Spatial and Spatio-temporal Epidemiology

IntroductionAtmospheric conditions under climate change increase pressure on healthcare systems. Especially, the intensive care units (ICU) are vulnerable due to low buffer capacity and high utilization rates. MethodsDaily ICU cases from 2009 to 2023 were derived from the German statutory health insurance data of eleven regional AOK insurances. Cases were stratified by age and sex. Generalized additive models were used to investigate the associations between daily ICU cases and lagged atmospheric variables. Thirteen intensive care relevant diseases were analyzed using disease-specific predictor sets. Analyses were conducted for regions derived from a human-biometeorological characterization of Germany. Model performance was assessed using (weighted) explained deviance. ResultsOver the 15-year study period, 9,970,548 ICU patients were recorded (44% women), 74.3% aged [≥]60 years. Trauma was the most common ICU-related disease, followed by non-ST elevation myocardial infarction (NSTEMI), pneumonia and ischemic stroke. ICU demand was most sensitive (p [≤] 0.05) to pressure-related factors, thermo-physiological parameters and ozone concentration. In terms of sex-age differences, atmospheric factors affected men more frequently, while women were more impacted by cold weather and particulate matter (PM10). Heat was more relevant for patients aged [≥]60 years. The NSTEMI model in Central Eastern Germany performed best (weighted explained deviance of 49.3%). In males [≥]60 years, heatwaves were associated with a reduced risk of ICU cases (Relative Risk = 0.94, 95%-Confidence Interval 0.89 to 0.99). ConclusionThe study identified key atmospheric factors for ICU, enabling the German healthcare system to prepare better for short-term impacts of meteorological and air quality factors. KEY MESSAGESWhat is already known on this topic: O_LIThe atmospheric changes have a direct impact on public health and the inpatient care, particularly in intensive care units. C_LIO_LIConsequently, there is a necessity to investigate the influence of atmospheric factors on intensive care in order to prepare the healthcare system for the new circumstances. C_LI What this study adds: O_LIThe study provides evidence that atmospheric factors influence the intensive care in Germany and describes age and sex-specific aspects. C_LIO_LIThe results offer valuable insights into how different atmospheric factors affect the demand for intensive care in hospitals. C_LI How this study might affect research, practice or policy: O_LIThe study enables the German healthcare system to better prepare for short-term effects of atmospheric factors, and structural or resource-related adjustments could be made in hospitals to anticipate for short-term fluctuations in intensive care demand. C_LI

Suicide is one of the worlds leading public health problems, with more than 720,000 deaths annually. Suicide has traditionally been studied from an individual perspective. However, research has increasingly highlighted the influence of community-level factors on suicide risk. This study aimed to (1) analyse the spatial distribution of suicide mortality at the provincial level in Spain (2018-2022); (2) perform stratified analyses by sex and age group; and (3) compare suicide risk across different phases of the COVID-19 pandemic. We used data from the Spanish National Institute of Statistics on 19,381 suicide deaths in 47 peninsular provinces between 2018 and 2022. Covariates included sociodemographic (e.g. aging rate, population density), economic (e.g. unemployment, GDP), and environmental (e.g. temperature) indicators. Bayesian hierarchical spatial Poisson regression models were fitted to estimate suicide risk and identify significant contextual variables. The general spatial model revealed a higher risk of suicide in provinces with lower population density, higher aging rates, and lower health expenditure. Other covariates such as gross domestic product, unemployment, or temperature were associated with specific sex or age groups. Suicide risk was highest in the northwestern provinces and lowest in the central regions. Stratified analyses showed similar patterns across gender and age groups, and between time periods, with some variations in spatial distribution. This study reveals significant spatial heterogeneity in suicide risk across Spanish regions, influenced by socio-demographic, economic, and environmental factors. These findings underline the importance of regionally tailored suicide prevention policies, especially in aging and low-density areas with low health investment. Key MessagesWe examined spatial patterns and socioeconomic and environmental determinants of suicide mortality in 50 Spanish provinces between 2018 and 2022. We found persistent geographical inequalities in suicide rates, with higher mortality in low-density provinces and those with older populations, and protective effects associated with health expenditure. These findings highlight the importance of place-based suicide prevention strategies that consider regional disparities and socioeconomic vulnerabilities.

