Medical Decision Making最新文献

{"title":"Shared Decision Making Is in Need of Effectiveness-Implementation Hybrid Studies.","authors":"Arwen H Pieterse, Leti van Bodegom-Vos","doi":"10.1177/0272989X241286516","DOIUrl":"https://doi.org/10.1177/0272989X241286516","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reframing SDM Using Implementation Science: SDM Is the Intervention.","authors":"Marla L Clayman, A Rani Elwy, Jason L Vassy","doi":"10.1177/0272989X241285418","DOIUrl":"https://doi.org/10.1177/0272989X241285418","url":null,"abstract":"","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating Social Determinants of Health in Infectious Disease Models: A Systematic Review of Guidelines.","authors":"Shehzad Ali, Zhe Li, Nasheed Moqueet, Seyed M Moghadas, Alison P Galvani, Lisa A Cooper, Saverio Stranges, Margaret Haworth-Brockman, Andrew D Pinto, Miqdad Asaria, David Champredon, Darren Hamilton, Marc Moulin, Ava A John-Baptiste","doi":"10.1177/0272989X241280611","DOIUrl":"https://doi.org/10.1177/0272989X241280611","url":null,"abstract":"<p><strong>Background: </strong>Infectious disease (ID) models have been the backbone of policy decisions during the COVID-19 pandemic. However, models often overlook variation in disease risk, health burden, and policy impact across social groups. Nonetheless, social determinants are becoming increasingly recognized as fundamental to the success of control strategies overall and to the mitigation of disparities.</p><p><strong>Methods: </strong>To underscore the importance of considering social heterogeneity in epidemiological modeling, we systematically reviewed ID modeling guidelines to identify reasons and recommendations for incorporating social determinants of health into models in relation to the conceptualization, implementation, and interpretations of models.</p><p><strong>Results: </strong>After identifying 1,372 citations, we found 19 guidelines, of which 14 directly referenced at least 1 social determinant. Age (<i>n</i> = 11), sex and gender (<i>n</i> = 5), and socioeconomic status (<i>n</i> = 5) were the most commonly discussed social determinants. Specific recommendations were identified to consider social determinants to 1) improve the predictive accuracy of models, 2) understand heterogeneity of disease burden and policy impact, 3) contextualize decision making, 4) address inequalities, and 5) assess implementation challenges.</p><p><strong>Conclusion: </strong>This study can support modelers and policy makers in taking into account social heterogeneity, to consider the distributional impact of infectious disease outbreaks across social groups as well as to tailor approaches to improve equitable access to prevention, diagnostics, and therapeutics.</p><p><strong>Highlights: </strong>Infectious disease (ID) models often overlook the role of social determinants of health (SDH) in understanding variation in disease risk, health burden, and policy impact across social groups.In this study, we systematically review ID guidelines and identify key areas to consider SDH in relation to the conceptualization, implementation, and interpretations of models.We identify specific recommendations to consider SDH to improve model accuracy, understand heterogeneity, estimate policy impact, address inequalities, and assess implementation challenges.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study.","authors":"Mathyn Vervaart","doi":"10.1177/0272989X241279459","DOIUrl":"https://doi.org/10.1177/0272989X241279459","url":null,"abstract":"<p><strong>Highlights: </strong>The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R.The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process.We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Nudge Strategies in Improving Physicians' Prescribing Behavior: A Systematic Review and Meta-analysis.","authors":"Maya Fey Hallett, Trine Kjær, Line Bjørnskov Pedersen","doi":"10.1177/0272989X241270001","DOIUrl":"https://doi.org/10.1177/0272989X241270001","url":null,"abstract":"<p><strong>Background: </strong>Nudges have been proposed as a method of influencing prescribing decisions.</p><p><strong>Purpose: </strong>The purpose of this article is to 1) investigate associations between nudges' characteristics and effectiveness, 2) assess the quality of the literature, 3) assess cost-effectiveness, and 4) create a synthesis with policy recommendations.</p><p><strong>Methods: </strong>We searched health and social science databases. We included studies that targeted prescribing decisions, included a nudge, and used prescribing behavior as the outcome. We recorded study characteristics, effect size of the primary outcomes, and information on cost-effectiveness. We performed a meta-analysis on the standardized mean difference of the studies' primary outcomes, tested for associations between effect size and key intervention characteristics, and created a funnel plot evaluating publication bias.</p><p><strong>Synthesis: </strong>We identified 21 studies containing 25 nudges. In total, 62 of 85 (73%) outcomes showed a statistically significant effect. The average effect size was -0.22 standardized mean difference. No studies included heterogeneity analyses. We found no associations between effects and selected study characteristics. Study quality varied and correlated with study design. A total of 7 of 21 (33%) studies included an evaluation of costs. These studies suggested that the interventions were cost-effective but considered only direct effects. We found evidence of publication bias.