Success Factors of European Syndromic Surveillance Systems: A Worked Example of Applying Qualitative Comparative Analysis

High Influenza Vaccine Effectiveness and Absence of Increased Influenza-like-Illness Epidemic Activity in the 2021-2022 Influenza Season in Catalonia (Spain) Based on Surveillance Data Collected by Sentinel Pharmacies

Influenza surveillance and influenza vaccination are the key activities for preventing and controlling influenza epidemics. The study assessed the influenza surveillance and influenza vaccination data obtained from sentinel pharmacies of Catalonia, Spain, in the 2021-2022 influenza season. The sentinel pharmacies were selected from all community pharmacies to report all influenza-like illness (ILI) cases detected during the 2021-2022 influenza season and collect influenza surveillance and influenza vaccination data. The ILI cases were identified based on European Centre for Disease Control (ECDC) criteria. The moving epidemic method (MEM) was used to assess the ILI epidemic activity. The screening method was used to assess influenza vaccination effectiveness in patients aged 65-or-more years old. The sentinel pharmacies reported 212 ILI cases with a negative COVID-19 test and a total number of 412 ILI cases. An absence of increased ILI epidemic activity was observed in the 2021-2022 influenza season based on two criteria: (1) Number of ILI cases reported per week in the 2021-2022 influenza season significantly lower than the MEM-based epidemic threshold. (2) Mean number of ILI cases reported per week in the 2021-2022 influenza season significantly lower than during the ILI/influenza epidemic periods detected from 2017 to 2020 using the same methodology. Influenza vaccination was effective in preventing ILI among patients aged 65-or-more-years old. The absence of the influenza epidemic during the 2021-2022 influenza season could be explained by influenza vaccination and COVID-19 prevention measures (wearing face masks, social distancing). The sentinel pharmacies provided influenza surveillance data not provided by traditional influenza surveillance systems.

Syndromic Surveillance Systems for Mass Gatherings: A Scoping Review

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.

Adapting Syndromic Surveillance Baselines After Public Health Interventions

BACKGROUND

Public health surveillance requires historical baselines to identify unusual activity. However, these baselines require adjustment after public health interventions. We describe an example of such an adjustment after the introduction of rotavirus vaccine in England in July 2013.

METHODS

We retrospectively measured the magnitude of differences between baselines and observed counts (residuals) before and after the introduction of a public health intervention, the introduction of a rotavirus vaccine in July 2013. We considered gastroenteritis, diarrhea, and vomiting to be indicators for national syndromic surveillance, including telephone calls to a telehealth system, emergency department visits, and unscheduled consultations with general practitioners. The start of the preintervention period varied depending on the availability of surveillance data: June 2005 for telehealth, November 2009 for emergency departments, and July 2010 for general practitioner data. The postintervention period was July 2013 to the second quarter of 2016. We then determined whether baselines incorporating a step-change reduction or a change in seasonality resulted in more accurate models of activity.

RESULTS

Residuals in the unadjusted baseline models increased by 42%-198% from preintervention to postintervention. Increases in residuals for vomiting indicators were 19%-44% higher than for diarrhea. Both step-change and seasonality adjustments improved the surveillance models; we found the greatest reduction in residuals in seasonally adjusted models (4%-75%).

CONCLUSION

Our results demonstrated the importance of adjusting surveillance baselines after public health interventions, particularly accounting for changes in seasonality. Adjusted baselines produced more representative expected values than did unadjusted baselines, resulting in fewer false alarms and a greater likelihood of detecting public health threats.

Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review

BACKGROUND

Syndromic surveillance can supplement conventional health surveillance by analyzing less-specific, near-real-time data for an indication of disease occurrence. Emergency medical call centre dispatch and ambulance data are examples of routinely and efficiently collected syndromic data that might assist in infectious disease surveillance. Scientific literature on the subject is scarce and an overview of results is lacking.

METHODS

A scoping review including (i) review of the peer-reviewed literature, (ii) review of grey literature and (iii) interviews with key informants.

