An analysis of the construct validity and responsiveness of the ICECAP-SCM capability wellbeing measure in a palliative care hospice setting.

BACKGROUND: For outcome measures to be useful in health and care decision-making, they need to have certain psychometric properties. The ICECAP-Supportive Care Measure (ICECAP-SCM), a seven attribute measure (1. Choice, 2. Love and affection, 3. Physical suffering, 4. Emotional suffering, 5. Dignity, 6. Being supported, 7. Preparation) developed for use in economic evaluation of end-of-life interventions, has face validity and is feasible to use. This study aimed to assess the construct validity and responsiveness of the ICECAP-SCM in hospice inpatient and outpatient settings. METHODS: A secondary analysis of data collated from two studies, one focusing on palliative care day services and the other on constipation management, undertaken in the same national hospice organisation across three UK hospices, was conducted. Other quality of life and wellbeing outcome measures used were the EQ-5D-5L, McGill Quality of Life Questionnaire - Expanded (MQOL-E), Patient Health Questionnaire-2 (PHQ-2) and Palliative Outcomes Scale Symptom list (POS-S). The construct validity of the ICECAP-SCM was assessed, following hypotheses generation, by calculating correlations between: (i) its domains and the domains of other outcome measures, (ii) its summary score and the other measures' domains, (iii) its summary score and the summary scores of the other measures. The responsiveness of the ICECAP-SCM was assessed using anchor-based methods to understand change over time. Statistical analysis consisted of Spearman and Pearson correlations for construct validity and paired t-tests for the responsiveness analysis. RESULTS: Sixty-eight participants were included in the baseline analysis. Five strong correlations were found with ICECAP-SCM attributes and items on the other measures: four with the Emotional suffering attribute (Anxiety/depression on EQ-5D-5L, Psychological and Burden on MQOL-E and Feeling down, depressed or hopeless on PHQ-2), and one with Physical suffering (Weakness or lack of energy on POS-S). ICECAP-SCM attributes and scores were most strongly associated with the MQOL-E measure (0.73 correlation coefficient between summary scores). The responsiveness analysis (n = 36) showed the ICECAP-SCM score was responsive to change when anchored to changes on the MQOL-E over time (p < 0.05). CONCLUSIONS: This study provides initial evidence of construct validity and responsiveness of the ICECAP-SCM in hospice settings and suggests its potential for use in end-of-life care research.

Exploring the costs, consequences and efficiency of three types of palliative care day services in the UK: a pragmatic before-and-after descriptive cohort study.

BACKGROUND: Palliative Care Day Services (PCDS) offer supportive care to people with advanced, progressive illness who may be approaching the end of life. Despite the growth of PCDS in recent years, evidence of their costs and effects is scarce. It is important to establish the value of such services so that health and care decision-makers can make evidence-based resource allocation decisions. This study examines and estimates the costs and effects of PCDS with different service configurations in three centres across the UK in England, Scotland and Northern Ireland. METHODS: People who had been referred to PCDS were recruited between June 2017 and September 2018. A pragmatic before-and-after descriptive cohort study design analysed data on costs and outcomes. Data on costs were collected on health and care use in the 4 weeks preceding PCDS attendance using adapted versions of the Client Service Receipt Inventory (CSRI). Outcomes, cost per attendee/day and volunteer contribution to PCDS were also estimated. Outcomes included quality of life (MQOL-E), health status (EQ-5D-5L) and capability wellbeing (ICECAP-SCM). RESULTS: Thirty-eight attendees were recruited and provided data at baseline and 4 weeks (centre 1: n = 8; centre 2: n = 8, centre 3: n = 22). The cost per attendee/day ranged from £121-£190 (excluding volunteer contribution) to £172-£264 (including volunteer contribution) across the three sites. Volunteering constituted between 28 and 38% of the total cost of PCDS provision. There was no significant mean change at 4 week follow-up from baseline for health and care costs (centre 1: £570, centre 2: -£1127, centre 3: £65), or outcomes: MQOL-E (centre 1: - 0.48, centre 2: 0.01, centre 3: 0.24); EQ-5D-5L (centre 1: 0.05, centre 2: 0.03, centre 3: - 0.03) and ICECAP-SCM (centre 1:0.00, centre 2: - 0.01, centre 3: 0.03). Centre costs variation is almost double per attendee when attendance rates are held constant in scenario analysis. CONCLUSIONS: This study highlights the contribution made by volunteers to PCDS provision. There is insufficient evidence on whether outcomes improved, or costs were reduced, in the three different service configurations for PCDS. We suggest how future research may overcome some of the challenges we encountered, to better address questions of cost-effectiveness in PCDS.

