Multimorbidity and patterns of chronic conditions in a primary care population in Switzerland: a cross-sectional study

Déruaz‐Luyet, Anouk; N’Goran, Alexandra; Senn, Nicolas; Bodenmann, Patrick; Pasquier, Jérôme; Widmer, Daniel; Tandjung, Ryan; Rosemann, Thomas; Frey, Peter; Streit, Sven; Zeller, Andreas; Haller, Dagmar M.; Excoffier, Sophie; Burnand, Bernard; Herzig, Lilli

doi:10.1136/bmjopen-2016-013664

Cited by 50 publications

(60 citation statements)

References 34 publications

(29 reference statements)

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“…Guisando‐Clavero et al use cluster analysis to identify six patterns of comorbidities in a study of 190 108 patients with multimorbidities. Several other researchers perform either cluster analysis or Latent Class Analysis (LCA, or exploratory factor analysis [EFA]) to identify patterns of multimorbidity . EFA and LCA are dimensionality reduction methods that collapse combinations of observed variables into unobserved factors.…”

Section: Methodsmentioning

confidence: 99%

“…Class Analysis (LCA, or exploratory factor analysis [EFA]) to identify patterns of multimorbidity. [21][22][23][24][25] EFA and LCA are dimensionality reduction methods that collapse combinations of observed variables into unobserved factors. These factors can then be used to represent subgroups.…”

Section: Several Other Researchers Perform Either Cluster Analysis Ormentioning

confidence: 99%

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Identifying obesity‐related multimorbidity combinations in the United States

Madlock‐Brown

Reynolds

2019

Clinical Obesity

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Summary Interest in understanding the effects of multimorbidity on outcomes has increased in recent years. This paper presents the most common obesity‐related groupings of multimorbidity in the United States. Using Cerner HealthFacts data, we applied the frequent pattern growth algorithm to identify prevalent multimorbidity groupings of 3 or more diseases (one being obesity) by race using a dataset of 574 172 patients with obesity from all over the United States. We set the minimum prevalence to 10% and identified groupings of ICD10‐CM diagnoses that occur in our dataset at or above the minimum prevalence level. We provide binomial proportion confidence interval estimates to demonstrate the validity of the proportions. We performed g‐test for independence to validate differences in prevalence by race. We found 18 multimorbidity combinations with prevalence higher than or equal to 10%. Our results indicate that there are multiple common multimorbidities groupings for patients with obesity. Each multimorbidity combination is composed of diseases from the following clinical categories: endocrine, nutritional and metabolic diseases; diseases of the circulatory system; diseases of the digestive system; diseases of the nervous system; and diseases of the musculoskeletal system and connective tissue. For each multimorbidity pattern, the prevalence was found to be significantly different by race according to the g‐test with P‐value < .001. Most frequent patterns include essential hypertension or disorder of lipid metabolism. This study identifies common groupings of multimorbidity. We believe our data can be useful for those developing integrated care plans, particularly for those serving diverse communities.

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Section: Methodsmentioning

confidence: 99%

Section: Several Other Researchers Perform Either Cluster Analysis Ormentioning

confidence: 99%

Identifying obesity‐related multimorbidity combinations in the United States

Madlock‐Brown

Reynolds

2019

Clinical Obesity

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“…Thus, in younger age groups, clusters focused around mental health are associated with most GP contact, in people aged 65-84, a cluster of mental health and coronary heart disease is associated with most GP contact, and other indicators of health service use, whereas in people aged over 85, the cluster representing most health service contact is dominated by cardiovascular conditions. In terms of relative importance of single conditions within multimorbid clusters, the predominance of mental health conditions and hypertension has been identified in previous work 9,10,17,26 . A novel finding in our work is the inclusion of pain in many of the clusters we identified.…”

Section: Summary Of Results and Comparison With Other Studiesmentioning

confidence: 79%

Characteristics, service use and mortality of clusters of multimorbid patients in England: a population-based study

