Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis

  • Wongsa Laohasiriwong | drwongsa@gmail.com Faculty of Public Health and Research and Training Center for Enhancing Quality of Life for Working Age People, Khon Kaen University, Khon Kaen, Thailand.
  • Nattapong Puttanapong Faculty of Economics, Thammasat University, Bangkok, Thailand.
  • Atthawit Singsalasang Faculty of Public Health, Khon Kaen University, Khon Kaen, Thailand.

Abstract

Spatial pattern detection can be a useful tool for understanding the geographical distribution of hypertension (HT). The aim of this study was to apply the technique of local indicators of spatial association statistics to examine the spatial patterns of HT in the 76 provinces of Thailand. Previous studies have demonstrated that socioeconomic status (SES), economic growth, population density and urbanization have effects on the occurrence of disease. Research has suggested that night-time light (NTL) can be used as a proxy for a number of variables, including urbanization, density, economic growth and SES. To date, there has not been any study on spatial patterns of HT and there is no information on how NTL might correlate with HT. Therefore, this study has investigated NTL as a parameter for detection of hotspots of HT in Thailand. It was found that HT clusters occurred in Bangkok and in metropolitan areas. In addition, significantly low-rate clusters were seen in some provinces in the Northeast and also in southern provinces. These findings should facilitate control and prevention of HT and, therefore, serve as support for researchers, decision-makers, academics and public health officials to propose more sound and effective strategies for the control of HT in Thailand and elsewhere.

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Published
2018-05-07
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Original Articles
Keywords:
Hypertension, Local indices of spatial association, Nighttime light, Thailand.
Statistics
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How to Cite
Laohasiriwong, W., Puttanapong, N., & Singsalasang, A. (2018). Prevalence of hypertension in Thailand: Hotspot clustering detected by spatial analysis. Geospatial Health, 13(1). https://doi.org/10.4081/gh.2018.608