Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force.

Importance: The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known. Objective: To estimate outcomes of various mammography screening strategies. Design, Setting, and Population: Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses. Exposures: Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment. Main Outcomes and Measures: Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women. Results: Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women. Conclusions: This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.

Analysis of Breast Cancer Mortality in the US-1975 to 2019.

Importance: Breast cancer mortality in the US declined between 1975 and 2019. The association of changes in metastatic breast cancer treatment with improved breast cancer mortality is unclear. Objective: To simulate the relative associations of breast cancer screening, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer with improved breast cancer mortality. Design, Setting, and Participants: Using aggregated observational and clinical trial data on the dissemination and effects of screening and treatment, 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models simulated US breast cancer mortality rates. Death due to breast cancer, overall and by estrogen receptor and ERBB2 (formerly HER2) status, among women aged 30 to 79 years in the US from 1975 to 2019 was simulated. Exposures: Screening mammography, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer. Main Outcomes and Measures: Model-estimated age-adjusted breast cancer mortality rate associated with screening, stage I to III treatment, and metastatic treatment relative to the absence of these exposures was assessed, as was model-estimated median survival after breast cancer metastatic recurrence. Results: The breast cancer mortality rate in the US (age adjusted) was 48/100 000 women in 1975 and 27/100 000 women in 2019. In 2019, the combination of screening, stage I to III treatment, and metastatic treatment was associated with a 58% reduction (model range, 55%-61%) in breast cancer mortality. Of this reduction, 29% (model range, 19%-33%) was associated with treatment of metastatic breast cancer, 47% (model range, 35%-60%) with treatment of stage I to III breast cancer, and 25% (model range, 21%-33%) with mammography screening. Based on simulations, the greatest change in survival after metastatic recurrence occurred between 2000 and 2019, from 1.9 years (model range, 1.0-2.7 years) to 3.2 years (model range, 2.0-4.9 years). Median survival for estrogen receptor (ER)-positive/ERBB2-positive breast cancer improved by 2.5 years (model range, 2.0-3.4 years), whereas median survival for ER-/ERBB2- breast cancer improved by 0.5 years (model range, 0.3-0.8 years). Conclusions and Relevance: According to 4 simulation models, breast cancer screening and treatment in 2019 were associated with a 58% reduction in US breast cancer mortality compared with interventions in 1975. Simulations suggested that treatment for stage I to III breast cancer was associated with approximately 47% of the mortality reduction, whereas treatment for metastatic breast cancer was associated with 29% of the reduction and screening with 25% of the reduction.

Modeling Thyroid Cancer Epidemiology in the United States Using Papillary Thyroid Carcinoma Microsimulation Model.

OBJECTIVES: Thyroid cancer incidence increased over 200% from 1992 to 2018, whereas mortality rates had not increased proportionately. The increased incidence has been attributed primarily to the detection of subclinical disease, raising important questions related to thyroid cancer control. We developed the Papillary Thyroid Carcinoma Microsimulation model (PATCAM) to answer them, including the impact of overdiagnosis on thyroid cancer incidence. METHODS: PATCAM simulates individuals from age 15 until death in birth cohorts starting from 1975 using 4 inter-related components, including natural history, detection, post-diagnosis, and other-cause mortality. PATCAM was built using high-quality data and calibrated against observed age-, sex-, and stage-specific incidence in the United States as reported by the Surveillance, Epidemiology, and End Results database. PATCAM was validated against US thyroid cancer mortality and 3 active surveillance studies, including the largest and longest running thyroid cancer active surveillance cohort in the world (from Japan) and 2 from the United States. RESULTS: PATCAM successfully replicated age- and stage-specific papillary thyroid cancers (PTC) incidence and mean tumor size at diagnosis and PTC mortality in the United States between 1975 and 2015. PATCAM accurately predicted the proportion of tumors that grew more than 3 mm and 5 mm in 5 years and 10 years, aligning with the 95% confidence intervals of the reported rates from active surveillance studies in most cases. CONCLUSIONS: PATCAM successfully reproduced observed US thyroid cancer incidence and mortality over time and was externally validated. PATCAM can be used to identify factors that influence the detection of subclinical PTCs.

