Introduction: The prevalence of Diabetes Mellitus (DM) is increasing, globally. However, studies on the association between Socioeconomic Status (SES) factors and DM have mostly been conducted in specific areas with rather small sample sizes or not with nationally representative samples. Their results have also been inconclusive regarding whether SES has any influence on DM or not. Aim:To determine the association between SES and DM in Thailand. Materials and Methods:This study utilized the data from the National socioeconomics survey, a cross-sectional study conducted by the National Statistical Office (NSO) in 2010 and 2012. A total of 17,045 and 16,903 participants respectively who met the inclusion criteria were included in this study. The information was collected by face-to-face interview with structured questionnaires. Multilevel mixed-effects logistic regression analysis was performed to determine the potential socioeconomic factors associated with DM. Results:The prevalence of DM was 3.70% (95% CI: 3.36 to 4.05) and 8.11% (95%CI: 6.25 to 9.74) in 2010 and 2012 respectively and the prevalence of DM in 2012 was 1.36 times (95% CI: 1.25 to 1.48) when compared with 2010. The multilevel mixed-effects logistic regression observed that odds of having DM were significantly higher among those who aged 55-64 years old in 2010 and 65 years old or greater in 2012 (OR adj = 18.13; 95%CI: 9.11 to 36.08, OR adj 31.69; 95%CI: 20.78 to 48.33, respectively), females (OR adj = 2.09; 95%CI: 1.66 to 2.62, OR adj = 1.77; 95%CI: 1.54 to 2.05, respectively), and had lower education attainment (OR adj = 5.87; 95%CI: 4.70 to 7.33, OR adj = 1.22; 95%CI: 1.04 to 1.45, respectively) were also found to be associated with DM . Conclusion:The study indicated that SES has been associated with DM. Those with female gender, old age and low educational attainment were vulnerable to DM.

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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Unbalanced regional development has been a persistent concern in Thailand, and satellite imageries can provide alternative data for examining the dynamics of regional development. This study validated the consistency between Defense Meteorological Satellite Program–Operational Linescan System (DMSP‐OLS) nighttime light (NTL) imageries and data from the official nationwide socio‐economic survey. It found that the two sources exhibited a statistically significant correlation in 1994–2013. On the basis of this finding, the NTL index was applied to the computations of the local indicators of spatial association (LISA) and Moran's I statistics. The results for LISA showed that Bangkok and its vicinity were highly concentrated development areas during the study period. However, the obtained Moran's I statistics indicated that the degree of concentration continuously decreased. NTL‐based Gini, Theil, and Atkinson inequality indices were also computed to validate the spatial expansion of regional development. All three NTL‐based indices showed that regional inequality decreased during the study period.

This study aimed to determine the association between socioeconomic determinants and Chronic Respiratory Diseases (CRDs) in Thailand. The data were used from the National Socioeconomics Survey (NSS), a cross-sectional study conducted by the National Statistical Office (NSO), in 2010 and 2012. The survey used stratified two-stage sampling to select a nationally representative sample to respond to a structured questionnaire. A total of 17,040 and 16,905 individuals in 2010 and 2012, respectively, were included in this analysis. Multiple logistic regressions were used to identify the association between socioeconomic factors while controlling for other covariates. The prevalence of CRDs was 3.81% and 2.79% in 2010 and 2012, respectively. The bivariate analysis indicated that gender, family size, geographic location, fuels used for cooking and smoking were significantly associated with CRDs in 2010, whereas education, family size, occupation, region, geographic location, and smoking were significantly associated with CRDs in 2012. Both in 2010 and 2012, the multiple logistic regression indicated that the odds of having CRDs were significantly higher among those who lived in urban areas, females, those aged ≥41-50 or ≥61 yr old, and smokers when controlling for other covariates. However, fuels used for cooking, wood and gas, are associated with CRDs in 2010.