scholarly journals Tracking environmental hazards and health outcomes to inform decision-making in the United States

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Heather Strosnider ◽  
Patrick Wall ◽  
Holly Wilson ◽  
Joseph Ralph ◽  
Fuyuen Yip

ObjectiveTo increase the availability and accessibility of standardized environmental health data for public health surveillance and decision-making.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 in environmental health surveillance described by the Pew Environmental Commission (1). The report cited gaps in our understanding of how the environment affects our health and attributed these gaps to a dearth of surveillance data for environmental hazards, human exposures, and health effects. 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. Accomplishing this mission requires a range of expertise from environmental health scientists to programmers to communicators employing the best practices and latest technical advances of their disciplines. Critical to this mission, the Tracking Program must identify and prioritize what data are needed, address any gaps found, and integrate the data into the network for ongoing surveillance.MethodsThe Tracking Program identifies important environmental health topics with data challenges based on the recommendations in the Pew Commission report as well as input from federal, state, territorial, tribal, and local partners. For each topic, the first step is to formulate the key surveillance question, which includes identifying the decision-maker or end user. Next, available data are evaluated to determine if the data can answer the question and, if not, what enhancements or new data are needed. Standards are developed to establish data requirements and to ensure consistency and comparability. Standardized data are then integrated into the network at national, state, and local levels. Standardized measures are calculated to translate the data into the information needed. These measures are then publically disseminated via national, state, and local web-based portals. Data are updated annually or as they are available and new data are added regularly. All data undergo a multi-step validation process that is semi-automated, routinized, and reproducible.ResultsThe first set of nationally consistent data and measures (NCDM) was released in 2008 and covered 8 environmental health topics. Since then the NCDM have grown to cover 14 topics. Additional standardized data and measures are integrated into the national network resulting in 23 topics with standardized 450 measures (Figure). On the national network, measures can be queried via the Data Explorer, viewed in the info-by-location application, or connected to via the network’s Application Program Interface (API). On average, 15,000 and 3300 queries are run every month on the Data Explorer and the API respectfully. Additional locally relevant data are available on state and local tracking networks.Gaps in data have been addressed through standards for new data collections, models to extend available data, new methodologies for using existing data, and expansion of the utility of non-traditional public health data. For example, the program has collaborated with the Environmental Protection Agency to develop daily estimates of fine particulate matter and ozone for every county in the conterminous US and to develop the first national database of standardized radon testing data. The program also collaborated with the National Aeronautics and Space Administration and its academic partners to transform satellite data into data products for public health.The Tracking Program has analyzed the data to address important gaps in our understanding of the relationship between negative health outcomes and environmental hazards. Data have been used in epidemiologic studies to better quantify the association between fine particulate matter, ozone, wildfire smoke, and extreme heat on emergency department visits and hospitalizations. Results are translated into measures of health burden for public dissemination and can be used to inform regulatory standards and public health interventions.ConclusionsThe scope of the Tracking Program’s mission and the volume of data within the network requires the program to merge traditional public health expertise and practices with current technical and scientific advances. Data integrated into the network can be used to (1) describe temporal and spatial trends in health outcomes and potential environmental exposures, (2) identify populations most affected, (3) generate hypotheses about associations between health and environmental exposures, and (4) develop, guide, and assess the environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors. The program continues to expand the data within the network and the applications deployed for others to access the data. Current data challenges include the need for more temporally and spatially resolved data to better understand the complex relationships between environmental hazards, health outcomes, and risk factors at a local level. National standards are in development for systematically generating, analyzing, and disseminating small area data and real-time data that will allow for comparisons between different datasets over geography and time.References1. Pew Environmental Health Tracking Project Team. America’s Environmental Health Gap: Why the Country Needs a Nationwide Health Tracking Network. Johns Hopkins School of Hygiene and Public Health, Department of Health Policy and Management; 2000.

Author(s):  
Arnold Kamis ◽  
Rui Cao ◽  
Yifan He ◽  
Yuan Tian ◽  
Chuyue Wu

In this research, we take a multivariate, multi-method approach to predicting the incidence of lung cancer in the United States. We obtain public health and ambient emission data from multiple sources in 2000–2013 to model lung cancer in the period 2013–2017. We compare several models using four sources of predictor variables: adult smoking, state, environmental quality index, and ambient emissions. The environmental quality index variables pertain to macro-level domains: air, land, water, socio-demographic, and built environment. The ambient emissions consist of Cyanide compounds, Carbon Monoxide, Carbon Disulfide, Diesel Exhaust, Nitrogen Dioxide, Tropospheric Ozone, Coarse Particulate Matter, Fine Particulate Matter, and Sulfur Dioxide. We compare various models and find that the best regression model has variance explained of 62 percent whereas the best machine learning model has 64 percent variance explained with 10% less error. The most hazardous ambient emissions are Coarse Particulate Matter, Fine Particulate Matter, Sulfur Dioxide, Carbon Monoxide, and Tropospheric Ozone. These ambient emissions could be curtailed to improve air quality, thus reducing the incidence of lung cancer. We interpret and discuss the implications of the model results, including the tradeoff between transparency and accuracy. We also review limitations of and directions for the current models in order to extend and refine them.


