- Email: [email protected]
Should we keep on measuring multimorbidity?
Journal of Clinical Epidemiology 71 (2016) 113e123
LETTERS TO THE EDITOR Should we keep on measuring multimorbidity? Based on recent studies [1e3] in this journal, Bernardini and Fracchia report a disillusion and even a substantial uselessness of multimorbidity tools. In the light of a continuously increasing incidence of chronic diseases and continuously increasing numbers of patients with multimorbidity, this may seem remarkable. But the measurement of multimorbidity is indeed prone to important challenges. Boeckxstaens et al. [1,4] indicate that the comprehensive Cumulative Illness Rating Scale [5] is not clearly superior to a simple disease count in terms of identifying patients with disability and frailty or in terms of predicting adverse outcomes. This disillusion of multimorbidity indices has been confirmed by Toson et al. [2] who state that the Charlson Comorbidity Index is a valid tool to predict mortality but not to predict resource utilization after hip fracture. And third, a systematic review of Yurkovich et al. [3] confirms again that the validity of a measure is highly dependent on the outcome of interest, the type of data available, and the study population. So, the question is whether we continue the search for the best measure in the market? Because, measuring the impact of multimorbidity by adding up diseases and conditions actually seems to be failing. It is highly questionable whether any measure, being simple or complicated, will actually ever be able to capture the complexity of multimorbidity and its impact on individual patients, health systems, and populations. Patients with the same morbidity burden still differ in terms of functional status and prognosis. The European General Practice Research Network (EGPRN) definition of multimorbidity [6] regards disability and frailty as major outcomes of multimorbidity, but the causeeeffect relationships in multimorbidity are far from univocal. Several modifiers of the effects of multimorbidity have been identified mostly in terms of negative modifiers that increase the risk of adverse outcomes such as disability, depressive symptoms, impaired cognition, lower level of education,. However, qualitative inquiries indicate that extensive disease lists do not necessarily match with impaired functional status and decreased quality of life. So, comprehensive assessments of patients with multimorbidity should maybe not only describe people’s impairments and deficiencies but also their resources and strengths. A comprehensive interpretation of research findings in this field [7] indicates that we should step back from a linear cause-consequence model of multimorbidity that aims for a measure of multimorbidity predicting every adverse outcome and providing 0895-4356/Ó 2016 Elsevier Inc. All rights reserved.
complete insight into the impact of multimorbidity at the individual patient. In general, multimorbidity research would benefit from a redirected attention from the search for quantitative disease-oriented measures that use a reductionist approach of ‘‘counting diseases’’ toward qualitative person-oriented assessments of people who comprehensively describe the whole patient rather than the complete disease list. We propose to assess functional status and determinants of vulnerability comprehensively, next to disease lists, rather than to consider disability and frailty as outcomes of multimorbidity. In Fig. 1, we proposednext to the traditional cause-consequence model of multimorbidity proposed by EGPRNdan integrative biopsychosocial model for patients with multimorbidity. This model is inspired by the model of International Classification of Functioning and disability in Health (ICF) [8], which also originated out of a more traditionaldmedicald cause-consequence model of disability (Fig. 1). In fact, the components of ICF allow to capture the individual’s functional status (both in terms of impairments and capabilities) and can accommodate determinants of frailty and other modifiers of the effects of multimorbidity (both in terms of barriers and facilitators). Throughout this inquiry, we believe it to be important to adopt a positive approach to patients with multiple chronic diseases by acknowledging and reinforcing patients’ individual capabilities, resources, and strengths instead of merely focus on impairments, deficiencies, and barriers. Pauline Boeckxstaens* Department of Family Medicine and Primary Health Care Ghent University (UG) UZ Gent 3K3 De Pintelaan 185 9000 Ghent, Belgium
An De Sutter Department of Family Medicine and Primary Health Care Ghent University (UG) UZ Gent 3K3 De Pintelaan 185 9000 Ghent, Belgium
Bert Vaes Institut de Recherche Sante et Societe Universite Catholique de Louvain (UCL) a Clos Chapelle-aux-champs 30 bte 30.15 - 1200 Woluwe-Saint-Lambert Brussels, Belgium;
114
Letters to the editor / Journal of Clinical Epidemiology 71 (2016) 113e123
Fig. 1. Towards a biopsychosocial understanding of multimorbidity. ADL, activities of daily living.
