Care Fragmentation, Social Determinants of Health, and Postoperative Mortality in Older Veterans

Abstract

Introduction: Veterans Affairs Surgical Quality Improvement Program (VASQIP) benchmarking algorithms helped the Veterans Health Administration (VHA) reduce postoperative mortality. Despite calls to consider social risk factors, these algorithms do not adjust for social determinants of health (SDoH) or account for services fragmented between the VHA and the private sector. This investigation examines how the addition of SDoH change model performance and quantifies associations between SDoH and 30-d postoperative mortality.

Methods: VASQIP (2013-2019) cohort study in patients ≥65 y old with 2-30-d inpatient stays. VASQIP was linked to other VHA and Medicare/Medicaid data. 30-d postoperative mortality was examined using multivariable logistic regression models, adjusting first for clinical variables, then adding SDoH.

Results: In adjusted analyses of 93,644 inpatient cases (97.7% male, 79.7% non-Hispanic White), higher proportions of non-veterans affairs care (adjusted odds ratio [aOR] = 1.02, 95% CI = 1.01-1.04) and living in highly deprived areas (aOR = 1.15, 95% CI = 1.02-1.29) were associated with increased postoperative mortality. Black race (aOR = 0.77, CI = 0.68-0.88) and rurality (aOR = 0.87, CI = 0.79-0.96) were associated with lower postoperative mortality. Adding SDoH to models with only clinical variables did not improve discrimination (c = 0.836 versus c = 0.835).

Conclusions: Postoperative mortality is worse among Veterans receiving more health care outside the VA and living in highly deprived neighborhoods. However, adjusting for SDoH is unlikely to improve existing mortality-benchmarking models. Reduction efforts for postoperative mortality could focus on alleviating care fragmentation and designing care pathways that consider area deprivation. The adjusted survival advantage for rural and Black Veterans may be of interest to private sector hospitals as they attempt to alleviate enduring health-care disparities.

Keywords: Area deprivation; Care fragmentation; Risk prediction modeling; Rurality; Surgical outcomes.

Figure 1.. Flow diagram of study cohort

HMO, Health Maintenance Organization; ICN, Integration Control Number; PR, Puerto Rico; scrSSN, scrambled Social Security Number; SDoH, social determinants of health; US, United States; VA, Veterans Affairs

Figure 2.. Multiple models quantifying the associations of Black vs. White race on 30-day mortality constructed to explore how adjustor variables alter the bivariate association.

The figure summarizes a series of simple models that isolate how each clinical variable affects the bivariate association between Black and White cases. The first model shows that Black race was associated with significantly higher odds of mortality compared to White race in a bivariate model. The addition of age to this bivariate model increased the odds of mortality slightly, but the increase was not statistically significant. The addition of care fragmentation, OSS, surgical specialty, ADI, rurality, or case status each reduced the odds of mortality, but not enough to eliminate the statistical significance of the association between Black race and higher mortality. The addition of either PASC or Gagne score reduced the association to statistical insignificance with the confidence intervals including 1. Only with the addition of RAI did Black race represent a statistically significant survival advantage. Abbreviations: aOR, adjusted odds ratio; ADI, Area Deprivation Index; OSS, Operative Stress Score; PASC, Preoperative Acute Serious Condition; RAI, Risk Analysis Index.