IntroductionObesity and related health conditions are unevenly distributed across neighborhoods, often co-occuring with multiple health challenges and socioeconomic disadvantages. Using an ecosyndemic framework, which integrates ecological and social dimensions that contribute to the clustering of health problems, this study examines how adverse obesity-related health outcomes spatially cluster in relation to obesogenic environments and socioeconomic position (SEP) across Dutch neighborhoods. MethodsNationwide neighborhood-level data on health outcomes, obesogenic environmental exposures (food environment, walkability, drivability, bikeability, sports facilities), and SEP were combined for all inhabited Dutch administrative neighborhoods in 2016 (N=12,420). Cluster analysis was used to identify distinct neighborhood profiles and descriptive statistics to characterize each cluster, with spatial patterns visualized using an interactive heatmap and principal component plots. ResultsFive neighborhood clusters were identified. The Ecosyndemic cluster (N=1,070 neighborhoods) exhibited the highest burden of obesity (17% [IQR 16;19), chronic diseases (36% [IQR 33;38%) and risk of anxiety/depression (55% [IQR 51;58]), unhealthy food environments and low SEP. In contrast, the Privileged cluster (N=6,425) had more favorable health outcomes and living conditions, including lower obesity prevalence (12% [IQR 11;14]). The Psychosocial Vulnerability cluster (N=991) was notable for elevated risk of anxiety/depression (47% [IQR 43;51]) combined with relatively low obesity (11% [IQR 8;12]). The Syndemic cluster (N=1,836; obesity 15% [IQR 14;17]) and Towards Privileged cluster (N=2,098; obesity 12% [IQR 10;13]) represented intermediate profiles. ConclusionObesity and related health issues frequently cluster with unfavorable environment and SEP at the neighborhood level. The ecosyndemic framework offers a novel approach for identifying high-risk areas and supports targeted, social and place-based interventions.

ImportancePrenatal cannabis exposure is increasing in prevalence, yet its associations with early brain development--particularly how the timing and frequency of exposure across gestation relate to neonatal brain structure--remain insufficiently understood. Clarifying these associations is essential for informing early risk identification and guiding perinatal care. ObjectiveTo examine associations between patterns of maternal prenatal cannabis exposure, including exposure presence, gestational timing, and frequency of exposure, and neonatal brain structure and microstructure during the first month of life. Design, Setting, and ParticipantsThis cohort study included 1,782 mother-infant dyads (221 with PCE) from the HEALthy Brain and Child Development Study. Mother-reported prenatal cannabis exposure was assessed using the validated Timeline Follow-back method. Infants underwent natural-sleep magnetic resonance imaging, including T2-weighted structural imaging and diffusion imaging, within the first month of life. Main Outcomes and MeasuresAssociations between prenatal cannabis exposure and regional T2-weighted volumes and diffusion white matter microstructure metrics examined (1) exposure presence, (2) gestational timing of exposure, and (3) frequency of exposure within exposed infants. ResultsAny prenatal cannabis exposure was associated with brain volume differences in cerebellar and subcortical limbic regions, including smaller amygdala, thalamic, and cerebellar vermis volumes and larger caudate, hippocampal, and cerebellar cortex volumes. Timing-specific analyses revealed divergent patterns: first trimester exposure was associated with smaller volumes in select regions, whereas exposure that continued into the third trimester was associated with larger volumes in overlapping structures, with additional subcortical volumetric differences observed. White matter microstructure alterations were observed only among infants with exposure that continued into the third trimester. Within the exposed subgroup, higher frequency of cannabis exposure was associated with larger cerebral white matter volumes and white matter microstructural differences in white matter regions. Conclusions and RelevanceIn infants with maternal prenatal cannabis exposure, we observed timing- and frequency-dependent differences in brain development within the first month of life. These findings underscore the importance of considering not only the presence of exposure, but also when and how much cannabis is used during pregnancy to support targeted prenatal counseling and early developmental monitoring for exposed infants. Key PointsO_ST_ABSQuestionC_ST_ABSIs prenatal cannabis exposure associated with brain development in the first month of life? FindingsIn a cohort[ABS] of 1,782 mother-infant dyads, prenatal cannabis exposure was associated with region-specific differences in neonatal brain volumes. Brain volume and diffusion white matter microstructure associations differed between exposure limited to the first trimester versus exposure that continued into the third trimester. Greater frequency of exposure across gestation was also associated with volumetric and microstructural differences. MeaningThe timing and frequency of prenatal cannabis exposure is associated with alterations in neonatal brain development, underscoring the importance of addressing cannabis use in pregnancy.

BackgroundAir pollution is a potentially modifiable risk factor for dementia with a population attributable risk fraction of 3%. Little is known about the causal mechanisms behind the association, so we aimed to investigate this. MethodsData from the UK Biobank were used to investigate the association between six measures of air pollution (NO2, NOx, PM2{middle dot}5-10, PM2{middle dot}5, PM2{middle dot}5 absorbance and PM10) and dementia incidence. Indirect pathways through four mediators (cardiovascular conditions, mental health treatment, insufficient exercise and social isolation) were explored. Logistic regression was used to model the associations between air pollution, mediators and dementia. Casual mediation analysis implemented using the g-formula was used to investigate the joint indirect effect through the mediators. FindingsExposure to the highest quintile of PM2{middle dot}5 (Rte:1{middle dot}14, 95% CI:1{middle dot}06-1{middle dot}23), NOx (Rte:1{middle dot}11, 95% CI:1{middle dot}03-1{middle dot}20) or NO2 (Rte:1{middle dot}08, 95% CI:0{middle dot}99-1{middle dot}16), compared to the lowest quintile, was associated with higher dementia risk. Most of the observed association resulted from the direct effect of air pollution, consisting of pathways not captured through considered mediators. Amongst those in the highest PM2{middle dot}5 quintile, jointly intervening on the four mediators would result in a 1% reduction in risk of dementia (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}01-1{middle dot}02). The randomised pure natural indirect effect was similar for NO2 (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}00-1{middle dot}01) and NOx (Rpnie:1{middle dot}01, 95% CI: 1{middle dot}01-1{middle dot}02). InterpretationMost of the association between dementia and PM2{middle dot}5, NO2 and NOx occurs through the direct effect of air pollution, or other unmeasured mediators, and not pathways through these four mediators. FundingMedical Research Council (Grant MR/W006774/1).

