BACKGROUND AND OBJECTIVE: Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the United States. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000-2008) to help predict asthma admissions during the 2009 H1N1 influenza pandemic. METHODS: We identified all hospitalizations between 1998 and 2008 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998-2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic. RESULTS: Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (P-value < 0.0001). Use of influenza data to predict asthma admissions during the 2009 H1N1 pandemic improved the mean squared prediction error by 60.2%. CONCLUSIONS: Influenza activity in the population is significantly associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may decrease hospitalizations of asthma patients.
BACKGROUND AND OBJECTIVE: Although influenza has been associated with asthma exacerbations, it is not clear the extent to which this association affects health care use in the United States. The first goal of this project was to determine whether, and to what extent, the incidence of asthma hospitalizations is associated with seasonal variation in influenza. Second, we used influenza trends (2000-2008) to help predict asthma admissions during the 2009 H1N1influenza pandemic. METHODS: We identified all hospitalizations between 1998 and 2008 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project during which a primary diagnosis of asthma was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We performed time series regression analyses to investigate the association of monthly asthma admissions with influenza incidence. Finally, we applied these time series regression models using 1998-2008 data, to forecast monthly asthma admissions during the 2009 influenza pandemic. RESULTS: Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of asthma hospitalizations (P-value < 0.0001). Use of influenza data to predict asthma admissions during the 2009 H1N1 pandemic improved the mean squared prediction error by 60.2%. CONCLUSIONS:Influenza activity in the population is significantly associated with asthma hospitalizations in the United States, and this association can be exploited to more accurately forecast asthma admissions. Our results suggest that improvements in influenza surveillance, prevention and treatment may decrease hospitalizations of asthmapatients.
Authors: William W Busse; Wayne J Morgan; Peter J Gergen; Herman E Mitchell; James E Gern; Andrew H Liu; Rebecca S Gruchalla; Meyer Kattan; Stephen J Teach; Jacqueline A Pongracic; James F Chmiel; Suzanne F Steinbach; Agustin Calatroni; Alkis Togias; Katherine M Thompson; Stanley J Szefler; Christine A Sorkness Journal: N Engl J Med Date: 2011-03-17 Impact factor: 91.245
Authors: Jeremy Ginsberg; Matthew H Mohebbi; Rajan S Patel; Lynnette Brammer; Mark S Smolinski; Larry Brilliant Journal: Nature Date: 2009-02-19 Impact factor: 49.962
Authors: Sue Smith; Roger Morbey; Richard G Pebody; Thomas C Hughes; Simon de Lusignan; F Alex Yeates; Helen Thomas; Sarah J O'Brien; Gillian E Smith; Alex J Elliot Journal: Emerg Infect Dis Date: 2017-11 Impact factor: 6.883
Authors: Catherine Lemiere; Camille Taillé; Jason Kihyuk Lee; Steven G Smith; Stephen Mallett; Frank C Albers; Eric S Bradford; Steven W Yancey; Mark C Liu Journal: Respir Res Date: 2021-06-22