Neovascular age-related macular degeneration (nAMD) imposes a substantial clinical and economic burden in the United States (US). Faricimab offers the potential for longer dosing intervals than aflibercept, but its economic value in the US setting has not been established. This study evaluated the cost utility of faricimab versus aflibercept for treating nAMD from the US payer perspective. A Markov model of five health states defined by best-corrected visual acuity (BCVA) simulated disease progression over 5 years. Clinical inputs were drawn from pooled 1-year data of the TENAYA and LUCERNE phase III trials (n = 1329), which compared faricimab with aflibercept for treating nAMD patients. Direct medical costs encompassed drug acquisition, intravitreal administration, monitoring, office visits, and management of endophthalmitis. Outcomes included total costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratio (ICER), and net monetary benefit (NMB) at a willingness-to-pay (WTP) threshold of US100,000/QALY. Scenario, deterministic, and probabilistic sensitivity analyses (10,000 iterations) were conducted. In the base case, faricimab yielded 2.80 QALYs for US52,797, whereas aflibercept produced 2.72 QALYs at US62,367. Faricimab was therefore dominant (− US9570; + 0.08 QALY), with an incremental NMB of US17,981. Faricimab required markedly fewer injections over 5 years (22.6 vs 34). Scenario analyses did not materially change the directionality of the base-case results, with faricimab being dominant or cost effective. The model was most sensitive to drug acquisition prices and BCVA-state utilities, yet faricimab remained below the WTP threshold across all plausible ranges. Probabilistic analysis showed a 97

Real option value (ROV) remains underexplored in epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) for non-small cell lung cancer (NSCLC) even though its impact on economic evaluation is being acknowledged. The objective of this study is to estimate the ROV of osimertinib compared with gefitinib/erlotinib in the treatment of advanced NSCLC. The ROV was estimated using a three-state Markov model comprising progression-free survival, progressed, and death. The trend approach using NSCLC patients from Surveillance, Epidemiology, and End Results (SEER), and Medicare data, diagnosed between 2011 and 2017, was used. Cox proportional hazards models were fitted in the first 3 years of diagnosis. We assumed that historical survival trends continued beyond 2017 to estimate post-2017 the lung cancer-specific hazard ratio. The estimated hazard ratio was then applied to the transition probabilities from the progressed disease to death to estimate costs and quality-adjusted life years (QALYs), accounting for future improvements in survival. A cycle length of 1 month and a lifetime horizon was used in the analysis. The analysis was conducted from the US health system’s perspective and a discount rate of 3

BACKGROUND:The CheckMate 76K trial analysis showed that the use of nivolumab as adjuvant therapy in the treatment of patients with resected stage IIB-IIC melanoma reduced recurrence-free survival (RFS). This study aimed to estimate the cost-effectiveness of nivolumab as adjuvant therapy in these patients from the United States (U.S.) payer perspective. RESEARCH DESIGN AND METHODS:A cohort-based Markov model with monthly cycles was developed to simulate RFS over a Five-year base-case horizon. Health states included recurrence-free without adverse events, recurrence-free with adverse events, and disease recurrence (absorbing). Transition probabilities were derived from parametric survival models fitted to reconstructed individual patient-level RFS data. Sensitivity and scenario analyses were applied. RESULTS:Over five years, nivolumab increased costs by USD 182,783 and yielded 0.444 additional quality-adjusted life-years (QALYs), resulting in an incremental cost-effectiveness ratio (ICER) of USD 411,958 per QALY. Nivolumab was not cost-effective at conventional U.S. willingness-to-pay thresholds in the base case. Sensitivity analyses confirmed robustness, while scenarios assuming greater durability of benefit improved cost-effectiveness but generally remained above thresholds. CONCLUSIONS:Within a conservative recurrence-focused framework, adjuvant nivolumab improves health outcomes but is unlikely to be cost-effective at current prices over a short-term horizon. Reassessment will be essential as longer-term evidence emerges.

