Environmental Public Health Tracking Network Research Articles

Public Health Data Applications Using the CDC Tracking Network: Augmenting Environmental Hazard Information With Lower‐Latency NASA Data

AbstractExposure to environmental hazards is an important determinant of health, and the frequency and severity of exposures is expected to be impacted by climate change. Through a partnership with the U.S. National Aeronautics and Space Administration, the U.S. Centers for Disease Control and Prevention's National Environmental Public Health Tracking Network is integrating timely observations and model data of priority environmental hazards into its publicly accessible Data Explorer (https://ephtracking.cdc.gov/DataExplorer/). Newly integrated data sets over the contiguous U.S. (CONUS) include: daily 5‐day forecasts of air quality based on the Goddard Earth Observing System Composition Forecast, daily historical (1980‐present) concentrations of speciated PM2.5 based on the modern era retrospective analysis for research and applications, version 2, and Moderate Resolution Imaging Spectroradiometer (MODIS) daily near real‐time maps of flooding (MCDWD). Data integrated into the CDC Tracking Network are broadly intended to improve community health through action by informing both research and early warning activities, including (a) describing temporal and spatial trends in disease and potential environmental exposures, (b) identifying populations most affected, (c) generating hypotheses about associations between health and environmental exposures, and (d) developing, guiding, and assessing environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors.

The Impact of Air Pollutants on a Chicagoland Cohort of Patients with Acute Myeloid Leukemia

Background Outcomes in acute myeloid leukemia (AML) can be impacted by cytogenetic/molecular features along with environmental factors such as previous exposure to cytotoxic therapy and county-level income. Our group previously identified structural racism, defined as systemic disadvantage experienced by certain groups of people, as a mediator of disparities within a cohort of 822 patients (pts) diagnosed with AML in the Chicagoland area even when accounting for genetic features of disease (Abraham et al, Blood 2022). Air pollution disproportionately impacts racial-ethnic minorities and has been demonstrated to impact DNA methylation patterns in pts which in turn influence disease biology (Tessum et al, Sci Advances 2021; Rider et al, Clin Epigenetics 2019). With that in mind we aimed to investigate the impact of air pollutant exposures that have been associated with both leukemia development/mortality and with air pollution disparities in historically redlined cities. Methods Adult AML (non-APL) pts diagnosed between 2012 and 2018 at six academic cancer centers in the Chicago area were included. Pts' census tracts were joined with measures of pollution obtained from the Center for Disease Control National Environmental Public Health Tracking Network and the 2014 National Air Toxics Assessment: 1,3-butadiene, benzene, diesel particulate matter (PM), polycyclic aromatic hydrocarbons and polycyclic organic matter (PAHPOM), and PM 2.5 µm (2014 averaged daily census tract measures). Pollutant exposures by racial/ethnic groups were determined using a Kruskall-Wallis rank sum test and pairwise Wilcoxon rank sum tests. Univariate logistic regressions for each pollutant were performed for 2010 European Leukemia Net (ELN) AML criteria and TP53 mutation status. Survival among pts in the most and least polluted census block quartiles for each pollutant was compared using Kaplan-Meier survival analysis. Results Our multi-center database contained 789 pts with AML with census tract data, allowing for air pollutant data collection. Median age of diagnosis was 62 years old. In addition, 59.9% of pts were non-Hispanic White (NHW), 15.3% were non-Hispanic Black (NHB), and 15% of pts were Hispanic. Additional characteristics are summarized in Table 1. There was significantly higher median exposure to 1,3-butadiene, benzene, diesel PM, PAHPOM, and PM2.5 in NHB and Hispanic pts when compared to NHW pts. Hispanic pts had significantly median higher exposure to 1,3-butadiene and benzene when compared to NHB pts while NHB pts had significantly higher median exposure to PM2.5 ( Table 2). With univariate analysis, no pollutant exposure was found to be significantly associated with ELN 2010 risk category or with TP53 mutation status. We also compared median OS (mOS) from AML diagnosis between the quartile with the lowest exposure (Q1) and quartile with the highest exposure (Q4). For 1,3-butadiene mOS was 2.52 years (Q1) vs. 2.96 years (Q4) (p=0.75), for benzene it was 2.65 years vs 3.0 years (p=0.38), for diesel PM it was 2.56 years vs 2.96 years (p=0.56), for PAHPOM it was 2.76 years vs. 2.96 years (p=0.31), and for PM2.5 it was 2.92 years vs. 2.70 years (p=0.32). Conclusions We analyzed the impact of 1,3-butadiene, benzene, diesel PM, PAHPOM, and PM2.5 on outcomes in pts with AML in the Chicagoland area. While there were significant differences in median exposure to pollutants by race/ethnicity, there was no significant association between pollutant exposure and ELN 2010 risk category or TP53 mutation analysis. We also analyzed overall survival between Q1 and Q4 of exposure for each pollutant and found no significant difference. While pollutant exposure disproportionately impacts racial-ethnic minorities, individual pollutants did not appear to be a major driver of outcomes in AML. Limitations include the source of our air pollutant data, where misclassification of pollutants may exist and impact the ability to detect true associations.

