The effect of socioeconomic and environmental factors on obesity: A spatial regression analysis
The effect of socioeconomic and environmental factors on obesity: A spatial regression analysis
This paper examined the effect of socio-economic and environmental factors on obesity in Cleveland (Ohio) using an OLS model and three spatial regression models: spatial error model, spatial lag model, and a spatial error model with a spatially lagged response (SEMSLR). Comparative assessment of the models showed that the SEMSLR and the spatial error models were the best models. The spatial effect from the various spatial regression models was statistically significant, indicating an essential spatial interaction among neighboring geographic units and the need to account for spatial dependency in obesity research. The authors also found a statistically significant positive association between the percentage of families below poverty, Black population, and SNAP recipient with obesity rate. The percentage of college-educated had a statistically significant negative association with the obesity rate. The study shows that health outcomes such as obesity are not randomly distributed but are more clustered in deprived and marginalized neighborhoods.
Yankey, O.
9965d053-8afb-462f-b7fe-b270e21f2ec1
Amegbor, P.M.
916b864c-e3f5-4ee1-8c0c-d0ab7866d326
Essah, M.
b57f4c3a-70c8-4343-9a42-5315162bdb4b
2021
Yankey, O.
9965d053-8afb-462f-b7fe-b270e21f2ec1
Amegbor, P.M.
916b864c-e3f5-4ee1-8c0c-d0ab7866d326
Essah, M.
b57f4c3a-70c8-4343-9a42-5315162bdb4b
Yankey, O., Amegbor, P.M. and Essah, M.
(2021)
The effect of socioeconomic and environmental factors on obesity: A spatial regression analysis.
International Journal of Applied Geospatial Research, 12 (4).
(doi:10.4018/IJAGR.2021100104).
Abstract
This paper examined the effect of socio-economic and environmental factors on obesity in Cleveland (Ohio) using an OLS model and three spatial regression models: spatial error model, spatial lag model, and a spatial error model with a spatially lagged response (SEMSLR). Comparative assessment of the models showed that the SEMSLR and the spatial error models were the best models. The spatial effect from the various spatial regression models was statistically significant, indicating an essential spatial interaction among neighboring geographic units and the need to account for spatial dependency in obesity research. The authors also found a statistically significant positive association between the percentage of families below poverty, Black population, and SNAP recipient with obesity rate. The percentage of college-educated had a statistically significant negative association with the obesity rate. The study shows that health outcomes such as obesity are not randomly distributed but are more clustered in deprived and marginalized neighborhoods.
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Published date: 2021
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Local EPrints ID: 468372
URI: http://eprints.soton.ac.uk/id/eprint/468372
ISSN: 1947-9654
PURE UUID: 323bc0af-d0da-4170-99f9-098c5016555a
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Date deposited: 11 Aug 2022 16:52
Last modified: 17 Mar 2024 04:11
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O. Yankey
Author:
P.M. Amegbor
Author:
M. Essah
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