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Measuring health disparities using a continuous social risk factor. | LitMetric

AI Article Synopsis

  • The study aims to introduce a new way to measure disparities at the hospital level by focusing on continuous polysocial risk factors and their impact on patient outcomes.
  • It analyzed Medicare data for patients aged 65 and older, focusing on hospital readmissions for common conditions, using methods that improve upon traditional measurements of social risk.
  • The results suggest that this novel approach provides a more nuanced understanding of disparities across hospitals and helps identify provider-level outcomes that better reflect social risk profiles.

Article Abstract

Objective: To propose and evaluate a novel approach for measuring hospital-level disparities according to the effect of a continuous, polysocial risk factor on those outcomes.

Study Setting: Our cohort consisted of Medicare Fee-for-Service (FFS) patients 65 years and older admitted to acute care hospitals for one of six common conditions or procedures. Medicare administrative claims data for six hospital readmission measures including hospitalizations from July 2015 to June 2018 were used.

Study Design: We adapted existing methodologies that were developed to report hospital-level disparities using dichotomous social risk factors (SRFs). The existing methods report disparities within and across hospitals; we developed and tested modified approaches for both methods using the Agency for Healthcare Research and Quality Socioeconomic Status Index. We applied the adapted methodologies to six 30-day hospital readmission measures included in the Centers for Medicare & Medicaid Services Hospital Readmissions Reduction Program measures. We compared the within- and across-hospital results for each to those obtained from using the original methods and dichotomizing the AHRQ SES Index into "low" and "high" scores.

Data Collection: We used Medicare FFS administrative claims data linked to U.S. Census data.

Principal Findings: For all six readmission measures we find that, when compared with the existing methods, the methods for continuous SRFs provide disparity results for more facilities though across a narrower range of values. Measures of disparity based on this approach are moderately to highly correlated with those based on a dichotomous version of the same risk factor, while reflecting a fuller spectrum of risk. This approach represents an opportunity for detection of provider-level results that more closely align with underlying social risk.

Conclusion: We have demonstrated the feasibility and utility of estimating hospital disparities of care using a continuous, polysocial risk factor. This approach expands the potential for reporting hospital-level disparities while better accounting for the multifactorial nature of social risk on hospital outcomes.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9836958PMC
http://dx.doi.org/10.1111/1475-6773.14048DOI Listing

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