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Studying the association between air pollution and lung cancer incidence in a large metropolitan area using a kernel density function

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  • Portnov, Boris A.
  • Dubnov, Jonathan
  • Barchana, Micha

Abstract

In the absence of patient-specific data, composite level data are often used in epidemiological studies. However, since individual exposure levels cannot accurately be inferred from aggregate data, such an approach may lead to erroneous estimates of health effects of potential environmental risk factors. In the present study, we attempt to address this information-loss problem by using the "kernel density function", which estimates the intensity of events across a surface, by calculating the overall number of cases situated within a given search radius from a target point. The present paper illustrates the use of this analytical technique for a study of association between the geographical distributions of lung cancer cases and SO2 air pollution estimates in the Greater Haifa Metropolitan Area (GHMA). In the analysis, the results obtained by kernel smoothing are contrasted with those obtained by areal aggregation techniques more commonly used in empirical studies.

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  • Portnov, Boris A. & Dubnov, Jonathan & Barchana, Micha, 2009. "Studying the association between air pollution and lung cancer incidence in a large metropolitan area using a kernel density function," Socio-Economic Planning Sciences, Elsevier, vol. 43(3), pages 141-150, September.
    • Handle: RePEc:eee:soceps:v:43:y:2009:i:3:p:141-150
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    References listed on IDEAS

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    1. מירי מונק, 2005. "העצמת נשים בדואיות בנגב," Working Papers 97, National Insurance Institute of Israel.
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    1. Wu, Xianhua & Tian, Zhiqing & Kuai, Yun & Song, Shunfeng & Marson, Stephen M., 2022. "Study on spatial correlation of air pollution and control effect of development plan for the city cluster in the Yangtze River Delta," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    2. Daniel Felsenstein & Eilat Elbaum & Tsafrir Levi & Ran Calvo, 2021. "Post-processing HAZUS earthquake damage and loss assessments for individual buildings," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 21-45, January.
    3. Hajiseyedjavadi, Seyedsaeed & Karimi, Hassan A. & Blackhurst, Michael, 2022. "Predicting lead water service lateral locations: Geospatial data science in support of municipal programming," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    4. Chinmay Mungi & Dejian Lai & Xianglin L. Du, 2019. "Spatial Analysis of Industrial Benzene Emissions and Cancer Incidence Rates in Texas," IJERPH, MDPI, vol. 16(15), pages 1-13, July.
    5. Marina Zusman & Dani Broitman & Boris A. Portnov, 2016. "Application of the double kernel density approach to the multivariate analysis of attributeless event point datasets," Letters in Spatial and Resource Sciences, Springer, vol. 9(3), pages 363-382, October.
    6. Yizhou Wu & Peilei Fan & Heyuan You, 2018. "Spatial Evolution of Producer Service Sectors and Its Influencing Factors in Cities: A Case Study of Hangzhou, China," Sustainability, MDPI, vol. 10(4), pages 1-23, March.

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    Keywords

    Kernel density Air pollution Cancer;

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