Applied Health Economics and Health Policy最新文献

{"title":"Cost-Utility Analysis of Genomic Profiling in Directing Targeted Therapy in Advanced NSCLC in Thailand.","authors":"Saowalak Turongkaravee, Surakit Nathisuwan, Thanyanan Baisamut, Jennis Meanwatthana","doi":"10.1007/s40258-025-00950-3","DOIUrl":"https://doi.org/10.1007/s40258-025-00950-3","url":null,"abstract":"<p><strong>Background: </strong>Sequential next-generation sequencing (NGS) testing has demonstrated cost-effectiveness in guiding targeted therapy with tyrosine kinase inhibitors (TKIs) for advanced non-small cell lung cancer (aNSCLC) in developed countries. However, its cost-effectiveness in developing countries remains uncertain.</p><p><strong>Objective: </strong>The aim was to conduct a cost-utility analysis comparing sequential NGS testing with the current approach of single epidermal growth factor receptor (EGFR) testing combined with first-line targeted therapy, as implemented under Thailand's Universal Health Coverage scheme for aNSCLC.</p><p><strong>Method: </strong>Hybrid decision tree and Markov models were developed to estimate the lifetime costs and quality-adjusted life years (QALYs) associated with each strategy. The models simulate cohorts of aNSCLC patients who receive platinum-based chemotherapy or TKIs based on identified gene alterations. Patients enter the model at 60 years of age. The incremental cost-effectiveness ratio (ICER) was computed from a societal perspective. The analysis employed a lifetime horizon and discounted costs and outcomes at a rate of 3%. Furthermore, uncertainty and scenario analyses were conducted.</p><p><strong>Findings: </strong>A sequential NGS testing strategy could identify an additional 19% of patients with biomarker-positive findings who subsequently received biomarker-driven targeted therapy compared to a single EGFR testing strategy. The number needed to screen to identify a single gene mutation and administer first-line TKI was six for the sequential NGS testing strategy. Compared to the single EGFR testing, the ICER of the sequential NGS testing strategy was 1,851,150 THB/QALY (US$51,335). At a willingness-to-pay threshold of 160,000 THB/QALY (US$4437), the single EGFR testing strategy demonstrated 100% cost-effectiveness. In contrast, the sequential NGS testing was not deemed cost-effective. The sensitivity of the ICER was influenced by the overall survival rates associated with anaplastic lymphoma kinase (ALK) inhibitors and platinum-based chemotherapy.</p><p><strong>Interpretation: </strong>Sequential NGS testing identified a greater number of patients with aNSCLC eligible for targeted therapies, resulting in improved survival rates and enhanced QALYs compared to single EGFR testing. However, in the context of Thailand, sequential NGS testing was not cost-effective. The single EGFR testing strategy emerged as the most cost-effective option for guiding first-line targeted therapy.</p>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Health Utilities Using Health Administrative Data: Leveraging Survey-linked Health Administrative Data from Ontario, Canada.","authors":"Yue Niu, Nazire Begen, Guangyong Zou, Sisira Sarma","doi":"10.1007/s40258-025-00947-y","DOIUrl":"https://doi.org/10.1007/s40258-025-00947-y","url":null,"abstract":"<p><strong>Background: </strong>The quality-adjusted life year (QALY) is widely used to measure health outcome that combines the length of life and health-related quality of life (HRQoL). To be a reliable QALY measure, HRQoL measurements with a preference-based scoring algorithm need to be converted into health utilities on a scale from zero (dead) to one (perfect health). However, preference-based health utility data are often not available. We address this gap by developing a predictive model for health utilities.</p><p><strong>Objectives: </strong>To develop a predictive model for health utilities using available demographic and morbidity variables in a health administrative dataset for non-institutionalised populations in Ontario, Canada.</p><p><strong>Methods: </strong>The data were obtained from the 2009 to 2010 Canadian Community Health Survey containing Health Utilities Index Mark3 (HUI3), a generic multi-attribute preference-based health utility instrument linked with Ontario health administrative (OHA) data that were collected for administrative or billing purposes for patient encounters with the health care system. We employed four regression models (linear, Tobit, single-part beta mixture, and two-part beta mixture) and a calibration technique to identify the best-fit regression model.</p><p><strong>Results: </strong>Our findings indicate that the two-part beta mixture model is the best-fit for predicting health utilities in the OHA data. The proposed predictive model reflects the original distribution of HUI3 in the population.