Assessing Zika virus infection during pregnancy and adverse fetal, infant, and child outcomes: The Zika virus (ZIKV) Individual Participant Data (IPD) Meta-analyses (MA) Phase II and the analysis of surveillance-based IPD data. (ZIKV-IPD-MA-2S)

Estimating the short- and especially the long-term- effects of Zika virus (ZIKV) infection during pregnancy is challenging. Potential ZIKV-related adverse outcomes are: congenital malformations (called as well Congenital Zika Syndrome or CZS), pregnancy loss, neonatal and child mortality, and developmental issues. However their assessment is threatened by the absence of, or lack of good quality data, and a series of biases that are not often accounted for when analyzing ZIKV-related data. Zika is a virus mostly transmitted to humans by the bites of infected Aedes mosquitoes, but can be transmitted vertically from pregnant individuals to their fetuses. In 2016, a public health emergency of international concern (PHEIC) was declared due to the increase of microcephaly in children born to ZIKV-infected pregnant individuals. After six years of the PHEIC, ZIKV is still a threat. Over 40,000 cases were reported in 14 countries of the Americas region in 2022 and still, robust estimation of the short- and long-term effects of Zika during pregnancy are lacking. We propose a research project to contribute to the estimation of ZIKV-related risks, using data from 64 estudies (22 countries) participating in the ZIKV- Individual Participant Data (IPD) Consortium. The ZIKV-IPD consortium is an initiative led by the World Health Organization (WHO), created in 2017 to bring together multidisciplinary researchers to conduct a synthesis of the available literature called IPD meta-analyses (IPD-MA), of ZIKV-related cohorts and surveillance-based. Our research includes harmonization of available data to be analyzed while addressing issues related to the heterogeneity of the data (e.g., definitions, study designs, type of measurements, etc), and developing and applying statistical methods to de-bias the data and providing robust epidemiological estimates. We will contribute to informe public policy and decision-making in the healthcare system and among pregnant individuals.