This study examined the association between patient-reported outcomes (PROs), medication-taking confidence, sociodemographic characteristics, and the frequency of 90-day hospital and emergency department (ED) visits among individuals with cardiovascular risk factors. A cross-sectional survey was administered to adults ≥ 18 years-old with hypertension, hyperlipidemia, or type-2 diabetes. Of the 1495 questionnaires distributed, 1275 were completed and included in the analysis. Bivariate ordered probit and ordinal logistic regression models were used to evaluate associations between patient-reported variables and healthcare utilization. Among respondents, 54.1

OBJECTIVES:This study aimed to develop and validate machine learning (ML) models to predict all-cause hospital admissions and 90-day readmissions using structured, patient-reported survey data. METHODS:A cross-sectional survey was conducted between 3 July 2021 and 18 December 2022, among US adults aged ≥18 years with at least one cardiovascular risk factor. Participants were recruited through social media, community pharmacies and outpatient clinics. The final sample included 1318 participants. Primary outcomes were any all-cause hospitalisation and readmission within 90 days. Eight supervised ML models were trained using an 80:20 train-test split and 10-fold cross-validation. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC), precision, recall, F1 score and calibration metrics. SHapley Additive exPlanations (SHAP) values identified key predictors. RESULTS:Among 1318 participants, 35.0% reported at least one hospitalisation and 10.4% reported a 90-day readmission. The Extra Trees (ET) model demonstrated the best performance across both outcomes. For hospitalisation, ET achieved an AUROC of 0.93, precision of 0.83 and recall of 0.87. For readmission, AUROC was 0.99 with precision of 0.95 and recall of 0.96. SHAP analysis identified heart disease, medication burden, race/ethnicity, employment and insurance status as the most influential predictors. DISCUSSION:Patient-reported data reflecting behavioural, social and clinical factors can predict hospitalisations with high accuracy, complementing traditional EHR-based models. CONCLUSIONS:Integrating such patient-reported and behavioural data into electronic health records could enable earlier identification of high-risk individuals and support targeted, preventive interventions to improve healthcare outcomes.

BACKGROUND:Real option value (ROV) offers an innovative paradigm to evaluate the dynamic value of medical technologies, particularly in cancer, by capturing the value of extending patient survival to access future innovations. Despite its potential, the application of ROV in medical technologies in oncology remains underexplored. OBJECTIVE:To synthesize existing evidence on the application of ROV in medical technologies in oncology. METHODS:A comprehensive search of PubMed, ScienceDirect, and Web of Science was conducted to identify peer-reviewed studies published in English from January 2000 to May 2024. In the search query, a combination of keywords related to "real option value" and "cancer" was used. Key data extracted included study characteristics, objectives, ROV modeling technique, and primary findings. The Consolidated Health Economic Evaluation Reporting Standards 2022 checklist was used for quality assessment of the studies. RESULTS:A total of 13 of 165 studies assessed the ROV of medical therapies, with a primary focus on melanoma, lung cancer, and prostate cancer. ROV was modeled from the ex post and ex ante perspectives. The methodologies employed vary, with common forecasting approaches including the Lee-Carter model to project future decreases in mortality rates, fitting Cox proportional regression models on administrative claims data, or estimating the approval likelihood of early pipeline drugs based on data from early randomized clinical trials. CONCLUSIONS:The ROV represents a critical dimension in evaluating medical technologies in oncology, where innovation is rapid. The implications of ROV extend beyond oncology, with the potential to influence funding, pricing, and access decisions in other disease areas as well. However, challenges such as oversimplification of assumptions for forecasting, methodological consistency, and lack of standardized framework remain pervasive. This systematic review underscores the need to integrate ROV into Health Technology Assessment practices to inform resource allocation and policy decisions.

