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Publication
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| Title of Article |
Spatial association between sociodemographic, environmental factors and prevalence of stroke among diabetes and hypertension patients in Thailand |
| Date of Acceptance |
19 October 2022 |
| Journal |
| Title of Journal |
The Open Public Health Journal |
| Standard |
SCOPUS |
| Institute of Journal |
Bentham Open |
| ISBN/ISSN |
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| Volume |
2022 |
| Issue |
15 |
| Month |
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| Year of Publication |
2022 |
| Page |
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| 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. There has been a hypothesis about whether sociodemographic and environmental factors could play a role in influencing stroke. The aim of this study was to investigates the spatial association between sociodemographic, environmental factors and the prevalence of stroke among diabetes and hypertension 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, environmental factors and prevalence of stoke among diabetes and hypertension 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 followed by southern part of the Northeast regions. The Bivariate Moran’s I indicated a spatial association between various factors and 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). In addition, the AIC of the SEM was slightly lower than that of the SLM (157.71 versus 161.92, respectively). SEM indicated that sociodemographic and environmental characteristics including: 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 prevalence of stroke in DM and HTN patients has been continuously rising in Thailand. Spatial autocorrections on prevalence of stoke had been significantly clustered in several provinces of Thailand. Distribution of alcohol store, density of tobacco store, concentration of elder people, increasing day temperature and density of NTL were likely to be associated with enhancing prevalence of stroke in the cluster and neighboring provinces of Thailand. The findings of this study will benefit public sectors or related organizations, policymakers, medical professionals and strategy developers to develop efficient measures to control stroke among diabetic and hyperresponsive patients in the country. |
| Keyword |
Stroke, Spatial analysis, Socioeconomic and environmental factors, Diabetes, Hypertension |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ได้รับการตอบรับให้ตีพิมพ์ |
| Level of Publication |
นานาชาติ |
| citation |
true |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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