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
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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4
5
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)
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.
Dabady, M., Blank, R. M., & Citro, C. F. (Eds.). (2004). Measuring racial discrimination. National Academies Press.
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Holland 1986; 2003; Rubin 1974, 1977, 1978; Pearl 2000.
Using counterfactual methods in
disparities studies
The causal effect α is a difference in
outcome Y between treatment (X=x1)and
control (X=x2)
α= E(Y | X=x1) - E(Y | X=x2)
Z
Randomized experiments
and quasi-experiments at the
population level allow us to
calculate average treatment
effects that estimate this
causal effect.
X
Y
U
Z
Randomization
X
Y
U
Dabady, M., Blank, R. M., & Citro, C. F. (Eds.). (2004). Measuring racial discrimination. National Academies Press.
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Holland 1986; 2003; Rubin 1974, 1977, 1978; Pearl 2000.
Using counterfactual methods in
disparities studies
The causal effect α is a difference in
outcome Y between treatment (X=x1)and
control (X=x2)
α= E(Y | X=x1) - E(Y | X=x2)
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.
Z
X
Y
U
Z
Randomization
X
Y
U
Dabady, M., Blank, R. M., & Citro, C. F. (Eds.). (2004). Measuring racial discrimination. National Academies Press.
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Holland 1986; 2003; Rubin 1974, 1977, 1978; Pearl 2000.
Using counterfactual methods disparities
studies
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.”
Z
Randomization
Race???
Y
U
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.
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improve identification of healthcare
disparities
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)…
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Measuring healthcare disparities: A
non-causal “counterfactual”
problem
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
Institute of Medicine,
2003
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Differences, Discrimination, and
Disparity
Difference
Clinical Need &
Appropriateness &
Patient Preferences
Blacks
Whites
Quality of care
The difference is due to:
IOM Unequal Treatment 2002
Healthcare Systems &
Legal / Regulatory
Systems
Discrimination:
Bias, Stereotyping, and
Uncertainty
Disparity
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Should differences due to all of
these factors be considered a
disparity?
Differences due to:
Income
Education
Rates of Substance Use
Age
Geography
Discrimination
Racism
Insurance
Employment
Comorbidities
 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?
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In Unequal Treatment, the IOM made a
distinction between allowable and
unallowable differences
Allowable / Justified
Unallowable / Unfair
Need for Care
(Substance abuse rates)
Prevalence of MI
Preferences for Care
Blacks
Whites
Minority
Difference
Non-Minority
Quality of Care
The IOM Definition
of Healthcare Disparities
Discrimination
Income
Education
Employment
Insurance
Clinical Need &
Appropriateness,
Patient Preferences
Healthcare Systems &
Legal / Regulatory
Systems
Disparity
Discrimination:
Bias, Stereotyping,
& Uncertainty
IOM, 2002
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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 studies
1) 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 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.
2.
3.
Fit a model
Transform HS distribution
Calculate predictions
Adjust Need (HS) “Index”
(Rank and Replace)
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Black
White
2.
Fit a model
Transform HS
3.
Calculate predictions
1.
distribution
Transform Distribution of Health Status
1.
2.
3.
Fit a model
Transform HS distribution
Calculate predictions
Propensity Score Weighting
 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.
1.
2.
3.
Fit a model
Transform HS distribution
Calculate predictions
Propensity Score Weighting
P(White)=β0+ β1(HS) = ê i(z)
After PS weighting
Before PS weighting
1.
2.
3.
Fit a model
Transform HS distribution
Calculate predictions
Does the method matter?
Different estimates,
similar variance
Mean Expenditure
Given Nonzero Expenditure
$
0
20 40 60 80
120
200
0
-400
-200
$
Unadjusted
Rank and Replace
Propensity Score
Recycled
RDE
Probability
Total Expenditure
0.00 0.02 0.04 0.06 0.08 0.10
Probability of Any Expenditure
White - Black
White - Latino
White - Black
White - Latino
Wh
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Does the method matter?
Different estimates
for linear models
Table 4. Comparison of methods of calculating black-white disparity in total
medical expenditure using linear and non-linear models
Adjustment for HS and
SES (RDE)1
Disparity
(SE)
Linear Models
Linear RDE
488.60
(78)
Non-Linear RDE
Non-linear models
841.75
(94)
Adjustment for HS
Disparity
2
(SE)
Oaxaca-Blinder Decomposition
912.63
(101)
RDE of Model with No SES
(98)
1125.72
Propensity Score
(411)
1284.72
Rank and Replace
(482)
1407.32
Combined Method
(491)
1454.25
Source: Combined Medical Expenditure Panel Survey (MEPS) data from 2003 and 2004
Similar
estimates
for IOM
concordant
methods
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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.
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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
bcook@cha.harvard.edu
@cmmhr
www.healthequityresearch.org
SAS and Stata code available for the “rank and
replace” adjustment of health status variables.
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