Modeled and perceived RF-EMF, noise and air pollution and symptoms in a population cohort. Is perception key in predicting symptoms?

A prospective exploration of the urban exposome in relation to headache in the Dutch population-based Occupational and environmental health cohort study (AMIGO)

OBJECTIVE

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).

MATERIAL AND METHODS

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.

RESULTS

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).

CONCLUSION

Our results suggested that exposure to air pollution and heat island effects contributed to the reporting of weekly headache episodes in the study population.

Objective and subjective measures of air pollution and self-rated health: the evidence from Chile

PURPOSE

The literature exploring individual differences in self-rated health has grown fast in recent years. Self-rated health (SRH) is a good indicator of general health status. This empirical study explores the association between outdoor air pollution and SRH in Chile. This type of analysis is infrequent in Latin America.

METHODS

We used objective and subjective air pollution measures. The first corresponds to PM2.5, and the latter to the perception of a high level of air pollution. Drawing on data from two independent and repeated nationwide surveys over the period 2006-2017 at the individual level in Chile, we performed repeated cross-sectional analyses for each year of survey application. Ordered Logit (OL) and Logit (L) multivariate models were used to investigate the association between SRH and air pollution measures, considering other socioeconomic and demographic covariates.

RESULTS

We found that the higher is the level of air pollution, the lower the SRH in Chile, regardless of whether air pollution is physically measured or perceived by respondents. The results were consistent over the years in the sign and significance of regression coefficients using two surveys and two forms of the outcome variable.

CONCLUSIONS

Our findings add evidence that air pollution is a relevant determinant of SRH. In addition, they show that subjective measures of air pollution can be as reliable as physical measures in the analysis of the association between air pollution and human health.

Dose-response associations of maternal prenatal noise exposure duration with antepartum depression status

BACKGROUND

Antepartum depression has been reported to be associated with the intensity of maternal prenatal noise exposure; however, the association between noise exposure duration and the development of antepartum depression has not been established. This study aimed to determine the total and trimester-specific association of prenatal noise exposure duration with the development of antepartum depression.

METHODS

From May 2018 to June 2021, we recruited 2,166 pregnant women from Shengjing Hospital, northeast China. We used a standardized questionnaire to assess women's prenatal noise exposure and used the Edinburgh Postnatal Depression Scale to assess pregnant women's antepartum depression during the 1st -, 2nd -, and 3rd - trimesters. We calculated a cumulative noise exposure score ranging from 0 to 3, with a higher score reflecting higher frequency and longer duration of noise exposure during pregnancy.

RESULTS

Women who were exposed to noise for ≥ 15 min per day had an increased risk of antepartum depression compared with women who were not exposed to noise during pregnancy [odds ratio (OR) = 1.83, 95%CI:1.18, 2.83]. Noise exposure in a specific trimester was associated with higher risk of depression in the same trimester and subsequent trimesters. We observed increases in antepartum depression risk with increasing cumulative noise exposure scores (P for trend < 0.05 for all). Pregnant women with the highest scores had the highest risk of antepartum depression during the first (OR = 1.30, 95%CI:1.02, 1.65), second (OR = 1.75, 95%CI:1.23, 2.50) trimesters. Women with a cumulative noise exposure score of 2 had the highest risk of antepartum depression during the third trimester (OR = 1.79, 95%CI:1.14, 2.80), as well as during the whole pregnancy (OR = 1.94, 95%CI:1.14, 3.30).

CONCLUSIONS

Maternal prenatal noise exposure duration was positively associated with antepartum depression risk in a dose-response manner. It is necessary to develop strategies by which pregnant women can avoid excessive exposure to noise to prevent antepartum depression.

Traffic-related air pollution, road traffic noise, and Parkinson's disease: Evaluations in two Dutch cohort studies

Background

Environmental factors such as air pollution have been associated with Parkinson's disease (PD), but findings have been inconsistent. We investigated the association between exposure to several air pollutants, road traffic noise, and PD risk in two Dutch cohorts.

