Predicting STI Diagnoses Amongst MSM and Young People Attending Sexual Health Clinics in England: Triage Algorithm Development and Validation Using Routine Clinical Data

Partnership types and coital frequency as predictors of gonorrhea and chlamydia among young MSM and young transgender women

BACKGROUND

Sexually transmitted infections pose a major public health challenge in the United States and this burden is especially acute in subpopulations like young men who have sex with men (YMSM) and young transgender women (YTW). Yet, the direct behavioral antecedents of these infections are not well understood making it difficult to identify the cause of recent increases in incidence. This study examines how variations in partnership rates and the number of condomless sex acts are associated with STI infections among YMSM-YTW.

METHOD

This study leveraged 3 years of data from a large longitudinal cohort of YMSM-YTW. A series of generalized linear mixed models examined the association between the number of condomless anal sex acts, number of one-time partners, number of casual partners, and number of main partners and chlamydia, gonorrhea, or any STI.

RESULTS

Results indicated the number of casual partners was associated with gonorrhea [aOR = 1.17 (95% CI: 1.08, 1.26)], chlamydia [aOR = 1.12 (95% CI: 1.05, 1.20)], and any STI [aOR = 1.14 (95% CI: 1.08, 1.21)] while the number of one-time partners was only associated with gonorrhea [aOR = 1.13 (95% CI: 1.02, 1.26)]. The number of condomless anal sex acts was not associated with any outcome.

CONCLUSION

These findings suggest the number of casual partners is a consistent predictor of STI infection among YMSM-YTW. This may reflect the quick saturation of risk within partnerships making the number of partners, rather than the number of acts, the more relevant factor for STI risk.

Computational methods applied to syphilis: where are we, and where are we going?

Syphilis is an infectious disease that can be diagnosed and treated cheaply. Despite being a curable condition, the syphilis rate is increasing worldwide. In this sense, computational methods can analyze data and assist managers in formulating new public policies for preventing and controlling sexually transmitted infections (STIs). Computational techniques can integrate knowledge from experiences and, through an inference mechanism, apply conditions to a database that seeks to explain data behavior. This systematic review analyzed studies that use computational methods to establish or improve syphilis-related aspects. Our review shows the usefulness of computational tools to promote the overall understanding of syphilis, a global problem, to guide public policy and practice, to target better public health interventions such as surveillance and prevention, health service delivery, and the optimal use of diagnostic tools. The review was conducted according to PRISMA 2020 Statement and used several quality criteria to include studies. The publications chosen to compose this review were gathered from Science Direct, Web of Science, Springer, Scopus, ACM Digital Library, and PubMed databases. Then, studies published between 2015 and 2022 were selected. The review identified 1,991 studies. After applying inclusion, exclusion, and study quality assessment criteria, 26 primary studies were included in the final analysis. The results show different computational approaches, including countless Machine Learning algorithmic models, and three sub-areas of application in the context of syphilis: surveillance (61.54%), diagnosis (34.62%), and health policy evaluation (3.85%). These computational approaches are promising and capable of being tools to support syphilis control and surveillance actions.

Ensuring equitable, inclusive and meaningful gender identity- and sexual orientation-related data collection in the healthcare sector: insights from a critical, pragmatic systematic review of the literature

In several countries, no gender identity- and sexual orientation-related data is routinely collected, if not for specific health or administrative/social purposes. Implementing and ensuring equitable and inclusive socio-demographic data collection is of paramount importance, given that the LGBTI community suffers from a disproportionate burden in terms of both communicable and non-communicable diseases. To the best of the authors' knowledge, there exists no systematic review addressing the methods that can be implemented in capturing gender identity- and sexual orientation-related data in the healthcare sector. A systematic literature review was conducted for filling in this gap of knowledge. Twenty-three articles were retained and analysed: two focussed on self-reported data, two on structured/semi-structured data, seven on text-mining, natural language processing, and other emerging artificial intelligence-based techniques, two on challenges in capturing sexual and gender-diverse populations, eight on the willingness to disclose gender identity and sexual orientation, and, finally, two on integrating structured and unstructured data. Our systematic literature review found that, despite the importance of collecting gender identity- and sexual orientation-related data and its increasing societal acceptance from the LGBTI community, several issues have to be addressed yet. Transgender, non-binary identities, and also intersex individuals remain often invisible and marginalized. In the last decades, there has been an increasing adoption of structured data. However, exploiting unstructured data seems to overperform in identifying LGBTI members, especially integrating structured and unstructured data. Self-declared/self-perceived/self-disclosed definitions, while being respectful of one's perception, may not completely be aligned with sexual behaviours and activities. Incorporating different levels of information (biological, socio-demographic, behavioural, and clinical) would enable overcoming this pitfall. A shift from a rigid/static nomenclature towards a more nuanced, dynamic, 'fuzzy' concept of a 'computable phenotype' has been proposed in the literature to capture the complexity of sexual identities and trajectories. On the other hand, excessive fragmentation has to be avoided considering that: (i) a full list of options including all gender identities and sexual orientations will never be available; (ii) these options should be easily understood by the general population, and (iii) these options should be consistent in such a way that can be compared among various studies and surveys. Only in this way, data collection can be clinically meaningful: that is to say, to impact clinical outcomes at the individual and population level, and to promote further research in the field.

