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Setting prices for life-saving medical or pharmaceutical products needs to consider multiple factors, e.g., affordability and health outcomes across different populations. When a group of buyers (e.g., countries) combine their purchasing power (e.g., via a group purchasing organization), the average procurement price decreases in the total volume. Decisions about what price to then charge to each member in a group are particularly challenging, considering the disparities in their respective ability and willingness to pay. Tiered pricing can be an effective way to set prices for a group of buyers, but its performance needs to be quantified and evaluated. We modeled the decision of setting prices of a medical product (for example, a vaccine) for a group of buyers using a mixed integer programming model, considering the buyers' ability and willingness to pay. The objective is to minimize the unit price disparity adjusted by the buyers' willingness to pay, subject to the constraint that the prices decrease in the buyers' ability to pay. We also developed an analogous subsidy allocation model that applies if the group receives philanthropic donations to support procurement. The models were illustrated with two case studies based on the Bacillus Calmette-Guerin (BCG) vaccine procurement by Gavi, the Vaccine Alliance and Pan American Health Organization , and the performances of uniform, tiered, and differentiated pricing schemes were examined. The adjusted unit price disparity is non-increasing in the number of price tiers allowed. The biggest decrease in the adjusted price disparity occurs when switching to two-tier pricing from uniform pricing. Tiered pricing performs better in the Gavi group compared to the PAHO group, in part because the ability to pay and willingness to pay have a higher degree of rank correlation within the former group of countries. This work provides a model for price-setting (subsidy allocation) decisions for a group of buyers and provides a quantitative comparison of different pricing schemes. The results of the case studies suggest that the performance of tiered pricing depends on various factors, including the disparities in the ability and willingness to pay across the buyers. This research has been supported in part by the Center for Health and Humanitarian Systems, the William W. George endowment, and the following benefactors at Georgia Tech: Andrea Laliberte, Richard Rick E. and Charlene Zalesky, and Claudia and Paul Raines. [ABSTRACT FROM AUTHOR]

We study the capacity planning and allocation decisions for multiple heterogeneous resources, considering potential demand scenarios, where each demand requests a subset of the available resource types simultaneously at a specified time, location, and duration (smRmD). We model this problem as a two-stage stochastic integer program and consider two variants for the objective function: (a) maximize the expected reward of demands met over all scenarios, subject to a budget B for resources, and (b) maximize the expected reward of demands met over all scenarios minus the cost of resources. Contributions of this work include (i) a thorough complexity analysis of smRmD and its variants, (ii) analysis of structural properties, (iii) development of various approximation algorithms using the unique structural properties of smRmD and its variants, and (iv) an extensive computational study to explore the ease with which exact and approximate solutions may be found. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This research has been supported in part by National Science Foundation (NSF) Graduate Research Fellowship [DGE-1650044], NSF [Grants CMMI-1538860, NSF-AF:1910423, and NSF-AF:1717947], and the following Georgia Tech benefactors: William W. George, Andrea Laliberte, Richard "Rick" E. & Charlene Zalesky, and Claudia & Paul Raines. [ABSTRACT FROM AUTHOR]

Pediatric immunization is important for preventing potentially life-threatening diseases in children. Over time, the number of recommended pediatric vaccines has increased and is likely to increase further as new vaccines are developed. Given the different number of doses for available vaccines and various constraints (e.g., the appropriate age for each dose of a vaccine or the time between doses), it is challenging to develop a recommended vaccination schedule or a catch-up schedule when a child falls behind on one or more vaccinations. We developed an integer programming optimization model, enabled by Python programming and embedded into an Excel-based decision tool, to recommend childhood vaccination schedules or personalized catch-up schedules. The model recommends a vaccination schedule that balances the goal of being as close as possible to the clinically-indicated dosing schedules and the goal of minimizing clinic visits, and gives users the ability to trade off between these two goals. We illustrated the broad applicability of our proposed model with commonly-faced vaccine scheduling challenges in the United States. The illustrative computational case study confirms our model's ability to create personalized schedules based on each child's age and vaccination history, and to adjust appropriately when a new vaccine becomes available. The model presented in this paper fills the need for an easy-to-use tool to recommend vaccination schedules for de novo and catch-up purposes. It provides straightforward recommendations that can be easily used by physicians, is flexible to handle the requirements varying by region, and can be updated as new vaccines are approved for use. [ABSTRACT FROM AUTHOR]

We consider the problem of allocating multiple heterogeneous resources geographically and over time to meet demands that require some subset of the available resource types simultaneously at a specified time, location, and duration. The objective is to maximize the total reward accrued from meeting (a subset of) demands. We model this problem as an integer program, show that it is NP-hard, and analyze the complexity of various special cases. We introduce approximation algorithms and an extension to our problem that considers travel costs. Finally, we test the performance of the integer programming model in an extensive computational study. [ABSTRACT FROM AUTHOR]

