Environmental Epidemiology Research Articles

Precarity and Resilience: The Wellbeing of U.S. Latinos in a Time of Mounting Climate Change

This environmental epidemiology article examines the understudied issue of Latino risk in the context of climate change. The largest ethnic minority group in the U.S., Latino socioeconomic characteristics put them disproportionately in harm’s way for the adverse health impacts of climate change, including being subject to diverse forms of discrimination in gaining access to government resources for prevention and recovery from climate impacts. The specific ways climate change is experienced in Latino communities varies by physical location, employment and residence patterns, and community resilience. Failure to address the threat of climate change, and to identify the specific needs of those who are most vulnerable, including not including them directly in preparedness planning, enhances the likelihood that official responses to climate change will perpetuate historic patterns of denial of Latino concerns about the environment, and existing patterns of inequality, and widen prevailing differences in climate-driven adversity.

Harmonization of transcriptomic and methylomic analysis in environmental epidemiology studies for potential application in chemical risk assessment.

Recent efforts have posited the utility of transcriptomic-based approaches to understand chemical-related perturbations in the context of human health risk assessment. Epigenetic modification (e.g., DNA methylation) can influence gene expression changes and is known to occur as a molecular response to some chemical exposures. Characterization of these methylation events is critical to understand the molecular consequences of chemical exposures. In this context, a novel workflow was developed to interrogate publicly available epidemiological transcriptomic and methylomic data to identify relevant pathway level changes in response to chemical exposure, using inorganic arsenic as a case study. Gene Set Enrichment Analysis (GSEA) was used to identify causal methylation events that result in concomitant downstream transcriptional deregulation. This analysis demonstrated an unequal distribution of differentially methylated regions across the human genome. After mapping these events to known genes, significant enrichment of a subset of these pathways suggested that arsenic-mediated methylation may be both specific and non-specific. Parallel GSEA performed on matched transcriptomic samples determined that a substantially reduced subset of these pathways are enriched and that not all chemically-induced methylation results in a downstream alteration in gene expression. The resulting pathways were found to be representative of well-established molecular events known to occur in response to arsenic exposure. The harmonization of enriched transcriptional patterns with those identified from the methylomic platform promoted the characterization of plausibly causal molecular signaling events. The workflow described here enables significant gene and methylation-specific pathways to be identified from whole blood samples of individuals exposed to environmentally relevant chemical levels. As future efforts solidify specific causal relationships between these molecular events and relevant apical endpoints, this novel workflow could aid risk assessments by identifying molecular targets serving as biomarkers of hazard, informing mechanistic understanding, and characterizing dose ranges that promote relevant molecular/epigenetic signaling events occuring in response to chemical exposures.

Secondary Metabolism Gene Clusters Exhibit Increasingly Dynamic and Differential Expression during Asexual Growth, Conidiation, and Sexual Development in Neurospora crassa.

