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Evaluating the environmental health impacts of urban policies is critical for developing and implementing policies that lead to more healthy and equitable cities. This article aims to (1) identify research questions commonly used when evaluating the health impacts of urban policies at different stages of the policy process, (2) describe commonly used methods, and (3) discuss challenges, opportunities, and future directions.In the diagnosis and design stages of the policy process, research questions aim to characterize environmental problems affecting human health and to estimate the potential impacts of new policies. Simulation methods using existing exposure-response information to estimate health impacts predominate at these stages of the policy process. In subsequent stages, e.g., during implementation, research questions aim to understand the actual policy impacts. Simulation methods or observational methods, which rely on experimental data gathered in the study area to assess the effectiveness of the policy, can be applied at these stages. Increasingly, novel techniques fuse both simulation and observational methods to enhance the robustness of impact evaluations assessing implemented policies. The policy process consists of interdependent stages, from inception to end, but most reviewed studies focus on single stages, neglecting the continuity of the policy life cycle. Studies assessing the health impacts of policies using a multi-stage approach are lacking. Most studies investigate intended impacts of policies; focusing also on unintended impacts may provide a more comprehensive evaluation of policies.

Studies suggest a link between PM2.5 and amyotrophic lateral sclerosis (ALS), but critical exposure windows have not been examined. We performed a case-control study on the Danish population spanning 1989 to 2013. Cases selected from the Danish National Patient Registry based on ICD codes. Five controls were randomly selected from the Danish Civil Registry and matched to a case on vital status, age, and sex. PM2.5 concentration was assigned at residential addresses using monthly predictions from a dispersion model. We used conditional logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI), adjusting for confounding. We evaluated exposure to averaged PM2.5 concentrations 12-24 months, 2-6, and 2-11 years pre-ALS diagnosis; annual lagged exposures up to 11 years pre-diagnosis; and cumulative associations in lags 1-5 and 1-10, allowing for varying association estimates by year. We identified 3983 cases and 19,915 controls. Cumulative exposure to PM2.5 in the period 2 to 6 years pre-diagnosis was associated with ALS (OR=1.06, 95% CI: 0.99–1.13). Exposures in the 2nd, 3rd, and 4th year preceding diagnosis were individually associated with higher odds of ALS (e.g., lag-1 OR=1.04, 95% CI: 1.00–1.08). Exposure to PM2.5 within six years before diagnosis may represent a critical exposure window.

Environmental health researchers often aim to identify sources/behaviors that give rise to potentially harmful exposures. We adapted principal component pursuit (PCP)-a robust technique for dimensionality reduction in computer vision and signal processing-to identify patterns in environmental mixtures. PCP decomposes the exposure mixture into a low-rank matrix containing consistent exposure patterns across pollutants and a sparse matrix isolating unique exposure events. We adapted PCP to accommodate non-negative and missing data, and values below a given limit of detection (LOD). We simulated data to represent environmental mixtures of two sizes with increasing proportions, Comment: 32 pages, 11 figures, 4 tables

Long-term exposure to fine particulate matter (PMWe leveraged 15 years of data from the New York Department of Health Statewide Planning and Research Cooperative System (2000-2014) to calculate annual ALS first hospitalizations in New York State. We used the first hospital admission as a surrogate of disease aggravation and a prediction model to estimate population-weighted annual black carbon, organic matter (OM), nitrate, sulfate, sea salt, and soil concentrations at the county level. We used a multi-pollutant mixed quasi-Poisson model with county-specific random intercepts to estimate rate ratios (RR) of 1-year exposure to each PMWe observed 5,655 first ALS-related hospitalizations. The annual average hospitalization count per county was 6.08 and the average PMOur findings suggest that PM

Background Long-term exposure to fine particulate matter (PM2.5) has been associated with neurodegenerative diseases, including disease aggravation in Parkinson's disease (PD), but associations with specific PM2.5 components have not been evaluated. Objective To characterize the association between specific PM2.5 components and PD first hospitalization, a surrogate for disease aggravation. Methods We obtained data on hospitalizations from the New York Department of Health Statewide Planning and Research Cooperative System (2000–2014) to calculate annual first PD hospitalization counts in New York State per county. We used well-validated prediction models at 1 km2 resolution to estimate county level population-weighted annual black carbon (BC), organic matter (OM), nitrate, sulfate, sea salt (SS), and soil particle concentrations. We then used a multi-pollutant mixed quasi-Poisson model with county-specific random intercepts to estimate rate ratios (RR) of one-year exposure to each PM2.5 component and PD disease aggravation. We evaluated potential nonlinear exposure–outcome relationships using penalized splines and accounted for potential confounders. Results We observed a total of 197,545 PD first hospitalizations in NYS from 2000 to 2014. The annual average count per county was 212 first hospitalizations. The RR (95% confidence interval) for PD aggravation was 1.06 (1.03, 1.10) per one standard deviation (SD) increase in nitrate concentrations and 1.06 (1.04, 1.09) for the corresponding increase in OM concentrations. We also found a nonlinear inverse association between PD aggravation and BC at concentrations above the 96th percentile. We found a marginal association with SS and no association with sulfate or soil exposure. Conclusion In this study, we detected associations between the PM2.5 components OM and nitrate with PD disease aggravation. Our findings support that PM2.5 adverse effects on PD may vary by particle composition.

