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Introduction Although Switzerland has one of the highest life expectancy in the world, this global indicator may mask significant disparities at a local level. The present study used a spatial cluster detection approach on individual mortality data to investigate the geographic footprint of life expectancy inequalities in the state of Geneva, Switzerland. Methods Individual-level mortality data (n=30,592) were obtained from the Geneva’s mortuary announcements (2003-2017). We measured life expectancy inequalities through Life Expectancy Difference (LED), defined as the difference between the individual’s age at death and their Life Expectancy at Birth. We assessed the spatial dependence of LED across the state of Geneva using spatial autocorrelation statistics (Local Moran’s I). To ensure the robustness of the discovered patterns, we ran the analysis for ten random subsets of 10,000 individuals drawn from the 30,592 deceased. We also repeated the spatial analysis for LED before and after controlling for nationality and neighborhood income.Results LED was not randomly distributed across the state of Geneva. The ten random subsets revealed no significant difference in the geographic footprint of LED and the population characteristics within Local Moran cluster types, suggesting the robustness of the spatial structure obtained. The proportion of women, the proportion of Swiss, and the median neighborhood income were significantly lower for populations within low LED clusters than for populations within high LED clusters. After controlling for nationality and neighborhood income, we observed a slight reduction in the low LED cluster footprints, but we found similar differences in population characteristics between cluster types.Conclusion To the best of our knowledge, this is the first study in Switzerland using spatial cluster detection methods to investigate small area inequalities in life expectancy. We identified a clear geographic footprint of LED, which may support further investigations and guide future public health interventions at the local level.

Background: The early detection of clusters of infectious diseases such as the SARS-CoV-2-related COVID-19 disease can promote timely testing recommendation compliance and help to prevent disease outbreaks. Prior research revealed the potential of COVID-19 participatory syndromic surveillance systems to complement traditional surveillance systems. However, most existing systems did not integrate geographic information at a local scale, which could improve the management of the SARS-CoV-2 pandemic., Objective: The aim of this study is to detect active and emerging spatiotemporal clusters of COVID-19-associated symptoms, and to examine (a posteriori) the association between the clusters' characteristics and sociodemographic and environmental determinants., Methods: This report presents the methodology and development of the @choum (English: "achoo") study, evaluating an epidemiological digital surveillance tool to detect and prevent clusters of individuals (target sample size, N=5000), aged 18 years or above, with COVID-19-associated symptoms living and/or working in the canton of Geneva, Switzerland. The tool is a 5-minute survey integrated into a free and secure mobile app (CoronApp-HUG). Participants are enrolled through a comprehensive communication campaign conducted throughout the 12-month data collection phase. Participants register to the tool by providing electronic informed consent and nonsensitive information (gender, age, geographically masked addresses). Symptomatic participants can then report COVID-19-associated symptoms at their onset (eg, symptoms type, test date) by tapping on the @choum button. Those who have not yet been tested are offered the possibility to be informed on their cluster status (information returned by daily automated clustering analysis). At each participation step, participants are redirected to the official COVID-19 recommendations websites. Geospatial clustering analyses are performed using the modified space-time density-based spatial clustering of applications with noise (MST-DBSCAN) algorithm., Results: The study began on September 1, 2020, and will be completed on February 28, 2022. Multiple tests performed at various time points throughout the 5-month preparation phase have helped to improve the tool's user experience and the accuracy of the clustering analyses. A 1-month pilot study performed among 38 pharmacists working in 7 Geneva-based pharmacies confirmed the proper functioning of the tool. Since the tool's launch to the entire population of Geneva on February 11, 2021, data are being collected and clusters are being carefully monitored. The primary study outcomes are expected to be published in mid-2022., Conclusions: The @choum study evaluates an innovative participatory epidemiological digital surveillance tool to detect and prevent clusters of COVID-19-associated symptoms. @choum collects precise geographic information while protecting the user's privacy by using geomasking methods. By providing an evidence base to inform citizens and local authorities on areas potentially facing a high COVID-19 burden, the tool supports the targeted allocation of public health resources and promotes testing.

