ANÁLISE DAS NOTIFICAÇÕES DE DENGUE NO PARANÁ: ESTUDO DE CASO A PARTIR DA ESTATÍSTICA DESCRITIVA E ANÁLISE DE CORRESPONDÊNCIA MÚLTIPLA.
Palavras-chave:
dengue, notificação de doença, sistemas de informação em saúde, análise multivariada, análise de correspondência múltipla
Resumo
O objetivo deste estudo é descrever as notificações de dengue no Paraná no ano epidemiológico 2019-2020. Os registros foram extraídos do Sistema de Agravo de Notificações (SINAN). Além da estatística descritiva, a análise de correspondência múltipla foi utilizada para explorar relações entre 51 variáveis, presentes no sistema, incluindo informações socioeconômicas e clínicas. O período representou recorde na série histórica para o estado, com 366.760 notificações, das quais 66,59% foram confirmadas. Óbitos pelo agravo foram 198, o que representa 0,054% dos notificados. O critério de confirmação adotado, na maior parte dos casos, foi o clínico-epidemiológico, utilizado em 65,88% do total. Exames laboratoriais específicos foram empregados como critério de confirmação em 27,31% dos indivíduos. Foram, também, identificado maior associação entre as variáveis de doenças pré-existentes e os níveis de casos alarmantes (DAS) e graves (DG) de dengue. Além disso, relações entre variáveis socioeconômicas específicas e sinais clínicos de dengue clássica foram observadas e estão descritas nos resultados. A pesquisa pretende contribuir para oferecer um panorama do registro das notificações de dengue no Paraná, para o ano epidemiológico abordado e sugerir outras possibilidades para análises exploratórias posteriores.
Referências
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23. Hung TM, Shepard DS, Bettis AA et al. Productivity costs from a dengue episode in Asia: a systematic literature review. BMC Infect. Dis. 2020; 20 (1): 1-18.
24. Teich V, Arinelli R, Fahham L, Aedes aegypti e sociedade: o impacto econômico das arboviroses no Brasil. JBES: Braz. J. of Health Econ. 2017; 9(3): 267-276.
25. Oliveira LNS, Itria A, Lima EC. Cost of illness and program of dengue: A systematic review. PloS One. 2019; 14(2): e0211401.
26. Rafikahmed S, Mateti UV, Subramanya C, Shetty S, Sunny A, Madhusoodanan A. Assessment of direct medical cost using cost of illness analysis in patients with dengue fever-Retrospective study. Clin. Epidemiol. and Gl. Health.2021;12: 100842.
27. Fares RCG, Souza KPR, Añez G, Rios M. Epidemiological scenario of dengue in Brazil. BioMed Res. Int. 2015;2015: 321873.
28. Martin BM, Evans AA, de Carvalho DS et al. Clinical outcomes of dengue virus infec-tion in pregnant and non-pregnant women of reproductive age: a retrospective cohort study from 2016 to 2019 in Paraná, Brazil. BMC Infect Dis. 2022; 22 (5).
29. Brigagão G, Corrêa NAB. Levantamento epidemiológico da dengue no estado do Pa-raná Brasil nos anos de 2011 a 2015. Arquivos de Ciências da Saúde da UNIPAR. 2017; 21(1).
30. RSTUDIO TEAM. RStudio: Integrated Delevopment for R. RStudio, Inc., Boston, 2020. Disponível em
31. Rencher AC, Christensen WF. Methods of multivariate analysis. 3ª ed. New Jersey: John Wiley & Sons, 2012. 781 p.
32. Abdi H, Béra M. Correspondence Analysis. In Encyclopedia of Research Design. Thousand Oaks. 2010.
33. Fogaça TK, Mendonça F. Distribuição espacial dos sorotipos de dengue e fluxos in-termunicipais no Paraná. Raega-O Espaç. Geográf. em Anál. 2019;46(2):101-115.
