RESEARCH ARTICLE
Sociodemographic correlates of HIV drug resistance and access to drug resistance testing in British Columbia, Canada Genevieve Rocheleau1,2, Conrado Franco-Villalobos2, Natalia Oliveira2, Zabrina L. Brumme2,3, Melanie Rusch4, Jeannie Shoveller2,5, Chanson J. Brumme2, P. Richard Harrigan1,2*
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1 Department of Medicine, University of British Columbia, Vancouver, Canada, 2 BC Centre for Excellence in HIV/AIDS, Vancouver, Canada, 3 Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada, 4 Vancouver Island Health Authority, Victoria, Canada, 5 School of Population and Public Health, University of British Columbia, Vancouver, Canada * [email protected]
Abstract OPEN ACCESS Citation: Rocheleau G, Franco-Villalobos C, Oliveira N, Brumme ZL, Rusch M, Shoveller J, et al. (2017) Sociodemographic correlates of HIV drug resistance and access to drug resistance testing in British Columbia, Canada. PLoS ONE 12(9): e0184848. https://doi.org/10.1371/journal. pone.0184848 Editor: Georgios K Nikolopoulos, University of Cyprus, CYPRUS Received: June 14, 2017 Accepted: August 31, 2017 Published: September 22, 2017 Copyright: © 2017 Rocheleau et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The dataset consists of very large numbers of sequences from a relatively restricted part of the world, and include multiple linked genes, consequently representing personally identifiable data. Releasing this information would breach the conditions under which consent was given and not be allowed by our Research Ethics Board. However, except for identifiable information and patient medical records, the authors are able to release all aspects of their research program, including software
Sociodemographic correlates of engagement in human immunodeficiency virus (HIV) care are well studied, however the association with accessing drug resistance testing (DRT) and the development of drug resistance have not been characterized. Between 1996– 2014, 11 801 HIV patients accessing therapy in British Columbia were observed longitudinally. A subset of 9456 patients had testable viral load; of these 8398 were linked to census data. Sociodemographic (census tract-level) and clinical (individual-level) correlates of DRT were assessed using multivariable General Estimating Equation logistic regression adjusted odds ratios (aOR). The mean number of tests per patient was 2.1 (Q1-Q3; 0–3). Separately, any drug resistance was determined using IAS-USA (2013) list for 5703 initially treatment naïve patients without baseline resistance; 5175 were census-linked (mean of 1.5 protease-reverse transcriptase sequences/patient, Q1-Q3; 0–2). Correlates of detecting drug resistance in this subset were analyzed using Cox PH regression adjusted hazard ratios (aHR). Our results indicate baseline CD4 250 copies/ mL) [11]. In each calendar year patients were considered eligible for testing when 1 sample was above the lower limit of detection of the DRT assay in use at that time (usually pVL >250 copies/mL). Patients with a physician-ordered DRT result available were considered to have accessed testing during that calendar year. Among the 11 801 patients initially considered, 9456 had eligible pVL in any year of the study. Among those eligible, a mean number of 2.1 (Q1-Q3; 0–3.0) resistance tests were ordered per patient over the course of follow-up.
PLOS ONE | https://doi.org/10.1371/journal.pone.0184848 September 22, 2017
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Sociodemographic correlates of HIV drug resistance and access to testing
Linking of census data and clinical data. Patient postal code or city of residence at time of Drug Treatment Program enrollment determined their census tract. Clinical data was linked with census tract-level data from the Census Canada Survey that was conducted closest to Drug Treatment Program enrollment date (census 1996, 2001, 2006, or 2011). If insufficient information was available to determine census tract, census metropolitan area/census agglomeration (or local health area) was used. Among patients with eligible pVL, 8398 had clinical data linked to census tract data. See S3 Table for a comparison of characteristics of included and excluded individuals. Plasma viral load testing. Testing is free of charge in BC when ordered by a physician, and occurs regularly as part of routine patient care [11]. Testing was completed at the St. Paul’s Hospital Virology Laboratory using Roche Molecular Diagnostics kits. Quantification of adherence to prescribed drug regimen. Prescription refill percentage, obtained from Drug Treatment Program ARV prescription records and calculated as the number of days with antiretroviral drugs dispensed divided by the number of days of follow up in the first year on therapy, was used as a crude estimate of adherence and has been demonstrated as a good predictor of future adherence [24]. Statistical methods. Unadjusted odds ratios (uOR), adjusted odds ratios (aOR), and 95% confidence intervals (CIs) were determined by Generalized Estimating Equations (GEE) logistic regression. The optimal multivariable explanatory model was selected using an Akaike Information Criterion (AIC)-based backward elimination procedure. Covariate selection was completed through backward elimination to minimize the Quasilikelihood Information Criterion (QICu). The model was adjusted for individual-level biological, transmission risk group and clinical covariates. All covariates were treated as categorical, and included sex at birth (male, female), transmission risk factor (men who have sex with men (MSM), people who inject drugs (PWID), heterosexual), ever diagnosed with hepatitis C (HCV), drug in first recorded regimen (Only nRTI -including mono-, dual-, or triple-therapy; NNRTI as third drug in regimen; or PI as third drug in regimen), suboptimal adherence (
