Unisanté Data repository

This cross-sectional study aimed to examine the experiences of trans and nonbinary (TNB) individuals regarding the documentation and communication of sex and gender identity in healthcare settings in Switzerland. Data were collected through an online survey assessing structural factors (documentation accuracy, form inclusivity, ease of change, provider knowledge), interpersonal factors (respectful communication, perceived rejection, breach of confidentiality, medical harm), individual factors (disclosure safety, disclosure of gender identity, negative expectations, non-disclosure behavior), and healthcare avoidance. Recruitment was conducted through professional medical organizations (n = 11), LGBTIQ+ community organizations (n = 22), a targeted Instagram campaign led by Unisanté and Transgender Network Switzerland (TGNS), and snowball sampling. Data collection took place from July 10 to September 4, 2025. The dataset includes responses from 156 participants.

This project aims to develop, validate, and disseminate a Job-Exposure Matrix (JEM) for night work exposure (NW‑JEM) to support epidemiological research and occupational health surveillance. The JEM provides standardized, reproducible estimates of the index of exposure to night work across occupational groups in Switzerland.

This scoping review aims to gather and analyze existing digital Patient Decision Aids (PtDAs) for cancer prevention targeting socially disadvantaged women.

Active smoking remains a major confounding factor in occupational epidemiology studies. When individual smoking data are unavailable or incomplete in registry-based studies or retrospective analyses, indirect adjustment using job-exposure matrices (JEMs) provides an approach to control for smoking confounding based on occupational and demographic characteristics. The Swiss Job-Exposure Matrix for Active Smoking (SJEM-T) is a validated quantitative tool providing smoking probability estimates for specific occupational groups. The SJEM-T was developed using Swiss Health Survey data from four waves (2007, 2012, 2017, 2022), comprising approximately 60,000 workers. Smoking probabilities were estimated using logistic regression with current smoking status as the dependent variable, stratified by occupation (ISCO-88), sex, age group, and year. The matrix provides estimates for 12,160 unique strata. Dual validation (internal and criterion in independent cohorts) was performed.