Abstract

Poor comparability of social groups is one of the major methodological problems that threatens the validity of health disparities (HD) research findings. We illustrate a methodological solution that can additionally unpack the mechanisms behind differential effects on depression and anxiety. We describe racial/ethnic differences in the prevalence of depression and anxiety scores between Black and White women using classic methods, and then we illustrate a 1:1 matching procedure that allows for building of individual-level difference scores, i.e., actual HD difference score variables, for each pair of comparable participants. We compare the prevalence of depression disorder between Black and White young women after matching them 1:1 on common socio-economic characteristics (age, employment, education, and marital status). In essence, we follow matching or stratification methods, but make a step further and match cases 1:1 on propensity scores, i.e., we create Black–White ‘dyads’. Instead of concluding from plain comparisons that 11% more White young women (18–30 years old) report a depressive disorder than Black young women, the matched data confirms the trend, but provides more nuances. In 27% of the pairs of comparable pairs the White woman was depressed (and the comparable Black woman was not), while in 15% of the pairs the Black woman was depressed (and the comparable White woman was not). We find that Black-to-White disparities in neighborhood disorder do not predict depression differences (HDs), while such an effect is evident for anxiety HDs. The 1:1 matching approach allows us to examine more complex HD effects, like differential mediational or resilience mechanisms that appear to be protective of Black women’s mental health.

Highlights

  • Understanding the underlying causes of health disparities (HD) is a major research objective in the US and abroad, because it promises to uncover efficient solutions for health equity [1,2]

  • We provide a method that puts in practice this imaginary ‘what if’ (or counter-factual (CF) [5]) exercise that increases comparability and potentially reduces confounding [6], by directly matching 1:1 participants on all background factors, re-assessing the range of differences in health outcomes

  • We propose that what is missing from analytical modeling of HDs is the direct specification of HDs

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Summary

Introduction

Understanding the underlying causes of health disparities (HD) is a major research objective in the US and abroad, because it promises to uncover efficient solutions for health equity [1,2]. HDs are differences in health outcomes between groups that are avoidable; this implies that if one knows what causes them, one can avoid, or at least reduce them. When such disparities in health are not reduced even when causal mechanisms are understood, HDs qualify as inequities, and they are unfair [4]. Brain Sci. 2018, 8, 207 happen to members of a disadvantaged racial/ethnic group, had they been members of the privileged group; such imaginary exercises have to assume that members of different R/E groups are ‘exchangeable’, or that one can infer what could have happened to a person from one group had they switched roles with an ‘identical’ person from another group. We provide a method that puts in practice this imaginary ‘what if’ (or counter-factual (CF) [5]) exercise that increases comparability and potentially reduces confounding [6], by directly matching 1:1 participants on all background factors, re-assessing the range of differences in health outcomes

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