BackgroundCannabis use is highly prevalent among people who use unregulated drugs. While daily cannabis use has been hypothesized to provide protective effects through substitution or tolerance mechanisms, the relationship between cannabis use frequency and overdose risk remains poorly understood, particularly for infrequent users. MethodsWe conducted a secondary analysis of cross-sectional interview data from people who use unregulated drugs in Vancouver, British Columbia, collected during the fentanyl crisis (November 2019-July 2021; n=657). Binary logistic regression examined associations between self-reported cannabis use frequency (five categories: less than monthly, 1-3 times per month, weekly, more than weekly and daily) and non-fatal overdose in the preceding six months. Daily use served as the reference category. Models adjusted for age, gender, ethnicity, homelessness, mental health, HIV status, incarceration and daily use of alcohol, opioids, fentanyl, cocaine and stimulants. ResultsAmong 657 participants, 95 (14.5%) reported non-fatal overdose in the past six months. In adjusted models with daily cannabis use as the reference, infrequent cannabis use was associated with significantly increased odds of overdose: use 1-3 times per month (aOR=3.17, 95% CI: 1.50-6.69, p=.002) and more than weekly use (aOR=3.13, 95% CI: 1.70-5.76, p<.001) showed approximately three-fold increased odds compared to daily use. Less frequent use showed non-significant trends in the same direction (less than monthly: aOR=1.73, 95% CI: 0.89-3.37, p=.109; weekly: aOR=1.44, 95% CI: 0.59-3.51, p=.421). Sensitivity analysis restricted to participants with daily stimulant or fentanyl use (n=148) revealed even stronger associations. ConclusionsInfrequent cannabis use was associated with substantially increased overdose risk compared to daily use. This frequency-dependent relationship, with infrequent users at highest risk, likely reflects tolerance differences: infrequent users lack tolerance to synergistic cannabis-opioid effects. These findings were completely obscured in preliminary analyses that dichotomized cannabis use as daily versus less-than-daily, demonstrating how analytical choices can mask critical public health insights. Current harm reduction approaches, including cannabis distribution programs, should incorporate frequency-dependent risk communication and develop strategies to protect infrequent users who may be at heightened overdose risk.

