Dataset Information

Harnessing the potential of blood donation archives for influenza surveillance and control.

ABSTRACT: Many blood donation services around the globe maintain large archives of serum and/or plasma specimens of blood donations which could potentially be used for serologic surveillance and risk assessment of influenza. Harnessing this potential requires robust evidence that the outcomes of influenza serology in plasma, which is rarely used, is consistent with that in serum, which is the conventional choice of specimens for influenza serology. We harvested EDTA-plasma specimens from the blood donation archives of Hong Kong Red Cross Transfusion Services, where EDTA is the type of anticoagulant used for plasma collection, compared their antibody titers and responses to that in serum. Influenza A/H1N1/California/7/2009 and A/H3N2/Victoria/208/2009 were the test strains. Our results showed that antibody titers in 609 matched serum/EDTA-plasma specimens (i.e. obtained from the same donor at the same time) had good agreement inferred by Intraclass Correlation Coefficient, the value of which was 0.82 (95% CI: 0.77-0.86) for hemagglutination inhibition assay and 0.95 (95% CI: 0.93-0.96) for microneutralization assay; seroconversion rates (based on hemagglutination inhibition titers) during the 2010 and 2011 influenza seasons in Hong Kong inferred from paired EDTA-plasma were similar to that inferred from paired sera. Our study provided the proof-of-concept that blood donation archives could be leveraged as a valuable source of longitudinal blood specimens for the surveillance, control and risk assessment of both pandemic and seasonal influenza.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7259782 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Harnessing the potential of blood donation archives for influenza surveillance and control.

Zhang Yanyu Y Leung Kathy K Perera Ranawaka A P M RAPM Lee Cheuk-Kwong CK Peiris J S Malik JSM Wu Joseph T JT

PloS one 20200529 5

Many blood donation services around the globe maintain large archives of serum and/or plasma specimens of blood donations which could potentially be used for serologic surveillance and risk assessment of influenza. Harnessing this potential requires robust evidence that the outcomes of influenza serology in plasma, which is rarely used, is consistent with that in serum, which is the conventional choice of specimens for influenza serology. We harvested EDTA-plasma specimens from the blood donatio ...[more]

PMID: 32470010

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Project description:BackgroundAcute infections can cause an individual to have an elevated resting heart rate (RHR) and change their routine daily activities due to the physiological response to the inflammatory insult. Consequently, we aimed to evaluate if population trends of seasonal respiratory infections, such as influenza, could be identified through wearable sensors that collect RHR and sleep data.MethodsWe obtained de-identified sensor data from 200 000 individuals who used a Fitbit wearable device from March 1, 2016, to March 1, 2018, in the USA. We included users who wore a Fitbit for at least 60 days and used the same wearable throughout the entire period, and focused on the top five states with the most Fitbit users in the dataset: California, Texas, New York, Illinois, and Pennsylvania. Inclusion criteria included having a self-reported birth year between 1930 and 2004, height greater than 1 m, and weight greater than 20 kg. We excluded daily measurements with missing RHR, missing wear time, and wear time less than 1000 min per day. We compared sensor data with weekly estimates of influenza-like illness (ILI) rates at the state level, as reported by the US Centers for Disease Control and Prevention (CDC), by identifying weeks in which Fitbit users displayed elevated RHRs and increased sleep levels. For each state, we modelled ILI case counts with a negative binomial model that included 3-week lagged CDC ILI rate data (null model) and the proportion of weekly Fitbit users with elevated RHR and increased sleep duration above a specified threshold (full model). We also evaluated weekly change in ILI rate by linear regression using change in proportion of elevated Fitbit data. Pearson correlation was used to compare predicted versus CDC reported ILI rates.FindingsWe identified 47 249 users in the top five states who wore a Fitbit consistently during the study period, including more than 13·3 million total RHR and sleep measures. We found the Fitbit data significantly improved ILI predictions in all five states, with an average increase in Pearson correlation of 0·12 (SD 0·07) over baseline models, corresponding to an improvement of 6·3-32·9%. Correlations of the final models with the CDC ILI rates ranged from 0·84 to 0·97. Week-to-week changes in the proportion of Fitbit users with abnormal data were associated with week-to-week changes in ILI rates in most cases.InterpretationActivity and physiological trackers are increasingly used in the USA and globally to monitor individual health. By accessing these data, it could be possible to improve real-time and geographically refined influenza surveillance. This information could be vital to enact timely outbreak response measures to prevent further transmission of influenza cases during outbreaks.FundingPartly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.

Dataset Information

Harnessing the potential of blood donation archives for influenza surveillance and control.

Publications

Harnessing the potential of blood donation archives for influenza surveillance and control.

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