Spatial analysis of 10-year predicted risk and incident atherosclerotic cardiovascular disease: the CoLaus cohort.

Whether cardiovascular risk scores geographically aggregate and inform on spatial development of atherosclerotic cardiovascular diseases (ASCVD) remains unknown. Our aim is to determine the spatial distribution of 10-year predicted cardiovascular risk and ASCVD, and to compare the overlap of the resulting spatial distributions. Using prospective data from the CoLaus|PsyCoLaus cohort study (2003-2021) we computed SCORE2 in participants free from ASCVD. Geographical distributions of predicted risk and events were determined using the Gi* Getis-Ord autocorrelation statistic. 6203 individuals (54% women, mean age 52.5 ± SD 10.7, ASCVD incidence rate 5.7%) were included. We identified clusters of high versus low predicted risk (4%, 6%, respectively) and ASCVD (5%, 5% respectively) at baseline. They persisted at follow-up. Overlap of SCORE2 and ASCVD clusters was marginal. Body-mass index and alcohol consumption explained most of the predicted risk distribution. For ASCVD, high clusters persisted or were reinforced after multivariate adjustment, while low incidence clusters were reduced, multifactorial determinants. Incidence rate of ASCVD was 2.5% higher (IC 95%, 1.4-3.7) in clusters of higher incidence of ASCVD. To develop up-to-date, geographically targeted prevention strategies, there is a need to study novel geographically risk factors affecting ASCVD and to update commonly used prediction models for a population approach.

Multiple genomic solutions for local adaptation in two closely related species (sheep and goats) facing the same climatic constraints.

The question of how local adaptation takes place remains a fundamental question in evolutionary biology. The variation of allele frequencies in genes under selection over environmental gradients remains mainly theoretical and its empirical assessment would help understanding how adaptation happens over environmental clines. To bring new insights to this issue we set up a broad framework which aimed to compare the adaptive trajectories over environmental clines in two domesticated mammal species co-distributed in diversified landscapes. We sequenced the genomes of 160 sheep and 161 goats extensively managed along environmental gradients, including temperature, rainfall, seasonality and altitude, to identify genes and biological processes shaping local adaptation. Allele frequencies at putatively adaptive loci were rarely found to vary gradually along environmental gradients, but rather displayed a discontinuous shift at the extremities of environmental clines. Of the 430 candidate adaptive genes identified, only 6 were orthologous between sheep and goats and those responded differently to environmental pressures, suggesting different putative mechanisms involved in local adaptation in these two closely related species. Interestingly, the genomes of the 2 species were impacted differently by the environment, genes related to signatures of selection were most related to altitude, slope and rainfall seasonality for sheep, and summer temperature and spring rainfall for goats. The diversity of candidate adaptive pathways may result from a high number of biological functions involved in the adaptations to multiple eco-climatic gradients, and a differential role of climatic drivers on the two species, despite their co-distribution along the same environmental gradients. This study describes empirical examples of clinal variation in putatively adaptive alleles with different patterns in allele frequency distributions over continuous environmental gradients, thus showing the diversity of genetic responses in adaptive landscapes and opening new horizons for understanding genomics of adaptation in mammalian species and beyond.

Measuring Nurses' Knowledge and Awareness of Climate Change and Climate-Associated Diseases: Protocol for a Systematic Review of Existing Instruments.

