Using GIS to link SEER-Medicare and California pesticide data: a population-based case-control study of pesticide exposure and hepatocellular carcinoma risk

Geographic information systems (GIS), used to analyze spatial data, represent a powerful method to study human health. This research demonstrates the usage of GIS in (1) designing a pesticide exposure metric and (2) linking population-based data sources to conduct an epidemiologic study examining the association between pesticide exposure and hepatocellular carcinoma (HCC). The first study presents a new GIS method to estimate individual-level agricultural pesticide exposure in California. Landsat remotely sensed satellite images were classified into crop fields and matched to California Pesticide Use Report (PUR) agricultural pesticide application data. Pesticide exposure was calculated using pesticide-treated crop fields intersecting a 500-meter buffer around geocoded locations. Compared to the standard GIS method of matching PUR data to infrequently updated crop land use surveys (LUS’s), our method was able to match significantly more PUR temporary crop pesticide applications to Landsat vs. LUS crops (65.4% vs. 52.4%; n=2,466; McNemar’s p<0.0001). The second study explored different ways of scaling up Public Land Survey System (PLSS) section pesticide data, the geographic level of reporting for PURs, to the ZIP Code level. We observed substantial agreement between area-weighted ZIP Code pesticide application rates and gold standard census block rates in rural areas (weighted kappa 0.63; 95% confidence interval [CI] 0.57, 0.69). Area weighting was used to estimate pesticide exposure in the third study. The third (and primary) study was a population-based case-control study examining the association between agricultural pesticide exposure and hepatocellular carcinoma in California via implementing a novel data linkage between Surveillance, Epidemiology, and End Results (SEER)-Medicare and PURs using Medicare ZIP Codes in a GIS. Among rural California residents, previous annual ZIP Code exposure to over 0.06 applied organochlorine pounds per acre significantly increased the risk of developing HCC after adjusting for liver disease and diabetes (odds ratio 1.52; 95% CI 1.02, 2.28; p=0.0415). This is the first epidemiologic study using GIS to examine pesticide exposure and HCC. The public health significance of this research is related to using epidemiologic, GIS, and biostatistical methods to form a better understanding of pesticides as a potential risk factor for HCC, which is increasing in incidence

Ambient ultraviolet radiation exposure and hepatocellular carcinoma incidence in the United States

Background: Hepatocellular carcinoma (HCC), the most commonly occurring type of primary liver cancer, has been increasing in incidence worldwide. Vitamin D, acquired from sunlight exposure, diet, and dietary supplements, has been hypothesized to impact hepatocarcinogenesis. However, previous epidemiologic studies examining the associations between dietary and serum vitamin D reported mixed results. The purpose of this study was to examine the association between ambient ultraviolet (UV) radiation exposure and HCC risk in the U.S. Methods: The Surveillance, Epidemiology, and End Results (SEER) database provided information on HCC cases diagnosed between 2000 and 2014 from 16 population-based cancer registries across the U.S. Ambient UV exposure was estimated by linking the SEER county with a spatiotemporal UV exposure model using a geographic information system. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between ambient UV exposure per interquartile range (IQR) increase (32.4 mW/m2) and HCC risk adjusting for age at diagnosis, sex, race, year of diagnosis, SEER registry, and county-level information on prevalence of health conditions, lifestyle, socioeconomic, and environmental factors. Results: Higher levels of ambient UV exposure were associated with statistically significant lower HCC risk (n = 56,245 cases; adjusted IRR per IQR increase: 0.83, 95% CI 0.77, 0.90; p<0.01). A statistically significant inverse association between ambient UV and HCC risk was observed among males (p for interaction = 0.01) and whites (p for interaction = 0.01). Conclusions: Higher ambient UV exposure was associated with a decreased risk of HCC in the U.S. UV exposure may be a potential modifiable risk factor for HCC that should be explored in future research

Sociodemographic Patterns of Exposure to Civil Aircraft Noise in the United States

