The influence of cold weather on the usage of emergency link calls: a case study in Hong Kong

BMC Medical Informatics and Decision Making

Research Article

Paul SF Yip¹  Feng Chen² 

[1] Department of Social Work and Social Administration, The University of Hong Kong, Pokfulam Road, Hong Kong, China;Department of Statistics, The University of New South Wales, Sydney, Australia;

关键词: Auto-regression;    Count data;    Generalized linear auto-regressive moving-average (GLARMA) model;    Generalized linear model (GLM);    negative binomial;    Nonlinear effect;    Overdispersion;    Poisson regression;    Time series;   

DOI  :  10.1186/s12911-015-0191-1

 received in 2014-03-08, accepted in 2015-07-22,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundIn response to an unexpected long cold spell in February 1996 which killed more than 100 older adults (mostly living alone) in Hong Kong, the Hong Kong Senior Citizen Home Safety Association established a Personal Emergency Link Service to provide emergency contact to the older adults, which uses a telephone system to render emergency relief and total care service around the clock. To facilitate the dynamic and efficient allocation of service resources, it is crucial to understand the factors linked with use of the services and number of hospital admissions arising from PE link service.MethodsWe initially use the Poisson generalized linear model (GLM) with polynomial effect functions of relevant covariates. If the time series of residuals from fitting the Poisson GLM reveals significant serial correlation, a Poisson generalized linear autoregressive moving average (GLARMA) model is refitted to the data to account for the auto-correlation among the time series of daily call numbers. If the data is overdispersed relative to the best fitting Poisson GLARMA model, then the negative binomial GLARMA model is refitted to account for any overdispersion. In all the models, dummy variables for weekdays and months are included to account for any cyclic trends due weekday effect or month of the year effect. The secular time trend is modeled by a polynomial function of calendar time over the study period. Finally any critical temperatures are identified by visually inspecting the graph of the effect function of temperature.ResultsThe weekday and month effects are both significant with Monday seeing more PE Link calls than Sunday and June seeing less than January. Temperature has significant effect on the PE Link call rate with the effect highly nonlinear. A critical temperature, below which excessive increase in PE link calls that lead to hospital admissions, is identified to be around 15 °C.ConclusionIdentifying a threshold temperature which generates an excessive increase in the expected number of PE Link calls would be useful in service provision planning and support for elderly in need of hospital admission.

【 授权许可】

CC BY   
© Chen and Yip. 2015