BACKGROUND:In 2024, Alzheimer's disease affected approximately 6.9 million Americans aged 65 and older. Current therapies include acetylcholinesterase inhibitors, N-methyl-D-aspartate receptor inhibitors, and monoclonal antibodies. With an economic burden surpassing $345 billion, $222 billion borne by Medicare and Medicaid, this study evaluates lecanemab's budgetary impact for early AD, including subgroup analyses by gender and race. METHODS:A budget impact model evaluated LECA for early Alzheimer's disease, comparing scenarios with and without the therapy. Inputs included market share, costs, duration, and compliance. Gender and race subgroup analyses, annual costs, PMPM, PTMPM, and sensitivity analysis outcomes were assessed to explore parameter-driven variability comprehensively. RESULTS:Introducing LECA for over 3.5 million eligible early AD patients in the U.S. may generate a three-year budget impact of $4.1 billion for Medicare. Incremental PMPM savings were $1.4, and PTMPM savings reached $24.1. Subgroup analyses revealed no significant gender or racial differences in PMPM and PTMPM, with variability only in overall budget impact. Sensitivity analyses indicate that enhanced healthcare resource utilization, reduced disease severity, and improved cost-efficiency among males contribute to strengthening Medicare's budget sustainability. CONCLUSIONS:Utilizing LECA as a treatment for early AD is expected to be cost saving with respect to Medicare budgets in the U.S.

BACKGROUND: Bipolar disorder is a severe recurrent, episodic psychiatric condition with a worldwide prevalence of approximately 1%, affecting more than 5 million adults in the United States in 2020. A subtype, bipolar I disorder (BP-I), which accounts for approximately one-quarter of cases, is associated with impairments in psychosocial functioning and quality of life. Recommended treatment options include daily oral, or long-acting injectable, antipsychotics, including the aripiprazole once every month formulation, which has been shown to improve adherence compared with oral treatments. A new formulation of aripiprazole for administration once every 2 months ready to use (Ari 2MRTU) has been shown to have similar efficacy to monthly treatment, with only 6 doses annually. OBJECTIVE: To estimate the financial impact of introducing the new formulation of aripiprazole as a treatment option for adults diagnosed with BP-I in the United States. METHODS: A cohort of eligible patients with BP-I was selected from a hypothetical US health plan of 1 million members, and the treatment costs modeled with a 3-year time horizon, in scenarios with or without the addition of Ari 2MRTU. Inputs into the model included user-definable estimates of the current and projected market share of the available antipsychotics, forecast uptake of aripiprazole new formulation, acquisition, initiation, and administration costs, hospitalization costs, time on treatment, and patient adherence. The budget impact was estimated as the difference in the annual cost for the total cohort for the current and new scenarios, the cost per member per month, and the cost per treated member per month. Deterministic sensitivity analyses were also conducted to examine the extent to which the model results were affected by variations in individual input parameters RESULTS: The total budget impact of introducing a formulation of Ari2MRTU as maintenance monotherapy for treating a cohort of eligible patients with BP-I in the United States from a health plan of 1 million members was estimated to be $898,930 over 3 years, representing a per member per month cost saving of $0.025 and a per treated member per month cost saving of $2.43. The sensitivity analysis supports a modest reduction in budget, with the main driver being adherence with medication regimen. CONCLUSIONS: The introduction of Ari 2MRTU as a maintenance treatment for adults with BP-I is expected to have a neutral effect on payer budgets in the United States and is a potentially favorable option for patients who prefer less frequent dosing.