BACKGROUND: Climate change is a health emergency. Each year, it is estimated to cost more than 230 million years of life expectancy, with 4-9 million premature deaths associated with air pollution, and 9 million excess deaths due to non-optimal temperatures, representing 7% more temperature-related deaths since 2015 and 66% more since 2000. OBJECTIVE: Identify and evaluate the reliability, fidelity, and validity of instruments measuring nurses' knowledge and awareness of climate change and climate-associated diseases. METHODS: A systematic literature review will retrieve and assess studies examining instruments measuring nurses' knowledge and awareness of climate change and climate-associated diseases. Using predefined search terms for nurses, climate change, literacy and scales or tools, we will search for published articles recorded in the following electronic databases, with no language or date restrictions, from their inception until 31 October 2023: Medline Ovid SP (from 1946), PubMed (NOT Medline[sb], from 1996), Embase.com (from 1947), CINAHL Ebesco (from 1937), the Cochrane Library Wiley (from 1992), Web of Science Core Collection (from 1900), the Trip Database (from 1997), JBI OVID SP (from 1998), and the GreenFILE EBSCO. We will also hand-search relevant articles' bibliographies and search for unpublished studies using Google Scholar, ProQuest Dissertations and Theses Global, and DART-EUrope.eu. This will be completed by exploring the gray literature in OpenGrey and the Grey Literature Report, from inception until 31 October 2023, in collaboration with a librarian. Twelve bibliographic databases will be searched for publications up to 31 October 2023. The papers selected will be assessed for their quality. RESULTS: The electronic database searches were completed in May 2023. Retrieved articles are being screened, and the study will be completed by October 2023. After removing duplicates, our search strategy has retrieved 3449 references. CONCLUSIONS: This systematic review will provide specific knowledge about instruments to measure nurses' knowledge, awareness, motivation, attitudes, behaviors, beliefs, skills, and competencies regarding climate change and climate-associated diseases.

Small area vulnerability, household food insecurity and child malnutrition in Medellin, Colombia: results from a repeated cross-sectional study.

Background: Malnutrition and food insecurity might be driven not only by individual factors but also by contextual conditions, such as area-level deprivation or vulnerability. This study aimed to analyze the association between area-level vulnerability and i) household food insecurity and ii) malnutrition in children in Medellin, Colombia, during the years 2017 and 2018. Methods: We obtained data from two different sources: the Living Standards Measurement Survey (LSMS) and the nutrition surveillance system of Medellin. The main outcomes were food insecurity in households with children and anthropometric indicators for children under five. The main predictor was area-level vulnerability. Mixed effects Poisson regression with robust standard errors models were conducted to test the association of quintiles of deprivation with each outcome. Findings: Households with children living in areas with the highest deprivation had 1.9 times the prevalence of food insecurity as compared to those living in areas with the lowest deprivation (PR 1.91, 95% CI 1.42-2.57). Similar results were observed for underweight/risk of underweight (PR 1.26, 95% CI 1.11-1.42), stunting/risk of stunting (PR 1.36, 95% CI 1.22-1.53) and stunting (PR 1.93 95% CI 1.55-2.39) among children under five. We found no consistent associations with wasting/risk of wasting or excess weight/risk of overweight across quintiles of deprivation. Interpretation: This study sheds light on the role of area-level vulnerability on malnutrition in children in Medellin, Colombia, showing a pattern of increasing prevalence of food insecurity, underweight and stunting by quintile of deprivation. Funding: Swiss School of Public Health (SSPH+) and Centre for Global Health Inequalities Research (CHAIN).

Climate Change, Environmental Health, and Challenges for Nursing Discipline.

Current data and scientific predictions about the consequences of climate change are accurate in suggesting disaster. Since 2019, climate change has become a threat to human health, and major consequences on health and health systems are already observed. Climate change is a central concern for the nursing discipline, even though nursing theorists' understanding of the environment has led to problematic gaps that impact the current context. Today, nursing discipline is facing new challenges. Nurses are strategically placed to respond to the impacts of climate change through their practice, research, and training in developing, implementing, and sustaining innovation towards climate change mitigation and adaptation. It is urgent for them to adapt their practice to this reality to become agents of change.

Vertical distribution and seasonal dynamics of planktonic cyanobacteria communities in a water column of deep mesotrophic Lake Geneva.

