Multimorbidity in Diverse Populations: Stratified Analysis of Race/Ethnicity, Age, Obesity, and Healthcare Costs

This research aims to fill an essential gap in understanding how Body Mass Index (BMI) cutoffs relate to multimorbidity across races in the United States (US). Given the significant and growing rates of obesity and multimorbidity, as well as the known differences in healthy fat distribution among different races, this is an important area of research. BMI is a widely used but imperfect measure of obesity, as it does not account for differences in body composition. However, it is still used as a diagnostic tool. It is vital to ensure that the cutoffs used to define obesity are appropriate for all populations, particularly given the racial disparities in multimorbidity rates. This proposed framework for evaluating BMI cutoffs across races for multimorbidity considered a range of measures, such as, including incidence rates of prevalent diseases, age, gender, type of patient visits, and type of health insurance to arrive at questioning the current World Health Organization (WHO) BMI cutoffs in the US. This research demonstrated that having the exact BMI cutoffs across all races does not serve all populations ideally through three assessments. First, it assessed differences in the prevalence of multimorbidity by race. It identified disease combinations shared by all races/ethnicities, shared by some, and those unique to one group for each age/obesity level. These findings demonstrated that despite controlling for age and obesity, there are differences in multimorbidity prevalence across races. Second, the study developed models to project total charges for the most common multimorbidity combinations in the US and evaluated the accuracy of these models across different racial and ethnic groups and multimorbidity patterns. The relationship between healthcare costs and multimorbidity varied for each racial group and depended on the specific combination of chronic conditions, age, and obesity status. Third, it assessed the relationship between BMI and healthcare burden across race and healthcare utilization among middle-aged patients in the US. It demonstrated that the relationship between BMI and healthcare burden varied across races within the same healthcare care utilization category. This research can improve health outcomes and reduce the risk of chronic diseases associated with obesity and multimorbidity, particularly among vulnerable populations. It will also be essential to consider the potential implications of any new BMI cutoffs on clinical practice and health policies related to obesity and multimorbidity in serving unique clinical needs. More work must be done to understand how multimorbidity, BMI, age, and healthcare burden associate across races

Racial differences in healthcare expenditures for prevalent multimorbidity combinations in the USA: a cross-sectional study

Abstract Background We aimed to model total charges for the most prevalent multimorbidity combinations in the USA and assess model accuracy across Asian/Pacific Islander, African American, Biracial, Caucasian, Hispanic, and Native American populations. Methods We used Cerner HealthFacts data from 2016 to 2017 to model the cost of previously identified prevalent multimorbidity combinations among 38 major diagnostic categories for cohorts stratified by age (45–64 and 65 +). Examples of prevalent multimorbidity combinations include lipedema with hypertension or hypertension with diabetes. We applied generalized linear models (GLM) with gamma distribution and log link function to total charges for all cohorts and assessed model accuracy using residual analysis. In addition to 38 major diagnostic categories, our adjusted model incorporated demographic, BMI, hospital, and census division information. Results The mean ages were 55 (45–64 cohort, N = 333,094) and 75 (65 + cohort, N = 327,260), respectively. We found actual total charges to be highest for African Americans (means $78,544 [45–64],$176,274 [65 +]) and lowest for Hispanics (means $29,597 [45–64],$66,911 [65 +]). African American race was strongly predictive of higher costs (p < 0.05 [45–64]; p < 0.05 [65 +]). Each total charge model had a good fit. With African American as the index race, only Asian/Pacific Islander and Biracial were non-significant in the 45–64 cohort and Biracial in the 65 + cohort. Mean residuals were lowest for Hispanics in both cohorts, highest in African Americans for the 45–64 cohort, and highest in Caucasians for the 65 + cohort. Model accuracy varied substantially by race when multimorbidity grouping was considered. For example, costs were markedly overestimated for 65 + Caucasians with multimorbidity combinations that included heart disease (e.g., hypertension + heart disease and lipidemia + hypertension + heart disease). Additionally, model residuals varied by age/obesity status. For instance, model estimates for Hispanic patients were highly underestimated for most multimorbidity combinations in the 65 + with obesity cohort compared with other age/obesity status groupings. Conclusions Our finding demonstrates the need for more robust models to ensure the healthcare system can better serve all populations. Future cost modeling efforts will likely benefit from factoring in multimorbidity type stratified by race/ethnicity and age/obesity status