Introduction To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. Methods We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. Results Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. Conclusion Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies., Graphical abstract Unlabelled Image

Background: Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited. Objective: We examined the potential association between long-term exposure to particulate matter ≤2.5μm in aerodynamic diameter [fine particulate matter (PM2.5)] and disease aggravation in Alzheimer’s (AD) and Parkinson’s (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation. Methods: We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000–2014) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual PM2.5 concentrations estimated by a prediction model at a 1-km2 resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y PM2.5 exposure. We allowed for nonlinear exposure–outcome relationships using penalized splines and accounted for potential confounders. Results: We found a positive nonlinear PM2.5–PD association that plateaued above 11 μg/m3 (RR=1.09, 95% CI: 1.04, 1.14 for a PM2.5 increase from 8.1 to 10.4 μg/m3). We also found a linear PM2.5–ALS positive association (RR=1.05, 95% CI: 1.01, 1.09 per 1-μg/m3 PM2.5 increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients

Statistical learning (SL) includes methods that extract knowledge from complex data. SL methods beyond generalized linear models are being increasingly implemented in public health research and epidemiology because they can perform better in instances with complex or high-dimensional data---settings when traditional statistical methods fail. These novel methods, however, often include random sampling which may induce variability in results. Best practices in data science can help to ensure robustness. As a case study, we included four SL models that have been applied previously to analyze the relationship between environmental mixtures and health outcomes. We ran each model across 100 initializing values for random number generation, or "seeds," and assessed variability in resulting estimation and inference. All methods exhibited some seed-dependent variability in results. The degree of variability differed across methods and exposure of interest. Any SL method reliant on a random seed will exhibit some degree of seed sensitivity. We recommend that researchers repeat their analysis with various seeds as a sensitivity analysis when implementing these methods to enhance interpretability and robustness of results., 19 pages, 5 figures, 1 table. For associated code, visit https://github.com/yanellinunez/Commentary-to-mixture-methods-paper

Amyotrophic lateral sclerosis (ALS) is the most common adult-onset paralytic disorder. It is characterized by progressive degeneration of the motor neurons controlling voluntary movement. The underlying mechanisms remain elusive, a fact that has precluded development of effective treatments. ALS presents as a sporadic condition 90-95% of the time, i.e., without familial history or obvious genetic mutation. This suggests that ALS has a strong environmental component. Organophosphates (OPs) are prime candidate neurotoxicants in the etiology of ALS, as exposure to OPs was linked to higher ALS incidence among farmers, soccer players, and Gulf War veterans. In addition, polymorphisms in paraoxonase 1, an enzyme that detoxifies OPs, may increase individual vulnerability both to OP poisoning and to the risk of developing ALS. Furthermore, exposure to high doses of OPs can give rise to OP-induced delayed neuropathy (OPIDN), a debilitating condition akin to ALS characterized by similar motor impairment and paralysis. The question we pose in this review is: "what can we learn from acute exposure to high doses of neurotoxicants (OPIDN) that could help our understanding of chronic diseases resulting from potentially decades of silent exposure (ALS)?" The resemblances between OPIDN and ALS are striking at the clinical, etiological, neuropathological, cellular, and potentially molecular levels. Here, we critically present available evidence, discuss current limitations, and posit future research. In the search for the environmental origin of ALS, OPIDN offers an exciting trail to follow, which can hopefully lead to the development of novel strategies to prevent and cure these dreadful disorders.

Autism spectrum disorders (ASD) are common, complex and heterogeneous neurodevelopmental disorders. Cellular and molecular mechanisms responsible for ASD pathogenesis have been proposed based on genetic studies, brain pathology and imaging, but a major impediment to testing ASD hypotheses is the lack of human cell models. Here, we reprogrammed fibroblasts to generate induced pluripotent stem cells, neural progenitor cells (NPCs) and neurons from ASD individuals with early brain overgrowth and non-ASD controls with normal brain size. ASD-derived NPCs display increased cell proliferation because of dysregulation of a β-catenin/BRN2 transcriptional cascade. ASD-derived neurons display abnormal neurogenesis and reduced synaptogenesis leading to functional defects in neuronal networks. Interestingly, defects in neuronal networks could be rescued by insulin growth factor 1 (IGF-1), a drug that is currently in clinical trials for ASD. This work demonstrates that selection of ASD subjects based on endophenotypes unraveled biologically relevant pathway disruption and revealed a potential cellular mechanism for the therapeutic effect of IGF-1.

