Discrete-event Micro-simulation Model Research Articles

Abstract 5941: A simulation modeling study to support personalized breast cancer prevention and early detection in high-risk women

Abstract Purpose: To evaluate the impact of early detection with screening and primary prevention with risk-reducing medication to provide personalized data that will help identify women who are more likely to benefit from various interventions or combinations of interventions with the least harms. Methods: We adapted the CISNET microsimulation model G-E of breast cancer natural history to evaluate the harms and benefits of annual mammography and risk reducing medication among high-risk women (i.e., 5-year risk greater than or equal to 3%). Model G-E is a discrete event microsimulation model that follows millions of women from birth to death and captures the variability in distributions of each event. Each simulated woman is assigned a cohort-specific life expectancy which is used to select a date of breast cancer death. For this study, we dynamically updated the risk of developing breast cancer for each simulated woman based on her family history, breast density, age and history of biopsy. We used large observational and clinical trial data to derive input parameters for cohort-specific birth rates, incidence and stage without screening, other cause mortality by age, screening performance (sensitivity/specificity), survival by age, stage, and subtype without treatment, treatment efficacy, and other cause mortality. We compared model outcomes for screening alone vs. screening with a 5-year course of risk reducing medication. We modeled various screening strategies including annual or biennial screening starting at ages 35, 40, 45 and stopping at ages 65 and 74 years. Model outcomes for each strategy included, the benefits of risk-reducing drugs (avoiding breast cancer) and screening (breast cancer stage, breast cancer-specific survival), and harms of screening (false positives, overdiagnosis). We also conducted sensitivity analysis to estimate the effects of uncertainty in model inputs or assumptions on results. Results: We found that risk reducing medication could result in an additional 28% decrease in invasive breast cancer incidence, 20% decrease in stage IV diagnosis, and a 30% decrease in breast cancer death if screening started at age 35 in a high-risk woman. However, potential breast density changes due to risk reducing medication among high-risk women could result in a 19% increase in false positive screening results compared to screening alone. The results varied by the starting age of screening. Conclusions: Simulation modeling is useful in assessing the relative benefits and harms of screening and risk reducing medication in high-risk women. Citation Format: Jinani Jayasekera, Amy Zhao. A simulation modeling study to support personalized breast cancer prevention and early detection in high-risk women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5941.

Its experimental applications in a freight interchange platform

The logistics and transportation sectors research new ways, new strategies and new technologies to improve the efficiency and the competitiveness, reducing the waste and increasing the services quality. The ITS (Intelligent Transportation Systems) allow an efficient management of the logistics processes and represent useful tools for companies to produce new knowledge, new organizational models and new business cultures in order to carry out competitive advantage. The paper aims to highlight the ITS contribution in the management of the freight interchange nodes. Four case studies are proposed, in which the current system is compared with some project scenarios involving ITS in the logistics activities. The reference system is an agri-food platform and the study concerns the processes related to the inbound activities. The analysis has been carried out using a dynamic, stochastic, discrete-event micro-simulation model, conveniently specified and calibrated.

Simulation modeling as a tool to support clinical guidelines and care for breast cancer prevention and early detection in high-risk women.

