Joel Mintz1,2, Matthew S Duprey3, Andrew R Zullo3,4, Yoojin Lee3, Douglas P Kiel2,5, Lori A Daiello3, Kenneth E Rodriguez6, Arjun K Venkatesh7, Sarah D Berry2,5. 1. Nova Southeastern University College of Allopathic Medicine, Davie, Florida, USA. 2. Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, Massachusetts, USA. 3. Department of Health Services, Policy, and Practice, Brown University, Providence, Rhode Island, USA. 4. Center of Innovation in Long-Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, Rhode Island, USA. 5. Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA. 6. Department of Orthopedic Trauma Surgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA. 7. Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut, USA.
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.
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.
Authors: Nicole C Wright; Shanette G Daigle; Mary E Melton; Elizabeth S Delzell; Akhila Balasubramanian; Jeffrey R Curtis Journal: J Bone Miner Res Date: 2019-08-05 Impact factor: 6.741
Authors: Joel Mintz; Alexandra Lee; Meryl Gold; Emily J Hecker; Cathleen Colón-Emeric; Sarah D Berry Journal: J Am Geriatr Soc Date: 2020-12-07 Impact factor: 5.562
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