Obtaining bio-oil and activated carbon from waste pomegranate peels by pyrolysis.

This study aims to produce beneficial products with pomegranate peel waste through pyrolysis. For this purpose, the usability of the liquid product as a biofuel and the solid product as an adsorbent for dye removal was investigated. To characterize the bio-oil and biochar produced under the best pyrolysis conditions, Fourier transforms infrared spectroscopy (FT-IR), Gas chromatography-mass spectrometry (GC-MS), calorific value, Brunauer-Emmett-Teller (BET), and Scanning electron microscopy (SEM) analyses were conducted. When we examine the FT-IR spectrum of the bio-oil, the presence of phenol, alcohol, ketone, and aldehyde groups is seen in the structure. The GC-MS analysis demonstrated that phenol content was 27.9%, aldehyde content was 19%, acid compound content was 18.28%, ketone content was 8.7%, and aromatic compound content was 8.4%. The lower calorific value of bio-oil was determined as 27.33 MJ/kg. It was observed that activated carbon produced from biochar at a 3:1 KOH/biochar impregnation ratio and a carbonization temperature of 800 °C exhibited the highest surface area (1307 m2/g). In adsorption analysis, it was observed that the adsorption efficiency was higher at pH 9 and 35 °C and with 150 ppm initial concentration. Langmuir and Freundlich adsorption isotherms were determined, and the high R2 (0.99) was consistent with the Langmuir methylene blue (MB) adsorption model.

Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks.

OBJECTIVES: Machine learning (ML)-based emulators improve the calibration of decision-analytical models, but their performance in complex microsimulation models is yet to be determined. METHODS: We demonstrated the use of an ML-based emulator with the Colorectal Cancer (CRC)-Adenoma Incidence and Mortality (CRC-AIM) model, which includes 23 unknown natural history input parameters to replicate the CRC epidemiology in the United States. We first generated 15,000 input combinations and ran the CRC-AIM model to evaluate CRC incidence, adenoma size distribution, and the percentage of small adenoma detected by colonoscopy. We then used this data set to train several ML algorithms, including deep neural network (DNN), random forest, and several gradient boosting variants (i.e., XGBoost, LightGBM, CatBoost) and compared their performance. We evaluated 10 million potential input combinations using the selected emulator and examined input combinations that best estimated observed calibration targets. Furthermore, we cross-validated outcomes generated by the CRC-AIM model with those made by CISNET models. The calibrated CRC-AIM model was externally validated using the United Kingdom Flexible Sigmoidoscopy Screening Trial (UKFSST). RESULTS: The DNN with proper preprocessing outperformed other tested ML algorithms and successfully predicted all 8 outcomes for different input combinations. It took 473 s for the trained DNN to predict outcomes for 10 million inputs, which would have required 190 CPU-years without our DNN. The overall calibration process took 104 CPU-days, which included building the data set, training, selecting, and hyperparameter tuning of the ML algorithms. While 7 input combinations had acceptable fit to the targets, a combination that best fits all outcomes was selected as the best vector. Almost all of the predictions made by the best vector laid within those from the CISNET models, demonstrating CRC-AIM's cross-model validity. Similarly, CRC-AIM accurately predicted the hazard ratios of CRC incidence and mortality as reported by UKFSST, demonstrating its external validity. Examination of the impact of calibration targets suggested that the selection of the calibration target had a substantial impact on model outcomes in terms of life-year gains with screening. CONCLUSIONS: Emulators such as a DNN that is meticulously selected and trained can substantially reduce the computational burden of calibrating complex microsimulation models. HIGHLIGHTS: Calibrating a microsimulation model, a process to find unobservable parameters so that the model fits observed data, is computationally complex.We used a deep neural network model, a popular machine learning algorithm, to calibrate the Colorectal Cancer Adenoma Incidence and Mortality (CRC-AIM) model.We demonstrated that our approach provides an efficient and accurate method to significantly speed up calibration in microsimulation models.The calibration process successfully provided cross-model validation of CRC-AIM against 3 established CISNET models and also externally validated against a randomized controlled trial.

