Identifying critical windows of prenatal phenol, paraben, and pesticide exposure and child neurodevelopment: Findings from a prospective cohort study

BACKGROUND

This study aimed to investigate how exposure to a mixture of endocrine disrupting chemicals (EDCs) during two points in pregnancy affects early childhood neurodevelopment.

METHODS

We analyzed publicly-available data from a high-risk cohort of mothers and their children (2007-2014) that measured six EDCs including methyl-, ethyl- and propyl parabens (MEPB, ETPB, PRPB), Bisphenol-A (BPA), 3,5,6-trichloro-2-pyridinol (TCPy), 3-phenoxybenzoic acid (3-PBA) in prenatal urine samples during the second and third trimesters. Neurodevelopmental scores were assessed using Mullen Scales of Early Learning (MSEL) at age 3. We used mean field variational Bayes for lagged kernel machine regression (MFVB-LKMR) to investigate the association between trimester-specific co-exposure to the six EDCs and MSEL scores at age 3, stratified by sex.

RESULTS

The analysis included 130 children. For females, the relationship between BPA and 3PBA with MSEL score varied between the two trimesters. In the second trimester, effect estimates for BPA were null but inversely correlated with MSEL score in the third trimester. 3PBA had a negative relationship with MSEL in the second trimester and positive correlation in the third trimester. For males, effect estimates for all EDCs were in opposing directions across trimesters. MFVB-LKMR analysis identified significant two-way interaction between EDCs for MSEL scores in both trimesters. For example, in females, the MSEL scores associated with increased exposure to TCPy were 1.75 units (95%credible interval -0.04, -3.47) lower in the 2nd trimester and 4.61 (95%CI -3.39, -5.84) lower in the third trimester when PRPB was fixed at the 75th percentile compared to when PRPB was fixed at the 25th percentile.

CONCLUSION

Our study provides evidence that timing of EDC exposure within the prenatal period may impact neurodevelopmental outcomes in children. More of these varying effects were identified among females. Future research is needed to explore EDC mixtures and the timing of exposure during pregnancy to enhance our understanding of how these chemicals impact child health.

A methodological study of exposome based on an open database: Association analysis between exposure to metal mixtures and hyperuricemia

BACKGROUND

Exposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods.

OBJECTS

We aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data.

METHODS

Pre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA.

RESULTS

The prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02-1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13-1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA.

CONCLUSIONS

Heavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field.

Urban environment and health behaviours in children from six European countries

BACKGROUND

Urban environmental design is increasingly considered influential for health and wellbeing, but evidence is mostly based on adults and single exposure studies. We evaluated the association between a wide range of urban environment characteristics and health behaviours in childhood.

METHODS

We estimated exposure to 32 urban environment characteristics (related to the built environment, traffic, and natural spaces) for home and school addresses of 1,581 children aged 6-11 years from six European cohorts. We collected information on health behaviours including total amount of overall moderate-to-vigorous physical activity, physical activity outside school hours, active transport, sedentary behaviours and sleep duration, and developed patterns of behaviours with principal component analysis. We used an exposure-wide association study to screen all exposure-outcome associations, and the deletion-substitution-addition algorithm to build a final multi-exposure model.

RESULTS

In multi-exposure models, green spaces (Normalized Difference Vegetation Index, NDVI) were positively associated with active transport, and inversely associated with sedentary time (22.71 min/day less (95 %CI -39.90, -5.51) per interquartile range increase in NDVI). Residence in densely built areas was associated with more physical activity and less sedentary time, and densely populated areas with less physical activity outside school hours and more sedentary time. Presence of a major road was associated with lower sleep duration (-4.80 min/day (95 %CI -9.11, -0.48); compared with no major road). Results for the behavioural patterns were similar.

CONCLUSIONS

This multicohort study suggests that areas with more vegetation, more building density, less population density and without major roads are associated with improved health behaviours in childhood.

