title: Application of an autoregressive integrated moving average model for predicting injury mortality in Xiamen, China.
abstract: OBJECTIVE
  Injury is currently an increasing public health problem in China. Reducing the loss due to injuries has become a main priority of public health policies. Early warning of injury mortality based on surveillance information is essential for reducing or controlling the disease burden of injuries. We conducted this study to find the possibility of applying autoregressive integrated moving average (ARIMA) models to predict mortality from injuries in Xiamen.
  
  
  METHODS
  The monthly mortality data on injuries in Xiamen (1 January 2002 to 31 December 2013) were used to fit the ARIMA model with the conditional least-squares method. The values p, q and d in the ARIMA (p, d, q) model refer to the numbers of autoregressive lags, moving average lags and differences, respectively. The Ljung-Box test was used to measure the 'white noise' and residuals. The mean absolute percentage error (MAPE) between observed and fitted values was used to evaluate the predicted accuracy of the constructed models.
  
  
  RESULTS
  A total of 8274 injury-related deaths in Xiamen were identified during the study period; the average annual mortality rate was 40.99/100,000 persons. Three models, ARIMA (0, 1, 1), ARIMA (4, 1, 0) and ARIMA (1, 1, (2)), passed the parameter (p<0.01) and residual (p>0.05) tests, with MAPE 11.91%, 11.96% and 11.90%, respectively. We chose ARIMA (0, 1, 1) as the optimum model, the MAPE value for which was similar to that of other models but with the fewest parameters. According to the model, there would be 54 persons dying from injuries each month in Xiamen in 2014.
  
  
  CONCLUSIONS
  The ARIMA (0, 1, 1) model could be applied to predict mortality from injuries in Xiamen.
authors.affiliation: Department of Chronic and Non-communicable Diseases Control and Prevention, Xiamen Center for Disease Control and Prevention, Xiamen, China.
authors.forename: Yilan
authors.initials: Y
authors.lastname: Lin
authors.affiliation: Department of Chronic and Non-communicable Diseases Control and Prevention, Xiamen Center for Disease Control and Prevention, Xiamen, China.
authors.forename: Min
authors.initials: M
authors.lastname: Chen
authors.affiliation: Department of Chronic and Non-communicable Diseases Control and Prevention, Xiamen Center for Disease Control and Prevention, Xiamen, China.
authors.forename: Guowei
authors.initials: G
authors.lastname: Chen
authors.affiliation: Department of Chronic and Non-communicable Diseases Control and Prevention, Xiamen Center for Disease Control and Prevention, Xiamen, China.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Wu
authors.affiliation: Department of Pharmacy, Xiamen Municipal Maternal and Child Health Hospital, Xiamen, China.
authors.forename: Tianquan
authors.initials: T
authors.lastname: Lin
mesh_terms: D017677:Age Distribution; D002681:China / Q000453:epidemiology; D005544:Forecasting; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D011159:Population Surveillance; D011237:Predictive Value of Tests; D011634:Public Health; D017678:Sex Distribution; D014947:Wounds and Injuries / Q000401:mortality* / Q000517:prevention & control
keywords: ARIMA; injury; prediction; time serial