What is a star worth to Medicare beneficiaries? A discrete choice experiment of hospital quality ratings.

Hospital quality ratings are widely available to help Medicare beneficiaries make an informed choice about where to receive care. However, how beneficiaries' trade-off between different quality domains (clinical outcomes, patient experience, safety, efficiency) and other considerations (out-of-pocket cost, travel distance) is not well understood. We sought to study how beneficiaries make trade-offs when choosing a hypothetical hospital. We administered an online survey that included a discrete choice experiment to a nationally representative sample of 1025 Medicare beneficiaries. On average, beneficiaries were willing to pay $1698 more for a hospital with a 1-star higher rating on clinical outcomes. This was over twice the value of the patient experience ($691) and safety ($615) domains and nearly 8 times the value of the efficiency domain ($218). We also found that the value of a 1-star improvement depends not only on the quality domain but also the baseline level of performance of the hospital. Generally, it is more valuable for low-performing hospitals to achieve average performance than for average hospitals to achieve excellence.

PDB NextGen Archive: centralizing access to integrated annotations and enriched structural information by the Worldwide Protein Data Bank.

The Protein Data Bank (PDB) is the global repository for public-domain experimentally determined 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation (or NextGen) Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the NextGen Archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, domain annotations from Pfam, SCOP2 and CATH databases and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date and easily accessible structural annotations. Database URL: http://www.wwpdb.org/ftp/pdb-nextgen-archive-site.

Barriers and Facilitators of Using R for Decision Analytic Modeling in Health Technology Assessment: Focus Group Results.

BACKGROUND AND OBJECTIVE: Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators. METHODS: We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes. RESULTS: Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures. CONCLUSIONS: There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R.

Implementation of an audit and feedback module targeting low-value clinical practices in a provincial trauma quality assurance program: a cost-effectiveness study.

BACKGROUND: Audit and Feedback (A&F) interventions based on quality indicators have been shown to lead to significant improvements in compliance with evidence-based care including de-adoption of low-value practices (LVPs). Our primary aim was to evaluate the cost-effectiveness of adding a hypothetical A&F module targeting LVPs for trauma admissions to an existing quality assurance intervention targeting high-value care and risk-adjusted outcomes. A secondary aim was to assess how certain A&F characteristics might influence its cost-effectiveness. METHODS: We conducted a cost-effectiveness analysis using a probabilistic static decision analytic model in the Québec trauma care continuum. We considered the Québec Ministry of Health perspective. Our economic evaluation compared a hypothetical scenario in which the A&F module targeting LVPs is implemented in a Canadian provincial trauma quality assurance program to a status quo scenario in which the A&F module is not implemented. In scenarios analyses we assessed the impact of A&F characteristics on its cost-effectiveness. Results are presented in terms of incremental costs per LVP avoided. RESULTS: Results suggest that the implementation of A&F module (Cost = $1,480,850; Number of LVPs = 6,005) is associated with higher costs and higher effectiveness compared to status quo (Cost = $1,124,661; Number of LVPs = 8,228). The A&F module would cost $160 per LVP avoided compared to status quo. The A&F module becomes more cost-effective with the addition of facilitation visits; more frequent evaluation; and when only high-volume trauma centers are considered. CONCLUSION: A&F module targeting LVPs is associated with higher costs and higher effectiveness than status quo and has the potential to be cost-effective if the decision-makers' willingness-to-pay is at least $160 per LVP avoided. This likely represents an underestimate of true ICER due to underestimated costs or missed opportunity costs. Results suggest that virtual facilitation visits, frequent evaluation, and implementing the module in high-volume centers can improve cost-effectiveness.

Restraint validation of biomolecular structures determined by NMR in the Protein Data Bank.

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NEF and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB restraint violation report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.

IHMCIF: An Extension of the PDBx/mmCIF Data Standard for Integrative Structure Determination Methods.