2019 ◽  
Author(s):  
Jaakko Kukkonen ◽  
Mikko Savolahti ◽  
Yuliia Palamarchuk ◽  
Timo Lanki ◽  
Väinö Nurmi ◽  
...  

Abstract. We have developed an integrated tool of assessment that can be used for evaluating the public health costs caused by the concentrations of fine particulate matter (PM2.5) in ambient air. The model can be used in assessing the impacts of various alternative air quality abatement measures, policies and strategies. The model has been applied for the evaluation of the costs of the domestic emissions that influence the concentrations of PM2.5 in Finland in 2015. The model includes the impacts on human health; however, it does not address the impacts on climate change or the state of the environment. First, the national Finnish emissions were evaluated using the Finnish Regional Emission Scenarios model (FRES) on a resolution of 250 × 250 m2 for the whole of Finland. Second, the atmospheric dispersion was analyzed by using the chemical transport model SILAM and the source-receptor matrices contained in the FRES model. Third, the health impacts were assessed by combining the spatially resolved concentration and population datasets, and by analyzing the impacts for various health outcomes. Fourth, the economic impacts for the health outcomes were evaluated. The model can be used to evaluate the costs of the health damages for various emission source categories, for a unit of emissions of PM2.5. It was found that economically the most effective measures would be the reduction of the emissions in urban areas of (i) road transport, (ii) non-road vehicles and machinery, and (iii) residential wood combustion. The reduction of the precursor emissions of PM2.5 was clearly less effective, compared with reducing directly the emissions of PM2.5. We have also designed a user-friendly web-based tool of assessment that is available open access.


2020 ◽  
Vol 20 (15) ◽  
pp. 9371-9391
Author(s):  
Jaakko Kukkonen ◽  
Mikko Savolahti ◽  
Yuliia Palamarchuk ◽  
Timo Lanki ◽  
Väinö Nurmi ◽  
...  

Abstract. We have developed an integrated assessment tool that can be used for evaluating the public health costs caused by the concentrations of fine particulate matter (PM2.5) in ambient air. The model can be used to assess the impacts of various alternative air quality abatement measures, policies and strategies. The model has been applied to evaluate the costs of the domestic emissions that influence the concentrations of PM2.5 in Finland in 2015. The model includes the impacts on human health; however, it does not address the impacts on climate change or the state of the environment. First, the national Finnish emissions were evaluated using the Finnish Regional Emission Scenarios (FRESs) model on a resolution of 250×250 m2 for the whole of Finland. Second, the atmospheric dispersion was analysed by using the chemical transport model, namely the System for Integrated modeLling of Atmospheric coMposition (SILAM) model, and the source receptor matrices contained in the FRES model. Third, the health impacts were assessed by combining the spatially resolved concentration and population data sets and by analysing the impacts for various health outcomes. Fourth, the economic impacts of the health outcomes were evaluated. The model can be used to evaluate the costs of the health damages for various emission source categories and for a unit of emissions of PM2.5. It was found that the economic benefits, in terms of avoided public health costs, were largest for measures that will reduce the emissions of (i) road transport, (ii) non-road vehicles and machinery, and (iii) residential wood combustion. The reduction in the precursor emissions of PM2.5 resulted in clearly lower benefits when compared with directly reducing the emissions of PM2.5. We have also designed a user-friendly, web-based assessment tool that is open access.


Author(s):  
Jason Reece

Housing quality, stability, and affordability have a direct relationship to socioemotional and physical health. Both city planning and public health have long recognized the role of housing in health, but the complexity of this relationship in regard to infant and maternal health is less understood. Focusing on literature specifically relevant to U.S. metropolitan areas, I conduct a multidisciplinary literature review to understand the influence of housing factors and interventions that impact infant and maternal health. The paper seeks to achieve three primary goals. First, to identify the primary “pathways” by which housing influences infant and maternal health. Second, the review focuses on the role and influence of historical housing discrimination on maternal health outcomes. Third, the review identifies emergent practice-based housing interventions in planning and public health practice to support infant and maternal health. The literature suggests that the impact of housing on infant health is complex, multifaceted, and intergenerational. Historical housing discrimination also directly impacts contemporary infant and maternal health outcomes. Policy interventions to support infant health through housing are just emerging but demonstrate promising outcomes. Structural barriers to housing affordability in the United States will require new resources to foster greater collaboration between the housing and the health sectors.


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