The Spillover Effects of Extending Liver Transplantation to Patients with Colorectal Liver Metastases: A Discrete Event Simulation Analysis.

BACKGROUND: Liver transplantation is an alternative treatment for patients with nonresectable colorectal cancer liver-only metastases (CRLM); however, the potential effects on wait-list time and life expectancy to other patients on the transplant waiting list have not been considered. We explored the potential effects of expanding liver transplantation eligibility to include patients with CRLM on wait-list time and life expectancy in Norway. METHODS: We developed a discrete event simulation model to reflect the Norwegian liver transplantation waiting list process and included 2 groups: 1) patients currently eligible for liver transplantation and 2) CRLM patients. Under 2 alternative CRLM-patient transplant eligibility criteria, we simulated 2 strategies: 1) inclusion of only currently eligible patients (CRLM patients received standard-of-care palliative chemotherapy) and 2) expanding waiting list eligibility to include CRLM patients under 2 eligibility criteria. Model outcomes included median waiting list time, life expectancy, and total life-years. RESULTS: For every additional CRLM patient listed per year, the overall median wait-list time, initially 52 d, increased by 8% to 11%. Adding 2 additional CRLM patients under the most restrictive eligibility criteria increased the CRLM patients' average life expectancy by 10.64 y and decreased the average life expectancy for currently eligible patients by 0.05 y. Under these assumptions, there was a net gain of 149.61 life-years over a 10-y programmatic period, which continued to increase under scenarios of adding 10 CRLM patients to the wait-list. Health gains were lower under less restrictive CRLM eligibility criteria. For example, adding 4 additional CRLM patients under the less restrictive eligibility criteria increased the CRLM patients' average life expectancy by 5.64 y and decreased the average life expectancy for currently eligible patients by 0.12 y. Under these assumptions, there was a net gain of 96.36 life-years over a 10-y programmatic period, which continued to increase up to 7 CRLM patients. CONCLUSIONS: Our model-based analysis enabled the consideration of the potential effects of enlisting Norwegian CRLM patients for liver transplantation on wait-list time and life expectancy. Enlisting CRLM patients is expected to increase the total health effects, which supports the implementation of liver transplantation for CRLM patients in Norway. HIGHLIGHTS: Given the Norwegian donor liver availability, adding patients with nonresectable colorectal cancer liver-only metastases (CRLM) to the liver transplantation waiting list had an overall modest, but varying, impact on total waiting list time.Survival gains for selected CRLM patients treated with liver transplantation would likely outweigh the losses incurred to patients listed currently.To improve the total life-years gained in the population, Norway should consider expanding the treatment options for CRLM patients to include liver transplantation.Other countries may also have an opportunity to gain total life-years by extending the waiting list eligibility criteria; however, country-specific analyses are required.

Integrating decision modeling and machine learning to inform treatment stratification.

There is increasing interest in moving away from "one size fits all (OSFA)" approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules ('policies') that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for "standard" and "intensive" blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

A Review of the Cost-Effectiveness Evidence for FDA-Approved Cell and Gene Therapies.

Cell and gene therapy (CGT) innovations have provided several significant breakthroughs in recent years. However, CGTs often come with a high upfront cost, raising questions about patient access, affordability, and long-term value. This study reviewed cost-effectiveness analysis (CEA) studies that have attempted to assess the long-term value of Food and Drug Administration (FDA)-approved CGTs. Two reviewers independently searched the Tufts Medical Center CEA Registry to identify all studies for FDA-approved CGTs, per January 2023. A data extraction template was used to summarize the evidence in terms of the incremental cost-effectiveness ratio expressed as the cost per quality-adjusted life year (QALY) and essential modeling assumptions, combined with a template to extract the adherence to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. The review identified 26 CEA studies for seven CGTs. Around half of the base-case cost-effectiveness results indicated that the cost per QALY was below $100,000-$150,000, often used as a threshold for reasonable cost-effectiveness in the United States. However, the results varied substantially across studies for the same treatment, ranging from being considered very cost-effective to far from cost-effective. Most models were based on data from single-arm trials with relatively short follow-ups, and different long-term extrapolations between studies caused large differences in the modeled cost-effectiveness results. In sum, this review showed that, despite the high upfront costs, many CGTs have cost-effectiveness evidence that can support long-term value. Nonetheless, substantial uncertainty regarding long-term value exists because so much of the modeling results are driven by uncertain extrapolations beyond the clinical trial data.