Selective photoelectrocatalytic oxidation of 3-methylpyridine to vitamin B3 by WO3 decorated nanotube-structured TiO2.

Nanotube-structured TiO2 electrodes on Ti plates were formed in ethylene glycol solution by the anodic oxidation method applied for different times and calcined at 500 °C. Different amounts of WO3 were decorated on the nanotube surfaces electrochemically. The electrodes were characterized, and the effects of the nanotube length on the Ti plate, decorated WO3 amount, electrolyte concentration, applied potential, and type of radiation source on the oxidation of 3-methylpyridine were investigated, together with the product distribution/selectivity. In a photoelectrocatalytic system, the vitamin B3 yield increased significantly (ca. 17 fold) under UVA by decorating nanotube-structured TiO2 with WO3, whilst low reaction rates and no products were found under Vis irradiation, as only unselective photolytic reactions occurred. This unexpected result was clarified for the first time in the literature.

Validating agent-based simulation model of hospital-associated Clostridioides difficile infection using primary hospital data.

As agent-based models (ABMs) are increasingly used for modeling infectious diseases, model validation is becoming more crucial. In this study, we present an alternate approach to validating hospital ABMs that focuses on replicating hospital-specific conditions and proposes a new metric for validating the social-environmental network structure of ABMs. We adapted an established ABM representing Clostridioides difficile infection (CDI) spread in a generic hospital to a 426-bed Midwestern academic hospital. We incorporated hospital-specific layout, agent behaviors, and input parameters estimated from primary hospital data into the model, referred to as H-ABM. We compared the predicted CDI rate against the observed rate from 2013-2018. We used colonization pressure, a measure of nearby infectious agents, to validate the socio-environmental agent networks in the ABM. Finally, we conducted additional experiments to compare the performance of individual infection control interventions in the H-ABM and the generic model. We find that the H-ABM is able to replicate CDI trends during 2013-2018, including a roughly 46% drop during a period of greater infection control investment. High CDI burden in socio-environmental networks was associated with a significantly increased risk of C. difficile colonization or infection (Risk ratio: 1.37; 95% CI: [1.17, 1.59]). Finally, we found that several high-impact infection control interventions have diminished impact in the H-ABM. This study presents an alternate approach to validation of ABMs when large-scale calibration is not appropriate for specific settings and proposes a new metric for validating socio-environmental network structure of ABMs. Our findings also demonstrate the utility of hospital-specific modeling.

Breast cancer risk for women with diabetes and the impact of metformin: A meta-analysis.

BACKGROUND: Diabetes mellitus has been associated with increased breast cancer (BC) risk; however, the magnitude of this effect is uncertain. This study focused on BC risk for women with type 2 diabetes mellitus (T2DM). METHODS: Two separate meta-analyses were conducted (1) to estimate the relative risk (RR) of BC for women with T2DM and (2) to evaluate the risk of BC for women with T2DM associated with the use of metformin, a common diabetes treatment. In addition, subgroup analyses adjusting for obesity as measured by body mass index (BMI) and menopausal status were also performed. Studies were identified via PubMed/Scopus database and manual search through April 2021. RESULTS: A total of 30 and 15 studies were included in the first and second meta-analyses, respectively. The summary RR of BC for women with T2DM was 1.15 (95% confidence interval [CI], 1.09-1.21). The subgroup analyses adjusting BMI and adjusting BMI and menopause resulted in a summary RR of 1.22 (95% CI, 1.15-1.30) and 1.20 (95% CI, 1.05-1.36), respectively. For women with T2DM, the summary RR of BC was 0.82 (95% CI, 0.60-1.12) for metformin users compared with nonmetformin users. CONCLUSIONS: Women with T2DM were more likely to be diagnosed with BC and this association was strengthened by adjusting for BMI and menopausal status. No statistically significant reduction of BC risk was observed among metformin users. IMPACT: These two meta-analyses can inform decision-making for women with type 2 diabetes regarding their use of metformin and the use of screening mammography for early detection of breast cancer.

Team Science Principles Enhance Cancer Care Delivery Quality Improvement: Interdisciplinary Implementation of Breast Cancer Screening Shared Decision Making.