Methods

Data from 50,087 participants from two Dutch population-based cohort studies, European Prospective Investigation into Cancer and Nutrition in the Netherlands and Arbeid, Milieu en Gezondheid Onderzoek were analyzed. In these cohorts, 235 PD cases were ascertained based on a previously validated algorithm combining self-reported information (diagnosis, medication, and symptoms) and registry data. We assigned the following traffic-related exposures to residential addresses at baseline: NO2, NOx, particulate matter (PM)2.5absorbance (as a marker for black carbon exposure), PM with aerodynamic diameter ≤2.5 µm (PM2.5), ≤10 µm (PM10), PMcoarse (size fraction 2.5-10 µm), ultrafine particles <0.1 µm (UFP), and road traffic noise (Lden). Logistic regression models were applied to investigate the associations with PD, adjusted for possible confounders.

Results

Both single- and two-pollutant models indicated associations between exposure to NOx, road traffic noise, and increasing odds of developing PD. Odds ratios of fully adjusted two-pollutant models in the highest compared with the lowest exposure quartile were 1.62 (95% CI = 1.02, 2.62) for NOx and 1.47 (95% CI = 0.97, 2.25) for road traffic noise, with clear trends across exposure categories.

Conclusions

Our findings suggest that NOx and road traffic noise are associated with an increased risk of PD. While the association with NOx has been shown before, further investigation into the possible role of environmental noise on PD is warranted.

Associations between the urban exposome and type 2 diabetes: Results from penalised regression by least absolute shrinkage and selection operator and random forest models

BACKGROUND

Type 2 diabetes (T2D) is thought to be influenced by environmental stressors such as air pollution and noise. Although environmental factors are interrelated, studies considering the exposome are lacking. We simultaneously assessed a variety of exposures in their association with prevalent T2D by applying penalised regression Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Artificial Neural Networks (ANN) approaches. We contrasted the findings with single-exposure models including consistently associated risk factors reported by previous studies.

METHODS

Baseline data (n = 14,829) of the Occupational and Environmental Health Cohort study (AMIGO) were enriched with 85 exposome factors (air pollution, noise, built environment, neighbourhood socio-economic factors etc.) using the home addresses of participants. Questionnaires were used to identify participants with T2D (n = 676(4.6 %)). Models in all applied statistical approaches were adjusted for individual-level socio-demographic variables.

RESULTS

Lower average home values, higher share of non-Western immigrants and higher surface temperatures were related to higher risk of T2D in the multivariable models (LASSO, RF). Selected variables differed between the two multi-variable approaches, especially for weaker predictors. Some established risk factors (air pollutants) appeared in univariate analysis but were not among the most important factors in multivariable analysis. Other established factors (green space) did not appear in univariate, but appeared in multivariable analysis (RF). Average estimates of the prediction error (logLoss) from nested cross-validation showed that the LASSO outperformed both RF and ANN approaches.

CONCLUSIONS

Neighbourhood socio-economic and socio-demographic characteristics and surface temperature were consistently associated with the risk of T2D. For other physical-chemical factors associations differed per analytical approach.

Concentration-dependent increase in symptoms due to diesel exhaust in a controlled human exposure study

Background

Traffic-related air pollution (TRAP) exposure causes adverse effects on wellbeing and quality of life, which can be studied non-invasively using self-reported symptoms. However, little is known about the effects of different TRAP concentrations on symptoms following controlled exposures, where acute responses can be studied with limited confounding. We investigated the concentration–response relationship between diesel exhaust (DE) exposure, as a model TRAP, and self-reported symptoms.

Methods

We recruited 17 healthy non-smokers into a double-blind crossover study where they were exposed to filtered air (FA) and DE standardized to 20, 50, 150 µg/m3 PM2.5 for 4 h, with a ≥ 4-week washout between exposures. Immediately before, and at 4 h and 24 h from the beginning of the exposure, we administered visual analog scale (VAS) questionnaires and grouped responses into chest, constitutional, eye, neurological, and nasal categories. Additionally, we assessed how the symptom response was related to exposure perception and airway function.

