Lin Yilan, Chen Min, Chen Guowei, Wu Xiaoqing, Lin Tianquan
Department of Chronic and Non-communicable Diseases Control and Prevention, Xiamen Center for Disease Control and Prevention, Xiamen, China.
Department of Pharmacy, Xiamen Municipal Maternal and Child Health Hospital, Xiamen, China.
BMJ Open. 2015 Dec 9;5(12):e008491. doi: 10.1136/bmjopen-2015-008491.
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.
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.
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.
The ARIMA (0, 1, 1) model could be applied to predict mortality from injuries in Xiamen.
伤害目前在中国是一个日益严重的公共卫生问题。减少伤害造成的损失已成为公共卫生政策的主要优先事项。基于监测信息对伤害死亡率进行早期预警对于减少或控制伤害的疾病负担至关重要。我们开展本研究以探寻应用自回归积分滑动平均（ARIMA）模型预测厦门伤害死亡率的可能性。
采用厦门2002年1月1日至2013年12月31日期间伤害的月度死亡率数据，运用条件最小二乘法拟合ARIMA模型。ARIMA（p，d，q）模型中的p、q和d值分别指自回归滞后项数、移动平均滞后项数和差分次数。使用Ljung-Box检验来衡量“白噪声”和残差。观测值与拟合值之间的平均绝对百分比误差（MAPE）用于评估所构建模型的预测准确性。
研究期间厦门共确认8274例与伤害相关的死亡；年均死亡率为40.99/10万人口。三个模型，即ARIMA（0，1，1）、ARIMA（4，1，0）和ARIMA（1，1，（2）），通过了参数检验（p<0.01）和残差检验（p>0.05），MAPE分别为11.91%、11.96%和11.90%。我们选择ARIMA（0，1，1）作为最优模型，其MAPE值与其他模型相近，但参数最少。根据该模型，2014年厦门每月将有54人死于伤害。
ARIMA（0，1，1）模型可用于预测厦门的伤害死亡率。
