The use of mixed generalized additive modeling to assess the effect of temperature on the usage of emergency electrocardiography examination among the elderly in Shanghai

Abstract

Background: Acute coronary artery diseases have been observed to be associated with some meteorological variables. But few of the previous studies considered autocorrelated outcomes. Electrocardiography is a widely used tool in the initial diagnosis of acute cardiovascular events, and emergency electrocardiography counts were shown to be highly correlated with acute myocardial infarction in our pilot study, hence a good index of prediction for acute cardiovascular events morbidity among the elderly. To indirectly assess the impact of temperature on the number of acute cardiovascular events, we studied the association between temperature and emergency electrocardiography counts while considering autocorrelated nature of the response variables.

Methods: We collected daily emergency electrocardiography counts for elderly females and males in Shanghai from 2007 to middle 2012, and studied temperature and other effects on these data using Mixed Generalized Additive Modelling methods. Delayed temperature effect distribution was described as the weighted average of the temperatures within 3 days before the counts was recorded. Autoregressive random effects were used in the model to describe the autocorrelation of the response variables.

Main results: Temperature effect was observed to be piecewise linearly associated with the logarithm of emergency electrocardiography counts. The optimal weights of the delayed temperature effect distribution were obtained from the model estimation. The weights of lag-1 were the maximums, significantly greater than the weights of lag-2 and lag-3 for both females and males. The model showed good fit with R2 values of 0.860 for females and 0.856 for males.

Conclusion: From the mixed generalized additive model, we infer that during cold and mild days, the number of emergency electrocardiography counts increase as temperature effect decreases, while during hot days, counts increase as temperature effect increases. Similar properties could be inferred for the occurrence of cardiovascular events.

Figure 1. Scatter plot s of ECG counts and Temperature v. s. Time.

A. Daily emergency ECG counts in female (red dots) and male (blue dots) elderly group from 2007 to middle 2012, with the solid lines: Lowess estimation of ECG counts. B. Daily averaged temperature from 2007 to middle 2012, with the solid line: Lowess estimation of averaged temperature.

Figure 2. Scatter plot s of MI cases numbers v. s. ECG counts.

A. Scatter plot of daily MI cases numbers v. s. daily emergency ECG counts in female (red triangle) and male (blue square) elderly group in the month of July 2011. B. Scatter plot of daily MI cases numbers v. s. daily emergency ECG counts in female (red triangle) and male (blue square) elderly group in the month of December 2011.

Figure 3. Scatter plot of BD counts v. s. Temperature from 2007 to middle 2012.

A. Scatter plot of BD counts v. s. Temperature in female elderly group, with the solid black line: Lowess estimator of the ECG counts against daily average temperature. B. Scatter plot of BD counts v. s. Temperature in male elderly group, with the solid black line: Lowess estimator of the ECG counts against daily average temperature.

Figure 4. Residual autocorrelation and partial autocorrelation of GAM and MGAM in female elderly group.

Upper left is the autocorrelation function (ACF) of GAM residuals. Upper right is the ACF of MGAM residuals. Lower left is the partial autocorrelation function (PACF) of GAM residuals. Lower right is the PACF of MGAM residuals.

Figure 5. Residual autocorrelation and partial autocorrelation of GAM and MGAM in male elderly group.

Upper left is the autocorrelation function (ACF) of GAM residuals. Upper right is the ACF of MGAM residuals. Lower left is the partial autocorrelation function (PACF) of GAM residuals. Lower right is the PACF of MGAM residuals.

Figure 6. Estimated spline for temperature effect of MGAM and GAM in both data sets.

A. Temperature effect of MGAM and GAM estimated from female elderly group data. B. Temperature effect of MGAM and GAM estimated from male elderly group data. The red solid lines in both plots are the estimated spline for temperature effects on ECG counts from MGAM model, and the black dashed lines are the estimated spline for temperature effects on ECG counts from GAM model.

Figure 7. Exponential of DOW effect estimated by MGAM.

A. Exponential of DOW effect from female elderly group. B. Exponential of DOW effect from male elderly group.