Common and Unique Barriers to the Exchange of Administrative Healthcare Data in Environmental Public Health Tracking Program

CDC’s National Environmental Public Health Tracking Program (Tracking Program) receives administrative data annually from 25–30 states to track potential environmental exposures and to make data available for public access. In 2019, the CDC Tracking Program conducted a cross-sectional survey among principal investigators or program managers of the 26 funded programs to improve access to timely, accurate, and local data. All 26 funding recipients reported having access to hospital inpatient data, and most states (69.2%) regularly update data user agreements to receive the data. Among the respondents, 15 receive record-level data with protected health information (PHI) and seven receive record-level data without PHI. Regarding geospatial resolution, approximately 50.0% of recipients have access to the street address or census tract information, 34.6% have access to ZIP code, and 11.5% have other sub-county geographies (e.g., town). Only three states receive administrative data for their residents from all border states. The survey results will help the Tracking Program to identify knowledge gaps and perceived barriers to the use and accessibility of administrative data for the CDC Tracking Program. The information collected will inform the development of resources that can provide solutions for more efficient and timely data exchange.

Groundwater Arsenic Distribution in India by Machine Learning Geospatial Modeling

Groundwater is a critical resource in India for the supply of drinking water and for irrigation. Its usage is limited not only by its quantity but also by its quality. Among the most important contaminants of groundwater in India is arsenic, which naturally accumulates in some aquifers. In this study we create a random forest model with over 145,000 arsenic concentration measurements and over two dozen predictor variables of surface environmental parameters to produce hazard and exposure maps of the areas and populations potentially exposed to high arsenic concentrations (>10 µg/L) in groundwater. Statistical relationships found between the predictor variables and arsenic measurements are broadly consistent with major geochemical processes known to mobilize arsenic in aquifers. In addition to known high arsenic areas, such as along the Ganges and Brahmaputra rivers, we have identified several other areas around the country that have hitherto not been identified as potential arsenic hotspots. Based on recent reported rates of household groundwater use for rural and urban areas, we estimate that between about 18–30 million people in India are currently at risk of high exposure to arsenic through their drinking water supply. The hazard models here can be used to inform prioritization of groundwater quality testing and environmental public health tracking programs.

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.

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.