Zhu

Edwards

Mant

et al. 2019

Preprint

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Background Multimorbidity is one of the principal challenges facing health systems worldwide. To help understand the changes to services and policies that are required to deliver better care, we used a novel approach to investigate which diseases co-occur and how combinations are associated with mortality and service use. Methods Linked primary and secondary care electronic health records contributed by 382 general practices in England to the Clinical Practice Research Datalink (CPRD) were used. The study included a representative set of multimorbid adults (18 years old or more) with two or more long-term conditions (N=113,211). A random set of 80% of the multimorbid patients (N=90,571) were stratified by age and clustered using latent class analysis. Consistency between obtained disease profiles, classification quality and associations with demographic characteristics and primary outcomes (hospitalisation, polypharmacy and mortality) was validated in the remaining 20% of multimorbid patients (N=22,640). Findings We identified twenty patient clusters across four age strata. The clusters with the highest mortality comprised psychoactive substance and alcohol misuse (aged 18-64), coronary heart disease, depression and pain (aged 65-84) and coronary heart disease, heart failure and atrial fibrillation (aged 85+). The clusters with the highest service use coincided with those with highest mortality for people aged over 65. For people aged 18-64, the cluster with the highest service use comprised depression, anxiety and pain. The majority of 85+ year olds multimorbid patients belonged to the cluster with the lowest service use and mortality for that age range. Pain featured in thirteen clusters. Interpretation This work has highlighted patterns of multimorbidity that have implications for health services. These include the importance of psychoactive substance and alcohol misuse in people under the age of 65; of co-morbid depression and coronary heart disease in people aged 65-84, and of cardiovascular disease in people aged 85+.

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“…Data for this study were extracted from the cross-sectional MultiMorbidity Study in Family Medicine (MMFM), conducted in Switzerland between January and September 2015. The study's detailed protocol and its first results have been described elsewhere (20,21). In summary, a convenience sample of 100 GPs randomly enrolled 888 patients, aged 18 years and over, with at least three chronic conditions identified from a pre-established list of 75 conditions (22).…”

Section: Study Design and Settingmentioning

confidence: 99%

“…In the complete Andersen model, not having an informal caregiver (OR 0.50, 95%CI 0.28-0.88), using more medications (OR 1.13, 95%CI 1.05-1. 21), and being less independent (OR 2.47, 95%CI 1. 36-4.51) were all factors positively associated with homecare services use.…”

Section: Introductionmentioning

confidence: 99%

A cross-sectional study of ambulatory care services use by multimorbid patients in primary care in the light of the Andersen model

Messi

Müeller

Haller

et al. 2020

Preprint

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Background: Multimorbidity is frequently encountered in primary care and is associated with an increasing use of healthcare services. The Andersen Behavioral Model of Health Services Use is a multilevel framework classifying societal, contextual, and individual characteristics about the use of healthcare services into three categories: 1. predisposing factors, 2. enabling factors, and 3. need factors. The present study aimed to explore multimorbid patients’ use of ambulatory healthcare in terms of homecare and other allied health services, visits to GPs, and number of specialists involved. A secondary aim was to apply Andersen’s model to explore factors associated with this use. Method: In a cross-sectional study, 100 Swiss GPs enrolled up to 10 multimorbid patients each. After descriptive analyses, we tested the associations of each determinant and outcome variable of healthcare use, according to the Andersen model’s three categories : predisposing factors ( patient’s age, sex, marital status, educational level), enabling factors (health literacy (HLS EU 6), deprivation (DipCare)), and need factors (patient’s quality of life (EQ-5D-3L), treatment burden (TBQ), severity index (CIRS), number of chronic conditions and number of medications). Logistic regressions (for dichotomous variables) and negative binomial regressions (for count variables) were calculated to identify predictors of multimorbid patients’ healthcare use. Results: Analyses included 843 multimorbid patients; mean age 73.0 (SD 12.0); 48.3% men; 15.1% (127/843) used homecare; 34.9% (294/843) used other health services; median patient–GP contacts (previous year), 11 (IQR 7–16); mean number of specialist’s involved 1.9 (SD 1.4). In the complete Andersen model, not having an informal caregiver (OR 0.50, 95%CI 0.28–0.88), using more medications (OR 1.13, 95%CI 1.05–1.21), and being less independent (OR 2.47, 95%CI 1.36–4.51) were all factors positively associated with homecare services use. Social deprivation was related with a decrease in homecare services use (OR 0.75, 95%CI 0.62–0.89).” Conclusion: Multimorbid patients in primary care reported a high use of ambulatory healthcare services variably associated with the Andersen model’s factors: Healthcare use was associated with objective medical needs, but also on contextual or individual predisposing or enabling factors. These findings emphasize the importance of adapting care coordination to individual patients’ profile

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Multimorbidity and patterns of chronic conditions in a primary care population in Switzerland: a cross-sectional study

Cited by 50 publications

References 34 publications

Identifying obesity‐related multimorbidity combinations in the United States

Identifying obesity‐related multimorbidity combinations in the United States

Characteristics, service use and mortality of clusters of multimorbid patients in England: a population-based study

A cross-sectional study of ambulatory care services use by multimorbid patients in primary care in the light of the Andersen model

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