Journal of Medical Economics

Background/Objectives: Older adults are at high risk of influenza-related severe disease and complications. HD-IIV, currently recommended for adults [≥]65 years, is egg-based and may be subject to egg-adapted mutations. An mRNA-based vaccine for influenza, mRNA-1010, has been approved by the Food and Drug Administration. Given the clinical benefits observed in mRNA-1010 clinical trials and potential to avoid egg-adapted mutations, this analysis explores the clinical impact of mRNA-1010 compared to HD-IIV in those [≥]65 years in the United States. Methods: This study used a decision-analytic model of influenza infection and downstream outcomes to project influenza burden with each vaccine over a one-year timeframe. Vaccine coverage, HD-IIV vaccine effectiveness (VE) and influenza epidemiology was estimated based upon Centers for Disease Control and Prevention data. The relative VE (rVE) for mRNA-1010 vs. HD-IIV was modelled based on data from the clinical trial and published literature on the impact of egg adaptation. Results: The base-case analysis suggests mRNA-1010 may prevent an additional 232,200 symptomatic influenza infections, 130,000 outpatient cases, 21,100 influenza-related hospitalizations, and 2,200 influenza-related deaths compared to HD-IIV. The number needed to vaccination to prevent one hospitalization was 514 or mRNA-1010 vs no vaccine and 404 for HD-IIV versus no vaccine, for an incremental NNV of 110. Scenario and deterministic sensitivity analyses results indicate that clinical outcomes prevented by mRNA-1010 are most sensitive to influenza incidence and VE. Conclusions: mRNA-1010 may improve protection against influenza in US adults aged [≥]65 years, leading to meaningful reductions in the clinical influenza-related disease burden in older adults.

Background: While health system costs of COVID-19 are well documented, evidence on out-of-pocket costs to patients and families, including non-medical and time burden, is limited. This study aimed to estimate out-of-pocket medical, non-medical, and time burden of COVID-19 illness directly from patients and caregivers. Methods: Two cross-sectional surveys were fielded to national nonprobability samples of U.S. adults in Fall 2023: a personal burden survey (adult sample) and a caregiver burden survey (child sample). Surveys collected data on care setting, visit duration including travel and wait time, and medical and non-medical out-of-pocket costs. After applying quality-based exclusion criteria, 409 adult sample and 52 child sample responses were analyzed. Results: Over 87% of respondents were insured. Median out-of-pocket medical costs per visit were low, ranging from $5 (emergency department) to $10 (urgent care) in the adult sample, and from $0 (emergency department) to $75 (hospitalization) in the child sample. Median non-medical costs were $28 or less across care settings. Median time spent receiving care ranged from 90 minutes (primary care, child sample) to 300 minutes (emergency department, adult sample). Despite the relatively low reported out-of-pocket costs, COVID-related financial challenges were reported by 34% of the adult sample and 29% of the child sample, with the most common difficulty being paying non-medical bills (11% and 15%, respectively). Twelve percent in both groups reported problems paying COVID-19-related medical bills. Conclusions: In this post-pandemic non-probability sample, individual out-of-pocket costs for COVID-19 illness were relatively modest. However, time burden was substantial and one-third of respondents reported financial hardship. These estimates, including non-medical and time burden not captured in administrative data, can support more robust economic evaluations of COVID-19 prevention and treatment interventions.

Abstract Background Estimates of the healthcare costs associated with influenza treatment are essential for cost-effectiveness analyses that inform preventative and treatment strategies such as vaccination and non-pharmaceutical interventions. Existing evidence varies across treatment settings, age groups, and countries and there is little data from low- and middle-income settings. Methods We conducted an umbrella review of systematic reviews and meta-analyses reporting direct admitted and non-admitted hospitalisation costs of seasonal influenza treatment in children, adults, and older people. We supplemented an evidence gap with a systematised review of studies reporting adult non-admitted treatment costs. Costs were extracted and adjusted to 2023 USD. We fitted a Bayesian mixed-effects model to estimate influenza treatment costs using national economic indicators, age group, and treatment setting as a priori predictors. We predicted admitted and non-admitted treatment costs for children, adults, and older people in 195 countries and territories. Results Seven reviews were included in the umbrella review, supplemented by 17 primary studies for adult non-admitted care. Extracted data points most frequently described admitted treatment costs and came from high-income countries. The best-fitting model used healthcare expenditure per capita to predict treatment costs, with country-level random effects. Estimated costs increased with healthcare expenditure per capita, were higher for admitted than non-admitted care, and were highest for adults and lowest for children. Conclusions This study provides novel estimates of influenza-associated healthcare costs across age groups and treatment settings by synthesising the available global evidence. By extrapolating to settings without data, our estimates can inform future economic evaluation studies of influenza interventions worldwide.

