https://doi.org/10.4081/gh.2020.854
Determining the association between dengue and social inequality factors in north-eastern Brazil: A spatial modelling
Submitted: 15 December 2019
Accepted: 15 April 2020
Published: 17 June 2020
Accepted: 15 April 2020
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Dengue is a global public health problem. The Dengue Virus (DENV) serotypes are transmitted by an Aedes aegypti mosquito. Vector control is among the primary methods to prevent the disease, especially in tropical countries. This study aimed to analyze the spatial distribution of dengue and its relationship with social inequalities using spatial modelling. An ecological study with temporal and spatial analysis was conducted in the state of Sergipe, Northeast Brazil, over a period of 18 years. Spatial modelling was used to determine the influence of space on dengue incidence and social inequalities. The epidemic rates in 2008, 2012, and 2015 were identified. Spatial modelling explained 40% of the influence of social inequalities on dengue incidence in the state. The main social inequalities related to the occurrence of dengue were the percentage of people living in extreme poverty and inadequate sanitation. The epidemic situation even increased the risk of dengue in the population of the state of Sergipe. These results demonstrate the potential of spatial modelling in determining the factors associated with dengue epidemics and are useful in planning the intersectoral public health policies.
Lutomiah J, Barrera R, Makio A, Mutisya J, Koka H, Owaka S, et al., 2016 Dengue Outbreak in Mombasa City, Kenya, 2013–2014: Entomologic Investigations. PLoS Negl Trop Dis 10: e0004981. doi: 10.1371/journal.pntd.0004981
DOI: https://doi.org/10.1371/journal.pntd.0004981
Restrepo BN, Beatty ME, Goez Y, Ramirez RE, Diaz FJ, Piedrahita LD et al, 2014. Frequency and clinical manifestations of dengue in urban Medellin, Colombia. J Trop Med 872608. doi: 10.1155/2014/872608.
DOI: https://doi.org/10.1155/2014/872608
Stewart-Ibarra AM, Ryan SJ, Kenneson A, King CA, Abbott M, Barbachano-Guerrero A, et al, 2018. The burden of dengue fever and chikungunya in southern coastal Ecuador: epidemiology, clinical presentation, and phylogenetics from the first two years of a prospective study. Am J Trop Med. Hyg 98: 1444.-1459. doi: 10.4269/ajtmh.17-0762.
DOI: https://doi.org/10.4269/ajtmh.17-0762
World Health Organization, 2015. Dengue and dengue haemorrhagic fever. Fact sheet no. 117.
Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al, 2013. The global distribution and burden of dengue. Nature 496: 504-507.
DOI: https://doi.org/10.1038/nature12060
Araújo VEM, Bezerra JMT, Amâncio FF, Passos VMA, Carneiro M, 2017. Increase in the burden of dengue in Brazil and federated units, 2000 and 2015: analysis of the Global Burden of Disease Study 2015. Rev Bras Epidemiol 20: 205-216.
DOI: https://doi.org/10.1590/1980-5497201700050017
Guo XX, Zhu XJ, Li CX, 2013. Vector competence of Aedes albopictus and Aedes aegypti (Diptera: Culicidae) for Den 2-43 and New Guinea C virus strains of dengue 2 virus. Acta Trop 128: 566-570.
DOI: https://doi.org/10.1016/j.actatropica.2013.08.006
Moyes CL, Vontas J, Martins AJ, Ng LC, Koou SY, Dusfour I, et al, 2017. Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans. PLoS Negl Trop Dis 11: e0005625.
DOI: https://doi.org/10.1371/journal.pntd.0005625
Francis S, Saavedra-Rodriguez K, Perera R, Paine M, Black WC IV, Delgoda R, 2017. Insecticide resistance to permethrin and malathion and associated mechanisms in Aedes aegypti mosquitoes from St. Andrew Jamaica. PLoS ONE 12: e0179673.
DOI: https://doi.org/10.1371/journal.pone.0179673
Frenk J, Moon S, 2013. Governance challenges in global health. N Engl J Med 368: 936-942.
DOI: https://doi.org/10.1056/NEJMra1109339
Christofferson RC, Mores CN, 2015. A role for vector control in dengue vaccine programs. Vaccine 33: 7069-7074.
DOI: https://doi.org/10.1016/j.vaccine.2015.09.114
Zara ALSA, Santos SM, Fernandes-Oliveira ES, Carvalho RG, Coelho GE, 2016. Aedes aegypti control strategies: a review. Epidemiologia e Serviços de Saúde 25: 391-404.