ABSTRACTSecondary metabolite clusters (SMCs) encode the machinery for fungal toxin production. However, understanding their function and analyzing their products requires investigation of the developmental and environmental conditions in which they are expressed. Gene expression is often restricted to specific and unexamined stages of the life cycle. Therefore, we applied comparative genomics analyses to identify SMCs in Neurospora crassa and analyzed extensive transcriptomic data spanning nine independent experiments from diverse developmental and environmental conditions to reveal their life cycle-specific gene expression patterns. We reported 20 SMCs comprising 177 genes—a manageable set for investigation of the roles of SMCs across the life cycle of the fungal model N. crassa—as well as gene sets coordinately expressed in 18 predicted SMCs during asexual and sexual growth under three nutritional and two temperature conditions. Divergent activity of SMCs between asexual and sexual development was reported. Of 126 SMC genes that we examined for knockout phenotypes, al-2 and al-3 exhibited phenotypes in asexual growth and conidiation, whereas os-5, poi-2, and pmd-1 exhibited phenotypes in sexual development. SMCs with annotated function in mating and crossing were actively regulated during the switch between asexual and sexual growth. Our discoveries call for attention to roles that SMCs may play in the regulatory switches controlling mode of development, as well as the ecological associations of those developmental stages that may influence expression of SMCs.IMPORTANCE Secondary metabolites (SMs) are low-molecular-weight compounds that often mediate interactions between fungi and their environments. Fungi enriched with SMs are of significant research interest to agriculture and medicine, especially from the aspects of pathogen ecology and environmental epidemiology. However, SM clusters (SMCs) that have been predicted by comparative genomics alone have typically been poorly defined and insufficiently functionally annotated. Therefore, we have investigated coordinate expression in SMCs in the model system N. crassa, and our results suggest that SMCs respond to environmental signals and to stress that are associated with development. This study examined SMC regulation at the level of RNA to integrate observations and knowledge of these genes in various growth and development conditions, supporting combining comparative genomics and inclusive transcriptomics to improve computational annotation of SMCs. Our findings call for detailed study of the function of SMCs during the asexual-sexual switch, a key, often-overlooked developmental stage.

PREVALENCE OF DENGUE HEMORRHAGIC FEVER IN BALI FROM 2015 TO 2020 AND DURING THE COVID-19 PANDEMIC

Background: Dengue hemorrhagic fever cases increased significantly during the COVID-19 pandemic phase. Purpose: This study defines, describes, and evaluates the dengue hemorrhagic fever incidence in Bali Province from 2015 to 2020 and in the context of the COVID-19 pandemic. Methods: The various types of descriptive study with case series design. The study used an environmental epidemiology approach to analyze the health profile of Bali Province. The number of DHF cases in 2015-2020, DHF-related morbidity and death rates, larva-free rates, climate, population and population density, and gender were variables in this study. The study took place in May and June 2021. Results: The prevalence of dengue hemorrhagic fever in the province of Bali fluctuated between 2015 and 2020. The highest number of dengue cases in 2016 was 20,306, and they fell from 2017 to 2019. Cases doubled in 2020, up by 12,173. The endemic areas with the highest prevalence of cases are in the districts of Buleleng and Badung. The highest incidence rate was in 2016 at 483/100,000 population, and for the last five years, it has not met the national target of 49/100,000 population. The highest CFR figure in 2020 was 0.43%, an increase of 1.5 times during the COVID-19 pandemic. Conclusion: The prevalence of dengue hemorrhagic fever cases in Bali in 2015 – 2020 fluctuated significantly, and mortality due to dengue during the COVID-19 pandemic experienced a significant increase.

Assessing seasonality and the role of its potential drivers in environmental epidemiology: a tutorial.

Several methods have been used to assess the seasonality of health outcomes in epidemiological studies. However, little information is available on the methods to study the changes in seasonality before and after adjusting for environmental or other known seasonally varying factors. Such investigations will help us understand the role of these factors in seasonal variation in health outcomes and further identify currently unknown or unmeasured risk factors. This tutorial illustrates a statistical procedure for examining the seasonality of health outcomes and their changes, after adjusting for potential environmental drivers by assessing and comparing shape, timings and size. We recommend a three-step procedure, each carried out and compared before and after adjustment: (i) inspecting the fitted seasonal curve to determine the broad shape of seasonality; (ii) identifying the peak and trough of seasonality to determine the timings of seasonality; and (iii) estimating the peak-to-trough ratio and attributable fraction to measure the size of seasonality. Reporting changes in these features on adjusting for potential drivers allows readers to understand their role in seasonality and the nature of any residual seasonal pattern. Furthermore, the proposed approach can be extended to other health outcomes and environmental drivers.

New problems of environmental ecology: ticks and tick-borne pathogens in city parks of Ukraine.

