Emergency departments as under-utilized venues to provide HIV prevention services to female sex workers in Nairobi, Kenya
Bibliographic record
Abstract
BACKGROUND: Female sex workers (FSW) in sub-Saharan Africa are disproportionately affected by HIV and remain a key target population for efforts to reduce transmission. While HIV prevention tools such as PEP and PrEP are available through outpatient FSW clinics, these services are underused. Emergency medicine is a rapidly expanding field in Kenya and may provide a novel venue for initiating or optimizing HIV prevention services. This study examined the characteristics of FSW from Nairobi, Kenya, who had utilized an emergency department (ED) during the past year to broaden our understanding of the patient factors related to usage. METHODS: An anonymous questionnaire was administered to a convenience sample of 220 Nairobi FSW attending dedicated clinics from June to July 2019. The participants were categorized into those who attended an ED over the past year (acute care users) and clinic-only users (control). A modified version of the WHO Violence Against Women Instrument assessed gender-based violence. Multivariable negative binomial logistic regressions evaluated predictors of health care use among these populations. RESULTS: Of the total 220 women (median [IQR] age 32 [27-39]), 101 and 116 were acute care and control populations, respectively. Acute care users had 12.7 ± 8.5 healthcare visits over a 12-month period, and the control population had 9.1 ± 7.0 (p < 0.05). ED attendance did not improve the PrEP usage, with 48.5%, and 51% of acute care and clinic users indicated appropriate PrEP use. Patient factors that correlated with health care utilization among acute care users included client sexual violence (OR 2.2 [1.64-2.94], p < 0.01), PrEP use (OR 1.54 (1.25-1.91), < 0.01), and client HIV status (OR 1.35 (1.02-1.69), p < 0.01). CONCLUSIONS: Many FSW at high risk for HIV were not accessing HIV prevention tools despite attending a dedicated FSW clinic offering such services. FSW who had attended an ED over the past year had a higher prevalence of HIV risk factors, demonstrating that emergency departments may be important acute intervention venues to prevent HIV transmission in this population. These results can guide policy design, health care provider training, and facility preparedness to support strategies aimed at improving HIV prevention strategies for FSW in Kenyan ED's.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".