Barriers to eliminating HIV transmission in England by 2030
Bibliographic record
Abstract
One of the key obstacles to achieving the UNAIDS goal of zero HIV transmissions by 2030 is the current inability to reliably identify infected but undiagnosed individuals. This is crucial for not only initiating treatment with the goals of viral suppression and halting onwards transmission, but also for assessing the true burden of HIV, including how close we are to achieving the elimination of HIV. The inability to reliably identify undiagnosed individuals results in suboptimal resource allocation because it is unclear which groups need to be targeted for interventions aimed at harm reduction, increasing awareness about HIV, and improving willingness to test. New methods to further identify and define these undiagnosed groups are crucial for reducing the burden of HIV and measuring our progress towards HIV elimination. In The Lancet Public Health, Anne M Presanis and colleagues used a Bayesian approach to further expand methodologies to estimate the prevalence of undiagnosed HIV infection in England.1Presanis AM Harris RJ Kirwan PD et al.Trends in undiagnosed HIV prevalence in England and implications for eliminating HIV transmission by 2030: an evidence synthesis model.Lancet Public Health. 2021; 6: e739-51Summary Full Text Full Text PDF PubMed Scopus (4) Google Scholar The method itself generates similar results if compared with other approaches to estimating this number of undiagnosed individuals,2Brizzi F Birrell PJ Plummer MT et al.Extending Bayesian back-calculation to estimate age and time specific HIV incidence.Lifetime Data Anal. 2019; 25: 757-780Crossref PubMed Scopus (6) Google Scholar, 3Brizzi F Birrell PJ Kirwan P et al.Tracking elimination of HIV transmission in men who have sex with men in England: a modelling study.Lancet HIV. 2021; 8: e440-e448Summary Full Text Full Text PDF PubMed Scopus (3) Google Scholar and then takes these estimates several steps further by enabling meaningful disaggregation of results. Presanis and colleagues used an extensive array of data sources to improve estimations of numbers of undiagnosed individuals in England by subgroup, including age, sex, region, history of clinic attendance, and exposure group. The authors found that the number of undiagnosed individuals was halved between 2013 and 2019, showing substantial progress towards achieving HIV elimination in England. However, the rate at which undiagnosed individuals were identified differed by region (ie, London vs outside of London), age group, and history of clinic attendance. These disparities show important gaps, on which efforts should be focused to identify currently undiagnosed individuals and how future resources can be efficiently allocated to close these gaps. Another strategy to improve timely HIV diagnosis, particularly among typically low-incidence groups, is testing based on indicator condition. Indicator conditions are defined as diseases associated with an undiagnosed HIV prevalence of more than 0·1%, and have been shown to be successful in improving timely HIV diagnosis, specifically in Europe.4Bert F Gualano MR Biancone P et al.Cost-effectiveness of HIV screening in high-income countries: a systematic review.Health Policy. 2018; 122: 533-547Crossref PubMed Scopus (20) Google Scholar, 5Omland L H Legarth R Ahlström MG Sørensen HT Obel N Five-year risk of HIV diagnosis subsequent to 147 hospital-based indicator diseases: a Danish nationwide population-based cohort study.Clin Epidemiol. 2016; 8: 333-340Crossref PubMed Scopus (6) Google Scholar Given the differences that Presanis and colleagues identified, with a greater relative reduction of the undiagnosed population in London compared with other areas of England, efforts to understand and target populations living outside of London and other major metropolitan areas will be key to developing strategies to reach the undiagnosed individuals in such settings. A difference in the rates of timely diagnosis between urban and rural settings has been seen in the Netherlands. A published qualitative study from the Netherlands6Bedert M Davidovich U de Bree G et al.Understanding reasons for HIV late diagnosis: a qualitative study among hiv-positive individuals in Amsterdam, the Netherlands.AIDS Behav. 2021; (published online March 31.)https://doi.org/10.1007/S10461-021-03239-3Crossref PubMed Scopus (1) Google Scholar found that both psychosocial and health-system factors seemed to contribute to this difference in timely diagnosis; finding ways to mitigate these factors could aid in achieving the goal of zero HIV transmission by 2030. For clinic attendance, Presanis and colleagues estimate the rate of undiagnosed infection to be up to 30 times greater in individuals who had recently visited a clinic compared with people who had not. Studies from other countries confirm that undiagnosed individuals do attend clinics, but are not necessarily tested for HIV, even if testing is indicated.7Dovel K Balakasi K Gupta S et al.Frequency of visits to health facilities and HIV services offered to men, Malawi.Bull World Health Organ. 2021; (published online June 29.)https://cdn.who.int/media/docs/default-source/bulletin/online-first/blt.20.278994.pdf?sfvrsn=2b1b3c9a_5Crossref PubMed Scopus (2) Google Scholar Approaches involving self-testing for HIV at a clinic have been shown to be effective and cost-effective ways to identify previously undiagnosed individuals in low-income and middle-income countries.8Dovel K Shaba F Offorjebe OA et al.Effect of facility-based HIV self-testing on uptake of testing among outpatients in Malawi: a cluster-randomised trial.Lancet Glob Health. 2020; 8: e276-e287Summary Full Text Full Text PDF PubMed Scopus (39) Google Scholar, 9Nichols BE Offorjebe OA Cele R et al.Economic evaluation of facility-based HIV self-testing among adult outpatients in Malawi.J Int AIDS Soc. 2020; 23e25612Crossref PubMed Scopus (6) Google Scholar Similar but increasingly targeted and refined approaches that can be applied in an English setting could be effective strategies to reduce the proportion of undiagnosed individuals. As noted by Presanis and colleagues, risk groups with a small number of remaining undiagnosed individuals are increasingly difficult to quantify with reasonable certainty. It is therefore likely that, as the number of undiagnosed individuals decreases in the future, the ability to accurately estimate the number remaining will become more difficult. This challenge could be one limitation in measuring progress towards zero transmissions with certainty, along with the fact that onward transmission will continue to occur, through either the reintroduction of infections from outside of England, or continued transmission originating from people who are either undiagnosed or on-treatment but not virally suppressed. Presanis and colleagues’ study provides a useful roadmap for understanding how to target services in the future to reach undiagnosed people with HIV. Novel interventions to diagnose infections in populations that typically have a very low HIV incidence, and the further scale up and refinement of existing strategies, will be required to work towards achieving the target of zero transmissions by 2030. MvdV reports grants and personal fees from AbbVie, Gilead Sciences, Merck Sharp & Dohme, and ViiV, outside the submitted work. BEN declares no competing interests. Trends in undiagnosed HIV prevalence in England and implications for eliminating HIV transmission by 2030: an evidence synthesis modelThe UNAIDS target of diagnosing 90% of people living with HIV by 2020 was reached by 2016 in England, with the country on track to achieve the new target of 95% diagnosed by 2025. Reductions in transmission and undiagnosed prevalence have corresponded to large scale-up of testing in key populations and early diagnosis and treatment. Additional and intensified prevention measures are required to eliminate transmission of HIV among the communities that have experienced slower declines than other subgroups, despite having very low prevalences of HIV. Full-Text PDF Open Access
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".