**Background:** Attention-deficit/hyperactivity disorder (ADHD) affects approximately 4.4% of US adults. ADHD is associated with high-risk driving behavior and costly motor vehicle accidents. DYANAVEL XR (DXR) (Tris Pharma, Inc.) is a once-daily fast-acting amphetamine developed for ADHD treatment. A randomized controlled trial showed that DXR patients were 43% less likely to crash during a driving simulation than individuals taking placebo. Study outcomes suggest a DXR crash rate similar to that of a driver without ADHD, while patients treated with the current standard of care (SOC) have a 52% higher crash risk than non-ADHD drivers. **Objective:** The aim was to evaluate the economic benefits attributable to improved driving abilities and avoided crashes in DXR patients compared with patients treated with the SOC or those who are untreated. **Methods:** A cost-impact model estimated 1-year crash-related cost outcomes for DXR-treated patients compared with SOC-treated and untreated ADHD patients. SOC was assumed to consist of a combination of short-, intermediate-, and long-acting ADHD stimulant and non-stimulant medications. DXR crash risk was assumed equivalent to the non-ADHD population risk, as supported by trial data. Crash risk for untreated and SOC-treated ADHD patients were assumed to be 99% and 52% higher than the general US population, respectively. Model outcomes included the cost impact (medication- and crash-related costs) and the number of crashes, injuries, and fatalities avoided with DXR. **Results:** Treatment with DXR would avoid 0.82 crashes, 0.016 injuries, and 0.036 fatalities per year compared with untreated patients, and 0.036 crashes, 0.007 injuries, and 0.0001 fatalities per year compared with SOC-treated patients. Compared with a population of 25% SOC-treated patients and 75% untreated patients, DXR use would save an average of $4581 per person per year across all age groups when priced at $80 per month, assuming all SOC-treated and untreated patients utilized DXR. When the value of quality-of-life improvement is considered, savings increase over 7-fold. **Discussion:** Outcomes suggest that DXR may be an economically beneficial treatment compared with SOC for ADHD patients. **Conclusions:** The economic model showed that DXR is cost-saving compared with no treatment and SOC by reducing the number of motor vehicle crashes in the ADHD population.

**Background:** Early detection of lung cancer is crucial for improving patient outcomes. Although advances in diagnostic technologies have significantly enhanced the ability to identify lung cancer in earlier stages, there are still limitations. The alarming rate of false positives has resulted in unnecessary utilization of medical resources and increased risk of adverse events from invasive procedures. Consequently, there is a critical need for advanced diagnostics after an initial low-dose computed tomography (LDCT) scan. **Objectives:** This study evaluated the potential cost savings for US payers of CyPath® Lung, a novel diagnostic tool utilizing flow cytometry and machine learning for the early detection of lung cancer, in patients with positive LDCT scans with indeterminate pulmonary nodules (IPNs) ranging from 6 to 29 mm. **Methods:** A cost offset model was developed to evaluate the net expected savings associated with the use of CyPath® Lung relative to the current standard of care for individuals whose IPNs range from 6 to 29 mm. Perspectives from both Medicare and private payers in a US setting are included, with a 1-year time horizon. Cost calculations included procedure expenses, complication costs, and diagnostic assessment costs per patient. Primary outcomes of this analysis include cost savings per cohort and cost savings per patient. **Results:** Our analysis showed positive cost savings from a private payer’s perspective, with expected savings of $895 202 311 per cohort and $6460 per patient, across all patients. Scenario analysis resulted in cost savings of $890 829 889 per cohort, and $6429 per patient. Similarly, savings of $378 689 020 per cohort or $2733 per patient were yielded for Medicare payers, across all patients. In addition, scenario analysis accounting for false negative patients from a Medicare payer perspective yielded savings of $376 902 203 per cohort and $2720 per patient. **Discussion:** The results suggest substantial cost savings, primarily due to reductions in follow-up diagnostic assessments and procedures, and highlight the importance of accurate diagnostic tools in reducing unnecessary healthcare expenditures. **Conclusion:** CyPath® Lung utilization yields savings for private and Medicare payers relative to the current standard of care in a US setting for individuals with 6 to 20 mm IPNs.

Background There is currently a need for additional diagnostic information to help guide treatment decisions and to properly determine the best treatment pathway for patients identified with indeterminate pulmonary nodules (IPNs). The aim of this study was to demonstrate the incremental cost-effectiveness of LungLB compared to the current clinical diagnostic pathway (CDP) in the management of patients with IPNs, from a US payer’s perspective.Methods A decision tree and Markov model hybrid was chosen from a payer perspective in the US setting, based on published literature, to assess the incremental cost-effectiveness of LungLB compared to the current CDP in the management of patients with IPNs. Primary endpoints of the analysis include expected costs, life years (LYs), and quality-adjusted life years (QALYs) for each arm of the model, as well as an incremental cost-effectiveness ratio (ICER), which is calculated as the incremental costs per QALY, and net monetary benefit (NMB).Results We find that, with the inclusion of LungLB to the current CDP diagnostic pathway, expected LYs over the typical patient’s lifespan increase by 0.07 years and QALYs increase by 0.06. The average patient in the CDP arm will pay approximately $44,310 over their lifespan, while a patient in the LungLB arm will pay $48,492, resulting in a difference of $4,182. The differentials between the CDP and LungLB arms of the model in costs and QALYs yield an ICER of $75,740 per QALY and an incremental NMB of $1,339.Conclusion This analysis provides evidence that LungLB, in conjunction with CDP, is a cost-effective alternative compared to the current CDP alone in a US setting for individuals with IPNs.

