomous states and valuations • These incorporate many levels of disability into life expectancy estimates and count time spent with each level of disability. • Polytomous model (three or more health states defined: weights assigned to each; generally 0 to 1.0. These may be added together and compared across diseases) • = Health-adjusted life expectancy (HALE) • First calculated for Canada by Wilkins. Four levels of severity & arbitrary weights. • Recent work uses utility weights. E.g. from Health Utilities Index, Quality of Well-Being Scale, EUROQoL, etc.

24. Polytomous Curves Showing Quality of Survival Survivors Deaths This diagram illustrates several classes of disability, each having a separate severity weighting. The area ‘H’ again includes healthy people, but the definition may have changed. The top curve shows deaths; intermediate curves represent various levels of disability. Age

25. Health Expectancy by Income Level and Sex, Canada, 1978 (Wilkins) Years Severely disabled Restricted Minor limitations Healthy Low High Income Quintiles Males Females

26. Relationship between Life Expectancy, Health Expectancy and Health-Adjusted Life Expectancy Life Expectancy Health-Adjusted Life Expectancy Healthy Life Expectancy By down-weighting the various levels of disability, the HALE falls between LE and HLE

27. Some HALE Results for Canada • Wolfson & Wilkins at Statistics Canada used data from the National Population Health Survey to calculate HALEs, using the “Health Utilities Index” to weight different levels of imperfect health • The difference between LE and HALE is 11% for men, and 15% for women, because women live longer and suffer more chronic disease at older ages • They recalculated HALEs, deleting certain types of disability, and found that sensory problems (eyesight, hearing) were the major contributor in Canada to lost years. Vision problem have a very minor effect on health status, but are very common… Pain was the second largest cause • They also showed that less educated people both live shorter lives, and also experience more disability • Source: Wolfson MC. Health Reports 1986;8(1):41-46

28. Gap Measures: QALYs & DALYs • Gap measures can also use a weighting for intermediate health states. This is necessary to combine time lost due to ill health with time lost due to premature mortality • Quality Adjusted Life Years (QALYs) lost • Common outcome measurement in clinical trials, program evaluation • Record extra years of life provided by therapy and quality of that life • Typically use utility scale running from 0 to 1 • DALYS (disability-adjusted life years) lost

29. Complementarity of Health Expectancies and Health Gaps Gaps Age Expectancies LE = Life Expectancy; SLE = Standard LE; HALE = Health-Adjusted LE; HLE = Healthy LE; SEYLL = Standard Expected Years of Life Lost HALY = Health-Adjusted Life Years Lost

30. 4. When do we Use Each Type of Measure? Towards a Functional Classification

31. Recall our Classification of Measures: • Descriptive measures: • Current health status • Evaluative measures • Analytic measures: • Predictive methods that look forward; • Explanatory measures that look backwards.

32. Characteristics of Descriptive Measures • Intuitively simple – cover themes of interest to people in general (“quality of life”, etc) • Reflect values; possible political influence • Time frame = present • Emphasis on modifiable themes • Goal = to make broad classifications

33. Characteristics of Evaluative Measures • Fine-grained: select indicators that sample densely from relevant level of severity • Need to be sensitive to change produced by particular intervention • Content tailored to intervention; usually not comprehensive • Common emphasis on summary score • But should also cover potential side-effects

34. Sensitivity of a Measurement: Metaphor of the combs Evaluative Descriptive

35. Match the Instrument to the Application Population Monitoring Outcomes Research Patient Management 4 4 4 3 3 3 2 2 2 1 1 1 Source: John Ware, October 2000

36. Characteristics of Predictive Measures • Content can be selective rather than comprehensive • Items not necessarily modifiable, or even very important • If derived from discriminant analysis, likely to be parsimonious • Focus on algorithmic scoring and interpretation (e.g., either x or y, plus z in the absence of w)

37. Characteristics of Explanatory Measures • Can combine various types of measures & classifications, ranging from distal to proximal • Based on a conceptual model, rather than empirically based • There can therefore be rival explanatory approaches • Content not necessarily modifiable factors, but these would be desirable

38. 5. Environmental Measures Compositional vs. Contextual Measures

39. Compositional • Demographics; age, ethnic composition, lone parents, dependency ratios, etc • Population resources: wealth, educational levels, etc • Community: social cohesion, watch programs, participation (voting, donations, etc)

40. Contextual • Neighbourhood type, quality; amenities, transportation • Employment opportunities • Access to care • Environmental quality: pollution levels: air, water, noise • Climate • Equity

41. 6. Global Measures Income inequalities, Health inequalities.

42. Some Examples of Global Measures • Social solidarity; sense of identity; artistic output; public interest in health issues, etc. • Indicators of societal support: the “safety net” • Quality of social institutions for health (health protection laws, etc.) • Social cohesion, neighbourhood quality, social capital

43. Canadian Social Health Index Composite Indicator, including: Homicides Alcohol-related fatalities Affordable housing Income equity Child poverty Child abuse IMR Teen suicide Drug abuse High school drop-out rate Unemployment Avg. weekly earnings Seniors’ poverty rate Uninsured health costs for seniors Source: Human Resources Development CanadaApplied Research Bulletin 1997;3:6-8

44. Distributional Measures: Health Inequalities (I) • Index of Dissimilarity: Absolute number or percentage of all cases that must be redistributed to obtain the same mortality rate for all SES groups. • Index of Dissimilarity in Length of Life: The absolute number or proportion of person-years of life that should be redistributed among SES strata to achieve equal length of life in all.

45. Measures of Health Inequalities (II) • Relative Index of Inequality: Ratio of morbidity or mortality rates between those at bottom of SES range to those at top. This is estimated using regression and corrects for other factors. • Slope Index of Inequality: Expresses health inequality between top and bottom of social hierarchy in terms of rate differences rather than rate ratios

46. Gini Coefficient: Measure of Income Inequality L(s) lies below line of equality when income inequality favours the rich Gini coefficient is twice the area between the curve and the line of equality % of income 100 L(s) 0 100 % of population

47. Standardized Index of Health Inequality L(s) lies above line of equality when ill-health is concentrated among poor. L*(s) is indirectly standardized curve indicating unavoidable inequality (e.g., due to age-sex distribution) Inequality favours rich if L(s) lies above L*(s) Cum % of ill-health 100 L(s) L*(s) 100 0 Cum. % of population ordered by income

48. Measures of Impact of Interventions to Reduce Inequalities • Population attributable risk: The reduction in health gap that would occur if everyone experienced the rates in the highest socioeconomic group • Population attributable life lost index: The absolute or proportional increase in life expectancy if everyone experienced the life expectancy of the highest SES group