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Grant Details

Grant Number: 5R21CA235153-02 Interpret this number
Primary Investigator: Liu, Bian
Organization: Icahn School Of Medicine At Mount Sinai
Project Title: Exploration of Dynamic Spatiotemporal Exposure Profiles Via Patient Residential and Healthcare Utilization History
Fiscal Year: 2020


PROJECT SUMMARY The places people reside throughout their lives play an important role in their health and in their propensity to develop diseases such as cancer. However, the longitudinal spatiotemporal contexts of where people live are not commonly incorporated into cancer studies. Recent advances in information technology and “big data” and associated analytic approaches have made it possible for cancer registries and researchers to capture residential histories at the population level. We propose to develop a large multi-dimensional database for cancer patients using multiple data sources to reconstruct their longitudinal residential and exposure histories, and to identify potential patient exposure profiles using data mining techniques guided by scientific evidence from the cancer epidemiology and environmental health literature. We will demonstrate the feasibility and identify advantages and challenges of such an approach by using mesothelioma as an example. We hypothesize that there are distinct spatiotemporal environmental exposure trajectories and exposure profiles among mesothelioma patients that can be identified using residential histories. Our specific aims are: Aim 1: Develop an optimal algorithm to streamline the process of compiling, cleaning, verifying, and constructing the residential histories of mesothelioma patients diagnosed between 2011 and 2015 in New York, as reported to the New York State Cancer Registry (NYSCR), utilizing multiple commercial and governmental data sources; Aim 2: Develop an optimal algorithm to streamline the process of compiling, cleaning, verifying, and constructing the exposure history associated with each mesothelioma patient's residential history by leveraging exposure proxies at the individual residence level and area-level information associated with patient's residential addresses, utilizing multiple commercial and governmental data sources; and Aim 3: Visualize the spatiotemporal dynamics of patients' residential and exposure histories, and identify predictors of their exposure profiles, using advanced data mining techniques such as cluster analysis, latent class analysis, and network analysis. The proposal is innovative in both the methods for constructing the database and the analytical methods for uncovering important exposure profiles, such as critical exposure windows, environmental clusters/hotspots, and the relative contributions of exposures across space and time. To our knowledge, no similar database exists at present. The residential data compiled in this project will be permanently stored within the NYSCR to allow future use, the first such example by any cancer registry. The identified exposure phenotypes will contribute to better understanding of the role environmental exposure plays in mesothelioma disease development. The methods developed can be tested, scaled up, replicated by other states, and adopted to other cancers and non-cancer related conditions. This life-course perspective approach holds great potential for advancing cancer research as well as for routine cancer registry surveillance.


Utilizing residential histories to assess environmental exposure and socioeconomic status over the life course among mesothelioma patients.
Authors: Liu B. , Niu L. , Lee F.F. .
Source: Journal of thoracic disease, 2023-11-30; 15(11), p. 6126-6139.
EPub date: 2023-11-07.
PMID: 38090310
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Time trends in cancer and dementia related hospital admissions among Medicare fee-for-service population, 2013-2018.
Authors: Li L. , Zhan S. , Naasan G. , Ornstein K.A. , Taioli E. , Mazumdar M. , Jebakumar J. , McCardle K. , Liu B. .
Source: Journal of geriatric oncology, 2022 Sep; 13(7), p. 1058-1061.
EPub date: 2022-05-03.
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Correlates of cancer prevalence across census tracts in the United States: A Bayesian machine learning approach.
Authors: Niu L. , Hu L. , Li Y. , Liu B. .
Source: Spatial and spatio-temporal epidemiology, 2022 Aug; 42, p. 100522.
EPub date: 2022-05-27.
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Estimating uncertainty in a socioeconomic index derived from the American community survey.
Authors: Boscoe F.P. , Liu B. , Lafantasie J. , Niu L. , Lee F.F. .
Source: SSM - population health, 2022 Jun; 18, p. 101078.
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High ambient temperature and child emergency and hospital visits in New York City.
Authors: Niu L. , Herrera M.T. , Girma B. , Liu B. , Schinasi L. , Clougherty J.E. , Sheffield P.E. .
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Predictors of Survival among Male and Female Patients with Malignant Pleural Mesothelioma: A Random Survival Forest Analysis of Data from the 2000-2017 Surveillance, Epidemiology, and End Results Program.
Authors: Liu B. , Niu L. , Boscoe F. , Lee F.F. .
Source: Journal of registry management, 2021 Fall; 48(3), p. 118-125.
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Internet-Based Health Care Communication Among Cancer Survivors, 2011-2018 National Health Interview Survey.
Authors: Liu B. , Yabroff K.R. , Zheng Z. , Tamler R. , Han X. .
Source: Preventing chronic disease, 2021-09-09; 18, p. E87.
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Trends of hospitalizations among patients with both cancer and dementia diagnoses in New York 2007-2017.
Authors: Liu B. , Ornstein K.A. , Alpert N. , Schwartz R.M. , Dharmarajan K.V. , Kelley A.S. , Taioli E. .
Source: Healthcare (Amsterdam, Netherlands), 2021 Sep; 9(3), p. 100565.
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Examining the role of healthcare access in racial/ethnic disparities in receipt of provider-patient discussions about smoking: A latent class analysis.
Authors: Li L. , Zhan S. , Hu L. , Wilson K.M. , Mazumdar M. , Liu B. .
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The Impact of Dementia on Cancer Treatment Decision-Making, Cancer Treatment, and Mortality: A Mixed Studies Review.
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Source: JNCI cancer spectrum, 2021 Jun; 5(3), .
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A comparison of two neighborhood-level socioeconomic indexes in the United States.
Authors: Boscoe F.P. , Liu B. , Lee F. .
Source: Spatial and spatio-temporal epidemiology, 2021 Jun; 37, p. 100412.
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The Influence of Increasing Levels of Provider-Patient Discussion on Quit Behavior: An Instrumental Variable Analysis of a National Survey.
Authors: Liu B. , Zhan S. , Wilson K.M. , Mazumdar M. , Li L. .
Source: International journal of environmental research and public health, 2021-04-26; 18(9), .
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Ranking sociodemographic, health behavior, prevention, and environmental factors in predicting neighborhood cardiovascular health: A Bayesian machine learning approach.
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Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.
Authors: Hu L. , Liu B. , Ji J. , Li Y. .
Source: Journal of the American Heart Association, 2020-11-17; 9(22), p. e016745.
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Residential mobility among adult cancer survivors in the United States.
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