Examining the relationship between health-related quality of life and increasing numbers of diagnoses

Development of a core outcome set for multimorbidity trials in low/middle-income countries (COSMOS): study protocol

INTRODUCTION

'Multimorbidity' describes the presence of two or more long-term conditions, which can include communicable, non-communicable diseases, and mental disorders. The rising global burden from multimorbidity is well documented, but trial evidence for effective interventions in low-/middle-income countries (LMICs) is limited. Selection of appropriate outcomes is fundamental to trial design to ensure cross-study comparability, but there is currently no agreement on a core outcome set (COS) to include in trials investigating multimorbidity specifically in LMICs. Our aim is to develop international consensus on two COSs for trials of interventions to prevent and treat multimorbidity in LMIC settings.

METHODS AND ANALYSIS

Following methods recommended by the Core Outcome Measures in Effectiveness Trials initiative, the development of these two COSs will occur in parallel in three stages: (1) generation of a long list of potential outcomes for inclusion; (2) two-round online Delphi surveys and (3) consensus meetings. First, to generate an initial list of outcomes, we will conduct a systematic review of multimorbidity intervention and prevention trials and interviews with people living with multimorbidity and their caregivers in LMICs. Outcomes will be classified using an outcome taxonomy. Two-round Delphi surveys will be used to elicit importance scores for these outcomes from people living with multimorbidity, caregivers, healthcare professionals, policy makers and researchers in LMICs. Finally, consensus meetings including all of these stakeholders will be held to agree outcomes for inclusion in the two COSs.

ETHICS AND DISSEMINATION

The study has been approved by the Research Governance Committee of the Department of Health Sciences, University of York, UK (HSRGC/2020/409/D:COSMOS). Each participating country/research group will obtain local ethics board approval. Informed consent will be obtained from all participants. We will disseminate findings through peer-reviewed open access publications, and presentations at global conferences selected to reach a wide range of LMIC stakeholders.

PROSPERO REGISTATION NUMBER

CRD42020197293.

Bayesian Hierarchical Models for Meta-Analysis of Quality-of-Life Outcomes: An Application in Multimorbidity

BACKGROUND

Health-related quality of life (HRQoL) is a key outcome in cost-utility analyses, which are commonly used to inform healthcare decisions. Different instruments exist to evaluate HRQoL, however while some jurisdictions have a preferred system, no gold standard exists. Standard meta-analysis struggles with the variety of outcome measures, which may result in the exclusion of potentially relevant evidence.

OBJECTIVE

Using a case study in multimorbidity, the objective of this analysis is to illustrate how a Bayesian hierarchical model can be used to combine data across different instruments. The outcome of interest is the slope relating HRQoL to the number of coexisting conditions.

METHODS

We propose a three-level Bayesian hierarchical model to systematically include a large number of studies evaluating HRQoL using multiple instruments. Random effects assumptions yield instrument-level estimates benefitting from borrowing strength across the evidence base. This is particularly useful where little evidence is available for the outcome of choice for further evaluation.

RESULTS

Our analysis estimated a reduction in quality of life of 3.8-4.1% per additional condition depending on HRQoL instrument. Uncertainty was reduced by approximately 80% for the instrument with the least evidence.

CONCLUSION

Bayesian hierarchical models may provide a useful modelling approach to systematically synthesize data from HRQoL studies.

Multimorbidity and quality of life: Systematic literature review and meta-analysis

Multimorbidity is typically defined as the co-existence of two or more chronic diseases within an individual. Its prevalence is highest among the elderly, with poor quality of life (QoL) being one of the major consequences. This study aims to: (1) understand the relationship between multimorbidity and QoL or health-related quality of life (HRQoL) through systematic literature review; (2) explore the strength of this association by conducting the first meta-analysis on the subject. Following PRISMA, Medline/PubMed, Embase, CINAHL and PsycINFO were searched for studies published through September 1st, 2018. Original studies with clear operationalization of multimorbidity and validated QoL (or HRQoL) measurement were retained. For random-effect meta-analysis, a minimum of three studies with the same multimorbidity tool (e.g. number of diseases or equal comorbidity index) and the same QoL tool were required. Number of diseases was most common and the only measure on which meta-analysis was carried out. The outcome of interest was the linear regression slope between increasing number of diseases and QoL. Heterogeneity was explored with meta-regression. Out of 25,890 studies initially identified, 74 studies were retained for systematic review (total of 2,500,772 participants), of which 39 were included in the meta-analysis. The mean decrease in HRQoL per each added disease, depending on the scale, ranged from: -1.55% (95%CI: -2.97%, -0.13%) for the mental component summary score of pooled SF-36, -12 and -8 scales to -4.37% (95%CI: -7.13%, -1.61%) for WHOQoL-BREF physical health domain. Additional studies considering severity, duration and patterns of diseases are required to further clarify this association.

Estimating Joint Health Condition Utility Values

OBJECTIVES

To predict health state utility values (HSUVs) for individuals with up to 4 conditions simultaneously.

METHODS

Person-level data were taken from the General Practice Patient Survey, a national survey of adult patients registered with general practices in England. Individuals reported whether they had any 1 of 16 chronic conditions and completed the 3-level EuroQol 5-dimensional questionnaire. Four nonparametric methods (additive, multiplicative, minimum, and the adjusted decrement estimator) and 1 parametric estimator (the linear index) were used to predict HSUVs for individuals with a joint health condition (JHC). Predicted and actual utility scores were compared for precision using root mean square error and mean absolute error. Bias was assessed using mean error.

RESULTS

The analysis included 929,565 individuals, of which 30.5% had at least 2 conditions. Of the nonparametric estimators, the multiplicative approach produced estimates with the lowest bias and most precision for 2 JHCs. For populations with a long-term mental health condition within the JHC, the multiplicative approach overestimated utility scores. All nonparametric methods produced biased results when estimating HSUVs for 3 or 4 JHCs. The linear index generally produced unbiased results with the highest precision.

CONCLUSIONS

The multiplicative approach was the best nonparametric estimator when estimating HSUVs for 2 JHCs. None of the nonparametric approaches for estimating HSUVs can be recommended with more than 2 JHCs. The linear index was found to have good predictive properties but needs external validation before being recommended for routine use.