BACKGROUND Extensive research has documented medication abortion safety. Large-scale insurance claims data offer an important complement to clinical studies by assessing real-world outcomes in routine practice. In claims data, researchers must infer mifepristone use, clinical outcomes, and event severity from diagnosis, treatment, and utilization codes. Understanding how these analytic choices influence safety and effectiveness estimates is important for interpreting evidence used to inform care delivery and medication policy. METHODS We conducted a retrospective cohort study of mifepristone use with national multipayer claims data, both medical and pharmacy, from 2017 through 2023. We identified both a broad cohort and a more specific medication-abortion cohort after excluding evidence of other indications. We classified potential safety events, effectiveness outcomes, and health care utilization within 45 days of mifepristone use. Serious adverse events required claims-observable consequences consistent with FDA seriousness criteria. RESULTS The broad cohort included 869,409 mifepristone uses among 698,780 unique patients for medication abortion or other indications. After restriction to uses with additional evidence of medication abortion, 775,931 uses among 626,443 unique patients remained. In the medication-abortion cohort, 3,306 uses were followed by at least one serious adverse event, resulting in an absolute 45-day risk of 0.43%. Serious bleeding or hemorrhage occurred after 0.24% of uses, serious infection or sepsis after 0.05%, serious hypersensitivity or anaphylaxis after 0.01%, and serious ectopic-pregnancy outcomes after 0.05%. CONCLUSIONS Serious adverse events after mifepristone use were rare in this national study. Applying established, clinically grounded criteria markedly reduced the number of events classified as serious.

Importance: With a recent shift towards summer seasonal predominance of COVID-19 across much of the US, debate continues over optimal COVID-19 vaccination strategies regarding timing (including summer vaccination), eligibility, and dosing frequency. We evaluated the population-level impact of alternative vaccine strategies to inform US and state vaccine policy. Objective: To compare reductions in COVID-19 hospitalizations under different vaccination timing, eligibility, and dosing frequency. Design: Decision analytical model using a stochastic individual-based model of SARS-CoV-2 transmission, calibrated to US COVID-19 hospitalization incidence from May 2024-April 2025. Setting: Simulated US national population, with additional scenarios representing state-level seasonal patterns. Participants: Simulated population of 5 million individuals with US age structure, stratified by risk group (immunocompetent, high risk with comorbid conditions, or immunocompromised). Exposures: Vaccination timing, universal or age- and risk-based eligibility, and dosing frequency, including a hybrid strategy of universal annual vaccination with a second dose for adults [≥]65 years and immunocompromised adults (US vaccine policy during the study period). Main Outcomes and Measures: COVID-19 hospitalization risk per 100,000 persons by age-risk group, measured among vaccinated individuals and in the total population. Results: If summer COVID-19 vaccination timing is feasible, annual universal vaccination in July minimized risk (441 [95% UI: 423-463] COVID-19 hospitalizations per 100,000 persons among adults [≥]65 years) compared to a counterfactual scenario with no additional vaccination given during the study period (578 [553-601]). If timing does not change, hybrid vaccination minimized risk, followed by semiannual risk-based vaccination (460 [440-487] and 477 [454-499] hospitalizations per 100,000 in adults [≥]65 years, respectively). The advantage of summer timing diminished if vaccine coverage decreased by 5-10 absolute percentage points (25-50% relative decrease). The magnitude of benefit from summer-timed vaccination and semiannual doses in the highest-risk groups varied regionally. Conclusions and Relevance: Although annual COVID-19 vaccination has historically been timed for winter transmission, summer vaccination may avert more hospitalizations if coverage is maintained, especially in states with predominantly summer transmission including Western and Southern states. The comparative impact of vaccination strategies, especially the benefit of a semiannual dose in the highest-risk groups, may vary regionally. COVID-19 vaccination timing and eligibility may warrant reconsideration.

Background/Aims This work aimed to estimate the annual tertiary healthcare and societal costs associated with paediatric congenital heart disease (CHD) in the United Kingdom using real-world data from a Level 1 cardiac surgical centre in the National Health Service (NHS). Methods A prevalence-based cost-of-illness analysis was conducted using patient-level inpatient and outpatient activity data from Evelina London Children's Hospital between 1 June 2023 and 31 May 2024. Costs were estimated from NHS and societal perspectives and extrapolated to national level using National Congenital Heart Disease Audit (NCHDA) procedural activity data. Healthcare costs included tertiary centre care costs. Societal costs included parental productivity losses and impacts on lifetime earnings for children. Deterministic sensitivity analysis explored parameter uncertainty. Results Approximately 9.6% of UK paediatric CHD procedures were undertaken at Evelina London Children's Hospital at a cost of {pound}15.0 million per annum, corresponding to a national estimate of {pound}156.4 million. Single-ventricle procedures accounted for 4.8% of inpatient activity but 19.4% of inpatient costs. Sensitivity analysis produced national healthcare cost estimates ranging from {pound}125.1 million to {pound}187.7 million. National productivity losses associated with parental absence from work were estimated at {pound}82.4 million annually. Individuals with CHD were estimated to experience a mean reduction in lifetime earnings of {pound}81,509. Conclusion Paediatric CHD imposes a substantial economic cost on the UK healthcare system and wider society. This study provides the first real-world national estimate of the economic impact of paediatric CHD in the UK and may inform future service planning, research investment, and innovation adoption strategies.

