Epidemiological description and trajectories of patients with prostate cancer in Denmark: an observational study of 7448 patients.

OBJECTIVE: Identification of patients at high risk of aggressive prostate cancer is a major clinical challenge. With the view of developing artificial intelligence-based methods for identification of these patients, we are constructing a comprehensive clinical database including 7448 prostate cancer (PCa) Danish patients. In this paper we provide an epidemiological description and patients' trajectories of this retrospective observational population, to contribute to the understanding of the characteristics and pathways of PCa patients in Denmark. RESULTS: Individuals receiving a PCa diagnosis during 2008-2014 in Region Southern Denmark were identified, and all diagnoses, operations, investigations, and biochemistry analyses, from 4 years prior, to 5 years after PCa diagnosis were obtained. About 85.1% were not diagnosed with metastatic PCa during the study period (unaggressive PCa); 9.2% were simultaneously diagnosed with PCa and metastasis (aggressive-advanced PCa), while 5.7% were not diagnosed with metastatic PCa at first, but they were diagnosed with metastasis at some point during the 5 years follow-up (aggressive-not advanced PCa). Patients with unaggressive PCa had more clinical investigations directly related to PCa detection (prostate ultrasounds and biopsies) during the 4 years prior to PCa diagnosis, compared to patients with aggressive PCa, which may have contributed to the early detection of PCa.

Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health.

Atmospheric ammonia (NH3) released from agriculture is contributing significantly to acidification and atmospheric NH3 may have on human health is much less readily available. The potential direct impact of NH3 on the health of the general public is under-represented in scientific literature, though there have been several studies which indicate that NH3 has a direct effect on the respiratory health of those who handle livestock. These health impacts can include a reduced lung function, irritation to the throat and eyes, and increased coughing and phlegm expulsion. More recent studies have indicated that agricultural NH3 may directly influence the early on-set of asthma in young children. In addition to the potential direct impact of ammonia, it is also a substantial contributor to the fine particulate matter (PM2.5) fraction (namely the US and Europe); where it accounts for the formation of 30% and 50% of all PM2.5 respectively. PM2.5 has the ability to penetrate deep into the lungs and cause long term illnesses such as Chronic Obstructive Pulmonary Disease (COPD) and lung cancer. Hence, PM2.5 causes economic losses which equate to billions of dollars (US) to the global economy annually. Both premature deaths associated with the health impacts from PM2.5 and economic losses could be mitigated with a reduction in NH3 emissions resulting from agriculture. As agriculture contributes to more than 81% of all global NH3 emissions, it is imperative that food production does not come at a cost to the world's ability to breathe; where reductions in NH3 emissions can be easier to achieve than other associated pollutants.

Artificial intelligence outperforms standard blood-based scores in identifying liver fibrosis patients in primary care.

For years, hepatologists have been seeking non-invasive methods able to detect significant liver fibrosis. However, no previous algorithm using routine blood markers has proven to be clinically appropriate in primary care. We present a novel approach based on artificial intelligence, able to predict significant liver fibrosis in low-prevalence populations using routinely available patient data. We built six ensemble learning models (LiverAID) with different complexities using a prospective screening cohort of 3352 asymptomatic subjects. 463 patients were at a significant risk that justified performing a liver biopsy. Using an unseen hold-out dataset, we conducted a head-to-head comparison with conventional methods: standard blood-based indices (FIB-4, Forns and APRI) and transient elastography (TE). LiverAID models appropriately identified patients with significant liver stiffness (> 8 kPa) (AUC of 0.86, 0.89, 0.91, 0.92, 0.92 and 0.94, and NPV ≥ 0.98), and had a significantly superior discriminative ability (p < 0.01) than conventional blood-based indices (AUC = 0.60-0.76). Compared to TE, LiverAID models showed a good ability to rule out significant biopsy-assessed fibrosis stages. Given the ready availability of the required data and the relatively high performance, our artificial intelligence-based models are valuable screening tools that could be used clinically for early identification of patients with asymptomatic chronic liver diseases in primary care.

Video-based eye tracking performance for computer-assisted diagnostic support of diabetic neuropathy.

