Evaluating the complex interactions between malaria and cholera prevalence,neglected tropical disease comorbidities,and community perception of health risks of climate change

The burgeoning literature on the climate change–human health nexus has focused almost exclusively on the health impacts of climate change with little attention to how ill-health and disease influence public perception of the health risks of climate change. Based on a cross-sectional survey of 1,253 individuals, linear regression was used to examine the independent effects of malaria and cholera prevalence, and neglected tropical disease comorbidities on perceived health risks of climate change. Individuals who reported more comorbidities had higher scores on perceived health risks of climate change compared with those who did not report any comorbidities. Unexpectedly, at the multivariate level, there were no statistically significant relationships between age of respondents, gender, and educational attainment on the one hand, and perceived health risks of climate change on the other hand. Individuals who were diagnosed with cholera in the past 12 months had higher scores on perceived health risks of climate change but there was no relationship between diagnosis with malaria in the past 12 months and perceived health risks. Individuals who had attained secondary education had lower scores on perceived health risks of climate change compared with those without any formal education. Given that this relationship did not exist at the bivariate level, it indicates that biosocial and sociocultural factors suppressed the relationship between secondary education attainment and perceived health risks of climate change. The findings underscore the complex relationship between perceived health risks of climate change and infectious disease, comorbidities, compositional, and contextual factors at the multivariate level.     

Preference-Order Recursion for Finding Relevant Pure,Admissible and Optimal Statistical Decision Functions*

A preference-order recursion algorithm for obtaining a relevant subset of pure, admissible (non-dominated, efficient) decision functions which converges towards an optimal solution in statistical decision problems is proposed. The procedure permits a decision maker to interactively express strong binary preferences for partial decision functions at each stage of the recursion, from which an imprecise probability and/or utility function is imputed and used as one of several pruning mechanisms to obtain a reduced relevant subset of admissible decision functions or to converge on an optimal one. The computational and measurement burden is thereby mitigated significantly, for example, by not requiring explicit or full probability and utility information from the decision maker. The algorithm is applicable to both linear and nonlinear utility functions. The results of behavioral and computational experimentation show that the approach is viable, efficient, and robust.     

COMPLEX MULTIECHELON INVENTORY SYSTEM MANAGEMENT USING A DYNAMIC SIMULATION MODEL

A dynamic modeling approach to management of multiechelon, multi-indenture inventory systems with repair is addressed. The structure of the model follows that of the U.S. Air Force Reparable Asset Management System. The model is used as a vehicle to discuss the structure of typical multiechelon systems and to illustrate the advantages of a dynamic modeling approach to such systems.     

Distribution of Aggregate Utility Using Stochastic Elements of Additive Multiattribute Utility Models

Conventionally, elements of a multiattribute utility model characterizing a decision maker's preferences, such as attribute weights and attribute utilities, are treated as deterministic, which may be unrealistic because assessment of such elements can be imprecise and erroneous, or differ among a group of individuals. Moreover, attempting to make precise assessments can be time consuming and cognitively demanding. We propose to treat such elements as stochastic variables to account for inconsistency and imprecision in such assessments. Under these assumptions, we develop procedures for computing the probability distribution of aggregate utility for an additive multiattribute utility function (MAUF), based on the Edgeworth expansion. When the distributions of aggregate utility for all alternatives in a decision problem are known, stochastic dominance can then be invoked to filter inferior alternatives. We show that, under certain mild conditions, the aggregate utility distribution approaches normality as the number of attributes increases. Thus, only a few terms from the Edgeworth expansion with a standard normal density as the base function will be sufficient for approximating an aggregate utility distribution in practice. Moreover, the more symmetric the attribute utility distributions, the fewer the attributes to achieve normality. The Edgeworth expansion thus can provide a basis for a computationally viable approach for representing an aggregate utility distribution with imprecisely specified attribute weights and utilities assessments (or differing weights and utilities across individuals). Practical guidelines for using the Edgeworth approximation are given. The proposed methodology is illustrated using a vendor selection problem.     

Investing: the case for recognition as an independent capacity

In this article, we provide support for the need to recognize investing as an independent capacity. A comparison of the definitions and models of financial and investing capacities revealed significant differences between them. A review of the status of investing capacity assessment revealed that there are currently no investing capacity specific assessment instruments (ICSAIs). Implications for researchers and clinicians resulting from the lack of recognition of investing as an independent capacity are discussed and used as a rational for the need to develop ICSAIs. The benefits of ICSAI development for financial, legal, and clinical professionals as well as for investors are discussed, and a direction for future investing capacity research is proposed.     

Optimal Selection Strategy for the Group Interview Problem

We propose a more generalized version of the secretary problem, called the group interview problem, in which each group contains several alternatives and each group of alternatives is presented and evaluated sequentially over time. Using the assumptions corresponding to the classical secretary problem, we derive an optimal selection strategy which maximizes the probability of winning or selecting the single best choice in a given sequence of groups. We further address the problem of choosing at the beginning of the evaluation process a sequence of groups to maximize the winning probability. Because of formidable computational requirements to obtain an optimal solution to this sequencing problem, we then develop a heuristic algorithm based on several properties inherent in an optimal selection strategy. The heuristic procedure is evaluated experimentally using Monte Carlo simulation and is shown to be effective in obtaining near-optimal (within 5 percent) solutions.     

Public Ethnography; Ethnography as Public Sociology

Since ethnography is arguably the kind of sociology of most appeal to the lay public, public ethnography, particularly participant observation research, should be a major form of public sociology. Public ethnography differs from academic ethnography when its sites and subjects are relevant to what the lay public wants and needs to know, and when it is written in non technical English. This article spells out the requirements, conditions and processes involved in making relevant ethnography acceptable to the lay public and thus turning it into public ethnography.     

Predicting Advancement to Senior Management from Competencies and Personality Data: A Seven-year Follow-up Study

This study tracked the career progress of general managers over a seven-year period. The main aim was to identify those competencies (skills, abilities, values) and personality characteristics assessed seven years ago which are associated with current success and rate of advancement. The sample consisted of 72 out of 100 managers who had been assessed on the Henley General Management Course in 1988/89. Comparisons were made between their current salary and responsibilities and the equivalent data gathered in 1988/89. Factor analysis revealed two main criteria of success: current seniority and rate of advancement. Correlations were computed between these two factors and assessments of competencies (by the managers and their boss) and their personality profiles derived from the occupational personality questionnaire. The sample was also divided into discrete groups of ‘high fliers’ and ‘low fliers’, based on their respective rate of advancement. Almost all of the ‘high flier’ group are now directors, and receive an average salary of £80 000 (compared to the mean for the total group of £29 000 in 1988/89). Ten characteristics differentiated the ‘high-fliers’. These included: willingness to take risks; an exceptional ability to manage and motivate staff; a need to achieve really demanding targets and a strong competitive streak.