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DTSTART;TZID=America/Denver:20231112T163000
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UID:submissions.supercomputing.org_SC23_sess430_ws_cafcws106@linklings.com
SUMMARY:Environmental Factors and Lung Cancer:  A Predictive Spatial Appro
 ach
DESCRIPTION:Wenhuan Tan, Xiange Wang, and Silvia Crivelli (Lawrence Berkel
 ey National Laboratory (LBNL)) and Xinlian Liu (National Institutes of Hea
 lth (NIH), National Institute of Child Health and Human Development; Lawre
 nce Berkeley National Laboratory (LBNL))\n\nLung cancer has witnessed a su
 bstantial increase in prevalence over the past few decades. While studies 
 have established that the environment is the primary cause of most lung ca
 ncer cases, the development of lung cancer may be the result of the combin
 ed impact of multiple environmental factors.\n\nOur objective is to invest
 igate the relationship between lung cancer incidence and various physical 
 ambient factors, including climatology, air quality, meteorological condit
 ions, and soil vegetation. To address the issue of missing data on lung ca
 ncer cases at the county level in 2020, we use a Bayesian spatial and temp
 oral modeling approach to mapping geographic variation in lung cancer mort
 ality rates for subnational areas with R-INLA.\n\nOur predictive model is 
 constructed using multiple independent variables obtained from various sat
 ellite sources, covering the period from 1960 to 2016. Climate data such a
 s heatwaves, extreme temperatures are from National Oceanic and Atmospheri
 c Administration (NOAA) and PRISM climate group (PRISM). Air quality indic
 ators such as PM2.5, NO2, and ozone levels are sourced from NASA's Earth D
 ata. Observational meteorological data, encompassing temperature, dew poin
 t, wind direction, wind speed, cloud cover, cloud layers, ceiling height, 
 visibility, current weather, and precipitation amount, are obtained from t
 he EPA's high-resolution gridded dataset. Soil vegetation and cropland dat
 a are acquired from the United States Department of Agriculture (USDA) usi
 ng satellite imagery. Furthermore, we explore additional geophysical data 
 available through the Google Earth Engine platform.\n\nOur predictive mode
 l reveals an increasing positive association between multiple environmenta
 l factors and lung cancer incidence over the years. We apply a linear mode
 l with group fixed effects to 2012-2016 data, assessing lung cancer's rela
 tive risk and generating a 2017-2021 environmental vulnerability map. This
  work highlights AI and integrated data analysis' potential in interpretin
 g and predicting complex health phenomena like lung cancer.\n\nTag: Applic
 ations, State of the Practice\n\nRegistration Category: Workshop Reg Pass\
 n\nSession Chairs: Lynn Borkon (Frederick National Laboratory for Cancer R
 esearch); Sally Ellingson (University of Kentucky); Sean Hanlon (National 
 Institutes of Health (NIH), National Cancer Institute (NCI)); Patricia Kov
 atch (Icahn School of Medicine at Mount Sinai); and Eric Stahlberg (MD And
 erson Cancer Center, University of Texas)\n\n
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