Department of Public and Primary HealthCare Katholieke Universiteit Leuven (KUL) Kapucijnenvoer 35 blok d box 7001 B-3000, Leuven, Belgium
[5]
[6]
Jean-Marie Degryse Institut de Recherche Sante et Societe Universite Catholique de Louvain (UCL) a Clos Chapelle-aux-champs 30 bte 30.15 - 1200 Woluwe-Saint-Lambert Brussels, Belgium; Department of Public and Primary HealthCare Katholieke Universiteit Leuven (KUL) Kapucijnenvoer 35 blok d box 7001 B-3000, Leuven, Belgium *Corresponding author. Tel.: 00329322747; fax: 00323324967. E-mail address: [email protected] (P. Boeckxstaens)
References [1] Boeckxstaens P, Vaes B, Van Pottelbergh G, De Sutter A, Legrand D, Adriaensen W, et al. Multimorbidity measures were poor predictors of adverse events in patients aged 80 years: a prospective cohort study. J Clin Epidemiol 2015;68:220e7. [2] Toson B, Harvey LA, Close JC. The ICD-10 Charlson Comorbidity Index predicted mortality but not resource utilization following hip fracture. J Clin Epidemiol 2015;68:44e51. [3] Yurkovich M, Avina-Zubieta JA, Thomas J, Gorenchtein M, Lacaille D. A systematic review identifies valid comorbidity indices derived from administrative health data. J Clin Epidemiol 2015;68:3e14. [4] Boeckxstaens P, Vaes B, Legrand D, Dalleur O, De Sutter A, Degryse JM. The relationship of multimorbidity with disability and frailty in the oldest patients: a cross-sectional analysis of three
[7] [8]
measures of multimorbidity in the BELFRAIL cohort. Eur J Gen Pract 2015;21:39e44. Hudon C, Fortin M, Vanasse A. Cumulative Illness Rating Scale was a reliable and valid index in a family practice context. J Clin Epidemiol 2005;58:603e8. Le Reste JY, Nabbe P, Lygidakis C, Doerr C, Lingner H, Czachowski S, et al. A research group from the European General Practice Research Network (EGPRN) explores the concept of multimorbidity for further research into long term care. J Am Med Dir Assoc 2013;14:132e3. Boeckxstaens P. Multimorbidity: a quantitative and qualitative exploration in primary care. Ghent University, Ghent; 2014. WHO. International Classification of Functioning and Disability in health (ICF) 2001.
http://dx.doi.org/10.1016/j.jclinepi.2015.05.015
Comorbidity indices disillusion Recently, some interesting studies about comorbidity appeared on this journal. As geriatricians, we strongly believe that comorbidity is an increasing issue, especially in our field, because aging is connected with a growing incidence of chronic diseases, which become prevalent and synergic in oldest patients [1]. Some agreement emerges from the studies by Boeckxstaens et al. [2], Toson et al. [3], and Yurkovich et al. [4], particularly about the poor value of the more widespread comorbidity tools in predicting adverse clinical events (ACEs) or functional decline after hospitalization. Having been created with calibration on mortality, which is the strongest outcome to use and also the easiest to consider when creating a prognostic score, it is quite predictable that comorbidity indices (e.g., Cumulative Illness
LETTERS TO THE EDITOR Should we keep on measuring multimorbidity? Based on recent studies [1e3] in this journal, Bernardini and Fracchia report a disillusion and even a substantial uselessness of multimorbidity tools. In the light of a continuously increasing incidence of chronic diseases and continuously increasing numbers of patients with multimorbidity, this may seem remarkable. But the measurement of multimorbidity is indeed prone to important challenges. Boeckxstaens et al. [1,4] indicate that the comprehensive Cumulative Illness Rating Scale [5] is not clearly superior to a simple disease count in terms of identifying patients with disability and frailty or in terms of predicting adverse outcomes. This disillusion of multimorbidity indices has been confirmed by Toson et al. [2] who state that the Charlson Comorbidity Index is a valid tool to predict mortality but not to predict resource utilization after hip fracture. And third, a systematic review of Yurkovich et al. [3] confirms again that the validity of a measure is highly dependent on the outcome of interest, the type of data available, and the study population. So, the question is whether we continue the search for the best measure in the market? Because, measuring the impact of multimorbidity by adding up diseases and conditions actually seems to be failing. It is highly questionable whether any measure, being simple or complicated, will actually ever be able to capture the complexity of multimorbidity and its impact on individual patients, health systems, and populations. Patients with the same morbidity burden still differ in terms of functional status and prognosis. The European General Practice Research Network (EGPRN) definition of multimorbidity [6] regards disability and frailty as major outcomes of multimorbidity, but the causeeeffect relationships in multimorbidity are far from univocal. Several modifiers of the effects of multimorbidity have been identified mostly in terms of negative modifiers that increase the risk of adverse outcomes such as disability, depressive symptoms, impaired cognition, lower level of education,. However, qualitative inquiries indicate that extensive disease lists do not necessarily match with impaired functional status and decreased quality of life. So, comprehensive assessments of patients with multimorbidity should maybe not only describe people’s impairments and deficiencies but also their resources and strengths. A comprehensive interpretation of research findings in this field [7] indicates that we should step back from a linear cause-consequence model of multimorbidity that aims for a measure of multimorbidity predicting every adverse outcome and providing 0895-4356/Ó 2016 Elsevier Inc. All rights reserved.
