A survey on use of rapid tests and tuberculosis diagnostic practices by primary health care providers in South Africa: implications for the development of new point-of-care tests

BACKGROUND: Effective infectious disease control requires early diagnosis and treatment initiation. Point-of-care testing offers rapid turn-around-times, facilitating same day clinical management decisions. To maximize the benefits of such POC testing programs, we need to understand how rapid tests are used in everyday clinical practice. METHODS: In this cross-sectional survey study, 400 primary healthcare providers in two cities in South Africa were interviewed on their use of rapid tests in general, and tuberculosis diagnostic practices, between September 2012 and June 2013. Public healthcare facilities were selected using probability-sampling techniques and private healthcare providers were randomly selected from the Health Professional Council of South Africa list. To ascertain differences between the two healthcare sectors 2-sample z-tests were used to compare sample proportions. RESULTS: The numbers of providers interviewed were equally distributed between the public (n = 200) and private sector (n = 200). The most frequently reported tests in the private sector include blood pressure (99.5%), glucose finger prick (89.5%) and urine dipstick (38.5%); and in the public sector were pregnancy (100%), urine dipstick (100%), blood pressure (100%), glucose finger prick (99%) and HIV rapid test (98%). The majority of TB testing occurs in the public sector, where significantly more providers prefer Xpert MTB/RIF assay, the designated clinical TB diagnostic tool by the national TB program, as compared to the private sector (87% versus 71%, p-value >0.0001). Challenges with regard to TB diagnosis included the long laboratory turn-around-time, difficulty in obtaining sputum samples and lost results. All providers indicated that a new POC test for TB should be rapid and cheap, have good sensitivity and specificity, ease of sample acquisition, detect drug-resistance and work in HIV-infected persons. Conclusion/significance The existing centralized laboratory services, poor quality assurance, and lack of staff capacity deter the use of more rapid tests at POC. Further research into the practices and choices of these providers is necessary to aid the development of new POC tests

Determinants and reasons for switching anti-retroviral regimen among HIV-infected youth in a large township of South Africa (2002-2019).

BACKGROUND: There are limited data exploring antiretroviral therapy (ART) changes and time to change among South Africa young people living with HIV/AIDS. OBJECTIVE: We describe the time to first drug switch, which includes ART regimen change (three drug switch) and substitutions (single drug switch). We describe common reasons for ART switch among young people aged 10 to 24 years in South Africa. METHODS: We conducted a retrospective cohort study at a primary health care clinic in Cape Town, South Africa, providing ART to HIV-infected adolescents and adults since 2002. Those aged 10 to 24 years at ART initiation, who accessed care clinic between September 2002 and April 2019. Data was retrieved from electronic information systems: ART regimens, ART changes, dates for initiation or stop of each drug/regimen, laboratory results (viral loads, haemoglobin, liver enzyme results, and creatinine to support the reason for ART switch. From written records, we abstracted reason for single drug switch or regimen change, as well as socio demographic and clinical data. We fitted cox regression models to determine factors associated with ART switch (Having a change in one or more drugs in ART combination) and the rate of occurrence. RESULTS: Of 2601 adolescents included, 605 (24.9%) adolescents switched ART over 5090.5 person years at risk (PYAR), a rate of 11.9 /100PYAR. Median follow-up time was 4.4 (± 3.2) years. At multivariable analysis, the older age group was protective of the risk of ART switch: adjusted Hazard Ratio [aHR] 0.78, 95% CI 0.62-0.98, transfer status [transferred out 1.42 [1.11-1.82]. The hazard of ART switch increased with more severe HIV-disease at ART start, as observed by increasing WHO clinical stage or reduced CD4 count at baseline. The primary reasons for ART switch were side effects (20.0%), virological failure (17.9%) and formulation switch (27.8%). Others reasons included pregnancy, Hepatitis B, tuberculosis and psychosis. CONCLUSION: ART switches are frequent and occur at a consistent rate across 7.5 years from initiation. The main reasons for ART switch were virological failure and drug side effects

Characteristics of TB diagnosis in the public and private healthcare sectors.

<p>Characteristics of TB diagnosis in the public and private healthcare sectors.</p

Choice of criteria for an ideal POC test for TB stratified by importance, as indicated by healthcare providers.

<p>Choice of criteria for an ideal POC test for TB stratified by importance, as indicated by healthcare providers.</p

Time requirements of a rapid POC test according to both private and public healthcare provider.

<p>Time requirements of a rapid POC test according to both private and public healthcare provider.</p

Urine tenofovir-monitoring predicts HIV viremia in patients treated with high genetic-barrier regimens.

