Ethnic and geographic variations in multimorbidty: Evidence from three large cohorts

A common characteristic of patients seen at the Veterans Health Administration (VHA) is a high number of concurrent comorbidities (i.e. multimorbidity). This study (i) examines the magnitude and patterns of multimorbidity by race/ethnicity and geography; (ii) compares the level of variation explained by these factors in three multimorbidity measures across three large cohorts. We created three national cohorts for Veterans with chronic kidney disease (CKD:n = 2,190,564), traumatic brain injury (TBI:n = 167,954) and diabetes-mellitus (DM:n = 1,263,906). Multimorbidity was measured by Charlson-Deyo, Elixhauser and Walraven-Elixhauser scores. Multimorbidity differences by race/ethnicity and geography were compared using generalized linear models (GLM). Latent class analysis (LCA) was used to identify groups of conditions that are highly associated with race/ethnic groups. Differences in age (CKD,74.5, TBI,49.7, DM, 66.9 years), race (CKD,80.9%, TBI,76.4%, DM, 63.8% NHW) and geography (CKD,64.4%, TBI,70%, DM, 70.9% urban) were observed among the three cohorts. Accounting for these differences, GLM results showed that risk of multimorbidity in non-Hispanic blacks (NHB) with CKD were 1.16 times higher in urban areas and 1.10 times higher in rural areas compared to non-Hispanic whites (NHW) with CKD. DM and TBI showed similar results with risk for NHB, 1.05 higher in urban areas and 0.97 lower in rural areas for both diseases. Overall, our results show that (i) multimorbidity risk was higher for NHB in urban areas compared to rural areas in all three cohorts; (ii) multimorbidity risk was higher for Hispanics in urban areas compared to rural areas in the DM and CKD cohorts; and (iii) the highest overall multimorbidity risk of any race group or location exists for Hispanics in insular islands for all three disease cohorts. These findings are consistent among the three multimorbidity measures. In fact, our LCA also showed that a three class LC model based on Elixhauser or Charlson provides good discrimination by type and extent of multimorbidity.