[Display omitted] • Antibody testing as part of a COVID-19 vaccine distribution plan is effective. • Antibody testing should be used to prioritize vaccination when supply is limited. • Prioritization reduces infection attack rates and deaths. • The relative time between vaccination start and infection peak is important. To assess the value of using SARS-CoV-2 specific antibody testing to prioritize the vaccination of susceptible individuals as part of a COVID-19 vaccine distribution plan when vaccine supply is limited. An extended susceptible-infected-recovered (SIR) compartmental model was used to simulate COVID-19 spread when considering diagnosis, isolation, and vaccination of a cohort of 1 million individuals. The scenarios modeled represented 4 pandemic severity scenarios and various times when the vaccine becomes available during the pandemic. Eligible individuals have a probability p of receiving antibody testing prior to vaccination (p = 0, 0.25, 0.5, 0.75, and 1). The vaccine was modeled as a single dose vaccine with 90% and 70% efficacy. The value of serology testing was evaluated by comparing the infection attack rate, peak infections, peak day, and deaths. The use of antibody testing to prioritize the allocation of limited vaccines reduces infection attack rates and deaths. The size of the reduction depends on when the vaccine becomes available relative to the infection peak day. The largest percentage reduction in cases and deaths occurs when the vaccine is deployed before and close to the infection peak day. The reduction in the number of cases and deaths diminishes as vaccine deployment is delayed. Antibody testing as part of the vaccination plan is an effective method to maximize the benefit of a COVID-19 vaccine. Decision-makers need to consider relative timing between the infection peak day and when the vaccine becomes available. [ABSTRACT FROM AUTHOR]

As the spread of COVID19 in the US continues to grow, local and state officials face difficult decisions about when and how to transition to a "new normal." The goal of this study is to project the number of COVID19 infections and resulting severe outcomes, and the need for hospital capacity under social distancing, particularly, shelter-in-place and voluntary quarantine for the State of Georgia. We developed an agent-based simulation model to project the infection spread. The model utilizes COVID19-specific parameters and data from Georgia on population interactions and demographics. The simulation study covered a seven and a half-month period, testing different social distancing scenarios, including baselines (no-intervention or school closure only) and combinations of shelter-in-place and voluntary quarantine with different timelines and compliance levels. The following outcomes are compared at the state and community levels: the number and percentage of cumulative and daily new symptomatic and asymptomatic infections, hospitalizations, and deaths; COVID19-related demand for hospital beds, ICU beds, and ventilators. The results suggest that shelter-in-place followed by voluntary quarantine reduced peak infections from approximately 180K under no intervention and 113K under school closure, respectively, to below 53K, and delayed the peak from April to July or later. Increasing shelter-in-place duration from four to five weeks yielded 2–9% and 3–11% decrease in cumulative infection and deaths, respectively. Regardless of the shelter-in-place duration, increasing voluntary quarantine compliance decreased daily new infections from almost 53K to 25K, and decreased cumulative infections by about 50%. The cumulative number of deaths ranged from 6,660 to 19,430 under different scenarios. Peak infection date varied across scenarios and counties; on average, increasing shelter-in-place duration delayed the peak day by 6 days. Overall, shelter-in-place followed by voluntary quarantine substantially reduced COVID19 infections, healthcare resource needs, and severe outcomes. [ABSTRACT FROM AUTHOR]

Despite high vaccine coverage rates in children and efficacy of pneumococcal conjugate vaccines, invasive pneumococcal disease (IPD) episodes due to serotypes included in the vaccine following completion of the recommended course of immunisation (i.e. vaccine failure) have been reported. We used data gathered from a population-based enhanced passive surveillance for IPD in children under 18 years of age in Massachusetts and an ensemble model composed of three machine-learning algorithms to predict probability of 13-valent pneumococcal conjugated vaccine (PCV13) failure and to evaluate potential associated features including age, underlying comorbidity, clinical presentation, and vaccine schedule. Vaccine failure was defined as diagnosis of IPD due to vaccine serotype (VST), in a child who received age recommended doses recommended by Advisory Committee of Immunization Practices. During the 7-year study period, between April 01, 2010 and March 31, 2017, we identified 296 IPD cases. There were 107 (36%) IPD cases caused by VST, mostly serotype 19A (49, 17%), 7F (21, 7%), and 3 (18, 6%). Thirty-seven (34%) were in children who were completely vaccinated representing 13% of all IPD cases. Vaccine failure was more likely among children older than 60 months (predicted probability 0.40, observed prevalence 0.37, model prediction accuracy 79%), children presenting with pneumonia (predicted probability 0.27, observed prevalence 0.31, model accuracy 77%), and children with underlying comorbidity (predicted probability 0.24, observed prevalence 0.23, model accuracy 96%). Vaccine failure probability for those >60 months of age and had an underlying risk factor was 45% (observed prevalence 0.33, model accuracy 82%). The likelihood of vaccine failure was lowest among children who had completed 3 primary doses plus one booster dose PCV13 (predicted probability 0.14, observed prevalence 0.14, model prediction accuracy 100%). PCV13 vaccine failure is more frequent among older children with underlying comorbidity, and among those who present with pneumococcal pneumonia. Our study provides a preliminary framework to predict the patterns of vaccine failures and may contribute to decision-making processes to optimize PCV immunization schedules. [ABSTRACT FROM AUTHOR]

