Measuring Healthcare Disparities

Third North American Congress of Epidemiology Montreal, Quebec, June 21-24, 2011

James P. ScanlanAttorney at LawWashington, DC

jps@jpscanlan.com

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)

• Scanlan’s Rule page of jpscanlan.com and its twenty sub-pages (especially the Immunization Disparities sub-page)

• Mortality and Survival page of jpscanlan.com

• Measurement Problems in the National Healthcare Disparities Report (APHA 2007)

• “Can We actually Measure Health Disparities?,” Chance 2006

• “Race and Mortality,” Society 2000

• “Divining Difference,” Chance 1994

• “The Perils of Provocative Statistics,” Public Interest 1991

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.

• 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

0

1

2

3

4

5

1 3 5 10 20 30 40 50 60 70 80 90 95 97 99

Cutoffs Defined by AG Success Rate

Rat

ios

(Ab

s P

erc

Pn

t D

iff)

(1) AG Succ Rate/DG Succ Rate

(2) Ratio DG Fail Rate/AG Fail Rate

(3) DG Fail Odds/AG Fail Odds

(4) Absolute Diff Betw Rates

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

0

10

20

30

40

50

1 3 5 10 20 30 40 50 60 70 80 90 95 97 99

Cutoffs Defined by AG Success Rate

Per

cen

tag

e P

oin

ts

Absolute Diff Betw Rates

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 2006. Latter shows how decreasing poverty increases proportion blacks make up of the poor and of the non-poor.

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

Cutoffs

-0.3

-0.2

-0.1

0

1 3 5 10 20 30 40 50 60 70 80 90 95 97 99

Cutoffs Defined by AG Success Rate

Co

nce

ntr

atio

n I

nd

ex

Fail Concentration Index

Success ConcentrationIndex

Other Illustrative Data

• Income data (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. 2011 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)

Period Grade YearWhiteRate

BlackRate

FavRatio

AdvRatio AbsDf EES

PreRq 5 1996 8% 3% 2.67 1.05 0.05 0.47

Post 1 5 1997 46% 33% 1.39 1.24 0.13 0.34

Post 2 5 1998 50% 39% 1.28 1.22 0.11 0.29

PreRq 9 1996 46% 32% 1.44 1.26 0.14 0.37

Post 1 9 1997 89% 84% 1.06 1.45 0.05 0.24

Post 2 9 1998 93% 89% 1.04 1.57 0.04 0.26

Table 2: Illustration of Appraisals of the Comparative Degree of Employer Bias Using Different Measures of Disparities in Selection/Rejection

Employer AG Sel Rate DG Sel Rate RR Selection RR Rejection AbsDf OR

A 20.0% 9.0% 2.22 (1) 1.14 (4) 0.11 (4) 2.53 (1)

B 40.1% 22.7% 1.77 (2) 1.29 (3) 0.17(2) 2.29 (3)

C 59.9% 40.5% 1.48 (3) 1.48 (2) 0.19 (1) 2.19 (4)

D 90.0% 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