Sociodemographic correlates of HIV drug resistance and access to drug resistance testing in British Columbia, Canada Genevieve Rocheleau1,2, Conrado Franco-Villalobos2, Natalia Oliveira2, Zabrina L. Brumme2,3, Melanie Rusch4, Jeannie Shoveller2,5, Chanson J. Brumme2, P. Richard Harrigan1,2*
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1 Department of Medicine, University of British Columbia, Vancouver, Canada, 2 BC Centre for Excellence in HIV/AIDS, Vancouver, Canada, 3 Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada, 4 Vancouver Island Health Authority, Victoria, Canada, 5 School of Population and Public Health, University of British Columbia, Vancouver, Canada * [email protected]
Abstract OPEN ACCESS Citation: Rocheleau G, Franco-Villalobos C, Oliveira N, Brumme ZL, Rusch M, Shoveller J, et al. (2017) Sociodemographic correlates of HIV drug resistance and access to drug resistance testing in British Columbia, Canada. PLoS ONE 12(9): e0184848. https://doi.org/10.1371/journal. pone.0184848 Editor: Georgios K Nikolopoulos, University of Cyprus, CYPRUS Received: June 14, 2017 Accepted: August 31, 2017 Published: September 22, 2017 Copyright: © 2017 Rocheleau et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The dataset consists of very large numbers of sequences from a relatively restricted part of the world, and include multiple linked genes, consequently representing personally identifiable data. Releasing this information would breach the conditions under which consent was given and not be allowed by our Research Ethics Board. However, except for identifiable information and patient medical records, the authors are able to release all aspects of their research program, including software
Sociodemographic correlates of engagement in human immunodeficiency virus (HIV) care are well studied, however the association with accessing drug resistance testing (DRT) and the development of drug resistance have not been characterized. Between 1996– 2014, 11 801 HIV patients accessing therapy in British Columbia were observed longitudinally. A subset of 9456 patients had testable viral load; of these 8398 were linked to census data. Sociodemographic (census tract-level) and clinical (individual-level) correlates of DRT were assessed using multivariable General Estimating Equation logistic regression adjusted odds ratios (aOR). The mean number of tests per patient was 2.1 (Q1-Q3; 0–3). Separately, any drug resistance was determined using IAS-USA (2013) list for 5703 initially treatment naïve patients without baseline resistance; 5175 were census-linked (mean of 1.5 protease-reverse transcriptase sequences/patient, Q1-Q3; 0–2). Correlates of detecting drug resistance in this subset were analyzed using Cox PH regression adjusted hazard ratios (aHR). Our results indicate baseline CD4 250 copies/ mL) [11]. In each calendar year patients were considered eligible for testing when 1 sample was above the lower limit of detection of the DRT assay in use at that time (usually pVL >250 copies/mL). Patients with a physician-ordered DRT result available were considered to have accessed testing during that calendar year. Among the 11 801 patients initially considered, 9456 had eligible pVL in any year of the study. Among those eligible, a mean number of 2.1 (Q1-Q3; 0–3.0) resistance tests were ordered per patient over the course of follow-up.
PLOS ONE | https://doi.org/10.1371/journal.pone.0184848 September 22, 2017
2 / 15
Sociodemographic correlates of HIV drug resistance and access to testing
Linking of census data and clinical data. Patient postal code or city of residence at time of Drug Treatment Program enrollment determined their census tract. Clinical data was linked with census tract-level data from the Census Canada Survey that was conducted closest to Drug Treatment Program enrollment date (census 1996, 2001, 2006, or 2011). If insufficient information was available to determine census tract, census metropolitan area/census agglomeration (or local health area) was used. Among patients with eligible pVL, 8398 had clinical data linked to census tract data. See S3 Table for a comparison of characteristics of included and excluded individuals. Plasma viral load testing. Testing is free of charge in BC when ordered by a physician, and occurs regularly as part of routine patient care [11]. Testing was completed at the St. Paul’s Hospital Virology Laboratory using Roche Molecular Diagnostics kits. Quantification of adherence to prescribed drug regimen. Prescription refill percentage, obtained from Drug Treatment Program ARV prescription records and calculated as the number of days with antiretroviral drugs dispensed divided by the number of days of follow up in the first year on therapy, was used as a crude estimate of adherence and has been demonstrated as a good predictor of future adherence [24]. Statistical methods. Unadjusted odds ratios (uOR), adjusted odds ratios (aOR), and 95% confidence intervals (CIs) were determined by Generalized Estimating Equations (GEE) logistic regression. The optimal multivariable explanatory model was selected using an Akaike Information Criterion (AIC)-based backward elimination procedure. Covariate selection was completed through backward elimination to minimize the Quasilikelihood Information Criterion (QICu). The model was adjusted for individual-level biological, transmission risk group and clinical covariates. All covariates were treated as categorical, and included sex at birth (male, female), transmission risk factor (men who have sex with men (MSM), people who inject drugs (PWID), heterosexual), ever diagnosed with hepatitis C (HCV), drug in first recorded regimen (Only nRTI -including mono-, dual-, or triple-therapy; NNRTI as third drug in regimen; or PI as third drug in regimen), suboptimal adherence (
