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

Do Australian sexual health clinics have the capacity to meet demand? A mixed methods survey of directors of sexual health clinics in Australia

Background The study describes the capacity of publicly funded sexual health clinics in Australia and explores the challenges they face. Methods We sent a survey to the directors of publicly funded sexual health clinics across Australia between January and March 2024. The survey asked about how their clinics were managing the current clinical demand. Results Twenty-seven of 35 directors of sexual health clinics responded. These 27 clinics offered a median of 35 (IQR: 20-60) bookings each day, but only a median of 10 (IQR: 2-15) walk-in consultations for symptomatic patients. The average proportion of days that clinics were able to see all patients who presented with symptoms was 70.1% (95% CI 55.4, 84.9) during summer versus 75.4% (95% CI 62.2, 88.5) during winter. For patients without symptoms, the corresponding proportions were 53.3% (95% CI 37.9, 68.8) during summer versus 57.7% (95% CI 41.7, 73.7) during winter. If these percentages were adjusted for the number of consultations that the clinic provided, then the corresponding numbers for symptomatic individuals was 51.0% for summer and 65.2% for winter, and for asymptomatic individuals it was 48.1% and 49.8%, respectively. The catchment population of the clinics for each consultation they provided ranged from as low as 3696 to a maximum of 5 million (median 521,077). Conclusions The high proportion of days on which sexual health clinics were not able to see all patients is likely to delay testing and treatment of individuals at high risk of STIs and impede effective STI control.

Impact of Result Displays in an Anogenital Symptom Checker App on Health-seeking Behaviours: A Cross-sectional, Vignette-based Study

Introduction

The Melbourne Sexual Health Centre developed an artificial intelligence-powered app called AiSTI to help the public identify potential sexually transmitted infection (STI)-related anogenital lesions. This research sought to explore how individuals respond to the application's result displays and recommendations and how this might affect their health-seeking behavior.

Methods

From April to July 2024, participants completed an anonymous online survey, responding to hypothetical scenarios related to STI and non-STI conditions before and after viewing the randomized AiSTI application's result displays. They were asked how soon they would seek healthcare and how concerned they would be about each scenario. We reported descriptive statistics and used logistic regression analyses to explore associations between result displays and health-seeking behaviors.

Results

Our study included 512 participants (median age, 32 years; interquartile range: 25-40.5). Approximately 65% (n = 330) were assigned male at birth. For the STI scenario, intention to seek care within 24 hours increased from 75% to >90% after viewing probable STI diagnosis displays (P < .001). For the non-STI scenario, 46% initially intended to seek urgent care, but this was significantly reduced to below 25% after viewing non-STI result displays (P < .001). All result displays (concise text, full text, and meter) significantly increased the likelihood of seeking care within 24 hours for the STI scenario (adjusted odds ratios: 3.6-4.0, P < .001) and within a week for the non-STI scenario (adjusted odds ratios: 2.4-2.5, P < .001).

Conclusions

Our study found that digital health interventions with effective result displays could encourage urgent care-seeking for STI cases.

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

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.

User Views on Online Sexual Health Symptom Checker Tool: Qualitative Research

BACKGROUND

Delayed diagnosis and treatment of sexually transmitted infections (STIs) contributes to poorer health outcomes and onward transmission to sexual partners. Access to best-practice sexual health care may be limited by barriers such as cost, distance to care providers, sexual stigma, and trust in health care providers. Online assessments of risk offer a novel means of supporting access to evidence-based sexual health information, testing, and treatment by providing more individualized sexual health information based on user inputs.

OBJECTIVE

This developmental evaluation aims to find potential users' views and experiences in relation to an online assessment of risk, called iSpySTI (Melbourne Sexual Health Center), including the likely impacts of use.

METHODS

Individuals presenting with urogenital symptoms to a specialist sexual health clinic were given the opportunity to trial a web-based, Bayesian-powered tool that provides a list of 2 to 4 potential causes of their symptoms based on inputs of known STI risk factors and symptoms. Those who tried the tool were invited to participate in a once-off, semistructured research interview. Descriptive, action, and emotion coding informed the comparative analysis of individual cases.

RESULTS

Findings from interviews with 14 people who had used the iSpySTI tool support the superiority of the online assessment of STI risk compared to existing sources of sexual health information (eg, internet search engines) in providing trusted and probabilistic information to users. Additionally, potential users reported benefits to their emotional well-being in the intervening period between noticing symptoms and being able to access care. Differences in current and imagined urgency of health care seeking and emotional impacts were found based on clinical diagnosis (eg, non-STI, curable and incurable but treatable STIs) and whether participants were born in Australia or elsewhere.

CONCLUSIONS

Online assessments of risk provide users experiencing urogenital symptoms with more individualized and evidence-based health information that can improve their health care-seeking and provide reassurance in the period before they can access care.

Exploring Factors That Prolong the Diagnosis of Myasthenia Gravis

Background and Objectives

Myasthenia gravis (MG) is a condition with significant phenotypic variability, posing a diagnostic challenge to many clinicians worldwide. Prolonged diagnosis can lead to reduced remission rates and morbidity. This study aimed to identify factors leading to a longer time to diagnosis in MG that could be addressed in future to optimize diagnosis time.

Methods

One hundred and ten patients from 3 institutions in Melbourne, Australia, were included in this retrospective cohort study. Demographic and clinical data were collected for these patients over the first 5 years from diagnosis and at 10 years. Nonparametric statistical analysis was used to identify factors contributing to a longer diagnosis time.

Results

The median time for MG diagnosis was 102 (345) days. 90% of patients were diagnosed before 1 year. Female patients took longer than male patients to be diagnosed (p = 0.013). The time taken for first presentation after symptom onset contributed most to diagnosis time (median 17 [141] days), with female patients and not working as contributory factors. Neurology referral took longer if patients had diplopia (p = 0.022), respiratory (p = 0.026) symptoms, or saw an ophthalmologist first (p < 0.001). Outpatient management compared with inpatient was associated with a longer time to be seen by a neurologist from referral (p < 0.001), for the first diagnostic result to return (p = 0.001), and for the result to be reviewed (p < 0.001). Ocular MG had a median greater time to neurologist review than generalized MG (median 5 [25] days vs 1 [13] days, p = 0.035). Electrophysiology tests took longer for outpatients than inpatients (median 21 [35] days vs 2 [8] days, p < 0.001). Outpatients were also started on treatment later than inpatients (p < 0.001). There was no association of MG severity, ethnicity, age, medical and ocular comorbidities, and public or private health service on diagnosis time. There was also no impact of time to diagnosis on Myasthenia Gravis Foundation of America outcomes, number of follow-ups or hospitalizations, or prevalence of treatments used. This study is limited by low patient numbers and its retrospective nature.

Discussion

This study identified several factors that can contribute to a prolonged diagnosis time of MG. Patient and clinician education about MG and outpatient diagnostic efficiency needs emphasis. Further studies are also needed to explore the delayed presentation time of women and nonworking patients in MG.