Surgical waiting lists and queue management in a Brazilian tertiary public hospital.

BACKGROUND: Centralized management of queues helps to reduce the surgical waiting time in the publicly funded healthcare system, but this is not a reality in the Brazilian Unified Healthcare System (BUHS). We describe the implementation of the "Patients with Surgical Indication" (PSI) in a Brazilian public tertiary hospital, the impact on waiting time, and its use in rationing oncological surgeries during the COVID-19 Pandemic. METHODS: Retrospective observational study of elective surgical requests (2016-2022) in a Brazilian general, public, tertiary university hospital. We recovered information regarding the inflows (indications), outflows and their reasons, the number of patients, and waiting time in queue. RESULTS: We enrolled 82,844 indications in the PSI (2016-2022). The waiting time (median and interquartile range) in days decreased from 98(48;168) in 2016 to 14(3;152) in 2022 (p < 0.01). The same occurred with the backlog that ranged from 6,884 in 2016 to 844 in 2022 (p < 001). During the Pandemic, there was a reduction in the number of non-oncological surgeries per month (95% confidence interval) of -10.9(-18.0;-3.8) during Phase I (January 2019-March 2020), maintenance in Phase II (April 2020-August 2021) 0.1(-10.0;10.4) and increment in Phase III (September 2021-December 2022) of 23.0(15.3;30.8). In the oncological conditions, these numbers were 0.6(-2.1;3.3) for Phase I, an increase of 3.2(0.7;5.6) in Phase II and 3.9(1,4;6,4) in Phase III. CONCLUSION: Implementing a centralized list of surgical indications and developing queue management principles proved feasible, with effective rationing. It unprecedentedly demonstrated the decrease in the median waiting time in Brazil.

Classifying Winning Performances in International Women's Rugby Union.

PURPOSE: The efficacy of isolated and relative performance indicators (PIs) has been compared in rugby union; the latter more effective at discerning match outcomes. However, this methodology has not been applied in women's rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative data sets, in women's rugby union. METHODS: Twenty-six PIs were selected from 110 women's international rugby matches between 2017 and 2022 to form an isolated data set, with relative data sets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both data sets, and feature selection and importance were used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. RESULTS: The isolated full model correctly classified 75% of outcomes (CI, 65%-82%), whereas the relative full model correctly classified 78% (CI, 69%-86%). Reduced respective models correctly classified 74% (CI, 65%-82%) and 76% (CI, 67%-84%). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test data sets, respectively. No significant difference in accuracy was found between data sets. In the relative reduced model, meters made, clean breaks, missed tackles, lineouts lost, carries, and kicks from hand were significant. CONCLUSIONS: Increased relative meters made, clean breaks, carries, and kicks from hand and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women's rugby.

The cost-effectiveness of a virtual intervention to prevent eating disorders in young women in Sweden.

OBJECTIVE: To determine the cost-effectiveness of a virtual version of the Body Project (vBP), a cognitive dissonance-based program, to prevent eating disorders (ED) among young women with a subjective sense of body dissatisfaction in the Swedish context. METHOD: A decision tree combined with a Markov model was developed to estimate the cost-effectiveness of the vBP in a clinical trial population of 149 young women (mean age 17 years) with body image concerns. Treatment effect was modeled using data from a trial investigating the effects of vBP compared to expressive writing (EW) and a do-nothing alternative. Population characteristics and intervention costs were sourced from the trial. Other parameters, including utilities, treatment costs for ED, and mortality were sourced from the literature. The model predicted the costs and quality-adjusted life years (QALYs) related to the prevention of incidence of ED in the modeled population until they reached 25 years of age. The study used both a cost-utility and return on investment (ROI) framework. RESULTS: In total, vBP yielded lower costs and larger QALYs than the alternatives. The ROI analysis denoted a return of US $152 for every USD invested in vBP over 8 years against the do-nothing alternative and US $105 against EW. DISCUSSION: vBP is likely to be cost-effective compared to both EW and a do-nothing alternative. The ROI from vBP is substantial and could be attractive information for decision makers for implementation of this intervention for young females at risk of developing ED. PUBLIC SIGNIFICANCE: This study estimates that the vBP is cost-effective for the prevention of eating disorders among young women in the Swedish setting, and thus is a good investment of public resources.

Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input.

OBJECTIVES: A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates. METHODS: The original study estimated QoL of patients in need of commonly performed procedures in live expert-panel meetings. This study replicated this procedure using a web-based Delphi approach in a different hospital. The new QoL scores were compared with the original scores using mixed effects linear regression. The ranking of surgical procedures based on combined QoL values from the validation and original study was compared to the ranking based solely on the original QoL values. RESULTS: The overall mean difference in QoL estimates between the validation study and the original study was - 0.11 (95% CI: -0.12 - -0.10). The model output (DALY/month delay) based on QoL data from both studies was similar to the model output based on the original data only: The Spearman's correlation coefficient between the ranking of all procedures before and after including the new QoL estimates was 0.988. DISCUSSION: Even though the new QoL estimates were systematically lower than the values from the original study, the ranking for urgency based on health loss per unit of time delay of procedures was consistent. This underscores the robustness and generalizability of the decision model for prioritization of surgical procedures.

Model-Projected Cost-Effectiveness of Adult Hearing Screening in the USA.

BACKGROUND: While 60% of older adults have hearing loss (HL), the majority have never had their hearing tested. OBJECTIVE: We sought to estimate long-term clinical and economic effects of alternative adult hearing screening schedules in the USA. DESIGN: Model-based cost-effectiveness analysis simulating Current Detection (CD) and linkage of persons with HL to hearing healthcare, compared to alternative screening schedules varying by age at first screen (45 to 75 years) and screening frequency (every 1 or 5 years). Simulated persons experience yearly age- and sex-specific probabilities of acquiring HL, and subsequent hearing aid uptake (0.5-8%/year) and discontinuation (13-4%). Quality-adjusted life-years (QALYs) were estimated according to hearing level and treatment status. Costs from a health system perspective include screening ($30-120; 2020 USD), HL diagnosis ($300), and hearing aid devices ($3690 year 1, $910/subsequent year). Data sources were published estimates from NHANES and clinical trials of adult hearing screening. PARTICIPANTS: Forty-year-old persons in US primary care across their lifetime. INTERVENTION: Alternative screening schedules that increase baseline probabilities of hearing aid uptake (base-case 1.62-fold; range 1.05-2.25-fold). MAIN MEASURES: Lifetime undiscounted and discounted (3%/year) costs and QALYs and incremental cost-effectiveness ratios (ICERs). KEY RESULTS: CD resulted in 1.20 average person-years of hearing aid use compared to 1.27-1.68 with the screening schedules. Lifetime total per-person undiscounted costs were $3300 for CD and ranged from $3630 for 5-yearly screening beginning at age 75 to $6490 for yearly screening beginning at age 45. In cost-effectiveness analysis, yearly screening beginning at ages 75, 65, and 55 years had ICERs of $39,100/QALY, $48,900/QALY, and $96,900/QALY, respectively. Results were most sensitive to variations in hearing aid utility benefit and screening effectiveness. LIMITATION: Input uncertainty around screening effectiveness. CONCLUSIONS: We project that yearly hearing screening beginning at age 55+ is cost-effective by US standards.

Residual Disease Burden After Neoadjuvant Therapy Among US Patients With High-Risk HER2-positive Early-Stage Breast Cancer: A Population Effectiveness Model.