Background: Temperate subalpine lakes recovering from eutrophication in central Europe are experiencing harmful blooms due to the proliferation of Planktothrix rubescens, a potentially toxic cyanobacteria. To optimize the management of cyanobacteria blooms there is the need to better comprehend the combination of factors influencing the diversity and dominance of cyanobacteria and their impact on the lake's ecology. The goal of this study was to characterize the diversity and seasonal dynamics of cyanobacteria communities found in a water column of Lake Geneva, as well as the associated changes on bacterioplankton abundance and composition. Methods: We used 16S rRNA amplicon high throughput sequencing on more than 200 water samples collected from surface to 100 meters deep monthly over 18 months. Bacterioplankton abundance was determined by quantitative PCR and PICRUSt predictions were used to explore the functional pathways present in the community and to calculate functional diversity indices. Results: The obtained results confirmed that the most dominant cyanobacteria in Lake Geneva during autumn and winter was Planktothrix (corresponding to P. rubescens). Our data also showed an unexpectedly high relative abundance of picocyanobacterial genus Cyanobium, particularly during summertime. Multidimensional scaling of Bray Curtis dissimilarity revealed that the dominance of P. rubescens was coincident with a shift in the bacterioplankton community composition and a significant decline in bacterioplankton abundance, as well as a temporary reduction in the taxonomic and PICRUSt2 predicted functional diversity. Conclusion: Overall, this study expands our fundamental understanding of the seasonal dynamics of cyanobacteria communities along a vertical column in Lake Geneva and the ecology of P. rubescens, ultimately contributing to improve our preparedness against the potential occurrence of toxic blooms in the largest lake of western Europe.

Detection of SARS-CoV-2 infection clusters: The useful combination of spatiotemporal clustering and genomic analyses.

Background: The need for effective public health surveillance systems to track virus spread for targeted interventions was highlighted during the COVID-19 pandemic. It spurred an interest in the use of spatiotemporal clustering and genomic analyses to identify high-risk areas and track the spread of the SARS-CoV-2 virus. However, these two approaches are rarely combined in surveillance systems to complement each one's limitations; spatiotemporal clustering approaches usually consider only one source of virus transmission (i.e., the residential setting) to detect case clusters, while genomic studies require significant resources and processing time that can delay decision-making. Here, we clarify the differences and possible synergies of these two approaches in the context of infectious disease surveillance systems by investigating to what extent geographically-defined clusters are confirmed as transmission clusters based on genome sequences, and how genomic-based analyses can improve the epidemiological investigations associated with spatiotemporal cluster detection. Methods: For this purpose, we sequenced the SARS-CoV-2 genomes of 172 cases that were part of a collection of spatiotemporal clusters found in a Swiss state (Vaud) during the first epidemic wave. We subsequently examined intra-cluster genetic similarities and spatiotemporal distributions across virus genotypes. Results: Our results suggest that the congruence between the two approaches might depend on geographic features of the area (rural/urban) and epidemic context (e.g., lockdown). We also identified two potential superspreading events that started from cases in the main urban area of the state, leading to smaller spreading events in neighboring regions, as well as a large spreading in a geographically-isolated area. These superspreading events were characterized by specific mutations assumed to originate from Mulhouse and Milan, respectively. Our analyses propose synergistic benefits of using two complementary approaches in public health surveillance, saving resources and improving surveillance efficiency.

The neighborhood environment and its association with the spatio-temporal footprint of tobacco consumption and changes in smoking-related behaviors in a Swiss urban area.

This study aimed to evaluate the association of the neighborhood environment with the spatio-temporal dependence of tobacco consumption and changes in smoking-related behaviors in a Swiss urban area. Data were obtained from the CoLaus cohort (2003-2006, 2009-2012, and 2014-2017) in Lausanne, Switzerland. Local Moran's I was performed to assess the spatial dependence of tobacco consumption. Prospective changes in tobacco consumption and the location of residence of participants were assessed through Cox regressions. Analyses were adjusted by individual and neighborhood data. The neighborhood environment was spatially associated with tobacco consumption and changes in smoking-related behaviors independently of individual factors.

Association of neighbourhood disadvantage and individual socioeconomic position with all-cause mortality: a longitudinal multicohort analysis.

