Modeling the Adenoma and Serrated Pathway to Colorectal CAncer (ASCCA)

Several colorectal cancer (CRC) screening models have been developed describing the progression of adenomas to CRC. Currently, there is increasing evidence that serrated lesions can also develop into CRC. It is not clear whether screening tests have the same test characteristics for serrated lesions as for adenomas, but lower sensitivities have been suggested. Models that ignore this type of colorectal lesions may provide overly optimistic predictions of the screen‐induced reduction in CRC incidence. To address this issue, we have developed the Adenoma and Serrated pathway to Colorectal CAncer (ASCCA) model that includes the adenoma‐carcinoma pathway and the serrated pathway to CRC as well as characteristics of colorectal lesions. The model structure and the calibration procedure are described in detail. Calibration resulted in 19 parameter sets for the adenoma‐carcinoma pathway and 13 for the serrated pathway that match the age‐ and sex‐specific adenoma and serrated lesion prevalence in the COlonoscopy versus COlonography Screening (COCOS) trial, Dutch CRC incidence and mortality rates, and a number of other intermediate outcomes concerning characteristics of colorectal lesions. As an example, we simulated outcomes for a biennial fecal immunochemical test screening program and a hypothetical one‐time colonoscopy screening program. Inclusion of the serrated pathway influenced the predicted effectiveness of screening when serrated lesions are associated with lower screening test sensitivity or when they are not removed. To our knowledge, this is the first model that explicitly includes the serrated pathway and characteristics of colorectal lesions. It is suitable for the evaluation of the (cost)effectiveness of potential screening strategies for CRC.     

Revenue Management of Reusable Resources with Advanced Reservations

We consider a revenue management problem wherein the seller is endowed with a single type resource with a finite capacity and the resource can be repeatedly used to serve customers. There are multiple classes of customers arriving according to a multi‐class Poisson process. Each customer, upon arrival, submits a service request that specifies his service start time and end time. Our model allows customer advanced reservation times and services times in each class to be arbitrarily distributed and correlated. Upon arrival of each customer, the seller must instantaneously decide whether to accept this customer's service request. A customer whose request is denied leaves the system. A customer whose request is accepted is allocated with a specific item of the resource at his service start time. The resource unit occupied by a customer becomes available to other customers after serving this customer. The seller aims to design an admission control policy that maximizes her expected long‐run average revenue. We propose a policy called the ε‐perturbation class selection policy (ε‐CSP), based on the optimal solution in the fluid setting wherein customers are infinitesimal and customer arrival processes are deterministic, under the restriction that the seller can utilize at most (1 − ε) of her capacity for any ε ∈ (0, 1). We prove that the ε‐CSP is near‐optimal. More precisely, we develop an upper bound of the performance loss of the ε‐CSP relative to the seller's optimal revenue, and show that it converges to zero with a square‐root convergence rate in the asymptotic regime wherein the arrival rates and the capacity grow up proportionally and the capacity buffer level ε decays to zero.     

Assessment of the Effectiveness of Radon Screening Programs in Reducing Lung Cancer Mortality

The present study was aimed at assessing the health consequences of the presence of radon in Quebec homes and the possible impact of various screening programs on lung cancer mortality. Lung cancer risk due to this radioactive gas was estimated according to the cancer risk model developed by the Sixth Committee on Biological Effects of Ionizing Radiations. Objective data on residential radon exposure, population mobility, and tobacco use in the study population were integrated into a Monte‐Carlo‐type model. Participation rates to radon screening programs were estimated from published data. According to the model used, approximately 10% of deaths due to lung cancer are attributable to residential radon exposure on a yearly basis in Quebec. In the long term, the promotion of a universal screening program would prevent less than one death/year on a province‐wide scale (0.8 case; IC 99%: –3.6 to 5.2 cases/year), for an overall reduction of 0.19% in radon‐related mortality. Reductions in mortality due to radon by (1) the implementation of a targeted screening program in the region with the highest concentrations, (2) the promotion of screening on a local basis with financial support, or (3) the realization of systematic investigations in primary and secondary schools would increase to 1%, 14%, and 16.4%, respectively, in the each of the populations targeted by these scenarios. Other than the battle against tobacco use, radon screening in public buildings thus currently appears as the most promising screening policy for reducing radon‐related lung cancer.     

