Objective: Myocardial infarction has been associated with both transportation noise and air pollution. We examined residential exposure to aircraft noise and mortality from myocardial infarction, ...taking air pollution into account. Methods: We analyzed the Swiss National Cohort, which includes geocoded information on residence. Exposure to aircraft noise and air pollution was determined based on geospatial noise and airpollution (PM₁₀) models and distance to major roads. We used Cox proportional hazard models, with age as the timescale. We compared the risk of death across categories of A-weighted sound pressure levels (dB(A)) and by duration of living in exposed corridors, adjusting for PM₁₀ levels, distance to major roads, sex, education, and socioeconomic position of the municipality. Results: We analyzed 4.6 million persons older than 30 years who were followed from near the end of 2000 through December 2005, including 15,532 deaths from myocardial infarction (ICD-10 codes I 21, I 22). Mortality increased with increasing level and duration of aircraft noise. The adjusted hazard ratio comparing ≥60 dB(A) with <45 dB(A) was 1.3 (95% confidence interval = 0.96-1.7) overall, and 1.5 (1.0-2.2) in persons who had lived at the same place for at least 15 years. None of the other endpoints (mortality from all causes, all circulatory disease, cerebrovascular disease, stroke, and lung cancer) was associated with aircraft noise. Conclusion: Aircraft noise was associated with mortality from myocardial infarction, with a dose-response relationship for level and duration of exposure. The association does not appear to be explained by exposure to particulate matter air pollution, education, or socioeconomic status of the municipality.
Full text
Available for:
BFBNIB, CMK, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
•Prospective exploration of environmental determinants of headache.•Headache appears to be a transient condition in the population.•Air pollution and urban temperature contribute to the reporting of ...headache.•The largest effect was observed for NO2, PM10, and heat island effect.
Headache is one of the most prevalent and disabling health conditions globally. We prospectively explored the urban exposome in relation to weekly occurrence of headache episodes using data from the Dutch population-based Occupational and Environmental Health Cohort Study (AMIGO).
Participants (N = 7,339) completed baseline and follow-up questionnaires in 2011 and 2015, reporting headache frequency. Information on the urban exposome covered 80 exposures across 10 domains, such as air pollution, electromagnetic fields, and lifestyle and socio-demographic characteristics. We first identified all relevant exposures using the Boruta algorithm and then, for each exposure separately, we estimated the average treatment effect (ATE) and related standard error (SE) by training causal forests adjusted for age, depression diagnosis, painkiller use, general health indicator, sleep disturbance index and weekly occurrence of headache episodes at baseline.
Occurrence of weekly headache was 12.5 % at baseline and 11.1 % at follow-up. Boruta selected five air pollutants (NO2, NOX, PM10, silicon in PM10, iron in PM2.5) and one urban temperature measure (heat island effect) as factors contributing to the occurrence of weekly headache episodes at follow-up. The estimated causal effect of each exposure on weekly headache indicated positive associations. NO2 showed the largest effect (ATE = 0.007 per interquartile range (IQR) increase; SE = 0.004), followed by PM10 (ATE = 0.006 per IQR increase; SE = 0.004), heat island effect (ATE = 0.006 per one-degree Celsius increase; SE = 0.007), NOx (ATE = 0.004 per IQR increase; SE = 0.004), iron in PM2.5 (ATE = 0.003 per IQR increase; SE = 0.004), and silicon in PM10 (ATE = 0.003 per IQR increase; SE = 0.004).
Our results suggested that exposure to air pollution and heat island effects contributed to the reporting of weekly headache episodes in the study population.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Display omitted
•Cross-sectional analysis of 253 Ugandan smallholder farmers in a low-income context.•Increased risk of sleep problems among farmers exposed to pesticides.•Female farmers at higher ...risk for sleep problems after recent pesticide exposure.•Use of mancozeb and glyphosate leads to increased risk of sleep problems.
