Primary Drug Resistance in South Africa: Data from 10 Years of Surveys

Africa Centre for Health and Population Studies, Doris Duke Medical Research Institute, Nelson Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa.
AIDS research and human retroviruses (Impact Factor: 2.33). 01/2012; 28(6):558-65. DOI: 10.1089/AID.2011.0284
Source: PubMed


HIV-1 transmitted drug resistance (TDR) could reverse the gains of antiretroviral rollout. To ensure that current first-line therapies remain effective, TDR levels in recently infected treatment-naive patients need to be monitored. A literature review and data mining exercise was carried out to determine the temporal trends in TDR in South Africa. In addition, 72 sequences from seroconvertors identified from Africa Centre's 2010 HIV surveillance round were also examined for TDR. Publicly available data on TDR were retrieved from GenBank, curated in RegaDB, and analyzed using the Calibrated Population Resistance Program. There was no evidence of TDR from the 2010 rural KwaZulu Natal samples. Ten datasets with a total of 1618 sequences collected between 2000 and 2010 were pooled to provide a temporal analysis of TDR. The year with the highest TDR rate was 2002 [6.67%, 95% confidence interval (CI): 3.09-13.79%; n=6/90]. After 2002, TDR levels returned to <5% (WHO low-level threshold) and showed no statistically significant increase in the interval between 2002 and 2010. The most common mutations were associated with NNRTI resistance, K103N, followed by Y181C and Y188C/L. Five sequences had multiple resistance mutations associated with NNRTI resistance. There is no evidence of TDR in rural KwaZulu-Natal. TDR levels in South Africa have remained low following a downward trend since 2003. Continuous vigilance in monitoring of TDR is needed as more patients are initiated and maintained onto antiretroviral therapy.

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    • "Copyright: ©2015Lucic et al.Citation: Lucic D, McClernon A, Fangling Xu, Liang X, Weaver S, Dong J, Cloherty GA, McClernon D (United States have suppressed viral loads[8]. If antiretroviral viral drug levels are suboptimal, the risk of developing ARV resistance is high due to the high rate of HIV-1 replication and the lack of proofreading capacity in the transcriptase enzyme[2]. "

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    • "Under SATuRN, >7000 genotypes with treatment and monitoring data have been collected. Using the built-in customized report and query functionality, data of specific attributes are selected, analysed and used to answer specific clinical and research questions (de Oliveira et al., 2010; Manasa et al., 2012). In addition, members of the SATuRN project recently published a book (Rossouw et al., 2013) containing a series of case studies used for training. "
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    ABSTRACT: Summary: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface.Availability and implementation: Source code, binaries and documentation are available on RegaDB is written in the Java programming language, using a web-service-oriented architecture.Contact:
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    ABSTRACT: ISSUES: Paper-based structured clinical records are widely used for monitoring and evaluating the public sector HIV antiretroviral (ART) treatment program in South Africa. Computerized systems are sometimes used for operational data capture but are usually limited to individual clinics and hospitals. Important information also exists in other databases. A requirement exists to collate information into electronic format and integrate data from a variety of sources to facilitate reporting and quality controls. DESCRIPTION: Handheld computers (PDA's) were used to collate data from paper records at urban and rural sites during the first nine months of the ART roll-out program in the Free State province of South Africa. Thereafter a commercial online computer system (Meditech) was implemented. A Microsoft SQL Server 2000 database server was used to deploy the information into a relational and dimensional format using Microsoft Data Transformation Services. External laboratory and resistance data were integrated as well. After 18 months 19013 patients were registered on the system, of which 2743 were ART patients. A total of 143751 forms have been captured with a median of 4 forms/non- ARV patient and 26 forms/ARV patient. Six quality categories with a total of 32 quality routines were implemented. The system has also been used to compile quarterly reports and national indicators. LESSONS LEARNED: PDAs compare favourably with online systems for data collection from remote rural and urban sites and are useful where online systems or connectivity are lacking. Dimensional data modelling and data warehousing techniques are fundamental for creating databases that are optimized for querying, aggregated reporting, longitudinal analysis and research. RECOMMENDATIONS: Data quality remains the most challenging aspect of information systems for monitoring and evaluation. The next phase will be to feed information back to the health care workers and patients to give them the benefit of information systems and to encourage better input data quality.
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