Is Earth a perfect square? Repetition increases the perceived truth of highly implausible statements

A single exposure to statements is typically enough to increase their perceived truth. This Truth-by-Repetition (TBR) effect has long been assumed to occur only with statements whose truth value is unknown to participants. Contrary to this hypothesis, recent research has found that statements contradicting participants' prior knowledge (as established from a first sample of participants) show a TBR effect following their repetition (in a second, independent sample of participants). As for now, however, attempts at finding a TBR effect for blatantly false (i.e., highly implausible) statements have failed. Here, we reasoned that highly implausible statements such as Elephants run faster than cheetahs may show repetition effects, provided a sensitive truth measure is used and statements are repeated more than just once. In a preregistered experiment, participants judged on a 100-point scale the truth of highly implausible statements that were either new to them or had been presented five times before judgment. We observed an effect of repetition: repeated statements were judged more true than new ones, although all judgments were judged below the scale midpoint. Exploratory analyses additionally show that about half the participants showed no or even a reversed effect of repetition. The results provide the first empirical evidence that repetition can increase perceived truth even for highly implausible statements, although not equally so for all participants and not to the point of making the statements look true.

Effects of accumulated environmental, social and host exposures on early childhood educational outcomes

BACKGROUND

Persistent disparities in academic performance may result from a confluence of adverse exposures accruing disproportionately to specific subpopulations.

OBJECTIVE

Our overarching objective was to investigate how multiple exposures experienced over time affect early childhood educational outcomes. We were specifically interested in whether there were: racial/ethnic disparities in prevalence of adverse exposures; racial/ethnic disparities in associations observed between adverse exposures and early childhood educational outcomes; and interactions between exposures, suggesting that one exposure augments susceptibility to adverse effects of another exposure.

METHODS

We link geocoded North Carolina birth data for non-Hispanic white (NHW) and non-Hispanic black (NHB) children to blood lead surveillance data and 4th grade end-of-grade (EOG) standardized test scores (n = 65,151). We construct a local, spatial index of racial isolation (RI) of NHB at the block group level. We fit race-stratified multi-level models of reading and mathematics EOG scores regressed on birthweight percentile for gestational age, blood lead level, maternal smoking, economic disadvantage, and RI, adjusting for maternal- and child-level covariates and median household income.

RESULTS

There were marked racial/ethnic disparities in prevalence of adverse exposures. Specifically, NHB children were more likely than NHW children to be economically disadvantaged (80% vs. 40%), live in block groups with the highest quintile of RI (46% vs. 5%), have higher blood lead levels (4.6 vs. 3.7 μg/dL), and lower birthweight percentile for gestational age (mean: 39th percentile vs. 51st percentile). NHB children were less likely to have mothers who reported smoking during pregnancy (11% and 22%). We observed associations between key adverse exposures and reading and math EOG scores in 4th grade. Higher birthweight percentile for gestational age was associated with higher EOG scores, while economic disadvantage, maternal smoking, and elevated blood lead levels were associated with lower EOG scores. Associations observed for NHB and NHW children were generally not statistically different from one another, with the exception of neighborhood RI. NHB children residing in block groups in the highest RI quintile had reading and math scores 1.54 (0.74, 2.34) and 1.12 (0.38, 1.87) points lower, respectively, compared to those in the lowest RI quintile; statistically significant decrements in EOG scores associated with RI were not observed for NHW children. We did not find evidence of multiplicative interactions between exposures for NHB or NHW children.

DISCUSSION

Key adverse host, environmental, and social exposures accrue disproportionately to NHB children. Decrements in test scores associated with key adverse exposures were often but not always larger for NHB children, but were not significantly different from those estimated for NHW children. While we did not observe interactive effects, NHB children on average experience more deleterious combined exposures, resulting in larger decrements to test scores compared to NHW children.

Interdisciplinary data science to advance environmental health research and improve birth outcomes

Rates of preterm birth and low birthweight continue to rise in the United States and pose a significant public health problem. Although a variety of environmental exposures are known to contribute to these and other adverse birth outcomes, there has been a limited success in developing policies to prevent these outcomes. A better characterization of the complexities between multiple exposures and their biological responses can provide the evidence needed to inform public health policy and strengthen preventative population-level interventions. In order to achieve this, we encourage the establishment of an interdisciplinary data science framework that integrates epidemiology, toxicology and bioinformatics with biomarker-based research to better define how population-level exposures contribute to these adverse birth outcomes. The proposed interdisciplinary research framework would 1) facilitate data-driven analyses using existing data from health registries and environmental monitoring programs; 2) develop novel algorithms with the ability to predict which exposures are driving, in this case, adverse birth outcomes in the context of simultaneous exposures; and 3) refine biomarker-based research, ultimately leading to new policies and interventions to reduce the incidence of adverse birth outcomes.

