Journal of Medical Economics最新文献

Background: Lung cancer caused 2.2 million new cases and 1.8 million deaths globally in 2020, accounting for 11.4% of global cancer. In the Asia-Pacific (AP) region, lung cancer is the leading cause of cancer mortality, contributing to 60% of global lung cancer deaths. This study assesses the economic impact of premature lung cancer deaths in 12 AP countries with different economic profiles by estimating productivity losses.

Methods: The human capital approach was used to estimate productivity losses from premature lung cancer deaths (ICD-10 C33-34) across Singapore, Australia, Hong Kong, New Zealand, South Korea, Taiwan, Malaysia, Thailand, Indonesia, Philippines, Vietnam, and India. Years of productive life lost (YPLL) and the present value of future lost productivity (PVFLP) were calculated based on age-specific mortality, wage, and employment data. Results were analyzed by grouping countries into high- and middle-income categories.

Findings: In 2019, 221,293 lung cancer deaths led to 617,574 YPLL and over $2.3 billion in productivity losses across the 12 AP countries. High-income countries experienced greater losses ($1.5 billion, $34,359 per death) compared to middle-income countries ($816 million, $4,660 per death).

Limitations: The analysis excluded direct healthcare costs and productivity losses from morbidity and caregiver burden. Assumptions such as uniform labor force participation and mortality distribution may limit precision.

Interpretation: Lung cancer imposes a significant burden across the AP region, with economic disparities between high- and middle-income countries. Findings highlight the need for continued investment in prevention, early detection, and equitable access to treatment, especially in middle-income nations.

Aims: The aim of this study was to systematically review and descriptively synthesize published cost-effectiveness evidence for biologic versus non-biologic treatments as well as comparisons among different biologic therapies and treatment sequencing strategies, in inflammatory bowel disease, highlighting patterns and variability across disease types, treatment strategies, and study designs.

Materials and methods: We conducted a systematic review following the PRISMA guidelines, searching the Web of Science and PubMed databases for studies published between 2013 and 2024. Studies comparing the cost-effectiveness of treatments for inflammatory bowel disease were included.

Results: Eighteen studies met the inclusion criteria, covering Crohn's disease and/or ulcerative colitis. Biologic therapies were generally associated with superior health outcomes compared with conventional treatments. Reported quality-adjusted life years (QALYs) ranged from 2.23 to 18.12 for biologic therapies and from 1.69 to 17.99 for non-biologic treatments. Although biologic therapies have higher costs, they are generally considered cost-effective. For Crohn's disease, infliximab was reported as a cost-effective biologic option, while findings for ulcerative colitis varied. Surgical intervention (colectomy) was identified as a cost-effective in selected clinical scenarios.

Conclusion: Biologic therapies for inflammatory bowel disease are cost-effective, providing significant health benefits that offset higher costs. Substantial methodological heterogeneity and reliance on model-based economic evaluations limit direct comparability across studies. Future economic evaluations should focus on methodological consistency and transparency in assumptions to strengthen the interpretability of cost-effectiveness evidence.

Objective: Exagamglogene autotemcel (exa-cel) is a one-time nonviral gene-edited therapy approved in the United States (US) for treatment of patients aged ≥12 years with sickle cell disease (SCD) with recurrent vaso-occlusive crises (VOCs). Standard of care (SOC) for SCD includes symptomatic care, hydroxyurea and/or red blood cell transfusions. This study estimated the long-term clinical outcomes and cost-effectiveness of exa-cel relative to SOC among patients with SCD with recurrent VOCs.

Methods: A Markov model was used to compare the expected lifetime costs and clinical outcomes of patients with SCD with recurrent VOCs treated with exa-cel versus SOC from the US payer and societal perspectives. The model structure is based on disease severity, characterized by VOC frequency, which impacts the risk of developing SCD-related complications and mortality. The model incorporated data from the phase 3 pivotal CLIMB SCD-121 trial alongside published literature. Model outcomes included number of VOCs and other acute complications, proportion of patients developing chronic complications, life years (LYs), quality-adjusted LYs (QALYs), costs, and incremental cost-effectiveness ratios (ICERs).

Results: Over a lifetime horizon, exa-cel was projected to improve survival by 30.8 years (mean age of death, exa-cel: 74.5 vs. SOC: 43.6), reduce the number of VOC events by 77 (7 vs. 84), and reduce undiscounted disease-related costs by $3.34 M ($0.55 M vs. $3.89 M) compared to treatment with SOC. Patients treated with exa-cel also were less likely to experience acute complications or develop chronic complications compared to SOC. The ICER per discounted QALY for exa-cel versus SOC was $16,800 from the payer perspective; exa-cel was dominant (less costly, more effective than SOC) from the societal perspective.