Diabetes is currently one of the major public health threats. The essential components for effective treatment of diabetes include early diagnosis and regular monitoring. However, health-care providers are often short of human resources to closely monitor populations at risk. In this work, a video-based eye-tracking method is proposed as a low-cost alternative for detection of diabetic neuropathy. The method is based on the tracking of the eye-trajectories recorded on videos while the subject follows a target on a screen, forcing saccadic movements. Upon extraction of the eye trajectories, representation of the obtained time-series is made with the help of heteroscedastic ARX (H-ARX) models, which capture the dynamics and latency on the subject's response, while features based on the H-ARX model's predictive ability are subsequently used for classification. The methodology is evaluated on a population constituted by 11 control and 20 insulin-treated diabetic individuals suffering from diverse diabetic complications including neuropathy and retinopathy. Results show significant differences on latency and eye movement precision between the populations of control subjects and diabetics, while simultaneously demonstrating that both groups can be classified with an accuracy of 95%. Although this study is limited by the small sample size, the results align with other findings in the literature and encourage further research.

Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality.

Rubeosis faciei diabeticorum, caused by microangiopathy and characterized by a chronic facial erythema, is associated with diabetic neuropathy. In clinical practice, facial erythema of patients with diabetes is evaluated based on subjective observations of visible redness, which often goes unnoticed leading to microangiopathic complications. To address this major shortcoming, we designed a contactless, non-invasive diagnostic point-of-care-device (POCD) consisting of a digital camera and a screen. Our solution relies on (1) recording videos of subject's face (2) applying Eulerian video magnification to videos to reveal important subtle color changes in subject's skin that fall outside human visual limits (3) obtaining spatio-temporal tensor expression profile of these variations (4) studying empirical spectral density (ESD) function of the largest eigenvalues of the tensors using random matrix theory (5) quantifying ESD functions by modeling the tails and decay rates using power law in systems exhibiting self-organized-criticality and (6) designing an optimal ensemble of learners to classify subjects into those with diabetic neuropathy and those of a control group. By analyzing a short video, we obtained a sensitivity of 100% in detecting subjects diagnosed with diabetic neuropathy. Our POCD paves the way towards the development of an inexpensive home-based solution for early detection of diabetic neuropathy and its associated complications.

Non-invasive detection of diabetic complications via pattern analysis of temporal facial colour variations.

BACKGROUND AND OBJECTIVE: Diabetes mellitus is a common disorder amounting to 400 million patients worldwide. It is often accompanied by a number of complications, including neuropathy, nephropathy, and cardiovascular diseases. For example, peripheral neuropathy is present among 20-30% of diabetics before the diagnosis is substantiated. For this reason, a reliable detection method for diabetic complications is crucial and attracts a lot of research attention. METHODS: In this paper, we introduce a non-invasive detection framework for patients with diabetic complications that only requires short video recordings of faces from a standard commercial camera. We employed multiple image processing and pattern recognition techniques to process video frames, extract relevant information, and predict the health status. To evaluate our framework, we collected a dataset of 114 video files from diabetic patients, who were diagnosed with diabetes for years and 60 video files from the control group. Extracted features from videos were tested using two conceptually different classifiers. RESULTS: We found that our proposed framework correctly identifies patients with diabetic complications with 92.86% accuracy, 100% sensitivity, and 80% specificity. CONCLUSIONS: Our study brings a novel perspective on diagnosis procedures in this field. We used multiple techniques from image processing, pattern recognition, and machine learning to robustly process video frames and predict the health status of our subjects with high efficiency.

Influence of urban forest on traffic air pollution and children respiratory health.

This study aimed to assess the air quality, the prevalence of child respiratory morbidity, and the association between them, in urban areas where concentrations of pollutants are expected to be below national limits. The monitoring of PM10, NO2 and O3 was performed in five schools, during 9 months. Information about respiratory diseases and associated symptoms were collected from each student using a questionnaire based on the International Study of Asthma and Allergies in Childhood. The PM10 and NO2 concentrations were higher at points closer to roads and avenues with intense vehicle flow and lower at the point closer to a park, with dense vegetation. All sampling points exceeded the annual limit established by WHO for PM10. Some maximum PM10 concentrations recorded close to the road was six times higher than the international limit. In total, 340 answered questionnaires were collected (68% response rate). Respiratory symptoms such as wheezing, sneezing, running nose, tearing, and itchy eyes had positive and strong correlation to the primary pollutants (0.70 to 0.87), but the frequency of some symptoms was lower close to the urban forest. Therefore, our results confirm the importance of creating and maintaining green areas in urban space, considering all ecosystem services provided by them, especially the improvement of air quality. In addition, a continuous program to monitor and control atmospheric pollution is required in mid-sized counties located nearby important roads, with growing fleets of vehicles.

