Self-Reported COVID-19 Vaccine and Booster Acceptance and Hesitancy Among Autistic Adults in Pennsylvania: Cross-Sectional Analysis of Survey Data.

BACKGROUND: The autistic population is rapidly increasing; meanwhile, autistic adults face disproportionate risks for adverse COVID-19 outcomes. Limited research indicates that autistic individuals have been accepting of initial vaccination, but research has yet to document this population's perceptions and acceptance of COVID-19 boosters. OBJECTIVE: This study aims to identify person-level and community characteristics associated with COVID-19 vaccination and booster acceptance among autistic adults, along with self-reported reasons for their stated preferences. Understanding this information is crucial in supporting this vulnerable population given evolving booster guidelines and the ending of the public health emergency for the COVID-19 pandemic. METHODS: Data are from a survey conducted in Pennsylvania from April 11 to September 12, 2022. Demographic characteristics, COVID-19 experiences, and COVID-19 vaccine decisions were compared across vaccination status groups. Chi-square analyses and 1-way ANOVA were conducted to test for significant differences. Vaccination reasons were ranked by frequency; co-occurrence was identified using phi coefficient correlation plots. RESULTS: Most autistic adults (193/266, 72.6%) intended to receive or received the vaccine and booster, 15% (40/266) did not receive or intend to receive any vaccine, and 12.4% (33/266) received or intended to receive the initial dose but were hesitant to accept booster doses. Reasons for vaccine acceptance or hesitancy varied by demographic factors and COVID-19 experiences. The most significant were previously contracting COVID-19, desire to access information about COVID-19, and discomfort with others not wearing a mask (all P=.001). County-level factors, including population density (P=.02) and percentage of the county that voted for President Biden (P=.001) were also significantly associated with differing vaccination acceptance levels. Reasons for accepting the initial COVID-19 vaccine differed among those who were or were not hesitant to accept a booster. Those who accepted a booster were more likely to endorse protecting others and trusting the vaccine as the basis for their acceptance, whereas those who were hesitant about the booster indicated that their initial vaccine acceptance came from encouragement from someone they trusted. Among the minority of those hesitant to any vaccination, believing that the vaccine was unsafe and would make them feel unwell were the most often reported reasons. CONCLUSIONS: Intention to receive or receiving the COVID-19 vaccination and booster was higher among autistic adults than the population that received vaccines in Pennsylvania. Autistic individuals who accepted vaccines prioritized protecting others, while autistic individuals who were vaccine hesitant had safety concerns about vaccines. These findings inform public health opportunities and strategies to further increase vaccination and booster rates among generally accepting autistic adults, to better support the already strained autism services and support system landscape. Vaccination uptake could be improved by leveraging passive information diffusion to combat vaccination misinformation among those not actively seeking COVID-19 information to better alleviate safety concerns.

Intervention Optimization: A Paradigm Shift and Its Potential Implications for Clinical Psychology.

To build a coherent knowledge base about what psychological intervention strategies work, develop interventions that have positive societal impact, and maintain and increase this impact over time, it is necessary to replace the classical treatment package research paradigm. The multiphase optimization strategy (MOST) is an alternative paradigm that integrates ideas from behavioral science, engineering, implementation science, economics, and decision science. MOST enables optimization of interventions to strategically balance effectiveness, affordability, scalability, and efficiency. In this review we provide an overview of MOST, discuss several experimental designs that can be used in intervention optimization, consider how the investigator can use experimental results to select components for inclusion in the optimized intervention, discuss the application of MOST in implementation science, and list future issues in this rapidly evolving field. We highlight the feasibility of adopting this new research paradigm as well as its potential to hasten the progress of psychological intervention science.

Optimizing Interventions for Equitability: Some Initial Ideas.

