Cargando…
LB17. Age-Related Differences in Influenza Type/Subtype Among Patients Hospitalized with Influenza, FluSurv-NET—2017–2018
BACKGROUND: The 2017–2018 influenza season had the highest rates of influenza hospitalizations since the 2009 H1N1 pandemic. We used data from the Influenza Hospitalization Surveillance Network (FluSurv-NET) to identify unique characteristics of the 2017–2018 season. METHODS: We included all patient...
Autores principales: | , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6254194/ http://dx.doi.org/10.1093/ofid/ofy229.2191 |
_version_ | 1783373668789780480 |
---|---|
author | Garg, Shikha O’Halloran, Alissa Cummings, Charisse Nitura Anderson, Evan J Alden, Nisha Bennett, Nancy M Billing, Laurie Chai, Shua J Kim, Sue Lynfield, Ruth Muse, Alison Price, Andrea Ryan, Patricia Talbot, H Keipp Torres, Salina Yousey-Hindes, Kimberly Thomas, Ann Reed, Carrie |
author_facet | Garg, Shikha O’Halloran, Alissa Cummings, Charisse Nitura Anderson, Evan J Alden, Nisha Bennett, Nancy M Billing, Laurie Chai, Shua J Kim, Sue Lynfield, Ruth Muse, Alison Price, Andrea Ryan, Patricia Talbot, H Keipp Torres, Salina Yousey-Hindes, Kimberly Thomas, Ann Reed, Carrie |
author_sort | Garg, Shikha |
collection | PubMed |
description | BACKGROUND: The 2017–2018 influenza season had the highest rates of influenza hospitalizations since the 2009 H1N1 pandemic. We used data from the Influenza Hospitalization Surveillance Network (FluSurv-NET) to identify unique characteristics of the 2017–2018 season. METHODS: We included all patients residing within a FluSurv-NET catchment area, and hospitalized with laboratory-confirmed influenza during 2017–2018. We used multiple imputation, including age, surveillance site, and month of hospital admission as predictors, to impute influenza A subtype for 40–64% of cases across seasons with an unknown subtype. We calculated influenza hospitalization rates by type/subtype per 100,000 population. We compared 2017–2018 rates to rates during 4 prior seasons: 2016–2017, 2015–2016, 2014–2015, and 2013–2014. RESULTS: The overall unadjusted hospitalization rates per 100,000 population varied from 31.5 during 2015–2016 to 105.1 during 2017–2018. After imputing A subtype, the 2017–2018 season had the highest rates observed for H3N2 (62.8) and B (28.5) than in any previous season, and the third highest rate of H1N1 (13.5) (Figure 1A). During 2017–2018, rates in adult ≥65 years peaked 3 weeks before they peaked in children 0–4 years. In contrast, during the four prior seasons, rates in adults ≥65 years peaked during the same week or 1 week after they peaked in children 0–4 years. During 2017–2018, the distribution of influenza type/subtypes varied significantly by age group (P < 0.0001); for example, the proportion of cases with H1N1 ranged from 19 to 29% in those <65 years to only 7% in those ≥65 years. During 2017–2018, H1N1 (the nonpredominant A virus) contributed >25% of A cases across all age groups (except ≥65 years) vs. all prior seasons where the nonpredominant A virus contributed <10% of A cases across all age groups (except ≥65 years) (Figure 1B–F). CONCLUSIONS: Several unique characteristics may have contributed to the high hospitalization rates observed during 2017–2018. Rates in older adults, who were predominantly infected with H3N2, peaked several weeks prior to children in contrast to prior seasons. Higher overall rates of H3N2 and B were observed in 2017–2018 compared with these prior seasons and substantial H1N1 co-circulation also occurred with marked variability by age group. DISCLOSURES: E. J. Anderson, NovaVax: Grant Investigator, Research grant. Pfizer: Grant Investigator, Research grant. AbbVie: Consultant, Consulting fee. MedImmune: Investigator, Research support. PaxVax: Investigator, Research support. Micron: Investigator, Research support. H. K. Talbot, Sanofi Pasteur: Investigator, Research grant. Gilead: Investigator, Research grant. MedImmune: Investigator, Research grant. Vaxinnate: Safety Board, none. Seqirus: Safety Board, none. [Image: see text] |
format | Online Article Text |
id | pubmed-6254194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-62541942018-11-28 LB17. Age-Related Differences in Influenza Type/Subtype Among Patients Hospitalized with Influenza, FluSurv-NET—2017–2018 Garg, Shikha O’Halloran, Alissa Cummings, Charisse Nitura Anderson, Evan J Alden, Nisha Bennett, Nancy M Billing, Laurie Chai, Shua J Kim, Sue Lynfield, Ruth Muse, Alison Price, Andrea Ryan, Patricia Talbot, H Keipp Torres, Salina Yousey-Hindes, Kimberly Thomas, Ann Reed, Carrie Open Forum Infect Dis Abstracts BACKGROUND: The 2017–2018 influenza season had the highest rates of influenza hospitalizations since the 2009 H1N1 pandemic. We used data from the Influenza Hospitalization Surveillance Network (FluSurv-NET) to identify unique characteristics of the 2017–2018 season. METHODS: We included all patients residing within a FluSurv-NET catchment area, and hospitalized with laboratory-confirmed influenza during 2017–2018. We used multiple imputation, including age, surveillance site, and month of hospital