1. Applying Causal Inference Methods to Improve Identification of Health and Healthcare Disparities, and the Underlying Mediators and Moderators of Disparities healthequityresearch.org @cmmhr December 10, 2015

2. Overview Identifying healthcare disparities: applying concepts from a causal inference framework (Cook) Brief background on race and causal inference A framework that uses the notion of the “counterfactual” to measure healthcare disparities. Identifying Pathways Amenable to Disparities Reduction (Valeri) A causal inference perspective Example of racial disparities in cancer survival 2

3. Identifying Health Disparities and Pathways Amenable for Interventions to Reduce Disparities 3

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6. Quantifying Disparities and How They Arise Jones CP et al. J Health Care Poor Underserved 2009 6

7. Using counterfactual methods in disparities studies α= E(Y | X=x 1 ) - E(Y | X=x 2 ) 7 Dabady, M., Blank, R. M., & Citro, C. F. (Eds.). (2004). Measuring racial discrimination. National Academies Press. Holland 1986; 2003; Rubin 1974, 1977, 1978; Pearl 2000. The causal effect α is a difference in outcome Y between treatment (X=x1)and control (X=x2) Difference between an individual receiving treatment and the same individual not receiving treatment. Because the individual can only take one of these values, one of these is a counterfactual.

8. Using counterfactual methods in disparities studies α= E(Y | X=x 1 ) - E(Y | X=x 2 ) 8 X Z U Y X Z U Y Randomization Dabady, M., Blank, R. M., & Citro, C. F. (Eds.). (2004). Measuring racial discrimination. National Academies Press. Holland 1986; 2003; Rubin 1974, 1977, 1978; Pearl 2000. The causal effect α is a difference in outcome Y between treatment (X=x1)and control (X=x2) Randomized experiments and quasi-experiments at the population level allow us to calculate average treatment effects that estimate this causal effect.

9. Using counterfactual methods in disparities studies α= E(Y | X=x 1 ) - E(Y | X=x 2 ) 9 X Z U Y X Z U Y Randomization Randomization breaks the link between X and all other observables (Z) and unobserved variables (U) except the outcome (Y) By randomizing at the population level, we are able to infer the difference between the outcome if an individual received the treatment and the outcome if the same individual did not receive the treatment. Remember that one of these is a counterfactual. Dabady, M., Blank, R. M., & Citro, C. F. (Eds.). (2004). Measuring racial discrimination. National Academies Press. Holland 1986; 2003; Rubin 1974, 1977, 1978; Pearl 2000. The causal effect α is a difference in outcome Y between treatment (X=x1)and control (X=x2)

10. Using counterfactual methods disparities studies 10 VanderWeele, T. J., & Robinson, W. R. (2014). On the causal interpretation of race in regressions adjusting for confounding and mediating variables.Epidemiology, 25(4), 473-484. see Krieger letter to editor and response.  For causation to occur, manipulability of the potential causal variable is required (Holland 2003)  Is race manipulable?  “Racial categories, differential perceptions and treatment of racial groups, and associations between race and health outcomes are modifiable.” Race??? Z U Y Randomization

11. 11  In disparities studies, minority race is the “treatment” of interest.  Ideally, the counterfactual group is a group identical in all aspects to the minority group except for minority race status.  “Balancing” can be achieved (i.e., videos with actors (Schulman 1999), job applications given names typical of blacks and whites ( Bertrand and Mullainathan 2004 )).  Implementing the IOM definition of healthcare disparities requires a hypothetical group with counterfactual distributions of health status variables (Cook et al. 2009)… Using counterfactual methods to improve identification of healthcare disparities

12. Measuring healthcare disparities: A non-causal “counterfactual” problem 12 Institute of Medicine, 2003  Unequal Treatment defines disparities: “all differences except those due to clinical appropriateness and need and patient preferences”  Disparities do include differences due to SES (differential impact of healthcare systems and the legal/ regulatory climate), and discrimination.  In short, a comparison between whites and counterfactual group of blacks with white health status

13. Defining Healthcare Disparity: Differences, Discrimination, and Disparity 13 Quality of care Whites Blacks Difference Clinical Need & Appropriateness & Patient Preferences Healthcare Systems & Legal / Regulatory Systems Discrimination: Bias, Stereotyping, and Uncertainty The difference is due to: Disparity IOM Unequal Treatment 2002

