BackgroundSeveral studies have investigated the association between asthma exacerbations and exposures to ambient temperature and precipitation. However, limited data exists regarding how extreme events, projected to grow in frequency, intensity, and duration in the future in response to our changing climate, will impact the risk of hospitalization for asthma. The objective of our study was to quantify the association between frequency of extreme heat and precipitation events and increased risk of hospitalization for asthma in Maryland between 2000 and 2012.MethodsWe used a time-stratified case-crossover design to examine the association between exposure to extreme heat and precipitation events and risk of hospitalization for asthma (ICD-9 code 493, n = 115,923).ResultsOccurrence of extreme heat events in Maryland increased the risk of same day hospitalization for asthma (lag 0) by 3 % (Odds Ratio (OR): 1.03, 95 % Confidence Interval (CI): 1.00, 1.07), with a considerably higher risk observed for extreme heat events that occur during summer months (OR: 1.23, 95 % CI: 1.15, 1.33). Likewise, summertime extreme precipitation events increased the risk of hospitalization for asthma by 11 % in Maryland (OR: 1.11, 95 % CI: 1.06, 1.17). Across age groups, increase in risk for asthma hospitalization from exposure to extreme heat event during the summer months was most pronounced among youth and adults, while those related to extreme precipitation event was highest among ≤4 year olds.ConclusionExposure to extreme heat and extreme precipitation events, particularly during summertime, is associated with increased risk of hospitalization for asthma in Maryland. Our results suggest that projected increases in frequency of extreme heat and precipitation event will have significant impact on public health.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0142-z) contains supplementary material, which is available to authorized users.
Irrigation water contaminated with Salmonella enterica and Listeria monocytogenes may provide a route of contamination of raw or minimally processed fruits and vegetables. While previous work has surveyed specific and singular types of agricultural irrigation water for bacterial pathogens, few studies have simultaneously surveyed different water sources repeatedly over an extended period of time. This study quantified S. enterica and L. monocytogenes levels (MPN/L) at 6 sites, including river waters: tidal freshwater river (MA04, n = 34), non-tidal freshwater river, (MA05, n = 32), one reclaimed water holding pond (MA06, n = 25), two pond water sites (MA10, n = 35; MA11, n = 34), and one produce wash water site (MA12, n = 10) from September 2016-October 2018. Overall, 50% (84/168) and 31% (53/ 170) of sampling events recovered S. enterica and L. monocytogenes, respectively. Results showed that river waters supported significantly (p < 0.05) greater levels of S. enterica than pond or reclaimed waters. The non-tidal river water sites (MA05) with the lowest water temperature supported significantly greater level of L. monocytogenes compared to all other sites; L. monocytogenes levels were also lower in winter and spring compared to summer seasons. Filtering 10 L of water through a modified Moore swab (MMS) was 43.5 (Odds ratio, p < 0.001) and 25.5 (p < 0.001) times more likely to recover S. enterica than filtering 1 L and 0.1 L, respectively; filtering 10 L was 4.8 (p < 0.05) and 3.9 (p < 0.05) times more likely to recover L. monocytogenes than 1L and 0.1 L, respectively. Work presented here shows that S. enterica and L. monocytogenes levels are higher in river waters compared to pond or
Background:People’s time-location patterns are important in air pollution exposure assessment because pollution levels may vary considerably by location. A growing number of studies are using global positioning systems (GPS) to track people’s time-location patterns. Many portable GPS units that archive location are commercially available at a cost that makes their use feasible for epidemiological studies.Methods:We evaluated the performance of five portable GPS data loggers and two GPS cell phones by examining positional accuracy in typical locations (indoor, outdoor, in-vehicle) and factors that influence satellite reception (building material, building type), acquisition time (cold and warm start), battery life, and adequacy of memory for data storage. We examined stationary locations (eg, indoor, outdoor) and mobile environments (eg, walking, traveling by vehicle or bus) and compared GPS locations to highly-resolved US Geological Survey (USGS) and Digital Orthophoto Quarter Quadrangle (DOQQ) maps.Results:The battery life of our tested instruments ranged from <9 hours to 48 hours. The acquisition of location time after startup ranged from a few seconds to >20 minutes and varied significantly by building structure type and by cold or warm start. No GPS device was found to have consistently superior performance with regard to spatial accuracy and signal loss. At fixed outdoor locations, 65%–95% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices. At fixed indoor locations, 50%–80% of GPS points fell within 20-m of the corresponding DOQQ locations for all the devices except one. Most of the GPS devices performed well during commuting on a freeway, with >80% of points within 10-m of the DOQQ route, but the performance was significantly impacted by surrounding structures on surface streets in highly urbanized areas.Conclusions:All the tested GPS devices had limitations, but we identified several devices which showed promising performance for tracking subjects’ time location patterns in epidemiological studies.
BackgroundAir pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data.MethodsWe developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model.ResultsIndoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data.ConclusionsOur models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.
To our knowledge, this is the first empirical evidence showing that extreme temperature/precipitation events-that are expected to be more frequent and intense in coming decades-are disproportionately impacting coastal communities with regard to salmonellosis. Adaptation strategies need to account for this differential burden, particularly in light of ever increasing coastal populations.
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