Search

The 2009 H1N1 outbreak provides an opportunity to identify strengths and weaknesses of disease surveillance and notification systems that have been implemented in the past decade.Drawing on a systematic review of the scientific literature, official documents, websites, and news reports, we constructed a timeline differentiating three kinds of events: (1) the emergence and spread of the pH1N1 virus, (2) local health officials' awareness and understanding of the outbreak, and (3) notifications about the events and their implications. We then conducted a "critical event" analysis of the surveillance process to ascertain when health officials became aware of the epidemiologic facts of the unfolding pandemic and whether advances in surveillance notification systems hastened detection.This analysis revealed three critical events. First, medical personnel identified pH1N1in California children because of an experimental surveillance program, leading to a novel viral strain being identified by CDC. Second, Mexican officials recognized that unconnected outbreaks represented a single phenomenon. Finally, the identification of a pH1N1 outbreak in a New York City high school was hastened by awareness of the emerging pandemic. Analysis of the timeline suggests that at best the global response could have been about one week earlier (which would not have stopped spread to other countries), and could have been much later.This analysis shows that investments in global surveillance and notification systems made an important difference in the 2009 H1N1 pandemic. In particular, enhanced laboratory capacity in the U.S. and Canada led to earlier detection and characterization of the 2009 H1N1. This includes enhanced capacity at the federal, state, and local levels in the U.S., as well as a trilateral agreement enabling collaboration among U.S., Canada, and Mexico. In addition, improved global notification systems contributed by helping health officials understand the relevance and importance of their own information.

The 2009 H1N1 outbreak provides an opportunity to learn about the strengths and weaknesses of current U.S. public health surveillance systems and to identify implications for measuring public health emergency preparedness.We adopted a "triangulation" approach in which multiple contemporary data sources, each with different expected biases, are compared to identify time patterns that are likely to reflect biases versus those that are more likely to be indicative of actual infection rates. This approach is grounded in the understanding that surveillance data are the result of a series of decisions made by patients, health care providers, and public health professionals about seeking and providing health care and about reporting cases to health authorities. Although limited by the lack of a gold standard, this analysis suggests that children and young adults are over-represented in many pH1N1 surveillance systems, especially in the spring wave. In addition, the nearly two-month delay between the Northeast and the South in the Fall peak in some surveillance data seems to at least partially reflect regional differences in concerns about pH1N1 rather than real differences in pH1N1 infection rates.Although the extent of the biases suggested by this analysis cannot be known precisely, the analysis identifies underlying problems with surveillance systems--in particular their dependence on patient and provider behavior, which is influenced by a changing information environment--that could limit situational awareness in future public health emergencies. To improve situational awareness in future health emergencies, population-based surveillance systems such as telephone surveys of representative population samples and seroprevalence surveys in well-defined population cohorts are needed.

Timely and accurate data on COVID-19 cases and COVID-19‒related deaths are essential for making decisions with significant health, economic, and policy implications. A new report from the National Academies of Sciences, Engineering, and Medicine proposes a uniform national framework for data collection to more accurately quantify disaster-related deaths, injuries, and illnesses. This article describes how following the report's recommendations could help improve the quality and timeliness of public health surveillance data during pandemics, with special attention to addressing gaps in the data necessary to understand pandemic-related health disparities.

Zika virus provides an example for which public health surveillance is based primarily on health care provider notifications to health departments of potential cases. This case-based surveillance is commonly used to understand the spread of disease in a population. However, case-based surveillance is often biased-whether testing is done and which tests are used and the accuracy of the results depend on a variety of factors including test availability, patient demand, perceptions of transmission, and patient and provider awareness, leading to surveillance artifacts that can provide misleading information on the spread of a disease in a population and have significant public health practice implications. To better understand this challenge, we first summarize the process that health departments use to generate surveillance reports, then describe factors influencing testing and reporting patterns at the patient, provider, and contextual level. We then describe public health activities, including active surveillance, that influence both patient and provider behavior as well as surveillance reports, and conclude with a discussion about the interpretation of surveillance data and approaches that could improve the validity of surveillance reports.

Public health authorities have adopted entry screening and subsequent restrictions on travelers from Ebola-affected West African countries as a strategy to prevent importation of Ebola virus disease (EVD) cases. We analyzed international, federal, and state policies-principally based on the policy documents themselves and media reports-to evaluate policy variability. We employed means-ends fit analysis to elucidate policy objectives. We found substantial variation in the specific approaches favored by WHO, CDC, and various American states. Several US states impose compulsory quarantine on a broader range of travelers or require more extensive monitoring than recommended by CDC or WHO. Observed differences likely partially resulted from different actors having different policy goals-particularly the federal government having to balance foreign policy objectives less salient to states. Further, some state-level variation appears to be motivated by short-term political goals. We propose recommendations to improve future policies, which include the following: (1) actors should explicitly clarify their objectives, (2) legal authority should be modernized and clarified, and (3) the federal government should consider preempting state approaches that imperil its goals.