Mpox stigma in the UK and implications for future outbreak control: a cross-sectional mixed methods study
Bibliographic record
Abstract
Abstract Background Stigma emerged as a prominent public health challenge in the global mpox outbreak that began in 2022, impeding outbreak control efforts and the well-being of affected individuals. Addressing stigma is important for improving infection prevention and control. Despite frequent mention in public and policy discourse, robust assessment of mpox stigma is lacking. This study investigated the causes, manifestations, and impacts of mpox-related stigma in the UK, focusing on anticipated stigma among directly and indirectly affected communities. Methods We conducted an online, mixed-methods cross-sectional survey to assess mpox stigma. We developed and content validated a new tool, the Stigma Survey and Community-based Assessment for New and Re-emerging outbreaks (Stigma-SCANR) for this purpose. Through quota sampling, the survey targeted populations most affected by mpox at the time of data collection (March–July 2024), including gay, bisexual, and other men who have sex with men (GBMSM), and healthcare workers. The survey primarily explored anticipated stigma. Respondents with previous mpox diagnoses were asked about personal experiences of stigma. Results Of 479 respondents who initiated the survey, 437 (91%) were included in analyses. In modules related to drivers of stigma, pre-existing prejudices towards associated groups such as GBMSM and migrants were emphasised, alongside fear and misinformation. On average, respondents anticipated higher levels of negative judgement and unwarranted avoidance compared to other forms of social stigma, particularly from casual partners and the public. Among the 13 respondents who reported a previous mpox diagnosis, 11 (85%) had experienced mpox-related stigma. Nearly a quarter of respondents (24%) said they would not, or were unlikely to, tell a recent sexual partner about an mpox diagnosis. Feelings of shame were considered the most common barrier to care-seeking (299 respondents, 68%). Conclusions This analysis of mpox stigma in the UK offers insights for international outbreak response, particularly in countries with similarly affected communities. Lessons learnt may also be transferable to other disease outbreaks. We propose practical recommendations for reducing stigma in future outbreaks, including peer support initiatives, distributing accessible information about safe timelines for returning to socialising and work or school, and co-designing public communications and contact tracing programmes with affected community members.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.046 | 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".