Missing values occur in almost all real-world medical data. Sometimes, more information is available for the missing values due to technical measurement limits. This was also the case for some sports medical data set where several laboratory measurements below a lower limit of quantification (LLOQ) were faced and supposed to be used in a multiple linear regression model. When studying the literature, the problem arises in several disciplines (environmental epidemiology, pharmacokinetic studies etc.) and different statistical methods are suggested. However, only very limited work on a method comparison is available, especially in the multivariable linear regression settting. Therefore, we compare statistical methods for addressing values below a LLOQ in multiple linear regression modeling by a simulation study. We consider both the case that the variable below the LLOQ is among one of the independent variables and that it is the dependent variable in the regression model. We also vary different underlying assumptions, such as distributions, sample sizes, proportions of missing values, correlations, or linearity assumptions. Overall, the two compartment model showed the best performance in terms of bias and coverage when the LLOQ occurred in the independent variable and no big collinearity issue was present. When the variable subject to the LLOQ is the dependent variable, tobit showed the lowest bias and highest coverage for censoring proportions up to 0.8. When facing a data set with values below a lower limit of quantification and a multiple linear regression model is chosen as analysis model, a conscious choice for dealing with those left-censored data should be made. In this article, we provide guidance on the performance of different established methods.

Co-exposure to multiple fine particulate matter constituents and adults obstructive sleep apnoea (OSA) in Southern China.

Fluoride exposure disrupts creatinine homeostasis and undermines the reliability of urinary metabolomics normalization.

The development of the Human Health Exposure Analysis Resource (HHEAR) Data Repository for environmental epidemiology research.

Air pollution, particularly exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2), is a serious health problem that may impair lung function in high-risk groups such as gas station fuel operators. This study aimed to analyze the relationship between exposure to PM2.5, NO2, and individual characteristics with lung function disorders among gas station workers in Padang City. This research used an analytical descriptive design with a cross-sectional approach, applying environmental health risk analysis (EHRA) and environmental health epidemiology (EHE) methods. A total of 79 respondents from seven gas stations were selected, with data collected through interviews, measurement of PM2.5 and NO2 concentrations, spirometry testing, and assessment of individual characteristics. The findings showed that PM2.5 and NO2 concentrations were below the regulatory threshold; however, 30.4% of workers experienced impaired lung function. Statistical analysis revealed significant associations between lung function and age, gender, and length of employment, whereas pollutant exposure levels, vehicle density, mask use, and smoking status were not significantly associated. It can be concluded that individual characteristics had a stronger influence on lung function disorders compared to direct pollutant exposure. Routine health monitoring, education on the use of personal protective equipment, and work-hour regulation are recommended to reduce health risks among gas station workers.

Cancer is a complicated, multi-factorial disease, involving genetics, molecules, clinical factors, lifestyle, and environment. Precision oncology has advanced with genomics-based classification and AI-assisted diagnosis, but most existing models of personalized treatment are predominantly and usually only biologically driven and do not account for environmental conditions such as air pollution, toxic chemicals, climate stress, workplace, and social ecologies etc. Evidence published by environmental health and cancer epidemiology research shows that these exposures affect the development of cancer, its progression, the response to treatment, and survival. Combining big data analytics, artificial intelligence, multi-omics, advanced imaging, and environmental informatics offers an opportunity to create precision oncology, considering the environmental context. This study provides an AI-powered big data framework, aggregating the data collected from electronic health records, multi-omics data such as genomics, transcriptomics, proteomics, metabolomics, AI-improved imaging, and exposomics data of monitoring systems and geographic data. Machine-learning, deep-learning, predictive modeling, and explainable AI-based approaches are adopted to explain complex associations of genes with the environment, enhance the early detection of cancer, refine the risk assessment process, and customize treatments. By considering the latest publications, this paper presents the state-of-the-art AI-driven precision oncology, environmental health analytics, and exposomics, as well as some technical and ethical concerns, while laying out a potential scalable architecture for environmentally-aware personalized cancer care. The findings show that inclusion of environmental exposure information in AI-enabled oncology workflow leads to increased diagnostic accuracy, therapeutic uniqueness, and health equity in addition to promoting sustainable and preventive strategies against cancer. This work is a step forward in research in environmental precision oncology, and can provide useful recommendations for clinicians, researchers, and policy makers.