PURPOSE: Implementing shared decision making (SDM), recommended in screening mammography by national guidelines for women age 40-49 years, faces challenges that innovations in quality improvement and team science (TS) are poised to address. We aimed to improve the effectiveness, patient-centeredness, and efficiency of SDM in primary care for breast cancer screening. METHODS: Our interdisciplinary team included primary and specialty care, psychology, epidemiology, communication science, engineering, and stakeholders (patients and clinicians). Over a 6-year period, we executed two iterative cycles of plan-do-study-act (PDSA) to develop, revise, and implement a SDM tool using TS principles. Patient and physician surveys and retrospective analysis of tool performance informed our first PDSA cycle. Patient and physician surveys, toolkit use, and clinical outcomes in the second PDSA cycle supported SDM implementation. We gathered team member assessments on the importance of individual TS activities. RESULTS: Our first PDSA cycle successfully generated a SDM tool called Breast Cancer Risk Estimator, deemed valuable by 87% of patients surveyed. Our second PDSA cycle increased Breast Cancer Risk Estimator utilization, from 2,000 sessions in 2017 to 4,097 sessions in 2019 while maintaining early-stage breast cancer diagnoses. Although TS activities such as culture, trust, and communication needed to be sustained throughout the project, shared goals, research/data infrastructure support, and leadership were more important earlier in the project and persisted in the later stages of the project. CONCLUSION: Combining rigorous quality improvement and TS principles can support the complex, interdependent, and interdisciplinary activities necessary to improve cancer care delivery exemplified by our implementation of a breast cancer screening SDM tool.

Diagnostic Accuracy of Fine-Needle Biopsy in the Detection of Thyroid Malignancy: A Systematic Review and Meta-analysis.

Importance: Fine-needle biopsy (FNB) became a critical part of thyroid nodule evaluation in the 1970s. It is not clear how diagnostic accuracy of FNB has changed over time. Objective: To conduct a systematic review and meta-analysis estimating the accuracy of thyroid FNB for diagnosis of malignancy in adults with a newly diagnosed thyroid nodule and to characterize changes in accuracy over time. Data Sources: PubMed, SCOPUS, and Cochrane Central Register of Controlled Trials were searched from 1975 to 2020 using search terms related to FNB accuracy in the thyroid. Study Selection: English-language reports of cohort studies or randomized trials of adult patients undergoing thyroid FNB with sample size of 20 or greater and using a reference standard of surgical histopathology or clinical follow-up were included. Articles that examined only patients with known thyroid disease or focused on accuracy of novel adjuncts, such as molecular tests, were excluded. Two investigators screened each article and resolved conflicts by consensus. A total of 36 of 1023 studies met selection criteria. Data Extraction and Synthesis: The MOOSE guidelines were used for data abstraction and assessing data quality and validity. Two investigators abstracted data using a standard form. Studies were grouped into epochs by median data collection year (1975 to 1990, 1990 to 2000, 2000 to 2010, and 2010 to 2020). Data were pooled using a bivariate mixed-effects model. Main Outcomes and Measures: The primary outcome was accuracy of FNB for diagnosis of malignancy. Accuracy was hypothesized to increase in later time periods, a hypothesis formulated prior to data collection. Results: Of 16 597 included patients, 12 974 (79.2%) were female, and the mean (SD) age was 47.3 (12.9) years. The sensitivity of FNB was 85.6% (95% CI, 79.9-89.5), the specificity was 71.4% (95% CI, 61.1-79.8), the positive likelihood ratio was 3.0 (95% CI, 2.3-4.1), and the negative likelihood ratio was 0.2 (95% CI, 0.2-0.3). The area under the receiver operating characteristic curve was 86.1%. Epoch was not significantly associated with accuracy. None of the available covariates could explain observed heterogeneity. Conclusions and Relevance: Accuracy of thyroid FNB has not significantly changed over time. Important developments in technique, preparation, and interpretation may have occurred too heterogeneously to capture a consistent uptrend over time. FNB remains a reliable test for thyroid cancer diagnosis.

Prevalence of Subclinical Papillary Thyroid Cancer by Age: Meta-analysis of Autopsy Studies.

