Modeling the Impact of COVID-19 Mitigation Strategies in Pennsylvania, USA.

Purpose. To estimate the impact on mortality of nonpharmaceutical interventions (NPIs) implemented early in the COVID-19 pandemic. Methods. We implemented an agent-based modified SEIR model of COVID-19, calibrated to match death numbers reported in Pennsylvania from January 2020 to April 2021 and including representations of NPIs implemented in Pennsylvania. To investigate the impact of these strategies, we ran the calibrated model with no interventions and with varying combinations, timings, and levels of interventions. Results. The model closely replicated death outcomes data for Pennsylvania. Without NPIs, deaths in the early months of the pandemic were estimated to be much higher (67,718 deaths compared to actual 6,969). Voluntary interventions alone were relatively ineffective at decreasing mortality. Delaying implementation of interventions led to higher deaths (∼9,000 more deaths with just a 1-week delay). School closure was insufficient as a single intervention but was an important part of a combined intervention strategy. Conclusions. NPIs were effective at reducing deaths early in the COVID-19 pandemic. Agent-based models can incorporate substantial detail on infectious disease spread and the impact of mitigations. Policy Implications. The model supports the importance and effectiveness of NPIs to decrease morbidity from respiratory pathogens. This is particularly important for emerging pathogens for which no vaccines or treatments exist, but such strategies are applicable to a variety of respiratory pathogens. Highlights: Nonpharmaceutical interventions were used extensively during the early period of the COVID-19 pandemic, but their use has remained controversial.Agent-based modeling of the impact of these mitigation strategies early in the COVID-19 pandemic supports the effectiveness of nonpharmaceutical interventions at decreasing mortality.Since such interventions are not specific to a particular pathogen, they can be used to protect against any respiratory pathogen, known or emerging. They can be applied rapidly when conditions warrant.

The Impact of the Good Behavior Game on Risk for Drug Use Disorder in an Agent-Based Model of Southern Sweden.

OBJECTIVE: Drug use disorder (DUD) is a worldwide problem, and strategies to reduce its incidence are central to decreasing its burden. This investigation seeks to provide a proof of concept for the ability of agent-based modeling to predict the impact of the introduction of an effective school-based intervention, the Good Behavior Game (GBG), on reducing DUD in Scania, Sweden, primarily through increasing school achievement. METHOD: We modified an existing agent-based simulation model of opioid use disorder to represent DUD in Scania County, southern Sweden. The model represents every individual in the population and is calibrated with the linked individual data from multiple sources including demographics, education, medical care, and criminal history. Risks for developing DUD were estimated from the population in Scania. Scenarios estimated the impact of introducing the GBG in schools located in disadvantaged areas. RESULTS: The model accurately reflected the growth of DUD in Scania over a multiyear period and reproduced the levels of affected individuals in various socioeconomic strata over time. The GBG was estimated to improve school achievement and lower DUD registrations over time in males residing in disadvantaged areas by 10%, reflecting a decrease of 540 cases of DUD. Effects were considerably smaller in females. CONCLUSIONS: This work provides support for the impact of improving school achievement on long-term risks of developing DUD. It also demonstrated the value of using simulation modeling calibrated with data from a real population to estimate the impact of an intervention applied at a population level.

Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden.

Introduction: Current influenza vaccines have limited effectiveness. COVID-19 vaccines using mRNA technology have demonstrated very high efficacy, suggesting that mRNA vaccines could be more effective for influenza. Several such influenza vaccines are in development. FRED, an agent-based modeling platform, was used to estimate the impact of more effective influenza vaccines on seasonal influenza burden. Methods: Simulations were performed using an agent-based model of influenza that included varying levels of vaccination efficacy (40-95 % effective). In some simulations, level of infectiousness and/or length of infectious period in agents with breakthrough infections was also decreased. Impact of increased and decreased levels of vaccine uptake were also modeled. Outcomes included number of symptomatic influenza cases estimated for the US. Results: Highly effective vaccines significantly reduced estimated influenza cases in the model. When vaccine efficacy was increased from 40 % to a maximum of 95 %, estimated influenza cases in the US decreased by 43 % to > 99 %. The base simulation (40 % efficacy) resulted in âˆ¼ 28 million total yearly cases in the US, while the most effective vaccine modeled (95 % efficacy) decreased estimated cases to âˆ¼ 22,000. Discussion: Highly effective vaccines could dramatically reduce influenza burden. Model estimates suggest that even modest increases in vaccine efficacy could dramatically reduce seasonal influenza disease burden.