Climate change has increasingly altered global ecological systems, influencing the dynamics of infectious diseases. Rising temperatures, changing precipitation patterns, and the expansion of suitable habitats for vectors create favorable conditions for viral transmission. This review examines the upward trend of epidemic viral spread in the context of climate change, with a focus on vector-borne and respiratory viruses. Drawing on recent epidemiological data and climate models, the study analyzes the correlation between environmental shifts and viral outbreaks.Our findings indicate that warming temperatures accelerate the geographic expansion of arboviruses such as dengue, Zika, and chikungunya, while altered rainfall patterns foster mosquito breeding cycles. Moreover, extreme weather events disrupt healthcare infrastructure and increase population displacement, facilitating respiratory virus transmission such as influenza and coronaviruses. The convergence of climate-driven ecological changes and global mobility amplifies the risk of cross-border viral epidemics. These findings underscore an urgent imperative: efforts to address climate change cannot be separated from strategies for infectious disease preparedness. Surveillance systems must be reinforced and designed to detect early signals of climate-sensitive outbreaks, while stronger intersectoral collaboration can bridge gaps between environmental science, epidemiology, and health policy. Integrating climate-informed frameworks into public health decision-making offers a pathway to reduce the growing burden of viral diseases shaped by environmental change.

Interrupted time series (ITS) designs are increasingly used in environmental health to evaluate impacts of extreme weather events or policies. This paper aims to introduce traditional and contemporary ITS approaches, including machine learning algorithms and Bayesian frameworks, which enhance flexibility in modeling complex temporal patterns (e.g., seasonality and nonlinear trends) and spatially heterogeneous treatment effects. We present a comparative analysis of methods such as ARIMA, machine learning models, and Bayesian ITS, using a real-world case study: estimating excess respiratory hospitalizations during the 2018 wildfire smoke event in San Francisco. Our study demonstrates the practical application of these methods and provides a guide for selecting and implementing ITS designs in environmental epidemiology. To ensure reproducibility, we share annotated datasets and R scripts, allowing researchers to replicate analyses and adapt workflows. While focused on environmental applications, particularly acute exposures like wildfire smoke, the framework is broadly applicable to public health interventions. This work advances ITS methodology by integrating contemporary statistical innovations and emphasizing actionable guidance for causal inference in complex, real-world settings.

Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke.

Wastewater-based epidemiology (WBE) represents a valuable tool for assessing viral circulation at the community level. Human bocavirus (HBoV), a member of the Parvoviridae family with four genotypes (HBoV1-4), has been detected in respiratory and enteric samples, although its environmental epidemiology remains poorly characterized. This study aimed to investigate the presence and diversity of HBoV in wastewater samples from Córdoba, Argentina, during 2020-2021. A total of 84 raw sewage samples collected at the city's wastewater treatment plant were analyzed by nested PCR and sequencing of the VP1/VP2 and NP1 regions. HBoV DNA was detected in 44% of samples, with higher circulation in 2020 (69.7%) compared to 2021 (27.4%), and a seasonal trend peaking in winter and spring. Phylogenetic analyses revealed 15 sequences clustering with HBoV2 and 11 with HBoV3, while three HBoV1-positive samples were confirmed by NP1 sequencing. To our knowledge, this is the first report of HBoV detection in environmental samples from Argentina, documenting the co-circulation of three genotypes (HBoV1-3) in a single urban setting. These findings underscore the usefulness of WBE for monitoring bocavirus diversity and circulation in the community.

There are disparities in cancer incidence, mortality, and survival by race/ethnicity. As a result of structural mechanisms of discrimination, minoritized racial/ethnic groups are disproportionately exposed to higher levels of environmental carcinogens. Increased risk of exposure to harmful environmental pollutants may contribute to observed cancer disparities by race/ethnicity, but few studies have examined this effect. How race/ethnicity is operationalized in epidemiologic studies can impact interpretation of associations and potentially mask disparities, preventing the development of targeted public health interventions. We conducted a systematic review of epidemiologic studies on ambient environmental pollution and cancer outcomes in US adults and assessed how race/ethnicity was operationalized. A total of 3,346 studies were identified. We found that of 172 studies that included race/ethnicity, 85/172 (49%) only considered race/ethnicity as a confounder. Of the remaining 87 studies, 60/87 (69%) stratified analyses by race/ethnicity, 9/87 (10%) were minority health studies that included one non-White racial/ethnic group, 18/87 (21%) examined estimated cancer risk as an outcome with race/ethnicity as the main exposure. Despite these limited analyses, many of these studies found stronger associations among racial/ethnic minority groups. One study examined environmental exposures as a causal mediator to explain potential disparities in cancer outcomes. There is a need for more research on racial/ethnic cancer disparities related to environmental pollutants. Researchers should consider developing data sources and leverage existing databases with robust racial/ethnic diversity and put ethical consideration into how race/ethnicity is included in conceptual frameworks to ensure fairness, equity, and clarity.

