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.

• Main Authors: Wei-ping Ma, Shuo Gu, Yi Wang, Xian-jing Zhang, Ai-rong Wang, Nai-qing Zhao, Yan-yan Song
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2014-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC4061122?pdf=render

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.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.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.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.