Enhancing surveillance of sexually transmitted infections in England with gender identity and behavioural data: the GUMCAD STI Surveillance System
Bibliographic record
Abstract
Abstract Background There has been an increasing trend in bacterial sexually transmitted infections (STIs) in England since the early 2000s. Since 2008, surveillance of STIs in England has been conducted using the Genitourinary Medicine Clinic Activity Dataset (hereafter referred to as ‘GUMCAD’), a depersonalised dataset of all attendances at all publicly-commissioned sexual health services (SHS). The aim is this article is to describe and evaluate the impact of the piloting and rollout of an enhanced specification of GUMCAD at SHS in England. Methods GUMCAD was enhanced in 2019 to allow SHS to report the gender identity (whether cisgender, transgender, gender diverse) of service users, and selected behavioural information collected during routine sexual history-taking such as the number of recent sex partners (last 3 months). Results Feasibility and acceptability of reporting these new data were confirmed in the pilot stage. 2023 was the first year over which most SHS (93%, 224/241) submitted enhanced GUMCAD data. Of all 4,610,410 consultations at SHS in 2023, gender identity (96% of consultations) and data on whether this varied from the sex registered at birth (88% of consultations) were well reported. Transgender women, transgender men, and gender-diverse individuals (identifying as non-binary or in any other way) respectively comprised 0.4%, 0.4%, and 0.5% of all consultations in 2023. There was less complete reporting of recent sex partners but, where reported, gay, bisexual and other men who have sex with men were more likely to report multiple recent sex partners. Conclusions These enhancements provide novel insights into sexual health need relevant to targeting existing and novel preventative interventions for STIs such as 4CMenB vaccination for gonorrhoea and doxycycline post-exposure prophylaxis (doxyPEP) for syphilis in England. The reporting of these new STI surveillance data also raise new complexities in interpretation, and behavioural data completeness will require further support and development.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".