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Objective: We used the results from the first three screening rounds of Bahcesehir Mammography Screening Project (BMSP), a 10-year (2009-2019) and the first organized population-based screening program implemented in a county of Istanbul, Turkey, to assess the potential cost-effectiveness of a population-based mammography screening program in Turkey.Materials and Methods: Two screening strategies were compared: BMSP (includes three biennial screens for women between 40-69) and Turkish National Breast Cancer Registry Program (TNBCRP) which includes no organized population-based screening. Costs were estimated using direct data from the BMSP project and the reimbursement rates of Turkish Social Security Administration. The life-years saved by BMSP were estimated using the stage distribution observed with BMSP and TNBCRP.Results: A total of 67 women (out of 7234 screened women) were diagnosed with breast cancer in BMSP. The stage distribution for AJCC stages O, I, II, III, IV was 19.4%, 50.8%, 20.9%, 7.5%, 1.5% and 4.9%, 26.6%, 44.9%, 20.8%, 2.8% with BMSP and TNBCRP, respectively. The BMSP program is expected to save 279.46 life years over TNBCRP with an additional cost of $677.171, which implies an incremental cost-effectiveness ratio (ICER) of $2.423 per saved life year. Since the ICER is smaller than the Gross Demostic Product (GDP) per capita in Turkey ($10.515 in 2014), BMSP program is highly cost-effective and remains cost-effective in the sensitivity analysis.Conclusion: Mammography screening may change the stage distribution of breast cancer in Turkey. Furthermore, an organized population-based screening program may be cost-effective in Turkey and in other developing countries. More research is needed to better estimate life-years saved with screening and further validate the findings of our study.

Abstract 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.

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.

Abstract Background Clostridioides difficile infection (CDI) is commonly associated with outcomes like recurrence and readmission. The effect of social determinants of health, such as ‘neighborhood’ socioeconomic disadvantage, on a CDI patient’s health outcomes is unclear. Living in a disadvantaged neighborhood could interfere with a CDI patient’s ability to follow post-discharge care recommendations and the success probability of these recommendations, thereby increasing risk of readmission. We hypothesized that neighborhood disadvantage was associated with 30-day readmission risk in Medicare patients with CDI. Methods In this retrospective cohort study, odds of 30-day readmission for CDI patients are evaluated controlling for patient sociodemographics, comorbidities, and hospital and stay-level variables. The cohort was created from a random 20% national sample of Medicare patients during the first 11 months of 2014. Results From the cohort of 19,490 patients (39% male; 80% white; 83% 65 years or older), 22% were readmitted within 30 days of an index stay. Unadjusted analyses showed that patients from the most disadvantaged neighborhoods were readmitted at a higher rate than those from less disadvantaged neighborhoods (26% vs. 21% rate: unadjusted OR = 1.32 [1.20, 1.45]). This relationship held in adjusted analyses, in which residence in the most disadvantaged neighborhoods was associated with 16% increased odds of readmission (adjusted OR = 1.16 [1.04, 1.28]). Conclusions Residence in disadvantaged neighborhoods poses a significantly increased risk of readmission in CDI patients. Further research should focus on in-depth assessments of this population to better understand the mechanisms underlying these risks and if these findings apply to other infectious diseases.

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.

IntroductionVaccination 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.MethodsWe 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.ResultsThe 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.DiscussionIn 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.

