Geospatial modeling of microcephaly and zika virus spread patterns in Brazil
Autoři:
Pedro Amaral aff001; Lucas Resende de Carvalho aff001; Thiago Augusto Hernandes Rocha aff002; Núbia Cristina da Silva aff003; João Ricardo Nickenig Vissoci aff004
Působiště autorů:
CEDEPLAR/UFMG, Center for Development and Regional Planning, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
aff001; PAHO/WHO, Brasília, Federal District, Brazil
aff002; CEPEAD/UFMG, Center of Higher Studies and Research in Administration, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
aff003; Duke University, Duke School of Medicine, Department of Surgery, Division of Emergency Medicine, Durham, North Carolina, United States of America
aff004
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222668
Souhrn
Microcephaly and Zika Virus infection (ZIKV) were declared Public Health Emergencies of International Concern by the World Health Organization in 2016. Brazil was considered the epicenter of the outbreak. However, the occurrence of both ZIKV and microcephaly in Brazil was not evenly distributed across the country. To better understand this phenomenon, we investigate regional characteristics at the municipal level that can be associated with the incidence of microcephaly, our response variable, and its relationship with ZIKV and other predictors. All epidemiological data in this study was provided by the Ministry of Health official database (DATASUS). Microcephaly was only confirmed after birth and the diagnostic was made regardless of the mother’s ZIKV status. Using exploratory spatial data analysis and spatial autoregressive Tobit models, our results show that microcephaly incidence is significantly, at 95% confidence level, related not only to ZIKV, but also to access to primary care, population size, gross national product, mobility and environmental attributes of the municipalities. There is also a significant spatial autocorrelation of the dependent variable. The results indicate that municipalities that show a high incidence of microcephaly tend to be clustered in space and that incidence of microcephaly varies considerably across regions when correlated only with ZIKV, i.e. that ZIKV alone cannot explain the differences in microcephaly across regions and their correlation is mediated by regional attributes.
Klíčová slova:
Brazil – Infants – Primary care – Sanitation – Microcephaly – Zika virus – Spatial autocorrelation – Chikungunya infection
Zdroje
1. World Health Organization. Zika virus and complications: Public Health Emergency of International Concern. World Heal. Organ.—Program. 2016. http://www.who.int/emergencies/zika-virus/en/
2. European Centre for Disease Prevention and Control. Current Zika transmission worldwide [Internet]. 2017. https://ecdc.europa.eu/en/publications-data/current-zika-transmission-worldwide
3. Brasil-SINAN/SUS. Ministry of Health. Sistema de informação de agravos de notificação (SINAN/SUS). 2019. Accessed 15 nov. 2018.
4. Brasil P, Sequeira PC, Freitas AD, Zogbi HE, Calvet GA, de Souza RV, et al. Guillain-Barré syndrome associated with Zika virus infection. Lancet [Internet]. 2016; (10026):1482. Available from: http://dx.doi.org/10.1016/S0140-6736(16)30058-7 27115821
5. França GVA, Schuler-Faccini L, Oliveira WK, Henriques CMP, Carmo EH, Pedi VD, et al. Congenital Zika virus syndrome in Brazil: a case series of the first 1501 livebirths with complete investigation. Lancet (London, England). 2016; 388(10047):891–897.
6. Garcez PP, Loiola EC, Madeiro da Costa R, Higa LM, Trindade P, Delvecchio R, et al. Zika virus impairs growth in human neurospheres and brain organoids. Science. 2016; 352(6287):816–818. Available from: http://www.sciencemag.org/cgi/doi/10.1126/science.aaf6116 27064148
7. Calvet G, Aguiar RS, Melo ASO, Sampaio SA, de Filippis I, Fabri A, et al. Detection and sequencing of Zika virus from amniotic fluid of fetuses with microcephaly in Brazil: a case study. Lancet Infect. Dis. [Internet]. Elsevier. 2017; 16(6):653–660. Available from: http://dx.doi.org/10.1016/S1473-3099(16)00095-5
8. Castro M. C., Han Q. C., Carvalho L. R., Victora C. G., & França G. V. Implications of Zika virus and congenital Zika syndrome for the number of live births in Brazil. Proceedings of the National Academy of Sciences, 2018; 115(24), 6177–6182.
