Article Figures & Data
Tables
- Table 1
Characteristics and Application of the Most Commonly Studied Multimorbidity Measures in Outpatient Settings
Measure Original Derivation/Validation Populations Information Needed Original Purpose of Score How Information Is Used Comments Disease count Not applicable (varies for different studies) Clinician-rated disease counts derived from medical records or clinician diagnosis
Self-reported disease counts based on questionnaires or interviewsNot applicable (varies for different studies) Single diseases added to give a total number of diseases/conditions per individual No weighting of diseases regarding severity or prognosis Chronic Disease Score (CDS)/RxRisk Model15–17 Original CDS15: adult HMO enrollees from a single US HMO
Revised CDS16 derived and validated in 254,694 adult members of a US HMO.
RxRisk17 derived and validated in large samples of US HMO enrolleesAutomated pharmacy data during a 1-year period To develop a stable measure of chronic disease status using routine pharmacy data rather than chart review Original CDS considered 17 disease states, weighted by an expert panel
Score based on history of dispensed drugs for 1 year, adjusted for age and sex
Subsequent versions used empirically derived weights and expanded number of diseases16,17Limited number of disease states
Weighting of original CDS based on consensus rather than empirical data (addressed by subsequent versions)Charlson Index18 Derived in 559 US medical inpatients
Validated in 685 women receiving treatment for breast cancerVarious versions are available; 17 to 22 disease categories, including age
In different forms, can be administered by a health professional on paper or electronically or self-completed as a questionnaire
FreeTo predict 1-year mortality among patients admitted to hospital
Later adapted to predict costs19Each disease is given a weighting of 1 to 6 and weighted scores are summated; this score can also be combined with age
Variations have been developed to use ICD-9 data, namely, Romano et al (Dartmouth- Manitoba score),20 Deyo et al,21 D’Hoore et al,22 Ghali et al,23 Rius et al24Limited number of diseases
Prognoses vary between cancers yet have similar rating
Needs information about severity of some conditions
Prognosis for some conditions has improved since index developedAdjusted Clinical Groups (ACG) System25 Derived and validated in US using large HMO databases
Validation sample also included 30,000 Medicaid recipientsAge, sex, and diagnosis codes from medical records or insurance claims coded using the ICD or Read code systems
Data entered into ACG System software available at cost under licenseOriginally devised to predict morbidity burden and use of health care resources
System developed to provide a number of tools with different purposesCollapsed into Initial Diagnosis Codes then to calculate ADGs (32); CADGs (12); MACs (26); ACGs (102). Each ACG includes individuals with a similar pattern of morbidity and similar expected resource use Need to purchase bespoke software
Based on records or claims data so dependent on reliability of those dataCumulative Index Illness Rating Scale (CIRS)26,27 Hospitalized men in the United States26 and subsequently older adults in ambulatory settings27 A rating scale consisting of 14 body systems categories that can be filled in by trained assessors directly during clinical consultation or from medical records.
Free accessTo assess the medical burden of chronic illness Each body system has a severity rating of 0 to 4, which are summated to create a total score (0–56), or presented as an index based on the number of categories scoring 2 or more.
Several variations existRequires training based on a manual. Broad body system groups
Prognoses vary among types of condition and may have improved since index was devised(Duke Severity Illness Check-list (DUSOI) index28–30 Developed in 249 adult patients attending a family practice in the United States Severity of illness checklist for measuring a person’s illness severity
Can be filled in during clinical consultation or from medical records
Available from authorTo quantify the burden of illness as measured by the physician Each diagnosis is rated on 4 levels: symptom, complication, prognosis without treatment, prognosis with treatment
Various severity scores are calculated using the ratings (from 0 to 4) for each parameter of every diagnosisSubjective judgment is required on the part of the assessor
Requires training-
ADGs=Adjusted Diagnosis Groups; CADGS = collapsed Aggregated Diagnosis Groups; HMO=health maintenance organization, ICD=International Classification of Diseases; MACs=major Adjusted Categories.
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Diagnosis Based Measures Medication Based Measures Adjusted Clinical Groups (ACG) System version25 Chronic Disease Score (CDS)/Rx-Risk15–17 Charlson index18 Simple count of drug types prescribed43 Cumulative Index Illness Rating Scale (CIRS)26,27 Morbidity Drug Burden Index (MDBI)44,45 Disease count Nursing home multimorbidity matrix46 Duke Severity of Illness Checklist (DUSOI )28 Elixhauser index31 Functional Comorbidity Index (FCI)32–34 Geriatric Index of Comorbidity (GIC)35 Hierarchical Coexisting Conditions (HCCs)36 Index of Co-Existent Disease (ICED)37 Seattle Index of Comorbidity (SIC)38,39 Self-Administered Comorbidity Questionnaire (SCQ)40 Disease Burden (Bayliss)41,42 -
Note: Only key references given in this table.
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Additional Files
The Article in Brief
Measures of Multimorbidity and Morbidity Burden for Use in Primary Care and Community Settings: A Systematic Review and Guide
Chris Salisbury , and colleagues
Background Many primary care patients have multiple medical conditions (multimorbidity). To assess the impact of muilitmorbidity, it is necessary to measure it. This analysis of existing research identifies measures of multimorbidity and morbidity (illness) burden suitable for use in research in primary care and community populations and investigates their validity.
What This Study Found This systematic review identifies 17 different measures. The measures most commonly used in primary care, and for which there is greatest evidence of validity, are disease counts, the Charlson index, and the Adjusted Clinical Groups (ACG) System. Different measures are most appropriate according to the outcome being studied and the type of data available. For example, researchers interested in the relationship between multimorbidity and health care utilization will find most evidence for the validity of the Charlson Index, the ACG System and disease counts, but evidence is strongest for the ACG System in relation to costs, for Charlson index in relation to mortality, and for disease counts or Charlson index in relation to quality of life. Other measures, such as the Cumulative Index Illness Rating Scale and Duke Severity of Illness Checklist, are more complex to administer and their advantages over easier methods have not been well established.
Implications
- Research is needed to directly compare the performance of different measures.
Supplemental Appendix, Tables, & Figure
Supplemental Appendix. Multimorbidity in the Primary Care Setting Search Strategy; Supplemental Table 1. Index of Studies With Demonstrated Associations Between Patient Sociodemographic Characteristics and Multimorbidity or Morbidity Burden Using Different Measures; Supplemental Table 2. Index of Studies Which Have Demonstrated Relationships Between Multimorbidity or Morbidity Burden and Cost or Process of Care Using Different Measures; Supplemental Table 3. Index of Studies Which Have Demonstrated Relationships Between Multimorbidity or Morbidity Burden and Patient Health Outcomes Using Different Measures; Supplemental Figure 1. PRISMA diagram
Files in this Data Supplement:
- Supplemental data: Appendix - PDF file, 2 pages
- Supplemental data: Tables 1-3 - PDF file, 10 pages
- Supplemental data: Figure - PDF file, 1 page