BACKGROUND: Approximately half of patients with high-risk HER2-positive early-stage breast cancer (ESBC) do not have pathologic complete response (pCR) after neoadjuvant therapy. The residual burden of disease among this population has not been previously quantified. MATERIALS AND METHODS: We used decision-modeling techniques to simulate recurrence, progression from locoregional to distant cancer, breast cancer-related mortality, and mortality from other causes over a 10-year period in a hypothetical cohort. We derived progression probabilities primarily from the KATHERINE trial of T-DM1 (ado-trastuzumab emtansine) and mortality outcomes from the published literature. Modeled outcomes included recurrences, breast cancer deaths, deaths from other causes, direct medical costs, and costs due to lost productivity. To estimate the residual disease burden, we compared outcomes from a cohort of patients treated with T-DM1 versus a hypothetical cohort with no disease recurrence. RESULTS: We estimated that 9,300 people would experience incident high-risk HER2-positive ESBC in the United States in 2021 based on cancer surveillance databases, clinical trial data, and expert opinion. We estimated that, in this group, 2,118 would experience disease recurrence, including 1,576 distant recurrences, and 1,358 would experience breast cancer deaths. This residual disease burden resulted in 6,435 life-years lost versus the recurrence-free cohort, and healthcare-related costs totaling $644 million, primarily associated with treating distant cancers. CONCLUSION: Patients with HER2-positive ESBC who do not achieve pCR after neoadjuvant therapy are at ongoing risk of recurrence despite the effectiveness of neoadjuvant treatment. There is substantial clinical and economic value in further reducing the residual disease burden in this population.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.

Cost-effectiveness frameworks for comparing genome and exome sequencing versus conventional diagnostic pathways: A scoping review and recommended methods.

PURPOSE: Methodological challenges have limited economic evaluations of genome sequencing (GS) and exome sequencing (ES). Our objective was to develop conceptual frameworks for model-based cost-effectiveness analyses (CEAs) of diagnostic GS/ES. METHODS: We conducted a scoping review of economic analyses to develop and iterate with experts a set of conceptual CEA frameworks for GS/ES for prenatal testing, early diagnosis in pediatrics, diagnosis of delayed-onset disorders in pediatrics, genetic testing in cancer, screening of newborns, and general population screening. RESULTS: Reflecting on 57 studies meeting inclusion criteria, we recommend the following considerations for each clinical scenario. For prenatal testing, performing comparative analyses of costs of ES strategies and postpartum care, as well as genetic diagnoses and pregnancy outcomes. For early diagnosis in pediatrics, modeling quality-adjusted life years (QALYs) and costs over ≥20 years for rapid turnaround GS/ES. For hereditary cancer syndrome testing, modeling cumulative costs and QALYs for the individual tested and first/second/third-degree relatives. For tumor profiling, not restricting to treatment uptake or response and including QALYs and costs of downstream outcomes. For screening, modeling lifetime costs and QALYs and considering consequences of low penetrance and GS/ES reanalysis. CONCLUSION: Our frameworks can guide the design of model-based CEAs and ultimately foster robust evidence for the economic value of GS/ES.

Validation of the Decision model of the Burden of Hearing loss Across the Lifespan (DeciBHAL) in Chile, India, and Nigeria.

Background: There is no published decision model for informing hearing health care resource allocation across the lifespan in low- and middle-income countries. We sought to validate the Decision model of the Burden of Hearing loss Across the Lifespan International (DeciBHAL-I) in Chile, India, and Nigeria. Methods: DeciBHAL-I simulates bilateral sensorineural hearing loss (SNHL) and conductive hearing loss (CHL) acquisition, SNHL progression, and hearing loss treatment. To inform model inputs, we identified setting-specific estimates including SNHL prevalence from the Global Burden of Disease (GBD) studies, acute otitis media (AOM) incidence and prevalence of otitis-media related CHL from a systematic review, and setting-specific pediatric and adult hearing aid use prevalence. We considered a coefficient of variance root mean square error (CV-RMSE) of ≤15% to indicate good model fit. Findings: The model-estimated prevalence of bilateral SNHL closely matched GBD estimates, (CV-RMSEs: 3.2-7.4%). Age-specific AOM incidences from DeciBHAL-I also achieved good fit (CV-RMSEs=5.0-7.5%). Model-projected chronic suppurative otitis media prevalence (1.5% in Chile, 4.9% in India, and 3.4% in Nigeria) was consistent with setting-specific estimates, and the incidence of otitis media-related CHL was calibrated to attain adequate model fit. DeciBHAL-projected adult hearing aid use in Chile (3.2-19.7% ages 65-85 years) was within the 95% confidence intervals of published estimates. Adult hearing aid prevalence from the model in India was 1.4-2.3%, and 1.1-1.3% in Nigeria, consistent with literature-based and expert estimates. Interpretation: DeciBHAL-I reasonably simulates hearing loss natural history, detection, and treatment in Chile, India, and Nigeria. Future cost-effectiveness analyses might use DeciBHAL-I to inform global hearing health policy. Funding: National Institutes of Health (3UL1-TR002553-03S3 and F30 DC019846).

