Background Extragenital gonorrhea (GC) and chlamydia (CT) are usually asymptomatic and only detected through screening. Ceftriaxone plus azithromycin is the recommended GC treatment; monotherapy (azithromycin or doxycycline) is recommended for CT. In urethral CT-positive/urethral GC-negative persons who are not screened extragenitally, CT monotherapy can lead to GC undertreatment and may foster the development of gonococcal antimicrobial resistance. We assessed urethral and extragenital GC and CT positivity among men who have sex with men (MSM) attending sexually transmitted disease clinics. Methods We included visit data for MSM tested for GC and CT at 30 sexually transmitted disease clinics in 10 jurisdictions during January 1, 2015, and June 30, 2019. Using an inverse-variance random effects model to account for heterogeneity between jurisdictions, we calculated weighted test visit positivity estimates and 95% confidence intervals (CI) for GC and CT at urethral and extragenital sites, and extragenital GC among urethral CT-positive/GC-negative test visits. Results Of 139,718 GC and CT test visits, we calculated overall positivity (GC, 16.7% [95% CI, 14.4–19.1]; CT, 13.3% [95% CI, 12.7–13.9]); urethral positivity (GC, 7.5% [95% CI, 5.7–9.3]; CT, 5.2% [95% CI, 4.6–5.8]); rectal positivity (GC, 11.8% [95% CI, 10.4–13.2]; CT, 12.6% [95% CI, 11.8–13.4]); and pharyngeal positivity (GC, 9.1% [95% CI, 7.9–10.3]; CT, 1.8% [95% CI, 1.6–2.0]). Of 4566 urethral CT-positive/GC-negative test visits with extragenital testing, extragenital GC positivity was 12.5% (95% CI, 10.9–14.1). Conclusions Extragenital GC and CT were common among MSM. Without extragenital screening of MSM with urethral CT, extragenital GC would have been undetected and undertreated in approximately 13% of these men. Undertreatment could potentially select for antimicrobial resistance. These findings underscore the importance of extragenital screening in MSM.

In Massachusetts, a large proportion of bacterial STIs are reported from a small subpopulation, many of whom have repeated infections and are likely to have higher impact on STI and HIV rates. Public health can play a crucial role in reaching high-volume repeaters whose STI histories may be hidden from clinicians due to fragmented care.

Women who experience intimate partner violence (IPV) are at increased risk for HIV infection. To further the understanding of the dyadic factors that impact condom use among women, we investigated the impact of three relationship factors (i.e., power, fear, and dependence) on the association between HIV-related information, motivation, and behavioral skills [constructs from the Information-Motivation-Behavioral Skills (IMB) model] and condom use among abused women. Data from 133 urban, low-income women recruited from several community-based agencies (e.g., domestic violence agencies, women’s health organizations, hospitals, Department of Health and Human Services, and Family Court) showed that these women experienced high levels of IPV and that relationship power, fear of abuse, and partner dependence were all associated with condom use. Multivariable models revealed that fear of abuse and partner dependence moderated that association between IMB constructs and condom use but relationship power did not. Results highlight the critical need to incorporate strategies to address relationship factors in HIV prevention programs with abused women.

Many methodological challenges were identified among these studies. This review contributes to the home birth published work by identifying key strengths and limitations that need to be accounted for in the interpretation of study findings and the development of future studies. Based on this review, the key variables that would strengthen future studies are birth attendant identification, documented planned birth location, and specification of the birth risk level. Uniformity of data collection and minimizing missing data are also critical.

Background A substantial fraction of sexually transmitted infections (STIs) occur in patients who have previously been treated for an STI. We assessed whether routine electronic health record (EHR) data can predict which patients presenting with an incident STI are at greatest risk for additional STIs in the next 1 to 2 years. Methods We used structured EHR data on patients 15 years or older who acquired an incident STI diagnosis in 2008 to 2015 in eastern Massachusetts. We applied machine learning algorithms to model risk of acquiring ≥1 or ≥2 additional STIs diagnoses within 365 or 730 days after the initial diagnosis using more than 180 different EHR variables. We performed sensitivity analysis incorporating state health department surveillance data to assess whether improving the accuracy of identifying STI cases improved algorithm performance. Results We identified 8723 incident episodes of laboratory-confirmed gonorrhea, chlamydia, or syphilis. Bayesian Additive Regression Trees, the best-performing algorithm of any single method, had a cross-validated area under the receiver operating curve of 0.75. Receiver operating curves for this algorithm showed a poor balance between sensitivity and positive predictive value (PPV). A predictive probability threshold with a sensitivity of 91.5% had a corresponding PPV of 3.9%. A higher threshold with a PPV of 29.5% had a sensitivity of 11.7%. Attempting to improve the classification of patients with and without repeat STIs diagnoses by incorporating health department surveillance data had minimal impact on cross-validated area under the receiver operating curve. Conclusions Machine algorithms using structured EHR data did not differentiate well between patients with and without repeat STIs diagnosis. Alternative strategies, able to account for sociobehavioral characteristics, could be explored.