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. Hightlights Operationalisation of two count-based multimorbidity measures using linked electronic hospitalepisode data was evaluated (Charlson and Tonelli). First study to compare the Tonelli measure with another measure for investigating multimor-bidity in hospitalised patients. Multimorbidity prevalence differed depending on measure used, but both multimorbidity mea-sures identified those at risk of poor outcomes. Operationalised multimorbidity tools have uses for future multimorbidity research and use insecondary care data systems. Multimorbidity measures are not interchangeable, and choice of measure should depend onpurpose.

BackgroundMultimorbidity is recognised as a complex and growing health challenge. Currently there is no “gold standard” multimorbidity measure and few studies have compared measures in hospitalised patients. ObjectivesWe aimed to evaluate two published multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland. MethodsWe used the Scottish Morbidity Record (SMR) data for the years 2009-2016. We included all adults admitted to hospital in the Grampian region of Scotland (population 588,100) during 2014. Morbidities were identified from inpatient admissions during the five years prior to admission date in 2014 (ICD-10 codes). Two multimorbidity measures were used: Charlson (Quan 2005), and Tonelli et al (2015); and multimorbidity was defined as ≥2 morbidities. Kappa statistics assessed agreement between the two measures in classifying patients as multimorbid. The association between multimorbidity and mortality, readmissions, and length of stay was examined using regression methods with odds ratios (OR) or incidence rate ratios (IRR) calculated as appropriate. FindingsIn 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). After adjusting for covariates, multimorbidity was associated with an increased risk of longer length of stay, (Charlson IRR 1.10 (1.03, 1.18; p=0.005); Tonelli IRR 1.11 (1.04, 1.18; p<0.001)) and readmission (Charlson OR 2.06 (1.94, 2.19; p<0.001); Tonelli OR 2.12 (2.01, 2.22; p<0.001)). Multimorbidity had a higher risk of mortality when measured using Charlson (Charlson OR 2.71 (2.52, 2.92; p=<0.001); Tonelli OR (1.84 (1.72, 1.98; p<0.001)). ConclusionsMultimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and will be useful for future multimorbidity research and use in secondary care data systems.

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