34. Preto, C. de Mello AM, Maluf EMCP, Krainski ET, Graeff G, de Sousa GA et al. Vaccination coverage and adherence to a dengue vaccination program in the state of Parana, Brazil. Vaccine. 2021;39(4): 711-719.
35. KRAEMER, Moritz UG et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. elife, v. 4, p. e08347, 2015.
36. Ossani PC, Cirillo MA. MVar: Multivariate analysis. Disponível em: URL
2. Wilder-Smith A, Ooi EE, Horstick O, Wills B. Dengue. The Lancet. 2019; 393(10169): 350-363.
3. Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, et al. The global distribution and burden of dengue. Nature. 2013; 496 (7446):504-507.
4. Yaseen HM, Simon F, Deparis X, Marimoutou C. Identification of initial severity determinants to predict arthritis after chikungunya infection in a cohort of French gendarmes. BMC musculoskeletal disord. 2014; 15 (1):1-8.
5. Higuera-Mendieta D R, Cortés-Corrales S, Quintero J, González-Uribe C. KAP surveys and dengue control in Colombia: disentangling the effect of sociodemographic factors using multiple correspondence analysis. PLoS Neglect. Trop. Diseas. 2016; 10(9):e0005016.
6. Fritzell C, Raude J, Adde L, Dusfour I, Quenel P, Flamand C. Knowledge, attitude, and practices of vector-borne disease prevention during the emergence of a new arbovirus: implications for the control of Chikungunya virus in French Guiana. PLoS Neglect Trop Disea. 2016; 10(11): e0005081.
7. Wu C, Guo X, Zhao J, Lv Q, Li H, McNeil E, et al. Behaviors related to mosquito-borne diseases among different ethnic minority groups along the China-laos border ar-eas. Int J of Environm Res and Pub Health. 2017; 14(10): 1227.
8. Siswantining T, Windesia Y, Soemartojo MS, Ariyanto MM, Shahab M R. Predicting the risk of hospitalization to six diagnoses with highest costs based on outpatient claims. In: AIP Conference Proceedings. AIP Publishing LLC, 2018: 020067.
9. Nava-Doctor JE, Sandoval-Ruiz CA, Fernández-Crispín A. Knowledge, attitudes, and practices regarding vector-borne diseases in central Mexico. J of Ethnobiol and Ehnomed. 2021;17(1):1-14.
10. Wichmann O, Gascon J, Schunk M, Puente S, Saiikamaki H, Gjorup I, et al. Severe dengue virus infection in travelers: risk factors and laboratory indicators. The J of Infect Diseases. 2007; 195(8): 1089-1096.
11. Souza-Neto JA, Powell JR, Bonizzoni M. Aedes aegypti vector competence studies: A review. Infec., Genet. and Evol. 2019; 67: 191-209.
12. Flipse J, Smit JM. The complexity of a dengue vaccine: a review of the human antibody response. PLoS neglect. Trop. Diseas..2015; 9 (6): e0003749.
13. WORLD HEALTH ORGANIZATION. Handbook for clinical management of dengue. 2012.
14. Ahmed S, Ali N, Ashraf S, Ilyas M, Tariq W, Chotani R. Dengue fever outbreak: a clinical management experience. J Coll Physic Surg Pak. 2088; 18(1):8-12.
15. Tanner L, Schreiber M, Low JGH, Ong A, Tolfvenstam T, Lai YL, et al. Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis. 2008; 2(3): e196.
16. Thomas SJ, Yoon I. A review of Dengvaxia®: Development to deployment. Human vacc. and immunotherapeutics. 2019; 15 (10): 2295-2314.
17. Stanaway JD, Shepard DS, Undurraga EA, Halasa YA, Coffeng LE, Brady OJ, et al. The global burden of dengue: an analysis from the Global Burden of Disease Study 2013. The Lancet Infect. Diseas. 2016; 16 (6): 712-723.