Interventions (including behavioral, biobehavioral, biomedical, and social-structural interventions) hold tremendous potential not only to improve public health overall but also to reduce health disparities and promote health equity. In this study, we introduce one way in which interventions can be optimized for health equity in a principled fashion using the multiphase optimization strategy (MOST). Specifically, we define intervention equitability as the extent to which the health benefits provided by an intervention are distributed evenly versus concentrated among those who are already advantaged, and we suggest that, if intervention equitability is acknowledged to be a priority, then equitability should be a key criterion that is balanced with other criteria (effectiveness overall, as well as affordability, scalability, and/or efficiency) in intervention optimization. Using a hypothetical case study and simulated data, we show how MOST can be applied to achieve a strategic balance that incorporates equitability. We also show how the composition of an optimized intervention can differ when equitability is considered versus when it is not. We conclude with a vision for next steps to build on this initial foray into optimizing interventions for equitability.

Racial and ethnic disparities in emergency department transfers to public hospitals.

OBJECTIVE: To examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences. DATA SOURCES AND STUDY SETTING: ED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010-2019); American Hospital Association Annual Survey (2009-2018). STUDY DESIGN: Logistic regression examined race/ethnicity and payer on the likelihood of transfer to a public hospital among transferred ED patients. The base model was controlled for patient and hospital characteristics and year fixed effects. Models II and III added urbanicity and hospital referral region (HRR), respectively. Model IV used hospital fixed effects, which compares patients within the same hospital. Models V and VI stratified Model IV by payer and condition, respectively. Conditions were classified as emergency care sensitive conditions (ECSCs), where transfer is protocolized, and non-ECSCs. We reported marginal effects at the means. DATA COLLECTION/EXTRACTION METHODS: We examined 1,265,588 adult ED patients transferred from 187 hospitals. PRINCIPAL FINDINGS: Black patients were more likely to be transferred to public hospitals compared with White patients in all models except ECSC patients within the same initial hospital (except trauma). Black patients were 0.5-1.3 percentage points (pp) more likely to be transferred to public hospitals than White patients in the same hospital with the same payer. In the base model, Hispanic patients were more likely to be transferred to public hospitals compared with White patients, but this difference reversed after controlling for HRR. Hispanic patients were - 0.6 pp to -1.2 pp less likely to be transferred to public hospitals than White patients in the same hospital with the same payer. CONCLUSIONS: Large population-level differences in whether ED patients of different races/ethnicities were transferred to public hospitals were largely explained by hospital market and the initial hospital, suggesting that they may play a larger role in explaining differences in transfer to public hospitals, compared with other external factors.

Using decision analysis for intervention value efficiency to select optimized interventions in the multiphase optimization strategy.

OBJECTIVE: Optimizing multicomponent behavioral and biobehavioral interventions presents a complex decision problem. To arrive at an intervention that is both effective and readily implementable, it may be necessary to weigh effectiveness against implementability when deciding which components to select for inclusion. Different components may have differential effectiveness on an array of outcome variables. Moreover, different decision-makers will approach this problem with different objectives and preferences. Recent advances in decision-making methodology in the multiphase optimization strategy (MOST) have opened new possibilities for intervention scientists to optimize interventions based on a wide variety of decision-maker preferences, including those that involve multiple outcome variables. In this study, we introduce decision analysis for intervention value efficiency (DAIVE), a decision-making framework for use in MOST that incorporates these new decision-making methods. We apply DAIVE to select optimized interventions based on empirical data from a factorial optimization trial. METHOD: We define various sets of hypothetical decision-maker preferences, and we apply DAIVE to identify optimized interventions appropriate to each case. RESULTS: We demonstrate how DAIVE can be used to make decisions about the composition of optimized interventions and how the choice of optimized intervention can differ according to decision-maker preferences and objectives. CONCLUSIONS: We offer recommendations for intervention scientists who want to apply DAIVE to select optimized interventions based on data from their own factorial optimization trials. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reassessing surgical guidelines for papillary thyroid cancer impact on survival: Expanding indications for lobectomy.

