Cataloging health state utility estimates for Duchenne muscular dystrophy and related conditions.

BACKGROUND: Duchenne muscular dystrophy (DMD) is a genetic disease resulting in progressive muscle weakness, loss of ambulation, and cardiorespiratory complications. Direct estimation of health-related quality of life for patients with DMD is challenging, highlighting the need for proxy measures. This study aims to catalog and compare existing published health state utility estimates for DMD and related conditions. METHODS: Using two search strategies, relevant utilities were extracted from the Tufts Cost-Effectiveness Analysis Registry, including health states, utility estimates, and study and patient characteristics. Analysis One identified health states with comparable utility estimates to a set of published US patient population utility estimates for DMD. A minimal clinically important difference of ± 0.03 was applied to each DMD utility estimate to establish a range, and the registry was searched to identify other health states with associated utilities that fell within each range. Analysis Two used pre-defined search terms to identify health states clinically similar to DMD. Mapping was based on the degree of clinical similarity. RESULTS: Analysis One identified 4,308 unique utilities across 2,322 cost-effectiveness publications. The health states captured a wide range of acute and chronic conditions; 34% of utility records were extrapolated for US populations (n = 1,451); 1% were related to pediatric populations (n = 61). Analysis Two identified 153 utilities with health states clinically similar to DMD. The median utility estimates varied among identified health states. Health states similar to the early non-ambulatory DMD phase exhibited the greatest difference between the median estimate of the sample (0.39) and the existing estimate from published literature (0.21). CONCLUSIONS: When available estimates are limited, using novel search strategies to identify utilities of clinically similar conditions could be an approach for overcoming the information gap. However, it requires careful evaluation of the utility instruments, tariffs, and raters (proxy or self).

Developing Criteria for Health Economic Quality Evaluation Tool.

OBJECTIVES: Because existing publication guidelines and checklists have limitations when used to assess the quality of cost-effectiveness analysis, we developed a novel quality assessment tool for cost-effectiveness analyses, differentiating methods and reporting quality and incorporating the relative importance of different quality attributes. METHODS: We defined 15 quality domains from a scoping review and identified 72 methods and reporting quality attributes (36 each). After designing a best-worst scaling survey, we fielded an online survey to researchers and practitioners to estimate the relative importance of the attributes in February 2021. We analyzed the survey data using a sequential conditional logit model. The final tool included 48 quality attributes deemed most important for assessing methods and reporting quality (24 each), accompanied by a free and web-based scoring system. RESULTS: A total of 524 participants completed the methodology section, and 372 completed both methodology and reporting sections. Quality attributes pertaining to the "modeling" and "data inputs and evidence synthesis" domains were deemed most important for methods quality, including "structure of the model reflects the underlying condition and intervention's impact" and "model validation is conducted." Quality attributes pertaining to "modeling" and "Intervention/comparator(s)" domains were considered most important for reporting quality, including "model descriptions are detailed enough for replication." Despite its growing prominence, "equity considerations" were not deemed as important as other quality attributes. CONCLUSIONS: The Criteria for Health Economic Quality Evaluation tool allows users to differentiate methods and reporting as well as quantifies the relative importance of quality attributes. Alongside other considerations, it could help assess and improve the quality of cost-effectiveness evidence to inform value-based decisions.

An Evidence Review of Low-Value Care Recommendations: Inconsistency and Lack of Economic Evidence Considered.

