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Seroprevalence for dengue virus in a hyperendemic area and associated socioeconomic and demographic factors using a cross-sectional design and a geostatistical approach, state of São Paulo, Brazil

Authors
  • Chiaravalloti-Neto, Francisco1
  • da Silva, Rafael Alves2
  • Zini, Nathalia2
  • da Silva, Gislaine Celestino Dutra2
  • da Silva, Natal Santos3
  • Parra, Maisa Carla Pereira2
  • Dibo, Margareth Regina4
  • Estofolete, Cassia Fernanda2
  • Fávaro, Eliane Aparecida2
  • Dutra, Karina Rocha2
  • Mota, Manlio Tasso Oliveira2
  • Guimarães, Georgia Freitas2
  • Terzian, Ana Carolina Bernardes2
  • Blangiardo, Marta5
  • Nogueira, Mauricio Lacerda2
  • 1 Universidade de São Paulo (USP), Departamento de Epidemiologia, Faculdade de Saúde Pública, Avenida Doutor Arnaldo 715, São Paulo, SP, 01246-904, Brazil , São Paulo (Brazil)
  • 2 Faculdade de Medicina de São José do Rio Preto (FAMERP), Laboratório de Pesquisas em Virologia, Departamento de Doenças Dermatológicas Infecciosas e Parasitárias, Avenida Brigadeiro Faria Lima, 5416, São José do Rio Preto, SP, 15090-000, Brazil , São José do Rio Preto (Brazil)
  • 3 Faculdade de Medicina, União das Faculdades dos Grandes Lagos, Laboratório de Modelagens Matemática e Estatística em Medicina, Rua Doutor Eduardo Nielsen 960, São José do Rio Preto, SP, 15030-070, Brazil , São José do Rio Preto (Brazil)
  • 4 Superintendência de Controle de Endemias, Laboratório de Entomologia, Rua Cardeal Arcoverde 2878, São Paulo, SP, 05408-003, Brazil , São Paulo (Brazil)
  • 5 Imperial College, MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, St. Mary’s Campus, Norfolk Place, London, W2 1PG, UK , London (United Kingdom)
Type
Published Article
Journal
BMC Infectious Diseases
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 20, 2019
Volume
19
Issue
1
Identifiers
DOI: 10.1186/s12879-019-4074-4
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundSão José do Rio Preto is one of the cities of the state of São Paulo, Brazil, that is hyperendemic for dengue, with the presence of the four dengue serotypes.Objectives: to calculate dengue seroprevalence in a neighbourhood of São José do Rio Preto and identify if socioeconomic and demographic covariates are associated with dengue seropositivity.MethodsA cohort study to evaluate dengue seroprevalence and incidence and associated factors on people aged 10 years or older, was assembled in Vila Toninho neighbourhood, São José do Rio Preto. The participant enrolment occurred from October 2015 to March 2016 (the first wave of the cohort study), when blood samples were collected for serological test (ELISA IgG anti-DENV) and questionnaires were administrated on socio-demographic variables. We evaluated the data collected in this first wave using a cross-sectional design. We considered seropositive the participants that were positive in the serological test (seronegative otherwise). We modelled the seroprevalence with a logistic regression in a geostatistical approach. The Bayesian inference was made using integrated nested Laplace approximations (INLA) coupled with the Stochastic Partial Differential Equation method (SPDE).ResultsWe found 986 seropositive individuals for DENV in 1322 individuals surveyed in the study area in the first wave of the cohort study, corresponding to a seroprevalence of 74.6% (95%CI: 72.2–76.9). Between the population that said never had dengue fever, 68.4% (566/828) were dengue seropositive. Older people, non-white and living in a house (instead of in an apartment), were positively associated with dengue seropositivity. We adjusted for the other socioeconomic and demographic covariates, and accounted for residual spatial dependence between observations, which was found to present up to 800 m.ConclusionsOnly one in four people aged 10 years or older did not have contact with any of the serotypes of dengue virus in Vila Toninho neighbourhood in São José do Rio Preto. Age, race and type of house were associated with the occurrence of the disease. The use of INLA in a geostatistical approach in a Bayesian context allowed us to take into account the spatial dependence between the observations and identify the associated covariates to dengue seroprevalence.

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