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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 3  |  Page : 150-156

A predictive logistic regression model for periodontal diseases


Department of Preventive Dental Sciences, College of Dentistry, Najran University, Najran, Kingdom of Saudi Arabia

Date of Submission22-Nov-2020
Date of Decision22-Oct-2021
Date of Acceptance05-Nov-2021
Date of Web Publication21-Dec-2021

Correspondence Address:
Dr. Md Zahid Hossain
Department of Preventive Dental Sciences, Division of Periodontics, College of Dentistry, Najran University, King Abdulaziz Road, Najran
Kingdom of Saudi Arabia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/sjoralsci.sjoralsci_123_20

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  Abstract 


Introduction: Periodontal diseases (gingivitis and periodontitis) are one of the main concerns for oral health affecting around 20%–50% of the world population.
Aims: The aim of this study was to formulate a predictive model for periodontal diseases in a selected population.
Materials and Methods: A hospital-based analytical study was carried out. Seven hundred male patients having different forms of periodontal diseases were included to explore the common features and possible risk factors related to periodontal diseases. Chi-squared test and t-test were performed for univariate analysis, and binary logistic regression model was adapted for multivariate analysis using SPSS v23.
Results and Discussion: Four hundred and seventy (67%) and 230 (33%) patients suffered from gingivitis and periodontitis, respectively. The mean age of patients with periodontitis (37.17 ± 11.52 years) was significantly higher than those with gingivitis (26.04 ± 10.83 years). Univariate analysis showed that plaque and calculus had statistically significant relationship with gingivitis 451 (72%). Systemic diseases 18 (72%) and patients' habits 39 (76%) had statistically significant relationship with periodontitis (P < 0.05). A logistic regression model was formulated including age, risk factors, and nationality. The model was tested, and its sensitivity, specificity, and accuracy for detecting periodontal diseases were equal to 83.3%, 67.2%, and 78.0%, respectively.
Conclusions: This model had a good fit and explained a significant proportion of variance in the outcome variable (periodontitis) R2 = 0.40, (χ2 (9) = 238.32, P < 0.001).

Keywords: Gingivitis, logistic regression, Najran, periodontitis, risk factors, Saudi Arabia


How to cite this article:
Hossain MZ, Alshahrani MA, Alasmari AS, Hyderah KM, Alshabab AZ, Hassan MA, Abdulrazzaq AM. A predictive logistic regression model for periodontal diseases. Saudi J Oral Sci 2021;8:150-6

How to cite this URL:
Hossain MZ, Alshahrani MA, Alasmari AS, Hyderah KM, Alshabab AZ, Hassan MA, Abdulrazzaq AM. A predictive logistic regression model for periodontal diseases. Saudi J Oral Sci [serial online] 2021 [cited 2022 Nov 30];8:150-6. Available from: https://www.saudijos.org/text.asp?2021/8/3/150/333011




  Introduction Top


Periodontal diseases (gingivitis and periodontitis) are among the most prevalent oral conditions that affect about 20%–50% of global human population.[1],[2] The high prevalence of periodontal disease in adolescents, adults, and older individuals has made it a public health concern.[3]

The recent Global Burden of Disease studies indicate that the global burden of periodontal disease was increased by 57.3% from 1990 to 2010.[4],[5],[6] Studies also indicate that severe periodontitis is the 6th most prevalent disease worldwide, with an overall prevalence of 11.2% affecting around 743 million people.[7],[8] Saudi studies reported that the highest prevalence of gingivitis was among younger age groups, whereas the highest prevalence of periodontitis was among older age groups with plaque and gingivitis at 87.9% and 73.9%, respectively.[9],[10] A Yemeni study on university students observed that 31.4%, 51.7%, and 4.4% had bleeding, calculus, and shallow pocket, respectively.[11]

Individuals are at the risk of multiple tooth loss, edentulism, and masticatory dysfunction because periodontitis is the major cause of tooth loss in adult population worldwide, with all these factors affecting their nutrition, quality of life (QoL), and self-esteem as well as imposing huge socioeconomic impacts and health-care costs.[12],[13],[14],[15] Periodontitis is a chronic noncommunicable disease (NCD) that shares social determinants and risk factors, with the major NCDs that cause around two-thirds of deaths such as heart disease, diabetes, cancer, and chronic respiratory disease.[5],[14],[15] Systemic conditions and disorders are now considered to be the secondary factors modulating disease initiation or progression instead of acting as primary etiological factors. They may be hormonal, nutritional, genetic, or related to age or comprise drug intake or such habits as smoking.[10],[16],[17]

