Dataset Information

Early Growth Parameters as Predictors of Developmental Delay Among Children Conceived During the 2015-2016 Zika Virus Outbreak in Northeastern Brazil.

ABSTRACT:

Background

Identifying infants with congenital infection for early intervention will likely be challenging in future Zika virus outbreaks. We investigated indicators of risk for developmental delay among children born with and without obvious manifestations of congenital Zika virus infection.

Methods

We evaluated 120 children conceived during the 2015-2016 Zika virus outbreak in Paraíba, Brazil. We analyzed data from children at birth; ages 1-7 months and approximately 24 months, using medical records (i.e., anthropometric measurements diagnoses), medical evaluation (i.e., Zika/other laboratory tests, dysmorphic features), and parent report (seizures, developmental delay). We used a Bayesian modeling approach to identify predictors of developmental delay.

Results

Head circumference (HC) and length at birth and rates of growth for HC and length at follow-up were consistent across domains of developmental delay; (e.g., for every 1 cm per month decrease in HC growth rate; there was a corresponding decrease in the gross motor z-score). Modeling results indicated that HC and length at birth, and follow-up HC and length rates of growth, were predictive of developmental delay.

Conclusion

These findings suggest that accurate measurement and frequent monitoring of HC and length, especially in the first few months of life, may be useful for identifying children possibly congenitally exposed to Zika virus who could benefit from early intervention services.

SUBMITTER: Rose CE

PROVIDER: S-EPMC7709658 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Early Growth Parameters as Predictors of Developmental Delay Among Children Conceived During the 2015-2016 Zika Virus Outbreak in Northeastern Brazil.

Rose Charles E CE Bertolli Jeanne J Attell Jacob Elijah JE Moore Cynthia A CA Melo Flavio F Kotzky Kim K Krishna Nevin N Satterfield-Nash Ashley A Pereira Isabela Ornelas IO Pessoa Andre A Smith Donna Camille DC Santelli Ana Carolina Faria E Silva ACFES Peacock Georgina G

Tropical medicine and infectious disease 20201001 4

<h4>Background</h4>Identifying infants with congenital infection for early intervention will likely be challenging in future Zika virus outbreaks. We investigated indicators of risk for developmental delay among children born with and without obvious manifestations of congenital Zika virus infection.<h4>Methods</h4>We evaluated 120 children conceived during the 2015-2016 Zika virus outbreak in Paraíba, Brazil. We analyzed data from children at birth; ages 1-7 months and approximately 24 months, ...[more]

PMID: 33019699

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Project description:BACKGROUND:Between January 2015 and August 2016, two epidemic waves of Zika virus (ZIKV) disease swept the Northeastern (NE) region of Brazil. As a result, two waves of Guillain-Barré Syndrome (GBS) were observed concurrently. The mandatory reporting of ZIKV disease began region-wide in February 2016, and it is believed that ZIKV cases were significantly under-reported before that. The changing reporting rate has made it difficult to estimate the ZIKV infection attack rate, and studies in the literature vary widely from 17% to > 50%. The same applies to other key epidemiological parameters. In contrast, the diagnosis and reporting of GBS cases were reasonably reliable given the severity and easy recognition of the disease symptoms. In this paper, we aim to estimate the real number of ZIKV cases (i.e., the infection attack rate) and their dynamics in time, by scaling up from GBS surveillance data in NE Brazil. METHODOLOGY:A mathematical compartmental model is constructed that makes it possible to infer the true epidemic dynamics of ZIKV cases based on surveillance data of excess GBS cases. The model includes the possibility that asymptomatic ZIKV cases are infectious. The model is fitted to the GBS surveillance data and the key epidemiological parameters are inferred by using a plug-and-play likelihood-based estimation. We make use of regional weather data to determine possible climate-driven impacts on the reproductive number [Formula: see text], and to infer the true ZIKV epidemic dynamics. FINDINGS AND CONCLUSIONS:The GBS surveillance data can be used to study ZIKV epidemics and may be appropriate when ZIKV reporting rates are not well understood. The overall infection attack rate (IAR) of ZIKV is estimated to be 24.1% (95% confidence interval: 17.1%-29.3%) of the population. By examining various asymptomatic scenarios, the IAR is likely to be lower than 33% over the two ZIKV waves. The risk rate from symptomatic ZIKV infection to develop GBS was estimated as ? = 0.0061% (95% CI: 0.0050%-0.0086%) which is significantly less than current estimates. We found a positive association between local temperature and the basic reproduction number, [Formula: see text]. Our analysis revealed that asymptomatic infections affect the estimation of ZIKV epidemics and need to also be carefully considered in related modelling studies. According to the estimated effective reproduction number and population wide susceptibility, we comment that a ZIKV outbreak would be unlikely in NE Brazil in the near future.

Dataset Information

Early Growth Parameters as Predictors of Developmental Delay Among Children Conceived During the 2015-2016 Zika Virus Outbreak in Northeastern Brazil.

Background

Methods

Results

Conclusion

Publications

Early Growth Parameters as Predictors of Developmental Delay Among Children Conceived During the 2015-2016 Zika Virus Outbreak in Northeastern Brazil.

Similar Datasets

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