Self-reported data in environmental health studies: mail vs. web-based surveys.

BACKGROUND: Internet has been broadly employed as a facilitator for epidemiological surveys, as a way to provide a more economical and practical alternative to traditional survey modes. A current trend in survey research is to combine Web-based surveys with other survey modes by offering the participant the possibility of choosing his/her preferred response method (i.e. mixed-mode approach). However, studies have also demonstrated that the use of different survey modes may produce different responses to the same questions, posing potential challenges on the use of mixed-mode approaches. METHODS: In this paper, we have implemented a statistical comparison between mixed-mode survey responses collected via mail (i.e. paper) and Web methods obtained from a cross-sectional study in non-urban areas of Denmark. Responses provided by mail and Web participants were compared in terms of: 1) the impact of reminder letters in increasing response rates; 2) differences in socio-demographic characteristics between response groups; 3) changes on the likelihood of reporting health symptoms and negative attitudes towards environmental stressors. Comparisons were mainly performed by two sample t-test, Pearson's Chi-squared test and multinomial logistic regression models. RESULTS: Among 3104 contacted households, 1066 residents decided to participate on the study. Out of those, 971 selected to respond via mail, whereas 275 preferred the Web method. The majority of socio-demographic characteristics between these two groups of respondents were shown to be statistically different. The use of mailed surveys increased the likelihood of reporting health symptoms and negative attitudes towards environmental stressors, even after controlling for demographic characteristics. Furthermore, the use of reminder letters had a higher positive impact in increasing responses of Web surveys when compared to mail surveys. CONCLUSIONS: Our main findings suggest that the use of mail and Web surveys may produce different responses to the same questions posed to participants, but, at the same time, may reach different groups of respondents, given that the overall characteristics of both groups considerably differ. Therefore, the tradeoff between using mixed-mode survey as a way to increase response rate and obtaining undesirable measurement changes may be attentively considered in future survey studies.

Invisible Color Variations of Facial Erythema: A Novel Early Marker for Diabetic Complications?

AIM: (1) To quantify the invisible variations of facial erythema that occur as the blood flows in and out of the face of diabetic patients, during the blood pulse wave using an innovative image processing method, on videos recorded with a conventional digital camera and (2) to determine whether this "unveiled" facial red coloration and its periodic variations present specific characteristics in diabetic patients different from those in control subjects. METHODS: We video recorded the faces of 20 diabetic patients with peripheral neuropathy, retinopathy, and/or nephropathy and 10 nondiabetic control subjects, using a Canon EOS camera, for 240 s. Only one participant presented visible facial erythema. We applied novel image processing methods to make the facial redness and its variations visible and automatically detected and extracted the redness intensity of eight facial patches, from each frame. We compared average and standard deviations of redness in the two groups using t-tests. RESULTS: Facial redness varies, imperceptibly and periodically, between redder and paler, following the heart pulsation. This variation is consistently and significantly larger in diabetic patients compared to controls (p value < 0.001). CONCLUSIONS: Our study and its results (i.e., larger variations of facial redness with the heartbeats in diabetic patients) are unprecedented. One limitation is the sample size. Confirmation in a larger study would ground the development of a noninvasive cost-effective automatic tool for early detection of diabetic complications, based on measuring invisible redness variations, by image processing of facial videos captured at home with the patient's smartphone.

Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning.

BACKGROUND: Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populations. Before it can be widely applied, significant research priorities need to be addressed. We present two innovative data science algorithms which can considerably improve acquisition and analysis of relevant data on colorectal polyps obtained from capsule endoscopy. MATERIAL AND METHODS: A fully paired study was performed (2015-2016), where 255 participants from the Danish national screening program had CCE, colonoscopy, and histopathology of all detected polyps. We developed: (1) a new algorithm to match CCE and colonoscopy polyps, based on objective measures of similarity between polyps, and (2) a deep convolutional neural network (CNN) for autonomous detection and localization of colorectal polyps in colon capsule endoscopy. RESULTS AND CONCLUSION: Unlike previous matching methods, our matching algorithm is able to objectively quantify the similarity between CCE and colonoscopy polyps based on their size, morphology and location, and provides a one-to-one unequivocal match between CCE and colonoscopy polyps. Compared to previous methods, the autonomous detection algorithm showed unprecedented high accuracy (96.4%), sensitivity (97.1%) and specificity (93.3%), calculated in respect to the number of polyps detected by trained nurses and gastroenterologists after visualizing frame-by-frame the CCE videos.

