Identifying Risk Factors and Spatial Clustering of HIV Infection Among Female Sex Workers in India
Bibliographic record
Abstract
Background: The human immunodeficiency virus (HIV) epidemic in India is generally considered to be more concentrated, with the focus on high-risk groups including female sex workers (FSWs). The Integrated Biological and Behavioral Surveillance (IBBS), the first nationwide surveillance conducted during 2014-2015, collected many key indicators, including indicators related to HIV/STI transmission. The purpose of this study was to develop an index score for each domain surveyed and to identify focus areas for interventions among FSWs. Methods: The study population consisted of 27,007 FSWs. Forty high-risk related covariates of HIV/STI transmission, demographic characteristics, sexual history, condom practices, knowledge of HIV/STI and biological variables were considered. The original data set was examined using the correlation matrix and was reduced to 15 highly-correlated factors using principal component analysis. The factors were further improved using varimax rotation and the percentage of variation was used as weights to obtain the initial score for each domain, which were then standardized for comparison. Bartlett’s test of sphericity was examined before the factor extraction. Results: Six factors were extracted, which together explained about 73% of the total variation. The factors were: (1) more number of clients; (2) younger FSW and started selling sex at younger age; (3) experiencing condom breakage; (4) having occasional clients and poor HIV/AIDS knowledge; (5) illiteracy; and (6) a longer period of sex work. Six domains with an index score of above 80, from the states of Maharashtra, Rajasthan, Arunachal Pradesh, Uttar Pradesh, and Jharkhand need greater intervention. Conclusion and Implications for Translation: FSWs’ current age, age at commencement of sex work, and the number of clients were the indicators most-associated with HIV infection. Therefore, program and policy interventions should focus on FSWs who are younger than <25 years, who started selling sex at <22 years, and who have >10 clients. Key words: • Female Sex Worker • Kriged Map • Factor Analysis • Principle Component Analysis • HIV • Sexually Transmitted Infections Copyright © 2021 Elangovan et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in this journal, is properly cited.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".