BACKGROUND: Estimates of the prevalence of transmitted HIV drug resistance (TDR) in a population are derived from resistance tests performed on samples from patients thought to be naïve to antiretroviral treatment (ART). Much of the debate over reliability of estimates of the prevalence of TDR has focused on whether the sample population is representative. However estimates of the prevalence of TDR will also be distorted if some ART-experienced patients are misclassified as ART-naïve. METHODS: The impact of misclassification bias on the rate of TDR was examined. We developed methods to obtain adjusted estimates of the prevalence of TDR for different misclassification rates, and conducted sensitivity analyses of trends in the prevalence of TDR over time using data from the UK HIV Drug Resistance Database. Logistic regression was used to examine trends in the prevalence of TDR over time. RESULTS: The observed rate of TDR was higher than true TDR when misclassification was present and increased as the proportion of misclassification increased. As the number of naïve patients with a resistance test relative to the number of experienced patients with a test increased, the difference between true and observed TDR decreased. The observed prevalence of TDR in the UK reached a peak of 11.3% in 2002 (odds of TDR increased by 1.10 (95% CI 1.02, 1.19, p(linear trend) = 0.02) per year 1997-2002) before decreasing to 7.0% in 2007 (odds of TDR decreased by 0.90 (95% CI 0.87, 0.94, p(linear trend) < 0.001) per year 2002-2007. Trends in adjusted TDR were altered as the misclassification rate increased; the significant downward trend between 2002-2007 was lost when the misclassification increased to over 4%. CONCLUSION: The effect of misclassification of ART on estimates of the prevalence of TDR may be appreciable, and depends on the number of naïve tests relative to the number of experienced tests. Researchers can examine the effect of ART misclassification on their estimates of the prevalence of TDR if such a bias is suspected.
BACKGROUND: Estimates of the prevalence of transmitted HIV drug resistance (TDR) in a population are derived from resistance tests performed on samples from patients thought to be naïve to antiretroviral treatment (ART). Much of the debate over reliability of estimates of the prevalence of TDR has focused on whether the sample population is representative. However estimates of the prevalence of TDR will also be distorted if some ART-experienced patients are misclassified as ART-naïve. METHODS: The impact of misclassification bias on the rate of TDR was examined. We developed methods to obtain adjusted estimates of the prevalence of TDR for different misclassification rates, and conducted sensitivity analyses of trends in the prevalence of TDR over time using data from the UK HIV Drug Resistance Database. Logistic regression was used to examine trends in the prevalence of TDR over time. RESULTS: The observed rate of TDR was higher than true TDR when misclassification was present and increased as the proportion of misclassification increased. As the number of naïve patients with a resistance test relative to the number of experienced patients with a test increased, the difference between true and observed TDR decreased. The observed prevalence of TDR in the UK reached a peak of 11.3% in 2002 (odds of TDR increased by 1.10 (95% CI 1.02, 1.19, p(linear trend) = 0.02) per year 1997-2002) before decreasing to 7.0% in 2007 (odds of TDR decreased by 0.90 (95% CI 0.87, 0.94, p(linear trend) < 0.001) per year 2002-2007. Trends in adjusted TDR were altered as the misclassification rate increased; the significant downward trend between 2002-2007 was lost when the misclassification increased to over 4%. CONCLUSION: The effect of misclassification of ART on estimates of the prevalence of TDR may be appreciable, and depends on the number of naïve tests relative to the number of experienced tests. Researchers can examine the effect of ART misclassification on their estimates of the prevalence of TDR if such a bias is suspected.
Authors: Julie Fox; Samantha Hill; Steve Kaye; Simon Dustan; Myra McClure; Sarah Fidler; Nicola E Mackie Journal: AIDS Date: 2007-01-11 Impact factor: 4.177
Authors: Victoria A Johnson; Francoise Brun-Vezinet; Bonaventura Clotet; Huldrych F Gunthard; Daniel R Kuritzkes; Deenan Pillay; Jonathan M Schapiro; Douglas D Richman Journal: Top HIV Med Date: 2009-12
Authors: Vivek Jain; Maria C Sucupira; Peter Bacchetti; Wendy Hartogensis; Ricardo S Diaz; Esper G Kallas; Luiz M Janini; Teri Liegler; Christopher D Pilcher; Robert M Grant; Rodrigo Cortes; Steven G Deeks; Frederick M Hecht Journal: J Infect Dis Date: 2011-04-15 Impact factor: 5.226
Authors: Patricia Cane; Ian Chrystie; David Dunn; Barry Evans; Anna Maria Geretti; Hannah Green; Andrew Phillips; Deenan Pillay; Kholoud Porter; Anton Pozniak; Caroline Sabin; Erasmus Smit; Jonathan Weber; Mark Zuckerman Journal: BMJ Date: 2005-11-18
Authors: David Etoori; Alison Wringe; Chodziwadziwa Whiteson Kabudula; Jenny Renju; Brian Rice; F Xavier Gomez-Olive; Georges Reniers Journal: Front Public Health Date: 2020-04-07
Authors: David Dolling; Caroline Sabin; Valerie Delpech; Erasmus Smit; Anton Pozniak; David Asboe; Andrew Leigh Brown; Duncan Churchill; Ian Williams; Anna Maria Geretti; Andrew Phillips; Nicola Mackie; Gary Murphy; Hannah Castro; Deenan Pillay; Patricia Cane; David Dunn; David Dolling Journal: BMJ Date: 2012-08-21