</p><p><strong>Conclusion: </strong>Our proposed predictive model generates reasonably accurate health utility predictions from OHA data. Our model-based prediction approach is a useful strategy for real-world applications, particularly when preference-based utility data are unavailable.</p>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Economic Evaluations of Obesity Interventions: Expert Views on How We Can Identify, Interpret, Analyse and Translate Effects","authors":"Samira B. Jabakhanji,&nbsp;Gintare Valentelyte,&nbsp;Fabian Manke-Reimers,&nbsp;Vidar Halsteinli,&nbsp;Rønnaug Ødegård,&nbsp;Adam Martin,&nbsp;Grace O’Malley,&nbsp;Jan Sorensen,&nbsp;Emilia Hagman","doi":"10.1007/s40258-025-00946-z","DOIUrl":"10.1007/s40258-025-00946-z","url":null,"abstract":"<div><p>Economic evaluations of obesity interventions are critical to informing policymakers and clinical practitioners about best-value prevention and treatment interventions. However, existing studies often fail to measure appropriate outcomes over sufficient time periods and to adequately address the complexity of data, environments and outcomes. An international, multidisciplinary workshop in Ireland (May 2023) addressed these issues through scientific presentations on obesity modelling, group discussions and interactive small-group exercises. Nineteen presenters and participants co-created a list of research needs, priorities and strategies for the long-term study of obesity and its complications. To support availability of relevant outcome and cost data for health economic analyses, participants highlighted a need to define standards for data collection, data sharing, modelling, and integrating a systems perspective. For example, regarding data collection, careful consideration must be given to selecting valid and relevant health-related outcomes for determining future health risk. Although these issues have been previously highlighted, they remain critical barriers to comprehensive economic obesity studies. To identify best-value obesity interventions, researchers should prioritise strategies to overcome these barriers. This includes early engagement with multidisciplinary stakeholders to integrate diverse perspectives. Developing infrastructure to support international collaborations between researchers, policymakers and patient representatives was also recommended. Additionally, establishing best-practice guidelines could help researchers navigate the complexities of obesity data, environments and outcomes, particularly in data-scarce research environments. The creation of a core outcomes set for obesity would standardise measures for economic evaluations, thereby facilitating more robust cross-country comparisons of intervention effects and improving the evidence base and overall quality of future obesity research.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 2","pages":"161 - 169"},"PeriodicalIF":3.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40258-025-00946-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143073496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contextual Factors that Influence Antibiotic Prescribing: A Discrete Choice Experiment of GP Registrars","authors":"Gregory Merlo,&nbsp;Lisa Hall,&nbsp;Parker Magin,&nbsp;Amanda Tapley,&nbsp;Katie J. Mulquiney,&nbsp;Alison Fielding,&nbsp;Andrew Davey,&nbsp;Joshua Davies,&nbsp;Mieke van Driel","doi":"10.1007/s40258-025-00944-1","DOIUrl":"10.1007/s40258-025-00944-1","url":null,"abstract":"<div><h3>Introduction</h3><p>Antimicrobial resistance is a global emergency related to overprescribing of antibiotics. Few studies have explored how prescribing behaviours may change as the consequence of changing resistance. Understanding how contextual factors influence antibiotic prescribing will facilitate improved communication strategies to promote appropriate antibiotic prescribing. We aimed to develop and conduct a discrete choice experiment (DCE) to measure how contextual factors influence intended antibiotic prescribing of general practitioner (GP) registrars.</p><h3>Methods</h3><p>Factors included as attributes in the DCE were level of antibiotic resistance, requirement for an authority to prescribe, existence of a Practice Incentives Program (PIP) for low prescribing and supervisor support for low prescribing. The survey was administered in an online format for GP registrars undergoing training between 2020 and 2021. Regression analysis using a conditional logit model with interaction effects was used on the basis of the assumptions of independence of irrelevant alternatives, independence of error terms and no preference heterogeneity.