A scoping review to create a framework for the steps in developing condition-specific preference-based instruments de novo or from an existing non-preference-based instrument: use of item response theory or Rasch analysis.

BACKGROUND: There is no widely accepted framework to guide the development of condition-specific preference-based instruments (CSPBIs) that includes both de novo and from existing non-preference-based instruments. The purpose of this study was to address this gap by reviewing the published literature on CSPBIs, with particular attention to the application of item response theory (IRT) and Rasch analysis in their development. METHODS: A scoping review of the literature covering the concepts of all phases of CSPBI development and evaluation was performed from MEDLINE, Embase, PsychInfo, CINAHL, and the Cochrane Library, from inception to December 30, 2022. RESULTS: The titles and abstracts of 1,967 unique references were reviewed. After retrieving and reviewing 154 full-text articles, data were extracted from 109 articles, representing 41 CSPBIs covering 21 diseases or conditions. The development of CSPBIs was conceptualized as a 15-step framework, covering four phases: 1) develop initial questionnaire items (when no suitable non-preference-based instrument exists), 2) establish the dimensional structure, 3) reduce items per dimension, 4) value and model health state utilities. Thirty-nine instruments used a type of Rasch model and two instruments used IRT models in phase 3. CONCLUSION: We present an expanded framework that outlines the development of CSPBIs, both from existing non-preference-based instruments and de novo when no suitable non-preference-based instrument exists, using IRT and Rasch analysis. For items that fit the Rasch model, developers selected one item per dimension and explored item response level reduction. This framework will guide researchers who are developing or assessing CSPBIs.

Creating a Multiply Imputed Value Set for the EQ-5D-5L in Canada: State-Level Misspecification Terms Are Needed to Characterize Parameter Uncertainty Correctly.

BACKGROUND: Parameter uncertainty in EQ-5D-5L value sets often exceeds the instrument's minimum important difference, yet this is routinely ignored. Multiple imputation (MI) accounts for parameter uncertainty in the value set; however, no valuation study has implemented this methodology. Our objective was to create a Canadian MI value set for the EQ-5D-5L, thus enabling users to account for parameter uncertainty in the value set. METHODS: Using the Canadian EQ-5D-5L valuation study (N = 1,073), we first refit the original model followed by models with state-level misspecification. Models were compared based on the adequacy of 95% credible interval (CrI) coverage for out-of-sample predictions. Using the best-fitting model, we took 100 draws from the posterior distribution to create 100 imputed value sets. We examined how much the standard error of the estimated mean health utilities increased after accounting for parameter uncertainty in the value set by using the MI and original value sets to score 2 data sets: 1) a sample of 1,208 individuals from the Canadian general public and 2) a sample of 401 women with breast cancer. RESULTS: The selected model with state-level misspecification outperformed the original model (95% CrI coverage: 94.2% v. 11.6%). We observed wider standard errors for the estimated mean utilities on using the MI value set for both the Canadian general public (MI: 0.0091; original: 0.0035) and patients with breast cancer (MI: 0.0169; original: 0.0066). DISCUSSION AND CONCLUSIONS: We provide 1) the first MI value sets for the EQ-5D-5L and 2) code to construct MI value sets while accounting for state-level model misspecification. Our study suggests that ignoring parameter uncertainty in value sets leads to falsely narrow SEs. HIGHLIGHTS: Value sets for health state utility instruments are estimated subject to parameter uncertainty; this parameter uncertainty may exceed the minimum important difference of the instrument, yet it is not fully captured using current methods.This study creates the first multiply imputed value set for a multiattribute utility instrument, the EQ-5D-5L, to fully capture this parameter uncertainty.We apply the multiply imputed value set to 2 data sets from 1) the Canadian general public and 2) women with invasive breast cancer.Scoring the EQ-5D-5L using a multiply imputed value set led to wider standard error estimates, suggesting that the current practice of ignoring parameter uncertainty in the value set leads to falsely low standard errors.Our work will be of interest to methodologists and developers of the EQ-5D-5L and users of the EQ-5D-5L, such as health economists, researchers, and policy makers.

The impact of the early COVID-19 pandemic on healthcare system resource use and costs in two provinces in Canada: An interrupted time series analysis.