CONTEXT: It is not known how underlying subclinical papillary thyroid cancer (PTC) differs by age. This meta-analysis of autopsy studies investigates how subclinical PTC prevalence changes over the lifetime. METHODS: We searched PubMed, Embase, and Web of Science databases from inception to May 2021 for studies that reported the prevalence of PTC found at autopsy. Two investigators extracted the number of subclinical PTCs detected in selected age groups and extent of examination. A quality assessment tool was used to assess bias. Logistic regression models with random intercepts were used to pool the age-specific subclinical PTC prevalence estimates. RESULTS: Of 1773 studies screened, 16 studies with age-specific data met the inclusion criteria (n = 6286 autopsies). The pooled subclinical PTC prevalence was 12.9% (95% CI 7.8-16.8) in whole gland and 4.6% (2.5- 6.6) in partial gland examination. Age-specific prevalence estimates were ≤40 years, 11.5% (6.8-16.1); 41-60 years, 12.1% (7.6-16.5); 61-80 years, 12.7% (8-17.5); and 81+ years, 13.4% (7.9-18.9). Sex did not affect age-specific prevalence and there was no difference in prevalence between men and women in any age group. In the regression model, the OR of prevalence increasing by age group was 1.06 (0.92-1.2, P = .37). CONCLUSION: This meta-analysis shows the prevalence of subclinical PTC is stable across the lifespan. There is not a higher subclinical PTC prevalence in middle age, in contrast to higher observed incidence rates in this age group. These findings offer unique insights into the prevalence of subclinical PTC and its relationship to age.

A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis.

Overdiagnosis of breast cancer, defined as diagnosing a cancer that would otherwise not cause symptoms or death in a patient's lifetime, costs U.S. health care system over $1.2 billion annually. Overdiagnosis rates, estimated to be around 10%-40%, may be reduced if indolent breast findings can be identified and followed with noninvasive imaging rather than biopsy. However, there are no validated guidelines for radiologists to decide when to choose imaging options recognizing cancer grades and types. The aim of this study is to optimize breast cancer diagnostic decisions based on cancer types using a large-scale finite-horizon Markov decision process (MDP) model with 4.6 million states to help reduce overdiagnosis. We prove the optimality of a divide-and-search algorithm that relies on tight upper bounds on the optimal decision thresholds to find an exact optimal solution. We project the high-dimensional MDP onto two lower dimensional MDPs and obtain feasible upper bounds on the optimal decision thresholds. We use real data from two private mammography databases and demonstrate our model performance through a previously validated simulation model that has been used by the policy makers to set the national screening guidelines in the United States. We find that a decision-analytical framework optimizing diagnostic decisions while accounting for breast cancer types has a strong potential to improve the quality of life and alleviate the immense costs of overdiagnosis. Our model leads to a 20 % reduction in overdiagnosis on the screening population, which translates into an annual savings of approximately $300 million for the U.S. health care system.

Optimal breast cancer risk reduction policies tailored to personal risk level.

Depending on personal and hereditary factors, each woman has a different risk of developing breast cancer, one of the leading causes of death for women. For women with a high-risk of breast cancer, their risk can be reduced by two main therapeutic approaches: 1) preventive treatments such as hormonal therapies (i.e., tamoxifen, raloxifene, exemestane); or 2) a risk reduction surgery (i.e., mastectomy). Existing national clinical guidelines either fail to incorporate or have limited use of the personal risk of developing breast cancer in their proposed risk reduction strategies. As a result, they do not provide enough resolution on the benefit-risk trade-off of an intervention policy as personal risk changes. In addressing this problem, we develop a discrete-time, finite-horizon Markov decision process (MDP) model with the objective of maximizing the patient's total expected quality-adjusted life years. We find several useful insights some of which contradict the existing national breast cancer risk reduction recommendations. For example, we find that mastectomy is the optimal choice for the border-line high-risk women who are between ages 22 and 38. Additionally, in contrast to the National Comprehensive Cancer Network recommendations, we find that exemestane is a plausible, in fact, the best, option for high-risk postmenopausal women.

Breast Cancer Screening Strategies for Women With ATM, CHEK2, and PALB2 Pathogenic Variants: A Comparative Modeling Analysis.