IHMCIF (github.com/ihmwg/IHMCIF) is a data information framework that supports archiving and disseminating macromolecular structures determined by integrative or hybrid modeling (IHM), and making them Findable, Accessible, Interoperable, and Reusable (FAIR). IHMCIF is an extension of the Protein Data Bank Exchange/macromolecular Crystallographic Information Framework (PDBx/mmCIF) that serves as the framework for the Protein Data Bank (PDB) to archive experimentally determined atomic structures of biological macromolecules and their complexes with one another and small molecule ligands (e.g., enzyme cofactors and drugs). IHMCIF serves as the foundational data standard for the PDB-Dev prototype system, developed for archiving and disseminating integrative structures. It utilizes a flexible data representation to describe integrative structures that span multiple spatiotemporal scales and structural states with definitions for restraints from a variety of experimental methods contributing to integrative structural biology. The IHMCIF extension was created with the benefit of considerable community input and recommendations gathered by the Worldwide Protein Data Bank (wwPDB) Task Force for Integrative or Hybrid Methods (wwpdb.org/task/hybrid). Herein, we describe the development of IHMCIF to support evolving methodologies and ongoing advancements in integrative structural biology. Ultimately, IHMCIF will facilitate the unification of PDB-Dev data and tools with the PDB archive so that integrative structures can be archived and disseminated through PDB.

Cancer incidence among Armenians in California.

INTRODUCTION: California is home to the largest population of Armenians in the United States. The historical categorization of Armenians as 'White' or 'Some Other Race' in population databases has likely masked cancer incidence patterns in this population. This is the first study considering cancer incidence among Armenians in California. METHODS: We used the Armenian Surname List and birthplace information in the California Cancer Registry to identify Armenians with cancer diagnosed during 1988-2019. We calculated proportional incidence ratios (PIR) among Armenians compared with non-Hispanic Whites (NHWs). As an exploratory analysis, we calculated incidence rate ratios (IRR) during 2006-2015 using Armenian population denominators from the American Community Survey (ACS). We selected PIR as our primary method given uncertainty regarding the use of ACS population estimates for rate calculations. RESULTS: There were 27,212 cancer diagnoses among Armenians in California, 13,754 among males and 13,458 among females. Armenian males had notably higher proportions of stomach (PIR = 2.39), thyroid (PIR = 1.45), and tobacco-related cancers including bladder (PIR = 1.53), colorectal (PIR = 1.29), and lung (PIR = 1.16) cancers. Higher proportional incidence of cancers including stomach (PIR = 3.24), thyroid (PIR = 1.47), and colorectal (PIR = 1.29) were observed among Armenian females. Exploratory IRR analyses showed higher stomach (IRR = 1.78), bladder (IRR = 1.13), and colorectal (IRR = 1.12) cancers among Armenian males and higher stomach (IRR = 2.54) cancer among Armenian females. CONCLUSION: We observed higher stomach, colorectal and thyroid cancer incidence among males and females, and tobacco-related cancers among males. Further research is needed to refine Armenian population estimates and understand and address risk factors associated with specific cancers among Armenians in California.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Restraint Validation of Biomolecular Structures Determined by NMR in the Protein Data Bank.

Biomolecular structure analysis from experimental NMR studies generally relies on restraints derived from a combination of experimental and knowledge-based data. A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing the establishment of uniform methods of model-vs-data structure validation against restraints and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data deposition-validation-biocuration system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints.

Cost-Effectiveness Analysis of a Community-Based Telewellness Weight Loss Program.

Introduction: The purpose of this study was to perform a cost-effectiveness analysis of the Koa Family Program, a community-based telewellness weight reduction intervention for overweight and obese women aged 21-45 years with low income. The Koa Family Program resulted in an approximately 8-pound weight loss as demonstrated in an RCT published previously. Methods: Estimates for the cost-effectiveness were derived from the prospective 25-week RCT including 70 women (25 kg/m2≤BMI<40 kg/m2). The analysis was from a program-funder perspective. Base case costs, as well as low and high scenario costs, were estimated from the services provided to intervention participants. The incremental costs were compared with the incremental effectiveness, with weight loss being the outcome of interest. Costs were in 2021 U.S. dollars. Cost-effectiveness was assessed using the incremental cost-effectiveness ratio and the incremental net benefit. The statistical uncertainty was characterized using an incremental net benefit by willingness-to-pay plot and a cost-effectiveness acceptability curve. Results: The base case average cost per participant was $564.39. The low and high scenario average costs per participant were $407.34 and $726.22, respectively. Over the 25-week study timeframe, participants lost an average 7.7 pounds, yielding a base case incremental cost-effectiveness ratio of approximately $73 per extra pound lost. The probability that the Koa Family Program is cost-effective is 90%, assuming a willingness-to-pay of $115 for a 1-pound reduction, and is 95%, assuming a willingness-to-pay of $140. Conclusions: The Koa Family Program provides good value with cost-effectiveness in line with other weight-loss interventions. This is a striking finding given that the Koa Family Program serves a more vulnerable population than is typically engaged in weight loss research studies.

