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The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock, and wildlife

Bacillus anthracis is a spore-forming, Gram-positive bacterium responsible for anthrax, an acute and commonly lethal infection that most significantly affects grazing livestock, wild ungulates and other herbivorous mammals, but also poses a serious threat to human health1, 2. The geographic extent of B. anthracis endemism is still poorly understood, despite multi-decade research on anthrax epizootic and epidemic dynamics around the world3, 4. Several biogeographic studies have focused on modeling environmental suitability for anthrax at local or national scales5-9, but many countries have limited or inadequate surveillance systems, even within known endemic regions. Here we compile an extensive global occurrence dataset for B. anthracis, drawing on confirmed human, livestock, and wildlife anthrax outbreaks. With these records, we use boosted regression trees10, 11 to produce the first map of the global distribution of B. anthracis as a proxy for anthrax risk. Variable contributions to the model support pre-existing hypotheses that environmental suitability for B. anthracis depends most strongly on soil characteristics such as pH that affect spore persistence, and the extent of seasonal fluctuations in vegetation, which plays a key role in transmission for herbivores12, 13. We apply the global model to estimate that 1.83 billion people (95% credible interval: 0.59--4.16 billion) live within regions of anthrax risk, but most of that population faces little occupational exposure to anthrax. More informatively, a global total of 63.8 million rural poor livestock keepers (95% CI: 17.5--168.6 million) and 1.1 billion livestock (95% CI: 0.4--2.3 billion) live within vulnerable regions. Human risk is concentrated in rural areas, and human and livestock vulnerability are both concentrated in rainfed systems throughout arid and temperate land across Eurasia, Africa, and North America. We conclude by mapping where anthrax risk overlaps with vulnerable wild ungulate populations, and therefore could disrupt sensitive conservation efforts for species like bison, pronghorn, and saiga that coincide with anthrax-prone, mixed-agricultural landscapes. Anthrax is a zoonotic disease caused by the Gram-positive bacterium Bacillus anthracis, a generalist soil-transmitted pathogen found on every inhabited continent14, and several islands including Haiti and parts of the Philippines and Indonesia. Worldwide, an estimated 20,000 to 100,000 cases of anthrax occur annually, mostly in poor rural areas15. In clinical presentations of anthrax, case fatality rates are a function of exposure pathway. Respiratory exposure from spore inhalation is important the context of bioterrorism, but is highly uncommon, and accounts for a negligible fraction of the global burden of anthrax cases. Cutaneous exposure to B. anthracis accounts for the majority of human cases worldwide, and typically presents with low mortality; gastrointestinal exposure accounts for the remainder and presents with intermediate to high fatality rates. Cutaneous and gastrointestinal cases of anthrax are most commonly caused by handling and slaughtering infected livestock, or butchering and eating contaminated meat; untreated gastrointestinal cases likely account for most human mortality from anthrax.14-16

epidemiology

Persistent hotspots of Schistosoma mansoni transmission in the Kenyan waters of Lake Victoria: a search for snail-related answers beneath the waves

Following a four-year annual praziquantel treatment campaign the resulting prevalence of S. mansoni was seen to differ among individual villages along the Kenyan shore of Lake Victoria. We have investigated possible inherent differences in snail-related aspects of transmission among such 10 villages, including six persistent hotspot (PHS) villages ([≤]30% reduction in prevalence following repeated treatments) located along the west-facing shore of the lake, and four PZQ-responding (RESP) villages (>30% prevalence reduction following repeated treatment) along Winam Gulf. When taking into account all sampling sites and times and water hyacinth presence/absence, shoreline-associated B. sudanica from PHS and RESP villages did not differ in relative abundance or prevalence of S. mansoni infection. Water hyacinth intrusions were associated with increased B. sudanica abundance. The deeper water snail Biomphalaria choanomphala was significantly more abundant in the PHS villages and prevalence of S. mansoni among villages both before and after control was positively correlated with B. choanomphala abundance. Worm recoveries from sentinel mice did not differ between PHS and RESP villages, and abundance of non-schistosome trematode species was not associated with S. mansoni abundance. Biomphalaria choanomphala provides an alternative, deepwater mode of transmission that may favor greater persistence of S. mansoni in PHS villages. As we found evidence for ongoing S. mansoni transmission in all 10 villages, we conclude conditions conducive for transmission and reinfection occur ubiquitously. This argues for an integrated, basin-wide plan for schistosomiasis control to counteract rapid reinfections facilitated by large snail populations and movements of infected people around the lake.

