Alex Z Fu1, Michael W Kattan. 1. Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 44195, USA. fuz@ccf.org
Abstract
OBJECTIVE: To document the racial and ethnic differences in individuals' perception of their general health status assessed by preference-based measures. METHODS: Using the 2003 Medical Expenditure Panel Survey (MEPS), a nationally representative sample of 20 428 people with reported concurrent EuroQol (EQ-5D) US scores were included in the study. Given the upper-bound of preference-based scores at 1.0, a two-part model was derived to identify the relationship between race/ethnicity and the preference-based score after controlling for individual demographic covariates, comorbidity profile, and functional and activity limitations. In order to generalize the results to the whole US population, the complex survey sampling design of the MEPS was taken into account using the specified sample weight, variance estimation stratum, and primary sampling unit. RESULTS: In the fully adjusted model, Asians were more likely to report being in full health (score of 1.0) than Whites by 4.2 percentage points (p < 0.05), whereas no differences were identified for Blacks and Hispanics compared to Whites. Beyond health and disease conditions, education and income explained the racial/ethnic difference for EQ-5D score for Blacks and Hispanics relative to Whites, but this was not the case between Asians and Whites. No clinically important differences were identified between racial/ethnic groups for individuals not reporting full health. CONCLUSIONS: This study adds to the literature of health-related quality of life (HRQoL) by providing additional empirical evidence at the US national level to demonstrate racial/ethnic differences assessed by preference-based measures. Healthcare researchers and clinicians need to be aware that Asians are more likely to perceive a higher preference-based score than Whites, given the same health and disease conditions. Subgroup analysis may be considered regarding the optimal decision making and conclusions based on cost-effectiveness analysis.
OBJECTIVE: To document the racial and ethnic differences in individuals' perception of their general health status assessed by preference-based measures. METHODS: Using the 2003 Medical Expenditure Panel Survey (MEPS), a nationally representative sample of 20 428 people with reported concurrent EuroQol (EQ-5D) US scores were included in the study. Given the upper-bound of preference-based scores at 1.0, a two-part model was derived to identify the relationship between race/ethnicity and the preference-based score after controlling for individual demographic covariates, comorbidity profile, and functional and activity limitations. In order to generalize the results to the whole US population, the complex survey sampling design of the MEPS was taken into account using the specified sample weight, variance estimation stratum, and primary sampling unit. RESULTS: In the fully adjusted model, Asians were more likely to report being in full health (score of 1.0) than Whites by 4.2 percentage points (p < 0.05), whereas no differences were identified for Blacks and Hispanics compared to Whites. Beyond health and disease conditions, education and income explained the racial/ethnic difference for EQ-5D score for Blacks and Hispanics relative to Whites, but this was not the case between Asians and Whites. No clinically important differences were identified between racial/ethnic groups for individuals not reporting full health. CONCLUSIONS: This study adds to the literature of health-related quality of life (HRQoL) by providing additional empirical evidence at the US national level to demonstrate racial/ethnic differences assessed by preference-based measures. Healthcare researchers and clinicians need to be aware that Asians are more likely to perceive a higher preference-based score than Whites, given the same health and disease conditions. Subgroup analysis may be considered regarding the optimal decision making and conclusions based on cost-effectiveness analysis.
Authors: Norah E Mulvaney-Day; Marcela Horvitz-Lennon; Chih-Nan Chen; Mara Laderman; Margarita Alegría Journal: Qual Life Res Date: 2010-08-01 Impact factor: 4.147
Authors: Alex Z Fu; Kristi D Graves; Roxanne E Jensen; John L Marshall; Margaret Formoso; Arnold L Potosky Journal: J Cancer Res Clin Oncol Date: 2015-11-18 Impact factor: 4.553