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. 2024 Apr 25;19(4):e0293024.
doi: 10.1371/journal.pone.0293024. eCollection 2024.

Association between dental clinical measures and oral health-related quality of life among Iraqi schoolchildren: A cross-sectional study

Affiliations

Association between dental clinical measures and oral health-related quality of life among Iraqi schoolchildren: A cross-sectional study

Hanan Fadhil Alautry et al. PLoS One. .

Abstract

Objective: The aim of this study was to assess the association between dental clinical measures and oral health-related quality of life (OHRQoL) together with the potential mediating role of sociodemographic factors and oral health behaviours on this association in a group of Iraqi children.

Methods: The target population for this cross-sectional study consisted of 372 primary school children aged 8-10 years in the city of Kut, Iraq, during the year 2022. The participants were selected using a multi-stage random sampling technique. Detailed information about the children was collected through a comprehensive questionnaire that included demographic characteristics, oral health-related behaviors, the Arabic version of the Child Perceptions Questionnaire for 8-10-year-olds (CPQ8-10), and parental knowledge regarding oral health. Additionally, clinical dental evaluations were conducted, which included assessments of decayed, missing, and filled surfaces (DMFS, dmfs) as well as teeth (DMFT, dmft). Simplified oral hygiene index (OHI-S), bleeding on probing (BOP), and the community periodontal index (CPI) were also recorded for each participant using the recommended methodology by the World Health Organization (WHO). The statistical analysis included the Chi-square test, independent t-test, and simple and multiple linear regressions.

Results: The mean age of the children was 9.0 (± 0.82) years. About one-third of children reported brushing at least two times per day and consuming a sweet snack once a day. Visiting a dentist during the past year was reported by 21% of children. Oral health behaviours demonstrated a significant association with the total CPQ8-10 scores (p < 0.001). Based on adjusted effects (β and 95% CI) from the multiple linear regressions, untreated dental caries (dt > 0, DT > 0) had a negative impact on the total CPQ8-10 score (adjusted β = 2.3 (95% CI: 0.67 to 3.91) and 3.4 (95% CI: 2.14 to 4.56), respectively). Decayed surfaces (DS), and teeth (DT) were associated with the total score of the CPQ8-10 and all its subscales (adjusted β range = 0.1 (95% C.I.: 0.03 to 0.19)-1.0 (95% CI: 0.72 to 1.26) and 0.2 (95% CI: 0.004 to 0.40)-1.2 (95% CI: 0.91 to 1.67), respectively). There was an association between oral hygiene index and total CPQ8-10 scores (adjusted β = 1.8 (95% CI: 0.62 to 3.02)), especially the functional limitations and emotional well-being subscales.

Conclusion: Findings of this study emphasizes the negative impact of dental caries and poor oral hygiene on children's OHRQoL. This association is highlighted more when considering that over two-thirds of these children do not comply with favourable levels of oral health behaviour. Enhancing the level of OHRQoL among these children, therefore, necessitates comprehensive programs for decreasing the volume of unmet oral health needs and improving children's adherence to recommended oral health behaviours.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Forest plot for the effect of dental clinical measures on total CPQ8-10 scores by multiple linear regression models.
Fig 2
Fig 2. Forest plot for the effect of dental clinical measures on oral symptoms subscale score by multiple linear regression models.
Fig 3
Fig 3. Forest plot for the effect of dental clinical measures on functional limitations subscale score by multiple linear regression models.
Fig 4
Fig 4. Forest plot for the effect of dental clinical measures on emotional well-being subscale score by multiple linear regression models.
Fig 5
Fig 5. Forest plot for the effect of dental clinical measures on social well-being subscale score by multiple linear regression models.

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