Methods for Early Assessment of the Societal Value of Health Technologies: A Scoping Review and Proposal for Classification.

OBJECTIVES: Early assessments of health technologies help to better align and integrate their development and assessment. Such assessments can take many forms and serve different purposes, hampering users in their selection of the most appropriate method for a specific goal. The aim of this scoping review was to structure the large set of methods according to their specific goal. METHODS: A scoping review was conducted using PubMed and reference lists of retrieved articles, to identify review studies with a methodological focus. From the included reviews, all individual methods were listed. Based on additional literature and examples, we extracted the specific goal of each method. All goals were clustered to derive a set of subclasses and methods were grouped into these subclasses. RESULTS: Of the 404 screened, 5 reviews were included, and 1 was added when searching reference lists. The reviews described 56 methods, of which 43 (77%) were included and classified as methods to (1) explore the nature and magnitude of the problem, (2) estimate the nature and magnitude of the expected (societal) value, (3) identify conditions for the potential value to materialize, and (4) help develop and design the type of research that is needed. CONCLUSIONS: The wide range of methods for exploring the societal value of health technologies at an early stage of development can be subdivided into a limited number of classes, distinguishing methods according to their specific objective. This facilitates selection of appropriate methods, depending on the specific needs and aims.

Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation.

BACKGROUND: The expected value of sample information (EVSI) calculates the value of collecting additional information through a research study with a given design. However, standard EVSI analyses do not account for the slow and often incomplete implementation of the treatment recommendations that follow research. Thus, standard EVSI analyses do not correctly capture the value of the study. Previous research has developed measures to calculate the research value while adjusting for implementation challenges, but estimating these measures is a challenge. METHODS: Based on a method that assumes the implementation level is related to the strength of evidence in favor of the treatment, 2 implementation-adjusted EVSI calculation methods are developed. These novel methods circumvent the need for analytical calculations, which were restricted to settings in which normality could be assumed. The first method developed in this article uses computationally demanding nested simulations, based on the definition of the implementation-adjusted EVSI. The second method is based on adapting the moment matching method, a recently developed efficient EVSI computation method, to adjust for imperfect implementation. The implementation-adjusted EVSI is then calculated with the 2 methods across 3 examples. RESULTS: The maximum difference between the 2 methods is at most 6% in all examples. The efficient computation method is between 6 and 60 times faster than the nested simulation method in this case study and could be used in practice. CONCLUSIONS: This article permits the calculation of an implementation-adjusted EVSI using realistic assumptions. The efficient estimation method is accurate and can estimate the implementation-adjusted EVSI in practice. By adapting standard EVSI estimation methods, adjustments for imperfect implementation can be made with the same computational cost as a standard EVSI analysis. HIGHLIGHTS: Standard expected value of sample information (EVSI) analyses do not account for the fact that treatment implementation following research is often slow and incomplete, meaning they incorrectly capture the value of the study.Two methods, based on nested Monte Carlo sampling and the moment matching EVSI calculation method, are developed to adjust EVSI calculations for imperfect implementation when the speed and level of the implementation of a new treatment depends on the strength of evidence in favor of the treatment.The 2 methods we develop provide similar estimates for the implementation-adjusted EVSI.Our methods extend current EVSI calculation algorithms and thus require limited additional computational complexity.

A machine learning approach for modeling decisions in the out of hospital cardiac arrest care workflow.

