Zhang Zhihong, Luo Jiebo
School of Nursing, University of Rochester, Rochester, NY, USA.
Institute for Data Science, Columbia University, New York City, NY, USA.
Pediatr Res. 2025 Jun 2. doi: 10.1038/s41390-025-04054-5.
This study aims to conduct a time series analysis to assess the effect of COVID-19 on infant and neonatal mortality in its first three years.
Linked infant birth and death certificate data from the United States between 2016 and 2022 were used. Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA)/ARIMA with Exogenous Variables (ARIMAX) were employed to predict monthly infant and neonatal mortality trends in 2022 with and without incorporating COVID-19 as a feature. Interrupted Time Series Analysis was conducted to measure monthly rate changes before (pre-March 2020) and during the COVID-19 period (March 2020-December 2022).
Infant and neonatal deaths declined from 2016 to 2020/2021, with increases observed starting in May 2022. Incorporating COVID-19 as a variable into prediction models reduced the predictive performance of both LSTM and ARIMA/ARIMAX models. Compared to the pre-COVID-19 period, ITS showed no significant changes in monthly infant and neonatal death rates. However, significant increases were noted in low birth weight, low Apgar scores, and infrequent prenatal visits during 2021-2022.
COVID-19 altered the previously continuous decline in infant and neonatal deaths in 2022. Despite this late increase, the overall impact of COVID-19 on infant and neonatal mortality remains minimal.
Infant and neonatal deaths showed a steady decline from 2016 through 2020/2021. However, an increase in infant and neonatal deaths emerged in the later stages of the COVID-19 pandemic (2022). Rates of risk factors associated with infant and neonatal deaths, such as low birth weight and inadequate prenatal care, increased during the COVID-19 period. Understanding the shifts in infant and neonatal mortality and related risk factors during the COVID-19 pandemic provides valuable insights into its impact and enhances preparedness for future pandemics.
本研究旨在进行时间序列分析,以评估新冠疫情头三年对婴儿和新生儿死亡率的影响。
使用了2016年至2022年期间美国关联的婴儿出生和死亡证明数据。采用长短期记忆网络(LSTM)以及自回归积分滑动平均模型(ARIMA)/带外生变量的自回归积分滑动平均模型(ARIMAX),在纳入和不纳入新冠疫情作为特征的情况下,预测2022年每月的婴儿和新生儿死亡率趋势。进行了中断时间序列分析,以衡量2020年3月之前(新冠疫情前)和新冠疫情期间(2020年3月至2022年12月)每月的死亡率变化。
2016年至2020/2021年期间,婴儿和新生儿死亡人数下降,2022年5月开始出现上升。将新冠疫情作为变量纳入预测模型降低了LSTM和ARIMA/ARIMAX模型的预测性能。与新冠疫情前时期相比,中断时间序列分析显示每月婴儿和新生儿死亡率没有显著变化。然而,2021年至2022年期间,低出生体重、低阿氏评分和产前检查不频繁的情况显著增加。
新冠疫情改变了2022年婴儿和新生儿死亡人数此前持续下降的趋势。尽管后期有所增加,但新冠疫情对婴儿和新生儿死亡率的总体影响仍然很小。
2016年至2020/2021年期间,婴儿和新生儿死亡人数呈稳步下降趋势。然而,在新冠疫情后期(2022年),婴儿和新生儿死亡人数出现增加。新冠疫情期间,与婴儿和新生儿死亡相关的风险因素发生率,如低出生体重和产前护理不足,有所上升。了解新冠疫情期间婴儿和新生儿死亡率的变化以及相关风险因素,有助于深入了解其影响,并增强对未来大流行的防范能力。