To date, no systematic reviews have found fluoride to be effective in preventing dental caries in adults. The objective of this meta-analysis was to examine the effectiveness of self- and professionally applied fluoride and water fluoridation among adults. We used a random-effects model to estimate the effect size of fluoride (absolute difference in annual caries increment or relative risk ratio) for all adults aged 20+ years and for adults aged 40+ years. Twenty studies were included in the final body of evidence. Among studies published after/during 1980, any fluoride (self- and professionally applied or water fluoridation) annually averted 0.29 (95%CI: 0.16-0.42) carious coronal and 0.22 (95%CI: 0.08-0.37) carious root surfaces. The prevented fraction for water fluoridation was 27% (95%CI: 19%-34%). These findings suggest that fluoride prevents caries among adults of all ages.

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Public officials with the authority to order hurricane evacuations face a difficult trade-off between risks to life and costly false alarms. Evacuation decisions must be made on the basis of imperfect information, in the form of forecasts. The quality of these decisions can be improved if they are also informed by measures of uncertainty about the forecast, including estimates of the value of waiting for updated, more accurate, forecasts. Using a stochastic model of storm motion derived from historic tracks, this paper explores the relationship between lead time and track uncertainty for Atlantic hurricanes and the implications of this relationship for evacuation decisions. Typical evacuation clearance times and track uncertainty imply that public officials who require no more than a 10% probability of failing to evacuate before a striking hurricane (a false negative) must accept that at least 76%--and for some locations over 90%--of evacuations will be false alarms. Reducing decision lead times from 72 to 48 hours for major population centers could save an average of hundreds of millions of dollars in evacuation costs annually, with substantial geographic variation in savings.decision analysis, risk, natural systems, disaster planning, public evacuations

The decision to prepare for an oncoming hurricane is typically framed as a static cost:loss problem, based on a strike-probability forecast. The value of waiting for updated forecasts is therefore neglected. In this paper, the problem is reframed as a sequence of interrelated decisions that more accurately represents the situation faced by a decision maker monitoring an evolving tropical cyclone. A key feature of the decision model is that the decision maker explicitly anticipates and plans for future forecasts whose accuracy improves as lead time declines. A discrete Markov model of hurricane travel is derived from historical tropical cyclone tracks and combined with the dynamic decision model to estimate the additional value that can be extracted from existing forecasts by anticipating updated forecasts, rather than incurring an irreversible preparation cost based on the instantaneous strike probability. The value of anticipating forecasts depends on the specific alternatives and cost profile of each decision maker, but conceptual examples for targets at Norfolk, Virginia, and Galveston, Texas, yield expected savings ranging up to 8% relative to repeated static decisions. In real-time decision making, forecasts of improving information quality could be used in combination with strike-probability forecasts to evaluate the trade-off between lead time and forecast accuracy, estimate the value of waiting for improving forecasts, and thereby reduce the frequency of false alarms.

Markov models of disease progression are widely used to model transitions in patients' health state over time. Usually, patients' health status may be classified according to a set of ordered health states. Modelers lump together similar health states into a finite and usually small, number of health states that form the basis of a Markov chain disease-progression model. This increases the number of observations used to estimate each parameter in the transition probability matrix. However, lumping together observably distinct health states also obscures distinctions among them and may reduce the predictive power of the model. Moreover, as we demonstrate, precision in estimating the model parameters does not necessarily improve as the number of states in the model declines. This paper explores the tradeoff between lumping error introduced by grouping distinct health states and sampling error that arises when there are insufficient patient data to precisely estimate the transition probability matrix.

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Governmental organizations play a major role in disaster relief operations. Supply chains set up to respond to disasters differ dramatically in many dimensions that affect the cost of relief efforts. One factor that has been described recently is self-sustainment, which occurs when supplies consumed by intermediate stages of a supply chain must be provided via the chain itself because they are not locally available. This article applies the concept of self-sustainment to response supply chains. A mathematical model of a self-sustaining response supply chain is developed. Analysis of this model yields insights about the relationships and interactions among self-sustainment, speed of disaster onset, dispersion of impact, and the cost of the relief efforts.

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