Longitudinal Associations of Neighborhood Crime and Perceived Safety with Blood Pressure: The Multi-Ethnic Study of Atherosclerosis (MESA)

Background: High neighborhood crime and low perceptions of safety may influence blood pressure (BP) through chronic stress. Few studies have examined these associations using longitudinal data. Methods: We used longitudinal data from 528 participants of the Multi-Ethnic Study of Atherosclerosis (aged 45-84, nonhypertensive at baseline) who lived in Chicago, Illinois. We examined associations of changes in individual-level perceived safety, aggregated neighborhood-level perceived safety, and past-year rates of police-recorded crime in a 1, =, or = mile buffer per 1,000 population with changes in systolic and diastolic BPs using fixed-effects linear regression. BP was measured five times between 2000 and 2012 and was adjusted for antihypertensive medication use (+10 mm Hg added to systolic and +5 mm Hg added to diastolic BP for participants on medication). Models were adjusted for time-varying sociodemographic and healthrelated characteristics and neighborhood socioeconomic status. We assessed differences by sex. Results: A standard deviation increase in individual-level perceived safety was associated with a 1.54 mm Hg reduction in systolic BP overall (95% confidence interval [CI]: 0.25, 2.83), and with a 1.24 mm Hg reduction in diastolic BP among women only (95% CI: 0.37, 2.12) in adjusted models. Increased neighborhood-level safety was not associated with BP change. An increase in police-recorded crime was associated with a reduction in systolic and diastolic BPs among women only, but results were sensitive to neighborhood buffer size. Conclusions: Results suggest individual perception of neighborhood safety may be particularly salient for systolic BP reduction relative to more objective neighborhood exposures

Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values.

Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification? Location: The Netherlands. Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three-dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations. Results and Conclusions: With indicator values, most relevés were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models

Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding

Durden J, Schoening T, Althaus F, et al. Perspectives in Visual Imaging for Marine Biology and Ecology: From Acquisition to Understanding. In: Hughes RN, Hughes DJ, Smith IP, Dale AC, eds. Oceanography and Marine Biology: An Annual Review. 54. Boca Raton: CRC Press; 2016: 1-72

Use of SMS texts for facilitating access to online alcohol interventions: a feasibility study

A41 Use of SMS texts for facilitating access to online alcohol interventions: a feasibility study In: Addiction Science & Clinical Practice 2017, 12(Suppl 1): A4

Physiological Ecology of Lichens

x,293 hal,;ill,;21 c