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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.

Ma WP, Gu S, Wang Y, Zhang XJ, Wang AR, Zhao NQ, Song YY - PLoS ONE (2014)

Bottom Line: Delayed temperature effect distribution was described as the weighted average of the temperatures within 3 days before the counts was recorded.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Social Medicine, School of Public Health, Fudan University, Shanghai, China.

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.

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Related in: MedlinePlus

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.
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pone-0100284-g006: 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.

Mentions: The temperature effect curves were displayed in Figure 6. The temperature effects in the left panel were estimated from female ECG counts, while the right panel was from male ECG counts. For the female ECG counts (Figure 6A), we can see that the temperature curve of the Mixed GAM was V-shaped with the minimums occurring at 27–30°C. The curve went down from 0°C to 27°C, and rose up from 30°C to 35°C. The decreasing part followed a piecewise linear trend with the slope being −0.0024 from 0°C to 11°C (corresponding Risk Ratio (RR) for a 1°C change in temperature was 0.998 and 0.988 for a 5°C change) and −0.0116 from 11°C to 27°C (corresponding RR 0.988 for a 1°C change and 0.944 for a 5°C change). The increasing part also followed a linear trend with the slope being 0.0113 from 30°C to 35°C (corresponding RR 1.011 for a 1°C change or 1.058 for a 5°C change).


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.

Ma WP, Gu S, Wang Y, Zhang XJ, Wang AR, Zhao NQ, Song YY - PLoS ONE (2014)

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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4061122&req=5

pone-0100284-g006: 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.
Mentions: The temperature effect curves were displayed in Figure 6. The temperature effects in the left panel were estimated from female ECG counts, while the right panel was from male ECG counts. For the female ECG counts (Figure 6A), we can see that the temperature curve of the Mixed GAM was V-shaped with the minimums occurring at 27–30°C. The curve went down from 0°C to 27°C, and rose up from 30°C to 35°C. The decreasing part followed a piecewise linear trend with the slope being −0.0024 from 0°C to 11°C (corresponding Risk Ratio (RR) for a 1°C change in temperature was 0.998 and 0.988 for a 5°C change) and −0.0116 from 11°C to 27°C (corresponding RR 0.988 for a 1°C change and 0.944 for a 5°C change). The increasing part also followed a linear trend with the slope being 0.0113 from 30°C to 35°C (corresponding RR 1.011 for a 1°C change or 1.058 for a 5°C change).

Bottom Line: Delayed temperature effect distribution was described as the weighted average of the temperatures within 3 days before the counts was recorded.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Social Medicine, School of Public Health, Fudan University, Shanghai, China.

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.

Show MeSH
Related in: MedlinePlus