Evaluating the Effect of Socio-Economic Status on DMFT Index in Children Aged 12 in Iran through Zero-Inflated Poisson Regression
Archives of Advances in Biosciences,
Vol. 11 No. 2 (2020),
16 May 2020
,
Page 47-53
Abstract
Introduction: The most common index in dental studies is the decayed, missing, or filled teeth (dmft)/DMFT. Risk factor evaluation in order to investigate the significant factors that affect this DMFT in children has an important role in dental epidemiological studies. The aim of this study was to investigate the association between socioeconomic factors and dental caries.
Materials and Methods: This cross-sectional study was a part of a national survey for assessing the oral health status of Iranian citizens in 2012. The target population was children aged. The data and oral examination results were collected by the clinical examination form and trained calibrated dental group (dentists and hygienists). A zero-inflated Poisson regression model (ZIP) with a random effect was utilized for evaluating the effect of socioeconomic status on DMFT.
Results: In general, 1564 subjects were studied. From the entire subject in this study, the frequency of zero was 4176 (67%). The result of the ZIP model with random effect in zero part showed that socioeconomic status (OR = 1.97; P-value <0.001) had a significant effect on zero DMFT occurrences. The variance component of the random intercept in zero part was significant too (σ2 =17.11, P < 0.001).
Conclusion: The zero-inflated Poisson model with random effect in zero part was fitted to this data. Children from lower socioeconomic classes experienced more DMFT.
- Zero-inflated
- Dental Caries
- DMFT
How to Cite
References
Selwitz RH, Ismail AI, Pitts NBJTL. Dental caries. 2007; 369(9555):51-9.
Preisser JS, Stamm JW, Long DL, Kincade MEJCr. Review and recommendations for zero-inflated count regression modeling of dental caries indices in epidemiological studies. 2012; 46(4):413-23.
Chen KJ, Gao SS, Duangthip D, Li SKY, Lo ECM, Chu CHJBOH. Dental caries status and its associated factors among 5-year-old Hong Kong children: a cross-sectional study. 2017; 17(1):121.
Evans CA, Kleinman DVJTJotADA. The Surgeon General's report on America's oral health: opportunities for the dental profession. 2000; 131(12):1721-8.
Petersen PE, Bourgeois D, Ogawa H, Estupinan-Day S, Ndiaye C. The global burden of oral diseases and risks to oral health. Bulletin of the World Health Organization. 2005;83:661-9.
Batra M, Shah AF, Rajput P, Shah IA. Comparison of linear and zero-inflated negative binomial regression models for appraisal of risk factors associated with dental caries. Journal of Indian Society of Pedodontics Preventive Dentistry. 2016; 34(1):71.
Matranga D, Campus G, Castiglia P, Strohmenger L, Solinas G. Italian deprivation index and dental caries in 12-year-old children: a multilevel Bayesian analysis. Caries research. 2014; 48(6):584-93.
Böhning D, Dietz E, Schlattmann P, Mendonça L, Kirchner U. The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology. Journal of the Royal Statistical Society. 1999; Ser. A.162 (2):195-209.
Hall DB. Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study. Biometrics. 2000(December):1030-9.
Hilbe JM. Negative binomial regression: Cambridge University Press; 2011.
Hu M-C, Pavlicova M, Nunes EV. Zero-Inflated and Hurdle Models of Count Data with Extra Zeros: Examples from an HIV-Risk Reduction Intervention Trial. The American Journal of Drug and Alcohol Abuse. 2011; 37(5):367-75.
Sanders AE, Spencer AJ. Social Inequality: Social inequality in perceived oral health among adults in Australia. Australian New Zealand journal of public health. 2004; 28(2):159-66.
Petersen PE, Baez RJ. Oral health surveys: basic methods. World Health Organization. 2013.
Grainger R, Reid D. Distribution of dental caries in children. Journal of dental research. 1954; 33(5):613-23.
Hinde J, Demétrio CG. Overdispersion: models and estimation. Computational Statistics Data Analysis. 1998; 27(2):151-70.
Barry SC, Welsh AH. Generalized additive modelling and zero inflated count data. Ecological Modelling. 2002; 157(2-3):179-88.
McCullagh P, Nelder J. Generalized linear models New York Chapman & Hall. 1989.
Berk R, MacDonald JM. Overdispersion and Poisson regression. Journal of Quantitative Criminology. 2008; 24(3):269-84.
Solinas G, Campus G, Maida C, Sotgiu G, Cagetti MG, Lesaffre E, et al. What statistical method should be used to evaluate risk factors associated with dmfs index? Evidence from the National Pathfinder Survey of 4‐year‐old Italian children. Community dentistry oral epidemiology. 2009; 37(6):539-46.
Bedos C, Brodeur J-M, Arpin S, Nicolau B. Dental caries experience: a two-generation study. Journal of dental research. 2005; 84(10):931-6.
Nunn ME, Dietrich T, Singh HK, Henshaw MM, Kressin NR. Prevalence of early childhood caries among very young urban Boston children compared with US children. Journal of public health dentistry. 2009; 69(3):156-62.
Sakeenabi B, Swamy HS, Mohammed RN. Association between obesity, dental caries and socioeconomic status in 6-and 13-year-old school children. Oral health preventive dentistry. 2012; 10(3).
Costa LR, Daher A, Queiroz MG, health p. Early childhood caries and body mass index in young children from low income families. International journal of environmental research. 2013; 10(3):867-78.
Schwendicke F, Dörfer C, Schlattmann P, Page LF, Thomson W, Paris S. Socioeconomic inequality and caries: a systematic review and meta-analysis. Journal of dental research. 2015; 94(1):10-8.
Ghorbani Z, Ahmady AE, Ghasemi E, Zwi AB. Socioeconomic inequalities in oral health among adults in Tehran, Iran. Community dental health. 2015; 32(1):26-31.
Locker D, Slade G. Association between clinical and subjective indicators of oral health status in an older adult population. Gerodontology. 1994; 11(2):108-14.
- Abstract Viewed: 141 times
- PDF Downloaded: 104 times