Research shows that HIV treatment in the Western Cape is working
The human immunodeficiency virus (HIV) is a particular threat to developing countries in Africa. These researchers created a computer program that uses data routinely collected when patients go for treatment, to predict how well they are managing their HIV.
The world is trying to eliminate HIV. The best way to monitor progress in 2020 was through surveys. However, surveys are expensive and take time to conduct.
HIV treatment programmes gather data about patients, but essential information, like date of birth, is often missing. This makes it difficult to identify individuals and track their illness. These researchers created a computer model that can use this data to predict how well patients and carers in a particular area manage HIV.
The researchers collected information from many public treatment centres in the Western Cape province of South Africa from 2008 to 2018. They then created a model that matches patient data across all their sources. It also predicts how well the treatment works for these patients.
Their model predicted that the percentage of patients who no longer show symptoms of HIV increased from 85% in 2008 to 90% in 2018. It also predicted that half of the people with HIV in the Western Cape no longer showed symptoms in 2018, up from only 12% in 2008.
The researchers' new model showed that the Western Cape's treatment for HIV is effective. The model can be a powerful tool to plan future efforts to eliminate HIV. For instance, the model predicted that we must focus on men and people under 24, especially young children, because the current treatment system does not seem to be as effective for them as for others.
The model seems accurate for the Western Cape because the results are similar to other studies and surveys. However, future studies should focus on adapting the model for more areas.
This work by South African and Swiss researchers is important because record-keeping systems in Africa are often neglected and incomplete because of the many burdens it faces. Their new model adjusts for this and gives estimates for information that would otherwise not be available.
Introduction: There are few population-wide data on viral suppression (VS) that can be used to monitor programmatic targets in sub-Saharan Africa. We describe how routinely collected viral load (VL) data from antiretroviral therapy (ART) programmes can be extrapolated to estimate population VS and validate this using a combination of empiric and model-based estimates.
Methods: VL test results from were matched using a record linkage algorithm to obtain linked results for individuals. Test-level and individual-level VS rates were based on test VL values <1000 cps/mL, and individual VL <1000 cps/mL in a calendar year, respectively. We calculated population VS among people living with HIV (PLWH) in the province by combining census-derived midyear population estimates, HIV prevalence estimates and individual level VS estimates from routine VL data.
Results: Approximately 1.9 million VL test results between 2008 and 2018 were analysed. Among individuals in care, VS increased from 85.5% in 2008 to 90% in 2018. Population VS among all PLWH in the province increased from 12.2% in 2008 to 51.0% in 2017. The estimates derived from this method are comparable to those from other published studies. Sensitivity analyses showed that the results are robust to variations in linkage method, but sensitive to the extreme combinations of assumed VL testing coverage and population HIV prevalence.
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