Spatial integration framework of solar, wind, and hydropower energy potential in Southeast Asia.

Amid its massive increase in energy demand, Southeast Asia has pledged to increase its use of renewable energy by up to 23% by 2025. Geospatial technology approaches that integrate statistical data, spatial models, earth observation satellite data, and climate modeling can be used to conduct strategic analyses for understanding the potential and efficiency of renewable energy development. This study aims to create the first spatial model of its kind in Southeast Asia to develop multi-renewable energy from solar, wind, and hydropower, further broken down into residential and agricultural areas. The novelty of this study is the development of a new priority model for renewable energy development resulting from the integration of area suitability analysis and the estimation of the amount of potential energy. Areas with high potential power estimations for the combination of the three types of energy are mostly located in northern Southeast Asia. Areas close to the equator, have a lower potential than the northern countries, except for southern regions. Solar photovoltaic (PV) plant construction is the most area-intensive type of energy generation among the considered energy sources, requiring 143,901,600 ha (61.71%), followed by wind (39,618,300 ha; 16.98%); a combination of solar PV and wind (37,302,500 ha; 16%); hydro (7,665,200 ha; 3.28%); a combination of hydro and solar PV (3,792,500 ha; 1.62%); and a combination of hydro and wind (582,700 ha; 0.25%). This study is timely and important because it will inform policies and regional strategies for transitioning to renewable energy, with consideration of the different characteristics present in Southeast Asia.

Spatial association of socio-demographic, environmental factors and prevalence of diabetes mellitus in middle-aged and elderly people in Thailand.

The burden of diabetes mellitus (DM), one of the major noncommunicable diseases (NCDs), has been significantly rising globally. In the Asia-Pacific region, Thailand ranks within the top ten of diabetic patient populations and the disease has increased from 2.3% in 1991 to 8.0% in 2015. This study applied local indicators of spatial association (LISA) and spatial regression to examine the local associations in Thailand with night-time light, spatial density of alcohol/convenience stores, concentration of elderly population and prevalence of DM among middle-aged and elderly people. Univariate LISA identified the statistically significant cluster of DM prevalence in the upper north-eastern region. For multivariate spatial analysis, the obtained R2 values of the spatial lag model (SLM) and spatial error model (SEM) were 0.310 and 0.316, respectively. These two models indicated a statistical significant association of several sociodemographic and environmental characteristics with the DM prevalence: food shops (SLM coefficient = 9.625, p<0.001; SEM coefficient = 9.695, p<0.001), alcohol stores (SLM coefficient = 1.936, p<0.05; SEM coefficient = 1.894, p<0.05), population density of elderly people (SLM coefficient = 0.156, p<0.05; SEM coefficient = 0.188, p<0.05) and night-time light density (SLM coefficient = -0.437, p<0.001; SEM coefficient = -0.437, p<0.001). These findings are useful for policymakers and public health professionals in formulating measures aimed at reducing DM burden in the country.

Spatial association between COVID-19 incidence rate and nighttime light index.

This study statistically identified the localised association between socioeconomic conditions and the coronavirus disease 2019 (COVID-19) incidence rate in Thailand on the basis of the 1,727,336 confirmed cases reported nationwide during the first major wave of the pandemic (March-May 2020) and the second one (July 2021-September 2021). The nighttime light (NTL) index, formulated using satellite imagery, was used as a provincial proxy of monthly socioeconomic conditions. Local indicators of spatial association statistics were applied to identify the localised bivariate association between COVID-19 incidence rate and the year-on-year change of NTL index. A statistically significant negative association was observed between the COVID-19 incidence rate and the NTL index in some central and southern provinces in both major pandemic waves. Regression analyses were also conducted using the spatial lag model (SLM) and the spatial error model (SEM). The obtained slope coefficient, for both major waves of the pandemic, revealed a statistically significant negative association between the year-on-year change of NTL index and COVID-19 incidence rate (SLM: coefficient= âˆ'0.0078 and âˆ'0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= âˆ'0.0086 and âˆ'0.0083 with P=0.067 and 0.056, respectively). All of the obtained results confirmed the negative association between the COVID-19 pandemic and socioeconomic activity revealing the future extensive applications of satellite imagery as an alternative data source for the timely monitoring of the multidimensional impacts of the pandemic.

Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015.

This study statistically identified the association of remotely sensed environmental factors, such as Land Surface Temperature (LST), Night Time Light (NTL), rainfall, the Normalised Difference Vegetation Index (NDVI) and elevation with the incidence of leptospirosis in Thailand based on the nationwide 7,495 confirmed cases reported during 2013-2015. This work also established prediction models based on empirical findings. Panel regression models with random-effect and fixed-effect specifications were used to investigate the association between the remotely sensed environmental factors and the leptospirosis incidence. The Local Indicators of Spatial Association (LISA) statistics were also applied to detect the spatial patterns of leptospirosis and similar results were found (the R2 values of the random-effect and fixed-effect models were 0.3686 and 0.3684, respectively). The outcome thus indicates that remotely sensed environmental factors possess statistically significant contribution in predicting this disease. The highest association in 3 years was observed in LST (random- effect coefficient = -9.787, P<0.001; fixed-effect coefficient = -10.340, P=0.005) followed by rainfall (random-effect coefficient = 1.353, P<0.001; fixed-effect coefficient = 1.347, P<0.001) and NTL density (random-effect coefficient = -0.569, P=0.004; fixed-effect coefficient = -0.564, P=0.001). All results obtained from the bivariate LISA statistics indicated the localised associations between remotely sensed environmental factors and the incidence of leptospirosis. Particularly, LISA's results showed that the border provinces in the northeast, the northern and the southern regions displayed clusters of high leptospirosis incidence. All obtained outcomes thus show that remotely sensed environmental factors can be applied to panel regression models for incidence prediction, and these indicators can also identify the spatial concentration of leptospirosis in Thailand.

