Key result
Adding socioeconomic status to readmission models improves nephrectomy rankings for safety-net hospitals.
Why the study?
Safety-net hospitals care for more low socioeconomic status patients and are disproportionately penalized under SES-naive Medicare readmission risk-adjustment models, necessitating development of SES-inclusive risk adjustment frameworks.
Does incorporating socioeconomic status into risk-adjustment models affect hospital rankings for urologic oncology surgery readmissions?
Observational (n=22,463)
Yes
Does incorporating socioeconomic status into risk-adjustment models affect hospital rankings for urologic oncology surgery readmissions?
Incorporating socioeconomic status into readmission risk-adjustment models significantly alters hospital rankings, protecting safety-net hospitals from disproportionate penalties in certain urologic surgeries.
May protect safety-net hospitals from unfair penalties in nephrectomy rankings; supports socioeconomic adjustment in models but requires prospective validation.
BACKGROUND: Safety-net hospitals (SNHs) care for more patients of low socioeconomic status (SES) than non-SNHs and are disproportionately punished under SES-naive Medicare readmission risk-adjustment models. This study was designed to develop a risk-adjustment framework that incorporates SES and to assess the impact on readmission rates. METHODS: California Office of Statewide Health Planning and Development data from 2007 to 2011 were used to identify patients undergoing radical cystectomy (RC) for bladder cancer (n = 3771) or partial nephrectomy (PN; n = 5556) or radical nephrectomy (RN; n = 13,136) for kidney cancer. Unadjusted hospital rankings and predicted rankings under models simulating the Medicare Hospital Readmissions Reduction Program were compared with predicted rankings under models incorporating SES and hospital factors. SES, derived from a multifactorial neighborhood score, was calculated from US Census data. RESULTS: The 30-day readmission rate was 26.1% for RC, 8.3% for RN, and 9.5% for PN. The addition of SES, geographic, and hospital factors changed hospital rankings significantly in comparison with the base model (P < .01) except for SES for RC (P = .07) and SES and rural factors for PN (P = .12). For RN and PN, the addition of SES predicted lower percentile ranks for SNHs and thus improved observed-to-expected rankings (P < .01). For RC, there were no changes in hospital rankings. CONCLUSIONS: SES is important for risk adjustments for complex surgical procedures such as RC. Patient SES affects overall hospital rankings across cohorts, and critically, it differentially and punitively affects rankings for SNHs for some procedures. Cancer 2018.
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Odisho et al. (2018) conducted an observational in Bladder cancer or kidney cancer (n=22,463). Risk-adjustment models incorporating socioeconomic status (SES) vs. SES-naive Medicare readmission risk-adjustment models was evaluated on Change in hospital rankings (p=<0.01). Incorporating socioeconomic status into readmission risk-adjustment models significantly improved observed-to-expected hospital rankings for safety-net hospitals performing nephrectomy (P<0.01).
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