BACKGROUND:Endocrine therapy is the mainstay treatment for breast cancer (BC) to reduce BC recurrence risk. During the first year of endocrine therapy use, nearly 30% of BC survivors are nonadherent, which may increase BC recurrence risk. This study is to examine the association between endocrine therapy adherence trajectories and BC recurrence risk in nonmetastatic BC survivors. METHODS:This retrospective cohort study included Medicare beneficiaries in the United States (US) with incident nonmetastatic BC followed by endocrine therapy initiation in 2010-2019 US Surveillance, Epidemiology, and End Results linked Medicare data. We calculated monthly fill-based proportion of days covered in the first year of endocrine therapy. We applied group-based trajectory models to identify distinct endocrine therapy adherence patterns. After the end of the first-year endocrine therapy trajectory measurement period, we estimated the risk of time to first treated BC recurrence within 4 years using Cox proportional hazards models. RESULTS:We identified 5 trajectories of adherence to endocrine therapy in BC Stages 0-I subgroup (n = 28,042) and in Stages II-III subgroup (n = 7781). A trajectory of discontinuation before 6 months accounted for 7.0% in Stages 0-I and 5.8% in Stages II-III subgroups, and this trajectory was associated with an increased treated BC recurrence risk compared to nearly perfect adherence (Stages 0-I: adjusted hazard [aHR] = 1.84, 95% CI = 1.46-2.33; Stages II-III: aHR = 1.38, 95% CI = 1.07-1.77). CONCLUSIONS:Nearly 7% of BC survivors who discontinued before completing 6 months of treatment was associated with an increased treated BC recurrence risk compared to those with nearly perfect adherence among Medicare nonmetastatic BC survivors.

BACKGROUND:The cost of medically attended RSV LRI (lower respiratory infection) is critical in determining the economic value of new RSV immunoprophylaxes. However, most studies have focused on intermittent RSV encounters, not the episode of care that captures the entirety of RSV illness. METHODS:We created age- and condition-specific cohorts of children under 5 years of age using MarketScan® data (2015-2019). We contrasted aggregating healthcare costs over RSV-LRTI episodes to ascertaining costs based on RSV-specific encounters only. Economic burden was estimated by multiplying costs per encounter or per episode by their respective incidence rates. RESULTS:Average cost was higher per episode than per encounter regardless of settings (inpatient: $28,586 vs. $18,056 and outpatient/ED: $2099 vs. $407 for infants). Across ages, the economic burden was highest for infants and RSV-LRTI requiring inpatient care, but the burden in outpatient/ED settings was disproportionately higher than costs due to higher incidence rates (for inpatient vs. outpatient episodes: $226,403 vs. $101,269; for inpatient vs. outpatient encounters: $151,878 vs. $38,819 per 1000 infant-years). For high-risk children, cost and burden were up to 3-10 times higher, respectively. CONCLUSIONS:With a comprehensive stratification by settings and risk condition, the encounter- versus episode-based estimates provide a robust range for policymakers' economic appraisal of new RSV immunoprophylaxes.

To examine the association between prescription opioid use trajectories and risk of opioid use disorder (OUD) or overdose among nonmetastatic breast cancer survivors by treatment type. This retrospective cohort study included female nonmetastatic breast cancer survivors with at least 1 opioid prescription fill in 2010–2019 Surveillance, Epidemiology and End Results linked Medicare data. Opioid mean daily morphine milligram equivalents (MME) calculated within 1.5 years after initiating active breast cancer therapy. Group-based trajectory models identified distinct opioid use trajectory patterns. Risk of time to first OUD/overdose event within 1 year after the trajectory period was calculated for distinct trajectory groups using Cox proportional hazards models. Analyses were stratified by treatment type. Four opioid use trajectories were identified for each treatment group. For 38,030 survivors with systemic endocrine therapy, 3 trajectories were associated with increased OUD/overdose risk compared with early discontinuation: minimal dose (< 5 MME; adjusted hazard ratio [aHR] = 1.73 [95