Bounds in Competing Risks Models and the War on Cancer

In 1971, President Nixon declared war on cancer. Thirty years later, many declared this war a failure: the age‐adjusted mortality rate from cancer in 2000 was essentially the same as in the early 1970s. Meanwhile the age‐adjusted mortality rate from cardiovascular disease fell dramatically. Since the causes that underlie cancer and cardiovascular disease are likely dependent, the decline in mortality rates from cardiovascular disease may partially explain the lack of progress in cancer mortality. Because competing risks models (used to model mortality from multiple causes) are fundamentally unidentified, it is difficult to estimate cancer trends. We derive bounds for aspects of the underlying distributions without assuming that the underlying risks are independent. We then estimate changes in cancer and cardiovascular mortality since 1970. The bounds for the change in duration until death for either cause are fairly tight and suggest much larger improvements in cancer than previously estimated.     

Dread and Risk Elimination Premium for the Value of a Statistical Life

The value of a statistical life (VSL) is a widely used measure for the value of mortality risk reduction. As VSL should reflect preferences and attitudes to risk, there are reasons to believe that it varies depending on the type of risk involved. It has been argued that cancer should be considered a “dread disease,” which supports the use of a “cancer premium.” The objective of this study is to investigate the existence of a cancer premium (for pancreatic cancer and multiple myeloma) in relation to road traffic accidents, sudden cardiac arrest, and amyotrophic lateral sclerosis (ALS). Data were collected from 500 individuals in the Swedish general population of 50–74‐year olds using a web‐based questionnaire. Preferences were elicited using the contingent valuation method, and a split‐sample design was applied to test scale sensitivity. VSL differs significantly between contexts, being highest for ALS and lowest for road traffic accidents. A premium (92–113%) for cancer was found in relation to road traffic accidents. The premium was higher for cancer with a shorter time from diagnosis to death. A premium was also found for sudden cardiac arrest (73%) and ALS (118%) in relation to road traffic accidents. Eliminating risk was associated with a premium of around 20%. This study provides additional evidence that there exist a dread premium and risk elimination premium. These factors should be considered when searching for an appropriate value for economic evaluation and health technology assessment.     

Resource allocation for restoration of compromised systems

Computer systems are constantly under the threats of being attacked and in many cases these attacks succeed. Today’s networked systems are thus built to be intrusion tolerant. In a large scale, the progresses of compromising the networked system and recovering the damage will carry on in parallel, allowing services to be continued (at a degraded level). One of the key problems in the restoration procedure regards to the resource allocation strategies and the cost associated with, specifically, a minimal cost is desired. In this paper we model the cost as a sum of service loss and resource expense that incur during the restoration procedure. We investigate the achievable minimal total cost and corresponding resource allocation strategy for different situations. The situations include both constant rates and time-variant rates in terms of the speed of compromising and recovering. We also consider the fact that the restoration rate is constrained by the resource allocated. The relationship can be either linear or obeying the law of diminishing marginal utility. We present both analytical and numerical results in the paper. The results show the impact from various system parameters on the critical conditions for a successful system restoration and on the minimal cost. Dr. Ray is currently with Google Inc., 604 Arizona Avenue, Santa Monica, CA 90401, USA. His e-mail contact is sibu@google.com, siburay@gmail.com     

One-Hit Models of Carcinogenesis: Conservative or Not?

One-hit formulas are widely believed to be "conservative" when used to analyze carcinogenesis bioassays, in the sense that they will rarely underestimate risks of cancer at low exposures. Such formulas are generally applied to the lifetime incidence of cancer at a specific site, with risks estimated from animal data at zero dose (control), and two or more additional doses that are appreciable fractions of a maximum tolerated dose. No empirical study has demonstrated that the one-hit formula is conservative in the sense described. The Carcinogenesis Bioassay Database System contains data on 1212 separate bioassays of 308 chemical substances tested at exactly three evaluable doses. These provided sufficient data to examine 8432 specific combinations of cancer site with sex, species, and chemical. For each of these we fitted a one-hit formula to the zero and maximum dose data points, then examined the relation of the fitted curve to the incidence rate observed at the mid-dose, with and without adjustment for intercurrent mortality. Both underestimates and overestimates of risk at mid-dose occurred substantially more often than expected by chance. We cannot tell whether such underestimates would occur at lower doses, but offer six biological reasons why underestimates might be expected. In a high percentage of animal bioassays, the one-hit formula is not conservative when applied in the usual way to animal data. It remains possible that the one-hit formula may indeed be conservative at sufficiently low doses (below the observational range), but the usual procedure, applied to the usual dose range, can be nonconservative in estimating the slope of the formula at such low doses. Risk assessments for regulation of carcinogens should incorporate some measure of additional uncertainty.     