RESULTS

Forty-four records were selected: 20 peer reviewed and 24 grey publications describing 44 studies and systems. Most publications focused on detecting respiratory illnesses or on outbreak detection at mass gatherings. Most used retrospective data; some described outcomes of temporary systems; only two described continuously active dispatch- and ambulance-based syndromic surveillance. Key informants interviewed valued dispatch- and ambulance-based syndromic surveillance as a potentially useful addition to infectious disease surveillance. Perceived benefits were its potential timeliness, standardization of data and clinical value of the data.

CONCLUSIONS

Various dispatch- and ambulance-based syndromic surveillance systems for infectious diseases have been reported, although only roughly half are documented in peer-reviewed literature and most concerned retrospective research instead of continuously active surveillance systems. Dispatch- and ambulance-based syndromic data were mostly assessed in relation to respiratory illnesses; reported use for other infectious disease syndromes is limited. They are perceived by experts in the field of emergency surveillance to achieve time gains in detection of infectious disease outbreaks and to provide a useful addition to traditional surveillance efforts.

Evaluation of the trends in the incidence of infectious diseases using the syndromic surveillance system, early warning and response unit, Mongolia, from 2009 to 2017: a retrospective descriptive multi-year analytical study

Background

In recent times, emerging and re-emerging infectious diseases are posing a public health threat in developing countries, and vigilant surveillance is necessary to prepare against these threats. Analyses of multi-year comprehensive infectious disease syndrome data are required in Mongolia, but have not been conducted till date. This study aimed to describe the trends in the incidence of infectious disease syndromes in Mongolia during 2009–2017 using a nationwide syndrome surveillance system for infectious diseases established in 2009.

Methods

We analyzed time trends using monthly data on the incidence of infectious disease syndromes such as acute fever with rash (AFR), acute fever with vesicular rash (AFVR), acute jaundice (AJ), acute watery diarrhea (AWD), acute bloody diarrhea (ABD), foodborne disease (FD) and nosocomial infection (NI) reported from January 1, 2009 to December 31, 2017. Time series forecasting models based on the data up to 2017 estimated the future trends in the incidence of syndromes up to December 2020.

Results

During the study, the overall prevalence of infectious disease syndromes was 71.8/10,000 population nationwide. The average number of reported infectious disease syndromes was 14,519 (5229-55,132) per year. The major types were AFR (38.7%), AFVR (31.7%), AJ (13.9%), ABD (10.2%), and AWD (1.8%), accounting for 96.4% of all reported syndromes. The most prevalent syndromes were AJ between 2009 and 2012 (59.5–48.7%), AFVR between 2013 and 2014 (54.5–59%), AFR between 2015 and 2016 (67.6–65.9%), and AFVR in 2017 (62.2%). There were increases in the prevalence of AFR, with the monthly number of cases being 37.7 ± 6.1 during 2015–2016; this could be related to the measles outbreak in Mongolia during that period. The AFVR incidence rate showed winter’s multiplicative seasonal fluctuations with a peak of 10.6 ± 2 cases per 10,000 population in 2017. AJ outbreaks were identified in 2010, 2011, and 2012, and these could be associated with hepatitis A outbreaks. Prospective time series forecasting showed increasing trends in the rates of AFVR and ABD.

Conclusions

The evidence-based method for infectious disease syndromes was useful in gaining an understanding of the current situation, and predicting the future trends of various infectious diseases in Mongolia.

Partnering for enhanced digital surveillance of influenza-like disease and the effect of antivirals and vaccines (PEDSIDEA)

BACKGROUND

Standardised clinical outcome measures are urgently needed for the surveillance of influenza and influenza-like illness (ILI) based on individual patient data (IPD).

OBJECTIVES

We report a multicentre prospective cohort using a predefined disease severity score in routine care.