City parks of Vinnytsia, Ukraine, were examined for the occurrence of host-seeking ticks and collected specimens were examined for presence of rickettsial pathogens. In the summer season, the only collected individuals were Dermacentor reticulatus adults (n= 203). DNA identification of pathogens was conducted with the use of 16S rRNA gene amplification. Molecular analyses confirmed the presence of Rickettsia raoultii in ticks from all parks, with infection prevalence ranging from 5% to 68% across parks. These results indicate that city parks in Vinnytsia have become a new habitat for ticks of high medical and veterinary importance. Due to the risk of acquiring tick-borne pathogens, the urban environment such as parks and home gardens, should be monitored for the presence of ticks and their infection with human pathogens. Screening of these areas is an important preventive element of environmental epidemiology.

Mitigating spatial confounding by explicitly correlating Gaussian random fields

AbstractSpatial models are used in a variety of research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in such spatial regression models is spatial confounding. This phenomenon is observed when spatially indexed covariates modeling the mean of the response are correlated with a spatial random effect included in the model, for example, as a proxy of unobserved spatial confounders. As a result, estimates for regression coefficients of the covariates can be severely biased and interpretation of these is no longer valid. Recent literature has shown that typical solutions for reducing spatial confounding can lead to misleading and counterintuitive results. In this article, we develop a computationally efficient spatial model that explicitly correlates a Gaussian random field for the covariate of interest with the Gaussian random field in the main model equation and integrates novel prior structures to reduce spatial confounding. Starting from the univariate case, we extend our prior structure also to the case of multiple spatially confounded covariates. In simulation studies, we show that our novel model flexibly detects and reduces spatial confounding in spatial datasets, and it performs better than typically used methods such as restricted spatial regression. These results are promising for any applied researcher who wishes to interpret covariate effects in spatial regression models. As a real data illustration, we study the effect of elevation and temperature on the mean of monthly precipitation in Germany.

Extended two-stage designs for environmental research

BackgroundThe two-stage design has become a standard tool in environmental epidemiology to model multi-location data. However, its standard form is rather inflexible and poses important limitations for modelling complex risks associated with environmental factors. In this contribution, we illustrate multiple design extensions of the classical two-stage method, all implemented within a unified analytic framework.MethodsWe extended standard two-stage meta-analytic models along the lines of linear mixed-effects models, by allowing location-specific estimates to be pooled through flexible fixed and random-effects structures. This permits the analysis of associations characterised by combinations of multivariate outcomes, hierarchical geographical structures, repeated measures, and/or longitudinal settings. The analytic framework and inferential procedures are implemented in the R package mixmeta.ResultsThe design extensions are illustrated in examples using multi-city time series data collected as part of the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Specifically, four case studies demonstrate applications for modelling complex associations with air pollution and temperature, including non-linear exposure–response relationships, effects clustered at multiple geographical levels, differential risks by age, and effect modification by air conditioning in a longitudinal analysis.ConclusionsThe definition of several design extensions of the classical two-stage design within a unified framework, along with its implementation in freely-available software, will provide researchers with a flexible tool to address novel research questions in two-stage analyses of environmental health risks.

Association of contralateral breast cancer risk with mammographic density defined at higher-than-conventional intensity thresholds.

Mammographic dense area (MDA) is an established predictor of future breast cancer risk. Recent studies have found that risk prediction might be improved by redefining MDA in effect at higher-than-conventional intensity thresholds. We assessed whether such higher-intensity MDA measures gave stronger prediction of subsequent contralateral breast cancer (CBC) risk using the Women's Environment, Cancer, and Radiation Epidemiology (WECARE) Study, a population-based CBC case-control study of ≥1year survivors of unilateral breast cancer diagnosed between 1990 and 2008. Three measures of MDA for the unaffected contralateral breast were made at the conventional intensity threshold ("Cumulus") and at two sequentially higher-intensity thresholds ("Altocumulus" and "Cirrocumulus") using the CUMULUS software and mammograms taken up to 3 years prior to the first breast cancer diagnosis. The measures were fitted separately and together in multivariable-adjusted logistic regression models of CBC (252 CBC cases and 271 unilateral breast cancer controls). The strongest association with CBC was MDA defined using the highest intensity threshold, Cirrocumulus (odds ratio per adjusted SD [OPERA] 1.40, 95% CI 1.13-1.73); and the weakest association was MDA defined at the conventional threshold, Cumulus (1.32, 95% CI 1.05-1.66). In a model fitting the three measures together, the association of CBC with Cirrocumulus was unchanged (1.40, 95% CI 0.97-2.05), and the lower brightness measures did not contribute to the CBC model fit. These results suggest that MDA defined at a high-intensity threshold is a better predictor of CBC risk and has the potential to improve CBC risk stratification beyond conventional MDA measures.

