Qual Life Res. 2026 Jun 15;35(8):195. doi: 10.1007/s11136-026-04300-1.
ABSTRACT
PURPOSE: To examine associations between cardiometabolic conditions and health-related quality of life (HRQoL) and evaluate temporal trends in condition-associated HRQoL decrements from 2001 to 2022.
METHODS: We analyzed nationally representative data from U.S. adults aged ≥ 18 years in the Medical Expenditure Panel Survey (2001-2022), excluding years without BMI data collection (2017, 2019, 2021). HRQoL was measured using EQ-5D utilities mapped from SF-12 scores with a validated algorithm. For each year, survey-weighted multivariable regression models estimated associations of sociodemographic characteristics, BMI, and six cardiometabolic conditions with HRQoL. Temporal trends in condition-associated HRQoL decrements were assessed using meta-regression. To estimate recent average associations, we pooled data from 2015, 2016, 2018, and 2022.
RESULTS: HRQoL improved over time, with lower values in 2001-2012 than 2013-2022 and an increase from its lowest value in 2012 (0.873) to highest in 2018 (0.888). Stroke contributed the greatest adjusted HRQoL decrement, followed by heart disease, diabetes, high blood pressure, obesity, and high cholesterol. Diabetes- and heart disease-associated decrements attenuated linearly over time (-0.0500 in 2001 to -0.0414 in 2022 and -0.0611 to -0.0487, respectively) , whereas high blood pressure-associated decrement was greatest around 2012 (-0.0361 in 2001, -0.0404 in 2012, and - 0.0313 in 2022) and obesity-associated decrement was smallest around 2012 (- 0.0311, - 0.0290, and - 0.0370).
CONCLUSIONS: Changes in condition-associated HRQoL decrements over time suggest that utility values may not remain constant across calendar years. Smaller decrements for diabetes and heart disease may reflect better treatment and management, whereas the growing obesity-related decrement may indicate changes in the national severity of obesity. These patterns highlight the need for current, nationally representative utility estimates in population health research.
PMID:42295494 | DOI:10.1007/s11136-026-04300-1