IMPORTANCE: Screening mammography and magnetic resonance imaging (MRI) are recommended for women with ATM, CHEK2, and PALB2 pathogenic variants. However, there are few data to guide screening regimens for these women. OBJECTIVE: To estimate the benefits and harms of breast cancer screening strategies using mammography and MRI at various start ages for women with ATM, CHEK2, and PALB2 pathogenic variants. DESIGN, SETTING, AND PARTICIPANTS: This comparative modeling analysis used 2 established breast cancer microsimulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) to evaluate different screening strategies. Age-specific breast cancer risks were estimated using aggregated data from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium for 32 247 cases and 32 544 controls in 12 population-based studies. Data on screening performance for mammography and MRI were estimated from published literature. The models simulated US women with ATM, CHEK2, or PALB2 pathogenic variants born in 1985. INTERVENTIONS: Screening strategies with combinations of annual mammography alone and with MRI starting at age 25, 30, 35, or 40 years until age 74 years. MAIN OUTCOMES AND MEASURES: Estimated lifetime breast cancer mortality reduction, life-years gained, breast cancer deaths averted, total screening examinations, false-positive screenings, and benign biopsies per 1000 women screened. Results are reported as model mean values and ranges. RESULTS: The mean model-estimated lifetime breast cancer risk was 20.9% (18.1%-23.7%) for women with ATM pathogenic variants, 27.6% (23.4%-31.7%) for women with CHEK2 pathogenic variants, and 39.5% (35.6%-43.3%) for women with PALB2 pathogenic variants. Across pathogenic variants, annual mammography alone from 40 to 74 years was estimated to reduce breast cancer mortality by 36.4% (34.6%-38.2%) to 38.5% (37.8%-39.2%) compared with no screening. Screening with annual MRI starting at 35 years followed by annual mammography and MRI at 40 years was estimated to reduce breast cancer mortality by 54.4% (54.2%-54.7%) to 57.6% (57.2%-58.0%), with 4661 (4635-4688) to 5001 (4979-5023) false-positive screenings and 1280 (1272-1287) to 1368 (1362-1374) benign biopsies per 1000 women. Annual MRI starting at 30 years followed by mammography and MRI at 40 years was estimated to reduce mortality by 55.4% (55.3%-55.4%) to 59.5% (58.5%-60.4%), with 5075 (5057-5093) to 5415 (5393-5437) false-positive screenings and 1439 (1429-1449) to 1528 (1517-1538) benign biopsies per 1000 women. When starting MRI at 30 years, initiating annual mammography starting at 30 vs 40 years did not meaningfully reduce mean mortality rates (0.1% [0.1%-0.2%] to 0.3% [0.2%-0.3%]) but was estimated to add 649 (602-695) to 650 (603-696) false-positive screenings and 58 (41-76) to 59 (41-76) benign biopsies per 1000 women. CONCLUSIONS AND RELEVANCE: This analysis suggests that annual MRI screening starting at 30 to 35 years followed by annual MRI and mammography at 40 years may reduce breast cancer mortality by more than 50% for women with ATM, CHEK2, and PALB2 pathogenic variants. In the setting of MRI screening, mammography prior to 40 years may offer little additional benefit.

Breast Cancer Screening Among Childhood Cancer Survivors Treated Without Chest Radiation: Clinical Benefits and Cost-Effectiveness.

BACKGROUND: Early initiation of breast cancer screening is recommended for high-risk women, including survivors of childhood cancer treated with chest radiation. Recent studies suggest that female survivors of childhood leukemia or sarcoma treated without chest radiation are also at elevated early onset breast cancer risk. However, the potential clinical benefits and cost-effectiveness of early breast cancer screening among these women are uncertain. METHODS: Using data from the Childhood Cancer Survivor Study, we adapted 2 Cancer Intervention and Surveillance Modeling Network simulation models to reflect the elevated risks of breast cancer and competing mortality among leukemia and sarcoma survivors. Costs and utility weights were based on published studies and databases. Outcomes included breast cancer deaths averted, false-positive screening results, benign biopsies, and incremental cost-effectiveness ratios. RESULTS: In the absence of screening, the lifetime risk of dying from breast cancer among survivors was 6.8% to 7.0% across models. Early initiation of annual mammography with breast magnetic resonance imaging screening between ages 25 and 40 years would avert 52.6% to 64.3% of breast cancer deaths. When costs and quality-of-life impacts were considered, screening starting at age 40 years was the only strategy with an incremental cost-effectiveness ratio below the $100 000 per quality-adjusted life-year (QALY) gained cost-effectiveness threshold ($27 680 to $44 380 per QALY gained across models). CONCLUSIONS: Among survivors of childhood leukemia or sarcoma, early initiation of breast cancer screening at age 40 years may reduce breast cancer deaths by half and is cost-effective. These findings could help inform screening guidelines for survivors treated without chest radiation.