Abstract 18866: Binge Alcohol Drinking and Myocardial Infarction Mortality in the United States

Introduction: Although moderate alcohol intake is related to decrease all cause and ischemic heart disease mortality, intake of large amounts at a time remains controversial. Some studies have shown that binge drinking (BD) could have a protective effect. Hypothesis: The aim of this study is to study life-style risk factors associated with cardiovascular mortality in the U.S. population. Methods: The CDC National Environmental Public Health Tracking Network was used to identify age-adjusted mortality rate due to myocardial infarction (MMI) per county among people ≥35 years between 2018 and 2019. County's age-adjusted prevalence of current smoking (CS), binge alcohol drinking (BD), no-leisure time physical activity (NLTPA) and obesity were also identified as potential community life-style risk factors for MMI. Multivariable regression model predicting MMI rate was used to identify relationship between county MMI rate and those 4 life-style risk factors. Results: A total of 3106 counties were included in this study. Median county age-adjusted MMI rate was 61.00 [46.30, 85.38] per 100.000. Median county age-adjusted prevalence of CS was 19.80% [17.40, 22.80], BD 17.70% [15.70, 19.90], Obesity 35.60% [32.70, 38.10], and NLTPA 26.50% [23.30, 30.68]. In a multivariable regression model, CS prevalence above 20% increased the MMI rate by 9.83 (6.82 to 12.84, p<0.001) per 100.000, NLTPA prevalence above 29% increased the MMI rate by 21.57 (18.12 to 25.02, p<0.001) per 100.000, and obesity prevalence above 36% increased the MMI rate by 8.30 (5.54 to 11.07, p<0.001) per 100.000. On the other hand, BD prevalence above 18% was not associated with a significant increase in the MMI rate. [0.67 (-2.11 to 3.44, p=0.638) per 100.000]. Conclusions: Community life-style risk-factors associated with myocardial infarction mortality include current smoking, no-leisure physical activity, and obesity. Alcohol binge drinking did not reduce myocardial infarction mortality rate.

Per- and Polyfluoroalkyl Substances (PFAS) in Community Water Systems (CWS) and the Risk of Thyroid Cancer: An Ecological Study.

Thyroid cancer incidence has been steadily increasing over the past decade in the United States (US). A discussion exists regarding the potential contribution of exposure to endocrine-disrupting chemicals, encompassing certain per- and poly-fluoroalkyl substances (PFASs). This ecological study evaluated the potential correlation between PFAS levels in drinking water and thyroid cancer incidence in the US. Data on age-adjusted thyroid cancer incidence rate (per 100,000 persons) by county were obtained from the Centers for Disease Control and Prevention (CDC) for US counties with available data in 2015-2019. Data on PFAS concentrations in the drinking water of selected community water systems (CWSs) were obtained from the CDC National Environmental Public Health Tracking Network in 2013-2015. The correlation between PFASs in CWSs and thyroid cancer incidence was calculated using Spearman correlation. A statistically significant correlation was found between perfluorooctanoic acid (PFOA) (r = 0.031; p = 0.043), perfluorononanoic acid (PFNA) (r = 0.058; p ≤ 0.001), and thyroid cancer incidence. The results suggest a potential link between certain PFAS exposures and thyroid cancer risk. However, due to the nature of an ecological study, no conclusions can be drawn at the individual level or causality. More research is needed, particularly on an individual level to allow for more detailed exposure assessment.

