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Objectives. We analyzed singleton births to determine the relationship between birth weight and altitude exposure. Methods. We analyzed 715 213 singleton births across 74 counties from the western states of Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, and Washington from January 1, 2000, to December 31, 2000. Birth data were obtained from the Division of Vital Statistics, National Center for Health Statistics, for registered births. Results. Regression analyses supported previous research by showing that a 1000-meter increase in maternal altitude exposure in pregnancy was associated with a 75.9-gram reduction in birth weight (95% confidence interval = -84.1, -67.6). Quantile regression models indicated significant and near-uniform depressant effects from altitude exposure across the conditional distribution of birth weight. Bivariate sample-selection models showed that a 1000-meter increase in altitude exposure, over and above baseline residential altitude, decreased birth weight by an additional 58.8 grams (95% confidence interval = -98.4, -19.2). Conclusions. Because of calculable health care-related costs associated with lower birth weight, our reported results might be of interest to clinicians practicing at higher altitudes. [ABSTRACT FROM AUTHOR]

Lindeman et al. [12] provide a unique solution to the relative importance of correlated predictors in multiple regression by averaging squared semi-partial correlations obtained for each predictor across all p! orderings. In this paper, we propose a series of predictor sensitivity statistics that complement the variance decomposition procedure advanced by Lindeman et al. [12]. First, we detail the logic of averaging over orderings as a technique of variance partitioning. Second, we assess predictors by conditional dominance analysis, a qualitative procedure designed to overcome defects in the Lindeman et al. [12] variance decomposition solution. Third, we introduce a suite of indices to assess the sensitivity of a predictor to model specification, advancing a series of sensitivity-adjusted contribution statistics that allow for more definite quantification of predictor relevance. Fourth, we describe the analytic efficiency of our proposed technique against the Budescu conditional dominance solution to the uneven contribution of predictors across all p! orderings. [ABSTRACT FROM AUTHOR]

Abstract: We investigate the relationship between maternal exposure to benzene and birth weight outcomes for resident births in the United States in 1996 and 1999, taking advantage of a natural experiment afforded by the regulation of benzene content of gasoline in various American cities. Regression results show that a unit increase (μg/m3) in maternal exposure to benzene reduces birth weight by 16.5g (95% CI, 17.6 to 15.4). A unit increase in benzene exposure increases the odds of a low birth weight event by 7%. Similarly, a 1μg/m3 increase in benzene concentration increases the odds of very low birth weight event by a multiplicative factor of 1.23 (95% CI, 1.19 to 1.28). Difference-in-differences analyses show that birth weight increased by 13.7g (95% CI, 10.7 to 16.8) and the risk of low birth weight decreased by a factor of .95 (95% CI, .93 to .98) in counties experiencing a 25% decline in benzene concentrations from 1996 to 1999. Public health policy and economic implications of results are discussed. [Copyright &y& Elsevier]

We analyze household flood insurance purchases in Florida from 1999 to 2005, and the extent to which household insurance purchases correspond with flood mitigation activities by local governments involved in the Federal Emergency Management Agency's (FEMA) Community Rating System (CRS). Regression results indicate that household flood insurance purchases correlate strongly with local government mitigation activities, adjusting for hazard experience, hazard proximity, and community demography. Policy implications of this observed relationship are discussed, assuming four temporal order and floodplain development scenarios, with particular attention to the congruence of outcomes relative to policy objectives. [Copyright &y& Elsevier]

Studies on the impacts of hurricanes, tropical storms, and tornados indicate that poor communities of colour suffer disproportionately in human death and injury. 2 Few quantitative studies have been conducted on the degree to which flood events affect socially vulnerable populations. We address this research void by analysing 832 countywide flood events in Texas from 1997–2001. Specifically, we examine whether geographic localities characterised by high percentages of socially vulnerable populations experience significantly more casualties due to flood events, adjusting for characteristics of the natural and built environment. Zero-inflated negative binomial regression models indicate that the odds of a flood casualty increase with the level of precipitation on the day of a flood event, flood duration, property damage caused by the flood, population density, and the presence of socially vulnerable populations. Odds decrease with the number of dams, the level of precipitation on the day before a recorded flood event, and the extent to which localities have enacted flood mitigation strategies. The study concludes with comments on hazard-resilient communities and protection of casualty-prone populations. [ABSTRACT FROM AUTHOR]

Although there is a growing body of research examining public perceptions of global climate change, little work has focused on the role of place and proximity in shaping these perceptions. This study extends previous conceptual models explaining risk perception associated with global climate change by adding a spatial dimension. Specifically, Geographic Information Systems and spatial analytical techniques are used to map and measure survey respondents' physical risk associated with expected climate change. Using existing spatial data, multiple measures of climate change vulnerability are analyzed along with demographic, attitudinal, and social contextual variables derived from a representative national survey to predict variation in risk perception. Bivariate correlation and multivariate regression analyses are used to identify and explain the most important indicators shaping individual risk perception. Analysis of the data suggests that the relationship between actual and perceived risk is driven by specific types of physical conditions and experiences. [ABSTRACT FROM AUTHOR]

This article adapts a theory of ecological modernization to characterize countries by their economic, political, and cultural development to estimate their structural and strategic readiness to commit and comply with the Kyoto Protocol. Hypotheses logically derived from ecological modernization theory perform as expected, with substantial variation in Kyoto Protocol ratification explained by our variable pool. Results from logistic and Cox proportional hazards regression tests show that democratic openness, gross domestic product (GDP) output per unit of energy input, CO2 emissions per capita, and record of international cooperation on transboundary environmental problems significantly predict whether or not a country is party to the Protocol, and how quickly a country submits an instrument of ratification. [ABSTRACT FROM AUTHOR]