epidemiology

A Data-driven Individual-based Model of Infectious Disease in Livestock Operation: A Validation Study for Paratuberculosis

Chronic livestock diseases cause large financial loss and affect the animal health and welfare. Controlling these diseases mostly requires precise information on both individual animal and population dynamics to inform farmers decision. Mathematical models provide opportunities to test different control and elimination options rather implementing them in real herds, but these models require valid parameter estimation and validation. Fitting these models to data is a difficult task due to heterogeneities in livestock processes. In this paper, we develop an infectious disease modeling framework for a livestock disease (paratuberculosis) that is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Infection with MAP leads to reduced milk production, pregnancy rates, and slaughter value and increased culling rates in cattle and causes significant economic losses to the dairy industry in the US. These economic effects are particularly important motivations in the control and elimination of MAP. In this framework, an individual-based model (IBM) of a dairy herd was built and a MAP infection was integrated on top of it. Once the model produced realistic dynamics of MAP infection, we implemented an evaluation method by fitting it to data from three dairy herds from the Northeast region of the US. The model fitting exercises used least-squares and parameter space searching methods to obtain the best-fitted values of selected parameters. The best set of parameters were used to model the effect of interventions. The results show that the presented model can complement real herd statistics where the intervention strategies suggested a reduction in MAP but no elimination was observed. Overall, this research not only provides a complete model for MAP infection dynamics in a cattle herd, but also offers a method for estimating parameter by fitting IBM models.

epidemiology

Prescription Opioid Analgesic Use and Mortality in Systemic Lupus Erythematosus

ObjectivesThis research investigated the prevalence of opioid analgesic use in patients with systemic lupus erythematosus (SLE).\n\nMethodsThis 5-year prospective cohort study of 275 SLE patients focused on prescription opioid use and 5-year outcome. Associations were determined with univariable regression analysis and then multivariable models were created to determine independent effects on dependent variables\n\nResultsPrescription opioid use was common in SLE with 24% using opioid analgesics chronically and 76% not using opioids. Opioid users had a higher rate of tobacco use (p<0.01), cocaine use (p<0.002), mean pain scores (p<0.001), disease activity (SLEDAI-2K) (p<0.001), disease damage (SLICC/ACRDI) (p<0.001), non-adherence to medical therapy (p<0.01), and total deaths at 5 years (opioids: 48.0%, no opioids 19.0%, p<0.001). Logistic regression analysis predicting death revealed opioid use (hazard ratio 2.6, p<0.001) and SLEDAI-2K (1.1, p<0.001) respectively; and opioid use (hazard ratio 2.5, p<0.002), SLEDAI-2K (hazard ratio 1.1, p<0.001), and non-adherence (hazard ratio 1.6, p=0.11), respectively. Multivariable Cox Model analysis estimating probability of death with covariates: opioid use (hazard ratio 2.6, p<0.001) and SLEDAI-2K (hazard ratio 1.1, p<0.001); opioid use (hazard ratios 3.0, p<0.001), and cocaine use (hazard ratio 3.2, p<0.001). The Kaplan-Meir survival analysis revealed a significantly higher probability of death for SLE patients using opioid analgesics.\n\nConclusionsPrescription opioid analgesic use is common in SLE and is associated with markedly increased mortality. Preferably, non-opioid approaches to treat chronic pain should be used in SLE patients.\n\nClinical trial registration numberThis was not a clinical trial.\n\nKEY MESSAGES1. Chronic opioid analgesic use is common in SLE (24%).\n\n2. Opioid use is associated with greater disease severity, tobacco use, non-adherence, and increased mortality.\n\n3. Opioids should be used cautiously in SLE; alternative non-opioid management of pain is recommended.\n\nACKNOWLEDGMENTS AND FUNDING INFORMATIONThis work was supported by US National Institutes of Health research grants to Dr. Sibbitt (R01 NS035708) and to the Clinical and Translational Research Center (UL1TR001449).

