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.

Living donors are a significant and increasing source of livers for transplantation, mainly because of the insufficient supply of cadaveric organs. We consider the problem of optimally timing a living-donor liver transplant to maximize the patient's total reward, such as quality-adjusted life expectancy. We formulate a Markov decision process (MDP) model in which the state of the process is described by patient health. We derive structural properties of the MDP model, including a set of intuitive conditions that ensure the existence of a control-limit optimal policy. We use clinical data in our computational experiments, which show that the optimal policy is typically of control-limit type.medical decision making, Markov decision processes, control-limit policy, organ transplantation, liver transplantation, service operations

Background Chronic hepatitis C virus (HCV) infection causes substantial health and economic burden in the United States (US). With the availability of direct-acting antiviral agents (DAAs), recently approved and other therapies under development and 1-time birth-cohort screening, the burden of HCV disease is expected to decrease. Objective To predict the impact of new therapies and screening on chronic HCV cases and associated disease outcomes. Design Individual-level state-transition model. Setting Existing and anticipated HCV therapies and screening in the US. Patients Total HCV-infected population in the US. Measurements Chronic HCV cases and advanced-stage HCV outcomes. Results The number of chronic HCV cases decreased from 3.2 million in 2001 to 2.3 million in 2013. One-time birth-cohort screening beginning in 2013 is expected to identify 487 000 HCV cases in the next 10 years. In contrast, 1-time universal screening could identify 933 700 HCV cases. With the availability of highly effective therapies, HCV could become a rare disease in the next 22 years. The adoption of recently approved HCV therapies and one-time birth-cohort screening can prevent approximately 124 200 cases of decompensated cirrhosis, 78 800 cases of hepatocellular carcinoma, 126 500 liver-related deaths and 9900 liver transplants by 2050. Increasing the treatment capacity would further reduce the burden of HCV-related disease. Limitations Empirical data on the effectiveness of the future HCV therapies, on the future annual incidence of HCV, and on HCV treatment capacity are lacking. Conclusions New HCV therapies along with widespread implementation of screening and treatment will play an important role in reducing the burden of HCV disease. More aggressive screening recommendations are needed to identify a large pool of infected patients. Funding source National Institutes of Health.

Alcohol consumption is associated with decreased antiretroviral adherence, and decreased adherence results in poorer outcomes. However the magnitude of alcohol's impact on survival is unknown. Our objective was to use a calibrated and validated simulation of HIV disease to estimate the impact of alcohol on survival. We incorporated clinical data describing the temporal and dose-response relationships between alcohol consumption and adherence in a large observational cohort (N = 2,702). Individuals were categorized as nondrinkers (no alcohol consumption), hazardous drinkers (consume ≥5 standard drinks on drinking days), and nonhazardous drinkers (consume <5 standard drinks on drinking days). Our results showed that nonhazardous alcohol consumption decreased survival by more than 1 year if the frequency of consumption was once per week or greater, and by 3.3 years (from 21.7 years to 18.4 years) with daily consumption. Hazardous alcohol consumption decreased overall survival by more than 3 years if frequency of consumption was once per week or greater, and by 6.4 years (From 16.1 years to 9.7 years) with daily consumption. Our results suggest that alcohol is an underappreciated yet modifiable risk factor for poor survival among individuals with HIV.

We provide a tutorial on the construction and evaluation of Markov decision processes (MDPs), which are powerful analytical tools used for sequential decision making under uncertainty that have been widely used in many industrial and manufacturing applications but are underutilized in medical decision making (MDM). We demonstrate the use of an MDP to solve a sequential clinical treatment problem under uncertainty. Markov decision processes generalize standard Markov models in that a decision process is embedded in the model and multiple decisions are made over time. Furthermore, they have significant advantages over standard decision analysis. We compare MDPs to standard Markov-based simulation models by solving the problem of the optimal timing of living-donor liver transplantation using both methods. Both models result in the same optimal transplantation policy and the same total life expectancies for the same patient and living donor. The computation time for solving the MDP model is significantly smaller than that for solving the Markov model. We briefly describe the growing literature of MDPs applied to medical decisions.

O perating room (OR) scheduling is an important operational problem for most hospitals. In this study, we present a novel two-stage stochastic mixed-integer programming model to minimize total expected operating cost given that scheduling decisions are made before the resolution of uncertainty in surgery durations. We use this model to quantify the benefit of pooling ORs as a shared resource and to illustrate the impact of parallel surgery processing on surgery schedules. Decisions in our model include the number of ORs to open each day, the allocation of surgeries to ORs, the sequence of surgeries within each OR, and the start time for each surgeon. Realistic-sized instances of our model are difficult or impossible to solve with standard stochastic programming techniques. Therefore, we exploit several structural properties of the model to achieve computational advantages. Furthermore, we describe a novel set of widely applicable valid inequalities that make it possible to solve practical instances. Based on our results for different resource usage schemes, we conclude that the impact of parallel surgery processing and the benefit of OR pooling are significant. The latter may lead to total cost reductions between 21% and 59% on average.

IntroductionThe ability to preserve organs prior to transplant is essential to the organ allocation process.ObjectiveThe purpose of this study is to describe the functional relationship between cold-ischemia time (CIT) and primary nonfunction (PNF), patient and graft survival in liver transplant.MethodsTo identify relevant articles Medline, EMBASE and the Cochrane database, including the non-English literature identified in these databases, was searched from 1966 to April 2008. Two independent reviewers screened and extracted the data. CIT was analyzed both as a continuous variable and stratified by clinically relevant intervals. Nondichotomous variables were weighted by sample size. Percent variables were weighted by the inverse of the binomial variance.ResultsTwenty-six studies met criteria. Functionally, PNF% = −6.678281+0.9134701*CIT Mean+0.1250879*(CIT Mean−9.89535)2−0.0067663*(CIT Mean−9.89535)3, r2 = .625, , p<.0001. Mean patient survival: 93 % (1 month), 88 % (3 months), 83 % (6 months) and 83 % (12 months). Mean graft survival: 85.9 % (1 month), 80.5 % (3 months), 78.1 % (6 months) and 76.8 % (12 months). Maximum patient and graft survival occurred with CITs between 7.5–12.5 hrs at each survival interval. PNF was also significantly correlated with ICU time, % first time grafts and % immunologic mismatches.ConclusionThe results of this work imply that CIT may be the most important pre-transplant information needed in the decision to accept an organ.

The question of when to initiate HIV treatment is considered the most important question in HIV care today. Benefits of delaying therapy include avoiding the negative side effects and toxicities associated with the drugs, delaying selective pressures that induce the development of resistant strains of the virus, and preserving a limited number of treatment options. On the other hand, the risks of delayed therapy include the possibility of irreversible damage to the immune system, development of AIDS-related complications, and death. We use Markov decision processes to develop the first HIV optimization models that aim to maximize the expected lifetime or quality-adjusted lifetime of a patient. We prove conditions that establish structural properties of the optimal solution and compare them to our data and results. Model solutions, based on clinical data, support a strategy of treating HIV earlier in its course as opposed to recent trends toward treating it later.