BACKGROUND:One of the goals established by the United States National Action Plan to Combat Antibiotic-Resistant Bacteria is to reduce inappropriate outpatient antibiotic prescriptions by 50% by 2020. Recent data on the achievement of this goal is lacking. The objective of our study was to examine recent trends in the appropriateness of oral antibiotic prescriptions dispensed to a commercially insured population in outpatient settings in the United States to quantify the relative trend in inappropriate antibiotic prescribing from 2010 to 2018. METHODS:Our cross-sectional analysis examined oral antibiotic prescriptions dispensed in outpatient settings using the IBM MarketScan Commercial Data from January 2010 to December 2018. Trends in the annual proportion of antibiotic prescriptions classified as appropriate, potentially appropriate, inappropriate, or without any medical visit during a 7 days look-back period were estimated using multivariable generalized linear models with Poisson distribution adjusting for beneficiaries' demographic and infectious conditions. RESULTS:Approximately 170 million oral antibiotic prescriptions were dispensed to 86 million beneficiaries during 2010 to 2018. The mean age of the study population was 34.5 (±19.1) years, with 58.4% females and 24.6% children. We observed a 12.9% (95% Confidence Interval [CI] = 12.6%-13.2%; p < 0.01) decline in rates of antibiotic use, from 832 to 727 prescriptions per 1000 beneficiaries, from 2010 to 2018. The proportion of prescriptions classified as appropriate increased by 36.7% (95% CI = 36.4%-36.9%; p < 0.01); potentially appropriate prescriptions increased by 9.3% (95% CI = 9.1%-9.4%; p < 0.01); whereas inappropriate prescriptions and those without a medical visit declined by 11.3% (95% CI = 11.2%-11.4%; p < 0.01) and 14.0% (95% CI = 13.9%-14.2%; p < 0.01), respectively. Similar declining trends were observed in use and proportion of inappropriate prescriptions for broad-spectrum antibiotics. In 2018, amoxicillin and azithromycin were the most common appropriate and inappropriate prescription fills, respectively. CONCLUSION:Although antibiotic use and inappropriate prescribing declined steadily from 2010 to 2018 in the United States, this study demonstrates that we have not achieved the national goal of reducing inappropriate antibiotic prescribing by 50%.

Evaluating healthcare interventions for their impacts beyond health outcomes may result in recognition of changes in human capital, income level, tax revenue, and government spending, which could affect economic growth and population health. In this paper, we document instances where current health technology assessment (HTA) practices fail to account for the impacts of healthcare interventions on broader society beyond the healthcare sector. We propose a novel conceptual framework, highlighting its three components (distributional cost-effectiveness analysis [DCEA], input-output model, and voting scheme) and their contributions to capturing the economic and societal ripple effects of healthcare interventions. This manuscript also outlines a case study in which the framework is applied to the reassessment of a previously evaluated digital health therapeutic for the treatment of opioid use disorder (OUD) compared with standard of care, demonstrating its practical application. The DCEA health value metric indicates that digital therapeutic is more equitable, favoring socioeconomically disadvantaged groups, while standard of care exacerbates health inequality by benefiting the already advantaged. Additionally, digital therapeutic shows potential for boosting productivity, raising income, and creating jobs, supporting its consideration by employer-sponsored health plans to optimize resource allocation for treating OUD. The conceptual framework provides insights for enhancing HTAs to incorporate the broader economic and societal impacts of healthcare interventions. By integrating DCEA, extended HTA analysis with input-output modeling, and a voting scheme, decision makers can make informed choices aligned with societal priorities, although further research and validation are necessary for practical implementation across diverse healthcare contexts.

BACKGROUND:Limited research exists on pricing policies from a bibliometric and visualization perspective, and there is a lack of understanding of their typology. This study aims to address these gaps in knowledge and provide a deeper understanding of the research topics and development trends in this field. METHODS:A bibliometric study was conducted on drug pricing approaches in healthcare literature, published between 2000 and June 2023. The literature was identified through an extensive search of healthcare databases and was then classified based on the year of publication, research topics, corresponding authors, location of corresponding authors, and journal titles. The citation data analysis was conducted using Bibliometrix, which consisted of descriptive, geographical, and time-series analyses and visualization. RESULTS:Between 2000 and June 2023, 173 articles were disseminated across 98 distinct publication sources. During the analysis, we observed a significant and consistent rise in literature reports on drug pricing approaches in healthcare, especially in 2010. The research topics were distributed almost equally, discussing improvement or issues with drug pricing models and addressing drug pricing applications. Our analysis revealed that the top ten corresponding authors were responsible for 19% of the total articles, with those based in the United States being the most productive. Furthermore, the "Health Economics" journal ranked first among the top ten journals. These findings align with the overall publication trends of drug pricing methods reported in other fields. CONCLUSIONS:The current study offers a comprehensive overview of drug pricing techniques utilized in medicine through visualization and bibliometric techniques. Analysis of authors, journals, institutions, and countries could serve as a reference for new researchers and guide them differently. Researchers can also consider emerging trends when determining the focus of their studies.

