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Spatial Non-Stationarity Effects of Unhealthy Food Environments and Green Spaces for Type-2 Diabetes in Toronto

Author

Listed:
  • Haoxuan Ge

    (Department of Geography, Geomatics and Environment, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada)

  • Jue Wang

    (Department of Geography, Geomatics and Environment, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
    Department of Geography and Planning, University of Toronto, 100 St. George St., Toronto, ON M5S 3G3, Canada)

Abstract

Environmental factors may operate differently when relations are measured across different geographical locations, a phenomenon known as spatial non-stationarity. This study investigates the spatial non-stationarity effect of unhealthy food environments and green spaces on the T2DM prevalence rate at the neighborhood level in Toronto. This study also compares how the results vary between age groups, classified as all adults (20 and above), young adults (from 20 to 44), middle adulthood (from 45 to 64), and seniors (65 and above). The geographically weighted regression model is utilized to explore the impacts of spatial non-stationarity effects on the research results, which may lead to biased conclusions, which have often been ignored in past studies. The results from this study reveal that environmental variables dissimilarly affect T2DM prevalence rates among different age groups and neighborhoods in Toronto after controlling for socioeconomic factors. For example, the green space density yields positive associations with diabetes prevalence rates for elder generations but negative relationships for younger age groups in twenty-two and four neighborhoods, respectively, around Toronto East. The observed associations will provide beneficial suggestions to support government and public health authorities in designing education, prevention, and intervention programs targeting different neighborhoods to control the burden of diabetes.

Suggested Citation

  • Haoxuan Ge & Jue Wang, 2023. "Spatial Non-Stationarity Effects of Unhealthy Food Environments and Green Spaces for Type-2 Diabetes in Toronto," Sustainability, MDPI, vol. 15(3), pages 1-25, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1762-:d:1038481
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    References listed on IDEAS

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    1. Piccolo, Rebecca S. & Duncan, Dustin T. & Pearce, Neil & McKinlay, John B., 2015. "The role of neighborhood characteristics in racial/ethnic disparities in type 2 diabetes: Results from the Boston Area Community Health (BACH) Survey," Social Science & Medicine, Elsevier, vol. 130(C), pages 79-90.
    2. Salois, Matthew J., 2012. "Obesity and diabetes, the built environment, and the ‘local’ food economy in the United States, 2007," Economics & Human Biology, Elsevier, vol. 10(1), pages 35-42.
    3. Tashi Dendup & Xiaoqi Feng & Stephanie Clingan & Thomas Astell-Burt, 2018. "Environmental Risk Factors for Developing Type 2 Diabetes Mellitus: A Systematic Review," IJERPH, MDPI, vol. 15(1), pages 1-25, January.
    4. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    5. Felipe De la Fuente & María Angélica Saldías & Camila Cubillos & Gabriela Mery & Daniela Carvajal & Martín Bowen & María Paz Bertoglia, 2020. "Green Space Exposure Association with Type 2 Diabetes Mellitus, Physical Activity, and Obesity: A Systematic Review," IJERPH, MDPI, vol. 18(1), pages 1-18, December.
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