Quality of life and burden of morbidity in primary care users with multimorbidity
Authors Peters M, Kelly L, Potter CM, Jenkinson C, Gibbons E, Forder J, Fitzpatrick R
Received 4 August 2017
Accepted for publication 27 October 2017
Published 16 February 2018 Volume 2018:9 Pages 103—113
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Colin Mak
Peer reviewer comments 3
Editor who approved publication: Dr Robert Howland
Michele Peters,1 Laura Kelly,1 Caroline M Potter,1 Crispin Jenkinson,1 Elizabeth Gibbons,1 Julien Forder,2 Ray Fitzpatrick1
1Health Services Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, 2Personal Social Services Research Unit, School of Social Policy Sociology and Social Research, University of Kent, Canterbury, UK
Purpose: The aim of this study was to assess the quality of life, number of diseases and burden of morbidity of multimorbid primary care users and whether a simple disease count or a multimorbidity burden score is more predictive of quality of life.
Patients and methods: Primary care patients with at least 1 of 11 specified chronic conditions were invited to participate in a postal survey. Participants completed the Disease Burden Impact Scale (DBIS) questionnaire, the five dimension-five level Euro-Qol (EQ-5D-5L) and standard demographics questions. The DBIS asks participants to self-report chronic conditions and to rate the impact of each condition. Descriptive statistics and analysis of variance were used to determine quality of life, count of diseases and burden of morbidity. Multiple linear regression analyses determined whether disease count or the DBIS, adjusted for demographics, was more predictive of the EQ-5D-5L scores.
Results: Thirty-one percent (n=917) responded, from which 69 were excluded as they reported no or only one condition, leaving 848 (92%) in the analysis. Slightly more women (50.9%) participated; the mean age was 67.0 (SD 13.9) and the mean number of conditions was 6.5 (SD 3.49). The mean scores were: DBIS 15.5 (SD 12.00; score range 0–140, with higher scores indicating higher multimorbidity burden), EQ-5D-5L score 0.69 (SD 0.28; score range −0.28 [a state worse than death] to 1 [best possible health state]) and EQ-5D Visual Analog Scale (EQ-VAS) 65.44 (SD 23.66; score range 0–100 with higher scores meaning better health). The model using the DBIS score was more predictive of the EQ-5D-5L score and EQ-VAS than the model using the disease count (R2adj=0.53 using DBIS and R2adj=0.42 using disease count for EQ-5D-5L score, and R2adj=0.44 using DBIS versus R2adj=0.34 using disease count for EQ-VAS). All models were statistically significant (p<0.001).
Conclusion: The DBIS is a useful measure for assessing multimorbidity from the perspective of primary care users in particular, as it is more predictive of health outcomes than a simple count of conditions.
Keywords: multimorbidity, quality of life, chronic disease, disease burden, patient-reported outcomes
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