INTRODUCTION: The aim of our study was to assess the initial impact of COVID-19 on total publicly-funded direct healthcare costs and health services use in two Canadian provinces, Ontario and British Columbia (BC). METHODS: This retrospective repeated cross-sectional study used population-based administrative datasets, linked within each province, from January 1, 2018 to December 27, 2020. Interrupted time series analysis was used to estimate changes in the level and trends of weekly resource use and costs, with March 16-22, 2020 as the first pandemic week. Also, in each week of 2020, we identified cases with their first positive SARS-CoV-2 test and estimated their healthcare costs until death or December 27, 2020. RESULTS: The resources with the largest level declines (95% confidence interval) in use in the first pandemic week compared to the previous week were physician services [Ontario: -43% (-49%,-37%); BC: -24% (-30%,-19%) (both p<0.001)] and emergency department visits [Ontario: -41% (-47%,-35%); BC: -29% (-35%,-23%) (both p<0.001)]. Hospital admissions declined by 27% (-32%,-23%) in Ontario and 21% (-26%,-16%) in BC (both p<0.001). Resource use subsequently rose but did not return to pre-pandemic levels. Only home care and dialysis clinic visits did not significantly decrease compared to pre-pandemic. Costs for COVID-19 cases represented 1.3% and 0.7% of total direct healthcare costs in 2020 in Ontario and BC, respectively. CONCLUSIONS: Reduced utilization of healthcare services in the overall population outweighed utilization by COVID-19 patients in 2020. Meeting the needs of all patients across all services is essential to maintain resilient healthcare systems.

Developing the Breast Utility Instrument to Measure Health-Related Quality-of-Life Preferences in Patients with Breast Cancer: Selecting the Item for Each Dimension.

Introduction. Generic preference-based instruments inadequately measure breast cancer (BrC) health-related quality-of-life preferences given advances in therapy. Our overall purpose is to develop the Breast Utility Instrument (BUI), a BrC-specific preference-based instrument. This study describes the selection of the BUI items. Methods. A total of 408 patients from diverse BrC health states completed the EORTC QLQ-C30 and BR45 (breast module). For each of 10 dimensions previously assessed with confirmatory factor analysis, we evaluated data fit to the Rasch model based on global model and item fit, including threshold ordering, item residuals, infit and outfit, differential item functioning (age), and unidimensionality. Misfitting items were removed iteratively, and the model fit was reassessed. From items fitting the Rasch model, we selected 1 item per dimension based on high patient- and clinician-rated item importance, breadth of item thresholds, and clinical relevance. Results. Global model fit was good in 7 and borderline in 3 dimensions. Separation index was acceptable in 4 dimensions. Item selection criteria were maximized for the following items: 1) physical functioning (trouble taking a long walk), 2) emotional functioning (worry), 3) social functioning (interfering with social activities), 4) pain (having pain), 5) fatigue (tired), 6) body image (dissatisfied with your body), 7) systemic therapy side effects (hair loss), 8) sexual functioning (interest in sex), 9) breast symptoms (oversensitive breast), and 10) endocrine therapy symptoms (problems with your joints). Conclusions. We propose 10 items for the BUI. Our next steps include assessing the measurement properties prior to eliciting preference weights of the BUI. Highlights: A previous confirmatory factor analysis established 10 dimensions of the European Organisation for Research and Treatment of Cancer (EORTC) core quality of life questionnaire (QLQ-C30) and its breast module (BR45).In this study, we selected 1 item per dimension based on fit to the Rasch model, patient- and clinician-rated item importance, breadth of item thresholds, and clinical relevance.These items form the core of the future Breast Utility Instrument (BUI).The future BUI will be a novel breast cancer-specific preference-based instrument that potentially will better reflect women's preferences in clinical decision making and cost utility analyses.

Initial health care costs for COVID-19 in British Columbia and Ontario, Canada: an interprovincial population-based cohort study.

BACKGROUND: COVID-19 imposed substantial health and economic burdens. Comprehensive population-based estimates of health care costs for COVID-19 are essential for planning and policy evaluation. We estimated publicly funded health care costs in 2 Canadian provinces during the pandemic's first wave. METHODS: In this historical cohort study, we linked patients with their first positive SARS-CoV-2 test result by June 30, 2020, in 2 Canadian provinces (British Columbia and Ontario) to health care administrative databases and matched to negative or untested controls. We stratified patients by highest level of initial care: community, long-term care, hospital (without admission to the intensive care unit [ICU]) and ICU. Mean publicly funded health care costs for patients and controls, mean net (attributable to COVID-19) costs and total costs were estimated from 30 days before to 120 days after the index date, or to July 31, 2020, in 30-day periods for patients still being followed by the start of each period. RESULTS: We identified 2465 matched people with a positive test result for SARS-CoV-2 in BC and 28 893 in Ontario. Mean age was 53.4 (standard deviation [SD] 21.8) years (BC) and 53.7 (SD 22.7) years (Ontario); 55.7% (BC) and 56.1% (Ontario) were female. Net costs in the first 30 days after the index date were $22 010 (95% confidence interval [CI] 19 512 to 24 509) and $15 750 (95% CI 15 354 to 16 147) for patients admitted to hospital, and $65 828 (95% CI 58 535 to 73 122) and $56 088 (95% CI 53 721 to 58 455) for ICU patients in BC and Ontario, respectively. In the community and long-term care settings, net costs were near 0. Total costs for all people, from 30 days before to 30 days after the index date, were $22 128 330 (BC) and $175 778 210 (Ontario). INTERPRETATION: During the first wave, we found that mean costs attributable to COVID-19 were highest for patients with ICU admission and higher in BC than Ontario. Reducing the number of people who acquire COVID-19 and severity of illness are required to mitigate the economic impact of COVID-19.