The flowtime network restoration problem was introduced by Averbakh and Pereira (2012) who presented a Minimum Spanning Tree heuristic, two local search procedures, and an exact branch-and-bound algorithm. This note corrects the computational results in Averbakh and Pereira (2012). [ABSTRACT FROM AUTHOR]

In addition to improved sanitation, hygiene, and better access to safe water, oral cholera vaccines can help to control the spread of cholera in the short term. However, there is currently no systematic method for determining the best allocation of oral cholera vaccines to minimize disease incidence in a population where the disease is endemic and resources are limited. We present a mathematical model for optimally allocating vaccines in a region under varying levels of demographic and incidence data availability. The model addresses the questions of where, when, and how many doses of vaccines to send. Considering vaccine efficacies (which may vary based on age and the number of years since vaccination), we analyze distribution strategies which allocate vaccines over multiple years. Results indicate that, given appropriate surveillance data, targeting age groups and regions with the highest disease incidence should be the first priority, followed by other groups primarily in order of disease incidence, as this approach is the most life-saving and cost-effective. A lack of detailed incidence data results in distribution strategies which are not cost-effective and can lead to thousands more deaths from the disease. The mathematical model allows for what-if analysis for various vaccine distribution strategies by providing the ability to easily vary parameters such as numbers and sizes of regions and age groups, risk levels, vaccine price, vaccine efficacy, production capacity and budget. [ABSTRACT FROM AUTHOR]

Widespread product shortages during the COVID-19 pandemic and other emergencies have prompted several large studies of how to make supply chains more resilient. In this article we leverage these studies, as well as the academic literature, to provide a review of our state of knowledge about supply chain resilience. To do this, we (i) classify the failure modes of a supply chain, (ii) quantitatively evaluate the level of resilience needed in a supply chain to achieve desired business or societal outcomes, (iii) describe a structured framework of actions to enhance supply chain resilience, and (iv) use the resulting conceptual paradigm to review the academic literature on supply chain risk and resilience. In each step, we summarize key insights from our current state of understanding, as well as gaps that present opportunities for research and practice. [ABSTRACT FROM AUTHOR]

Background: During the 2009 H1N1 influenza pandemic, concerns arose about the potential negative effects of mass public gatherings and travel on the course of the pandemic. Better understanding the potential effects of temporal changes in social mixing patterns could help public officials determine if and when to cancel large public gatherings or enforce regional travel restrictions, advisories, or surveillance during an epidemic. Methods: We develop a computer simulation model using detailed data from the state of Georgia to explore how various changes in social mixing and contact patterns, representing mass gatherings and holiday traveling, may affect the course of an influenza pandemic. Various scenarios with different combinations of the length of the mass gatherings or traveling period (range: 0.5 to 5 days), the proportion of the population attending the mass gathering events or on travel (range: 1% to 50%), and the initial reproduction numbers R0 (1.3, 1.5, 1.8) are explored. Results: Mass gatherings that occur within 10 days before the epidemic peak can result in as high as a 10% relative increase in the peak prevalence and the total attack rate, and may have even worse impacts on local communities and travelers' families. Holiday traveling can lead to a second epidemic peak under certain scenarios. Conversely, mass traveling or gatherings may have little effect when occurring much earlier or later than the epidemic peak, e.g., more than 40 days earlier or 20 days later than the peak when the initial R0 = 1.5. Conclusions: Our results suggest that monitoring, postponing, or cancelling large public gatherings may be warranted close to the epidemic peak but not earlier or later during the epidemic. Influenza activity should also be closely monitored for a potential second peak if holiday traveling occurs when prevalence is high. [ABSTRACT FROM AUTHOR]

In an environment where a buyer procures capacity from a capacitated supplier through a wholesale price contract, the impact of timing of the decisions and of additional demand information on the supplier's pricing and the buyer's procurement decisions are investigated. As the selling season approaches, the buyer and the supplier have better information about the demand process. It is questioned as to what degree waiting until the demand uncertainty is resolved—given that such an option is feasible—is the best alternative for the supply chain partners. The main model is a two-period dynamic pricing/procurement game where the supplier sets wholesale prices dynamically; he sets the second period price after seeing the demand state as well as the buyer's first period procurement quantity. Two single-period models based on the information available to the players prior to their decisions are also studied. For each model, the subgame perfect Nash equilibrium is found in a closed form and the players' behaviors are analyzed to determine the impact of the additional information and trading periods on their welfare. The supplier's optimal capacity decision is also considered. Several additional insights are obtained through numerical experiments. [ABSTRACT FROM AUTHOR]