Results

An increase in DE concentration raised total (β ± standard error = 0.05 ± 0.03, P = 0.04), constitutional (0.01 ± 0.01, P = 0.03) and eye (0.02 ± 0.01, P = 0.05) symptoms at 4 h, modified by perception of temperature, noise, and anxiety. These symptoms were also correlated with airway inflammation. Compared to FA, symptoms were significantly increased at 150 µg/m3 for the total (8.45 ± 3.92, P = 0.04) and eye (3.18 ± 1.55, P = 0.05) categories, with trends towards higher values in the constitutional (1.49 ± 0.86, P = 0.09) and nasal (1.71 ± 0.96, P = 0.08) categories.

Conclusion

DE exposure induced a concentration-dependent increase in symptoms, primarily in the eyes and body, that was modified by environmental perception. These observations emphasize the inflammatory and sensory effects of TRAP, with a potential threshold below 150 µg/m3 PM2.5. We demonstrate VAS questionnaires as a useful tool for health monitoring and provide insight into the TRAP concentration–response at exposure levels relevant to public health policy.

Review of the scientific evidence on the individual sensitivity to electromagnetic fields (EHS)

Part of the population considers themselves as sensitive to the man-made electromagnetic radiation (EMF) emitted by powerlines, electric wiring, electric home appliance and the wireless communication devices and networks. Sensitivity is characterized by a broad variety of non-specific symptoms that the sensitive people claim to experience when exposed to EMF. While the experienced symptoms are currently considered as a real life impairment, the factor causing these symptoms remains unclear. So far, scientists were unable to find causality link between symptoms experienced by sensitive persons and the exposures to EMF. However, as presented in this review, the executed to-date scientific studies, examining sensitivity to EMF, are of poor quality to find the link between EMF exposures and sensitivity symptoms of some people. It is logical to consider that the sensitivity to EMF exists but the scientific methodology used to find it is of insufficient quality. It is time to drop out psychology driven provocation studies that ask about feelings-based non-specific symptoms experienced by volunteers under EMF exposure. Such research approach produces only subjective and therefore highly unreliable data that is insufficient to prove, or to disprove, causality link between EHS and EMF. There is a need for a new direction in studying sensitivity to EMF. The basis for it is the notion of a commonly known phenomenon of individual sensitivity, where individuals' responses to EMF depend on the genetic and epigenetic properties of the individual. It is proposed here that new studies, combining provocation approach, where volunteers are exposed to EMF, and high-throughput technologies of transcriptomics and proteomics are used to generate objective data, detecting molecular level biochemical responses of human body to EMF.

Environmental Noise and Effects on Sleep: An Update to the WHO Systematic Review and Meta-Analysis

BACKGROUND

Nighttime noise carries a significant disease burden. The World Health Organization (WHO) recently published guidelines for the regulation of environmental noise based on a review of evidence published up to the year 2015 on the effects of environmental noise on sleep.

OBJECTIVES

This systematic review and meta-analysis will update the WHO evidence review on the effects of environmental noise on sleep disturbance to include more recent studies.

METHODS

Investigations of self-reported sleep among residents exposed to environmental traffic noise at home were identified using Scopus, PubMed, Embase, and PsycINFO. Awakenings, falling asleep, and sleep disturbance were the three outcomes included. Extracted data were used to derive exposure-response relationships for the probability of being highly sleep disturbed by nighttime noise [average outdoor A-weighted noise level (Lnight) 2300-0700 hours] for aircraft, road, and rail traffic noise, individually. The overall quality of evidence was assessed using Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria.

RESULTS

Eleven studies (n=109,070 responses) were included in addition to 25 studies (n=64,090 responses) from the original WHO analysis. When sleep disturbance questions specifically mentioned noise as the source of disturbance, there was moderate quality of evidence for the probability of being highly sleep disturbed per 10-dB increase in Lnight for aircraft [odds ratio (OR)=2.18; 95% confidence interval (CI): 2.01, 2.36], road (OR=2.52; 95% CI: 2.28, 2.79), and railway (OR=2.97; 95% CI: 2.57, 3.43) noise. When noise was not mentioned, there was low to very low quality of evidence for being sleep disturbed per 10-dB increase in Lnight for aircraft (OR=1.52; 95% CI: 1.20, 1.93), road (OR=1.14; 95% CI: 1.08, 1.21), and railway (OR=1.17; 95% CI: 0.91, 1.49) noise. Compared with the original WHO review, the exposure-response relationships closely agreed at low (40 dB Lnight) levels for all traffic types but indicated greater disturbance by aircraft traffic at high noise levels. Sleep disturbance was not significantly different between European and non-European studies.

