Alyna T Chien1,2, Joseph P Newhouse3,4,5,6, Lisa I Iezzoni7,8, Carter R Petty9, Sharon-Lise T Normand3,10, Mark A Schuster11,2. 1. Division of General Pediatrics, Department of Medicine and alyna.chien@childrens.harvard.edu. 2. Departments of Pediatrics. 3. Health Care Policy, and. 4. Departments of Health Policy and Management and. 5. John F. Kennedy School of Government, Harvard University, Cambridge, Massachusetts. 6. National Bureau of Economic Research, Cambridge, Massachusetts; and. 7. Medicine, Harvard Medical School. 8. Mongan Institute Health Policy Center, Massachusetts General Hospital, Boston, Massachusetts. 9. Clinical Research Center, Boston Children's Hospital, Boston, Massachusetts. 10. Biostatistics, Harvard T. H. Chan School of Public Health, and. 11. Division of General Pediatrics, Department of Medicine and.
Abstract
BACKGROUND: Risk-adjustment algorithms typically incorporate demographic and clinical variables to equalize compensation to insurers for enrollees who vary in expected cost, but including information about enrollees' socioeconomic background is controversial. METHODS: We studied 1 182 847 continuously insured 0 to 19-year-olds using 2008-2012 Blue Cross Blue Shield of Massachusetts and American Community Survey data. We characterized enrollees' socioeconomic background using the validated area-based socioeconomic measure and calculated annual plan payments using paid claims. We evaluated the relationship between annual plan payments and geocoded socioeconomic background using generalized estimating equations (γ distribution and log link). We expressed outcomes as the percentage difference in spending and utilization between enrollees with high and low socioeconomic backgrounds. RESULTS: Geocoded socioeconomic background had a significant, positive association with annual plan payments after applying standard adjusters. Every 1 SD increase in socioeconomic background was associated with a 7.8% (95% confidence interval, 7.2% to 8.3%; P < .001) increase in spending. High socioeconomic background enrollees used higher-priced outpatient and pharmacy services more frequently than their counterparts from low socioeconomic backgrounds (eg, 25% more outpatient encounters annually; 8% higher price per encounter; P < .001), which outweighed greater emergency department spending among low socioeconomic background enrollees. CONCLUSIONS: Higher socioeconomic background is associated with greater levels of pediatric health care spending in commercially insured children. Including socioeconomic information in risk-adjustment algorithms may address concerns about adverse selection from an economic perspective, but it would direct funds away from those caring for children and adolescents from lower socioeconomic backgrounds who are at greater risk of poor health.
BACKGROUND: Risk-adjustment algorithms typically incorporate demographic and clinical variables to equalize compensation to insurers for enrollees who vary in expected cost, but including information about enrollees' socioeconomic background is controversial. METHODS: We studied 1 182 847 continuously insured 0 to 19-year-olds using 2008-2012 Blue Cross Blue Shield of Massachusetts and American Community Survey data. We characterized enrollees' socioeconomic background using the validated area-based socioeconomic measure and calculated annual plan payments using paid claims. We evaluated the relationship between annual plan payments and geocoded socioeconomic background using generalized estimating equations (γ distribution and log link). We expressed outcomes as the percentage difference in spending and utilization between enrollees with high and low socioeconomic backgrounds. RESULTS: Geocoded socioeconomic background had a significant, positive association with annual plan payments after applying standard adjusters. Every 1 SD increase in socioeconomic background was associated with a 7.8% (95% confidence interval, 7.2% to 8.3%; P < .001) increase in spending. High socioeconomic background enrollees used higher-priced outpatient and pharmacy services more frequently than their counterparts from low socioeconomic backgrounds (eg, 25% more outpatient encounters annually; 8% higher price per encounter; P < .001), which outweighed greater emergency department spending among low socioeconomic background enrollees. CONCLUSIONS: Higher socioeconomic background is associated with greater levels of pediatric health care spending in commercially insured children. Including socioeconomic information in risk-adjustment algorithms may address concerns about adverse selection from an economic perspective, but it would direct funds away from those caring for children and adolescents from lower socioeconomic backgrounds who are at greater risk of poor health.
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