A Validation Study of the EQ-5D-5L and ICEpop Capability Measure for Older People Among Older Individuals With Depressive Symptoms in Brazil.

OBJECTIVES: This study aimed to assess the known-groups validity of the EQ-5D-5L and the ICEpop Capability Measure for Older People (ICECAP-O), 2 outcome measures used in economic evaluation, among older adults with depressive symptoms in socioeconomically deprived areas of Brazil. We also explored the role of education and income on responses to these measures. METHODS: This cross-sectional study used baseline data from PROACTIVE, a cluster randomized controlled trial to evaluate a psychosocial intervention for late-life depression among older adults. Participants aged ≥60 years with a 9-item Patient Health Questionnaire score ≥10 were recruited from 20 primary healthcare clinics. Ordered logistic regression models assessed the association between depressive symptoms severity, income, and education and dimension-level responses on the EQ-5D-5L and ICECAP-O. Multivariable regression models investigated the ability of EQ-5D-5L and ICECAP-O scores to discriminate between depressive symptoms severity levels and other characteristics, including education level and household income. RESULTS: A total of 715 participants were included in the study. Depressive symptoms severity was associated with all EQ-5D-5L and ICECAP-O dimensions, except the ICECAP-O enjoyment attribute. In contrast, household income was only associated with the ICECAP-O security attribute. Higher severity of depressive symptoms (9-item Patient Health Questionnaire scores) was also strongly associated with lower (ie, worse) scores on both measures in all models. Education level and household income showed no association with either EQ-5D-5L or ICECAP-O scores. CONCLUSIONS: To best of our knowledge, this is the first study that investigated the validity of these 2 measures among older adults in Brazil. Both EQ-5D-5L and ICECAP-O showed evidence of validity in differentiating depressive symptom severity.

Choice of statistical model for cost-effectiveness analysis and covariate adjustment: empirical application of prominent models and assessment of their results.

CONTEXT: Statistical models employed in analysing patient-level cost and effectiveness data need to be flexible enough to adjust for any imbalanced covariates, account for correlations between key parameters, and accommodate potential skewed distributions of costs and/or effects. We compare prominent statistical models for cost-effectiveness analysis alongside randomised controlled trials (RCTs) and covariate adjustment to assess their performance and accuracy using data from a large RCT. METHOD: Seemingly unrelated regressions, linear regression of net monetary benefits, and Bayesian generalized linear models with various distributional assumptions were used to analyse data from the TASMINH2 trial. Each model adjusted for covariates prognostic of costs and outcomes. RESULTS: Cost-effectiveness results were notably sensitive to model choice. Models assuming normally distributed costs and effects provided a poor fit to the data, and potentially misleading inference. Allowing for a beta distribution captured the true incremental difference in effects and changed the decision as to which treatment is preferable. CONCLUSIONS: Our findings suggest that Bayesian generalized linear models which allow for non-normality in estimation offer an attractive tool for researchers undertaking cost-effectiveness analyses. The flexibility provided by such methods allows the researcher to analyse patient-level data which are not necessarily normally distributed, while at the same time it enables assessing the effect of various baseline covariates on cost-effectiveness results.

Predicting the ICECAP-O capability index from the WOMAC osteoarthritis index: is mapping onto capability from condition-specific health status questionnaires feasible?

BACKGROUND: There is a growing interest in the application of the capability approach in health economic analysis. The aim of the research reported here is to assess the feasibility of mapping from a condition-specific questionnaire (Western Ontario and McMaster Universities [WOMAC] Osteoarthritis Index) to a capability well-being questionnaire (ICEpop CAPability measure for Older people [ICECAP-O]). METHODS: . One hundred five osteoarthritis patients requiring joint replacement completed the 5 attributes on the ICECAP-O (attachment, security, role, enjoyment, and control) and the 3 WOMAC categories (pain, stiffness, and physical function). The prediction data set consisted of baseline scores, whereas follow-up data were used to validate the predictions. The mapping algorithms used ordinary least squares and multinomial logistic regression models to predict the relationship between WOMAC scores, categories, or items and ICECAP-O scores or the 5 ICECAP-O attributes. RESULTS: . ICECAP-O scores predicted from WOMAC category scores produced the lowest mapping error statistics (mean absolute error = 0.0832; mean squared error = 0.0142) as well as highest goodness of fit (R(2) = 0.3976). Prediction of ICECAP-O attributes from WOMAC category scores was possible for the majority of capability dimensions. The "control" attribute and physical function WOMAC category exhibited the strongest relationship (R(2) = 0.2143). The "attachment" attribute proved difficult to predict from any WOMAC category, which is in line with intuition given this attribute captures psychological well-being rather than pain, stiffness, or physical function. CONCLUSION: . This is the first study to investigate the predictive ability of a condition-specific measure of health onto capability. The results presented here suggest it is feasible to map from condition-specific measures to an overall capability index, although WOMAC cannot predict individual capability in its entirety. Although the results here are encouraging for those interested in using ICECAP-O, given the small validation sample size applied, further research will be required to verify these findings.