Tag Archives: public health

Developing and Applying the Geographic Aggregation Tool (GAT) at NYS Department of Health

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“R”-based software application is used by public health researchers and data analysts

While often much of the Empire State GIS software discussion evolves around the use of the ESRI platform, there continues to be a steady dose and discussion of alternative geospatial client and web-based products. For example, staff at the New York State Department of Health (NYSDOH) use ArcGIS, but also other GIS programs including MapInfo, Map Marker, SatScan, and QGIS, as well as statistical programs like SAS, SPSS and Tableau for research and surveillance projects.

Supported by funding from the Centers for Disease Control and Prevention (CDC), staff from the NYSDOH Environmental Public Health Tracking (EPHT) Program – also known as “Tracking” – has leveraged the free and open-source software program “R” to develop and maintain the Geographic Aggregation Tool (GAT).  R has grown primarily out of the statistics and data analysis space which is very popular and used extensively in public health research.

GAT is currently maintained by Abigail Stamm at NYSDOH who supports a GAT webpage on GitHub containing an extensive listing of documentation, developer tools, metadata, tutorials and more.  According to Stamm, EPHT staff like using R for a couple of reasons including:

  • R is free,  easily accessible to the public, and enables staff to share the GAT package with agencies that do not have an ArcGIS license or the training and resources to use any of the other geographical software. (Legacy versions of GAT, including a SAS version, are archived on GitHub.)
  • The GAT package automates everything.  This is particularly valuable especially if users include scripting options which bypass the Graphic User Interface (series of pop-up windows) and reduces likelihood of mistakes and makes recording and reproducing workflows and updating much easier.

GAT documentation and content on GitHub.

At its core, GAT aggregates, or dissolves, geographic areas (most commonly used is census tract geography) based on numeric values for each area, such as case or population numbers, as well as other demographic values such as median income.  Health researchers often want subject data at a higher granularity than the county-level, which can conceal or mask variation, especially in counties with a mix of urban and rural populations.  Also, showing data at town level won’t work because many rural towns have very small populations.  Areas with small populations are likely to have few cases, resulting in unstable rates and also putting confidentiality of cases at risk.

To overcome these limitations NYSDOH developed GAT to join neighboring geographic areas together until a user defined population and/or number of cases is reached to support the statistical analysis desired. This allows local health departments and others to use rates to identify hot spots for targeted interventions. GAT can also be used to produce maps at varying geographic resolutions required by the user.

How GAT Works

GAT requests user inputs through a series of dialogs, including menus, checkboxes, and text boxes, so no programming knowledge is necessary.  GAT reads in a polygon shapefile which must contain, at minimum, a character variable that uniquely identifies areas and a numeric variable to sum for aggregation.  A series of dialog boxes allows the user to select:

  1. A variable to uniquely identify areas
  2. One or two aggregation variables
  3. Optionally, a variable of areas within which merging will be preferred (ex. county)
  4. The value (sum) to which the selected aggregation variable(s) should be aggregated
  5. The preferred aggregation method: closest geographic or population-weighted centroid, least value, or ratio of two values

Depending on the specifics of the data and the type of analysis of interest to the user, GAT offers four types of aggregation methods:

  1. Closest geographic centroid
  2. Closest population – weighted centroid
  3. Neighbor with the lowest count
  4. Most similar neighbor

Applying different rules or criteria to the different GAT aggregation results produce contrasting results. These are samples of the GAT aggregation tool when applied to total population numbers in towns in Hamilton and Fulton (NY) Counties. Sample code for producing these maps can be accessed here.

GAT produces two shapefiles, an aggregated file and a crosswalk. GAT also produces a PDF of maps and a log of the entire process, including user settings, any warnings, and a brief data dictionary. (The PDF and log provide much more information than being shown in this article).   These files are designed to help evaluate and report aggregation results and standardize user process.  NYSDOH staff developed GAT to standardize and automate how to aggregate New York’s 4900 census tracts.

Using the different aggregation methods may affect the values of resulting aggregated areas in different ways. For example, it was found when testing GAT that in cases where a small corner of a census tract contained most of its population, aggregating by geographic versus population weighted centroid could provide very different results. Also, when developing aggregated areas for the Tracking portal, it was recognized the portal would be displaying disease rates.  NYSDOH staff felt the most suitable aggregation method for these population-based measures would be to aggregate to the closest population weighted centroid.  To check for areas with smaller populations and unusually large numbers of cases, users have the option to aggregate by case count rather than population (or in addition to population).

While access to health data and resolution of data varies across the NYSDOH (point/address, census tract, zip code, or municipal level), staff in the Tracking program receive hospitalization and emergency room visit data at point level (resident address) from the Statewide Planning and Research Cooperative System (SPARCS). Staff geocode these datasets using multiple programs including MapMarker, SAM in ArcGIS, and NYCGBAT and assign the encompassing census tract to the point data. Methods to assign to tract based on zip code or town and to impute tracts for records that cannot be geocoded due to incomplete address have also been developed.