Identifying environmental exposure profiles associated with timing of menarche: A two-step machine learning approach to examine multiple environmental exposures

BACKGROUND

Variation in the timing of menarche has been linked with adverse health outcomes in later life. There is evidence that exposure to hormonally active agents (or endocrine disrupting chemicals; EDCs) during childhood may play a role in accelerating or delaying menarche. The goal of this study was to generate hypotheses on the relationship between exposure to multiple EDCs and timing of menarche by applying a two-stage machine learning approach.

METHODS

We used data from the National Health and Nutrition Examination Survey (NHANES) for years 2005-2008. Data were analyzed for 229 female participants 12-16 years of age who had blood and urine biomarker measures of 41 environmental exposures, all with >70% above limit of detection, in seven classes of chemicals. We modeled risk for earlier menarche (<12 years of age vs older) with exposure biomarkers. We applied a two-stage approach consisting of a random forest (RF) to identify important exposure combinations associated with timing of menarche followed by multivariable modified Poisson regression to quantify associations between exposure profiles ("combinations") and timing of menarche.

RESULTS

RF identified urinary concentrations of monoethylhexyl phthalate (MEHP) as the most important feature in partitioning girls into homogenous subgroups followed by bisphenol A (BPA) and 2,4-dichlorophenol (2,4-DCP). In this first stage, we identified 11 distinct exposure biomarker profiles, containing five different classes of EDCs associated with earlier menarche. MEHP appeared in all 11 exposure biomarker profiles and phenols appeared in five. Using these profiles in the second-stage of analysis, we found a relationship between lower MEHP and earlier menarche (MEHP ≤ 2.36 ng/mL vs >2.36 ng/mL: adjusted PR = 1.36, 95% CI: 1.02, 1.80). Combinations of lower MEHP with benzophenone-3, 2,4-DCP, and BPA had similar associations with earlier menarche, though slightly weaker in those smaller subgroups. For girls not having lower MEHP, exposure profiles included other biomarkers (BPA, enterodiol, monobenzyl phthalate, triclosan, and 1-hydroxypyrene); these showed largely null associations in the second-stage analysis. Adjustment for covariates did not materially change the estimates or CIs of these models. We observed weak or null effect estimates for some exposure biomarker profiles and relevant profiles consisted of no more than two EDCs, possibly due to small sample sizes in subgroups.

CONCLUSION

A two-stage approach incorporating machine learning was able to identify interpretable combinations of biomarkers in relation to timing of menarche; these should be further explored in prospective studies. Machine learning methods can serve as a valuable tool to identify patterns within data and generate hypotheses that can be investigated within future, targeted analyses.

Overlapping-sample Mendelian randomisation with multiple exposures: a Bayesian approach

BACKGROUND

Mendelian randomization (MR) has been widely applied to causal inference in medical research. It uses genetic variants as instrumental variables (IVs) to investigate putative causal relationship between an exposure and an outcome. Traditional MR methods have mainly focussed on a two-sample setting in which IV-exposure association study and IV-outcome association study are independent. However, it is not uncommon that participants from the two studies fully overlap (one-sample) or partly overlap (overlapping-sample).

METHODS

We proposed a Bayesian method that is applicable to all the three sample settings. In essence, we converted a two- or overlapping- sample MR to a one-sample MR where data were partly unmeasured. Assume that all study individuals were drawn from the same population and unmeasured data were missing at random. Then the missing data were treated au pair with the model parameters as unknown quantities, and thus, were imputed iteratively conditioning on the observed data and estimated parameters using Markov chain Monte Carlo. We generalised our model to allow for pleiotropy and multiple exposures and assessed its performance by a number of simulations using four metrics: mean, standard deviation, coverage and power. We also compared our method with classic MR methods.

RESULTS

In our proposed method, higher sample overlapping rate and instrument strength led to more precise estimated causal effects with higher power. Pleiotropy had a notably negative impact on the estimates. Nevertheless, the coverages were high and our model performed well in all the sample settings overall. In comparison with classic MR, our method provided estimates with higher precision. When the true causal effects were non-zero, power of their estimates was consistently higher from our method. The performance of our method was similar to classic MR in terms of coverage.

CONCLUSIONS

Our model offers the flexibility of being applicable to any of the sample settings. It is an important addition to the MR literature which has restricted to one- or two- sample scenarios. Given the nature of Bayesian inference, it can be easily extended to more complex MR analysis in medical research.

