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

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

Methods: 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.

Results: 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.

Conclusion: 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.
medical statistics, epidemiological methods, cohort studies, hospital admissions, multiple-failure, survival analysis, risk factor, older people
1471-2288
Westbury, L.D.
5ed45df3-3df7-4bf9-bbad-07b63cd4b281
Syddall, H.E.
a0181a93-8fc3-4998-a996-7963f0128328
Simmonds, S.J.
2214e6b5-868a-4dae-8491-fca5d5a8ecb8
Cooper, C.
e05f5612-b493-4273-9b71-9e0ce32bdad6
Aihie Sayer, Avan
fb4c2053-6d51-4fc1-9489-c3cb431b0ffb
Westbury, L.D.
5ed45df3-3df7-4bf9-bbad-07b63cd4b281
Syddall, H.E.
a0181a93-8fc3-4998-a996-7963f0128328
Simmonds, S.J.
2214e6b5-868a-4dae-8491-fca5d5a8ecb8
Cooper, C.
e05f5612-b493-4273-9b71-9e0ce32bdad6
Aihie Sayer, Avan
fb4c2053-6d51-4fc1-9489-c3cb431b0ffb

Westbury, L.D., Syddall, H.E., Simmonds, S.J., Cooper, C. and Aihie Sayer, Avan (2016) Identification of risk factors for hospital admission using multiple-failure survival models: a toolkit for researchers. BMC Medical Research Methodology, 16, [46]. (doi:10.1186/s12874-016-0147-x). (PMID:27117081)

Record type: Article

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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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More information

Accepted/In Press date: 17 March 2016
e-pub ahead of print date: 26 April 2016
Published date: 26 April 2016
Keywords: medical statistics, epidemiological methods, cohort studies, hospital admissions, multiple-failure, survival analysis, risk factor, older people
Organisations: Faculty of Medicine

Identifiers

Local EPrints ID: 390203
URI: http://eprints.soton.ac.uk/id/eprint/390203
ISSN: 1471-2288
PURE UUID: be09a601-3a43-42b3-a160-e7bb8e6f3a62
ORCID for H.E. Syddall: ORCID iD orcid.org/0000-0003-0171-0306
ORCID for C. Cooper: ORCID iD orcid.org/0000-0003-3510-0709

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Date deposited: 22 Mar 2016 10:17
Last modified: 29 Jul 2020 01:33

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Contributors

Author: L.D. Westbury
Author: H.E. Syddall ORCID iD
Author: S.J. Simmonds
Author: C. Cooper ORCID iD
Author: Avan Aihie Sayer

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