</p><p><strong>Limitations: </strong>Heterogeneity and few studies limit the possibilities of statistical inference about effectiveness.</p><p><strong>Conclusions: </strong>Nudges may be effective at directing prescribing decisions, but effects are small and health effects and cost-effectiveness are unclear. Future nudge studies should contain a rationale for the chosen nudge, prioritize the use of high-quality study designs, and include evaluations of heterogeneity, cost-effectiveness, and health outcomes to inform decision makers. Moreover, preregistration of the protocol is warranted to limit publication bias.</p><p><strong>Highlights: </strong>Nudging as a method to improve prescribing decisions has gained popularity during the past decade.We find that nudging can improve prescribing decisions, but effect sizes are mostly small, and the size of derived health outcomes is unclear.Most studies use feedback and error-stopping nudges to target excessive opioid or antibiotic prescribing, making heterogeneity analyses across nudge types difficult.Further research on the cost-effectiveness of nudges and generalizability is needed to guide decision makers considering nudging as a tool to guide prescribing decisions.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Frequencies Improve Public Understanding of Medical Test Results: An Experimental Study on Various Bayesian Inference Tasks with Multiple Scoring Methods and Non-Bayesian Reasoning Strategies","authors":"Soyun Kim","doi":"10.1177/0272989x241275191","DOIUrl":"https://doi.org/10.1177/0272989x241275191","url":null,"abstract":"BackgroundIt is well established that the natural frequencies (NF) format is cognitively more beneficial for Bayesian inference than the conditional probabilities (CP) format. However, empirical studies have suggested that the NF facilitation effect might be limited to specific groups of individuals. Unlike previous studies that focused on a limited number of Bayesian inference problems evaluated by a single scoring method, it was essential to examine multiple Bayesian problems using various scoring metrics. This study also explored the impact of numeracy on Bayesian inference and assessed non-Bayesian cognitive strategies using the numerical information in problem solving.MethodsIn a Web-based experimental survey, 175 South Korean adults were randomly assigned to 1 of 2 format groups (NF v. CP). After completing numeracy scales, participants were asked to estimate 4 Bayesian inference problems and document the numerical information used in their problem-solving process. Four scoring methods—strict rounding, loose rounding, absolute deviation, and 50-Split—were used to evaluate participants’ estimations.ResultsThe NF format generally outperformed the CP format across all problems, except in a chorionic villus sampling test problem when evaluated using the 50-Split method. In addition, numeracy levels significantly influenced Bayesian inference; participants with higher numeracy demonstrated better performance. In addition, participants used various non-Bayesian strategies influenced by the format and the nature of the problems.ConclusionsThe NF facilitation effect was consistently observed across multiple Bayesian problems and scoring methods. Individuals with higher numeracy levels benefited more from the NF format. The use of various non-Bayesian strategies varied with the formats and nature of specific tasks.HighlightsThe natural frequencies (NF) format is known to foster understanding of medical test results compared with the conditional probabilities (CP) format, but some studies have reported that this benefit is either nonexistent or limited to specific groups. This study aims to replicate previous empirical studies using various Bayesian problems using multiple scoring methods. The NF format fosters understanding of medical test results across all Bayesian problems by all scoring methods, except in the CVS problem when using a 50-Split scoring method. Participants with high numeracy perform better Bayesian inference than those with lower numeracy. Particularly, higher numerates benefit more in the NF format than lower numerates do. In addition, the public tend to use various non-Bayesian reasoning strategies depending on the format and the nature of the tasks.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Much Information Is Too Much? An Experimental Examination of How Information Disclosures May Unintentionally Encourage the Withholding of Health Information","authors":"Helen Colby, Deidre Popovich, Tony Stovall","doi":"10.1177/0272989x241275645","DOIUrl":"https://doi.org/10.1177/0272989x241275645","url":null,"abstract":"IntroductionInformation disclosures are used in medicine to provide patients with relevant information. This research examines whether patients are less likely to discuss medical conditions with their physicians after seeing an insurance information disclosure.MethodsThree experimental studies with nonprobability online samples (n<jats:sub>total</jats:sub> = 875 US adult participants) examined the impact of information disclosures on patients’ likelihood of disclosing symptoms to providers, using new symptoms and preexisting chronic conditions. The effects of insurance disclosures were also compared to those of pharmaceutical discount disclosures.ResultsThese studies demonstrate that information disclosures can result in unintended consequences for patients and providers. Results showed that information