Predicting Risk of Long-Term Institutionalization Among Community Dwelling Veterans Before the COVID-19 Pandemic.

Objective: To identify risk of long-term institutionalization (LTI) among Veterans receiving care in the Veterans Health Administration (VA).

Study Setting And Design: We developed the "Predicted Long-term Institutionalization" (PLI) risk model for Veterans alive in the community at the end of fiscal-year (FY) 2017 followed for LTI in nursing home (cumulative NH days allowing any acute care and up to 7 days in community > 90 days) during FY2018-FY2019.

Data Sources And Analytic Sample: PLI used demographics, diagnoses, prior hospital and nursing home (NH) use, and risk indices for death and frailty from VA and Medicare claims and Minimum Data Set data. Development of PLI used multiple iterations to maximize sensitivity, constrained by achieving a number needed to screen (≤ 8), including age normalization to minimize algorithmic bias. We combined the elevated risk (ER) and common risk (CR) strata-specific predictions from the logistic regression models to identify three tiers of PLI: low risk, moderate risk, and high risk. We describe Veterans' outcomes in FY2018/2019 (LTI, death, hospitalization and VA cost) across the three PLI tiers.

Principal Findings: For identifying Veterans in LTI, compared to a baseline model that used only VA data as predictors (sensitivity 23%, specificity 98%), calibrating separate ER and CR strata increased sensitivity to 30%, the addition of Medicare data increased sensitivity to 33%, and age-normalization with differential risk strata thresholds increased sensitivity to 41% (specificity 96.6%). The final PLI model (c-statistic = 0.87) identified 3.5% of Veterans in PLI-high risk (13% LTI rate), who accounted for 41% of new LTI, 22% of decedents, 19% of VA cost, and 11% of hospitalizations in FY2018-2019.

Conclusions: The PLI score identifies Veterans at high risk of LTI for further assessment and targeting of resources to support continued community residence.