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BackgroundIn 2012, Colorado experienced one of its worst wildfire seasons of the past decade. The goal of this study was to investigate the relationship of local PM2.5 levels, modeled using the Weather Research and Forecasting Model with Chemistry, with emergency department visits and acute hospitalizations for respiratory and cardiovascular outcomes during the 2012 Colorado wildfires.MethodsConditional logistic regression was used to assess the relationship between both continuous and categorical PM2.5 and emergency department visits during the wildfire period, from June 5th to July 6th 2012.ResultsFor respiratory outcomes, we observed positive relationships between lag 0 PM2.5 and asthma/wheeze (1 h max OR 1.01, 95 % CI (1.00, 1.01) per 10 μg/m3; 24 h mean OR 1.04 95 % CI (1.02, 1.06) per 5 μg/m3), and COPD (1 h max OR 1.01 95 % CI (1.00, 1.02) per 10 μg/m3; 24 h mean OR 1.05 95 % CI (1.02, 1.08) per 5 μg/m3). These associations were also positive for 2-day and 3-day moving average lag periods. When PM2.5 was modeled as a categorical variable, bronchitis also showed elevated effect estimates over the referent groups for lag 0 24 h average concentration. Cardiovascular results were consistent with no association.ConclusionsWe observed positive associations between PM2.5 from wildfire and respiratory diseases, supporting evidence from previous research that wildfire PM2.5 is an important source for adverse respiratory health outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0146-8) contains supplementary material, which is available to authorized users.

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BackgroundTo better understand potential transmission risks from contact with the body fluids of children, we monitored the presence and amount of CMV shedding over time in healthy CMV-seropositive children.MethodsThrough screening we identified 36 children from the Atlanta, Georgia area who were CMV-seropositive, including 23 who were shedding CMV at the time of screening. Each child received 12 weekly in-home visits at which field workers collected saliva and urine. During the final two weeks, parents also collected saliva and urine daily.ResultsPrevalence of shedding was highly correlated with initial shedding status: children shedding at the screening visit had CMV DNA in 84% of follow-up saliva specimens (455/543) and 28% of follow-up urine specimens (151/539); those not shedding at the screening visit had CMV DNA in 16% of follow-up saliva specimens (47/303) and 5% of follow-up urine specimens (16/305). Among positive specimens we found median viral loads of 82,900 copies/mL in saliva and 34,730 copies/mL in urine (P = 0.01), while the viral load for the 75th percentile was nearly 1.5 million copies/mL for saliva compared to 86,800 copies/mL for urine. Younger age was significantly associated with higher viral loads, especially for saliva (P < 0.001). Shedding prevalence and viral loads were relatively stable over time. All children who were shedding at the screening visit were still shedding at least some days during weeks 11 and 12, and median and mean viral loads did not change substantially over time.ConclusionsHealthy CMV-seropositive children can shed CMV for months at high, relatively stable levels. These data suggest that behavioral prevention messages need to address transmission via both saliva and urine, but also need to be informed by the potentially higher risks posed by saliva and by exposures to younger children.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-014-0569-1) contains supplementary material, which is available to authorized users.

Regulatory monitoring networks are often too sparse to support community-scale PM 2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM 2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM 2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM 2.5 concentrations at 1-km spatial resolution using three different PM 2.5 datasets (AQS-only, IVAN-only, and AQS/ IVAN combined). The results showed that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM 2.5 prediction with an increase of crossvalidation (CV) R 2 by ~0.2. The IVAN measurements also contributed to the increased importance *

Early studies of weather, seasonality, and environmental influences on COVID-19 have yielded inconsistent and confusing results. To provide policy-makers and the public with meaningful and actionable environmentally-informed COVID-19 risk estimates, the research community must meet robust methodological and communication standards.

BackgroundCongenital cytomegalovirus (CMV) is the leading infectious cause of birth defects in the United States. To better understand factors that may influence CMV transmission risk, we compared viral and immunological factors in healthy children and their mothers.MethodsWe screened for CMV IgG antibodies in a convenience sample of 161 children aged 0-47 months from the Atlanta, Georgia metropolitan area, along with 32 mothers of children who screened CMV-seropositive. We assessed CMV shedding via PCR using saliva collected with oral swabs (children and mothers) and urine collected from diapers using filter paper inserts (children only).ResultsCMV IgG was present in 31% (50/161) of the children. Half (25/50) of seropositive children were shedding in at least one fluid. The proportion of seropositive children who shed in saliva was 100% (8/8) among the 4-12 month-olds, 64% (9/14) among 13-24 month-olds, and 40% (6/15) among 25-47 month-olds (P for trend = 0.003). Seropositive mothers had a lower proportion of saliva shedding (21% [6/29]) than children (P < 0.001). Among children who were shedding CMV, viral loads in saliva were significantly higher in younger children (P <0.001); on average, the saliva viral load of infants (i.e., <12 months) was approximately 300 times that of two year-olds (i.e., 24-35 months). Median CMV viral loads were similar in children's saliva and urine but were 10-50 times higher (P < 0.001) than the median viral load of the mothers' saliva. However, very high viral loads (> one million copies/mL) were only found in children's saliva (31% of those shedding); children's urine and mothers' saliva specimens all had fewer than 100,000 copies/mL. Low IgG avidity, a marker of primary infection, was associated with younger age (p = 0.03), higher viral loads in saliva (p = 0.02), and lower antibody titers (p = 0.005).ConclusionsYoung CMV seropositive children, especially those less than one year-old may present high-risk CMV exposures to pregnant women, especially via saliva, though further research is needed to see if this finding can be generalized across racial or other demographic strata.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-014-0568-2) contains supplementary material, which is available to authorized users.

Predicting long-term spatiotemporal characteristics of fine particulate matter (PM 2.5 ) is important in China to understand historical levels of PM 2.5 , to support health effects research of both long-term and short-term exposures to PM 2.5 , and to evaluate the efficacy of air pollution control policies. Satellite-retrieved aerosol optical depth (AOD) provides a unique opportunity to characterize the long-term trends of ground-level PM 2.5 at high spatial resolution. However, the missing rate of AOD in Northeastern China (NEC) is very high, especially in winter, and challenges the accuracy of long-term predictions of PM 2.5 if left unresolved. Using random forest algorithms, this study developed a gap-filling approach combing satellite AOD, meteorological data, land use parameters, population and visibility in the NEC during 2005-2016. The model, including all predictors, combined with a model without AOD was able to fill the gap of PM 2.5 predictions caused by missing AOD at 1-km resolution. The R 2 (RMSE) of the full-coverage predictions was 0.81 (18.5 μg/m 3 ) at the daily level. Gap-filled PM 2.5 predictions on days with missing AOD reduced the relative prediction error from 28% to 2.5% in winter. The leave-one year-out-cross-validation R 2 (RMSE) of the full-coverage predictions was 0.65 (16.3 μg/m 3 ) at the monthly level, indicating relatively high accuracy of predicted historical PM 2.5 concentrations. Our results suggested that AOD helped increase the reliability of historical PM 2.5 prediction when ground PM 2.5 measurements were unavailable, even though predictions from the AOD model only accounted for approximate 37% of the whole dataset. Predicted PM 2.5 level in NEC have