On service degrade at a discount: Capacity, demand pooling, and optimal discounting

In this paper we study the capacity investment decisions and operational strategies of a firm providing two-class services facing uncertain demands. The capacity decisions of the resources are made before demands are observed. Each service can be implemented by its corresponding resource. Should a mismatch between the capacity and the actual demand for the services arise, the low-class resource can be used as a substitute for the high-class service. We introduce an operational strategy called degrade-at-a-discount, where a price discount is offered to motivate customers to accept a lower class service when their original choice is out of capacity. By formulating the problem as a one-period, two-stage stochastic problem, we analyze how to set up the optimal capacity and the optimal discount. We also conduct a comprehensive numerical study to show the benefits of the degrade-at-a-discount strategy.     

Using Fairness Models to Improve Equity in Health Delivery Fleet Management

Inefficiency and inequity are two challenges that plague humanitarian operations and health delivery in resource‐limited regions. Increasing capacity in humanitarian and health delivery supply chains is one option that has the potential to improve equity while maintaining efficiency. For example, the nonprofit organization Riders for Health has worked to increase capacity by providing reliable transportation to health workers in rural parts of sub‐Saharan Africa; with more motorcycle hours at their disposal, health workers can perform more outreach to outlying communities. We develop a model using a family of fairness function to quantify the efficiency and equity of health delivery as capacity is increased via development programs. We present optimal resource allocations under utilitarian, proportionally fair, and egalitarian objectives and extend the model to include dual modes of transport and diminishing returns of subsequent outreach visits. Finally, we demonstrate how to apply our model at a regional level to provide support for humanitarian decision makers such as Riders for Health. We use data from the baseline phase of our evaluation trial of Riders for Health in Zambia to quantify efficiency and equity for one real‐world scenario.     

Capacity Allocation and Pricing Strategies for New Wireless Services

Indoor cell phone users often suffer poor connectivity. One promising solution to this issue, femtocell technology, has been rapidly developed and deployed over the past few years. One of the biggest challenges facing femtocell deployment is the lack of a clear business model. This study investigates the economic incentive for cellular operators (also called macrocell operators) to enable femtocell service by leasing spectrum resources to independent femtocell operators. We model the interactions between a macrocell operator, a femtocell operator, and end‐users as a three‐stage dynamic game, and derive the equilibrium pricing and capacity allocation decisions. We show that when spectrum resources are very limited, the macrocell operator has more incentive to lease spectrum to the femtocell operator, as femtocell services can help cover more users and improve the utilization efficiency of the limited spectrum resource. However, when the total spectrum resource is large, femtocell service offers significant competition to macrocell service and, as a result, the macrocell operator has less incentive to enable femtocell service. We also show the impact of the additional operational costs and limited coverage of femtocell service on equilibrium decisions, consumer surplus, and social welfare.     

Pricing,Allocation, and Overbooking in Dynamic Service Network Competition When Demand Is Uncertain

We study the problem of combined pricing, resource allocation, and overbooking by service providers involved in dynamic noncooperative oligopolistic competition on a network that represents the relationships of the providers to one another and to their customers when service demand is uncertain. We propose, analyze, and compute solutions for a model that is more general than other models reported in the revenue management literature to date. In particular, previous models typically consider only three or four of five key revenue management features that we have purposely built into our model: (1) pricing, (2) resource allocation, (3) dynamic competition, (4) an explicit network, and (5) uncertain demand. Illustrative realizations of the abstract problem we study are those of airline revenue management and service provision by companies facing resource constraints. Under fairly general regularity conditions, we prove existence and uniqueness of a pure strategy Nash equilibrium for dynamic oligopolistic service network competition described by our model. We also show, for an appropriate notion of regularity, that competition leads to the underpricing of network services, a finding numerically illustrated by an example of intermediate size. Our proposed algorithm can be implemented using well‐known off‐the‐shelf commercial software.     

