Comparing Multi-level and Ordinary Logistic Regression Models in Evaluating Factors Related to Periodontal Clinical Attachment Loss

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DEHGHANI S.; ABADI A.; NAMDARI M.; GHORBANI Z.

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Journal: Iranian Journal of Epidemiology Year:2019 | Volume:14 | Issue:4 Page(s): 359-365

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Title

Comparing Multi-level and Ordinary Logistic Regression Models in Evaluating Factors Related to Periodontal Clinical Attachment Loss

Author(s)

DEHGHANI S. | ABADI A. | NAMDARI M. | GHORBANI Z. | Issue Writer Certificate

Keywords

Multilevel logistic regression modelQ1

Hierarchical structureQ1

Clinical attachment lossQ1

Abstract

Background and Objectives: Periodontal disease is one of the most common oral health problems. Clinical attachment loss occurs in sever periodontal cases (CAL>3). In this study, we applied a classic regression model and the models that consider the Hierarchical structure of the data to estimate and compare the effect of different factors on CAL. Methods: This cross-sectional study was performed in 375 pregnant women and 192 mothers of three-year-old children. The data were gathered from 16 health networks of Shahid Beheshti University of Medical Sciences, Tehran, Iran. CAL was determined for 6 teeth per person by a dentist according to WHO standard oral health examination form. Three-level and ordinary logistic regression analyses were applied for data analysis using the STATA software 14. Results: Of 3, 402 examined teeth, 6. 3% had CAL> 3mm. Based on the obtained results, the odds of CAL>3mm were 2. 4 in the third semester compared to non-pregnant women. The odds of CAL>3mm were 2. 86 in women without daily floss use compared to women with routine daily floss use. Posterior teeth were more likely to have CAL>3m than anterior teeth (OR = 1. 65) (P-value < 0. 05). Conclusion: According to the AIC index, multi-level logistic regression model has a better fit than ordinary logistic regression model and can estimate the coefficients of factors related to CAL>3mm more precisely. The use of the ordinary logistic regression model in hierarchical data can result in underestimated standard errors of the estimated parameters.

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APA: Copy

DEHGHANI, S., ABADI, A., NAMDARI, M., & GHORBANI, Z.. (2019). Comparing Multi-level and Ordinary Logistic Regression Models in Evaluating Factors Related to Periodontal Clinical Attachment Loss. IRANIAN JOURNAL OF EPIDEMIOLOGY, 14(4 ), 359-365. SID. https://sid.ir/paper/377364/en

Vancouver: Copy

DEHGHANI S., ABADI A., NAMDARI M., GHORBANI Z.. Comparing Multi-level and Ordinary Logistic Regression Models in Evaluating Factors Related to Periodontal Clinical Attachment Loss. IRANIAN JOURNAL OF EPIDEMIOLOGY[Internet]. 2019;14(4 ):359-365. Available from: https://sid.ir/paper/377364/en

IEEE: Copy

S. DEHGHANI, A. ABADI, M. NAMDARI, and Z. GHORBANI, “Comparing Multi-level and Ordinary Logistic Regression Models in Evaluating Factors Related to Periodontal Clinical Attachment Loss,” IRANIAN JOURNAL OF EPIDEMIOLOGY, vol. 14, no. 4 , pp. 359–365, 2019, [Online]. Available: https://sid.ir/paper/377364/en

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