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美国感染性心内膜炎住院患者中注射吸毒的错误分类调整患病率:2007-2016 年全国住院患者样本的系列横断面分析。

Misclassification Error-Adjusted Prevalence of Injection Drug Use Among Infective Endocarditis Hospitalizations in the United States: A Serial Cross-Sectional Analysis of the 2007-2016 National Inpatient Sample.

出版信息

Am J Epidemiol. 2021 Apr 6;190(4):588-599. doi: 10.1093/aje/kwaa207.

Abstract

Administrative health databases have been used to monitor trends in infective endocarditis hospitalization related to nonprescription injection drug use (IDU) using International Classification of Diseases (ICD) code algorithms. Because no ICD code for IDU exists, drug dependence and hepatitis C virus (HCV) have been used as surrogate measures for IDU, making misclassification error (ME) a threat to the accuracy of existing estimates. In a serial cross-sectional analysis, we compared the unadjusted and ME-adjusted prevalences of IDU among 70,899 unweighted endocarditis hospitalizations in the 2007-2016 National Inpatient Sample. The unadjusted prevalence of IDU was estimated with a drug algorithm, an HCV algorithm, and a combination algorithm (drug and HCV). Bayesian latent class models were used to estimate the median IDU prevalence and 95% Bayesian credible intervals and ICD algorithm sensitivity and specificity. Sex- and age group-stratified IDU prevalences were also estimated. Compared with the misclassification-adjusted prevalence, unadjusted estimates were lower using the drug algorithm and higher using the combination algorithm. The median ME-adjusted IDU prevalence increased from 9.7% (95% Bayesian credible interval (BCI): 6.3, 14.8) in 2008 to 32.5% (95% BCI: 26.5, 38.2) in 2016. Among persons aged 18-34 years, IDU prevalence was higher in females than in males. ME adjustment in ICD-based studies of injection-related endocarditis is recommended.

摘要

行政健康数据库已被用于使用国际疾病分类(ICD)代码算法监测与非处方注射药物使用(IDU)相关的感染性心内膜炎住院趋势。由于不存在 IDU 的 ICD 代码,因此药物依赖和丙型肝炎病毒(HCV)已被用作 IDU 的替代指标,这使得错误分类误差(ME)成为现有估计准确性的威胁。在一项连续横断面分析中,我们比较了未经调整和 ME 调整后的 70899 例未加权心内膜炎住院患者中 IDU 的患病率。未经调整的 IDU 患病率是使用药物算法、HCV 算法和组合算法(药物和 HCV)估计的。贝叶斯潜在类别模型用于估计 IDU 的中位数患病率和 95%贝叶斯可信区间以及 ICD 算法的敏感性和特异性。还估计了按性别和年龄组分层的 IDU 患病率。与错误分类调整后的患病率相比,药物算法的未调整估计值较低,而组合算法的未调整估计值较高。从 2008 年的 9.7%(95%贝叶斯可信区间[BCI]:6.3,14.8)到 2016 年的 32.5%(95%BCI:26.5,38.2),经 ME 调整的 IDU 患病率增加。在 18-34 岁的人群中,女性 IDU 患病率高于男性。建议在基于 ICD 的注射相关性心内膜炎研究中进行 ME 调整。

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