Motivated by a real application, we consider the following aircraft scheduling problem. At any time, the aircraft are at different locations or are serving a customer and new customer requests arrive, each consisting of a departure location, departure time, and destination. Our objective is to satify these requests (by subcontracting extra aircraft if necessary) at minimum cost under additional constraints of maintenance requirements and previously scheduled trips. We show that the jet aircraft scheduling problem is NP complete and discuss three special cases. We show that the second and third special cases are also NP complete. We provide a polynomial time network flow based algorithm for the first special case and a pseudo-polynomial time dynamic programming algorithm for the second special case. We formulate the problem as a 0-1 integer program and observe that most small and medium size problems can be solved by Cplex. For larger and difficult instances, we provide a fast heuristic with good performance. [ABSTRACT FROM AUTHOR]

• A natural history model was introduced where probabilities were assigned to each health state based on patient characteristics such as age, gender, and history of depression. • Annual screening of all adults 18 years old or older for depression would be $11,134 and $34,065 cost per QALY gained for females and males, respectively. In comparison, screening every 5-years resulted in ICERs of $8627 for females and $26,892 for males. • The age-specific analysis showed that screening of old populations had higher ICERs than the young and middle age groups. • Our baseline model could be utilized to evaluate the potential consequences of medication strategies or alternative intervention scenarios. Depression is a treatable disease, and untreated depression can lead to serious health complications and decrease the quality of life. Therefore, prevention, early identification, and treatment efforts are essential. Screening has an essential role in preventive medicine in the general population. Ideally, screening tools detect patients early enough to manage the disease and reduce symptoms. We aimed to determine the cost-effectiveness of routine screening schedules. We used a discrete-time nonstationary Markov model to simulate the progression of depression. We used Monte Carlo techniques to simulate the stochastic model for 20 years or during the lifetime of individuals. Baseline and screening scenario models with screening frequencies of annual, 2-year, and 5-year strategies were compared based on incremental cost-effectiveness ratios (ICER). Monte Carlo (MC) simulation and one-way sensitivity analysis were conducted to manage uncertainties. In the general population, all screening strategies were cost-effective compared to the baseline. However, male and female populations differed based on cost over quality-adjusted life years (QALY). Females had lower ICERs, and annual screening had the highest ICER for females, with 11,134$/QALY gained. In contrast, males had around three times higher ICER, with annual screening costs of 34,065$/QALY gained. We assumed that the screening frequency was not changing at any time during the screening scenario. In our calculations, false-positive cases were not taking into account. Considering the high lifetime prevalence and recurrence rates of depression, detection and prevention efforts can be one critical cornerstone to support required care. Our analysis combined the expected benefits and costs of screening and assessed the effectiveness of screening scenarios. We conclude that routine screening is cost-effective for all age groups of females and young, middle-aged males. [ABSTRACT FROM AUTHOR]

Background: Depression is a treatable disease, and untreated depression can lead to serious health complications and decrease the quality of life. Therefore, prevention, early identification, and treatment efforts are essential. Screening has an essential role in preventive medicine in the general population. Ideally, screening tools detect patients early enough to manage the disease and reduce symptoms. We aimed to determine the cost-effectiveness of routine screening schedules.Methods: We used a discrete-time nonstationary Markov model to simulate the progression of depression. We used Monte Carlo techniques to simulate the stochastic model for 20 years or during the lifetime of individuals. Baseline and screening scenario models with screening frequencies of annual, 2-year, and 5-year strategies were compared based on incremental cost-effectiveness ratios (ICER). Monte Carlo (MC) simulation and one-way sensitivity analysis were conducted to manage uncertainties.Results: In the general population, all screening strategies were cost-effective compared to the baseline. However, male and female populations differed based on cost over quality-adjusted life years (QALY). Females had lower ICERs, and annual screening had the highest ICER for females, with 11,134$/QALY gained. In contrast, males had around three times higher ICER, with annual screening costs of 34,065$/QALY gained.Limitations: We assumed that the screening frequency was not changing at any time during the screening scenario. In our calculations, false-positive cases were not taking into account.Conclusions: Considering the high lifetime prevalence and recurrence rates of depression, detection and prevention efforts can be one critical cornerstone to support required care. Our analysis combined the expected benefits and costs of screening and assessed the effectiveness of screening scenarios. We conclude that routine screening is cost-effective for all age groups of females and young, middle-aged males. [ABSTRACT FROM AUTHOR]