Aims and objectives of the study

”Severe periodontal disease, which may result in tooth loss, and was the 11th most prevalent disease globally in 2016.”[2] This statement contributed to our decision to conduct this study, the first of its kind, in the College of Dentistry (COD), Najran University (NU), Kingdom of Saudi Arabia (KSA), on periodontal diseases among the patients attending the dental outpatient department (OPD). The aims of the study were to (i) describe characteristics of patients attended the OPD who presented with periodontal diseases (gingivitis or periodontitis), (ii) assess the relationship of the following selected factors of age, nationality of the patients, and other factors to periodontal disease types using univariate analysis, and (iii) test these factors as an independent predictive factor for periodontal disease types using binary logistic regression analysis;[18],[19] in order to formulate a logistic model that can be used as a valuable tool for predicting periodontal diseases in the selected population.


  Materials and Methods Top


Study design

A hospital-based descriptive and analytical study was carried out to explore the common features and possible risk factors related to periodontal diseases (gingivitis and periodontitis) occurring among the patients who attended the OPD at COD, NU, Najran, KSA. The study duration was between January 1, 2018 and March 30, 2018 and to formulate a predictive logistic model of periodontal diseases.

Sampling criteria

A convenience sampling method was used, in which all well-documented patients' records kept in OPD were included irrespective of age, race, ethnicity, systemic conditions, and habits. Only male patients had access to this hospital, and therefore, 700 male patients whose records indicated that they had periodontal diseases were included in this study. Any patient without well-documented record was excluded from this study. To avoid selection bias, the authors exerted much effort in establishing strict inclusion and exclusion criteria.

Disease measurement

Periodontal parameters were recorded using a University of Michigan 'O' probe to diagnose gingivitis and periodontitis cases.

The study population was recruited from the patients' records kept in the OPD. All participants signed the informed consent before their enrollment into the study. Each case was diagnosed as no periodontal disease (gingivitis or periodontitis) and any other based on periodontal parameters recorded. Data included the patients' age, nationality, periodontal diseases, and their possible risk factors.

According to the Canadian Health Measures Survey 2007–2009, the measurement of loss of periodontal ligament attachment is considered the gold standard in reporting the prevalence of periodontal disease.[20] The National Health and Nutrition Examination Survey (NHANES) has determined that attachment loss (AL) and probing depth (PD) at six sites of all teeth (excluding third molars) was a measure for the estimation of periodontal disease in the United States of America.[21]

Gingival index (GI) was recorded using Loe and Silness (1963) method.[22]

0 = No inflammation.

1 = Mild inflammation, slight change in color, slight edema, and no bleeding on probing.

2 = Moderate inflammation and bleeding on probing.

3 = Severe inflammation, marked redness and edema, ulceration, and tendency toward spontaneous bleeding.

Plaque index was recorded by Silness and Loe (1964) method.[23]

  • 0 = No plaque in the gingival area
  • 1 = A film of plaque adhering to the free gingival margin (GM) and adjacent area of the tooth
  • 2 = Moderate accumulation of soft deposits within the gingival pocket and on the GM and/or adjacent tooth surface that can be seen by naked eye
  • 3 = Abundance of soft matter within the gingival pocket and/or on the GM and adjacent tooth surface.


PD was measured as the distance from the GM to the base of the gingival sulcus/periodontal pocket. The periodontal probe was also used to measure the distance between the cementoenamel junction (CEJ) and the GM.

Clinical attachment level (CAL) was calculated according to the standard formula CAL = probing pocket depth + (GM – CEJ).

Localized and generalized gingivitis/periodontitis cases were diagnosed with the ≤30% and >30% teeth involvements, respectively. Mild, moderate, and severe types of periodontitis were diagnosed using CAL as 1–2 mm, 3-4 mm, and >5 mm, respectively.

Statistical Analysis

Obtained data were coded, entered, and analyzed using the SPSS software (version 23, IBM Corp, Armonk, NY, USA). Descriptive analysis was done to summarize data as numbers and percentages. Significance testing of differences was done using the Chi-squared test and t-test to compare data of nominal or interval level, respectively. Binary logistic regression analysis[18],[19] was performed to formulate a predictive model for periodontitis. Factors tested for being independent predictive factors regarding periodontitis and gingivitis were the patients' age, nationality, and risk factors. The dependent outcome factors were the gingivitis and periodontitis. All tests were two tailed, and a P < 0.05 was considered statistically significant.


