Sexually Transmitted Infections最新文献

Objectives: Across Canada, in the last decade, incidence rates of sexually transmitted and blood-borne infections (STBBI) have peaked (syphilis) or plateaued (hepatitis C virus (HCV) and HIV). Key populations (gay, bisexual and other men who have sex with men, trans and gender-diverse people, and people who use injection drugs) are at greater risk for these STBBIs, so correctly predicting risk before screening potentially infected individuals is crucial. We developed and validated a diagnostic clinical risk prediction model (CRPM) estimating HIV, HCV and syphilis risk for two key populations in two Canadian provinces.

Methods: We used 20 variables and STBBI test results from a cross-sectional study evaluating multiplexed testing (detection of coinfections) in New Brunswick and Quebec (n=400) to develop our CRPM. We randomly split the data into development (n=300) and validation (n=100) datasets using clinic-stratified sampling. We used Bayesian predictive projection with development data to select ranked STBBI predictors. We obtained the ORs of the highest performing submodel measured as area under the receiver operating curve (AUC), sensitivity and specificity with 89% credible intervals (89% CrI) using validation data. Analyses were performed in R (≥V.4.2.3).

Results: Out of 400 participants, 73 were infected with HIV (n=16), HCV (n=60), and/or syphilis (n=5). An internally validated submodel with two predictors (past drug injection, type of past sexually transmitted infection) displayed the highest AUC (0.79; 89% CrI 0.66 to 0.79), sensitivity (0.85; 89% CrI 0.79 to 0.91) and specificity (0.30; 89% CrI 0.15 to 0.50). The predictor contributing most to STBBI risk was past drug injection (OR=7.62; 89% CrI 4.41 to 13.07).

Conclusions: This Bayesian-based CRPM is the first to identify high-risk individuals for HIV, HCV and syphilis with an overall good performance that minimises case missing. After additional validation, it could serve as a promising novel tool for prescreening key populations and improve Canadian STBBI multiplexed screening strategies.

Objectives: This study aimed to develop robust machine learning (ML)-based and deep learning (DL)-based models capable of detecting mpox cases for surveillance efforts using clinical notes.

Methods: As part of a learning health system initiative, we conducted a retrospective study of clinical encounters at the Columbia University Irving Medical Center in New York City. We included patients with mpox diagnoses confirmed by PCR testing between 15 May 2022 and 15 October 2022 and three matched controls for each case based on patient age, sex, race, ethnicity and visit month. We trained three mpox surveillance models using: (1) logistic regression with L1 regularisation (least absolute shrinkage and selection operator (LASSO)), (2) ClinicalBERT and (3) ClinicalLongformer. We evaluated model performance using precision, recall, F1 score, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) and recall at 80% precision (RP80).

Results: The study included 228 PCR-confirmed mpox cases and 698 controls. LASSO regression outperformed the DL models with a precision, recall and F1 score of 0.93, AUROC of 0.97, AUPRC of 0.93 and RP80 of 0.89. ClinicalBERT achieved a precision of 0.88, recall of 0.89, F1 score of 0.88 and AUROC of 0.93. ClinicalLongformer achieved a precision of 0.87, recall of 0.88, F1 score of 0.87 and AUROC of 0.92. Phrases related to symptoms (eg, lesions and pain) were among the most predictive features in LASSO regression.

Conclusions: ML and DL models based on clinical notes show promise for identifying mpox cases. In this study, LASSO regression outperformed DL models and excelled in minimising false positives. These findings highlight the potential for ML and DL methods to support case surveillance for mpox and other infectious diseases. These methods may also prove helpful for flagging missed or delayed diagnoses as part of continuous quality improvement.

Objectives: Despite parallel global trends of increasing incarceration rates and sexually transmitted infections (STIs) among women, STI epidemiological data for this vulnerable at-risk population are limited. The study objective was to characterise patterns of STI symptoms and explore covariates and drivers of indicating STI symptoms using syndemic theory among a population of incarcerated women in Peru.

Methods: In a cross-sectional study, a sample of 249 incarcerated women responded to a questionnaire on substance use, depression, sexual behaviour, STI symptoms and violence, among other variables, between May and July 2015 in Santa Manica Prison (Lima, Peru). Univariate and bivariate analyses informed a latent profile analysis (LPA) and logistic regression.

