Impact evaluation of Zika epidemic on congenital anomalies registration in Brazil: An interrupted time series analysis

Abstract

This study aimed to assess the impact of the Zika epidemic on the registration of birth defects in Brazil. We used an interrupted time series analysis design to identify changes in the trends in the registration of congenital anomalies. We obtained monthly data from Brazilian Live Birth Information System and used two outcome definitions: 1) rate of congenital malformation of the brain and eye (likely to be affected by Zika and its complications) 2) rate of congenital malformation not related to the brain or eye unlikely to be causally affected by Zika. The period between maternal infection with Zika and diagnosis of congenital abnormality attributable to the infection is around six months. We therefore used September 2015 as the interruption point in the time series, six months following March 2015 when cases of Zika started to increase. For the purposes of this analysis, we considered the period from January 2010 to September 2015 to be “pre-Zika event,” and the period from just after September 2015 to December 2017 to be “post-Zika event.” We found that immediately after the interruption point, there was a great increase in the notification rate of congenital anomalies of 14.9/10,000 live births in the brain and eye group and of 5.2/10,000 live births in the group not related with brain or eye malformations. This increase in reporting was in all regions of the country (except in the South) and especially in the Northeast. In the period “post-Zika event”, unlike the brain and eye group which showed a monthly decrease, the group without brain or eye malformations showed a slow but significant increase (relative to the pre-Zika trend) of 0.2/10,000 live births. These findings suggest an overall improvement in the registration of birth malformations, including malformations that were not attributed to Zika, during and after the Zika epidemic.

Spatiotemporal Analysis of the Population Risk of Congenital Microcephaly in Pernambuco State, Brazil

“…Also, the data on Zika in Pernambuco in the study time frame have limitations in that the surveillance systems were still being developed, and diagnostic laboratory tests were limited [14]. However, at the descriptive level, the spatial and temporal patterns of microcephaly shown here coincide with those of Zika in Brazil shown by, for example, Brady et al from December 2015 onwards [14], and Paixão et al in an interrupted time series analysis by region [31].…”

Early maternal Zika infection predicts severe neonatal neurological damage: results from the prospective Natural History of Zika Virus Infection in Gestation cohort study

“…29 Before 2015, the overall and brain/eye congenital malformation rates reported in Brazil were 0.8-1% and 0.08%, respectively. 30 An inverse association between the gestational age of ZIKV infection and the severity of fetal outcomes has been reported, with the worst outcomes resulting from first-trimester infection. 6,7,9,[31][32][33] However, no study has yet systematically and prospectively approached the exact gestational age of ZIKV infection or explored this as a continuous variable.…”

“…We observed five to eight times higher adverse pregnancy outcome rates in the ZIKV‐infected women who were studied by us than was shown in baseline vital statistical data before the ZIKV outbreak (2010–2014) for the same area, which showed an 0.83% pregnancy loss rate and a congenital anomaly rate of 1.3% among 100 000 pregnancies 29 . Before 2015, the overall and brain/eye congenital malformation rates reported in Brazil were 0.8–1% and 0.08%, respectively 30 …”

Impact of ICD-9-CM to ICD-10-CM coding transition on trauma hospitalization trends among young adults in 12 states

“…We conducted an interrupted time series (ITS) analysis using discharge data on traumatic injury-related hospitalizations that occurred between January 2011 and December 2017 in 12 U.S. states. ITS is a quasi-experimental design that is useful for examining the impact of well-defined policy changes, interventions or events, on population-level outcomes (Slavova et al 2018 ; Paixão et al 2019 ; Penfold and Zhang 2013 ; Salemi et al 2019 ). Data were aggregated at the month level, based on the date of discharge.…”