Development and Validation of the Medication-Based Disease Burden Index

George, Johnson; Vuong, Tam; Bailey, Michael; Kong, David Chee Ming; Marriott, Jennifer Lillian; Stewart, Kay

doi:10.1345/aph.1g204

Cited by 50 publications

(40 citation statements)

References 21 publications

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“…The weighed Rx‐Risk‐V score can be negative because some conditions appeared to be protective against mortality in this study 10 . Weights for MDBI were those used in the original MDBI study 13 that were derived from the Global Burden of Disease study and the Victorian Burden of Disease study. The scoring is done electronically, thus it is not time intensive.…”

Section: Methodsmentioning

confidence: 99%

“…A new medication‐based co‐morbidity index, the Medication‐Based Disease Burden Index (MDBI) has been developed in Australia 13 . The MDBI consists of specific medications for 20 chronic medical conditions.…”

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confidence: 99%

“…A validation study in 317 patients from two major hospitals showed a modest, but significant correlation with the Charlson index and the Chronic Disease Score (CDS). The MDBI score could successfully predict death and planned or unplanned hospital readmissions 13 …”

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confidence: 99%

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Validity of medication‐based co‐morbidity indices in the Australian elderly population

Vitry

Wong

Roughead

et al. 2009

Australian and New Zealand Journal of Public Health

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Objectives: To determine the validity of two medication‐based co‐morbidity indices, the Medicines Disease Burden Index (MDBI) and Rx‐Risk‐V in the Australian elderly population. Methods: In Phase I, the sensitivity and specificity of both indices were determined in 767 respondents from wave 6 of the Australian Longitudinal Study of Ageing (ALSA). Medication‐defined index disease categories were compared to self‐reported medical conditions. Correlation with self‐rated health was examined and Cox proportional hazards models were used to assess the predictive validity for mortality. Phase II verified the predictive ability of Rx‐Risk‐V in a sample of 213,191 veterans from Australian Department of Veterans' Affairs (DVA) database. Results: MDBI and Rx‐Risk‐V scores could be calculated for 28% and 73% of the ALSA sample respectively. Both indices had high specificities and low to moderate sensitivities compared to self‐reported medical conditions. Total weighted scores were significantly related to self‐rated health (p<0.001). Both indices were predictive of mortality (Hazard Ratio (HR) =3.690 (95% CI 2.264‐6.015) for MDBI and HR 1.079 (95% CI 1.045‐1.114) for Rx‐Risk‐V. The predictive validity for mortality of Rx‐Risk‐V was confirmed using DVA data (HR= 1.090, 95% CI 1.088‐1.092). Conclusions: Medication‐based co‐morbidity indices Rx‐Risk‐V and MDBI are valid measures of co‐morbidity. However, Rx‐Risk‐V detects more comorbidity in the Australian elderly population and is likely to be a more suitable index to use in administrative datasets, particularly where studies include large numbers of outpatients.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Validity of medication‐based co‐morbidity indices in the Australian elderly population

Vitry

Wong

Roughead

et al. 2009

Australian and New Zealand Journal of Public Health

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“…Adjusted Clinical Groups (ACG) System version 25 Charlson index 18 Cumulative Index Illness Rating Scale (CIRS) 26,27 Disease count Duke Severity of Illness Checklist (DUSOI ) 28 Elixhauser index 31 Functional Comorbidity Index (FCI) [32][33][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 Chronic Disease Score (CDS) / Rx-Risk [15][16][17] Simple count of drug types prescribed 43 Morbidity Drug Burden Index (MDBI) 44,45 Nursing home multimorbidity matrix 46 Note: Only key references given in this table.…”

Section: Appendix Measures Of Multimorbidity Identifi Ed Diagnosis Bmentioning

confidence: 99%

Measures of Multimorbidity and Morbidity Burden for Use in Primary Care and Community Settings: A Systematic Review and Guide

Huntley¹,

Johnson²,

Purdy³

et al. 2012

The Annals of Family Medicine

520

469

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PURPOSE Many patients consulting in primary care have multiple conditions (multimorbidity). Aims of this review were to identify measures of multimorbidity and morbidity burden suitable for use in research in primary care and community populations, and to investigate their validity in relation to anticipated associations with patient characteristics, process measures, and health outcomes.METHODS Studies were identifi ed using searches in MEDLINE and EMBASE from inception to December 2009 and bibliographies.RESULTS Included were 194 articles describing 17 different measures. Commonly used measures included disease counts (n = 98), Chronic Disease Score (CDS) / RxRisk (n = 17), Adjusted Clinical Groups (ACG) System (n = 25), the Charlson index (n = 38), the Cumulative Index Illness Rating Scale (CIRS; n = 10) and the Duke Severity of Illness Checklist (DUSOI; n = 6). Studies that compared measures suggest their predictive validity for the same outcome differs only slightly. Evidence is strongest for the ACG System, Charlson index, or disease counts in relation to care utilization; 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. Simple counts of diseases or medications perform almost as well as complex measures in predicting most outcomes. Combining measures can improve validity. CONCLUSIONSThe measures most commonly used in primary care and community settings are disease counts, Charlson index, ACG System, CIRS, CDS, and DUSOI. Different measures are most appropriate according to the outcome of interest. Choice of measure will also depend on the type of data available. More research is needed to directly compare performance of different measures.

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“…Increasing regimen complexity is associated with a higher likelihood of non-adherence, adverse events, economic burden and declines in quality of life for patients, and with greater strain and depression in caregivers [52,84–88]. Clinical feasibility and patient preferences should inform treatment decisions.…”

Section: Introductionmentioning

confidence: 99%

Patient-centered care for people living with multimorbidity

Boyd

Lucas

2014

Current Opinion in HIV and AIDS

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Purpose of review The purpose of this review is to consider a patient-centred approach to the care of people living with HIV (PLWH) who have multimorbidity, irrespective of the specific conditions. Recent findings Interdisciplinary care to achieve patient-centred care for people with multimorbidity is recognized as important, but the evaluation of models designed to achieve this goal are needed. Key elements of such approaches include patient preferences, interpretation of the evidence, prognosis as a tool to inform patient-centred care, clinical feasibility and optimization of treatment regimens. Summary Developing and evaluating the best models of patient-centred care for PLWH who also have multimorbidity is essential. This challenge represents an opportunity to leverage the lessons learned from the care of people with multimorbidity in general, and vice versa.

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Development and Validation of the Medication-Based Disease Burden Index

Abstract: Initial testing suggests that MDBI could offer an alternative low-cost and convenient method for quantifying disease burden and predicting health outcomes.

Cited by 50 publications

References 21 publications

Validity of medication‐based co‐morbidity indices in the Australian elderly population

Validity of medication‐based co‐morbidity indices in the Australian elderly population

Measures of Multimorbidity and Morbidity Burden for Use in Primary Care and Community Settings: A Systematic Review and Guide

Patient-centered care for people living with multimorbidity

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