tetano
Editor, Senior Moderator
Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China
Eric HY Lau, Jiandong Zheng, Tim K Tsang, Qiaohong Liao, Bryan Lewis, John S Brownstein, Sharon Sanders, Jessica Y Wong, Sumiko R Mekaru, Caitlin Rivers, Peng Wu, Hui Jiang, Yu Li, Jianxing Yu, Qian Zhang, Zhaorui Chang, Fengfeng Liu, Zhibin Peng, Gabriel M Leung, Luzhao Feng, Benjamin J Cowling and Hongjie Yu
Author Affiliations
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BMC Medicine 2014, 12:88 doi:10.1186/1741-7015-12-88
Published: 28 May 2014
Abstract (provisional)
Background
Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of 'line lists' with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus.
Methods
We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by.., Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure.
Results
Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou.
Conclusions
We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries.
full text
http://www.biomedcentral.com/content/pdf/1741-7015-12-88.pdf
Eric HY Lau, Jiandong Zheng, Tim K Tsang, Qiaohong Liao, Bryan Lewis, John S Brownstein, Sharon Sanders, Jessica Y Wong, Sumiko R Mekaru, Caitlin Rivers, Peng Wu, Hui Jiang, Yu Li, Jianxing Yu, Qian Zhang, Zhaorui Chang, Fengfeng Liu, Zhibin Peng, Gabriel M Leung, Luzhao Feng, Benjamin J Cowling and Hongjie Yu
Author Affiliations
For all author emails, please log on.
BMC Medicine 2014, 12:88 doi:10.1186/1741-7015-12-88
Published: 28 May 2014
Abstract (provisional)
Background
Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of 'line lists' with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus.
Methods
We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by.., Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure.
Results
Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou.
Conclusions
We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries.
full text
http://www.biomedcentral.com/content/pdf/1741-7015-12-88.pdf