Community recommendations on cryoEM data archiving and validation.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

A Tutorial on Net Benefit Regression for Real-World Cost-Effectiveness Analysis Using Censored Data from Randomized or Observational Studies.

HIGHLIGHTS: We illustrate the steps involved in carrying out cost-effectiveness analysis using net benefit regressions with possibly censored demo data by providing step-by-step guidance and code applied to a data set.We demonstrate the importance of these new methods by illustrating how naïve methods for handling censoring can lead to biased cost-effectiveness results.

SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.

Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.

Community recommendations on cryoEM data archiving and validation: Outcomes of a wwPDB/EMDB workshop on cryoEM data management, deposition and validation.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and consensus recommendations resulting from the workshop. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

Estimating the Cost-Effectiveness of HIV Self-Testing in the United States Using Net Benefit Regression.

BACKGROUND: Cost-effectiveness analysis of HIV self-testing using patient-level data from a randomized clinical trial can inform HIV prevention funding decisions. Cost-effectiveness analysis using net-benefit regression addresses the sampling uncertainty in the trial data and the variability of policymakers' willingness to pay (WTP). METHODS: We used published data from a 12-month longitudinal randomized clinical trial that enrolled 2665 men who had sex with men randomly assigned to the self-testing arm (participants receiving self-test kits) and control arm (participants receiving standard-of-care), and the self-testing arm identified 48 additional new HIV cases. We used net-benefit regression to investigate the cost-effectiveness of an HIV self-testing intervention, which compared the incremental cost per new HIV diagnosis with policymakers' WTP thresholds. We addressed the uncertainties in estimating the incremental cost and the policymakers' WTP per new diagnosis through the incremental net-benefit (INB) regression and cost-effectiveness acceptability curve (CEAC) analyses. RESULTS: From the health care provider's perspective, the INB analysis showed a positive net benefit of HIV self-testing compared with standard-of-care when policymakers' WTP per new HIV diagnosis was $9365 (95% confidence interval: $5700 to $25,500) or higher. The CEAC showed that the probability of HIV self-testing being cost-effective compared with standard-of-care was 58% and >99% at a WTP of $10 000 and $50 000 per new HIV diagnosis, respectively. CONCLUSION: The INB and CEAC analyses suggest that HIV self-testing has the potential to be cost-effective for relatively low values of policymakers' WTP.

Announcing the launch of Protein Data Bank China as an Associate Member of the Worldwide Protein Data Bank Partnership.

The Protein Data Bank (PDB) is the single global archive of atomic-level, three-dimensional structures of biological macromolecules experimentally determined by macromolecular crystallography, nuclear magnetic resonance spectroscopy or three-dimensional cryo-electron microscopy. The PDB is growing continuously, with a recent rapid increase in new structure depositions from Asia. In 2022, the Worldwide Protein Data Bank (wwPDB; https://www.wwpdb.org/) partners welcomed Protein Data Bank China (PDBc; https://www.pdbc.org.cn) to the organization as an Associate Member. PDBc is based in the National Facility for Protein Science in Shanghai which is associated with the Shanghai Advanced Research Institute of Chinese Academy of Sciences, the Shanghai Institute for Advanced Immunochemical Studies and the iHuman Institute of ShanghaiTech University. This letter describes the history of the wwPDB, recently established mechanisms for adding new wwPDB data centers and the processes developed to bring PDBc into the partnership.

Potential Avoidable Costs of Low-Value Clinical Practices in Acute Injury Care in an Integrated Canadian Provincial Trauma System.

This economic evaluation estimated the direct health care costs associated with 11 low-value clinical practices in acute trauma care in the integrated health care system of Quebec, Canada.

Minimum information guidelines for experiments structurally characterizing intrinsically disordered protein regions.

An unambiguous description of an experiment, and the subsequent biological observation, is vital for accurate data interpretation. Minimum information guidelines define the fundamental complement of data that can support an unambiguous conclusion based on experimental observations. We present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the parameters required for the wider scientific community to understand the findings of an experiment studying the structural properties of intrinsically disordered regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data. The MIADE guidelines will improve the interpretability of experimental results for data consumers, facilitate direct data submission, simplify data curation, improve data exchange among repositories and standardize the dissemination of the key metadata on an IDR experiment by IDR data sources.