10525 Background: To evaluate the incremental short- and long-term benefits and harms of primary prevention with risk reducing medication in high-risk women receiving screening mammography. Methods: We adapted an established, validated Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer discrete event microsimulation model developed to synthesize data the impact of using risk-reducing medication and annual mammography among women with a 3% or higher five-year risk of developing breast cancer. We also examined the effects of supplemental MRI. The model follows a simulated cohort of millions of US women from birth to death. We used large observational and clinical trial data to derive input parameters for cohort-specific birth rates, breast cancer risk, incidence and stage, screening performance, survival by age, stage, and subtype, treatment efficacy, and other cause mortality. Breast cancer risk was modeled based on family history, breast density, age and history of past breast biopsy. We compared two strategies, annual 3D mammography alone vs. annual 3D mammography and a 5-year course of risk reducing medication at various starting ages, and adding MRI to each approach. Outcomes included the benefits of risk-reducing drugs (avoiding breast cancer) and screening (stage, breast cancer death). Harms included drug side effects and screening false positives and overdiagnosis. Sensitivity analysis tested the impact of uncertainty in model inputs and assumptions on results. Results: Compared to mammography alone, adding risk reducing medication could decrease invasive breast cancer incidence by 30%, and breast cancer deaths by 30% (Table). However, due to reduction in breast cancer incidence, risk reducing medication could result in a 3% increase in false positive results; adding MRI increases benefits but also increases false-positive results. Benefits and harms of risk reducing medication and breast cancer screening strategies for women at high-risk of developing breast cancer. Conclusions: Risk-reducing mediation reduces the risk of hormone-receptor positive breast cancer, and combining this with mammography (and/or MRI) improves earlier detection. Additional work is ongoing to incorporate adverse effects of therapy. Simulation modeling can be used to provide individualized data to facilitate discussions about breast cancer prevention and early detection among high-risk women seen in clinical practice.[Table: see text]

Adjuvant trastuzumab chemotherapy in early breast cancer: meta-analysis of randomised trials and cost-effectiveness analysis.

Trastuzumab has a large financial impact on the average cost of breast cancer treatment. This study reassessed the cost-effectiveness of listing the drug on the subsidised Australian Pharmaceutical Benefits Scheme. Using a continuous-time, discrete-event microsimulation model, we examined the effect of 1-year trastuzumab on the total number of disability-adjusted life-years (DALYs) averted among Australian women with human epidermal growth factor receptor-2 (HER2) positive early breast cancer. Target population was women aged 30–100 years and newly diagnosed with the disease in 2003. The model adjusted for tumour size and followed the women up until death or age 100 years. Uncertainty was examined in univariate and probabilistic multivariate sensitivity analyses. The incremental cost-effectiveness ratio (ICER) was A$132,537 (95% confidence interval 91,172–200,485) per DALY averted. Results were sensitive to restriction of trastuzumab to high-risk (large tumour) and/or high-benefit (young) patients. Suitable combinations of tumour size and age restrictions would improve the cost-effectiveness of trastuzumab. Specifically, restricting trastuzumab to women aged 40 years or younger with tumour sizes 40+ mm reduced the ICER to A$35,290 per DALY averted. Trastuzumab for HER2-positive early breast cancer had a high ICER. It is unclear why the Pharmaceutical Benefits Scheme listing does not use restrictions to improve the cost-effectiveness of the drug.

The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update.

The University of Wisconsin Breast Cancer Epidemiology Simulation Model (UWBCS), also referred to as Model W, is a discrete-event microsimulation model that uses a systems engineering approach to replicate breast cancer epidemiology in the US over time. This population-based model simulates the lifetimes of individual women through 4 main model components: breast cancer natural history, detection, treatment, and mortality. A key feature of the UWBCS is that, in addition to specifying a population distribution in tumor growth rates, the model allows for heterogeneity in tumor behavior, with some tumors having limited malignant potential (i.e., would never become fatal in a woman's lifetime if left untreated) and some tumors being very aggressive based on metastatic spread early in their onset. The model is calibrated to Surveillance, Epidemiology, and End Results (SEER) breast cancer incidence and mortality data from 1975 to 2010, and cross-validated against data from the Wisconsin cancer reporting system. The UWBCS model generates detailed outputs including underlying disease states and observed clinical outcomes by age and calendar year, as well as costs, resource usage, and quality of life associated with screening and treatment. The UWBCS has been recently updated to account for differences in breast cancer detection, treatment, and survival by molecular subtypes (defined by ER/HER2 status), to reflect the recent advances in screening and treatment, and to consider a range of breast cancer risk factors, including breast density, race, body-mass-index, and the use of postmenopausal hormone therapy. Therefore, the model can evaluate novel screening strategies, such as risk-based screening, and can assess breast cancer outcomes by breast cancer molecular subtype. In this article, we describe the most up-to-date version of the UWBCS.

