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Identification of risk factors for hospital admission using multiple-failure survival models: a toolkit for researchers

Overview of attention for article published in BMC Medical Research Methodology, April 2016
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Title
Identification of risk factors for hospital admission using multiple-failure survival models: a toolkit for researchers
Published in
BMC Medical Research Methodology, April 2016
DOI 10.1186/s12874-016-0147-x
Pubmed ID
Authors

Leo D. Westbury, Holly E. Syddall, Shirley J. Simmonds, Cyrus Cooper, Avan Aihie Sayer

Abstract

The UK population is ageing; improved understanding of risk factors for hospital admission is required. Linkage of the Hertfordshire Cohort Study (HCS) with Hospital Episode Statistics (HES) data has created a multiple-failure survival dataset detailing the characteristics of 2,997 individuals at baseline (1998-2004, average age 66 years) and their hospital admissions (regarded as 'failure events') over a 10 year follow-up. Analysis of risk factors using logistic regression or time to first event Cox modelling wastes information as an individual's admissions after their first are disregarded. Sophisticated analysis techniques are established to examine risk factors for admission in such datasets but are not commonly implemented. We review analysis techniques for multiple-failure survival datasets (logistic regression; time to first event Cox modelling; and the Andersen and Gill [AG] and Prentice, Williams and Peterson Total Time [PWP-TT] multiple-failure models), outline their implementation in Stata, and compare their results in an analysis of housing tenure (a marker of socioeconomic position) as a risk factor for different types of hospital admission (any; emergency; elective; >7 days). The AG and PWP-TT models include full admissions histories in the analysis of risk factors for admission and account for within-subject correlation of failure times. The PWP-TT model is also stratified on the number of previous failure events, allowing an individual's baseline risk of admission to increase with their number of previous admissions. All models yielded broadly similar results: not owner-occupying one's home was associated with increased risk of hospital admission. Estimated effect sizes were smaller from the PWP-TT model in comparison with other models owing to it having accounted for an increase in risk of admission with number of previous admissions. For example, hazard ratios [HR] from time to first event Cox models were 1.67(95 % CI: 1.36,2.04) and 1.63(95 % CI:1.36,1.95) for not owner-occupying one's home in relation to risk of emergency admission or death among women and men respectively; corresponding HRs from the PWP-TT model were 1.34(95 % CI:1.15,1.56) for women and 1.23(95 % CI:1.07,1.41) for men. The PWP-TT model may be implemented using routine statistical software and is recommended for the analysis of multiple-failure survival datasets which detail repeated hospital admissions among older people.

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Mendeley readers

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The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 24%
Researcher 9 20%
Student > Master 6 13%
Other 4 9%
Professor > Associate Professor 3 7%
Other 5 11%
Unknown 7 16%
Readers by discipline Count As %
Medicine and Dentistry 14 31%
Nursing and Health Professions 5 11%
Computer Science 3 7%
Decision Sciences 2 4%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 7 16%
Unknown 12 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 April 2016.
All research outputs
#17,799,386
of 22,865,319 outputs
Outputs from BMC Medical Research Methodology
#1,683
of 2,019 outputs
Outputs of similar age
#204,928
of 298,924 outputs
Outputs of similar age from BMC Medical Research Methodology
#29
of 32 outputs
Altmetric has tracked 22,865,319 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,019 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.1. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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