BACKGROUND: Comparisons of lobectomy versus total thyroidectomy for papillary thyroid cancer have not addressed significant threats to valid inference from observational data. The purpose of this study was to compare survival after lobectomy versus total thyroidectomy for papillary thyroid cancer while addressing bias from unmeasured confounding. METHODS: This retrospective cohort study included 84,300 patients treated with lobectomy or total thyroidectomy for papillary thyroid cancer in the National Cancer Database from 2004 to 2017. The primary outcome was overall survival evaluated by flexible parametric survival models and inverse probability weighting on the propensity score. Bias from unobserved confounding was assessed using two-way deterministic sensitivity analysis and 2-stage least squares regression. RESULTS: The median age of treated patients was 48 years (interquartile range, 37-59), 78% were women, and 76% were white. We found no statistically significant differences in overall survival or 5- and 10-year survival between patients treated with lobectomy or total thyroidectomy. Additionally, we found no statistically significant difference in survival by subgroups, including tumor size (<4 cm or ≥4 cm), age (<65 or ≥65), or estimated risk of mortality. Sensitivity analyses suggested that an unmeasured confounder would need to have an extremely large effect to change the primary finding. CONCLUSION: This is the first study to compare lobectomy and total thyroidectomy outcomes while adjusting for and quantifying the potential effects of unmeasured confounding variables on observational data. The findings suggest that total thyroidectomy is unlikely to offer a survival advantage over lobectomy regardless of tumor size, patient age, or overall risk of death.

Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations.

BACKGROUND: Missing data in costs and/or health outcomes and in confounding variables can create bias in the inference of health economics and outcomes research studies, which in turn can lead to inappropriate policies. Most of the literature focuses on handling missing data in randomized controlled trials, which are not necessarily always the data used in health economics and outcomes research. OBJECTIVES: We aimed to provide an overview on missing data issues and how to address incomplete data and report the findings of a systematic literature review of methods used to deal with missing data in health economics and outcomes research studies that focused on cost, utility, and patient-reported outcomes. METHODS: A systematic search of papers published in English language until the end of the year 2020 was carried out in PubMed. Studies using statistical methods to handle missing data for analyses of cost, utility, or patient-reported outcome data were included, as were reviews and guidance papers on handling missing data for those outcomes. The data extraction was conducted with a focus on the context of the study, the type of missing data, and the methods used to tackle missing data. RESULTS: From 1433 identified records, 40 papers were included. Thirteen studies were economic evaluations. Thirty studies used multiple imputation with 17 studies using multiple imputation by chained equation, while 15 studies used a complete-case analysis. Seventeen studies addressed missing cost data and 23 studies dealt with missing outcome data. Eleven studies reported a single method while 20 studies used multiple methods to address missing data. CONCLUSIONS: Several health economics and outcomes research studies did not offer a justification of their approach of handling missing data and some used only a single method without a sensitivity analysis. This systematic literature review highlights the importance of considering the missingness mechanism and including sensitivity analyses when planning, analyzing, and reporting health economics and outcomes research studies.

A posterior expected value approach to decision-making in the multiphase optimization strategy for intervention science.

In current practice, intervention scientists applying the multiphase optimization strategy (MOST) with a 2k factorial optimization trial use a component screening approach (CSA) to select intervention components for inclusion in an optimized intervention. In this approach, scientists review all estimated main effects and interactions to identify the important ones based on a fixed threshold, and then base decisions about component selection on these important effects. We propose an alternative posterior expected value approach based on Bayesian decision theory. This new approach aims to be easier to apply and more readily extensible to a variety of intervention optimization problems. We used Monte Carlo simulation to evaluate the performance of a posterior expected value approach and CSA (automated for simulation purposes) relative to two benchmarks: random component selection, and the classical treatment package approach. We found that both the posterior expected value approach and CSA yielded substantial performance gains relative to the benchmarks. We also found that the posterior expected value approach outperformed CSA modestly but consistently in terms of overall accuracy, sensitivity, and specificity, across a wide range of realistic variations in simulated factorial optimization trials. We discuss implications for intervention optimization and promising future directions in the use of posterior expected value to make decisions in MOST. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The influence of anatomic stage and receptor status on first recurrence for breast cancer within 5 years (AFT-01).