epidemiology

Clustering Analysis To Explore Cohorts In Comorbid Patients

1.Multimorbidities are associated with significant burden on the healthcare system and the lack of accurate and pertinent statistical exploratory techniques have often limited their analysis. Here we employ exploratory hierarchal agglomerative clustering (HAC) of multimorbidities in the inpatient population in the state of Ohio. The examination exposed the presence of ten discrete, clinically pertinent groups of multimorbidities within the Ohio inpatient population. This method offers an assessable empirical exploration of the multimorbidities present in a specific geographic populace.

epidemiology

Direct estimation of HDL-mediated cholesterol efflux capacity from serum

High-density lipoprotein mediated cholesterol efflux capacity (HDL-CEC) is a functional attribute that may have a protective role in atherogenesis. However, the estimation of HDL-CEC is based on in vitro cell assays that are laborious and hamper large-scale phenotyping. Here, we present a cost-effective high-throughput nuclear magnetic resonance (NMR) spectroscopy method to estimate HDL-CEC directly from serum. We applied the new method in a population-based study of 7,603 individuals including 574 who developed incident coronary heart disease (CHD) during 15 years of follow-up, making this the largest quantitative study for HDL-CEC. As estimated by NMR-spectroscopy, a 1-SD higher HDL-CEC was associated with a lower risk of incident CHD (hazards ratio 0.86; 95%CI 0.79-0.93, adjusted for traditional risk factors and HDL-C). These findings are consistent with published associations based on in vitro cell assays. These corroborative large-scale findings provide further support for a potential protective role of HDL-CEC in CHD, and substantiate this new method and its future applications.

epidemiology

Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance

Influenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making.

epidemiology

Designing a Study of Correlates of Risk for Ebola Vaccination

The rVSV Ebola vaccine was shown to be very efficacious in a novel ring vaccination trial in Guinea. However, no correlates of vaccine protection have been established for Ebola vaccines. Several Ebola vaccine candidates are available, but conducting randomized trials of additional candidates in outbreak situations has become difficult. Establishing correlates of vaccine protection would be useful in helping vaccine candidates become licensed. In this note, we explore power and sample calculations to study potential correlates of risk (protection) during an Ebola vaccination campaign in an outbreak situation under a number of assumptions. At an overall vaccine efficacy of 75%, 50 Ebola endpoints in the vaccinees provided good power. At an overall vaccine efficacy of 90%, 20 Ebola endpoints gave good power under certain assumptions. In the May - July 2018 Ebola outbreak in DRC, over 3000 individuals were vaccinated, with no reported cases in vaccinated individuals. To be feasible, this type of study need Ebola endpoints in vaccinated individuals.

epidemiology

Trajectories of inflammatory biomarkers over the eighth decade and their associations with immune cell counts and epigenetic ageing

BACKGROUNDEpigenetic age acceleration (an older methylation age compared to chronological age) correlates strongly with various age-related morbidities and mortality. Chronic systemic inflammation is thought to be a hallmark of ageing but the relationship between an increased epigenetic age and this likely key phenotype of ageing has not yet been extensively investigated.\n\nMETHODSWe modelled the trajectories of the inflammatory biomarkers C-reactive protein (CRP; measured using both a high- and low-sensitivity assay), and interleukin-6 (IL-6) over the 8th decade in the Lothian Birth Cohort 1936. We additionally investigated the association between CRP and imputed leukocyte counts. Using linear mixed models we examined the cross-sectional and longitudinal association between the inflammatory biomarkers and two measures of epigenetic age acceleration, derived from the Horvath and Hannum epigenetic clocks.\n\nRESULTSLow-sensitivity CRP declined, high-sensitivity CRP did not change, and IL-6 increased over time. CRP levels inversely associated with total counts of CD8+T cells and CD4+T cells, and positively associated with senescent CD8+T cells, plasmablasts and granulocytes. Cross-sectionally, the Hannum, but not the Horvath, measure of age acceleration was positively associated with low-sensitivity CRP, high-sensitivity CRP, IL-6 and a restricted measure of CRP ([&le;]10mg/L) likely reflecting levels relevant to chronic inflammation.\n\nCONCLUSIONSWe found a divergent relationship between inflammation and immune system parameters in older age. We additionally report the Hannum measure of epigenetic age acceleration associated with an elevated inflammatory profile cross-sectionally, but not longitudinally.