BACKGROUND:There is significant over-prescription of antibiotics for suspected community-acquired pneumonia (CAP) patients as bacterial and viral pathogens are difficult to differentiate. To address this issue, a host response diagnostic called MeMed BV (MMBV) was developed that accurately differentiates bacterial from viral infection at the point of need by integrating measurements of multiple biomarkers. A literature-based cost-impact model was developed that compared the cost impact and clinical benefits between using the standard of care diagnostics combined with MMBV relative to standard of care diagnostics alone.METHODS:The patient population was stratified according to the pneumonia severity index, and cost savings were considered from payer and provider perspectives. Four scenarios were considered. The main analysis considers the cost impact of differences in antibiotic stewardship and resulting adverse events. The first, second, and third scenarios combine the impacts on antibiotic stewardship with changes in hospital admission probability, length of hospital stay and diagnosis related group (DRG) reallocation, and hospital admission probability, length of stay, and DRG reallocation in combination, respectively.RESULTS:The main analysis results show overall per-patient savings of $37 for payers and $223 for providers. Scenarios 1, 2, and 3 produced savings of $137, $189, and $293 for payers, and $339, $713, and $809 for providers, respectively.LIMITATIONS:Models are simulations of real-world clinical processes, and are not sensitive to variations in clinical practice driven by differences in physician practice styles, differences in facility-level practice patterns, and patient comorbidities expected to exacerbate the clinical impact of CAP. Hospital models are limited to costs and do not consider differences in revenue associated with each approach.CONCLUSIONS:Introducing MMBV to the current SOC diagnostic process is likely to be cost-saving to both hospitals and payers when considering impacts on antibiotic distribution, hospital admission rate, hospital LOS, and DRG reallocation.

Background Sepsis is a life-threatening organ dysfunction in response to infection. Early recognition and rapid treatment are critical to patient outcomes and cost savings, but sepsis is difficult to diagnose because of its non-specific symptoms. Biomarkers such as pancreatic stone protein (PSP) offer rapid results with greater sensitivity and specificity than standard laboratory tests. Methods This study developed a decision tree model to compare a rapid PSP test to standard of care in the emergency department (ED) and intensive care unit (ICU) to diagnose patients with suspected sepsis. Key model parameters included length of hospital and ICU stay, readmission due to infection, cost of sepsis testing, length of antibiotic treatment, antibiotic resistance, and clostridium difficile infections. Model inputs were determined by review of sepsis literature. Results The rapid PSP test was found to reduce costs by $1688 per patient in the ED and $3315 per patient in the ICU compared to standard of care. Cost reductions were primarily driven by the specificity of PSP in the ED and the sensitivity of PSP in the ICU. Conclusions The results of the model indicate that PSP testing is cost saving compared to standard of care in diagnosis of sepsis. The abundance of sepsis cases in the ED and ICU make these findings important in the clinical field and further support the potential of sensitive and specific markers of sepsis to not only improve patient outcomes but also reduce healthcare expenditures.

Background The study evaluated the cost of baroreflex activation therapy plus guideline directed therapy (BAT + GDT) compared to GDT alone for HF patients with reduced ejection fraction and New York Heart Association Class III or II (with a recent history of III). Baroreflex activation therapy (BAT) is delivered by an implantable device that stimulates the baroreceptors through an electrode attached to the outside of the carotid artery, which rebalances the autonomic nervous system to regain cardiovascular (CV) homeostasis. The BeAT-HF trial evaluated the safety and effectiveness of BAT. Methods A cost impact model was developed from a U.S. health care payer or integrated delivery network perspective over a 3-year period for BAT + GDT versus GDT alone. Expected costs were calculated by utilizing 6-month data from the BeAT-HF trial and existing literature. HF hospitalization rates were extrapolated based on improvement in NT-proBNP. Results At baseline the expected cost of BAT + GDT were $29,526 per patient more than GDT alone due to BAT device and implantation costs. After 3 years, the predicted cost per patient was $9521 less expensive for BAT + GDT versus GDT alone due to lower rates of significant HF hospitalizations, CV non-HF hospitalizations, and resource intensive late-stage procedures (LVADs and heart transplants) among the BAT + GDT group. Conclusions BAT + GDT treatment becomes less costly than GDT alone beginning between years 1 and 2 and becomes less costly cumulatively between years 2 and 3, potentially providing significant savings over time. As additional BeAT-HF trial data become available, the model can be updated to show longer term effects.

Reaching the goals set by the Health Care Payment and Learning Action Network requires an unyielding and unrelenting focus on encouraging providers to adopt advanced alternative payment models (APMs). Many of these models will continue to be voluntary because they either are in early stages or have not yet proven their effectiveness. The models that have proven their effectiveness should become permanent, comprising the new way that providers are paid in the Medicare program. Either way, getting today's high performers into those programs and keeping them engaged to continue to innovate and set new benchmarks is as important as attracting and improving the performance of poorer performers. That will require a shift in Medicare's policy on pricing and evaluating APMs.

