Recent evidence has shown that fine particulate matter (PM2.5) may be an important environmental risk factor for chronic kidney disease (CKD), but few studies have examined this association for individual patients using fine spatial data. To investigate the association between PM2.5 and CKD (estimated glomerular filtration rate [eGFR]<45 ml/min/1.73 m2) in the Twin-Cities area in Minnesota using a large electronic health care database (2012–2019). We estimated the previous 1-year average PM2.5 from the first eGFR (measured with the CKD Epidemiology Collaboration equation using the first available creatinine measure during the baseline period [2012–2014]) using Environmental Protection Agency downscaler modeling data at the census tract level. We evaluated the spatial relative risk and clustering of CKD prevalence using a K-function test statistic. We assessed the prevalence ratio of the PM2.5 association with CKD incidence using a mixed effect Cox model, respectively. Patients (n = 20,289) in the fourth (PM2.5 > 10.4), third (10.3 < PM2.5 < 10.8) and second quartile (9.9 < PM2.5 < 10.3) vs. the first quartile (<9.9 μg/m3) had a 2.52[2.21, 2.87], 2.18[1.95, 2.45], and 1.72[1.52, 1.97] hazard rate of developing CKD in the fully adjusted models, respectively. We identified spatial heterogeneities and evidence of CKD clustering across our study region, but this spatial variation was accounted for by air pollution and individual covariates. Exposure to higher PM2.5 is associated with a greater risk for incident CKD. Improvements in air quality, specifically at hotspots, may reduce CKD.

BACKGROUND AND AIM: Studies have shown an acute association between the occurrence of thunderstorm conditions in the presence of high pollen and severe asthma, but minimal work has studied these ev...

BACKGROUND AND AIM: Drought represents a globally relevant natural disaster linked to adverse health. Evidence has shown agricultural communities to be particularly susceptible to drought condition...

The COVID-19 global pandemic has likely affected air quality due to extreme changes in human behavior. We assessed air quality during the COVID-19 pandemic for fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the continental United States from January 8th-April 21st in 2017–2020. We considered pollution during the COVID-19 period (March 13–April 21st) and the pre-COVID-19 period (January 8th-March 12th) with 2020 representing ‘current’ data and 2017–2019 representing ‘historical’ data. County-level pollution concentrations were compared between historical versus current periods, and counties were stratified by institution of early or late non-essential business closures. Statistically significant NO2 declines were observed during the current COVID-19 period compared to historical data: a 25.5% reduction with absolute decrease of 4.8 ppb. PM2.5 also showed decreases during the COVID-19 period, and the reduction is statistically significant in urban counties and counties from states instituting early non-essential business closures. Understanding how air pollution is affected during COVID-19 pandemic will provide important clues regarding health effects and control of emissions. Further investigation is warranted to link this finding with health implications.

Sampling campaign design is a crucial aspect of air pollution exposure studies. Selection of both monitor numbers and locations is important for maximizing measured information, while minimizing bias and costs. We developed a two-stage geostatistical-based method using pilot NO 2 samples from Lanzhou, China with the goal of improving sample design decision-making, including monitor numbers and spatial pattern. In the first step, we evaluate how additional monitors change prediction precision through minimized kriging variance. This was assessed in a Monte Carlo fashion by adding up to 50 new monitors to our existing sites with assigned concentrations based on conditionally simulated NO 2 surfaces. After identifying a number of additional sample sites, a second step evaluates their potential placement using a similar Monte Carlo scheme. Evaluations are based on prediction precision and accuracy. Costs are also considered in the analysis. It was determined that adding 28-locations to the existing Lanzhou NO 2 sampling campaign captured 73.5% of the total kriged variance improvement and resulted in predictions that were on average within 10.9 μg/m 3 of measured values, while using 56% of the potential budget. Additional monitor sites improved kriging variance in a nonlinear fashion. This method development allows for informed sampling design by quantifying prediction improvement (accuracy and precision) against the costs of monitor deployment.

Indoor concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and carbon monoxide (CO) were measured across 16 urban public schools in three different seasons. Exceedance of the WHO guidelines for indoor air was observed, mainly for the hourly average NO2 concentrations. Seasonal variability was statistically significant for indoor NO2 and CO concentrations, with higher exposures in fall and winter. An extensive list of potential factors at the outdoor environment, school, and room level that may explain the variability in indoor exposure was examined. Factors with significant contributions to indoor exposure were mostly related to the outdoor pollution sources. This is evidenced by the strong associations between indoor concentration of CO and NO2 and factors including outdoor PM2.5 and NO2 concentrations, including length of the nearby roads and the number of nearby industrial facilities. Additionally, we found that poor conditions of the buildings (a prevalent phenomenon in the studied urban area), including physical defects and lack of proper ventilation, contributed to poor air quality in schools. The results suggest that improving building conditions and facilities as well as a consideration of the school surroundings may improve indoor air quality in schools.