OBJECTIVE: Access to viral load measurements is constrained in resource-limited settings. A lateral flow urine tenofovir (TFV) rapid assay (UTRA) for patients whose regimens include TFV offers an affordable approach to frequent adherence monitoring. DESIGN: We conducted a cross-sectional study of patients to assess the utility of UTRA to predict virologic failure, defined as a viral load greater than 400 copies/ml. METHODS: We assessed urine TFV among 113 participants at increased risk of viral failure (who had previous viral failure on this regimen or had previously been ≥30 days out of care), comparing low genetic-barrier efavirenz (EFV) regimens (n = 60) to dolutegravir (DTG)-boosted or ritonavir-boosted protease inhibitor (PI/r)-based high genetic-barrier regimens (n = 53). Dried blood spots (DBS) for TFV-diphosphate and plasma for TFV concentrations were collected, with drug resistance assessed if viral failure present. RESULTS: Among 113 participants, 17 of 53 received DTG or PI/r had viral failure at the cross-sectional visit, with 11 (64.7%) demonstrating an undetectable urine TFV; the negative-predictive value (NPV) of undetectable UTRA for viral failure was 85% (34/40); none of the 16 sequenced had dual class drug resistance. In those treated with EFV regimens the sensitivity was lower, as only 1 (4.8%) of 21 with viral failure had an undetectable UTRA (P &lt; 0.001). CONCLUSIONS: Urine tenofovir-testing had a high negative-predictive value for viral failure in patients treated with DTG or ritonavir-boosted protease inhibitor regimens, where viral failure was largely explained by poor drug adherence. Frequent monitoring with inexpensive lateral flow urine TFV testing should be investigated prospectively in between viral load visits to improve viral load suppression on DTG-based first-line therapy in resource-limited settings

Drug Resistance, Rather than Low Tenofovir Levels in Blood or Urine, Is Associated with Tenofovir, Emtricitabine, and Efavirenz Failure in Resource-Limited Settings

The high cost of viral load (VL) testing limits its use for antiretroviral therapy (ART) adherence support. A low-cost lateral flow urine tenofovir (TFV) rapid assay predicts pre-exposure prophylaxis breakthroughs, but has not yet been investigated in HIV treatment. We therefore evaluated its utility in a pilot cross-sectional study of TFV-containing ART recipients at an increased risk of virologic failure (VF). Participants who had a treatment interruption ≥30 days or had ≥1 episode of viremia (VL ≥400 copies/mL) in the previous year were recruited from a public health setting in Cape Town, South Africa. Self-reported adherence data were collected, the urine TFV assay performed, and concurrent TFV-diphosphate analyzed in dried blood spots. VL testing was done concurrently and, if viremic, genotypic HIV drug resistance testing was performed. Of 48 participants, 18 (37.5%) had VL (&gt;400 copies/mL) at the time of the study, including 16 of 39 receiving efavirenz (EFV), 2 of 6 receiving protease inhibitors, and 0 of 3 receiving dolutegravir. Resistance testing succeeded in 17/18, of which 14 had significant mutations compromising ≥2 agents of the current EFV-based regimen. Of these 14, all had detected urine TFV. Urine TFV was undetectable in two out of three without regimen-relevant resistance; p = .02. In participants on EFV-based regimens returning to care, VF was largely due to viral resistance, where detectable urine TFV had 100% sensitivity (14/14 participants) in predicting resistance. Conversely, when undetectable, the urine-based assay could be used to preclude participants with poor adherence from undergoing costly HIV drug resistance testing

Where do HIV-infected adolescents go after transfer? – Tracking transition/transfer of HIV-infected adolescents using linkage of cohort data to a health information system platform

Introduction: To evaluate long-term outcomes in HIV-infected adolescents, it is important to identify ways of tracking outcomes after transfer to a different health facility. The Department of Health (DoH) in the Western Cape Province (WCP) of South Africa uses a single unique identifier for all patients across the health service platform. We examined adolescent outcomes after transfer by linking data from four International epidemiology Databases to Evaluate AIDS Southern Africa (IeDEA-SA) cohorts in the WCP with DoH data

2018 update to the HIV-TRePS system: The development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings

Objectives: Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping. Methods: Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system. Results: The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotypemodels achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55-0.63, and were marginallymore accurate than previousmodels. The models were able to identify alternative regimens that were predicted to be effective for the vastmajority of cases inwhich the newregimen prescribed in the clinic failed. Conclusions: These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings