Factors related to periodontal disease in a rural population

Braz Oral Res. 2006 Jul-Sep;20(3):257-62. doi: 10.1590/s1806-83242006000300014.

Abstract

To estimate the prevalence and related aspects of periodontitis in a rural area of the State of Bahia, Brazil, this cross-sectional study was carried out in the village of Matinha dos Pretos, Feira de Santana County, Bahia, among 172 subjects ranging from 20 to 60 years of age. During household visits, a full-mouth periodontal exam was performed on each subject, who also answered a questionnaire about socio-demographic, economic and health-related issues. The factors assessed were plaque index, bleeding on probing index, probing depth, gingival recession or hyperplasia measurements. Clinical attachment loss was also calculated. The multivariate logistic regression method was used to evaluate the relative contribution of these factors to the periodontitis condition. The prevalence of periodontitis was 24.4%. The following factors were all positively associated with the presence of periodontitis: being male (OR = 1.58; 1.00 - 2.53), being 30 years of age or older (OR = 2.80; 1.00 - 7.39), living in a house where there was more than one person per room (OR = 1.53; 0.96 - 2.45), being a cigarette or pipe smoker or ex-smoker (OR = 1.49; 0.92 - 2.39), having a plaque index of over 65% (OR = 2.97; 2.72 - 7.39) and more than four missing teeth (OR = 1.51; 0.82 - 2.78). The authors concluded that socioeconomic and biological factors, especially poor oral hygiene and older age, are positively associated with periodontitis in the rural population of a small village in the county of Feira de Santana, State of Bahia, Brazil.

MeSH terms

  • Adult
  • Brazil / epidemiology
  • Dental Plaque / epidemiology
  • Dental Plaque Index
  • Epidemiologic Methods
  • Female
  • Humans
  • Male
  • Middle Aged
  • Oral Health*
  • Oral Hygiene
  • Periodontal Attachment Loss / epidemiology
  • Periodontitis / diagnosis
  • Periodontitis / epidemiology*
  • Rural Population / statistics & numerical data*
  • Socioeconomic Factors