BACKGROUND: Risk-stratified follow-up guidelines that account for the absolute risk and timing of recurrence may improve the quality and efficiency of breast cancer follow-up. The objective of this study was to assess the relationship of anatomic stage and receptor status with timing of the first recurrence for patients with local-regional breast cancer and generate risk-stratified follow-up recommendations. METHODS: The authors conducted a secondary analysis of 8007 patients with stage I-III breast cancer who enrolled in nine Alliance legacy clinical trials from 1997 to 2013 (ClinicalTrials.gov identifier NCT02171078). Patients who received standard-of-care therapy were included. Patients who were missing stage or receptor status were excluded. The primary outcome was days from the earliest treatment start date to the date of first recurrence. The primary explanatory variable was anatomic stage. The analysis was stratified by receptor type. Cox proportional-hazards regression models produced cumulative probabilities of recurrence. A dynamic programming algorithm approach was used to optimize the timing of follow-up intervals based on the timing of recurrence events. RESULTS: The time to first recurrence varied significantly between receptor types (p < .0001). Within each receptor type, stage influenced the time to recurrence (p < .0001). The risk of recurrence was highest and occurred earliest for estrogen receptor (ER)-negative/progesterone receptor (PR)-negative/Her2neu-negative tumors (stage III; 5-year probability of recurrence, 45.5%). The risk of recurrence was lower for ER-positive/PR-positive/Her2neu-positive tumors (stage III; 5-year probability of recurrence, 15.3%), with recurrences distributed over time. Model-generated follow-up recommendations by stage and receptor type were created. CONCLUSIONS: This study supports considering both anatomic stage and receptor status in follow-up recommendations. The implementation of risk-stratified guidelines based on these data has the potential to improve the quality and efficiency of follow-up.

A Conceptual Framework for Optimizing the Equity of Hospital-Based Emergency Care: The Structure of Hospital Transfer Networks.

Policy Points Current pay-for-performance and other payment policies ignore hospital transfers for emergency conditions, which may exacerbate disparities. No conceptual framework currently exists that offers a patient-centered, population-based perspective for the structure of hospital transfer networks. The hospital transfer network equity-quality framework highlights the external and internal factors that determine the structure of hospital transfer networks, including structural inequity and racism. CONTEXT: Emergency care includes two key components: initial stabilization and transfer to a higher level of care. Significant work has focused on ensuring that local facilities can stabilize patients. However, less is understood about transfers for definitive care. To better understand how transfer network structure impacts population health and equity in emergency care, we proposea conceptual framework, the hospital transfer network equity-quality model (NET-EQUITY). NET-EQUITY can help optimize population outcomes, decrease disparities, and enhance planning by supporting a framework for understanding emergency department transfers. METHODS: To develop the NET-EQUITY framework, we synthesized work on health systems and quality of health care (Donabedian, the Institute of Medicine, Ferlie, and Shortell) and the research framework of the National Institute on Minority Health and Health Disparities with legal and empirical research. FINDINGS: The central thesis of our framework is that the structure of hospital transfer networks influences patient outcomes, as defined by the Institute of Medicine, which includes equity. The structure of hospital transfer networks is shaped by internal and external factors. The four main external factors are the regulatory, economic environment, provider, and sociocultural and physical/built environment. These environments all implicate issues of equity that are important to understand to foster an equitable population-based system of emergency care. The framework highlights external and internal factors that determine the structure of hospital transfer networks, including structural racism and inequity. CONCLUSIONS: The NET-EQUITY framework provides a patient-centered, equity-focused framework for understanding the health of populations and how the structure of hospital transfer networks can influence the quality of care that patients receive.

Local/Regional Recurrence Rates After Breast-Conserving Therapy in Patients Enrolled in Legacy Trials of the Alliance for Clinical Trials in Oncology (AFT-01).