Marcondes CB, Ximenes MFFM, 2016. Zika virus in Brazil and the danger of infestation by Aedes (Stegomyia) mosquitoes. Rev Soc Bras Med Trop 49: 4-10.
DOI: https://doi.org/10.1590/0037-8682-0220-2015
Vazquez-Prokopec GM, Kitron U, Montgomery B, Horne P, Ritchie SA, 2010. Quantifying the spatial dimension of dengue virus epidemic spread within a tropical urban environment. PLoS Negl Trop Dis 4: e920.
DOI: https://doi.org/10.1371/journal.pntd.0000920
Instituto Brasileiro de Geografia e EstatÃstica. Brasil, 2017. http://cidades.ibge.gov.br/xtras/uf.php?coduf=28.
Sergipe. State Secretariat of Health, 2011. A sanitary and managerial reform of the SUS in the state of Sergipe: knowledge and technologies to implement a policy. Apprentice's Book 1.
Kim H, Fay MP, Feuer EJ, Midthune DN, 2000. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med 19(3):335–351.
DOI: https://doi.org/10.1002/(SICI)1097-0258(20000215)19:3<335::AID-SIM336>3.0.CO;2-Z
Brasil MS, 2007. Sistemas de Informações Geográficas e Análise Espacial na Saúde Pública. Ministério da Saúde, Ministry of Health, Brazil, 148 pp.
Druck S, Carvalho MS, Câmara G, Monteiro AMV, 2004. Análise espacial de dados geográficos. BrasÃlia (DF): Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA).
Moran PAP, 1948. The interpretation of statistical maps. J R Stat Soc Ser B 10: 243-251.
DOI: https://doi.org/10.1111/j.2517-6161.1948.tb00012.x
Câmara G, Monteiro AMV, 2001. Geocomputation techniques for spatial analysis: are they relevant to health data? CadSaude Publica, Rio Janeiro 17: 1059-1081.
DOI: https://doi.org/10.1590/S0102-311X2001000500002
Anselim L, 2005. Exploring a spatial data with GeoDaTM: a workbook. Rev. version. Urbana (IL): Center for Spatially Integrated Social Science of University of Illinois., USA.
Weaver SC, Reisen WK, 2010. Present and future arboviral threats. Antiviral Res 85: 328-345.
DOI: https://doi.org/10.1016/j.antiviral.2009.10.008
World Health Organization, 2008. The world health report 2008: primary health care now more than ever. Available at: https://www.who.int/whr/2008/en/ (accessed April 2020)
Ren H, Ning W, Lu L, Zhuang D, Liu Q, 2015. Characterization of dengue epidemics in mainland China over the past decade. J Infect Dev Ctries 9: 970-976.
DOI: https://doi.org/10.3855/jidc.5998
Lippi CA, Stewart-Ibarra AM, Muñoz ÃG, et al, 2018. The social and spatial ecology of dengue presence and burden during an outbreak in Guayaquil, Ecuador, 2012. Int J Environ Res Public Health 15: E827.
DOI: https://doi.org/10.3390/ijerph15040827
Mondini A, Chiaravalloti-Neto F, 2008. Spatial correlation of incidence of dengue with socioeconomic, demographic and environmental variables in a Brazilian city. Sci Total Environ 393: 241-248.
DOI: https://doi.org/10.1016/j.scitotenv.2008.01.010
Hagenlocher M, Delmelle E, Casas I, Kienberger S, 2013. Assessing socioeconomic vulnerability to dengue fever in Cali, Colombia: statistical vs expert-based modelling. Int J Health Geogr 12: 36. doi: 10.1186/1476-072X-12-36.
DOI: https://doi.org/10.1186/1476-072X-12-36
Soghaier MA, Himatt S, Osman KE, Okoued SI, Seidahmed OE, Beatty ME, et al, 2015. Cross-sectional community-based study of the socio-demographic factors associated with the prevalence of dengue in the eastern part of Sudan in 2011. BMC Public Health 15: 558. doi: 10.1186/s12889-015-1913-0.
DOI: https://doi.org/10.1186/s12889-015-1913-0
Braga C, Luna CF, Martelli CM, de Souza WV, Cordeiro MT, Alexander N, et al, 2010. Seroprevalence and risk factors for dengue infection in socio-economically distinct areas of Recife, Brazil. Acta Trop 113: 234-240.
DOI: https://doi.org/10.1016/j.actatropica.2009.10.021
Costa JV, Donalisio MR, Silveira LV, 2013. Spatial distribution of dengue incidence and socio-environmental conditions in Campinas, Sao Paulo State, Brazil, 2007. Cad Saude Publica 29: 1522-1532.
DOI: https://doi.org/10.1590/S0102-311X2013001200005
Khormi HM, Kumar L, 2011. Modelling dengue fever risk based on socioeconomic parameters, nationality and age groups: GIS and remote sensing based case study. Sci Total Environ 409: 4713-4719.
DOI: https://doi.org/10.1016/j.scitotenv.2011.08.028
Heukelbach J, de Oliveira FA, Kerr-Pontes LR, Feldmeier H, 2001. Risk factors associated with an outbreak of dengue fever in a favela in Fortaleza, north-east Brazil. Trop Med Int Health 6: 635-642.
DOI: https://doi.org/10.1046/j.1365-3156.2001.00762.x
Seidahmed OM, Hassan SA, Soghaier MA, Siam HA, Ahmed FT, Elkarsany MM, et al, 2012. Spatial and temporal patterns of dengue transmission along a Red Sea coastline: a longitudinal entomological and serological survey in Port Sudan city. PLoS Negl Trop Dis 6: e1821.
DOI: https://doi.org/10.1371/journal.pntd.0001821
Mulligan K, Dixon J, Joanna Sinn CL, Elliott SJ, 2015. Is dengue a disease of poverty? A systematic review. Pathog Glob Health 109: 10-18.
DOI: https://doi.org/10.1179/2047773214Y.0000000168
Lima SVMA, Santos AD, Duque AM, Oliveira-Goes MA, Silva MVP, Araújo DC, et al, 2019. Spatial and temporal analysis of tuberculosis in an area of social inequality in Northeast Brazil. BMC Public Health 873: 19. doi: 10.1186/s12889-019-7224-0.
DOI: https://doi.org/10.1186/s12889-019-7224-0
Marteis LS, Stefller LM, Araújo KCGM, Santos RLC, 2013. Identificação e distribuição espacial de imóveischave de Aedes aegypti no bairro Porto Dantas, Aracaju, Sergipe, Brasil entre 2007 e 2008. Cad. Saúde Pública, Rio de Janeiro 29: 368-378.
DOI: https://doi.org/10.1590/S0102-311X2013000600023
Akter R, Naish S, Hu W, Tong S, 2017. Socio-demographic, ecological factors and dengue infection trends in Australia. PLoS ONE 12: e0185551.
DOI: https://doi.org/10.1371/journal.pone.0185551
Stewart Ibarra AM, Ryan SJ, Beltrán E, MejÃa R, Silva M, Muñoz Ã, 2013. Dengue vector dynamics (Aedes aegypti) influenced by climate and social factors in Ecuador: Implications for targeted control. PLoS ONE 8: e78263.
DOI: https://doi.org/10.1371/journal.pone.0078263
Stewart-Ibarra AM, Muñoz ÃG, Ryan SJ, Ayala EB, Borbor-Cordova MJ, Finkelstein JL, et al, 2014. Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010. BMC Infect Dis 14: 610. doi: 10.1186/s12879-014-0610-4.
DOI: https://doi.org/10.1186/s12879-014-0610-4
Schmidt WP, Suzuki M, Dinh Thiem V, White RG, Tsuzuki A, Yoshida LM, et al, 2011. Population density, water supply, and the risk of dengue fever in Vietnam: Cohort study and spatial analysis. PLoS Med 8: e1001082.
DOI: https://doi.org/10.1371/journal.pmed.1001082
Souza VMM, Hoffmann JL, Freitas MM, Brant JL, Araújo WN. Avaliação do conhecimento, atitudes e práticas sobre dengue no MunicÃpio de Pedro Canário, Estado do EspÃrito Santo, Brasil, 2009: um perfil ainda atual. Rev Pan-Amaz Saude 2012; 3(1):37-43.
DOI: https://doi.org/10.5123/S2176-62232012000100006
How to Cite
da Conceição Araújo, D. ., dos Santos, A. D., Lima, S. V. M. A., Vaez, A. C., Cunha, J. O., & Conceição Gomes Machado de Araújo, K. (2020). Determining the association between dengue and social inequality factors in north-eastern Brazil: A spatial modelling. Geospatial Health, 15(1). https://doi.org/10.4081/gh.2020.854
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