An economic reappraisal of hepatitis B virus testing strategy for blood donors in Taiwan.

BACKGROUND AND OBJECTIVES: Taiwan is among the few hepatitis B virus (HBV) high-endemic countries that implement universal mini-pool nucleic acid testing (MP-NAT) and hepatitis B surface antigen (HBsAg) testing together with confirmatory individual donor nucleic acid testing (ID-NAT) for its blood supply since 2013. The aim of this study was to reappraise the value of HBsAg test in Taiwan's HBV testing strategy. MATERIALS AND METHODS: A Markov model was constructed, and cost-effectiveness analysis was conducted in order to reappraise the existing HBV screening strategy in Taiwan. RESULTS: The incremental cost-effectiveness ratio (ICER) for the current testing strategy in Taiwan was estimated to be $US 443 154 per quality-adjusted life year (QALY) gained. This is almost six times the willingness-to-pay (WTP) threshold that reflects local preferences. CONCLUSION: Universal HBsAg and MP-8-NAT together with confirmatory ID-NAT testing prevents a significant amount of HBV infections from entering the Taiwan blood supply. However, this comes at a disproportionate increase in cost.

Effects of socioeconomic status on cancer patient survival: counterfactual event-based mediation analysis.

PURPOSE: This study investigated the direct and indirect effects of socioeconomic status (SES) on the survival time of cancer patients by using cancer stage to create a pathway from SES to health outcomes and facilitate a mechanistic inference. METHODS: Both a traditional mediation analysis and a counterfactual event-based mediation analysis were applied to SEER (The Surveillance, Epidemiology, and End Results) data from the National Cancer Institute of the United States. A Cox proportional hazards model for survival analysis was performed in the mediation analysis. RESULTS: The counterfactual event-based mediation analysis showed that the effect of SES on survival time was partially mediated by stage at diagnosis in lung (12%), liver (14.33%), and colorectal (9%) cancers. Investigation of the fundamental mechanism involved thus established the direct effect of SES on survival time and the indirect effect of SES on survival time through stage at diagnosis. Moreover, the mediation analysis also revealed that the disparity in timely diagnosis (i.e., stage at diagnosis) caused by SES was slightly significant. CONCLUSIONS: SES can either affect cancer survival directly or indirectly through stage at diagnosis. Opportunities to reduce cancer disparity exist in the design of early detection policies or mechanisms for patients with varying resources.

Effects of meteorological factors on blood donation.

BACKGROUND AND OBJECTIVES: Blood donation can be affected by various factors including time, weather, and other events (such as disasters). To better understand how meteorological factors affect blood donation, this study builds a novel predictive model for blood supply in North Taiwan to facilitate better blood supply chain and inventory management. MATERIALS AND METHODS: This study uses a two-step prediction model that combines generalized additive models (GAM) and time series models to investigate the meteorological and the day-of-the-week effects on blood donation. For the period 2010-2014, blood donation data and weather information of Big Taipei area are described and analyzed. A bootstrap method was also implemented on the residuals produced by the two-step model to construct the prediction intervals. RESULTS: Daily average temperature and daily cumulative precipitation have non-linear effects on blood donation. By including weather information into the model, the prediction model not only can account for the fluctuation affected by the day-of-the-week effects but also the weather (e.g., temperature and rainfall) effects. The prediction intervals can be used to detect the anomaly donation patterns or to signal possible shortages when there is a forecast of an extreme weather event. CONCLUSION: The findings underscore the need for quantitatively modeling meteorological effects in blood supply chain management, and provide important implications for the authority of a blood center to take necessary intervention and measures to reduce shortages and excessive expirations.

Optimal collecting policy for apheresis platelets in a regional blood center.

BACKGROUND AND OBJECTIVES: Planning platelet collection and inventory must rely not only on adequate forecasts of transfusion demand but also sophisticated mathematical modeling techniques. This research aims to develop a better demand forecasting model of apheresis platelets and a mathematical programming model to determine the best target amounts of apheresis platelet collection. MATERIALS AND METHODS: Time series data of apheresis platelets collected from donors and platelets supplied to hospitals daily in Taipei Blood Center from January 2014 to December 2015 was used to fit a forecasting model which combines a regression-type model for formulating the deterministic trends and seasonal variation and an autoregressive moving average model (ARMA) for explaining remaining serial correlations. A seasonal autoregressive integrated moving average (SARIMA) model was also used for benchmarking the prediction performance. A linear programming model was then formulated to solve for the optimal daily target collection volumes that maximize the total social benefits. RESULTS: The time series model achieved good predictive power with a mean absolute percentage error less than 10%. The appropriateness of the proposed target collection volumes was also verified by using a simulation model, and the proportion of the total platelets requested by hospitals that can be filled by collected apheresis platelets can increase significantly by using the new policy. CONCLUSION: The methods proposed in this study can be easily implemented to enhance the management efficiency of blood collecting and supplying of a blood center, and to decrease the costs of the blood outdates and shortages.

