Evolving Failures in the Delivery of Human Immunodeficiency Virus Care: Lessons From a Ugandan Meningitis Cohort 2006–2016
Bibliographic record
Abstract
Abstract Background Because of investments in human immunodeficiency virus (HIV) care in sub-Saharan Africa, the number of people aware of their status and receiving antiretroviral therapy (ART) has increased; however, HIV/acquired immune deficiency syndrome (AIDS) mortality still remains high. Methods We performed retrospective analysis of 3 sequential prospective cohorts of HIV-infected Ugandan adults presenting with AIDS and meningitis from 2006 to 2009, 2010 to 2012, and 2013 to 2016. Participants were categorized as follows: (1) unknown HIV status; (2) known HIV+ without ART; (3) known HIV+ with previous ART. We further categorized 2006 and 2013 cohort participants by duration of HIV-status knowledge and of ART receipt. Results We screened 1353 persons with suspected meningitis. Cryptococcus was the most common pathogen (63%). Over the decade, we observed an absolute increase of 37% in HIV status knowledge and 59% in antecedent ART receipt at screening. The 2006 cohort participants were new/recent HIV diagnoses (65%) or known HIV+ but not receiving ART (35%). Many 2013 cohort participants were new/recent HIV diagnoses (34%) and known HIV+ with <1 month ART (20%), but a significant proportion were receiving ART 1–4 months (11%) and >4 months (30%). Four percent of participants discontinued ART. From 2010 to 2016, meningitis cases per month increased by 33%. Conclusions Although improved HIV screening and ART access remain much-needed interventions in resource-limited settings, greater investment in viral suppression and opportunistic infection care among the growing HIV-infected population receiving ART is essential to reducing ongoing AIDS mortality.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".