Perspectives Toward Long-Term Care Measurement From Frontline Workers in Brazil

The Brazilian long-term care (LTC) sector remains poorly structured and underdeveloped. COVID-19 did not bring unprecedented focus to the sector just because of the high mortality; it also affected the quality of care. In this pilot study, we evaluated the perspectives toward WE-THRIVE LTC measurements from Brazilian frontline workers in five long-term care facilities. For the four WE-THRIVE domains of LTC measurement (workforce and staffing, person-centered care, organizational context, and care outcomes), respondents used a 4-point Likert scale to rate their importance and answered open-ended questions about how these aspects of care changed since COVID-19. With few exceptions, respondents rated these aspects of LTC as extremely important or very important. Qualitative results highlighted concerns about and impacts of COVID-19, such as challenges related to the isolation of residents. The assessed measurement domains are confirmed to be important by frontline staff in Brazil. Measurement adoption must account for current issues.

Mapping expanded prostate cancer index composite to EQ5D utilities to inform economic evaluations in prostate cancer: Secondary analysis of NRG/RTOG 0415

PURPOSE

The Expanded Prostate Cancer Index Composite (EPIC) is the most commonly used patient reported outcome (PRO) tool in prostate cancer (PC) clinical trials, but health utilities associated with the different health states assessed with this tool are unknown, limiting our ability to perform cost-utility analyses. This study aimed to map EPIC tool to EuroQoL-5D-3L (EQ5D) to generate EQ5D health utilities.

METHODS AND MATERIALS

This is a secondary analysis of a prospective, randomized non-inferiority clinical trial, conducted between 04/2006 and 12/2009 at cancer centers across the United States, Canada, and Switzerland. Eligible patients included men >18 years with a known diagnosis of low-risk PC. Patient HRQoL data were collected using EPIC and health utilities were obtained using EQ5D. Data were divided into an estimation sample (n = 765, 70%) and a validation sample (n = 327, 30%). The mapping algorithms that capture the relationship between the instruments were estimated using ordinary least squares (OLS), Tobit, and two-part models. Five-fold cross-validation (in-sample) was used to compare the predictive performance of the estimated models. Final models were selected based on root mean square error (RMSE).

RESULTS

A total of 565 patients in the estimation sample had complete information on both EPIC and EQ5D questionnaires at baseline. Mean observed EQ5D utility was 0.90±0.13 (range: 0.28-1) with 55% of patients in full health. OLS models outperformed their counterpart Tobit and two-part models for all pre-determined model specifications. The best model fit was: "EQ5D utility = 0.248541 + 0.000748*(Urinary Function) + 0.001134*(Urinary Bother) + 0.000968*(Hormonal Function) + 0.004404*(Hormonal Bother)- 0.376487*(Zubrod) + 0.003562*(Urinary Function*Zubrod)"; RMSE was 0.10462.

CONCLUSIONS

This is the first study to identify a comprehensive set of mapping algorithms to generate EQ5D utilities from EPIC domain/ sub-domain scores. The study results will help estimate quality-adjusted life-years in PC economic evaluations.

Longitudinal predictive ability of mapping algorithms: Secondary analysis of NRG Oncology/RTOG 0415

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Background: Mapping algorithms informing economic evaluations are often derived using baseline data from clinical trials. It is unclear if these algorithms can predict health utilities accurately in post-intervention data. Thus, this study examines the longitudinal predictive ability of mapping algorithms derived from baseline trial data and explores the factors associated with prediction errors. Methods: This methodological study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer (PC), conducted by NRG Oncology (NCT00331773). In addition to patient demographic and clinical data, this study utilized PRO data collected at baseline and 6, 12 and 24 months post-intervention. The Expanded Prostate Cancer Index Composite (EPIC) questionnaire measures health-related quality-of-life (HRQoL) and has four domains (urinary, sexual, hormonal, and bowel) and two subdomains per domain (function and bother); EuroQOL-5D-3L (EQ5D) captures health utilities. Ordinary Least Squares (OLS) regression models were used to map EPIC scores to EQ5D utilities in the baseline data through 5-fold cross-validation. Predictive performance was tested in the post-intervention data; predicted and reported utilities were compared using t-tests, and the absolute prediction error was modeled using fixed effects, as a function of baseline demographic and clinical covariates, as well as observed and predicted EQ5D utilities. Results: A total of 267 (199) patients had complete EQ5D and EPIC domain (or subdomain) data at baseline and all subsequent assessments. In the EPIC domain sample, mean ± standard deviation observed EQ5D utility was 0.90±0.13 at baseline, 0.92±0.11 at 6 months, 0.90±0.13 at 12 months and 0.89±0.14 at 24 months. Mean absolute differences (MDs) between reported and predicted were lower for models using EPIC subdomain data compared to EPIC domain data, and generally decreased as the time of assessment increased. The mapping functions over-predicted utilities for patients in perfect health while the prediction errors were increasingly negative for lower reported EQ5D scores. According to the fixed effects model for EPIC domain data, lower observed and predicted baseline EQ5D scores, and time of assessment were significant predictors of the absolute prediction error; for EPIC subdomain data, lower observed and predicted baseline EQ5D scores, hormonal bother and function, and bowel function significantly predicted the absolute prediction error. Conclusions: This study is the first to demonstrate the longitudinal validity of EPIC questionnaire, and builds upon existing research on longitudinal validity of mapping functions. The low MDs in prediction errors in post-intervention data indicate that the mapping functions are sensitive to treatment effect, thereby increasing confidence in their use in economic evaluations in PC.

A methodological comparison of mapping algorithms to obtain health utilities derived using cross-sectional and longitudinal data: Secondary analysis of NRG/RTOG 0415

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Background: To compare the predictive ability of health utility mapping algorithms derived using cross-sectional and longitudinal data specific to the Expanded Prostate Cancer Index Composite (EPIC). Methods: This mapping study utilized data from an international, multicenter, randomized controlled trial of patients with low-risk prostate cancer conducted by NRG Oncology (NCT00331773). Health-related quality-of-life (HRQoL) data were collected using EPIC, and health utilities were obtained using EuroQOL-5D (EQ5D) at baseline and 6, 12 and 24 months post-intervention. Data were split into an estimation sample (70%) and a validation sample (30%). Ordinary Least Squares (OLS) regression models were estimated using baseline cross-sectional data as well as pooled data from all assessment periods. Random effects (RE) specifications that explicitly model the longitudinal nature of the data were also estimated. Candidate models were selected based on root mean square error (RMSE). Results: A total of 196 (147) patients in the estimation sample had complete EQ5D and EPIC domain (subdomain) data at all time points. OLS models using combined data outperformed the counter-part RE models as well as OLS models using baseline data in the five-fold cross-validation. Addition of covariates to the models resulted in improved predictive ability. In the external validation, when only EPIC domain/ subdomain data are available, the OLS model using combined data predicted EQ5D utilities better than the counterpart RE model and OLS model using baseline data (RMSE=0.121108 & 0.111345). OLS model using baseline data outperformed other model types for algorithms with EPIC domains and demographics (RMSE=0.121757), while RE models outperformed the other two model types for algorithms with EPIC subdomains and demographic data, (0.112782) and for algorithms with EPIC domains/ subdomains, demographics, and clinical covariates (RMSE=0.123589 & 0.163093). Conclusions: While algorithms using pooled data outperformed other model types in internal validation, RE models showed better predictive ability in external validation for algorithms with covariates. Clinical trial information: NCT00331773.