OBJECTIVE: We sought to evaluate local/regional recurrence rates after breast-conserving surgery in a cohort of patients enrolled in legacy trials of the Alliance for Clinical Trials in Oncology and to evaluate variation in recurrence rates by receptor subtype. BACKGROUND: Multiple randomized controlled trials have demonstrated equivalent survival between breast conservation and mastectomy, albeit with higher local/regional recurrence rates after breast conservation. However, absolute rates of local/regional recurrence have been declining with multi-modality treatment. METHODS: Data from 5 Alliance for Clinical Trials in Oncology legacy trials that enrolled women diagnosed with breast cancer between 1997 and 2010 were included. Women who underwent breast-conserving surgery and standard systemic therapies (n=4,404) were included. Five-year rates of local/regional recurrence were estimated from Kaplan-Meier curves. Patients were censored at the time of distant recurrence (if recorded as the first recurrence), death, or last follow-up. Multivariable Cox proportional hazards models were used to identify factors associated with time to local/regional recurrence, including patient age, tumor size, lymph node status, and receptor subtype. RESULTS: Overall 5-year recurrence was 4.6% (95% CI=4.0-5.4%). Five-year recurrence rates were lowest in those with ER+ or PR+ tumors (Her2+ 3.4% [95% CI 2.0-5.7%], Her2- 4.0% [95% CI 3.2-4.9%]) and highest in the triple-negative subtype (7.1% [95% CI 5.4-9.3%]). On multivariable analysis, increasing nodal involvement and triple-negative subtype were positively associated with recurrence ( P <0.0001). CONCLUSIONS: Rates of local/regional recurrence after breast conservation in women with breast cancer enrolled in legacy trials of the Alliance for Clinical Trials in Oncology are significantly lower than historic estimates. This data can better inform patient discussions and surgical decision-making.

Surveillance Imaging vs Symptomatic Recurrence Detection and Survival in Stage II-III Breast Cancer (AFT-01).

BACKGROUND: Guidelines for follow-up after locoregional breast cancer treatment recommend imaging for distant metastases only in the presence of patient signs and/or symptoms. However, guidelines have not been updated to reflect advances in imaging, systemic therapy, or the understanding of biological subtype. We assessed the association between mode of distant recurrence detection and survival. METHODS: In this observational study, a stage-stratified random sample of women with stage II-III breast cancer in 2006-2007 and followed through 2016 was selected, including up to 10 women from each of 1217 Commission on Cancer facilities (n = 10 076). The explanatory variable was mode of recurrence detection (asymptomatic imaging vs signs and/or symptoms). The outcome was time from initial cancer diagnosis to death. Registrars abstracted scan type, intent (cancer-related vs not, asymptomatic surveillance vs not), and recurrence. Data were merged with each patient's National Cancer Database record. RESULTS: Surveillance imaging detected 23.3% (284 of 1220) of distant recurrences (76.7%, 936 of 1220 by signs and/or symptoms). Based on propensity-weighted multivariable Cox proportional hazards models, patients with asymptomatic imaging compared with sign and/or symptom detected recurrences had a lower risk of death if estrogen receptor (ER) and progesterone receptor (PR) negative, HER2 negative (triple negative; hazard ratio [HR] = 0.73, 95% confidence interval [CI] = 0.54 to 0.99), or HER2 positive (HR = 0.51, 95% CI = 0.33 to 0.80). No association was observed for ER- or PR-positive, HER2-negative (HR = 1.14, 95% CI = 0.91 to 1.44) cancers. CONCLUSIONS: Recurrence detection by asymptomatic imaging compared with signs and/or symptoms was associated with lower risk of death for triple-negative and HER2-positive, but not ER- or PR-positive, HER2-negative cancers. A randomized trial is warranted to evaluate imaging surveillance for metastases results in these subgroups.

Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force.

Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.

Self-reported COVID-19 vaccination acceptance and hesitancy among autistic adults.

