Geographic variations in the incidence of congenital hypothyroidism in China: a retrospective study based on 92 million newborns screened in 2013-2018 / 中华医学杂志(英文版)

BACKGROUND@#Although congenital hypothyroidism (CH) has been widely studied in Western countries, CH incidence at different administrative levels in China during the past decade remains unknown. This study aimed to update the incidence and revealed the spatial pattern of CH incidence in the mainland of China, which could be helpful in the planning and implementation of preventative measures.@*METHODS@#The data used in our study were derived from 245 newborns screening centers that cover 30 provinces of the Chinese Newborn Screening Information System. Spatial auto-correlation was analyzed by Global Moran I and Getis-Ord Gi statistics at the provincial level. Kriging interpolation methods were applied to estimate a further detailed spatial distribution of CH incidence at city level throughout the mainland of China, and Kulldorff space scanning statistical methods were used to identify the spatial clusters of CH cases at the city level.@*RESULTS@#A total of 91,921,334 neonates were screened from 2013 to 2018 and 42,861 cases of primary CH were identified, yielding an incidence of 4.66 per 10,000 newborns screened (95% confidence interval [CI]: 4.62-4.71). Neonates in central (risk ratio [RR] = 0.84, 95% CI: 0.82-0.85) and western districts (RR = 0.71, 95% CI: 0.69-0.73) had lower probability of CH cases compared with the eastern region. The CH incidence indicated a moderate positive global spatial autocorrelation (Global Moran I value = 0.394, P  < 0.05), and the CH cases were significantly clustered in spatial distribution. A most likely city-cluster (log-likelihood ratio [LLR] = 588.82, RR = 2.36, P  < 0.01) and 25 secondary city-clusters of high incidence were scanned. The incidence of each province and each city in the mainland of China was estimated by kriging interpolation, revealing the most affected province and city to be Zhejiang Province and Hangzhou city, respectively.@*CONCLUSION@#This study offers an insight into the space clustering of CH incidence at provincial and city scales. Future work on environmental factors need to focus on the effects of CH occurrence.

Tree-Augmented NaÏve Bayesian network model for predicting prostate cancer / 中华男科学杂志

<p><b>Objective</b>To evaluate the integrated performance of age, serum PSA, and transrectal ultrasound images in the prediction of prostate cancer using a Tree-Augmented NaÏve (TAN) Bayesian network model.</p><p><b>METHODS</b>We collected such data as age, serum PSA, transrectal ultrasound findings, and pathological diagnoses from 941 male patients who underwent prostate biopsy from January 2008 to September 2011. Using a TAN Bayesian network model, we analyzed the data for predicting prostate cancer, and compared them with the gold standards of pathological diagnosis.</p><p><b>RESULTS</b>The accuracy, sensitivity, specificity, positive prediction rate, and negative prediction rate of the TAN Bayesian network model were 85.11%, 88.37%, 83.67%, 70.37%, and 94.25%, respectively.</p><p><b>CONCLUSIONS</b>Based on age, serum PSA, and transrectal ultrasound images, the TAN Bayesian network model has a high value for the prediction of prostate cancer, and can help improve the clinical screening and diagnosis of the disease.</p>