Feasibility and Preferences for Home-based Self-Testing for HIV, Diabetes, and Hypertension in Kenya, South Africa, and Zambia
Bibliographic record
Abstract
Abstract Home-based self-testing may improve individual health outcomes and public health disease surveillance by lowering the barriers associated with clinic-based diagnostic testing in low- and middle-income countries (LMICs). We assessed the feasibility of conducting home-based, self-testing for key communicable and non-communicable diseases, including HIV, diabetes, and hypertension, in sub-Saharan Africa. We enrolled participants (≧15 years) from households in peri-urban and rural communities of Kenya, South Africa, and Zambia. Participants opted in to self-directed rapid testing for HIV and blood glucose and had their blood pressure measured by a research team member. Our primary measures included HIV status and testing history, HIV and blood glucose rapid test results, blood pressure, self-reported usability and acceptability of self-testing, and participant preferences for future self-testing. Among the 526 participants from 100 households enrolled in each country, the average age was 41 years and 63% were female. Overall, 16% of participants reported living with HIV. Over half of participants (52%) had last tested for HIV >12 months ago or had never tested for HIV, and 8% of participants were unsure of their HIV status. Among participants who self-tested, 2% (N=6) tested positive for HIV and 4% (N=18) had high blood glucose, while 26% (N=131) had high blood pressure measured by the clinical team. Only 13% of study participants reported previously using a rapid test. Most (>90%) participants rated all procedures for HIV and blood glucose tests as either “very easy” or “fairly easy” to use. Most participants (88%, N=458) preferred home-based testing. Home-based self-testing for HIV and blood glucose and testing for blood pressure were feasible and preferred in peri-urban and rural areas of Kenya, South Africa, and Zambia. Self-testing has potential to expand and accelerate access to healthcare delivery in LMICs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".