BACKGROUND: Rapid access to a trauma centre for severely injured road accident victims, conceptualised as the Golden Hour, links access time to definitive treatment within 1 hour of trauma with reduced risks of morbidity and mortality. Access times have not been studied in Switzerland. The aim of this work was to model the transport time by ambulance of seriously injured road traffic accident victims to one of the 12 trauma centres in Switzerland and to investigate whether this time influenced mortality., METHODS: Isochronous travel curves in 10-minute increments were modelled around each of the 12 Swiss trauma centres to assess travel times at the Swiss national level, based on the shortest travel time from the location of a serious road accident to the nearest trauma centre. We used the national database of the Federal Roads Office, which provided the geolocation of these accidents occurring between 2011 and 2017. The association between mortality and transport time to the nearest trauma centre was then analysed., RESULTS: The current distribution of trauma centres allowed access time within the Golden Hour for accidents occurring on the Swiss plateau, but the time was more prolonged in the Alps or the Jura. An association existed between mortality and prehospital transport time from the site of an accident to the nearest trauma centre. For each additional 10-minute isochrone, an average increase of 0.4% in mortality was observed., CONCLUSION: This work showed an adequate distribution of trauma centres in Switzerland and suggests a positive relationship between transport time to the nearest trauma centre and mortality. The numerous confounding factors not systematically collected in publicly available databases limit the robustness of our results. This study confirms the importance of having a national trauma registry to allow quality analyses to guide public health decisions.

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 geospatial approach. 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 policymakers to develop locally tailored interventions such as targeted prevention campaigns and pave the way for precision public health delivery.

Living in deprived neighbourhoods may have biological consequences, but few studies have assessed this empirically. We examined the association between neighbourhood deprivation and allostatic load, a biological marker of wear and tear, taking into account individual's socioeconomic position. We analysed data from three cohort studies (CoLaus-Switzerland; EPIPorto-Portugal; Whitehall II-UK) comprising 16,364 participants. We defined allostatic load using ten biomarkers of dysregulated metabolic, cardiovascular, and inflammatory systems (body mass index; waist circumference; total, high and low density lipoprotein cholesterol; triglycerides; glucose; systolic and diastolic blood pressure; C-reactive protein). Mixed Poisson regression models were fitted to examine associations with neighbourhood deprivation (in quintiles, Q1-least deprived as reference). After adjustment for confounding variables, participants living in the most deprived quintile had 1.13 times higher allostatic load than those living in the least deprived quintile (Relative Risk, RR, for Q2 RR = 1.06, 95% CI 1.03-1.09; Q3 = 1.06, 1.03-1.10; Q4 = 1.09, 1.06-1.12; Q5 = 1.13, 1.09-1.16). This association was partially modified by individual's socioeconomic position, such that the relative risk was higher in participants with low socioeconomic position (Q5 vs Q1 1.16, 1.11-1.22) than those with high socioeconomic position (Q5 vs Q1 1.07, 1.01-1.13). Neighbourhood deprivation is associated with biological wear and tear, suggesting that neighbourhood-level interventions may yield health gains. Living in deprived neighbourhoods may have biological consequences, but few studies have assessed this empirically. We examined the association between neighbourhood deprivation and allostatic load, a biological marker of wear and tear, taking into account individual's socioeconomic position. We analysed data from three cohort studies (CoLaus-Switzerland; EPIPorto-Portugal; Whitehall II-UK) comprising 16,364 participants. We defined allostatic load using ten biomarkers of dysregulated metabolic, cardiovascular, and inflammatory systems (body mass index; waist circumference; total, high and low density lipoprotein cholesterol; trig lycerides; glucose; systolic and diastolic blood pressure; C-reactive protein). Mixed Poisson regression models were fitted to examine associations with neighbourhood deprivation (in quintiles, Q1-least deprived as reference). After adjustment for confounding variables, participants living in the most deprived quintile had 1.13 times higher allostatic load than those living in the least deprived quintile (Relative Risk, RR, for Q2 RR = 1.06, 95%CI 1.03-1.09; Q3 = 1.06, 1.03-1.10; Q4 = 1.09, 1.06-1.12; Q5 = 1.13, 1.09-1.16). This association was partially modified by individual's socioeconomic position, such that the relative risk was higher in participants with low socioeconomic position (Q5 vs Q11.16, 1.11-1.22) than those with high socioeconomic position (Q5 vs Q1 1.07, 1.11-1.13). Neighbourhood deprivation is associated with biological wear and tear, suggesting that neighbourhood-level interventions may yield health gains.

