Characteristics of Genital Dissatisfaction Among a Nationally Representative Sample of U.S. Women.

BackgroundFemale genital self-image is an important aspect of psychosocial and sexual health. The Female Genital Self-Image Scale (FGSIS) is a validated instrument that has been used to characterize women's level of genital dissatisfaction.AimIn this report, we assess genital dissatisfaction using the FGSIS in a nationally representative sample of U.S. women.MethodsWe conducted a nationally representative survey of non-institutionalized adults aged 18-65 years residing in the United States. The survey included questions about demographics, sexual behavior, and the FGSIS.OutcomesDemographic characteristics were found to significantly correlate to women's perceived genital dissatisfaction.ResultsIn total, 3,372 women completed the survey and 3,143 (93.2%) completed the FGSIS. The mean age was 46 years, and there was broad representation across the United States in terms of age, education, and location. On bivariate analysis, women's genital dissatisfaction was significantly correlated to their age, race, location, and education. Women who were sexually active were less likely to report genital dissatisfaction than women who were not sexually active (76% vs 62%, respectively, P < .001). The frequency of sexual activity was negatively correlated with genital dissatisfaction (P = .002). Women who reported genital dissatisfaction were less likely than those who reported satisfaction to engage in receptive vaginal sex (83% vs 88%, respectively, P = .03). There were no other significant associations between genital dissatisfaction and types of sexual activity. On multivariate analysis, women were less likely to report genital dissatisfaction if they were older, of black race, had an education level of high school or above, and/or lived in the Northeastern or Midwestern United States. There was no association between genital dissatisfaction and relationship status or gender of sexual partner.Clinical translationFemale genital dissatisfaction may be related to age, race, education, and geography.ConclusionsThis is the first nationally representative sample of U.S. women focusing on genital and self-image and dissatisfaction. These data may not apply outside the United States. These data may help providers who provide information for women and manage concerns related to genital self-image. Rowen TS, Gaither TW, Shindel AW, et al. Characteristics of Genital Dissatisfaction Among a Nationally Representative Sample of U.S. Women. J Sex Med 2018;15:698-704

Synchronous genitourinary lichen sclerosus signals a distinct urinary microbiome profile in men with urethral stricture disease.

PurposeAlterations in the urinary microbiome have been associated with urological diseases. The microbiome of patients with urethral stricture disease (USD) remains unknown. Our objective is to examine the microbiome of USD with a focus on inflammatory USD caused by lichen sclerosus (LS).MethodsWe collected mid-stream urine samples from men with LS-USD (cases; n = 22) and non-LS USD (controls; n = 76). DNA extraction, PCR amplification of the V4 hypervariable region of the 16S rRNA gene, and sequencing was done on the samples. Operational taxonomic units (OTUs) were defined using a > 97% sequence similarity threshold. Alpha diversity measurements of diversity, including microbiome richness (number of different OTUs) and evenness (distribution of OTUs) were calculated and compared. Microbiome beta diversity (difference between microbial communities) relationships with cases and controls were also assessed.ResultsFifty specimens (13 cases and 37 controls) produced a 16S rRNA amplicon. Mean sample richness was 25.9 vs. 16.8 (p = 0.076) for LS-USD vs. non-LS USD, respectively. LS-USD had a unique profile of bacteria by taxonomic order including Bacillales, Bacteroidales and Pasteurellales enriched urine. The beta variation of observed bacterial communities was best explained by the richness.ConclusionsMen with LS-USD may have a unique microbiologic richness, specifically inclusive of Bacillales, Bacteroidales and Pasteurellales enriched urine compared to those with non-LS USD. Further work will be required to elucidate the clinical relevance of these variations in the urinary microbiome

Cycling and Female Sexual and Urinary Function: Results From a Large, Multinational, Cross-Sectional Study.

BACKGROUND:Bicycle riding has become an increasingly popular mode of transportation and exercise, especially among women, and previous studies have demonstrated a relationship between cycling and sexual dysfunction, albeit using non-validated questionnaires. AIM:We aimed to explore the relationship between cycling and sexual and urinary dysfunction. METHODS:Cyclists were recruited to complete a survey through Facebook advertisements and outreach to sporting clubs across 5 English-speaking countries. Swimmers and runners were recruited as a comparison group. OUTCOMES:Participants were queried using validated questionnaires, including the Female Sexual Function Index, the American Urological Association Symptom Index, and non-validated questions about history of urinary tract infections (UTIs), genital numbness, and genital saddle sores (all self-reported). RESULTS:3,118 (53.3%) Women completed the survey, comprising 1,053 (34%) non-cyclists, 1,656 (53%) low-intensity cyclists, and 409 (13%) high-intensity cyclists. After adjusting for age, body mass index, hypertension, diabetes, ischemic heart disease, tobacco use, race, marital status, urinary symptoms, and sexual activity, high-intensity cyclists had lower odds of self-reported sexual dysfunction compared to non-cyclists (adjusted odds ratio [aOR] 0.7, P = .02). There were no statistically significant differences in urinary symptoms across groups. Compared to non-cyclists, both low- and high-intensity cyclists had higher odds of reporting a previous UTI (aOR 1.4, P < .001, and aOR 1.4, P = .009, respectively), genital numbness (odds ratio [OR] 6.5, P < .001, and OR 9.1, P < .001, respectively), and saddle sores (OR 6.3, P < .001, and OR 22.7, P < .001, respectively). CLINICAL TRANSLATION:Women cyclists were more likely to report other genitourinary conditions, including UTIs, genital numbness, and saddle sores. CONCLUSIONS:This is the largest study comparing cyclists to other athletes with respect to sexual and urinary function. The study is limited by its cross-sectional design and sampling methods. We found that women cyclists were no more likely to report sexual dysfunction or urinary symptoms than swimmers or runners. Gaither TW, Awad MA, Murphy GP, et al. Cycling and Female Sexual and Urinary Function: Results From a Large, Multinational, Cross-Sectional Study. J Sex Med 2018;15:510-518

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages