NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #27

Presenter Information
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First Name: Michael









Middle Initial: {Empty}









Last Name: Leung









Degree(s): PhD









Position/Title/Career Status: Postdoctoral Research Fellow









Organization: Harvard T.H. Chan School of Public Health









Organization Address:
Boston










Email: mleung@hsph.harvard.edu









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Abstract Information
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Abstract Category: Building specific disease cohorts, or visualization techniques









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Abstract Title: Investigating prenatal environmental stressors and pediatric cancer risk using linked birth-cancer registry data in New Jersey









Abstract Summary:
Pediatric cancer is the leading cause of disease-related deaths in children. Its environmental origins are not well understood, but there is emerging evidence that points to the etiologic importance of several environmental exposures, such as air pollution and extreme temperature. However, only a few studies have systematically examined the relationship between prenatal environmental stressors and pediatric cancer, largely due to limitations in linked longitudinal datasets.

Using a novel, individual-level dataset linking birth certificates to the cancer registry in New Jersey, we plan to examine associations between climate-related exposures and pediatric cancer incidence. Our project aims to:
1.	Characterize prenatal exposures to environmental stressors (e.g., air pollution, background radiation etc.) using geocoded residential addresses and publicly available environmental datasets. 
2.	Link these exposures to cancer outcomes (e.g., acute lymphoblastic leukemia) using the linked birth-cancer registry data.
3.	Use flexible statistical models, including distributed lag and spatiotemporal survival models, to evaluate exposure–outcome associations, accounting for key confounders.

Our data is housed at Rutgers University and is protected by a data use agreement, and so it is not publicly available to share. However, we can share a simulated example for the Data Jamboree. We also have not yet assembled a team for the Data Jamboree. If our proposed dataset does not work, I am eager to join a team conducting work on building cohorts using registry data, or the employment of statistical methods used to analyze exposure-outcome associations in pediatric cancer epidemiology.

This project aligns with the goals of the Data Jamboree to encourage the reuse of high-value cancer datasets and foster interdisciplinary collaboration. I bring expertise in epidemiology, environmental health, and big data analysis and am excited to lead or contribute to a team working on pediatric cancer, environmental health and/or big data.











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