Prevalence of viral load suppression, predictors of virological failure and patterns of HIV drug resistance after 12 and 48 months on first-line antiretroviral therapy: a national cross-sectional survey in Uganda.

OBJECTIVES: We implemented the WHO cross-sectional survey protocol to determine rates of HIV viral load (VL) suppression (VLS), and weighted prevalence, predictors and patterns of acquired drug resistance (ADR) in individuals with virological failure (VF) defined as VL ≥1000 copies/mL. METHODS: We enrolled 547 and 1064 adult participants on first-line ART for 12 (±3) months (ADR12) and ≥48 months (ADR48), respectively. Dried blood spots and plasma specimens were collected for VL testing and genotyping among the VFs. RESULTS: VLS was 95.0% (95% CI 93.4%-96.5%) in the ADR12 group and 87.9% (95% CI 85.0%-90.9%) in the ADR48 group. The weighted prevalence of ADR was 96.1% (95% CI 72.9%-99.6%) in the ADR12 and 90.4% (95% CI 73.6-96.8%) in the ADR48 group, out of the 30 and 95 successful genotypes in the respective groups. Initiation on a zidovudine-based regimen compared with a tenofovir-based regimen was significantly associated with VF in the ADR48 group; adjusted OR (AOR) 1.96 (95% CI 1.13-3.39). Independent predictors of ADR in the ADR48 group were initiation on a zidovudine-based regimen compared with tenofovir-based regimens, AOR 3.16 (95% CI 1.34-7.46) and ART duration of ≥82 months compared with <82 months, AOR 1.92 (95% CI 1.03-3.59). CONCLUSIONS: While good VLS was observed, the high prevalence of ADR among the VFs before they underwent the recommended three intensive adherence counselling (IAC) sessions followed by repeat VL testing implies that IAC prior to treatment switching may be of limited benefit in improving VLS

HIV drug resistance among adults initiating antiretroviral therapy in Uganda.

BACKGROUND: WHO revised their HIV drug resistance (HIVDR) monitoring strategy in 2014, enabling countries to generate nationally representative HIVDR prevalence estimates from surveys conducted using this methodology. In 2016, we adopted this strategy in Uganda and conducted an HIVDR survey among adults initiating or reinitiating ART. METHODS: A cross-sectional survey of adults aged ≥18 years initiating or reinitiating ART was conducted at 23 sites using a two-stage cluster design sampling method. Participants provided written informed consent prior to enrolment. Whole blood collected in EDTA vacutainer tubes was used for preparation of dried blood spot (DBS) specimens or plasma. Samples were shipped from the sites to the Central Public Health Laboratory (CPHL) for temporary storage before transfer to the Uganda Virus Research Institute (UVRI) for genotyping. Prevalence of HIVDR among adults initiating or reinitiating ART was determined. RESULTS: Specimens from 491 participants (median age 32 years and 61.5% female) were collected between August and December 2016. Specimens from 351 participants were successfully genotyped. Forty-nine had drug resistance mutations, yielding an overall weighted HIVDR prevalence of 18.2% with the highest noted for NNRTIs at 14.1%. CONCLUSIONS: We observed a high HIVDR prevalence for NNRTIs among adults prior to initiating or reinitiating ART in Uganda. This is above WHO's recommended threshold of 10% when countries should consider changing from NNRTI- to dolutegravir-based first-line regimens. This recommendation was adopted in the revised Ugandan ART guidelines. Dolutegravir-containing ART regimens are preferred for first- and second-line ART regimens

Producing HIV estimates: from global advocacy to country planning and impact measurement

Background: The development of global HIV estimates has been critical for understanding, advocating for and funding the HIV response. The process of generating HIV estimates has been cited as the gold standard for public health estimates. Objective: This paper provides important lessons from an international scientific collaboration and provides a useful model for those producing public health estimates in other fields. Design: Through the compilation and review of published journal articles, United Nations reports, other documents and personal experience we compiled historical information about the estimates and identified potential lessons for other public health estimation efforts. Results: Through the development of core partnerships with country teams, implementers, demographers, mathematicians, epidemiologists and international organizations, UNAIDS has led a process to develop the capacity of country teams to produce internationally comparable HIV estimates. The guidance provided by these experts has led to refinements in the estimated numbers of people living with HIV, new HIV infections and AIDS-related deaths over the past 20 years. A number of important updates to the methods since 1997 resulted in fluctuations in the estimated levels, trends and impact of HIV. The largest correction occurred between the 2005 and 2007 rounds with the additions of household survey data into the models. In 2001 the UNAIDS models at that time estimated there were 40 million people living with HIV. In 2016, improved models estimate there were 30 million (27.6–32.7 million) people living with HIV in 2001. Conclusions: Country ownership of the estimation tools has allowed for additional uses of the results than had the results been produced by researchers or a team in Geneva. Guidance from a reference group and input from country teams have led to critical improvements in the models over time. Those changes have improved countries’ and stakeholders’ understanding of the HIV epidemic

Uganda's new national laboratory sample transport system: a successful model for improving access to diagnostic services for Early Infant HIV Diagnosis and other programs.

