Measuring multimorbidity in hospitalised patients using linked hospital episode data

comparison of two measures

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Abstract

Introduction

Multimorbidity is a complex and growing health challenge. There is no accepted “gold standard” multimorbidity measure for hospital resource planning, and few studies have compared measures in hospitalised patients.


Aim

To evaluate operationalisation of two multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland.


Methods

Linked hospital episode data (Scottish Morbidity Record (SMR)) for the years 2009-2016 were used. Adults admitted to hospital as a general/acute inpatient during 2014 were included. Conditions (ICD-10) were identified from general/acute (SMR01) and psychiatric (SMR04) admissions during the five years prior to first admission in 2014. Two count-based multimorbidity measures were used (Charlson Comorbidity Index and Tonelli et al.), and multimorbidity was defined as ≥2 conditions. Kappa statistics assessed agreement. The association between multimorbidity and length of stay, readmission and mortality was assessed using logistic and negative binomial regression as appropriate.


Results

In 41,545 adults (median age 62 years, 52.6% female), multimorbidity prevalence was 15.1% (95% CI 14.8%, 15.5%) using Charlson and 27.4% (27.0%, 27.8%) using Tonelli – agreement 85.1% (Kappa 0.57). Multimorbidity prevalence, using both measures, increased with age. Multimorbidity was higher in males (16.5%) than females (13.9%) using the Charlson measure, but similar across genders when measured with Tonelli. After adjusting for covariates, multimorbidity remained associated with longer length of stay (Charlson IRR 1.1 (1.0, 1.2); Tonelli IRR 1.1 (1.0, 1.2)) and readmission (Charlson OR 2.1 (1.9, 2.2); Tonelli OR 2.1 (2.0, 2.2)). Multimorbidity had a stronger association with mortality when measured using Charlson (OR 2.7 (2.5, 2.9)), than using Tonelli (OR (1.8 (1.7, 2.0)).


Conclusions

Multimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and such operationalised tools will be useful for future multimorbidity research and use in secondary care data systems. Multimorbidity measures are not interchangeable, and the choice of measure should depend on the purpose.
Original languageEnglish
Article number02
Number of pages13
JournalInternational Journal of Population Data Science
Volume4
Issue number1
Early online date21 Jan 2019
DOIs
Publication statusPublished - Jan 2019

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Comorbidity
Length of Stay
Hospital Planning
Secondary Care
Mortality
Scotland
International Classification of Diseases
Information Systems
Psychiatry
Inpatients
Morbidity

Cite this

@article{c9762dccc5d344b8b16779be9b1c99e7,
title = "Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures",
abstract = "IntroductionMultimorbidity is a complex and growing health challenge. There is no accepted “gold standard” multimorbidity measure for hospital resource planning, and few studies have compared measures in hospitalised patients.AimTo evaluate operationalisation of two multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland.MethodsLinked hospital episode data (Scottish Morbidity Record (SMR)) for the years 2009-2016 were used. Adults admitted to hospital as a general/acute inpatient during 2014 were included. Conditions (ICD-10) were identified from general/acute (SMR01) and psychiatric (SMR04) admissions during the five years prior to first admission in 2014. Two count-based multimorbidity measures were used (Charlson Comorbidity Index and Tonelli et al.), and multimorbidity was defined as ≥2 conditions. Kappa statistics assessed agreement. The association between multimorbidity and length of stay, readmission and mortality was assessed using logistic and negative binomial regression as appropriate.ResultsIn 41,545 adults (median age 62 years, 52.6{\%} female), multimorbidity prevalence was 15.1{\%} (95{\%} CI 14.8{\%}, 15.5{\%}) using Charlson and 27.4{\%} (27.0{\%}, 27.8{\%}) using Tonelli – agreement 85.1{\%} (Kappa 0.57). Multimorbidity prevalence, using both measures, increased with age. Multimorbidity was higher in males (16.5{\%}) than females (13.9{\%}) using the Charlson measure, but similar across genders when measured with Tonelli. After adjusting for covariates, multimorbidity remained associated with longer length of stay (Charlson IRR 1.1 (1.0, 1.2); Tonelli IRR 1.1 (1.0, 1.2)) and readmission (Charlson OR 2.1 (1.9, 2.2); Tonelli OR 2.1 (2.0, 2.2)). Multimorbidity had a stronger association with mortality when measured using Charlson (OR 2.7 (2.5, 2.9)), than using Tonelli (OR (1.8 (1.7, 2.0)).ConclusionsMultimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and such operationalised tools will be useful for future multimorbidity research and use in secondary care data systems. Multimorbidity measures are not interchangeable, and the choice of measure should depend on the purpose.",
author = "Lynn Robertson and Dolapo Ayansina and Marjorie Johnston and Angharad Marks and Corri Black",
note = "Acknowledgements: This work was funded by NHS Grampian. We thank NHS Grampian who provided data and also the Grampian Data Safe Haven, who hosted the data and provided data management support and the linkage service. We acknowledge the support from The Farr Institute of Health Informatics Research, Scotland. The Farr Institute is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates), the Wellcome Trust, (MRC Grant Nos: Scotland MR/K007017/1). We also acknowledge the support of our Study Steering Committee, which included clinical, epidemiological and health intelligence representation. Funding: The study was funded by NHS Grampian, Public Health Directorate.",
year = "2019",
month = "1",
doi = "10.23889/ijpds.v4i1.461",
language = "English",
volume = "4",
journal = "International Journal of Population Data Science",
issn = "2399-4908",
number = "1",

