Predicting chronic obstructive pulmonary disease hospitalizations based on concurrent influenza activity

Abstract

Although influenza has been associated with chronic obstructive pulmonary disease (COPD) exacerbations, it is not clear the extent to which this association affects healthcare use in the United States. The first goal of this project was to determine to what extent the incidence of COPD hospitalizations is associated with seasonal influenza. Second, as a natural experiment, we used influenza activity to help predict COPD admissions during the 2009 H1N1 influenza pandemic. To do this, we identified all hospitalizations between 1998 and 2010 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project (HCUP) during which a primary diagnosis of COPD was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We formulated time series regression models to investigate the association of monthly COPD admissions with influenza incidence. Finally, we applied these models, fit using 1998-2008 data, to forecast monthly COPD admissions during the 2009 pandemic. Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of COPD hospitalizations (p-value < 0.0001). The association is especially strong among older patients requiring mechanical ventilation. Use of influenza data to predict COPD admissions during the 2009 H1N1 pandemic reduced the mean-squared prediction error by 29.9%. We conclude that influenza activity is significantly associated with COPD hospitalizations in the United States and influenza activity can be exploited to more accurately forecast COPD admissions. Our results suggest that improvements in influenza surveillance, prevention, and treatment may decrease hospitalizations of patients diagnosed with COPD.

Figure 1. COPD admissions (upper panel) and influenza admissions (lower panel) by month from January 1998 to July 2010

In the upper panel, prior to 2009, the red series represents the fitted values based on the time series model with concurrent influenza activity as an explanatory variable. After 2009, the dotted red series represents forecasts of COPD admissions with influenza; the dotted blue series represents forecasts of COPD admissions without influenza.

Figure 2. Time series forecast for the COPD admissions during July 2009 through June 2010

In the upper panel, the black series represents the actual COPD series ; the dotted red series represents forecasts of COPD admissions with influenza and the dotted blue series represents forecasts of COPD admissions without influenza. The corresponding influenza series is shown in the lower panel. Importantly, the last 6 months of 2009 include the second wave of the 2009 influenza pandemic. Note: The right vertical axis represents monthly COPD admissions in terms of the percentage of peak monthly COPD admissions during the forecasting period. Thus, the peak month corresponds to 100%. For example, in December 2009, the forecasting error with influenza is roughly 1%, and the error without influenza is approximately 15% (where the percentage is relative to peak admissions during the forecasting period).

Figure 3. Time series forecast for AECOPD admissions during July 2009 through June 2010

In the upper panel, the black series represents the actual AECOPD series; the dotted red series represents forecasts of AECOPD admissions with influenza and the dotted blue series represents forecasts of AECOPD admissions without influenza. The corresponding influenza series is shown in the lower panel. Importantly, the last 6 months of 2009 include the second wave of the 2009 influenza pandemic. Note: The right vertical axis represents monthly AECOPD admissions in terms of the percentage of peak monthly AECOPD admissions during the forecasting period. Thus, the peak month corresponds to 100%. For example, in December 2009, the forecasting error with influenza is roughly 2–3%, and the error without influenza is approximately 15% (where the percentage is relative to peak admissions during the forecasting period).