BACKGROUND: Breast cancer is the most diagnosed cancer in the United States, and half of breast cancer survivors experience major depressive disorders (hereafter depression). Healthcare Effectiveness Data and Information Set (HEDIS) quality measures evaluating depression treatment practices recommend uninterrupted antidepressant treatment for 3 months in the acute phase and 3 months in the continuation phase for the general population. However, little is known about the extent of and trends in antidepressant nonadherence among breast cancer survivors with depression, which may impact adherence to breast cancer treatment, potentially leading to breast cancer recurrence and other adverse outcomes. OBJECTIVE: To examine the trends and characteristics associated with antidepressant nonadherence among breast cancer survivors with depression in the United States. METHODS: We conducted cross-sectional analyses of Surveillance, Epidemiology, and End Results linked with Medicare data (2010-2019) for women with breast cancer and depression who newly initiated antidepressant use. Using HEDIS measures of nonadherence (ie, antidepressant prescription coverage ≤84 days of the 114-day acute phase or ≤180 days of the 231-day continuation phase), we calculated the annual crude prevalence of antidepressant nonadherence and examined trends using unadjusted logistic regression. Multivariable logistic regression identified characteristics associated with antidepressant nonadherence. RESULTS: Among 9,452 eligible breast cancer survivors with depression (aged ≥65 years = 84% and White race = 82%), the crude prevalence of antidepressant nonadherence decreased from 2010 to 2019 for both the acute (49% to 40%; Ptrend<0.001) and continuation (67% to 57%; Ptrend<0.001) phases. Factors significantly associated with higher odds of antidepressant nonadherence in both the acute and continuation phases included Black race (odds ratios [ORs] [95% CI] for the acute/continuation phases: 2.0 [1.7-2.4]/2.0 [1.7-2.3]) and Hispanic ethnicity (1.5 [1.1-1.9]/2.2 [1.6-2.9]) compared with White race; receiving the first antidepressant from an oncologist vs a psychiatrist (1.4 [1.1-1.8]/1.6 [1.2-2.0]); and using antidepressants not recommended for older adults by the Beers criteria (2.2 [1.6-2.9]/2.0 [1.4-2.7]). Factors associated with lower odds of antidepressant nonadherence in both phases included receiving lymph node dissection (0.7 [0.5-0.9]/0.7 [0.5-0.9]), receiving endocrine therapy (0.9 [0.8-0.9]/0.8 [0.7-0.9]), having a higher National Cancer Institute comorbid index (0.8 [0.7-0.8]/0.9 [0.8-0.9]), having a follow-up visit with a psychiatrist (0.9 [0.8-0.9]/0.9 [0.8-0.9]), and switching to different antidepressants (0.7 [0.6-0.8]/0.7 [0.7-0.8]). CONCLUSIONS: Despite antidepressant nonadherence prevalence decreasing from 2010 to 2019, over half of breast cancer survivors with depression and Medicare were nonadherent in the continuation phase. Patients with identified nonadherence risk factors may benefit from close monitoring and targeted interventions. DISCLOSURES: Wei-Hsuan Lo-Ciganic reported grants from the National Institute on Drug Abuse (R01DA044985 and R01DA050676), the National Institute on Aging (R21AG060308), the National Institute of Mental Health (R01MH121907), Merck Sharp & Dohme, Bristol Myers Squibb, the Richard King Mellon Foundation at the University of Pittsburgh, the Clinical and Translational Science Institute of the University of Florida, the Pharmaceutical Research and Manufacturers of America (PhRMA) Foundation, and the US Department of Veterans Affairs outside the submitted work; in addition, Wei-Hsuan Lo-Ciganic has a patent pending for U1195.70174US00. Haesuk Park reported grants from Bristol Myers Squibb/Pfizer Alliance American Thrombosis Investigator Initiated Research Program (ARISTA-USA) outside the submitted work. Juan M. Hincapie-Castillo reported grants from Merck outside the submitted work. Debbie Wilson reported grants from the National Institute on Drug Abuse, the National Institute on Aging, Merck Sharp & Dohme, and Bristol Myers Squibb outside the submitted work; and serving as an editorial board member for the Journal of Pharmacy Technology. Ching-Yuan Chang's contributions to this manuscript were made while at the University of Florida College of Pharmacy. Ching-Yuan Chang is currently employed by Vertex Pharmaceuticals, Inc. Vertex did not fund or have any involvement in this study or publication. Vakaramoko Diaby is currently employed by Otsuka, Inc. Otsuka did not fund or have any involvement in this study or publication. No other disclosures were reported.