epidemiology

Multiple Exposures, Reinfection, and Risk of Progression to Active Tuberculosis

A recent study reported on a tuberculosis outbreak in a largely Inuit village. Among recently infected individuals, exposure to additional active cases was associated with an increasing probability of developing active disease within a year. Using binomial risk models, we evaluated two potential mechanisms by which multiple infections during the first year following initial infection could account for increasing disease risk with increasing exposures. In the reinfection model, multiple exposures have an independent risk of becoming an infection, and infections contribute independently to active disease. In the threshold model, disease risk follows a sigmoidal function with small numbers of exposures conferring a low risk of active disease and large numbers of exposures conferring a high risk. To determine the dynamic impact of reinfection during the early phase of infection, we performed simulations from a modified Reed-Frost model of TB dynamics following spread from an initial number of cases. We parameterized this model with the maximum likelihood estimates from the reinfection and threshold models in addition to the observed distribution of exposures among recent infections. We find that both models can plausibly account for the observed increase in disease risk with increasing exposures, but the threshold model confers a better fit than a nested model without a threshold (p=0.04). Our simulations indicate that multiple exposures during this critical time period can lead to dramatic increases in outbreak size. In order to decrease TB burden in high-prevalence settings, it may be necessary to implement measures aimed at preventing repeated exposures, in addition to preventing primary infection.

epidemiology

Detecting overlapping spatial clusters of high sugar-sweetened beverage intake and high body mass index in a general population: a cross-sectional study

ObjectiveTo identify populations and areas presenting higher consumption of sugar-sweetened beverages (SSB) and their overlap with populations and areas presenting higher body mass index (BMI).\n\nDesignCross-sectional population-based study.\n\nSettingState of Geneva, Switzerland.\n\nParticipants15,767 non-institutionalized residents aged between 35 and 74 years (20 and 74 since 2011) of the state of Geneva, Switzerland.\n\nMain outcome measuresSpatial indices of sugar-sweetened beverage intake frequency and body mass index. Median regression analysis was used to control for characteristics of patients.\n\nResultsThe SSB intake frequency and the BMI were not randomly distributed across the state. Among the 15,423 participants retained for the analyses, 2,034 (13.2%) were within clusters of high SSB intake frequency and 1,651 (10.7%) was within clusters of low SSB intake frequency, 11,738 (76.1%) showed no spatial dependence. We also identified clusters of BMI, 4,014 (26.0%) participants were within clusters of high BMI and 3,591 (23.3%) were within clusters of low BMI, 7,818 (50.7%) showed no spatial dependence. We found that clusters of SSB intake frequency and BMI overlap in specific areas. 1,719 (11.1%) participants were within high SSB intake frequency and high BMI clusters. After adjustment for covariates (education level, gender, age, nationality, and the median income of the area), the identified clusters persisted and were only slightly attenuated.\n\nConclusionA fine-scale spatial approach allows identifying specific populations and areas presenting higher SSB consumption and, for some areas, higher SSB consumption associated with higher BMI. These findings could guide legislators to develop targeted interventions such as prevention campaigns and pave the way for precision public health.\n\nWhat is already known on this topicO_LIThe consumption of sugar-sweetened beverages (SSBs) is an important contributory factor of obesity and obesity-related diseases.\nC_LIO_LISSB consumption varies according to socioeconomic status, which could explain the higher prevalence of obesity in specific areas.\nC_LIO_LISSB taxation faces resistance in many countries due to its potential regressive nature.\nC_LI\n\nWhat this study addsO_LIThe spatial analysis of individual-level SSB consumption in the state of Geneva provides a clear identification of populations and areas presenting higher SSB consumption and, for some areas, higher SSB consumption along with higher body mass index (BMI).\nC_LIO_LIThe results demonstrate the persistence of SSB clustering in the geographic space after adjusting for education level, gender, nationality, age, and neighborhood-level median income.\nC_LIO_LIThe findings provide guidance for future public health interventions to reduce SSB consumption by better targeting vulnerable populations.\nC_LI

epidemiology

The association between adiposity and inpatient hospital costs in the UK Biobank cohort