Modeling the effects of multiple exposures with unknown group memberships: a Bayesian latent variable approach

We propose a Bayesian latent variable model to allow estimation of the covariate-adjusted relationships between an outcome and a small number of latent exposure variables, using data from multiple observed exposures. Each latent variable is assumed to be represented by multiple exposures, where membership of the observed exposures to latent groups is unknown. Our model assumes that one measured exposure variable can be considered as a sentinel marker for each latent variable, while membership of the other measured exposures is estimated using MCMC sampling based on a classical measurement error model framework. We illustrate our model using data on multiple cytokines and birth weight from the Seychelles Child Development Study, and evaluate the performance of our model in a simulation study. Classification of cytokines into Th1 and Th2 cytokine classes in the Seychelles study revealed some differences from standard Th1/Th2 classifications. In simulations, our model correctly classified measured exposures into latent groups, and estimated model parameters with little bias and with coverage that was similar to the oracle model.

Multiple exposures to organophosphate flame retardants alter urinary oxidative stress biomarkers among children: The Hokkaido Study

Organophosphate flame retardants (PFRs) are used as additives in plastics and other applications such as curtains and carpets as a replacement for brominated flame retardants. As such, exposure to PFR mixtures is widespread, with children being more vulnerable than adults to associated health risks such as allergies and inflammation. Oxidative stress is thought to be able to modulate the development of childhood airway inflammation and atopic dermatitis. To evaluate these associations, the present study investigated the relationship between urinary PFR metabolites, their mixtures and urinary oxidative stress biomarkers in children as part of the Hokkaido Study on Environment and Children's Health. The levels of the oxidative stress biomarkers, such as 8-hydroxy-2'-deoxyguanosine (8-OHdG), hexanoyl-lysine (HEL), and 4-hydroxynonenal (HNE), and of 14 PFR metabolites were measured in morning spot urine samples of 7-year-old children (n = 400). Associations between PFR metabolites or PFR metabolite mixtures and oxidative stress biomarkers were examined by multiple regression analysis and weighted quantile sum regression analysis, respectively. We found that the non-chlorinated PFR metabolites, 2-ethylhexyl phenyl phosphate (EHPHP), bis(2-butoxyethyl) phosphate (BBOEP), and diphenyl phosphate (DPHP) were associated with increased levels of oxidative stress biomarkers. Furthermore, the PFR metabolite mixture was associated with increased levels of HEL and HNE, but not 8-OHdG. The combination of elevated top 2 PFR metabolites was not associated with higher urinary oxidative stress marker levels. This is the first study to report associations between urinary PFR metabolites and oxidative stress biomarkers among children.

Multiple Exposures and Coexposures to Occupational Hazards Among Agricultural Workers: A Systematic Review of Observational Studies

BACKGROUND

Workers may be exposed to various types of occupational hazards at the same time, potentially increasing the risk of adverse health outcomes. The aim of this review was to analyze the effects of multiple occupational exposures and coexposures to chemical, biomechanical, and physical hazards on adverse health outcomes among agricultural workers.

METHODS

Articles published in English between 1990 and 2015 were identified using five popular databases and two complementary sources. The quality of the included publications was assessed using the methodology developed by the Effective Public Health Practice Project assessment tool for quantitative studies.

RESULTS

Fifteen articles were included in the review. Multiple chemical exposures were significantly associated with an increased risk of respiratory diseases, cancer, and DNA and cytogenetic damage. Multiple physical exposures seemed to increase the risk of hearing loss, whereas coexposures to physical and biomechanical hazards were associated with an increased risk of musculoskeletal disorders among agricultural workers.

CONCLUSION

Few studies have explored the impact of multiple occupational exposures on the health of agricultural workers. A very limited number of studies have investigated the effect of coexposures among biomechanical, physical, and chemical hazards on occupational health, which indicates a need for further research in this area.

Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression

BACKGROUND

Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables.

METHODS

This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code.

RESULTS

Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability.

CONCLUSIONS

This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health.

Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression

In environmental and nutritional epidemiology and in many other fields, there is increasing interest in estimating the effect of simultaneous exposure to several agents (e.g., multiple nutrients, pesticides, or air pollutants) on a health outcome. We consider estimating the effect of a multivariate exposure that includes several continuous agents and their interactions-on an outcome, when the true confounding variables are an unknown subset of a potentially large (relative to sample size) set of measured covariates. Our approach is rooted in the ideas of Bayesian model averaging: the exposure effect is estimated as a weighted average of the estimated exposure effects obtained under several linear regression models that include different sets of the potential confounders. We introduce a data-driven prior that assigns to the likely confounders a higher probability of being included into the regression model. We show that our approach can also be formulated as a penalized likelihood formulation with an interpretable tuning parameter. Through a simulation study, we demonstrate that the proposed approach identifies parsimonious models that are fully adjusted for observed confounding and estimates the multivariate exposure effect with smaller mean squared error compared to several alternatives. We apply the method to an Environmental Wide Association Study using National Heath and Nutrition Examination Survey to estimate the effect of mixtures of nutrients and pesticides on lipid levels.

Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall

BACKGROUND

Accounting for daily mobility allows assessment of multiple exposure to environments. This study compares spatial data obtained (i) from an interactive map-based questionnaire on regular activity locations (VERITAS) and (ii) from GPS tracking.

METHODS

234 participants of the RECORD GPS Study completed the VERITAS questionnaire and wore a GPS tracker for 7 days. Analyses illustrate the spatial match between both datasets.

RESULTS

For half of the sample, 85.5% of GPS data fell within 500 m of a VERITAS location. The median minimum distance between a VERITAS location and a GPS coordinate ranged from 0.4 m for home to slightly over 100 m for a recreational destination.

CONCLUSIONS

There is a spatial correspondence between destinations collected through VERITAS and 7-day GPS tracking. Both collection methods offer complementary ways to assess daily mobilities, useful to study environmental determinants of health and health inequities.

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children

Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was -1.19 points (95% CI -1.94, -0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data.

The utility of multiple synthesized views in the recognition of unfamiliar faces

The ability to recognize an unfamiliar individual on the basis of prior exposure to a photograph is notoriously poor and prone to errors, but recognition accuracy is improved when multiple photographs are available. In applied situations, when only limited real images are available (e.g., from a mugshot or CCTV image), the generation of new images might provide a technological prosthesis for otherwise fallible human recognition. We report two experiments examining the effects of providing computer-generated additional views of a target face. In Experiment 1, provision of computer-generated views supported better target face recognition than exposure to the target image alone and equivalent performance to that for exposure of multiple photograph views. Experiment 2 replicated the advantage of providing generated views, but also indicated an advantage for multiple viewings of the single target photograph. These results strengthen the claim that identifying a target face can be improved by providing multiple synthesized views based on a single target image. In addition, our results suggest that the degree of advantage provided by synthesized views may be affected by the quality of synthesized material.

Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining

OBJECTIVES

Traditional studies on effects of outdoor pollution on asthma have been criticized for questionable statistical validity and inefficacy in exploring the effects of multiple air pollutants, alone and in combination. Association rule mining (ARM), a method easily interpretable and suitable for the analysis of the effects of multiple exposures, could be of use, but the traditional interest metrics of support and confidence need to be substituted with metrics that focus on risk variations caused by different exposures.

METHODS

We present an ARM-based methodology that produces rules associated with relevant odds ratios and limits the number of final rules even at very low support levels (0.5%), thanks to post-pruning criteria that limit rule redundancy and control for statistical significance. The methodology has been applied to a case-crossover study to explore the effects of multiple air pollutants on risk of asthma in pediatric subjects.

RESULTS

We identified 27 rules with interesting odds ratio among more than 10,000 having the required support. The only rule including only one chemical is exposure to ozone on the previous day of the reported asthma attack (OR=1.14). 26 combinatory rules highlight the limitations of air quality policies based on single pollutant thresholds and suggest that exposure to mixtures of chemicals is more harmful, with odds ratio as high as 1.54 (associated with the combination day0 SO₂, day0 NO, day0 NO₂, day1 PM).

CONCLUSIONS

The proposed method can be used to analyze risk variations caused by single and multiple exposures. The method is reliable and requires fewer assumptions on the data than parametric approaches. Rules including more than one pollutant highlight interactions that deserve further investigation, while helping to limit the search field.

Re-creating daily mobility histories for health research from raw GPS tracks: Validation of a kernel-based algorithm using real-life data

BACKGROUND

GPS tracking is increasingly used to document daily mobility, allowing refined analysis of daily exposures and health behaviour. Validation of algorithms processing raw GPS data to identify activity locations and trips are lacking.