Objective To develop and validate a tool to predict patients with ischaemic heart disease (IHD) at risk of excessive healthcare resource utilisation.Design A retrospective cohort study.Setting We identified patients through the State of Florida Agency for Health Care Administration (N=586 518) inpatient dataset.Participants Adult patients (at least 40 years of age) admitted to the hospital with a diagnosis of IHD between 1 January 2007 and 31 December 2016.Primary outcome measures We identified patients whose healthcare utilisation is higher than presumed (analysis of residuals) and used logistic regression (binary and multinomial) in estimating the predictive models to classify individual as high-need, high-care (HNHC) patients relative to inpatient visits (frequency of hospitalisation), cost and hospital length of stay. Discrimination power, prediction accuracy and model improvement for the binary logistic model were assessed using receiver operating characteristic statistic, the Brier score and the log-likelihood (LL)-based pseudo-R2, respectively. LL-based pseudo-R2 and Brier score were used for multinomial logistic models.Results The binary logistic model had good discrimination power (c-statistic=0.6496), an accuracy of probabilistic predictions (Brier score) of 0.0621 and an LL-based pseudo-R2 of 0.0338 in the development cohort. The model performed similarly in the validation cohort (c-statistic=0.6480), an accuracy of probabilistic predictions (Brier score) of 0.0620 and an LL-based pseudo-R2 of 0.0380. A user-friendly Excel-based HNHC risk predictive tool was developed and readily available for clinicians and policy decision-makers.Conclusions The Excel-based HNHC risk predictive tool can accurately identify at-risk patients for HNHC based on three measures of healthcare expenditures.

INTRODUCTION:The objective of this study was to estimate the economic impact of providing universal hepatitis C virus testing in commercially insured middle-aged persons who inject drugs in the U.S.METHODS:This study developed a dynamic 10-year economic model to project the clinical and economic outcomes associated with hepatitis C virus testing among middle-aged adult persons who inject drugs, from a payer's perspective. Costs related to hepatitis C virus testing, direct-acting antiviral, and liver-related outcomes between the (1) current hepatitis C virus testing rate (i.e., 8%) and (2) universal hepatitis C virus testing rate (i.e., 100%) were compared. Among patients testing positive, 21% of those without cirrhosis and 48% of those with cirrhosis were assumed to initiate direct-acting antivirals. Sensitivity analyses were performed to identify variables (e.g., direct-acting antiviral drug costs, hepatitis C virus testing costs, direct-acting antiviral treatment rate) influencing this study's conclusion.RESULTS:The model predicts that during the 10-year period, universal hepatitis C virus testing will cost an additional $242 per person who injects drugs to the payers' healthcare budgets compared with the current scenario. Sensitivity analyses showed values ranging from $1,656 additional costs to $1,085 cost savings across all varied parameters and scenarios. A total of 80% of the current direct-acting antiviral costs indicated that cost savings will be $383 per person who injects drugs.CONCLUSIONS:Universal hepatitis C virus testing among persons who inject drugs would not achieve cost savings within 10 years, with the cost of direct-acting antivirals contributing the most to the spending. To promote universal hepatitis C virus testing among persons who inject drugs, decreasing direct-acting antiviral costs and sustainable funding streams for hepatitis C virus testing should be considered.