 Other Applications of GAT

Public Use in New York State

 Working towards NYSDOH’s goal of making sub-county data more accessible, NYS EPHT is developing a platform which will display environmental health outcomes and exposures in an interactive mapping application.  The current EPHT data portal displays county-level indicators but EPHT staff are redesigning it to include subcounty data using the sub-county aggregated areas created by GAT.  This will assist local agencies with targeting interventions while maintaining privacy and confidentiality.

Tracking is also working with Health Data NY (HDNY) to display the sub-county data. The HDNY platform will serve as a data repository displaying data for environmental health outcomes including asthma, chronic obstructive pulmonary disease (COPD), and myocardial infarction (MI; heart attacks), since these are the health outcomes local agencies are most interested in when planning extreme weather-related mitigation and resource allocation. The data will be available for public download along with the shapefile and a brief description of how the shapefile was developed.

The CDC is also using GAT to develop sub-county areas for various health outcome indicators having piloted it for data from other EPHT grantee states for health outcomes including asthma, MI, and COPD.

Sampling Design

 GAT is also being used in the development and selection of sampling areas as part of Biomonitoring NY, a statewide biomonitoring project.  In the first year of sampling this effort has focused on the sampling of households on Long Island. To this end, the group aggregated 2010 census block groups within census tracts so that each aggregated area had at least 440 households. In the next step, remaining tracts that were too small to meet the household minimum were aggregated to neighboring tracts. After completing the final aggregation, study staff randomly selected 25 aggregated areas and mailed postcards and invitation packets soliciting participants in households in the block groups within the selected aggregated areas.  Read more about the project.

Biomonitoring is a way of measuring the amount of environmental chemicals found in the human body. It is an important part of New York statewide epidemiological research that seeks to determine levels of chemical exposure in the human body and help better understand whether chemical exposures are associated with health effects in humans.

Informing Policy

In 2019, NYS passed the nation-leading Climate Leadership and Community Protection Act (CLCPA; aka “Climate Act”) to empower New York residents to fight climate change at home, at work, and in their communities.  NYSDOH, along with several other state agencies, is providing support and assisting in the implementation of the Climate Act. One effort includes identifying disadvantaged communities (DAC) that could benefit from mitigation efforts and allocation of investments. GAT was used to create sub-county areas for several climate-impacted health indicators that will be analyzed with multiple other variables to define DACs.

Learn More About GAT

A variety of documentation is available on the use and development of GAT.  Slides (pdf) from a 2020 and National Association of Health Data Organizations (NAHDO) presentation by NYSDOH staff are available here including a YouTube video.  GAT slides and a video from the 2021 useR Conference are also available.   Most recently staff presented at the 2021 Place and Health Conference in November which included epidemiologists, health geographers, social and behavioral scientists, statisticians, data scientists, and public health professionals from all levels of government.

GAT poster that was presented at the 2021 Place and Health Conference (download here). A lightning talk is also available.

Contact:

Abigail Stamm, Research Scientist
Bureau of Environmental and Occupational Epidemiology
New York State Department of Health
abigail.stamm@health.ny.gov

GAT was written in R-2.9.2 under Windows XP and was revised and converted to a package in R-3.4.3 under Windows 10 using RStudio-1.4.1103 and devtools-2.3.2. The latest version of GAT was compiled in R-3.6.1 and runs in R-3.5.3 through R-4.1.1.

https://github.com/ajstamm/gatpkg

Social Determinants of Health: How Place Can Affect Health Outcomes

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Why does location matter for health?

In 1948, the World Health Organization defined Health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” This definition has evolved over time to recognize additional factors that have been found to impact health. Driven in part by improved understanding of socioeconomic influences, technological changes, and increased attention on health policy, there has been a growing effort to more thoroughly elucidate factors that determine health. Genetics, individual behavior, social factors, the physical environment, health services and governmental policies all have been found to play a role in health outcomes. Crafting effective policy depends upon a solid understanding of each of these factors, as well as their complex interactions.

Social determinants of health—as distinct from medical care—are increasingly recognized as influencing a broad range of health matters, from access to outcome. According to America’s Health Rankings, when compared with other developed and many developing nations, the U.S. ranks at or near the bottom for health outcomes related to life expectancy. However, data from the Organisation for Economic Co-operation and Development (OECD), which seeks to understand what drives economic, social and environmental change across governments, show that the U.S. continues to exceed other countries in healthcare spending.

While the United States trails other developed countries in life expectancy (years), it still leads all others in healthcare spending.

