Immunity to hepatitis A among men who have sex with men attending a large sexual health clinic in Melbourne, Australia, 2012–2018

Vodstrcil, Lenka A; Fairley, Christopher K; Williamson, Deborah A.; Bradshaw, Catriona S; Chen, Marcus Y; Chow, Eric P F

doi:10.1136/sextrans-2019-054327

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…This is similar to a recent study from Victoria, Australia, where they found that in their single tertiary sexual health service the susceptibility was 56%. 9 These estimates of susceptibility are worryingly above the predicted level at which outbreaks can occur. As rates of STI increase, more effort is needed to provide adequate herd immunity in MSM for hepatitis A.…”

Section: Discussionmentioning

confidence: 98%

Hepatitis A susceptibility in newly attending men who have sex with men to an urban sexual health centre

et al. 2020

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Within the UK, the majority of hepatitis A occurs in high risk groups such as men who have sex with men (MSM). It has been estimated that 70% of MSM need immunity to provide adequate herd immunity. We aimed to estimate the proportion of hepatitis A susceptibility in MSM throughout a 10-year period (2010–2019), and explore associated demographic factors. Using our Electronic Patient Record system, we extracted anonymous clinical data between for MSM at their first attendance; including hepatitis A IgG result, age, country of birth and diagnosis of an STI. Overall, 1401/6884(20%) were tested for hepatitis A IgG at their first attendance, with 626/1401 (45%, 95% CI = 42%–47%) showing susceptibility. Testing rates increased between 2010–2019 (OR = 67.79, 95%CI = 39.09–117.60, p = <0.0001); however, susceptibility remained similar (OR = 0.98, 95%CI = 0.33–2.89, p = 0.98). MSM aged 35 and under had significantly higher susceptibility vs MSM aged over 35 (OR 3.4176, 95%CI = 2.71–4.31, p = <0.0001). UK-born had significantly higher susceptibility vs non-UK born (OR 1.5, 95%CI = 1.2147–1.8618, p = 0.0002). Susceptibility of hepatitis A in MSM may be higher than necessary to control future outbreaks. It is important that effective targeting of MSM, particularly young MSM, occur at all levels of healthcare and not solely rely on opportunistic presentation at a sexual health clinic.

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Section: Discussionmentioning

confidence: 98%

Hepatitis A susceptibility in newly attending men who have sex with men to an urban sexual health centre

et al. 2020

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“…Melbourne Sexual Health Centre (MSHC) is Victoria’s largest public sexual health clinic in Australia and offers free HIV/STI testing and management 34 . Men who reported having sex with men in the past 12 months were defined as MSM.…”

Section: Methodsmentioning

confidence: 99%

Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages

Fairley

Chow

et al. 2022

Sci Rep

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Timely and regular testing for HIV and sexually transmitted infections (STI) is important for controlling HIV and STI (HIV/STI) among men who have sex with men (MSM). We established multiple machine learning models (e.g., logistic regression, lasso regression, ridge regression, elastic net regression, support vector machine, k-nearest neighbour, naïve bayes, random forest, gradient boosting machine, XGBoost, and multi-layer perceptron) to predict timely (i.e., within 30 days) clinic attendance and HIV/STI testing uptake after receiving a reminder message via short message service (SMS) or email). Our study used 3044 clinic consultations among MSM within 12 months after receiving an email or SMS reminder at the Melbourne Sexual Health Centre between April 11, 2019, and April 30, 2020. About 29.5% [899/3044] were timely clinic attendance post reminder messages, and 84.6% [761/899] had HIV/STI testing. The XGBoost model performed best in predicting timely clinic attendance [mean [SD] AUC 62.8% (3.2%); F1 score 70.8% (1.2%)]. The elastic net regression model performed best in predicting HIV/STI testing within 30 days [AUC 82.7% (6.3%); F1 score 85.3% (1.8%)]. The machine learning approach is helpful in predicting timely clinic attendance and HIV/STI re-testing. Our predictive models could be incorporated into clinic websites to inform sexual health care or follow-up service.

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“…The Melbourne Sexual Health Centre (MSHC) is the largest public sexual health center in Victoria, Australia and offers free HIV and STI testing and management [32]. At the MSHC, individuals' demographic information and sexual practices are recorded using a computer-assisted self-interview (CASI) at each visit, at least 3 months apart [33].…”

Section: Study Populationmentioning

confidence: 99%

Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study

Xu¹,

Yu²,

Ge³

et al. 2022

J Med Internet Res

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Background HIV and sexually transmitted infections (STIs) are major global public health concerns. Over 1 million curable STIs occur every day among people aged 15 years to 49 years worldwide. Insufficient testing or screening substantially impedes the elimination of HIV and STI transmission. Objective The aim of our study was to develop an HIV and STI risk prediction tool using machine learning algorithms. Methods We used clinic consultations that tested for HIV and STIs at the Melbourne Sexual Health Centre between March 2, 2015, and December 31, 2018, as the development data set (training and testing data set). We also used 2 external validation data sets, including data from 2019 as external “validation data 1” and data from January 2020 and January 2021 as external “validation data 2.” We developed 34 machine learning models to assess the risk of acquiring HIV, syphilis, gonorrhea, and chlamydia. We created an online tool to generate an individual’s risk of HIV or an STI. Results The important predictors for HIV and STI risk were gender, age, men who reported having sex with men, number of casual sexual partners, and condom use. Our machine learning–based risk prediction tool, named MySTIRisk, performed at an acceptable or excellent level on testing data sets (area under the curve [AUC] for HIV=0.78; AUC for syphilis=0.84; AUC for gonorrhea=0.78; AUC for chlamydia=0.70) and had stable performance on both external validation data from 2019 (AUC for HIV=0.79; AUC for syphilis=0.85; AUC for gonorrhea=0.81; AUC for chlamydia=0.69) and data from 2020-2021 (AUC for HIV=0.71; AUC for syphilis=0.84; AUC for gonorrhea=0.79; AUC for chlamydia=0.69). Conclusions Our web-based risk prediction tool could accurately predict the risk of HIV and STIs for clinic attendees using simple self-reported questions. MySTIRisk could serve as an HIV and STI screening tool on clinic websites or digital health platforms to encourage individuals at risk of HIV or an STI to be tested or start HIV pre-exposure prophylaxis. The public can use this tool to assess their risk and then decide if they would attend a clinic for testing. Clinicians or public health workers can use this tool to identify high-risk individuals for further interventions.

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Immunity to hepatitis A among men who have sex with men attending a large sexual health clinic in Melbourne, Australia, 2012–2018

Cited by 4 publications

References 22 publications

Hepatitis A susceptibility in newly attending men who have sex with men to an urban sexual health centre

Hepatitis A susceptibility in newly attending men who have sex with men to an urban sexual health centre

Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages

Web-Based Risk Prediction Tool for an Individual's Risk of HIV and Sexually Transmitted Infections Using Machine Learning Algorithms: Development and External Validation Study

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