DISCUSSION

Available evidence suggests that transportation noise is negatively associated with self-reported sleep. Sleep disturbance in this updated meta-analysis was comparable to the original WHO review at low nighttime noise levels. These low levels correspond to the recent WHO noise limit recommendations for nighttime noise, and so these findings do not suggest these WHO recommendations need revisiting. Deviations from the WHO review in this updated analysis suggest that populations exposed to high levels of aircraft noise may be at greater risk of sleep disturbance than determined previously. https://doi.org/10.1289/EHP10197.

Machine learning approaches to characterize the obesogenic urban exposome

BACKGROUND

Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously.

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

DISCUSSION

The pluralistic analysis on environmental obesogens strengthens the existing evidence on the role of neighborhood socioeconomic position, urbanicity and air pollution.

Narrative review of citizen science in environmental epidemiology: Setting the stage for co-created research projects in environmental epidemiology

Several citizen science (CS) initiatives have been adopted in environmental science to monitor air and noise pollution, and water quality related to civic concerns. Nevertheless, CS projects in environmental epidemiology remain scarce. This is because little attention has been paid to evaluate associations of environmental exposures with health effects directly. This narrative review aims to promote the understanding and application of CS in environmental epidemiology. There are many commonalities between CS and other participatory approaches in environmental epidemiology. Yet, CS can foster the democratization of scientific governance and enhance the sustainability of research projects more effectively than other existing participatory approaches. This is especially the case in projects where citizens are invited to participate, engage and become involved throughout all the phases of a research project (co-created projects). This paper identifies various challenges and opportunities specific to the implementation of co-created CS projects in environmental epidemiology. The development of more locally relevant research designs, using local knowledge, obtaining medical ethical clearance, and co-analysing the association between exposure and health, are examples of opportunities and challenges that require epidemiologists to go beyond the traditional research framework and include more outreach activities. Continued efforts, particularly the sharing of information about projects' collaborative processes, are needed to make CS a more concrete and cohesive approach in environmental epidemiology.

Perceived environmental pollution and subjective cognitive decline (SCD) or SCD-related functional difficulties among the general population

This study aimed to determine whether perceived environmental pollution is associated with subjective cognitive decline (SCD) or SCD-related functional difficulties. We conducted a cross-sectional study using data from a nationwide sample of 191,054 individuals aged ≥19 years from the 2018 Korea Community Health Survey. Perceived air, water, soil, noise, and green space pollution was assessed. To investigate the dose-response effect of the amount of perceived environmental pollution, we determined whether an increasing number of types of perceived environmental pollution also increased the odds of having SCD or SCD-related functional difficulties. Among the 191,054 individuals, the prevalence of SCD and SCD-related functional difficulties was 17.7% (N = 33,853) and 2.2% (N = 4139), respectively. Perception of air, soil, or noise pollution was significantly associated with SCD. However, the perception of any type of environmental pollution was not significantly associated with SCD-related functional difficulties. With regard to a dose-response effect, individuals perceiving a greater number of types of environmental pollution had significantly higher odds of SCD or SCD-related functional difficulties. This association was notably more evident in the younger age group.

Environmental exposures and sleep outcomes: A review of evidence, potential mechanisms, and implications

Environmental exposures and poor sleep outcomes are known to have consequential effects on human health. This integrative review first seeks to present and synthesize existing literature investigating the relationship between exposure to various environmental factors and sleep health. We then present potential mechanisms of action as well as implications for policy and future research for each environmental exposure. Broadly, although studies are still emerging, empirical evidence has begun to show a positive association between adverse effects of heavy metal, noise pollution, light pollution, second-hand smoke, and air pollution exposures and various sleep problems. Specifically, these negative sleep outcomes range from subjective sleep manifestations, such as general sleep quality, sleep duration, daytime dysfunction, and daytime sleepiness, as well as objective sleep measures, including difficulties with sleep onset and maintenance, sleep stage or circadian rhythm interference, sleep arousal, REM activity, and sleep disordered breathing. However, the association between light exposure and sleep is less clear. Potential toxicological mechanisms are thought to include the direct effect of various environmental toxicants on the nervous, respiratory, and cardiovascular systems, oxidative stress, and inflammation. Nevertheless, future research is required to tease out the exact pathways of action to explain the associations between each environmental factor and sleep, to inform possible therapies to negate the detrimental effects, and to increase efforts in decreasing exposure to these harmful environmental factors to improve health.

