基于流行病学调查的综合荟萃分析和数学建模方法预测中国大陆儿童龋病流行趋势。

Predicting trend of early childhood caries in mainland China: a combined meta-analytic and mathematical modelling approach based on epidemiological surveys.

机构信息

College of Stomatology, Chongqing Medical University, Chongqing, China.

Chongqing key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, China.

出版信息

Sci Rep. 2017 Jul 26;7(1):6507. doi: 10.1038/s41598-017-06626-w.

Early childhood caries (ECC) is the most common chronic disease in young children. A reliable predictive model for ECC prevalence is needed in China as a decision supportive tool for planning health resources. In this study, we first established the autoregressive integrated moving average (ARIMA) model and grey predictive model (GM) based on the estimated national prevalence of ECC with meta-analysis from the published articles. The pooled data from 1988 to 2010 were used to establish the model, while the data from 2011 to 2013 were used to validate the models. The fitting and prediction accuracy of the two models were evaluated by mean absolute error (MAE) and mean absolute percentage error (MAPE). Then, we forecasted the annual prevalence from 2014 to 2018, which was 55.8%, 53.5%, 54.0%, 52.9%, 51.2% by ARIMA model and 52.8%, 52.0%, 51.2%, 50.4%, 49.6% by GM. The declining trend in ECC prevalence may be attributed to the socioeconomic developments and improved public health service in China. In conclusion, both ARIMA and GM models can be well applied to forecast and analyze the trend of ECC; the fitting and testing errors generated by the ARIMA model were lower than those obtained from GM.

婴幼儿龋（ECC）是幼儿最常见的慢性疾病。中国需要建立一种可靠的 ECC 患病率预测模型，作为规划卫生资源的决策支持工具。在本研究中，我们首先基于已发表文献的荟萃分析，使用全国 ECC 患病率的估算数据，分别建立自回归综合移动平均（ARIMA）模型和灰色预测模型（GM）。使用 1988 年至 2010 年的数据建立模型，而 2011 年至 2013 年的数据用于验证模型。通过平均绝对误差（MAE）和平均绝对百分比误差（MAPE）评估两种模型的拟合和预测精度。然后，我们预测了 2014 年至 2018 年的年度患病率，ARIMA 模型预测的患病率分别为 55.8%、53.5%、54.0%、52.9%、51.2%，GM 模型预测的患病率分别为 52.8%、52.0%、51.2%、50.4%、49.6%。ECC 患病率的下降趋势可能归因于中国的社会经济发展和公共卫生服务的改善。总之，ARIMA 和 GM 模型都可以很好地应用于预测和分析 ECC 的趋势；ARIMA 模型产生的拟合和检验误差低于 GM 模型。