Evaluating the Reliability and Robustness of Racial and Ethnic Health Disparities in Cardiometabolic Disease in NHANES, NHIS, and BRFSS (2015-2021).

BACKGROUND

The United States uses the National Health Interview Survey (NHIS), Behavioral Risk Factor Surveillance System (BRFSS), and National Health and Nutrition Examination Survey to monitor disease trends and inform clinical care/prevention research. These 3 surveys share similar national estimates. However, the consistency of each survey's estimates by race has not been examined. Here, we compare prevalence estimates and disparities in cardiometabolic diseases across 5 aggregated racial and ethnic groups.

METHODS

We examined the age- and fully-adjusted prevalence of cardiovascular disease and diabetes among non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian, and "Other" race respondents aged 30 years or older. Cardiovascular disease included self-reported physician diagnosis of heart attack, stroke, and coronary heart disease.

RESULTS

Although overall national population estimates were similar, there was heterogeneity in estimates by survey. For heart attack and diabetes, each racial group had a higher prevalence in BRFSS than NHIS (eg, Heart Attack: Hispanic BRFSS: 3.4% [95% CI, 3.2-3.6], NHIS: 2.0% [95% CI, 1.8, 2.2]; non-Hispanic Black BRFSS: 3.8% [95% CI, 3.6, 3.9]; NHIS: 3.0% [95% CI, 2.7, 3.2]). Non-Hispanic Asian people had the lowest general cardiovascular disease prevalence across all 3 data sets (NHIS: 5.9%, National Health and Nutrition Examination Survey: 5.3%, BRFSS: 6.9%), while Other/multi-racial respondents had the highest prevalence (NHIS: 9.9%, National Health and Nutrition Examination Survey: 13.1%, BRFSS: 10.7%). However, the magnitude of these differences across data sets was small.

CONCLUSIONS

Prevalence estimates for heart attack and diabetes were heterogeneous by race across surveys. These results highlight the importance of improving the representation of racially minoritized groups within national surveys to produce more precise estimates.