Association of long-term exposure to ambient air pollution with the incidence of sleep disorders: A cohort study in China

BACKGROUND

Sleep disorders have been verified to be associated with adverse health outcomes. Recent studies have linked ambient air pollution to sleep disorders. However, evidence with large sample size and especially prospective studies is very limited.

METHODS

We used data from a prospective cohort study established from 2015 to 2018 in Ningbo, China. Participants were followed-up after baseline survey through linkage to the regional Health Information System (HIS). Sleep disorders were defined based on International Classification of Disease 10 (ICD-10). Spatial-temporal land-use regression (LUR) models were used to estimate the annual exposure to particulate matter with diameter ≤ 2.5 µm (PM_2.5), ≤ 10 µm (PM₁₀) and nitrogen dioxides (NO₂). The associations between long-term exposure to air pollutants and prevalence of sleep disorders were examined using logistic regression models, and Cox regression models for the effects of air pollution on the incidence of sleep disorders. A generalized weighted quantile sum (gWQS) regression was used in the multipollutant analysis.

RESULTS

A total of 38,775 participants were included in the final analysis. Based on baseline data, we observed significant positive associations between air pollution exposure and increased odds of prevalent sleep disorders (Odds Ratio (OR)= 1.48, 95% confidence interval (CI): 1.41-1.55 for PM_2.5; OR= 1.47,95%CI:1.38-1.57 for PM₁₀; OR= 1.38, 95%CI:1.31-1.46 for NO₂). In the longitudinal analysis, hazard ratios for incident sleep disorders associated with per interquartile range (IQR) increase in PM_2.5, PM₁₀ and NO₂ were 1.14 (1.03, 1.25), 1.13 (1.01, 1.27) and 1.13 (1.04, 1.23), respectively. A gWQS regression analysis showed significant association between air pollution mixture and incident sleep disorders (OR=1.11, 95%CI: 1.03-1.20).

CONCLUSIONS

Long-term exposure to PM_2.5, PM₁₀ and NO₂ were associated with increased risk of sleep disorders in a Chinese population. Our findings could provide evidence for a more general role in the adverse health impact of air pollution.

Association between perceived environmental pollution and poor sleep quality: results from nationwide general population sample of 162,797 people

OBJECTIVE/BACKGROUND

Perceived environmental pollution may play a significant role in understanding environmentally induced health-related symptoms. This study aimed to determine whether perceived environmental pollution is associated with poor sleep quality.

METHODS

We conducted a cross-sectional study using data from a nationwide sample of 162,797 individuals aged ≥19 years from the 2018 Korea Community Health Survey. The Pittsburgh Sleep Quality Index was used for assessing sleep quality. Five types of perceived environmental pollutants involving air, water, soil, noise, and green space were assessed. We investigate the association between perceived environmental pollution and poor sleep quality. We also investigated whether an increasing number of perceived environmental pollutants magnified the odds of poor sleep quality.

RESULTS

The prevalence of poor sleep quality was 42.7% (n = 69,554), and 15.6%, 10.1%, 11.9%, 23.0%, and 11.5% reported perceived environmental pollution concerning air, water, soil, noise, and green space, respectively. A perception of air, soil, or noise pollution was significantly associated with poor sleep quality. In addition, those perceiving a greater number of environmental pollutants had significantly higher odds of poor sleep quality. Notably, this association was magnified in individuals living in rural areas.

CONCLUSIONS

Perceived environmental pollution was significantly associated with poor sleep quality. Our results suggest that a more comprehensive exposure to environmental pollution may not only have a worse effect on health outcomes including sleep quality.