Abstract Background Mendelian Randomization is a type of instrumental variable (IV) analysis that uses inherited genetic variants as instruments to estimate causal effects attributable to genetic factors. This study aims to estimate the impact of obesity on annual inpatient healthcare costs in the UK using linked data from the UK Biobank and Hospital Episode Statistics (HES). Methods UK Biobank data for 482,127 subjects was linked with HES inpatient admission records, and costs were assigned to episodes of care. A two-stage least squares (TSLS) IV model and a TSLS two-part cost model were compared to a naïve regression of inpatient healthcare costs on body mass index (BMI). Results The naïve analysis of annual cost on continuous BMI predicted an annual cost of £21.61 [95% CI £20.33 – £22.89] greater cost per unit increase in BMI. The TSLS IV model predicted an annual cost of £14.36 [95% CI £0.31 – £28.42] greater cost per unit increase in BMI. Modelled with a binary obesity variable, the naïve analysis predicted that obese subjects incurred £205.53 [95% CI £191.45 – £219.60] greater costs than non-obese subjects. The TSLS model predicted a cost £201.58 [95% CI £4.32 – £398.84] greater for obese subjects compared to non-obese subjects. Conclusions The IV models provide evidence for a causal relationship between obesity and higher inpatient healthcare costs. Compared to the naïve models, the binary IV model found a slightly smaller marginal effect of obesity, and the continuous IV model found a slightly smaller marginal effect of a single unit increase in BMI.