Can a Two-Dose Influenza Vaccine Regimen Better Protect Older Adults? An Agent-Based Modeling Study.

Older adults (age ≥ 65) are at high risk of influenza morbidity and mortality. This study evaluated the impact of a hypothetical two-dose influenza vaccine regimen per season to reduce symptomatic flu cases by providing preseason (first dose) and mid-season (second dose) protection to offset waning vaccine effectiveness (VE). The Framework for Reconstructing Epidemiological Dynamics (FRED), an agent-based modeling platform, was used to compare typical one-dose vaccination to a two-dose vaccination strategy. Primary models incorporated waning VE of 10% per month and varied influenza season timing (December through March) to estimate cases and hospitalizations in older adults. Additional scenarios modeled reductions in uptake and VE of the second dose, and overall waning. In seasons with later peaks, two vaccine doses had the largest potential to reduce cases (14.4% with February peak, 18.7% with March peak) and hospitalizations (13.1% with February peak, 16.8% with March peak). Reductions in cases and hospitalizations still resulted but decreased when 30% of individuals failed to receive a second dose, second dose VE was reduced, or overall waning was reduced to 7% per month. Agent-based modeling indicates that two influenza vaccine doses could decrease cases and hospitalizations in older individuals. The highest impact occurred in the more frequently observed late-peak seasons. The beneficial impact of the two-dose regimen persisted despite model scenarios of reduced uptake of the second dose, decreased VE of the second dose, or overall VE waning.

Measuring quality effects in equilibrium.

Unlike demand studies in other industries, models of provider demand in health care often must omit a price, or any other factor that equilibrates the market such as a waiting time. Estimates of the consumer response to quality may consequently be attenuated, if the limited capacity of individual physicians prevents some consumers from obtaining higher quality. We propose a tractable method to address this problem by adding a congestion effect to standard discrete-choice models. We show analytically how this can improve forecasts of the consumer response to quality. We then apply this method to the market for heart surgery, and find that the attenuation bias in estimated quality effects can be important empirically.

Impact of Low Rates of Influenza on Next-Season Influenza Infections.

INTRODUCTION: Interventions to curb the spread of COVID-19 during the 2020-2021 influenza season essentially eliminated influenza during that season. Given waning antibody titers over time, future residual population immunity against influenza will be reduced. The implication for the subsequent 2021-2022 influenza season is unknown. METHODS: An agent-based model of influenza implemented in the Framework for Reconstructing Epidemiological Dynamics simulation platform was used to estimate cases and hospitalizations over 2 successive influenza seasons. The impact of reduced residual immunity owing to protective measures in the first season was estimated over varying levels of similarity (cross-immunity) between influenza strains over the seasons. RESULTS: When cross-immunity between first- and second-season strains was low, a decreased first season had limited impact on second-season cases. High levels of cross-immunity resulted in a greater impact on the second season. This impact was modified by the transmissibility of strains in the 2 seasons. The model estimated a possible increase of 13.52%-46.95% in cases relative to that in a normal season when strains have the same transmissibility and 40%-50% cross-immunity in a season after a very low one. CONCLUSIONS: Given the light 2020-2021 influenza season, cases may increase by as much as 50% in 2021-2022, although the increase could be much less, depending on cross-immunity from past infection and transmissibility of strains. Enhanced vaccine coverage or continued interventions to reduce transmission could reduce this high season. Young children may have a higher risk in 2021-2022 owing to limited exposure to infection in the previous year.