Cost-effectiveness of telehealth with remote patient monitoring for postpartum hypertension.

OBJECTIVE: Evaluate cost-effectiveness of telehealth with remote monitoring for postpartum hypertensive disorders from the hospital's perspective. METHODS: A decision tree was developed using results from a non-randomized controlled trial comparing telehealth to standard outpatient blood pressure monitoring. At discharge, postpartum women with a hypertensive disorder received a Bluetooth tablet, blood pressure monitor, and scale to submit vitals daily for 6 weeks. Women were managed and treated with a standard protocol. We performed a cost-effectiveness analysis using data from the hospital, device manufacturer supplied charges, and utilities. A cost-effectiveness threshold was set at $100,000/quality-adjusted life years. One-way and two-way sensitivity analyses were performed to evaluate the robustness of our results compared to baseline assumptions. RESULTS: Telehealth monitoring significantly reduced postpartum readmissions, 3.7% (8/214) versus 0.5% (1/214), and resulted in higher quality-adjusted life years. Telehealth monitoring was cost-effective and cost-saving. Average cost of telehealth per patient was $309, and was cost-effective to a cost of $420 per patient. Telehealth monitoring remained cost-effective down to an admission cost of $10,999 compared to our baseline-estimate for the average admission cost of $14,401. Telehealth monitoring also remained cost-effective when the postpartum readmission rate was 3.0% or higher with standard monitoring. With a cost saving of $93 per patient and an estimated 333,253 pregnant women with hypertension in the US a year, telehealth could reduce health care costs in the US by approximately $31 million a year. CONCLUSIONS: This study demonstrates telehealth with remote blood pressure monitoring may be a cost-effective and cost-saving solution for management of postpartum hypertension.

A risk-based framework for assessing real-time lung cancer screening eligibility that incorporates life expectancy and past screening findings.

BACKGROUND: Current lung cancer risk-based screening approaches use a single risk-threshold, disregard life-expectancy, and ignore past screening findings. We address these limitations with a comprehensive analytical framework, the individualized lung cancer screening decision (ENGAGE) tool that aims to optimize lung cancer screening for US ever-smokers under dynamic risk assessment by incorporating life expectancy and past screening findings over time. METHODS: ENGAGE employs a partially observable Markov decision process framework that integrates published risk prediction and disease progression models, to dynamically assess the trade-off between the expected health benefits and harms associated with screening. ENGAGE evaluates lung cancer risk annually and provides real-time screening eligibility that maximizes the expected quality-adjusted life-years (QALYs) of ever-smokers. We compare ENGAGE against the 2013 U.S. Preventive Services Task Force (USPSTF) lung cancer screening guideline and single-threshold risk-based screening paradigms. RESULTS: Compared with the 2013 USPSTF guidelines, ENGAGE expands screening coverage among ever-smokers (ENGAGE: 78%, USPSTF: 61%), while reducing the number of screening examinations per person (ENGAGE:10.43, USPSTF:12.07, P < .001), yields higher effectiveness in terms of increased lung cancer-specific mortality reduction (ENGAGE: 19%, USPSTF: 15%, P < .001) and improves screening efficiency (ENGAGE: 696, USPSTF: 819 screens per death avoided, P < .001). When compared against a single-threshold risk-based screening strategy, ENGAGE increases QALY requiring 30% fewer screens per death avoided (ENGAGE: 696, single-threshold: 889, P < .001), and reduces false positives by 40%. CONCLUSIONS: ENGAGE provides a comprehensive framework for dynamic risk-based assessment of lung cancer screening eligibility by incorporating life expectancy and past screening findings that can serve to guide future policies on the effectiveness and efficiency of screening. LAY SUMMARY: A novel decision-analytical screening framework was developed for lung cancer, the individualized lung cancer screening decision (ENGAGE) tool to provide personalized screening schedules for ever-smokers. ENGAGE captures the dynamic nature of lung cancer risk and incorporates life expectancy into the screening decision-making process. ENGAGE integrates past screening findings and changes in smoking behavior of individuals and provides informed screening decisions that outperform existing screening guidelines and single-threshold risk-based screening approaches. A personalized lung cancer screening program facilitated by a tool such as ENGAGE could enhance the efficiency of lung cancer screening.

