Spatial distribution and influencing factors of thyroid cancer hospitalization rate among rural residents in Fujian Province, China from 2012 to 2016.

This study aimed to explore the spatial distribution and influencing factors of thyroid cancer hospitalization rates in Fujian Province from 2012 to 2016. Hospitalization reimbursement records for thyroid cancer were obtained from 2025 hospitals in Fujian Province from 2012 to 2016. The Moran's I method was used for spatial autocorrelation analysis and to further draw a spatial cluster map in Fujian. Geographic detectors were used to explore the effect of risk factors on spatial heterogeneity of inpatient service utilization for thyroid cancer. The study showed that there was obvious temporal and spatial heterogeneity in the utilization rate of inpatient services for thyroid cancer in Fujian Province, which were mainly concentrated in Fuzhou, with Lianjiang County as the center, and the gathering area involves 26 counties and cities. Among a variety of environmental factors, air quality index (AQI) (q = 0.481), carbon sequestration (q = 0.161), and carbon emissions (q = 0.155) were the main factors affecting the hospitalization rates. AQI and carbon emissions were generally positively correlated with hospitalization rates, and carbon sequestration was negatively correlated. After the interaction of the two factors, the interpretation of the hospitalization rate was enhanced. The obvious spatial heterogeneity will help the relevant departments to adjust measures to local conditions and allocate medical resources rationally to ease the pressure of seeking medical attention in high-demand areas.

Risk Prediction for Non-alcoholic Fatty Liver Disease Based on Biochemical and Dietary Variables in a Chinese Han Population.

Nonalcoholic fatty liver disease (NAFLD) is a common liver disease globally, but there are no optimal methods for its prediction or diagnosis. The present cross-sectional study proposes a non-invasive tool for NAFLD screening. The study included 2,446 individuals, of whom 574 were NAFLD patients. Multivariable logistic regression analysis was used to identify risk factors for NAFLD and incorporate them in a risk prediction nomogram model; the variables included both clinical and lifestyle-related variables. Following stepwise regression, BMI, waist circumference, serum triglyceride, high-density lipoprotein cholesterol, alanine aminotransferase, presence of diabetes and hyperuricemia, tuber and fried food consumption were identified as significant risk factors and used in the model. The final nomogram was found to have good discrimination ability (area under the receiver operating characteristic curve = 0.843 [95% CI: 0.819-0.867]), and reasonable accuracy for the prediction of NAFLD risk. A cut-off score of <180 for the nomogram was found to have high sensitivity and predictivity for the exclusion of individuals from screening. The model can be used as a non-invasive tool for mass screening.