9. Brady Oliver J., et al. “The association between Zika virus infection and microcephaly in Brazil 2015–2017: An observational analysis of over 4 million births.” PLoS medicine. 2019; 16(3), e1002755. doi: 10.1371/journal.pmed.1002755 30835728
10. Vissoci JRN, Rocha TAH, da Silva NC, de Sousa Queiroz RC, Thomaz EBAF, Amaral PVM, et al. Zika virus infection and microcephaly: Evidence regarding geospatial associations. PLoS Negl. Trop. Dis. 2018. 12(4):e0006392. Available from: http://dx.plos.org/10.1371/journal.pntd.0006392 29694351
11. Macintyre S, Ellaway A, Cummins S. Place effects on health: How can we conceptualise, operationalise and measure them? Soc. Sci. Med. 2002; 55(1):125–139. doi: 10.1016/s0277-9536(01)00214-3 12137182
12. Xavier DR, Magalhães M de AFM, Gracie R, dos Reis IC, de Matos VP, Barcellos C. Difusão espaço-tempo do dengue no Município do Rio de Janeiro, Brasil, no período de 2000–2013. Cad. Saude Publica. 2017; 33(2). Available from: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2017000205006&lng=pt&tlng=pt
13. Nsoesie EO, Ricketts RP, Brown HE, Fish D, Durham DP, Ndeffo Mbah ML, et al. Spatial and temporal clustering of chikungunya virus transmission in dominica. PLoS Negl. Trop. Dis. 2015; 9(8): 1–11.
14. Jaenisch T, Rosenberger KD, Brito C, Brady O, Brasil P, Marques ET. Risk of microcephaly after Zika virus infection in Brazil, 2015 to 2016. Bull. World Health Organ. [Internet]. 2017; 95(3):191–198. Available from: http://www.who.int/entity/bulletin/volumes/95/3/16-178608.pdf 28250532
15. Benchimol E. I., Smeeth L., Guttmann A., Harron K., Moher D., Petersen I., … & RECORD Working Committee. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS medicine. 2015; 12(10), e1001885. doi: 10.1371/journal.pmed.1001885 26440803
16. Paim J, Travassos C, Almeida C, Bahia L, MacInko J. The Brazilian health system: History, advances, and challenges. Lancet. 2011; 377(9779):1778–97. doi: 10.1016/S0140-6736(11)60054-8 21561655
17. Laguardia J, Domingues CMA, Carvalho C, Lauerman CR, Macário E, Glatt R. Sistema de Informação de Agravos de Notificação (Sinan): desafios no desenvolvimento de um sistema de informação em saúde. Epidemiol. e Serviços Saúde. 2004; 13(3):135–47.
18. Brasil-IBGE. Estimativas de População. Instituto Brasileiro de Geografia e Estatística. 2016. http://www.ibge.gov.br/home/estatistica/populacao/estimativa2016/default.shtm. Accessed 15 nov. 2018.
19. Ribeiro LC de Q, Ribeiro MG. IBEU Municipal—Índice de bem-estar urbano dos municípios brasileiros. Rio de Janeiro; 2013.
20. Brasil. IBGE. Produto interno bruto dos municípios. Instituto Brasileiro de Geografia e Estatística. 2013. https://ww2.ibge.gov.br/home/estatistica/economia/pibmunicipios/2014/default_base.shtm. Accessed 23 abr. 2018.
21. Ord J.K. and Getis A. Local Spatial Autocorrelation Statistics: distributional Issues and an Application. Geographical Analysis. 1995; 27(4): 286–306.
22. ESRI. ArcGIS Desktop: Release 10.3. Redlands CA. 2014. p. Environmental Systems Research Institute.
23. Tobin James. "Estimation of relationships for limited dependent variables." Econometrica: journal of the Econometric Society. 1958; 26(1): 24–36.
24. Amemiya T. Advanced econometrics. Harvard University Press; 1985.
25. Amaral P V., Anselin L. Finite sample properties of Moran’s I test for spatial autocorrelation in tobit models. Pap. Reg. Sci., 2014; 93(4):773–781.
26. LeSage J., & Pace R. K. Introduction to spatial econometrics. Chapman and Hall/CRC. 2009.
27. Wilhelm S, de Matos MG. Estimating Spatial Probit Models in R. R Journal. 2013; 5(1):130–144. Available from: https://journal.r-project.org/archive/2013/RJ-2013-013/RJ-2013-013.pdf
28. Gelman A., Carlin J., Stern H. and Rubin D. Bayesian Data Analysis, London:Chapman & Hall; 1995.
29. Kesrouani A, Fallet C, Vuillard E, Jacqz-Aigrain E, Sibony O, Oury JF, et al. Pathologic and laboratory correlation in microcephaly associated with prenatal cocaine exposure. Early Hum Dev. 2001; 63(2): 79–81 doi: 10.1016/s0378-3782(01)00123-2 11408096
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