1. Incorporating Residential Histories into Space-Time Models for Health Geographic Analysis
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.
This project was funded by the National Science Foundation award #1560888
Goals of the project:
Objective 1. Develop and test a new statistical approach that incorporates mobility data using the publically available simulated EpiSims dataset.
Objective 2. Obtain cancer data from the New Jersey State Cancer Registry and Rutgers University as part of a subcontract on this NSF project.
Objective 3. Modify, test, and validate the new statistical approach using both cancer data and birth data that were linked to residential history data and associated exposures at those residential locations.
Publications: To date, we have published two papers that demonstrate the integration of residential histories with cancer registry data. Our work is among the first to link residential histories with statewide cancer registry data in the United States.
Our project has demonstrated the feasibility of integrating residential histories into a large population-based cohort of incident cancer cases by linking cancer registry data with public record databases while also maintaining confidentiality.
Below are links to our two main publications and titles of papers submitted for publication or ongoing.
| Wiese D*, Stroup AM, Maiti A, Harris G, Lynch SM, Vucetic S, Henry KA. Residential Mobility and Geospatial Disparities in Colon Cancer Survival [published online ahead of print, 2020 Aug 5]. Cancer Epidemiol Biomarkers Prev. 2020;10.1158/1055-9965.EPI-20-0772. doi:10.1158/1055-9965.EPI-20-0772
Wiese D*, Stroup AM, Maiti A, Harris G, Lynch SM, Vucetic S, Henry KA. Socioeconomic Disparities in Colon Cancer Survival: Revisiting Neighborhood Poverty Using Residential Histories. Epidemiology. 2020 Sep;31(5):728-735. doi: 10.1097/EDE.0000000000001216. PMID: 32459665
Wiese D*, Stroup AM, Maiti A, Harris G, Lynch SM, Vucetic S, Henry KA. Examining Socio-Spatial Mobility in Colon Cancer Patients Henry KA, Wiese D, Stroup AM, Maiti A, Harris G, Lynch SM, Vucetic S, Stroup AM. Incorporating residential histories to study geographic clustering of CTCL in New Jersey
Maiti A*, Wiese D, Stroup AM, Maiti A, Harris G, Lynch SM, Vucetic S, Henry KA. Modeling spatial risks using residential data using Spatially Regularized Logistic Regression |
*doctoral student
Python code for processing residential histories data (will be posted)