495 Radon and Fracking Exposures and Lymphoma Risk in a Canine Model of non-Hodgkin Lymphoma

OBJECTIVES/GOALS: The objective of this study was to determine whether residential radon and proximity to horizontal oil and drilling (fracking) are risk factors for the development of multicentric lymphoma in pet dogs, a spontaneous, immunocompetent model for non-occupational risk for NHL in humans. METHODS/STUDY POPULATION: Two case-control populations of dogs with multicentric lymphoma were utilized, with a focus on two dog breeds at high risk for lymphoma. Control dogs were matched for age, breed, and sex. Home addresses were collected for 54 Golden retrievers with lymphoma and 108 Golden retriever controls, and for 56 boxer dogs with lymphoma and 84 unaffected boxer controls. Counties of residence were matched to radon zones and percentage of home radon tests that exceeded the actionable level of 4 pCi/L, available by county through the EPA and the CDC National Environmental Public Health Tracking Network from 2008 to 2017. Locations of horizontal oil and gas wells were obtained from the Enverus Database, and distances from dog homes to the closest well, and well density by county, were calculated for each case and control. RESULTS/ANTICIPATED RESULTS: We found no significant differences in radon zones, county level radon measurements, or residential proximity to active fracking wells between dogs with lymphoma and unaffected controls in either the Golden Retriever or boxer populations. DISCUSSION/SIGNIFICANCE: Canine multicentric lymphoma resembles human NHL and is a valuable model of non-occupational environmental risk for NHL in people. Although we did not find geographic associations between radon and fracking wells, follow-up studies will measure household radon, as well as household air, water, and dog urine for potentially genotoxic chemicals.

A Multistate Study on Housing Factors Influential to Heat-Related Illness in the United States.

As climate change increases the frequency and intensity of devastating and unpredictable extreme heat events, developments to the built environment should consider instigating practices that minimize the likelihood of indoor overheating during hot weather. Heatwaves are the leading cause of death among weather-related causes worldwide, including in developed and developing countries. In this empirical study, a four-step approach was used to collect, extract and analyze data from twenty-seven states in the United States. Three housing characteristic categories (i.e., general housing conditions, living conditions, and housing thermal inertia) and eight variables were extracted from the American Housing Survey database, ResStock database and CDC's National Environmental Public Health Tracking Network. Multivariable regression models were used to understand the influential variables, a multicollinearity test was used to determine the dependence of those variables, and then a logistic model was used to verify the results. Three variables-housing age (HA), housing crowding ratio (HCR), and roof condition (RC)-were found to be correlated with the risk of heat-related illness (HRI) indexes. Then, a logistic regression model was generated using the three variables to predict the risk of heat-related emergency department visits (EDV) and heat-related mortality (MORD) on a state level. The results indicate that the proposed logistic regression model correctly predicted 100% of the high-risk states for MORD for the eight states tested. Overall, this analysis provides additional evidence about the housing character variables that influence HRI. The outcomes also reinforce the concept of the built environment determined health and demonstrate that the built environment, especially housing, should be considered in techniques for mitigating climate change-exacerbated health conditions.

Social vulnerability, medical care access and asthma related emergency department visits and hospitalization: An observational study

BackgroundThe Social Vulnerability Index (SVI) is a publicly available dataset to identify communities in greatest need of resources. ObjectiveTo examine the utility of using the county-level SVI as predictors of asthma-related outcomes. MethodsWe used the American Community Survey-derived SVI and the National Environmental Public Health Tracking Network – Query Tool to retrieve data for all counties with available SVI data and at least one matched outcome of interest. Then, we tested SVI as a predictor for emergency department visits (EDV) and hospitalizations, with investigating disparities in primary care physician (PCP) density and emergency department physicians (EDP) density. Linear and logistic regression models were used. ResultsCompared to counties of the lowest SVI quartile, counties of mid-low, mid-high, and highest SVI quartiles had 1%, 4%, and 5% higher odds of asthma-related EDV per 10,000 population, respectively, and 4%, 21%, and 24% higher odds of asthma-related hospitalization per 10,000 population, respectively. Moreover, the data showed an apparent resources mismatch between the EDP densities per 10,000 populations and the SVI quartiles, and the effect of the county level SVI on the asthma-related EDV and hospitalization is not strongly affected by PCP or EDP densities. ConclusionThe counties with the highest SVI -and the most vulnerable to asthma hazards- have a lower coverage of PCP and EDP. Interventions directed to address persistent social vulnerability would offer the opportunity of primary prevention with less exhaustion for the medical resources.

