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Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility. We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003–2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models. PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene having the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects. We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.

Studies in areas with relatively high levels of air pollution have found some positive associations between exposures to ambient levels of air pollution and several birth outcomes including low birth weight (LBW). The purpose of this study was to examine the association between LBW among term infants and ambient air pollution, by trimester of exposure, in a region of lower level exposures. The relationship between LBW and ambient levels of particulate matter up to 10 um in diameter (PM10), sulfur dioxide (SO2) and ground-level ozone (O3) was evaluated using the Nova Scotia Atlee Perinatal Database and ambient air monitoring data from the Environment Canada National Air Pollution Surveillance Network and the Nova Scotia Department of Environment. The cohort consisted of live singleton births (≥37 weeks of gestation) between January1,1988 and December31,2000. Maternal exposures to air pollution were assigned to women living within 25 km of a monitoring station at the time of birth. Air pollution was evaluated as a continuous and categorical variable (using quartile exposures) for each trimester and relative risks were estimated from logistic regression, adjusted for confounding variables. There were 74,284 women with a term, singleton birth during the study period and with exposure data. In the analyses unadjusted for year of birth, first trimester exposures in the highest quartile for SO2 and PM10suggested an increased risk of delivering a LBW infant (relative risk = 1.36, 95% confidence interval = 1.04 to 1.78 for SO2 exposure and relative risk = 1.33, 95% confidence interval = 1.02 to 1.74 for PM10). After adjustment for birth year, the relative risks were attenuated somewhat and not statistically significant. A dose-response relationship for SO2 was noted with increasing levels of exposure. No statistically significant effects were noted for ozone. Our results suggest that exposure during the first trimester to relatively low levels of some air pollutants may be associated with a reduction in birth weight in term-born infants. These findings have implications for the development of effective risk management strategies to minimize the public health impacts for pregnant women.

To investigate air pollution effects during pregnancy or in the first weeks of life, models are needed that capture both the spatial and temporal variability of air pollution exposures. We developed a time-space exposure model for ambient NO2 concentrations in Bern, Switzerland. We used NO2 data from passive monitoring conducted between 1998 and 2009: 101 rural sites (24,499 biweekly measurements) and 45 urban sites (4350 monthly measurements). We evaluated spatial predictors (land use; roads; traffic; population; annual NO2 from a dispersion model) and temporal predictors (meteorological conditions; NO2 from continuous monitoring station). Separate rural and urban models were developed by multivariable regression techniques. We performed ten-fold internal cross-validation, and an external validation using 57 NO2 passive measurements obtained at study participant’s homes. Traffic related explanatory variables and fixed site NO2 measurements were the most relevant predictors in both models. The coefficient of determination (R2) for the log transformed models were 0.63 (rural) and 0.54 (urban); cross-validation R2s were unchanged indicating robust coefficient estimates. External validation showed R2s of 0.54 (rural) and 0.67 (urban). This approach is suitable for air pollution exposure prediction in epidemiologic research with time-vulnerable health effects such as those occurring during pregnancy or in the first weeks of life.

Nitrate is converted to nitrite in the human body and subsequently can react with amines and amides in the gastrointestinal tract to form N-nitroso compounds (NOCs), which are known to be carcinogenic in animals. Humans can be exposed to nitrate via consumption of drinking water and diet, especially green leafy vegetables and cured meat. The contribution of nitrate from drinking water in combination with meat intake has not been investigated thoroughly. Therefore, in the present pilot study, we examined the effect of nitrate from drinking water, and its interaction with the consumption of white and processed red meat, on the endogenous formation of NOCs, taking into account the intake of vitamin C, a nitrosation inhibitor. Twenty healthy subjects were randomly assigned to two groups consuming either 3.75 g/kg body weight (maximum 300 g per day) processed red meat or unprocessed white meat per day for two weeks. Drinking water nitrate levels were kept low during the first week (< 1.5 mg/L), whereas in week 2, nitrate levels in drinking water were adjusted to the acceptable daily intake level of 3.7 mg/kg bodyweight. At baseline, after 1 and 2 weeks, faeces and 24 h urine samples were collected for analyses of nitrate, apparent total N-nitroso compounds (ATNC), compliance markers, and genotoxic potential in human colonic Caco-2 cells. Urinary nitrate excretion was significantly increased during the high drinking water nitrate period for both meat types. Furthermore, levels of compliance markers for meat intake were significantly increased in urine from subjects consuming processed red meat (i.e. 1-Methylhistidine levels), or unprocessed white meat (i.e. 3-Methylhistidine). ATNC levels significantly increased during the high drinking water nitrate period, which was more pronounced in the processed red meat group. Genotoxicity in Caco-2 cells exposed to faecal water resulted in increased genotoxicity after the interventions, but results were only significant in the low drinking water nitrate period in subjects consuming processed red meat. Furthermore, a positive correlation was found between the ratio of nitrate/vitamin C intake (including drinking water) and the level of ATNC in faecal water of subjects in the processed red meat group, but this was not statistically significant. Drinking water nitrate significantly contributed to the endogenous formation of NOC, independent of the meat type consumed. This implies that drinking water nitrate levels should be taken into account when evaluating the effect of meat consumption on endogenous formation of NOC. Dutch Trialregister: 29707 . Registered 19th of October 2018. Retrospectively registered.

