Point-of-Care Testing as an Influenza Surveillance Tool: Methodology and Lessons Learned from Implementation

Rapid influenza molecular testing in secondary care and influenza surveillance in England: Any impact?

INTRODUCTION

The use of rapid molecular testing for influenza diagnosis is becoming increasingly popular. Used at the point of care or in a clinical laboratory, these tests detect influenza A and B viruses, though many do not distinguish between influenza A subtypes. The UK Severe Influenza Surveillance System (USISS) collects surveillance data on laboratory-confirmed influenza admissions to secondary care in England. This study set out to understand how rapid influenza molecular testing was being used and how it might influence the availability of subtyping data collected on influenza cases admitted to secondary care in England.

METHODS

At the end of the 2017/2018 and 2018/2019 influenza seasons, a questionnaire was sent to all National Health Service Hospital Trusts in England to evaluate the use of rapid influenza testing. Surveillance data collected through USISS was analysed from 2011/2012 to 2020/2021.

RESULTS

Of responding trusts, 42% (13/31) in 2017/2018 and 55% (9/17) in 2018/2019 used rapid influenza molecular tests, either alone or in combination with other testing. The majority of rapid tests used did not subtype the influenza A result, and limited follow-up testing occurred. Surveillance data showed significant proportions of influenza A hospital and intensive care unit/high dependency unit admissions without subtyping information, increasing by approximately 35% between 2012/2013 and 2020/2021.

CONCLUSIONS

The use of rapid influenza molecular tests is a likely contributing factor to the large proportion of influenza A hospitalisations in England that were unsubtyped. Given their clear clinical advantages, further work must be done to reinforce these data for public health through integrated genomic surveillance.

Front-Line Emergency Department Clinician Acceptability and Use of a Prototype Real-Time Cloud-Based Influenza Surveillance System

Objectives: To assess emergency department (ED) clinicians' perceptions of a novel real-time influenza surveillance system using a pre- and post-implementation structured survey.

Methods: We created and implemented a laboratory-based real-time influenza surveillance system at two EDs at the beginning of the 2013-2014 influenza season. Patients with acute respiratory illness were tested for influenza using rapid PCR-based Cepheid Xpert Flu assay. Results were instantaneously uploaded to a cloud-based data aggregation system made available to clinicians via a web-based dashboard. Clinicians received bimonthly email updates summating year-to-date results. Clinicians were surveyed prior to, and after the influenza season, to assess their views regarding acceptability and utility of the surveillance system data which were shared via dashboard and email updates.

Results: The pre-implementation survey revealed that the majority (82%) of the 151 ED clinicians responded that they “sporadically” or “don't,” actively seek influenza-related information during the season. However, most (75%) reported that they would find additional information regarding influenza prevalence useful. Following implementation, there was an overall increase in the frequency of clinician self-reporting increased access to surveillance information from 50 to 63%, with the majority (75%) indicating that the surveillance emails impacted their general awareness of influenza. Clinicians reported that the additional real-time surveillance data impacted their testing (65%) and treatment (51%) practices.

Conclusions: The majority of ED clinicians found surveillance data useful and indicated the additional information impacted their clinical practice. Accurate and timely surveillance information, distributed in a provider-friendly format could impact ED clinician management of patients with suspected influenza.

Integrating molecular point-of-care testing for influenza into primary care: a mixed-methods feasibility study

BACKGROUND

Molecular point-of-care testing (POCT) for influenza in primary care could influence clinical care and patient outcomes.

AIM

To assess the feasibility of incorporating influenza POCT into general practice in England.

DESIGN AND SETTING

A mixed-methods study conducted in six general practices that had not previously participated in respiratory virology sampling, which are part of the Royal College of General Practitioners Research and Surveillance Centre English sentinel surveillance network, from February 2019 to May 2019.

METHOD

A sociotechnical perspective was adopted using the Public Health England POCT implementation toolkit and business process modelling notation to inform qualitative analysis. Quantitative data were collected about the number of samples taken, their representativeness, and the virology results obtained, comparing them with the rest of the sentinel system over the same weeks.

RESULTS

A total of 312 POCTs were performed; 276 were used for quantitative analysis, of which 60 were positive for influenza and 216 were negative. The average swabbing rate was 0.4 per 1000 population and swab positivity was between 16.7% (n = 14/84) and 41.4% (n = 12/29). Given a positive influenza POCT result, the odds ratio of receiving an antiviral was 14.1 (95% confidence intervals [CI] = 2.9 to 70.0, P<0.001) and of receiving an antibiotic was 0.4 (95% CI = 0.2 to 0.8, P = 0.01), compared with patients with a negative result. Qualitative analysis showed that it was feasible for practices to implement POCT, but there is considerable variation in the processes used.

CONCLUSION

Testing for influenza using POCT is feasible in primary care and may improve antimicrobial use. However, further evidence from randomised trials of influenza POCT in general practice is needed.

Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study

BACKGROUND

Acute 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.

METHODS

We 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.

FINDINGS

We 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.

INTERPRETATION

Activity 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.

FUNDING

Partly supported by the US National Institutes of Health National Center for Advancing Translational Sciences.

Analytical performance of the BD veritor™ system for rapid detection of influenza virus A and B in a primary healthcare setting

Background

Infections with influenza A virus cannot be clinically differentiated from infections caused by influenza B virus or other respiratory viruses. Additionally, although antiviral treatment is available for influenza A virus, it is not effective for the other viruses and must be initiated early in the course of disease for it to be effective. For these reasons, there is a need for a rapid, accurate diagnostic test for use in physicians’ offices at the time patients are seen. We report the first field performance of BD Veritor™ System for Rapid Detection of Flu A + B test compared to real time PCR. The performance of this test was compared to real time PCR performed in the Istanbul University Influenza Reference Laboratory.

Method

A single-blinded cross sectional study was conducted in nine different family medicine centers in Istanbul, Turkey between 01 November 2014 and 01 May 2015. For every patient, two specimens were collected, one for real time PCR and one for the Veritor test. Specimens for the Veritor test were immediately tested at the participating clinic according to the manufacturer’s instructions. The specimens for real time PCR were transferred to the reference laboratory.

Results

A total of 238 persons were included in the study: 72 (30 %) of the patients included in the study were below 19 years old and accepted as childhood group. Mean age of adults was 42.4 and children 10.2 years. A total of 122 patients out of 238 were positive for influenza. The clinical sensitivity and specificity of the Veritor test in all age groups was determined to be 80 and 94 %, respectively. Positive predictive value was 93 % and the negative one was 81 %.

Conclusion

Field performance of the rapid influenza test was high and found to be useful with respect to rational antiviral use, avoiding unnecessary antibiotic usage and the management of cases by the family physicians.