Influence of socioeconomic status on oral disease burden: a population-based study

Abstract

Background Dental caries is associated with Biological, behavioral, socioeconomic, and environmental factors; however, socioeconomic status is a distal determinant of dental caries development that modulates exposure to risk and protective factors. This study aimed to analyze the socioeconomic factors associated with the concentration of oral diseases in a population-based study in Brazil. Methods This is a quantitative, analytical, cross-sectional study based on secondary data from the SB São Paulo 2015 epidemiological survey. A total of 17,560 subjects were included. The concentration of oral disease in the population was estimated by the oral disease burden (ODB) variable. The ODB consists of four components: dental caries; tooth loss; need for dental prosthesis and periodontal condition. Thus, the total score on the ODB could vary between 0 and 4, with the highest score indicating the worst possible situation. ODB was analyzed in multivariate negative binomial regression, and multivariate binary logistic regression analysis. The following factors were included as independent variables: age group, skin color, socioeconomic factors, family income and Oral Impact on Daily Performance (OIDP). Results In the sample, 86.9% had no minimum ODP component. Negative multivariate binomial regression showed a statistically significant relationship (p < 0.005) between ODB and all variables analyzed (skin color, family income, education, OIDP results and age range). The adjusted multivariate binary logistic regression showed that the individuals most likely to have at least one component of ODB were nonwhite (25.5%), had a family income of up to R$ 1500.00/month (19.6%), had only completed primary education (19.1%), and reported that their oral health had an impact on their daily activities (57.6%). Older adults individuals were two times more likely than adolescents to have an ODB component. Conclusions ODB is associated with factors related to social inequality. Adults and older adults individuals had the highest cumulative number of ODB components.

Association between migration and oral health-related quality of life: results from a nationally representative online survey

“…To measure oral health-related quality of life, we used the well-known Oral Health Impact Profile (OHIP-G5 [ 19 ]) which encompasses four factors [ 1 ] oral function, [ 2 ] orofacial pain, [ 3 ] appearance and [ 4 ] psychosocial impact. Favorable psychometric characteristics of the OHIP-G5 have been shown [ 19 ].…”

“…The items were as follows (in each case: 0-never, 1-hardly ever, 2-occasionally, 3-fairly often, and 4-very often): [ 1 ] Have you had difficulty chewing any foods because of problems with your teeth, mouth, dentures or jaw? [ 2 ] Have you felt that there has been less flavor in your food because of problems with your teeth, mouth, dentures or jaws? [ 3 ] Have you had painful aching in your mouth?…”

“…Inequalities in oral diseases can be seen in marginalized and poor groups of society [ 1 ]. Non-white race and ethnicity, low levels of education, low family income are risk factors for suffering from oral diseases (caries, tooth loss, periodontitis, need for dental prosthesis) [ 2 ]. A migration background does not per se cause disease initiation/development, but the accumulation of risk factors are especially seen in migrant groups that are often socioeconomically disadvantaged [ 3 ].…”

Influence of gender and oral health knowledge on DMFT index: a cross sectional study among school children in Kaski District, Nepal

“…Oral health problems are usually multifactorial. Socioeconomic factors are attributable to the severity and wider distribution of oral diseases [ 4 ]. In addition, family environment, including socio-behavioral and environmental factors are strongly related with the oral health condition of the children.…”

Professionals’ perspectives on how to address persistent oral health inequality among young children: an exploratory multi-stakeholder analysis in a disadvantaged neighbourhood of Amsterdam, the Netherlands

“…To tackle the burden of poor oral health in young children, it is necessary to gain a better understanding of the determinants causing oral health inequalities [ 11 , 12 ]. Previous research has mainly focused on individual-level determinants resulting in behaviour change interventions, such as preventive strategies for oral hygiene and sugar control [ 8 , 13 ].…”