Epidemiological And Health Systems Implications Of Evolving HIV And Hypertension In South Africa And Kenya
Bibliographic record
Abstract
Health systems in sub-Saharan Africa are facing an ongoing HIV epidemic and increasing burden of noncommunicable disease. With the focus shifting to the development of comprehensive primary health care and chronic disease treatment, multidisease modeling is integral to estimating future health care needs. We extended an established agent-based model of HIV transmission to include hypertension in two rural settings: KwaZulu-Natal, South Africa, and western Kenya. We estimated that from 2018 to 2028 hypertension prevalence would increase from 40 percent to 46 percent in KwaZulu-Natal and from 29 percent to 35 percent in western Kenya, while HIV prevalence is stabilizing and predicted to decrease. As the health system burden in sub-Saharan Africa is changing, innovative chronic disease treatment and the broadening of successful programs, such as integrated HIV and noncommunicable disease care, are necessary to reach universal health care coverage.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".