Incidence of depression in relation to transportation noise exposure and noise annoyance in the SAPALDIA study

Prospective evidence on the risk of depression in relation to transportation noise exposure and noise annoyance is limited and mixed. We aimed to investigate the associations of long-term exposure to source-specific transportation noise and noise annoyance with incidence of depression in the SAPALDIA (Swiss cohort study on air pollution and lung and heart diseases in adults) cohort. We investigated 4,581 SAPALDIA participants without depression in the year 2001/2002. Corresponding one-year mean road, railway and aircraft day-evening-night noise (Lden) was calculated at the most exposed façade of the participants' residential floors, and transportation noise annoyance was assessed on an 11-point scale. Incident cases of depression were identified in 2010/2011, and comprised participants reporting physician diagnosis, intake of antidepressant medication or having a short form-36 mental health score < 50. We used robust Poisson regressions to estimate the mutually adjusted relative risks (RR) and 95% confidence intervals (CI) of depression, independent of traffic-related air pollution and other potential confounders. Incidence of depression was 11 cases per 1,000 person-years. In single exposure models, we observed positive but in part, statistically non-significant associations (per 10 dB) of road traffic Lden [RR: 1.06 (0.93, 1.22)] and aircraft Lden [RR: 1.19 (0.93, 1.53)], and (per 1-point difference) of noise annoyance [RR: 1.05 (1.02, 1.08)] with depression risk. In multi-exposure model, noise annoyance effect remained unchanged, with weaker effects of road traffic Lden [(RR: 1.02 (0.89, 1.17)] and aircraft Lden [(RR: 1.17 (0.90, 1.50)]. However, there were statistically significant indirect effects of road traffic Lden [(β: 0.02 (0.01, 0.03)] and aircraft Lden [β: 0.01 (0.002, 0.02)] via noise annoyance. There were no associations with railway Lden in the single and multi-exposure models [(RR_{both models}: 0.88 (0.75, 1.03)]. We made similar findings among 2,885 non-movers, where the effect modification and cumulative risks were more distinct. Noise annoyance effect in non-movers was stronger among the insufficiently active (RR: 1.09; 95%CI: 1.02, 1.17; p_interaction = 0.07) and those with daytime sleepiness [RR: 1.07 (1.02, 1.12); p_interaction = 0.008]. Cumulative risks of Lden in non-movers showed additive tendencies for the linear cumulative risk [(RR_{per 10dB of combined sources}: 1.31 (0.90, 1.91)] and the categorical cumulative risk [(RR_{triple- vs. zero-source ≥45 dB}: 2.29 (1.02, 5.14)], and remained stable to noise annoyance. Transportation noise level and noise annoyance may jointly and independently influence the risk of depression. Combined long-term exposures to noise level seems to be most detrimental, largely acting via annoyance. The moderation of noise annoyance effect by daytime sleepiness and physical activity further contribute to clarifying the involved mechanisms. More evidence is needed to confirm these findings for effective public health control of depression and noise exposure burden.

Air pollution exposure and adverse sleep health across the life course: A systematic review

An increasing number of epidemiological studies have examined air pollution as a possible contributor to adverse sleep health, but results are mixed. The aims of this systematic review are to investigate and summarize the associations between exposures to air pollutants and various sleep measures across the lifespan. PubMed, CINAHL, Cochrane, Scopus, Web of Science, and PsycInfo were searched through October 2019 to identify original data-based research examining direct epidemiological associations between ambient and indoor air pollution exposures and various sleep health measures, including sleep quality, sleep duration, sleep disturbances, and daytime sleepiness. Twenty-two articles from 2010 to 2019 were selected for inclusion in this review, including a wide range of study populations (from early childhood to elderly) and locations (10 Asian, 4 North American, 3 European, 5 other). Due to variation in both exposure and outcome assessments, conducting a meta-analysis was not plausible. Twenty-one studies reported a generally positive association between exposure and poor sleep quality. While most studies focused on ambient air pollutants, five assessed the specific effect of indoor exposure. In children and adolescents, increased exposure to both ambient and indoor pollutants is associated with increased respiratory sleep problems and a variety of additional adverse sleep outcomes. In adults, air pollution exposure was most notably related to sleep disordered breathing. Existing literature generally shows a negative relationship between exposures to air pollution and sleep health in populations across different age groups, countries, and measures. While many associations between air pollution and sleep outcomes have been investigated, the mixed study methods and use of subjective air pollution and sleep measures result in a wide range of specific associations. Plausible toxicological mechanisms remain inconclusive. Future studies utilizing objective sleep measures and controlling for all air pollution exposures and individual encounters may help ameliorate variability in the results reported by current published literature.