Moreover, evidence illustrating the powerful role of social factors in determining health suggests “the effects of medical care may be more limited than commonly thought, particularly in determining who becomes sick or injured in the first place.” Studies have shown that states with a higher ratio of social to health spending had significantly better health outcomes for adult obesity, asthma, mentally unhealthy days, days with activity limitations, and mortality rates for lung cancer, acute myocardial infarction, and type 2 diabetes. Some studies have estimated that medical care is only responsible for 10-15% of preventable mortality in the U.S., indicating that social factors can lead to—or help prevent—poor health outcomes and inequalities.

To address this, the World Health Organization created the Commission on Social Determinants of Health in 2005. By 2008, the Commission concluded in their final report that “inequities are killing people on a grand scale”. Recommendations included improvement of daily living conditions, addressing the inequitable distribution of power, money and resources, measuring and understanding the problem, and assessing impact of action. Growing awareness also led to U.S. Centers for Disease Control (CDC) research, and implementation of a program to advance health equity called Healthy People 2020 which advocates for the creation of “social and physical environments that promote good health for all”, one of their four overreaching goals for the decade.

The Role of GIS

Location affects health. We see dramatic differences in life expectancy between countries, as well as between zip codes. Socioeconomic differences between countries are tied to disparities in health outcomes. Within the U.S., poverty and transportation affects access to healthcare. At a smaller scale, we know that safer neighborhoods, healthy and secure housing, and access to healthy food and open space are associated with healthier populations. U.S. Census data, American Community Survey data, and other sources can help tie such social factors to geography, allowing for better understanding, measurement and tracking of social determinants of health. Universities, governmental and non-profit organizations now routinely collect such data and utilize GIS to help inform analysis and better decision-making, leading to stronger policies. The Healthy People 2020 Approach to Social Determinants of Health employs a “place-based” organizing framework, organized around Economic Stability, Education, Social and Community Context, Health and Healthcare, and the Neighborhood and Built Environment.  Many factors reflecting economic stability, such as poverty and employment, can be mapped within a GIS framework. Mapping education levels, literacy and language, quality of housing, environmental contaminants and areas with high concentrations of chronic disease can help to identify vulnerable populations, reallocate resources and better plan interventions and public education and awareness campaigns. Much of this data is readily available and free of charge. Furthermore, GIS lends itself well to Disparities Analytics, the development of analysis and visualization methods to monitor and report health disparities.

The Westchester Community Foundation and the Westchester Index

For 40 years, the Westchester Community Foundation, a division of the New York Community Trust, has worked to improve local and regional quality of life in Westchester County, NY by addressing community issues, promoting responsible philanthropy, and connecting donors to critical, local needs. In this capacity, the Foundation, together with the Community Healthcare Association of New York State (CHCANYS) and Westchester County GIS recently developed and released the Westchester Index, a tool developed to help understand social determinants of health in Westchester County.  The collaboration relied on data from American Community Survey, the U.S. Census survey, the New York State Department of Health as well as school districts for select health outcomes and socioeconomic indicators. The CDC Social Vulnerability Index (SVI), created by the Geospatial Research, Analysis, and Services Program (GRASP) of the CDC, was also included in the Westchester County Index. The SVI uses 15 U.S. census variables at the census tract level grouped into 4 themes (Socioeconomic Status, Household Composition, Race/Ethnicity/Language, and Housing/Transportation) to help identify communities that may need support in preparing for hazards or recovering from disaster.  Westchester County GIS provided detailed data on public transportation, land use

A collaborative effort between Westchester County GIS and the Westchester Community Foundation, the Westchester Index contains data on over 200 indicators reflecting social determinants of health.

categories, as well as locations of environmental hazards. In addition, zip code, census tract and block, school district, municipality, County Legislative district, New York State Senate and Assembly district, and U.S. Congressional district boundaries were provided by Westchester County GIS, allowing for a user to further tailor an analysis. Overall, the Westchester Index contains data on over 200 indicators reflecting social determinants of health. The interactive tool is expected to be used by planning officials, community healthcare networks, school districts, non-profit organizations and others to explore the data and better understand the factors that drive health outcomes in Westchester County, leading to more informed decision-making for greater health equity.  View the Westchester Index Story Map here.

Editor’s Note:  This article was prepared by Anjali Sauthoff.  Ms. Sauthoff is an environmental health scientist currently working with Westchester County GIS to develop integrated approaches that address climate adaptation and community resilience. Her previous research at the Energy Institute at the University of Wisconsin at Madison focused on potential mitigation strategies for reducing transportation-related climate emissions. She received her PhD from Columbia University, where she developed an air pollution exposure model and received training in the health effects of environmental exposures. Her Master’s degree is in Neurobiology from SUNY Stony Brook. She can be reached at ap768@columbia.edu.