Tackling an emerging epidemic: the burden of non-communicable diseases among people living with HIV/AIDS in sub-Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa (SSA) is at a crossroad. Over the last decade, successes in the scale up of HIV care and treatment programs has led to a burgeoning number of people living with HIV (PLHIV) in care. At the same time, an epidemiologic shift has been witnessed with a concomitant rise in non-communicable diseases (NCD) related morbidity and mortality. Against low levels of domestic financing and strained healthcare delivery platforms, the NCD-HIV syndemic threatens to reverse gains made in care of people living with HIV (PLHIV). NCDs are the global health disruptor of the future. In this review, we draw three proposals for low and middle-income countries (LMICs) based on existing literature, that if contextually adopted would mitigate against impending poor NCD-HIV care outcomes. First, we call for an adoption of universal health coverage by countries in SSA. Secondly, we recommend leveraging on comparably formidable HIV healthcare delivery platforms through integration. Lastly, we advocate for institutional-response building through a multi-stakeholder governance and coordination mechanism. Based on our synthesis of existing literature, adoption of these three strategies would be pivotal to sustain gains made so far for NCD-HIV care in SSA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".