Frailty predicts referral for elder abuse evaluation in a nationwide healthcare system-Results from a case-control study.

BACKGROUND

Elder abuse (EA) is common and has devastating health impacts. Frailty may increase susceptibility to and consequences of EA for older adults, making healthcare system detection more likely, but this relationship has been difficult to study. We examined the association between a recently validated frailty index and referral to social work (SW) for EA evaluation in the Veterans Administration (VA) healthcare system.

METHODS

We conducted a case-control study of veterans aged ≥60 years evaluated by SW for suspected EA between 2010 and 2018 (n = 14,723) and controls receiving VA primary care services in the same 60-day window (n = 58,369). We used VA and Medicare claims data to measure frailty (VA Frailty Index) and comorbidity burden (the Elixhauser Comorbidity Index) in the 2 years prior to the index. We used adjusted logistic regression models to examine the association of frailty or comorbidity burden with referral to SW for EA evaluation. We used Akaike Information Criterion (AIC) values to evaluate model fit and likelihood ratio (LR) tests to assess the statistical significance of including frailty and comorbidity in the same model.

RESULTS

The sample (n = 73,092) had a mean age 72 years; 14% were Black, and 6% were Hispanic. More cases (67%) than controls (36%) were frail. LR tests comparing the nested models were highly significant (p < 0.001), and AIC values indicated superior model fit when including both frailty and comorbidity in the same model. In a model adjusting for comorbidity and all covariates, pre-frailty (aOR vs. robust 1.7; 95% CI 1.5-1.8) and frailty (aOR vs. robust 3.6; 95% CI 3.3-3.9) were independently associated with referral for EA evaluation.

CONCLUSIONS

A claims-based measure of frailty predicted referral to SW for EA evaluation in a national healthcare system, independent of comorbidity burden. Electronic health record measures of frailty may facilitate EA risk assessment and detection for this important but under-recognized phenomenon.