Analysis of birth defects surveillance in Urumqi from 2018 to 2023 and application of three kinds of model in prediction

IF 2.3 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Clinical Epidemiology and Global Health Pub Date : 2025-03-29 DOI:10.1016/j.cegh.2025.102016

Luhan Zhang , Jinfang He , Shuyuan Xue , Rui Shi , Guifeng Ding

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引用次数: 0

Abstract

Objective

This study aimed to assess the incidence and risk factors of perinatal birth defects in Urumqi (2018–2023), and compare the predictive accuracy of Joinpoint regression, Prophet, Grey Model (GM(1,1)), and a Bayesian-optimized hybrid model.

Methods

Data were extracted from the Xinjiang Maternal and Child Health Cloud Platform. After quality control, we constructed the database in Excel, and analyses were performed using JMP 14.0 and R 4.4.1. Using population-based surveillance data from 36 midwifery institutions, we conducted trend analysis (Joinpoint regression) and time-series forecasting (Prophet, GM(1,1), and a Bayesian-weighted hybrid model). Model performance was evaluated by MAE, RMSE, MAPE, and R².

Results

The overall incidence of birth defects was 149.47 per 10,000, with a significant upward trend (χ²_trend = 25.268, P < 0.001). Congenital heart disease (53.65 %) was the most prevalent defect. Higher incidence rates were observed in male infants, urban areas, and mothers aged≥35 years. The Grey Model showed the lowest prediction error (MAE = 21.8, MAPE = 15.86 %), while the Combined model achieved the highest R²(0.82) and lowest RMSE (29.34).

Conclusion

The rising incidence of perinatal birth defects in Urumqi underscores the need for enhanced monitoring. Our findings advocate a tiered public health surveillance strategy: GM(1,1) for immediate-term (0–12 months), the hybrid model for medium-term (13–24 months), and Prophet for long-term (>25 months) planning, enabling resource prioritization in low-resource settings.

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乌鲁木齐市2018 - 2023年出生缺陷监测分析及三种模型预测应用

目的评估乌鲁木齐市2018-2023年围产期出生缺陷发生率及危险因素，比较结合点回归、先知灰色模型（GM(1,1)）和贝叶斯优化混合模型的预测准确率。方法数据来源于新疆省妇幼健康云平台。质量控制完成后，在Excel中建立数据库，使用JMP 14.0和r4.4.1进行分析。利用36家助产机构基于人口的监测数据，我们进行了趋势分析（Joinpoint回归）和时间序列预测（Prophet， GM（1,1）和贝叶斯加权混合模型）。采用MAE、RMSE、MAPE和R2评价模型的性能。结果新生儿出生缺陷总发生率为149.47 /万人，呈显著上升趋势(χ2趋势= 25.268,P <；0.001)。先天性心脏病（53.65%）是最常见的缺陷。男性婴儿、城市地区和年龄≥35岁的母亲的发病率较高。灰色模型的预测误差最小（MAE = 21.8, MAPE = 15.86%），而组合模型的R2最高（0.82），RMSE最低（29.34）。结论乌鲁木齐市围产期出生缺陷发生率上升，需要加强监测。我们的研究结果提倡采用分层公共卫生监测策略：GM（1,1）用于短期（0-12个月），混合模型用于中期（13-24个月），Prophet用于长期（25个月）规划，从而在资源不足的情况下实现资源优先排序。

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来源期刊

Clinical Epidemiology and Global Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

4.60

自引率

7.70%

发文量

218

审稿时长

66 days

期刊介绍： Clinical Epidemiology and Global Health (CEGH) is a multidisciplinary journal and it is published four times (March, June, September, December) a year. The mandate of CEGH is to promote articles on clinical epidemiology with focus on developing countries in the context of global health. We also accept articles from other countries. It publishes original research work across all disciplines of medicine and allied sciences, related to clinical epidemiology and global health. The journal publishes Original articles, Review articles, Evidence Summaries, Letters to the Editor. All articles published in CEGH are peer-reviewed and published online for immediate access and citation.