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.

A selenoureido-iminoglycolipid transported by zeolitic-imidazolate framework nanoparticles: a novel antioxidant therapeutic approach.

A selenium-containing metal-organic framework with remarkable antioxidant capacity and ROS-scavenging activity was constructed by a controlled de novo encapsulation approach of a glycoconjugate mimetic, specifically a sp2-iminoglycolipid bearing a selenoureido fragment (DSeU), within a zeolitic-imidazolate framework exoskeleton. Biocompatible and homogeneous nanosized particles of ∼70 nm (DSeU@ZIF8) were obtained, which could be efficiently internalized in cells, overcoming the poor solubility in biological media and limited bioavailability of glycolipids. The ZIF-particle served as nanocarrier for the intracellular delivery of the selenocompound to cells, promoted by the acidic pH inside endosomes/lysosomes. As demonstrated by in vitro studies, the designed DSeU@ZIF8 nanoparticles displayed a high antioxidant activity at low doses; lower intracellular ROS levels were observed upon the uptake of DSeU@ZIF8 by human endothelial cells. Even more interesting was the finding that these DSeU@ZIF8 particles were able to reverse to a certain level the oxidative stress induced in cells by pre-treatment with an oxidizing agent. This possibility of modulating the oxidative stress in living cells may have important implications in the treatment of diverse pathological complications that are generally accompanied with elevated ROS levels.

Benefits and concerns of expanded carrier screening: what do pregnant Latina women in Texas think?

The American College of Medical Genetics and Genomics (ACMG) recommends carrier screening for all pregnant women regardless of race or ethnicity. In recent years, the ACMG broadened the guidelines to include expanded carrier screening (ECS) which can screen for 112 conditions. This study seeks to explore the perceptions of pregnant Latina women about the benefits and concerns related to ECS use. Partnering with prenatal clinics in Texas, we conducted semi-structured qualitative interviews with 32 pregnant Latina women in their second or third trimester of pregnancy. NVivo 8 was used to conduct content analysis and emergent coding of the data. Participants reported the benefits of ECS as helping them prepare for the baby's arrival, informing them of the baby's risk for genetic conditions, ensuring the health of their baby, and preventing diseases before birth. The ECS-related concerns expressed by the participants included worries surrounding potential positive ECS results, insufficient knowledge about the genetic diseases screened for by ECS, the accuracy of the ECS, the potential harm ECS may cause the baby, and the affordability of ECS. After weighing both their perceived benefits and concerns, nearly all the participants believed that ECS should be offered to all pregnant women. This study contributes to an understudied research area in the genetic/genomic field. Our findings can help increase the awareness of obstetricians, genetic professionals, and other healthcare providers regarding pregnant Latina women's views on ECS and inform the design of culturally appropriate care as ECS is adopted into routine clinical practice.

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.

Machine learning methods for imbalanced data set for prediction of faecal contamination in beach waters.

Predicting water contamination by statistical models is a useful tool to manage health risk in recreational beaches. Extreme contamination events, i.e. those exceeding normative are generally rare with respect to bathing conditions and thus the data is said to be imbalanced. Modeling and predicting those rare events present unique challenges. Here we introduce and evaluate several machine learning techniques and metrics to model imbalanced data and evaluate model performance. We do so by using a) simulated data-sets and b) a real data base with records of faecal coliform abundance monitored for 10 years in 21 recreational beaches in Uruguay (N ≈ 19000) using in situ and meteorological variables. We discuss advantages and disadvantages of the methods and provide a simple guide to perform models for a general audience. We also provide R codes to reproduce model fitting and testing. We found that most Machine Learning techniques are sensitive to imbalance and require specific data pre-treatment (e.g. upsampling) to improve performance. Accuracy (i.e. correctly classified cases over total cases) is not adequate to evaluate model performance on imbalanced data set. Instead, true positive rates (TPR) and false positive rates (FPR) are recommended. Among the 52 possible candidate algorithms tested, the stratified Random forest presented the better performance improving TPR in 50% with respect to baseline (0.4) and outperformed baseline in the evaluated metrics. Support vector machines combined with upsampling method or synthetic minority oversampling technique (SMOTE) performed well, similar to Adaboost with SMOTE. These results suggests that combining modeling strategies is necessary to improve our capacity to anticipate water contamination and avoid health risk.

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.

Perfil de voluntarios para la asistencia en emergencias y catástrofes: la empatía y el entrenamiento en primera atención psicológica / Volunteers' profile for the emergencies and catastrophes`assitence: empathy and training en psychological first aid

La empatía constituye un predictor del comportamiento socialmente responsable (Castejón Costa, 2016, Anué, Abal y Attorresi, 2016), que incluye factores cognitivos y emocionales (Mestre Escrivá, Navarro y García, 2004) cobrando interés en relación al perfil de voluntarios en Primera Ayuda Psicológica (Montero, 2014; Moreto, Blasco y Piñero 2017). Se aborda la asociación entre la empatía (reactividad interpersonal) y la eficacia en el entrenamiento para Primera Ayuda Psicológica (PAP). Metodología: estudio descriptivo, correlacional, comparativo, transversal en 139 voluntarios. Resultados: se encontraron asociaciones significativas (p_valor<0.05) entre las dimensiones de empatía, el género, la edad y la experiencia previa. La eficacia en el entrenamiento presentó relación con el género, la edad y la capacitación. La empatía funciona como moduladora entre el perfil del voluntario y su eficacia en la aplicación de PAP.

Empathy is a predictor of the social responsible behaviour (Castejón Costa, 2016; Anué, Abal & Attorresi, 2016). It's analyzed in relation with the professional profile of the volunteers (Montero, 2014; Moreto, Blasco & Piñero 2017) from a multidimensional perspective that includes cognitive and emotional factors (Escrivá, Navarro & García, 2004). It is analyzed the association between empathy, understood as interpersonal reactivity and to the efficacy in the training for Psychological First Aid (PFA). Methodology: descriptive, correlacional and comparative study of transversal cut in 139 volunteers. Results: there were found significative associations (p_value<0.05) among the dimensions of Interpersonal Reactivity, the gender, age and previous experience. At the same time, the efficacy in the training programme showed relation with the gender, age and training. The analyses by group allow to see that the empathy `dimensions function as modulators in the volunteer ´profile and its efficacy in the application of PFA.

Intonation and Evidentiality in Spanish Polar Interrogatives.

Three different nuclear pitch accents can be found in Castilian Spanish polar interrogatives. In addition to the 'canonical' low-rise pattern, there are two marked interrogative contours featuring high-rise and rise-fall pitch accents. The aim of this paper is to explain how each contour contributes to the interpretation of the utterance in which they occur. I argue that this contribution is to be sought at the semantic, not at the pragmatic-illocutionary, attitudinal-level. My proposal is that the low-rise contour is the expression of unspecified sentence polarity (corresponding to the interrogative operator), whereas the two marked contours add indications about the information source-that is, they encode evidential distinctions. The high-rise pattern indicates that the Self is the source of the information; the rise-fall tone indicates that Other is the source. The whole range of pragmatic interpretations that have been described in the literature can be easily accommodated into the present proposal as inferential developments of the encoded meaning together with contextual information. This view has implications for a theory of interrogatives, for the phonology of intonation and for the articulation of the semantics/pragmatics interface.

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.