Population Health ManagementVol. 24, No. 3 Points of ViewFree AccessGenetic Variant Reinterpretation: Economic and Population Health Management ChallengesJosé A. Pagán, Henry Shelton Brown, John Rowe, John E. Schneider, David L. Veenstra, Avni Gupta, Sara M. Berger, Wendy K. Chung, and Paul S. AppelbaumJosé A. PagánAddress correspondence to: José A. Pagán, PhD, Department of Public Health Policy and Management, School of Global Public Health, New York University, 715 Broadway, New York, NY 10003, USA E-mail Address: jose.pagan@nyu.eduDepartment of Public Health Policy and Management, School of Global Public Health, New York University, New York, New York, USA.Search for more papers by this author, Henry Shelton BrownUTHealth School of Public Health, Austin Regional Campus, University of Texas Health Science Center at Houston, Austin, Texas, USA.Search for more papers by this author, John RoweDepartment of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, New York, USA.Search for more papers by this author, John E. SchneiderAvalon Health Economics LLC, Morristown, New Jersey, USA.Search for more papers by this author, David L. VeenstraComparative Health Outcomes, Policy and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, Washington, USA.Search for more papers by this author, Avni GuptaDepartment of Public Health Policy and Management, School of Global Public Health, New York University, New York, New York, USA.Search for more papers by this author, Sara M. BergerDivision of Clinical Genetics, Department of Pediatrics, New York Presbyterian Hospital, Columbia University Irving Medical Center, New York, New York, USA.Search for more papers by this author, Wendy K. ChungDepartments of Pediatrics and Medicine, Columbia University Irving Medical Center, New York, New York, USA.Search for more papers by this author, and Paul S. AppelbaumDepartment of Psychiatry, Columbia University Vagelos College of Physicians & Surgeons, NY State Psychiatric Institute, New York, New York, USA.Search for more papers by this authorPublished Online:8 Jun 2021https://doi.org/10.1089/pop.2020.0115AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail The use of genetic testing for screening and treatment decisions for many health conditions is growing rapidly. There are thousands of genetic tests on the market today and common clinical domains include prenatal testing, pharmacogenetics, rare diseases, cancer diagnostics, treatment, and risk predictions.1 Genetic testing includes panels of genes for a single clinical indication, such as breast cancer or cardiomyopathy, and genomic tests—including chromosome microarrays, exome and genome sequencing—largely for pediatric conditions such as intellectual disability, birth defects, and undiagnosed disorders. Genetic testing costs can range from $100 to $10,000 depending on the specific test.2 Recent estimates suggest that the global genetic testing market will be valued at US$22 billion by 2024.3Our understanding of the human genome is still in flux and for many human genetic variants we do not have enough information to know whether they are associated with disease (ie, pathogenic) or benign. These are classified as variants of uncertain significance. This means that many economic analyses and models of costs of diseases (eg, cancer) must be modified to include the possibility of reinterpretation over time as our understanding of a genetic variant changes. Expected future health care costs can increase or decrease depending on whether effective prevention is pursued. Other economic challenges include issues such as whether insurers should cover the costs of reinterpretation. Economic implications also differ for public and private insurance providers.For genomic tests, the clinical yield of reanalysis of previously nondiagnostic exome sequencing about 1 to 3 years after initial testing can be as high as 23.1% because of the identification of new disease genes over time.4 Changes over time in the interpretation of genetic variants means that a person who had a genetic test done 10 years ago could take the same test with the same lab today but receive a different interpretation of the same previously identified genetic variant. It is difficult to estimate precisely the cost of variant reinterpretation as it depends on the extent to which the data are organized to support reinterpretation and automation in a given laboratory. That is, the cost of variant reinterpretation includes work related to evidence review as well as work interpreting experimental/functional data. As far as we know, no systematic data exist on the costs of variant reinterpretation across laboratories.For genetic testing, accurate and up-to-date variant interpretation is necessary to inform clinical decision-making for the physician, the patient and their family. Variant reinterpretation challenges become more pronounced as clinical labs sequence larger panels of genes, exomes, and genomes in a growing number of patients. As a result, variant reclassification raises important clinical, ethical, legal, and economic issues. This article discusses the economic and population health management implications of variant reinterpretation.Complexity of Variant ReinterpretationThe clinical, ethical, legal, and economic aspects of variant reinterpretation are important because a change in the risk of developing a given health condition may bring a shift in recommended health care. Variant reinterpretation also may provide new information that could affect family members and the health care services they do or do not need. Currently, it is unclear how the cost of reinterpretation can be incorporated into economic decision-making models as well as how it may influence payment and reimbursement options.There are 2 primary pathways for reinterpretation. First, an initial genetic test may reveal an uncertain variant or may not identify any variants associated with a predisposition for a health condition. Reinterpretation done at a later point in time may reveal that a previously identified genetic variant is in fact associated with a medical condition or predisposition. This can be thought of as an initial false negative. Alternatively, although far less frequent, there may be a variant identified on the initial genetic test that is thought to be associated with a genetic predisposition for a health condition. Reinterpretation done at a later point in time may reveal that the genetic variant is no longer considered to be related to that medical condition or predisposition. This can be thought of as an initial false positive. If reinterpretation detects false positives, insurers may be more likely to cover the cost of reinterpretation. For false negatives, third-party payers may be less likely to want to pay for the reinterpretation because that would lead to higher future health care costs, although these actions also could lead to disease prevention or early diagnosis. However, in any given case, the presence and direction of changes in interpretation will not be known until the process is undertaken and the costs of reinterpretation are incurred.Economic ConsiderationsThe frequency of reinterpretation should be informed by the pace of increasing knowledge in variant pathogenicity, and this will vary by clinical indication. Furthermore, both the clinical and economic impact of reinterpretation should inform policies. Developing pragmatic coverage and reimbursement policies will thus be particularly challenging. For example, reinterpretation for a woman who initially was found to have a variant of uncertain significance for inherited breast cancer risk would be important within a time frame that is sufficient to prevent future disease. Yet, a patient receiving drug therapy that costs hundreds of thousands of dollars a year potentially could undergo reinterpretation periodically to detect false positives.The complexity of assessing the clinical and economic value of reinterpretation suggests that decision-analytic policy models will be helpful in quantifying these outcomes and their associated uncertainty. Decision-analytic models are used by health care policy makers to assess value in health care technologies as well as to inform coverage and reimbursement policies. There also is a need to develop general guidelines that can be used to inform cost-effectiveness analysis of reinterpretation. Economic analyses to assess the adoption of new technologies rely on the use of structured economic models to assess the benefits and costs of adopting a given technology, understand complexity, assess impact over time, and evaluate the effects of a care process or treatment on different populations and health conditions. Thus, guidelines that can inform the development of economic models are needed to address issues such as how often variants are likely to be reinterpreted over time, what the expected cost impact of different treatment changes are, and what time horizon should be considered.DisparitiesOur understanding of the human genome is incomplete, particularly for individuals who are not of European ancestry given the available reference populations used to evaluate variants. This has potential consequences for health disparities. For example, the frequency of variants of uncertain significance in the BRCA1/2 genes has been estimated to be 4.4% for Caucasians, 8.9% for African Americans, and 8.0% for Hispanics.5 For larger panels of hereditary cancer genes, one laboratory has reported frequencies of variants of uncertain significance to be 22.1% for Caucasians, 30.3% for African Americans, and 24.9% for Hispanics.6The main issue here is that the economic and health implications of reinterpretation may disproportionately impact populations with poorer health outcomes and a lower ability to pay. This would exacerbate health disparities and likely will be of interest to communities in which the impact could be relatively high (eg, states with large populations served by Medicaid).Paying for Variant ReinterpretationVariant reinterpretation also presents an important challenge when it comes to deciding who pays for the service to reassess genetic variants over time. For instance, providers with capitation payment arrangements with insurers may be considered to be responsible for covering reinterpretation costs as findings from variant reinterpretation could result in health care utilization pathways that are different from what was planned following the initial genomic test results. Payer coverage and reimbursement policies play a critical role in the adoption of new genomic technologies in clinical care because they provide the financial resources not only for testing but also for subsequent health care utilization associated with genetic testing. As such, the decision to pay for variant reinterpretation becomes particularly relevant because reinterpretation can lead to recommendations for health care treatments that were not considered to be necessary before results were made available, but were afterward.The challenge of how to pay for variant reinterpretation is particularly significant in the United States given its highly fragmented health insurance market. Americans get their health insurance coverage from multiple sources (mostly Medicare, Medicaid, and employer-sponsored private insurance). A health insurer in the United States typically will offer multiple plans with different types of coverage, varying reimbursement rules, and different target patient populations. The US payer community is diffuse and it will be difficult to achieve consensus on a multifaceted issue such as reimbursement for variant reinterpretation.The Need to Make the Case for Paying for ReinterpretationVariant reinterpretation is likely to influence health care utilization in unexpected and highly unpredictable ways. Payers certainly will be unlikely to cover variant reinterpretation if its impact on health care utilization cannot be assessed over a reasonable period of time.One approach that may prove useful to assess the case for variant reinterpretation is to think of the problem from the perspective of the budget impact on a payer, as insurers routinely consider the costs of new diagnostic or treatment approaches in their decision-making regarding coverage.7 The costs of variant reinterpretation to a given payer, employer, or provider—though potentially large for certain subpopulations or individual patients—may have a small budget impact in a large population of covered lives if few people receive genetic testing and if few variants are of uncertain significance.Budget impact analysis could be used to estimate the likely change in expenditures to a specific budget holder (eg, an employer) resulting from a decision to reimburse a new health care intervention at the population level. A budget impact analysis would measure the trade-offs over time between the costs of reinterpretation and the cost impact of potential savings associated with reinterpretation; for example, the savings associated with early detection and treatment.Population Health Management ConsiderationsGenomic sequencing and variant reinterpretation are well aligned with different elements of population health management. Genetic testing can be congruent with patient-centered medical care models in the sense that this testing fosters patient autonomy, is consistent with treatment tailoring, requires team-based care, and empowers patients by providing them with information critical to various health care choices. There is evidence that screening the general population for a given gene mutation (eg, in BRCA1/2) may be more cost-effective than screening high-risk groups, such as people with a family history of a disease.8Although there are efforts to integrate genomic information with clinical and environmental data (eg, the Healthy Nevada Project), the success of these initiatives hinges on understanding how variant reinterpretation may change the use of genetic data in population health management.9 For example, interactions among genetic, clinical, and environmental information may affect cancer risk in particular ways but estimates of cancer risk may be highly sensitive to accurate variant interpretation.Another important population health management consideration is that results of variants of uncertain significance in genetic testing are more frequent for ethnic and racial minority groups than whites.10 Although many variants of uncertain significance are later reclassified as benign, clinicians may incorrectly manage these results the same way as pathogenic variants.11 What this implies is that ethnic and racial minority populations may be more likely to receive inappropriate medical care given the differences in the frequency of variants of uncertain significance across different groups.Lastly, the focus on big data and predictive analytics is evident in both genetic testing and population health management. There may be synergies in these 2 areas when it comes to how data are analyzed to understand health care utilization and better manage health care expenditures.Next StepsThe use of genetic testing has been growing faster than our level of knowledge about genetic variants and their connection with health conditions at any given time. Genetic variant reinterpretation is becoming increasingly important to assess clinical care options — with patients, their families, clinicians, testing laboratories, payers, and policy makers all being interested in learning more about its clinical and financial consequences. Next steps that need to take place include the need for guidance to understand not only how often variants should be reinterpreted but also what is the financial impact of different treatment options and how different populations are affected. Close collaboration between all stakeholders is required to develop this guidance, which will be critical to develop a variant reinterpretation business case for payers. Moving forward will require an increased awareness about the economic challenges of genetic variant reinterpretation and holistic guidance on how to counter these challenges in order to maximize the value of genetic testing and match it with its speed of use in medical care.Author Disclosure StatementDr. Veenstra's organization and department have received support from Foundation Medicine to assess the value of precision medicine in oncology. The remaining authors declare that there are no conflicts of interest.Funding InformationThis work was supported by a grant (R01HG010365) from the National Human Genome Research Institute (NHGRI). Drs. Chung and Appelbaum also received support from NHGRI grants RM1HG007257 and U01HG008680. Dr. Veenstra also received support from NHGRI grant R01HG009694.References1. Phillips KA, Deverka PA, Hooker GW, Douglas MP. Genetic test availability and spending: where are we now? Where are we going? Health aff (Millwood) 2018;37:710–716. Crossref, Medline, Google Scholar2. Lister Hill National Center for Biomedical Communications. What is the cost of genetic testing, and how long does it take to get the results? 2020. https://ghr.nlm.nih.gov/primer/testing/costresults Accessed September 1, 2020. Google Scholar3. Market Watch. $22 Billion by 2024 Genetic Testing Market set for massive growth. 2019. https://www.marketwatch.com/press-release/22-billion-by-2024-genetic-testing-market-set-for-massive-growth-2019-04-03 Accessed December 8, 2019. Google Scholar4. Wenger AM, Harendra G, Bernstein JA, Bejerano G. Systematic reanalysis of clinical exome data yields additional diagnoses: implications for providers. Genet Med 2017;19:209–214. Crossref, Medline, Google Scholar5. Panos LTS, V; Dolinsky , JS; Panchani, K; LaDuca , H. Variant of unknown significance rates vary by ethnicity and genes analyzed. Paper presented at American Society of Human Genetics, Maryland, 2015. Google Scholar6. Carter NJ, Hiraki S, Yackowski L, et al. Yield of pathogenic/likely pathogenic variants in breast cancer patients undergoing an inherited cancer panel based upon ethnic background. Paper presented at American College of Medical Genetics and Genomics (ACMG), Tampa, FL, 2016. Google Scholar7. Mauskopf JA, Sullivan SD, Annemans L, et al. Principles of good practice for budget impact analysis: report of the ISPOR Task Force on good research practices—budget impact analysis. Value Health 2007;10:336–347. Crossref, Medline, Google Scholar8. Manchanda R, Patel S, Gordeev VS, et al. Cost-effectiveness of population-based BRCA1, BRCA2, RAD51C, RAD51D, BRIP1, PALB2 mutation testing in unselected general population women. J Natl Cancer Inst 2018;110:714–725. Crossref, Medline, Google Scholar9. Health IT Analytics. Healthy Nevada study combines genomics, population health. 2018. https://healthitanalytics.com/news/healthy-nevada-study-combines-genomics-population-health. Accessed December 8, 2019. Google Scholar10. Caswell-Jin JL, Gupta T, Hall E, et al. Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk. Genet Med 2018;20:234–239. Crossref, Medline, Google Scholar11. Kurian AW, Li Y, Hamilton AS, et al. Gaps in incorporating germline genetic testing into treatment decision-making for early-stage breast cancer. J Clin Oncol 2017;35:2232–2239. Crossref, Medline, Google ScholarFiguresReferencesRelatedDetailsCited byDoes the law require reinterpretation and return of revised genomic results?8 January 2021 | Genetics in Medicine, Vol. 23, No. 5 Volume 24Issue 3Jun 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:José A. Pagán, Henry Shelton Brown, John Rowe, John E. Schneider, David L. Veenstra, Avni Gupta, Sara M. Berger, Wendy K. Chung, and Paul S. Appelbaum.Genetic Variant Reinterpretation: Economic and Population Health Management Challenges.Population Health Management.Jun 2021.310-313.http://doi.org/10.1089/pop.2020.0115Published in Volume: 24 Issue 3: June 8, 2021Online Ahead of Print:September 9, 2020Keywordseconomicspopulation health managementgenetic variant reinterpretationPDF download