Between 1962 and 1971, the US Air Force sprayed Agent Orange across Vietnam, exposing many soldiers to this dioxin-containing herbicide. Several negative health outcomes have been linked to Agent Orange exposure, but data is lacking on the effects this chemical has on the genome. Therefore, we sought to characterize the impact of Agent Orange exposure on DNA methylation in the whole blood and adipose tissue of veterans enrolled in the Air Force Health Study (AFHS). We received adipose tissue (n = 37) and whole blood (n = 42) from veterans in the AFHS. Study participants were grouped as having low, moderate, or high TCDD body burden based on their previously measured serum levels of dioxin. DNA methylation was assessed using the Illumina 450 K platform. Epigenome-wide analysis indicated that there were no FDR-significantly methylated CpGs in either tissue with TCDD burden. However, 3 CpGs in the adipose tissue (contained within SLC9A3, LYNX1, and TNRC18) were marginally significantly (q < 0.1) hypomethylated, and 1 CpG in whole blood (contained within PTPRN2) was marginally significantly (q < 0.1) hypermethylated with high TCDD burden. Analysis for differentially methylated DNA regions yielded SLC9A3, among other regions in adipose tissue, to be significantly differentially methylated with higher TCDD burden. Comparing whole blood data to a study of dioxin exposed adults from Alabama identified a CpG within the gene SMO that was hypomethylated with dioxin exposure in both studies. We found limited evidence of dioxin associated DNA methylation in adipose tissue and whole blood in this pilot study of Vietnam War veterans. Nevertheless, loci in the genes of SLC9A3 in adipose tissue, and PTPRN2 and SMO in whole blood, should be included in future exposure analyses.

The electromagnetic fields (EMFs) emitted by the technologies affect the homeostatic systems (nervous, endocrine, and immune systems) and consequently the health. In a previous work, we observed that men and women, after 2 months of using a bed with a registered HOGO system, that prevents and drain EMFs, improved their immunity, redox and inflammatory states and rejuvenated their rate of aging or biological age. Since, EMFs can act as a chronic stressor stimulus, and affect the sleep quality. The objective of this work was to study in men and women (23–73 years old) the effect of sleeping for 2 months on that bed in the blood concentrations of several hormones related to stress response and sleep quality as well as to corroborate the rejuvenation of their biological age. In 18 men and women, plasma concentration of cortisol, dehydroepiandrosterone (DHEA), catecholamines (epinephrine, norepinephrine and dopamine), serotonin, oxytocin and melatonin were analyzed before and after 2 months of using the HOGO beds. A group of 10 people was used as placebo control. In another cohort of 25 men (20 experimental and 5 placebo), the effects of rest on the HOGO system on the concentration of cortisol and testosterone in plasma were studied. In all these volunteers, the biological age was analyzed using the Immunity Clock model. There is a significant increase in plasma concentration of DHEA, norepinephrine, serotonin, oxytocin, and melatonin as well as in testosterone, after resting for 2 months in that bed with the EMFs avoiding system. In addition, decreases in Cortisol/DHEA and Testosterone/cortisol ratio and plasma dopamine concentration were observed. No differences were found in placebo groups. In all participants that slept on HOGO beds, the biological age was reduced. Sleeping in a bed that isolates from EMFs and drain them can be a possible strategy to improve the secretion of hormones related to a better response to stress and sleep quality, which means a better endocrine system, and consequently better homeostasis and maintenance of health. This fact was confirmed with the slowdown in the rate of aging checked with a rejuvenation of the biological age.

