RESEARCH ARTICLE
Spatial Association Between Sociodemographic, Environmental Factors and Prevalence of Stroke Among Diabetes and Hypertension Patients in Thailand
Krittiyanee Thammasarn1, Wongsa Loahasiriwong2, *, Roshan Kumar Mahato2, Kittipong Sornlom3
Article Information
Identifiers and Pagination:
Year: 2022Volume: 15
E-location ID: e187494452210191
Publisher ID: e187494452210191
DOI: 10.2174/18749445-v15-e2210191
Article History:
Received Date: 6/7/2022Revision Received Date: 8/9/2022
Acceptance Date: 19/9/2022
Electronic publication date: 02/12/2022
Collection year: 2022
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Stroke is one of the top leading causes of death and disability among adults and the elderly worldwide. Hypertension (HT) and Diabetes Mellitus (DM) are the most common contributory risk factors of stroke, accounting for up to 75% of all cases. This study aimed to investigate the spatial association between sociodemographic and environmental factors and the prevalence of stroke among DM and HT patients in Thailand.
Methods:
This spatial study applied global Moran’s I, the local indicators of spatial association (LISA) and spatial regression to examine the localised associations of sociodemographic and environmental factors and the prevalence of stroke among DM and HT patients in Thailand.
Results:
The univariate Moran’s I scatter plot of the annual prevalence of stroke in Thailand’s provinces observed significant positive spatial autocorrelation with the Moran’s I value of 0.454 (p < 0.05). The High-High clusters of strokes were mostly located in the center. The Bivariate Moran’s I indicated a spatial association between various factors and the prevalence of stroke in which the LISA analysis indicated; 16 Hot-spots or High-High clusters (HH) and 4 Cold-spot or low-low clusters (LL) with alcohol store density, 17 HH and 4 LL clusters with tobacco store density, 9 HH and 9 LL clusters with elderly population density, 5 HH and 3 LL clusters of primary care per population ratio, 16 HH and 3 LL clusters with LST, and 10 HH and 5 LL clusters with NTL. The Spatial Error Model (SEM) of spatial regression analysis has been observed to be the best model that could predict the variation in the prevalence of stroke by 50.80% (R2=0.508). SEM indicated tobacco store density (coefficient=0.065, P<0.05), elderly population density (coefficient=0.013, P<0.001, LST (day) (coefficient=1.417, P<0.05), and NTL (coefficient=0.021, P<0.05) were statistically significant associated with the prevalence of stroke among DM and HT patients in Thailand.
Conclusion:
Our study observed that the distribution of alcohol stores, density of tobacco stores, concentration of older adults, increasing day temperature and density of NTL were likely to be associated with enhancing the prevalence of stroke in the cluster and neighboring provinces of Thailand. The findings of this study will benefit public sectors or related organizations to develop efficient measures to control stroke.
