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Seroprevalence of Chlamydia trachomatis and Associated Factors among Vulnerable Riverine in the Brazilian Amazon.

José Jorge da Silva GalvãoCarlos Leonardo Figueiredo CunhaEllen Christiane Correa PinhoDavid de Jesus da Silva PaivaNádile Juliane Costa de CastroValéria Gabriele Caldas NascimentoWanderson Santiago de Azevedo JuniorRichardson Augusto Rosendo da SilvaRosimar Neris Martins FeitosaAntonio Carlos Rosário VallinotoEliã Pinheiro BotelhoGlenda Roberta Oliveira Naiff Ferreira
Published in: International journal of environmental research and public health (2022)
Due to social and individual conditions and access to health services, Amazonian riverside populations are highly vulnerable to sexually transmitted infections, including Chlamydia trachomatis . The aim is to estimate the seroprevalence of Chlamydia trachomatis and analyze the associated factors among riverside dwellers in a capital city in the Brazilian Amazon. A cross-sectional study was carried out with residents of the Combu Island, Belém. The study sample was calculated using the population survey technique in the EPI INFO. Only people aged 18 and over were included. ELISA serology was performed to detect antibodies against Chlamydia trachomatis . For data collection, a form containing vulnerability factor questions was applied. Binary regression analysis was performed using the Minitab 20 program. The study sample consisted of 325 participants. The prevalence of IgG/IgM antibodies against Chlamydia trachomatis was 22.2% and 5.5%, respectively. In the multiple regression, only participants with a broken condom were more likely to have antibodies against the bacteria (OR: 1.90; 95% CI: 1.01; 3.37; p = 0.046). Seroprevalence was associated with condom breakage. This factor demonstrates that despite having an attitude towards condom use, probably, they may have inadequate knowledge about the correct practice of introduction.
Keyphrases
  • healthcare
  • men who have sex with men
  • risk factors
  • mental health
  • climate change
  • physical activity
  • electronic health record
  • deep learning