Using Decomposition Analysis to Identify Modifiable Racial Disparities in the Distribution of Blood Pressure in the United States.
Bottom Line: In the present study, we used decomposition methods to identify how population-level reductions in key risk factors for hypertension could reshape entire population distributions of blood pressure and associated disparities among racial/ethnic groups.We compared blood pressure distributions among non-Hispanic white, non-Hispanic black, and Mexican-American persons using data from the US National Health and Nutrition Examination Survey (2003-2010).Decomposition offers an approach to understand how modifying risk factors might alter population-level health disparities in overall outcome distributions that can be obscured by standard regression analyses.
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Mentions: First, we constructed age-adjusted, sex-specific estimates of blood pressure distributions among each racial/ethnic group (Figure 1) (23). Next, we measured and plotted the difference in blood pressure along each point of the distribution. To assess the contribution of each risk factor independently and of the risk factors jointly, we constructed counterfactual distributions that reflect how one group's distribution of blood pressure would be expected to change if its risk factor profile looked more like that of a comparator group (e.g., how much the distribution of systolic blood pressure among black participants would be expected to shift if that had the same risk factor profiles as white participants). These counterfactual distributions were defined by first writing the marginal distribution of blood pressure as the joint distribution of blood pressure integrated over risk factors for each group. Then, applying the law of iterated expectations (24), we expressed the group-wise marginal distribution of blood pressure as the product of the conditional blood pressure density and conditional risk factor densities. This allowed us to consider a number of counterfactual situations such as the blood pressure outcome for black participants if they had the risk factor profiles of white participants (Figure 2).Figure 1.