BackgroundDengue fever is a major neglected tropical disease with a rapidly rising global burden, and localized outbreaks are increasingly reported in southern subtropical China. Fujian Province, a coastal subtropical region with favorable ecological conditions for Aedes albopictus breeding and frequent cross-border exchanges with dengue-endemic areas, has had continuous local dengue cases for over a decade, raising concerns about the establishment of a stable natural endemic focus. Sustained local dengue transmission is defined by four core criteria, but no systematic assessment of these criteria has been conducted for Fujian using long-term multi-dimensional surveillance data. We aimed to evaluate whether a natural endemic focus for sustained local dengue transmission has been established in Fujian Province from 2014 to 2024 using four core evidence dimensions. MethodsWe extracted data on imported and locally acquired dengue cases in Fujian from 2014 to 2024 from Chinas National Notifiable Disease Reporting System (NNDRS). Serological surveillance for dengue IgG antibodies and virological surveillance for dengue virus in Aedes albopictus were conducted at seven sentinel sites. The study period was stratified into three phases based on the impact of COVID-19 non-pharmacological interventions: pre-pandemic (2014-2019), pandemic(2020-2022), and post-pandemic(2023-2024). Descriptive epidemiological analysis and data visualization were performed using R software (version 4.4.1), with t-tests for continuous variables and {chi}{superscript 2} tests for categorical variables. ResultsA total of 3,606 dengue cases were reported in Fujian during the study period, including 1,229 imported and 2,377 locally acquired cases. Key findings were as follows: (1) Temporal distribution: Local dengue transmission was completely interrupted during the 2020-2022 COVID-19 pandemic (0 local cases, only 26 imported cases), and resumed at a low level in 2023-2024 (160 local cases). (2) Serology: The overall population dengue IgG antibody positivity rate was 4.2% (66/15,736), with no statistically significant difference between pre-epidemic (3.8%, 30/7,835) and post-epidemic seasons (4.5%, 36/7,901; P=0.48), and no year with a positivity rate exceeding 10%. (3) Vector surveillance: Only one dengue virus-positive sample was detected among 385,000 Aedes albopictus mosquitoes collected during routine surveillance (Taijiang District, Fuzhou, October 2017), with no viral nucleic acid detected in all other samples. (4) Age distribution: The mean age of locally acquired cases (46.1{+/-}19.8 years) was significantly higher than that of imported cases (35.8{+/-}11.2 years, P<0.001), and local cases were concentrated in the middle-aged group (40-60 years) with no child-dominant pattern observed. ConclusionsFujian Province has not established a stable natural endemic focus for sustained local dengue transmission, and imported cases are the primary driver of local outbreaks in the region. Strengthened surveillance and early management of imported cases, integrated vector control targeting Aedes albopictus, and targeted public health education are critical and essential strategies to prevent the establishment of a dengue natural endemic focus in Fujian and other subtropical coastal regions with similar epidemiological characteristics. Author SummaryDengue fever is a rapidly spreading neglected tropical disease worldwide, and southern China faces persistent threats of local transmission due to favorable ecological conditions for mosquito breeding and frequent cross-border travel. Fujian Province, a subtropical coastal region in southeastern China, has reported annual local dengue cases for over a decade, raising public health concerns about the potential establishment of a stable natural endemic focus--where the virus circulates sustainably without relying on imported cases. To address this critical question, we conducted a comprehensive 11-year assessment (2014-2024) of dengue transmission in Fujian using four key evidence dimensions defined for identifying dengue endemic foci: the continuity of local cases independent of imported sources, population antibody levels, dengue virus detection in local mosquitoes (Aedes albopictus), and the age distribution of infected patients. We also leveraged the COVID-19 pandemic(2020-2022) as a unique natural experiment, during which strict travel restrictions drastically reduced imported dengue cases, to test whether local transmission could persist on its own. Our findings showed that local dengue transmission in Fujian completely stopped during the COVID-19 pandemic and only resumed when cross-border travel and imported cases recovered, confirming local transmission is entirely dependent on imported virus sources. Additionally, the local population had a very low dengue antibody positivity rate (4.2%), dengue virus was detected in only one mosquito sample over 11 years of surveillance, and local cases were concentrated in middle-aged adults (not children--the typical group affected in endemic areas). Together, these results confirm that Fujian Province has not established a stable natural endemic focus for dengue fever. While no endemic focus exists yet, Fujian remains at high risk of imported-driven local outbreaks due to its climate and cross-border exchanges. Our study highlights three critical strategies to prevent the future establishment of a dengue endemic focus in Fujian and other similar subtropical coastal regions: strengthening surveillance and early response for imported dengue cases, implementing targeted mosquito control measures during peak transmission seasons, and conducting public health education to raise awareness of dengue prevention. These evidence-based interventions are key to blocking the formation of sustained local dengue transmission and protecting regional population health.

ABSTRACT/SUMMARYO_ST_ABSObjectiveC_ST_ABSCannabis use during pregnancy is increasing; associations with neonatal growth may be confounded by nicotine. We evaluated prenatal cannabis exposure (PreCE) and neonatal outcomes in a prospective cohort with biochemical control for nicotine exposure. MethodsIn the Cannabis Use During Early Life and Development (CUDDEL) study, pregnant women with a lifetime history of cannabis use were classified as PreCE if they self-reported use or had urine THC-COOH positivity at any trimester (n=297) and as unexposed if they reported no use and tested negative (n=151). Linear regression and modified Poisson models estimated associations with birthweight and small for gestational age (SGA; <10th and <5th percentiles), adjusting for sociodemographic factors, gestational age, maternal age and BMI, and urinary cotinine. Analyses stratified by cannabis use frequency (>weekly vs <monthly) and cotinine status. ResultsParticipants (N=448; 18-41 years; 85.3% non-Hispanic Black) had lower birthweight with PreCE in adjusted models (Beta=-0.08; padj=0.041). High-frequency PreCE was associated with lower birthweight compared with unexposed pregnancies (Beta=-0.13; padj=0.03), whereas low-frequency PreCE was not. Cotinine-positive PreCE showed the greatest birthweight reduction versus unexposed (Beta=-0.20; padj<0.001). PreCE was also associated with higher likelihood of SGA <5th percentile; risk was highest in PreCE+Nicotine compared with both unexposed and PreCE-Nicotine groups. ConclusionsPrenatal cannabis exposure was associated with reduced birthweight and SGA in this cohort. Nicotine co-exposure intensified these associations, yet effects persisted without cotinine, supporting cannabis as an independent perinatal risk factor and emphasizing the value of cotinine assessment in populations where blunt use or secondhand exposure is common.