There is currently no clinical data for the effectiveness of the only licensed Ebola virus disease vaccine (Ervebo) against Bundibugyo virus disease. A number of studies have shown immune cross-reactivity with Bundibugyo virus after Ervebo vaccination. However, antibody recognition of Bundibugyo is around 2.8-fold lower than recognition of Ebola virus. Here, we aimed to infer how a 2.8-fold drop in immune recognition may affect Ervebo protection against Bundibugyo virus disease based on analysis of data on Ervebo immunogenicity and protection from Ebola virus disease. This work has three main components. Firstly, we analysed the timing of vaccine protection in the Ervebo pivotal ring vaccination clinical trial. Secondly, we performed a systematic review and meta-analysis of antibody responses to Ebola virus after Ervebo vaccination over time. Thirdly, we combined these data with previously reported data on the cross-reactivity to Bundibugyo virus to infer protection of Ervebo against Bundibugyo virus disease. We find that the clinical trial data for Ervebo supports vaccine protection beginning earlier than 10 days after vaccination. Secondly, binding antibody levels against Ebola virus on day 28 post vaccination (peak responses) were on average 12.9-fold (95% confidence interval, CI: 6.4 - 26.0) higher than on day 7, 6.3-fold (95% CI: 3.7 -10.8) higher than on day 10, and 2.5-fold (95% CI: 1.4 - 4.4) higher than on day 14 (neutralising antibodies showed similar results). Finally, we consider the scenarios where antibody levels to Ebola virus at day 7 or 10 are putative protective thresholds against Ebola virus disease, and we assume these thresholds also apply for Bundibugyo virus disease. Then, with a 2.8-fold reduction of responses to Bundibugyo compared to Ebola virus, we predict antibody responses to Bundibugyo virus after Ervebo vaccination are likely above these putative protective thresholds.

Importance Several influenza vaccine products are available for older adults, but evidence combining both their absolute effectiveness and head-to-head comparisons within the same large population remains scarce, limiting product-specific vaccination guidance for this high-risk group. Objective To estimate the relative and absolute effectiveness of influenza vaccines against influenza-related hospitalization and in-hospital mortality among older adults. Design, Setting, and Participants Nationwide cohort study using the French National Health Data System (SNDS), including all adults born in 1958 or earlier, living in metropolitan France, at the start of the 2023-2024 campaign. Relative effectiveness (rVE) was assessed using a landmark design with exposure fixed before epidemic onset. Absolute effectiveness (VE) was assessed using a sequential trial emulation with weekly matching of newly vaccinated individuals to yet unvaccinated controls. Follow-up ran from December 5, 2023 to April 19, 2024. Exposures Vaccination with a high-dose (Efluelda) or one of three standard-dose quadrivalent vaccines (Influvac, Fluarix, Vaxigrip), compared individually or by dosage category, and against no vaccination. Main Outcomes and Measures Hospitalization with a diagnosis of influenza and death during such hospitalization. Effectiveness was estimated as 1 minus the hazard ratio from weighted Cox models, with corresponding numbers needed to vaccinate to prevent one case. Results Among 13.8 million eligible individuals, 5.7 million (41%) were vaccinated at the beginning of follow-up. The high-dose vaccine showed greater rVE than standard-dose vaccines against hospitalization (27% [95%CI, 23 to 31%]) and in-hospital death (27% [95%CI,14 to 38%]). Absolute VE was 39% (95%CI, 38 to 41%) against hospitalization and 45% (95%CI, 40 to 49%) against in-hospital death, and was higher for the high-dose vaccine (50%) than standard-dose vaccines (36%) against hospitalization. This difference was most evident among adults 70 years and older, while overall effectiveness declined with age. Conclusions and Relevance In this study, influenza vaccination conferred moderate protection against severe outcomes in older adults, and high-dose vaccination was associated with significantly greater effectiveness than standard-dose vaccines, particularly among adults 70 years and older. These findings support product-level differentiation in vaccination strategy for this population.