Results: Most women (93.5%) were Peruvian; 86.6% had prison sentences <5 years; the median age was 37 years (range 18-70 years); 2.6% were pregnant, 7.2% had children residing with them in prison; most women (78.7%) had a high school degree; >1/3 of the sample had ≥2 STI symptoms. The LPA analysis revealed that 39% of the sample had a 'syndemic' profile (co-occurrence of multiple STI symptoms, experiences of violence and substance use). Approximately 87% of women who were characterised by the syndemic profile were <50 years of age. The 'syndemic' profile was associated with double the prevalence of having multiple STI symptoms (≥2 symptoms: Prevalence Ratio (PR)=1.88 (95% CI 1.18, 2.99); ≥3 symptoms: PR=2.55 (95% CI 1.32, 4.93)).

Conclusions: To address this syndemic, younger incarcerated women presenting with co-occurring STI symptoms (>2) can be clinically screened for diagnosis and treatment and assessed for substance use and risk of violence. Further research in this area may help stem and prevent deleterious health outcomes, including STIs, abuse and substance misuse, that can impact the individual and families.

Background: Few studies have examined the sexual practices of female sex workers (FSWs) outside of their sex work and their association with the detection of bacterial sexually transmitted infections (STIs). This study aimed to investigate the annual trends in the number of non-work sexual partners and condom use among FSWs and their association with STIs.

Methods: We conducted a repeated cross-sectional study of FSWs attending a sexual health clinic in Melbourne for the first time between January 2011 and December 2020. Data on sexual practices with non-work male and female sexual partners, condom use and STI diagnoses among FSWs were extracted.

Results: Of the 3075 FSWs included in the analysis, 70% (n=2167) reported having non-work sexual partners in the past 12 months, 66% (n=2041) reported having non-work male sexual partners, 20% (n=608) reported having non-work female sexual partners and 18% (n=556) reported having both non-work male and female sexual partners. From 2011 to 2020, the proportion of FSWs who had non-work casual male sexual partners increased from 37% (115/315) to 61% (69/113) (p_trend<0.001). Condomless sex with non-work casual male sexual partners increased from 43% (50/115) to 67% (46/69) (p_trend<0.001). The positivity of any STIs increased from 5% (16/312) to 13% (14/110) (p_trend<0.001). Multivariable analyses showed that FSWs who had condomless sex with non-work casual male sexual partners had higher odds of having any STIs (adjusted OR 1.41; 95% CI: 1.01, 1.95) compared with those who consistently used condoms with these sexual partners.

Conclusion: Our study highlights a shift in the sexual practices of FSWs outside of sex work, in line with the trends observed in the general population. These changes may contribute to changes in STI transmission dynamics in this population. Overall, we found that the STI positivity in FSWs is low.

Objectives: Neisseria gonorrhoeae (NG), a bacterium characterised by multidrug resistance, is traditionally treated with ceftriaxone, either as a monotherapy or in conjunction with azithromycin. However, the emergence of strains resistant to these antibiotics presents a considerable challenge to current therapeutic approaches, which necessitates the exploration of alternative treatment strategies.

Methods: Agar dilution and broth microdilution to determine the minimum inhibitory concentrations (MICs) of antibiotics and 7,8-dihydroxyflavone. The development of 7,8-dihydroxyflavone drug resistance was detected under the treatment of sublethal concentration for 30 passages. Genotypic analysis was conducted to examine penA, 23S rRNA alleles and multilocus sequence typing.

Results: 55 clinical NG isolates emphasised the high-level rates of the current antimicrobial resistance, with a notable prevalence of the penA-60.001 FC428 clone and high-level azithromycin-resistant clones characterised by the 23S rRNA-1 (A2059G) and 23S rRNA-2 (C2611T) alleles. Among the strains that are representative of clinical circulation, 7,8-dihydroxyflavone demonstrated inhibitory activity, with MIC₅₀ and MIC₉₀ values of 10 mg/L and 20 mg/L, respectively, which are unlikely to induce drug resistance in NG.

Conclusions: 7,8-dihydroxyflavone may represent a promising antimicrobial agent in the context of escalating antibiotic resistance.