The need for validation of statistical methods for estimating respiratory virus-attributable hospitalization

Abstract

Public policy regarding influenza has been based largely on the burden of hospitalization estimated through ecologic studies applying increasingly sophisticated statistical methods to administrative databases. None are known to have been validated by observational studies. The authors illustrated how 6 commonly applied statistical methods estimate virus-attributable hospitalization of children 6-23 months of age and compared the estimates with results obtained from a prospective study using virologic assessment. The proportions of pneumonia and influenza and of bronchiolitis hospitalizations attributable to respiratory syncytial virus and/or influenza were derived by using Serfling regression, periseason differences, Poisson regression with log link, negative binomial regression with identity link, and a Box-Jenkins transfer function. No method provided accurate or consistent estimates for both viruses and outcomes. Virus-attributable hospitalization estimates varied widely between statistical methods and between seasons, with greater between-season variation for admissions attributed to influenza compared with respiratory syncytial virus. Sophistication of statistical methods may have been interpreted as assurance that results are more accurate. Without validation against epidemiologic data, with viral etiology confirmed in individual patients, the accuracy of statistical methods in ecologic studies is simply not known. Until these methods are validated, their methodological limitations should be made explicit and proxy estimates used cautiously in guiding public policy.