Background: Nigeria introduced single-dose HPV vaccination for girls aged 9-14 in 2023, reaching approximately 60% aggregate coverage by 2025. While an important achievement, this remains short of the 90% coverage called for under the WHO's 90-70-90 cervical cancer elimination targets, and further scale-up depends on reaching out-of-school girls. In this study we used a model of HPV transmission in Nigeria to quantify the residual cervical cancer burden under continued adolescent delivery, assess how additional screening scale-up and equity assumptions modify it, and identify the effective coverage requirements for an infant HPV vaccination program to be non-inferior to the existing adolescent strategy. Methods: We used HPVsim, an agent-based microsimulation calibrated to Nigerian sexual behavior, HPV prevalence, and cervical cancer incidence, to project new cervical cancer cases and age-standardized incidence over 2025-2100 under scenarios which vary vaccination delivery mode (status-quo adolescent, WHO 90-70-90 adolescent scale-up, and 60/75/90% infant delivery), screening coverage (opportunistic 15% vs. scaled 70% aggregate), and the degree to which primary school completion correlated with vaccination (odds ratio 1 vs. 5) and with screening (odds ratio 1 vs. 3). Infant delivery scenarios were run at hypothetical effective-vaccine-efficacy-at-exposure values of 50%, 70%, and 95% - scenario inputs bracketing the plausible waning range, not evidence-based estimates of infant vaccine performance (no HPV vaccine trial has enrolled anyone below age 10). Each scenario was run at 50 replicates (10 calibration draws, 5 seeds); reported outcomes are the median across replicates and uncertainty ranges are interquartile (25th-75th percentile). Results: Continuation of Nigeria's current adolescent program is projected to avert 1.82 million cervical cancer cases (IQR 1.65-1.90 million; 95% UI 1.39-2.02 million) over 2025-2100 (a 53% reduction relative to no vaccination, IQR 51-55%; 95% UI 46-58%), with age-standardized incidence falling from 19.7 to 7.8 per 100,000 by 2100. However, 82% of the near-term (2025-2075) burden is projected to fall on the pre-2015 birth cohort, i.e. women already beyond the vaccination target age, leaving approximately 945,000 residual cases (IQR 905K-1.00M; 95% UI 835K-1.14M) in this cohort. Scaling up screening to WHO targets was estimated to avert a further ~387K cases (IQR 331-424K; 95% UI 269-515K), roughly 25% of the residual burden; results were about 6-8% better if the scale-up instead reached all women equitably regardless of education. Under a hypothetical scenario of infant vaccination at 90% coverage and 95% effective vaccine efficacy at exposure (matching adolescent-age performance), outcomes were comparable to full WHO adolescent scale-up (49% vs. 54% reduction in vaccine-targetable cohort cancers), but under a hypothetical 50% effective efficacy at Nigeria's DTP3-anchored 62% coverage the strategy averted only a fraction of these cancers. Conclusions: The near-term cervical cancer burden in Nigeria is dominated by cohorts vaccination cannot reach; scaling screening for them is the more consequential lever over the coming three decades and rests on established evidence. Findings on infant HPV delivery are exploratory and hypothesis-generating: they depend on three unverified empirical prerequisites - infant HPV vaccine immunogenicity in the first months of life, durability of protection across the ~15-25-year interval to peak HPV exposure, and coverage achievable through Nigeria's routine childhood immunization platform - and should not be read as a basis for any policy recommendation contingent on infant HPV delivery until those prerequisites are established.

Introduction: This study compared the health and economic impact of the 21-valent pneumococcal conjugate vaccine (aPCV21) vs the 20-valent pneumococcal conjugate vaccine (PCV20) on invasive pneumococcal disease (IPD) and post-meningitis sequelae (PMS) among adults aged 65 years and older in the United Kingdom (UK). Methods: A published Markov model was used to simulate a single-cohort of vaccine-naive adults who became eligible for age-based vaccination at age 65 years, with uptake occurring over time. The cohort was followed until a maximum age of 100 years. Model inputs were derived from publicly available national health data and published reports. Outcomes were discounted at 3.5% annually, and costs were adjusted to the 2024 Great Britain Pound ({pound}). Additional analyses explored the impact of changing the age-based recommendation from 65 years to 75 years. Scenarios involving instantaneous vaccine uptake rates were included as part of the sensitivity analyses. A deterministic sensitivity analysis was performed on the incremental direct medical costs between vaccinating with aPCV21 vs PCV20 under the age-based recommendation of 65 years. Results: Using an age-based recommendation of 65 years, aPCV21 led to fewer cases, deaths, and costs than PCV20. Compared with PCV20, aPCV21 averted an additional 138 IPD cases, 1 PMS case, and 22 IPD deaths, saving {pound}1.2 million in direct medical costs. Raising the age-based recommendation from 65 to 75 years and using aPCV21 (or PCV20, respectively), resulted in 120 (92) additional IPD cases, 2 (1) additional PMS case(s), 19 (14) additional IPD death(s), and an extra {pound}1.1 ({pound}0.8) million in direct medical costs. The deterministic sensitivity analysis showed that aPCV21 reduced direct medical costs across all parameter ranges. Conclusions: Compared with PCV20, aPCV21 prevented more IPD cases, IPD deaths, and direct medical costs among older adults in the UK. Changing the age-based recommendation from age 65 to 75 resulted in worse health and economic outcomes. Key words. Pneumococcal infections; pneumococcal vaccines; vaccines, conjugate; serotype; United Kingdom; health costs