</p><h3>Results</h3><p>In total, 617 unique respondents answered at least one choice set question. Respondents showed significant preference for avoiding prescribing antibiotics when antibiotic resistance was 25–35% or 40–60% compared with 5–8%. There was also a significant preference for avoiding prescribing when an authority to prescribe was required, or when there was supervisory support of low antibiotic prescribing. In the main effects analysis, respondents were significantly less likely to choose a prescribing option if there was a PIP; however, when interaction effects were included in the regression analysis there was a significant interaction between PIP and resistance rates, but the preference weights for PIP was no longer significant.</p><h3>Conclusions</h3><p>Knowledge about community resistance impacts the stated intention of GP registrars to prescribe antibiotics. The use of the DCE may have made it possible to determine factors influencing prescribing that would not be detected using other survey methods. These findings provide guidance for producing, explaining and communicating issues regarding antibiotic prescribing to GP registrars.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 2","pages":"311 - 317"},"PeriodicalIF":3.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s40258-025-00944-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approaches to Incorporation of Preferences into Health Economic Models of Genomic Medicine: A Critical Interpretive Synthesis and Conceptual Framework.","authors":"Hadley Stevens Smith, Dean A Regier, Ilias Goranitis, Mackenzie Bourke, Maarten J IJzerman, Koen Degeling, Taylor Montgomery, Kathryn A Phillips, Sarah Wordsworth, James Buchanan, Deborah A Marshall","doi":"10.1007/s40258-025-00945-0","DOIUrl":"https://doi.org/10.1007/s40258-025-00945-0","url":null,"abstract":"<p><strong>Introduction: </strong>Genomic medicine has features that make it preference sensitive and amenable to model-based health economic evaluation. Preferences of patients, caregivers, and clinicians related to the uptake and delivery of genomic medicine technologies and services that are not captured in health state utility weights can affect the intervention's cost-effectiveness and budget impact. However, there is currently no established or agreed-on approach for integrating preference information into economic evaluations. The objective of this study was to explore approaches for incorporating preferences into model-based economic evaluations of genomic medicine and to develop a conceptual framework to consider preferences in health economic models.</p><p><strong>Methods: </strong>We conducted a critical interpretive synthesis of published literature guided by the following question: how have preferences been incorporated into model-based economic evaluations of genomic medicine interventions? We integrated findings from the literature and expert opinion to develop a conceptual framework of ways in which preferences influence economic value in the context of genomic medicine.</p><p><strong>Results: </strong>Our synthesis included 14 articles. Revealed and stated preference data were used to estimate choice probabilities and to value outcomes. Our conceptual framework situates preference data in the context of health system, patient, clinician, and family characteristics. Preference data were sourced from clinicians, patients and families impacted by a condition or intervention, and the general public. Evaluations employed various types of models, including discrete event simulation, microsimulation, Markov, and decision tree models.</p><p><strong>Conclusion: </strong>When evaluating the broad benefits and costs of implementing new interventions, sufficiently accounting for preferences in the form of model inputs and valuation of outcomes in economic evaluations is important to avoid biased implementation decisions. Incorporation of preference data may improve alignment between predicted and real-world uptake and more accurately estimate welfare impacts, and this study provides critical insights to support researchers who seek to incorporate preference information into model-based health economic evaluations.</p>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Productivity Losses due to Health Problems Arising from COVID-19 Pandemic: A Systematic Review of Population-Level Studies Worldwide","authors":"Paweł Niewiadomski,&nbsp;Marta Ortega-Ortega,&nbsp;Błażej Łyszczarz","doi":"10.1007/s40258-024-00935-8","DOIUrl":"10.1007/s40258-024-00935-8","url":null,"abstract":"<div><h3>Aim</h3><p>To systematically review the evidence on productivity losses due to health problems arising from the COVID-19 pandemic based on evidence from population-level studies.</p><h3>Methods</h3><p>Following PRISMA statement, we conducted a systematic review using Medline, Embase, Scopus, Web of Science, EconLit, WHO COVID-19 Research and EuropePMC databases and a grey literature search. We included population-level studies using secondary data and qualitatively assessed eligible studies. For a quantitative cross-study comparison, we calculated losses in 2020 international dollars and as a share of gross domestic product. PROSPERO registration number: CRD42023478059.