PATIENTS/METHODS

The Vienna Vaccine Safety initiative (ViVI) Disease Severity Score ("ViVI Score") was made available as an android-based mobile application to three paediatric hospitals in Berlin and Athens between 2013 and 2016. Healthcare professionals assessed ILI patients at the point of care including severity, risk factors and use of antibiotics/antivirals/vaccines. RT-PCR for influenza A/B viruses was performed at the Hellenic Pasteur Institute and the Robert Koch Institute. PCR testing was blinded to severity scoring and vice versa.

RESULTS

A total of 1615 children aged 0-5 years (54.4% males) were assessed at the three sites. The mean age was 1.7 years (SD 1.5; range 0-5.9). The success rate (completion of the scoring without disruption to the ER workflow) was 100%. ViVI Disease Severity Scores ranged from 0 to 35 (mean 13.72). Disease severity in the Berlin Cohort was slightly higher (mean 15.26) compared to the Athens Cohorts (mean 10.86 and 11.13). The administration of antibiotics was most prevalent in the Berlin Cohort, with 41.2% on antibiotics (predominantly cefuroxime) as opposed to only 0.5% on neuraminidase inhibitors. Overall, Risk-adjusted ViVI Scores were significantly linked to the prescription of both, antibiotics and antivirals.

CONCLUSIONS

The Risk-adjusted ViVI Score enables a precision medicine approach to managing ILI in multicentre settings. Using mobile applications, severity data will be obtained in real time with important implications for the evaluation of antiviral/vaccine use.

Real-time estimation of inpatient beds required in emergency departments

BACKGROUND

Long boarding time in emergency department (ED) leads to increased morbidity and mortality. Prediction of admissions upon triage could improve ED care efficiency and decrease boarding time.

OBJECTIVE

To develop a real-time automated model (MA) to predict admissions upon triage and compare this model with triage nurse prediction (TNP).

PATIENTS AND METHODS

A cross-sectional study was conducted in four EDs during 1 month. MA used only variables available upon triage and included in the national French Electronic Emergency Department Abstract. For each patient, the triage nurse assessed the hospitalization risk on a 10-point Likert scale. Performances of MA and TNP were compared using the area under the receiver operating characteristic curves, the accuracy, and the daily and hourly mean difference between predicted and observed number of admission.

RESULTS

A total of 11 653 patients visited the EDs, and 19.5-24.7% were admitted according to the emergency. The area under the curves (AUCs) of TNP [0.815 (0.805-0.826)] and MA [0.815 (0.805-0.825)] were similar. Across EDs, the AUCs of TNP were significantly different (P < 0.001) in all EDs, whereas AUCs of MA were all similar (P>0.2). Originally, using daily and hourly aggregated data, the percentage of errors concerning the number of predicted admission were 8.7 and 34.4%, respectively, for MA and 9.9 and 35.4%, respectively, for TNP.

CONCLUSION

A simple model using variables available in all EDs in France performed well to predict admission upon triage. However, when analyzed at an hourly level, it overestimated the number of inpatient beds needed by a third. More research is needed to define adequate use of these models.

Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials

INTRODUCTION

Influenza-Like Illness is a leading cause of hospitalization in children. Disease burden due to influenza and other respiratory viral infections is reported on a population level, but clinical scores measuring individual changes in disease severity are urgently needed. Areas covered: We present a composite clinical score allowing individual patient data analyses of disease severity based on systematic literature review and WHO-criteria for uncomplicated and complicated disease. The 22-item ViVI Disease Severity Score showed a normal distribution in a pediatric cohort of 6073 children aged 0-18 years (mean age 3.13; S.D. 3.89; range: 0 to 18.79). Expert commentary: The ViVI Score was correlated with risk of antibiotic use as well as need for hospitalization and intensive care. The ViVI Score was used to track children with influenza, respiratory syncytial virus, human metapneumovirus, human rhinovirus, and adenovirus infections and is fully compliant with regulatory data standards. The ViVI Disease Severity Score mobile application allows physicians to measure disease severity at the point-of care thereby taking clinical trials to the next level.