Background Evidence on {beta}-thalassaemia carrier prevalence is fragmented across heterogeneous surveys and national summaries, leaving within-country variation and geographic concentration of carriers poorly resolved. This study characterises {beta}-thalassaemia carrier prevalence and carrier populations at high spatial resolution and identifies where additional data would most improve geographic coverage. Methods Using carrier-prevalence records curated through ITHANET, a global haemoglobinopathy resource, Bayesian geostatistical modelling generated high-resolution predictions across 25 countries. Predictions were integrated with population data to estimate national and subnational carrier numbers. Prediction uncertainty and gaps in existing survey coverage informed prioritisation of healthcare sites for additional surveys. Findings Predicted prevalence varied markedly within countries, and highest-prevalence areas did not coincide with the largest carrier populations. National mean prevalence was highest in Cyprus (12.99%), whereas India, China, and Pakistan had the largest carrier populations, together accounting for 81.21% of estimated carriers. Random five-fold cross-validation favoured a model capturing broad geographic patterns and location-specific variation. Predictive performance deteriorated when geographically clustered records were withheld together, indicating reduced reliability across larger gaps in geographic sampling and the need for additional data from poorly characterised areas. Survey prioritisation identified 183 healthcare sites across 17 countries for future carrier-prevalence surveys. Interpretation By distinguishing areas of high carrier prevalence, large carrier populations, and evidence gaps, this updatable framework provides a subnational evidence base to inform {beta}-thalassaemia prevention planning and guide future data collection, screening, and genetic counselling.

Objectives To evaluate the impact of COVID-19 vaccination on the prevention of long COVID (LC) in the Omicron era through an updated systematic review and meta-analysis. Methods We performed an update of our previously published systematic review and meta-analysis of vaccination and LC risk (searches between 1 January 2022 and 1 March 2024). Databases (Embase, MEDLINE, PubMed, Europe PMC, LILACS, Cochrane COVID-19 Study Register) were searched for studies published between 1 March 2024 and 13 July 2026. Observational studies assessing the impact of vaccination on the risk of LC four or more weeks after SARS-CoV-2 infection were included. Vaccines were restricted to those centrally authorised in the European Union. Random-effects meta-analyses, restricted to adults, were performed to calculate pooled odds ratios (OR) of LC in three comparisons: vaccinated (any dose) versus unvaccinated; booster vaccination versus unvaccinated; and booster vaccination versus primary course vaccination only. Results Nineteen new studies were identified, of which ten were included in the updated meta-analyses alongside eight studies from the original review. The pooled OR of LC in vaccinated adults (any dose) versus unvaccinated was 0.75 (95% confidence interval [CI] 0.67-0.84; 13 studies). ORs were also lower for booster vaccination versus unvaccinated (0.73; 95% CI 0.65-0.82; 6 studies), and booster vaccination versus primary course vaccination (0.63; 95% CI 0.53-0.75; 6 studies). Significant associations between vaccination and lower LC odds were observed in subgroup analyses of mRNA-only vaccination and LC outcomes three or more months after infection. Conclusions This updated review and meta-analysis reinforces our previous finding that vaccination may reduce the burden of LC in the Omicron era, with booster doses potentially providing additional protection. Although evidence in high-risk groups now prioritised for vaccination in most countries remains limited, these findings clearly demonstrate the benefits of vaccination and support current seasonal vaccination strategies.