Background: Major depression is a treatable disease, and untreated depression can lead to serious health complications. Therefore, prevention, early identification, and treatment efforts are essential. Natural history models can be utilized to make informed decisions about interventions and treatments of major depression.Methods: We propose a natural history model of major depression. We use steady-state analysis to study the discrete-time Markov chain model. For this purpose, we solved the system of linear equations and tested the parameter and transition probabilities empirically.Results: We showed that bias in parameters might collectively cause a significant mismatch in a model. If incidence is correct, then lifetime prevalence is 33.2% for females and 20.5% for males, which is higher than reported values. If prevalence is correct, then incidence is .0008 for females and .00065 for males, which is lower than reported values. The model can achieve feasibility if incidence is at low levels and recall bias of the lifetime prevalence is quantified to be 31.9% for females and 16.3% for males.Limitations: This model is limited to major depression, and patients who have other types of depression are assumed healthy. We assume that transition probabilities (except incidence rates) are correct.Conclusion: We constructed a preliminary model for the natural history of major depression. We determined the lifetime prevalences are underestimated and the average incidence rates may be underestimated for males. We conclude that recall bias needs to be accounted for in modeling or burden estimates, where the recall bias should increase with age. [ABSTRACT FROM AUTHOR]

We quantify the effect of a set of interventions including asthma self-management education, influenza vaccination, spacers, and nebulizers on healthcare utilization and expenditures for Medicaid-enrolled children with asthma in New York and Michigan. We obtained patients' data from Medicaid Analytic eXtract files and evaluated patients with persistent asthma in 2010 and 2011. We used difference-in-difference regression to quantify the effect of the intervention on the probability of asthma-related healthcare utilization, asthma medication, and utilization costs. We estimated the average change in outcome measures from pre-intervention/intervention (2010) to post-intervention (2011) periods for the intervention group by comparing this with the average change in the control group over the same time horizon. All of the interventions reduced both utilization and asthma medication costs. Asthma self-management education, nebulizer, and spacer interventions reduced the probability of emergency department (20.8–1.5%, 95%CI 19.7–21.9% vs. 0.5–2.5%, respectively) and inpatient (3.5–0.8%, 95%CI 2.1–4.9% vs. 0.4–1.2%, respectively) utilizations. Influenza vaccine decreased the probability of primary care physician (6–3.5%, 95%CI 4.4–7.6% vs. 1.5–5.5%, respectively) visit. The reductions varied by state and intervention. Promoting asthma self-management education, influenza vaccinations, nebulizers, and spacers can decrease the frequency of healthcare utilization and asthma-related expenditures while improving medication adherence. [ABSTRACT FROM AUTHOR]

Background: Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19. This study evaluates the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of NPIs for COVID19 and being homebound (i.e., refraining from interactions outside of the household).Methods: An agent-based simulation model, which captures the natural history of the disease at the individual level, and the infection spread via a contact network assuming heterogeneous population mixing in households, peer groups (workplaces, schools), and communities, is adapted to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in-place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020.Results: Compared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 4.5, 23.1, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 119 K-248 K, 465 K-499 K, 5388 K-5389 K, respectively. Compared to no intervention, school closure only reduced the percentage of the population infected by less than 16% while more than doubling the peak number of adults homebound.Conclusions: Voluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place. [ABSTRACT FROM AUTHOR]

Priorities of the Centers for Disease Control and Prevention's 6|18 Initiative include outpatient asthma self-management education (ASME) and home-based asthma visits (home visit) as interventions for children with poorly-controlled asthma. ASME and home visit intervention programs are currently not widely available. This project was to assess the economic sustainability of these programs for state asthma control programs reimbursed by Medicaid. We used a simulation model based on parameters from the literature and Medicaid claims, controlling for regression to the mean. We modeled scenarios under various selection criteria based on healthcare utilization and age to forecast the return on investment (ROI) using data from New York. The resulting tool is available in Excel or Python. Our model projected health improvement and cost savings for all simulated interventions. Compared against home visits alone, the simulated ASME alone intervention had a higher ROI for all healthcare utilization and age scenarios. Savings were primarily highest in simulated program participants who had two or more asthma-related emergency department visits or one inpatient visit compared to those participants who had one or more asthma-related emergency department visits. Segmenting the selection criteria by age did not significantly change the results. This model forecasts reduced healthcare costs and improved health outcomes as a result of ASME and home visits for children with high urgent healthcare utilization (more than two emergency department visits or one inpatient hospitalization) for asthma. Utilizing specific selection criteria, state based asthma control programs can improve health and reduce healthcare costs. [ABSTRACT FROM AUTHOR]

