实用医学杂志 ›› 2022, Vol. 38 ›› Issue (11): 1378-1384.doi: 10.3969/j.issn.1006⁃5725.2022.11.014

• 临床研究 • 上一篇    下一篇

灰色模型GM(1,1)在广西出生缺陷预测中的应用

彭振仁 韦洁 黄秀宁 宋鹏书 梁丽芳 黄柏华 何嘉嘉 陈碧艳 丘小霞 何升    

  1. 广西壮族自治区妇幼保健院,广西出生缺陷预防控制研究所,广西生殖健康与出生缺陷防治重点实验室, 广西出生缺陷防治基础研究重点实验室(南宁530000)

  • 出版日期:2022-06-10 发布日期:2022-06-10
  • 通讯作者: 何升 E⁃mail:heshengbiol@163.com
  • 基金资助:
    广西卫生健康委员会自筹课题(编号:Z20210056);广西医学高层次骨干人才“139”计划(编号:G202003023)

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

摘要:

目的 应用灰色模型 GM(1,1)构建不同水平的出生缺陷发生率预测模型,并对预测模型进 行评估,探索灰色模型 GM(1,1)在出生缺陷发生率中的预测效果。方法 基于 2016-2020 年广西出生缺 陷监测数据,以年、季度、月共 3 个层次构建总出生缺陷发生率及前 5 种出生缺陷的灰色模型 GM(1,1),并 通过模型综合评价指标对不同层次的灰色模型预测效果进行评估。结果 在使用灰色模型预测总出生 缺陷年度、季度、月发生率时,平均相对误差分别为-0.12%、0.49%、0.79%,后验差比值 C 分别为 0.002 0.285、0.392,预测效果分别为好、好、合格;前 5 种出生缺陷(先天性心脏病、多指(趾)、马蹄内翻足、外耳 其他畸形、并指(趾))的年度发生率灰色模型拟合效果明显优于季度和月份的。结论 灰色模型的预测 效果可能与原始序列数据波动性有关,在对出生缺陷发生率进行预测时以年度为单位的预测效果可能 更佳。

关键词:

出生缺陷, 灰色模型, 预测

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