On the treatment of uncertainty and variability in making decisions about risk.

Much attention has been paid to the treatment of dependence and to the characterization of uncertainty and variability (including the issue of dependence among inputs) in performing risk assessments to avoid misleading results. However, with relatively little progress in communicating about the effects and implications of dependence, the effort involved in performing relatively sophisticated risk analyses (e.g., two-dimensional Monte Carlo analyses that separate variability from uncertainty) may be largely wasted, if the implications of those analyses are not clearly understood by decisionmakers. This article emphasizes that epistemic uncertainty can introduce dependence among related risks (e.g., risks to different individuals, or at different facilities), and illustrates the potential importance of such dependence in the context of two important types of decisions--evaluations of risk acceptability for a single technology, and comparisons of the risks for two or more technologies. We also present some preliminary ideas on how to communicate the effects of dependence to decisionmakers in a clear and easily comprehensible manner, and suggest future research directions in this area.

Should the model for risk-informed regulation be game theory rather than decision theory?

Risk analysts frequently view the regulation of risks as being largely a matter of decision theory. According to this view, risk analysis methods provide information on the likelihood and severity of various possible outcomes; this information should then be assessed using a decision-theoretic approach (such as cost/benefit analysis) to determine whether the risks are acceptable, and whether additional regulation is warranted. However, this view ignores the fact that in many industries (particularly industries that are technologically sophisticated and employ specialized risk and safety experts), risk analyses may be done by regulated firms, not by the regulator. Moreover, those firms may have more knowledge about the levels of safety at their own facilities than the regulator does. This creates a situation in which the regulated firm has both the opportunity-and often also the motive-to provide inaccurate (in particular, favorably biased) risk information to the regulator, and hence the regulator has reason to doubt the accuracy of the risk information provided by regulated parties. Researchers have argued that decision theory is capable of dealing with many such strategic interactions as well as game theory can. This is especially true in two-player, two-stage games in which the follower has a unique best strategy in response to the leader's strategy, as appears to be the case in the situation analyzed in this article. However, even in such cases, we agree with Cox that game-theoretic methods and concepts can still be useful. In particular, the tools of mechanism design, and especially the revelation principle, can simplify the analysis of such games because the revelation principle provides rigorous assurance that it is sufficient to analyze only games in which licensees truthfully report their risk levels, making the problem more manageable. Without that, it would generally be necessary to consider much more complicated forms of strategic behavior (including deception), to identify optimal regulatory strategies. Therefore, we believe that the types of regulatory interactions analyzed in this article are better modeled using game theory rather than decision theory. In particular, the goals of this article are to review the relevant literature in game theory and regulatory economics (to stimulate interest in this area among risk analysts), and to present illustrative results showing how the application of game theory can provide useful insights into the theory and practice of risk-informed regulation.

Analysis of treatment delivery errors in brachytherapy using formal risk analysis techniques.

PURPOSE: To identify hazardous situations in treatments, analyze the nature of errors committed, and assess the value of several analysis techniques. MATERIALS AND METHODS: The study applied several risk analysis techniques to brachytherapy events (misadministrations) reported to the U.S. Nuclear Regulatory Commission and the International Atomic Energy Agency. RESULTS: (1) Events usually have multiple causes. (2) Failure to consider human performance in the design of equipment led to a large fraction of the events. (3) Verification procedures often were ineffectual. (4) Many events followed the failure of persons involved to detect that the situation was abnormal, often even though many indications pointed to that fact. Once the event was identified, the response often included actions appropriate for normal conditions, but inappropriate for the conditions of the event. (5) Events tended to happen most with actions having the least time available. (6) Lack of training and procedures covering unusual conditions frequently contributed to events. (7) New procedures or new persons joining a case in the middle present increased hazards. CONCLUSIONS: Risk analysis tools common in industry provide useful information for error reduction in medical settings, although not as effectively, and modification of such techniques could improve their efficacy.