Dedicated clinics can be established in an influenza pandemic to isolate people and potentially reduce opportunities for influenza transmission. However, their operation requires resources and their existence may attract the worried-well. In this study, we quantify the impact of opening dedicated influenza clinics during a pandemic based on an agent-based simulation model across a time-varying social network of households, workplaces, schools, community locations, and health facilities in the state of Georgia. We calculate performance measures, including peak prevalence and total attack rate, while accounting for clinic operations, including timing and location. We find that opening clinics can reduce disease spread and hospitalizations even when visited by the worried-well, open for limited weeks, or open in limited locations, and especially when the clinics are in operation during times of highest prevalence. Specifically, peak prevalence, total attack rate, and hospitalization reduced 0.07–0.32%, 0.40–1.51%, 0.02–0.09%, respectively, by operating clinics for the pandemic duration. [ABSTRACT FROM AUTHOR]

There is increasing interest in gait evaluations in clinical settings given the associations between gait and health outcomes. However, efforts examining implementation of gait evaluation in neurological clinics are lacking. Herein, gait implementation within a cognitive neurology clinic is presented. Over a 21-month period, a gait evaluation was collected on 81% of eligible patients (n = 2,622; mean age 73.2±9.5; age range 49-94 years; 47% female). Patients and staff reported being satisfied with the gait assessment. These finding have implications for gait evaluations in clinical settings and for clinical research aimed at understanding the impact of cognitive symptomatology on gait. [ABSTRACT FROM AUTHOR]

Objective: To understand the value of information on vaccine inventory levels during an influenza pandemic, we propose a simulation study to compare vaccine allocation strategies using: (i) only population information (pro-rata, or population-based, PB), (ii) both population and vaccine inventory information (population and inventory-based, PIB). Methods: We adapt an agent-based simulation model to predict the spread of the disease both geographically and temporally. We study PB and PIB when uptake rates vary geographically. The simulation study is done from 2015 to 2017, using population and commuting data from the state of Georgia from the United States census. Findings: Compared to PB under reasonable scenarios, PIB reduces the infection attack rate from 23.4% to 22.4%, decreases the amount of leftover inventory from 827 to 152 thousand, and maintains or increases the percentage of vaccinated population. Conclusions: Our results indicate the need for greater vaccine inventory visibility in public health supply chains, especially when supply is limited, and uptake rates vary geographically. Such visibility has a potential to decrease the number of infections, help identify locations with low uptake rates and to motivate public awareness efforts. [ABSTRACT FROM AUTHOR]

With over 100,000 patients on the kidney transplant waitlist in 2019, it is important to understand if and how the functional status of a patient may change while on the waitlist. Recorded both at registration and just prior to transplantation, the Karnofsky Performance Score measures a patient's functional status and takes on values ranging from 0 to 100 in increments of 10. Using machine learning techniques, we built a gradient boosting regression model to predict a patient's pre-transplant functional status based on information known at the time of waitlist registration. The model's predictions result in an average root mean squared error of 12.99 based on 5 rolling origin cross validations and 12.94 in a separate out-of-time test. In comparison, predicting that the pre-transplant functional status remains the same as the status at registration, results in average root mean squared errors of 14.50 and 14.11 respectively. The analysis is based on 118,401 transplant records from 2007 to 2019. To the best of our knowledge, there has been no previously published research on building a model to predict kidney pre-transplant functional status. We also find that functional status at registration and total serum albumin, have the most impact in predicting the pre-transplant functional status. [ABSTRACT FROM AUTHOR]

Background: Thousands of school systems have struggled with the decisions about how to deliver education safely and effectively amid the COVID19 pandemic. This study evaluates the public health impact of various school reopening scenarios (when, and how to return to in-person instruction) on the spread of COVID19.Methods: An agent-based simulation model was adapted and used to project the impact of various school reopening strategies on the number of infections, hospitalizations, and deaths in the state of Georgia during the study period, i.e., February 18th-November 24th, 2020. The tested strategies include (i) schools closed, i.e., all students receive online instruction, (ii) alternating school day, i.e., half of the students receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iii) alternating school day for children, i.e., half of the children (ages 0-9) receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iv) children only, i.e., only children receive in-person instruction, (v) regular, i.e., all students return to in-person instruction. We also tested the impact of universal masking in schools.Results: Across all scenarios, the number of COVID19-related deaths ranged from approximately 8.8 to 9.9 thousand, the number of cumulative infections ranged from 1.76 to 1.96 million for adults and 625 to 771 thousand for children and youth, and the number of COVID19-related hospitalizations ranged from approximately 71 to 80 thousand during the study period. Compared to schools reopening August 10 with a regular reopening strategy, the percentage of the population infected reduced by 13%, 11%, 9%, and 6% in the schools closed, alternating school day for children, children only, and alternating school day reopening strategies, respectively. Universal masking in schools for all students further reduced outcome measures.Conclusions: Reopening schools following a regular reopening strategy would lead to higher deaths, hospitalizations, and infections. Hybrid in-person and online reopening strategies, especially if offered as an option to families and teachers who prefer to opt-in, provide a good balance in reducing the infection spread compared to the regular reopening strategy, while ensuring access to in-person education. [ABSTRACT FROM AUTHOR]