Poorly educated smallholder farmers in low-income countries are highly exposed to pesticides. This can result in adverse mental health issues, of which sleep problems might be an underlying indicator. We aim to examine the association between sleep problems and pesticide exposure among smallholder farmers in Uganda.
A cross-sectional survey with 253 smallholder farmers was conducted between October and December 2019. Sleep problems were assessed during the week before the visit using the Medical Outcomes Study Sleep Scale (MOS-SS). Exposure to pesticides was assessed as application days of any pesticide and as use of 2,4-D, glyphosate, mancozeb, organophosphates & carbamates, pyrethroids and other pesticides during the week and year prior to the visit. Associations were assessed using adjusted multivariable logistic regression models.
Increased odds ratio (OR) for the sleep problem index 6-items (OR 95% Confidence Interval 1.99 1.04; 3.84 and 3.21 1.33; 7.82), sleep inadequacy (1.94 1.04; 3.66 and 2.49 1.05–6.22) and snoring (3.17 1.12; 9.41 and 4.07 1.04; 15.14) were observed for farmers who respectively applied pesticides up to two days and three or more days in the past week compared to farmers who did not apply during the past week. Gender-stratified analyses showed a higher OR for female applicators (4.27 1.76–11.16) than for male applicators (1.82 0.91–3.79) for the association between the sleep problem index 6-items and pesticide use in the week before the visit. Increased ORs were also observed for the association between the sleep problem index 6-item and mancozeb exposure during the past year 2.28 1.12–4.71 and past week 2.51 0.86–7.55 and glyphosate exposure during the past week 3.75 1.24–11.8 compared to non-applicators.
Our findings suggest an increased risk of sleep problems among smallholder farmers in a pesticide-exposure-dependent way in a low-income context. Further gender-stratified, longitudinal investigations are warranted to confirm these findings.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Type 2 diabetes is one of the major chronic diseases accounting for a substantial proportion of disease burden in Western countries. The majority of the burden of type 2 diabetes is attributed to ...environmental risks and modifiable risk factors such as lifestyle. The environment we live in, and changes to it, can thus contribute substantially to the prevention of type 2 diabetes at a population level. The ‘exposome’ represents the (measurable) totality of environmental, i.e. nongenetic, drivers of health and disease. The external exposome comprises aspects of the built environment, the social environment, the physico-chemical environment and the lifestyle/food environment. The internal exposome comprises measurements at the epigenetic, transcript, proteome, microbiome or metabolome level to study either the exposures directly, the imprints these exposures leave in the biological system, the potential of the body to combat environmental insults and/or the biology itself. In this review, we describe the evidence for environmental risk factors of type 2 diabetes, focusing on both the general external exposome and imprints of this on the internal exposome. Studies provided established associations of air pollution, residential noise and area-level socioeconomic deprivation with an increased risk of type 2 diabetes, while neighbourhood walkability and green space are consistently associated with a reduced risk of type 2 diabetes. There is little or inconsistent evidence on the contribution of the food environment, other aspects of the social environment and outdoor temperature. These environmental factors are thought to affect type 2 diabetes risk mainly through mechanisms incorporating lifestyle factors such as physical activity or diet, the microbiome, inflammation or chronic stress. To further assess causality of these associations, future studies should focus on investigating the longitudinal effects of our environment (and changes to it) in relation to type 2 diabetes risk and whether these associations are explained by these proposed mechanisms.
Graphical abstract
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•Residential pre-natal exposure to pesticides can result in adverse birth outcomes.•We explored associations between 139 pesticides and several birth outcomes.•We found indications of such ...associations for five pesticides.•Fluroxypyr-meptyl, glufosinate-ammonium, linuron, vinclozolin and picoxystrobin.•Confirmatory investigations on these and/or similar compounds is warrented.
Maternal occupational exposure to pesticides has been linked to adverse birth outcomes but associations with residential pesticide exposures are inconclusive.
To explore associations between residential exposure to specific pesticides and birth outcomes using individual level exposure and pregnancy/birth data.
From all 2009–2013 singleton births in the Dutch birth registry, we selected mothers > 16 years old living in non-urban areas, who had complete address history and changed addresses at most once during pregnancy (N = 339,947). We estimated amount (kg) of 139 active ingredients (AI) used within buffers of 50, 100, 250 and 500 m around each mother's home during pregnancy. We used generalized linear models to investigate associations between 12 AIs with evidence of reproductive toxicity and gestational age (GA), birth weight (BW), perinatal mortality, child́s sex, prematurity, low birth weight (LBW), small for gestational age (SGA) and large for gestational age (LGA), adjusting for individual and area-level confounders. For the remainder 127 AIs, we used minimax concave penalty with a stability selection step to identify those that could be related to birth outcomes.
Regression analyses showed that maternal residential exposure to fluroxypyr-meptyl was associated with longer GA, glufosinate-ammonium with higher risk of LBW, linuron with higher BW and higher odds of LGA, thiacloprid with lower odds of perinatal mortality and vinclozolin with longer GA. Variable selection analysis revealed that picoxystrobin was associated with higher odds of LGA. We found no evidence of associations with other AIs. Sensitivity and additional analysis supported these results except for thiacloprid.
In this exploratory study, pregnant women residing near crops where fluroxypyr-meptyl, glufosinate-ammonium, linuron, vinclozolin and picoxystrobin were applied had higher risk for certain potentially adverse birth outcomes. Our findings provide leads for confirmatory investigations on these compounds and/or compounds with similar modes of action.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Clinical trials and meta-analyses have produced conflicting results of the efficacy of unconjugated pneumococcal polysaccharide vaccine in adults. We sought to evaluate the vaccine's efficacy on ...clinical outcomes as well as the methodologic quality of the trials.
We searched several databases and all bibliographies of reviews and meta-analyses for clinical trials that compared pneumococcal polysaccharide vaccine with a control. We examined rates of pneumonia and death, taking the methodologic quality of the trials into consideration.
We included 22 trials involving 101 507 participants: 11 trials reported on presumptive pneumococcal pneumonia, 19 on all-cause pneumonia and 12 on all-cause mortality. The current 23-valent vaccine was used in 8 trials. The relative risk (RR) was 0.64 (95% confidence interval CI 0.43-0.96) for presumptive pneumococcal pneumonia and 0.73 (95% CI 0.56-0.94) for all-cause pneumonia. There was significant heterogeneity between the trials reporting on presumptive pneumonia (I(2) = 74%, p < 0.001) and between those reporting on all-cause pneumonia (I(2) = 90%, p < 0.001). The RR for all-cause mortality was 0.97 (95% CI 0.87-1.09), with moderate heterogeneity between trials (I(2) = 44%, p = 0.053). Trial quality, especially regarding double blinding, explained a substantial proportion of the heterogeneity in the trials reporting on presumptive pneumonia and all-cause pneumonia. There was little evidence of vaccine protection in trials of higher methodologic quality (RR 1.20, 95% CI 0.75-1.92, for presumptive pneumonia; and 1.19, 95% CI 0.95-1.49, for all-cause pneumonia in double-blind trials; p for heterogeneity > 0.05). The results for all-cause mortality in double-blind trials were similar to those in all trials combined. There was little evidence of vaccine protection among elderly patients or adults with chronic illness in analyses of all trials (RR 1.04, 95% CI 0.78-1.38, for presumptive pneumococcal pneumonia; 0.89, 95% CI 0.69-1.14, for all-cause pneumonia; and 1.00, 95% CI 0.87-1.14, for all-cause mortality).
Pneumococcal vaccination does not appear to be effective in preventing pneumonia, even in populations for whom the vaccine is currently recommended.
