| NSF Org: |
BCS Division of Behavioral and Cognitive Sciences |
| Recipient: |
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| Initial Amendment Date: | March 10, 2016 |
| Latest Amendment Date: | September 2, 2016 |
| Award Number: | 1560888 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
Scott Freundschuh
BCS Division of Behavioral and Cognitive Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
| Start Date: | June 1, 2016 |
| End Date: | August 31, 2020 (Estimated) |
| Total Intended Award Amount: | $300,000.00 |
| Total Awarded Amount to Date: | $300,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
1805 N BROAD ST PHILADELPHIA PA US 19122-6104 (215)707-7547 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3340 N. Broad Street, SFC 427 Philadelphia PA US 19140-5104 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Geography and Spatial Sciences |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
This research project will develop and test new spatial statistical methods for health and disease mapping that incorporate residential history data. These statistical methods will enable researchers to assess risk of chronic diseases, such as cancer, and other health outcomes, such as pre-term births, as a function of the geographic-specific exposures associated with residential history. The new statistical methods will provide researchers with a robust and powerful tool for using residential histories when they test hypotheses about geographic exposures over time and space and their impacts on health and disease. The project will increase basic understanding of the amount of information bias introduced when residential histories are ignored. Project results will provide empirical examples for geographers, public officials, and other scientists that demonstrate why residential history data should be used in health and disease surveillance systems, how these data can be incorporated, and why this information should be included when conducting health geographic analysis. Project methods and findings will assist those addressing a broader set of health-related issues. The use of the data from a state cancer registry and a birth registry will provide insights regarding the use of administrative databases to obtain residential histories for health geographic analysis. The new methods will be adaptable for use by researchers and by public health practitioners and medical personnel in addressing problems besides long-latency diseases. By analyzing daily-scale movement, for example, it will be possible to map acute diseases like salmonella or health events like asthma attacks. The project also will provide education and training opportunities for undergraduate and graduate students in health and medical geography, computer science, and epidemiology.
Common methods for assessing risk factors based on geographic-specific exposures or the clustering of health and disease events generally have relied on static data limited to a single point in time and space, such as a person's location at the time of diagnosis. Ignoring residential history is a significant shortcoming in such analyses because of the latency period between causative exposures and resulting health and disease events. The investigators will address this shortcoming by providing a framework for combining multipoint, longitudinal residential history data with health and disease data that is normally based on a single time point. Building upon previous research in health geography, geographical information sciences, and data mining, they will develop hierarchical Bayes models that assess risk of disease while accounting for latency and temporally changing social and environmental exposures, geographic uncertainty, and missing data. They will test and demonstrate these new statistical models using both synthetic data and empirical secondary datasets of cancer and birth outcomes that include residential histories.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This project's main goals were to link population-based cancer data to residential history data and develop a novel spatial statistical method for health and disease mapping that uses these data. We linked residential histories to non-Hodgkin lymphoma (NHL) and colon cancer data collected by the New Jersey State Cancer Registry and performed several case studies to demonstrate our approach. This is a significant contribution because most research on geographic disparities of chronic diseases like cancer is based solely on the patient's residential location at the time of diagnosis. Using only a single residential location introduces uncertainty about neighborhood-based environmental and socioeconomic exposures (e.g., pollutants and poverty). It assumes the person remained at the same residence leading up to his or her diagnosis of cancer and, in turn, experienced uniform neighborhood-based exposures or socioeconomic risk factors over time. We successfully demonstrated the feasibility of moving beyond the use of a single residential location by linking population-based cancer registry data with public record databases to obtain residential histories of the cancer cases while also maintaining confidentiality.
More specifically, using this unique dataset of residential histories, we 1) examined geographic clustering of a subsite of NHL called cutaneous T-cell lymphoma (CTCL), 2) measured geographic variation of patient survival after a colon cancer diagnosis, and 3) developed a new statistical approach called spatially regularized logistic regression, which models overall disease risk as the weighted average of location-specific disease risk measured through residential histories during the latency period leading up to diagnosis. Our results We also developed algorithms, written in python and SAS, to clean and process the residential history data.
Overall, the project has benefited a broad set of disciplines, including public health, geography, and epidemiology. The results from our research show how residential histories can be used to routinely integrate longitudinal epidemiological approaches into population-based cancer research without the need to conduct thousands of expensive and time-consuming interviews to obtain residential histories. The results also demonstrate how integrating residential histories with cancer registry data can improve our understanding of geospatial disparities in cancer outcomes across the cancer control and prevention continuum (cancer development, detection, diagnosis, treatment, mortality, and survivorship). Finally, this research also provides an easily reproducible methodological approach of how other cancer registries and researchers in the United States can integrate residential histories into their research.
To date, this funded research project has resulted in several publications and numerous presentations at professional meetings. In addition, two doctoral students and two undergraduate students were trained by the grant.
Last Modified: 12/13/2020
Modified by: Kevin Henry
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