“Multimorbidity” as the manifestation of network disturbances. From nosology to the Meikirch model

Bircher, J; Hahn, Eckhart G.

doi:10.1111/jep.12633

Cited by 7 publications

(10 citation statements)

References 12 publications

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“…In the fifth commentary within the current Forum, we come to the contribution by Bircher and Hahn . For these commentators, it is certainly broadly justified to examine the problem of multimorbidity in the manner described by Sturmberg et al However, the commentators find one question so far unanswered.…”

Section: Multimorbidity As the Manifestation Of Network Disturbancesmentioning

confidence: 99%

“…For these commentators, it is certainly broadly justified to examine the problem of multimorbidity in the manner described by Sturmberg et al However, the commentators find one question so far unanswered. Is it needed and possible, they ask, “to develop the whole diagnostic picture from molecular genetics to the person and [his/her] surroundings in each case?” In their paper, Bircher and Hahn explore the possibility of analysing patients in a more focussed manner through posing the questions “Is it possible to do the best for the patients, when applying a recently described model of health and disease, the Meikirch model? Can it identify the factors that are predominant in rendering a patient diseased, and can it help the patient to evolve further and to emerge into a better state of health?”…”

Section: Multimorbidity As the Manifestation Of Network Disturbancesmentioning

confidence: 99%

“…Bircher and Hahn proceed to describe the Meikirch model to explain its applicability to the understanding and management of multimorbidity and to compare it with the network model advanced by Sturmberg et al The Meikirch model consists of 5 components and 10 interactions, some or all of which may need to be explored in the context of the patient with multimorbid illness so that the overall process can generate a single, though inevitably complex, assessment of the condition of the given individual patient. Following this procedure, questions can be asked about what has happened, and what can be done, so that a range of options (by no means all of which will be strictly “medical”) become available for discussion with the patient and from which a selection can be made on the basis of priorities for early action.…”

Section: Multimorbidity As the Manifestation Of Network Disturbancesmentioning

confidence: 99%

“…Following this procedure, questions can be asked about what has happened, and what can be done, so that a range of options (by no means all of which will be strictly “medical”) become available for discussion with the patient and from which a selection can be made on the basis of priorities for early action. As Bircher and Hahn observe, the nature of the model is that it looks at health and the lack of it in the broadest sense and as part of a strong patient‐physician relationship/partnership. As the commentators say, this renders a strictly nosological approach less discerning, though the viability of the model in practice will need to be tested through rigorous empirical and mixed methods research.…”

Section: Multimorbidity As the Manifestation Of Network Disturbancesmentioning

confidence: 99%

“…In the fifth commentary within the current Forum, we come to the contribution by Bircher and Hahn. 6 For these commentators, it is certainly broadly justified to examine the problem of multimorbidity in the manner described by Sturmberg et al 1 can be done, so that a range of options (by no means all of which will be strictly "medical") become available for discussion with the patient and from which a selection can be made on the basis of priorities for early action. As Bircher and Hahn 6 observe, the nature of the model is that it looks at health and the lack of it in the broadest sense and as part of a strong patient-physician relationship/partnership.…”

Section: Multimorbidity As the Manifestation Of Network Disturbancementioning

confidence: 99%

See 4 more Smart Citations

Multimorbidity—A manifestation of network disturbances? How to investigate? How to treat?

Miles¹,

Asbridge

2017

Evaluation Clinical Practice

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Section: Multimorbidity As the Manifestation Of Network Disturbancesmentioning

confidence: 99%

Section: Multimorbidity As the Manifestation Of Network Disturbancesmentioning

confidence: 99%

Section: Multimorbidity As the Manifestation Of Network Disturbancesmentioning

confidence: 99%

Section: Multimorbidity As the Manifestation Of Network Disturbancesmentioning

confidence: 99%

Section: Multimorbidity As the Manifestation Of Network Disturbancementioning

confidence: 99%

See 3 more Smart Citations

Multimorbidity—A manifestation of network disturbances? How to investigate? How to treat?