Tobacco use disorder (TUD) remains a leading cause of preventable mortality, and existing pharmacotherapies yield 12-month abstinence rates below 30%. As cannabis legalization expands, approximately 18-22% of people who use tobacco report concurrent cannabis use, yet the impact of co-use on cessation outcomes and the therapeutic potential of endocannabinoid system (ECS) modulation remain unclear. We conducted a translational systematic review and meta-analysis following PRISMA 2020 guidelines, searching Ovid MEDLINE, Embase, APA PsycInfo, and Web of Science through January 2026 (PROSPERO: CRD420250652724). Three study categories were eligible: observational studies of cannabis co-use and cessation outcomes; preclinical studies of cannabinoid modulators on nicotine-related behaviors; and human experimental studies of ECS-targeted interventions. Of 4,869 records screened, 52 studies met inclusion criteria. Meta-analysis of 18 observational studies (N=229,630) revealed that cannabis use was associated with 35% lower odds of achieving tobacco smoking cessation (OR=0.65; 95% CI: 0.55-0.78; p<0.0001; I{superscript 2}=88.1%). Preclinical evidence (15 studies) demonstrated that CB1 receptor antagonists robustly reduced nicotine self-administration and reinstatement, while cannabidiol (CBD) attenuated both nicotine intake and withdrawal without affecting food reinforcement. Clinical translation of CB1 receptor inverse agonists failed due to psychiatric adverse effects, but CBD showed promise by reducing cigarette consumption by 40%, reversing attentional bias to smoking cues, and alleviating withdrawal severity. These findings distinguish naturalistic cannabis exposure from potentially beneficial targeted ECS modulation, and support CBD as a promising candidate for adequately powered tobacco cessation trials.

We describe a fast, noninvasive, low-cost survey method designed to understand the mode of transmission of an emerging pathogen. It is inspired from the standard household prevalence survey consisting in sampling households and counting the total number of people infected in each household, but refines it with the aim of improving diagnosis and estimating more parameters of the model of intra-household transmission. The survey was carried out in May-June 2020, during part of the first national French lockdown and received responses from more than 6,000 households involving a total of 20,000 people. We explain how we conceived the questionnaire, how we disseminated it, to the public through an open website hosted by CNRS, marketed through media and social media, and to a socially representative panel hosted by two survey institutes (BVA, Bilendi). We used the data obtained from the representative panel to correct for sampling biases in the CNRS survey using a classical raking procedure. Our results indicate that raking correctly canceled statistical biases between the two populations. We obtain the empirical distribution in households of the number and nature of symptoms. The main factors affecting the presence of symptoms are age, gender, body mass index (BMI), household size, but not necessarily in the expected direction. Our study shows that combining self-reporting and representative surveys allows investigators to obtain information on prevalence and household transmission mechanisms on emerging diseases at low cost.

The COVID-19 pandemic exposed major vulnerabilities of hospital capacity and management worldwide, particularly in intensive care units (ICUs) and emergency rooms (ER), imposing prompt adaptation and resource reallocation. Although SARS-CoV-2 is no longer endangering healthcare systems, winter seasons continue to bring recurrent overload of critical care services, primarily due to respiratory infections. In France e.g., this pattern led to the reactivation of the national emergency response plan during the 2024-2025 seasonal influenza peak, highlighting the continuous need for improved predictive tools. However, forecasting hospitalization surges at a local scale remains a methodological challenge because the (very) low incidence numbers are subject to strong stochasticity and therefore require additional input of information and dedicated approaches. This study investigates the potential for early forecasting of respiratory infection peaks by analyzing ER visit trends. By clustering all-cause ER visits during the 2023-2025 winter seasons from the Nimes University Hospital (France), we identified a strong temporal correlation between early pediatric hospitalizations ([&le;]5 years old) and the following weeks adult hospitalization incidence for respiratory infections. The results suggest that tracking hospital admissions of pediatric ER visits, even without hospital care needs, can serve as a valuable early warning signal for upcoming peaks in respiratory-related hospitalizations. This predictive approach could improve hospital preparedness and resource management during seasonal influenza outbreaks. Author summaryThe epidemics of respiratory viruses present a significant challenge to hospitals in the temperate zone on an annual basis. Frequently, the hospital overload is mitigated by the late reactive allocation of human and material resources that are, hence, suboptimal. This study proposes a statistical framework to assist hospitals in anticipating bed requirements during seasonal influenza waves, despite high noise at the local level, by enhancing hospitalization forecasting with emergency room (ER) visit data. The prediction of the adult epidemic peak is possible through the analysis of the respiratory pediatric ER visits, which facilitates hospital management.