BACKGROUND: Low-value care, typically defined as health services that provide little or no benefit, has potential to cause harm, incur unnecessary costs, and waste limited resources. Although evidence-based guidelines identifying low-value care have increased, the guidelines differ in the type of evidence they cite to support recommendations against its routine use. OBJECTIVE: We examined the evidentiary rationale underlying recommendations against low-value interventions. DESIGN: We identified 1167 "low-value care" recommendations across five US organizations: the US Preventive Services Task Force (USPSTF), the "Choosing Wisely" Initiative, American College of Physicians (ACP), American College of Cardiology/American Heart Association (ACC/AHA), and American Society of Clinical Oncology (ASCO). For each recommendation, we classified the reported evidentiary rationale into five groups: (1) low economic value; (2) no net clinical benefit; (3) little or no absolute clinical benefit; (4) insufficient evidence; (5) no reason mentioned. We further investigated whether any cited or otherwise available cost-effectiveness evidence was consistent with conventional low economic value benchmarks (e.g., exceeding $100,000 per quality-adjusted life-year). RESULTS: Of the identified low-value care recommendations, Choosing Wisely contributed the most (N=582, 50%), followed by ACC/AHA (N=250, 21%). The services deemed "low value" differed substantially across organizations. "No net clinical benefit" (N=428, 37%) and "little or no clinical benefit" (N=296, 25%) were the most commonly reported reasons for classifying an intervention as low value. Consideration of economic value was less frequently reported (N=171, 15%). When relevant cost-effectiveness studies were available, their results were mostly consistent with low-value care recommendations. CONCLUSIONS: Our study found that evidentiary rationales for low-value care vary substantially, with most recommendations relying on clinical evidence. Broadening the evidence base to incorporate cost-effectiveness evidence can help refine the definition of "low-value" care to reflect whether an intervention's costs are worth the benefits. Developing a consensus grading structure on the strength and evidentiary rationale may help improve de-implementation efforts for low-value care.

State-Level Variation In Low-Value Care For Commercially Insured And Medicare Advantage Populations.

Low-value care is a major source of health care inefficiency in the US. Our analysis of 2009-19 administrative claims data from OptumLabs Data Warehouse found that low-value care and associated spending remain prevalent among commercially insured and Medicare Advantage enrollees. The aggregated prevalence of twenty-three low-value services was 1,920 per 100,000 eligible enrollees, which amounted to $3.7 billion in wasteful expenditures during the study period. State-level variation in spending was greater than variation in utilization, and much of the variation in spending was driven by differences in average procedure prices. If the average price for twenty-three low-value services among the top ten states in spending were set to the national average, their spending would decrease by 19.8 percent (from $735,000 to $590,000 per 100,000 eligible enrollees). State-level actions to improve the routine measurement and reporting of low-value care could identify sources of variation and help design state-specific policies that lead to better patient-centered outcomes, enhanced equity, and more efficient spending.

Bridging the gap: aligning economic research with disease burden.

INTRODUCTION: Cost-effectiveness analysis (CEA) is critical for identifying high-value interventions that address significant unmet need. This study examines whether CEA study volume is proportionate to the burden associated with 21 major disease categories. METHODS: We searched the Tufts Medical Center CEA and Global Health CEA Registries for studies published between 2010 and 2019 that measured cost per quality-adjusted life-year or cost per disability-adjusted life-year (DALY). Stratified by geographical region and country income level, the relationship between literature volume and disease burden (as measured by 2019 Global Burden of Disease estimates of population DALYs) was analysed using ordinary least squares linear regression. Additionally, the number of CEAs per intervention deemed 'essential' for universal health coverage by the Disease Control Priorities Network was assessed to evaluate how many interventions are supported by cost-effectiveness evidence. RESULTS: The results located below the regression line but with relatively high burden suggested disease areas that were 'understudied' compared with expected study volume. Understudied disease areas varied by region. Higher-income and upper-middle-income country (HUMIC) CEA volume for non-communicable diseases (eg, mental/behavioural disorders) was 100-fold higher than that in low-income and lower-middle-income countries (LLMICs). LLMIC study volume remained concentrated in HIV/AIDS as well as other communicable and neglected tropical diseases. Across 60 essential interventions, only 33 had any supporting CEA evidence, and only 21 had a decision context involving a low-income or middle-income country. With the exception of one intervention, available CEA evidence revealed the 21 interventions to be cost-effective, with base-case findings less than three times the GDP per capita. CONCLUSION: Our analysis highlights disease areas that require significant policy attention. Research gaps for highly prevalent, lethal or disabling diseases, as well as essential interventions may be stifling potential efficiency gains. Large research disparities between HUMICs and LLMICs suggest funding opportunities for improving allocative efficiency in LLMIC health systems.