Cancer survivors with sub-optimal patient-centered communication before and during the early COVID-19 pandemic.

OBJECTIVES: Patient-Centered Communication (PCC) is an essential element of patient-centered cancer care. Thus, this study aimed to examine the prevalence of and factors associated with optimal PCC among cancer survivors during COVID-19, which has been less studied. METHODS: We used national survey (Health Information National Trends Survey) among cancer survivors (n = 2579) to calculate the prevalence (%) of optimal PCC in all 6 PCC domains and overall (mean) by time (before COVID-19, 2017-19 vs. COVID-19, 2020). Multivariable logistic regressions were performed to explore the associations of sociodemographic (age, birth gender, race/ethnicity, income, education, usual source of care), and health status (general health, depression/anxiety symptoms, time since diagnosis, cancer type) factors with optimal PCC. RESULTS: The prevalence of optimal PCC decreased during COVID-19 overall, with the greatest decrease in managing uncertainty (7.3%). Those with no usual source of care (odd ratios, ORs =1.53-2.29), poor general health (ORs=1.40-1.66), depression/anxiety symptoms (ORs=1.73-2.17) were less likely to have optimal PCC in most domains and overall PCC. CONCLUSIONS: We observed that the decreased prevalence of optimal PCC, and identified those with suboptimal PCC during COVID-19. PRACTICE IMPLICATIONS: More efforts to raise awareness and improve PCC are suggested, including education and guidelines, given the decreased prevalence during this public health emergency.

Medicare Beneficiaries' Perspectives on the Quality of Hospital Care and Their Implications for Value-Based Payment.

Importance: Medicare's Hospital Value-Based Purchasing (HVBP) program adjusts hospital payments according to performance on 4 equally weighted quality domains: clinical outcomes, safety, patient experience, and efficiency. The assumption that performance on each domain is equally important may not reflect the preferences of Medicare beneficiaries. Objective: To estimate the relative importance (ie, weight) of the 4 quality domains in the HVBP program from the perspective of Medicare beneficiaries and the impact of using beneficiary value weights on incentive payments for hospitals enrolled in fiscal year 2019. Design, Setting, and Participants: An online survey was conducted in March 2022. A nationally representative sample of Medicare beneficiaries was recruited through Ipsos KnowledgePanel. Value weights were estimated using a discrete choice experiment that asked respondents to choose between 2 hospitals and indicate which they preferred. Hospitals were described using 6 attributes, including (1) clinical outcomes, (2) patient experience, (3) safety, (4) Medicare spending per patient, (5) distance, and (6) out-of-pocket cost. Data analysis was performed from April to November 2022. Main Outcomes and Measures: An effects-coded mixed logit regression model was used to estimate the relative importance of quality domains. HVBP program performance was linked to Medicare payment data in the Medicare Inpatient Hospitals by Provider and Service data set and hospital characteristics from the American Hospital Association Annual Survey data set, and the estimated impact of using beneficiary value weights on hospital payments was estimated. Results: A total of 1025 Medicare beneficiaries (518 women [51%]; 879 individuals [86%] aged ≥65 years; 717 White individuals [70%]) responded to the survey. A hospital's performance on clinical outcomes was most highly valued by beneficiaries (49%), followed by safety (22%), patient experience (21%), and efficiency (8%). Nearly twice as many hospitals would see a payment reduction when using beneficiary value weights than would see an increase (1830 vs 922 hospitals); however, the average net decrease was smaller (mean [SD], -$46 978 [$71 211]; median [IQR], -$24 628 [-$53 507 to -$9562]) than the comparable increase (mean [SD], $93 243 [$190 654]; median [IQR], $35 358 [$9906 to $97 348]). Hospitals seeing a net reduction with beneficiary value weights were more likely to be smaller, lower volume, nonteaching, and non-safety-net hospitals located in more deprived areas that served less complex patients. Conclusions and Relevance: This survey study of Medicare beneficiaries found that current HVBP program value weights do not reflect beneficiary preferences, suggesting that the use of beneficiary value weights may exacerbate disparities by rewarding larger, high-volume hospitals.