Identifying factors associated with COVID-19 vaccination acceptance among vulnerable groups, including autistic individuals, can increase vaccination rates and support public health. The purpose of this study was to determine differences among autistic adults who reported COVID-19 vaccination acceptance from those who did not. In this study we describe COVID-19 vaccination status and self-reported preferences among autistic adults and identify related factors. Vaccine accepters were more likely to report increased loneliness during COVID-19, lived in more populous counties (p = 0.02), and lived in counties won by President Biden in the 2020 US presidential election (p < 0.001). Positive correlations were found between desire to protect others, concern about contracting COVID-19, and trusting vaccine safety (p < 0.001). Concern about vaccine safety was common among the vaccine hesitant, while lack of concern about COVID-19 overall was not. Identifying health promotion strategies based on self-reported, lived experiences about COVID-19 among vulnerable groups is key for public health impact.

End-of-Life Care in Patients Undergoing Allogeneic Hematopoietic Cell Transplantation.

Background: Patients receiving allogeneic hematopoietic cell transplantation (HCT) have high morbidity and mortality risk, but literature is limited on factors associated with end-of-life (EOL) care intensity. Objectives: Describe EOL care in patients after allogeneic HCT and examine association of patient and clinical characteristics with intense EOL care. Design: Retrospective chart review. Setting/Subjects: A total of 113 patients who received allogeneic HCT at Mayo Clinic Arizona between 2013 and 2017 and died before November 2019. Measurements: A composite EOL care intensity measure included five markers: (1) no hospice enrollment, (2) intensive care unit (ICU) stay in the last month, (3) hospitalization >14 days in last month, (4) chemotherapy use in the last two weeks, and (5) cardiopulmonary resuscitation, hemodialysis, or mechanical ventilation in the last week of life. Multivariable logistic regression modeling assessed associations of having ≥1 intensity marker with sociodemographic and disease characteristics, palliative care consultation, and advance directive documentation. Results: Seventy-six percent of patients in our cohort had ≥1 intensity marker, with 43% receiving ICU care in the last month of life. Median hospital stay in the last month of life was 15 days. Sixty-five percent of patients died in hospice; median enrollment was 4 days. Patients with higher education were less likely to have ≥1 intensity marker (odds ratio 0.28, p = 0.02). Patients who died >100 days after HCT were less likely to have ≥1 intensity marker than patients who died ≤100 days of HCT (p = 0.04). Conclusions: Death within 100 days of HCT and lower educational attainment were associated with higher likelihood of intense EOL care.

One view of the next decade of research on behavioral and biobehavioral approaches to cancer prevention and control: intervention optimization.

As a new decade begins, we propose that the time is right to reexamine current methods and procedures and look for opportunities to accelerate progress in cancer prevention and control. In this article we offer our view of the next decade of research on behavioral and biobehavioral interventions for cancer prevention and control. We begin by discussing and questioning several implicit conventions. We then briefly introduce an alternative research framework: the multiphase optimization strategy (MOST). MOST, a principled framework for intervention development, optimization, and evaluation, stresses not only intervention effectiveness, but also intervention affordability, scalability, and efficiency. We review some current limitations of MOST along with future directions for methodological work in this area, and suggest some changes in the scientific environment we believe would permit wider adoption of intervention optimization. We propose that wider adoption of intervention optimization would have a positive impact on development and successful implementation of interventions for cancer prevention and control and on intervention science more broadly, including accumulation of a coherent base of knowledge about what works and what does not; establishment of an empirical basis for adaptation of interventions to different settings with different levels and types of resources; and, in the long run, acceleration of progress from Stage 0 to Stage V in the National Institutes of Health Model of Stages of Intervention Development.

Estimating (quality-adjusted) life-year losses associated with deaths: With application to COVID-19.

