Meltoncoffey9414
Sickle cell disease (SCD) is a complex, chronic condition that impairs health-related quality of life of affected individuals and their caregivers. As curative therapies emerge, comprehensive cost-effectiveness models will inform their value. These models will require descriptions of health states and their corresponding utility values that accurately reflect health-related quality of life over the disease trajectory. The objectives of this systematic review were to develop a catalog of health state utility (HSU) values for SCD, identify research gaps, and provide future directions for preference elicitation.
Records were identified through searches of PubMed and Embase, Tufts Medical Center Cost-Effectiveness Analysis Registry, reference lists of relevant articles, and consultation with SCD experts (2008-2020). We removed duplicate records and excluded ineligible studies. selleck compound For included studies, we summarized the study characteristics, methods used for eliciting HSUs, and HSU values.
Five studies empirically elicited utilities using indirect methods (EQ-5D) (n= 3) and Short Form-6 Dimension (n= 2); these represent health states associated with general SCD (n= 1), SCD complications (n= 2), and SCD treatments (n= 3). Additionally, we extracted HSUs from 7 quality-adjusted life-years-based outcome research studies. The HSU among patients with general SCD without specifying complications ranged from 0.64 to 0.887. Only 36% of the HSUs used in the quality-adjusted life-year-based outcomes research studies were derived from individuals with SCD. No study estimated HSUs in caregivers.
There is a dearth of literature of HSUs for use in SCD models. Future empirical studies should elicit a comprehensive set of HSUs from individuals with SCD and their caregivers.
There is a dearth of literature of HSUs for use in SCD models. Future empirical studies should elicit a comprehensive set of HSUs from individuals with SCD and their caregivers.
To investigate the extent to which stated preferences for treatment criteria elicited using multicriteria decision analysis (MCDA) methods are consistent with the trade-offs (implicitly) applied in cost-effectiveness analysis (CEA), and the impact of any differences on the prioritization of treatments.
We used existing MCDA and CEA models developed to evaluate interventions for knee osteoarthritis in the New Zealand population. We established equivalent input parameters for each model, for the criteria "treatment effectiveness," "cost," "risk of serious harms," and "risk of mild-to-moderate harms" across a comprehensive range of (hypothetical) interventions to produce a complete ranking of interventions from each model. We evaluated the consistency of these rankings between the 2 models and investigated any systematic differences between the (implied) weight placed on each criterion in determining rankings.
There was an overall moderate-to-strong correlation in intervention rankings between the MCDA and CEA models (Spearman correlation coefficient= 0.51). Nevertheless, there were systematic differences in the evaluation of trade-offs between intervention attributes and the resulting weights placed on each criterion. The CEA model placed lower weights on risks of harm and much greater weight on cost (at all accepted levels of willingness-to-pay per quality-adjusted life-year than did respondents to the MCDA survey.
MCDA and CEA approaches to inform intervention prioritization may give systematically different results, even when considering the same criteria and input data. These differences should be considered when designing and interpreting such studies to inform treatment prioritization decisions.
MCDA and CEA approaches to inform intervention prioritization may give systematically different results, even when considering the same criteria and input data. These differences should be considered when designing and interpreting such studies to inform treatment prioritization decisions.
Relatively few studies to date have examined the preferences of members of the general population as potential future consumers of long-term aged care services. This study aimed to use discrete choice experiment methodology to compare the preferences of 3 groups the general population, residents, and family members of people living in long-term aged care.
A total of 6 salient attributes describing the physical and psychosocial care in long-term residential aged care were drawn from qualitative research with people with a lived experience of aged care and were used to develop the discrete choice experiment questionnaire. The 6 attributes included the level of time care staff spent with residents, homeliness of shared spaces, the homeliness of their own rooms, access to outside and gardens, frequency of meaningful activities, and flexibility with care routines. The questionnaire was administered to 1243 respondents including consumers (residents [n= 126], family member carers [n= 416]), and members of the general population (n= 701).
For both the general population and resident samples, having their own room feeling "home-like" exhibited the largest impact upon overall preferences. For the family member sample, care staff being able to spend enough time exhibited the largest impact. Tests of poolability indicated that the resident and general population samples estimates could be pooled. The null hypothesis of equal parameters between the groups was rejected for the family members, indicating significant differences in preferences relative to the resident and the general population samples.
This study illustrates that preferences for residential aged care delivery may vary depending upon perspective and experience.
This study illustrates that preferences for residential aged care delivery may vary depending upon perspective and experience.