BackgroundHigh adiposity is associated with higher risks for a variety of adverse health outcomes, including higher rates of age-adjusted mortality and increased morbidity. This has important implications for the management of healthcare systems, since the endocrinal, cardiometabolic and other changes associated with increased adiposity may be associated with substantial healthcare costs.\n\nMethodsWe studied the association between various measures of adiposity and inpatient hospital costs through record linkage between UK Biobank and records of inpatient care in England and Wales. UK Biobank is a large prospective cohort study that aimed to recruit men and women aged between 40 and 69 from 2006 to 2010. We applied generalised linear models to cost per person year to estimate the marginal effect and averaged adjusted predicted cost of adiposity on inpatient costs.\n\nResultsValid cost and body mass index (BMI) data from 457,689 participants were available for inferential analysis. Some 54.4% of individuals included in the analysis sample had positive inpatient healthcare costs over the period of follow-up. Median hospital costs per person year of follow-up were {pound}89, compared to mean costs of {pound}481. Mean BMI overall was 27.4 kg/m2 (standard deviation 4.8). The marginal effect of a unit increase in BMI was {pound}13.61 (99% confidence interval: {pound}12.60 to {pound}14.63) per person year of follow up. The marginal effect of a standard deviation increase in BMI was {pound}69.20 (99% confidence interval: {pound}64.98 to {pound}73.42). The marginal effect of becoming obese was {pound}136.35 (99% confidence interval: {pound}124.62 to {pound}148.08). Average adjusted predicted inpatient hospital costs increased almost linearly when modelled using continuous measure of adiposity. Sensitivity analysis of different scenarios did not substantially change these conclusions, although there was some evidence of attenuation of the effects of adiposity when controlling for waist-hip ratios, and when individuals who self-reported any pre-existing conditions were excluded from analysis.\n\nConclusionsHigher adiposity is associated with higher inpatient hospital costs. Further scrutiny using causal inferential methods is warranted to establish if further public health investments are required to manage the large healthcare costs observationally associated with overweight and obesity.

epidemiology

Self-Limiting Factors in Pandemics and Multi-Disease Syndemics

The potential for an infectious disease outbreak that is much worse than those which have been observed in human history, whether engineered or natural, has been the focus of significant concern in biosecurity. Fundamental dynamics of disease spread make such outbreaks much less likely than they first appear. Here we present a slightly modified formulation of the typical SEIR model that illustrates these dynamics more clearly, and shows the unlikely cases where concern may still be warranted. This is then applied to an extreme version of proposed pandemic risk, multi-disease syndemics, to show that (absent much clearer reasons for concern) the suggested dangers are overstated.\n\nThe models used in this paper are available here: https://github.com/davidmanheim/Infectious-Disease-Models

epidemiology

Quantifying the impact of dengue containment activities using high-resolution observational data

Dengue virus causes over 96 million cases worldwide per year and is ex-panding rapidly in geographic range, especially in urban areas. Containment activities are an essential part of reducing the public health burden caused by dengue, but systematic evidence on the comparative efficacy of activities from the field is lacking. To our knowledge, the effect of containment activities on local (sub-city) scale disease dynamics has never been systematically characterized using empirical containment and case data. We combine data from a comprehensive dengue containment monitoring system with confirmed dengue case data from the local government hospitals to estimate the efficacy of seven common containment activities in two urban areas in Pakistan. We use a modified version of the time series Suspected Infected Recovered frame-work to estimate how the reproductive number, R0, of the outbreak changed in relation to deployment of each containment activity. We also estimate the spatial dependence of cases based on deployment of each containment activity. Both analyses suggest that activities aimed at the adult phase of the mosquito lifecycle have the highest efficacy, with fogging having the largest quantifiable effect in reducing cases immediately after deployment. In examining the efficacy of containment activities contemporaneously deployed in the same locations, results here can guide recommendations for future deployment of resources during dengue outbreaks in urban settings.