BACKGROUND: Few studies have examined the interactions between individual socioeconomic position and neighbourhood deprivation and the findings so far are heterogeneous. Using a large sample of diverse cohorts, we investigated the interaction effect of neighbourhood socioeconomic deprivation and individual socioeconomic position, assessed using education, on mortality. METHODS: We did a longitudinal multicohort analysis that included six cohort studies participating in the European LIFEPATH consortium: the CoLaus (Lausanne, Switzerland), E3N (France), EPIC-Turin (Turin, Italy), EPIPorto (Porto, Portugal), Melbourne Collaborative Cohort Study (Melbourne, VIC, Australia), and Whitehall II (London, UK) cohorts. All participants with data on mortality, educational attainment, and neighbourhood deprivation were included in the present study. The data sources were the databases of each cohort study. Poisson regression was used to estimate the mortality rates and associations (relative risk, 95% CIs) with neighbourhood deprivation (Q1 being least deprived to Q5 being the most deprived). Baseline educational attainment was used as an indicator of individual socioeconomic position. Estimates were combined using pooled analysis and the relative excess risk due to the interaction was computed to identify additive interactions. FINDINGS: The cohorts comprised a total population of 168 801 individuals. The recruitment dates were 2003-06 for CoLaus, 1989-91 for E3N, 1992-98 for EPIC-Turin, 1999-2003 for EPIPorto, 1990-94 for MCCS, and 1991-94 for Whitehall II. We use baseline data only and mortality data obtained using record linkage. Age-adjusted mortality rates were higher among participants residing in more deprived neighbourhoods than those in the least deprived neighbourhoods (Q1 least deprived neighbourhoods, 369·7 per 100 000 person-years [95% CI 356·4-383·2] vs Q5-most deprived neighbourhoods 445·7 per 100 000 person-years [430·2-461·7]), but the magnitude of the association varied according to educational attainment (relative excess risk due to interaction=0·18, 95% CI 0·08-0·28). The relative risk for Q5 versus Q1 was 1·31 (1·23-1·40) among individuals with primary education or less, but less pronounced among those with secondary education (1·12; 1·04-1·21) and tertiary education (1·16; 1·07-1·27). Associations remained after adjustment for individual-level factors, such as smoking, physical activity, and alcohol intake, among others. INTERPRETATION: Our study suggests that the detrimental health effect of living in disadvantaged neighbourhoods is more pronounced among individuals with low education attainment, amplifying social inequalities in health. This finding is relevant to policies aimed at reducing health inequalities, suggesting that these issues should be addressed at both the individual level and the community level. FUNDING: The European Commission, European Regional Development Fund, the Portugese Foundation for Science and Technology.

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

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, well-being, 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 Specchio-COVID-19 platform, social media posts, press releases and through regular scientific dissemination methods (open-access articles, conferences).

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area.

INTRODUCTION: Evidence suggests that the built environment can influence the intensity of physical activity. However, despite the importance of the geographic context, most of the studies do not consider the spatial framework of this association. We aimed to assess individual spatial dependence of objectively measured moderate and vigorous physical activity (MVPA) and describe the characteristics of the built environment among spatial clusters of MVPA. METHODS: Cross-sectional data from the second follow-up (2014-2017) of CoLaus|PsyCoLaus, a longitudinal population-based study of the Lausanne area (Switzerland), was used to objectively measure MVPA using accelerometers. Local Moran's I was used to assess the spatial dependence of MVPA and detect geographic clusters of low and high MVPA. Additionally, the characteristics of the built environment observed in the clusters based on raw MVPA and MVPA adjusted for socioeconomic and demographic factors were compared. RESULTS: Data from 1,889 participants (median age 63, 55% women) were used. The geographic distribution of MVPA and the characteristics of the built environment among clusters were similar for raw and adjusted MVPA. In the adjusted model, we found a low concentration of individuals within spatial clusters of high MVPA (median: 38.5mins; 3% of the studied population) and low MVPA (median: 10.9 mins; 2% of the studied population). Yet, clear differences were found in both models between clusters regarding the built environment; high MVPA clusters were located in areas where specific compositions of the built environment favor physical activity. CONCLUSIONS: Our results suggest the built environment may influence local spatial patterns of MVPA independently of socioeconomic and demographic factors. Interventions in the built environment should be considered to promote physically active behaviors in urban areas.