Summary Background We used sequential target trial emulation (TTE) to examine associations between opioid analgesic initiation and adverse maternal and neonatal outcomes while accounting for gestational timing, time at risk, and potential confounding. Methods Using the Early Life Course platform, comprising linked population-based data for births in New South Wales, Australia (2013-2019), we emulated a new trial at each gestational week, comparing women who initiated a prescribed opioid analgesic with eligible non-initiators. Baseline covariates were balanced using inverse probability weighting. Weighted logistic regression estimated odds ratios (ORs) for neonatal outcomes, postpartum haemorrhage, and severe maternal morbidity. Pooled logistic regression estimated hazard ratios (HRs) for other maternal outcomes, combined across gestational weeks using fixed-effects meta-analysis. Sensitivity analyses assessed unmeasured confounding, exposure misclassification, and potential reverse causation. Findings Among 511,357 pregnancies, over 80% of opioid initiators received only one dispensing. Opioid initiation was not associated with increased risks of placental abruption (HR 0.94, 95% CI 0.75-1.16), severe maternal morbidity (OR 1.05, 0.97-1.11), postpartum haemorrhage (OR 0.98, 0.95-1.02), low Apgar score (OR 0.99, 0.87-1.08), small for gestational age (OR 0.90, 0.85-0.95), or neonatal abstinence syndrome (OR 0.98, 0.72-1.30). Risks were elevated for PPROM (HR 1.10, 0.98-1.23), preterm birth (HR 1.26, 1.19-1.33), severe neonatal morbidity (OR 1.08, 1.02-1.17), and perinatal death (OR 1.29, 1.10-1.52). Perinatal death estimates attenuated in analyses addressing potential reverse causation. Interpretation Using a TTE approach that accounted for gestational timing and time at risk, we found no appreciable increase in most maternal or neonatal outcomes following predominantly short-term opioid initiation. Elevated risks of preterm birth, perinatal death, and third-trimester PPROM may reflect causal effects or confounding by pregnancy-related indications. Studies using time-appropriate designs and detailed information on prescribing indications and their timing are needed to clarify these associations.

Background: Cost-effectiveness analyses (CEA) built on simulation models are usually delivered through project-specific scripts. Each new model, and often each new question, rebuilds the same scaffolding: parameter management, calibration, base-case analysis, sensitivity analysis and export. This is costly, and is where traceability between an estimate and the model version producing it is lost. We present THALASSA, a web application providing this infrastructure once, for any model satisfying a small programming contract. Methods: A simulation model is connected by one file implementing a fixed contract of R functions that determine its behaviour. THALASSA exposes the complete CEA workflow in a browser: a generated overview of the connected model, parameter editing, calibration, base-case comparison with the efficiency curve, and deterministic and probabilistic sensitivity analysis at a willingness-to-pay threshold, each with timestamped file export carrying the parameter values it was run with. Every session is bound to a repository version, so results carry their generating model version. Results: We demonstrate THALASSA on two public models of contrasting difficulty: a simple tutorial model and a generic cancer screening model with two competing natural-history pathways and a 21-dimensional calibration deliberately built to be more complex. Both ran on the same unmodified setup, implementing the same contract, with no code changes. Conclusions: Separating analysis infrastructure from model logic makes the CEA workflow reusable across projects and reproducible by construction. A modelling team can move from a versioned repository to audit-ready, exportable CEA outputs without writing analysis code. The integration effort does not grow with model size and one installation serves unrelated projects.

Background: Pregnancy is a uniquely complex ethical and clinical context in which precision medicine approaches, including genomics-driven risk prediction tools, biomarker-based stratification, and data-intensive analytics, are rapidly evolving. There is a need for community consultation on the ethical, social, and practical implications of precision medicine in pregnancy research and healthcare. Aim: The primary aim of this study was to engage healthcare consumers to build consensus on the acceptability of precision medicine approaches in pregnancy research and healthcare. We also sought to identify consumer priorities for future research. Methods: A two-round modified Delphi study was conducted to determine consumer opinions on the use of precision medicine approaches in pregnancy research and healthcare. Seven in-person workshops (round one) were held, with purposeful recruitment to ensure a representative group of healthcare consumers who engage with the healthcare system during pregnancy. These included first-time parents, culturally and linguistically diverse women, Aboriginal and Torres Strait Islander women, and women and their partners who had experienced previous pregnancy complications. Recruitment was conducted through established advocacy networks, and each workshop was facilitated by representatives from each organization. A second-round online survey, based on workshop content, was completed to assess the acceptability of precision medicine and to determine consumer priorities for future precision medicine research on pregnancy health. Results: Precision medicine research and the implementation of new technologies in clinical care were acceptable to participants; however, the need for transparency and trust between participants and researchers regarding the use and storage of genetic material was highlighted, particularly among Aboriginal and culturally and linguistically diverse participants. Outcomes related to survival and perinatal complications (stillbirth, preterm birth, and labor complications) were the highest-rated consumer research priorities, along with maternal mental health and breastfeeding. When asked whether participants would like to see PM available as part of routine pregnancy care for conditions with available treatments, complete consensus (100%) was reached. Conclusion: Healthcare consumers strongly support the use of precision medicine in pregnancy research and its translation into routine clinical care, particularly when effective interventions are available. However, implementation must be underpinned by transparency, trust, culturally appropriate governance, and clear communication about the collection, storage, and use of genetic material. These findings provide a consumer-informed framework for prioritizing future precision medicine research and guiding its responsible translation into pregnancy care.