The association between air pollution and childhood asthma: United States, 2010–2015

Objective The current population-based study examines the association between county-level ambient air pollution and childhood asthma. Methods Data from the nationally representative 2010–2015 National Health Interview Survey were linked to nationwide fine particulate matter (PM2.5) air pollution data at the county-level from the National Environmental Public Health Tracking Network which utilizes air quality monitoring stations and modeled PM2.5 measurements (Downscaler model data) and adjusted by county-level socioeconomic characteristics data from the 2010–2015 American Community Survey. Multilevel modeling techniques were used to assess the association between PM2.5 annual concentrations (quartiles < 8.11, 8.11–9.50, 9.51–10.59, ≥ 10.60 µg/m3) and current childhood asthma along with two asthma outcomes (episode in the past year, emergency room (ER) visit due to asthma). Results From 2010 to 2015, there were significant declines in PM2.5 concentrations and asthma outcomes. In unadjusted models, children living in areas with higher PM2.5 concentrations were more likely to have current asthma, ≥1 asthma episode in the past year, and ≥1 ER visit due to asthma compared with children living in areas with the lowest quartile (< 8.11 µg/m3). After adjusting for characteristics at the county, geographic, and child and family-level, significant associations remained for asthma episode, and ER visit among children living in areas with PM2.5 annual concentrations between 9.51 and 10.59 µg/m3 (3rd quartile) compared with children living in areas with the lowest quartile. Conclusions This study adds to the limited literature by incorporating nationally representative county-, child-, and family-level data to provide a multi-level analysis of the associations between air pollution and childhood asthma in the U.S.

Aggregated cumulative county arsenic in drinking water and associations with bladder, colorectal, and kidney cancers, accounting for population served.

Many studies neglect to account for variation in population served by community water systems (CWSs) when aggregating CWS-level contaminant concentrations to county level. In an ecological epidemiologic analysis, we explored two methods-unweighted and weighted (proportion of CWS population served by county population)-to account for population served by CWS in association between arsenic and three cancers to determine the impact of population served on aggregated measures of exposure. CWS arsenic concentration data for 19 states were obtained from Centers for Disease Control and Prevention (CDC) National Environmental Public Health Tracking Network for 2000-10, aggregated to county level, and linked to county-level cancer data for 2011-5 from National Cancer Institute and CDC State Cancer Profiles. Negative binomial regression models estimated adjusted risk ratios (aRR) and 95% confidence intervals (CI) between county-level bladder, colorectal, and kidney cancers and quartiles of aggregated cumulative county-level arsenic concentration (ppb-years). We observed positive associations between the highest quartileof exposure, compared to the lowest, ofaggregated cumulative county-level arsenic concentration (ppb-year) for bladder [weighted aRR: 1.89(1.53, 2.35)], colorectal [1.64(1.33, 2.01)], and kidney [1.69(1.37, 2.09)] cancers. We observed stronger associations utilizing the weighted exposure assessment method. However, inferences from this study are limited due to the ecologic nature of the analyses and different analytic study designs are needed to assess the utility that the weighted by CWS population served metric has for exposure assessment. Weighting by CWS population served accounts for some potential exposure assignment error in epidemiologic analysis.

Availability, access, analysis and dissemination of small-area data

In this era of ‘big data’, there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualizations, allowing small-area data to be seen and understood by non-expert audiences, are revolutionizing public and researcher interactions with data. The UK Small Area Health Statistics Unit’s Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and ‘mash-ups’, and user-generated inputs from social media, mobile devices and wearable tech are new data streams that will find utility in future studies, and bring novel dimensions with respect to ethical use of small-area data.

Ambient air pollution and in vitro fertilization treatment outcomes.