We discuss the evaluation of expected tardiness of an order at the time of arrival in an M/M/c queuing system with N priority classes, considering both nonpreemptive and preemptive service disciplines. Upon arrival, a customer order is quoted a lead time of d, and placed in the queue according to the priority class of the customer. Orders within the same priority class are processed on a first-come, first-served basis. We derive the Laplace transforms of the expected tardiness of the order given the quoted lead time, priority class of the order, and system status. For the special case of single priority class, the Laplace transform can be inverted into a closed-form expression. For the case with multiple priority classes, a closed-form expression cannot be obtained, hence, we develop three customized numerical inverse Laplace transformation algorithms. Two of these algorithms provide upper and lower bounds for the expected tardiness under a simple condition on system parameters. Using this property, we obtain error bounds for our customized algorithms; such bounds are not available for general purpose numerical inversion algorithms in the literature. Next, we develop a novel methodology to compare the precision of general purpose numerical inversion algorithms and analyze the performances of three algorithms from the literature. Finally, we provide a recommendation scheme given computational time and error tolerances of the decision maker. The methods developed in this paper for the accurate estimation of expected tardiness establish an important step toward developing due date quotation policies in a multiclass queue, contributing to the due date quotation literature that has been largely focused on single-class queues. [ABSTRACT FROM AUTHOR]

We study a firm which serves customers that are sensitive to quoted price and leadtime, with pricing and leadtime decisions being made by the marketing and production departments, respectively. We analyze the inefficiencies created by the decentralization of the price and leadtime decisions. In the decentralized setting, the total demand generated is larger, leadtimes are longer, quoted prices are lower, and the firm's profits are lower as compared to the centralized setting. We show that coordination can be achieved using a transfer price contract with bonus payments. We also provide insights on the sensitivity of the optimal decisions with respect to market characteristics, sequence of decisions and the firm's capacity level. [ABSTRACT FROM AUTHOR]

In this paper, we study integrated order selection and scheduling decisions, where the manufacturer has the flexibility to choose leadtimes. Our main goal is to provide a mechanism for coordinating order selection, leadtime and scheduling decisions and to determine under what conditions leadtime flexibility is most useful for increasing the manufacturer's profits. Through numerical analyses, we compare and contrast the benefits of leadtime flexibility and the flexibility to partially fulfill orders in different demand environments defined by the congestion level (or demand load), seasonality of the demand and order size. We consider both the cases where the manufacturer has and does not have the flexibility to produce orders early before they are committed. [ABSTRACT FROM AUTHOR]

Introduction: Over 19% of deceased organ donors are labeled increased risk for disease transmission (IRD) for viral blood‐borne disease transmission. Many potential organ recipients need to decide between accepting an IRD organ offer and waiting for a non–IRD organ. Methods: Using machine learning and simulation, we built transplant and waitlist survival models and compared the survival for patients accepting IRD organ offers or waiting for non–IRD organs for the heart, liver, and lung. The simulation consisted of generating 20 000 different scenarios of a recipient either receiving an IRD organ or waiting and receiving a non–IRD organ. Results: In the simulations, the 5‐year survival probabilities of heart, liver, and lung recipients who accepted IRD organ offers increased on average by 10.2%, 12.7%, and 7.2%, respectively, compared with receiving a non–IRD organ after average wait times (190, 228, and 223 days, respectively). When the estimated waitlist time was at least 5 days for the liver, and 1 day for the heart and lung, 50% or more of the simulations resulted in a higher chance of 5‐year survival when the patient received an IRD organ versus when the patient remained on the waitlist. We also developed a simple equation to estimate the benefits, in terms of 5‐year survival probabilities, of receiving an IRD organ versus waiting for a non–IRD organ, for a particular set of recipient/donor characteristics. Conclusion: For all three organs, the majority of patients are predicted to have higher 5‐year survival accepting an IRD organ offer compared with waiting for a non–IRD organ. [ABSTRACT FROM AUTHOR]

We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell’s concordance index, than the Estimated Post Transplant Survival model used in the kidney allocation system in the U.S., and other models published recently in the literature. The model has a five-year concordance index of 0.724 (in comparison, the concordance index is 0.697 for the Estimated Post Transplant Survival model, the state of the art currently in use). It combines predictions from random survival forests with a Cox proportional hazards model. The rankings of importance for the model’s variables differ by transplant recipient age. Better survival predictions could eventually lead to more efficient allocation of kidneys and improve patient outcomes. [ABSTRACT FROM AUTHOR]

A pilot MD-facing alert was associated with increase compliance of an SBS of <=-2 for all high UE risk pediatric patients with no increase in CAP-D scores. Clinical decision support was implemented in the electronic health record to (1) alert RTs when UE risk charting was not complete in a timely fashion for an intubated pediatric patient and (2) encourage MDs in one PICU to order a sedation behavioral scale (SBS) of <=-2 and avoid sedation holidays in high UE risk patients. [Extracted from the article]

