3.2. The Pooled Prevalence
The pooled prevalence rate is depicted by the red dashed line in the plot, which was 46.2% in this instance (
Figure 2). This is a common value that can be used to judge study-specific ripe figures. The blue dotted lines indicate the 95% confidence limits.
The large studies clustered at the top of the plot have smaller standard errors, whereas at the bottom, the small studies have larger standard errors and are more widely spread.
The collection of studies included in the review (
Table 2) shows that periodontal disease is widespread, especially among at-risk groups such as diabetics, young, obese, and very old. This review highlights that the prevalence of periodontal disease, particularly in specific regions, such as Najran and the Eastern Province, is staggeringly high, ranging from 39% to 52.1%. It also identified several risk factors that contribute to the high prevalence of periodontal disease. These include poor oral hygiene, smoking, and socioeconomic factors. These could be referred to as “Biopsychosocial factors”, with the biological determinants associated with inadequate brushing, the psychological factors pertaining to tobacco addiction, and the social determinants connected with one stage of life.
Table 3 shows that the prevalence of periodontal disease, more specifically gingivitis and periodontitis, varies from study to study in Saudi Arabia. The only report on the prevalence of gingivitis by Alshabab et al. (2021) [
30] indicated a high rate of 61% among diverse populations in Najran Province. In addition, periodontitis has a wider prevalence distribution. The highest number documented to date for any population is that reported by Thomas et al. (2020) [
27]—71.3%. This study examined young adults living in Zulfi, an area of Saudi Arabia where a predominately high ratio of bread is found in the local diet. The findings are alarming, not only for the young adults studied but also because the high prevalence points to an urgent health issue that needs to be addressed.
In the Eastern Province, Tabassum et al. (2022) [
31] showed that 52.1% of individuals suffer from periodontitis. They linked this condition primarily to diabetes and poor oral hygiene. This is not surprising, given the established associations between these diseases and periodontitis. Similarly, individuals with poor oral hygiene are more likely to be afflicted with periodontitis. Alshabab et al. (2021) [
30] showed that periodontitis has a lower prevalence (39%) in Najran Province, suggesting regional differences in health behaviors and access to dental care.
More broadly, AlGhamdi et al. (2020) [
32] found periodontitis in 8.6 percent of high school students. They attributed this lower rate to less than stellar oral hygiene. Therefore, this finding connects periodontitis with something that can be improved, namely the educational level of teenagers regarding oral health.
The pooled prevalence of periodontal diseases in separate studies is intriguing as shown in
Figure 3. The dashed red line indicates a pooled prevalence of 46.2%. This serves as a benchmark for evaluating the findings of individual studies. It is a central place around which one can see that all individual studies are grouped.
The studies report very different prevalence rates. This stems from the methodology used in these studies. A critical choice that greatly affects the conduction of the study is the index used to measure disease. The two key indices in this context are the Community Periodontal Index (CPI) and the Periodontal Disease Index (PDI). These two indices are significantly different. They measure periodontal disease using different methods. As a result, when studies used these different indices, they reported very different prevalence rates.
The evaluations presented in
Table 4 pinpoint the vital factor of methodological rigor in determining treatment effectiveness. By looking closely at design, population selection, and outcome measurement, we get a glimpse of the potential biases that could compromise the validity of treatment effect findings.
The research carried out by Farshori et al. (2020) [
28] exhibited an average bias leaning toward positive treatment results. Similarly, research by Afsheen Tabassum et al. (2022) [
31] showed the same tendencies.
Evaluating the quality of the studies revealed that they were predominantly low-to-moderately biased. This assessment is crucial because the findings are only as reliable as the studies from which they were derived. Two studies, Thomas et al. (2020) [
27] and Almas et al. (1996) [
29], stood out due to their rigorous methodologies and serve as valuable reference points. To evaluate potential publication bias, funnel plots were employed. The observed asymmetry in these plots may suggest the presence of small-study effects, which could result from publication bias or other factors. It is important to acknowledge that funnel plot asymmetry can also arise due to between-study heterogeneity, the choice of effect metric, or chance. Therefore, while funnel plots are useful diagnostic tools, their interpretation should be approached with caution, considering these alternative explanations. This study reports a good range of prevalence from various studies that have been conducted. These studies contribute to the overall knowledge regarding the prevalence of periodontal disease in Saudi Arabia with a representative sample size. The contribution of different risk factors and the quality of the studies that assess them highlight the need for a thorough approach to periodontal health.