PP137 Colorectal Cancer Screening In The Philippines: Cost-Utility Analysis

Introduction:Colorectal cancer (CRC) is the fourth leading cause of cancer deaths in the Philippines. In 2014, the Philippine Health Insurance Corporation (PhilHealth) created a CRC treatment package. The study aimed to determine the cost-utility and budget impact of CRC screening strategies.Methods:A discrete-event microsimulation model was used to simulate four screening modalities: (i) guaiac-fecal occult blood test (gFOBT) followed by colonoscopy every 10 years; (ii) fecal immunochemical test (FIT) followed by colonoscopy every 10 years; (iii) FIT followed by flexible sigmoidoscopy; and (iv) colonoscopy screening every 10 years. These interventions were all compared to no screening. Parameter values were taken from a rapid review of the medical literature and primary data collection from a nationally representative sample of tertiary hospitals.Results:All screening modalities were very cost effective considering that the incremental cost-effective ratios (ICERs) were lower than the gross domestic product per capita threshold suggested by the World Health Organization. Sensitivity analysis showed that the ICERs of all screening modalities evaluated remained below this threshold. The strategy of using FIT followed by colonoscopy every 10 years had an ICER of USD 6,025, with an annual budget impact of USD 6.5 million, assuming low compliance. With moderate compliance this could increase to USD 18.7 million annually.Conclusions:PhilHealth may introduce a benefit package for outpatient screening of colorectal cancer using the screening modality of annual FIT followed by colonoscopy every 10 years.

Cancer care coordinators in stage III colon cancer: a cost-utility analysis.

BackgroundThere is momentum internationally to improve coordination of complex care pathways. Robust evaluations of such interventions are scarce. This paper evaluates the cost-utility of cancer care coordinators for stage III colon cancer patients, who generally require surgery followed by chemotherapy.MethodsWe compared a hospital-based nurse cancer care coordinator (CCC) with ‘business-as-usual’ (no dedicated coordination service) in stage III colon cancer patients in New Zealand. A discrete event microsimulation model was constructed to estimate quality-adjusted life-years (QALYs) and costs from a health system perspective. We used New Zealand data on colon cancer incidence, survival, and mortality as baseline input parameters for the model. We specified intervention input parameters using available literature and expert estimates. For example, that a CCC would improve the coverage of chemotherapy by 33 % (ranging from 9 to 65 %), reduce the time to surgery by 20 % (3 to 48 %), reduce the time to chemotherapy by 20 % (3 to 48 %), and reduce patient anxiety (reduction in disability weight of 33 %, ranging from 0 to 55 %).ResultsMuch of the direct cost of a nurse CCC was balanced by savings in business-as-usual care coordination. Much of the health gain was through increased coverage of chemotherapy with a CCC (especially older patients), and reduced time to chemotherapy. Compared to ‘business-as-usual’, the cost per QALY of the CCC programme was $NZ 18,900 (≈ $US 15,600; 95 % UI: $NZ 13,400 to 24,600). By age, the CCC intervention was more cost-effective for colon cancer patients < 65 years ($NZ 9,400 per QALY). By ethnicity, the health gains were larger for Māori, but so too were the costs, meaning the cost-effectiveness was roughly comparable between ethnic groups.ConclusionsSuch a nurse-led CCC intervention in New Zealand has acceptable cost-effectiveness for stage III colon cancer, meaning it probably merits funding. Each CCC programme will differ in its likely health gains and costs, making generalisation from this evaluation to other CCC interventions difficult. However, this evaluation suggests that CCC interventions that increase coverage of, and reduce time to, effective treatments may be cost-effective.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-015-0970-5) contains supplementary material, which is available to authorized users.

