Their First Real Test: Did Advanced Biosurveillance Systems Help Detect and Provide Situational Awareness for the 2009 H1N1 Pandemic?
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Abstract
Background
The 2009 H1N1 pandemic provided the first serious test of advanced biosurveillance systems, including indicator-based and event-based surveillance systems and social media, designed to detect disease outbreaks and to provide real-time situational.
Objective
To examine the performance of biosurveillance systems in practice, identify their strengths and weaknesses, and offer recommendations to improve their use in public health practice.
Methods
This study is based on a retrospective analysis of reports from U.S., Mexico, Hong Kong, and global surveillance systems, published reports based on surveillance data, key informant interviews, and a Bayesian statistical model applied to Hong Kong influenza surveillance data.
Results
Coming at the end of the flu season, the initial “signal†of the 2009 H1N1 outbreak in Mexico and the United States was too diffuse to have triggered automated outbreak detection algorithms. Mexican authorities, however, successfully used syndromic data to help confirm indications of an outbreak from standard surveillance systems. Global reporting and notification systems helped Mexican authorities recognize that a series of apparently unconnected outbreaks were actually manifestations of the pandemic H1N1 virus first characterized in the United States.
In the United States, syndromic surveillance systems tracking influenza-like illness in primary care and hospital emergency departments and Google Flu Trends data were primarily used to provide situational awareness about the age distribution and geographic spread of H1N1 infections. A process analysis of these systems identified evidence of potential reporting biases related to patient and healthcare provider awareness of H1N1 in the media. As a result, conclusions that children were at “higher risk†and about geographical spread may be incorrect.
In Hong Kong, influenza surveillance is conducted through a standard hospital-centered network and syndromic systems drawing on sentinel physicians, clinics, and institutions. During the H1N1 outbreak each surveillance system exhibited unique susceptibilities to the informational and policy environment. Increased media coverage and public awareness of the pandemic not only brought the “worried well†to hospitals, but also a higher percentage of “worried ill†who otherwise would have not sought medical attention. Institutional surveillance, such as fever screening at the elderly homes and daycare centers, was less affected by the informational environment.
Conclusions
The development and widespread implementation of advanced biosurveillance systems represents an important step towards preparedness, but the 2009 H1N1 experience identified limitations that must be addressed. In particular, we must be aware of potential reporting biases reflecting public and provider awareness about the outbreak in interpreting surveillance data.
The 2009 H1N1 pandemic reminds us that public health crises are often characterized by scientific uncertainty. In this context, syndromic surveillance and similar advanced biosurveillance systems are unlikely, by themselves, to detect future events and provide enough information about them early enough to initiate and guide a public health response. These systems can, however, provide information critical to inform an effective response, such as confirming an outbreak detected through other means. Similarly, global reporting and notification systems are important because they help provide information needed to interpret local information and “connect the dots†rather than communicating established facts.
The 2009 H1N1 pandemic provided the first serious test of advanced biosurveillance systems, including indicator-based and event-based surveillance systems and social media, designed to detect disease outbreaks and to provide real-time situational.
Objective
To examine the performance of biosurveillance systems in practice, identify their strengths and weaknesses, and offer recommendations to improve their use in public health practice.
Methods
This study is based on a retrospective analysis of reports from U.S., Mexico, Hong Kong, and global surveillance systems, published reports based on surveillance data, key informant interviews, and a Bayesian statistical model applied to Hong Kong influenza surveillance data.
Results
Coming at the end of the flu season, the initial “signal†of the 2009 H1N1 outbreak in Mexico and the United States was too diffuse to have triggered automated outbreak detection algorithms. Mexican authorities, however, successfully used syndromic data to help confirm indications of an outbreak from standard surveillance systems. Global reporting and notification systems helped Mexican authorities recognize that a series of apparently unconnected outbreaks were actually manifestations of the pandemic H1N1 virus first characterized in the United States.
In the United States, syndromic surveillance systems tracking influenza-like illness in primary care and hospital emergency departments and Google Flu Trends data were primarily used to provide situational awareness about the age distribution and geographic spread of H1N1 infections. A process analysis of these systems identified evidence of potential reporting biases related to patient and healthcare provider awareness of H1N1 in the media. As a result, conclusions that children were at “higher risk†and about geographical spread may be incorrect.
In Hong Kong, influenza surveillance is conducted through a standard hospital-centered network and syndromic systems drawing on sentinel physicians, clinics, and institutions. During the H1N1 outbreak each surveillance system exhibited unique susceptibilities to the informational and policy environment. Increased media coverage and public awareness of the pandemic not only brought the “worried well†to hospitals, but also a higher percentage of “worried ill†who otherwise would have not sought medical attention. Institutional surveillance, such as fever screening at the elderly homes and daycare centers, was less affected by the informational environment.
Conclusions
The development and widespread implementation of advanced biosurveillance systems represents an important step towards preparedness, but the 2009 H1N1 experience identified limitations that must be addressed. In particular, we must be aware of potential reporting biases reflecting public and provider awareness about the outbreak in interpreting surveillance data.
The 2009 H1N1 pandemic reminds us that public health crises are often characterized by scientific uncertainty. In this context, syndromic surveillance and similar advanced biosurveillance systems are unlikely, by themselves, to detect future events and provide enough information about them early enough to initiate and guide a public health response. These systems can, however, provide information critical to inform an effective response, such as confirming an outbreak detected through other means. Similarly, global reporting and notification systems are important because they help provide information needed to interpret local information and “connect the dots†rather than communicating established facts.
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