Prevalence of sexually transmitted infections and immunization status among registered sex workers: A pilot study in lower Bavaria, Germany

by Fabian Standl, Lena Senger, Heribert Stich

Background

Sex workers are often considered at elevated risk for sexually transmitted infections (STIs). This pilot study describes the socio‑epidemiological characteristics of registered sex workers in a rural German setting, estimates the prevalence of four STIs (HIV, hepatitis B [HBV], hepatitis C [HCV], and syphilis [lues]), compares these with the local population, and assesses HBV immunization coverage.

Methods

Under §10 of the Prostitute Protection Act (ProstSchG), annual health consultations are mandatory; voluntary serologic testing is permitted under §19 of the Infection Protection Act. We conducted a retrospective observational monocentric pilot study using routine consultation records and voluntary serologic results from the Public Health Service (PHS) of Landshut (2017–2021). In total, 523 consultations were documented; 99 blood samples from 48 registered sex workers (2019–2021) were analyzed. Primary screening assays were followed by confirmatory tests when indicated. Crude point/period prevalences and 95% confidence intervals (95% CI) were calculated. HBV immunization was defined according to Standing Committee on Vaccination (STIKO) recommendations.

Results

The cohort was predominantly female (n = 47; 97.9%), mean age 34.8 ± 11.2 years; 85.3% (n = 41) had a migration background (n = 27; 56.3% from Eastern EU countries). No acute HIV, HBV, or HCV infection was detected. Evidence of past HBV infection (anti‑HBc) was found in n = 7 (14.6%; 95% CI: 6.8–26.5), past HCV in n = 1 (2.1%; 95% CI: 0.2–9.3). Syphilis serology was reactive in 12.5% (n = 6), with n = 2 (4.2%; 95% CI: 0.9–12.7) meeting criteria for treatment‑requiring infection. HBV vaccine‑induced immunity was documented in 43.8%; only 29.2% achieved titers ≥100 mIU/ml. Compared with regional surveillance data, the prevalence of acute notifiable STIs among sex workers was not increased.

Conclusions

In this rural setting, acute notifiable STIs were uncommon among registered sex workers, while past HBV infection and suboptimal HBV immunization were frequent. Public health efforts should prioritize HBV vaccination and syphilis prevention or treatment, and expand low‑threshold, trusted services tailored to this workforce.

Lagging behind in health and work expectancies: Increasing disadvantage of individuals with lower educational level in Germany

by Juliane Tetzlaff, Fabian Tetzlaff, Marc Luy

Background

Many governments increased the retirement age in response to population ageing. Against this backdrop, it remains unclear whether the development in healthy life years can keep pace with the increase in working life years and whether people with lower socio-economic status are left behind. We investigated time trends in healthy life years and healthy working life years and how trends differ between educational groups in Germany.

Methods

Temporal trends in partial life expectancy between age 30 and 69 were assessed using data from the German Socio-Economic Panel (GSOEP, N=40,150) of three educational groups. Based on this, education-specific (Un)Healthy Life Expectancy ((U)HLE) and (Un)Healthy Working Life Expectancy ((U)HWLE) were calculated using the Sullivan method. Health is assessed on the basis of two health indicators: the physical and the mental score of health-related quality of life (p/mHRQoL). Both has been shown to be important indicators for working-age health.

Results

With respect to pHRQoL, HLE increased among men and women with higher educational attainment while it decreased in men with lower educational level. HWLE increased stronger in men and women with higher than with lower educational attainment. UHWLE increased strongest in persons with lower educational attainment. In terms of mHRQoL, HLE increased in all educational groups except for the group of women with lower educational attainment. UHLE decreased among men and women with middle and higher educational level. HWLE increased in all groups, with increases being strongest among higher educated individuals. UHLE increased in women with lower educational attainment but decreased in men and women with higher educational level.

Discussion

We found polarising trends, with healthy life years and healthy working years developing less favourably among people with lower than with higher educational level. This applies to both the physical and mental component of HRQoL. The study shows that people with lower educational level are less able to keep pace with the prolonged working life from a health perspective and that more effective prevention is needed to stop the widening of health inequalities in working age.

Automatic uterus segmentation in transvaginal ultrasound using U-Net and nnU-Net

by Dilara Tank, Bianca G. S. Schor, Lisa M. Trommelen, Judith A. F. Huirne, Iacer Calixto, Robert A. de Leeuw

Purpose

Transvaginal ultrasound (TVUS) is pivotal for diagnosing reproductive pathologies in individuals assigned female at birth, often serving as the primary imaging method for gynecologic evaluation. Despite recent advancements in AI-driven segmentation, its application to gynecological ultrasound still needs further attention. Our study aims to bridge this gap by training and evaluating two state-of-the-art deep learning (DL) segmentation models on TVUS data.

Materials and methods

An experienced gynecological expert manually segmented the uterus in our TVUS dataset of 124 patients with adenomyosis, comprising still images (n = 122), video screenshots (n = 472), and 3D volume screenshots (n = 452). Two popular DL segmentation models, U-Net and nnU-Net, were trained on the entire dataset, and each imaging type was trained separately. Optimization for U-Net included varying batch size, image resolution, pre-processing, and augmentation. Model performance was measured using the Dice score (DSC).

Results

U-Net and nnU-Net had good mean segmentation performances on the TVUS uterus segmentation dataset (0.75 to 0.97 DSC). We observed that training on specific imaging types (still images, video screenshots, 3D volume screenshots) tended to yield better segmentation performance than training on the complete dataset for both models. Furthermore, nnU-Net outperformed the U-Net across all imaging types. Lastly, we report the best results using the U-Net model with limited pre-processing and augmentations.

Conclusions

TVUS datasets are well-suited for DL-based segmentation. nnU-Net training was faster and yielded higher segmentation performance; thus, it is recommended over manual U-Net tuning. We also recommend creating TVUS datasets that include only one imaging type and are as clutter-free as possible. The nnU-Net strongly benefited from being trained on 3D volume screenshots in our dataset, likely due to their lack of clutter. Further validation is needed to confirm the robustness of these models on TVUS datasets. Our code is available on https://github.com/dilaratank/UtiSeg.