Time to healthcare seeking following the onset of symptoms among men and women attending a sexual health clinic in Melbourne, Australia

Farquharson, Rebecca; Fairley, Christopher K; Abraham, Esha; Bradshaw, Catriona S; Plummer, Erica L; Ong, Jason J.; Vodstrcil, Lenka A; Chen, Marcus Y; Phillips, Tiffany R; Chow, Eric P F

doi:10.3389/fmed.2022.915399

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…This result is not surprising, as past studies have shown that MSW are less likely to seek health care compared with women. The reasons, however, are complex and may include a culture that promotes a unrealistic view of masculinity and self-reliance, [14][15][16][17][18][19] but also women may be more likely to take care of their sexual health because of other reasons such as pregnancies, regular cervical screening, and contraception 20 which they may more proactively attempt to gain a better understanding of sexual health issues. As such, this may result in women being more likely to consent to SHAVE to have a record documenting their sexual health.…”

Section: Discussionmentioning

confidence: 99%

Factors associated with consenting to receive an automated email summary following a sexual health clinic visit

Huang¹,

Fairley²,

Lee

et al. 2023

Sexual Trans Dis

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Background Electronic health records allow for inexpensive communication with patients. In March 2021, the Melbourne Sexual Health Centre (MSHC) implemented an automated email summary (“Sexual Health Automated Visit Email” (SHAVE)) of a client’s visit. This study evaluates the proportion of attendees at a sexual health service who opted in or out of SHAVE. Methods This study was conducted at MSHC in Australia between March 2021 and June 2022. Univariable and multivariable logistic regression analyses were used to examine the client characteristics associated with consenting to SHAVE. Results There were 18,528 clients (men: 12,700, women: 5,828) included in the final analysis and 55.2% (n = 10,233) consented to receiving SHAVE. Comparing to those who did not have a new STI diagnosis, clients with a new diagnosis of an STI, but not HIV, had lower odds of consenting to receiving SHAVE (chlamydia [aOR: 0.64; 95% CI: 0.57-0.72]; gonorrhoea [aOR: 0.71; 95% CI: 0.62-0.82]; syphilis [aOR: 0.75; 95% CI: 0.59-0.96]). Men had lower odds of consenting when compared to women (MSW [aOR: 0.77; 95% CI: 0.71-0.84] and MSM [aOR: 0.68; 95% CI: 0.62-0.75]). Comparing to those born in Australia or Oceania, clients born in Europe had lower odds of consenting (aOR: 0.81; 95% CI: 0.70-0.94) , whilst those born in Latin America or Caribbean had higher odds of consenting (aOR: 1.25; 95% CI: 1.04-1.51). Conclusion Email summaries may serve as a valuable strategy to improve health communication and record keeping for clients. Understanding the client characteristics associated with consenting SHAVE will allow for the implementation of strategies to better communicate with clients.

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Section: Discussionmentioning

confidence: 99%

Factors associated with consenting to receive an automated email summary following a sexual health clinic visit

Huang¹,

Fairley²,

Lee

et al. 2023

Sexual Trans Dis

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“…Other studies have found that delayed health care-seeking behavior, defined as symptom duration greater than seven days following the onset of STI-associated symptoms, is common (28-82% of cases). 28 Such a delay can be due to multiple factors, like the asymptomatic nature of some STIs, lack of knowledge, stigma, easy accessibility to pharmacies, self-medication, and superstition beliefs in our part of the world.…”

Section: Discussionmentioning

confidence: 99%

Awareness and Pattern of Sexually Transmitted Diseases: A Hospital Based Study

Thapa,

Rana

2023

Nepal J Dermatol Venereol & Leprol

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Introduction: Sexually transmitted diseases (STDs) are infections that occurs due to sexual contact. There are various pathogens contributing for STDs. There is a rising trend of STDs globally, but actual reported cases are low due to low screening and reporting. Materials and Methods: This study was a prospective hospital-based study. Patients were included in the study according to the inclusion criteria. All data were recorded in a preset proforma. Routine laboratory investigations along with STD workup was done in patients. Results: A total of 62 patients were included in the study according to the inclusion criteria. The age ranged from 17-52years. There were 63% males and 37% females. The majority of patients were employed (60%). Regarding awareness about symptoms of STDs, the patients responded as Infertility in 22%, ulcers in 17%, followed by swelling, genital discharge, dyspareunia, and 24% gave multiple response. Regarding attitude about awareness of prevention of STDs, maximum patients (43%) responded that, through condom use, prevention of the STDs was possible. Out of the total patients 58% Spouse were the commonest partner in 27%. The patients presented with symptoms like urethral discharge in 26%, followed by genital ulcers, genital growth, mixed infections and vaginal discharge. The commonest diagnosis was Syphilis followed by Gonococcal urethritis, non-gonococcal urethritis, warts, Genital herpes, vulvovaginal candidiasis, molluscum contagiosum, and mixed infections. Conclusion: Syphilis is the most prevalent STD, according to the current study. Due to the dearth of knowledge regarding STDs, the STD patients, who visit our facility may only be the tip of the iceberg. It is necessary to spread awareness about STDs among individuals through social media and educational institutions, starting at the local level and moving up to the national level.

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Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms

Soe,

Towns,

Latt

et al. 2024

BMC Infect Dis

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Background A significant proportion of individuals with symptoms of sexually transmitted infection (STI) delay or avoid seeking healthcare, and digital diagnostic tools may prompt them to seek healthcare earlier. Unfortunately, none of the currently available tools fully mimic clinical assessment or cover a wide range of STIs. Methods We prospectively invited attendees presenting with STI-related symptoms at Melbourne Sexual Health Centre to answer gender-specific questionnaires covering the symptoms of 12 common STIs using a computer-assisted self-interviewing system between 2015 and 2018. Then, we developed an online symptom checker (iSpySTI.org) using Bayesian networks. In this study, various machine learning algorithms were trained and evaluated for their ability to predict these STI and anogenital conditions. We used the Z-test to compare their average area under the ROC curve (AUC) scores with the Bayesian networks for diagnostic accuracy. Results The study population included 6,162 men (median age 30, IQR: 26–38; approximately 40% of whom had sex with men in the past 12 months) and 4,358 women (median age 27, IQR: 24–31). Non-gonococcal urethritis (NGU) (23.6%, 1447/6121), genital warts (11.7%, 718/6121) and balanitis (8.9%, 546/6121) were the most common conditions in men. Candidiasis (16.6%, 722/4538) and bacterial vaginosis (16.2%, 707/4538) were the most common conditions in women. During evaluation with unseen datasets, machine learning models performed well for most male conditions, with the AUC ranging from 0.81 to 0.95, except for urinary tract infections (UTI) (AUC 0.72). Similarly, the models achieved AUCs ranging from 0.75 to 0.95 for female conditions, except for cervicitis (AUC 0.58). Urethral discharge and other urinary symptoms were important features for predicting urethral gonorrhoea, NGU and UTIs. Similarly, participants selected skin images that were similar to their own lesions, and the location of the anogenital skin lesions were also strong predictors. The vaginal discharge (odour, colour) and itchiness were important predictors for bacterial vaginosis and candidiasis. The performance of the machine learning models was significantly better than Bayesian models for male balanitis, molluscum contagiosum and genital warts (P < 0.05) but was similar for the other conditions. Conclusions Both machine learning and Bayesian models could predict correct diagnoses with reasonable accuracy using prospectively collected data for 12 STIs and other common anogenital conditions. Further work should expand the number of anogenital conditions and seek ways to improve the accuracy, potentially using patient collected images to supplement questionnaire data.

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Time to healthcare seeking following the onset of symptoms among men and women attending a sexual health clinic in Melbourne, Australia

Cited by 5 publications

References 31 publications

Factors associated with consenting to receive an automated email summary following a sexual health clinic visit

Factors associated with consenting to receive an automated email summary following a sexual health clinic visit

Awareness and Pattern of Sexually Transmitted Diseases: A Hospital Based Study

Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms

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