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Forecasting and predicting intussusception in children younger than 48 months in Suzhou using a seasonal

Wan-Liang Guo1, Jia Geng2, Yang Zhan1

  • 1Department of Radiology, Children's Hospital of Soochow University, Suzhou, China.

BMJ Open
|February 21, 2019
PubMed

Insights

This study highlights intussusception epidemiology in children under 48 months in Suzhou. An autoregressive integrated moving average (ARIMA) model effectively forecasts intussusception cases, aiding early detection and prevention.

Area of Science:

  • Pediatric Epidemiology
  • Time-Series Analysis
  • Public Health Surveillance

Background:

  • Intussusception is a significant cause of intestinal obstruction in infants and young children.
  • Understanding epidemiological patterns is crucial for effective public health management.
  • Accurate forecasting models can aid in resource allocation and intervention planning.

Purpose of the Study:

  • To analyze the epidemiological characteristics of intussusception in children under 48 months in Suzhou.
  • To develop and validate a predictive model for intussusception occurrence in this age group.
  • To explore the utility of time-series analysis for intussusception surveillance.

Main Methods:

  • Retrospective analysis of 13,887 intussusception cases from 2007-2017.
  • Application of the Box-Jenkins approach to develop a seasonal autoregressive integrated moving average (ARIMA) model.
  • Validation of the ARIMA (1,0,1 1,1,1)12 model using 2017 data.

Main Results:

  • Intussusception cases occurred year-round, with seasonal peaks in late spring and early summer.
  • The most affected age group was children younger than 36 months.
  • The developed ARIMA model demonstrated good agreement between predicted and actual 2017 intussusception cases.

Conclusions:

  • Autoregressive integrated moving average (ARIMA) models are valuable tools for monitoring pediatric intussusception.
  • These models can predict future intussusception cases in Suzhou, supporting early warning systems.
  • Effective forecasting aids in timely treatment and prevention of severe complications.
Abstract

Keywords:
epidemiologyforecastingintussusceptiontime series

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