</p><h3>Results</h3><p>Thirty-eight studies were eligible for review, most of which reported losses in high-income countries and the European region. COVID-19 was a focus of 33 studies while 3 studies investigated losses from both long COVID and excess mortality. The Human Capital Approach dominated (30 studies) and no study used the Friction Cost Approach. Most studies (84%) reported on premature mortality losses and a quarter provided estimates of losses due to absenteeism. Of the 33 studies eligible for quantitative comparison, we found that the productivity losses ranged from 0 to 2.1% of gross domestic product; the greatest losses were in the high-income countries and for those aged 40–59 years; and losses among men contributed to around 3/4 of the total burden.</p><h3>Conclusion</h3><p>The available evidence on the topic is limited, particularly considering the methodological approaches used. Thus, more research is needed to reach a more comprehensive understanding of economy-level productivity losses resulting from the recent COVID-19 pandemic.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 2","pages":"231 - 251"},"PeriodicalIF":3.1,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Australian Preferences for Prenatal Screening: A Discrete Choice Experiment Comparing Metropolitan and Rural/Regional Areas.","authors":"Amber Salisbury, Sarah Norris, Alison Pearce, Kirsten Howard","doi":"10.1007/s40258-024-00938-5","DOIUrl":"https://doi.org/10.1007/s40258-024-00938-5","url":null,"abstract":"<p><strong>Background: </strong>Non-invasive prenatal testing has the potential to be a useful genetic screening tool in Australia. However, concerns have been raised about its cost, commercial provision, the psychological impacts of the screening process, and disparities in access experienced by rural and regional communities.</p><p><strong>Aims: </strong>The aims of this study are (1) to estimate Australian preferences for features of prenatal screening; (2) to explore potential variations in preferences between metropolitan and rural/regional communities; (3) to estimate the extent to which respondents are willing to trade-off between attributes, using willingness to pay (WTP) and willingness to wait estimates.</p><p><strong>Methods: </strong>A discrete choice experiment (DCE) was conducted with 12 choice tasks. The DCE recruited participants from metropolitan (n = 160) and rural/regional (n = 168) locations across Australia. Mixed logit and latent class analyses were conducted and WTP and willingness to wait were calculated.</p><p><strong>Results: </strong>Both metropolitan and rural/regional preferences were significantly impacted by the false-positive rate, false-negative rate, and cost. In addition, rural preferences were significantly impacted by the scope of the conditions covered, the inconclusive rate, and wait times. The number of screening tests and revealing the sex of the foetus were not significant within either group. Willingness to pay estimates ranged from AU$13 to avoid a test with a 1% increase in the false-positive rate to AU$323 to screen for a wide range of conditions.</p><p><strong>Conclusions: </strong>This study highlights the importance of considering differing preferences between rural and metropolitan populations when delivering prenatal screening. Further, this study provides Australian-specific WTP estimates to be incorporated into economic evaluations.</p>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods and Practical Considerations for Conducting Budget Impact Analysis for Non-Pharmaceutical Interventions","authors":"Xuanqian Xie,&nbsp;Jennifer Guo,&nbsp;Alexis K. Schaink,&nbsp;Kamilla Guliyeva,&nbsp;Chunmei Li,&nbsp;Wendy J. Ungar","doi":"10.1007/s40258-024-00943-8","DOIUrl":"10.1007/s40258-024-00943-8","url":null,"abstract":"<div><h3>Background</h3><p>Health technology assessment (HTA) can be conducted at the national, provincial, or hospital level. Although provincial and hospital-based HTAs often focus on non-pharmaceutical interventions, budget impact analysis (BIA) methods for non-pharmaceutical interventions have received less attention in the literature.</p><h3>Methods</h3><p>We reviewed HTAs of non-pharmaceutical interventions published since 2015 by a Canadian provincial HTA agency, evaluating the characteristics and challenges of conducting a BIA.