Is air pollution associated with IVF treatment outcomes in the USA? We did not find clear evidence of a meaningful association between reproductive outcomes and average daily concentrations of particulate matter with an aerodynamic diameter ≤2.5μm (PM2.5) and ozone (O3). Maternal exposure to air pollution such as PM2.5, nitrogen oxides, carbon monoxide or O3 may increase risks for adverse perinatal outcomes. Findings from the few studies using data from IVF populations to investigate associations between specific pollutants and treatment outcomes are inconclusive. Retrospective cohort study of 253 528 non-cancelled fresh, autologous IVF cycles including 230 243 fresh, autologous IVF cycles with a transfer of ≥1 embryo was performed between 2010 and 2012. We linked 2010-2012 National ART Surveillance System data for fresh, autologous IVF cycles with the ambient air pollution data generated using a Bayesian fusion model available through the Centers for Disease Control and Prevention's Environmental Public Health Tracking Network. We calculated county-level average daily PM2.5 and O3 concentrations for three time periods: cycle start to oocyte retrieval (T1), oocyte retrieval to embryo transfer (T2) and embryo transfer +14days (T3). Multivariable predicted marginal proportions from logistic and log-linear regression models were used to estimate adjusted risk ratios (aRR) and 95% CI for the association between reproductive outcomes (implantation rate, pregnancy and live birth) and interquartile increases in PM2.5 and O3. The multipollutant models were also adjusted for patients and treatment characteristics and accounted for clustering by clinic and county of residence. For all exposure periods, O3 was weakly positively associated with implantation (aRR 1.01, 95% CI 1.001-1.02 for T1; aRR 1.01, 95% CI 1.001-1.02 for T2 and aRR 1.01, 95% CI 1.001-1.02 for T3) and live birth (aRR 1.01, 95% CI 1.002-1.02 for T1; aRR 1.01, 95% CI 1.004-1.02 for T2 and aRR 1.02, 95% CI 1.004-1.03 for T3). PM2.5 was not associated with any of the reproductive outcomes assessed. The main limitation of this study is the use of aggregated air pollution data as proxies for individual exposure. The weak positive associations found in this study might be related to confounding by factors that we were unable to assess and may not reflect clinically meaningful differences. More research is needed to assess the impact of air pollution on reproductive function. None.

Estimating health-related costs of ten US climate-sensitive events in 2012: a retrospective study

BackgroundClimate change threatens human health, but there is a lack of evidence on the economic toll of these risks. Health-related cost estimates for climate-sensitive impacts illuminate an understudied impact of climate change and highlight how this threat is affecting sectors beyond property and infrastructure. Furthermore, cost estimates can guide health interventions and help public health professionals assess whether climate change adaptation measures are achieving their intended benefits. MethodsWe estimate mortality and morbidity costs associated with ten climate-sensitive case studies spanning eleven US states in 2012 (when the country experienced some of its warmest weather to date); the case studies include wildfires, extreme heat, West Nile Virus and Lyme disease, extreme precipitation and flooding, harmful algal blooms, allergenic pollen, ozone air pollution, and a major hurricane. We estimate the mortality and morbidity associated with each event using data reported in peer-reviewed epidemiology studies, state and federal data, and environmental public health tracking network data collected by state public health agencies. We augment this information with imputed incidence data from national healthcare utilisation statistics. FindingsApplying value of a statistical life and cost-of-illness approaches to publicly-available data from the Agency for Healthcare Research and Quality, we estimate total health-related costs associated with these case studies (from 917 deaths, 20 568 hospitalisations and 17 857 emergency department visits) of about $10·0 billion in 2018 dollars, with a sensitivity range of $2·7 billion to $24·6 billion. InterpretationBy better characterising these health damages in economic terms, this work helps characterise the burden that climate change already places on human health. In doing so, we provide a quantitative framework for broader estimation of climate-sensitive health-related costs. Expanded quantification of the budgetary pressures posed by climate change can help decision makers engage with the scale of the challenge and prioritise efficient adaptation strategies to protect public health. FundingNone.

Community drinking water data on the National Environmental Public Health Tracking Network: a surveillance summary of data from 2000 to 2010

This report describes the available drinking water quality monitoring data on the Centers for Disease Control and Prevention (CDC) National Environmental Public Health Tracking Network (Tracking Network). This surveillance summary serves to identify the degree to which ten drinking water contaminants are present in finished water delivered to populations served by community water systems (CWS) in 24 states from 2000 to 2010. For each state, data were collected from every CWS. CWS are sampled on a monitoring schedule established by the Environmental Protection Agency (EPA) for each contaminant monitored. Annual mean and maximum concentrations by CWS for ten water contaminants were summarized from 2000 to 2010 for 24 states. For each contaminant, we calculated the number and percent of CWS with mean and maximum concentrations above the maximum contaminant level (MCL) and the number and percent of population served by CWS with mean and maximum concentrations above the MCL by year and then calculated the median number of those exceedances for the 11-year period. We also summarized these measures by CWS size and by state and identified the source water used by those CWS with exceedances of the MCL. The contaminants that occur more frequently in CWS with annual mean and annual maximum concentrations greater than the MCL include the disinfection byproducts, total trihalomethanes (TTHM), and haloacetic acids (HAA5); arsenic; nitrate; radium and uranium. A very high proportion of exceedances based on MCLs occurred mostly in very small and small CWS, which serve a year-round population of 3,300 or less. Arsenic in New Mexico and disinfection byproducts HAA5 and TTHM, represent the greatest health risk in terms of exposure to regulated drinking water contaminants. Very small and small CWS are the systems’ greatest difficulty in achieving compliance.

