Measuring Healthcare Disparities Third North American Congress of Epidemiology Montreal, Quebec, June 21-24, 2011 James P. Scanlan Attorney at Law Washington, DC

Key Points Standard measures of differences between outcome rates (proportions) are problematic for measuring health and healthcare disparities because each is affected by the overall prevalence of an outcome. Healthcare disparities research is in disarray because of observers’ reliance on various measures without recognition of the way each measure is affected by the overall prevalence of an outcome. There exists only one answer to whether a disparity has increased or decreased over time or is otherwise larger in one setting than another. Fourth, that answer can be divined, albeit imperfectly, by deriving from each pair of outcome rates the difference between means of the underlying risk distributions.

References Measuring Health Disparities page (MHD) of jpscanlan.com (especially the Pay for Performance, Solutions sub-pages and Section E.7) Measuring Health DisparitiesPay for Performance Solutions Section E.7 Scanlan’s Rule page of jpscanlan.com and its twenty sub-pages (especially the Immunization Disparities sub-page) Scanlan’s RuleImmunization Disparities Mortality and Survival page of jpscanlan.com Mortality and Survival Measurement Problems in the National Healthcare Disparities Report (APHA 2007) Measurement Problems in the National Healthcare Disparities Report “Can We actually Measure Health Disparities?,” Chance 2006Can We actually Measure Health Disparities? “Race and Mortality,” Society 2000Race and Mortality “Divining Difference,” Chance 1994Divining Difference “The Perils of Provocative Statistics,” Public Interest 1991The Perils of Provocative Statistics

Patterns of Distributionally-Driven Changes in Standard Measures of Differences Between Rates as an Outcome Increases in Overall Prevalence Relative differences in experiencing the outcome tend to decrease. Relative differences in failing to experience the outcome tend to increase. Absolute differences between rates tend to increase to the point where the first group’s rate reaches 50%; behave inconsistently until the second group’s rate reaches 50%; then decline. Absolute differences tend also to move in the same direction of the smaller relative difference. See Introduction to Scanlan’s Rule page for nuances.Scanlan’s Rule Differences measured by odds ratios tend to change in the opposite direction of absolute differences (hence to track the larger relative difference).

Fig 1: Ratios of (1) Advantaged Group (AG) Success Rate to Disadvantaged Group (DG) Success Rate, (2) DG Fail Rate to AG Fail Rate, and (3) DG Fail Odds to AG Fails Odds; and (4) Absolute Difference Between Rates

Fig 2: Absolute Difference Between Success (or Failure) Rates of AG and DG at Various Cutoffs

Patterns of Distributionally-Driven Changes in the Concentration Index as an Outcome Increases in Overall Prevalence Concentration index value adverse to the disadvantaged group for failing to experience the outcome tends to decrease (i.e., failure to experience the outcome becomes more concentrated in the disadvantaged group). Concentration index value adverse to the disadvantaged group for experiencing the outcome tends to decrease (i.e., outcome becomes less concentrated in the advantaged group). See Concentration Index sub-page of MHD and Table 1 of Chance Latter shows how decreasing poverty increases proportion blacks make up of the poor and of the non-poor.Concentration Index Chance 2006

Fig 3: Concentration Index Values Adverse to Disadvantage Group for Failure and Success at Various Cutoffs

Other Illustrative Data Income data (Chance 2006)Chance 2006 NHANES Illustrations Framingham Illustrations Life Table Illustration Other types of data: test scores of any sort, foot race results, propensity score data, mortgage eligibility ratings, etc.

Reminder One It does not matter that one observes departures from the described prevalence- related (distributionally-driven) patterns. Actual patterns are functions of both (a) the prevalence-related forces and (b) the differences between the underlying distributions in the settings being compared.

Reminder Two That the prevalence-related forces may depart from those I describe (e.g., distributions may be irregular) may indeed complicate efforts to appraise the size of disparities. But such possibility cannot justify reliance on standard measures of differences between outcome rates without consideration of the prevalence- related forces.

Key Government Approaches to Disparities Measurement National Center for Health Statistics (Health People 2010, 2020 etc) – relative differences in adverse outcomes Agency for Healthcare Research and Quality HRQ (National Healthcare Disparities Report) – whichever relative difference (favorable or adverse) is larger Centers for Disease Control and Prevention (Jan Health Disparities and Inequalities Report) – absolute differences between rates

Table 1: Illustration Based on Morita et. al. (Pediatrics 2008) Data on Black and White Hepatitis Vaccination Rates Pre and Post School-Entry Vaccination Requirement (see Comment on Morita)Comment on Morita PeriodGradeYear White Rate Black Rate Fav Ratio Adv RatioAbsDfEES PreRq519968%3% Post %33% Post %39% PreRq %32% Post %84% Post %89%

Table 2: Illustration of Appraisals of the Comparative Degree of Employer Bias Using Different Measures of Disparities in Selection/Rejection EmployerAG Sel RateDG Sel RateRR SelectionRR RejectionAbsDfOR A20.0%9.0%2.22 (1)1.14 (4)0.11 (4)2.53 (1) B40.1%22.7%1.77 (2)1.29 (3)0.17(2)2.29 (3) C59.9%40.5%1.48 (3)1.48 (2)0.19 (1)2.19 (4) D90.0%78.2%1.15 (4)2.18 (1)0.12 (3)2.50 (2) parenthetical numbers reflect the rankings of most to least discriminatory employer using the particular measure.

Larger Implications Pay-for-Performance Subgroup Effects Meta-Analysis Case Control Studies