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Estimation of the Ecological Fallacy in the Geographical Analysis of the Association of Socio-Economic Deprivation and Cancer Incidence

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  • Katarina Lokar

    (Institute of Oncology Ljubljana, Epidemiology and Cancer Registry, 1000 Ljubljana, Slovenia
    Faculty of Medicine, University of Ljubljana, 1000 Ljubljana, Slovenia)

  • Tina Zagar

    (Institute of Oncology Ljubljana, Epidemiology and Cancer Registry, 1000 Ljubljana, Slovenia)

  • Vesna Zadnik

    (Institute of Oncology Ljubljana, Epidemiology and Cancer Registry, 1000 Ljubljana, Slovenia)

Abstract

Ecological deprivation indices at the level of spatial units are often used to measure and monitor inequalities in health despite the possibility of ecological fallacy. For the purpose of this study, the European Deprivation Index (EDI) was used, which is based on Townsend theorization of relative deprivation. The Slovenian version of EDI (SI-EDI) at the aggregated level (SI-EDI-A) was calculated to the level of the national assembly polling stations. The SI-EDI was also calculated at the individual level (SI-EDI-I) by the method that represents a methodological innovation. The degree of ecological fallacy was estimated with the Receiver Operating Characteristics (ROC) curves. By calculating the area under the ROC curve, the ecological fallacy was evaluated numerically. Agreement between measuring deprivation with SI-EDI-A and SI-EDI-I was analysed by graphical methods and formal testing. The association of the socio-economic status and the cancer risk was analysed in all first cancer cases diagnosed in Slovenia at age 16 and older in the period 2011–2013. Analysis was done for each level separately, for SI-EDI-I and for SI-EDI-A. The Poisson regression model was implemented in both settings but adapted specifically for aggregated and individual data. The study clearly shows that ecological fallacy is unavoidable. However, although the association of cancer incidence and socio-economic deprivation at individual and aggregated levels was not the same for all cancer sites, the results were very similar for the majority of investigated cancer sites and especially for cancers associated with unhealthy lifestyles. The results confirm the assumptions from authors’ previous research that using the level of the national assembly polling stations would be the acceptable way to aggregate data when explaining inequalities in health in Slovenia in ecological studies.

Suggested Citation

  • Katarina Lokar & Tina Zagar & Vesna Zadnik, 2019. "Estimation of the Ecological Fallacy in the Geographical Analysis of the Association of Socio-Economic Deprivation and Cancer Incidence," IJERPH, MDPI, vol. 16(3), pages 1-18, January.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:3:p:296-:d:199898
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    References listed on IDEAS

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    1. Schwartz, S., 1994. "The fallacy of the ecological fallacy: The potential misuse of a concept and the consequences," American Journal of Public Health, American Public Health Association, vol. 84(5), pages 819-824.
    2. Rehkopf, D.H. & Haughton, L.T. & Chen, J.T. & Waterman, P.D. & Subramanian, S.V. & Krieger, N., 2006. "Monitoring socioeconomic disparities in death: Comparing individual-level education and area-based socioeconomic measures," American Journal of Public Health, American Public Health Association, vol. 96(12), pages 2135-2138.
    3. Krieger, N., 1992. "Overcoming the absence of socioeconomic data in medical records: Validation and application of a census-based methodology," American Journal of Public Health, American Public Health Association, vol. 82(5), pages 703-710.
    4. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    5. Marion Albouy-Llaty & Frédérike Limousi & Camille Carles & Antoine Dupuis & Sylvie Rabouan & Virginie Migeot, 2016. "Association between Exposure to Endocrine Disruptors in Drinking Water and Preterm Birth, Taking Neighborhood Deprivation into Account: A Historic Cohort Study," IJERPH, MDPI, vol. 13(8), pages 1-16, August.
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