BACKGROUND Violence is a leading cause of death and an important public health threat, particularly among adolescents and young adults. However, the environmental causes of violent behavior are not well understood. Emerging evidence suggests exposure to air pollution may be associated with aggressive or impulsive reactions in people. METHODS We applied a two-stage hierarchical time series model to estimate change in risk of violent and non-violent criminal behavior associated with short-term air pollution in U.S. counties (2000-2013). We used daily monitoring data for ozone and fine particulate matter (PM2.5) from the Environmental Protection Agency and daily crime counts from the Federal Bureau of Investigation. We evaluated the exposure-response relationship and assessed differences in risk by community characteristics of poverty, urbanicity, race, and age. RESULTS Our analysis spans 301 counties in 34 states, representing 86.1 million people and 721,674 days. Each 10µg/m change in daily PM2.5 was associated with a 1.17% (95% CI: 0.90, 1.43) and a 10ppb change in ozone with a 0.59% (95% CI: 0.41, 0.78) relative risk increase (RRI) for violent crime. However, we observed no risk increase for non-violent property crime due to PM2.5 (RRI: 0.11%; 95% CI: -0.09, 0.31) or ozone (RRI: -0.05%; 95% CI: -0.22, 0.12). Our results were robust across all community types, except rural regions. Exposure-response curves indicated increased violent crime risk at concentrations below regulatory standards. CONCLUSIONS Our results suggest that short-term changes in ambient air pollution may be associated with greater risk of violent behavior, regardless of community type.

© 2019 Elsevier Inc. Background: Land use regression (LUR) models have been widely used to estimate air pollution exposures at high spatial resolution. However, few LUR models were developed for rapidly developing urban cores, which have substantially higher densities of population and built-up areas than the surrounding areas within a city's administrative boundary. Further, few studies incorporated vertical variations of air pollution in exposure assessment, which might be important to estimate exposures for people living in high-rise buildings. Objective: A LUR model was developed for the urban core of Lanzhou, China, along with a model of vertical concentration gradients in high-rise buildings. Methods: In each of four seasons in 2016–2017, NO2 was measured using Ogawa badges for 2 weeks at 75 ground-level sites. PM2.5 was measured using DataRAM for shorter time intervals at a subset (N = 38) of the 75 sites. Vertical profile measurements were conducted on 9 stories at 2 high-rise buildings (N = 18), with one building facing traffic and another facing away from traffic. The average seasonal concentrations of NO2 and PM2.5 at ground level were regressed against spatial predictors, including elevation, population, road network, land cover, and land use. The vertical variations were investigated and linked to ground-level predictions with exponential models. Results: We developed robust LUR models at the ground level for estimated annual averages of NO2 (R2: 0.71, adjusted R2: 0.67, and Leave-One-Out Cross Validation (LOOCV) R2: 0.64) and PM2.5 (R2: 0.77, adjusted R2: of 0.73, and LOOCV R2: 0.67) in the urban core of Lanzhou, China. The LUR models for the estimated seasonal averages of NO2 showed similar patterns. Vertical variation of NO2 and PM2.5 differed by windows orientation with respect to traffic, by season or by time of a day. Vertical variation functions incorporated the ground-level LUR predictions, in a form that could allow for exposure assessment in future epidemiological investigations. Conclusions: Ground-level NO2 and PM2.5 showed substantial spatial variations, explained by traffic and land use patterns. Further, vertical variation of air pollution levels is significant under certain conditions, suggesting that exposure misclassification could occur with traditional LUR that ignores vertical variation. More studies are needed to fully characterize three-dimensional concentration patterns to accurately estimate air pollution exposures for residents in high-rise buildings, but our LUR models reinforce that concentration heterogeneity is not captured by the limited government monitors in the Lanzhou urban area.

