The Journal of Practical Medicine ›› 2022, Vol. 38 ›› Issue (11): 1378-1384.doi: 10.3969/j.issn.1006⁃5725.2022.11.014

• Clinical Research • Previous Articles     Next Articles

Effect of gray model（1，1）on prediction of birth defects among the population of Guangxi Zhuang Autono⁃ mous Region 

PENG Zhenren，WEI Jie，HUANG Xiuning，SONG Pengshu，LIANG Lifang，HUANG Bohua，HE Jiajia，CHEN Biyan，QIU Xiaoxia，HE Sheng.   

  1. The Maternal & Child Health Hospital of Guangxi Zhuang Autono⁃ mous Region，Guangxi Birth Defects Research and Prevention Institute，Guangxi Key Laboratory of Reproductive Health and Birth Defect Prevention，Guangxi Key Laboratory of Birth Defects Research and Prevention，Nanning 530000，China 

  • Online:2022-06-10 Published:2022-06-10
  • Contact: HE Sheng E⁃mail：heshengbiol@163.com

Abstract:

Objective To study the effect of gray model GM（1，1）in predicting birth defects（BD）preva⁃ lence at different levels among the population of Guangxi Zhuang Autonomous Region. Methods Based on the monitoring data of BD in Guangxi from 2016 to 2020，the gray mode GM（1，1）was used to predict the overall BD prevalence and the top five main BD at three levels（year，quarter，and month）. We compared the prediction effect of different levels of gray models. Results The average relative errors for yearly，quarterly and monthly over⁃ all BD prevalence were ⁃0.12%，0.49% and 0.79%，respectively，and the posterior odds ratios were 0.002，0.285 and 0.392，respectively，and the prediction results were good，good and qualified，respectively. The yearly gray model prediction effect of the top five main BD（congenital heart disease，polydactyly，talipes equinovarus，other external ear deformities，and syndactyly）was significantly better than those of quarterly and monthly. Conclusion The prediction effect of GM（1，1）may be related to the volatility of original sequence data. It may be better for predicting the BD prevalence by year.

Key words: birth defects； , gray model； , predicting