Objective: To investigate the association between socioeconomic deprivation and the persistence of SARS-CoV-2 clusters., Methods: We analyzed 3,355 SARS-CoV-2 positive test results in the state of Geneva (Switzerland) from February 26 to April 30, 2020. We used a spatiotemporal cluster detection algorithm to monitor SARS-CoV-2 transmission dynamics and defined spatial cluster persistence as the time in days from emergence to disappearance. Using spatial cluster persistence measured outcome and a deprivation index based on neighborhood-level census socioeconomic data, stratified survival functions were estimated using the Kaplan-Meier estimator. Population density adjusted Cox proportional hazards (PH) regression models were then used to examine the association between neighborhood socioeconomic deprivation and persistence of SARS-CoV-2 clusters., Results: SARS-CoV-2 clusters persisted significantly longer in socioeconomically disadvantaged neighborhoods. In the Cox PH model, the standardized deprivation index was associated with an increased spatial cluster persistence (hazard ratio [HR], 1.43 [95% CI, 1.28-1.59]). The adjusted tercile-specific deprivation index HR was 1.82 [95% CI, 1.56-2.17]., Conclusions: The increased risk of infection of disadvantaged individuals may also be due to the persistence of community transmission. These findings further highlight the need for interventions mitigating inequalities in the risk of SARS-CoV-2 infection and thus, of serious illness and mortality.

BACKGROUND The etiology of Parkinson's disease (PD) remains unknown. To approach the issue of PD's risk factors from a new perspective, we hypothesized that coupling the geographic distribution of PD with spatial statistics may provide new insights into environmental epidemiology research. The aim of this case-control study was to examine the spatial dependence of PD prevalence in the Canton of Geneva, Switzerland (population = 474,211). METHODS PD cases were identified through Geneva University Hospitals, private neurologists and nursing homes medical records (n = 1115). Controls derived from a population-based study (n = 12,614) and a comprehensive population census dataset (n = 237,771). All individuals were geographically localized based on their place of residence. Spatial Getis-Ord Gi* statistics were used to identify clusters of high versus low disease prevalence. Confounder-adjustment was performed for age, sex, nationality and income. Tukey's honestly significant difference was used to determine whether nitrogen dioxide and particulate matters PM10 concentrations were different within PD hotspots, coldspots or neutral areas. RESULTS Confounder-adjustment greatly reduced greatly the spatial association. Characteristics of the geographic space influenced PD prevalence in 6% of patients. PD hotspots were concentrated in the urban centre. There was a significant difference in mean annual nitrogen dioxide and PM10 levels (+3.6 μg/m3 [p

The transhumance system, which consists in moving animals to high mountain pastures during summer, plays a considerable role in preserving both local biodiversity and traditions, as well as protecting against natural hazard. In cows, particularly, milk production is observed to decline as a response to food shortage and climatic stress, leading to atypical lactation curves that are barely described by current lactation models. Here, we relied on 5 million monthly milk records from over 200 000 Braunvieh and Original Braunvieh cows to devise a new model accounting for transhumance, and test the influence of environmental, physiological and morphological factors on cattle productivity. Counter to expectations, environmental conditions in the mountain showed a globally limited impact on milk production during transhumance, with cows in favourable conditions producing only 10% more compared with cows living in detrimental conditions, and with precipitation in spring and altitude revealing to be the most production-affecting variables. Conversely, physiological factors such as lactation number and pregnancy stage presented an important impact over the whole lactation cycle with 20% difference in milk production, and alter the way animals respond to transhumance. Finally, the considered morphological factors (cow height and foot angle) presented a smaller impact during the whole lactation cycle (10% difference in milk production). The present findings help to anticipate the effect of climate change and to identify problematic environmental conditions by comparing their impact with the effect of factors that are known to influence lactation.

1.SummaryThe transhumance system, which consists in moving animals to high mountain pastures during summer, plays a considerable role in preserving both local biodiversity and traditions, as well as protecting against natural hazard. In cows, particularly, milk production is observed to decline as a response to food shortage and climatic stress, leading to atypical lactation curves that are barely described by current lactation models. Here, we relied on five million monthly milk records from over 200,000 Braunvieh and Original Braunvieh cows to devise a new model accounting for transhumance, and test the influence of environmental, physiological, and morphological factors on cattle productivity. Counter to expectations, environmental conditions in the mountain showed a globally limited impact on milk production during transhumance, with cows in favourable conditions producing only 10% less compared to cows living in adverse conditions, and with precipitation in spring and altitude revealing to be the most production-affecting variables. Conversely, physiological factors as lactation number and pregnancy stage presented an important impact over the whole lactation cycle with 20% difference in milk production, and may therefore alter the way animals respond to transhumance. Finally, the considered morphological factors (cow height and foot angle) presented a smaller impact during the whole lactation cycle (10% difference in milk production). The present findings can help farmers to establish sustainable strategies for alleviating the negative effects of transhumance on productivity and preserving this important livestock practice.

