Stéphane Cullati
PhD, Privat Docent - Habilitation
stephane.cullati@unifr.ch
+41 26 300 9474
https://orcid.org/0000-0002-3881-446X
Social determinants of health; Biological embedding of social life; Life course; Ageing; Prevention; Health and Social Policies; Quality and security of health care
Senior Researcher
Medicine Section
Hours of reception
Biography
Stéphane Cullati is a sociologist and Senior Lecturer in Epidemiology and Public Health at the University of Fribourg. He trained in sociology and social sciences at the Universities of Lausanne, Geneva and Grenoble, and obtained a doctorate (PhD) in the sociology of health and the life course from the University of Geneva and a habilitation in social epidemiology from the University of Fribourg. He carried out post-doctoral research at University College London Research Department of Epidemiology & Public Health. His research focuses on the relationship between health and society, from the perspective of social inequalities in health, the life course, social policies and reserves. His research has examined the impact of public policy on health inequalities, the (historical) evolution of social inequalities in health, and inequalities in cancer screening.
Research and publications
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Research projects
‘PREVENT TOO’. Reducing social and life course inequalities in preventive practices: a comparative study of the effects of health and social policies
Status: OngoingStart 01.04.2024 End 31.03.2028 Funding SNSF Open project sheet Background and rationale: Prevention is key to healthy ageing in middle and early old age but social inequalities in preventive practices, such as cancer screening, have remained persistently stable for decades in high-income countries. Health and social policies can reduce social inequalities, and their intersections, in preventive practices, however little research explored their impact by varying their characteristics (social rights, social expenditure). Furthermore, research has not yet examined how changes in policies (introduction of a policy, change in an existing policy) can modify inequalities in preventive practices. Following an eco-social and institutional approach, we assume that the moderation effect of health and social policies is modified by the history of preventive practices promotion and by the characteristics of the primary healthcare system of the country. Moreover, while research has focused on preventive practices at a given point in time (cross-sectionally) in the lives of individuals, little has been done to study their individual prevention trajectories (e.g., whether the use of preventive practices is stable, declines, or increases), how these trajectories vary according to individuals’ past social trajectories (disadvantaged childhood living conditions, non-normative family and employment trajectories), and how policies can modify the influence individuals’ past social trajectories on prevention trajectories. Overall objective and specific aims: the overall objective is to better understand how health and social policies can reduce social and life course inequalities in prevention in middle and early old age. Two specific aims will examine (1) the impact of characteristics and change in health and social policies on social inequalities in preventive practices, and if this impact is modified by the history of preventive practices promotion and characteristics of the primary healthcare system of the country; (2) the influences of socio-economic conditions at different moment of the life course, employment and family life trajectories on preventive practices trajectories, and if these life course influences are modified by the country's policies. The project will be divided into 2 Work Packages (WPs), 1 per aim. Methods: WP1 will use (cross-)nationally representative repeated cross-sectional health surveys (EHIS, Belgium, England, Finland, Germany, Sweden, Switzerland, United States), covering a period from several decades (variable baseline depending on surveys) to present. Main outcomes will be use of healthcare services (screening for dyslipidaemia, breast cancer, etc.) to prevent chronic diseases (cancer and cardiometabolic diseases). Countries’ policies characteristics and their evolution during the study period will be retrieved from the Comparative Welfare Entitlements Dataset, the Inter-university Consortium for Political and Social Research, and the Social Policy Indicators. The country’s history of preventive practices promotion will be built using documentations from country’s health promotion agencies and literature searches in bibliographic databases. Characteristics of the primary healthcare system will be retrieved from the OECD database, the European Primary Care Activity Monitor and a typology of healthcare professionalism. Analyses will include sequence analysis, multilevel regressions, and regression discontinuity. WP2 will use three longitudinal studies of ageing (SHARE, ELSA, HRS) with life history calendars. Countries’ policies characteristics will be retrieved from Institutional Rules Explorer of the G2AD, in combination with the SPLASH and the Social Policy Indicators databases. Analyses will include sequence and latent class growth analysis. Expected results: We expect to observe (i) smaller social inequalities in preventive practices among policies with more specified objectives and targeted populations and as a result of improved social policies (aim 1); (ii) smaller negative impact of disadvantaged and non-normative trajectories on preventive trajectories when policies have specified objectives and targeted populations (aim 2). Impact: Provide robust evidence that policies can reduce inequalities in prevention in middle and early old age. Social inequalities in the gut microbiome: individual participants meta-analyse of population-level data
Status: CompletedStart 01.12.2019 End 30.11.2020 Funding SNSF Open project sheet Background: Despite substantial improvement in population health in the past five decades, health inequalities remain stable and consistent across time and space, if not increasing. If the link between social characteristics and health is well-established, the biological pathways between social exposures and health are less clear. Epigenetic factors and the stress model (McEwen 1998) attracted most of the empirical evidence on biological pathways. Recent advances in basic and clinical researches have highlighted the important role of the gut micro-biome for health. We hypothesis that the gut micro-biome could be another biological pathway mediating the social exposures and health link. Objective: To examine the associations between social variables and gut micro-biome biomarkers signatures. Method: individual participants meta-analyse of general population-level gut microbiome. Studies will be included if 1) participants are healthy patients or from the general population, 2) they collected data on the gut microbiome and 3) social information (social status and socioeconomic) on participants. Studies will be identified through literature review of studies on healthy participants, repositories of gut microbiome data (Human Microbiome Project Data Portal, European Genome-phenome Archive), and citizen science microbiome projects (e.g., Microsetta Initiative). Studies’ principal investigators (PI) will be contacted and screened for possible inclusion in the meta-analyse. First, analyses will be performed in each study, separately. Association between socioeconomic (primary objective) and social status (secondary objective) variables and gut biomarkers signatures will be estimated by generating odds ratios and 95% confidence intervals estimated with logistic regression analysis. Dichotomisation of biomarkers will be made following clinical guidelines, corrected for sex and age strata when appropriate, and checked by an expert in microbiology collaborating with our team. When appropriate, the analyses will be stratified by sex. When gut biomarkers will be measured repeatedly, the difference between baseline and follow-up will be estimated, and attrition will be included in the estimation. Second, estimates will be pooled in a meta-analysis. Fixed or random effect models will be determined depending on studies heterogeneity. Heterogeneity between studies will be computed with I2 and t2 statistics and 95% prediction intervals for odds ratios will be used to account for t2. Funnel plots will be assessed to identify if biases are operating in the findings. Robustness and sensitivity analyses will be conducted (molecular techniques bias, selection bias). Expected results: we expect cross-sectional associations between socioeconomic status and gut micro-biome signatures, suggesting a social gradient in the gut micro-biome of the general population. Limitations: the gut micro-biome is expected to be a potential, new, biological mediator between social exposures and health, however further researches collecting longitudinally data of gut micro-biome, dietary habits, anthropometrics measures, lifestyles and diseases information, social and life course information, will be needed to confirm this hypothesis.