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BackgroundSedentary behavior is a worldwide public health concern. There is consistent and growing evidence linking sedentary behavior to mortality and morbidity. Early monitoring and assessment of environmental factors associated with sedentary behaviors at a young age are important initial steps for understanding children's sedentary time and identifying pertinent interventions.ObjectiveThis study examines the association between daily temperature (maximum, mean, minimum, and diurnal variation) and all-day sedentary time among 4-6 year old children in Mexico City (n = 559) from the year 2013 to 2015.MethodsWe developed a spatiotemporally resolved hybrid satellite-based land use regression temperature model and calculated percent daily sedentary time from aggregating 10-second epoch vertical counts captured by accelerometers that participants wore for one week. We modeled generalized additive models (GAMs), one for each temperature type as a covariate (maximum, mean, minimum, and diurnal variation). All GAMs included percent all-day sedentary time as the outcome and participant-level random intercepts to account for repeated measures of sedentary time. Our models were adjusted for demographic factors and environmental exposures.ResultsDaily maximum temperature, mean temperature, and diurnal variation have significant negative linear relationships with all-day sedentary time (pConclusionsThese results are contrary to our hypothesis in which we expected a curvilinear relationship between temperature (maximum, mean, minimum, and diurnal variation) and sedentary time. Our findings suggest that temperature is an important environmental factor that influences children's sedentary behavior.

INTRODUCTION:This study aims to determine the association between temperature and preeclampsia and whether it is affected by seasonality and rural/urban lifestyle. METHODS:This cohort study included women who delivered at our medical center from 2004 to 2013 (31,101 women, 64,566 deliveries). Temperature values were obtained from a spatiotemporally resolved estimation model performing predictions at a 1×1km spatial resolution. In "Warm" pregnancies >50% of gestation occurred during the spring-summer period. In cold pregnancies >50% of gestation occurred during the fall and winter. Generalized estimating equation multivariable models were used to estimate the association between temperature and incidence of preeclampsia. RESULTS:1) The incidence of preeclampsia in at least one pregnancy was 7% (2173/64,566); 2) during "warm" pregnancies, an elevation of one IQR of the average temperature in the 1st or the 3rd trimesters was associated with an increased risk to develop preeclampsia [patients with Jewish ethnicity: 1st trimester: relative risk (RR) of 2.38(95%CI 1.50; 3.80), 3rd trimester 1.94(95%CI 1.34;2.81); Bedouins: 1st trimester: RR = 2.91(95%CI 1.98;4.28), 3rd trimester: RR = 2.37(95%CI 1.75;3.20)]; 3) In "cold" pregnancies, an elevation of one IQR of average temperature was associated with a lower risk to develop preeclampsia among patients with Bedouin-Arab ethnicity RR = 0.68 (95% CI 0.49-0.94) for 1st trimester and RR = 0.62 (95% CI 0.44-0.87) for 3rd trimester. CONCLUSIONS:1) Elevated averaged temperature during the 1st or 3rd trimesters in "warm" pregnancies confer an increased risk for the development of preeclampsia, especially in nomadic patients; 2) Of interest, during cold pregnancies, elevated averaged temperature was associated with a lower risk to develop preeclampsia for nomadic patients. 3) These findings suggest temperature might be associated with perturbations in maternal heat homeostasis resulting in reallocation of energy resources and their availability to the fetus that may increase the risk for preeclampsia. This observation is especially relevant in the context of global warming and its effects on maternal/fetal reproductive health.

BackgroundMany studies have identified an inequitable distribution of exposure to PM2.5 (particulate matter less than 2.5 microns) by race. We investigated the association of PM2.5 and cardiovascular mortality considering both the decedents' race and neighborhood racial composition as potential modifiers.MethodsWe obtained geocoded cardiovascular mortality records of all black and white decedents from urban block-groups in Massachusetts between 2001 and 2011 (n = 130,863). We examined the association between PM2.5 and cardiovascular mortality, and assessed effect modification by three types of racial modifiers: decedents' race, census block-group percent black and white, and two novel measures of racial segregation. The Racial Residential Segregation (RRS) quantifies the concentration of non-Hispanic blacks and whites in each block-group. The Index of Racial Dissimilarity measures dissimilarity in non-Hispanic black and white racial distribution between the smaller census block-group and larger tract.ResultsWe found a 2.35%(95%CI: 0.92%;3.79%) increase in mortality for each 10μg/m3 increase in two-day average exposure to PM2.5. The effect was modified by the block-group racial composition, with higher risks in block-groups with the highest percentage of black residents (interaction p-value = 0.04), and in block-groups with the lowest RRS (i.e. higher black to white resident ratio, interaction p-value = 0.072). Racial dissimilarity did not modify the associations.ConclusionCurrent levels of PM2.5 are associated with increased cardiovascular deaths in Massachusetts, with different risks between areas with different racial composition and segregation. This suggests that pollution reductions in neighborhoods with the highest percentage of non-Hispanic blacks would be most beneficial in reducing cardiovascular mortality and disparities.