INTRODUCTION: Uganda scaled-up Early HIV Infant Diagnosis (EID) when simplified methods for testing of infants using dried blood spots (DBS) were adopted in 2006 and sample transport and management was therefore made feasible in rural settings. Before this time only 35% of the facilities that were providing EID services were reached through the national postal courier system, Posta Uganda. The transportation of samples during this scale-up, therefore, quickly became a challenge and varied from facility to facility as different methods were used to transport the samples. This study evaluates a novel specimen transport network system for EID testing. METHODS: A retrospective study was done in mid-2012 on 19 pilot hubs serving 616 health facilities in Uganda. The effect on sample-result turnaround time (TAT) and the cost of DBS sample transport on 876 sample-results was analyzed. RESULTS: The HUB network system provided increased access to EID services ranging from 36% to 51%, drastically reduced transportation costs by 62%, reduced turn-around times by 46.9% and by a further 46.2% through introduction of SMS printers. CONCLUSIONS: The HUB model provides a functional, reliable and efficient national referral network against which other health system strengthening initiatives can be built to increase access to critical diagnostic and treatment monitoring services, improve the quality of laboratory and diagnostic services, with reduced turn-around times and improved quality of prevention and treatment programs thereby reducing long-term costs

Population-based monitoring of HIV drug resistance early warning indicators in Uganda: A nationally representative survey following revised WHO recommendations: S1 Data

With the scale-up of antiretroviral therapy (ART) there is a need to monitor programme performance to maximize ART efficacy and to prevent emergence of HIV drug resistance (HIVDR). In keeping with the elements of the World Health Organisation (WHO) guidance we carried out a nationally representative assessment of early warning indicators (EWI) at 304 randomly selected ART service outlets in Uganda

Correcting for selection bias in HIV prevalence estimates: an application of sample selection models using data from population-based HIV surveys in seven sub-Saharan African countries.

INTRODUCTION: Population-based biomarker surveys are the gold standard for estimating HIV prevalence but are susceptible to substantial non-participation (up to 30%). Analytical missing data methods, including inverse-probability weighting (IPW) and multiple imputation (MI), are biased when data are missing-not-at-random, for example when people living with HIV more frequently decline participation. Heckman-type selection models can, under certain assumptions, recover unbiased prevalence estimates in such scenarios. METHODS: We pooled data from 142,706 participants aged 15-49 years from nationally representative cross-sectional Population-based HIV Impact Assessments in seven countries in sub-Saharan Africa, conducted between 2015 and 2018 in Tanzania, Uganda, Malawi, Zambia, Zimbabwe, Lesotho and Eswatini. We compared sex-stratified HIV prevalence estimates from unadjusted, IPW, MI and selection models, controlling for household and individual-level predictors of non-participation, and assessed the sensitivity of selection models to the copula function specifying the correlation between study participation and HIV status. RESULTS: In total, 84.1% of participants provided a blood sample to determine HIV serostatus (range: 76% in Malawi to 95% in Uganda). HIV prevalence estimates from selection models diverged from IPW and MI models by up to 5% in Lesotho, without substantial precision loss. In Tanzania, the IPW model yielded lower HIV prevalence estimates among males than the best-fitting copula selection model (3.8% vs. 7.9%). CONCLUSIONS: We demonstrate how HIV prevalence estimates from selection models can differ from those obtained under missing-at-random assumptions. Further benefits include exploration of plausible relationships between participation and outcome. While selection models require additional assumptions and careful specification, they are an important tool for triangulating prevalence estimates in surveys with substantial missing data due to non-participation

How Turnaround Time reduced; sample and result transit time dropped from 49 days before lab consolidation to 26 day and from 26 to 14 days due to the NSRTN.

<p>How Turnaround Time reduced; sample and result transit time dropped from 49 days before lab consolidation to 26 day and from 26 to 14 days due to the NSRTN.</p

The cost at each of the 19 hubs before and after the initiation of the National sample referral transport network (NSRTN).

<p>The cost at each of the 19 hubs before and after the initiation of the National sample referral transport network (NSRTN).</p