"Bring the Hoses to Where the Fire Is!": Differential Impacts of Marginalization and Socioeconomic Status on COVID-19 Case Counts and Healthcare Costs.

OBJECTIVES: Local health leaders and the Director General of the World Health Organization alike have observed that COVID-19 "does not discriminate." Nevertheless, the disproportionate representation of people of low socioeconomic status among those infected resembles discrimination. This population-based retrospective cohort study examined COVID-19 case counts and publicly funded healthcare costs in Ontario, Canada, with a focus on marginalization. METHODS: Individuals with their first positive severe acute respiratory syndrome coronavirus 2 test from January 1, 2020 to June 30, 2020, were linked to administrative databases and matched to negative/untested controls. Mean net (COVID-19-attributable) costs were estimated for 30 days before and after diagnosis, and differences among strata of age, sex, comorbidity, and measures of marginalization were assessed using analysis of variance tests. RESULTS: We included 28 893 COVID-19 cases (mean age 54 years, 56% female). Most cases remained in the community (20 545, 71.1%) or in long-term care facilities (4478, 15.5%), whereas 944 (3.3%) and 2926 (10.1%) were hospitalized, with and without intensive care unit, respectively. Case counts were skewed across marginalization strata with 2 to 7 times more cases in neighborhoods with low income, high material deprivation, and highest ethnic concentration. Mean net costs after diagnosis were higher for males ($4752 vs $2520 for females) and for cases with higher comorbidity ($1394-$7751) (both P < .001) but were similar across levels of most marginalization dimensions (range $3232-$3737, all P ≥ .19). CONCLUSIONS: This study suggests that allocating resources unequally to marginalized individuals may improve equality in outcomes. It highlights the importance of reducing risk of COVID-19 infection among marginalized individuals to reduce overall costs and increase system capacity.

Developing the Breast Utility Instrument, a preference-based instrument to measure health-related quality of life in women with breast cancer: Confirmatory factor analysis of the EORTC QLQ-C30 and BR45 to establish dimensions.

OBJECTIVES: Breast cancer (BrC) and its treatments impair health-related quality of life (HRQoL). Utility is a measure of HRQoL that includes preferences for health outcomes, used in treatment decision-making. Generic preference-based instruments lack BrC-specific concerns, indicating the need for a BrC-specific preference-based instrument. Our objective was to determine dimensions of the European Organisation for Research and Treatment of Cancer (EORTC) general cancer (QLQ-C30) and breast module (BR45) instruments, the first step in our development of the novel Breast Utility Instrument (BUI). METHODS: Patients (n = 408) attending outpatient BrC clinics at an urban cancer centre, and representing a spectrum of BrC health states, completed the QLQ-C30 and BR45. We performed confirmatory factor analysis of the combined QLQ-C30 and BR45 using mean-and variance-adjusted unweighted least squares estimation. The hypothesized factor model was based on clinical relevance, item distributions, missing data, item-importance, and internal reliability of dimensions. Models were evaluated based on global and item fit, local areas of strain, and likelihood ratio tests of nested models. RESULTS: Our final model had 10 dimensions: physical and role functioning, emotional functioning, social functioning, body image, pain, fatigue, systemic therapy side effects, sexual functioning and enjoyment, arm and breast symptoms, and endocrine therapy symptoms. Good overall model fit was achieved: χ2/df: 1.45, Tucker-Lewis index: 0.946, comparative fit index: 0.951, standardized root-mean-square residual: 0.069, root-mean-square error of approximation: 0.033 (0.030-0.037). All items had salient factor loadings (λ>0.4, p<0.001). CONCLUSIONS: We identified important BrC HRQoL dimensions to develop the BUI, a BrC-specific preference-based instrument.