disclosures about insurance claims significantly negatively affected willingness to discuss health information with providers. This effect was consistent for both new health concerns, b = −0.661, P &lt; 0.001 (study 1, n = 250) and b = −0.893, P &lt; 0.001 (study 3, n = 375), as well as chronic conditions, b = −1.175, P &lt; .001 (study 2, n = 250); all studies were conducted in January 2023. Information provided to patients about pharmaceutical savings did not similarly affect willingness to discuss symptoms with providers.LimitationsThese were experimental studies with hypothetical scenarios. Future research is needed to understand how patients react to information disclosures in a physician’s office. Future research is also needed to examine the role of specific wording and tone used in information disclosures.ConclusionsPrior research has shown that patients prefer more information and to be involved in their medical decisions; however, these studies demonstrate that some information disclosures can discourage full communication between patients and physicians.ImplicationsThis research has important implications for the potential consequences of information disclosures in health care settings. Information disclosures should be presented in a way that will not discourage candid discussions of patient symptoms.HighlightsThis research found that information disclosures about insurance claims can negatively affect patient willingness to discuss health information with providers. Information disclosures may sometimes fall short of their intended purpose of aiding patient decisions with the goal of improved well-being. When information disclosures are focused on warning about potential new costs, patients may feel uncomfortable discussing new symptoms with their providers. Findings suggest patients may often be more concerned with costs than with addressing their ongoing health problems.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"General Population Mortality Adjustment in Survival Extrapolation of Cancer Trials: Exploring Plausibility and Implications for Cost-Effectiveness Analyses in HER2-Positive Breast Cancer in Sweden.","authors":"Kun Kim,Michael Sweeting,Nils Wilking,Linus Jönsson","doi":"10.1177/0272989x241275969","DOIUrl":"https://doi.org/10.1177/0272989x241275969","url":null,"abstract":"BACKGROUNDIn economic evaluations of novel therapies, assessing lifetime effects based on trial data often necessitates survival extrapolation, with the choice of model affecting outcomes. The aim of this study was to assess accuracy and variability between alternative approaches to survival extrapolation.METHODSData on HER2-positive breast cancer patients from the Swedish National Breast Cancer Register were used to fit standard parametric distribution (SPD) models and excess hazard (EH) models adjusting the survival projections based on general population mortality (GPM). Models were fitted using 6-y data for stage I and II, 4-y data for stage III, and 2-y data for stage IV cancer reflecting an early data cutoff while maintaining sufficient events for comparison of model estimates with actual long-term outcomes. We compared model projections of 15-y survival and restricted mean survival time (RMST) to 15-y registry data and explored the variability between models in extrapolations of long-term survival.RESULTSAmong 11,224 patients compared with the observed registry 15-y RMST estimates across the disease stages, EH cure models provided the most accurate estimates in patients with stage I to III cancer, whereas EH models without cure most closely matched survival in patients with stage IV cancer, in which cure assumption was less plausible. The Akaike information criterion-averaged model projections varied as follows: -8.2% to +5.3% for SPD models, -4.9% to +5.2% for the EH model without a cure assumption, and -19.3% to -0.2% for the EH model with a cure assumption. EH models significantly reduced between-model variance in the predicted RMSTs over a 50-y time horizon compared with SPD models.CONCLUSIONSEH models may be considered as alternatives to SPD models to produce more accurate and plausible survival extrapolation that accounts for general population mortality.HIGHLIGHTSExcess hazard (EH) methods have been suggested as an approach to incorporate background mortality rates in economic evaluation using survival extrapolation.We highlight that EH models with or without a cure assumption can produce more accurate survival projections and significantly reduce between-model variability in comparison with standard parametric distribution models across cancer stages.EH models may be a preferred modeling method to reduce model uncertainty in health economic modeling since models that would otherwise have produced implausible extrapolations are constrained by the EH framework.Reduced uncertainty in economic evaluations will enhance the application of evidence-based health care decision making.