Conclusions: Compared to SOC, exa-cel was projected to considerably reduce the number of VOCs, improve survival, and reduce disease-related costs in patients with SCD. Exa-cel was projected to be a cost-effective treatment option.

Aim: To compare healthcare utilization and spending among women enrolled in an employer-sponsored, artificial intelligence (AI) structured pelvic care program with those receiving usual in-person care for pelvic floor dysfunction (PFD) in routine clinical settings.

Methods: This retrospective payor-perspective economic evaluation used exact and propensity score-matched cohorts derived from a third-party U.S. nationwide claims database from July 2022 to May 2025. Eligible participants were adult females with a pelvic-related condition, at least 24 months of continuous health-insurance coverage, and a minimum of one pelvic claim in the prior year. Intervention group (IG) comprised women who participated in the AI pelvic care program (consisting of biofeedback-mediated pelvic floor muscle training asynchronously monitored by a physical therapist specialized in pelvic health). Comparator group (CG) included women who sought a medical or physical therapy evaluation visit for PFD. Self-reported clinical outcomes available for the IG were assessed using latent-basis growth analysis.

Results: The matched cohort included 602 women (301 per group). Relative to CG, IG patients had substantially lower healthcare spending over 12 months, with mean gross per-person pelvic-related savings of $3,082.4 (95% CI $1,270.2 to $4,894.7, p<.001). Savings were primarily associated with fewer surgical procedures (per-person difference of $2,534.2; 95% CI $831.2 to $4,237.2, p=.004), with differences also noted in medical office visits and imaging utilization. IG participants demonstrated significant improvements in pelvic floor symptom burden, work productivity, and mental health.

Limitations: Claims-based analyses cannot exclude unmeasured confounding, misclassification, or selection bias. The one-year follow-up limits assessment of long-term economic impact.

Conclusions: Participation in this AI pelvic care program was associated with markedly lower healthcare utilization and spending compared with usual care, largely linked to fewer surgical interventions. These findings highlight the potential of accessible, guideline-concordant AI pelvic care to lessen healthcare spending associated with PFD and inform payor-oriented care delivery models.

Aims: This study compares deep learning architectures with traditional regression and tree-based models for individual-level healthcare cost prediction, with particular attention to performance differences across data contexts.

Methods: We conducted a preregistered systematic review (PROSPERO CRD420251129440). Web of Science, PubMed, Embase, and Scopus were searched through August 2025. Eligible studies used real-world individual-level data (claims, electronic health records, or registries) to predict cost-related outcomes with at least one deep learning architecture and one classical regression comparator, and reported quantitative performance. Data were extracted on population, predictors, outcome horizon, model type, validation strategy, performance metrics, calibration, and interpretability.

Results: Eight studies met inclusion criteria, spanning the United States, Europe, and Asia. In longitudinal designs-such as multi-year claims prediction and medication or hospitalization time-series forecasting-sequential deep learning models, particularly LSTM and CNN-LSTM hybrids, consistently outperformed regression and tree-based algorithms. Reported gains included approximately 10-20% reductions in RMSE/MAE, R² improvements of 0.01-0.15, and AUROC values up to 0.78 for high-risk classification. Across studies, prior costs and utilization were consistently the strongest predictors, while social determinants and free-text features were rarely incorporated. In contrast, for low-dimensional, structured, cross-sectional medical data, generalized linear models and tree-based approaches remain robust baseline models due to their interpretability and calibration stability.

Limitations: Evidence is based on a small and heterogeneous set of eight studies, with limited external or temporal validation, short prediction horizons, and sparse assessment of calibration, economic interpretability, and fairness, warranting cautious interpretation.

Conclusions: Deep learning offers clear gains for longitudinal, sequence-rich cost forecasting, whereas tree-based methods remain highly competitive for cross-sectional tabular prediction. Overall, these findings are consistent with the proposed Complexity-Performance Hypothesis, which posits that the predictive advantages of deep learning emerge primarily when model capacity is well matched to data complexity.

Background: Underreporting of infections, hospitalizations, and deaths can pose challenges to accurately estimating the true burden of COVID-19. Consequently, health burden assessments and economic evaluations may underestimate the public health impact of interventions such as vaccination.

Methods: This targeted literature review summarized economic evaluations of COVID-19 that reported having adjusted for underreporting of epidemiological burden. Searches were performed in PubMed through 08/31/2025 with no geographic restrictions. Key study characteristics extracted: country, time period, population, parameters adjusted for underreporting, and the adjustment multipliers used. A high-level quality assessment of evidence was conducted, building on Drummond checklist and CHEERS. Given the qualitative nature of the question and the expected heterogeneity in study designs, the results were summarized qualitatively.

