ArticleScientific reports2017
Predicting trend of early childhood caries in mainland China: a combined meta-analytic and mathematical modelling approach based on epidemiological surveys.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Longitudinal host-microbiome dynamics of metatranscription identify hallmarks of progression in periodontitis.Microbiome · 2025Article
- Multivariable Projections of Caries-Free Prevalence and the Associated Factors from 2019 to 2030 among Schoolchildren Aged 6, 12 and 16-Year-Old in Malaysia.Children (Basel, Switzerland) · 2023Article
- Caries-Free Prevalence among Schoolchildren in Malaysia-Time-Series Analysis of Trends and Projections from 1996 to 2030.Children (Basel, Switzerland) · 2023Article
- Projecting the burden of dental caries and periodontal diseases among the adult population in the United Kingdom using a multi-state population model.Frontiers in public health · 2023Article
- Improving Caries Risk Prediction Modeling: A Call for Action.Journal of dental research · 2020Article
- Temporal trends analysis of human brucellosis incidence in mainland China from 2004 to 2018.Scientific reports · 2018Article
- Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013⁻2014.Bioengineering (Basel, Switzerland) · 2018Article
- Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model.PloS one · 2018Article
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5 authors.
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Abstract
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.
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