Spatial-Temporal Analysis of Factors Influencing the Median Urine Iodine Concentration of 8-10-year-old Children in Xinjiang, China 25 Years after Implementation of the Salt Iodization Policy.

The iodine status of children has improved and stabilized since China implemented salt iodization measures 25 years ago, but routine monitoring of iodine cannot reflect regional factors that influence the iodine level in children. Therefore, we conducted a regional spatial-temporal analysis of children's median urinary iodine concentration (MUIC) and searched for possible factors that might affect children's iodine levels by mining monitoring data. We analyzed data from Xinjiang collected as part of the "Iodine Deficiency Disease National Monitoring Program" from 2017 to 2020. The study population consisted of 76,268 children who participated in the study. We used global autocorrelation analysis to determine whether the MUIC of children was spatially clustered, local autocorrelation analysis to identify specific clustering areas, local cold and hot spot analysis to verify the reliability of the local autocorrelation results, and a spatial lag model to identify factors affecting the children's MUIC. The MUIC values were 217.70, 227.00, 230.67, and 230.67 µg/L in 2017, 2018, 2019, and 2020, respectively. Global autocorrelation analysis showed that the MUIC of all children in the study was significantly related to region (Z scores all > 1.96, P values all < 0.05) from 2017 to 2020. Partial auto-correlation analysis showed that counties with clusters of high values were mostly concentrated in the southwestern region of Xinjiang, whereas counties with clusters of low values were located in the northern part of Xinjiang. Partial cold spot and hot spot analysis showed the same trend, and there are more overlapping districts and counties in 4 years. Three-dimensional trend analysis indicated that children from the western part of Xinjiang had high levels of urinary iodine. According to spatial lag model, urine iodine concentration level is positively correlated with thyroid volume, average salary, and urbanization rate classification. The MUIC of 8-10-year-old children in Xinjiang was spatially clustered and related to geographic region. Our results show that spatial analysis of survey data combined with geographic technology and public health data can accurately identify areas with abnormal iodine concentrations in children. Additionally, understanding the factors that influence iodine levels in the human population is conducive to improving monitoring methods.