Persistent spatial clusters of high body mass index in a Swiss urban population as revealed by the 5-year GeoCoLaus longitudinal study

OBJECTIVE Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether and how BMI clusters evolve over time in a population is currently unknown. We aimed to determine the spatial dependence of BMI and its 5-year evolution in a Swiss general adult urban population, taking into account the neighborhood- and individual-level characteristics. DESIGN Cohort study SETTING Swiss general urban population PARTICIPANTS 6,481 geo-referenced individuals from the CoLaus cohort at baseline (age range 35–74 years, period=2003-2006) and 4,460 at follow-up (period=2009-2012). OUTCOME MEASURES Body weight and height were measured by trained health care professionals with participants standing without shoes in light indoor clothing. BMI was calculated as weight (kg) divided by height squared (m2). Participants were geocoded using their postal address (geographic coordinates of the place of residence). Getis-Ord Gi statistic was used to measure the spatial dependence of BMI values at baseline and its evolution at follow-up. RESULTS BMI was not randomly distributed across the city. At baseline and at follow-up, significant clusters of high versus low BMIs were identified and remained stable during the two periods. These clusters were meaningfully attenuated after adjustment for neighborhood-level income but not individual-level characteristics. Similar results were observed among participants who developed obesity. CONCLUSIONS To our knowledge, this is the first study to report longitudinal changes in BMI clusters in adults from a general population. Spatial clusters of high BMI persisted over a 5-year period and were mainly influenced by neighborhood-level income. ARTICLE SUMMARY STRENGTHS AND LIMITATIONS OF THE STUDY • As far as we know, this is the first study to report the persistence of spatial clusters of high BMI values over a 5-year period in adults from a general population • The observed east to west pattern of BMI clustering fits known socio-economic and ethno-cultural differences distinguishing these opposite regions of the city of Lausanne, Switzerland • A consequence of the social policy applied by the city is likely to fix populations with modest income in subsidized housing located in specific areas • While recruitment methods of the CoLaus study aimed at collecting information on a representative sample of the general population, adult participants and non-participants to the CoLaus study may differ and participation bias cannot be excluded • We considered several individual-level covariates but data on individual income was missing. We used instead the median income of the including city statistical sector.

Specchio-COVID19 cohort study: a longitudinal follow-up of SARS-CoV-2 serosurvey participants in the canton of Geneva, Switzerland

Idris Guessous, Stéphane Joost

Introduction The COVID-19 pandemic has affected billions of people around the world both directly through the infection itself and indirectly through its economic, social and sanitary impact. Collecting data over time is essential for the understanding of the disease spread, the incidence of COVID-19-like symptoms, the level and dynamics of immunity, as well as the long-term impact of the pandemic. The objective of the study was to set up a longitudinal follow-up of adult participants of serosurveys carried out in the canton of Geneva, Switzerland, during the COVID-19 pandemic. This follow-up aims at monitoring COVID-19 related symptoms and SARS-CoV-2 seroconversion, as well as the overall impact of the pandemic on several dimensions of health and on socioeconomic factors over a period of at least 2 years. Methods and analysis Serosurvey participants were invited to create an account on the dedicated digital platform Specchio-COVID19 (https://www.specchio-covid19.ch/) . On registration, an initial questionnaire assessed sociodemographic and lifestyle characteristics (including housing conditions, physical activity, diet, alcohol and tobacco consumption), anthropometry, general health and experience related to COVID-19 (symptoms, COVID-19 test results, quarantines, hospitalisations). Weekly, participants were invited to fill in a short questionnaire with updates on self-reported COVID-19-compatible symptoms, SARS-CoV-2 infection testing and vaccination. A more detailed questionnaire about mental health, wellbeing, risk perception and changes in working conditions was proposed monthly. Supplementary questionnaires were proposed at regular intervals to assess more in depth the impact of the pandemic on physical and mental health, vaccination adherence, healthcare consumption and changes in health behaviours. At baseline, serology testing allowed to assess the spread of SARS-CoV-2 infection among the general population and subgroups of workers. Additionally, seropositive participants and a sample of randomly selected participants were invited for serologic testing at regular intervals in order to monitor both the seropersistance of anti-SARS-CoV-2 antibodies and the seroprevalence of anti-SARS-CoV-2 antibodies in the population of the canton of Geneva. Ethics and dissemination The study was approved by the Cantonal Research Ethics Commission of Geneva, Switzerland (CCER Project ID 2020-00881). Results will be disseminated in a variety of ways, via the SpecchioCOVID-19 platform, social media posts, press releases and through regular scientific dissemination methods (openaccess articles, conferences).