We studied associations between prenatal exposure to particulate matter with diameter ≤ 2.5 μm (PM2.5) and postpartum psychological functioning in a lower income, ethnically mixed sample of urban US women enrolled in a pregnancy cohort study. Analyses included 557 mothers who delivered at ≥37 weeks gestation. Daily estimates of residential PM2.5 over gestation were derived using a satellite-based spatio-temporally resolved model. Outcomes included the Edinburgh Postnatal Depression Scale (EPDS) score from 6 or 12 months postpartum and subscale scores for anhedonia, depressive and anxiety symptoms. Associations were also examined within racial/ethnic groups. Distributed lag models (DLMs) were implemented to identify windows of vulnerability during pregnancy.Most mothers had less than a high school education (64%) and were primarily Hispanic (55%) and Black (29%). In the overall sample, a DLM adjusted for age, race, education, prenatal smoking, and season of delivery, we found significant associations between higher PM2.5 exposure in the second trimester and increased anhedonia subscale scores postpartum. In race stratified analyses, mid-pregnancy PM2.5 exposure was significantly associated with increased total EPDS scores as well as higher anhedonia and depressive symptom subscale scores among Black women.Increased PM2.5 exposure in mid-pregnancy was associated with increased depressive and anhedonia symptoms, particularly in Black women.

Dust storms are a common phenomenon in arid and semi-arid areas, and their impacts on both physical and human environments are of great interest. Number of studies have associated atmospheric PM pollution in urban environments with origin in natural soil/dust, but less evaluated the dust spatial patterns over a city. We aimed to analyze the spatial-temporal behavior of PM concentrations over the city of Beer Sheva, in southern Israel, where dust storms are quite frequent. PM data were recorded during the peak of each dust episode simultaneously in 23 predetermined fixed points around the city. Data were analyzed for both dust days and non-dust days (background). The database was constructed using Geographic Information System and includes distributions of PM that were derived using inverse distance weighted (IDW) interpolation. The results show that the daily averages of atmospheric PM10 concentrations during the background period are within a narrow range of 31 to 48 μg m-3 with low variations. During dust days however, the temporal variations are significant and can range from an hourly PM10 concentration of 100 μg m-3 to more than 1280 μg m-3 during strong storms. IDW analysis demonstrates that during the peak time of the storm the spatial variations in PM between locations in the city can reach 400 μg m-3. An analysis of site and storm contribution to total PM concentration revealed that higher concentrations are found in parts of the city that are proximal to dust sources. The results improve the understanding of the dynamics of natural PM and the dependence on wind direction. This may have implications for environmental and health outcomes.

The underlying mechanisms of the association between ambient temperature and cardiovascular morbidity and mortality are not well understood, particularly for daily temperature variability. We evaluated if daily mean temperature and standard deviation of temperature was associated with heart rate-corrected QT interval (QTc) duration, a marker of ventricular repolarization in a prospective cohort of older men.This longitudinal analysis included 487 older men participating in the VA Normative Aging Study with up to three visits between 2000-2008 (n = 743). We analyzed associations between QTc and moving averages (1-7, 14, 21, and 28 days) of the 24-hour mean and standard deviation of temperature as measured from a local weather monitor, and the 24-hour mean temperature estimated from a spatiotemporal prediction model, in time-varying linear mixed-effect regression. Effect modification by season, diabetes, coronary heart disease, obesity, and age was also evaluated.Higher mean temperature as measured from the local monitor, and estimated from the prediction model, was associated with longer QTc at moving averages of 21 and 28 days. Increased 24-hr standard deviation of temperature was associated with longer QTc at moving averages from 4 and up to 28 days; a 1.9°C interquartile range increase in 4-day moving average standard deviation of temperature was associated with a 2.8 msec (95%CI: 0.4, 5.2) longer QTc. Associations between 24-hr standard deviation of temperature and QTc were stronger in colder months, and in participants with diabetes and coronary heart disease.In this sample of older men, elevated mean temperature was associated with longer QTc, and increased variability of temperature was associated with longer QTc, particularly during colder months and among individuals with diabetes and coronary heart disease. These findings may offer insight of an important underlying mechanism of temperature-related cardiovascular morbidity and mortality in an older population.

Many studies report significant associations between PM(2.5) (particulate matter

BackgroundMany studies have reported significant associations between exposure to PM(2.5) and hospital admissions, but all have focused on the effects of short-term exposure. In addition all these studies have relied on a limited number of PM(2.5) monitors in their study regions, which introduces exposure error, and excludes rural and suburban populations from locations in which monitors are not available, reducing generalizability and potentially creating selection bias.MethodsUsing our novel prediction models for exposure combining land use regression with physical measurements (satellite aerosol optical depth) we investigated both the long and short term effects of PM(2.5) exposures on hospital admissions across New-England for all residents aged 65 and older. We performed separate Poisson regression analysis for each admission type: all respiratory, cardiovascular disease (CVD), stroke and diabetes. Daily admission counts in each zip code were regressed against long and short-term PM(2.5) exposure, temperature, socio-economic data and a spline of time to control for seasonal trends in baseline risk.ResultsWe observed associations between both short-term and long-term exposure to PM(2.5) and hospitalization for all of the outcomes examined. In example, for respiratory diseases, for every 10-µg/m(3) increase in short-term PM(2.5) exposure there is a 0.70 percent increase in admissions (CI = 0.35 to 0.52) while concurrently for every 10-µg/m(3) increase in long-term PM(2.5) exposure there is a 4.22 percent increase in admissions (CI = 1.06 to 4.75).ConclusionsAs with mortality studies, chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models.