Multimorbidity and comorbidity revisited: refining the concepts for international health research

Nicholson, Kathryn; Makovski, Tatjana T.; Griffith, Lauren; Raina, Parminder; Stranges, Saverio; Akker, Marjan van den

doi:10.1016/j.jclinepi.2018.09.008

Cited by 113 publications

(73 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The treatment of single chronic diseases has thereby been significantly improved. However, as the prevalence of multimorbidity is rising this development poses great challenges for the health professionals who must navigate in the complexity of multiple care pathways when treating patients with co-and multimorbidity 3 (the presence of more than one chronic condition in a single individual 4,5 ). Consequently, patients with multimorbidity face increasingly complicated medical lifestyle with fragmented appointments, investigations, polypharmacy, and medical regimes.…”

Section: Introductionmentioning

confidence: 99%

<p>Patterns of Multimorbidity and Differences in Healthcare Utilization and Complexity Among Acutely Hospitalized Medical Patients (≥65 Years) – A Latent Class Approach</p>

Juul‐Larsen¹,

Christensen²,

Bandholm³

et al. 2020

CLEP

View full text Add to dashboard Cite

The majority of acutely admitted older medical patients are multimorbid, receive multiple drugs, and experience a complex treatment regime. To be able to optimize treatment and care, we need more knowledge of the association between different patterns of multimorbidity and healthcare utilization and the complexity thereof. The purpose was therefore to investigate patterns of multimorbidity in a Danish national cohort of acutely hospitalized medical patients aged 65 and older and to determine the association between these multimorbid patterns with the healthcare utilization and complexity. Patients and Methods: Longitudinal cohort study of 129,900 (53% women) patients. Latent class analysis (LCA) was used to develop patterns of multimorbidity based on 22 chronic conditions ascertained from Danish national registers. A latent class regression was used to test for differences in healthcare utilization and healthcare complexity among the patterns measured in the year leading up to the index admission. Results: LCA identified eight distinct multimorbid patterns. Patients belonging to multimorbid patterns including the major chronic conditions; diabetes and chronic obstructive pulmonary disease was associated with higher odds of healthcare utilization and complexity than the reference pattern ("Minimal chronic conditions"). The pattern with the highest number of chronic conditions did not show the highest healthcare utilization nor complexity. Conclusion: Our study showed that chronic conditions cluster together and that these patterns differ in healthcare utilization and complexity. Patterns of multimorbidity have the potential to be used in epidemiological studies of healthcare planning but should be confirmed in other population-based studies.

show abstract

Section: Introductionmentioning

confidence: 99%

<p>Patterns of Multimorbidity and Differences in Healthcare Utilization and Complexity Among Acutely Hospitalized Medical Patients (≥65 Years) – A Latent Class Approach</p>

Juul‐Larsen¹,

Christensen²,

Bandholm³

et al. 2020

CLEP

View full text Add to dashboard Cite

show abstract

“…Comorbidity or multimorbidity is defined as living with multiple health‐care issues 37,38 . Of course, if the issues are very similar (or there is only one disease), the comorbidity is low, while more diverse combinations will result in higher comorbidity 38 .…”

Section: Suggested Methods For Overcoming Problemsmentioning

confidence: 99%

Processing new types of quality data

Marmor

Bashkansky

2020

Quality & Reliability Eng

View full text Add to dashboard Cite

Quality engineers are increasingly faced with the need to deal with new types of data, which are significantly different from ordinary numerical data by virtue of their nature and the operations that can be performed with them. Basic concepts related to processing of such data, ie, data similarity, measurement system analysis, variation analysis, and data fusion, need to be thoroughly rethought. Reviewing recent publications in the field, we suggest a common approach to processing all data types on the basis of the idea of defining the distance metric for the appropriate data space. The article discusses six types of quality data (nominal, ordinal, preference chains, strings, tree structured, and product/process distribution) and four data processing aspects (calculating data similarity, error description, data fusion, and intradispersion and interdispersion studies). Necessary information and recommendations are given for each combination of data type and problem. They are also summarized in a table that refers the reader to various sections of the article. Any other data type for which the distance metric is definable can be included into the framework of the proposed unified approach.

