Prevalence of and risk factors for suicidal ideation in adolescents during the COVID-19 pandemic: a cross-sectional study.

BACKGROUND AND OBJECTIVES: Pandemic-related life changes may have had a deleterious impact on suicidal behaviours. Early detection of suicidal ideation and identification of subgroups at increased risk could help prevent suicide, one of the leading causes of death among adolescents worldwide. Here, we aimed to investigate the prevalence of and risk factors for suicidal ideation in adolescents using a population-based sample from Switzerland, two years into the pandemic. METHODS: Between December 2021 and June 2022, adolescents aged 14 to 17 years already enrolled in a population-based cohort study (State of Geneva, Switzerland) were asked about suicidal ideation over the previous year. In addition to a regression model, we conducted a network analysis of exposures which identified direct and indirect risk factors for suicidal ideation (i.e. those connected through intermediate risk factors) using mixed graphical models. RESULTS: Among 492 adolescents, 14.4% (95% CI: 11.5-17.8) declared having experienced suicidal ideation over the previous year. Using network analysis, we found that high psychological distress, low self-esteem, identifying as lesbian, gay or bisexual, suffering from bullying, extensive screen time and a severe COVID-19 pandemic impact were major risk factors for suicidal ideation, with parent-adolescent relationship having the highest centrality strength in the network. CONCLUSION: Our results show that a significant proportion of adolescents experience suicidal ideation, yet these rates are comparable with pre-pandemic results. Providing psychological support is fundamental, with a focus on improving parent-adolescent relationships.

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.

The abiotic removal of organic micropollutants with iron and manganese oxides in rapid sand filters for groundwater treatment.

Rapid sand filters (RSFs) have shown potential for removing organic micropollutants (OMPs) from groundwater. However, the abiotic removal mechanisms are not well understood. In this study, we collect sand from two field RSFs that are operated in series. The sand from the primary filter abiotically removes 87.5% of salicylic acid, 81.4% of paracetamol, and 80.2% of benzotriazole, while the sand from the secondary filter only removes paracetamol (84.6%). The field collected sand is coated by a blend of iron oxides (FeOx) and manganese oxides (MnOx) combined with organic matter, phosphate, and calcium. FeOx adsorbs salicylic acid via bonding of carboxyl group with FeOx. The desorption of salicylic acid from field sand indicates that salicylic acid is not oxidized by FeOx. MnOx adsorbs paracetamol through electrostatic interactions, and further transforms it into p-benzoquinone imine through hydrolysis-oxidation. FeOx significantly adsorbs organic matter, calcium, and phosphate, which in turn influences OMP removal. Organic matter on field sand surfaces limits OMP removal by blocking sorption sites on the oxides. However, calcium and phosphate on field sand support benzotriazole removal via surface complexation and hydrogen bonding. This paper provides further insight into the abiotic removal mechanisms of OMPs in field RSFs.

Microbiome, resistome and mobilome of chlorine-free drinking water treatment systems.

Drinking water treatment plants (DWTPs) are designed to remove physical, chemical, and biological contaminants. However, until recently, the role of DWTPs in minimizing the cycling of antibiotic resistance determinants has got limited attention. In particular, the risk of selecting antibiotic-resistant bacteria (ARB) is largely overlooked in chlorine-free DWTPs where biological processes are applied. Here, we combined high-throughput quantitative PCR and metagenomics to analyze the abundance and dynamics of microbial communities, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs) across the treatment trains of two chlorine-free DWTPs involving dune-based and reservoir-based systems. The microbial diversity of the water increased after all biological unit operations, namely rapid and slow sand filtration (SSF), and granular activated carbon filtration. Both DWTPs reduced the concentration of ARGs and MGEs in the water by circa 2.5 log gene copies mL-1, despite their relative increase in the disinfection sub-units (SSF in dune-based and UV treatment in reservoir-based DWTPs). The total microbial concentration was also reduced (2.5 log units), and none of the DWTPs enriched for bacteria containing genes linked to antibiotic resistance. Our findings highlight the effectiveness of chlorine-free DWTPs in supplying safe drinking water while reducing the concentration of antibiotic resistance determinants. To the best of our knowledge, this is the first study that monitors the presence and dynamics of antibiotic resistance determinants in chlorine-free DWTPs.

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.

Exploring organic micropollutant biodegradation under dynamic substrate loading in rapid sand filters.