The purpose of this study was to assess the concordance in categorization and radiologic T staging using Liver Imaging Reporting and Data System (LI-RADS, LR) version 2017 (v2017), version 2018 (v2018), and the Organ Procurement and Transplantation Network (OPTN) criteria. All magnetic resonance imaging and computed tomography reports using a standardized LI-RADS macro between April 2015 and March 2018 were identified retrospectively. The major features (size, arterial phase hyperenhancement, washout, enhancing capsule, or threshold growth) were extracted from the report for each LR-3, LR-4, and LR-5 observation. Each observation was assigned a new category based on LI-RADS v2017, v2018, and OPTN criteria. Radiologic T stage was calculated based on the size and number of LR-5 or OPTN class 5 observations. Categories and T stages assigned by each system were compared descriptively. There were 398 patients (66.6% male; mean age, 63.4 years) with 641 observations (median size, 14 mm) who were included. A total of 73/182 (40.1%) observations categorized LR-4 by LI-RADS v2017 were up-categorized to LR-5 by LI-RADS v2018 due to changes in the LR-5 criteria, and 4/196 (2.0%) observations categorized as LR-5 by LI-RADS v2017 were down-categorized to LR-4 by LI-RADS v2018 due to changes in the threshold growth definition. The T stage was higher by LI-RADS v2018 than LI-RADS v2017 in 49/398 (12.3%) patients. Compared with the OPTN stage, 12/398 (3.0%) patients were upstaged by LI-RADS v2017 and 60/398 (15.1%) by LI-RADS v2018. Of 101 patients, 5 (5.0%) patients with T2 stage based on LI-RADS v2017 and 10/102 (9.8%) patients with T2 stage based on LI-RADS v2018 did not meet the T2 criteria based on the OPTN criteria. Of the 98 patients with a T2 stage based on OPTN criteria, 2 (2.0%) had a T stage ≥3 based on LI-RADS v2017 and 6 (6.1%) had a T stage ≥3 based on LI-RADS v2018.

School facility conditions, environment, and perceptions of safety and learning have been investigated for their impact on child development. However, it is important to consider how the environment separately influences academic performance and attendance after controlling for school and community factors. Using results from the Maryland School Assessment, we considered outcomes of school-level proficiency in reading and math plus attendance and chronic absences, defined as missing 20 or more days, for grades 3–5 and 6–8 at 158 urban schools. Characteristics of the environment included school facility conditions, density of nearby roads, and an index industrial air pollution. Perceptions of school safety, learning, and institutional environment were acquired from a School Climate Survey. Also considered were neighborhood factors at the community statistical area, including demographics, crime, and poverty based on school location. Poisson regression adjusted for over-dispersion was used to model academic achievement and multiple linear models were used for attendance. Each 10-unit change in facility condition index, denoting worse quality buildings, was associated with a decrease in reading (1.0% (95% CI: 0.1–1.9%) and math scores (0.21% (95% CI: 0.20-0.40), while chronic absences increased by 0.75% (95% CI: 0.30–1.39). Each log increase the EPA's Risk Screening Environmental Indicator (RSEI) value for industrial hazards, resulted in a marginally significant trend of increasing absenteeism (p < 0.06), but no association was observed with academic achievement. All results were robust to school-level measures of racial composition, free and reduced meals eligibility, and community poverty and crime. These findings provide empirical evidence for the importance of the community and school environment, including building conditions and neighborhood toxic substance risk, on academic achievement and attendance.

Objectives To design a method that uses preliminary hazard mapping data to optimize the number and location of sensors within a network for a long-term assessment of occupational concentrations, while preserving temporal variability, accuracy, and precision of predicted hazards. Methods Particle number concentrations (PNCs) and respirable mass concentrations (RMCs) were measured with direct-reading instruments in a large heavy-vehicle manufacturing facility at 80-82 locations during 7 mapping events, stratified by day and season. Using kriged hazard mapping, a statistical approach identified optimal orders for removing locations to capture temporal variability and high prediction precision of PNC and RMC concentrations. We compared optimal-removal, random-removal, and least-optimal-removal orders to bound prediction performance. Results The temporal variability of PNC was found to be higher than RMC with low correlation between the two particulate metrics (ρ = 0.30). Optimal-removal orders resulted in more accurate PNC kriged estimates (root mean square error [RMSE] = 49.2) at sample locations compared with random-removal order (RMSE = 55.7). For estimates at locations having concentrations in the upper 10th percentile, the optimal-removal order preserved average estimated concentrations better than random- or least-optimal-removal orders (P < 0.01). However, estimated average concentrations using an optimal-removal were not statistically different than random-removal when averaged over the entire facility. No statistical difference was observed for optimal- and random-removal methods for RMCs that were less variable in time and space than PNCs. Conclusions Optimized removal performed better than random-removal in preserving high temporal variability and accuracy of hazard map for PNC, but not for the more spatially homogeneous RMC. These results can be used to reduce the number of locations used in a network of static sensors for long-term monitoring of hazards in the workplace, without sacrificing prediction performance.

Strongyloidiasis is an emerging parasitic infection with intriguing epidemiology, presentation, and clinical management. We report a case of hyperinfection syndrome complicated by E. coli bacteremia and meningitis with one of the isolates showing a unique resistance pattern recently being recognized. This report describes the aspect of invasive bacterial infections in strongyloidiasis and highlights the unique susceptibility pattern of the E. coli isolate and the extreme caution required during the antibiotic therapy.