Seeing Data in a New Light: Data Visualization Techniques to Improve Understanding

ObjectiveThe presenter will demonstrate complex health and environment surveillance data visualization techniques within the CDC’s Environmental Public Health Tracking Network.IntroductionIn 2002, the United States (US) Centers for Disease Control and Prevention (CDC) launched the National Environmental Public Health Tracking Program (Tracking Program) to address the challenges and gaps in the nation’s environmental health surveillance infrastructure. The Tracking Program’s mission is to provide information from a nationwide network of integrated health and environmental data that drives actions to improve the health of communities. As a primary objective of the Tracking Program, the Environmental Public Health Tracking Network (Tracking Network) was developed as an online surveillance system with data available for 23 topics and over 450 different health, environmental, and population measures. The integration and display of such disparate data can be challenging. For data consumers without scientific training, or even scientists and public health professionals with limited time, it can be difficult to examine and explore the data in an online surveillance system. Additionally, casual data consumers may not require complex data details; a big picture perspective may be appropriate to their needs. The Tracking Network – which applies standardized data, a modern user interface, techniques catering to a variety of data consumers, and best practices in data visualization – provides a dynamic data query system that allows users to visualize different types of environmental health data in numerous ways including a variety of charting, mapping, and graphing options.MethodsAfter the Tracking Program identifies important environmental health topics, data standards are developed to aid in data validation and to ensure consistency and comparability of the data. The data are aggregated into standardized stratifications, summarized, and used to calculate environmental health measures. Visualization requirements for each measure are determined and developed on the Tracking Network. In addition, public health content are developed to provide important context for the user. The final step is the release of the environmental health measures onto the National Tracking Network, where they can be queried, visualized, compared, and analyzed with all of the other environmental health measures on the Tracking Network.ResultsLaunched in 2009, the Tracking Network developed at CDC is home to over 450 standardized environmental health measures spanning 23 topics and multiple years, and can be displayed at a geographic level of state, county, and census tract. The Tracking Network allows data consumers to interpret this data visually using tools including dynamic timeline maps, infographics, advanced charting and a streamlined user interface designed to be simple to use. With varying user levels in mind, this collection of tools provides a variety of avenues for the users to explore the data. Visualization results can be exported and embedded into other websites with data interpretation statements, benchmarks, and other visual cues that allow a broad audience to be able to access to environmental public health surveillance data.ConclusionsWhile the internet contains a wealth of health and environmental datasets, the Tracking Network provides a centralized location to access over 450 environmental health measures and provides a variety of visualization tools to translate the data into useful information. The speaker will present a range of display options available on the Tracking Network, highlight ways to present data for easy understanding and consumption, and provide a brief look into the future of data visualization.

175 Association of particulate matter air pollution and itch: a digital epidemiology approach

Air pollution secondary to industrialization is a growing health concern with the potential to cause skin toxicity. Among skin conditions, itch may be especially susceptible to environmental modulation given that free nerve endings of sensory neurons are located in the epidermis. While several air pollutants are postulated to impact health, one well-studied marker of air quality is atmospheric particulate matter ≤2.5μm (PM2.5). We hypothesized that increasing PM2.5 levels are associated with increased population level search interest in itch and employed Google Trends data to aggregate search queries across various regions with values recorded for search volume index (SVI). SVI data were evaluated within the United States in 2012 for ‘itch’, ‘rash’, and for comparison purposes ‘acne’ and ‘rosacea’ within each state. We obtained data on annual state averages for atmospheric particulate matter less than 2.5 microns in diameter (PM2.5) for 2012 from the National Environmental Public Health Tracking Network. Multivariate regression was performed to examine the association between SVI for itch and PM2.5, adjusted for temperature. Across the US, SVIs for the terms ‘itch’ and ‘rash’ were correlated with atmospheric PM2.5 concentration (R2 = 0.52 and 0.41, both p < 0.001). ‘Acne’ and ‘rosacea’ SVIs were not correlated with PM2.5 concentration (R2 = 0.04, p = 0.169; R2 = 0.001, p = 0.848). No significant associations were found between carbon dioxide emission levels and SVIs for any search terms. Our results demonstrate that increasing levels of fine particulate matter air pollution are associated with higher search interest in itch and rash, highlighting the potential role of environmental modulation in these conditions. These findings may be explained by pollutant-induced sensitization of cutaneous sensory nerves. Further research is needed to elucidate the pathophysiology of skin changes caused by air pollution and to identify additional inciting environmental agents.