Previous studies established a causal relationship between occupational benzene exposure and acute myeloid leukemia (AML). However, mixed results have been reported for associations between benzene ...exposure and other myeloid and lymphoid malignancies. Our work examined whether occupational benzene exposure is associated with increased mortality from overall lymphohaematopoietic (LH) cancer and major subtypes.
Mortality records were linked to a Swiss census-based cohort from two national censuses in 1990 and 2000. Cases were defined as having any LH cancers registered in death certificates. We assessed occupational exposure by applying a quantitative benzene job-exposure matrix (BEN-JEM) to census-reported occupations. Exposure was calculated as the products of exposure proportions and levels (P × L). Cox proportional hazards models were used to calculate LH cancer death hazard ratios (HR) and 95% confidence intervals (CI) associated with benzene exposure, continuously and in ordinal categories.
Our study included approximately 2.97 million persons and 13 415 LH cancer cases, including 3055 cases with benzene exposure. We observed increased mortality risks per unit (P × L) increase in continuous benzene exposure for AML (HR 1.03, 95% CI 1.00-1.06) and diffuse large B-cell lymphoma (HR 1.09, 95% CI 1.04-1.14). When exposure was assessed categorically, increasing trends in risks were observed with increasing benzene exposure for AML (P=0.04), diffuse large B-cell lymphoma (P=0.02), and follicular lymphoma (P=0.05).
In a national cohort from Switzerland, we found that occupational exposure to benzene is associated with elevated mortality risks for AML, diffuse large B-cell lymphoma, and possibly follicular lymphoma.
There is some evidence to suggest an association between ambient air pollution and development of Parkinson's disease (PD). However, the small number of studies published to date has reported ...inconsistent findings.
To assess the association between long-term exposure to ambient air pollution constituents and the development of PD.
Air pollution exposures (particulate matter with aerodynamic diameter <10 μm PM10, <2.5 μm PM2.5, between 2.5 μm and 10 μm PMcoarse, black carbon, and nitrogen oxides NO2 and NOx) were predicted based on land-use regression models developed within the “European Study for Air Pollution Effects” (ESCAPE) study, for a Dutch PD case-control study. A total of 1290 subjects (436 cases and 854 controls). were included and 16 years of exposure were estimated (average participant starting age: 53). Exposures were categorized and conditional logistic regression models were applied to evaluate the association between ambient air pollution and PD.
Overall, no significant, positive relationship between ambient air pollutants and PD was observed. The odds ratio (OR) for PD associated with an increase from the first quartile of NO2 (<22.8 μg/m3) and the fourth (>30.4 μg/m3) was 0.87 (95% CI: 0.54, 1.41). For PM2.5 where the contrast in exposure was more limited, the OR associated with an increase from the first quartile PM2.5 (<21.2 μg/m3) to the fourth (>22.3 μg/m3) was 0.50 (95% CI: 0.24, 1.01). In a subset of the population with long-term residential stability (n = 632), an increased risk of PD was observed (e.g. OR for Q4 vs Q1 NO2:1.37, 95% CI: 0.71, 2.67).
We found no clear association between 16 years of residential exposure to ambient air pollution and the development of PD in The Netherlands.
•The role of air pollution in Parkinson's disease (PD) is poorly understood.•We examined 16 years of air pollution and PD in a case-control study of 1290 people.•No positive relationship between air pollution in the 16 years and PD was observed.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•COSMOS is a multi-national prospective cohort study of mobile phone use and health.•Earlier epidemiologic studies are limited by recall bias or crude exposure assessment.•COSMOS includes over 250000 ...participants, a large proportion are long-term users.•We found no evidence of increased risk of glioma, meningioma or acoustic neuroma.•Suggests that amount of mobile phone use is not associated with brain tumour risk.