  Results Top


Seven hundred male patients recorded and diagnosed with periodontal diseases were accessioned. Of these, 470 (67%) suffered from gingivitis, and the rest, 230 (33%), suffered from periodontitis. The mean age of the patients was 29.70 ± 12.23 (range: 4–70) years. Results of two-tailed independent t-test [Table 1] showed that there was a statistically significant difference, at the 0.05 level, in the mean age between gingivitis (M = 26.04, S = 10.83) and periodontitis (M = 37.17, S = 11.52), t (430.482) = -12.231, P < 0.05.
Table 1: Results of t-tests and descriptive statistics for periodontal disease by the age

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[Table 2] shows the distribution of periodontal diseases (gingivitis and periodontitis) among the different nationalities and the risk factors related to them. Saudi patients suffered more from gingivitis (82%) than periodontitis (18%). On the other hand, Bangladeshi suffered more from periodontitis (74%) than gingivitis (26%), P < 0.001. Plaque and calculus were more related to the occurrence of gingivitis (72%) than periodontitis (27%). Conversely, systemic factors were more related to occurrence of periodontitis (72%) than gingivitis (27%), P < 0.001. There were statistically significant relationships between periodontal diseases and the reported risk factors.
Table 2: Occurrence of periodontal diseases by nationality and risk factors

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Results of the analysis of variance (ANOVA) show that there were statistically significant differences in mean ages among different types of gingivitis [Table 3] and periodontitis [Table 4], P < 0.001.
Table 3: Analysis of variance results and descriptive statistics of gingivitis by the age

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Table 4: Analysis of variance results and descriptive statistics of periodontitis by the age

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Binary logistic regression analysis model[24] was adapted using 695 cases with periodontal diseases [Table 5]. Five cases were excluded because of inconsistent data. The disease type (gingivitis or periodontitis) was considered as dependent (outcome) variable and others were considered as explanatory variables such as age, nationality, and risk factors. Results of the binary logistic regression indicated that there was a significant association between age, nationality, and risk factors and periodontal disease type (χ2 (9) = 238.32, P < 0.001). According to the model, patients who were ≤26 years of age were 6.9 times more likely to have gingivitis than periodontitis. Patients with plaque and calculus had 8.1 times greater chance of developing gingivitis than periodontitis. This model also showed that the Bangladeshi nationals were 6.3 times more likely to suffer from periodontitis than gingivitis. On the other side, Saudi nationals were 2.8 times more likely to suffer from gingivitis than periodontitis.
Table 5: Logistic regression analysis of periodontal disease type on age, nationality and risk factors

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The model was tested and its sensitivity, specificity, and accuracy were equal to 83.3%, 67.2%, and 78.0%, respectively. It also showed a moderate (0.503) interrater reliability calculated by Cohen's kappa statistics. This model is fairly explanatory since 40% of the changes in the outcome can be explained by the changes in the explanatory variables [Table 5].


  Discussion Top


Periodontal diseases (gingivitis and periodontitis) are prevalent both in developed and developing countries and affect about 20%–50% of global population.[1],[2] Previous reports from Saudi Arabia and Yemen documented lower prevalence rates of periodontal diseases among Yemenis than the Saudis.[10],[11] In this study, the highest numbers of patients were the Saudis (388 cases; 55.4%) who suffered from periodontal diseases followed by Yemenis (196 cases; 28.0%) [Table 2]. It was also observed that Yemenis suffered more from periodontitis (101 cases; 43.9%) compared to Saudis (70 cases; 30.4%) (P < 0.001) [Table 2]. This higher rate of periodontitis among might be attributed to the presence of oral habits such as khat (Catha edulis) among Yemeni population.[11],[25]

High rate of gingivitis among young Saudis was also reported by El Tantawi et al., in which 73.9% of their series suffered from gingivitis.[10] Similarly, another study (2016) reported 72.8% of young Greek suffered from gingivitis.[26] In contrast, a Yemeni study (2018) reported 78.6% of 5–12-year-old Yemeni population suffered from gingivitis.[27] The present study revealed that about 55% of patients (mean age: 26.04 ± 10.83) suffered from gingivitis, in which 68% of Saudi patients suffered from gingivitis [Table 1] and [Table 2].

Murillo et al. found 99.6% of gingivitis participants among the three Latin American cities, in which plaque and calculus were the major causative factors.[28] Idrees et al. carried out a study on 385 adult dentate participants to find out the prevalence and severity of plaque-induced gingivitis in a Saudi adult population.[29] They found that plaque accumulation was strongly associated with high prevalence (100%) of moderate-to-severe gingivitis among 18–40-year-old Saudi participants. The current study revealed that 89.0% of patients suffered from periodontal diseases due to plaque and calculus [Table 2].

