High-Risk Areas for Congenital Zika Syndrome in Rio de Janeiro: Spatial Cluster Detection.
Danielle Amaral de FreitasMayumi Duarte WakimotoSonia Maria Ferreira DiasReinaldo Souza-SantosPublished in: Tropical medicine and infectious disease (2024)
Brazil reported 18,282 suspected congenital Zika syndrome (CZS) cases up to 2018 and accounts for 61.4% of the total reported Zika cases in the Americas in the period. To detect high-risk areas for children with CZS in the city of Rio de Janeiro, we used cluster detection and thematic maps. We analyzed data using a Poisson model in Satscan 10.1.3 software. We also analyzed the records of children with CZS from 2015 to 2016 to describe the clinical and epidemiological maternal and child profile, as well as live births in 2016 and the social development index (SDI) by neighborhood. In 2015 and 2016, the incidence rates of CZS were 8.84 and 46.96 per 100,000 live births in the city, respectively. Severe congenital findings such as microcephaly and brain damage, osteoarticular impairment, ocular abnormalities, and hearing loss were observed in 47 children. The spatial distribution of CZS was concentrated in the north and west zones in heterogeneous neighborhoods. The neighborhoods with the highest occurrence of CZS cases were found to have the worst SDIs. Stascan detected three spatial clusters in the north zone, where the SDI is lower. The clusters presented high relative risks for CZS (7.86, 1.46, and 2.08), although they were not statistically significant. Our findings highlight a higher occurrence of CZS in areas with less favorable socioeconomic conditions.
Keyphrases
- zika virus
- young adults
- dengue virus
- mental health
- aedes aegypti
- healthcare
- pulmonary embolism
- physical activity
- tertiary care
- risk factors
- oxidative stress
- big data
- multiple sclerosis
- pregnant women
- machine learning
- intellectual disability
- data analysis
- autism spectrum disorder
- real time pcr
- brain injury
- climate change
- early onset
- electronic health record
- label free
- preterm birth
- weight loss
- drug induced
- artificial intelligence
- birth weight