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Review
. 2016 Jan-Mar;34(1):71-5.
doi: 10.4103/0970-4388.175521.

Comparison of linear and zero-inflated negative binomial regression models for appraisal of risk factors associated with dental caries

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Review

Comparison of linear and zero-inflated negative binomial regression models for appraisal of risk factors associated with dental caries

Manu Batra et al. J Indian Soc Pedod Prev Dent. 2016 Jan-Mar.

Abstract

Context: Dental caries among children has been described as a pandemic disease with a multifactorial nature. Various sociodemographic factors and oral hygiene practices are commonly tested for their influence on dental caries. In recent years, a recent statistical model that allows for covariate adjustment has been developed and is commonly referred zero-inflated negative binomial (ZINB) models.

Aim: To compare the fit of the two models, the conventional linear regression (LR) model and ZINB model to assess the risk factors associated with dental caries.

Materials and methods: A cross-sectional survey was conducted on 1138 12-year-old school children in Moradabad Town, Uttar Pradesh during months of February-August 2014. Selected participants were interviewed using a questionnaire. Dental caries was assessed by recording decayed, missing, or filled teeth (DMFT) index.

Statistical analysis used: To assess the risk factor associated with dental caries in children, two approaches have been applied - LR model and ZINB model.

Results: The prevalence of caries-free subjects was 24.1%, and mean DMFT was 3.4 ± 1.8. In LR model, all the variables were statistically significant. Whereas in ZINB model, negative binomial part showed place of residence, father's education level, tooth brushing frequency, and dental visit statistically significant implying that the degree of being caries-free (DMFT = 0) increases for group of children who are living in urban, whose father is university pass out, who brushes twice a day and if have ever visited a dentist.

Conclusion: The current study report that the LR model is a poorly fitted model and may lead to spurious conclusions whereas ZINB model has shown better goodness of fit (Akaike information criterion values - LR: 3.94; ZINB: 2.39) and can be preferred if high variance and number of an excess of zeroes are present.

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