Food Safety Status on Poultry Meat and Egg in Afghanistan

Foodborne illness is one of the most widespread health problems in the world. In addition, the food safety of poultry products remains a severe problem in many countries around the world. Like in many other countries, the government handles the development of food safety standards in Afghanistan. The poultry industry in Afghanistan has been under dynamic and progressive development. Despite the advances in the poultry industry in Afghanistan, the issues of foodborne pathogens, drug and chemical residues remain. Meat inspection at poultry processing plants has been successful but has certain limitations. More importantly, drugs, pesticides, mycotoxins and other chemical residues must be monitored in poultry meat and eggs. This work must be carried out in close collaboration with human and environmental health professionals, analysts, epidemiologists, food producers, processors and traders. Poultry veterinarians and the Afghanistan Veterinary Organization, International Veterinary Organization (IVO) should play a decision-making role in developing the poultry industry. Poultry veterinarians and IVO have a dual responsibility; epidemiological surveillance of poultry diseases and ensuring the safety of meat and eggs. Through their presence on farms and appropriate collaboration with farmers, poultry veterinarians play a crucial role in ensuring that birds are kept under hygienic conditions, especially conditions with public health significance. Slaughterhouse inspection of live birds (ante mortem) and the carcass (post-mortem) plays a key role in the surveillance network for poultry diseases and zoonosis and ensures the safety of poultry meat.

An Epidemiologic Review of Menstrual Blood Loss as an Excretion Route for Per- and Polyfluoroalkyl Substances.

Menstrual blood loss, a common physiologic occurrence, provides an excretion route for per- and polyfluoroalkyl substances (PFAS) since these chemicals are bound to proteins in blood. To increase awareness of this relationship in environmental epidemiology, we reviewed the available epidemiologic data on menstrual bleeding and PFAS concentrations. Initial epidemiologic studies reported generally higher PFAS concentrations in men, menopausal women, and those with a history of hysterectomy compared to premenopausal women. Although subsequent studies investigating menstrual cycle characteristics observed somewhat discrepant results for menstrual irregularity and cycle length, consistent associations have been observed between heavy menstrual bleeding and lower PFAS concentrations. This review highlights the important role of menstrual bleeding on the excretion of PFAS. Given the high prevalence of menstrual bleeding in the population and the implications for environmental epidemiology, we provide recommendations to move this field forward.

Exploration of long-term exposure markers for phthalate esters in human hair using liquid chromatography-tandem mass spectrometry

Phthalate esters are a group of synthetic industrial chemicals that are widely used in plastics. Urinary phthalate metabolites are short-term exposure markers frequently used to represent exposure levels in environmental epidemiology studies. Human hair is an alternative matrix for recording long-term exposure, but there are still analytical challenges that need to be addressed. In this study, an analytical method was established for simultaneously measuring nine major phthalate metabolites in human hair and successfully applied to measure phthalate metabolites in 30 hair samples collected from 30 individual human volunteers without known occupational exposure to phthalates. Two portions of 25 mg of hair samples were extracted by acidified methanol and water in 240 min of ultrasonication and then analyzed using a liquid chromatography-tandem mass spectrometry system. The limit of quantification ranged from 0.72 to 10.7 ng/g hair for nine phthalate metabolites. All nine phthalate metabolites were detected in more than 70% of the 30 individual human hair samples. The measured levels of hair phthalate metabolites were (in descending order): MEHP > MMP ≫ MEP > MBP (MnBP + MiBP) > MiNP > MEHHP ≈ MEOHP ≈ MECPP. The primary metabolite, MEHP (692 ± 582 ng/g), is the major DEHP metabolite in hair. This result is consistent with the findings in blood but not in urine, in which the secondary metabolites are the major DEHP metabolites. This method is easy to foresee with a clinical application and applies to human biomonitoring studies to assess long-term environmental phthalate exposure.