Characteristics of personal exposure to metals in PM2.5 and their implications for epidemiological studies: New insights from a panel study of the elderly in Beijing.

Background:Quantile regression helps identify how associations vary across the outcome variable’s distribution. Using simulations and data from the Maternal-Infant Research on Environmental Chemicals study, we showed that frequentist quantile regression can produce implausible results where the point estimates are integers or rational numbers and the outcome variable is discrete, which is common in health research. Applying “dithering” (also known as jittering) or using Bayesian quantile regression can prevent such implausible results, but the optimal strategy is unclear.Methods:We conducted simulations with discrete outcomes to compare the bias and variability of point estimates of undithered frequentist, dithered frequentist, and Bayesian quantile regression. We also compared the coverage and interval-width variance of these methods’ confidence or credible intervals.Results:The dithered frequentist method generated point estimates that were less variable than the undithered frequentist method. The Bayesian method had the least variable point estimates, but when the sample size was low (n = 100), it exhibited bias when modeling a binary or discrete covariate. The dithered frequentist method with xy-bootstrapped confidence intervals had nominal coverage and produced intervals with relatively consistent widths. The Bayesian method with adjusted intervals also had nominal coverage, but more variable interval widths. The Bayesian method with unadjusted intervals had poor coverage.Conclusion:In our simulations with discrete outcomes, dithered frequentist quantile regression (particularly with xy-bootstrapped confidence intervals) had the best overall performance. The Bayesian method with adjusted intervals is an acceptable strategy, although it was biased under certain scenarios and generated credible intervals with more variable widths.

Background:Previous studies have demonstrated that in utero and early life heat exposure can influence neurodevelopment. However, to our knowledge, these investigations have not evaluated realistic counterfactual scenarios; instead, they have primarily relied on static, crude comparisons of extreme temperatures versus a reference temperature over an extended period.Methods:We employed the framework of longitudinal modified treatment policy to examine the impact of heat exposure during prenatal and postnatal periods on the linguistic development of two-year-old children in the Etude Longitudinale Française depuis l’Enfance birth cohort (N = 12,163). Heat exposure was defined as the number of periods when overall daytime and nighttime daily temperatures surpassed the 90th percentile (20.6, 27.5, and 15.3 °C, respectively) for at least two consecutive days. Context-specific counterfactual scenarios were constructed by increasing daily temperatures by 1, 2, or 3 °C, in line with projections from Intergovernmental Panel on Climate Change scenarios. Causal effects were estimated by comparing the population mean outcomes under hypothetical counterfactual scenarios vs. those actually observed in the data using a doubly robust estimation technique (targeted maximum likelihood estimation). A library of machine learning algorithms was employed to model the intricate relationships between covariates and both the exposure and outcome variables.Results:In counterfactual scenarios where daily temperature increases by one degree, mean differences in log-transformed population outcome did not reach statistical significance. A two-degree daily increase in nighttime temperature showed a decrease in linguistic development scores of 30% (P < 0.001). A three-degree increase in overall, daytime and nighttime daily temperatures showed a decrease in scores of at least 6% (P < 0.003).Conclusion:Our study revealed a negative impact of increased air temperatures on the linguistic development of 2-year-old children in counterfactual scenarios involving two- and three-degree temperature rises. The longitudinal modified treatment policy approach offers valuable new insights for causal inference in environmental epidemiology, particularly through its ability to directly assess the effects of anticipated, policy-relevant temperature changes.

