Volume 24, Issue 9 pp. 943-950
Original Report

Diagnostic accuracy of algorithms to identify hepatitis C status, AIDS status, alcohol consumption and illicit drug use among patients living with HIV in an administrative healthcare database

Madeleine Durand

Corresponding Author

Madeleine Durand

Department of Internal Medicine, Centre Hospitalier de l'Unvisersité de Montréal, Montréal, Canada

Correspondence to: M. Durand, CHUM Research Center, 850, rue Saint-Denis, Montreal, Québec, H2X 0A9, Canada. E-mail: [email protected]Search for more papers by this author
Yishu Wang

Yishu Wang

Department of Epidemiology and Biostatistics, McGill University, Montréal, Canada

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François Venne

François Venne

Department of Medicine, Université de Montréal, Montréal, Canada

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Jacques Lelorier

Jacques Lelorier

Department of Medicine, Université de Montréal, Montréal, Canada

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Cécile L. Tremblay

Cécile L. Tremblay

Department of Microbiology, CHUM Research Center, Montréal, Canada

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Michal Abrahamowicz

Michal Abrahamowicz

Department of Epidemiology and Biostatistics, McGill University, Montréal, Canada

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First published: 24 June 2015
Citations: 6
Prior presentations: This research was conducted at the centre de recherche de l'UHRESS du CHUM and at the Pharmacoeconomy and pharmacoepidemiology research unit of the CHUM, Montréal, Canada. Results have been presented at the Canadian association for AIDS research annual meeting in St-John, Newfoundland, Canada in April 2013 (poster presentation) and at the International Conference of Pharmacoepidemiology in Taipei, Taiwan in October 2014 (oral poster presentation).

Abstract

Purpose

This study aims to develop and evaluate diagnostic algorithms for AIDS, hepatitis C status, alcohol abuse and illicit drug use in the administrative healthcare database of the Province of Quebec, Canada (Régie de l'assurance-maladie du Québec (RAMQ)).

Methods

We selected HIV-positive patients contributing to both the RAMQ database and a local clinical database, which was used as gold standard. We developed algorithms to identify the diagnoses of interest in RAMQ using data from hospital discharge summaries and medical and pharmaceutical claims databases. We estimated and compared sensitivity, specificity, positive predictive and negative predictive values and area under receiver operating curve for each algorithm.

Results

Four hundred twenty patients contributed to both databases. Prevalence of conditions of interest in the clinical database was as follows: AIDS 233 (55%), hepatitis C infection 105 (25%), alcohol abuse 106 (25%), illicit drug use 144 (34%) and intravenous drug use 107 (25%). Sensitivity to detect AIDS, hepatitis C, alcohol abuse, illicit drug use and intravenous drug use was 46% [95%CI: 39–53], 26% [18–35], 50% [37–57], 64% [55–72] and 70% [61–79], respectively. Specificity to detect these conditions was 91% [86–95], 97% [94–98], 92% [88–95], 95% [92–97] and 90% [87–93], respectively. Positive predictive values were 87% [80–92], 71% [54–85], 68% [56–78], 87% [79–93] and 72% [62–80], respectively. Area under receiver operating curve varied from 0.62 [0.57–0.65] for hepatitis C to 0.80 [0.76–0.85] for intravenous drug use.

Conclusions

Sensitivity was low to detect AIDS, alcohol abuse, illicit drug use and especially hepatitis C in RAMQ. Researchers must be aware of the potential for residual confounding and must consider additional methods to control for confounding. Copyright © 2015 John Wiley & Sons, Ltd.

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