Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling

Subjective social integration and its spatially varying determinants of rural-to-urban migrants among Chinese cities

Social integration, a huge issue triggered by migration, leads to potential social fragmentation and confrontation. Focusing on the precise enhancement of "inner" subjective social integration is the ultimate urbanization solution to enhance people-centered well-being and promote full social integration. This article used data from the China Migrants Dynamic Survey 2017 (CMDS 2017) to reveal the spatial patterns and mechanisms of subjective social integration in Chinese cities. We make an innovative attempt to introduce multiscale geographically weighted regression (MGWR) to address the appropriateness of policy formulation by addressing the spatial variation in the factors. The results demonstrate that the influences on subjective social integration have a strong spatial heterogeneity in China, a vast and unevenly developed country. Expanding on the typical factors, household registration and political participation affect North China more than other regions; and housing and marriage have a greater impact in South China, especially in the Pearl River Delta and the Eastern Seaboard. Income, welfare, and healthcare are indiscriminately sweeping through most of China. Such a conclusion reminds the Chinese government that it needs to consider not only addressing some of the national constraints to subjective social integration but also imposing precise, site-specific changes for different regions.

Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model

BACKGROUND

Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals. Our research focused on analyzing the spatiotemporal clustering of Leptospirosis and developing a disease prevalence model as an essential focal point for public health policymakers, urging targeted interventions and strategies.

METHODS

The SaTScan and Maximum Entropy (MaxEnt) modeling methods were used to find the spatiotemporal clusters of the Leptospirosis and model the disease prevalence in Iran. We incorporated nine environmental covariates by employing a spatial resolution of 1 km x 1 km, the finest resolution ever implemented for modeling Human Leptospirosis in Iran. These covariates encompassed the Digital Elevation Model (DEM), slope, displacement areas, water bodies, and land cover, monthly recorded Normalized Difference Vegetation Index (NDVI), monthly recorded precipitation, monthly recorded mean and maximum temperature, contributing significantly to our disease modeling approach. The analysis using MaxEnt yielded the Area Under the Receiver Operating Characteristic Curve (AUC) metrics for the training and test data, to evaluate the accuracy of the implemented model.

RESULTS

The findings reveal a highly significant primary cluster (p-value < 0.05) located in the western regions of the Gilan province, spanning from July 2013 to July 2015 (p-value < 0.05). Moreover, there were four more clusters (p-value < 0.05) identified near Someh Sara, Neka, Gorgan and Rudbar. Furthermore, the risk mapping effectively illustrates the potential expansion of the disease into the western and northwestern regions. The AUC metrics of 0.956 and 0.952 for the training and test data, respectively, underscoring the robust accuracy of the implemented model. Interestingly, among the variables considered, the influence of slope and distance from water bodies appears to be minimal. However, altitude and precipitation stand out as the primary determinants that significantly contribute to the prevalence of the disease.

CONCLUSIONS

The risk map generated through this study carries significant potential to enhance public awareness and inform the formulation of impactful policies to combat Leptospirosis. These maps also play a crucial role in tracking disease incidents and strategically directing interventions toward the regions most susceptible.