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

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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

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Identifying Health Disparities and Pathways Amenable for Interventions

to Reduce Disparities

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5

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Quantifying Disparities and How They Arise

Jones CP et al. J Health Care Poor Underserved 2009

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Using counterfactual methods in disparities studies

α= E(Y | X=x1) - E(Y | X=x2)

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.

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Using counterfactual methods in disparities studies

α= E(Y | X=x1) - E(Y | X=x2)

X

Z

U

Y

X

Z

U

YRandomization

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.

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Using counterfactual methods in disparities studies

α= E(Y | X=x1) - E(Y | X=x2)

X

Z

U

Y

X

Z

U

YRandomization

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)

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Using counterfactual methods disparities studies

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

YRandomization

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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

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Measuring healthcare disparities: A non-causal “counterfactual” problem

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

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Defining Healthcare Disparity: Differences, Discrimination, and

DisparityQ

uali

ty o

f ca

re

Wh

ites

Bla

cks

Difference

Clinical Need & Appropriateness & Patient PreferencesHealthcare Systems & Legal / Regulatory SystemsDiscrimination: Bias, Stereotyping, and Uncertainty

The difference is due to:

Disparity

IOM Unequal Treatment 2002

IncomeEducation

Rates of Substance Use

AgeGeography

Discrimination Racism

Insurance

EmploymentComorbidities

Should differences due to all of these factors be

considered a disparity?

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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:

In Unequal Treatment, the IOM made a distinction between allowable and

unallowable differences

Allowable / JustifiedNeed for Care (Substance abuse

rates)Prevalence of MI

Preferences for Care

Non

- Min

orit

y

Min

orit

y

Qua

li ty

o f C

are Difference

Clinical Need & Appropriateness,

Patient Preferences

Healthcare Systems & Legal / Regulatory

Systems

Discrimination:Bias, Stereotyping,

& Uncertainty

Disparity

Qua

li ty

o f C

are Difference

Clinical Need & Appropriateness,

Patient Preferences

Healthcare Systems & Legal / Regulatory

Systems

Discrimination:Bias, Stereotyping,

& Uncertainty

Clinical Need & Appropriateness,

Patient Preferences

Healthcare Systems & Legal / Regulatory

Systems

Discrimination:Bias, Stereotyping,

& Uncertainty

Disparity

IOM, 2002

The IOM Definition The IOM Definition

of Healthcare Disparitiesof Healthcare Disparities

Whi

tes

Bla

cks

Unallowable / Unfair

DiscriminationIncome

EducationEmployment

Insurance

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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.

Commonly Used Disparities Methods

Typical method of measuring disparities using a regression framework from previous studies1) y=0+ RRACEi+ AAgei+ GGenderi+ε

2) y=0+ RRACEi+ AAgei+ GGenderi + HHealthi+ε

3) y=0+ RRACEi+ AAgei+ GGenderi + HHealthi + IIncomei+ε

Omitted variable bias - R difficult to interpret

Difficult to track this coefficient (or change in coefficient) over time and across studies

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

Implementing the IOM Definition

• Adjust for health status (clinical appropriateness/ need), but not SES variables (system level variables)

• In a regression framework: y=0+ RRACEi+HHealthi + SSESi+ε

White: yW=0+ RRACEWhite+ HHealthWhite + SSESWhite+ε

Black: yB=0+ RRACEBlack+ HHealthWhite+ SSESBlack+ε

Disparity: yW-yB

^ ^

Example: Fit a Model of MH Care Expenditures

Two-part modelAccess (Expenditure>0): Probit

Prob(y>0) = Ф(x'β)

Expenditures: GLM with quasi-likelihoodsE(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 model2. Transform HS

distribution3. Calculate

predictions

Adjust Need (HS) “Index”(Rank and Replace)

1. Fit a model2. Transform HS distribution

3. Calculate predictions

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White

Black

Transform Distribution of Health Status

1. Fit a model2. Transform HS

distribution3. Calculate

predictions

Weight each individual on the propensity of “being white” conditional on a vector of observed health status covariates. • Measure P(White)=β0+ β1(HS)+ε = ê (Hi)

Weight minority individuals by their probability to be White (ê(Hi)), and White individuals by their probability to be minority (1- ê(Hi)).

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(Hi) close to 0.5, whose health status distributions could be either White or Black.

Propensity Score Weighting

1. Fit a model2. Transform HS

distribution3. Calculate

predictions

Propensity Score Weighting

P(White)=β0+ β1(HS) = ê i(z)

1. Fit a model2. Transform HS

distribution3. Calculate predictions

Before PS weighting

After PS weighting

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Does the method matter?

Total ExpenditureUnadjustedRank and ReplacePropensity ScoreRecycled

White - Black White - Latino

Probability of Any Expenditure

Pro

babi

lity

0.00

0.02

0.04

0.06

0.08

0.10

White - Black White - Latino

Mean ExpenditureGiven Nonzero Expenditure

$

-400

-200

020

0

White - Black White - Latino

Predicted Expenditure

$

020

4060

8012

0

RDE

Different estimates, similar variance

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Disparity (SE) Disparity (SE)

488.60 (78) 912.63 (101)

1125.72 (98)

1284.72 (411)

1407.32 (482)

1454.25 (491)Source: Combined Medical Expenditure Panel Survey (MEPS) data from 2003 and 2004

2Five methods were used to transform Whites to look like Blacks in health status SES - socioeconomic status; RDE - residual direct effectSE - standard error; HS - health statusAll disparity estimates are significant a the p<.05 level

1 Two methods were used to transform Whites to look like Blacks in dependent variables related to health status and SES.

Rank and Replace

RDE of Model with No SES

Non-linear models

Combined Method841.75 (94)

Table 4. Comparison of methods of calculating black-white disparity in total medical expenditure using linear and non-linear models

Propensity Score

Adjustment for HS and

SES (RDE)1

Linear RDELinear Models

Non-Linear RDE

Adjustment for HS2

Oaxaca-Blinder Decomposition

Different estimates for linear models

Similar estimates for IOM concordant methods

Does the method matter?

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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.

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

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bcook@cha.harvard.edu@cmmhr

www.healthequityresearch.org

SAS and Stata code available for the “rank and replace” adjustment of health status

variables.