Abstract The transport sector is facing significant challenges in reacting to the need of sustainable, energy-efficient, and pollution-free mobility to improve planetary health. Environmental epidemiology and toxicology have confirmed substantial health impacts from air pollution of road traffic, accounting mainly for fine particles and nitrogen dioxide. Our recent analysis of transport modes, covering EU27, showed dominance of passenger cars regardless of the choice of indicator pollutant in the 2030 Current legislation scenario. Here we evaluate the potential benefits of full electrification of the passenger car fleet. We assume that all the combustion engines in passenger cars are replaced by plug-in rechargeable electric vehicles, eliminating all exhausts. Based on earlier analyses we assume that due to the higher mass, the electric vehicles cause +18% higher road abrasion and tire wear emissions, and that the regenerative breaking technology leads to a halving of the brake wear. We calculate health impacts for primary PM2.5 emissions. The results suggest that in 2030, the transport sector would be responsible for ca. 10,000 premature deaths due to primary PM2.5 and that road traffic and passenger cars cause 91% and 62% of this. Removing the exhaust emissions would save 1258 lives. Despite of benefits of reduced exhaust emissions in electrification of passenger car fleet, substantial air pollution health impacts remain. These estimates are based on general epidemiological evidence. In the next steps of ULTHRAS we consider also toxicological evidence on the exhausts. It is necessary to continue developing technologies and policies to reduce road dust emissions and promote carpooling, supported by autonomous vehicle technologies, public transport services and other exposure reducing polices, such as related to urban planning and construction technologies. Car free zones and active transport routes are success examples, especially in local contexts to reduce human exposure. Key messages • Passenger car electrification alone is not sufficient to remove health burden of traffic. • We need also to consider wider development of urban transport systems to develop healthy living environments.

These models account for the spatial effects resulting from the proximity of events. A compromise exists in the mathematical accuracy of model parameters when spatial correlations are present in the data of the phenomenon. Data reliant on spatial correlations are crucial in statistical modelling, especially in environmental science, economics, epidemiology, and various other disciplines. This study employs and compares three artificial intelligence approaches—the genetic algorithm (GA), the TABU search algorithm (TSA), and the binary firefly algorithm (Binary FFA)—to determine which is the most efficient for estimating the parametric regression function for spatially dependent data. The Mean Absolute Percentage Error numbers derived from the simulation demonstrated that the Binary FFA method yielded the most accurate estimations. This illustrates the superiority of the algorithm compared to conventional methods, as well as Genetic Algorithms (GA) and Tabu Search Algorithms (TSA), in environmental assessments (particularly, water pollution in the Euphrates River) and the estimation of regression models for geographically dependent data. The regression parameter analysis for spatially dependent environmental data about Euphrates River pollution indicates that the temperature variablity exerted little influence on total dissolved salts. Conversely, the variables calcium (Ca), magnesium (Mg), and potassium (K) exhibited a significant and advantageous influence on total dissolved salts. In contrast, the variable sodium (Na) displayed a distinctly detrimental effect simultaneously.

Foodborne bacterial infections caused by Vibrio species, Campylobacter species, and pathogenic Escherichia coli (STEC/EHEC) pose significant public health risks globally. Environmental factors, particularly ambient temperature and rainfall, play crucial roles in modulating the ecology, transmission, and epidemiology of these pathogens. This review examines recent scientific literature (post-2015) on the associations between these key weather variables and infections caused by Vibrio, Campylobacter, and STEC. Evidence confirms that rising temperatures strongly promote the proliferation and geographic expansion of Vibrio species in aquatic environments. For Campylobacter and STEC, temperature influences seasonality and environmental survival, often through indirect mechanisms affecting hosts, vectors, or human behaviour. Rainfall, particularly heavy rainfall events, is a major driver of pathogen transport via runoff, leading to contamination of water sources and agricultural produce, thereby increasing exposure risk for all three pathogen groups. Understanding these complex, pathogen-specific relationships is critical for developing effective public health strategies, including enhanced surveillance, predictive modelling, and climate change adaptation measures in food safety and water management. Integrated approaches combining environmental monitoring, epidemiology, and climate science are essential to mitigating the growing threat of weather-influenced foodborne diseases.

