Predicting Chronic Obstructive Pulmonary Disease Hospitalizations Based on Concurrent Influenza Activity

Figures & data

Table 1.  Fitted time series regression models with the COPD incidence series as the response series and concurrent influenza activity as the explanatory series

	Coefficients	Estimates	SE	p-value
COPD	AR1	1.5013	0.1278	< 0.0001
	AR2	–0.5023	0.1264	< 0.0001
	MA1	–0.8827	0.0760	< 0.0001
	SMA1	–0.8996	0.2097	< 0.0001
	FLU	0.0970	0.0108	< 0.0001
COPD Requiring Mechanical Ventilation	AR1	0.9879	0.0197	< 0.0001
	MA1	–0.5629	0.0969	< 0.0001
	SMA1	–0.7494	0.1116	< 0.0001
	FLU	0.0965	0.0118	< 0.0001
AECOPD	AR1	1.4741	0.1894	< 0.0001
	AR2	–0.4950	0.1754	0.0037
	MA1	–0.7648	0.1400	< 0.0001
	SMA1	–0.8153	0.1593	< 0.0001
	FLU	0.1007	0.0120	< 0.0001
AECOPD Requiring Mechanical Ventilation	AR1	0.9756	0.0324	< 0.0001
	MA1	–0.4901	0.1030	< 0.0001
	SMA1	–0.7306	0.1054	< 0.0001
	FLU	0.1026	0.0138	< 0.0001

AR1: Autoregressive component of order 1.AR2: Autoregressive component of order 2.

MA1: Moving average component of order 1.

SMA1: Seasonal moving average component of order 1 with a periodicity of 12.

FLU: Influenza activity.

Note: The autoregressive and moving average components are included in the models to account for autocorrelation in the residuals. The influenza coefficients represent the instantaneous associations between the influenza series and the COPD series. The instantaneous associations were confirmed through the cross-correlation functions.

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).

Table 2.  Attributable risk measures by age group for the four COPD series

	COPD	COPD Requiring Mechanical Ventilation	AECOPD	AECOPD Requiring Mechanical Ventilation
Under 65 years	0.0374	0.0363	0.0391	0.0375
65 years and over	0.0419	0.0489	0.0423	0.0503

Note: The measure reflects the proportion of the overall incidence of COPD hospitalizations that could be potentially eliminated if influenza activity during the peak month could be reduced to the baseline level corresponding to the average over the 11 non-peak months.