Overall Effects of Risk Factors Associated with Dental Caries Indices Using the Marginalized Zero-Inflated Negative Binomial Model

被引:4
作者
Bakhshi, Enayatollah [1 ]
Yazdanipour, Mohammad Ali [1 ]
Rahgozar, Mehdi [1 ]
Ghorbani, Zahra [2 ]
Deghatipour, Marzieh [2 ]
机构
[1] Univ Social Welf & Rehabil Sci, Dept Biostat, Kodakyar Ave,Daneshjo Blvd, Tehran 1985713834, Iran
[2] Shahid Beheshti Univ Med Sci, Dent Sch, Dept Community Oral Hlth, Tehran, Iran
关键词
Count data; Dental caries; Marginalized model; Negative binomial model; Zero-inflated model; ORAL-HEALTH; REGRESSION-MODEL; POISSON;
D O I
10.1159/000498892
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
The purpose of this paper is to identify risk factors for decayed, missing, and filled permanent teeth (DMFT) in a population of 12-15-year-old schoolchildren and to apply the marginalized zero-inflated negative binomial (MZINB) model for determination and estimation of the overall effects of the risk factors. A cross-sectional survey comprising 764 students aged between 12 and 15 years was used to analyze the association between caries in children and some background characteristics in children and their parents. Information on the samples' social, behavioral, and demographic status was obtained through a series of closed questions. The incidence rate ratios (IRR) were used to associate some risk factors with caries. In the entire sample, the frequency of zero was 194 (25.4%). The result of the shared-parameter marginalized zero-inflated negative binomial (SP-MZINB) model showed that being a girl (IRR = 1.18; p value = 0.021), higher dental visits frequency (IRR = 1.20; p value <0.001), lower tooth brushing frequency (IRR = 0.91; p value = 0.019), higher flossing frequency (IRR = 1.11; p value = 0.001), and lower mothers' education (IRR = 0.89; p value = 0.042) are associated with DMFT. Our results may provide better insights of the factors associated with DMFT, and health programs should focus their efforts on healthcare services, for both preventive and curative purposes. This regression model provides an appropriate fit and meaningful interpretation to handling zero-inflated count outcomes. Also, it provides direct estimates of the effects of risk factors on the overall mean that does not require postmodeling computations.
引用
收藏
页码:541 / 546
页数:6
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