BMJ PUBLISHING GROUP2022

A fine scale analysis of socio-economic and landscape variables for the assessment of health status in Geneva

Joël Chételat, Idris Guessous, Stéphane Joost, Abram Pointet

Significant differences in health status between inhabitants of different commune types of the Geneva area have recently been established in a study relying on a large health database managed by the Unit of Population Epidemiology of the Hôpitaux Universitaires de Genève (HUG). For instance, the mean Body Mass Index is lower in richer communes than in other urban areas. Moreover, suburban residents and commuting populations have a higher hypertension prevalence than other communities. Though socio-economic status appears to be an essential driver for quality of life and health, specific knowledge is lacking to define which variables are of main concern. Besides, the relationship between socio-economic conditions and neighbouring living environment is only implicit. The objective of the present study is to detail the spatial differences coarsely observed previously. More particularly, it aims to determine to which extent landscape indicators are related to health risk factors and to identify areas where prevalence of health risk factors is potentially high on a fine scale basis. The study reuses the “Bus santé” database containing health-related information for more than 10'000 participants collected since 1998.Stratified sampling by age and by gender was used for the survey to be representative of the population. Samples were geocoded so that they can be analysed in a Geographic Information System (GIS). The Geneva canton has been divided into some 1600 homogeneous zones called Microregions based on the number of inhabitants, jobs, buildings and dwellings. Demographic, socio-economic and landscape variables such as the proportion of built-in surface and volume or the Shannon land use diversity index inform each of the Microregions. Significant factors characterising them are identified using factor analysis in order to make a spatial typology. A multiple regression is applied to assess the relationship between a set of health variables (including blood pressure and hypertension, glucose level and diabetes) from the sampled individuals contained within the Microregions and the factors describing these. Results of this fine scale analysis show the importance of income and socio-cultural membership as primary variables related to lifestyle and potential health status. The chosen landscape composition and structure variables come as complementary explanatory factors of neighbourhood counting for well-being or on the contrary stress-related illness. As an environmental health research, this study directly contributes to address land planning issues and inform on how landscape design potentially impacts quality of life and population health. Moreover, it serves to identify spatial types where risk factors are high and locate them in the geographical space.

2012

Overlapping spatial clusters of sugar-sweetened beverage intake and body mass index in Geneva state, Switzerland

Beatrice Bacchilega, David Nicolas De Ridder, Idris Guessous, Stéphane Joost

Background: Obesity and obesity-related diseases represent a major public health concern. Recently, studies have substantiated the role of sugar-sweetened beverages (SSBs) consumption in the development of these diseases.The fine identification of populations and areas in need for public health intervention remains challenging. This study investigates the existence of spatial clustering of SSB intake frequency (SSB-IF) and body mass index (BMI), and their potential spatial overlap in a population of adults of the state of Geneva using a fine-scale geospatialapproach. Methods: We used data on self-reported SSB-IF and measured BMI from residents aged between 20 and 74 years of the state of Geneva (Switzerland) that participated in the Bus Santé cross-sectional population-based study (n=15,423). Getis-Ord Gi spatial indices were used to identify spatial clusters of SSB-IF and BMI in unadjusted models and models adjusted for individual covariates (education level, gender, age, nationality, and neighborhood-level median income). Results: We identified a significant spatial clustering of BMI and SSB-IF. 13.2% (n=2034) of the participants were within clusters of higher SSB-IF and 10.7% (n=1651) were within clusters of lower SSB-IF. We identified overlapping clusters of SSB-IF and BMI in specific areas where 11.1% (n=1719) of the participants resided. After adjustment, the identified clusters persisted and were only slightly attenuated indicating that additional neighborhood-level determinants influence the spatial distribution of SSB-IF and BMI. Conclusions: Our fine-scale spatial approach allowed to identify specific populations and areas presenting higher SSB-IF and highlighted the existence of an overlap between populations and areas of higher SSB-IF associated with higher BMI. These findings could guide policy makers to develop locally tailored interventions such as targeted prevention campaigns and pave the way for precision public health delivery.

Springer2019