Spatial and statistical analysis of leptospirosis in Thailand from 2013 to 2015.

This study analyzes the temporal pattern and spatial clustering of leptospirosis, a disease recognized as an emerging public health problem in Thailand. The majority of those infected are farmers and fishermen. Severe epidemics of leptospirosis in association with the rainy reason have occurred since 1996. Still, an understanding of the annual variation and spatial clustering of the disease is lacking. Data were collected from the Center of Epidemiological Information, Bureau of Epidemiology, Ministry of Public Health, covering the nationwide incidence of leptospirosis during the period 2013-2015. Clustering techniques, including local indicators of spatial association and local Getis-Ord Gi* statistic, were used for the analysis and evaluation of the annual spatial distribution of the disease. Both these statistics revealed similar results for the areas with the highest clustering patterns of leptospirosis. Specifically, there were persisting hotspots in north-eastern and southern parts of Thailand over the three years covered by the study. This outcome suggests that healthcare resources should be allocated to the areas characterized by leptospirosis clustering.

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

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.

Socioeconomic disparities and chronic respiratory diseases in Thailand: The National Socioeconomics Survey.

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.

Association between Socioeconomic Status and Diabetes Mellitus: The National Socioeconomics Survey, 2010 and 2012.

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 (ORadj = 18.13; 95%CI: 9.11 to 36.08, ORadj 31.69; 95%CI: 20.78 to 48.33, respectively), females (ORadj = 2.09; 95%CI: 1.66 to 2.62, ORadj = 1.77; 95%CI: 1.54 to 2.05, respectively), and had lower education attainment (ORadj = 5.87; 95%CI: 4.70 to 7.33, ORadj= 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.

A comparison of spatial heterogeneity with local cluster detection methods for chronic respiratory diseases in Thailand.

BACKGROUND: The Centers for Disease Control and Prevention reported that deaths from chronic respiratory diseases (CRDs) in Thailand increased by almost 13% in 2010, along with an increased burden related to the disease. Evaluating the geographical heterogeneity of CRDs is important for surveillance. Previous studies have indicated that socioeconomic status has an effect on disease, and that this can be measured with variables such as night-time lights (NTLs) and industrial density (ID). However, there is no understanding of how NTLs and ID correlate with CRDs. We compared spatial heterogeneity obtained by using local cluster detection methods for CRDs and by correlating NTLs and ID with CRDs. METHODS: We applied the spatial scan statistic in SaTScan, as well as local indices of spatial association (LISA), Getis and Ord's local Gi*(d) statistic, and Pearson correlation. In our analysis, data were collected on gender, age, household income, education, family size, occupation, region, residential area, housing construction materials, cooking fuels, smoking status and previously diagnosed CRDs by a physician from the National Socioeconomic Survey, which is a cross-sectional study conducted by the National Statistical Office of Thailand in 2010. RESULTS: According to our findings, the spatial scan statistic, LISA, and the local Gi*(d) statistic revealed similar results for areas with the highest clustering of CRDs. However, the hotspots for the spatial scan statistic covered a wider area than LISA and the local Gi*(d) statistic. In addition, there were persistent hotspots in Bangkok and the perimeter provinces. NTLs and ID have a positive correlation with CRDs. CONCLUSIONS: This study demonstrates that all the statistical methods used could detect spatial heterogeneity of CRDs. NTLs and ID can serve as new parameters for determining disease hotspots by representing the population and industrial boom that typically contributes to epidemics.

Socioeconomic disparities in income, education and geographic location for hypertension among Thai adults: Results from the National Socioeconomic Survey.

Background: Hypertension (HT) has been one of the leading global risk factors for health and the leading cause of death in Thailand for decades. The influence of socioeconomic factors on HT has been varied and inconclusive. The aim of this study was to determine the association between socioeconomic determinants and HT in Thailand. Methods: This study used data from the National Socioeconomic Survey, a cross-sectional study that was conducted by the National Statistical Office of Thailand in the years 2005, 2006 and 2007. In our analysis, data were collected on gender, age, marital status, smoking status, education, status of work, occupation, current liability (short-term debt), household monthly income, residential area, region and previously diagnosed HT by a physician. Results: The odds of having HT were significantly higher among those who had household monthly income, education, residential area and region. The participants who had monthly income of <10001 baht (2005: AOR = 3.19, 95%CI:1.47 - 6.92; 2006: AOR 2.53, 95%CI:1.37 - 4.69; 2007: AOR = 3.35, 95%CI: 1.97 - 7.00), were living in Bangkok compared with the Northeast region (2005: AOR = 1.72, 95%CI:1.37 - 2.17; 2006: AOR =  2.44, 95%CI: 1.89 - 3.13; 2007: AOR =  2.63, 95%CI 2.08 - 3.45), lived as an urban resident (2005: AOR= 1.32, 95%CI: 1.12 - 1.56; 2006: AOR= 1.21, 95%CI: 1.02 - 1.43; 2007: AOR= 1.47, 95%CI: 1.18 - 1.62), and finished primary education (2005: AOR =1.21, 95%CI: 1.03 - 1.43; 2006: AOR= 1.23, 95%CI: 1.04 - 1.46; 2007: AOR= 1.18, 95%CI: 1.01 - 1.38) when controlling for other covariates. Conclusion: This study indicated that socioeconomic disparity has an influence on HT. Those with low educational attainment, low income, lived in urban regions, and were metropolitan residents (Bangkok) were vulnerable to HT.