Microbial removal of trace organic micropollutants (OMPs) from drinking water sources remains challenging. Nitrifying and heterotrophic bacteria in rapid sand filters (RSFs) are capable of biodegrading OMPs while growing on ammonia and dissolved organic matter (DOM). The loading patterns of ammonia and DOM may therefore affect microbial activities as well as OMP biodegradation. So far, there is very limited information on the effect of substrate loading on OMP biodegradation at environmentally relevant concentrations (∼ 1 µg/L) in RSFs. We investigated the biodegradation rates of 16 OMPs at various substrate loading rates and/or empty bed contact times (EBCT). The presence of DOM improved the biodegradation of paracetamol (41.8%) by functioning as supplementary carbon source for the heterotrophic degrader, while hindering the biodegradation of 2,4-D, mecoprop and benzotriazole due to substrate competition. Lower loading ratios of DOM/benzotriazole benefited benzotriazole biodegradation by reducing substrate competition. Higher ammonia loading rates enhanced benzotriazole removal by stimulating nitrification-based co-metabolism. However, stimulating nitrification inhibited heterotrophic activity, which in turn inhibited the biodegradation of paracetamol, 2,4-D and mecoprop. A longer EBCT promoted metformin biodegradation as it is a slowly biodegradable compound, but suppressed the biodegradation of paracetamol and benzotriazole due to limited substrate supply. Therefore, the optimal substrate loading pattern is contingent on the type of OMP, which can be chosen based on the priority compounds in practice. The overall results contribute to understanding OMP biodegradation mechanisms at trace concentrations and offer a step towards enhancing microbial removal of OMPs from drinking water by optimally using RSFs.

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.

Perception of the Communication Campaign for @choum a Symptom Reporting App: Insights from Semi Structured Interviews.

As an attempt to identify cluster of spread of COVID, we have developed the @choum functionality enabling individuals to signal when they perceived COVID-19 symptoms. The associated communication campaign did not encounter the expected success and only a limited amount of person did download the app. As an attempt to understand the barriers of use we have recruited a sample of general population to perform semi structured interview. Interview transcripts were analyzed using thematic analysis. Results highlight 3 profiles, engaged, critics and disengaged. We observe that these 3 profiles have different perception of the communication campaign, engaged participants being much more convinced by its message whereas disengaged people lack strongly of trust. This study helped us to identify what messages must be emphasized in order to attract critic people that may be convinced to use the tool.

Seroprevalence of anti-SARS-CoV-2 IgG antibodies, risk factors for infection and associated symptoms in Geneva, Switzerland: a population-based study.

Aims: To assess SARS-CoV-2 seroprevalence over the first epidemic wave in the canton of Geneva, Switzerland, as well as risk factors for infection and symptoms associated with IgG seropositivity. Methods: Between April and June 2020, former participants of a representative survey of the 20-74-year-old population of canton Geneva were invited to participate in the study, along with household members aged over 5 years. Blood samples were tested for anti-SARS-CoV-2 immunoglobulin G. Questionnaires were self-administered. We estimated seroprevalence with a Bayesian model accounting for test performance and sampling design. Results: We included 8344 participants, with an overall adjusted seroprevalence of 7.8% (95% credible interval 6.8-8.9). Seroprevalence was highest among 18-49 year-olds (9.5%), and lowest in 5-9-year-old children (4.3%) and individuals >65 years (4.7-5.4%). Odds of seropositivity were significantly reduced for female retirees and unemployed men compared to employed individuals, and smokers compared to non-smokers. We found no significant association between occupation, level of education, neighborhood income and the risk of being seropositive. The symptom most strongly associated with seropositivity was anosmia/dysgeusia. Conclusions: Anti-SARS-CoV-2 population seroprevalence remained low after the first wave in Geneva. Socioeconomic factors were not associated with seropositivity in this sample. The elderly, young children and smokers were less frequently seropositive, although it is not clear how biology and behaviours shape these differences.

Unravelling the contribution of nitrifying and methanotrophic bacteria to micropollutant co-metabolism in rapid sand filters.

The presence of organic micropollutant (OMP) in groundwater threatens drinking water quality and public health. Rapid sand filter (RSF) rely on biofilms with nitrifying and methanotrophic bacteria to remove ammonia and methane during drinking water production. Previous research observed the partial removal of OMPs with active nitrification and methane oxidation due to co-metabolic conversion of OMPs. However, the contribution of indigenous nitrifying and methanotrophic communities from RSF has yet to be fully explored. Accordingly, experiments were carried out with biofilm-covered sand collected from field-scale RSF, to assess the removal of nine OMPs by nitrifying and methanotrophic bacteria. Results indicated that stimulating nitrification resulted in significantly more removal of caffeine, 2,4-dichlorophenoxyacetic acid and bentazone. Stimulating methanotrophic conditions enhanced the removal of caffeine, benzotriazole, 2,4-dichlorophenoxyacetic acid and bentazone. Microbial community analysis based on 16 S rRNA gene sequencing revealed Nitrosomonas and Nitrospira are the dominant genus in the community under nitrifying conditions. The three genera Methylobacter, Methylomonas and Methylotenera were enriched under methanotrophic conditions. This study highlights that nitrifying and methanotrophic bacteria play important roles during OMP removal in field RSF. Furthermore, results suggest that bioaugmentation with an enriched nitrifying and methanotrophic culture is a promising approach to improve OMP removal in RSF.

