Repeated Rapid Active Sampling Surveys Demonstrated a Rapidly Changing Zika Seroprevalence among Children in a Rural Dengue-endemic Region in Southwest Guatemala during the Zika Epidemic (2015-2016)

在寨卡病毒流行期间(2015-2016 年),对危地马拉西南部登革热流行农村地区儿童进行重复快速主动采样调查,结果显示寨卡病毒血清阳性率迅速变化。

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Abstract

Although Central America is largely dengue virus (DENV)-endemic, the 2015-2016 Zika virus (ZIKV) pandemic brought new urgency to develop surveillance approaches capable of characterizing the rapidly changing disease burden in resource-limited settings. We conducted a pediatric DENV surveillance study in rural Guatemala, including serial cross-sectional surveys from April through September 2015 (Survey 1), in October-November 2015 (Survey 2), and January-February 2016 (Survey 3). Serum underwent DENV IgM MAC ELISA and polymerase chain reaction testing. Using banked specimens from Surveys 2 and 3, we expanded testing to include DENV 1-4 and ZIKV microneutralization (MN50), DENV NS1 IgG ELISA, and ZIKV anti-NS1 antibody Blockage of Binding (BoB) ELISA testing. Demographic risk factors for ZIKV BoB positivity were explored using multivariable generalized linear regression models. Of Survey 2 and 3 samples available (N = 382), DENV seroprevalence slightly increased (+1%-10% depending on the assay) during the surveillance period and increased with age. In contrast, ZIKV seroprevalence consistently increased over the 3-month period, including from 6% to 34% (P < 0.0001) and 10%-37% (P < 0.0001) using the MN50 ≥100 and BoB ELISA assays, respectively. Independent risk factors for ZIKV seropositivity included older age (prevalence ratio (PR)/year = 1.12, 95% confidence interval (CI) = 1.07-1.17) and primary caregiver literacy (PR = 2.80, CI = 1.30-6.06). Rapid active surveillance (RAS) surveys demonstrated a nearly 30% increase in ZIKV prevalence and a slight (≤ 10%) increase in DENV seroprevalence from October to November 2015 to January to February 2016 in rural southwest Guatemala, regardless of serologic assay used. RAS surveys may be a useful "off-the-shelf" tool to characterize arboviruses and other emerging pathogens rapidly in resource-limited settings.

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