epidemiology

Heterogeneity in the respiratory symptoms of patients with mild-moderate COPD

BackgroundThe burden of symptoms varies markedly between patients with Chronic Obstructive Pulmonary Disease (COPD) and is only weakly correlated with lung function impairment. While heterogeneity in lung function decline and exacerbations have been previously studied, the extent of heterogeneity in symptoms and the factors associated with this heterogeneity are not well understood.\n\nMethodsA sample of the general Canadian population [&ge;]40 years with persistent airflow limitation was followed for up to 3 years. Participants reported whether they experienced chronic coughing, phlegm, wheezing, or dyspnea during visits at 18-month intervals. We used mixed-effect logistic regression models (separately for each symptom) to assess overall heterogeneity in the occurrence of symptoms between individuals, and the proportion of variation in symptom burden explained by lung function versus all other clinical characteristics of participants.\n\nResults548 participants (54% male, mean age 67 years) contributed 1,086 visits in total, and 82% of patients reported at least one symptom during follow-up. There was substantial heterogeneity in the individual-specific probabilities for the occurrence of symptoms. This heterogeneity was highest for dyspnea and lowest for phlegm (interquartile range of probabilities: 0.15-0.77 and <0.01-0.53, respectively). FEV1 explained 82% of the variation between individuals in the occurrence of phlegm, 26% for dyspnea, 3% for cough, and <0.1% for wheeze. All clinical characteristics of participants (including FEV1) explained between 86% of heterogeneity in the occurrence of phlegm to <1% for wheeze.\n\nConclusionThere is marked heterogeneity in the burden of respiratory symptoms between COPD patients. The ability of lung function and other commonly measured clinical characteristics to explain this heterogeneity differs between symptoms.

epidemiology

Development and validation of the Evaluation Platform In COPD (EPIC): a population-based outcomes model of COPD for Canada

BackgroundWe report the development, validation, and implementation of an open-source population-based outcomes model of Chronic Obstructive Pulmonary Disease (COPD) for Canada.\n\nMethodsEvaluation Platform in COPD (EPIC) is a discrete event simulation model of Canadians 40 years of age or older. Three core features of EPIC are its open-population design (incorporating projections of future population growth, aging, and smoking trends), its incorporation of heterogeneity in lung function decline and burden of exacerbations, and its modeling of the natural history of COPD from inception. Multiple original data analyses, as well as values reported in the literature, were used to populate the model. Extensive face validity as well as internal and external validity evaluations were performed.\n\nResultsThe model was internally validated on demographic projections, mortality rates, lung function trajectories, COPD exacerbations, and stability of COPD prevalence over time within strata of risk factors. In external validation, it moderately overestimated rate of overall exacerbations in two independent trials, but generated consistent estimates of rate of severe exacerbations and mortality.\n\nLimitationsIn its current version, EPIC does not consider uncertainty in the evidence. Several components such as additional (e.g., environmental and occupational) risk factors, treatment, symptoms, and comorbidity will have to be added in future iterations.\n\nConclusionsEPIC is the first multi-purpose outcome- and policy-focused model of COPD for Canada. By modeling the natural history of COPD from its inception, it is capable of modeling the outcomes of decisions across the entire care pathway of COPD. Platforms of this type have the capacity to be iteratively updated to incorporate the latest evidence and to project the outcomes of many different scenarios within a consistent framework.

epidemiology

Mapping drinking water and quality of life aspects in urban settings: an application of the urban exposome

Cities face rapid changes leading to increasing inequalities and emerging public health issues that require cost-effective interventions. The urban exposome framework constitutes a novel approach in tackling city-wide challenges, such as those of drinking water quality and quality of life. In this proof-of-concept study, we presented part of the urban exposome of Limassol (Cyprus) focusing on chemical and microbial drinking water quality parameters and their association with urban neighborhood indicators. A perceptions study and an urban population study was conducted. We mapped the water quality parameters and participants opinions on city life (i.e. neighborhood life, health care and green space access) using quarters (small administrative areas) as the reference unit of the city. In an exploratory environment-wide association study analysis, we used all variables (questionnaire responses and water quality metrics) to describe correlations between them accounting, also, for self-reported health status. Overall, urban drinking-water quality using conventional indicators of chemical (disinfection byproducts-trihalomethanes) and microbial (coliforms, E. coli, and Enterococci) quality did not raise particular concerns. The general health and chronic health status of the urban participants were significantly (all >FDR corrected p value of 0.1) associated with different health conditions such as hypertension and asthma, or having financial issues in access to dental care. Additionally, correlations between trihalomethanes and participant characteristics (e.g. household cleaning, drinking water habits) were documented. This proof-of-concept study showed the potential of using integrative approaches to develop urban exposomic profiles and identifying within-city differentiated environmental and health indicators. The characterization of the urban exposome of Limassol will be expanded via the inclusion of biomonitoring tools and untargeted metabolomics platforms.

epidemiology

Indication of spatially random infection of chlamydia-like organisms in Bufo bufo tadpoles from ponds located in the Geneva metropolitan area

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

epidemiology