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 ≥100 days who were continuously enrolled in Medicare Parts A and B in 2016. FRIs were identified using four 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 to 2,775) identified more FRIs across all injuries while CQ1 identified less (n=21 to 2,320). CQ2 identified more intracranial bleeds (1,028 v. 448) than CQ1. For non-fracture categories, few FRIs were identified using CQ1 (n= 20 to 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 0.82-0.99. Conclusions Claims-based algorithms applied to outpatient and provider claims identify more non-fracture FRIs. When identifying risk factors, stakeholders should select the algorithm(s) suitable for the FRI and study purpose.

中文翻译：

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使用行政索赔数据识别疗养院居民的跌倒相关伤害

背景 跌倒相关伤害 (FRIs) 是疗养院 (NH) 居民发病、死亡和费用的主要原因。在管理数据中仔细定义 FRI 对于改善减少伤害工作至关重要。我们开发了一系列基于索赔的新颖算法，用于识别长期居住的新罕布什尔州居民的 FRI。方法 这是一个在 NH 居住 ≥100 天的居民的回顾性队列，他们在 2016 年连续参加 Medicare A 和 B 部分。 FRI 使用四种基于索赔的病例合格 (CQ) 定义来确定 [住院患者 (CQ1)、门诊患者和提供者进行手术 (CQ2)、门诊患者和提供者跌倒 (CQ3)、或住院或门诊患者和提供者跌倒 (CQ4)]。使用 phi 相关系数计算相关性。结果 在 153,220 名居民（平均 [SD] 年龄 81.2 [12.1]，68.0% 女性）中，我们根据一项或多项 CQ 定义确定了 10,104 名居民至少具有一项 FRI。在 2,950 名患有髋部骨折的居民中，所有算法均识别出 1,852 名（62.8%）人。算法 CQ4（n=326 至 2,775）在所有伤害中识别出更多的 FRI，而 CQ1 识别出较少的 FRI（n=21 至 2,320）。CQ2 比 CQ1 发现更多颅内出血（1,028 例 vs. 448 例）。对于非骨折类别，使用 CQ1 识别出很少的 FRI（n = 20 至 488）。在 CQ1 识别的 2,320 名髋部骨折居民中，2,145 名 (92.5%) 有外因伤害代码。所有算法均强相关，phi 系数范围为 0.82-0.99。结论 适用于门诊和医疗服务提供者索赔的基于索赔的算法可识别出更多非骨折 FRI。在识别风险因素时，利益相关者应选择适合 FRI 和研究目的的算法。