Importance: The Medicare Physician Fee Schedule values ambulatory services using survey-based estimates of physician time, a costly process vulnerable to inaccuracy. EHR audit logs are passively and universally collected and likely meet the American Medical Association Relative Value Scale Update Committee (AMA RUC) Extant Database Standards for alternative data to inform service valuation estimates. However, audit logs' validity for estimating face-to-face time, a core component of many of the most used procedural codes, is unknown. Objective: To assess whether EHR audit logs can facilitate accurate estimation of physician-patient face-to-face time during ambulatory visits. Design: Single-site validation study pairing EHR audit logs with direct audio/video recordings of ambulatory primary care visits (reference standard). We assessed agreement between audit-log estimates and recorded visit duration with Bland-Altman analysis and mean absolute error. Setting: University of Pennsylvania Observer data repository, a multi-modal database of ambulatory visits to Penn Medicine primary care clinics including video and audio recordings, transcripts, clinical EHR data, and EHR audit-log data. Participants: 75 visits from 12 physicians with data collected between August 2023 and February 2025. Exposures: A rule-based algorithm estimated face-to-face time via clusters of physician EHR activity relative to patient check-in time. Inactivity thresholds were applied to differentiate intraservice time with no EHR activity (e.g., a physical exam) from the end of a visit, with longer thresholds used for longer visits. Main Outcomes and Measures: Duration of recorded face-to-face visit time. Results: Recorded face-to-face time averaged 31.6 minutes (SD, 13.9). The audit-log estimator was unbiased (mean bias: -0.63 minutes; 95% CI: -6.0 to 2.6; mean absolute error: 9.0 minutes) and estimated face-to-face time within approximately 2% (38 seconds) of the recorded visit time with no proportional bias (slope: 0.13; P = .25). The 95% limits of agreement were -25.7 to 24.4 minutes, and 37% of estimates fell within 10% of recorded duration. Conclusions and Relevance: In this validation study, EHR audit logs recovered unbiased estimates of ambulatory face-to-face time. Larger-scale studies are required to verify generalizability, but EHR audit logs offer a scalable means to improve the rigor of retrospective physician work relative value unit estimation. This study represents a first step in a potential paradigm shift for measuring components of the fee schedule across procedures, settings, and provider types.

Existing prediction models for obstetric anal sphincter injuries (OASIS) often rely on traditional regression, lack external validation, and, when fitted on multicenter data, fail to account for hospital-level clustering. We aimed to develop and temporally validate mixed-effects machine learning models for OASIS and compare them with traditional regression models. A secondary aim was to evaluate whether macrosomia ([&ge;]4000 g) could substitute for continuous birth weight. We conducted a retrospective nationwide cohort study of singleton, term ([&ge;]37 weeks), cephalic vaginal deliveries in the Dutch Perinatal Registry. Data from 2016 to 2019 were used for model development with three-fold cross-validation; data from 2020 were reserved for temporal validation. Candidate predictors included 18 demographic, obstetric, and neonatal factors. We developed six models: mixed-effects gradient boosting (MEGB), mixed-effects LASSO (ME-LASSO), and backward stepwise logistic regression, each with either birth weight or macrosomia. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC), calibration by calibration plots, and prediction accuracy by the Brier score. Among 614,255 deliveries, OASIS occurred in 2.6%. In temporal validation including macrosomia, MEGB achieved the highest discrimination (AUC 0.727; 95% CI 0.719-0.735), significantly outperforming ME-LASSO (AUC 0.719; 95% CI 0.710-0.727; P<.0001) and stepwise logistic regression (AUC 0.714; 95% CI 0.706-0.722; P<.0001). Including birth weight marginally improved performance, with macrosomia serving as an effective proxy. Consistently important predictors were nulliparity, prolonged second stage, mediolateral episiotomy, advanced maternal age, operative vaginal delivery, macrosomia, and previous cesarean delivery. Uncalibrated MEGB showed poorer calibration, but isotonic recalibration substantially improved it. After calibration, all models showed excellent agreement between predicted and observed risks. MEGB provided improved discrimination and comparable calibration relative to ME-LASSO and stepwise logistic regression. Replacing continuous birth weight with a binary macrosomia indicator resulted in minimal performance loss, suggesting it may suffice for clinical risk prediction across all three modeling approaches.

