ArticleTransactions of the Royal Society of Tropical Medicine and Hygiene2023
Identifying hidden Zika hotspots in Pernambuco, Brazil: a spatial analysis.
Article in Transactions of the Royal Society of Tropical Medicine and Hygiene, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Childbearing attitudes amid successive novel infectious disease epidemics: a mixed-method experimental approach.Social forces; a scientific medium of social study and interpretation · 2026Article
- Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental-socioeconomic factors.Scientific reports · 2023Article
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6 authors.
Funding
Abstract
backgroundNortheast Brazil has the world's highest rate of Zika-related microcephaly. However, Zika case counts cannot accurately describe burden because mandatory reporting was only established when the epidemic was declining in the region.
methodsTo advance the study of the Zika epidemic, we identified hotspots of Zika in Pernambuco state, Northeast Brazil, using Aedes-borne diseases (dengue, chikungunya and Zika) and microcephaly data. We used Kulldorff's Poisson purely spatial scan statistic to detect low- and high-risk clusters for Aedes-borne diseases (2014-2017) and for microcephaly (2015-2017), separately. Municipalities were classified according to a proposed gradient of Zika burden during the epidemic, based on the combination of cluster status in each analysis and considering the strength of the evidence.
resultsWe identified 26 Aedes-borne diseases clusters (11 high-risk) and 5 microcephaly clusters (3 high-risk) in Pernambuco. According to the proposed Zika burden gradient, our results indicate that the northeast of Pernambuco and the Sertão region were hit hardest by the Zika epidemic. The first is the most populous area of Pernambuco, while the second has one of the highest rates of social and economic inequality in Brazil.
conclusionWe successfully identified possible hidden Zika hotspots using a simple methodology combining Aedes-borne diseases and microcephaly information.
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