Many epidemiological models of the COVID-19 pandemic have focused on preventing deaths. Questions have been raised as to the frailty of those succumbing to the COVID-19 infection. In this paper we employ standard life table methods to illustrate how the potential quality-adjusted life-year (QALY) losses associated with COVID-19 fatalities could be estimated, while adjusting for comorbidities in terms of impact on both mortality and quality of life. Contrary to some suggestions in the media, we find that even relatively elderly patients with high levels of comorbidity can still lose substantial life years and QALYs. The simplicity of the method facilitates straightforward international comparisons as the pandemic evolves. In particular, we compare five different countries and show that differences in the average QALY losses for each COVID-19 fatality is driven mainly by differing age distributions for those dying of the disease.

A Health Opportunity Cost Threshold for Cost-Effectiveness Analysis in the United States.

BACKGROUND: Cost-effectiveness analysis is an important tool for informing treatment coverage and pricing decisions, yet no consensus exists about what threshold for the incremental cost-effectiveness ratio (ICER) in dollars per quality-adjusted life-year (QALY) gained indicates whether treatments are likely to be cost-effective in the United States. OBJECTIVE: To estimate a U.S. cost-effectiveness threshold based on health opportunity costs. DESIGN: Simulation of short-term mortality and morbidity attributable to persons dropping health insurance due to increased health care expenditures passed though as premium increases. Model inputs came from demographic data and the literature; 95% uncertainty intervals (UIs) were constructed. SETTING: Population-based. PARTICIPANTS: Simulated cohort of 100 000 individuals from the U.S. population with direct-purchase private health insurance. MEASUREMENTS: Number of persons dropping insurance coverage, number of additional deaths, and QALYs lost from increased mortality and morbidity, all per increase of $10 000 000 (2019 U.S. dollars) in population treatment cost. RESULTS: Per $10 000 000 increase in health care expenditures, 1860 persons (95% UI, 1080 to 2840 persons) were simulated to become uninsured, causing 5 deaths (UI, 3 to 11 deaths), 81 QALYs (UI, 40 to 170 QALYs) lost due to death, and 15 QALYs (UI, 6 to 32 QALYs) lost due to illness; this implies a cost-effectiveness threshold of $104 000 per QALY (UI, $51 000 to $209 000 per QALY) in 2019 U.S. dollars. Given available evidence, there is about 14% probability that the threshold exceeds $150 000 per QALY and about 48% probability that it lies below $100 000 per QALY. LIMITATIONS: Estimates were sensitive to inputs, most notably the effects of losing insurance on mortality and of premium increases on becoming uninsured. Health opportunity costs may vary by population. Nonhealth opportunity costs were excluded. CONCLUSION: Given current evidence, treatments with ICERs above the range $100 000 to $150 000 per QALY are unlikely to be cost-effective in the United States. PRIMARY FUNDING SOURCE: None.

60 Years after Kefauver: Household income required to buy prescription drugs in the United States and abroad.

BACKGROUND: Assessing drug prices relative to income in the US compared to other Organization for Economic Co-Operation and Development (OECD) countries provides context for policymakers seeking to improve access and affordability. METHODS: Using current drug p. rice and income data, we recreate a historical analysis presented in 1960 to the Senate Subcommittee on Antitrust and Monopoly led by Sen. Estes Kefauver. We identified frequently prescribed generic and brand name drugs for US and international comparison by drug price category (low-price generics, mid-price brands, and high-price specialty brands) as a function of income. We further extend our analysis to consider US prices relative to the current Federal Poverty Level (FPL). RESULTS: For the low-price drugs, all fell below 1% of all of the US income levels presented. Mid-price drugs were below 10% of income for those at the US median household income level but approached 30% of income for those at the FPL. High-price drugs varied greatly, reaching over 600% FPL for one product. CONCLUSIONS: Americans receive bargain prices on par with international comparators for many low-priced generics drugs. For commonly used mid-priced drugs or high-priced specialty products, whether or not drug prices are considered a bargain in the US compared to international markets may depend on individual income. External reference pricing policies may help inform the negotiation for some drug prices, but affordability may still be limited for lower wage earners.