Al Mugeiren assessed periodontal status among the patients who attended the private university hospitals in Riyadh city of Saudi Arabia.[9] He found the highest prevalence of gingivitis in younger age (20–34 years) group and the highest prevalence of periodontitis in older age (50–64 years) group. In the present study, mean age of the patients was 29.70 ± 12.23 years; of which patients of higher ages (mean age: 37.17 ± 11.52; range: 18–70 years) suffered from periodontitis compared to gingivitis patients (mean age 26.04 ± 10.83).

The main causes of periodontal disease are poor oral hygiene and tobacco use.[3],[4],[9] The smokers are three times more likely to have a severe form of periodontal disease than nonsmokers.[6] A considerable number (39 cases, 17.0%) of patients in this study had habits of chewing khat, tobacco, and smoking [Table 2] and they also had plaque and calculus.

Literature consistently shows that diabetes mellitus is one of the systemic risk factors for periodontal diseases which can play a major role in the initiation and progression of the disease.[30],[31],[32],[33] It has been observed that periodontal disease is likely to cause an increase in the risk of cardiovascular disease.[34],[35],[36] This study revealed that the 7.8% of patients suffering from periodontal diseases had diabetes, hypertension, and asthma and all of them had plaque and calculus [Table 2].

As periodontitis is the major cause of tooth loss in adult population worldwide, afflicted individuals are at the risk of multiple tooth loss, edentulism, and masticatory dysfunction, which invariably affect their nutrition, QoL, and self-esteem as well as imposing huge socioeconomic impacts and health-care costs.[12],[13],[14],[15] Of all patients, the present study found that 33% of the studied patients suffered from different forms of periodontitis [Table 2].

The use of logistic regression modeling has exploded during the past few decades and is now commonly applied in many fields including dental epidemiology. Logistic regression involves a prediction equation, in which one or more explanatory (predictor) variables is used to provide information about expected values of a binary response (dependent) variable.[18],[19] Regression methods have become an integral component of any data analysis concerned with the explanation of relationship between a response variable and one or more explanatory variables called covariates. In this study, a binary logistic regression analysis model was adapted using 695 cases with periodontal diseases [Table 5].

In this study, patients who were younger than or equal to 26 years of age had 6.9 times more chance of having gingivitis than periodontitis. This is in accord with the data of Al Mugeiren[9] study. Our study observed that the patients with plaque and calculus had 8.1 times greater chance of developing gingivitis than periodontitis, a finding similar to data from the study of Nazir[3] In this model, Bangladeshi nationals were 6.3 times more likely to suffer from periodontitis than gingivitis. On the other side, Saudi nationals were 2.8 times more likely to suffer from gingivitis than the periodontitis, which is similar to data from the study of Al Mugeiren.[9]

The model was tested and its sensitivity, specificity, and accuracy were equal to 83.3%, 67.2%, and 78.0%, respectively. Similarly, Montero et al. recommended that their model could be used as a reliable screening tool for periodontitis in primary medical care settings to facilitate referral of patients at risk for periodontal examination and diagnosis.[37],[38] Our study was based on data extracted from the record files of the OPD of the college. This means that there might be some important missing data related to patients' history, presence of systemic conditions, and final diagnoses.


  Conclusions Top


This study presents a period-limited picture of patients suffering from different forms of periodontal diseases (gingivitis and periodontitis) in Najran area and who attended the OPD of periodontics at the COD in NU of KSA. Patients who had gingivitis and periodontitis showed a pattern consistent with the global data. Khat chewing was observed as a habit along with tobacco chewing and smoking. Further study on a broad scale and over a longer period in a prospective study design along with more careful history taking, clinical examination, and diagnoses of diseases would most likely improve the applicability of this model. Almost all the patients had poor oral hygiene and comprehensive preventive programs would be needed to improve their oral health. Using the research results, a greater effort can be made in providing periodontal health information to the population at or around Najran province of KSA, which in turn facilitates taking necessary steps to meet up the treatment needs. We suggest that this model can be further tested in primary medical care settings as a reliable screening method for diagnosing and promoting referral of patients at risk for periodontal diseases.

Acknowledgment

The authors expressed their gratitude to the director of clinics and all staff members of the record-keeping section of the college.

Ethical policy and institutional review board statement

The Research and Ethics Committee of the COD, NU, Saudi Arabia approved the research project under process number 001/18 on January 1, 2018. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Patient declaration of consent:

This study was performed using the record files kept in the OPD. All patients signed in the record files while registered at the OPD. Hence, separate patient consent form was not used.

Financial support and sponsorship

Nil.

Conflicts

There are no conflicts of interest.



 
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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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