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Original Investigation |

Association of Social Determinants With Children’s Hospitals’ Preventable Readmissions Performance

Marion R. Sills, MD, MPH1; Matt Hall, PhD2; Jeffrey D. Colvin, MD, JD3; Michelle L. Macy, MD, MS4,5; Gretchen J. Cutler, PhD, MPH6; Jessica L. Bettenhausen, MD3; Rustin B. Morse, MD7; Katherine A. Auger, MD, MSc8; Jean L. Raphael, MD, MPH9; Laura M Gottlieb, MD, MPH10; Evan S. Fieldston, MD, MBA, MSHP11; Samir S. Shah, MD, MSCE8
[+] Author Affiliations
1Department of Pediatrics, University of Colorado School of Medicine, Aurora
2Children’s Hospital Association, Overland Park, Kansas
3Department of Pediatrics, University of Missouri–Kansas City School of Medicine, Kansas City
4Department of Emergency Medicine, University of Michigan, Ann Arbor
5Department of Pediatrics, University of Michigan, Ann Arbor
6Department of Pediatric Emergency Medicine, Children's Hospitals and Clinics of Minnesota, Minneapolis
7Children’s Health System of Texas, University of Texas Southwestern, Dallas
8Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
9Department of Pediatrics, Baylor College of Medicine, Houston, Texas
10Department of Family and Community Medicine, University of California at San Francisco
11Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
JAMA Pediatr. 2016;170(4):350-358. doi:10.1001/jamapediatrics.2015.4440.
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Importance  Performance-measure risk adjustment is of great interest to hospital stakeholders who face substantial financial penalties from readmissions pay-for-performance (P4P) measures. Despite evidence of the association between social determinants of health (SDH) and individual patient readmission risk, the effect of risk adjusting for SDH on readmissions P4P penalties to hospitals is not well understood.

Objective  To determine whether risk adjustment for commonly available SDH measures affects the readmissions-based P4P penalty status of a national cohort of children’s hospitals.

Design, Setting, and Participants  Retrospective cohort study of 43 free-standing children’s hospitals within the Pediatric Health Information System database in the calendar year 2013. We evaluated hospital discharges from 2013 that met criteria for 3M Health Information Systems’ potentially preventable readmissions measure for calendar year 2013. The analysis was conducted from July 2015 to August 2015.

Exposures  Two risk-adjustment models: a baseline model adjusted for severity of illness and an SDH-enhanced model that adjusted for severity of illness and the following 4 SDH variables: race, ethnicity, payer, and median household income for the patient’s home zip code.

Main Outcomes and Measures  Change in a hospital’s potentially preventable readmissions penalty status (ie, change in whether a hospital exceeded the penalty threshold) using an observed-to-expected potentially preventable readmissions ratio of 1.0 as a penalty threshold.

Results  For the 179 400 hospital discharges from the 43 hospitals meeting inclusion criteria, median (interquartile range [IQR]) hospital-level percentages for the SDH variables were 39.2% nonwhite (n = 71 300; IQR, 28.6%-54.6%), 17.9% Hispanic (n = 32 060; IQR, 6.7%-37.0%), and 58.7% publicly insured (n = 106 116; IQR, 50.4%-67.8%). The hospital median household income for the patient’s home zip code was $40 674 (IQR, $35 912-$46 190). When compared with the baseline model, adjustment for SDH resulted in a change in penalty status for 3 hospitals within the 15-day window (2 were no longer above the penalty threshold and 1 was newly penalized) and 5 hospitals within the 30-day window (3 were no longer above the penalty threshold and 2 were newly penalized).

Conclusions and Relevance  Risk adjustment for SDH changed hospitals’ penalty status on a readmissions-based P4P measure. Without adjusting P4P measures for SDH, hospitals may receive penalties partially related to patient SDH factors beyond the quality of hospital care.

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Figure 1.
Distribution of Adjusted Potentially Preventable Readmission Rate by Hospital

Box-and-whisker plots with the left and right margins of the box indicating the first and third quartiles. The band inside the box is the second quartile (the median), and the whiskers indicate the minimum and maximum.

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Figure 2.
15-Day and 30-Day Observed-to-Expected Ratio for Potentially Preventable Readmission Rate (PPR) by Hospital

Each hospital is indicated by a letter along the x-axis, in sequence, based on hospital observed-to-expected PPR ratio in the 15-day baseline model. In adjusting for social determinants of health, hospitals that change from penalized to not penalized are indicated by solid black vertical lines, and hospitals that change from not penalized to penalized are indicated by dotted black vertical lines.

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