BACKGROUND:Early identification of acute infections and sepsis remains an unmet medical need. While early detection and initiation of treatment reduces mortality, inappropriate treatment leads to adverse events and the development of antimicrobial resistance. Current diagnostic and prognostic solutions, including procalcitonin, lack required accuracy. A novel blood-based host response test, HostDx™ Sepsis by Inflammatix, Inc., assesses the likelihood of a bacterial infection, the likelihood of a viral infection, and the severity of the condition.OBJECTIVES:We estimated the economic impact of adopting HostDx Sepsis testing among patients with suspected acute respiratory tract infection (ARTI) in the emergency department (ED).METHODS:Our cost impact model estimated costs for adult ED patients with suspected ARTI under the standard of care versus with the adoption of HostDx Sepsis from the perspective of US payers. Included costs were those assumed to be associated with an episode of sepsis diagnosis, management, and treatment. Projected accuracies for test predictions, disease prevalence, and clinical parameters was derived from patient-level meta-analysis data of randomized trials, supplemented with published performance data for HostDx Sepsis. One-way sensitivity analysis was performed on key input parameters.RESULTS:Compared to standard of care including procalcitonin, the superior test characteristics of HostDx Sepsis resulted in an average cost savings of approximately US$1974 per patient (-31.3%) exclusive of the cost of HostDx Sepsis. Reductions in hospital days (-0.80 days, -36.7%), antibiotic days (-1.49 days, -29.5%), and percent 30-day mortality (-1.67%, -13.64%) were driven by HostDx Sepsis providing fewer "noninformative" moderate risk predictions and more "certain" low- or high-risk predictions compared to standard of care, especially for patients who were not severely ill. These results were robust to changes in key parameters, including disease prevalence.CONCLUSIONS:Our model shows substantial savings associated with introduction of HostDx Sepsis among patients with ARTIs in EDs. These results need confirmation in interventional trials.

