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Analysis Of Epidemiological Characteristics And Prediction Of Early Childhood Caries In Mainland China

Posted on:2017-01-26Degree:DoctorType:Dissertation
Country:ChinaCandidate:X N ZhangFull Text:PDF
GTID:1224330503990953Subject:Clinical Laboratory Science
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Background Early childhood caries(ECC) is the most common and highly prevalent chronic disease of young children. Untreated caries causes pain and infection, interfering with children’s normal biting habits and general heath. In addition, it affects children’s daily activities and psychological health. Dental caries has placed a big economic burden and become a major public health issue all the world. The reported ECC prevalence vary a lot in different countries and in different areas of China. The epidemiological characteristics of ECC in China still remain unclear.Objective The aim of this study is to evaluate the situation of ECC prevalence and treatment in mainland China during 1987-2013, to explore its current epidemiological characteristics, and to forecast its trend in future. The outcomes might be helpful to the prevention and treatment ECC, and provide evidence-based data for t health program planning and future research.Methods The literatures focused on ECC in mainland China were collected. Our study of the epidemiological situation of ECC divides into three parts:1. To explore the epidemiological distribution(population/areas/time) and trend of ECC in mainland China in the past 30 years, eligible articles were searched and studied with meta analyses according to PRISMA checklist,2. To reflect ECC spatial distribution, pooled prevalence estimates for 5-year-old children in each province during 2006–2013 were entered into the Arc GIS software, to form a prevalence map in mainland China.3. Two predictive models(ARIMA and Grey dynamics model) of ECC were established, with pooled prevalence estimates for 5-year-old children during 1988-2010. The fitting and predictive accuracy of these two models were compared. Then we tried to forecast the prevalence of ECC from 2013 to 2017 in mainland China using both models.Results 1. The characteristics of the included studiesA total of 102 eligible literatures were included and the total sample size was 349,215. Two national-level, 20 provincial-level, and 80 city-level articles were involved, covering 22 provinces, 4 municipalities and 4 autonomous regions in mainland China. Forty-four studies reported care index. Heterogeneity was obvious in this meta-analysis. The random effect model was employed, and subgroup analyses were adopted to explore possible factors including survey year, age, gender, and location.2. The epidemiological status of ECC in mainland China during 1987-20131) The pooled national prevalence and care index were 65.5% and 3.6%, respectively.2) The overall prevalence ranged from 81% in 1987 to 49.3% in 2013, and the prevalence of ECC at age 5 ranged from to 81.2% in 1988 to 56.1% in 2012,both indicating a declining trend as time progressed.3) The pooled ECC prevalence for children aged 1-6 years was 17.7%, 20.9%, 41.4%, 53.4%, 65.7% and 68.2%, indicating an increasing trend with age.4) There was no significant difference in prevalence between males(59.1%) and females(58.9%); and the care index was also similar(8.1% versus 7.7%).5) A much higher care index was reported in urban(6.0%) than in rural(1.6%)(RR=3.68, 95%CI: 2.54-5.35). Slightly higher ECC prevalence was observed in rural areas(63.5%) compared with urban areas(59.5%).4. Application of GIS in ECC prevalence The 2006–2013 map of ECC prevalence among 5-year-olds showed wide geographic variations in mainland China. Notably, the zone of the lowest ECC prevalence was consisted of four adjacent provinces(or municipality) in the middle and west, involving Chongqing(36.4%), Sichuan(38.9%), Hubei(40.9%), and Shaanxi(48.2%). The highest prevalence in mainland China was found in Fujian, Hebei, Tianjin, and Guangxi.4. The prediction of ECC prevalence in mainland China1) ARIMA(2, 1, 3) and GM(l, l) constructed based on meta- analyses, both fitted for the combined series data from 1988 to 2010, and predicted that the ECC prevalence would keep declining in 2013-2017. The predicted prevalence in 2015 was 51.89% and 51.99% respectively, using these two models.2) When comparing the fitting effect, the average error and average error rate in the ARIMA(2, 1, 3) were 3.63% and 5.74%, respectively; the average error and average error rate in GM(l, l) were 4.81% and 7.34%. When predicting the prevalence in 2011 and 2012, the error in the ARIMA(2, 1, 3) was 9.76% and 1.96%; the error in the GM(1, 1) was 12.76% and 2.81%, respectively. Generally speaking, both of these two models fitted for the series data and can be used in the prevalence forecasting. Comparatively, ARIAM was more precise than GM in fitting and predictive effect.Conclusion This study presents the epidemiological situation of the oral health status among children in mainland China over the last 30 years. Prevalence declined over time. However, the prevalence of ECC is still high and most of the caries remained untreated. ECC prevalence map showed a wide variation. There is an urgent need for proper prevention and treatment measures to guarantee the Chinese children’s oral health.ARIMA(2, 1, 3) and GM(l, l) both fitted for the combined series data from 1988 to 2010 and can be used in the forecasting ECC prevalence. The predictive prevalence would keep declining in the next five years. Comparatively, ARIMA had better effect than GM.Innovation China is a vast and most populous country. It is not possible to conduct the national epidemiological survey every year since it is costly and time-consuming. This study is the first systematic review on prevalence and care index of ECC in mainland China. We originally used the pooled prevalence with meta-analyses and constructed different mathematic models to predict the trend of ECC. This may provide important evidence for effective interventions to improve oral health of Chinese children.
Keywords/Search Tags:Epidemiology, Early childhood caries, Prevalence, Care index, GIS, ARIMA Model, Grey Model
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