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U.S. health agencies periodically evaluate transplant programs based on their patients' posttransplant survival outcomes, and a program is flagged for review if the number of unsuccessful transplants far exceeds what would be expected based on national averages. Some researchers have expressed concerns that these regulations might cause programs to reject high-risk patients, whereas others have questioned if such a response would be rational. In "Characterizing Rational Transplant Program Response to Outcome-Based Regulation," Mildebrath et al. use chance-constrained optimization to demonstrate that it may, in fact, be rational for a transplant program to become more selective when evaluating transplant candidates for admittance to the waitlist. They also demonstrate that the regulations may unfairly penalize medium-sized programs. Moreover, their model quantifies which patients may be most at risk for adverse selection by programs. Their results provide insights to policymakers by quantitatively characterizing the response of rational programs to outcome-based regulations. Organ transplantation is an increasingly common therapy for many types of end-stage organ failure, including lungs, hearts, kidneys, and livers. The last 20 years have seen increased scrutiny of posttransplant outcomes in the United States to ensure the efficient utilization of the scarce organ supply. Under regulations by the Organ Procurement Transplantation Network (OPTN) and Centers for Medicare and Medicaid Services (CMS), the United States has seen a rise in risk-averse patient selection among transplant programs, resulting in decreased transplantation volume for some programs. Despite this observed decrease, the response of transplant programs to OPTN/CMS regulations remains poorly understood. In this work, we consider the perspective of a transplant program that seeks to simultaneously maximize transplant volume and control the risk of OPTN/CMS penalization. Using a chance-constrained mixed-integer programming model, we demonstrate that under certain conditions, it may be rational for a transplant program to curtail its transplant volume to avoid penalization under OPTN/CMS regulations. This finding, which confirms empirical results observed in the clinical literature, suggests that such regulations may be inherently unsuitable for use in incentivizing improved program performance. We also highlight other structural shortcomings of OPTN/CMS regulations that have not been observed previously in the literature. Funding: This work was supported by the U.S. Department of Defense and the National Science Foundation [Grants CMMI-1826144, CMMI-1826297, and CMMI-1826323]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2018.0721. [ABSTRACT FROM AUTHOR]

ObjectiveWe sought to determine the impact of changes in breastfeeding rates on population health.Materials and methodsWe used a Monte Carlo simulation model to estimate the population-level changes in disease burden associated with marginal changes in rates of any breastfeeding at each month from birth to 12 months of life, and in rates of exclusive breastfeeding from birth to 6 months of life. We used these marginal estimates to construct an interactive online calculator (available at www.usbreastfeeding.org/saving-calc ). The Institutional Review Board of the Cambridge Health Alliance exempted the study.ResultsUsing our interactive online calculator, we found that a 5% point increase in breastfeeding rates was associated with statistically significant differences in child infectious morbidity for the U.S. population, including otitis media (101,952 cases, 95% confidence interval [CI] 77,929-131,894 cases) and gastrointestinal infection (236,073 cases, 95% CI 190,643-290,278 cases). Associated medical cost differences were $31,784,763 (95% CI $24,295,235-$41,119,548) for otitis media and $12,588,848 ($10,166,203-$15,479,352) for gastrointestinal infection. The state-level impact of attaining Healthy People 2020 goals varied by population size and current breastfeeding rates.ConclusionModest increases in breastfeeding rates substantially impact healthcare costs in the first year of life.

The aim of this study was to quantify the excess cases of pediatric and maternal disease, death, and costs attributable to suboptimal breastfeeding rates in the United States. Using the current literature on the associations between breastfeeding and health outcomes for nine pediatric and five maternal diseases, we created Monte Carlo simulations modeling a hypothetical cohort of U.S. women followed from age 15 to age 70 years and their children from birth to age 20 years. We examined disease outcomes using (a) 2012 breastfeeding rates and (b) assuming that 90% of infants were breastfed according to medical recommendations. We measured annual excess cases, deaths, and associated costs, in 2014 dollars, using a 2% discount rate. Annual excess deaths attributable to suboptimal breastfeeding total 3,340 (95% confidence interval [1,886 to 4,785]), 78% of which are maternal due to myocardial infarction (n = 986), breast cancer (n = 838), and diabetes (n = 473). Excess pediatric deaths total 721, mostly due to Sudden Infant Death Syndrome (n = 492) and necrotizing enterocolitis (n = 190). Medical costs total $3.0 billion, 79% of which are maternal. Costs of premature death total $14.2 billion. The number of women needed to breastfeed as medically recommended to prevent an infant gastrointestinal infection is 0.8; acute otitis media, 3; hospitalization for lower respiratory tract infection, 95; maternal hypertension, 55; diabetes, 162; and myocardial infarction, 235. For every 597 women who optimally breastfeed, one maternal or child death is prevented. Policies to increase optimal breastfeeding could result in substantial public health gains. Breastfeeding has a larger impact on women's health than previously appreciated.

Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens; one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. This paper presents a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we also propose chance constraints that guarantee reaching an operable tumor size with a high probability in a neoadjuvant setting. We present analytical results pertinent to the accuracy of the model in representing biological processes of chemotherapy and establish its potential for clinical applications through a numerical study of breast cancer. History: Accepted by Paul Brooks, Area Editor for Applications in Biology, Medicine, & Healthcare. Funding: This work was supported by the National Science Foundation [Grants CMMI-1933369 and CMMI-1933373]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0207) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0207). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

ObjectiveTo estimate risk of necrotizing enterocolitis (NEC) for extremely low birth weight (ELBW) infants as a function of preterm formula (PF) and maternal milk intake and calculate the impact of suboptimal feeding on the incidence and costs of NEC.Study designWe used aORs derived from the Glutamine Trial to perform Monte Carlo simulation of a cohort of ELBW infants under current suboptimal feeding practices, compared with a theoretical cohort in which 90% of infants received at least 98% human milk.ResultsNEC incidence among infants receiving ≥98% human milk was 1.3%; 11.1% among infants fed only PF; and 8.2% among infants fed a mixed diet (P = .002). In adjusted models, compared with infants fed predominantly human milk, we found an increased risk of NEC associated with exclusive PF (aOR = 12.1, 95% CI 1.5, 94.2), or a mixed diet (aOR 8.7, 95% CI 1.2-65.2). In Monte Carlo simulation, current feeding of ELBW infants was associated with 928 excess NEC cases and 121 excess deaths annually, compared with a model in which 90% of infants received ≥98% human milk. These models estimated an annual cost of suboptimal feeding of ELBW infants of $27.1 million (CI $24 million, $30.4 million) in direct medical costs, $563 655 (CI $476 191, $599 069) in indirect nonmedical costs, and $1.5 billion (CI $1.3 billion, $1.6 billion) in cost attributable to premature death.ConclusionsAmong ELBW infants, not being fed predominantly human milk is associated with an increased risk of NEC. Efforts to support milk production by mothers of ELBW infants may prevent infant deaths and reduce costs.

Abstract. Influenza (flu) is a serious public health concern. The first line of defense is the flu shot, whose composition is updated annually to adjust for frequent mutations of the [...]

Many patients face difficulties when accessing medical facilities, particularly in rural areas. To alleviate these concerns, medical centers may offer transportation to eligible patients. However, the operation of such services [...]

The global fight against HIV/AIDS is hindered by a lack of drugs in the developing world. When patients in these countries initiate treatment, they typically remain on it until death; [...]

Scheduling elective procedures in an operating suite is a formidable task because of competing performance metrics and uncertain surgery durations. In this paper, we present an optimization framework for batch [...]