Under-5 mortality rate (U5MR) is a key indicator of child health and overall development. In Thailand, despite significant steps made in child health, disparities in U5MR persist across different provinces. We examined various socio-economic variables, health service availability and environmental factors impacting U5MR in Thailand to model their influences through spatial analysis. Global and Local Moran’s I statistics for spatial autocorrelation of U5MR and its related factors were used on secondary data from the Ministry of Public Health, National Centers for Environmental Information, National Statistical Office, and the Office of the National Economic and Social Development Council in Thailand. The relationships between U5MR and these factors were modelled using ordinary least squares (OLS) estimation, spatial lag model (SLM) and spatial error model (SEM). There were significant spatial disparities in U5MR across Thailand. Factors such as low birth weight, unemployment rate, and proportion of land use for agricultural purposes exhibited significant positive spatial autocorrelation, directly influencing U5MR, while average years of education, community organizations, number of beds for inpatients per 1,000 population, and exclusive breastfeeding practices acted as protective factors against U5MR (R2 of SEM = 0.588).The findings underscore the need for comprehensive, multi-sectoral strategies to address the U5MR disparities in Thailand. Policy interventions should consider improving socioeconomic conditions, healthcare quality, health accessibility, and environmental health in high U5M areas. Overall, this study provides valuable insights into the spatial distribution of U5MR and its associated factors, which highlights the need for tailored and localized health policies and interventions.

Background: Dengue fever, a mosquito-borne viral infection caused by the dengue virus, has become a significant global public health concern, especially in tropical and subtropical regions. Nepal, with its diverse geography and climate, has witnessed a rapid escalation in dengue cases in recent years, with the highest number of cases and fatalities reported in 2022. Objectives: This study analyzed the spatial distribution of dengue in Nepal from 2020 to 2023, using Moran’s I spatial statistics to explore the relationship between environmental factors (such as vegetation indices, land surface temperature and precipitation) and dengue incidence. Methods: By utilizing Geographic Information System (GIS) and spatial analysis techniques, the study seeks to identify high-incidence clusters and examine environmental factors contributing to the spread of dengue. Results: This study examined dengue incidence in Nepal from 2020 to 2023, uncovering significant variations in disease patterns and their environmental correlations. Dengue cases peaked in 2022 (Moran’s I; 0.634, P-value; 0.001) before declining in 2023 (Moran’s I; 0.144, P-value; 0.036), likely due to targeted public health interventions. Spatial analysis revealed no significant patterns in 2020 (Moran’s I; −0.004, P-value; 0.288) and 2021 (Moran’s I; 0.006, P-value; 0.186), however, a focused spatial distribution emerged in 2022 and 2023. Environmental factors showed evolving relationships with dengue transmission: NDVI and LST showed negative correlations in 2020–2021, while NDWI and precipitation shifted from negative to positive correlations over the study period. Conclusion: The findings showed significant spatial clustering of dengue cases in urban areas with correlations between higher precipitation and increased dengue incidence. These results highlighted the importance of adaptive public health strategies that account for environmental factors.

Objectives: To identify the patterns of tuberculosis (TB) notification rates and examine their relationship with social and economic determinants in Nepal between 2020 and 2023. Design: Cross-sectional study. Setting: Nepal. Participants: All TB cases across all ages. Primary outcome: Prevalence of TB cases. Methods: This cross-sectional spatial analysis used the data set of the National Tuberculosis Control Centre, Nepal, covering the Fiscal Year (FY) 2020–2021 to 2022–2023. Moran’s I and Local Indicators of Spatial Association were employed to detect the spatial autocorrelation between the prevalence of TB and associated social and demographic factors. Results: The overall prevalence rate for TB in FY 2020–2021 was 98.08 per 100 000 population. This increased to 129.82 per 100 000 population in FY 2021–2022, followed by a slight decrease to 128.39 per 100 000 population in FY 2022–2023. The highest TB prevalence was observed in Kathmandu, with 146 cases per 100 000 population in 2020–2021, and in Dang district, the rate decreased from 215–191 per 100 000 population. We investigated the spatial patterns of TB prevalence and highlighted the geographic areas in each district in Nepal from 2021 to 2023 with Moran’s I of 0.558, 0.614 and 0.596, respectively. The consistent identification of High-High clusters in specific districts like Banke, Kapilbastu and Parsa across all 3 years periods highlighted persistent high-risk areas for TB transmission in Nepal. Conclusions: This study emphasised the strong spatial associations and the complex, diverse aspects of TB transmission shaped by demographic and socioeconomic factors. Our results highlighted the need for tailored public health approaches that account for specific social determinants to address TB effectively.

Chronic respiratory diseases (CRDs) constitute 4% of the global disease burden and cause 4 million deaths annually. This cross-sectional study used QGIS and GeoDa to explore the spatial pattern and heterogeneity of CRDs morbidity and spatial autocorrelation between socio-demographic factors and CRDs in Thailand from 2016 to 2019. We found an annual, positive, spatial autocorrelation (Moran’s I >0.66, p<0.001) showing a strong clustered distribution. The local indicators of spatial association (LISA) identified hotspots mostly in the northern region, while coldspots were mostly seen in the central and north-eastern regions throughout the study period. Of the socio-demographic factors, the density of population, households, vehicles, factories and agricultural areas, correlated with the CRD morbidity rate, with statistically significant negative spatial autocorrelations and coldspots in the north-eastern and central areas (except for agricultural land) and two hotspots between farm household density and CRD in the southern region in 2019. This study identified vulnerable provinces with high risk of CRDs and can guide prioritization of resource allocation and provide target interventions for policy makers.