Glucocorticoid metabolites in newborns: A marker for traffic noise related stress?

BACKGROUND: Traffic noise has been associated with an increased risk for several non-auditory health effects, which may be explained by a noise-induced release of stress hormones (e.g. glucocorticoids). Although several studies in children and adults have indicated an increased secretion of glucocorticoids after exposure to noise, information regarding newborns is scarce. OBJECTIVES: To investigate the association between residential exposure to road traffic noise and postnatal stress response, as assessed by the concentration of glucocorticoids at five weeks of age. METHODS: Residential noise exposure was estimated for each infant based on spatially detailed modeled data. Adjusted multivariable linear regression models were used to estimate the association between noise exposure and the concentration of nine glucocorticoid metabolites measured in urine of 165 infants from a prospective birth cohort in Bern, Switzerland. Noise exposure (Lden, dB) was categorized into tertiles: low (reference), medium and high. RESULTS: Indications of a positive association were found between high road traffic noise and cortisol (% change relative to the reference: 12.1% [95% confidence interval: -10.3, 40.1%]) and cortisone (22.6% [-1.8, 53.0%]), but just the latter was borderline significant. Borderline significant associations were also found between downstream metabolites and higher road traffic noise levels; associations were found to be both positive (i.e. for ß-cortolone (51.5% [-0.9, 131.5%])) and negative (i.e. for α-cortolone (-18.3% [-33.6, 0.6%]) and tetrahydrocortisol (-23.7% [-42.8, 1.9%])). CONCLUSIONS: Our findings suggest a potential association between exposure to higher road traffic noise levels and changes in glucocorticoid metabolism in early postnatal life. A possible physiological relevance and associations with short- and long-term adverse health effects in a larger study population need to be further investigated.

Capsule endoscopy vs. colonoscopy vs. histopathology in colorectal cancer screening: matched analyses of polyp size, morphology, and location estimates.

PURPOSE: Colon capsule endoscopy (CCE) is considered a potential alternative to optical colonoscopy (OC) for colorectal cancer screening. However, the accuracy of CCE in polyp size and morphology estimation is unknown. METHODS: A fully paired study was performed (2015-2016), where 255 participants from the Danish national screening program had CCE, OC, and histopathology (HP) of all detected polyps. We developed a new algorithm to match CCE and OC polyps, based on objective measures of similarity between polyps. We performed paired comparisons of size, morphology and location of CCE, and OC- and HP-matched polyps. We used cross-validation to develop a model able to predict HP polyp size, based on CCE. RESULTS: CCE overestimated size assessed by HP (by 4.3 mm; 95%CI 3.3-5.2 mm) and OC (by 2.7 mm; 95%CI 1.4-3.9 mm). Polyps were more likely to being assessed as "pedunculated" and less likely to being assessed as "flat" in CCE, compared to OC (p < 0.0001). Our model could predict HP polyp size ≥ 6 mm, solely using CCE-assessed size, location, and morphology as model inputs, with a sensitivity = 0.93 (95%CI 0.66-1.00) and specificity = 0.50 (95%CI 0.32-0.68). CONCLUSIONS: If CCE is to be used as a screening test, it is essential: (1) to translate CCE polyp estimations into histopathologic polyp sizes and (2) to consider that, compared to OC, CCE has a higher tendency to assess polyps as pedunculated and a lower tendency to assess them as flat. TRIAL REGISTRATION: Clinicaltrials.gov No. NCT02303756.

Comparative analysis of spatio-temporal exposure assessment methods for estimating odor-related responses in non-urban populations.