Approximations for Estimating Change in Life Expectancy Attributable to Air Pollution in Relation to Multiple Causes of Death Using a Cause Modified Life Table

There is considerable debate as to the most appropriate metric for characterizing the mortality impacts of air pollution. Life expectancy has been advocated as an informative measure. Although the life‐table calculus is relatively straightforward, it becomes increasingly cumbersome when repeated over large numbers of geographic areas and for multiple causes of death. Two simplifying assumptions were evaluated: linearity of the relation between excess rate ratio and change in life expectancy, and additivity of cause‐specific life‐table calculations. We employed excess rate ratios linking PM_2.5 and mortality from cerebrovascular disease, chronic obstructive pulmonary disease, ischemic heart disease, and lung cancer derived from a meta‐analysis of worldwide cohort studies. As a sensitivity analysis, we employed an integrated exposure response function based on the observed risk of PM_2.5 over a wide range of concentrations from ambient exposure, indoor exposure, second‐hand smoke, and personal smoking. Impacts were estimated in relation to a change in PM_2.5 from 19.5 μg/m³ estimated for Toronto to an estimated natural background concentration of 1.8 μg/m³. Estimated changes in life expectancy varied linearly with excess rate ratios, but at higher values the relationship was more accurately represented as a nonlinear function. Changes in life expectancy attributed to specific causes of death were additive with maximum error of 10%. Results were sensitive to assumptions about the air pollution concentration below which effects on mortality were not quantified. We have demonstrated valid approximations comprising expression of change in life expectancy as a function of excess mortality and summation across multiple causes of death.     

Schedule Recovery: Unplanned Absences in Service Operations*

The U.S. service sector loses 2.3% of all scheduled labor hours to unplanned absences, but in some industries, the total cost of unplanned absences approaches 20% of payroll expense. The principal reasons for unscheduled absences (personal illness and family issues) are unlikely to abate anytime soon. Despite this, most labor scheduling systems continue to assume perfect attendance. This oversight masks an important but rarely addressed issue in services management: how to recover from short‐notice, short‐term reductions in planned capacity. In this article, we model optimal responses to unplanned employee absences in multi‐server queueing systems that provide discrete, pay‐per‐use services for impatient customers. Our goal is to assess the performance of alternate absence recovery strategies under various staffing and scheduling regimes. We accomplish this by first developing optimal labor schedules for hypothetical service environments with unreliable workers. We then simulate unplanned employee absences, apply an absence recovery model, and compute system profits. Our absence recovery model utilizes recovery strategies such as holdover overtime, call‐ins, and temporary workers. We find that holdover overtime is an effective absence recovery strategy provided sufficient reserve capacity (maximum allowable work hours minus scheduled hours) exists. Otherwise, less precise and more costly absence recovery methods such as call‐ins and temporary help service workers may be needed. We also find that choices for initial staffing and scheduling policies, such as planned overtime and absence anticipation, significantly influence the likelihood of successful absence recovery. To predict the effectiveness of absence recovery policies under alternate staffing/scheduling strategies and operating environments, we propose an index based on initial capacity reserves.     

Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

The subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry. The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.     

Perspectives on Cancer Prevention

Cancer prevention is a major component of cancer control, which also comprises screening, treatment, rehabilitation and palliative care. Preventive approaches need to be congruent with those adopted for other chronic diseases, with a major impact in reduction of incidence and mortality of many common cancers to be expected from smoking control and dietary modification. Increasing interest is now being paid to other environmental causes of cancer, and to gene-environment interactions. However, one of the major research needs remains the evaluation of better ways to convince people to make the necessary changes in their lifestyle that will reduce their risk of cancer.     

Inventory Commitment and Prioritized Backlogging Clearance with Alternative Delivery Lead Times

We propose a model where customers are classified into two groups: short lead‐time customers who require the product immediately and long lead‐time customers to whom the supplier may deliver either immediately or in the next cycle. Unmet orders are backlogged with associated costs. Specifically, the supplier faces two problems: how the on‐hand inventories should be allocated between the two classes of customers and how the backlogged orders should be cleared when replenishments arrive. We treat the former as an inventory commitment problem and handle the latter with priority rules. We characterize and compare the inventory commitment policies with three priority rules in clearing backlogs. We also explore the optimal inventory replenishment decision and evaluate the performance of each priority rule.     

