Circulation. Cardiovascular quality and outcomes · 2021
Estimating Long-Term Health Utility Scores and Expenditures for Cardiovascular Disease From the Medical Expenditure Panel Survey.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Morey Jacob R, Jiang Shangqing, Klein Sharon, Max Wendy, Masharani Umesh, Fleischmann Kirsten E, Hunink M G Myriam, Ferket Bart S
Аннотация
BACKGROUND: Long-term health utility scores and costs used in cost-effectiveness analyses of cardiovascular disease prevention and management can be inconsistent, outdated, or invalid for the diverse population of the United States. Our aim was to develop a user friendly, standardized, publicly available code and catalog to derive more valid long-term values for health utility and expenditures following cardiovascular disease events.
METHODS: Individual-level Short Form-12 version 2 health-related quality of life and expenditure data were obtained from the pooled 2011 to 2016 Medical Expenditure Panel Surveys. We developed code using the R programming language to estimate preference-weighted Short Form-6D utility scores from the Short Form-12 for quality-adjusted life year calculations and predict annual health care expenditures. Result predictors included cardiovascular disease diagnosis (myocardial infarction, ischemic stroke, heart failure, cardiac dysrhythmias, angina pectoris, and peripheral artery disease), sociodemographic factors, and comorbidity variables.
RESULTS: The cardiovascular disease diagnoses with the lowest utility scores were heart failure (0.635 [95% CI, 0.615-0.655]), angina pectoris (0.649 [95% CI, 0.630-0.667]), and ischemic stroke (0.649 [95% CI, 0.635-0.663]). The highest annual expenditures were for heart failure ($20 764 [95% CI, $17 500-$24 027]), angina pectoris ($18 428 [95% CI, $16 102-$20 754]), and ischemic stroke ($16 925 [95% CI, $15 672-$20 616]).
CONCLUSIONS: The developed code and catalog may improve the quality and comparability of cost-effectiveness analyses by providing standardized methods for extracting long-term health utility scores and expenditures from Medical Expenditure Panel Survey data, which are more current and representative of the US population than previous sources.
METHODS: Individual-level Short Form-12 version 2 health-related quality of life and expenditure data were obtained from the pooled 2011 to 2016 Medical Expenditure Panel Surveys. We developed code using the R programming language to estimate preference-weighted Short Form-6D utility scores from the Short Form-12 for quality-adjusted life year calculations and predict annual health care expenditures. Result predictors included cardiovascular disease diagnosis (myocardial infarction, ischemic stroke, heart failure, cardiac dysrhythmias, angina pectoris, and peripheral artery disease), sociodemographic factors, and comorbidity variables.
RESULTS: The cardiovascular disease diagnoses with the lowest utility scores were heart failure (0.635 [95% CI, 0.615-0.655]), angina pectoris (0.649 [95% CI, 0.630-0.667]), and ischemic stroke (0.649 [95% CI, 0.635-0.663]). The highest annual expenditures were for heart failure ($20 764 [95% CI, $17 500-$24 027]), angina pectoris ($18 428 [95% CI, $16 102-$20 754]), and ischemic stroke ($16 925 [95% CI, $15 672-$20 616]).
CONCLUSIONS: The developed code and catalog may improve the quality and comparability of cost-effectiveness analyses by providing standardized methods for extracting long-term health utility scores and expenditures from Medical Expenditure Panel Survey data, which are more current and representative of the US population than previous sources.