Spatial variation in soil inorganic nitrogen across an arid urban ecosystem

We explored variations in inorganic soil nitrogen (N) concentrations across metropolitan Phoenix, Arizona, and the surrounding desert using a probability-based synoptic survey. Data were examined using spatial statistics on the entire region, as well as for the desert and urban sites separately. Concentrations of both NO₃-N and NH₄-N were markedly higher and more heterogeneous amongst urban compared to desert soils. Regional variation in soil NO₃-N concentration was best explained by latitude, land use history, population density, along with percent cover of impervious surfaces and lawn, whereas soil NH₄-N concentrations were related to only latitude and population density. Within the urban area, patterns in both soil NO₃-N and NH₄-N were best predicted by elevation, population density and type of irrigation in the surrounding neighborhood. Spatial autocorrelation of soil NO₃-N concentrations explained 49% of variation among desert sites but was absent between urban sites. We suggest that inorganic soil N concentrations are controlled by a number of ‘local’ or ‘neighborhood’ human-related drivers in the city, rather than factors related to an urban-rural gradient.     

Sample sizes for estimation in clinical research

A sample size justification should be given for all clinical investigations. However, sometimes the objective of a trial is to estimate an effect with a view to planning a later definitive study. This paper describes the calculations for designing studies where one wishes to adopt an estimation approach through using confidence intervals around the overall response. Calculations are given for data anticipated to take a Normal form. Copyright © 2004 John Wiley & Sons, Ltd.     

A Bayesian Adjustment for Covariate Misclassification with Correlated Binary Outcome Data

Estimated associations between an outcome variable and misclassified covariates tend to be biased when the methods of estimation that ignore the classification error are applied. Available methods to account for misclassification often require the use of a validation sample (i.e. a gold standard). In practice, however, such a gold standard may be unavailable or impractical. We propose a Bayesian approach to adjust for misclassification in a binary covariate in the random effect logistic model when a gold standard is not available. This Markov Chain Monte Carlo (MCMC) approach uses two imperfect measures of a dichotomous exposure under the assumptions of conditional independence and non-differential misclassification. A simulated numerical example and a real clinical example are given to illustrate the proposed approach. Our results suggest that the estimated log odds of inpatient care and the corresponding standard deviation are much larger in our proposed method compared with the models ignoring misclassification. Ignoring misclassification produces downwardly biased estimates and underestimate uncertainty.     

Generations,Identities, and the Collective Memory of Che Guevara1

Has the image of Che Guevara lost its power to evoke radical politics in the face of pervasive commodification? The commercialization of this 1960s political icon has called into question the power of the market to shape collective memories. Meanwhile, antisystemic movements of the left continue to erect his image at protest events. In light of this contest over how Che Guevara is remembered, we investigate, using data from a survey of Spanish citizens, who is most likely to recall him. We find qualified support for the theory of generational imprinting—Che is more often recalled by those generations who saw him rise to prominence during their formative years, although prominent as a collective symbol rather than as a living person. Our results also corroborate the claim that historical figures or events are more salient for, and therefore more likely to be remembered by, some subgenerational units than others. Thus, although the younger generations are in general more likely than their elders to recall Che, he is most frequently remembered by the highly educated leftists who espouse postmaterialist and posttraditionalist values and identify more with their local regions than with the nation of Spain. These patterns suggest that, in contrast to the dire predictions of mass culture theorists, the memory of Che Guevara has become increasingly tied to markers of social, ethnic‐regional, and political identity.     

Social Antecedents of Medical Fraud and Over-Servicing: What Makes A Doctor Criminal?

Medical fraud and overservicing are estimated to cost the Australian community between $130 and $200 million per annum, a figure far greater than the national cost of burglary and almost the same as the total property loss from all conventional crime. An examination of the social antecedents of medical fraud and overservicing suggests that the predisposition of some doctors to engage in these practices occurs because of the following: (1) medical training and professional socialization that orientate student doctors away from altruistic health issues towards narrower self-interested professional concerns; (2) career expectations of a high pattern of material consumption that are often frustrated by an increasingly competitive medical market place; and (3) professional medical organizations that lobby for national health policies which reflect the mercenary self-interest of doctors rather than the health interest of the nation.