Estimates of Quality-Adjusted Life-Year Loss for Injuries in the United States. (April 2023)

• Record Type: Journal Article
• Authors: Raich, William; Baxter, Jennifer; Sheahan, Megan; Goldhaber-Fiebert, Jeremy; Sullivan, Patrick; Hanmer, Janel
• Is Part Of: Medical decision making. Volume 43:Number 3(2023)
• Page Start: 288
• Page End: 298
• Publication Date: 2023-04
• Subjects: EQ-5D -- health measurement -- injury -- HRQoL -- MEPS -- NHIS -- QALY -- RQL -- SF-6D -- SF-12; Medical policy -- Periodicals; Clinical medicine -- Decision making -- Periodicals; Medicine -- Periodicals; Médecine clinique -- Prise de décision -- Périodiques; 362.1
• DOI: 10.1177/0272989X221141454
• Languages: English
• ISSNs: 0272-989X

Abstract

Purpose: The goal of this study is to develop an approach for estimating nationally representative quality-adjusted life-year (QALY) loss from injury and poisoning conditions using data collected in the Medical Expenditure Panel Survey (MEPS) and the National Health Interview Survey (NHIS). Methods: This study uses data from the 2002–2015 NHIS and MEPS surveys. Injuries were identified in the MEPS medical events file and through self-reporting of medical conditions. We restricted our model to 163, 731 adults, for which we predict a total of 294, 977 EQ-5D scores using responses to the self-administered questionnaire. EQ-5D scores were modeled using age, sex, comorbidities, and binary indicators of the presence and duration of injury at the time of the health status questionnaire. These models consider nonlinearity over time during the first 3 y following the injury event. Results: Injuries are identified in MEPS using medical events that provide a reasonable proxy for the date of injury occurrence. Health-related quality of life (HRQL) decrements can be estimated using binary indicators of injury during different time periods. When grouped into 29 injury categories, most categories were statistically significant predictors of HRQL scores in the first year after injury. For these groups of injuries, mean first-year QALY loss estimates range from 0.005 (sprains and strains of joints and adjacent muscles, n = 7067) to 0.109 (injury to nerves and spinal cord, n = 71). Fewer estimates are significant in the second and third years after injury, which may reflect a return to baseline HRQL. Conclusion: This research presents both a framework for estimating QALY loss for short-lived medical conditions and nationally representative, community-based HRQL scores associated with a wide variety of injury and poisoning conditions. Highlights: This research provides a catalog of nationally representative, preference-based EQ-5D score decrements associated with surviving a large set of injuries, based on patient-reported health status. Mean first-year QALY loss estimates range from 0.005 (sprains and strains of joints and adjacent muscles, n = 7067) to 0.109 (injury to nerves and spinal cord, n = 71). This article presents a novel methodology for assessing quality-of-life impacts for acute conditions by calculating the time elapsed between injury and health status elicitation. Researchers may explore adapting these methods to study other short-lived conditions and health states, such as COVID-19 or chemotherapy.