Development of a Metric to Detect and Decrease Low-Value Prescribing in Older Adults.

Importance: Metrics that detect low-value care in common forms of health care data, such as administrative claims or electronic health records, primarily focus on tests and procedures but not on medications, representing a major gap in the ability to systematically measure low-value prescribing. Objective: To develop a scalable and broadly applicable metric that contains a set of quality indicators (EVOLV-Rx) for use in health care data to detect and reduce low-value prescribing among older adults and that is informed by diverse stakeholders' perspectives. Design, Setting, and Participants: This qualitative study used an online modified-Delphi method to convene an expert panel of 15 physicians and pharmacists. This panel, comprising clinicians, health system leaders, and researchers, was tasked with rating and discussing candidate low-value prescribing practices that were derived from medication safety criteria; peer-reviewed literature; and qualitative studies of patient, caregiver, and physician perspectives. The RAND ExpertLens online platform was used to conduct the activities of the panel. The panelists were engaged for 3 rounds between January 1 and March 31, 2021. Main Outcomes and Measures: Panelists used a 9-point Likert scale to rate and then discuss the scientific validity and clinical usefulness of the criteria to detect low-value prescribing practices. Candidate low-value prescribing practices were rated as follows: 1 to 3, indicating low validity or usefulness; 3.5 to 6, uncertain validity or usefulness; and 6.5 to 9, high validity or usefulness. Agreement among panelists and the degree of scientific validity and clinical usefulness were assessed using the RAND/UCLA (University of California, Los Angeles) Appropriateness Method. Results: Of the 527 low-value prescribing recommendations identified, 27 discrete candidate low-value prescribing practices were considered for inclusion in EVOLV-Rx. After round 1, 18 candidate practices were rated by the panel as having high scientific validity and clinical usefulness (scores of ≥6.5). After round 2 panel deliberations, the criteria to detect 19 candidate practices were revised. After round 3, 18 candidate practices met the inclusion criteria, receiving final median scores of 6.5 or higher for both scientific validity and clinical usefulness. Of those practices that were not included in the final version of EVOLV-Rx, 3 received high scientific validity (scores ≥6.5) but uncertain clinical usefulness (scores <6.5) ratings, whereas 6 received uncertain scientific validity rating (scores <6.5). Conclusions and Relevance: This study culminated in the development of EVOLV-Rx and involved a panel of experts who identified the 18 most salient low-value prescribing practices in the care of older adults. Applying EVOLV-Rx may enhance the detection of low-value prescribing practices, reduce polypharmacy, and enable older adults to receive high-value care across the full spectrum of health services.

Estimating the Impact of Low Influenza Activity in 2020 on Population Immunity and Future Influenza Seasons in the United States.

BACKGROUND: Influenza activity in the 2020-2021 season was remarkably low, likely due to implementation of public health preventive measures such as social distancing, mask wearing, and school closure. With waning immunity, the impact of low influenza activity in the 2020-2021 season on the following season is unknown. METHODS: We built a multistrain compartmental model that captures immunity over multiple influenza seasons in the United States. Compared with the counterfactual case, where influenza activity remained at the normal level in 2020-2021, we estimated the change in the number of hospitalizations when the transmission rate was decreased by 20% in 2020-2021. We varied the level of vaccine uptake and effectiveness in 2021-2022. We measured the change in population immunity over time by varying the number of seasons with lowered influenza activity. RESULTS: With the lowered influenza activity in 2020-2021, the model estimated 102 000 (95% CI, 57 000-152 000) additional hospitalizations in 2021-2022, without changes in vaccine uptake and effectiveness. The estimated changes in hospitalizations varied depending on the level of vaccine uptake and effectiveness in the following year. Achieving a 50% increase in vaccine coverage was necessary to avert the expected increase in hospitalization in the next influenza season. If the low influenza activity were to continue over several seasons, population immunity would remain low during those seasons, with 48% of the population susceptible to influenza infection. CONCLUSIONS: Our study projected a large compensatory influenza season in 2021-2022 due to a light season in 2020-2021. However, higher influenza vaccine uptake would reduce this projected increase in influenza.

Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection.

BACKGROUND: Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole-genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively. METHODS: We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared with traditional IP practice during the same period. RESULTS: Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2-14 patients. At least 1 transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192 408-$692 532. CONCLUSIONS: EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.

Evaluating the effectiveness of problem-solving courts at preventing fatal and non-fatal opioid events: preliminary findings from Indiana.

BACKGROUND: Fatal and non-fatal events associated with drug misuse are skyrocketing in most United States jurisdictions, including Indiana. Historically, the role of the judiciary is to arrest, impose sanctions and protect society from harm. Adults arrested for drug abuse in Indiana can be sentenced to 1 of 17 correctional facilities. As an alternative, they may be eligible to participate in a problem-solving court (PSC) programme that refers individuals to treatment as a pretrial diversionary strategy. The aim of the study is to determine which interventions offered by PSCs and correctional facilities impact morbidity and mortality. The study began in 2019 and will end in 2023; therefore, the results in this manuscript are preliminary. METHODS: The study cohort included two populations arrested for drug misuse: (1) adults sentenced to Indianan correctional facilities (1 January 2018 to 30 June 2021) and (2) adults participating in an Indiana PSC programme (1 January 2018 to 30 June 2021). The study used a mixed-methods design that integrated qualitative interviews of deputy wardens, PSC team members and service providers with the following quantitative datasets: sentencing information, emergency department visits, inpatient hospitalization admissions, prescription drug monitoring programme data and death records. The individuals will be followed at 2-week, 4-week, 6-month and 1-year intervals post-release. Difference-in-difference and time-to-event analyses will identify impactful interventions. A model will be created to show the effect of impactful interventions in Indiana counties that do not have PSCs. RESULTS: Findings are preliminary. There is variability amongst correctional facilities regarding programme eligibility, provided services and provision of medication-assisted treatment. All correctional facilities were severely impacted by the COVID-19 pandemic. CONCLUSION: It is anticipated that the adoption of impactful interventions will lower opioid-related morbidity and mortality rates.

Prescriber perspectives on low-value prescribing: A qualitative study.

BACKGROUND: Health systems are increasingly implementing interventions to reduce older patients' use of low-value medications. However, prescribers' perspectives on medication value and the acceptability of interventions to reduce low-value prescribing are poorly understood. OBJECTIVE: To identify the characteristics that affect the value of a medication and those factors influencing low-value prescribing from the perspective of primary care physicians. DESIGN: Qualitative study using semi-structured interviews. SETTING: Academic and community primary care practices within University of Pittsburgh Medical Center health system. PARTICIPANTS: Sixteen primary care physicians. MEASUREMENTS: We elicited 16 prescribers' perspectives on definitions and examples of low-value prescribing in older adults, the factors that incentivize them to engage in such prescribing, and the characteristics of interventions that would make them less likely to engage in low-value prescribing. RESULTS: We identified three key themes. First, prescribers viewed low-value prescribing among older adults as common, characterized both by features of the medications themselves and of the particular patients to whom they were prescribed. Second, prescribers described the causes of low-value prescribing as multifactorial, with factors related to patients, prescribers, and the health system as a whole, making low-value prescribing a default practice pattern. Third, interventions addressing low-value prescribing must minimize the cognitive load and time pressures that make low-value prescribing common. Interventions increasing time pressure or cognitive load, such as increased documentation, were considered less acceptable. CONCLUSIONS: Our findings demonstrate that low-value prescribing is a well-recognized phenomenon, and that interventions to reduce low-value prescribing must consider physicians' perspectives and address the specific patient, prescriber and health system factors that make low-value prescribing a default practice.

