期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Modelling and predicting low count child asthma hospital readmissions using General Additive Models
1
作者 Don Vicendese Andriy Olenko +3 位作者 shyamali dharmage Mimi Tang Michael Abramson Bircan Erbas 《Open Journal of Epidemiology》 2013年第3期125-134,共10页
Background: Daily paediatric asthma readmissions within 28 days are a good example of a low count time series and not easily amenable to common time series methods used in studies of asthma seasonality and time trends... Background: Daily paediatric asthma readmissions within 28 days are a good example of a low count time series and not easily amenable to common time series methods used in studies of asthma seasonality and time trends. We sought to model and predict daily trends of childhood asthma readmissions over time inVictoria,Australia. Methods: We used a database of 75,000 childhood asthma admissions from the Department ofHealth,Victoria,Australiain 1997-2009. Daily admissions over time were modeled using a semi parametric Generalized Additive Model (GAM) and by sex and age group. Predictions were also estimated by using these models. Results: N = 2401 asthma readmissions within 28 days occurred during study period. Of these, n = 1358 (57%) were boys. Overall, seasonal peaks occurred in winter (30.5%) followed by autumn (28.6%) and then spring (24.6%) (p 展开更多
关键词 ASTHMA READMISSION SEMI-PARAMETRIC Models SEASONALITY TIME Trend Low COUNT TIME Series
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部