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project.

Small area data are key to better understanding the complex relationships between environmental health, health outcomes, and risk factors at a local level. In 2014, the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program (Tracking Program) conducted the Sub-County Data Pilot Project with grantees to consider integration of sub-county data into the National Environmental Public Health Tracking Network (Tracking Network). The Tracking Program and grantees developed sub-county-level data for several data sets during this pilot project, working to standardize processes for submitting data and creating required geographies. Grantees documented challenges they encountered during the pilot project and documented decisions. This article covers the challenges revealed during the project. It includes insights into geocoding, aggregation, population estimates, and data stability and provides recommendations for moving forward. National standards for generating, analyzing, and sharing sub-county data should be established to build a system of sub-county data that allow for comparison of outcomes, geographies, and time. Increasing the availability and accessibility of small area data will not only enhance the Tracking Network's capabilities but also contribute to an improved understanding of environmental health and informed decision making at a local level.

Developing an online tool for identifying at-risk populations to wildfire smoke hazards

Wildfire episodes pose a significant public health threat in the United States. Adverse health impacts associated with wildfires occur near the burn area as well as in places far downwind due to wildfire smoke exposures. Health effects associated with exposure to particulate matter arising from wildfires can range from mild eye and respiratory tract irritation to more serious outcomes such as asthma exacerbation, bronchitis, and decreased lung function. Real-time operational forecasts of wildfire smoke concentrations are available but they are not readily integrated with information on vulnerable populations necessary to identify at-risk communities during wildfire smoke episodes. Efforts are currently underway at the Centers for Disease Control and Prevention (CDC) to develop an online tool that utilizes short-term predictions and forecasts of smoke concentrations and integrates them with measures of population-level vulnerability for identifying at-risk populations to wildfire smoke hazards. The tool will be operationalized on a national scale, seeking input and assistance from several academic, federal, state, local, Tribal, and Territorial partners. The final product will then be incorporated into CDC's National Environmental Public Health Tracking Network (http://ephtracking.cdc.gov), providing users with access to a suite of mapping and display functionalities. A real-time vulnerability assessment tool incorporating standardized health and exposure datasets, and prevention guidelines related to wildfire smoke hazards is currently unavailable for public health practitioners and emergency responders. This tool could strengthen existing situational awareness competencies, and expedite future response and recovery efforts during wildfire episodes.

Health data for Delaware:: The path towards creating Delaware's Environmental Public Health Tracking Network.

The environment impacts health and contributes to factors that not only protect us from disease but also increase our risk for certain negative health outcomes. The Delaware Division of Public Health (DPH) embarked on an endeavor in 2014 to provide its communities with timely, high-quality data on the environment, risk and protective factors, and health outcomes through a data sharing platform. Through strong partnerships with the Department of Natural Resources and Environmental Control and key opportunities, these efforts coalesced into the development of Delaware’s Environmental Public Health Tracking Network. We share our analyses and presentation of asthma hospitalization and air quality data and describe our path towards creating Delaware’s Environmental Tracking Network.

CDC's National Environmental Public Health Tracking Program in Action: Case Studies From State and Local Health Departments

The Centers for Disease Control and Prevention's (CDC's) National Environmental Public Health Tracking Program (Tracking Program) is a multidisciplinary collaboration that involves the ongoing collection, integration, analysis, interpretation, and dissemination of data from environmental hazard monitoring, human exposure surveillance, and health effects surveillance. With a renewed focus on data-driven decision-making, the CDC's Tracking Program emphasizes dissemination of actionable data to public health practitioners, policy makers, and communities. The CDC's National Environmental Public Health Tracking Network (Tracking Network), a Web-based system with components at the national, state, and local levels, houses environmental public health data used to inform public health actions (PHAs) to improve community health. This article serves as a detailed landscape on the Tracking Program and Tracking Network and the Tracking Program's leading performance measure, "public health actions." Tracking PHAs are qualitative statements addressing a local problem or situation, the role of the state or local Tracking Program, how the problem or situation was addressed, and the action taken. More than 400 PHAs have been reported by funded state and local health departments since the Tracking Program began collecting PHAs in 2005. Three case studies are provided to illustrate the use of the Tracking Program resources and data on the Tracking Network, and the diversity of actions taken. Through a collaborative network of experts, data, and tools, the Tracking Program and its Tracking Network are actively informing state and local PHAs. In a time of competing priorities and limited funding, PHAs can serve as a powerful tool to advance environmental public health practice.

