Identification of Fall-Related Injuries in Nursing Home Residents Using Administrative Claims Data

Abstract

Background: Fall-related injuries (FRIs) are a leading cause of morbidity, mortality, and costs among nursing home (NH) residents. Carefully defining FRIs in administrative data is essential for improving injury-reduction efforts. We developed a series of novel claims-based algorithms for identifying FRIs in long-stay NH residents.

Methods: This is a retrospective cohort of residents of NH residing there for at least 100 days who were continuously enrolled in Medicare Parts A and B in 2016. FRIs were identified using 4 claims-based case-qualifying (CQ) definitions (Inpatient [CQ1], Outpatient and Provider with Procedure [CQ2], Outpatient and Provider with Fall [CQ3], or Inpatient or Outpatient and Provider with Fall [CQ4]). Correlation was calculated using phi correlation coefficients.

Results: Of 153 220 residents (mean [SD] age 81.2 [12.1], 68.0% female), we identified 10 104 with at least one FRI according to one or more CQ definition. Among 2 950 residents with hip fractures, 1 852 (62.8%) were identified by all algorithms. Algorithm CQ4 (n = 326-2 775) identified more FRIs across all injuries while CQ1 identified less (n = 21-2 320). CQ2 identified more intracranial bleeds (1 028 vs 448) than CQ1. For nonfracture categories, few FRIs were identified using CQ1 (n = 20-488). Of the 2 320 residents with hip fractures identified by CQ1, 2 145 (92.5%) had external cause of injury codes. All algorithms were strongly correlated, with phi coefficients ranging from 0.82 to 0.99.

Conclusions: Claims-based algorithms applied to outpatient and provider claims identify more nonfracture FRIs. When identifying risk factors, stakeholders should select the algorithm(s) suitable for the FRI and study purpose.

Keywords: Accidental falls; Algorithms; Fractures; Health care administrative claims; Nursing home.