考虑计算资源容量的云服务垄断定价研究

云服务是针对虚拟计算资源的访问服务，客户价值差异与资源能力约束是影响云服务定价决策的重要因素。针对云服务市场中按固定价格和按用量定价两种常用定价方式，提出一般性假设并构建旨在最大化垄断利润的非线性定价模型，对模型求解并对模型解的含义进行揭示。给出数值算例，从市场覆盖、计算资源影子价格、厂商利润及客户剩余等方面对两种定价方法进行比较，结果表明：客户类型分布影响厂商利润、客户剩余及计算资源需求，厂商应努力掌握客户类型分布信息；计算资源充足时应采用按用量定价方法；市场覆盖与客户类型分布及可用计算资源量有关，按用量定价时市场覆盖更大；增加计算资源有利于提升社会福利。本文为云服务厂商定价决策提供理论与方法支持。     

A STUDY OF THE UTILIZATION OF CAPACITY CONSTRAINED RESOURCES IN DRUM‐BUFFER‐ROPE SYSTEMS*

Goldratt, the originator of the Theory of Constraints (TOC), maintains that only the system's primary resource constraint(s) should be scheduled at 100% of capacity. All other resources should have excess capacity. This paper presents the results of a simulation experiment that studies how changes in the capacity utilization of a systems two most heavily utilized resources affect the performance of a drum‐buffer‐rope (DBR)scheduling system. The research demonstrates that 100% utilization of the primary constraint is not optimal. It also shows that DBR responds well to relatively low levels of increased capacity at the operations second most heavily utilized resource. This research also highlights several other issues related to capacity utilization that need further investigation.     

Modeling the Cost Effectiveness of Fire Protection Resource Allocation in the United States: Models and a 1980–2014 Case Study

The estimated cost of fire in the United States is about $329 billion a year, yet there are gaps in the literature to measure the effectiveness of investment and to allocate resources optimally in fire protection. This article fills these gaps by creating data‐driven empirical and theoretical models to study the effectiveness of nationwide fire protection investment in reducing economic and human losses. The regression between investment and loss vulnerability shows high R² values (≈0.93). This article also contributes to the literature by modeling strategic (national‐level or state‐level) resource allocation (RA) for fire protection with equity‐efficiency trade‐off considerations, while existing literature focuses on operational‐level RA. This model and its numerical analyses provide techniques and insights to aid the strategic decision‐making process. The results from this model are used to calculate fire risk scores for various geographic regions, which can be used as an indicator of fire risk. A case study of federal fire grant allocation is used to validate and show the utility of the optimal RA model. The results also identify potential underinvestment and overinvestment in fire protection in certain regions. This article presents scenarios in which the model presented outperforms the existing RA scheme, when compared in terms of the correlation of resources allocated with actual number of fire incidents. This article provides some novel insights to policymakers and analysts in fire protection and safety that would help in mitigating economic costs and saving lives.     

Diesel Engine Exhaust and Lung Cancer Mortality: Time‐Related Factors in Exposure and Risk

To develop a quantitative exposure‐response relationship between concentrations and durations of inhaled diesel engine exhaust (DEE) and increases in lung cancer risks, we examined the role of temporal factors in modifying the estimated effects of exposure to DEE on lung cancer mortality and characterized risk by mine type in the Diesel Exhaust in Miners Study (DEMS) cohort, which followed 12,315 workers through December 1997. We analyzed the data using parametric functions based on concepts of multistage carcinogenesis to directly estimate the hazard functions associated with estimated exposure to a surrogate marker of DEE, respirable elemental carbon (REC). The REC‐associated risk of lung cancer mortality in DEMS is driven by increased risk in only one of four mine types (limestone), with statistically significant heterogeneity by mine type and no significant exposure‐response relationship after removal of the limestone mine workers. Temporal factors, such as duration of exposure, play an important role in determining the risk of lung cancer mortality following exposure to REC, and the relative risk declines after exposure to REC stops. There is evidence of effect modification of risk by attained age. The modifying impact of temporal factors and effect modification by age should be addressed in any quantitative risk assessment (QRA) of DEE. Until there is a better understanding of why the risk appears to be confined to a single mine type, data from DEMS cannot reliably be used for QRA.