Prevalence of pretreatment HIV drug resistance in key populations: a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Introduction WHO’s 2019 report on HIV drug resistance (HIVDR) documents a high prevalence of pretreatment drug resistance (PDR) among populations initiating first‐line antiretroviral therapy (ART) in low‐ and middle‐income countries (LMIC). However, systematic evidence on the prevalence of PDR among key populations remains limited. We performed a systematic review to characterize levels of PDR in key population groups and compared them to levels of PDR in the “general population” across different geographical regions. Methods Ten electronic databases were searched for papers published until February 2019 that included predefined search terms. We included studies that reported the number of successfully tested genotypes and the number of genotypes with drug resistance mutations among antiretroviral therapy treatment naïve people, recently infected people, or people initiating first‐line ART from key populations. To assess the prevalence of PDR for each key population, we pooled estimates using random‐effects meta‐analysis of proportions. Where possible, we computed the differences in the odds of PDR (any, and by drug class) present in each key population compared to the “general population”. The I2 statistic (a measure of heterogeneity between studies) is reported. Results and discussion A total of 332 datasets (from 218 studies) and 63,111 people with successful HIVDR genotyping were included in the analysis. The pooled prevalence estimate of any PDR was high among men who have sex with men (13.0%, 95% CI 11.0 to 14.0%, I2 = 93.19), sex workers (17.0%, 95% CI 6.0 – 32.0, I2 = 87.31%) and people in prisons (18.0%, 95% CI 11.0 to 25.0, I2 = 70.18%), but less so among people who inject drugs (7.0%, 95% CI 5.0 to 10.0, I2 = 90.23). Overall, men who have sex with men were more likely to carry any PDR compared to the “general population,” a finding which was statistically significant (odds ratio (OR) 1.28, 95% CI 1.13 – 1.46, I2 48.9%). Conclusions High prevalence of PDR found in key populations highlights the need to increase access to effective first‐line HIV treatment. The low prevalence of nucleotide reverse transcriptase inhibitor (NRTI) PDR suggests that current WHO recommendations for pre‐exposure prophylaxis (PrEP) regimens will remain effective and can be scaled up to prevent new HIV infections in high‐risk groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".