BackgroundInhaled combusted cannabis and co-use of combusted cannabis and nicotine electronic cigarettes (nECIGs) are on the rise, yet their long-term cardiovascular risk is unclear due to the high prevalence of confounders in observational human studies. Using primary plasma and monocytes and a novel ex vivo mechanistic model of two early steps in atherogenesis, this study examined whether chronic combusted cannabis use is associated with atherogenic changes, as estimated by 1) monocyte transendothelial migration (MTEM), and 2) monocyte-derived foam cell formation (MDFCF), and whether nECIG co-use further amplifies this risk. MethodsA cross-sectional parallel group comparison study was conducted in healthy adults (21-30 years) who chronically 1) used combusted cannabis, 2) co-used both combusted cannabis and nECIGs, and 3) were non-using controls. Using our ex vivo atherogenesis assay, primary outcomes of MTEM, MDFCF, and median fluorescence intensity (MFI) of the lipid-staining fluorochrome BODIPY were determined using primary plasma and autologous primary monocytes from participants. Using flow cytometry and the fluorochrome CELLROX, cellular oxidative stress (COS) in monocytes was determined. ResultsOf the 134 participants, 59 used cannabis, 26 co-used cannabis/nECIG, and 49 were non-using controls. The groups had similar age, sex, and race. Median MTEM was 1.13 fold greater in people who used cannabis compared to non-users 27.8% (IQR 26.1:29.2%) vs 24.5%, (IQR 22.9:27.4%), p<0.0001, and tended to be greater in people who co-used cannabis/nECIG by 1.22-fold 34.1%, (IQR 29.9:38.3%, p=0.17). Median MDFCF and MFI were also increased in people who used cannabis compared to non-users (MDFCF 36.3%, IQR 31.8:35.8%, vs 26.6%, IQR 23.8:25.8%, 1.36-fold and MFI 1163.8, IQR 1042.8:1155.0, vs 940.2 IQR 849.9:1101.4, 1.24-fold) and were further increased in people who co-used cannabis/nECIG (MDFCF 48.7%, IQR 37.3:52.4%, 1.34-fold, MFI 1433.7, IQR 1263.8:1686.4, 1.23-fold; all comparisons p<0.008). Foam cell formation, but not transendothelial migration, was strongly positively correlated with COS. All primary outcomes increased with greater frequency of cannabis and/or nECIG use. ConclusionsIn healthy young adults, exclusive cannabis use is associated with increased atherogenic properties of monocytes and plasma, and this atherogenic effect is further amplified by co-use of nECIGs.

Introduction Heat waves are increasingly frequent and linked to higher mortality risks in Hong Kong. However, estimates of total excess mortality associated with heat waves remain unavailable. This study quantifies excess deaths associated with heat waves in Hong Kong from 2014 to 2023. Methods Daily age- and sex-specific mortality rates and population data were obtained from the Hong Kong Life Tables and Census and Statistics Department. Temperature data came from the Hong Kong Observatory, and relative risks were derived from local research. A Monte Carlo simulation was used to estimate heat-attributable deaths under different heat wave definitions, calculating total excess deaths and annualized death rates per 100,000 population. Results Between 2014 and 2023, heat exposure resulted in an estimated 1,455 (95% CI: 1,098-1,812) to 3,238 (95% CI: 3,234-3,242) excess deaths. In 2023, annualized excess death rates ranged from 2.95 (95% CI: 2.41-3.50) to 5.09 (95% CI: 5.07-5.12) per 100,000 people. Males and individuals aged 65 or older were disproportionately affected. Conclusion Over the 10-year study period, 1,455 to 3,238 excess deaths in Hong Kong were attributed to extreme heat. Heat waves now rank among the top ten causes of death in Hong Kong, with mortality rates comparable to diabetes. These findings underscore the need for urgent public health interventions to mitigate the impact of extreme heat.

Incidence of norovirus has strong seasonality in temperate and continental climates. Many studies have examined its association with climate variables, but evidence remains disparate. We address this gap by performing a systematic review to summarise and interpret the strength and directionality of associations between climate variables and norovirus incidence. Embase, Scopus, Web of Science and PubMed databases were screened for peer-reviewed studies on 2nd of December 2024. Articles were included if they described any climate or meteorological variable, in a categorical or numerical format, relative to a measurement of norovirus incidence risk in a human population, or prevalence or survivability outside the human host. Bias was assessed using a modified Critical Appraisal Skills Programme checklist. If dispersion of the effect in a human population was provided, the mean size was calculated using inverse variance weighting. The effect size outside the host was summarised as D-values, representing the time required to achieve a 90% reduction in the detected amount of virus. A total 139 studies were included. Predictors of risk were ambient and water temperature, relative and absolute humidity, anomalies of ambient temperature and precipitation, atmospheric and vapour pressure. High heterogeneity in direction and size of effects was observed due to regional differences in the factors driving norovirus seasonality and differences in outcome and exposure definitions. Our review suggests that the sensitivity of norovirus to individual climate variables is region and time specific, reflecting geographical differences in the relative importance of norovirus transmission via environmental pathways versus human-to-human contact. Plain Language SummaryNorovirus, a gastrointestinal virus, has a higher number of cases during specific months of the year. Regions with similar types of climate appear to have similar time periods when the increase in the number of infections occurs, which has been linked to norovirus case numbers being correlated to individual climate variables, such as temperature or rainfall. To understand how these associations compare globally and what are their potential explanations, we screened four major scientific databases, namely Embase, Scopus, Web of Science and PubMed. After the selection process, a total 139 peer-reviewed studies were included in this study. We found that ambient and water temperature, relative and absolute humidity, anomalies of ambient temperature and precipitation, atmospheric and vapour pressure were predictors of an increase in norovirus cases. However, the strength and direction of the relationships differed from region to region. A potential explanation is that geographies also differ in how important individual routes are for the transmission of norovirus, specifically via the environment as opposed to direct human-to-human contact, whereas climate is likely to have a greater influence on the former. Key pointsO_LIThe strength and direction of associations between climate variables and norovirus incidence varies by region and time period C_LIO_LIThe strength of associations vary across the transmission routes of norovirus, e.g., environmental versus human-to-human contact C_LIO_LIClimate variables impact norovirus survival and dissemination outside the host, which may inform models of environmental virus transmission C_LI