Background Numerous methods exist to analyze complex environmental mixtures in health studies. As an illustration of the different uses of mixture methods, we employed methods geared toward distinct research questions concerning persistent organic chemicals (POPs) as a mixture and leukocyte telomere length (LTL) as an outcome. Methods With information on 18 POPs and LTL among 1,003 U.S. adults (NHANES, 2001–2002), we used unsupervised methods including clustering to identify profiles of similarly exposed participants, and Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) to identify common exposure patterns. We also employed supervised learning techniques, including penalized, weighted quantile sum (WQS), and Bayesian kernel machine (BKMR) regressions, to identify potentially toxic agents, and characterize nonlinear associations, interactions, and the overall mixture effect. Results Clustering separated participants into high, medium, and low POP exposure groups; longer log-LTL was found among those with high exposure. The first PCA component represented overall POP exposure and was positively associated with log-LTL. Two EFA factors, one representing furans and the other PCBs 126 and 118, were positively associated with log-LTL. Penalized regression methods selected three congeners in common (PCB 126, PCB 118, and furan 2,3,4,7,8-pncdf) as potentially toxic agents. WQS found a positive overall effect of the POP mixture and identified six POPs as potentially toxic agents (furans 1,2,3,4,6,7,8-hxcdf, 2,3,4,7,8-pncdf, and 1,2,3,6,7,8-hxcdf, and PCBs 99, 126, 169). BKMR found a positive linear association with furan 2,3,4,7,8-pncdf, suggestive evidence of linear associations with PCBs 126 and 169, and a positive overall effect of the mixture, but no interactions among congeners. Conclusions Using different methods, we identified patterns of POP exposure, potentially toxic agents, the absence of interaction, and estimated the overall mixture effect. These applications and results may serve as a guide for mixture method selection based on specific research questions. Electronic supplementary material The online version of this article (10.1186/s12940-019-0515-1) contains supplementary material, which is available to authorized users.

An increasing number of genetic variants have been implicated in autism spectrum disorders (ASDs), and the functional study of such variants will be critical for the elucidation of autism pathophysiology. Here, we report a de novo balanced translocation disruption of TRPC6, a cation channel, in a non-syndromic autistic individual. Using multiple models, such as dental pulp cells, induced pluripotent stem cell (iPSC)-derived neuronal cells and mouse models, we demonstrate that TRPC6 reduction or haploinsufficiency leads to altered neuronal development, morphology and function. The observed neuronal phenotypes could then be rescued by TRPC6 complementation and by treatment with insulin-like growth factor-1 or hyperforin, a TRPC6-specific agonist, suggesting that ASD individuals with alterations in this pathway may benefit from these drugs. We also demonstrate that methyl CpG binding protein-2 (MeCP2) levels affect TRPC6 expression. Mutations in MeCP2 cause Rett syndrome, revealing common pathways among ASDs. Genetic sequencing of TRPC6 in 1041 ASD individuals and 2872 controls revealed significantly more nonsynonymous mutations in the ASD population, and identified loss-of-function mutations with incomplete penetrance in two patients. Taken together, these findings suggest that TRPC6 is a novel predisposing gene for ASD that may act in a multiple-hit model. This is the first study to use iPSC-derived human neurons to model non-syndromic ASD and illustrate the potential of modeling genetically complex sporadic diseases using such cells.

Supplemental description of methods and additional tables and figures. Table S1 Useful resources for included methods. Table S2 Overview of method characteristics. Table S3 POP characteristics. Table S4 Demographic Characteristics. Figure S1 Hierarchical clustering dendrogram. Figure S2 PCA loadings. Figure S3 EFA loadings. Figure S4 BKMR Interactions (Plot 1). Figure S5 BKMR Interactions (Plot 2). (PDF 1233 kb)

Supplemental description of methods and additional tables and figures. Table S1 Useful resources for included methods. Table S2 Overview of method characteristics. Table S3 POP characteristics. Table S4 Demographic Characteristics. Figure S1 Hierarchical clustering dendrogram. Figure S2 PCA loadings. Figure S3 EFA loadings. Figure S4 BKMR Interactions (Plot 1). Figure S5 BKMR Interactions (Plot 2). (PDF 1233 kb)

Supplemental description of methods and additional tables and figures. Table S1 Useful resources for included methods. Table S2 Overview of method characteristics. Table S3 POP characteristics. Table S4 Demographic Characteristics. Figure S1 Hierarchical clustering dendrogram. Figure S2 PCA loadings. Figure S3 EFA loadings. Figure S4 BKMR Interactions (Plot 1). Figure S5 BKMR Interactions (Plot 2). (PDF 1233 kb)

Supplemental description of methods and additional tables and figures. Table S1 Useful resources for included methods. Table S2 Overview of method characteristics. Table S3 POP characteristics. Table S4 Demographic Characteristics. Figure S1 Hierarchical clustering dendrogram. Figure S2 PCA loadings. Figure S3 EFA loadings. Figure S4 BKMR Interactions (Plot 1). Figure S5 BKMR Interactions (Plot 2). (PDF 1233 kb)