ObjectivesAdjuvant paclitaxel and trastuzumab has been shown to be an effective regimen with low risk of cancer recurrence and treatment-related toxicities in early-stage node-negative, HER2-positive breast cancer. We investigated the cost-effectiveness of this regimen.MethodsA Markov-based microsimulation model with six health states is used to simulate four adjuvant therapy options for women with early-stage node-negative, HER2-positive breast cancer at different age groups. The four treatment arms are 1) adjuvant paclitaxel and trastuzumab (TH), 2) doxorubicin, cyclophosphamide, paclitaxel and trastuzumab (ACTH), 3) docetaxel, carboplatin and trastuzumab (TCH), and 4) no adjuvant trastuzumab (NT). Data from randomized trials were used to estimate treatment efficacy. Societal perspective was used in this cost-effectiveness analysis. Costs were measured in 2016 US dollars (US$) and quality-adjusted life-years (QALYs) was used for health outcomes. Sensitivity analyses were performed to evaluate the impact of uncertainty in parameter estimation.ResultsWe found that 40-year-old women undergoing TH treatment would have an average of 16.17 QALYs for the cost of $178,650 when lifetime horizon is used. Compared to NT, TH has incremental cost-effectiveness ratios ranged from $10,584 (ages 40-49) to $84,981 (age 80+) per additional QALYs. The sensitivity analysis showed that TH is cheaper and leads to higher QALYs compared to both ACTH and TCH for all age groups and time horizons.ConclusionsTH is cost-effective for all age groups in the base case scenario and in the sensitivity analysis. In order to reduce the parameter uncertainty, clinical trials with longer follow-up times are needed.

Background: There are no publicly available tools designed specifically to assist policy makers to make informed decisions about the optimal ages of breast cancer screening initiation for different populations of US women. Objective: To use three established simulation models to develop a web-based tool called Mammo OUTPuT. Methods: The simulation models use the 1970 US birth cohort and common parameters for incidence, digital screening performance, and treatment effects. Outcomes include breast cancers diagnosed, breast cancer deaths averted, breast cancer mortality reduction, false-positive mammograms, benign biopsies, and overdiagnosis. The Mammo OUTPuT tool displays these outcomes for combinations of age at screening initiation (every year from 40 to 49), annual versus biennial interval, lifetime versus 10-year horizon, and breast density, compared to waiting to start biennial screening at age 50 and continuing to 74. The tool was piloted by decision makers (n = 16) who completed surveys. Results: The tool demonstrates that benefits in the 40s increase linearly with earlier initiation age, without a specific threshold age. Likewise, the harms of screening increase monotonically with earlier ages of initiation in the 40s. The tool also shows users how the balance of benefits and harms varies with breast density. Surveys revealed that 100% of users (16/16) liked the appearance of the site; 94% (15/16) found the tool helpful; and 94% (15/16) would recommend the tool to a colleague. Conclusions: This tool synthesizes a representative subset of the most current CISNET (Cancer Intervention and Surveillance Modeling Network) simulation model outcomes to provide policy makers with quantitative data on the benefits and harms of screening women in the 40s. Ultimate decisions will depend on program goals, the population served, and informed judgments about the weight of benefits and harms.

A 2% threshold, traditionally used as a level above which breast biopsy recommended, has been generalized to all patients from several specific situations analyzed in the literature. We use a sequential decision analytic model considering clinical and mammography features to determine the optimal general threshold for image guided breast biopsy and the sensitivity of this threshold to variation of these features.We built a decision analytical model called a Markov Decision Process (MDP) model, which determines the optimal threshold of breast cancer risk to perform breast biopsy in order to maximize a patient's total quality-adjusted life years (QALYs). The optimal biopsy threshold is determined based on a patient's probability of breast cancer estimated by a logistic regression model (LRM) which uses demographic risk factors (age, family history, and hormone use) and mammographic findings (described using the established lexicon-BI-RADS). We estimate the MDP model's parameters using SEER data (prevalence of invasive vs. in situ disease, stage at diagnosis, and survival), US life tables (all cause mortality), and the medical literature (biopsy disutility and treatment efficacy) to determine the optimal "base case" risk threshold for breast biopsy and perform sensitivity analysis. The base case MDP model reveals that 2% is the optimal threshold for breast biopsy for patients between 42 and 75 however the thresholds below age 42 is lower (1%) and above age 75 is higher (range of 3-5%). Our sensitivity analysis reveals that the optimal biopsy threshold varies most notably with changes in age and disutility of biopsy.Our MDP model validates the 2% threshold currently used for biopsy but shows this optimal threshold varies substantially with patient age and biopsy disutility.