A Long Way from Steubenville: Environmental Epidemiology in a Rapidly Changing World.

This commentary focuses on research that has long been at the core of environmental epidemiology: studies of the health effects of air pollution. It highlights publications in the American Journal of Epidemiology going back more than 50 years that have contributed to the debate about the validity of this research and its meaning for public policy. Technological advances have greatly expanded the toolbox of environmental epidemiologists in terms of measuring and analyzing complex exposures in large populations. Yet, discussions about biases in estimating air pollution health effects have always been and remain intense. Epidemiologists have brought new methodologies and concepts to this research, alleviating some but not all concerns. Here, the focus is on seminal epidemiologic work that established valid links between air pollution exposures and health outcomes and generated data for environmental policies and prevention. With this commentary, I hope to inspire epidemiologists to address many more of the burning environmental health questions-wildfires included-with a similar scientific doggedness. The rapidly changing conditions of our planet are challenging us to innovate and offer solutions, albeit perhaps a little bit faster this time around.

Retrospective exposure reconstruction using approximate Bayesian computation: A case study on perfluorooctanoic acid and preeclampsia

BackgroundIn environmental epidemiology, measurements of toxicants in biological samples are often used as individual exposure assignments. It is common to obtain only one or a few exposure biomarkers per person and use those measurements to represent each person's relevant toxicant exposure for a given health outcome, even though most exposure biomarkers can fluctuate over time. When the timing of the exposure reflected by the biomarker measurement is misaligned with disease development especially if it occurs after the disease outcome, results could be subject to reverse causality or exposure measurement error. ObjectiveThis study aimed to use an approximate Bayesian computation (ABC) method to improve PFOA exposure estimates and characterize the effects of PFOA on preeclampsia in the C8 Studies. MethodsSerum PFOA concentrations were measured in blood samples collected during 2005–2006 in West Virginia and Ohio (the C8 Studies), and residential and water use histories and pregnancy outcomes were obtained from self-reports. Our previous results may have been influenced by the choice of methods for characterizing PFOA exposures. Here we use an ABC method to combine measured PFOA serum concentrations and environmentally modeled PFOA concentrations to reconstruct historical PFOA exposures. We also expanded our previous work by assuming more realistic lognormal distributions for key input parameters in the exposure and pharmacokinetic models. ResultsCompared to using fixed values of model parameters and Monte Carlo simulations, ABC produced similar Spearman correlations between estimated and measured serum PFOA concentrations, yet substantially reduced the mean squared error by over 50%. Based on ABC, compared to previous studies, we found a similar adjusted odds ratio (AOR) for the association between PFOA and preeclampsia. ConclusionsBayesian combination of modeled exposure and measured biomarker concentrations can reduce exposure measurement error compared to modeled exposure.

Geographical distribution of trace elements (selenium, zinc, iron, copper) and case fatality rate of COVID-19: a national analysis across conterminous USA.