Impact of the COVID-19 Pandemic on Breast Cancer Mortality in the US: Estimates From Collaborative Simulation Modeling.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has disrupted breast cancer control through short-term declines in screening and delays in diagnosis and treatments. We projected the impact of COVID-19 on future breast cancer mortality between 2020 and 2030. METHODS: Three established Cancer Intervention and Surveillance Modeling Network breast cancer models modeled reductions in mammography screening use, delays in symptomatic cancer diagnosis, and reduced use of chemotherapy for women with early-stage disease for the first 6 months of the pandemic with return to prepandemic patterns after that time. Sensitivity analyses were performed to determine the effect of key model parameters, including the duration of the pandemic impact. RESULTS: By 2030, the models project 950 (model range = 860-1297) cumulative excess breast cancer deaths related to reduced screening, 1314 (model range = 266-1325) associated with delayed diagnosis of symptomatic cases, and 151 (model range = 146-207) associated with reduced chemotherapy use in women with hormone positive, early-stage cancer. Jointly, 2487 (model range = 1713-2575) excess breast cancer deaths were estimated, representing a 0.52% (model range = 0.36%-0.56%) cumulative increase over breast cancer deaths expected by 2030 in the absence of the pandemic's disruptions. Sensitivity analyses indicated that the breast cancer mortality impact would be approximately double if the modeled pandemic effects on screening, symptomatic diagnosis, and chemotherapy extended for 12 months. CONCLUSIONS: Initial pandemic-related disruptions in breast cancer care will have a small long-term cumulative impact on breast cancer mortality. Continued efforts to ensure prompt return to screening and minimize delays in evaluation of symptomatic women can largely mitigate the effects of the initial pandemic-associated disruptions.

The impact of vaccination to control COVID-19 burden in the United States: A simulation modeling approach.

INTRODUCTION: Vaccination programs aim to control the COVID-19 pandemic. However, the relative impacts of vaccine coverage, effectiveness, and capacity in the context of nonpharmaceutical interventions such as mask use and physical distancing on the spread of SARS-CoV-2 are unclear. Our objective was to examine the impact of vaccination on the control of SARS-CoV-2 using our previously developed agent-based simulation model. METHODS: We applied our agent-based model to replicate COVID-19-related events in 1) Dane County, Wisconsin; 2) Milwaukee metropolitan area, Wisconsin; 3) New York City (NYC). We evaluated the impact of vaccination considering the proportion of the population vaccinated, probability that a vaccinated individual gains immunity, vaccination capacity, and adherence to nonpharmaceutical interventions. We estimated the timing of pandemic control, defined as the date after which only a small number of new cases occur. RESULTS: The timing of pandemic control depends highly on vaccination coverage, effectiveness, and adherence to nonpharmaceutical interventions. In Dane County and Milwaukee, if 50% of the population is vaccinated with a daily vaccination capacity of 0.25% of the population, vaccine effectiveness of 90%, and the adherence to nonpharmaceutical interventions is 60%, controlled spread could be achieved by June 2021 versus October 2021 in Dane County and November 2021 in Milwaukee without vaccine. DISCUSSION: In controlling the spread of SARS-CoV-2, the impact of vaccination varies widely depending not only on effectiveness and coverage, but also concurrent adherence to nonpharmaceutical interventions.

Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.

Since 2000, the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.