health care disparities: impossible but essential Alan Zaslavsky, - PowerPoint PPT Presentation

Causal inference for health and health care disparities: impossible but essential Alan Zaslavsky, Ben Lê Cook Harvard Medical School

Outline • Three faces of “disparities” • Health disparities and healthcare disparities • “IOM definition” of health care disparities • Regression modeling to apply the IOM definition • Was this causal inference? Causal effect of what? – “No causality without intervention”? • Nonetheless …

What is a disparity? (“inequality”, …) • Difference between groups in treatment or outcomes • Socially/ethically/morally unjust/unacceptable

Three faces of disparities research • Descriptive/predictive: – Which groups are affected? – Correlates, mediators of intergroup differences – Partitioning of variation attributable (predictively) to different factors/actors • Normative: – Which differences are objectionable? • Causal: – What factors would change outcomes if modified? – … and which of these could be modified?

Health disparities & health care disparities Health disparities Health care disparities • Effects of exposures and • Effects of interactions with experiences over entire life specific systems in specific course (and before) episodes • Cumulative • Incremental (mostly) • Past is continuous with • Can define and control for present pre-treatment status • Recognize broad social • Identify specific responsible responsibility, then specific parties/subsystems, then actors broader patterns • Both general and specific • Specific causal factors and mechanisms mechanisms

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

Operationalization of IOM Definition • Disparities do not include differences related to health status (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.

Problematical aspects of IOM definition • Patient preferences – Shaped by past personal and group experiences – “Tuskegee effect” – Legally forced segregation in South until Medicare – Indistinguishable from effects of inadequate communication, etc. • Discriminatory effect of health care priorities – Which diseases, conditions get R&D? – Ancillary resources essential to health care – Regional disparities with racial/ethnic effects

“IOM definition” of Healthcare Disparity (modified) Difference Clinical Need & Appropriateness Whites Socioeconomic correlates of race Quality of care Blacks Disparity Direct racial/ethnic responses : Bias, Stereotyping, and Uncertainty IOM, Unequal Treatment 2002

Examples: Implementing the IOM Definition • 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.

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

Oaxaca-Blinder decomposition • Linear model in groups g = A,B • Apply to compare groups A, B – First term is difference predicted by covariate difference – Second term is difference predicted by difference in coefficients (single or interacted group effect)

Nonlinear Oaxaca-Blinder decomposition • Nonlinear model in groups g = A,B • Apply to compare groups A, B – First term is difference predicted by covariate difference – Second term is difference predicted by difference in coefficients (single or interacted group effect)

Apply to disparities calculation • Objective: adjust for differences in allowable variables (health status) but not disparity mediators • Estimate intergroup difference if (counterfactually): – Group B had group A distribution of health status – But retained group B distribution of race, SES, etc. • In nonlinear model, construct a joint distribution of race, SES, health status with given margins.

Rank and replace • Separate linear predictor into “allowed” (health status) and “disallowed” terms – Aggregate all health status covariates into combined effect – Observations in the A and B samples separately ranked by the linear predictor from the health status variables – Match by their respective rankings. – (similar to “ Fairlie method of non- linear decomposition”) • Replace A health status with matched B health status • Calculate adjusted comparison

Adjust Need (HS) “Index” (Rank and Replace) 100 Black White

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

IOM-Concordant Prediction Results: Any mental health care Access to Mental Health Care Among those in Need (PHQ-2>=3 or K6>=13) – 2004-2013 MEPS 60% 47.3% 50% 40% 32.7% 29.4% 30% 20% 10% 0% White Black Hispanic -10%

IOM-Concordant Prediction Results: HbA1c Check in Last Year HbA1c Check Among those with Diabetes – 2004-2013 MEPS 80% 66.4% 70% 60% 56.8% 52.7% 50% 40% 30% 20% 10% 0% White Black Hispanic -10%

What did we just do with regression? • “Prediction” in regression – What distribution for Y if: – (1) postulated values/distribution for X – (2) relationships are maintained – Does not require belief in scientific generality of model – Gives substantive interpretation of covariates • Prediction may be factual or counterfactual • If counterfactual, may be – Matched (observed values) – Interpolated (within range of observed data) – Extrapolated (beyond range of observed data)

Can this be called a causal effect? • Rubin: causality only meaningful for a modifiable factor – If unmodifiable, no experiment/intervention possible – What might be modifiable is the system response to race/ethnic ID or appearance – Descriptive inference still useful; • Which is causal? – The doctor refused pain meds because the patient was Black – The doctor refused pain meds because she was told that Black patients were more likely to abuse

Regression prediction → Causal inference? • Threats to validity – Extrapolation without strong conceptual basis – Relationships differ in another setting • Are effects the same for given variable with … – Natural variation – Natural variation with selection (observational study) – Experimental intervention – Program implementation – Social change

Generalizability (“External validity”) Health disparities Health care disparities • Mechanisms variable across • Mechanisms based in settings, subgroups invariant clinical processes (sometimes, somewhat) • Effects in natural variation, • Desired outcomes involve trials, program implement- major extrapolation from ation may be similar existing conditions • Natural variation in • Natural variation in major treatment by geography, psychosocial factors hard to providers, etc. identify • Can control for relevant • Lifetime effects, manifest background and subtle

No causality without … • Strong version: “No causality without intervention” – Need intervention to deduce causality • Weak version: “No causality without relevant variation” – Establish basis for generalizability – Causal inference should inform us regarding effects of possible intervention – Conversely, intervention should recognize nature of underlying variation

Effects of race? Effects of racism? • “Effects of racism” as an ultimate objective – Is there accessible variation? • “Effects of race” looks at interaction • To generalize, need to: – Examine generalizability of studied variation – Recognize when measures may be off the causal pathway – Consider relevance to plausible interventions

Thank you • And thanks to previous speakers and chair for their contributions • Responses, questions and discussion?