Each new generation of mobile phone technology has triggered discussions about potential carcinogenicity from exposure to radiofrequency electromagnetic fields (RF-EMF). Available evidence has been insufficient to conclude about long-term and heavy mobile phone use, limited by differential recall and selection bias, or crude exposure assessment. The Cohort Study on Mobile Phones and Health (COSMOS) was specifically designed to overcome these shortcomings.
We recruited participants in Denmark, Finland, the Netherlands, Sweden, and the UK 2007–2012. The baseline questionnaire assessed lifetime history of mobile phone use. Participants were followed through population-based cancer registers to identify glioma, meningioma, and acoustic neuroma cases during follow-up. Non-differential exposure misclassification was reduced by adjusting estimates of mobile phone call-time through regression calibration methods based on self-reported data and objective operator-recorded information at baseline. Hazard ratios (HR) and 95% confidence intervals (CI) for glioma, meningioma, and acoustic neuroma in relation to lifetime history of mobile phone use were estimated with Cox regression models with attained age as the underlying time-scale, adjusted for country, sex, educational level, and marital status.
264,574 participants accrued 1,836,479 person-years. During a median follow-up of 7.12 years, 149 glioma, 89 meningioma, and 29 incident cases of acoustic neuroma were diagnosed. The adjusted HR per 100 regression-calibrated cumulative hours of mobile phone call-time was 1.00 (95 % CI 0.98–1.02) for glioma, 1.01 (95 % CI 0.96–1.06) for meningioma, and 1.02 (95 % CI 0.99–1.06) for acoustic neuroma. For glioma, the HR for ≥ 1908 regression-calibrated cumulative hours (90th percentile cut-point) was 1.07 (95 % CI 0.62–1.86). Over 15 years of mobile phone use was not associated with an increased tumour risk; for glioma the HR was 0.97 (95 % CI 0.62–1.52).
Our findings suggest that the cumulative amount of mobile phone use is not associated with the risk of developing glioma, meningioma, or acoustic neuroma.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Display omitted
•Multi-model inference is advocated for analyzing multiple exposures.•The findings were consistent depending on linear or nonlinear statistical methods.•Urban obesogenic environments ...are driven by specific neighborhood characteristics.•Neighborhood SEP is related to BMI independently from individual-level SEP.•Oxidative potential of PM2.5 might be positively related to BMI.
Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously.
We aimed to explore what environmental factors of the urban exposome are related to body mass index (BMI), and evaluated the consistency of findings across multiple statistical approaches.
A cross-sectional analysis was conducted using baseline data from 14,829 participants of the Occupational and Environmental Health Cohort study. BMI was obtained from self-reported height and weight. Geocoded exposures linked to individual home addresses (using 6-digit postcode) of 86 environmental factors were estimated, including air pollution, traffic noise, green-space, built environmental and neighborhood socio-demographic characteristics. Exposure-obesity associations were identified using the following approaches: sparse group Partial Least Squares, Bayesian Model Averaging, penalized regression using the Minimax Concave Penalty, Generalized Additive Model-based boosting Random Forest, Extreme Gradient Boosting, and Multiple Linear Regression, as the most conventional approach. The models were adjusted for individual socio-demographic variables. Environmental factors were ranked according to variable importance scores attributed by each approach and median ranks were calculated across these scores to identify the most consistent associations.
The most consistent environmental factors associated with BMI were the average neighborhood value of the homes, oxidative potential of particulate matter air pollution (OP), healthy food outlets in the neighborhood (5 km buffer), low-income neighborhoods, and one-person households in the neighborhood. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households and smaller amount of healthy food retailers. Higher BMI levels were observed in low-income neighborhoods, with lower average house values, lower share of one-person households, smaller amounts of healthy food retailers and higher OP levels. Across the approaches, we observed consistent patterns of results based on model’s capacity to incorporate linear or nonlinear associations.
The pluralistic analysis on environmental obesogens strengthens the existing evidence on the role of neighborhood socioeconomic position, urbanicity and air pollution.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