Background The local social and physical environment influences health outcomes and individual behaviour. It remains unknown if the environmental influence is independent of individual socioeconomic status (SES). We studied the spatial distribution of mammography screening uptake in the Swiss urban population of Geneva and determined its independence from SES. Methods We used individual-level geo-referenced data (n = 5 002) from the participants in the Bus Santé study, a population-based cross-sectional study ongoing in Geneva since 1992. Data from 1992 to 2014 have been included in this study. Local indicators of spatial association (LISA) were calculated and used to assess the spatial dependence of mammography uptake (defined as having had a mammography in the past). We report unadjusted spatial clusters; adjusted for neighbourhood income and individual educational attainment; and for demographic variables (age and Swiss nationality). Furthermore, we evaluated the association between the distance to the nearest screening centre and the identified spatial clusters. Results Mammography uptake was not randomly distributed, with spatial clusters overlapping with those of SES. Spatial clusters were reduced to 56.2% of their initial size (n = 1,033) after adjustment for SES variables. Adjustment for age and nationality further reduced the proportion of individuals exhibiting spatially-dependent behaviour (to 36.5% of the initial size). Distance to the nearest screening centre was not associated with the spatial distribution of mammography uptake. Conclusions High definition spatial distribution studies using analysis of individual data allow identifying spatial clusters which are not based on pre-determined administrative units (e.g. postal code) but rather on individual behaviour. Persistent spatial clusters after adjustment for SES and demographic confounders suggest additional area-level determinants influencing the spatial distribution of mammography uptake. Key messages High definition spatial studies can help uncover new patterns of health outcomes distribution independent of SES and based on individual behaviour, rather than administrative units. While SES explains a great proportion of the spatial distribution of mammography uptake, other still elusive factors may be contributing to the observed spatial inequalities in mammography uptake.

PurposeLocal physical and social environment has a defining influence on individual behaviour and health-related outcomes. However, it remains undetermined if its impact is independent of individual socioeconomic status. In this study, we evaluated the spatial distribution of mammography adherence in the state of Geneva (Switzerland) using individual-level data and assessed its independence from socioeconomic status (SES).MethodsGeo-referenced individual-level data from the population-based cross-sectional Bus Santé study (n = 5,002) were used to calculate local indicators of spatial association (LISA) and investigate the spatial dependence of mammography adherence. Spatial clusters are reported without adjustment; adjusted for neighbourhood income and individual educational attainment; and demographic factors (age and Swiss nationality). The association between adjusted clusters and the proximity to the nearest screening centre was also evaluated.ResultsMammography adherence was not randomly distributed throughout Geneva with clusters geographically coinciding with known SES distributions. After adjustment for SES indicators, clusters were reduced to 56.2% of their original size (n = 1,033). Adjustment for age and nationality further reduced the number of individuals exhibiting spatially dependent behaviour (36.5% of the initial size). The identified SES-independent hot spots and cold-spots of mammography adherence were not explained by proximity to the nearest screening centre.ConclusionsSES and demographic factors play an important role in shaping the spatial distribution of mammography adherence. However, the spatial clusters persisted after confounder adjustment indicating that additional neighbourhood-level determinants could influence mammography adherence and be the object of targeted public health interventions.