Evidence Relating to Environmental Noise Exposure and Annoyance, Sleep Disturbance, Cardio-Vascular and Metabolic Health Outcomes in the Context of IGCB (N): A Scoping Review of New Evidence

WHO published the Environmental Noise Guidelines for the European Region in 2018, based on seven systematic reviews including studies published between 2000 and 2014. Since then, new studies were published. At the request of the UK Department for Environment, Food and Rural Affairs (DEFRA), a review on annoyance, sleep disturbance, cardiovascular and metabolic effects in relation to environmental noise was prepared. The aim was to advise the Interdepartmental Group on Costs and Benefits Noise Subject Group (IGCB(N)) whether this new evidence warrants an update of their recommendations. Four databases for observational studies were screened and data were extracted on design, type and measurements of exposures and outcomes and confounders and their associations. The quality of the studies was indirectly assessed for cardiovascular and metabolic effects by only including studies with a case control or cohort design. For studies on annoyance and sleep disturbance, the risk of bias was expressed in exposure misclassification, selective participation and confounding. The update yielded 87 papers, pertaining to 108 new studies of which 40 new studies were on annoyance, 42 on sleep disturbance and 26 concerning cardiovascular and metabolic effects. The number, size and quality of the new studies suggest new meta-analyses could be undertaken over the sources and effects included in the WHO reviews.

"Symptoms associated with environmental factors" (SAEF) - Towards a paradigm shift regarding "idiopathic environmental intolerance" and related phenomena

Health conditions characterized by symptoms associated with chemical, physical and biological environmental factors unrelated to objectifiable pathophysiological mechanisms are often labelled by the general term "idiopathic environmental intolerances". More specific, exposure-related terms are also used, e.g. "multiple chemical sensitivities", "electromagnetic hypersensitivity" and "candidiasis hypersensitivity". The prevalence of the conditions varies from a few up to more than 50%, depending on definitions and populations. Based on evolving knowledge within this field, we provide arguments for a paradigm shift from terms focusing on exposure and intolerance/(hyper-)sensitivity towards a term more in line with the perceptual elements that seem to underlie these phenomena. Symptoms caused by established pathophysiologic mechanisms should not be included, e.g. allergic or toxicological conditions, lactose intolerance or infections. We discuss different alternatives for a new term/concept and end up proposing an open and descriptive term, "symptoms associated with environmental factors" (SAEF), including a definition. "Symptoms associated with environmental factors" both is in line with the current knowledge and acknowledge the experiences of the afflicted persons. Thus, the proposed concept is likely to facilitate therapy and communication between health professionals and afflicted persons, and to provide a base for better understanding of such phenomena in healthcare, society and science.

Patients with overlapping diagnoses of asthma and COPD: is livestock exposure a risk factor for comorbidity and coexisting symptoms and infections?

BACKGROUND

Epidemiological research on health effects of livestock exposure in population subgroups with compromised respiratory health is still limited. The present study explored the association between livestock exposure and comorbid/concurrent conditions in patients with overlapping diagnoses of asthma and COPD.

METHODS

Electronic health record data from 23 general practices in the Netherlands were collected from 425 patients diagnosed with both asthma and COPD, living in rural areas with high livestock density ("study area"). Data of 341 patients with the same overlapping diagnoses, living in rural areas with lower livestock density ("control areas") were obtained from 19 general practices. First, the prevalence of comorbid disorders and symptoms/infections were compared between the study and control area. Second, the examined health outcomes were analyzed in relation to measures of individual livestock exposure.