admission as predictors, to impute influenza A subtype for 40–64% of cases across seasons with an unknown subtype. We calculated influenza hospitalization rates by type/subtype per 100,000 population. We compared 2017–2018 rates to rates during 4 prior seasons: 2016–2017, 2015–2016, 2014–2015, and 2013–2014. RESULTS: The overall unadjusted hospitalization rates per 100,000 population varied from 31.5 during 2015–2016 to 105.1 during 2017–2018. After imputing A subtype, the 2017–2018 season had the highest rates observed for H3N2 (62.8) and B (28.5) than in any previous season, and the third highest rate of H1N1 (13.5) (Figure 1A). During 2017–2018, rates in adult ≥65 years peaked 3 weeks before they peaked in children 0–4 years. In contrast, during the four prior seasons, rates in adults ≥65 years peaked during the same week or 1 week after they peaked in children 0–4 years. During 2017–2018, the distribution of influenza type/subtypes varied significantly by age group (P < 0.0001); for example, the proportion of cases with H1N1 ranged from 19 to 29% in those <65 years to only 7% in those ≥65 years. During 2017–2018, H1N1 (the nonpredominant A virus) contributed >25% of A cases across all age groups (except ≥65 years) vs. all prior seasons where the nonpredominant A virus contributed <10% of A cases across all age groups (except ≥65 years) (Figure 1B–F). CONCLUSIONS: Several unique characteristics may have contributed to the high hospitalization rates observed during 2017–2018. Rates in older adults, who were predominantly infected with H3N2, peaked several weeks prior to children in contrast to prior seasons. Higher overall rates of H3N2 and B were observed in 2017–2018 compared with these prior seasons and substantial H1N1 co-circulation also occurred with marked variability by age group. DISCLOSURES: E. J. Anderson, NovaVax: Grant Investigator, Research grant. Pfizer: Grant Investigator, Research grant. AbbVie: Consultant, Consulting fee. MedImmune: Investigator, Research support. PaxVax: Investigator, Research support. Micron: Investigator, Research support. H. K. Talbot, Sanofi Pasteur: Investigator, Research grant. Gilead: Investigator, Research grant. MedImmune: Investigator, Research grant. Vaxinnate: Safety Board, none. Seqirus: Safety Board, none. [Image: see text] Oxford University Press 2018-11-26 /pmc/articles/PMC6254194/ http://dx.doi.org/10.1093/ofid/ofy229.2191 Text en © The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Abstracts Garg, Shikha O’Halloran, Alissa Cummings, Charisse Nitura Anderson, Evan J Alden, Nisha Bennett, Nancy M Billing, Laurie Chai, Shua J Kim, Sue Lynfield, Ruth Muse, Alison Price, Andrea Ryan, Patricia Talbot, H Keipp Torres, Salina Yousey-Hindes, Kimberly Thomas, Ann Reed, Carrie LB17. Age-Related Differences in Influenza Type/Subtype Among Patients Hospitalized with Influenza, FluSurv-NET—2017–2018 |
title | LB17. Age-Related Differences in Influenza Type/Subtype Among Patients Hospitalized with Influenza, FluSurv-NET—2017–2018 |
title_full | LB17. Age-Related Differences in Influenza Type/Subtype Among Patients Hospitalized with Influenza, FluSurv-NET—2017–2018 |
title_fullStr | LB17. Age-Related Differences in Influenza Type/Subtype Among Patients Hospitalized with Influenza, FluSurv-NET—2017–2018 |
title_full_unstemmed | LB17. Age-Related Differences in Influenza Type/Subtype Among Patients Hospitalized with Influenza, FluSurv-NET—2017–2018 |
title_short | LB17. Age-Related Differences in Influenza Type/Subtype Among Patients Hospitalized with Influenza, FluSurv-NET—2017–2018 |
title_sort | lb17. age-related differences in influenza type/subtype among patients hospitalized with influenza, flusurv-net—2017–2018 |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6254194/ http://dx.doi.org/10.1093/ofid/ofy229.2191 |
work_keys_str_mv | AT gargshikha lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT ohalloranalissa lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT cummingscharissenitura lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT andersonevanj lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT aldennisha lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT bennettnancym lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT billinglaurie lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT chaishuaj lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT kimsue lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT lynfieldruth lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT musealison lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT priceandrea lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT ryanpatricia lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT talbothkeipp lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT torressalina lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT youseyhindeskimberly lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT thomasann lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 AT reedcarrie lb17agerelateddifferencesininfluenzatypesubtypeamongpatientshospitalizedwithinfluenzaflusurvnet20172018 |