ObjectiveTo identify populations and areas presenting higher consumption of sugar-sweetened beverages (SSB) and their overlap with populations and areas presenting higher body mass index (BMI).DesignCross-sectional population-based study.SettingState of Geneva, Switzerland.Participants15,767 non-institutionalized residents aged between 35 and 74 years (20 and 74 since 2011) of the state of Geneva, Switzerland.Main outcome measuresSpatial indices of sugar-sweetened beverage intake frequency and body mass index. Median regression analysis was used to control for characteristics of patients.ResultsThe SSB intake frequency and the BMI were not randomly distributed across the state. Among the 15,423 participants retained for the analyses, 2,034 (13.2%) were within clusters of high SSB intake frequency and 1,651 (10.7%) was within clusters of low SSB intake frequency, 11,738 (76.1%) showed no spatial dependence. We also identified clusters of BMI, 4,014 (26.0%) participants were within clusters of high BMI and 3,591 (23.3%) were within clusters of low BMI, 7,818 (50.7%) showed no spatial dependence. We found that clusters of SSB intake frequency and BMI overlap in specific areas. 1,719 (11.1%) participants were within high SSB intake frequency and high BMI clusters. After adjustment for covariates (education level, gender, age, nationality, and the median income of the area), the identified clusters persisted and were only slightly attenuated.ConclusionA fine-scale spatial approach allows identifying specific populations and areas presenting higher SSB consumption and, for some areas, higher SSB consumption associated with higher BMI. These findings could guide legislators to develop targeted interventions such as prevention campaigns and pave the way for precision public health.What is already known on this topicThe consumption of sugar-sweetened beverages (SSBs) is an important contributory factor of obesity and obesity-related diseases.SSB consumption varies according to socioeconomic status, which could explain the higher prevalence of obesity in specific areas.SSB taxation faces resistance in many countries due to its potential regressive nature.What this study addsThe spatial analysis of individual-level SSB consumption in the state of Geneva provides a clear identification of populations and areas presenting higher SSB consumption and, for some areas, higher SSB consumption along with higher body mass index (BMI).The results demonstrate the persistence of SSB clustering in the geographic space after adjusting for education level, gender, nationality, age, and neighborhood-level median income.The findings provide guidance for future public health interventions to reduce SSB consumption by better targeting vulnerable populations.

Occurrence of bacteria belonging to the order Chlamydiales was investigated for the first time in common toad (Bufo bufo) tadpole populations collected from 41 ponds in the Geneva metropolitan area, Switzerland. A Chlamydiales-specific Real-Time PCR was used to detect and amplify the Chlamydiales 16S rRNA-encoding gene from the tails of 375 tadpoles. We found the studied amphibian populations to be infected by “Chlamydia-like organisms” (CLOs) attributable to the genera Similichlamydia, Neochlamydia, Protochlamydia and Parachlamydia (belonging to the family Parachlamydiaceae), Simkania (family Simkaniaceae) and Estrella (family Criblamydiaceae); additionally, DNA from the genus Thermoanaerobacter (family Thermoanaerobacteriaceae) was detected. A global autocorrelation analysis did not reveal a spatial structure in the observed CLOs infection rates, and association tests involving land cover characteristics did not evidence any clear effect on CLOs infection rates in B. bufo. Despite preliminary, these results suggest a random and ubiquitous distribution of CLOs in the environment, which would support the biogeographical expectation “everything is everywhere” for the concerned microorganisms and their amoeba vectors.

Susceptibility to obesity in today’s environment has a strong genetic component. Lower socioeconomic position (SEP) is associated with a higher risk of obesity but it is not known if it accentuates genetic susceptibility to obesity. We aimed to use up to 120,000 individuals from the UK Biobank study to test the hypothesis that measures of socioeconomic position accentuate genetic susceptibility to obesity. We used the Townsend deprivation index (TDI) as the main measure of socioeconomic position, and a 69-variant genetic risk score (GRS) as a measure of genetic susceptibility to obesity. We also tested the hypothesis that interactions between BMI genetics and socioeconomic position would result in evidence of interaction with individual measures of the obesogenic environment and behaviours that correlate strongly with socioeconomic position, even if they have no obesogenic role. These measures included self-reported TV watching, diet and physical activity, and an objective measure of activity derived from accelerometers. We performed several negative control tests, including a simulated environment correlated with BMI but not TDI, and sun protection use. We found evidence of gene-environment interactions with TDI (Pinteraction=3×10−10) such that, within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. We also observed evidence of interaction between sun protection use and BMI genetics, suggesting that residual confounding may result in evidence of non-causal interactions. Our findings provide evidence that relative social deprivation best captures aspects of the obesogenic environment that accentuate the genetic predisposition to obesity in the UK.

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.

Past studies conducted in urban areas analyzed the impact of the presence of green spaces on public health, and highlighted in particular the psychological benefits of interacting with nature. To investigate a supposed relationship between overweight and dense built environment, we focused on the State of Geneva, Switzerland, and calculated the correlation between Body Mass Index (BMI) in a representative sample of 6663 adults and the percentage of natural areas at the locations where these individuals were living. To this end, we used population-based health data from the “Bus Sante” study (Geneva University Hospitals) and multi-scale land cover maps obtained by means of satellite images and LiDAR data classification. We found little correlation between BMI (as a proxy for health) and land cover data and were not able to verify the working hypothesis at local and regional scales. However, an important phenomenon highlighted here is the difference in the results obtained between the city center and the whole State.