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.

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.

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study.

BACKGROUND: Assessing the burden of COVID-19 on the basis of medically attended case numbers is suboptimal given its reliance on testing strategy, changing case definitions, and disease presentation. Population-based serosurveys measuring anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) antibodies provide one method for estimating infection rates and monitoring the progression of the epidemic. Here, we estimate weekly seroprevalence of anti-SARS-CoV-2 antibodies in the population of Geneva, Switzerland, during the epidemic. METHODS: The SEROCoV-POP study is a population-based study of former participants of the Bus Santé study and their household members. We planned a series of 12 consecutive weekly serosurveys among randomly selected participants from a previous population-representative survey, and their household members aged 5 years and older. We tested each participant for anti-SARS-CoV-2-IgG antibodies using a commercially available ELISA. We estimated seroprevalence using a Bayesian logistic regression model taking into account test performance and adjusting for the age and sex of Geneva's population. Here we present results from the first 5 weeks of the study. FINDINGS: Between April 6 and May 9, 2020, we enrolled 2766 participants from 1339 households, with a demographic distribution similar to that of the canton of Geneva. In the first week, we estimated a seroprevalence of 4·8% (95% CI 2·4-8·0, n=341). The estimate increased to 8·5% (5·9-11·4, n=469) in the second week, to 10·9% (7·9-14·4, n=577) in the third week, 6·6% (4·3-9·4, n=604) in the fourth week, and 10·8% (8·2-13·9, n=775) in the fifth week. Individuals aged 5-9 years (relative risk [RR] 0·32 [95% CI 0·11-0·63]) and those older than 65 years (RR 0·50 [0·28-0·78]) had a significantly lower risk of being seropositive than those aged 20-49 years. After accounting for the time to seroconversion, we estimated that for every reported confirmed case, there were 11·6 infections in the community. INTERPRETATION: These results suggest that most of the population of Geneva remained uninfected during this wave of the pandemic, despite the high prevalence of COVID-19 in the region (5000 reported clinical cases over <2·5 months in the population of half a million people). Assuming that the presence of IgG antibodies is associated with immunity, these results highlight that the epidemic is far from coming to an end by means of fewer susceptible people in the population. Further, a significantly lower seroprevalence was observed for children aged 5-9 years and adults older than 65 years, compared with those aged 10-64 years. These results will inform countries considering the easing of restrictions aimed at curbing transmission. FUNDING: Swiss Federal Office of Public Health, Swiss School of Public Health (Corona Immunitas research program), Fondation de Bienfaisance du Groupe Pictet, Fondation Ancrage, Fondation Privée des Hôpitaux Universitaires de Genève, and Center for Emerging Viral Diseases.

A reference map of the human binary protein interactome.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.

GeoLaus, une étude de l'influence des caractéristiques géo-environ- nementales sur la santé.

GeoLaus, a Study on the Influence of Geo-Environmental Characteristics on Population Health Abstract. Geographic information on risk factors for health or disease is increasingly being used to understand the determinants of health. GeoLaus is a project initiated in 2015 that studies the impact of living spaces and socio-economic situation, on physical and mental health and on different lifestyle habits. This paper discusses and illustrates the use of spatial information in CoLaus to understand the determinants of obesity and daytime sleepiness. The first results of the GeoLaus study open new perspectives on population health.

Résumé. Des informations géographiques sur les facteurs de risque pour la santé ou sur la maladie sont de plus en plus utilisées dans le but de mieux comprendre les déterminants de la santé. GeoLaus est un projet initié en 2015 qui étudie l'impact des lieux de vie et de la situation socio-économique sur la santé physique, mentale et sur les différentes habitudes de vie. Cet article discute et illustre l'utilisation de l'information spatiale dans CoLaus pour mieux comprendre les déterminants de l'obésité et de la somnolence. Les premiers résultats de l'étude GeoLaus ouvrent des nouvelles perspectives en matière de santé populationnelle.

Socioeconomically Disadvantaged Neighborhoods Face Increased Persistence of SARS-CoV-2 Clusters.

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.