BACKGROUND: A growing body of research has shown that machine learning (ML) can be a useful tool to predict how different variable combinations affect out-of-hospital cardiac arrest (OHCA) survival outcomes. However, there remain significant research gaps on the utilization of ML models for decision-making and their impact on survival outcomes. The purpose of this study was to develop ML models that effectively predict hospital's practice to perform coronary angiography (CA) in adult patients after OHCA and subsequent neurologic outcomes. METHODS: We utilized all (N = 2398) patients treated by the Chicago Fire Department Emergency Medical Services included in the Cardiac Arrest Registry to Enhance Survival (CARES) between 2013 and 2018 who survived to hospital admission to develop, test, and analyze ML models for decisions after return of spontaneous circulation (ROSC) and patient survival. ML classification models, including the Embedded Fully Convolutional Network (EFCN) model, were compared based on their ability to predict post-ROSC decisions and survival. RESULTS: The EFCN classification model achieved the best results across tested ML algorithms. The area under the receiver operating characteristic curve (AUROC) for CA and Survival were 0.908 and 0.896 respectively. Through cohort analyses, our model predicts that 18.3% (CI 16.4-20.2) of patients should receive a CA that did not originally, and 30.1% (CI 28.5-31.7) of these would experience improved survival outcomes. CONCLUSION: ML modeling effectively predicted hospital decisions and neurologic outcomes. ML modeling may serve as a quality improvement tool to inform system level OHCA policies and treatment protocols.

Hydraulic performance benchmarking for effective management of water distribution networks: An innovative composite index-based approach.

Hydraulic performance assessment and benchmarking of water distribution networks (WDNs) impose a major challenge to water utilities worldwide. Presently, benchmarking strategies for WDNs are not fully developed, especially for analyzing intermittent systems commonly encountered in non-developed nations. To overcome these limitations, this paper proposes an index-based benchmarking strategy for WDNs, comparing their actual hydraulic performance and expected serviceability. A robust Hydraulic Performance Index (HPI) is developed as a global metric to account for the combined impact of multiple hydraulic outputs, concerning their benchmark values. The applicability of this index is verified on a numerical benchmark network, and its usefulness is demonstrated on a real-world intermittent WDN located in Kolkata (India) by coupling the HPI-based framework with hydraulic models using the EPANET-MATLAB programmer's toolkit. A scenario-based analysis is conducted using extended-period simulation to obtain the HPI for diverse service levels and leakage conditions of the WDN models. The HPI is designed to effectively capture the localized pressure reduction during peak flow, prioritize hydraulic outputs based on regional constraints, and penalize systems with unsustainably high hydraulic output. The developed strategy is also effective in performance benchmarking of WDNs of different nations with diverse serviceability and threshold parameters on a common platform. Finally, the practical efficacy and generalizability of the HPI-based results in the context of case-specific performance management of WDNs, along with limitations, recommendations and future perspectives are elucidated upon.

The Use of Decision Analytic Modeling in the Evaluation of Surgical Innovations: A Scoping Review.

OBJECTIVES: The main objective of this review was to map how decision analytic models are used in surgical innovation (in which research phase, with what aim) and to understand how challenges related to the assessment of surgical interventions are incorporated. METHODS: We systematically searched PubMed, Embase, and the Cochrane Library for studies published in 2018. We included original articles using a decision analytic model to compare surgical strategies. We included modeling studies of surgical innovations. General, innovation, and modeling characteristics were extracted, as were outcomes, recommendations, and handling of challenges related to the assessment of surgical interventions (learning curve, incremental innovation, dynamic pricing, quality variation, organizational impact). RESULTS: We included 46 studies. The number of studies increased with each research phase, from 4% (n = 2) in the preclinical phase to 40% (n = 20) in phase 3 studies. Eighty-one studies were excluded because they investigated established surgical procedures, indicating that modeling is predominantly applied after the innovation process. Regardless of the research stage, the aim to determine cost-effectiveness was most frequently identified (n = 40, 87%), whereas exploratory aims (eg, exploring when a strategy becomes cost-effective) were less common (n = 9, 20%). Most challenges related to the assessment of surgical interventions were rarely incorporated in models (eg, learning curve [n = 1, 2%], organizational impact [n = 2, 4%], and incremental innovation [n = 1, 2%]), except for dynamic pricing (n = 10, 22%) and quality variation (n = 6, 13%). CONCLUSIONS: In surgical innovation, modeling is predominantly used in later research stages to assess cost-effectiveness. The exploratory use of modeling seems still largely overlooked in surgery; therefore, the opportunity to inform research and development may not be optimally used.