Rural and Urban Differences in Air Quality, 2008-2012, and Community Drinking Water Quality, 2010-2015 - United States.

Problem/ConditionThe places in which persons live, work, and play can contribute to the development of adverse health outcomes. Understanding the differences in risk factors in various environments can help to explain differences in the occurrence of these outcomes and can be used to develop public health programs, interventions, and policies. Efforts to characterize urban and rural differences have largely focused on social and demographic characteristics. A paucity of national standardized environmental data has hindered efforts to characterize differences in the physical aspects of urban and rural areas, such as air and water quality.Reporting Period2008–2012 for air quality and 2010–2015 for water quality.Description of SystemSince 2002, CDC’s National Environmental Public Health Tracking Program has collaborated with federal, state, and local partners to gather standardized environmental data by creating national data standards, collecting available data, and disseminating data to be used in developing public health actions. The National Environmental Public Health Tracking Network (i.e., the tracking network) collects data provided by national, state, and local partners and includes 21 health outcomes, exposures, and environmental hazards. To assess environmental factors that affect health, CDC analyzed three air-quality measures from the tracking network for all counties in the contiguous United States during 2008–2012 and one water-quality measure for 26 states during 2010–2015. The three air-quality measures include 1) total number of days with fine particulate matter (PM2.5) levels greater than the U.S. Environmental Protection Agency’s (EPA’s) National Ambient Air Quality Standards (NAAQS) for 24-hour average PM2.5 (PM2.5 days); 2) mean annual average ambient concentrations of PM2.5 in micrograms per cubic meter (mean PM2.5); and 3) total number of days with maximum 8-hour average ozone concentrations greater than the NAAQS (ozone days). The water-quality measure compared the annual mean concentration for a community water system (CWS) to the maximum contaminant level (MCL) defined by EPA for 10 contaminants: arsenic, atrazine, di(2-ethylhexyl) phthalate (DEHP), haloacetic acids (HAA5), nitrate, perchloroethene (PCE), radium, trichloroethene (TCE), total trihalomethanes (TTHM), and uranium. Findings are presented by urban-rural classification scheme: four metropolitan (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) and two nonmetropolitan (micropolitan and noncore) categories. Regression modeling was used to determine whether differences in the measures by urban-rural categories were statistically significant.ResultsPatterns for all three air-quality measures suggest that air quality improves as areas become more rural (or less urban). The mean total number of ozone days decreased from 47.54 days in large central metropolitan counties to 3.81 days in noncore counties, whereas the mean total number of PM2.5 days decreased from 11.21 in large central metropolitan counties to 0.95 in noncore counties. The mean average annual PM2.5 concentration decreased from 11.15 μg/m3 in large central metropolitan counties to 8.87 μg/m3 in noncore counties. Patterns for the water-quality measure suggest that water quality improves as areas become more urban (or less rural). Overall, 7% of CWSs reported at least one annual mean concentration greater than the MCL for all 10 contaminants combined. The percentage increased from 5.4% in large central metropolitan counties to 10% in noncore counties, a difference that was significant, adjusting for U.S. region, CWS size, water source, and potential spatial correlation. Similar results were found for two disinfection by-products, HAA5 and TTHM. Arsenic was the only other contaminant with a significant result. Medium metropolitan counties had 3.1% of CWSs reporting at least one annual mean greater than the MCL, compared with 2.4% in large central counties.InterpretationNoncore (rural) counties experienced fewer unhealthy air-quality days than large central metropolitan counties, likely because of fewer air pollution sources in the noncore counties. All categories of counties had a mean annual average PM2.5 concentration lower than the EPA standard. Among all CWSs analyzed, the number reporting one or more annual mean contaminant concentrations greater the MCL was small. The water-quality measure suggests that water quality worsens as counties become more rural, in regards to all contaminants combined and for the two disinfection by-products individually. Although significant differences were found for the water-quality measure, the odds ratios were very small, making it difficult to determine whether these differences have a meaningful effect on public health. These differences might be a result of variations in water treatment practices in rural versus urban counties.Public Health ActionUnderstanding the differences between rural and urban areas in air and water quality can help public health departments to identify, monitor, and prioritize potential environmental public health concerns and opportunities for action. These findings suggest a continued need to develop more geographically targeted, evidence-based interventions to prevent morbidity and mortality associated with poor air and water quality.