Results: A total of 20 studies met the inclusion criteria. Of these, 14 (70%) reported numerical adjustment factors, and the remaining 30% did not report a numerical factor. The studies covered diverse geographic regions and time frames, with adjustments applied to parameters such as infections, hospitalizations, and mortality. The study quality was moderate to high. The multipliers used ranged widely across studies: 1 to 5 for mortality, 1 to 5 for hospitalizations, and 1 to 10 for infections, where a value higher than 1.0 reflects an adjustment factor for underreporting. The methodologies used to estimate underreporting varied, including comparisons to excess mortality data, Monte Carlo simulations, and validation against external datasets.

Limitations: Most studies used pandemic time horizons.

Conclusions: This review identified 14 modelling studies reporting numerical adjustment factors. The studies used diverse approaches and adjustment factors, reflecting variability in data availability and estimation methods. Recognizing and standardizing these adjustments is crucial for improving the accuracy and comparability of health economic analyses that inform policy decisions. Further research could refine underreporting estimates and assess their impact on economic model outcomes.

Background: Septic shock is a life-threatening condition associated with high morbidity, mortality, and healthcare costs. Vasopressin (VA) is recommended as a second-line vasopressor in septic shock, but its cost-effectiveness-especially regarding the timing of administration-remains unclear in European settings.

Methods: A hybrid decision-analytic model combining a short-term decision tree and a long-term Markov model was developed to evaluate the cost-effectiveness of VA in adult patients with septic shock. The analysis was conducted from both a healthcare payer and societal perspective. Clinical efficacy inputs were derived from high-quality meta-analyses and systematic reviews. The model incorporated health-states such as end-stage renal-disease (ESRD) with need for renal replacement therapy (RRT), atrial fibrillation (AF), and mortality over a lifetime horizon. Two comparisons were analyzed: VA versus No VA, and early (within 3-12 h of shock onset) versus late VA administration. Outcomes included incremental cost-effectiveness ratio (ICER), life-years (LYs), quality-adjusted life-years (QALYs), and direct and indirect cost estimates.

Results: Adding VA was a dominant strategy, improving clinical outcomes while reducing lifetime costs by 10,570 €per patient and yielding 0.09 additional QALYs. VA therapy reduced RRT dependence by 2.5% and increased AF-free survival by 6.2%. Early VA administration was even more cost-effective, providing 0.55 additional QALYs, 0.77 extra LYs, and 4,746 €in additional savings compared to late administration.

Conclusion: Second-line VA is a cost-effective intervention for septic shock, notably when initiated early. These findings support guideline recommendations for early vasopressor use and emphasize the clinical and economic value of timely VA therapy.

Aim: Anti-PD-(L)1 agents, inhibitors of programmed cell death protein 1 (PD-1) or its ligand (PD-L1), are established therapies that improve cancer management as well as the disease and societal burden of specific metastatic and early-stage cancers. The aim of the study was to determine the impact of adopting anti-PD-(L)1 agents for the treatment of all eligible patients with early-stage cancers versus reserving anti-PD-(L)1 agents for patients with metastatic disease alone in Hungary.

Methods: This study evaluated two scenarios, one where anti-PD-(L)1 agents were used to treat all eligible early-stage disease cases (ESD scenario) of melanoma (stage IIB-C and III), renal cell carcinoma (RCC), and triple-negative breast cancer (TNBC) versus a reference scenario where anti-PD-(L)1 agents were only used to treat metastatic disease cases in Hungary (2024-2033). A Markov-modeling approach estimated the health outcomes and productivity losses from each scenario from a societal perspective. Outcomes included recurrence-/event-/disease-free life-years, total life-years, quality-adjusted life-years (QALYs), productive years (patients and caregivers), recurrences/events, active treatments for metastatic disease, and deaths. The cumulative health and productivity impact of ESD treatment with anti-PD-(L)1 agents in Hungary was the difference in health and productivity outcomes between the ESD and reference scenarios for the time horizon of the model.

Results: ESD treatment with anti-PD-(L)1 agents was estimated to increase recurrence-/event-/disease-free life-years (+13.8%), total life-years (+3.7%), and QALYs (+4.7%), as well as productive work years for patients (+39.6%) and caregivers (+27.6%). Concurrently, there would be fewer recurrences/events (-31.0%), active treatments for metastatic disease (-34.0%), post-recurrence deaths (-30.3%), and total deaths (-23.1%).

Conclusion: Investing in anti-PD-(L)1 agents for early-stage disease may not only increase the life expectancy and QALYs for patients in Hungary but also increase productive work years for both patients and caregivers in Hungary. In addition, it may also help to reduce metastatic disease treatments and cancer-related deaths.