Evidence gaps in economic analyses of hearing healthcare: A systematic review.

BACKGROUND: Hearing loss is a common and costly medical condition. This systematic review sought to identify evidence gaps in published model-based economic analyses addressing hearing loss to inform model development for an ongoing Lancet Commission. METHODS: We searched the published literature through 14 June 2020 and our inclusion criteria included decision model-based cost-effectiveness analyses that addressed diagnosis, treatment, or prevention of hearing loss. Two investigators screened articles for inclusion at the title, abstract, and full-text levels. Data were abstracted and the studies were assessed for the qualities of model structure, data assumptions, and reporting using a previously published quality scale. FINDINGS: Of 1437 articles identified by our search, 117 unique studies met the inclusion criteria. Most of these model-based analyses were set in high-income countries (n = 96, 82%). The evaluated interventions were hearing screening (n = 35, 30%), cochlear implantation (n = 34, 29%), hearing aid use (n = 28, 24%), vaccination (n = 22, 19%), and other interventions (n = 29, 25%); some studies included multiple interventions. Eighty-six studies reported the main outcome in quality-adjusted or disability-adjusted life-years, 24 of which derived their own utility values. The majority of the studies used decision tree (n = 72, 62%) or Markov (n = 41, 35%) models. Forty-one studies (35%) incorporated indirect economic effects. The median quality rating was 92/100 (IQR:72-100). INTERPRETATION: The review identified a large body of literature exploring the economic efficiency of hearing healthcare interventions. However, gaps in evidence remain in evaluation of hearing healthcare in low- and middle-income countries, as well as in investigating interventions across the lifespan. Additionally, considerable uncertainty remains around productivity benefits of hearing healthcare interventions as well as utility values for hearing-assisted health states. Future economic evaluations could address these limitations. FUNDING: NCATS 3UL1-TR002553-03S3.

Cost-Effectiveness Analysis of Transcatheter Arterial Embolization Techniques for the Treatment of Gastrointestinal Bleeding in the United States.

OBJECTIVES: Gastrointestinal (GI) bleeding is a common medical emergency associated with significant mortality. Transcatheter arterial embolization first was introduced by Rosch et al as an alternative to surgery for upper GI bleeding. The clinical success in patients with GI bleeding treated with transcatheter arterial embolization previously has been reported. However, there are no cost-effectiveness analyses reported to date. Here we report cost-effectiveness analysis of N-butyl 2-cyanoacrylate glue (NBCA) and ethylene-vinyl alcohol copolymer (Onyx) versus coil (gold standard) for treatment of GI bleeding from a healthcare payer perspective. METHODS: Fixed-effects modeling with a generalized linear mixed method was used in NBCA and coil intervention arms to determine the pooled probabilities of clinical success and mortality with complications with their confidence intervals, while the Clopper-Pearson model was used for Onyx to determine the same parameters. Models were provided by the "Meta-Analysis with R" software package. A decision tree was built for cost-effectiveness analysis, and Microsoft Excel was used for probabilistic sensitivity analysis. The cost-effective option was determined based on the incremental cost-effectiveness ratio and scatter plots of incremental cost versus incremental quality-adjusted life-years. RESULTS: Comparing scatter plots and incremental cost-effectiveness ratio results, -$1024 and -$1349 per quality-adjusted life-year for Onyx and N-butyl 2-cyanoacrylate glue, respectively, Onyx was the least expensive and most effective intervention. CONCLUSION: Onyx was the dominant strategy regardless of threshold values. Our analyses provide a framework for researchers to predict the target clinical effectiveness for early-stage TAE interventions and guide resource allocation decisions.

Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation.

We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transition microsimulation model (MICROSIM), and 4) discrete event simulation (DES). Relative to DEQ, the net monetary benefit for PGx testing (v. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) v. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and runtime but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of "jumpover" states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.