The Relationship of Oral Health Literacy and Self-Efficacy With Oral Health Status and Dental Neglect

Department of Pediatric Dentistry, University of North Carolina, Chapel Hill, NC, USA.
American Journal of Public Health (Impact Factor: 4.55). 10/2011; 102(5):923-9. DOI: 10.2105/AJPH.2011.300291
Source: PubMed


We examined the associations of oral health literacy (OHL) with oral health status (OHS) and dental neglect (DN), and we explored whether self-efficacy mediated or modified these associations.
We used interview data collected from 1280 female clients of the Special Supplemental Nutrition Program for Women, Infants and Children from 2007 to 2009 as part of the Carolina Oral Health Literacy Project. We measured OHL with a validated word recognition test (REALD-30), and we measured OHS with the self-reported National Health and Nutrition Examination Survey item. Analyses used descriptive, bivariate, and multivariate methods.
Less than one third of participants rated their OHS as very good or excellent. Higher OHL was associated with better OHS (for a 10-unit REALD increase: multivariate prevalence ratio = 1.29; 95% confidence interval = 1.08, 1.54). OHL was not correlated with DN, but self-efficacy showed a strong negative correlation with DN. Self-efficacy remained significantly associated with DN in a fully adjusted model that included OHL.
Increased OHL was associated with better OHS but not with DN. Self-efficacy was a strong correlate of DN and may mediate the effects of literacy on OHS.

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    • "This is contrary to some other studies conducted in an urban dental setting (2). Limited understanding of oral health information can lead to poor oral health status and dental neglect (21). "
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    ABSTRACT: Background Limited health literacy among adults is one of the many barriers to better oral health outcomes. It is not uncommon to find people who consider understanding oral health information a challenge. Therefore, the present study assessed oral health literacy among clients visiting Gian Sagar Dental College and Hospital, Rajpura. Materials and Methods A cross-sectional study was conducted on 450participants who visited the Out Patient Department (OPD) of Gian Sagar Dental College and Hospital for a period of two months (Nov–Dec, 2013). A questionnaire was given to each of the participants. Oral health literacy was graded on a 12-point Likert scale based on the total score. Oral Health Literacy of the participants was assessed as low, medium and high on the basis of responses. Statistical analysis was done using SPSS-15 statistical package. ANOVA and Student t-test were used to do comparisons between groups. Results Low oral health literacy scores were reported in 60.2% (271) participants. More than 60% of the study participants had knowledge about dental terms such as ‘dental caries,’ and ‘oral cancer.’ Only 22% of the graduates had a high literacy score. Mean oral health literacy score according to educational qualification was statistically significant (p<0.05), whereas there was no significant difference in terms of age and gender (p>0.05). Conclusion The majority of the participants had low literacy scores. There is a need to address these problems especially among rural population by health care providers and the government.
    Ethiopian journal of health sciences 07/2014; 24(3):261-8. DOI:10.4314/ejhs.v24i3.10
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    • "An accumulating body of evidence links low OHL with worse oral health outcomes such as oral health status [22,23], dental neglect [24] as well as sporadic dental attendance [25]. In a investigation among a group of Indigenous Australians, Parker and Jamieson [26] found that although low OHL was not associated with self-reported oral health status, it was associated with increased prevalence of OHIP-14 impacts (proportion of items reported fairly/very often). "
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    ABSTRACT: To investigate the association between oral health literacy (OHL) and oral health-related quality of life (OHRQoL) and explore the racial differences therein among a low-income community-based group of female WIC participants. Participants (N = 1,405) enrolled in the Carolina Oral Health Literacy (COHL) study completed the short form of the Oral Health Impact Profile Index (OHIP-14, a measure of OHRQoL) and REALD-30 (a word recognition literacy test). Socio-demographic and self-reported dental attendance data were collected via structured interviews. Severity (cumulative OHIP-14 score) and extent of impact (number of items reported fairly/very often) scores were calculated as measures of OHRQoL. OHL was assessed by the cumulative REALD-30 score. The association of OHL with OHRQoL was examined using descriptive and visual methods, and was quantified using Spearman's rho and zero-inflated negative binomial modeling. The study group included a substantial number of African Americans (AA = 41%) and American Indians (AI = 20%). The sample majority had a high school education or less and a mean age of 26.6 years. One-third of the participants reported at least one oral health impact. The OHIP-14 mean severity and extent scores were 10.6 [95% confidence limits (CL) = 10.0, 11.2] and 1.35 (95% CL = 1.21, 1.50), respectively. OHL scores were distributed normally with mean (standard deviation, SD) REALD-30 of 15.8 (5.3). OHL was weakly associated with OHRQoL: prevalence rho = -0.14 (95% CL = -0.20, -0.08); extent rho = -0.14 (95% CL = -0.19, -0.09); severity rho = -0.10 (95% CL = -0.16, -0.05). "Low" OHL (defined as < 13 REALD-30 score) was associated with worse OHRQoL, with increases in the prevalence of OHIP-14 impacts ranging from 11% for severity to 34% for extent. The inverse association of OHL with OHIP-14 impacts persisted in multivariate analysis: Problem Rate Ratio (PRR) = 0.91 (95% CL = 0.86, 0.98) for one SD change in OHL. Stratification by race revealed effect-measure modification: Whites--PRR = 1.01 (95% CL = 0.91, 1.11); AA--PRR = 0.86 (95% CL = 0.77, 0.96). Although the inverse association between OHL and OHRQoL across the entire sample was weak, subjects in the "low" OHL group reported significantly more OHRQoL impacts versus those with higher literacy. Our findings indicate that the association between OHL and OHRQoL may be modified by race.
    Health and Quality of Life Outcomes 12/2011; 9(1):108. DOI:10.1186/1477-7525-9-108 · 2.12 Impact Factor
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    ABSTRACT: To investigate the association of caregivers' oral health literacy (OHL) with their children's oral health related-quality of life (C-OHRQoL) and explore literacy as a modifier in the association between children's oral health status (COHS) and C-OHRQoL. This study relied upon data from structured interviews with 203 caregivers of children aged 3-5 from the Carolina Oral Health Literacy (COHL) Project. Data were collected for OHL using REALD-30, caregiver-reported COHS using the NHANES-item and C-OHRQoL using the Early Childhood Oral Health Impact Scale (ECOHIS). This study also measured oral health behaviors (OHBs) and socio-demographic characteristics and calculated overall/stratified summary estimates for OHL and C-OHRQoL. Spearman's rho and 95% confidence intervals (CI) were computed as measures of correlation of OHL and COHS with C-OHRQoL. To determine whether OHL modified the association between COHS and C-OHRQoL, this study compared literacy-specific summary and regression estimates. Reported COHS was: excellent-50%, very good-28%, good-14%, fair-6%, poor-2%. The aggregate C-OHRQoL mean score was 2.0 (95% CI: 1.4, 2.6), and the mean OHL score 15.9 (95% CI: 15.2, 16.7). There was an inverse relationship between COHS and C-OHRQoL: ρ = -0.32 (95% CI: -0.45, -0.18). There was no important association between OHL and C-OHRQoL; however, deleterious OHBs were associated with worse C-OHRQoL. Literacy-specific linear and Poisson regression estimates of the association between COHS and C-OHRQoL departed from homogeneity (Wald χ(2) p < 0.2). In this community-based sample of caregiver/child dyads, a strong correlation was found between OHS and C-OHRQoL. The association's magnitude and gradient were less pronounced among caregivers with low literacy.
    Acta odontologica Scandinavica 12/2011; 70(5):390-7. DOI:10.3109/00016357.2011.629627 · 1.03 Impact Factor
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