Standardizing disease-specific quality of life measures across multiple chronic conditions: development and initial evaluation of the QOL Disease Impact Scale (QDIS®).

John E Ware,Rick Guyer,Nina Deng,Barbara Gandek

doi:10.1186/s12955-016-0483-x

Abstract

BackgroundTo document the development and evaluation of the Quality of life Disease Impact Scale (QDIS®), a measure that standardizes item content and scoring across chronic conditions and provides a summary, norm-based QOL impact score for each disease.MethodsA bank of 49 disease impact items was constructed from previously-used descriptions of health impact to represent ten frequently-measured quality of life (QOL) content areas and operational definitions successfully utilized in generic QOL surveys. In contrast to health in general, all items were administered with attribution to a specific disease (osteoarthritis, rheumatoid arthritis, angina, myocardial infarction, congestive heart failure, chronic kidney disease (CKD), diabetes, asthma, or COPD). Responses from 5418 adults were analyzed as five disease groups: arthritis, cardiovascular, CKD, diabetes, and respiratory. Unidimensionality, item parameter and scale-level invariance, reliability, validity and responsiveness to change during 9-month follow-up were evaluated by disease group and for all groups combined using multi-group confirmatory factor analysis (MGCFA), item response theory (IRT) and analysis of variance methods. QDIS was normed in an independent chronically ill US population sample (N = 4120).ResultsMGCFA confirmed a 1-factor model, justifying a summary score estimated using equal parameters for each item across disease groups. In support of standardized IRT-based scoring, correlations were very high between disease-specific and standardized IRT item slopes (r = 0.88–0.96), thresholds (r = 0.93–0.99) and person-level scores (r ≥ 0.99). Internal consistency, test-retest and person-level IRT reliability were consistently satisfactory across groups. In support of interpreting QDIS as a disease-specific measure, in comparison with generic measures, QDIS consistently discriminated markedly better across disease severity levels, correlated higher with other disease-specific measures in cross-sectional tests, and was more responsive in comparisons of groups with better, same or worse evaluations of disease-specific outcomes at the 9-month follow-up.ConclusionsStandardization of content and scoring across diseases was shown to be justified psychometrically and enabled the first summary measure of disease-specific QOL impact normed in the chronically ill population. This disease-specific approach substantially improves discriminant validity and responsiveness over generic measures and provides a basis for better understanding the relative QOL impact of multiple chronic conditions in research and clinical practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s12955-016-0483-x) contains supplementary material, which is available to authorized users.

Highlights

To document the development and evaluation of the Quality of life Disease Impact Scale (QDIS®), a measure that standardizes item content and scoring across chronic conditions and provides a summary, normbased quality of life (QOL) impact score for each disease
Disease-specific measures of quality of life (QOL) have the advantage of frequently being more responsive and clinically useful than generic QOL measures which do not focus on any specific condition [1,2,3], while generic measures have the advantage of enabling comparisons of QOL burden and treatment benefit across diseases [4, 5]
We extended the adaptive logic at the 9-month follow-up by using computerized adaptive testing (CAT) only for those patients suspected of scoring in the most impaired range and relying on a noisier but unbiased single-item QDIS estimate (QL1) for those showing lower or no impact

Summary

Introduction

To document the development and evaluation of the Quality of life Disease Impact Scale (QDIS®), a measure that standardizes item content and scoring across chronic conditions and provides a summary, normbased QOL impact score for each disease. The advantages of disease-specific measures result in part from achieving specificity by measuring the frequency and severity of specific symptoms such as joint pain in arthritis [6] or dyspnea in respiratory disease [7]. Such symptoms capture QOL only to the extent that they are quantified in terms of their impact on life or its quality [8]. Content from generic QOL measures has been incorporated in disease-specific measures to the point that a primary difference between them is whether survey questions make attributions to health in general, a specific component of health (e.g., physical or mental), or a specific disease. A survey of QOL impact attributed to headache, comes close to doing so [12]

Methods

Results

Conclusion