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PLoS Curr. 2011 Aug 14;3:RRN1256.
Identification of Influenza Cases During the H1N1 Pandemic in Massachusetts Using Population-Based Hospital Discharge Data.
Placzek H, Madoff L.
Source
Doctoral Candidate in Clinical and Population Health Research at UMass Medical School and Division of Infectious Diseases and Immunology, University of Massachusetts Medical School. Division of Epidemiology and Immunization, Massachusetts Department of Public Health. ProMED-mail, International Society for Infectious Diseases.
Abstract
Objectives(1) To characterize the epidemiology of H1N1-related hospitalizations in Massachusetts; and (2) to compare characteristics of those hospitalized during periods of seasonal influenza activity and during the H1N1 pandemic. MethodsAuthors applied maximum and minimum criteria to the Massachusetts Hospital Discharge Database to identify H1N1-related hospitalizations. They constructed annual line graphs describing mean frequencies of influenza-like illness(ILI)-related discharges between 2005-2008, and compared these rates to early waves of H1N1 in 2009. ResultsDuring spring and summer 2009, there were significantly higher rates of ILI-related hospital discharges in Massachusetts compared to 2005-2008. Out of 359,344 total discharges between April 26-September 30,2009, H1N1-related hospitalizations ranged from 601 to 10,967 cases. Minimum criteria confirmed that H1N1 affected a younger population (50% were <18 years), with higher rates among African-Americans (18%) and Hispanics (23%) and higher rates of ICU admission (21%) compared to seasonal influenza (39%, 10%, 14%, and 17% respectively). ConclusionsThis is the first population-based assessment of epidemiological characteristics of hospitalized H1N1 cases in Massachusetts, and it is the first to include all possible hospitalized cases in the analysis. The authors confirm that large administrative data sets can detect hospitalizations for influenza during a pandemic, but estimated case counts vary widely depending on selection criteria used. Maximum criteria overestimated H1N1 activity, and those meeting minimum criteria resemble published accounts of H1N1-related hospitalizations closely.
PMID:
21858253
[PubMed - in process]
Identification of Influenza Cases During the H1N1 Pandemic in Massachusetts Using Population-Based Hospital Discharge Data.
Placzek H, Madoff L.
Source
Doctoral Candidate in Clinical and Population Health Research at UMass Medical School and Division of Infectious Diseases and Immunology, University of Massachusetts Medical School. Division of Epidemiology and Immunization, Massachusetts Department of Public Health. ProMED-mail, International Society for Infectious Diseases.
Abstract
Objectives(1) To characterize the epidemiology of H1N1-related hospitalizations in Massachusetts; and (2) to compare characteristics of those hospitalized during periods of seasonal influenza activity and during the H1N1 pandemic. MethodsAuthors applied maximum and minimum criteria to the Massachusetts Hospital Discharge Database to identify H1N1-related hospitalizations. They constructed annual line graphs describing mean frequencies of influenza-like illness(ILI)-related discharges between 2005-2008, and compared these rates to early waves of H1N1 in 2009. ResultsDuring spring and summer 2009, there were significantly higher rates of ILI-related hospital discharges in Massachusetts compared to 2005-2008. Out of 359,344 total discharges between April 26-September 30,2009, H1N1-related hospitalizations ranged from 601 to 10,967 cases. Minimum criteria confirmed that H1N1 affected a younger population (50% were <18 years), with higher rates among African-Americans (18%) and Hispanics (23%) and higher rates of ICU admission (21%) compared to seasonal influenza (39%, 10%, 14%, and 17% respectively). ConclusionsThis is the first population-based assessment of epidemiological characteristics of hospitalized H1N1 cases in Massachusetts, and it is the first to include all possible hospitalized cases in the analysis. The authors confirm that large administrative data sets can detect hospitalizations for influenza during a pandemic, but estimated case counts vary widely depending on selection criteria used. Maximum criteria overestimated H1N1 activity, and those meeting minimum criteria resemble published accounts of H1N1-related hospitalizations closely.
PMID:
21858253
[PubMed - in process]