Background: Acute respiratory infection (ARI) accounts for over two-thirds of total antibiotic prescriptions although most are caused by viruses that do not benefit from antibiotics. Most antibiotics are prescribed in the outpatients setting. Antibiotic overuse leads to antibiotic-related adverse events (AEs), inclusive of secondary infections, resistance, and increased costs. Point-of-care tests (POCT) may reduce unnecessary antibiotics. A cost analysis was performed to assess diagnostic POCT options to identify patients with an ARI that may benefit from antibiotics in a United Kingdom (UK) outpatient setting.Methods: Healthcare savings were estimated using a budget impact analysis based on UK National Institute for Health and Care Excellence (NICE) data and direct costs (antibiotics, AEs, POCTs) derived from published literature. Otitis media, sinusitis, pharyngitis and bronchitis were considered the most common ARIs. Antibiotic-related AE costs were calculated using re-consultation costs for anaphylaxis, Stevens-Johnson syndrome, allergies/diarrhea/nausea, C. difficile infection (CDI). Potential cost-savings from POCTs was assessed by evaluating NICE guideline-referenced POCTs (CRP, FebriDx, Sarasota, FL) as well as a target product profile (TPP).Results: Fifty-percent (7,718,283) of ARI consultations resulted in antibiotics while guideline-based prescribing suggest appropriate antibiotic prescriptions are warranted 9% (1,444,877) of ARI consultations. Direct antibiotic costs for actual ARI consultations associated with antibiotics was £24,003,866 vs. £4,493,568 for guideline-based, "appropriate" antibiotic prescriptions. Antibiotic-related AEs and re-consultations for actual vs. appropriate prescribing totaled £302,496,486 vs. £63,854,269. ARI prescribing plus AE costs totaled £326,729,943 annually without the use of delayed prescribing practices or POCT while the addition of delayed prescribing plus POCT totaled £60,114,564-£78,148,933 depending on the POCT.Conclusions: Adding POCT to outpatient triage of ARI can reduce unnecessary antibiotics and antibiotic-related AEs, resulting in substantial cost savings. Further, near patient diagnostic testing can benefit health systems and patients by avoiding exposure to unnecessary drugs, side effects and antibiotic resistant pathogens.Key points for decision makersMany patients are unnecessarily treated with antibiotics for respiratory infections.Antibiotic misuse leads to unnecessary adverse events, secondary infections, re-consultations, antimicrobial resistance and increased costs.Point-of-care diagnostic tests used to guide antibiotic prescriptions will avoid unnecessary adverse health effects and expenses.

Tobacco product waste (TPW) is one of the most ubiquitous forms of litter, accumulating in large amounts on streets, highways, sidewalks, beaches, parks, and other public places, and flowing into storm water drains, waste treatment plants, and solid waste collection facilities. In this paper, we evaluate the direct and indirect costs associated with TPW in the 30 largest U.S. cities. We first developed a conceptual framework for the analysis of direct and indirect costs of TPW abatement. Next, we applied a simulation model to estimate the total costs of TPW in major U.S. cities. This model includes data on city population, smoking prevalence rates, and per capita litter mitigation costs. Total annual TPW-attributable mean costs for large US cities range from US$4.7 million to US$90 million per year. Costs are generally proportional to population size, but there are exceptions in cities that have lower smoking prevalence rates. The annual mean per capita TPW cost for the 30 cities was US$6.46, and the total TPW cost for all 30 cities combined was US$264.5 million per year. These estimates for the TPW-attributable cost are an important data point in understanding the negative economic externalities created by cigarette smoking and resultant TPW cleanup costs. This model provides a useful tool for states, cities, and other jurisdictions with which to evaluate a new economic cost outcome of smoking and to develop new laws and regulations to reduce this burden.