Study Objective: To investigate risk of aortic aneurysm or dissection in patients using oral fluoroquinolones compared to those using macrolides in real-world clinical practice among a large US general population.Design: Retrospective cohort study design.Data Source: MarketScan commercial and Medicare supplemental databases.Patients: Adults patients with at least one prescription fill for fluoroquinolone or macrolide antibiotics.Intervention: Fluoroquinolone or macrolide antibiotics.Measurements and Main Results: The primary outcome was estimated incidence of aortic aneurysm or dissection associated with the use of fluoroquinolones compared with macrolides during a 60-day follow-up period in a 1:1 propensity score-matched cohort. We identified 3,174,620 patients (1,587,310 in each group) after 1:1 propensity score matching. Crude incidence of aortic aneurysm or dissection was 1.9 cases per 1000 person-years among fluoroquinolone users and 1.2 cases per 1000 person-years among macrolide users. In multivariable Cox regression, compared with macrolides, the use of fluoroquinolones was associated with an increased risk of aortic aneurysm or dissection (aHR: 1.34; 95% CI: 1.17-1.54). The association was primarily driven by a high incidence of aortic aneurysm cases (95.8%). Results of sensitivity (e.g., fluoroquinolone exposure ranging from 7 to 14 days (aHR: 1.47; 95% CI: 1.26-1.71)) and subgroup analyses (e.g., ciprofloxacin (aHR: 1.26; 95% CI: 1.07-1.49) and levofloxacin (aHR: 1.44; 95% CI: 1.19-1.52)) remained consistent with main findings.Conclusions: Fluoroquinolone use was associated with a 34% increased risk of aortic aneurysm or dissection compared with macrolide use among a general US population.

The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) provide guidance for health technology assessment bodies to use when reporting economic evaluations. The most recent edition, CHEERS 2022, is a 28-item reporting checklist that was developed in consultation with a broad range of stakeholders. CHEERS 2022 has been translated into French so it can be used more widely and improve consistency in economic evaluations among the international health technology assessment community.

ObjectivesExamine predictors of clinical and resource utilization outcomes associated with Alzheimer's disease and related dementias (ADRD), stratified by patient severity profiles.MethodsCross-sectional study of adults (30+ year old) with ADRD discharged from US hospitals to home health care (HHC) and identified from the 2010-2015 Nationwide Readmissions Database (NRD) using ICD 9(th)-10(th) codes. Outcomes of interest included 30-day hospital readmissions, in-hospital mortality, and hospital length of stay (LOS). Covariates consisted of sociodemographic and clinical variables. Multiple logistic regressions (for readmissions and mortality) and generalized linear regressions (for LOS) were used to examine associations between outcomes and study covariates, stratified by patient severity profiles.ResultsOf 164,598 ADRD patients, 3,848 were mild, 68803 were moderate, 72428 were severe, and 19,519 were extreme. The 30-day readmission rate was 3.2%, death rate was 14.5%, and LOS was 3.0 days, (95%, CI: 15.0, 17.0) to 5.0 days, (95%, CI: 18.0, 19.0), all with a p-value<0.0001. Across outcomes and severity levels, the top predictors included number of diagnoses, gender, hospital bed size, primary and secondary diagnoses, and income size.ConclusionsSevere and extreme stages of HHC discharge may lead to increased readmissions, death, LOS, and costs. Specialized care is needed to reduce these negative outcomes in the ADRD patient population.