The explosion at the Chernobyl Nuclear Power Plant was one of the greatest known nuclear disasters of the 20th century. To reduce individual exposure to ionizing radiation the Soviet Union government introduced a number of counter-measures. This article presents a description of how historical events conspired to disrupt these efforts and affect residents in exposed areas. This study employed an extensive review of data on radionuclide deposition, contamination patterns and lifestyle characteristics. Data were obtained from the Ukraine Ministry of Health and the Ukraine Research Center for Radiation Medicine. Data are presented on annual contamination rates in selected locales as well as data on local food consumption patterns. Historical factors including economic and political circumstances are also highlighted. Results show the diminution of individual doses between 1987 and 1991 and then an increase between 1991 and 1994 and the relationship between this increase and changes in the lifestyle of the local population. A number of factors played direct and indirect roles in contributing to the populace's cumulative radiation exposure. Future post-contamination studies need to consider these factors when estimating individual exposures.

The effect of heatwaves on adverse birth outcomes is not well understood and may vary by how heatwaves are defined. The study aims to examine acute associations between various heatwave definitions and preterm and early-term birth. Using national vital records from 50 metropolitan statistical areas (MSAs) between 1982 and 1988, singleton preterm (< 37 weeks) and early-term births (37–38 weeks) were matched (1:1) to controls who completed at least 37 weeks or 39 weeks of gestation, respectively. Matching variables were MSA, maternal race, and maternal education. Sixty heatwave definitions including binary indicators for exposure to sustained heat, number of high heat days, and measures of heat intensity (the average degrees over the threshold in the past 7 days) based on the 97.5th percentile of MSA-specific temperature metrics, or the 85th percentile of positive excessive heat factor (EHF) were created. Odds ratios (OR) for heatwave exposures in the week preceding birth (or corresponding gestational week for controls) were estimated using conditional logistic regression adjusting for maternal age, marital status, and seasonality. Effect modification by maternal education, age, race/ethnicity, child sex, and region was assessed. There were 615,329 preterm and 1,005,576 early-term case-control pairs in the analyses. For most definitions, exposure to heatwaves in the week before delivery was consistently associated with increased odds of early-term birth. Exposure to more high heat days and more degrees above the threshold yielded higher magnitude ORs. For exposure to 3 or more days over the 97.5th percentile of mean temperature in the past week compared to zero days, the OR was 1.027 for early-term birth (95%CI: 1.014, 1.039). Although we generally found null associations when assessing various heatwave definitions and preterm birth, ORs for both preterm and early-term birth were greater in magnitude among Hispanic and non-Hispanic black mothers. Although associations varied across metrics and heatwave definitions, heatwaves were more consistently associated with early-term birth than with preterm birth. This study’s findings may have implications for prevention programs targeting vulnerable subgroups as climate change progresses.

Emissions of high concentrations of antibiotics from manufacturing sites select for resistant bacteria and may contribute to the emergence of new forms of resistance in pathogens. Many scientists, industry, policy makers and other stakeholders recognize such pollution as an unnecessary and unacceptable risk to global public health. An attempt to assess and reduce such discharges, however, quickly meets with complex realities that need to be understood to identify effective ways to move forward. This paper charts relevant key actor-types, their main stakes and interests, incentives that can motivate them to act to improve the situation, as well as disincentives that may undermine such motivation. The actor types and their respective interests have been identified using research literature, publicly available documents, websites, and the knowledge of the authors. Thirty-three different actor-types were identified, representing e.g. commercial actors, public agencies, states and international institutions. These are in complex ways connected by interests that sometimes may conflict and sometimes pull in the same direction. Some actor types can act to create incentives and disincentives for others in this area. The analysis demonstrates and clarifies the challenges in addressing industrial emissions of antibiotics, notably the complexity of the relations between different types of actors, their international dependency and the need for transparency. The analysis however also suggests possible ways of initiating incentive-chains to eventually improve the prospects of motivating industry to reduce emissions. High-resource consumer states, especially in multinational cooperation, hold a key position to initiate such chains.