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首页> 外文期刊>Pharmacoepidemiology and drug safety >Identification of hospitalizations for intentional self-harm when E-codes are incompletely recorded.
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Identification of hospitalizations for intentional self-harm when E-codes are incompletely recorded.

机译:当E码未完整记录时,确定住院的故意自残行为。

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CONTEXT: Suicidal behavior has gained attention as an adverse outcome of prescription drug use. Hospitalizations for intentional self-harm, including suicide, can be identified in administrative claims databases using external cause of injury codes (E-codes). However, rates of E-code completeness in US government and commercial claims databases are low due to issues with hospital billing software. OBJECTIVE: To develop an algorithm to identify intentional self-harm hospitalizations using recorded injury and psychiatric diagnosis codes in the absence of E-code reporting. METHODS: We sampled hospitalizations with an injury diagnosis (ICD-9 800-995) from two databases with high rates of E-coding completeness: 1999-2001 British Columbia, Canada data and the 2004 US Nationwide Inpatient Sample. Our gold standard for intentional self-harm was a diagnosis of E950-E958. We constructed algorithms to identify these hospitalizations using information on type of injury and presence of specific psychiatric diagnoses. RESULTS: The algorithm that identified intentional self-harm hospitalizations with high sensitivity and specificity was a diagnosis of poisoning, toxic effects, open wound to elbow, wrist, or forearm, or asphyxiation; plus a diagnosis of depression, mania, personality disorder, psychotic disorder, or adjustment reaction. This had a sensitivity of 63%, specificity of 99% and positive predictive value (PPV) of 86% in the Canadian database. Values in the US data were 74, 98, and 73%. PPV was highest (80%) in patients under 25 and lowest those over 65 (44%). CONCLUSIONS: The proposed algorithm may be useful for researchers attempting to study intentional self-harm in claims databases with incomplete E-code reporting, especially among younger populations.
机译:背景:自杀行为已成为处方药使用的不良后果而受到关注。可以使用外部伤害原因代码(E代码)在行政理赔数据库中确定因故意自我伤害(包括自杀)而住院的情况。但是,由于医院计费软件的问题,美国政府和商业索赔数据库中的E-code完整性率很低。目的:开发一种在没有E码报告的情况下使用记录的伤害和精神病诊断码来识别故意自残住院的算法。方法:我们从两个具有较高E编码完整性率的数据库中抽取了带有损伤诊断的住院治疗(ICD-9 800-995):1999-2001不列颠哥伦比亚省,加拿大数据和2004年美国全国住院患者样本。我们有意自我伤害的金标准是对E950-E958的诊断。我们构建了使用伤害类型和特定精神病学诊断信息来识别这些住院治疗的算法。结果:以高灵敏度和高特异性识别故意自残住院的算法是诊断中毒,中毒影响,肘部,腕部或前臂开放性伤口或窒息;加上诊断为抑郁症,躁狂症,人格障碍,精神病或调节反应。在加拿大数据库中,其敏感性为63%,特异性为99%,阳性预测值(PPV)为86%。美国数据的价值分别为74%,98%和73%。 PPV在25岁以下的患者中最高(80%),而在65岁以上的患者中最低(44%)。结论:所提出的算法可能对试图研究具有不完整E代码报告的索赔数据库中的故意自我伤害的研究人员有用,特别是在年轻人群中。

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