Multi-Morbidity in Hospitalised Older Patients: Who Are the Complex Elderly?

Ruiz, M.; Bottle, Alex; Long, Susannah; Aylin, Paul

doi:10.1371/journal.pone.0145372

Cited by 58 publications

(48 citation statements)

References 23 publications

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“…This finding is not consistent with previous reports in primary care populations, where multimorbidity is generally more common in females [6,12,24]. Studies reporting the prevalence of multimorbidity by gender in hospitalised populations are limited, and report conflicting results [14,16,47,48]. We also noted some evidence of a different pattern of multimorbidity between males and females at different age groups.…”

Section: Discussioncontrasting

confidence: 99%

“…The prevalence of multimorbidity in hospitalised patients varies widely among studies (22% to 99.7%) [12][13][14][15][16][17]. Some of this difference is likely to be due to different methods used for measuring multimorbidity including how multimorbidity is defined, the number and type of conditions included, data sources and coding schemes, population studied, and setting [18].…”

Section: Introductionmentioning

confidence: 99%

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Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

Robertson

Ayansina

Johnston

et al. 2019

IJPDS

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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.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

Robertson

Ayansina

Johnston

et al. 2019

IJPDS

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show abstract

“…Finally, a significant factor impacting acute hospitals is the changing nature of the patients cared for in them: patients are older, frailer and have more complex conditions than in previous decades [15]. This shift to older, frailer patients in acute hospitals, changes the way a hospital delivers care and the skills mix needed within the hospital to deliver high quality care that ensures patients can be safely treated and discharged.…”

Section: The Problem Of 'Lack Of Integration'mentioning

confidence: 99%

Clinical care in the home and population health management: improving the care of patients with long-term health conditions

Asbridge¹,

Davies²

2017

EJPCH

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Clinical care in the home, utilized, administered and evaluated in terms of its clinical and economic outcomes within a population health management system, can help patients with long term conditions (LTCs) live healthier lives while reducing the costs of care across the system. This model, through the avoidance of emergency hospital admissions, reducing the need for hospitalisation to treat progressive illnesses and activating and empowering patients, supporting good primary care, ensuring continuity of care and providing on-going emotional, psychological and practical support, can greatly increase the overall significance and efficiency of clinical care in the home, delivered as part of a population health management delivery system. This article argues strongly for the innate superiority of home-based care approaches within the modern era, actively recommending such approaches for the good of individual patients and an integrity of the general clinical system. We present hard evidence for the durability of our approach in both clinical and economic terms and advance to the reader the merits thereof.

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“…Our literature search for studies reporting the prevalence of multimorbidity in hospitalized patients revealed that the majority of studies have been focussed on adults over 65 years and/or patients with high severity of illness or highly selected patients. [21][22][23][24][25][26][27][28][29][30] Studies including unselected younger patients have reported the overall prevalence of multimorbidity, [31][32][33][34] but less often in detail by gender or specific age groups, 32,33 and we did not identify any studies investigating the prevalence of multimorbidity by urban-rural area or SES. In Scotland, rurality and SES are taken into account for allocating resources to NHS Boards.…”

Section: Introductionmentioning

confidence: 99%

Urban–rural and socioeconomic status: Impact on multimorbidity prevalence in hospitalized patients

et al. 2020

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Objective: The aim of this study was to describe multimorbidity prevalence in hospitalized adults, by urban–rural area of residence and socioeconomic status (SES). Methods: Linked hospital episode data were used. Adults (≥18 years) admitted to hospital as an inpatient during 2014 in Grampian, Scotland, were included. Conditions were identified from admissions during the 5 years prior to the first admission in 2014. Multimorbidity was defined as ≥2 conditions and measured using Tonelli et al. based on International Classification of Diseases-10 coding (preselected list of 30 conditions). We used proportions and 95% confidence intervals (CIs) to summarize the prevalence of multimorbidity by age group, sex, urban–rural category and deprivation. The association between multimorbidity and patient characteristics was assessed using the χ 2 test. Results: Forty one thousand five hundred and forty-five patients were included (median age 62, 52.6% female). Overall, 27.4% (95% CI 27.0, 27.8) of patients were multimorbid. Multimorbidity prevalence was 28.8% (95% CI 28.1, 29.5) in large urban versus 22.0% (95% CI 20.9, 23.3) in remote rural areas and 28.7% (95% CI 27.2, 30.3) in the most deprived versus 26.0% (95% CI 25.2, 26.9) in the least deprived areas. This effect was consistent in all age groups, but not statistically significant in the age group 18–29 years. Multimorbidity increased with age but was similar for males and females. Conclusion: Given the scarcity of research into the effect of urban–rural area and SES on multimorbidity prevalence among hospitalized patients, these findings should inform future research into new models of care, including the consideration of urban–rural area and SES.

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Multi-Morbidity in Hospitalised Older Patients: Who Are the Complex Elderly?

Cited by 58 publications

References 23 publications

Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

Clinical care in the home and population health management: improving the care of patients with long-term health conditions

Urban–rural and socioeconomic status: Impact on multimorbidity prevalence in hospitalized patients

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