Miles¹,

Asbridge

2017

Evaluation Clinical Practice

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Magnitude, pattern and correlates of multimorbidity among patients attending chronic outpatient medical care in Bahir Dar, northwest Ethiopia: the application of latent class analysis model

Abebe

Schneider

Alemu

et al. 2021

Preprint

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Objective This study aimed to investigate the magnitude, pattern and associated factors of multimorbidity in Bahir Dar, Ethiopia. Methods A multi-centered facility based study was conducted among 1440 participants aged 40+ years attending chronic outpatient medical care. Two complimentary methods (interview and review of medical records) were employed to collect the data on socio-demographic, behavioral and disease related characteristics. The data were analyzed by STATA V.16 and R Software V.4.1.0. We run descriptive statistics and fitted logistic regression and latent class analyses (LCA) models to determine associated factors and patterns of multimorbidity. Statistical significance was considered at p-value ,<=0.05. Results The magnitude of individual chronic conditions ranged from 1.4% to 37.9%, and multimorbidity was identified in 54.8% (95% CI=52.2%-57.4%) of the sample. The likelihood of developing multimorbidity was higher among participants aged from 45-54 years (AOR: 1.5, 95%CI= 1.1, 2.1), 55-64 years (AOR: 2.5, 95%CI=1.7, 3.5) and 65 years or more (AOR: 2.4, 95%CI=1.7, 3.5), among individuals classified as overweight (AOR: 1.6, 95%CI=1.2, 2.1) or obese (AOR: 1.9, 95%CI=1.3, 3.0) and among those individuals who believe in external locus of control (AOR: 1.8, 95%CI=1.3, 2.5). Four patterns of multimorbidity were identified, the cardiovascular category being the largest class (50.2%), followed by the metabolic group (32.6%). Advanced age, overweight and obesity predicted latent class membership, adjusting for relevant confounding factors. Conclusion The magnitude of multimorbidity in this study was high. The most frequently diagnosed chronic conditions shaped the patterns of multimorbidity. Advanced age, overweight and obesity were the factors profoundly associated with multimorbidity. Health service organization and provision in the study area need to be oriented by the realities in disease burden and pattern of multimorbidity. Further research is required to better understand the impact of multimorbidity on individuals well-being, survival and health service delivery.

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Multimorbidity of chronic non-communicable diseases: burden, care provision and outcomes over time among patients attending chronic outpatient medical care in Bahir Dar, Ethiopia—a mixed methods study protocol

et al. 2021

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IntroductionMultimorbidity refers to the presence of two or more chronic non-communicable diseases (NCDs) in a given individual. It is associated with premature mortality, lower quality of life (QoL) and greater use of healthcare resources. The burden of multimorbidity could be huge in the low and middle-income countries (LMICs), including Ethiopia. However, there is limited evidence on the magnitude of multimorbidity, associated risk factors and its effect on QoL and functionality. In addition, the evidence base on the way health systems are organised to manage patients with multimorbidity is sparse. The knowledge gleaned from this study could have a timely and significant impact on the prevention, management and survival of patients with NCD multimorbidity in Ethiopia and in LMICs at large.Methods and analysisThis study has three phases: (1) a cross-sectional quantitative study to determine the magnitude of NCD multimorbidity and its effect on QoL and functionality, (2) a qualitative study to explore organisation of care for patients with multimorbidity, and (3) a longitudinal quantitative study to investigate disease progression and patient outcomes over time. A total of 1440 patients (≥40 years) on chronic care follow-up will be enrolled from different facilities for the quantitative studies. The quantitative data will be collected from multiple sources using the KoBo Toolbox software and analysed by STATA V.16. Multiple case study designs will be employed to collect the qualitative data. The qualitative data will be coded and analysed by Open Code software thematically.Ethics and disseminationEthical clearance has been obtained from the College of Medicine and Health Sciences, Bahir Dar University (protocol number 003/2021). Subjects who provide written consent will be recruited in the study. Confidentiality of data will be strictly maintained. Findings will be disseminated through publications in peer-reviewed journals and conference presentations.

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“Multimorbidity” as the manifestation of network disturbances. From nosology to the Meikirch model

Cited by 7 publications

References 12 publications

Multimorbidity—A manifestation of network disturbances? How to investigate? How to treat?

Multimorbidity—A manifestation of network disturbances? How to investigate? How to treat?

Magnitude, pattern and correlates of multimorbidity among patients attending chronic outpatient medical care in Bahir Dar, northwest Ethiopia: the application of latent class analysis model

Multimorbidity of chronic non-communicable diseases: burden, care provision and outcomes over time among patients attending chronic outpatient medical care in Bahir Dar, Ethiopia—a mixed methods study protocol

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