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  • Study design and study population: We conducted a cross-sectional survey in eight provinces or cities of China: Beijing, Jilin, Hunan, Guangxi, Sichuan, Guizhou, Yunnan and Xinjiang Province. The survey protocol was taken from the WHO recommended cross-sectional survey on acquired HIVDR in adult patients receiving ART. Patients included were 18 years or older, had begun free ART treatment in 2013, and had received first-line ART for 9–18 months at enrollment. Eligible patients were enrolled at routine clinic visits in 2014. All participants provided written informed consent. Institutional review board approval was granted by National Center for AIDS/STD Control and Prevention (NCAIDS), Chinese Center for Disease Control and Prevention (China CDC). An interview-administered questionnaire (S1 File) was used for data collection. The questionnaire was administered face to face by trained local health staff in a private room. Data on demographic characteristics, ART treatment, and medicine adherence were collected during the interview. Blood specimens were collected after the interview. CD4+ T cells were quantified using flow cytometry at local CDCs within 12 hours. Plasma was isolated and sent under cold chain to the laboratory at NCAIDS, China CDC where the HIV viral load was measured. Viral suppression was defined as an HIV RNA level <1000 copies/ml. In samples with a viral load ≥1000 copies/ml, HIV drug resistance genotyping was performed at the NCAIDS laboratory by using an in-house method [15, 16]. A drug resistance mutation was identified and interpreted by using the algorithm of the Stanford HIV Drug Resistance Database (http://hivdb.stanford.edu/pages/algs/sierra_sequence.html). HIV drug resistance mutations were defined as those conferring low-, intermediate, or high- level resistance [17, 18]. All questionnaire data were double-entered using Epidata 3.1 (The Epidata Association Odense, Denmark). Statistical Analyses (S1 Table) were performed using SAS V9.4 (SAS Institute Inc, Cary, North Carolina, USA). Univariate logistic regression models were constructed to explore factors associated with drug resistance. A stepwise multivariate logistic regression model was constructed to select the variables that were independently associated with drug resistance. A P value <0.05 was considered statistically significant, and all tests were two-sided.
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