title: Using winter 2009-2010 to assess the accuracy of methods which estimate influenza-related morbidity and mortality.
abstract: We used the winter of 2009-2010, which had minimal influenza circulation due to the earlier 2009 influenza A(H1N1) pandemic, to test the accuracy of ecological trend methods used to estimate influenza-related deaths and hospitalizations. We aggregated weekly counts of person-time, all-cause deaths, and hospitalizations for pneumonia/influenza and respiratory/circulatory conditions from seven healthcare systems. We predicted the incidence of the outcomes during the winter of 2009-2010 using three different methods: a cyclic (Serfling) regression model, a cyclic regression model with viral circulation data (virological regression), and an autoregressive, integrated moving average model with viral circulation data (ARIMAX). We compared predicted non-influenza incidence with actual winter incidence. All three models generally displayed high accuracy, with prediction errors for death ranging from -5% to -2%. For hospitalizations, errors ranged from -10% to -2% for pneumonia/influenza and from -3% to 0% for respiratory/circulatory. The Serfling and virological models consistently outperformed the ARIMAX model. The three methods tested could predict incidence of non-influenza deaths and hospitalizations during a winter with negligible influenza circulation. However, meaningful mis-estimation of the burden of influenza can still result with outcomes for which the contribution of influenza is low, such as all-cause mortality.
authors.affiliation: Group Health Research Institute,Group Health Cooperative,Seattle,WA,USA.
authors.forename: M L
authors.initials: ML
authors.lastname: Jackson
authors.affiliation: Group Health Research Institute,Group Health Cooperative,Seattle,WA,USA.
authors.forename: D
authors.initials: D
authors.lastname: Peterson
authors.affiliation: Group Health Research Institute,Group Health Cooperative,Seattle,WA,USA.
authors.forename: J C
authors.initials: JC
authors.lastname: Nelson
authors.affiliation: Department of Population Medicine,Harvard Medical School and Harvard Pilgrim Health Care Institute,Boston,MA,USA.
authors.forename: S K
authors.initials: SK
authors.lastname: Greene
authors.affiliation: Kaiser Permanente of Southern California,Los Angeles,CA,USA.
authors.forename: S J
authors.initials: SJ
authors.lastname: Jacobsen
authors.affiliation: Epidemiology Research Center, Marshfield Clinic Research Foundation,Marshfield,WI,USA.
authors.forename: E A
authors.initials: EA
authors.lastname: Belongia
authors.affiliation: Vaccine Study Center, Kaiser Permanente of Northern California,Oakland,CA,USA.
authors.forename: R
authors.initials: R
authors.lastname: Baxter
authors.affiliation: Group Health Research Institute,Group Health Cooperative,Seattle,WA,USA.
authors.forename: L A
authors.initials: LA
authors.lastname: Jackson
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000401:mortality; D015331:Cohort Studies; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology* / Q000401:mortality; D008297:Male; D015233:Models, Statistical; D011024:Pneumonia, Viral / Q000453:epidemiology* / Q000401:mortality; D012140:Respiratory Tract Diseases / Q000453:epidemiology / Q000401:mortality; D012621:Seasons; D014481:United States / Q000453:epidemiology
keywords: Influenza; modelling; statistics