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptation and Validation of the Psychological Consequences of Screening Questionnaire (PCQ) for Cognitive Screening in Primary Care.","authors":"Rebecca M Lovett,Sarah Filec,Jeimmy Hurtado,Mary Kwasny,Alissa Sideman,Stephen D Persell,Katherine Possin,Michael Wolf","doi":"10.1177/0272989x241275676","DOIUrl":"https://doi.org/10.1177/0272989x241275676","url":null,"abstract":"BACKGROUNDContext-specific measures with adequate external validity are needed to appropriately determine psychosocial effects related to screening for cognitive impairment.METHODSTwo-hundred adults aged ≥65 y recently completing routine, standardized cognitive screening as part of their Medicare annual wellness visit were administered an adapted version of the Psychological Consequences of Screening Questionnaire (PCQ), composed of negative (PCQ-Neg) and positive (PCQ-Pos) scales. Measure distribution, acceptability, internal consistency, factor structure, and external validity (construct, discriminative, criterion) were analyzed.RESULTSParticipants had a mean age of 73.3 y and were primarily female and socioeconomically advantaged. Most had a normal cognitive screening result (99.5%, n = 199). Overall PCQ scores were low (PCQ-Neg: x¯= 1.27, possible range 0-36; PCQ-Pos: x¯ = 7.63, possible range 0-30). Both scales demonstrated floor effects. Acceptability was satisfactory, although the PCQ-Pos had slightly more item missingness. Both scales had Cronbach alphas >0.80 and a single-factor structure. Spearman correlations between the PCQ-Neg with general measures of psychological distress (Impacts of Events Scale-Revised, Perceived Stress Scale, Kessler Distress Scale) ranged from 0.26 to 0.37 (P's < 0.001); the correlation with the World Health Organization-Five Well-Being Index was -0.19 (P < 0.01). The PCQ-Neg discriminated between those with and without a self-reported subjective cognitive complaint (x¯ = 2.73 v. 0.89, P < 0.001) and was associated with medical visit satisfaction (r = -0.24, P < 0.001) on the Patient Satisfaction Questionnaire. The PCQ-Pos predicted self-reported willingness to engage in future screening (x¯ = 8.00 v. 3.00, P = 0.03).CONCLUSIONSThe adapted PCQ-Neg is an overall valid measure of negative psychological consequences of cognitive screening; findings for the PCQ-Pos were more variable. Future studies should address measure performance among diverse samples and those with abnormal screening results.HIGHLIGHTSThe PCQ scale is an overall valid measure of psychological dysfunction related to cognitive screening in older adults receiving normal screen results.PCQ scale performance should be further validated in diverse populations and those with abnormal cognitive screening results.The adapted PCQ may be useful to both health research and policy stakeholders seeking improved assessment of psychological impacts of cognitive screening.","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142200972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Structural Differences on the Modeled Cost-Effectiveness of Noninvasive Prenatal Testing.","authors":"Amber Salisbury, Alison Pearce, Kirsten Howard, Sarah Norris","doi":"10.1177/0272989X241263368","DOIUrl":"10.1177/0272989X241263368","url":null,"abstract":"<p><strong>Background: </strong>Noninvasive prenatal testing (NIPT) was developed to improve the accuracy of prenatal screening to detect chromosomal abnormalities. Published economic analyses have yielded different incremental cost-effective ratios (ICERs), leading to conclusions of NIPT being dominant, cost-effective, and cost-ineffective. These analyses have used different model structures, and the extent to which these structural variations have contributed to differences in ICERs is unclear.</p><p><strong>Aim: </strong>To assess the impact of different model structures on the cost-effectiveness of NIPT for the detection of trisomy 21 (T21; Down syndrome).</p><p><strong>Methods: </strong>A systematic review identified economic models comparing NIPT to conventional screening. The key variations in identified model structures were the number of health states and modeling approach. New models with different structures were developed in TreeAge and populated with consistent parameters to enable a comparison of the impact of selected structural variations on results.</p><p><strong>Results: </strong>The review identified 34 economic models. Based on these findings, demonstration models were developed: 1) a decision tree with 3 health states, 2) a decision tree with 5 health states, 3) a microsimulation with 3 health states, and 4) a microsimulation with 5 health states. The base-case ICER from each model was 1) USD$34,474 (2023)/quality-adjusted life-year (QALY), 2) USD$14,990 (2023)/QALY, (3) USD$54,983 (2023)/QALY, and (4) NIPT was dominated.</p><p><strong>Conclusion: </strong>Model-structuring choices can have a large impact on the ICER and conclusions regarding cost-effectiveness, which may inadvertently affect policy decisions to support or not support funding for NIPT. The use of reference models could improve international consistency in health policy decision making for prenatal screening.</p><p><strong>Highlights: </strong>NIPT is a clinical area in which a variety of modeling approaches have been published, with wide variation in reported cost-effectiveness.This study shows that when broader contextual factors are held constant, varying the model structure yields results that range from NIPT being less effective and more expensive than conventional screening (i.e., NIPT was dominated) through to NIPT being more effective and more expensive than conventional screening with an ICER of USD$54,983 (2023)/QALY.Model-structuring choices may inadvertently affect policy decisions to support or not support funding of NIPT. Reference models could improve international consistency in health policy decision making for prenatal screening.</p>","PeriodicalId":49839,"journal":{"name":"Medical Decision Making","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}