This study showed a long-term beneficial effect on CVD mortality and medical expenditure associated with a switch from regular salt to potassium-enriched salt in a group of elderly veterans. The effect was likely due to a major increase in potassium and a moderate reduction in sodium intakes.

Recently, there has been a Renaissance for multi-level selection models to explain the persistence of unselfish behavior in social dilemmas, in which assortative/correlated matching plays an important role. In the current study of a multi-round prisoners’ dilemma experiment, we introduce two correlated matching procedures that match subjects with similar action histories together. We discover significant treatment effects, compared to the control procedure of random matching. Particularly with the weighted history matching procedure we find bifurcations regarding group outcomes. Some groups converge to the all-defection equilibrium even more pronouncedly than the control groups do, while other groups generate much higher rate of cooperation, which is also associated with higher relative reward for a typical cooperative action. All in all, the data show that cooperation does have a much better chance to persist in a correlated/assortative-matching environment, as predicted in the literature. Copyright Economic Science Association 2007Prisoners’ dilemma, Cooperation, Experiment, Unselfish behavior, Evolution, Assortative matching, Correlated matching, Multi-level selection,

Mortality improvement has become a major issue in ratemaking for insurance companies, and ratemaking is especially difficult in Taiwan. There are two reasons for this difficulty: population size and rapid improvement in mortality. Because the history of life insurance in Taiwan is relatively short, all life insurance products are typically offered based on the same experience life table, which is constructed based on the population purchasing all types of life insurance products in Taiwan. In this study, we used experience data from Taiwan life insurance companies to explore whether there are risk factors related to mortality rates. The experience data will also be used to evaluate whether the customers of life insurance companies possess mortality patterns similar to that of the overall population in Taiwan.

BackgroundThere are many applications for spatial cluster detection and more detection methods have been proposed in recent years. Most cluster detection methods are efficient in detecting circular (or circular-like) clusters, but the methods which can detect irregular-shaped clusters usually require a lot of computing time.MethodsWe propose a new spatial detection algorithm for lattice data. The proposed method can be separated into two stages: the first stage determines the significant cells with unusual occurrences (i.e., individual clustering) by applying the Choynowski’s test, and the second stage determines if there are clusters based on the information of the first stage by a binomial approximate method. We first use computer simulation to evaluate the performance of the proposed method and compare it with the scan statistics. Furthermore, we take the Taiwan Cancer data in 2000 to illustrate the detection results of the scan statistics and the proposed method.ResultsThe simulation results support using the proposed method when the population sizes are large and the study regions are irregular. However, in general, the scan statistics still have better power in detecting clusters, especially when the population sizes are not large. For the analysis of cancer data, the scan statistics tend to spot more clusters, and the clusters’ shapes are close to circular (or elliptic). On the other hand, the proposed methods only find one cluster and cannot detect small-sized clusters.ConclusionsIn brief, the proposed methods can detect both circular and non-circular clusters well when the significant cells are correctly detected by the Choynowski’s method. In addition, the binomial-based method can handle the problem of multiple testing and save the computing time. On the other hand, both the circular and elliptical scan statistics have good power in detecting clusters, but tend to detect more clusters and have lower accuracy in detecting non-circular clusters.

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