Income inequality and traumatic dental injuries in 12-year-old children: A multilevel analysis. 2017

Mario Vianna Vettore, and Salma Efhima, and Carolina Machuca, and Gabriela de Almeida Lamarca
Unit of Dental Public Health, School of Clinical Dentistry, University of Sheffield, Sheffield, UK.

OBJECTIVE Contextual socio-economic factors have been associated with traumatic dental injuries (TDIs). However, evidence concerning the role of income inequality on TDIs in children is scarce. The aim of this study was to investigate the association between contextual income inequality over a 10-year period and TDIs in Brazilian children. METHODS The study population comprised a representative sample of 5027 children aged 12 years who participated in the Brazilian oral health survey in 2010. City-level Gini Index was used to measure contextual income inequality in the years 2000 and 2010, as well as the variation in income inequality between 2000 and 2010. Covariates were gender, ethnicity, family income, number of people per room and incisal overjet. Clinical examinations were used to assess TDIs. Multivariable multilevel ordered multinomial logistic regression was used to estimate cumulative Odds Ratio (OR) and 95% confidence intervals between income inequality and TDIs. RESULTS The prevalence of children who had one tooth with TDI and two or more teeth with TDIs was 15.2% and 6.4%, respectively. The maxillary central and left lateral incisors were the teeth most affected by TDIs. Gini coefficient reduction between the years 2000 and 2010 decreased the odds of TDIs even after adjustment for demographic and socio-economic characteristics, and incisal overjet. The likelihood of more TDIs decreased 21% for each 0.05 unit decrease in the Gini coefficient between the years 2000 and 2010. Boys, brown skin colour, overcrowding and incisal overjet greater than 5 mm remained statistically associated with TDIs in the final model. CONCLUSIONS The decrease in income inequality over a 10-year period was inversely associated with TDIs among Brazilian children aged 12 years.

UI MeSH Term Description Entries
D007182 Income Revenues or receipts accruing from business enterprise, labor, or invested capital. Income Distribution,Income Generation Programs,Savings,Distribution, Income,Distributions, Income,Income Distributions,Income Generation Program,Incomes,Program, Income Generation,Programs, Income Generation
D008297 Male Males
D001938 Brazil A country located on the eastern coast of South America, located between Colombia and Peru, that borders the Atlantic Ocean. It is bordered on the north by Venezuela, Guyana, Suriname, and French Guiana, on the south by Uruguay, and on the west by Argentina. The capital is Brasilia.
D002648 Child A person 6 to 12 years of age. An individual 2 to 5 years old is CHILD, PRESCHOOL. Children
D003710 Demography Statistical interpretation and description of a population with reference to distribution, composition, or structure. Demographer,Demographic,Demographic and Health Survey,Population Distribution,Accounting, Demographic,Analyses, Demographic,Analyses, Multiregional,Analysis, Period,Brass Technic,Brass Technique,Demographers,Demographic Accounting,Demographic Analysis,Demographic Factor,Demographic Factors,Demographic Impact,Demographic Impacts,Demographic Survey,Demographic Surveys,Demographic and Health Surveys,Demographics,Demography, Historical,Demography, Prehistoric,Factor, Demographic,Factors, Demographic,Family Reconstitution,Historical Demography,Impact, Demographic,Impacts, Demographic,Multiregional Analysis,Period Analysis,Population Spatial Distribution,Prehistoric Demography,Reverse Survival Method,Stable Population Method,Survey, Demographic,Surveys, Demographic,Analyses, Period,Analysis, Demographic,Analysis, Multiregional,Demographic Analyses,Demographies, Historical,Demographies, Prehistoric,Distribution, Population,Distribution, Population Spatial,Distributions, Population,Distributions, Population Spatial,Family Reconstitutions,Historical Demographies,Method, Reverse Survival,Method, Stable Population,Methods, Reverse Survival,Methods, Stable Population,Multiregional Analyses,Period Analyses,Population Distributions,Population Methods, Stable,Population Spatial Distributions,Prehistoric Demographies,Reconstitution, Family,Reconstitutions, Family,Reverse Survival Methods,Spatial Distribution, Population,Spatial Distributions, Population,Stable Population Methods,Technic, Brass,Technique, Brass
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012307 Risk Factors An aspect of personal behavior or lifestyle, environmental exposure, inborn or inherited characteristic, which, based on epidemiological evidence, is known to be associated with a health-related condition considered important to prevent. Health Correlates,Risk Factor Scores,Risk Scores,Social Risk Factors,Population at Risk,Populations at Risk,Correlates, Health,Factor, Risk,Factor, Social Risk,Factors, Social Risk,Risk Factor,Risk Factor Score,Risk Factor, Social,Risk Factors, Social,Risk Score,Score, Risk,Score, Risk Factor,Social Risk Factor
D015995 Prevalence The total number of cases of a given disease in a specified population at a designated time. It is differentiated from INCIDENCE, which refers to the number of new cases in the population at a given time. Period Prevalence,Point Prevalence,Period Prevalences,Point Prevalences,Prevalence, Period,Prevalence, Point,Prevalences
D055361 Multilevel Analysis The statistical manipulation of hierarchically and non-hierarchically nested data. It includes clustered data, such as a sample of subjects within a group of schools. Prevalent in the social, behavioral sciences, and biomedical sciences, both linear and nonlinear regression models are applied. Analyses, Multilevel,Analysis, Multilevel,Multilevel Analyses

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