BACKGROUND: Tooth loss is still prevalent among older adults and may negatively affect their health and well-being. Previous evidence has shown that oral disease-related factors are the more consistent predictors of tooth loss. Although certain models have considered oral health behaviour as a key construct in explaining oral health inequalities, others have favoured the role of social structure and the social environment in determining oral health outcomes. OBJECTIVE: To determine the tooth loss incidence and its predictors among community-living older adults in a southern Brazilian city. METHODS: A cohort study was conducted using a simple random sample of 388 older people evaluated in 2004. The follow-up was carried out during 2008, with 273 older persons examined. Interviews and oral examinations were conducted. The incidence of tooth loss was modelled through a conceptual framework with the use of negative binomial regression. RESULTS: Some 67.8% of people lost one or more teeth, whereas 12.5% became edentulous during the follow-up period. In the multivariate model that controlled for baseline predictors, older age; male gender; living in a rural area; being married; less schooling; current smoking; and dissatisfaction with the access to health services were associated with tooth loss. Among the clinical variables, the use of partial dentures and gingival bleeding index were predictors, whereas higher saliva flow rate was a protective factor for the outcome. DISCUSSION: As proposed in the conceptual framework, demographic factors; primary determinants of health; behavioural; and clinical variables were predictors of tooth loss. These results are important, because understanding the causal association between lifestyle practices such as oral hygiene or smoking with tooth loss does not elucidate why individuals and communities engage in such practices. CONCLUSION: Interventions aiming to reduce tooth loss and edentulism in the elderly should account for social and environmental factors, in combination with clinical and behavioural components.
BACKGROUND:Tooth loss is still prevalent among older adults and may negatively affect their health and well-being. Previous evidence has shown that oral disease-related factors are the more consistent predictors of tooth loss. Although certain models have considered oral health behaviour as a key construct in explaining oral health inequalities, others have favoured the role of social structure and the social environment in determining oral health outcomes. OBJECTIVE: To determine the tooth loss incidence and its predictors among community-living older adults in a southern Brazilian city. METHODS: A cohort study was conducted using a simple random sample of 388 older people evaluated in 2004. The follow-up was carried out during 2008, with 273 older persons examined. Interviews and oral examinations were conducted. The incidence of tooth loss was modelled through a conceptual framework with the use of negative binomial regression. RESULTS: Some 67.8% of people lost one or more teeth, whereas 12.5% became edentulous during the follow-up period. In the multivariate model that controlled for baseline predictors, older age; male gender; living in a rural area; being married; less schooling; current smoking; and dissatisfaction with the access to health services were associated with tooth loss. Among the clinical variables, the use of partial dentures and gingival bleeding index were predictors, whereas higher saliva flow rate was a protective factor for the outcome. DISCUSSION: As proposed in the conceptual framework, demographic factors; primary determinants of health; behavioural; and clinical variables were predictors of tooth loss. These results are important, because understanding the causal association between lifestyle practices such as oral hygiene or smoking with tooth loss does not elucidate why individuals and communities engage in such practices. CONCLUSION: Interventions aiming to reduce tooth loss and edentulism in the elderly should account for social and environmental factors, in combination with clinical and behavioural components.
Authors: Leila Maria Marchi-Alves; Dayana Freitas; Denise de Andrade; Simone de Godoy; Adrielle Naiara Toneti; Isabel Amélia Costa Mendes Journal: Biomed Res Int Date: 2017-06-21 Impact factor: 3.411
Authors: Fernando Oliveira Costa; Luís Otávio Miranda Cota; José Roberto Cortelli; Sheila Cavalca Cortelli; Renata Magalhães Cyrino; Eugênio José Pereira Lages; Ana Paula Lima Oliveira Journal: PLoS One Date: 2015-10-23 Impact factor: 3.240