tetano
Editor, Senior Moderator
[h=2]Epidemiology and Infection[/h]
[h=3]Original Papers[/h] [h=3]Using winter 2009?2010 to assess the accuracy of methods which estimate influenza-related morbidity and mortality[/h] [h=3]M. L. JACKSON[SUP]a1[/SUP] [SUP]c1[/SUP], D. PETERSON[SUP]a1[/SUP], J. C. NELSON[SUP]a1[/SUP], S. K. GREENE[SUP]a2[/SUP], S. J. JACOBSEN[SUP]a3[/SUP], E. A. BELONGIA[SUP]a4[/SUP], R. BAXTER[SUP]a5[/SUP] and L. A. JACKSON[SUP]a1[/SUP][/h]
[SUP]a1 [/SUP] Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
[SUP]a2 [/SUP] Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
[SUP]a3 [/SUP] Kaiser Permanente of Southern California, Los Angeles, CA, USA
[SUP]a4 [/SUP] Epidemiology Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA
[SUP]a5 [/SUP] Vaccine Study Center, Kaiser Permanente of Northern California, Oakland, CA, USA
SUMMARY
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.
http://journals.cambridge.org/actio...e=Issue_Alert&utm_medium=RSS&utm_campaign=HYG
- Epidemiology and Infection / Volume 143 / Issue 11 / August 2015, pp 2399-2407
- Copyright ? Cambridge University Press 2014
- DOI: http://dx.doi.org/10.1017/S0950268814003276 (About DOI), Published online: 12 December 2014
[h=3]Original Papers[/h] [h=3]Using winter 2009?2010 to assess the accuracy of methods which estimate influenza-related morbidity and mortality[/h] [h=3]M. L. JACKSON[SUP]a1[/SUP] [SUP]c1[/SUP], D. PETERSON[SUP]a1[/SUP], J. C. NELSON[SUP]a1[/SUP], S. K. GREENE[SUP]a2[/SUP], S. J. JACOBSEN[SUP]a3[/SUP], E. A. BELONGIA[SUP]a4[/SUP], R. BAXTER[SUP]a5[/SUP] and L. A. JACKSON[SUP]a1[/SUP][/h]
[SUP]a1 [/SUP] Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
[SUP]a2 [/SUP] Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
[SUP]a3 [/SUP] Kaiser Permanente of Southern California, Los Angeles, CA, USA
[SUP]a4 [/SUP] Epidemiology Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA
[SUP]a5 [/SUP] Vaccine Study Center, Kaiser Permanente of Northern California, Oakland, CA, USA
SUMMARY
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.
http://journals.cambridge.org/actio...e=Issue_Alert&utm_medium=RSS&utm_campaign=HYG