2026/07/01 by Aaron Beuoy, Kelsey S. Goddard, Noelle K. Kurth +3 · 1 voice
Social Sciences · Medicine · #Health disparities and outcomes #Down syndrome and intellectual disability research #Chronic Disease Management Strategies #Behavioral Risk Factor Surveillance System #Mental health #Logistic regression #Depression (economics) #Operationalization #Identification (biology) #Odds #Multiple Chronic Conditions #Public health
paper · doi:10.1093/rescon/vmag123
published in Research Connections 1(3)
openalex publication_date 2026/07/01 · openalex created_date 2026/07/22 · openalex updated_date 2026/08/05
Abstract Background and aims In the USA, the American Community Survey six (ACS-6) is widely adopted across federal and state surveys to operationalize disability status. Evidence indicates the ACS-6 does not fully capture individuals whose limitations are driven by chronic health or mental health conditions. Methods We examined whether individuals with common long-term conditions are more likely to be identified by a single, broad disability question despite being classified as non-disabled by the ACS-6. Using data from the 2024 Massachusetts Behavioral Risk Factor Surveillance System (BRFSS), we conducted a binary logistic regression to understand if people who have chronic physical health or mental health conditions were more likely to endorse the single disability question. Results The average predicted probabilities indicate that approximately 1 in 20 people with asthma or cancer, 1 in 10 with arthritis, angina or coronary heart disease, or diabetes, and 1 in 5 people with depression said yes to the single, broad disability question. Conclusions This study indicates that a broad, duration-based disability identification item captures individuals who were missed by the ACS-6 in Massachusetts’s BRFSS data. This work contributes to ongoing efforts to improve disability surveillance and ensure that population-based data reflect the full range of long-term health-related limitations experienced by people with disabilities. Learning Points