Confidence regions for spatial excursion sets from repeated random field observations, with an application to climate.

The goal of this paper is to give confidence regions for the excursion set of a spatial function above a given threshold from repeated noisy observations on a fine grid of fixed locations. Given an asymptotically Gaussian estimator of the target function, a pair of data-dependent nested excursion sets are constructed that are sub- and super-sets of the true excursion set, respectively, with a desired confidence. Asymptotic coverage probabilities are determined via a multiplier bootstrap method, not requiring Gaussianity of the original data nor stationarity or smoothness of the limiting Gaussian field. The method is used to determine regions in North America where the mean summer and winter temperatures are expected to increase by mid 21st century by more than 2 degrees Celsius.

Characterizing urban vulnerability to heat stress using a spatially varying coefficient model.

Identifying and characterizing urban vulnerability to heat is a key step in designing intervention strategies to combat negative consequences of extreme heat on human health. This study combines excess non-accidental mortality counts, numerical weather simulations, US Census and parcel data into an assessment of vulnerability to heat in Houston, Texas. Specifically, a hierarchical model with spatially varying coefficients is used to account for differences in vulnerability among census block groups. Socio-economic and demographic variables from census and parcel data are selected via a forward selection algorithm where at each step the remaining variables are orthogonalized with respect to the chosen variables to account for collinearity. Daily minimum temperatures and composite heat indices (e.g. discomfort index) provide a better model fit than other ambient temperature measurements (e.g. maximum temperature, relative humidity). Positive interactions between elderly populations and heat exposure were found suggesting these populations are more responsive to increases in heat.

Environmental characteristics associated with pedestrian-motor vehicle collisions in Denver, Colorado.

OBJECTIVES: We examined patterns of pedestrian-motor vehicle collisions and associated environmental characteristics in Denver, Colorado. METHODS: We integrated publicly available data on motor vehicle collisions, liquor licenses, land use, and sociodemographic characteristics to analyze spatial patterns and other characteristics of collisions involving pedestrians. We developed both linear and spatially weighted regression models of these collisions. RESULTS: Spatial analysis revealed global clustering of pedestrian-motor vehicle collisions with concentrations in downtown, in a contiguous neighborhood, and along major arterial streets. Walking to work, population density, and liquor license outlet density all contributed significantly to both linear and spatial models of collisions involving pedestrians and were each significantly associated with these collisions. CONCLUSIONS: These models, constructed with data from Denver, identified conditions that likely contribute to patterns of pedestrian-motor vehicle collisions. Should these models be verified elsewhere, they will have implications for future research directions, public policy to enhance pedestrian safety, and public health programs aimed at decreasing unintentional injury from pedestrian-motor vehicle collisions and promoting walking as a routine physical activity.

Spectrum of genotype and clinical manifestations in cerebral cavernous malformations.

OBJECTIVE: Cerebral cavernous malformations (CCMs) are focal dysmorphic blood vessel anomalies predisposing individuals to hemorrhagic stroke and epilepsy. CCMs are sporadic or inherited as autosomal dominant disease with three known genes. The hypothesis that genetic heterogeneity would account for the remarkable variability in CCM manifestations was tested. METHODS: CCM cases were prospectively enrolled. Germline CCM1 gene mutations were sought in 89 CCM samples. Associations with clinical manifestations and lesion characteristics were made among 41 symptomatic familial cases, including one cohort of 26 cases with CCM1 mutations and a second cohort of 15 cases without identifiable CCM1 mutations. The 15 cases were screened for CCM2 and CCM3 mutations. RESULTS: CCM1 mutations were found in 34 out of 50 subjects with familial disease and in none of 39 sporadic CCM cases. CCM2 and CCM3 mutations were found in three out of 10 families screened without CCM1 mutations. Clinical manifestations in 22 Hispanic-American cases with identical CCM1 mutations were highly variable. Fewer CCM1 patients experienced hemorrhage than others with familial disease (P = 0.0139 for all cases and P = 0.0442 for symptomatic cases). Adjusting for sex and age improved the logistic regression model, suggesting decreased numbers of patients with hemorrhage in CCM1 familial disease (P = 0.003 for all cases and P = 0.014 for symptomatic cases). Hemorrhage differences were not related to size or number of lesions. CONCLUSION: Factors in addition to CCM1 germline mutation contribute to CCM clinical manifestations. However, this evidence suggests that familial cases with CCM1 mutations may have less severe clinical manifestations than other familial cases.