</p><h3>Results</h3><p>We summarized the unique characteristics of BIAs for different categories of interventions, including surgery and other procedures, diagnostic or screening tests, therapeutic programs, and digital health technologies. We then discussed specific methodological and practical considerations for conducting a BIA of a surgical or other hospital-based procedure. Critical points for BIA methods include the following: (1) when estimating the size of a target population, healthcare system capacity must be accounted for, and historical volumes may offer more realistic figures than prevalence and incidence rates; (2) factors that affect the uptake of a new intervention include guideline recommendations, labor and infrastructure requirements for implementation, and the target population size; (3) when interpreting a budget impact that shows cost savings, analysts must address where the savings are generated from and whether they can be reallocated. Some of the considerations discussed may also apply to HTAs of pharmaceuticals.</p><h3>Conclusions</h3><p>When conducting a BIA of a non-pharmaceutical intervention, addressing these methodological considerations may help in better predicting the financial impact of the new intervention for the public payer and guide appropriate budget allocation for healthcare system planning.</p></div>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 2","pages":"197 - 208"},"PeriodicalIF":3.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Test Utilization and Cost among Families of Children Evaluated for Genetic Conditions: An Analysis of USA Commercial Claims Data.","authors":"Hadley Stevens Smith, Matthew Lakoma, Madison R Hickingbotham, Dawn Cardeiro, Katharine P Callahan, Monica H Wojcik, Ann Chen Wu, Christine Y Lu","doi":"10.1007/s40258-024-00942-9","DOIUrl":"https://doi.org/10.1007/s40258-024-00942-9","url":null,"abstract":"<p><strong>Introduction: </strong>Healthcare payers in the USA increasingly cover genetic testing, including exome sequencing (ES), for pediatric indications. Analysis of claims data enables understanding of utilization and costs in real-world settings. The objective of this study was to describe genetic test utilization, diagnostic outcomes, and costs for children who received ES as well as for those who received less comprehensive forms of genetic testing, along with their families.</p><p><strong>Patients and methods: </strong>We analyzed linked family claims data for commercially insured members of a large regional health plan. The sample included children younger than 18 years of age who had at least 1 year of continuous plan enrollment and at least one claim for genetic testing from 2016 to 2022, as well as their family members. We compared outcomes for children who ever had a claim for ES (ES cohort) with those for children who had claims for only less comprehensive genetic testing (other genetic testing (OGT) cohort). We evaluated the frequency of ICD-10 codes indicating genetic diagnoses, health care utilization, and out-of-pocket costs in relation to the timing of the index genetic test using t-tests and inverse-probability-of-treatment weighted regression models to control for observable clinical and demographic characteristics associated with type of testing received.</p><p><strong>Results: </strong>Our sample included 182 children (mean comorbidity index 4.78) in the ES cohort and 1789 children in the OGT cohort (3.63; p < 0.001). ES led to an average of 1.44 (95% confidence interval [CI] 0.67-2.20) more new genetic diagnostic codes after testing than OGT. A larger proportion of the proband's family members had subsequent genetic testing in the ES cohort (mean 33.3%) than in the OGT cohort (0.5%; p < 0.001), but no differences in the number of new genetic diagnoses in family members were observed. Out-of-pocket costs for genetic testing did not differ between the two cohorts stratified by clinical severity.</p><p><strong>Conclusions: </strong>In our sample of commercially insured pediatric patients, claims for ES were less frequent and occurred among children with more clinical complexity than those for less comprehensive genetic testing. Children in the ES cohort had a higher number of new genetic diagnoses post-testing than those in the OGT cohort with no significant differences in out-of-pocket cost of testing to families. Our findings suggest that ES is being reimbursed for children who may be difficult to diagnose.</p>","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":" ","pages":""},"PeriodicalIF":3.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Economic Evaluations of Robotic-Assisted Surgery: Methods, Challenges and Opportunities","authors":"Tzu-Jung Lai,&nbsp;Robert Heggie,&nbsp;Hanin-Farhana Kamaruzaman,&nbsp;Janet Bouttell,&nbsp;Kathleen Boyd","doi":"10.1007/s40258-024-00941-w","DOIUrl":"10.1007/s40258-024-00941-w","url":null,"abstract":"","PeriodicalId":8065,"journal":{"name":"Applied Health Economics and Health Policy","volume":"23 2","pages":"329 - 329"},"PeriodicalIF":3.1,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142909104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}