During the period from January 1999 through December 2008, there were 1,219,150 MI-associated hospitalizations (median, 2421 per week; interquartile range [IQR], 2112–2578) in England, of which 62.5% occurred in male patients. The median weekly age-standardized rate was 2.81 cases per 100,000 persons. Over the same time period 410,204 MI-associated deaths (median, 777 deaths per week; IQR, 639–908 deaths per week) were reported in England and Wales. Both MI-associated deaths and hospitalizations demonstrated a marked winter peak. GP consultation rates for ILI varied from 0.8 to 270.8 consultations per 100,000 persons per week (mean, 16.2 consultations per 100,000 persons per week) and were highest in 1998–1999 and 1999–2000, corresponding to circulation of the A/Sydney/5/97 strain of influenza A H3N2 subtype. ILI consultations showed a similar distribution to the weekly percentage of specimens testing positive for influenza virus during the influenza season, which ranged from 0% to 100% (mean, 18.1%).
In Hong Kong, during the period from January 1998 through December 2008, there were 65,108 MI-associated hospitalizations (median, 110 per week [IQR, 97–126]; median weekly age-standardized rate, 1.11 cases per 100,000 persons), with 59.6% occurring in male patients and 18,780 MI-associated deaths (median, 32 deaths per week; IQR, 27–38 deaths per week). There was a large winter peak, as well as a smaller summer increase in the number of MIs. The percentage of specimens testing positive for influenza virus (measured throughout the year) varied from 0.3% to 51.9% (mean, 13%) per week. Corresponding ILI consultation rates in GP and outpatient clinics are shown in , as are additional descriptions of exposure variables. demonstrates the weaker correlation in Hong Kong between clinical ILI and laboratory isolation rates.
Description of Influenza and Meteorological Variables in England and Wales and Hong Kong Over the Study Period
Weekly general practitioner (GP) consultation rates for influenza-like illness and percentage of positive specimens. SARS, severe acute respiratory syndrome.
Association Between Influenza and MI-Associated Deaths
In England and Wales, a strong association was seen between GP consultations for ILI and MI-associated deaths (IRR, 1.051; 95% confidence interval [CI], 1.043–1.058; P < .001 for a 10th–90th percentile change in ILI consultations occurring 1 week later) after adjusting for environmental temperature and humidity. The best-fitting models included lags of either −1 week (as above) or −2 weeks (IRR, 1.056; 95% CI 1.049–1.064; P < .001), although a significant association remained in the model with no lag time (IRR, 1.036; 95% CI, 1.028–1.043; P < .001). An additional description of the lag time between ILI consultations and MI-associated deaths is shown in .
Figure 2. Schematic illustration of the interpretation of lag times in the analysis of associations between myocardial infarction (MI)–associated death and influenza-like illness (ILI) consultations. For example in the analysis with a lag time of −1 (more ...)
In Hong Kong, there was a similarly robust association between the proportion of specimens testing positive for influenza virus and MI-associated deaths occurring in the same week (IRR, 1.077; 95% CI, 1.013–1.145; P = .018) for a 10th–90th percentile change in proportion of positive specimens after adjusting for temperature and relative humidity. The best model fits were seen around lag 0, with similar results given by models including lags of −1 week (IRR, 1.076; 95% CI, 1.012–1.144; P = .02) and +1 week (IRR, 1.074; 95% CI 1.010–1.142; P = .023). Seasonal patterns of influenza circulation and MI-associated deaths are shown in for the 2 countries.
Weekly influenza circulation and number of myocardial infarction (MI)–associated deaths. ILI, influenza-like illness.
Association Between Influenza and MI-Associated Hospitalizations
In England and Wales, ILI consultations lagged by −1 to −3 weeks (representing the best model fits) were associated with MI-associated hospitalization after adjusting for environmental variables. There was strong evidence of a small effect (IRR for a lag of −1 week, 1.009 [95% CI, 1.003–1.015; P = .004]; IRR for a lag of −2 weeks, 1.013 [95% CI, 1.008–1.019; P < .001]; IRR for a lag of −3 weeks, 1.012 [95% CI, 1.006–1.019; P < .001]). There was no association between ILI consultation rates and MI-associated hospitalizations reported in the same week (adjusted IRR, 1.002; 95% CI, 0.996–1.003; P = .59).
In Hong Kong, an association was seen between the proportion of influenza positive specimens and MI-associated hospitalizations in the same week (IRR, 1.066; 95% CI, 1.024–1.109; P = .002) after adjustment for environmental variables. Similar model fits and results were given by models including lag times of −1 week (IRR, 1.067; 95% CI 1.025–1.110; P = .001) and +1 week (IRR, 1.066; 95% CI 1.024–1.109; P = .002). shows the effect of including different lag times for influenza reporting on MI-associated hospitalizations.
Associations Between Myocardial Infarction (MI) Events (Hospitalizations or Deaths) and Influenza Circulation, Lagged by Differing Numbers of Weeks, in England and Wales and in Hong Kong
Adjustments were made to the final model to test the robustness of our effect estimates. Modeling MI seasonality using alternative methods, such as spline functions or indicator variables, for month with a linear year variable had little effect on the magnitude and direction of influenza effect estimates. Including weekly mean temperature modeled as a linear term and as a low threshold effect gave similar results to the final model in which temperature was included as a natural cubic spline. The best model fits were seen at temperature lags of either 0 or 1 week, which gave similar results. Use of the mean of weekly maximum and then of weekly minimum temperatures modeled as natural cubic splines made little difference to effect estimates. In Hong Kong, use of the weekly percentage of positive specimens interpolated using a spline function rather than simple linear interpolation gave slightly lower point estimates but similar effects for both MI-associated deaths and MI-associated hospitalizations. Results of the main sensitivity analyses are shown in .
Sensitivity Analyses for Models of the Association Between Influenza and Myocardial Infarction (MI) Hospitalizations or Deaths in England and Wales and in Hong Kong Showing the Effect of Varying Seasonality, Temperature, and Measures of Exposure
Hospitalizations for colon cancer (adjusted IRR, 0.98; 95% CI, 0.94–1.01; P = .21) and fractured neck of femur (adjusted IRR, 0.99; 95% CI, 0.96–1.02; P = .68) were not associated with influenza circulation. In both countries, the strongest associations between influenza and MI were seen in the oldest age groups (ie, age of >80 years and, to a lesser extent, age of 60–79 years); see .
Predicted Percentage of MIs Attributable to Influenza
Proportions of MI-associated deaths attributed to influenza under the final models ranged from 3.9% to 5.6% for Hong Kong and 3.1% to 3.4% for England and Wales, depending on the model of seasonality used. Proportions of MI-associated hospitalizations attributed to influenza were smaller in both settings: 3.0%–3.3% and 0.7%–1.2%, respectively. In weeks in which influenza circulation was in the ≥90th percentile, 9.7%–13.6% of MI-associated deaths in Hong Kong and 10.7%–11.8% of MI-associated deaths in England and Wales were attributed to influenza. For MI-associated hospitalizations, the corresponding figures were 7.5%–8.2% and 2.5%–4.6%.