}

TY - JOUR

T1 - Measuring multimorbidity in hospitalised patients using linked hospital episode data

T2 - comparison of two measures

AU - Robertson, Lynn

AU - Ayansina, Dolapo

AU - Johnston, Marjorie

AU - Marks, Angharad

AU - Black, Corri

N1 - Acknowledgements: This work was funded by NHS Grampian. We thank NHS Grampian who provided data and also the Grampian Data Safe Haven, who hosted the data and provided data management support and the linkage service. We acknowledge the support from The Farr Institute of Health Informatics Research, Scotland. The Farr Institute is supported by a 10-funder consortium: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Assembly Government), the Chief Scientist Office (Scottish Government Health Directorates), the Wellcome Trust, (MRC Grant Nos: Scotland MR/K007017/1). We also acknowledge the support of our Study Steering Committee, which included clinical, epidemiological and health intelligence representation. Funding: The study was funded by NHS Grampian, Public Health Directorate.

PY - 2019/1

Y1 - 2019/1

N2 - IntroductionMultimorbidity is a complex and growing health challenge. There is no accepted “gold standard” multimorbidity measure for hospital resource planning, and few studies have compared measures in hospitalised patients.AimTo evaluate operationalisation of two multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland.MethodsLinked hospital episode data (Scottish Morbidity Record (SMR)) for the years 2009-2016 were used. Adults admitted to hospital as a general/acute inpatient during 2014 were included. Conditions (ICD-10) were identified from general/acute (SMR01) and psychiatric (SMR04) admissions during the five years prior to first admission in 2014. Two count-based multimorbidity measures were used (Charlson Comorbidity Index and Tonelli et al.), and multimorbidity was defined as ≥2 conditions. Kappa statistics assessed agreement. The association between multimorbidity and length of stay, readmission and mortality was assessed using logistic and negative binomial regression as appropriate.ResultsIn 41,545 adults (median age 62 years, 52.6% female), multimorbidity prevalence was 15.1% (95% CI 14.8%, 15.5%) using Charlson and 27.4% (27.0%, 27.8%) using Tonelli – agreement 85.1% (Kappa 0.57). Multimorbidity prevalence, using both measures, increased with age. Multimorbidity was higher in males (16.5%) than females (13.9%) using the Charlson measure, but similar across genders when measured with Tonelli. After adjusting for covariates, multimorbidity remained associated with longer length of stay (Charlson IRR 1.1 (1.0, 1.2); Tonelli IRR 1.1 (1.0, 1.2)) and readmission (Charlson OR 2.1 (1.9, 2.2); Tonelli OR 2.1 (2.0, 2.2)). Multimorbidity had a stronger association with mortality when measured using Charlson (OR 2.7 (2.5, 2.9)), than using Tonelli (OR (1.8 (1.7, 2.0)).ConclusionsMultimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and such operationalised tools will be useful for future multimorbidity research and use in secondary care data systems. Multimorbidity measures are not interchangeable, and the choice of measure should depend on the purpose.

AB - IntroductionMultimorbidity is a complex and growing health challenge. There is no accepted “gold standard” multimorbidity measure for hospital resource planning, and few studies have compared measures in hospitalised patients.AimTo evaluate operationalisation of two multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland.MethodsLinked hospital episode data (Scottish Morbidity Record (SMR)) for the years 2009-2016 were used. Adults admitted to hospital as a general/acute inpatient during 2014 were included. Conditions (ICD-10) were identified from general/acute (SMR01) and psychiatric (SMR04) admissions during the five years prior to first admission in 2014. Two count-based multimorbidity measures were used (Charlson Comorbidity Index and Tonelli et al.), and multimorbidity was defined as ≥2 conditions. Kappa statistics assessed agreement. The association between multimorbidity and length of stay, readmission and mortality was assessed using logistic and negative binomial regression as appropriate.ResultsIn 41,545 adults (median age 62 years, 52.6% female), multimorbidity prevalence was 15.1% (95% CI 14.8%, 15.5%) using Charlson and 27.4% (27.0%, 27.8%) using Tonelli – agreement 85.1% (Kappa 0.57). Multimorbidity prevalence, using both measures, increased with age. Multimorbidity was higher in males (16.5%) than females (13.9%) using the Charlson measure, but similar across genders when measured with Tonelli. After adjusting for covariates, multimorbidity remained associated with longer length of stay (Charlson IRR 1.1 (1.0, 1.2); Tonelli IRR 1.1 (1.0, 1.2)) and readmission (Charlson OR 2.1 (1.9, 2.2); Tonelli OR 2.1 (2.0, 2.2)). Multimorbidity had a stronger association with mortality when measured using Charlson (OR 2.7 (2.5, 2.9)), than using Tonelli (OR (1.8 (1.7, 2.0)).ConclusionsMultimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and such operationalised tools will be useful for future multimorbidity research and use in secondary care data systems. Multimorbidity measures are not interchangeable, and the choice of measure should depend on the purpose.

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DO - 10.23889/ijpds.v4i1.461

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JO - International Journal of Population Data Science

JF - International Journal of Population Data Science

SN - 2399-4908

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