Chronic hepatitis C (CHC) infection affects more than 70 million people worldwide and imposes considerable health and economic burdens on patients and society. This study estimated 2 understudied components of the economic burden, patient out-of-pocket (OOP) costs and time costs, in patients with CHC in a tertiary hospital clinic setting and a community clinic setting.
This was a multicenter, cross-sectional study with hospital-based (n= 174) and community-based (n= 101) cohorts. We used a standardized instrument to collect healthcare resource use, time, and OOP costs. OOP costs included patient-borne costs for medical services, nonprescription drugs, and nonmedical expenses related to healthcare visits. Patient and caregiver time costs were estimated using an hourly wage value derived from patient-reported employment income and, where missing, derived from the Canadian census. Sensitivity analysis explored alternative methods of valuing time. Costs were reported in 2020 Canadian dollars.
The mean 3-month OOP cost was $55 (95% confidence interval [CI] $21-$89) and $299 (95% CI $170-$427) for thecommunity and hospital cohorts, respectively. The mean 3-month patient time cost was $743 (95% CI $485-$1002) (community) and $465 (95% CI $248-$682) (hospital). The mean 3-month caregiver time cost was $31 (95% CI $0-$63) (community) and $277 (95% CI $174-$380) (hospital). Patients with decompensated cirrhosis bore the highest costs.
OOP costs and patient and caregiver time costs represent a considerable economic burden to patient with CHC, equivalent to 14% and 21% of the reported total 3-month income for the hospital-based and community-based cohorts, respectively.
OOP costs and patient and caregiver time costs represent a considerable economic burden to patient with CHC, equivalent to 14% and 21% of the reported total 3-month income for the hospital-based and community-based cohorts, respectively.
Improving health and financial risk protection (FRP, the prevention of medical impoverishment) and their distributions is a major objective of national health systems. Explicitly describing FRP and disaggregated (eg, across socioeconomic groups) impact of health interventions in economic evaluations can provide decision makers with a broader set of health and financial outcomes to compare and prioritize interventions against each other.
We propose methods to synthesize such a broader set of outcomes by estimating and comparing the distributions in both health and FRP benefits procured by health interventions. We build on benefit-cost analysis frameworks and utility-based models, and we illustrate our methods with the case study of universal public finance (financing by government regardless of whom an intervention is targeting) of disease treatment in a low- and middle-income country setting.
Two key findings seem to emerge FRP is critical when diseases are less lethal (eg, case fatality rates <1% or so), and quantitative valuation of inequality aversion across income groups matters greatly. We recommend the use of numerous sensitivity analyses and that all distributional health and financial outcomes be first presented in a disaggregated form (before potential subsequent aggregation).
Estimation approaches such as the one we propose provide explicit disaggregated considerations of equity, FRP, and poverty impact for the development of health sector policies, with high relevance for population-based preventive measures.
Estimation approaches such as the one we propose provide explicit disaggregated considerations of equity, FRP, and poverty impact for the development of health sector policies, with high relevance for population-based preventive measures.
This study aimed to demonstrate enhanced survival extrapolation methods using electronic health record-derived real-world data (RWD).
The study population included patients diagnosed of ER+/HER2- metastatic breast cancer who started first-line treatment with anastrozole or letrozole between November 18, 2014, and November 18, 2015. Two patient cohorts were constructed a clinical trial cohort from digitized MONARCH-3 clinical trial results and a RWD cohort from a deidentified electronic health record-derived database. RWD patients were weighted to trial baseline covariate distributions. Standard parametric approaches were applied to trial data and a "best-fit" model was selected. We demonstrate traditional and enhanced hybrid (pooling with weighted RWD at start, 75%, or end of trial) extrapolation approaches.
Observed and estimated 5-year progression-free survival (PFS) rates in extrapolating the trial control arm (n= 165) were comparable across all methods. Compared with the observed 5-year mean PFS in the RWD cohort (n= 118) of 20.4 months (95% confidence interval [CI] 16.9-23.8), there was some variation among studied methods. Best-fit standard parametric model (log-normal) had 5-year mean PFS of 21.3 months (95% CI 18.2-24.9), and for the hybrid methods in order of estimate conservativeness was start of trial (20.8 months; 95% CI 18.5-23.2), 75% of trial (21.3 months; 95% CI 18.1-24.5), and end of trial (21.8 months; 95% CI 18.8-25.2).
Our study leverages RWD to enhance long-term survival extrapolation. Future use cases should include applying patient eligibility criteria, weighting on baseline characteristics, and choice of time window to add RWD to trial data.
Our study leverages RWD to enhance long-term survival extrapolation. Future use cases should include applying patient eligibility criteria, weighting on baseline characteristics, and choice of time window to add RWD to trial data.