Are Discount Rates Too High? Population Health and Intergenerational Equity.

Increasing attention is being paid to policy decisions in which shorter-term benefits may be eclipsed by longer-term harms, such as environmental damage. Health policy decisions have largely been spared this scrutiny, even though they too may contribute to longer-term harms. Any healthy population or society must sustain itself through reproduction, and therefore, transgenerational outcomes should be of intrinsic importance from a societal perspective. Yet, the discount rates typically employed in cost-effectiveness analyses have the effect of minimizing the importance of transgenerational health outcomes. We argue that, because cost-effectiveness analysis is based on foundational axioms of decision theory, it should value transgenerational outcomes consistently with those axioms, which require discount rates substantially lower than 3%. We discuss why such lower rates may not violate the Cretin-Keeler paradox.

Economic analysis of screening for subclinical rejection in kidney transplantation using protocol biopsies and noninvasive biomarkers.

Subclinical rejection (SCR) screening in kidney transplantation (KT) using protocol biopsies and noninvasive biomarkers has not been evaluated from an economic perspective. We assessed cost-effectiveness from the health sector perspective of SCR screening in the first year after KT using a Markov model that compared no screening with screening using protocol biopsy or biomarker at 3 months, 12 months, 3 and 12 months, or 3, 6, and 12 months. We used 12% subclinical cellular rejection and 3% subclinical antibody-mediated rejection (SC-ABMR) for the base-case cohort. Results favored 1-time screening at peak SCR incidence rather than repeated screening. Screening 2 or 3 times was favored only with age <35 years and with high SC-ABMR incidence. Compared to biomarkers, protocol biopsy yielded more quality-adjusted life years (QALYs) at lower cost. A 12-month biopsy cost $13 318/QALY for the base-case cohort. Screening for cellular rejection in the absence of SC-ABMR was less cost effective with 12-month biopsy costing $46 370/QALY. Screening was less cost effective in patients >60 years. Using biomarker twice or thrice was cost effective only if biomarker cost was <$700. In conclusion, in KT, screening for SCR more than once during the first year is not economically reasonable. Screening with protocol biopsy was favored over biomarkers.

Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks.

BACKGROUND: Whole genome sequencing (WGS) surveillance and electronic health record data mining have the potential to greatly enhance the identification and control of hospital outbreaks. The objective was to develop methods for examining economic value of a WGS surveillance-based infection prevention (IP) program compared to standard of care (SoC). METHODS: The economic value of a WGS surveillance-based IP program was assessed from a hospital's perspective using historical outbreaks from 2011-2016. We used transmission network of outbreaks to estimate incremental cost per transmission averted. The number of transmissions averted depended on the effectiveness of intervening against transmission routes, time from transmission to positive culture results and time taken to obtain WGS results and intervene on the transmission route identified. The total cost of an IP program included cost of staffing, WGS, and treating infections. RESULTS: Approximately 41 out of 89 (46%) transmissions could have been averted under the WGS surveillance-based IP program, and it was found to be a less costly and more effective strategy than SoC. The results were most sensitive to the cost of performing WGS and the number of isolates sequenced per year under WGS surveillance. The probability of the WGS surveillance-based IP program being cost-effective was 80% if willingness to pay exceeded $2400 per transmission averted. CONCLUSIONS: The proposed economic analysis is a useful tool to examine economic value of a WGS surveillance-based IP program. These methods will be applied to a prospective evaluation of WGS surveillance compared to SoC.

Development of a Synthetic Population Model for Assessing Excess Risk for Cardiovascular Disease Death.