OBJECTIVE

Propose novel ways to evaluate GPS processing algorithms data while validating an existing kernel-based algorithm with real-life GPS tracks.

METHODS

Seven-day GPS tracking and GPS-prompted recall interviews were conducted among 234 adult participants of the RECORD GPS Study. Raw GPS data was transformed using a kernel-based algorithm. Two match and nine mismatch configurations are analysed. Algorithm detection of activity locations and trips were validated.

RESULTS

Some 95.8% of available GPS time was correctly classified as an activity location or a trip. The algorithm falsely identified a trip for 2.2% of the tracking time, and falsely identified an activity location 0.7% of time. Missed trips and missed activity locations counted for less than .4% of the time.

CONCLUSION

The tested kernel-based algorithm provides histories of activity locations and trips that are highly concordant with GPS-prompted follow-up interviews.

Multifactorial airborne exposures and respiratory hospital admissions--the example of Santiago de Chile

Our results provide evidence for respiratory effects of combined exposure to airborne pollutants in Santiago de Chile. Different pollutants account for varying adverse effects. Ozone was not found to be significantly associated with respiratory morbidity.

BACKGROUND

High concentrations of various air pollutants have been associated with hospitalization due to development and exacerbation of respiratory diseases. The findings of different studies vary in effect strength and are sometimes inconsistent.

OBJECTIVES

We aimed to assess associations between airborne exposures by particulate matter as well as gaseous air pollutants and hospital admissions due to respiratory disease groups under the special orographic and meteorological conditions of Santiago de Chile.

METHODS

The study was performed in the metropolitan area of Santiago de Chile during 2004-2007. We applied a time-stratified case-crossover analysis taking temporal variation, meteorological conditions and autocorrelation into account. We computed associations between daily ambient concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10 and PM2.5 - particulate matter with aerodynamic diameters less than 10 or 2.5 μm, respectively) or ozone (O3) and hospital admissions for respiratory illnesses.

RESULTS

We found for CO, NO2, PM10 and PM2.5 adverse relationships to respiratory admissions while effect strength and lag depended on the pollutant and on the disease group. By trend, in 1-pollutant models most adverse pollutants were CO and PM10 followed by PM2.5, while in 2-pollutant models effects of NO2 persisted in most cases whereas other effects weakened and significant effects remain for PM2.5, only. In addition the strongest effects seemed to be immediate or with a delay of up to one day, but effects were found until day 7, too. Adverse effects of ozone could not be detected.

CONCLUSIONS

Taking case numbers and effect strength of all cardiovascular diseases into account, mitigation measures should address all pollutants especially CO, NO2, and PM10.

Multiple exposures to airborne pollutants and hospital admissions due to diseases of the circulatory system in Santiago de Chile

BACKGROUND

High concentrations of various air pollutants have been associated with hospitalization due to development and exacerbation of cardiovascular diseases.

OBJECTIVES

We aimed to assess associations between airborne exposures by particulate matter as well as gaseous air pollutants and hospital admissions due to different cardiovascular disease groups in Santiago de Chile.

METHODS

The study was performed in the metropolitan area of Santiago de Chile during 2004-2007. We applied a time-stratified case-crossover analysis taking temporal variation, meteorological conditions and autocorrelation into account. We computed associations between daily ambient concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10 and PM2.5--particulate matter with aerodynamic diameters less than 10 or 2.5 μm, respectively) or ozone (O3) and hospital admissions for cardiovascular illnesses.

RESULTS

We found for CO, NO2, PM10 and PM2.5 adverse relationships to cardiovascular admissions while effect strength and lag depended on the pollutant and on the disease group. By trend, in 1-pollutant models most adverse pollutants were NO2 and particulate matter (PM10 and PM2.5) followed by CO, while in 2-pollutant models effects of PM10 persisted in most cases whereas other effects weakened. In addition the strongest effects seemed to be immediate or with a delay of up to 2 days. Adverse effects of ozone could not be detected.

CONCLUSIONS

Our results provided evidence for adverse health effects of combined exposure to airborne pollutants. Different pollutants accounted for varying adverse effects within different cardiovascular disease groups. Taking case numbers and effect strength of all cardiovascular diseases into account, mitigation measures should address all pollutants but especially NO2, PM10, and CO.