Performance of a tool to identify different types of self-reported sexual risk among women attending a contraception and sexual health clinic: results of a cross-sectional survey

INTRODUCTION

A clinical prediction rule (CPR) using psychosocial questions was previously derived to target sexual healthcare in general practice by identifying women at risk of unintended pregnancy (UIP) and sexually transmitted infections (STIs). This psychosocial CPR may help target resources within contraception and sexual health (CASH) services. This study investigated how well it predicted recent self-reported risk of UIP and STI acquisition among women attending a CASH clinic.

METHODS

Female patients aged 16-44 years attending a CASH clinic in South-East England were offered a questionnaire on arrival. This comprised psychosocial questions, and others addressing three sexual risks: (1) two or more male sexual partners in the last year (2+P), (2) risk of STI acquisition through most recent partner and (3) risk of UIP in the last 6 months. A CPR score was calculated for each participant and cross-tabulated against self-report of each sexual risk to estimate CPR sensitivity and specificity.

RESULTS

The psychosocial questions predicting 2+P had sensitivity 83.2% (95% CI 79.3% to 86.5%) and specificity 56.1% (95% CI 51.3%-60.6%). Those predicting combined 2+P and/or risk of STI acquisition through most recent partner had a sensitivity of 89.1% (95% CI 85.7%-91.8%) and specificity of 43.7% (95% CI 39.0%-48.5%). Questions predicting risk of UIP in the last 6 months had a sensitivity of 82.5% (95% CI 78.6%-86.0%) and specificity of 48.3% (95% CI 43.4%-53.1%).

CONCLUSIONS

The CPR demonstrated good sensitivity but low specificity, so may be suited to triaging or stratifying which interventions to offer CASH patients and by which mode (eg, online vs face-to-face). Further investigation of causal links between psychosocial factors and sexual risk is warranted to support development of psychosocial interventions for this patient group.

Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches

OBJECTIVES

We aimed to develop machine learning models and evaluate their performance in predicting HIV and sexually transmitted infections (STIs) diagnosis based on a cohort of Australian men who have sex with men (MSM).

METHODS

We collected clinical records of 21,273 Australian MSM during 2011-2017. We compared accuracies for predicting HIV and STIs (syphilis, gonorrhoea, chlamydia) diagnosis using four machine learning approaches against a multivariable logistic regression (MLR) model.

RESULTS

Machine learning approaches consistently outperformed MLR. Gradient boosting machine (GBM) achieved the highest area under the receiver operator characteristic curve for HIV (76.3%) and STIs (syphilis, 85.8%; gonorrhoea, 75.5%; chlamydia, 68.0%), followed by extreme gradient boosting (71.1%, 82.2%, 70.3%, 66.4%), random forest (72.0%, 81.9%, 67.2%, 64.3%), deep learning (75.8%, 81.0%, 67.5%, 65.4%) and MLR (69.8%, 80.1%, 67.2%, 63.2%). GBM models demonstrated the ten greatest predictors collectively explained 62.7-73.6% of variations in predicting HIV/STIs. STIs symptoms, past syphilis infection, age, time living in Australia, frequency of condom use with casual male sexual partners during receptive anal sex and the number of casual male sexual partners in the past 12 months were most commonly identified predictors.

CONCLUSIONS

Machine learning approaches are advantageous over multivariable logistic regression models in predicting HIV/STIs diagnosis.