Severe outcome particularly death is the largest burden of COVID-19. Clinical observations showed preliminary data that deficiency in certain trace elements, essential for the normal activity of immune system, may be associated with worse COVID-19 outcome. Relevant study of environmental epidemiology has yet to be explored. We investigated the geographical association between concentrations of Se, Zn, Fe and Cu in surface soils and case fatality rate of COVID-19 in USA. Two sets of database, including epidemiological data of COVID-19 (including case fatality rate, from the University of John Hopkinson) and geochemical concentration data of Se, Zn, Fe and Cu in surface soils (from the National Geochemical Survey), were mapped according to geographical location at the county level across conterminous USA. Characteristics of population, socio-demographics and residential environment by county were also collected. Seven cross-sectional sampling dates, with a 4-week interval between adjacent dates, constructed an observational investigation over 24 weeks from October 8, 2020, to March 25, 2021. Multivariable fractional (logit) outcome regression analyses were used to assess the association with adjustment for potential confounding factors. In USA counties with the lowest concentration of Zn, the case fatality rate of COVID-19 was the highest, after adjustment for other influencing factors. Associations of Se, Fe and Cu with case fatality rate of COVID-19 were either inconsistent over time or disappeared after adjustment for Zn. Our large study provides epidemiological evidence suggesting an association of Zn with COVID-19 severity, suggesting Zn deficiency should be avoided.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10653-022-01204-0.

Research is in the air in Valle del Serchio

In this paper, we will present an ongoing Citizen Science (CS) project in environmental epidemiology in five European countries focussing on the Italian pilot study conducted in the Serchio Valley (Tuscany). The area is rich in natural, cultural and historic significance, but it also suffers from environmental contamination from a variety of sources, including industry, which is a health concern for the local population. The study is framed as a Post-Normal Science (PNS) project, applying, in particular, the idea of extended peer community (EPC). The research team looked for and encouraged the engagement of local residents in all the phases of the project, from the framing of the problem to the definition of the specific research questions, the research design, the collection and analysis of data, the dissemination of results, and the drafting of policy recommendations. We discuss the novelty of our approach, the progress so far, the different types of challenges encountered and the strategies adopted to deal with them. Besides the totally unexpected problems generated by the Covid-19 pandemic, we focus on the difficulty in conforming to the requirements of standard medical ethics, which do not take into account the peculiarities of projects such as ours.

Estimation and false discovery control for the analysis of environmental mixtures.

The analysis of environmental mixtures is of growing importance in environmental epidemiology, and one of the key goals in such analyses is to identify exposures and their interactions that are associated with adverse health outcomes. Typical approaches utilize flexible regression models combined with variable selection to identify important exposures and estimate a potentially nonlinear relationship with the outcome of interest. Despite this surge in interest, no approaches to date can identify exposures and interactions while controlling any form of error rates with respect to exposure selection. We propose two novel approaches to estimating the health effects of environmental mixtures that simultaneously (i) estimate and provide valid inference for the overall mixture effect and (ii) identify important exposures and interactions while controlling the false discovery rate (FDR). We show that this can lead to substantial power gains to detect weak effects of environmental exposures. We apply our approaches to a study of persistent organic pollutants and find that controlling the FDR leads to substantially different conclusions.

Epigenetics, environmentand epidemiology: an interview with Karl Kelsey.

In this interview, Professor Karl Kelsey speaks with Storm Johnson, Commissioning Editor for Epigenomics, on his work to date in the field of environmental epigenomics and epidemiology. DrKarl Kelsey, MD, MOHis aProfessor of Epidemiology and Pathology and Laboratory Medicine at Brown University. He is theFounding Director of the Center for Environmental Health and Technology and Head of the Environmental Health Section atthe Department of Epidemiology. DrKelsey is interested in the application of laboratory-based biomarkers in environmental disease, with experience in chronic disease epidemiology and tumor biology. The goals of his work include a mechanistic understanding of individual susceptibility to exposure-related cancers. In addition, his laboratory is interested in tumor biology, investigating somatic alterations in tumor tissue from thepatients who have developed exposure-related cancers. This work involves theuse ofan epidemiologic approach to characterize epigenetic and genetic alteration of genes in the causal pathway for malignancy. Active work includes several studies of individual susceptibility to cancer. DrKelsey's laboratory mainlyinvestigatessusceptibility to smoking-related lung cancer andstudiesmulti-racial and ethnic populations. In addition, the laboratory is also involved with thestudy ofinherited susceptibility to brain tumors and pancreatic cancer. Major case control studies that are ongoing in the laboratory include studies designed to understand inherited and acquired susceptibility in head and neck cancers. The laboratory is also involved in a case control study of asbestos-associated mesothelioma, arsenic exposure, cigarette smoking and bladder cancer. Considerable work is being devoted to understanding the mechanisms of action of both asbestos and arsenic including their ability to affect promoter methylation and gene silencing in carcinogenesis. Recent laboratory studies includes an interest in using newly developed DNA methylation biomarkers to probe immune profiles from archived blood. Dr Kelsey received his MD from the University of Minnesota and Masters of Occupational Health from Harvard University.

Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression.

There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian group index regression by treating both regression effects and missing BDL observations as parameters in a model estimated through a Markov chain Monte Carlo algorithm that we refer to as pseudo-Gibbs imputation. We compare this with other Bayesian imputation methods found in the literature (Multiple Imputation by Chained Equations and Sequential Full Bayes imputation) as well as with a non-Bayesian single-imputation method. To evaluate our proposed method, we conduct simulation studies with varying percentages of BDL missingness and strengths of association. We apply our method to the California Childhood Leukemia Study (CCLS) to estimate concentrations of chemicals in house dust in a mixture analysis of potential environmental risk factors for childhood leukemia. Our results indicate that pseudo-Gibbs imputation has superior power for exposure effects and sensitivity for identifying individual chemicals at high percentages of BDL missing data. In the CCLS, we found a significant positive association between concentrations of polycyclic aromatic hydrocarbons (PAHs) in homes and childhood leukemia as well as significant positive associations for polychlorinated biphenyls (PCBs) and herbicides among children from the highest quartile of household income. In conclusion, pseudo-Gibbs imputation addresses a commonly encountered problem in environmental epidemiology, providing practitioners the ability to jointly estimate the effects of multiple chemical exposures with high levels of BDL missingness.

Co-creating a local environmental epidemiology study: the case of citizen science for investigating air pollution and related health risks in Barcelona, Spain

BackgroundWhile the health risks of air pollution attract considerable attention, both scholarly and within the general population, citizens are rarely involved in environmental health research, beyond participating as data subjects. Co-created citizen science is an approach that fosters collaboration between scientists and lay people to engage the latter in all phases of research. Currently, this approach is rare in environmental epidemiology and when co-creation processes do take place, they are often not documented. This paper describes the first stages of an ongoing co-created citizen science epidemiological project in Barcelona (Spain), that included identifying topics that citizens wish to investigate as regards air pollution and health, formulating their concerns into research questions and co-designing the study protocol. This paper also reflects key trade-offs between scientific rigor and public engagement and provides suggestions to consider when applying citizen science to environmental health studies.MethodsExperts created an online survey and analyzed responses with descriptive statistics and qualitative coding. A pop-up intervention was held to discuss with citizens their concerns about air pollution and health. Later on, a community meeting was organized to narrow down the research topics and list potential research questions. In an online survey, citizens were asked to vote for the research question they would like to investigate with the experts. A workshop was held to choose a study design in which citizens would like to partake to answer their preferred research question.ResultsAccording to 488 respondents from the first survey, cognitive and mental health were the main priorities of investigation. Based on the second survey, with 27% of the votes from 556 citizens, the most popular research question was, “How does air pollution together with noise and green/blue spaces affect mental health?”. The study design selected was an observational study in which citizens provide daily repeated measures of different cognitive and mental health outcomes and relate them to the air pollution concentrations.ConclusionsBased on the co-creation activities and the results obtained, we conclude that applying citizen science in an environmental health project is valuable for researchers despite some challenges such as engaging citizens and maximizing representativity.