Prevalence and correlates of common mental health problems and recent suicidal thoughts and behaviours among female sex workers in Nairobi, Kenya
Bibliographic record
Abstract
BACKGROUND: Adverse childhood experiences (ACEs), poverty, violence and harmful alcohol/substance use are associated with poor mental health outcomes, but few studies have examined these risks among Female Sex Workers (FSWs). We examine the prevalence and correlates of common mental health problems including suicidal thoughts and behaviours among FSWs in Kenya. METHODS: Maisha Fiti is a longitudinal study among FSWs randomly selected from Sex Worker Outreach Programme (SWOP) clinics across Nairobi. Baseline behavioural-biological survey (n = 1003) data were collected June-December 2019. Mental health problems were assessed using the Patient Health Questionnaire (PHQ-9) for depression, the Generalised Anxiety Disorder tool (GAD-7) for anxiety, the Harvard Trauma Questionnaire (HTQ-17) for Post-Traumatic Stress Disorder (PTSD) and a two-item tool to measure recent suicidal thoughts/behaviours. Other measurement tools included the WHO Adverse Childhood Experiences (ACE) score, WHO Violence Against Women questionnaire, and the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST). Descriptive statistics and multivariable logistic regression were conducted using a hierarchical modelling approach. RESULTS: Of 1039 eligible FSWs, 1003 FSWs participated in the study (response rate: 96%) with mean age 33.7 years. The prevalence of moderate/severe depression was 23.2%, moderate/severe anxiety 11.0%, PTSD 14.0% and recent suicidal thoughts/behaviours 10.2% (2.6% suicide attempt, 10.0% suicidal thoughts). Depression, anxiety, PTSD and recent suicidal thoughts/behaviours were all independently associated with higher ACE scores, recent hunger (missed a meal in last week due to financial difficulties), recent sexual/physical violence and increased harmful alcohol/substance. PTSD was additionally associated with increased chlamydia prevalence and recent suicidal thoughts/behaviours with low education and low socio-economic status. Mental health problems were less prevalent among women reporting social support. CONCLUSIONS: The high burden of mental health problems indicates a need for accessible services tailored for FSWs alongside structural interventions addressing poverty, harmful alcohol/substance use and violence. Given the high rates of ACEs, early childhood and family interventions should be considered to prevent poor mental health outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".