Are Quality-Adjusted Life Years a Good Proxy Measure of Individual Capabilities?

BACKGROUND: There is a debate in the health economics literature concerning the most appropriate way of applying Amartya Sen's capability approach in economic evaluation studies. Some suggest that quality-adjusted life years (QALYs) alone are adequate while others argue that this approach is too narrow and that direct measures of capability wellbeing provide a more extensive application of Sen's paradigm. OBJECTIVE: This paper empirically explores whether QALYs provide a good proxy for individual capabilities. METHODS: Data is taken from a multinational cross-sectional survey of individuals with seven health conditions (asthma, arthritis, cancer, depression, diabetes, hearing loss, heart disease) and a healthy population. Each individual completed the ICECAP-A measure of capability wellbeing for adults and six health utility instruments that are used to generate QALYs, including EQ-5D and SF-6D. Primary analysis examines how well health utility instruments can explain variation in the ICECAP-A using ordinary least squares regression. RESULTS: The findings show that all seven health conditions have a negative association on overall capability as measured by the ICECAP-A index. Inclusion of health utility instruments into separate regressions improves the predictive power of capability but on average, explains less than half of the variation in capability wellbeing. Individuals with arthritis appear to be less inhibited in terms of capability losses when accounting for health utility, yet those who have depression record significant reductions in capability relative to the healthy population even after accounting for the most commonly used health utility instruments. CONCLUSION: The study therefore casts doubt on the ability of QALYs to act as a reliable proxy measure of individuals' capability.

The Relative Impacts of Disease on Health Status and Capability Wellbeing: A Multi-Country Study.

BACKGROUND: Evaluations of the impact of interventions for resource allocation purposes commonly focus on health status. There is, however, also concern about broader impacts on wellbeing and, increasingly, on a person's capability. This study aims to compare the impact on health status and capability of seven major health conditions, and highlight differences in treatment priorities when outcomes are measured by capability as opposed to health status. METHODS: The study was a cross-sectional four country survey (n = 6650) of eight population groups: seven disease groups with: arthritis, asthma, cancer, depression, diabetes, hearing loss, and heart disease and one health population 'comparator' group. Two simple self-complete questionnaires were used to measure health status (EQ-5D-5L) and capability (ICECAP-A). Individuals were classified by illness severity using condition-specific questionnaires. Effect sizes were used to estimate: (i) the difference in health status and capability for those with conditions, relative to a healthy population; and (ii) the impact of the severity of the condition on health status and capability within each disease group. FINDINGS: 5248 individuals were included in the analysis. Individuals with depression have the greatest mean reduction in both health (effect size, 1.26) and capability (1.22) compared to the healthy population. The effect sizes for capability for depression are much greater than for all other conditions, which is not the case for health. For example, the arthritis group effect size for health (1.24) is also high and similar to that of depression, whereas for the same arthritis group, the effect size for capability is much lower than that for depression (0.55). In terms of severity within disease groups, individuals categorised as 'mild' have similar capability levels to the healthy population (effect sizes <0.2, excluding depression) but lower health status than the healthy population (≥0.4). CONCLUSION: Significant differences exist in the relative effect sizes across diseases when measured by health status and capability. In terms of treating morbidity, a shift in focus from health gain to capability gain would increase funding priorities for patients with depression specifically and severe illnesses more generally.