The assessment of air pollution exposures in epidemiological studies does not always account for spatio-temporal variability of pollutants concentrations. In the case of odor studies, a common approach is to use yearly averaged odorant exposure estimates with low spatial resolution, which may not capture the spatio-temporal variability of emissions and therefore distort the epidemiological results. This study explores the use of different exposure assessment methods for time-variant ammonia exposures with high spatial resolution, in rural communities exposed to odors from agricultural and livestock farming activities. Exposure estimations were based on monthly ammonia concentrations from emission-dispersion models. Seven time-dependent residential NH3 exposures variables were investigated: 1) Annual mean of NH3 exposures; 2) Maximum annual NH3 exposure; 3) Area under the exposure curve; 4) Peak area; 5) Peak-to-mean ratio; 6) Area above the baseline (annual mean of NH3 exposures); and 7) Maximum positive slope of the exposure curve. We developed binomial and multinomial logistic regression models for frequency of odor perception and odor annoyance responses based on each temporal exposure variable. Odor responses estimates, goodness of fit and predictive abilities derived from each model were compared. All time-dependent NH3 exposure variables, except peak-to-mean ratio, were positively associated with odor perception and odor annoyance, although the results differ considerably in terms of magnitude and precision. The best goodness of fit of the predictive binomial models was obtained when using maximum monthly NH3 exposure as exposure assessment variable, both for odor perception and annoyance. The best predictive performance for odor perception was found when annual mean was used as exposure variable (accuracy=71.82%, Cohen's Kappa=0.298) whereas odor annoyance was better predicted when using peak area (accuracy=68.07%, Cohen's Kappa=0.290). Our study highlights the importance of taking temporal variability into account when investigating odor-related responses in non-urban residential areas.

A novel approach for exposure assessment in air pollution epidemiological studies using neuro-fuzzy inference systems: Comparison of exposure estimates and exposure-health associations.

Many epidemiological studies have used proximity to sources as air pollution exposure assessment method. However, proximity measures are not generally good surrogates because of their complex non-linear relationship with exposures. Neuro-fuzzy inference systems (NFIS) can be used to map complex non-linear systems, but its usefulness in exposure assessment has not been extensively explored. We present a novel approach for exposure assessment using NFIS, where the inputs of the model were easily-obtainable proximity measures, and the output was residential exposure to an air pollutant. We applied it to a case-study on NH3 pollution, and compared health effects and exposures estimated from NFIS, with those obtained from emission-dispersion models, and linear and non-linear regression proximity models, using 10-fold cross validation. The agreement between emission-dispersion and NFIS exposures was high (Root-mean-square error (RMSE) =0.275, correlation coefficient (r)=0.91) and resulted in similar health effect estimates. Linear models showed poor performance (RMSE=0.527, r=0.59), while non-linear regression models resulted in heterocedasticity, non-normality and clustered data. NFIS could be a useful tool for estimating individual air pollution exposures in epidemiological studies on large populations, when emission-dispersion data are not available. The tradeoff between simplicity and accuracy needs to be considered.

Characterization of exposure in epidemiological studies on air pollution from biodegradable wastes: Misclassification and comparison of exposure assessment strategies.

The assignment of exposure is one of the main challenges faced by environmental epidemiologists. However, misclassification of exposures has not been explored in population epidemiological studies on air pollution from biodegradable wastes. The objective of this study was to investigate the use of different approaches for assessing exposure to air pollution from biodegradable wastes by analyzing (1) the misclassification of exposure that is committed by using these surrogates, (2) the existence of differential misclassification (3) the effects that misclassification may have on health effect estimates and the interpretation of epidemiological results, and (4) the ability of the exposure measures to predict health outcomes using 10-fold cross validation. Four different exposure assessment approaches were studied: ammonia concentrations at the residence (Metric I), distance to the closest source (Metric II), number of sources within certain distances from the residence (Metric IIIa,b) and location in a specific region (Metric IV). Exposure-response models based on Metric I provided the highest predictive ability (72.3%) and goodness-of-fit, followed by IV, III and II. When compared to Metric I, Metric IV yielded the best results for exposure misclassification analysis and interpretation of health effect estimates, followed by Metric IIIb, IIIa and II. The study showed that modelled NH3 concentrations provide more accurate estimations of true exposure than distances-based surrogates, and that distance-based surrogates (especially those based on distance to the closest point source) are imprecise methods to identify exposed populations, although they may be useful for initial studies.

Wind turbines and idiopathic symptoms: The confounding effect of concurrent environmental exposures.