Importance: Evaluating the association of social determinants of health with chronic diseases at the population level requires access to individual-level factors associated with disease, which are rarely available for large populations. Synthetic populations are a possible alternative for this purpose. Objective: To construct and validate a synthetic population that statistically mimics the characteristics and spatial disease distribution of a real population, using real and synthetic data. Design, Setting, and Participants: This population-based decision analytical model used data for Allegheny County, Pennsylvania, collected from January 2015 to December 2016, to build a semisynthetic population based on the synthetic population used by the modeling and simulation platform FRED (A Framework for Reconstructing Epidemiological Dynamics). Disease status was assigned to this population using health insurer claims data from the 3 major insurance providers in the county or from the National Health and Nutrition Examination Survey. Biological, social, and other variables were also obtained from the National Health Interview Survey, Allegheny County, and public databases. Data analysis was performed from November 2016 to February 2020. Exposures: Risk of cardiovascular disease (CVD) death. Main Outcomes and Measures: Difference between expected and observed CVD death risk. A validated risk equation was used to estimate CVD death risk. Results: The synthetic population comprised 1 188 112 individuals with demographic characteristics similar to those of the 2010 census population in the same county. In the synthetic population, the mean (SD) age was 40.6 (23.3) years, and 622 997 were female individuals (52.4%). Mean (SD) observed 4-year rate of excess CVD death risk at the census tract level was -40 (523) per 100 000 persons. The correlation of social determinant data with difference between expected and observed CVD death risk indicated that income- and education-based social determinants were associated with risk. Estimating improved social determinants of health and biological factors associated with disease did not entirely remove the excess in CVD death rates. That is, a 20% improvement in the most significant determinants still resulted in 105 census tracts with excess CVD death risk, which represented 24% of the county population. Conclusions and Relevance: The results of this study suggest that creating a geographically explicit synthetic population from real and synthetic data is feasible and that synthetic populations are useful for modeling disease in large populations and for estimating the outcome of interventions.

Age and generational patterns of overdose death risk from opioids and other drugs.

The ongoing substance misuse epidemic in the United States is complex and dynamic and should be approached as such in the development and evaluation of policy1. Drug overdose deaths (largely attributable to opioid misuse) in the United States have grown exponentially for almost four decades, but the mechanisms of this growth are poorly understood2. From analysis of 661,565 overdose deaths from 1999 to 2017, we show that the age-specific drug overdose mortality curve for each birth-year cohort rises and falls according to a Gaussian-shaped curve. The ascending portion of each successive birth-year cohort mortality curve is accelerated compared with that of all preceding birth-year cohorts. This acceleration can be attributed to either of two distinct processes: a stable peak age, with an increasing amplitude of mortality rate curves from one birth-year cohort to the next; or a youthward shift in the peak age of the mortality rate curves. The overdose epidemic emerged and increased in amplitude among the 1945-1964 cohort (Baby Boomers), shifted youthward among the 1965-1980 cohort (Generation X), and then resumed the pattern of increasing amplitude in the 1981-1990 Millennials. These shifting age and generational patterns are likely to be driven by socioeconomic factors and drug availability, the understanding of which is important for the development of effective overdose prevention measures.

Criteria and Process for Initiating and Developing an ISPOR Good Practices Task Force Report.

The International Society for Pharmacoeconomics and Outcomes Research (ISPOR)'s "Good Practices Task Force" reports are highly cited, multistakeholder perspective expert guidance reports that reflect international standards for health economics and outcomes research (HEOR) and their use in healthcare decision making. In this report, we discuss the criteria, development, and evaluation/consensus review and approval process for initiating a task force. The rationale for a task force must include a justification, including why this good practice guidance is important and its potential impact on the scientific community. The criteria include: (1) necessity (why is this task force required?); (2) a methodology-oriented focus (focus on research methods, approaches, analysis, interpretation, and dissemination); (3) relevance (to ISPOR's mission and its members); (4) durability over time; (5) broad applicability; and 6) an evidence-based approach. In addition, the proposal must be a priority specifically for ISPOR. These reports are valuable to researchers, academics, students, health technology assessors, medical technology developers and service providers, those working in other commercial entities, regulators, and payers. These stakeholder perspectives are represented in task force membership to ensure the report's overall usefulness and relevance to the global ISPOR membership. We hope that this discussion will bring transparency to the process of initiating, approving, and producing these task force reports and encourage participation from a diverse range of experts within and outside ISPOR.

