Acute Stroke, Hematocrit, and Blood Pressure.

A population-based study of the relation between hematocrit and stroke subtype was carried out among 2,077 individuals using the Lehigh Valley Stroke Register. This register identifies all stroke patients admitted to the 8 acute care hospitals serving the Lehigh Valley area of eastern Pennsylvania-western New Jersey. The mean hematocrit was higher in patients with lacunes than with thrombotic or embolic strokes (p = 0.02). However, when blood pressure was also considered the increase in hematocrit in patients with lacunar stroke was significant only when systolic hypertension (greater than or equal to 150 mm Hg) was also present (p = 0.029); no significant difference in hematocrit was found between stroke subtypes in normotensive individuals. Therefore, we cannot exclude the possibility that hypertension interacts with hematocrit in accounting for the observed association with lacunar infarcts. There was no trend for increased in-hospital mortality for stroke patients in either the low (less than or equal to 30, 30-36%) or high (greater than or equal to 47%) hematocrit groups

Gender and ethnicity differences in the prevalence of scleroderma-related autoantibodies

Autoantibodies to topoisomerase I (topo I), RNA polymerase III (RNAPIII), centromere, U3RNP/fibrillarin, Th, PM-Scl, and U1RNP found in scleroderma (SSc) are associated with unique clinical subsets. The effects of race and gender on autoantibody prevalence and clinical manifestations were examined. Autoantibodies in sera from 105 SSc (include 75 Caucasian, 24 African-American, 6 others; 89 females and 16 males) were analyzed by immunofluorescence and immunoprecipitation. Clinical information was from database. SSc-related autoantibodies seldom coexist except for anti-topo I and anti-U1RNP. Anti-topo I (35% vs 15%), anti-U3RNP (30% vs 3%, p 08= 080.0005), and anti-U1RNP (30% vs 13%) were more common in African-Americans vs Caucasians. Anti-centromere (17%) and anti-PM-Scl (only in 8% of female) were found only in Caucasians. In race/gender combination, all three African-American males had anti-topo I (p 08= 080.04). Anti-U3RNP (35% vs 3%, p 08= 080.0005) and anti-U1RNP were common in African-American females. In African-American, all nucleolar dominant staining sera had anti-U3RNP; nuclear pattern was topo I (50%), U1RNP (19%), and RNAPIII (13%). In Caucasian, nucleolar was anti-Th (43%) and PM-Scl (29%); nuclear pattern was RNAPIII (29%), topo I (24%), and U1RNP (18%). Anti-topo I, anti-RNAPIII, and anti-U3RNP were associated with diffuse SSc while anti-centromere, anti-Th, and anti-U1 with limited disease. Proximal scleroderma was less common in African-American with anti-topo I (38% vs 91% in Caucasian, p 08= 080.04). The production of SSc-related autoantibodies is gender and race dependent, and this can be highly relevant in understanding their clinical significance. \ua9 2011 Clinical Rheumatology

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided

Linking Distributive and Procedural Justice to Employee Engagement Through Social Exchange: A Field Study in India

Research linking justice perceptions to employee outcomes has referred to social exchange as its central theoretical premise. We tested a conceptual model linking distributive and procedural justice to employee engagement through social exchange mediators, namely, perceived organizational support and psychological contract, among 238 managers and executives from manufacturing and service sector firms in India. Findings suggest that perceived organizational support mediated the relationship between distributive justice and employee engagement, and both perceived organizational support and psychological contract mediated the relationship between procedural justice and employee engagement. Theoretical and practical implications with respect to organizational functions are discussed

The statistical evaluation of social network dynamics

A class of statistical models is proposed for longitudinal network data. The dependent variable is the changing (or evolving) relation network, represented by two or more observations of a directed graph with a fixed set of actors. The network evolution is modeled as the consequence of the actors making new choices, or withdrawing existing choices, on the basis of functions, with fixed and random components, that the actors try to maximize. Individual and dyadic exogenous variables can be used as covariates. The change in the network is modeled as the stochastic result of network effects (reciprocity, transitivity, etc.) and these covariates. The existing network structure is a dynamic constraint for the evolution of the structure itself. The models are continuous-time Markov chain models that can be implemented as simulation models. The model parameters are estimated from observed data. For estimating and testing these models, statistical procedures are proposed that are based on the method of moments. The statistical procedures are implemented using a stochastic approximation algorithm based on computer simulations of the network evolution process