Whether or not wind turbines pose a risk to human health is a matter of heated debate. Personal reactions to other environmental exposures occurring in the same settings as wind turbines may be responsible of the reported symptoms. However, these have not been accounted for in previous studies. We investigated whether there is an association between residential proximity to wind turbines and idiopathic symptoms, after controlling for personal reactions to other environmental co-exposures. We assessed wind turbine exposures in 454 residences as the distance to the closest wind turbine (Dw) and number of wind turbines <1000m (Nw1000). Information on symptoms, demographics and personal reactions to exposures was obtained by a blind questionnaire. We identified confounders using confounders' selection criteria and used adjusted logistic regression models to estimate associations. When controlling only for socio-demographic characteristics, log10Dw was associated with "unnatural fatigue" (ORadj=0.38, 95%CI=0.15-1.00) and "difficulty concentrating" (ORadj=0.26, 95%CI=0.08-0.83) and Nw1000 was associated with "unnatural fatigue" (ORadj=1.35, 95%CI=1.07-1.70) and "headache" (ORadj=1.26, 95%CI=1.00-1.58). After controlling for personal reactions to noise from sources different from wind turbines and agricultural odor exposure, we did not observe a significant relationship between residential proximity to wind turbines and symptoms and the parameter estimates were attenuated toward zero. Wind turbines-health associations can be confounded by personal reactions to other environmental co-exposures. Isolated associations reported in the literature may be due to confounding bias.

In vivo and in situ measurement and modelling of intra-body effective complex permittivity.

Radio frequency tracking of medical micro-robots in minimally invasive medicine is usually investigated upon the assumption that the human body is a homogeneous propagation medium. In this Letter, the authors conducted various trial programs to measure and model the effective complex permittivity ε in terms of refraction ε', absorption ε″ and their variations in gastrointestinal (GI) tract organs (i.e. oesophagus, stomach, small intestine and large intestine) and the porcine abdominal wall under in vivo and in situ conditions. They further investigated the effects of irregular and unsynchronised contractions and simulated peristaltic movements of the GI tract organs inside the abdominal cavity and in the presence of the abdominal wall on the measurements and variations of ε' and ε''. They advanced the previous models of effective complex permittivity of a multilayer inhomogeneous medium, by estimating an analytical model that accounts for reflections between the layers and calculates the attenuation that the wave encounters as it traverses the GI tract and the abdominal wall. They observed that deviation from the specified nominal layer thicknesses due to non-geometric boundaries of GI tract morphometric variables has an impact on the performance of the authors' model. Therefore, they derived statistical-based models for ε' and ε'' using their experimental measurements.

Characteristics of pollutant gas releases from swine, dairy, beef, and layer manure, and municipal wastewater.

Knowledge about characteristics of gas releases from various types of organic wastes can assist in developing gas pollution reduction technologies and establishing environmental regulations. Five different organic wastes, i.e., four types of animal manure (swine, beef, dairy, and layer hen) and municipal wastewater, were studied for their characteristics of ammonia (NH3), carbon dioxide (CO2), hydrogen sulfide (H2S), and sulfur dioxide (SO2) releases for 38 or 43 days in reactors under laboratory conditions. Weekly waste additions and continuous reactor headspace ventilation were supplied to simulate waste storage conditions. Results demonstrated that among the five waste types, layer hen manure and municipal wastewater had the highest and lowest NH3 release potentials, respectively. Layer manure had the highest and dairy manure had the lowest CO2 release potentials. Dairy manure and layer manure had the highest and lowest H2S release potentials, respectively. Beef manure and layer manure had the highest and lowest SO2 releases, respectively. The physicochemical characteristics of the different types of wastes, especially the total nitrogen, total ammoniacal nitrogen, dry matter, and pH, had strong influence on the releases of the four gases. Even for the same type of waste, the variation in physicochemical characteristics affected the gas releases remarkably.

Air pollution from biodegradable wastes and non-specific health symptoms among residents: direct or annoyance-mediated associations?

Adverse health effects of exposure to high levels of air pollutants from biodegradable wastes have been well-studied. However, few investigations have examined the potential effects of chronic exposure to low-to-moderate levels on non-specific health symptoms among residents. Besides, most studies have relied on distances to waste sites to assign exposure status, and have not investigated whether the exposure-symptoms associations are direct or mediated by odor annoyance. In this study, individual-level exposures to a proxy indicator of biodegradable waste pollution (ammonia, NH3) in non-urban residences (n=454) during 2005-2010 were characterized by data from emission-dispersion validated models. Logistic regression and mediating analyses were used to examine associations between exposures and questionnaire-based data on annoyance and non-specific symptoms, after adjusting by person-specific covariates. Strong dose-response associations were found between exposures and annoyance, and between annoyance and symptoms. Associations between exposures and symptoms (nausea, headache, dizziness, difficulty concentrating and unnatural fatigue) were indirect (annoyance-mediated). This study indicates that environmental exposures play an important role in the genesis of non-specific symptoms among residents exposed to low-to-moderate air pollution from biodegradable wastes, although the effects seem to be indirect, relayed through stress-related mechanisms.