Background and Objectives: Influenza vaccination has a strong safety record and is effective in reducing pediatric influenza-associated morbidity and mortality. However, pediatric vaccination coverage (VC) has declined since the 2019/20 influenza season in the United States. We estimated the impact of increased vaccine protection (through increased VC and/or vaccine effectiveness [VE]) on pediatric influenza-associated burden during the 2022/23-2024/25 seasons. Methods: We used a compartmental model and laboratory-confirmed influenza-associated hospitalization, VC, and VE data among children 6 months-17 years to estimate influenza burden from 2022/23-2024/25 (the 'baseline' scenario) and the impact of vaccination. We simulated scenarios that (1) increased VC by five percentage points; (2) increased VE by five percentage points; (3) increased VC and VE by five percentage points; and (4) increased VC to levels achieved in 2019/20 (the season with the highest pediatric VC to date). We estimated the difference in hospitalizations and deaths between each scenario and the baseline scenario. Results: Among children 6 months-17 years, an estimated 120,000 influenza-associated hospitalizations and 1,650 deaths occurred across all seasons at baseline. Increasing VC to 2019/20 levels had the greatest estimated impact, reducing hospitalizations by 8,670 (7.2%) and deaths by 130 (7.9%). Increasing both VC and VE by five percentage points reduced hospitalizations by 6.6% and deaths by 6.7%, whereas increasing VC or VE by five percentage points reduced hospitalizations by 3.1% and deaths by 3.2%. Conclusions: Increasing pediatric influenza vaccination coverage back to 2019/20 levels was estimated to significantly reduce pediatric influenza-associated morbidity and mortality.

As health AI tools enter clinical spaces, how they are perceived by providers in safety-net environments shapes whether their benefits reach underserved populations. We examined the degree and drivers of healthcare providers' trust in health AI, whether that trust moderates perceived benefits for safety-net populations, and how providers balance benefit against concern across four specific hypothetical AI tools. We used a two-study mixed-methods design: 18 semi-structured interviews with healthcare practitioners and research leaders, followed by a survey of 229 Texas healthcare providers incorporating four vignettes describing hypothetical AI tools, each rated for appeal and concern and followed by open-ended explanation. Responses were thematically coded and stratified by self-reported trust in health AI (47.6% high trust, 52.4% low trust). Low-trust respondents most often cited building trust (50.5%) and data bias and accuracy (33.0%) as drivers of their current trust level, while high-trust respondents more often cited efficient and effective care (23.5% vs. 3.7% low-trust, p<0.001) and accuracy of data (11.8% vs. 1.8% low-trust, p=0.004); concerns about data security, governance, and the need for human oversight were raised at similar rates by both groups. When asked how AI could specifically benefit safety-net populations, both groups converged on expanding access to care (45.9% each), and none of 25 benefit-related codes differed significantly by trust group. Across all four vignettes, mean appeal ratings exceeded mean concern ratings, and high-trust providers rated every tool as more appealing and, for three of four tools, less concerning, without changing which tool ranked where relative to others. Providers exhibit conditional optimism: support is contingent on governance, human oversight, and context-sensitive implementation rather than on the technology itself. Realizing AI's promise for safety-net populations will require human-in-the-loop models, localized implementation strategies, community-informed design, and engaging providers directly in policy design and tool development.

Objective: To estimate the impact of removing race-adjusted antihypertensive treatment recommendations for Black adults on health outcomes, healthcare utilization, and racial/ethnic disparities. Materials and methods: A discrete-time state-transition microsimulation model of hypertension natural history and health system utilization was developed to simulate lifetime trajectories among 100,000 non-Hispanic Black and non-Hispanic white 40-year-old adults. We evaluated the effect of removing race-adjusted antihypertensive treatment recommendations under two scenarios: one assuming racial heterogeneity in treatment effectiveness, reflecting evidence behind prior race-specific guidelines, and the other assuming no racial heterogeneity. Our main outcomes included life expectancy, cumulative incidence and duration of hypertension, and blood pressure control with antihypertensive treatment. Results: Under assumptions of heterogeneous treatment effectiveness by race, guidelines without race adjustment decreased life expectancy among non-Hispanic Black adults by 0.4 years, increasing the life expectancy gap with non-Hispanic white adults from 4.1 to 4.5 years. Hypertension control among non-Hispanic Black adults also worsened. Years with uncontrolled hypertension increased by 0.8 years, widening the disparity with non-Hispanic white adults from 1.7 to 2.5 years. Assuming no racial heterogeneity, these effects were diminished: life expectancy decreased by 0.1 years and years with uncontrolled hypertension increased by 0.2 years, less than one-half and one-quarter of the effects observed under assumptions of heterogeneous treatment effectiveness by race. Discussion: The potential effects of removing race-adjusted antihypertensive treatment guidelines depended on assumptions of racial heterogeneity in treatment effectiveness. Conclusion: Our findings highlight the importance of understanding drivers of previously observed racial differences when considering modifying antihypertensive treatment guidelines.