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Evaluating the Use of Physician Billing Data for Age and Setting Specific Influenza Surveillance

出版	McGill University, 2009
URL	http://books.google.com.hk/books?id=a8cymgEACAAJ&hl=&source=gbs_api

註釋Syndromic surveillance has emerged as a novel, automated approach to monitoring diseases using pre-diagnostic but often non-specific data sources. However, there is little consensus about the best data sources. Using physician billing data from community-based care settings and emergency departments in Quebec, Canada during 1998-2003, we evaluated the lead-lag relationship between ambulatory medical visits for influenza-like illnesses (ILI) and pneumonia and influenza (P& I) hospitalizations by age-group, visit setting, and influenza season. To do so, we applied ARIMA modeling methodology and computed the cross-correlation function (CCF) using the residuals. ILI visits in community settings by children aged 5-17 years tended to provide the greatest lead times (at least 2 but up to 3 weeks) over P&I hospitalizations. Lead times varied each season, possibly due to the circulation of different strains each season. These findings have important implications for syndromic surveillance of influenza, as well as epidemic control strategies such as vaccination and school closure policies.