complete insight into the impact of multimorbidity at the individual patient. In general, multimorbidity research would benefit from a redirected attention from the search for quantitative disease-oriented measures that use a reductionist approach of ‘‘counting diseases’’ toward qualitative person-oriented assessments of people who comprehensively describe the whole patient rather than the complete disease list. We propose to assess functional status and determinants of vulnerability comprehensively, next to disease lists, rather than to consider disability and frailty as outcomes of multimorbidity. In Fig. 1, we proposednext to the traditional cause-consequence model of multimorbidity proposed by EGPRNdan integrative biopsychosocial model for patients with multimorbidity. This model is inspired by the model of International Classification of Functioning and disability in Health (ICF) [8], which also originated out of a more traditionaldmedicald cause-consequence model of disability (Fig. 1). In fact, the components of ICF allow to capture the individual’s functional status (both in terms of impairments and capabilities) and can accommodate determinants of frailty and other modifiers of the effects of multimorbidity (both in terms of barriers and facilitators). Throughout this inquiry, we believe it to be important to adopt a positive approach to patients with multiple chronic diseases by acknowledging and reinforcing patients’ individual capabilities, resources, and strengths instead of merely focus on impairments, deficiencies, and barriers. Pauline Boeckxstaens* Department of Family Medicine and Primary Health Care Ghent University (UG) UZ Gent 3K3 De Pintelaan 185 9000 Ghent, Belgium
An De Sutter Department of Family Medicine and Primary Health Care Ghent University (UG) UZ Gent 3K3 De Pintelaan 185 9000 Ghent, Belgium
Bert Vaes Institut de Recherche Sante et Societe Universite Catholique de Louvain (UCL) a Clos Chapelle-aux-champs 30 bte 30.15 - 1200 Woluwe-Saint-Lambert Brussels, Belgium;
114
Letters to the editor / Journal of Clinical Epidemiology 71 (2016) 113e123
Fig. 1. Towards a biopsychosocial understanding of multimorbidity. ADL, activities of daily living.
Department of Public and Primary HealthCare Katholieke Universiteit Leuven (KUL) Kapucijnenvoer 35 blok d box 7001 B-3000, Leuven, Belgium
[5]
[6]
Jean-Marie Degryse Institut de Recherche Sante et Societe Universite Catholique de Louvain (UCL) a Clos Chapelle-aux-champs 30 bte 30.15 - 1200 Woluwe-Saint-Lambert Brussels, Belgium; Department of Public and Primary HealthCare Katholieke Universiteit Leuven (KUL) Kapucijnenvoer 35 blok d box 7001 B-3000, Leuven, Belgium *Corresponding author. Tel.: 00329322747; fax: 00323324967. E-mail address: [email protected] (P. Boeckxstaens)
References [1] Boeckxstaens P, Vaes B, Van Pottelbergh G, De Sutter A, Legrand D, Adriaensen W, et al. Multimorbidity measures were poor predictors of adverse events in patients aged 80 years: a prospective cohort study. J Clin Epidemiol 2015;68:220e7. [2] Toson B, Harvey LA, Close JC. The ICD-10 Charlson Comorbidity Index predicted mortality but not resource utilization following hip fracture. J Clin Epidemiol 2015;68:44e51. [3] Yurkovich M, Avina-Zubieta JA, Thomas J, Gorenchtein M, Lacaille D. A systematic review identifies valid comorbidity indices derived from administrative health data. J Clin Epidemiol 2015;68:3e14. [4] Boeckxstaens P, Vaes B, Legrand D, Dalleur O, De Sutter A, Degryse JM. The relationship of multimorbidity with disability and frailty in the oldest patients: a cross-sectional analysis of three
[7] [8]
measures of multimorbidity in the BELFRAIL cohort. Eur J Gen Pract 2015;21:39e44. Hudon C, Fortin M, Vanasse A. Cumulative Illness Rating Scale was a reliable and valid index in a family practice context. J Clin Epidemiol 2005;58:603e8. Le Reste JY, Nabbe P, Lygidakis C, Doerr C, Lingner H, Czachowski S, et al. A research group from the European General Practice Research Network (EGPRN) explores the concept of multimorbidity for further research into long term care. J Am Med Dir Assoc 2013;14:132e3. Boeckxstaens P. Multimorbidity: a quantitative and qualitative exploration in primary care. Ghent University, Ghent; 2014. WHO. International Classification of Functioning and Disability in health (ICF) 2001.
http://dx.doi.org/10.1016/j.jclinepi.2015.05.015
Comorbidity indices disillusion Recently, some interesting studies about comorbidity appeared on this journal. As geriatricians, we strongly believe that comorbidity is an increasing issue, especially in our field, because aging is connected with a growing incidence of chronic diseases, which become prevalent and synergic in oldest patients [1]. Some agreement emerges from the studies by Boeckxstaens et al. [2], Toson et al. [3], and Yurkovich et al. [4], particularly about the poor value of the more widespread comorbidity tools in predicting adverse clinical events (ACEs) or functional decline after hospitalization. Having been created with calibration on mortality, which is the strongest outcome to use and also the easiest to consider when creating a prognostic score, it is quite predictable that comorbidity indices (e.g., Cumulative Illness