An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation

Abstract

Background Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. Objective We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. Methods First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. Results Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data ( r =.86-.98). The 2 surveillance variables—TMUHcS-RITP and TMUHcS-IMU—showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. Conclusions Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for dat...

Usefulness of Clinical Definitions of Influenza for Public Health Surveillance Purposes

“…In our opinion, the impact of this limitation may be minimal, as other factors, such as individual and daily variations, the site of measurement, and the natural trend for physicians to round temperatures up or down, can influence the measured temperature [9]. Secondly, age, the influenza type and subtype [5], or the season [18] might be partially responsible for differences in the performance of the variables studied. However, we adjusted for age and season, and it seems improbable that our results are invalid.…”

“…The importance of integrating data from sentinel sites with that from other medical and non-medical sources to detect and assess influenza epidemics has been pointed out [18][19][20]. However, each component of the integrated system must be assessed separately so as to improve data interpretation.…”

Socioeconomic bias in influenza surveillance

“…A growing community of researchers and practitioners across public health, medicine, science, military, and non-governmental organizations are developing and deploying technology-enabled surveillance systems [22] to support adaptive management of infectious diseases [68] and deliver actionable forecasts [69][70][71][72][73][74][75][76]. Many of these efforts focused on improving the timeliness and accuracy of bioevent detection, situational awareness, and forecasting [34,77].…”

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