BACKGROUND Occurrence, severity and geographic extent of droughts are anticipated to increase under climate change, but the health consequences of drought conditions are unknown. We estimate risks of cardiovascular and respiratory-related hospitalization and mortality associated with drought conditions for the western U.S. elderly population. METHODS For counties in the western U.S. (N=618) and for the period 2000 to 2013, we use data from the U.S. Drought Monitor to identify: 1) full drought periods; 2) non-drought periods; and 3) worsening drought periods stratified by low- and high-severity. We use Medicare claims to calculate daily rates of cardiovascular admissions, respiratory admissions, and deaths among adults 65 years or older. Using a two-stage hierarchical model, we estimated the percentage change in health risks when comparing drought to non-drought period days controlling for daily weather and seasonal trends. FINDINGS On average there were 2·1 million days and 0·6 million days classified as non-drought periods and drought periods, respectively. Compared to non-drought periods, respiratory admissions significantly decreased by -1·99% (95% posterior interval (PI): -3·56, -0·38) during the full drought period, but not during worsening drought conditions. Mortality risk significantly increased by 1·55% (95% PI: 0·17, 2·95) during the high-severity worsening drought period, but not the full drought period. Cardiovascular admissions did not differ significantly during either drought or worsening drought periods. In counties where drought occurred less frequently, we found risks for cardiovascular disease and mortality to increase during worsening drought conditions. INTERPRETATIONS Drought conditions increased risk of mortality during high-severity worsening drought, but decreased the risk of respiratory admissions during full drought periods among older adults. Counties that experience fewer drought events show larger risk for mortality and cardiovascular disease. This research describes an understudied environmental association with global health significance.

Few studies have explored the relationship between coarse particles (PM10-2.5) and adverse birth outcomes. We examined associations between gestational exposure of PM10-2.5 and birth weight. U.S. birth certificates data (1999-2007) were acquired for 8,017,865 births. Gestational and trimester exposures of PM10-2.5 were estimated using co-located PM10 and PM2.5 monitors ≤35km from the population-weighted centroid of mothers' residential counties. A linear regression model was applied, adjusted by potential confounders. As sensitivity analyses, we explored alternative PM10-2.5 estimations, adjustment for PM2.5, and stratification by regions. Gestational exposure to PM10-2.5 was associated with 6.6g (95% Confidence Interval: 5.9, 7.2) lower birth weight per interquartile range increase (7.8μg/m(3)) in PM10-2.5 exposures. All three trimesters showed associations. Under different exposure methods for PM10-2.5, associations remained consistent but with different magnitudes. Results were robust after adjusting for PM2.5, and regional analyses showed associations in all four regions with larger estimates in the South. Our results suggest that PM10-2.5 is associated with birth weight in addition to PM2.5. Regional heterogeneity may reflect differences in population, measurement error, region-specific emission pattern, or different chemical composition within PM10-2.5. Most countries do not set health-based standards for PM10-2.5, but our findings indicate potentially important health effects of PM10-2.5.

<h3>Importance</h3>Optimization of surgical outcomes after colectomy continues to be actively studied, but most studies group right-sided and left-sided colectomies together.<h3>Objective</h3>To determine whether the complication rate differs between right-sided and left-sided colectomies for cancer. As a secondary analysis, we investigated hospital length of stay.<h3>Design</h3>We identified patients who underwent colectomy for colon cancer in the 2005-2008 American College of Surgeons National Surgical Quality Improvement Program database and stratified cases by right and left side. Preoperative, intraoperative, and postoperative factors were compared. Multivariable techniques were used to assess the impact of the side of colectomy on operative outcome measures, adjusting for covariates.<h3>Setting</h3>Hospitals within the American College of Surgeons National Surgical Quality Improvement Program database.<h3>Patients</h3>We identified 4875 patients who underwent elective laparoscopic or open colectomy for right-sided or left-sided colon cancer in the database.<h3>Main Outcomes and Measures</h3>Major complications and surgical site infection (SSI) rates.<h3>Results</h3>In the 4875 colectomies studied, a laparoscopic approach was used in 42% of cases and at similar frequency in right-sided and left-sided colectomies. Thirty-day mortality (1.5%) was similar in both groups. Major complications were seen in 17% of patients in each group. Superficial SSI was more likely to occur in patients who underwent left-sided colectomy (8.2% vs 5.9%). Among patients with postoperative sepsis or deep or organ space SSIs, more patients in the left-sided colectomy group underwent reoperation compared with the right-sided colectomy group (56% vs 30%). Laparoscopic right-sided colectomy patients were more likely to have a prolonged hospital length of stay than laparoscopic left-sided colectomy patients (odds ratio, 1.39; 95% CI, 1.09-1.78).<h3>Conclusions and Relevance</h3>The outcomes after colectomy for cancer are comparable in right-sided and left-sided resections, except for in the case of superficial SSI, which is less common in right-sided resections. Further research on SSI after colectomy should incorporate right vs left side as a potential preoperative risk factor.