Aims: The purpose of this study is to assess the economic cost differences and the associated treatment resource changes between the developing coronary artery disease (CAD) diagnostic tool fast strain-encoded cardiac imaging (Fast-SENC) and the current commonly used stress test single-photon emission computed tomography (SPECT). Materials and methods: A "payer perspective" model was created first, consisting of long-term and short-term components that used a hypothetical cohort of patients of average age (60.8 years) presenting with chest pain and suspected CAD to assess cost-impact. A cost impact model was then built that assessed likely savings from a "hospital perspective" from substituting Fast-SENC for a portion of SPECTs assuming an average number of annual SPECT tests performed in US hospitals. Results: In the payer model, using Fast-SENC followed by coronary angiography (CA) and percutaneous coronary intervention (PCI) treatment when necessary is less costly than the SPECT method when considering both direct and indirect costs of testing. Expected costs of the Fast-SENC were between $2,510 and $2,632 per correct diagnosis, while expected costs for the SPECT were between $3,157 and $4,078. Fast-SENC reduced false positives by 50% and false negatives by 86%, generating additional cost savings. The hospital model showed total costs per CAD patient visit of $825 for SPECT and $376 for Fast-SENC. Limitations: Limitations of this study are that clinical data are sourced from other published clinical trials on how CAD diagnostic strategies impact clinical outcome, and that necessary assumptions were made which impact health outcomes. Conclusion: The lower cost, higher sensitivity and specificity rates, and faster, less burdensome process for detecting CAD patients make Fast-SENC a more capable and economically beneficial stress test than SPECT. The payer model and hospital model demonstrate an alignment between payer and provider economics as Fast-SENC provides monetary savings for patients and resource benefits for hospitals.

INTRODUCTION:Heart failure (HF) is a major public health concern, prevalent in millions of people worldwide. The most widely-used HF diagnostic method, echocardiography, incurs a decreased diagnostic accuracy for heart failure disease progression when patients are asymptomatic compared to those who are symptomatic. The purpose of this study is to conduct a cost-effectiveness analysis of heart failure diagnosis comparing echocardiography to a novel myocardial strain assessment (Fast-SENC), which utilizes cardiac-tagged magnetic resonance imaging.METHODS:We develop two models, one from the perspective of payers and one from the perspective of purchasers (hospitals). The payer model is a cost-effectiveness model composed of a 1-year short-term model and a lifetime horizon model. The hospital/purchaser model is a cost impact model where expected costs are calculated by multiplying cost estimates of each subcomponent by the accompanying probability.RESULTS:The payer model shows lower healthcare costs for Fast-SENC in comparison to ECHO ($24,647 vs. $39,097) and a lifetime savings of 37% when utilizing Fast-SENC. Similarly, the hospital model revealed that the total cost per HF patient visit is $184 for ECHO and $209 for Fast-SENC, which results in hospital contribution margins of $81 and $115, respectively.CONCLUSIONS:Fast-SENC is associated with higher quality-adjusted life years and lower accumulated expected healthcare costs than echocardiogram patients. Fast-SENC also shows a significant short-term and lifetime cost-savings difference and a higher hospital contribution margin when compared to echocardiography. These results suggest that early discovery of heart failure with methods like Fast-SENC can be cost-effective when followed by the appropriate treatment.

The global burden of dementia is about $1 trillion, annually, and one of the most challenging aspects of dementia is accurate and timely diagnosis and monitoring. In this paper, we develop a conceptual model to identify the economic aspects of a novel dementia diagnostic (referred to as “DDx”). We describe the logic and evidence surrounding the “cost offset” aspects associated with the use of DDx due, in part, to its increased speed and accuracy of diagnosis which ultimately leads to earlier disease-modifying treatments. The conceptual model focuses on four potential cost-driver domains: (1) diagnosis, (2) treatment, (3) outcomes, and (4) institutional care. We adopt a U.S. payer perspective and depict a hypothetical medical decision whereby patients from the base cohort can be “primarily diagnosed” via either the standard of care arm or the DDx arm. The main hypotheses are that the two arms may not be mutually exclusive in terms of testing, yet DDx is reflective of a more accurate and efficient approach. We then develop a cost analysis based on the main cost drivers and the flow of our model, alongside our own reasonable assumptions and cost evidence found in the literature. Faster time-to-treatment results in three desirable outcomes: (1) creating more opportunity to deliver optimal treatment, including the optimal mix of pharmacological and non-pharmacological treatments; (2) a delay in institutional care facility (ICF) admission; and (3) faster time-to-treatment has also been shown to reduce avoidable hospitalizations, which is also an important component of the overall costs of dementia. Summing these effects, the net potential savings attributable to DDx is $11,156, which represents a 13.8% savings per episode of dementia, compared to the standard of care. Savings are driven by reduction in diagnostic costs, hospitalization costs, and ICF costs. The savings attributable to DDx will accrue to different stakeholders in the U.S. in different ways. Fully-integrated health systems would accrue savings directly to the payers, while commercial insurance markets would experience shared savings between commercial health insurers and long-term care.