Estimating and predicting snakebite risk in the Terai region of Nepal through a high-resolution geospatial and One Health approach.

Most efforts to understand snakebite burden in Nepal have been localized to relatively small areas and focused on humans through epidemiological studies. We present the outcomes of a geospatial analysis of the factors influencing snakebite risk in humans and animals, based on both a national-scale multi-cluster random survey and, environmental, climatic, and socio-economic gridded data for the Terai region of Nepal. The resulting Integrated Nested Laplace Approximation models highlight the importance of poverty as a fundamental risk-increasing factor, augmenting the snakebite odds in humans by 63.9 times. For animals, the minimum temperature of the coldest month was the most influential covariate, increasing the snakebite odds 23.4 times. Several risk hotspots were identified along the Terai, helping to visualize at multiple administrative levels the estimated population numbers exposed to different probability risk thresholds in 1 year. These analyses and findings could be replicable in other countries and for other diseases.

Evaluating urban greening scenarios for urban heat mitigation: a spatially explicit approach.

Urban green infrastructure, especially trees, are widely regarded as one of the most effective ways to reduce urban temperatures in heatwaves and alleviate the adverse impacts of extreme heat events on human health and well-being. Nevertheless, urban planners and decision-makers are still lacking methods and tools to spatially evaluate the cooling effects of urban green spaces and exploit them to assess greening strategies at the urban agglomeration scale. This article introduces a novel spatially explicit approach to simulate urban greening scenarios by increasing the tree canopy cover in the existing urban fabric and evaluating their heat mitigation potential. The latter is achieved by applying the InVEST urban cooling model to the synthetic land use/land cover maps generated for the greening scenarios. A case study in the urban agglomeration of Lausanne, Switzerland, illustrates the development of tree canopy scenarios following distinct spatial distribution strategies. The spatial pattern of the tree canopy strongly influences the human exposure to the highest temperatures, and small increases in the abundance of tree canopy cover with the appropriate spatial configuration can have major impacts on human health and well-being. The proposed approach supports urban planning and the design of nature-based solutions to enhance climate resilience.

Geographic footprints of life expectancy inequalities in the state of Geneva, Switzerland.

Though Switzerland has one of the highest life expectancies in the world, this global indicator may mask significant disparities at a local level. The present study used a spatial cluster detection approach based on individual death records to investigate the geographical footprint of life expectancy inequalities in the state of Geneva, Switzerland. Individual-level mortality data (n = 22,751) were obtained from Geneva's official death notices (2009-2016). We measured life expectancy inequalities using the years of potential life lost or gained (YPLLG) metric, defined as the difference between an individual's age at death and their life expectancy at birth. We assessed the spatial dependence of YPLLG across the state of Geneva using spatial autocorrelation statistics (Local Moran's I). To ensure the robustness of the patterns discovered, we ran the analyses for ten random subsets of 10,000 individuals taken from the 22,751 deceased. We also repeated the spatial analysis for YPLLG before and after controlling for individual-level and neighborhood-level covariates. The results showed that YPLLG was not randomly distributed across the state of Geneva. The ten random subsets revealed no significant difference with the geographic footprint of YPLLG and the population characteristics within Local Moran cluster types, suggesting robustness for the observed spatial structure. The proportion of women, the proportion of Swiss, the neighborhood median income, and the neighborhood median age were all significantly lower for populations in low YPLLG clusters when compared to populations in high YPLLG clusters. After controlling for individual-level and neighborhood-level covariates, we observed a reduction of 43% and 39% in the size of low and high YPLLG clusters, respectively. To our knowledge, this is the first study in Switzerland using spatial cluster detection methods to investigate inequalities in life expectancy at a local scale and based on individual data. We identified clear geographic footprints of YPLLG, which may support further investigations and guide future public health interventions at the local level.

The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock.

Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.

Modelling transport time to trauma centres and 30-day mortality in road accidents in Switzerland: an exploratory study.

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.

Detection of Spatiotemporal Clusters of COVID-19-Associated Symptoms and Prevention Using a Participatory Surveillance App: Protocol for the @choum Study.

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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30444.

