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Big Data to Knowledge Analytics Reveals the Zika Virus Epidemic as Only One of Multiple Factors Contributing to a Year-Over-Year 28-Fold Increase in Microcephaly Incidence

Author

Listed:
  • Myriam Patricia Cifuentes

    (Department of Mathematics, College of Sciences, Antonio Nariño University, Bogotá 111321, Colombia
    Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, OH 43210, USA)

  • Clara Mercedes Suarez

    (Maestria en Salud Pública, Universidad Santo Tomás, Bogota 150001, Colombia)

  • Ricardo Cifuentes

    (School of Medicine and Health Sciences, Universidad Militar Nueva Granada, Bogotá 110111, Colombia)

  • Noel Malod-Dognin

    (Department of Computer Science, University College London, London WC1E 6BT, UK)

  • Sam Windels

    (Department of Computer Science, University College London, London WC1E 6BT, UK)

  • Jose Fernando Valderrama

    (Subdirectorate of Transmissible Diseases, Ministry of Health and Social Protection, Bogotá 110311, Colombia)

  • Paul D. Juarez

    (Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA)

  • R. Burciaga Valdez

    (Department of Family & Community Medicine, School of Medicine, University of New Mexico, Albuquerque, NM 87106, USA)

  • Cynthia Colen

    (Department of Sociology, College of Arts and Sciences, Ohio State University, Columbus, OH 43210, USA)

  • Charles Phillips

    (Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA)

  • Aramandla Ramesh

    (Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, Meharry Medical College, Nashville, TN 37208, USA)

  • Wansoo Im

    (Department of Family and Community Medicine, Meharry Medical College, Nashville, TN 37208, USA)

  • Maureen Lichtveld

    (Department of Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA 15261, USA)

  • Charles Mouton

    (Department of Family Medicine, College of Medicine, University of Texas Medical Branch, Galveston, TX 77555, USA)

  • Nataša Pržulj

    (Department of Computer Science, University College London, London WC1E 6BT, UK)

  • Darryl B. Hood

    (Division of Environmental Health Sciences, College of Public Health, Ohio State University, Columbus, OH 43210, USA)

Abstract

During the 2015–2016 Zika Virus (ZIKV) epidemic in Brazil, the geographical distributions of ZIKV infection and microcephaly outbreaks did not align. This raised doubts about the virus as the single cause of the microcephaly outbreak and led to research hypotheses of alternative explanatory factors, such as environmental variables and factors, agrochemical use, or immunizations. We investigated context and the intermediate and structural determinants of health inequalities, as well as social environment factors, to determine their interaction with ZIKV-positive- and ZIKV-negative-related microcephaly. The results revealed the identification of 382 associations among 382 nonredundant variables of Zika surveillance, including multiple determinants of environmental public health factors and variables obtained from 5565 municipalities in Brazil. This study compared those factors and variables directly associated with microcephaly incidence positive to ZIKV and those associated with microcephaly incidence negative to ZIKV, respectively, and mapped them in case and control subnetworks. The subnetworks of factors and variables associated with low birth weight and birthweight where birth incidence served as an additional control were also mapped. Non-significant differences in factors and variables were observed, as were weights of associations between microcephaly incidence, both positive and negative to ZIKV, which revealed diagnostic inaccuracies that translated to the underestimation of the scope of the ZIKV outbreak. A detailed analysis of the patterns of association does not support a finding that vaccinations contributed to microcephaly, but it does raise concerns about the use of agrochemicals as a potential factor in the observed neurotoxicity arising from the presence of heavy metals in the environment and microcephaly not associated with ZIKV. Summary: A comparative network inferential analysis of the patterns of variables and factors associated with Zika virus infections in Brazil during 2015–2016 coinciding with a microcephaly epidemic identified multiple contributing determinants. This study advances our understanding of the cumulative interactive effects of exposures to chemical and non-chemical stressors in the built, natural, physical, and social environments on adverse pregnancy and health outcomes in vulnerable populations.

Suggested Citation

  • Myriam Patricia Cifuentes & Clara Mercedes Suarez & Ricardo Cifuentes & Noel Malod-Dognin & Sam Windels & Jose Fernando Valderrama & Paul D. Juarez & R. Burciaga Valdez & Cynthia Colen & Charles Phill, 2022. "Big Data to Knowledge Analytics Reveals the Zika Virus Epidemic as Only One of Multiple Factors Contributing to a Year-Over-Year 28-Fold Increase in Microcephaly Incidence," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9051-:d:871436
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    References listed on IDEAS

    as
    1. Declan Butler, 2016. "Brazil asks whether Zika acts alone to cause birth defects," Nature, Nature, vol. 535(7613), pages 475-476, July.
    2. Paul D. Juarez & Patricia Matthews-Juarez & Darryl B. Hood & Wansoo Im & Robert S. Levine & Barbara J. Kilbourne & Michael A. Langston & Mohammad Z. Al-Hamdan & William L. Crosson & Maurice G. Estes &, 2014. "The Public Health Exposome: A Population-Based, Exposure Science Approach to Health Disparities Research," IJERPH, MDPI, vol. 11(12), pages 1-30, December.
    3. Marlos Melo Martins & Antonio José Ledo Alves da Cunha & Jaqueline Rodrigues Robaina & Carlos Eduardo Raymundo & Arnaldo Prata Barbosa & Roberto de Andrade Medronho, 2021. "Fetal, neonatal, and infant outcomes associated with maternal Zika virus infection during pregnancy: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-26, February.
    4. Rebecca K. Borchering & Angkana T. Huang & Luis Mier-y-Teran-Romero & Diana P. Rojas & Isabel Rodriguez-Barraquer & Leah C. Katzelnick & Silvio D. Martinez & Gregory D. King & Stephanie C. Cinkovich &, 2019. "Impacts of Zika emergence in Latin America on endemic dengue transmission," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    5. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
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