ABSTRACT
Objective To explore the spatiotemporal distribution pattern, and identify risk cluster of esophageal cancer in Huai’an City so as to provide evidence for control and prevention of esophageal cancer. Methods Data of esophageal cancer incidence at township level in Huai’an City from 2011 to 2015 was collected. Spatial autocorrelation and local indications of spatial autocorrelation (LISA) were implemented to evaluate the spatial pattern of esophageal cancer incidence. Spatial scan statistics was used to examine spatio-temporal clustering of risk areas. Results The average incidence of esophageal cancer in Huai’an from 2011 to 2015 was 67.12/10 million, the incidence of male was significantly higher than that of female. The results of Moran’s I values implyed the spatial autocorrelation at township level. The results of LISA indicated that there were local hot spots and cold spots. The significant high-risk clusters included townships in Huai’an County, Huaiyin County and Jinhu County. The low-risk clusters were located in the main urban area and Xuyi County. Conclusions There are significant spatio-temporal aggregation for the distribution of incidence of esophageal cancer in Huai’an City and same spatiotemporal high-risk clusters between male and female. Our findings have a foundation to explore the multi-factorial etiology of esophageal cancer and have vital practical value for health services and policies implementation.
ABSTRACT
<p><b>OBJECTIVE</b>To develop a risk model for predicting later development of diabetic nephropathy (DN) in Chinese people with type 2 diabetes mellitus (T2DM) and evaluate its performance with independent validation.</p><p><b>METHODS</b>We used data collected from the project 'Comprehensive Research on the Prevention and Control of Diabetes', which was a community-based study conducted by the Jiangsu Center for Disease Control and Prevention in 2013. A total of 11,771 eligible participants were included in our study. The endpoint was a clear diagnosis of DN. Data was divided into two components: a training set for model development and a test set for validation. The Cox proportional hazard regression was used for survival analysis in men and women. The model's performance was evaluated by discrimination and calibration.</p><p><b>RESULTS</b>The incidence (cases per 10,000 person-years) of DN was 9.95 (95% CI; 8.66-11.43) in women and 11.28 (95% CI; 9.77-13.03) in men. Factors including diagnosis age, location, body mass index, high-density-lipoprotein cholesterol, creatinine, hypertension, dyslipidemia, retinopathy, diet control, and physical activity were significant in the final model. The model showed high discrimination and good calibration.</p><p><b>CONCLUSION</b>The risk model for predicting DN in people with T2DM can be used in clinical practice for improving the quality of risk management and intervention.</p>