Wastewater is increasingly being recognized as an important data stream that can contribute to infectious disease surveillance and forecasting. With this recognition, a growing number of statistical inference approaches are being developed to use wastewater data to provide quantitative insights into epidemiological dynamics. However, few existing approaches have allowed for systematic integration of data streams for inference, for example by combining case incidence data and/or serological data with wastewater data. Furthermore, only a subset of existing approaches have been able to handle missing data without imputation and to handle datasets with different sampling times or intervals. Here, we develop a statistically rigorous, yet lightweight, approach to infer and forecast time-varying effective reproduction numbers (Rt values) using longitudinal wastewater virus concentrations either alone or jointly with additional data streams including case incidence data and serological data. Our approach relies on a state-space modeling approach for inference and forecasting, within the context of a simple bootstrap particle filter. We first describe the structure of our underlying disease transmission process model as well as our observation models. Using a mock dataset, we then show that Rt can be accurately estimated by interfacing this model with case incidence data, wastewater data, or a combination of these two data streams using the bootstrap particle filter. Of note, we show that these data streams alone do not allow for reconstruction of underlying infection dynamics due to structural parameter unidentifiability. We then apply our particle filter to a previously analyzed SARS-CoV-2 dataset from Zurich that includes case data and wastewater data. Our analyses of these real-world datasets indicate that incorporation of process noise (in the form of environmental stochasticity) into the state space model greatly improves our ability to reconstruct the latent variables of the model. We further show that underlying infection dynamics can be made identifiable through the incorporation of serological data and that the bootstrap particle filter can be used to make forecasts of Rt, case incidence, and wastewater virus concentrations. We hope that the inference approach presented here will lead to greater reliance on wastewater data for disease surveillance and forecasting that will aid public health practitioners in responding to infectious disease threats.

Dengue is one of the worlds highest-burden arboviral diseases. Although classically considered an urban disease, many regions experience a substantial dengue burden in rural areas. The combined influence of long-term climate, short-term weather variation, local built environments, and land-use gradients on dengue dynamics in rural settings remains poorly understood, limiting our ability to predict shifting risk under global change. Here, we investigate these dynamics in Costa Rica to disentangle how these interacting socio-environmental factors shape rural dengue transmission. We first use 22 years of canton-level (admin-2) case data to establish that both dengue cases and incidence are consistently higher in rural than in urban districts. Then, using ten years of district-level (admin-3) monthly case data and a Bayesian hierarchical modeling framework, we identify the climatic and land-use features most strongly associated with dengue risk. Temperature underlies broad spatial patterns in dengues urban-rural distribution, while precipitation effects differ between coasts, reflecting intercoastal climate zone contrasts rather than interactions between urbanization and water availability. Given suitable climate, even modest levels of built infrastructure substantially increase risk, but the relationship plateaus at higher levels of building volume. Dengue risk is also elevated in areas with high agricultural crop cover at low and mid elevations but not at higher, cooler elevations. Together these results suggest that high risk of rural dengue in Costa Rica result from climate suitability aligning with baseline levels of built infrastructure, with agriculture potentially emerging as a distinct driver of rural dengue transmission.

The COVID-19 pandemic has presented severe challenges in understanding and predicting the spread of infectious diseases, necessitating innovative approaches beyond traditional epidemiological models. This study introduces an advanced method for automated model discovery using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm, leveraging a dataset from the COVID-19 outbreak in Thuringia, Germany, encompassing over 400,000 patient records and vaccination data. By analysing this dataset, we develop a flexible, data-driven model that captures many aspects of the complex dynamics of the pandemics spread. Our approach incorporates external factors and interventions into the mathematical framework, leading to more accurate modelling of the pandemics behaviour. The fixed coefficient values of the differential equation as globally determined by the SINDy were not found to be accurate for locally modelling the measured data. We therefore refined our technique based on the differential equations as found by SINDy, by investigating three modifications that account for recent local data. In a first approach, we re-optimized the coefficient values using seven days of past data, without changing the globally determined differential equation. In a second approach, we allowed a temporal dependence of the coefficient values fitted using all previous data in combination with regularization. As a last method, we kept the coefficients fixed to the original values but augmented the differential equation with a small neural network, locally optimized to the data of the past week. Our findings reveal the critical role of vaccination and public health measures in the pandemics trajectory. The proposed model offers a robust tool for policymakers and health professionals to mitigate future outbreaks, providing insights into the efficacy of intervention strategies and vaccination campaigns. This study advances the understanding of COVID-19 dynamics and lays the groundwork for future research in epidemic modelling, emphasising the importance of adaptive, data-informed approaches in public health planning.