show abstract

“…Multimorbidity, the co-occurrence of multiple, chronic conditions, where none is more central, 1 is increasingly prevalent and is becoming the norm. [2][3][4] Multimorbidity is associated with high healthcare utilisation 5 and challenges clinicians in a fragmented healthcare system, aided by single disease guidelines.…”

Section: Introductionmentioning

confidence: 99%

“…9 10 Multimorbidity alone may not imply a need for complex, multidisciplinary care. 1 Sociodemographic characteristics, individual health and social experiences, and mental and somatic health characteristics 11 increase patient complexity. The British National Institute for Health and Care Excellence (NICE) guideline 10 defines multimorbidity as two or more long-term, single-count health conditions and recommends a multimorbid approach to care in various contexts, strengths and limitations of this study ► The HUNT Study is a large total county population general health survey with a multitude of variables, suitable to estimate prevalences of multimorbidity and frailty by self-reports and clinical measurements.…”

Section: Introductionmentioning

confidence: 99%

Prevalence of multimorbidity with frailty and associations with socioeconomic position in an adult population: findings from the cross-sectional HUNT Study in Norway

et al. 2020

View full text Add to dashboard Cite

ObjectivesTo explore prevalences and occupational group inequalities of two measures of multimorbidity with frailty.DesignCross-sectional study.SettingThe Nord-Trøndelag Health Study (HUNT), Norway, a total county population health survey, 2006–2008.ParticipantsParticipants older than 25 years, with complete questionnaires, measurements and occupation data were included.Outcomes≥2 of 51 multimorbid conditions with ≥1 of 4 frailty measures (poor health, mental illness, physical impairment or social impairment) and ≥3 of 51 multimorbid conditions with ≥2 of 4 frailty measures.AnalysisLogistic regression models with age and occupational group were specified for each sex separately.ResultsOf 41 193 adults, 38 027 (55% female; 25–100 years old) were included. Of them, 39% had ≥2 multimorbid conditions with ≥1 frailty measure, and 17% had ≥3 multimorbid conditions with ≥2 frailty measures. Prevalence differences in percentage points (pp) with 95% confidence intervals of those in high versus low occupational group with ≥2 multimorbid conditions and ≥1 frailty measure were largest in women age 30 years, 17 (14 to 20) pp and 55 years, 15 (13 to 17) pp and in men age 55 years, 15 (13 to 17) pp and 80 years, 14 (9 to 18) pp. In those with ≥3 multimorbid conditions and ≥2 frailty measures, prevalence differences were largest in women age 30 years, 8 (6 to 10) pp and 55 years, 10 (8 to 11) ppand in men age 55 years, 9 (8 to 11) pp and 80 years, 6 (95% CI 1 to 10) pp.ConclusionMultimorbidity with frailty is common, and social inequalities persist until age 80 years in women and throughout the lifespan in men. To manage complex multimorbidity, strategies for proportionate universalism in medical education, healthcare, public health prevention and promotion seem necessary.

show abstract

Multimorbidity and comorbidity revisited: refining the concepts for international health research

Cited by 113 publications

References 30 publications

<p>Patterns of Multimorbidity and Differences in Healthcare Utilization and Complexity Among Acutely Hospitalized Medical Patients (≥65 Years) – A Latent Class Approach</p>

<p>Patterns of Multimorbidity and Differences in Healthcare Utilization and Complexity Among Acutely Hospitalized Medical Patients (≥65 Years) – A Latent Class Approach</p>

Processing new types of quality data

Prevalence of multimorbidity with frailty and associations with socioeconomic position in an adult population: findings from the cross-sectional HUNT Study in Norway

Contact Info

Product

Resources

About