Improved Health Outcomes from Hepatitis C Treatment Scale-Up in Spain's Prisons: A Cost-Effectiveness Study.

Hepatitis C virus (HCV) is 15 times more prevalent among persons in Spain's prisons than in the community. Recently, Spain initiated a pilot program, JAILFREE-C, to treat HCV in prisons using direct-acting antivirals (DAAs). Our aim was to identify a cost-effective strategy to scale-up HCV treatment in all prisons. Using a validated agent-based model, we simulated the HCV landscape in Spain's prisons considering disease transmission, screening, treatment, and prison-community dynamics. Costs and disease outcomes under status quo were compared with strategies to scale-up treatment in prisons considering prioritization (HCV fibrosis stage vs. HCV prevalence of prisons), treatment capacity (2,000/year vs. unlimited) and treatment initiation based on sentence lengths (>6 months vs. any). Scaling-up treatment by treating all incarcerated persons irrespective of their sentence length provided maximum health benefits-preventing 10,200 new cases of HCV, and 8,300 HCV-related deaths between 2019-2050; 90% deaths prevented would have occurred in the community. Compared with status quo, this strategy increased quality-adjusted life year (QALYs) by 69,700 and costs by €670 million, yielding an incremental cost-effectiveness ratio of €9,600/QALY. Scaling-up HCV treatment with DAAs for the entire Spanish prison population, irrespective of sentence length, is cost-effective and would reduce HCV burden.

Exclusion Criteria as Measurements I: Identifying Invalid Responses.

Background. In a systematic review, Engel et al. found large variation in the exclusion criteria used to remove responses held not to represent genuine preferences in health state valuation studies. We offer an empirical approach to characterizing the similarities and differences among such criteria. Setting. Our analyses use data from an online survey that elicited preferences for health states defined by domains from the Patient-Reported Outcomes Measurement Information System (PROMIS®), with a U.S. nationally representative sample (N = 1164). Methods. We use multidimensional scaling to investigate how 10 commonly used exclusion criteria classify participants and their responses. Results. We find that the effects of exclusion criteria do not always match the reasons advanced for applying them. For example, excluding very high and very low values has been justified as removing aberrant responses. However, people who give very high and very low values prove to be systematically different in ways suggesting that such responses may reflect different processes. Conclusions. Exclusion criteria intended to remove low-quality responses from health state valuation studies may actually remove deliberate but unusual ones. A companion article examines the effects of the exclusion criteria on societal utility estimates.

Exclusion Criteria as Measurements II: Effects on Utility Functions.

Background. Researchers often justify excluding some responses in studies eliciting valuations of health states as not representing respondents' true preferences. Here, we examine the effects of applying 8 common exclusion criteria on societal utility estimates. Setting. An online survey of a US nationally representative sample (N = 1164) used the standard gamble method to elicit preferences for health states defined by 7 health domains from the Patient-Reported Outcomes Measurement Information System (PROMIS®). Methods. We estimate the impacts of applying 8 commonly used exclusion criteria on mean utility values for each domain, using beta regression, a form of analysis suited to double-bounded scales, such as utility. Results. Exclusion criteria have varied effects on the utility functions for the different PROMIS health domains. As a result, applying those criteria would have varied effects on the value of treatments (and side effects) that change health status on those domains. Limitations. Although our method could be applied to any health utility judgments, the present estimates reflect the features of the study that produced them. Those features include the selected health domains, standard gamble method, and an online format that excluded some groups (e.g., visually impaired and illiterate individuals). We also examined only a subset of all possible exclusion criteria, selected to represent the space of possibilities, as characterized in a companion article. Conclusions. Exclusion criteria can affect estimates of the societal utility of health states. We use those effects, in conjunction with the results of the companion article, to make suggestions for selecting exclusion criteria in future studies.