Industries such as aviation, hospitality, and package tours often face both individual and batch bookings, I requiring one unit and multiple units of capacity, respectively. Using bid prices is a common practice in accepting or rejecting an incoming booking (or equivalently, deciding which price bucket to offer to the incoming bookings at a given time). Most of the literature and existing applications for making accept/reject decisions model the arrival stream as individual bookings. In this paper, we propose an effective approach to determine bid prices when there is a mixed demand pattern with individual and batch bookings. We propose decomposing the demand into "small" and "large" bookings, using dynamic programming for large bookings, and a fast, high-quality approximation for small bookings. We present an application of our approach in the air cargo industry, where cargo is often separated into two categories, namely, mail and packages (small cargo) and freight (large cargo). To obtain bid prices for small cargo, we approximate cargo booking requests with passenger arrival models and develop an efficient and effective algorithm to solve the probabilistic nonlinear formulation of the seat allocation problem from the passenger literature. To obtain bid prices for large cargo, we solve a dynamic program decomposed by flight leg, which is tractable due to the scattered arrivals and large sizes of the bookings. Our approach leads to a significant potential increase in revenues compared to the first-come, first-served (FCFS) approach and the solution from the deterministic formulation (DIP), two methods commonly used in practice. [ABSTRACT FROM AUTHOR]

The article discusses a study of the effect of a lean rounding process in a pediatric intensive care unit (PICU) at the Children's Healthcare of Atlanta in Georgia. The electronic medical record (EMR) from Epic Systems Corp. at Children's was used as a source of discharge order data from 2007 to 2008. Results show that lean rounding process can improve rounding efficiency. It stresses the benefit of lean tools to healthcare organizations.

Background: Between 2002 and 2013, the organs of 13 deceased donors with infectious encephalitis were transplanted, causing infections in 23 recipients. As a consequence, organs from donors showing symptoms of encephalitis (increased probability of infectious encephalitis (IPIE) organs) might be declined. We had previously characterized the risk of IPIE organs using data available to most transplant teams and not requiring special diagnostic tests. If the probability of infection is low, the benefits of a transplant from a donor with suspected infectious encephalitis might outweigh the risk and could be lifesaving for some transplant candidates. Methods: Using organ transplant data and Cox Proportional Hazards models, we determined liver donor and recipient characteristics predictive of post‐transplant or waitlist survival and generated 5‐year survival probability curves. We also calculated expected waiting times for an organ offer based on transplant candidate characteristics. Using a limited set of actual cases of infectious encephalitis transmission via transplant, we estimated post‐transplant survival curves given an organ from an IPIE donor. Results: 54% (1256) of patients registered from 2002‐2006 who died or were removed from the waiting list because of deteriorated condition within 1 year could have had an at least marginal estimated benefit by accepting an IPIE liver with some probability of infection, with the odds increasing to 86% of patients if the probability of infection was low (5% or less). Additionally, 54% (1252) were removed from the waiting list prior to their estimated waiting time for a non‐IPIE liver and could have benefited from an IPIE liver. Conclusion: Improved allocation and utilization of IPIE livers could be achieved by evaluating the patient‐specific trade‐offs between (a) accepting an IPIE liver and (b) remaining on the waitlist and accepting a non‐IPIE liver after the estimated waiting time. [ABSTRACT FROM AUTHOR]

Background: There were 13 documented clusters of infectious encephalitis transmission via organ transplant from deceased donors to recipients during 2002‐2013. Hence, organs from donors diagnosed with encephalitis are often declined because of concerns about the possibility of infection, given that there is no quick and simple test to detect causes of infectious encephalitis. Methods: We constructed a database containing cases of infectious and non‐infectious encephalitis. Using statistical imputation, cross‐validation, and regression techniques, we determined deceased organ donor characteristics, including demographics, signs, symptoms, physical exam, and laboratory findings, predictive of infectious vs non‐infectious encephalitis, and developed a calculator which assesses the risk of infection. Results: Using up to 12 predictive patient characteristics (with a minimum of 3, depending on what information is available), the calculator provides the probability that a donor may have infectious vs non‐infectious encephalitis, improving the prediction accuracy over current practices. These characteristics include gender, fever, immunocompromised state (other than HIV), cerebrospinal fluid elevation, altered mental status, psychiatric features, cranial nerve abnormality, meningeal signs, focal motor weakness, Babinski's sign, movement disorder, and sensory abnormalities. Conclusion: In the absence of definitive diagnostic testing in a potential organ donor, infectious encephalitis can be predicted with a risk score. The risk calculator presented in this paper represents a prototype, establishing a framework that can be expanded to other infectious diseases transmissible through solid organ transplantation. [ABSTRACT FROM AUTHOR]