Background: Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI).Methods: We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a ‘Westernized’ diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment.Results: We found gene–environment interactions with TDI (Pinteraction = 3 × 10–10), self-reported TV watching (Pinteraction = 7 × 10–5) and self-reported physical activity (Pinteraction = 5 × 10–6). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely.Conclusions: Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. Of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible.

BACKGROUND: Body mass index (BMI) may cluster in space among adults and be spatially dependent. Whether BMI clusters among children and how age-specific BMI clusters are related remains unknown. We aimed to identify and compare the spatial dependence of BMI in adults and children in a Swiss general population, taking into account the area's income level. METHODS: Geo-referenced data from the Bus Santé study (adults, n=6663) and Geneva School Health Service (children, n=3601) were used. We implemented global (Moran's I) and local (local indicators of spatial association (LISA)) indices of spatial autocorrelation to investigate the spatial dependence of BMI in adults (35-74 years) and children (6-7 years). Weight and height were measured using standardized procedures. Five spatial autocorrelation classes (LISA clusters) were defined including the high-high BMI class (high BMI participant's BMI value correlated with high BMI-neighbors' mean BMI values). The spatial distributions of clusters were compared between adults and children with and without adjustment for area's income level. RESULTS: In both adults and children, BMI was clearly not distributed at random across the State of Geneva. Both adults' and children's BMIs were associated with the mean BMI of their neighborhood. We found that the clusters of higher BMI in adults and children are located in close, yet different, areas of the state. Significant clusters of high versus low BMIs were clearly identified in both adults and children. Area's income level was associated with children's BMI clusters. CONCLUSIONS: BMI clusters show a specific spatial dependence in adults and children from the general population. Using a fine-scale spatial analytic approach, we identified life course-specific clusters that could guide tailored interventions.

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).

IntroductionSmoking bans were suggested to reduce smoking prevalence and increase quit ratio but their equity impact remains unclear. We aimed to characterise the socioeconomic status (SES)-related inequalities in smoking prevalence and quit ratio before and after the implementation of a public smoking ban.MethodsWe included data from 17 544 participants in the population-based cross-sectional Bus Santé study in Geneva, Switzerland, between 1995 and 2014. We considered educational attainment (primary, secondary and tertiary) as a SES indicator. Outcomes were smoking prevalence (proportion of current smokers) and quit ratio (ex-smokers to ever-smokers ratio). We used segmented linear regression to assess the overall impact of smoking ban on outcome trends. We calculated the relative (RII) and slope (SII, absolute difference) indexes of inequality, quantifying disparities between educational groups in outcomes overall (1995–2014), before and after ban implementation (November 2009).ResultsLeast educated participants displayed higher smoking prevalence (RII=2.04, Pbefore=1.84, Pafter=3.01, Pbefore=0.14, Pafter=0.19, Pbefore=−0.15, Pafter=−0.27, PConclusionsImplementation of a public smoking ban coincided with a short-lived decrease in smoking prevalence and increase in quit ratio but also with a widening in SES inequalities in smoking-related outcomes.

In developing countries, the waterborne transmission of hepatitis E virus (HEV), caused by HEV genotypes 1 (HEV-1) and 2 (HEV-2), leads to the onset of large recurrent outbreaks. HEV infections are of particular concern among pregnant women, due to very high mortality rates (up to 70%). Unfortunately, good understanding of the factors that trigger the occurrence of HEV epidemics is currently lacking; therefore, anticipating the onset of an outbreak is yet not possible. In order to map the geographical regions at higher risk of HEV epidemics and the conditions most favorable for the transmission of the virus, we compiled a dataset of HEV waterborne outbreaks and used it to obtain models of geographical suitability for HEV across the planet. The main three variables that best predict the geographical distribution of HEV outbreaks at global scale are population density, annual potential evapotranspiration and precipitation seasonality. At a regional scale, the temporal occurrence of HEV outbreaks in the Ganges watershed is negatively correlated with the discharge of the river (r = −0.77). Combined, our findings suggest that ultimately, population density and water balance are main parameters influencing the occurrence of HEV-1 and HEV-2 outbreaks. This study expands the current understanding of the combination of factors shaping the biogeography and seasonality of waterborne viral pathogens such as HEV-1 and HEV-2, and contributes to developing novel concepts for the prediction and control of human waterborne viruses in the near future.