A Micro-simulation Model for Performance Evaluation of a Logistics Platform

The paper focuses on the receiving area of a logistics platform operating in the agri-food sector. The main aim of this work is to propose a “what to” approach for the resolution of problems related to the vehicles and goods receiving. The approach is based on a dynamic, stochastic, discrete-event micro-simulation model, which was properly specified and calibrated.The work proposes the formulation of the mathematical model defining the receiving activities in the logistics platform. The aim of the objective function is to minimize the average trucks turnaround in accordance with constraints allowing to establish the optimal entry sequence of the inbound trucks. The problem formulation considers the introduction of ITS to support the receiving activities management.

A Micro-Simulation Model for an Intelligent Logistics Platform: Specification and Calibration Results

Intelligent warehouses are becoming a key to logistics systems thanks to their ability to optimize the transportation tasks and, consequently, reduce costs. This article introduces an analysis of some problems related to the functional management of a logistics platform, with the aim of improving the active processes for the treatment and handling of goods by using ITS (Intelligent Transportation Systems) and specific automation. In particular, the study proposes the specification, calibration, and test of a stochastic, dynamic, discrete-event micro-simulation model to analyze , the inbound trucks and goods management. The final goal is to reduce the time costs related to goods receiving.

Micro-simulation Model Supporting the Management of an Interregional Logistic Platform

The paper proposes a stochastic, dynamic discrete-event micro-simulation model to analyse the operation of a logistic platform in order to determine its performance in different system configurations by using appropriate efficiency indicators. The issues connected to the management and optimization of operational functions are studied at a global level in order to obtain a technical/economic analysis through aggregate evaluations, which provide information useful to a tactical/strategic planning. The model implementation consists of four sequential steps: survey and statistical analysis, specification, calibration and validation model. The micro-simulation model has been implemented by using Witness software. The paper proposes an application of the micro-simulation model on the Kuehne+Nagel logistic platform in order to analyse its operativity.

A Statistical Analysis for Micro-Simulation of Udc Operativity

An Urban Distribution Center (UDC) is a useful City Logistics policy instrument. A UDC is a logistic site dedicated to decompose and consolidate freight directed to the city, by planning the routes and service time, restricting the traffic of trucks and increasing the load factor of feeder vehicles. A UDC can produce interesting impacts on the dynamics of urban freight distribution, but its success depends on many factors: an appropriate location; a wellbalanced presence of spaces and equipment; an efficient and effective organization of internal services; a connection with the surrounding area and with the related transport services; a management structure that meets different and complementary requirements; a capacity to support itself. The paper proposes an analysis of the functional organization of a UDC through a “what if” micro-simulation approach aimed at the UDC efficiency control. After a review of the state of the art, a procedure to implement a discrete-event micro-simulation model is described. Probability functions concerning the times of UDC activities are proposed.

Simulation and Evaluation of Automated Vehicle Identification at Weigh-in-Motion Inspection Stations

This paper describes the development and validation of a discrete event microsimulation model that was applied to investigate the implementation of automated vehicle identification (AVI) technologies at Nordel Inspection Station in Delta, British Columbia. Current operational policies require commercial vehicles passing through the area to be inspected. The study, which includes an extensive field survey to collect validation data, determined that the implementation of a conservative industry participation of 10% in the AVI program would result in benefits ranging from $2.4 million to $7.9 million (2008 CAD), or benefit–cost ratios ranging from 11 to 47 for a range of net-present value project costs of $50 to $200,000. Benefits also include the reduction of emissions, with 5-year greenhouse gas (GHG) emissions reductions ranging from 2,200 to 7,200 metric tons, or a cost of $9.1 to $123 per reduction of 1 ton of GHG, for a range of project implementation scenarios.

The development of a simulation model of primary prevention strategies for coronary heart disease.

This paper describes the present state of development of a discrete-event micro-simulation model for coronary heart disease prevention. The model is intended to support health policy makers in assessing the impacts on health care resources of different primary prevention strategies. For each person, a set of times to disease events, conditional on the individual's risk factor profile, is sampled from a set of probability distributions that are derived from a new analysis of the Framingham cohort study on coronary heart disease. Methods used to model changes in behavioural and physiological risk factors are discussed and a description of the simulation logic is given. The model incorporates POST (Patient Oriented Simulation Technique) simulation routines.