Abstract
Administrative health databases have been used to monitor trends in infective endocarditis hospitalization related to non-prescription 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 a threat to the accuracy of existing estimates. This serial cross-sectional analysis compared the unadjusted and misclassification error-adjusted prevalences of IDU among 70,899 unweighted endocarditis hospitalizations in the 2007-2016 United States National Inpatient Sample. The unadjusted IDU prevalence was estimated with a drug algorithm, HCV algorithm, and combination algorithm (drug and HCV). Bayesian latent class models estimated the median IDU prevalence and 95% Bayesian credible intervals (BCI) and ICD algorithm sensitivity and specificity. Sex- and age group-stratified IDU prevalences were also estimated. Compared to the misclassification-adjusted prevalence, unadjusted estimates were lower using the drug algorithm and higher using the combination algorithm. The median misclassification error-adjusted IDU prevalence increased from 9.7% (95% BCI: 6.3%, 14.8%) in 2008 to 32.5% (95% BCI: 26.5, 38.2%) in 2016. IDU prevalence was higher in females than males among those aged 18-34 years. Misclassification error-adjustment in ICD-based studies of injection-related endocarditis is recommended.
Tracking socio-economic inequalities in healthcare utilization in Iran: a repeated cross-sectional analysis