RESULTS

Pneumonia was twice as common among patients living in areas with a high livestock density (OR 2.29, 99% CI 0.96-5.47); however, there were generally no statistically significant differences in the investigated outcomes between the study and control area. Significant associations were observed between presence of goats within 1000 m and allergic rhinitis (OR 5.71, 99% CI 1.11-29.3, p < 0.01), number of co-occurring symptoms (IRR 1.69, 99% CI 1.03-2.77, p < 0.01) and anxiety (OR 8.18, 99% 1.5-44.7, p < 0.01). Presence of cattle within 500 m was associated with pneumonia prevalence (OR 2.48, 99% CI 1.05-5.84, p < 0.01).

CONCLUSION

Livestock exposure is not associated with comorbid chronic conditions but appears to be a risk factor for symptomatic effects in patients with overlapping diagnoses of asthma and COPD.

The potential for impact of man-made super low and extremely low frequency electromagnetic fields on sleep

An ever-growing number of electromagnetic (EM) emission sources elicits health concerns, particularly stemming from the ubiquitous low to extremely low frequency fields from power lines and appliances, and the radiofrequency fields emitted from telecommunication devices. In this article we review the state of knowledge regarding possible impacts of electromagnetic fields on melatonin secretion and on sleep structure and the electroencephalogram of humans. Most of the studies on the effects of melatonin on humans have been conducted in the presence of EM fields, focusing on the effects of occupational or residential exposures. While some of the earlier studies indicated that EM fields may have a suppressive effect on melatonin, the results cannot be generalized because of the large variability in exposure conditions and other factors that may influence melatonin. For instance, exposure to radiofrequency EM fields on sleep architecture show little or no effect. However, a number of studies show that pulsating radiofrequency electromagnetic fields, such as those emitted from cellular phones, can alter brain physiology, increasing the electroencephalogram power in selective bands when administered immediately prior to or during sleep. Additional research is necessary that would include older populations and evaluate the interactions of EM fields in different frequency ranges to examine their effects on sleep in humans.

Transportation noise exposure, noise annoyance and respiratory health in adults: A repeated-measures study

Transportation noise leads to sleep disturbance and to psychological and physiological sustained stress reactions, which could impact respiratory health. However, epidemiologic evidence on associations of objective transportation noise exposure and also perceived noise annoyance with respiratory morbidity is limited. We investigated independent associations of transportation noise exposure and noise annoyance with prevalent respiratory symptoms and incident asthma in adults. Using 17,138 observations (from 7049 participants) from three SAPALDIA (Swiss Cohort Study on Lung and Heart Diseases in Adults) surveys, we assessed associations of transportation noise exposure and noise annoyance with prevalent respiratory symptoms, and with incident asthma (in 10,657 nested observations from 6377 participants). Annual day-evening-night transportation noise comprising road, railway and aircraft Lden (Transportation Lden) was calculated for the most exposed façade of participants' residence using Swiss noise models. Transportation noise annoyance was assessed using an 11-point scale, and participants reported respiratory symptoms and doctor-diagnosed asthma at each survey. We estimated associations with transportation Lden (as well as source-specific Lden) and noise annoyance, independent of air pollution and other potential confounders, using mutually-adjusted mixed logistic and Poisson models and applying random intercepts at the level of the participants. Prevalent respiratory symptoms ranged from 5% (nocturnal dyspnoea) to 23% (regular cough/phlegm). Transportation noise annoyance, but not Lden, was independently associated with respiratory symptoms and current asthma in all participants, with odds ratios (OR) and 95% confidence intervals (CI) ranging between 1.03 (95%CI: 1.01, 1.06) and 1.07 (95% CI: 1.04, 1.11) per 1-point difference in noise annoyance. Both noise annoyance and Lden showed independent associations with asthma symptoms among asthmatics, especially in those reporting adult-onset asthma [OR_Lden: 1.90 (95% CI: 1.25, 2.89) per 10 dB; p-value of interaction (adult-onset vs. childhood-onset): 0.03; OR_{noise annoyance}: 1.06 (95%CI: 0.97, 1.16) per 1-point difference; p-value of interaction: 0.06]. No associations were found with incident asthma. Transportation noise level and annoyance contributed to symptom exacerbation in adult asthma. This suggests both psychological and physiological noise reactions on the respiratory system, and could be relevant for asthma care. More studies are needed to better understand the effects of objective and perceived noise in asthma aetiology and overall respiratory health.