13-C-AJFF-BU-016This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license https://creativecommons.org/licenses/by/4.0/. Please cite this article as: Matthew C. Simon, Jaime E. Hart, Jonathan I. Levy, Trang VoPham, Andrew Malwitz, Daniel Nguyen, Matthew Bozigar, L. Adrienne Cupples, Peter James, Francine Laden, and Junenette L. Peters. 2022. Sociodemographic Patterns of Exposure to Civil Aircraft Noise in the United States Environmental Health Perspectives. 130:2 CID: 027009 https://doi.org/10.1289/EHP9307Background: Communities with lower socioeconomic status and higher prevalence of racial/ethnic minority populations are often more exposed to environmental pollutants. Although studies have shown associations between aircraft noise and property values and various health outcomes, little is known about how aircraft noise exposures are sociodemographically patterned. Objective: Our aim was to describe characteristics of populations exposed to aviation noise by race/ethnicity, education, and income in the United States

An overview of GeoAI applications in health and healthcare

Abstract The moulding together of artificial intelligence (AI) and the geographic/geographic information systems (GIS) dimension creates GeoAI. There is an emerging role for GeoAI in health and healthcare, as location is an integral part of both population and individual health. This article provides an overview of GeoAI technologies (methods, tools and software), and their current and potential applications in several disciplines within public health, precision medicine, and Internet of Things-powered smart healthy cities. The potential challenges currently facing GeoAI research and applications in health and healthcare are also briefly discussed

Food Environments and Hepatocellular Carcinoma Incidence

Research into the potential impact of the food environment on liver cancer incidence has been limited, though there is evidence showing that specific foods and nutrients may be potential risk or preventive factors. Data on hepatocellular carcinoma (HCC) cases were obtained from the Surveillance, Epidemiology, and End Results (SEER) cancer registries. The county-level food environment was assessed using the Modified Retail Food Environment Index (mRFEI), a continuous score that measures the number of healthy and less healthy food retailers within counties. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between mRFEI scores and HCC risk, adjusting for individual- and county-level factors. The county-level food environment was not associated with HCC risk after adjustment for individual-level age at diagnosis, sex, race/ethnicity, year, and SEER registry and county-level measures for health conditions, lifestyle factors, and socioeconomic status (adjusted IRR: 0.99, 95% CI: 0.96, 1.01). The county-level food environment, measured using mRFEI scores, was not associated with HCC risk

Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology

Abstract Geospatial artificial intelligence (geoAI) is an emerging scientific discipline that combines innovations in spatial science, artificial intelligence methods in machine learning (e.g., deep learning), data mining, and high-performance computing to extract knowledge from spatial big data. In environmental epidemiology, exposure modeling is a commonly used approach to conduct exposure assessment to determine the distribution of exposures in study populations. geoAI technologies provide important advantages for exposure modeling in environmental epidemiology, including the ability to incorporate large amounts of big spatial and temporal data in a variety of formats; computational efficiency; flexibility in algorithms and workflows to accommodate relevant characteristics of spatial (environmental) processes including spatial nonstationarity; and scalability to model other environmental exposures across different geographic areas. The objectives of this commentary are to provide an overview of key concepts surrounding the evolving and interdisciplinary field of geoAI including spatial data science, machine learning, deep learning, and data mining; recent geoAI applications in research; and potential future directions for geoAI in environmental epidemiology

Social Distancing Associations with COVID-19 Infection and Mortality Are Modified by Crowding and Socioeconomic Status

The SARS-CoV-2 virus is a public health emergency. Social distancing is a key approach to slowing disease transmission. However, more evidence is needed on its efficacy, and little is known on the types of areas where it is more or less effective. We obtained county-level data on COVID-19 incidence and mortality during the first wave, smartphone-based average social distancing (0–5, where higher numbers indicate more social distancing), and census data on demographics and socioeconomic status. Using generalized linear mixed models with a Poisson distribution, we modeled associations between social distancing and COVID-19 incidence and mortality, and multiplicative interaction terms to assess effect modification. In multivariable models, each unit increase in social distancing was associated with a 26% decrease (p &lt; 0.0001) in COVID-19 incidence and a 31% decrease (p &lt; 0.0001) in COVID-19 mortality. Percent crowding, minority population, and median household income were all statistically significant effect modifiers. County-level increases in social distancing led to reductions in COVID-19 incidence and mortality but were most effective in counties with lower percentages of black residents, higher median household incomes, and with lower levels of household crowding