18. Laserna A, Barahona-Correa J, Baquero L, Castañeda-Cardona C, Rosselli D. Eco-nomic impact of dengue fever in Latin America and the Caribbean: a systematic re-view. Ver. Panam. de Salud Pública. 2018;42:e111.
19. Montibeler E, Oliveira D. Dengue endemic and its impact on the gross national prod-uct of Brazilian’s economy. Acta tropic. 2018; 178: 318-326.
20. Bavia L, Melanda FN, de Arruda TB, Mosimann ALP, Silveira GF, Aoki MN, et al. Epidemiological study on dengue in southern Brazil under the perspective of climate and poverty. Scientific Reports, v. 10, n. 1, p. 1-16, 2020.
21. Shepard DS, Halasa E, Stanaway YJ. The global economic burden of dengue: a sys-tematic analysis. The Lancet Infect. Dis.2016;16 (8): 935-941.
22. Koopmanschap MA, Van Ineveld BM. Towards a new approach for estimating indi-rect costs of disease. Soc. Sci. and Med. 1992; 34 (9): 1005-1010.
23. Hung TM, Shepard DS, Bettis AA et al. Productivity costs from a dengue episode in Asia: a systematic literature review. BMC Infect. Dis. 2020; 20 (1): 1-18.
24. Teich V, Arinelli R, Fahham L, Aedes aegypti e sociedade: o impacto econômico das arboviroses no Brasil. JBES: Braz. J. of Health Econ. 2017; 9(3): 267-276.
25. Oliveira LNS, Itria A, Lima EC. Cost of illness and program of dengue: A systematic review. PloS One. 2019; 14(2): e0211401.
26. Rafikahmed S, Mateti UV, Subramanya C, Shetty S, Sunny A, Madhusoodanan A. Assessment of direct medical cost using cost of illness analysis in patients with dengue fever-Retrospective study. Clin. Epidemiol. and Gl. Health.2021;12: 100842.
27. Fares RCG, Souza KPR, Añez G, Rios M. Epidemiological scenario of dengue in Brazil. BioMed Res. Int. 2015;2015: 321873.
28. Martin BM, Evans AA, de Carvalho DS et al. Clinical outcomes of dengue virus infec-tion in pregnant and non-pregnant women of reproductive age: a retrospective cohort study from 2016 to 2019 in Paraná, Brazil. BMC Infect Dis. 2022; 22 (5).
29. Brigagão G, Corrêa NAB. Levantamento epidemiológico da dengue no estado do Pa-raná Brasil nos anos de 2011 a 2015. Arquivos de Ciências da Saúde da UNIPAR. 2017; 21(1).
30. RSTUDIO TEAM. RStudio: Integrated Delevopment for R. RStudio, Inc., Boston, 2020. Disponível em
31. Rencher AC, Christensen WF. Methods of multivariate analysis. 3ª ed. New Jersey: John Wiley & Sons, 2012. 781 p.
32. Abdi H, Béra M. Correspondence Analysis. In Encyclopedia of Research Design. Thousand Oaks. 2010.
33. Fogaça TK, Mendonça F. Distribuição espacial dos sorotipos de dengue e fluxos in-termunicipais no Paraná. Raega-O Espaç. Geográf. em Anál. 2019;46(2):101-115.
34. Preto, C. de Mello AM, Maluf EMCP, Krainski ET, Graeff G, de Sousa GA et al. Vaccination coverage and adherence to a dengue vaccination program in the state of Parana, Brazil. Vaccine. 2021;39(4): 711-719.
35. KRAEMER, Moritz UG et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. elife, v. 4, p. e08347, 2015.
36. Ossani PC, Cirillo MA. MVar: Multivariate analysis. Disponível em: URL
Publicado
2023-04-29
Seção
Artigos
Declaro que os conteúdos apresentados nos artigos e demais trabalhos são de minha autoria e tenho total responsabilidade sobre os mesmos, os quais estou cedendo os direitos autorais à Revista Saúde e Meio Ambiente, para fins de divulgação científica em qualquer meio disponível.
