Association between socio-environmental factors, coverage by family health teams, and rainfall in the spatial distribution of Zika virus infection in the city of Rio de Janeiro, Brazil, in 2015 and 2016

Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental-socioeconomic factors

Chikungunya and Zika have been neglected as emerging diseases. This study aimed to analyze the space-time patterns of their occurrence and co-occurrence and their associated environmental and socioeconomic factors. Univariate (individually) and multivariate (co-occurrence) scans were analyzed for 608,388 and 162,992 cases of chikungunya and Zika, respectively. These occurred more frequently in the summer and autumn. The clusters with the highest risk were initially located in the northeast, dispersed to the central-west and coastal areas of São Paulo and Rio de Janeiro (2018-2021), and then increased in the northeast (2019-2021). Chikungunya and Zika demonstrated decreasing trends of 13% and 40%, respectively, whereas clusters showed an increasing trend of 85% and 57%, respectively. Clusters with a high co-occurrence risk have been identified in some regions of Brazil. High temperatures are associated with areas at a greater risk of these diseases. Chikungunya was associated with low precipitation levels, more urbanized environments, and places with greater social inequalities, whereas Zika was associated with high precipitation levels and low sewage network coverage. In conclusion, to optimize the surveillance and control of chikungunya and Zika, this study's results revealed high-risk areas with increasing trends and priority months and the role of socioeconomic and environmental factors.

Democratizing Public Health: Participatory Policymaking Institutions, Mosquito Control, and Zika in the Americas

The Zika virus is a mosquito-borne virus spread primarily by Aedes mosquitoes. Zika cases have been detected throughout the mosquito's range, with an epidemic occurring from 2015 to 2017 in Brazil. Many Zika cases are mild or asymptomatic, but infections in pregnant women can cause microcephaly in children, and a small percentage of cases result in Guillan-Barré syndrome. There is currently little systematic information surrounding the municipal spread of the Zika Virus in Brazil. This article uses coarsened exact matching with negative binomial estimation and ordinary least squares estimation to assess the determinants of Zika incidence across the ~280,000 cases confirmed and recorded by Brazil's Ministry of Health in 2016 and 2017. These data come from Freedom of Information Act (FOIA) requests in Brazil and have not been published. We use data on the universe of individual Zika cases in Brazil and Geographic Information Systems (GIS) software to examine the virus at the municipal level across 5570 municipalities and construct a unique, unusually rich dataset covering daily Zika transmission. Additionally, our dataset includes corresponding local data on democratic governance, mosquito control efforts, and environmental conditions to estimate their relationship to Zika transmission. The results demonstrate that the presence of subnational democratic, participatory policymaking institutions and high levels of local state capacity are associated with low rates of Zika contraction. These models control for local healthcare spending and economic conditions, among other factors, that also influence Zika contraction rates. In turn, these findings provide a better understanding of what works for local health governance and mosquito control and makes important data public so that scholars and practitioners can perform their own analyses. Stronger models of Zika transmission will then inform mosquito abatement efforts across the Global South, as well as provide a blueprint for combatting Dengue fever, which is also transmitted by Aedes mosquitoes.

Using machine learning to understand microgeographic determinants of the Zika vector, Aedes aegypti

There are limited data on why the 2016 Zika outbreak in Miami-Dade County, Florida was confined to certain neighborhoods. In this research, Aedes aegypti, the primary vector of Zika virus, are studied to examine neighborhood-level differences in their population dynamics and underlying processes. Weekly mosquito data were acquired from the Miami-Dade County Mosquito Control Division from 2016 to 2020 from 172 traps deployed around Miami-Dade County. Using random forest, a machine learning method, predictive models of spatiotemporal dynamics of Ae. aegypti in response to meteorological conditions and neighborhood-specific socio-demographic and physical characteristics, such as land-use and land-cover type and income level, were created. The study area was divided into two groups: areas affected by local transmission of Zika during the 2016 outbreak and unaffected areas. Ae. aegypti populations in areas affected by Zika were more strongly influenced by 14- and 21-day lagged weather conditions. In the unaffected areas, mosquito populations were more strongly influenced by land-use and day-of-collection weather conditions. There are neighborhood-scale differences in Ae. aegypti population dynamics. These differences in turn influence vector-borne disease diffusion in a region. These results have implications for vector control experts to lead neighborhood-specific vector control strategies and for epidemiologists to guide vector-borne disease risk preparations, especially for containing the spread of vector-borne disease in response to ongoing climate change.

Socioeconomic risk markers of congenital Zika syndrome: a nationwide, registry-based study in Brazil

While it is well known that socioeconomic markers are associated with a higher risk of arbovirus infections, research on the relationship between socioeconomic factors and congenital Zika syndrome (CZS) remains limited. This study investigates the relationship between socioeconomic risk markers and live births with CZS in Brazil. We conducted a population-based study using data from all registered live births in Brazil (Live Births Information System) linked with the Public Health Event Record from 1 January 2015 to 31 December 2018. We used logistic regression models to estimate the OR and 95% CIs of CZS based on a three-level framework. In an analysis of 11 366 686 live births, of which 3353 had CZS, we observed that live births of self-identified black or mixed race/brown mothers (1.72 (95% CI 1.47 to 2.01) and 1.37 (95% CI 1.24 to 1.51)) were associated with a higher odds of CZS. Live births from single women compared with married women and those from women with less than 12 years of education compared with those with more than 12 years of education also had higher odds of CZS. In addition, live births following fewer prenatal care appointments had increased odds of CZS in the nationwide data. However, in the analyses conducted in the Northeast region (where the microcephaly epidemic started before the link with Zika virus was established and before preventive measures were known or disseminated), no statistical association was found between the number of prenatal care appointments and the odds of CZS. This study shows that live births of the most socially vulnerable women in Brazil had the greatest odds of CZS. This disproportionate distribution of risk places an even greater burden on already socioeconomically disadvantaged groups, and the lifelong disabilities caused by this syndrome may reinforce existing social and health inequalities.