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.

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.

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.

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.

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= −0.0078 and −0.0064 with P<0.001 and 0.056, respectively; and SEM: coefficient= −0.0086 and −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.