Healthcare provider and service user perspectives on STI risk reduction interventions for young people and MSM in the UK

OBJECTIVE

Behavioural interventions have been shown to reduce sexual behaviours associated with increased risk of sexually transmitted infections in young people (<25 years) and men who have sex with men (MSM) internationally, but evidence from England is limited. We aimed to explore service provider and user experiences and perspectives on behavioural interventions to reduce sexual behaviour risks, and the use of automated methods to triage individuals to these services.

METHODS

We conducted a sequential mixed methods study with sexual health service providers and users in 2015/2016. Qualitative interviews with providers and service users (heterosexual young people and MSM) in London and Brighton allowed us to explore a range of experiences and expectations. A subsequent national web-survey of service providers measured the feasibility of delivery within existing resources and preferences for intervention attributes.

RESULTS

We conducted 35 service user (15 heterosexual young people; 20 MSM) and 26 provider interviews and had 100 web-survey responses. We found considerable heterogeneity in prevention services offered. Service users and providers were broadly supportive of tailoring interventions offered, but service users raised concerns about automated, data-driven triage, particularly around equity and fairness of service delivery. Digital technologies, including social media or apps, were appealing to providers, being less resource intensive. However, one-to-one talking interventions remained popular with both service users and providers, being familiar, trustworthy and personal. Key tensions between desirability of interventions and availability of resources to deliver them were acknowledged/recognised by providers and users.

CONCLUSION

Overall, behavioural interventions to reduce sexual behaviour risks were viewed favourably by service providers and users, with key considerations including: privacy, personalisation and convenience. However, introducing desirable targeted interventions within heterogeneous sexual health settings will require resources to adapt interventions and research to fully understand the barriers and facilitators to use within routine services.

Sexual risk reduction interventions for patients attending sexual health clinics: a mixed-methods feasibility study

BACKGROUND

Sexually transmitted infections (STIs) continue to represent a major public health challenge. There is evidence that behavioural interventions to reduce risky sexual behaviours can reduce STI rates in patients attending sexual health (SH) services. However, it is not known if these interventions are effective when implemented at scale in SH settings in England.

OBJECTIVES

The study (Santé) had two main objectives - (1) to develop and pilot a package of evidence-based sexual risk reduction interventions that can be delivered through SH services and (2) to assess the feasibility of conducting a randomised controlled trial (RCT) to determine effectiveness against usual care.

DESIGN

The project was a multistage, mixed-methods study, with developmental and pilot RCT phases. Preparatory work included a systematic review, an analysis of national surveillance data, the development of a triage algorithm, and interviews and surveys with SH staff and patients to identify, select and adapt interventions. A pilot cluster RCT was planned for eight SH clinics; the intervention would be offered in four clinics, with qualitative and process evaluation to assess feasibility and acceptability. Four clinics acted as controls; in all clinics, participants would be consented to a 6-week follow-up STI screen.

SETTING

SH clinics in England.

PARTICIPANTS

Young people (aged 16-25 years), and men who have sex with men.

INTERVENTION

A three-part intervention package - (1) a triage tool to score patients as being at high or low risk of STI using routine data, (2) a study-designed web page with tailored SH information for all patients, regardless of risk and (3) a brief one-to-one session based on motivational interviewing for high-risk patients.

MAIN OUTCOME MEASURES

The three outcomes were (1) the acceptability of the intervention to patients and SH providers, (2) the feasibility of delivering the interventions within existing resources and (3) the feasibility of obtaining follow-up data on STI diagnoses (primary outcome in a full trial).

RESULTS

We identified 33 relevant trials from the systematic review, including videos, peer support, digital and brief one-to-one sessions. Patients and SH providers showed preferences for one-to-one and digital interventions, and providers indicated that these intervention types could feasibly be implemented in their settings. There were no appropriate digital interventions that could be adapted in time for the pilot; therefore, we created a placeholder for the purposes of the pilot. The intervention package was piloted in two SH settings, rather than the planned four. Several barriers were found to intervention implementation, including a lack of trained staff time and clinic space. The intervention package was theoretically acceptable, but we observed poor engagement. We recruited patients from six clinics for the follow-up, rather than eight. The completion rate for follow-up was lower than anticipated (16% vs. 46%).

LIMITATIONS

Fewer clinics were included in the pilot than planned, limiting the ability to make strong conclusions on the feasibility of the RCT.

CONCLUSION

We were unable to conclude whether or not a definitive RCT would be feasible because of challenges in implementation of a pilot, but have laid the groundwork for future research in the area.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN16738765.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 23, No. 12. See the NIHR Journals Library website for further project information.