BACKGROUND:Advances such as passive monitoring technology (PMT), which provides holistic supervision of chronically ill and elderly patients, enable and support improved monitoring and observation, thus empowering the growing population of older adults to live more independently while lowering health care expenses.AIMS:This study develops a conceptual model to estimate the potential savings associated with PMT.METHODS:We first develop a conceptual model to identify the main cost variables associated with independent living, focusing on three pathways: (1) PMT, (2) independent living supported by the current standard of care, and (3) facility-based care. We examined the impact on three outcomes [i.e., health care costs, institutional costs, and health-related quality of life (HRQoL)] along each of the three care pathways (i.e., PMT, independent living supported by the standard of care, and facility-based care) and developed a cost-benefit model to calculate the net costs and benefits associated with each care pathway.RESULTS:The cost-benefit model showed savings between approximately $425 per-member per-month (PMPM) for those using PMT compared to those on the standard of care pathway. Sensitivity analysis demonstrated that a 5% increase in nursing home utilization generates cost savings of more than 30% PMPM.DISCUSSION:The total projected cost savings for individuals on the PMT arm are projected to be more than $425 PMPM, with annual savings of $5069 per-person per-year, and over $5.1 million for a target population of 1000 individuals.CONCLUSIONS:The cost calculations in our cost-benefit simulation model clearly demonstrate the value of PMT and show the potential value to payers and integrated delivery systems in offering PMT to individuals who are likely to benefit the most from the services.

Background: Studies have shown that improvements in glycemic control are associated with avoidance or delayed onset of diabetes complications, improvements in health-related quality of life, and reductions in diabetes-related health care costs. Clinical practice guidelines recommend maintaining a hemoglobin A1c (HbA1c) level less than 7%, but among type 2 diabetes patients using insulin, two-thirds have HbA1c above 7% and one-third have HbA1c above 9%. Objectives: This study examined the use of insulin management services to enable patients to optimize insulin dosing to achieve HbA1c targets and subsequently reduce health care costs. Cost savings may be achieved through reduced complications and hospitalizations, as well as reduced outpatient, physician, and clinic costs. This study quantified the reduction in pharmaceutical expenses related to the use of an enhanced insulin management service to improve glycemic control. Methods: Two hundred seventeen insulin-reliant patients were enrolled in the d-Nav® Insulin Guidance Service through a participating insurance group. A prospective cost analysis was conducted using data from enrolled patients who completed the first 90 days of follow up. Results: Of the 192 patients who completed the 90-day study period, 54 (28.13%) were prescribed one or more expensive medications at baseline, but 45 (83.33%) of those patients were eligible for medication discontinuation after 90 days. At baseline, the annual cost of expensive medications per patient was $7564 (CI: $5191-$9938) and $1483 (CI: -$1463-$4429) at 90 days (p<0.001). Direct savings from medication elimination was estimated to be $145 per patient per month (PPPM) or $1736 per patient per year (PPPY) for all patients and $514 PPPM/$6172 PPPY for the target group. Patients that completed the 90-day period significantly reduced HbA1c levels from 9.37% (CI:7.72%-11.03%) at baseline to 7.71% (CI: 6.70%-8.73%) (p<0.001). A total of 170 (88.54%) patients had improved HbA1c at 90 days. Conclusions: Use of the insulin guidance service achieved improved glycemic control by optimizing insulin dosing, which enabled most patients using the service to reduce or eliminate the use of expensive diabetes medications. Further study is needed to assess the impact of optimized insulin dosing on other diabetes related health care costs in a usual practice setting.

Background: The proportion of outpatient surgeries performed in physician offices has been increasing over time, raising concern about the impact on outcomes. Objective: To use a private insurance claims database to compare 7-day and 30-day hospitalization rates following relatively complex outpatient surgical procedures across physician offices, freestanding ambulatory surgery centers (ASCs), and hospital outpatient departments (HOPDs). Methods: A multivariable logistic regression model was used to compare the risk-adjusted probability of hospitalization among patients after any of the 88 study outpatient procedures at physician offices, ASCs, and HOPDs over 2008-2012 in Florida. Results: Risk-adjusted hospitalization rates were higher following procedures performed in physician offices compared with ASCs for all procedures grouped together, for most procedures grouped by type, and for many individual procedures. Conclusions: Hospitalizations following surgery were more likely for procedures performed in physician offices compared with ASCs, which highlights the need for ongoing research on the safety and efficacy of office-based surgery.

In 2001, the U.S. government released a rule that allowed states to “opt-out” of the federal requirement that a physician supervise the administration of anesthesia by a nurse anesthetist. To date, 17 states have opted out. The majority of the opt-out states cited increased access to anesthesia care as the primary rationale for their decision. In this study, we assess the impact of state opt-out policy on access to and costs of surgeries and other procedures requiring anesthesia services. Our null hypothesis is that opt-out rule adoption had little or no effect on surgery access or costs. We estimate an inpatient model of surgeries and costs and an outpatient model of surgeries. Each model uses data from multiple years of U.S. inpatient hospital discharges and outpatient surgeries. For inpatient cost models, the coefficient of the opt-out variable was consistently positive and also statistically significant in most model specifications. In terms of access to inpatient surgical care, the opt-out rules did not increase or decrease access in opt-out states. The results for the outpatient access models are less consistent, with some model specifications indicating a reduction in access associated with opt-out status, while other model specifications suggesting no discernable change in access. Given the sensitivity of model findings to changes in model specification, the results do not provide support for the belief that opt-out policy improves access to outpatient surgical care, and may even reduce access to outpatient surgical care (among freestanding facilities).