14. Income Education Rates of Substance Use Age Geography Discrimination Racism Insurance Employment Comorbidities Should differences due to all of these factors be considered a disparity? 14 Are these allowable or justified differences? Should the health care system be held accountable for these differences in care? To track progress in a way that is useful for policy, do we count all these differences? Differences due to:

15. In Unequal Treatment, the IOM made a distinction between allowable and unallowable differences Allowable / Justified Need for Care (Substance abuse rates) Prevalence of MI Preferences for Care Unallowable / Unfair Discrimination Income Education Employment Insurance 15

16. Definition of Racial Disparities: IOM  Example 1: Difference overestimates disparity Hispanics are on average younger and therefore use less medical care. This is not an “unfair” difference.  Example 2: Difference underestimates disparity African-Americans are on average less healthy than Whites but may have very similar rates of utilization. If Blacks were made to be as healthy as Whites, we would see much less use for Blacks compared to Whites - an “unfair” difference.

17. Commonly Used Disparities Methods  Typical method of measuring disparities using a regression framework from previous studies 1) y=  0 +  R RACE i +  A Age i +  G Gender i +ε 2) y=  0 +  R RACE i +  A Age i +  G Gender i +  H Health i +ε 3) y=  0 +  R RACE i +  A Age i +  G Gender i +  H Health i +  I Income i +ε  Omitted variable bias -  R difficult to interpret  Difficult to track this coefficient (or change in coefficient) over time and across studies

18. Operationalizing the IOM Definition (1) Fit a model (2) Transform distribution of health status (not SES) (3) Calculate predictions for minorities with transformed health status - Average predictions by group and estimate disparities

19. Implementing the IOM Definition Adjust for health status (clinical appropriateness/ need), but not SES variables (system level variables) In a regression framework: y=  0 +  R RACE i +  H Health i +  S SES i +ε White: y W =  0 +  R RACE White +  H Health White +  S SES White +ε Black: y B =  0 +  R RACE Black +  H Health White +  S SES Black +ε Disparity: y W -y B ^^

20. Example: Fit a Model of MH Care Expenditures Two-part model Access (Expenditure>0): Probit Prob(y>0) = Ф(x'β) Expenditures: GLM with quasi-likelihoods E(y|x) = μ(x'β) and Var(y|x) = (μ(x'β)) λ with log link function and variance proportional to mean (λ=1) 1. Fit a model 2. Transform HS distribution 3. Calculate predictions

21. Adjust Need (HS) “Index” (Rank and Replace) 1. Fit a model 2. Transform HS distribution 3. Calculate predictions 100 White Black

22. Transform Distribution of Health Status 1. Fit a model 2. Transform HS distribution 3. Calculate predictions

23.  Weight each individual on the propensity of “being white” conditional on a vector of observed health status covariates. Measure P(White)=β 0 + β 1 (HS)+ε = ê (H i )  Weight minority individuals by their probability to be White (ê(H i )), and White individuals by their probability to be minority (1- ê(H i )).  Multiply PS weights by survey weights Conditional on the propensity score, the distributions of observed health status covariates are the same for minorities and Whites (Rubin 1997)  Places more emphasis on individuals with ê(H i ) close to 0.5, whose health status distributions could be either White or Black. Propensity Score Weighting 1. Fit a model 2. Transform HS distribution 3. Calculate predictions

24. Propensity Score Weighting P(White)=β 0 + β 1 (HS) = ê i (z) 1. Fit a model 2. Transform HS distribution 3. Calculate predictions Before PS weighting After PS weighting

25. Does the method matter? 25 Different estimates, similar variance

26. 26 Different estimates for linear models Similar estimates for IOM concordant methods Does the method matter?

27. Summary  Counterfactual: “What would the rates of healthcare access be for a group of Black individuals with a white distribution of health status?”  IOM-concordant methods adjust for health status (but not SES) in the presence of non-linear models and correlations between health status and SES. Similar to non-linear decomposition (Fairlie 2006) and can be used to “decompose disparities” (Saloner, Carson, Cook 2014)  The rank and replace method and the modified propensity score method are “IOM-concordant” Both methods had similar estimates and variance in separate empirical analyses. 27

28. Summary 2:1 disparities in access to mental health care Applicable in the context of measuring readmissions and accountable care organizations that incentivize health disparity reduction? In disparities measurement, make a choice about how to define disparity; What is the right counterfactual comparison group? A race coefficient may be insufficient

29. bcook@cha.harvard.edu @cmmhr www.healthequityresearch.org SAS and Stata code available for the “rank and replace” adjustment of health status variables. 29