The association between BMI and healthcare burden, stratified by race and healthcare utilization among middle-aged patients in the US - Manal J. Alshakhs; Patricia J Goedecke; Lokesh K Chinthala; Nicole G. Weiskopf; Charisse Madlock-Brown (Scientific Reports)
Abstract This study assesses the relationship between BMI and healthcare burden, stratified by race and healthcare utilization, among middle-aged patients in the US. We used data from the Cerner HealthFacts database for 2016–2017 as our study period. We employed regression analysis to maximize the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. This analysis assessed the relationship between BMI and healthcare burden using logistic regression and identified optimal BMI cutoffs stratified across different racial groups and levels of healthcare utilization by maximizing the AUC of the ROC curve. BMI was normalized between 0 and 1, and odds ratios were interpreted as the change in odds of a CCI score greater than zero associated with a full-range (min-to-max) increase in BMI. Results indicated that BMI was strongly associated with CCI across regular, low, and non-utilization cohorts. Specifically, in the regular healthcare utilizer cohort, a min-to-max increase in BMI was associated with a significantly higher likelihood of a CCI score greater than zero. The odds ratios for a min-to-max BMI change were notably high for the White and Asian/Pacific Islander groups (24.3 and 38.18, respectively), compared to 5.04 and 3.82 for the Black and Native American groups. The AUC analysis revealed the highest value for the Asian/Pacific Islander cohort (0.71) and the lowest for the Black cohort (0.63), with optimal BMI cutoffs identified as 34 for African Americans, 35 for American Indians/Alaska Natives, 32 for Whites, and 27 for Asians/Pacific Islanders. The findings underscore the necessity of stratifying patients by healthcare utilization, particularly as regular utilizers in the White and Asian/Pacific Islander populations had lower BMI cutoffs. This study advocates for a paradigm shift in obesity diagnosis, emphasizing the need for refined metrics and additional research on BMI’s role in healthcare burden across diverse populations.