Identifying Areas at Greatest Risk for Recent Zika Virus Importation — New York City, 2016

In this study, we used logistic regression15 to predict weekly nowcasts16 throughout the summer of 2016 of census tracts at greatest risk of recent ZIKV importation. Nowcast results were used to inform geographically targeted activities, including performing public education, enrolling additional healthcare facilities in a sentinel surveillance system for detecting local ZIKV transmission, interpreting syndromic surveillance signals suggesting possible ZIKV-like illness, and, when reviewed in conjunction with mosquito surveillance data, informing control of Aedes spp. mosquitoes and placement of traps for continued surveillance.3,17 Methods Data Sources The NYC population (an estimated >8.5 million persons as of July 2015)18 was eligible for analysis. The unit of analysis was 2010 census tract (n=2,123 in NYC with >25 residents), i.e., the finest geographic resolution available for all independent variables. We selected small geographic units to prioritize spatial precision in identifying areas at high risk, despite potential instability in estimates for some geographic units. Smaller units have more homogeneous risk factor distributions than larger units, minimizing inferential problems in ecologic analysis.19 Census tract-level sociodemographic data were obtained from the 2010 U.S. Census and the American Community Survey 2010–2014. De-identified ZIKV-related testing and case data were obtained from the disease surveillance database used by the DOHMH Bureau o...
Source: PLOS Currents Outbreaks - Category: Epidemiology Authors: Source Type: research