Work-related stress is a major public health issue affecting workers across various sectors. Individuals experiencing work-related stress are more likely to consume psychoactive substances, primarily alcohol, tobacco, and cannabis, as well as psychoactive medications, which may be used as coping mechanisms. Work-related stress is also associated with adverse outcomes such as burnout, depression, anxiety, and sleep disorders. In France, early childhood professionals, including "ATSEMs", "animateurs", and "agents dentretien", play a crucial role in the education, care, and well-being of children but are exposed to high levels of occupational stress due to the emotionally demanding nature of their work and the associated physical strain, making them vulnerable to substance use, burnout, depression, anxiety, and sleep disorders. This cross-sectional epidemiological study, conducted at a single time point, will be carried out among early childhood professionals working in schools for children in Orleans Metropole, Communaute de Communes des Terres du Val de Loire (CCTVL), and Fleury-les-Aubrais. Ethical approval for this study was obtained from the Ethics Committee of the Centre Hospitalier Universitaire dOrleans (assigned reference number is CERO 2511-02). The study aims to provide a better understanding of the relationship between work-related stress and the use of psychoactive substances and medications among early childhood professionals, as well as the association between work-related stress and burnout, depression, anxiety, and sleep disorders. Data will be collected anonymously using self-administered online questionnaires, accessed via a QR code printed on flyers distributed to participants. The same QR code will also provide access to an information sheet explaining that the study complies with ethical guidelines and that proceeding implies non-objection to participation. Based on calculations performed using BiostaTGV, a sample size of 265 participants is required. Statistical analysis will be conducted using SPSS software. Studying these associations is essential for informing the development of targeted interventions and prevention.

BackgroundSecondhand smoke (SHS) exposure remains a major public health concern among adolescents, particularly in low- and middle-income countries. Evidence from Zambia is limited, despite increasing tobacco use and existing tobacco control policies. This study examined the prevalence and correlates of SHS exposure among adolescents in Zambia. MethodsWe analyzed data from the 2021 Zambia Global Youth Tobacco Survey (GYTS), a nationally representative, school-based survey. The sample included 6,499 adolescents aged 11-17 years enrolled in grades 7-9. The primary outcome was any SHS exposure, defined as exposure to tobacco smoke at home, school, enclosed public places, or outdoor public places. Weighted prevalence estimates were calculated, and multivariable logistic regression was used to identify factors associated with SHS exposure, adjusting for demographic, social, environmental, and socioeconomic variables. ResultsOverall, 66.0% of adolescents reported exposure to SHS. Adolescents living with a parent or guardian who smoked had nearly three times higher odds of SHS exposure (adjusted odds ratio [AOR] = 2.76; 95% CI: 2.12-3.62; p < 0.001). Having friends who smoked tobacco (AOR = 1.86; 95% CI: 1.52-2.30; p < 0.001) and seeing teachers smoking at school (AOR = 1.88; 95% CI: 1.40-2.56; p < 0.001) were also significant predictors. Media exposure was important: seeing people use tobacco on television (AOR = 1.88; 95% CI: 1.63-2.17; p < 0.001) and exposure to tobacco advertisements (AOR = 1.38; 95% CI: 1.14-1.67; p = 0.001) increased odds of SHS exposure. Adolescents who had smoked cigarettes had higher odds of exposure (AOR = 2.80; 95% CI: 1.70-4.67; p < 0.001), as did those intending to use tobacco in the next five years (AOR = 1.64; 95% CI: 1.21-2.24; p = 0.002). Age, sex, and grade level were not independently associated with SHS exposure. ConclusionsSHS exposure among adolescents in Zambia is widespread and is largely driven by household smoking, peer influence, school environments, and media exposure. Strengthening enforcement of smoke-free policies, promoting smoke-free homes, and addressing social and media influences are critical to reducing adolescent SHS exposure.

Childhood adversities are common and linked to increased risk of premature mortality, including deaths from accidents in early adulthood. We examined associations between childhood adversity and specific types of lethal accidents using nationwide register data from 1,282,636 individuals in the DANish LIFE course (DANLIFE) cohort born between Jan 1, 1980, and Dec 31, 2001, who did not die or emigrate before age 16. Individuals were classified into five trajectory groups based on annual exposure to 12 adversities across three dimensions from ages 0-15. Accident mortality was categorised into traffic, narcotic and hallucinogenic, other poisoning, and other accidents. Individuals were followed through Dec 31, 2022. Relative and absolute risks were estimated using Cox proportional hazards and Aalen additive hazard models. Compared with the low-adversity group, individuals in one of the childhood adversity groups experienced 4.4 to 33.8 additional accident deaths per 100,000 person-years. The largest relative (HR=13.4 95% CI [9.9-18.6]) and absolute (HD=12.9 95%CI [10.0-15.8]) differences were identified for the high versus low adversity group. High childhood adversity is strongly associated with preventable accident mortality in early adulthood, underscoring the need for structural and social interventions to reduce adversity exposure and related excess mortality.