Abstract Background The Planetary Health approach, which explores the links between human health and the disruption of ecological systems, is increasingly recognized as crucial for addressing the health challenges of the 21st century. Effective educational training is essential to build a competent workforce, especially in countries like Italy, which faces significant environmental pressures. This study aims to identify key Planetary Health training needs among professionals from different backgrounds in Italian institutions. Methods A cross-sectional survey was conducted between August and September 2024 among 21 Italian institutions, including research centers, public health agencies, healthcare facilities, and regional authorities. Training needs, training formats, and professional staff backgrounds were assessed. Descriptive statistics were used to analyze the responses. Results Of the 21 institutions invited, 14 (66.7%) completed the survey. Respondents represented diverse organizations with staff from environmental and health sectors. Key training needs included understanding the health impacts of pollution (79%), stakeholder engagement (64%), and advanced environmental epidemiology (57%). The preferred formats were seminars (79%), targeted courses (71%), and distance learning (50%). Conclusions The survey revealed strong recognition of Planetary Health's importance among Italian institutions. These findings provide valuable insights for developing targeted training initiatives in Italy to enhance the knowledge, competence, and capacity of professionals to address current and future Planetary Health challenges. Key messages • The study highlights emerging Planetary Health training needs in Italy, such as epidemiology, pollution impacts, and stakeholder engagement. • Environment and health institutions demonstrate a clear preference for courses that are both structured and multidisciplinary, and that also incorporate practical elements.

BackgroundIn environmental epidemiology and many other fields, estimating the causal effects of multiple concurrent exposures holds great promise for driving public health interventions and policy changes. Given the predominant reliance on observational data, confounding remains a key consideration, and generalized propensity score (GPS) methods are widely used as causal models to control measured confounders. However, current GPS methods for multiple continuous exposures remain scarce.MethodsWe proposed a novel causal model for exposure mixtures, called nonparametric multivariate covariate balancing generalized propensity score (npmvCBGPS). A simulation study examined whether npmvCBGPS, an existing multivariate GPS (mvGPS) method, and a linear regression model for the outcome can accurately and precisely estimate the effects of exposure mixtures in a variety of common scenarios. An application study illustrated the analysis of the causal role of per- and polyfluoroalkyl substances (PFASs) on BMI.ResultsThe npmvCBGPS achieved acceptable covariate balance in all scenarios. The estimates were close to the true value as long as either the exposure or the outcome model was correctly specified, and the results were less impacted by correlations among mixture components. The accuracy and precision of mvGPS and the linear regression model relied on the correctly specified exposure model and outcome model, respectively. The npmvCBGPS outperformed mvGPS in all scenarios. The npmvCBGPS achieved better covariate balance than mvGPS and provided an overall inverse trend between the PFAS mixtures with BMI.ConclusionsIn this study, we proposed npmvCBGPS to accurately estimate the causal effects of multiple exposure mixtures on health outcomes. Our approach is applicable across various domains, with a particular emphasis on environmental epidemiology.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12874-025-02673-4.

This study investigated the association between temperature and hospitalizations for respiratory diseases (RD) among suburban farmers in Zhangye, Wuwei, Dingxi, and Tianshui in Gansu province. We collected the daily hospital admission data for RD in four cities from the local public hospitals, covering the period from January 1, 2018 to December 31, 2019. The association was estimated using a quasi-Poisson generalized additive model (GAM) and distributed lag nonlinear model (DLNM) to account for lagged and non-linear effects, and the association varies geographically. Our study found that both low and high temperatures were associated with RD morbidity, and had significant lag effects in four cities, the risk of temperature on RD morbidity increased significantly in Zhangye (low temperature: RR = 2.107, 95%CI: 1.749, 2.540; high temperature: RR = 2.407, 95%CI: 1.932, 2.998), Wuwei (low temperature: RR = 1.758, 95%CI: 1.134, 2.726; high temperature: RR = 1.936, 95%CI: 1.541, 2.431), Dingxi (low temperature: RR = 1.876, 95%CI: 1.593, 2.208; high temperature: RR = 2.432, 95%CI: 1.932, 3.061) and Tianshui (low temperature: RR = 1.083, 95%CI: 1.021, 1.150; high temperature: RR = 1.630, 95%CI: 1.191, 2.229). Susceptible demographics linked to RD morbidity differ by gender and age group in four cities. For Wuwei, Dingxi, and Tianshui, females exhibited higher adverse effects when exposed to both low and high temperatures than males. By contrast, males in Zhangye showed higher relative risks (RR) than females. Additionally, in Zhangye, Wuwei, and Tianshui, low temperature had a greater impact on patients aged < 65years than on those aged ≥ 65years. For high temperature, patients aged < 65years in Zhangye, Wuwei, and Dingxi were more susceptible. These findings emphasize the need for region-tailored early warning systems and targeted preventive measures for vulnerable groups. The application of distributed lag non-linear modeling in a suburban agricultural population offers novel insights into environmental epidemiology in resource-constrained settings. Future research should prioritize refining temperature-health threshold definitions and leveraging micro-level exposure data to inform adaptive public health strategies.