Spatial clusters of daily tobacco consumption before and after a smoke-free policy implementation.

This study assessed the spatial dependence of daily tobacco consumption and how it is spatially impacted by individual and neighborhood socioeconomic determinants, and tobacco consumption facilities before and after a smoke-free implementation. Individual data was obtained from the Bus Santé, a cross-sectional survey in Geneva. Spatial clusters of high and low tobacco consumption were assessed using Getis-Ord Gi*. Daily tobacco consumption was not randomly clustered in Geneva and may be impacted by tobacco consumption facilities independently of socioeconomic factors and a smoking ban. Spatial analysis should be considered to highlight the impact of smoke-free policies and guide public health interventions.

Geospatial Analysis of Sodium and Potassium Intake: A Swiss Population-Based Study.

Inadequate sodium and potassium dietary intakes are associated with major, yet preventable, health consequences. Local public health interventions can be facilitated and informed by fine-scale geospatial analyses. In this study, we assess the existence of spatial clustering (i.e., an unusual concentration of individuals with a specific outcome in space) of estimated sodium (Na), potassium (K) intakes, and Na:K ratio in the Bus Santé 1992-2018 annual population-based surveys, including 22,495 participants aged 20-74 years, residing in the canton of Geneva, using the local Moran's I spatial statistics. We also investigate whether socio-demographic and food environment characteristics are associated with identified spatial clustering, using both global ordinary least squares (OLS) and local geographically weighted regression (GWR) modeling. We identified clear spatial clustering of Na:K ratio, Na, and K intakes. The GWR outperformed the OLS models and revealed spatial variations in the associations between explanatory and outcome variables. Older age, being a woman, higher education, and having a lower access to supermarkets were associated with higher Na:K ratio, while the opposite was seen for having the Swiss nationality. Socio-demographic characteristics explained a major part of the identified clusters. Socio-demographic and food environment characteristics significantly differed between individuals in spatial clusters of high and low Na:K ratio, Na, and K intakes. These findings could guide prioritized place-based interventions tailored to the characteristics of the identified populations.

Size and duration of COVID-19 clusters go along with a high SARS-CoV-2 viral load: A spatio-temporal investigation in Vaud state, Switzerland.

To understand the geographical and temporal spread of SARS-CoV-2 during the first documented wave of infection in the state of Vaud, Switzerland, we analyzed clusters of positive cases using the precise residential location of 33,651 individuals tested (RT-PCR) between January 10 and June 30, 2020. We used a prospective Poisson space-time scan statistic (SaTScan) and a Modified Space-Time Density-Based Spatial Clustering of Application with Noise (MST-DBSCAN) to identify both space-time and transmission clusters, and estimated cluster duration, transmission behavior (emergence, growth, reduction, etc.) and relative risk. For each cluster, we computed the number of individuals, the median age of individuals and their viral load. Among the 1684 space-time clusters identified, 457 (27.1%) were significant (p ≤ 0.05), such that they harbored a higher relative risk of infection within the cluster than compared to regions outside the cluster. Clusters lasted a median of 11 days (IQR 7-13) and included a median of 12 individuals per cluster (IQR 5-20). The majority of significant clusters (n = 260; 56.9%) had at least one person with an extremely high viral load (>1 billion copies/ml). Those clusters were considerably larger (median of 17 infected individuals, p < 0.001) than clusters with individuals showing a viral load below 1 million copies/ml (median of three infected individuals). The highest viral loads were found in clusters with the lowest average age group considered in the investigation, while clusters with the highest average age had low to middle viral load. In 20 significant clusters, the viral load of the three first cases was below 100,000 copies/ml, suggesting that subjects with fewer than 100,000 copies/ml may still be contagious. Notably, the dynamics of transmission clusters made it possible to identify three diffusion zones, which predominantly differentiated between rural and urban areas, the latter being more prone to persistence and expansion, which may result in the emergence of new clusters nearby. The use of geographic information is key for public health decision makers in mitigating the spread of the SARS-CoV-2 virus. This study suggests that early localization of clusters may help implement targeted protective measures limiting the spread of the virus.