Intersecting Syndemics of Household Water Insecurity and HIV/AIDS: Implications for Dermatological Health among People Living with HIV in Kenya
Bibliographic record
Abstract
Objectives In tropical and resource-poor settings where household water insecurity is prevalent, the risk of dermatological conditions is significantly high among People living with HIV (PLHIV). However, no study has examined the nature of this relationship in Kenya. Thus, this study assessed the effect of water insecurity on dermatological conditions among PLHIV in Kenya. Methods Data for this study were drawn from the Resource Insecurity and Well-being Study with a focus on PLHIV in Kenya (N = 1,132). Data collected included measures of household water insecurity experiences, dermatological conditions, use of improved/unimproved water sources, environmental risk factors, and sociodemographic factors. Following descriptive and bivariate analysis, complementary log-log regression models assessed the effect of water insecurity on each skin condition in three multivariate models. This was followed by predicted probabilities that examined the intersecting syndemics of water insecurity over water source and gender. Results Of the 1,132 PLHIV, 16.2% reported having skin infections, 16.3% experienced skin itching, and 9.4% experienced skin sores in the last month. In the multivariate models, water insecurity was a significant predictor of skin infections (OR: 1.03; 95% CI: 1.01, 1.04), skin itching (OR: 1.02, 95% CI: 1.01, 1.03) and skin sores (OR: 1.04, 95% CI: 1.02, 1.06). The interaction between water insecurity and improved water sources showed decreased skin sores and itching odds. Conclusions Household water insecurity is a significant predictor of dermatological conditions among PLHIV. Policies aimed at improving access to clean water and sanitation are essential in promoting their well-being.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".