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. The early childhood caries (ECC), the tooth decay occurred in any primary tooth in a child 71 months of age or younger 1 , has been reported as the most prevalent infectious disease of children. The ECC prevalence in mainland China is comparatively high, 65.5% for 1-6-year-olds and 66.1% for 5-year-olds 2 , far from the target by WHO "half of the 6-year-old children are caries-free" 3 . It has an adverse impact not only on children's nutrition intake, speech, and daily routine activities, but also on their physiological health 4, 5 . China is a rapidly developing country with the largest children population in the world 6 . Children's oral health has become a major public health problem in China. Hence, to better aid an explicit and quantitative direction for the future oral health plan among these population, a reliable prediction method to understand the trend of ECC is needed. Forecasting techniques, which have been extensively applied to analyze the occurrences, development, and future trends of diseases, such as tuberculosis 7 , malaria 8 , hepatitis 9 , diabetics 10 and influenza 11 , serve as a policy-supportive tool effectively. The first step to establish the forecasting models was to acquire the time sequence 12 . Traditionally, the data used for forecasting came from the regional reports or surveillance data. However, we can hardly get the series data of ECC prevalence by year. Since 1980's, the Chinese Ministry of Health has invested large human and financial resources to conduct national oral epidemiology survey every decade. The first national survey was conducted in 1982, and the surveyed population were mainly students from primary and secondary schools. In the second and third national oral surveys in 1995 and 2005 13, 14 , 11 and 30 Published: xx xx xxxx OPEN www.nature.com/scientificreports/ 2 SCIENTIFIC REPORtS | 7: 6507 |
A related work that suggests CADx to improve oral health prediction is developed by Zhang et al. REF , where an autoregressive integrated moving average model is performed and a grey predictive model for the estimation of the national prevalence of early childhood caries from 2014 to 2018, finding that the highest annual prevalence would be of 55.8%, being attributed to the socioeconomic developments and the public health service.
Predicting trend of early childhood caries in mainland China: a combined meta-analytic and mathematical modelling approach based on epidemiological surveys
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