The analysis in
Table 5 highlights the pronounced differences in the extent of periodontal disease among the various subgroups. The comparisons are striking; obese adults have a 71.3% rate of periodontal disease, while the 15–19 age group has a rate of only 8.6%, suggesting that poor health and periodontal disease tend to go hand in hand.
Obesity, with prevalence rates ranging from 39% to 61%, underscores the importance of population characteristics, such as age, lifestyle, and health conditions, which are known to influence periodontal disease risk. These factors point to the necessity of public health strategies that are better targeted and address the specific needs of our diverse population.
Table 6 shows the complex interactions between many risk factors related to periodontitis. Smoking was one of the risk factors that has been consistently highlighted in five studies. In addition to smoking, inadequate oral care is a primary factor in the development of periodontal diseases. Six studies underscored the critical role that insufficient oral hygiene plays in this context. When people do not take care of their mouths, plaque and calculus build up on their teeth, engendering a home for pathogenic bacteria. The environment becomes so hospitable that the normal range of bacteria has been eliminated, and the infected area is replete with a collection of harmful microbes believed to be the basis for the various forms of periodontal disease.
Four studies identified diabetes as a clear example of a two-way relationship between systemic health and oral diseases. The impact of this disorder on periodontal health is potent and direct, making patients with poorly controlled diabetes more likely to have periodontal disease. Another important variable is age, with four of the studies reviewed indicating that the prevalence of periodontitis moves with the age curve. This may reflect a cumulative exposure to risk factors that one has over time, as well as some not-so-great changes in immune function and tissue regeneration that seem to occur as we age. Understanding this relationship is important for developing non-silent, age-appropriate, and preventive strategies.
Two studies mentioned the markers of obesity and inflammation as being contributing factors to periodontal disease. They provided further evidence of the multifactorial nature of the disease. Obesity is associated with periodontal health and may be associated with systemic inflammation. This type of inflammation can amplify the destruction of periodontal tissues. The association is not yet clear, and it is probably not a way in which periodontal disease works in all cases. However, it seems to be, at least for some people, a way in which periodontal disease works.
In summary, the findings shown in
Table 3 further support the idea that several variables influence periodontal disease. These include the following dimensions in individuals’ lives: lifestyle selection, health conditions that affect the entire body, and demographic traits. Addressing these factors comprehensively and doing so in an effective way might help improve individuals’ periodontal health and, as a corollary, their oral health status.
The meta-analysis results offer a detailed examination of periodontal diseases across various demographic subgroups. The overall pooled prevalence rate was 46.2% (95% CI: 40.5–51.8), which means that almost half of the populations studied had periodontal disease.
The prevalence rates showed astonishing differences between subgroups (
Table 7). The highest rate was found in obese adults, with a prevalence of 71.3% (95% CI: 65.4–77.1), indicating a very strong association. The rate is sufficiently high that it seems more than just coincidental, given the runs of unfortunate inflammatory events that are hallmarks of both obesity and periodontal disease. Indeed, the finding that links the two together has moderate heterogeneity (i
2 = 72%,
p = 0.01), which means it is stable across different studies.
The significant prevalence of periodontal disease in adults with diabetes was 52.1% (95% CI: 45.8–58.4), with substantial heterogeneity (I2 = 80%, p < 0.001). The well-documented relationship between diabetes and periodontal disease has a solid biological basis. Hyperglycemia can destroy periodontal tissue and slow the normal wound-healing response.
In contrast, teenagers aged 15 to 19 years showed a much lower prevalence of 8.6% (95% CI: 5.2–12.1). The subgroup analysis uses a fixed-effects model, which suggests that there is a more consistent prevalence rate among this age group across different studies. This seems to be a good model because the I2 value is 40%, which is neither high nor low, and the p-value is 0.12.
The population subgroup we classified as “mixed” presented a prevalence range of 39–61%. We pooled this as a 50% prevalence (95% CI: 43.0–57.0), with high heterogeneity (I2 = 88%, p < 0.001). We believe that this heterogeneity primarily reflects the diverse population characteristics of the included studies.
The considerable heterogeneity observed across almost all subgroups indicates that several factors affect the prevalence of periodontal diseases. These factors include the appearance and constitution of the studied populations, variety of study designs, and appearance of “periodontal disease” in the studied groups. The nature of the reported prevalence is also influenced by study duration, study location, use of periodontitis stage and grade, and so on. The appearance of “low periodontal health” in a studied group clearly influences the appearance of “high periodontal disease prevalence” in the studied group.