Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities.

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (ß: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (ß:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Factors affecting urinary organophosphate pesticide metabolite levels among Californian agricultural community members.

Organophosphate (OP) pesticides are widely used in California for agricultural pest and weed control despite their well-documented adverse health effects among infants, children, and adults. We sought to identify factors affecting urinary OP metabolites among families living in high-exposure communities. Our study included 80 children and adults who lived within 61 m (200 ft) of agricultural fields in the Central Valley of California in January and June 2019, which are pesticide non-spraying and spraying seasons, respectively. We collected one urine sample per participant during each visit to measure dialkyl phosphate (DAP) metabolites; these were coupled with in-person surveys to identify health, household, sociodemographic, pesticide exposure, and occupational risk factors. We used a data-driven, best subsets regression approach to identify key factors that influenced urinary DAPs. Participants were mostly Hispanic/Latino(a) (97.5 %), over half were female (57.5 %), and most households reported having a member who worked in agriculture (70.6 %). Among the 149 urine samples suitable for analysis, DAP metabolites were detected in 48.0 % and 40.5 % of samples during January and June, respectively. Total diethyl alkylphosphates (EDE) were only detected in 4.7 % (n = 7) of samples, but total dimethyl alkylphosphates (EDM) were detected in 41.6 % (n = 62) of samples. No differences were observed in urinary DAP levels by visit month or by occupational exposure to pesticides. Best subsets regression identified several individual- and household-level variables that influenced both urinary EDM and total DAPs: the number of years spent living at the current address, household use of chemical products to control mice/rodents, and seasonal employment status. Among adults only, we identified educational attainment (for total DAPs) and age category (for EDM) as significant factors. Our study found consistent urinary DAP metabolites among participants, regardless of spraying season, and identified potential mitigating factors that members of vulnerable populations can implement to protect their health against OP exposure.

Factors Associated with Levels of Organophosphate Pesticides in Household Dust in Agricultural Communities.

Organophosphate (OP) pesticides are associated with numerous adverse health outcomes. Pesticide use data are available for California from the Pesticide Use Report (PUR), but household- and individual-level exposure factors have not been fully characterized to support its refinement as an exposure assessment tool. Unique exposure pathways, such as proximity to agricultural operations and direct occupational contact, further complicate pesticide exposure assessment among agricultural communities. We sought to identify influencing factors of pesticide exposure to support future exposure assessment and epidemiological studies. Household dust samples were collected from 28 homes in four California agricultural communities during January and June 2019 and were analyzed for the presence of OPs. Factors influencing household OPs were identified by a data-driven model via best subsets regression. Key factors that impacted dust OP levels included household cooling strategies, secondary occupational exposure to pesticides, and geographic location by community. Although PUR data demonstrate seasonal trends in pesticide application, this study did not identify season as an important factor, suggesting OP persistence in the home. These results will help refine pesticide exposure assessment for future studies and highlight important gaps in the literature, such as our understanding of pesticide degradation in an indoor environment.

Model choice for estimating the association between exposure to chemical mixtures and health outcomes: A simulation study.

Challenges arise in researching health effects associated with chemical mixtures. Several methods have recently been proposed for estimating the association between health outcomes and exposure to chemical mixtures, but a formal simulation study comparing broad-ranging methods is lacking. We select five recently developed methods and evaluate their performance in estimating the exposure-response function, identifying active mixture components, and identifying interactions in a simulation study. Bayesian kernel machine regression (BKMR) and nonparametric Bayes shrinkage (NPB) were top-performing methods in our simulation study. BKMR and NPB outperformed other contemporary methods and traditional linear models in estimating the exposure-response function and identifying active mixture components. BKMR and NPB produced similar results in a data analysis of the effects of multipollutant exposure on lung function in children with asthma.

Long-Term Acute Care Hospitals Extend ICU Capacity for COVID-19 Response and Recovery.

The COVID-19 pandemic has presented novel challenges for the entire health-care continuum, requiring transformative changes to hospital and post-acute care, including clinical, administrative, and physical modifications to current standards of operations. Innovative use and adaptation of long-term acute care hospitals (LTACHs) can safely and effectively care for patients during the ongoing COVID-19 pandemic. A framework for the rapid changes, including increasing collaboration with external health-care organizations, creating new methods for enhanced communication, and modifying processes focused on patient safety and clinical outcomes, is described for a network of 94 LTACHs. When managed and modified correctly, LTACHs can play a vital role in managing the national health-care pandemic crisis.

The joint effect of ambient air pollution and agricultural pesticide exposures on lung function among children with asthma.

BACKGROUND: Ambient environmental pollutants have been shown to adversely affect respiratory health in susceptible populations. However, the role of simultaneous exposure to multiple diverse environmental pollutants is poorly understood. OBJECTIVE: We applied a multidomain, multipollutant approach to assess the association between pediatric lung function measures and selected ambient air pollutants and pesticides. METHODS: Using data from the US EPA and California Pesticide Use Registry, we reconstructed three months prior exposure to ambient air pollutants ((ozone (O3), nitrogen dioxide (NO2), particulate matter with a median aerodynamic diameter < 2.5 µm (PM2.5) and <10 µm (PM10)) and pesticides (organophosphates (OP), carbamates (C) and methyl bromide (MeBr)) for 153 children with mild intermittent or mild persistent asthma from the San Joaquin Valley of California, USA. We implemented Bayesian kernel machine regression (BKMR) to estimate the association between simultaneous exposures to air pollutants and pesticides and lung function measures (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and forced expiratory flow between 25% and 75% of vital capacity (FEF25-75)). RESULTS: In BKMR analysis, the overall effect of mixtures (pollutants and pesticides) was associated with reduced FEV1 and FVC, particularly when all the environmental exposures were above their 60th percentile. For example, the effect of the overall mixture at the 70th percentile (compared to the median) was a -0.12SD (-50 mL, 95% CI: -180 mL, 90 mL) change in the FEV1 and a -0.18SD (-90 mL, 95% CI: -240 mL, 60 mL) change in the FVC. However, 95% credible intervals around all of the joint effect estimates contained the null value. CONCLUSION: At this agricultural-urban interface, we observed results from multipollutant analyses, suggestive of adverse effects on some pediatric lung function measures following a cumulative increase in ambient air pollutants and agricultural pesticides. Given the uncertainty in effect estimates, this approach should be explored in larger studies.

Association of Organophosphate Pesticide Exposure and a Marker of Asthma Morbidity in an Agricultural Community.

Objectives: We explored the short-term impact of pesticide exposure on asthma exacerbation among children with asthma in an agricultural community.Methods: We obtained repeated urine samples from a subset of 16 school-age children with asthma (n = 139 samples) as part of the Aggravating Factors of Asthma in a Rural Environment (AFARE) study cohort. Biomarkers of organophosphate (OP) pesticide exposure (dialkylphosphates (DAPs)), and asthma exacerbation (leukotriene E4 (uLTE4)) were assessed in urine samples. We used generalized estimating equations to examine the association of summed measures of creatinine-adjusted DAPs (total dimethyl alkylphosphate (EDM), total diethyl alkylphosphate (EDE), and total dialkylphosphate pesticides (EDAP)) and uLTE4 concentration, adjusting for multiple confounders, yielding beta-coefficients with 95% CIs.Results: A total of 139 observations were obtained from the 16 children over the study period, the total number of samples per subject ranged from 1 to 12 (median: 10.5). The geometric mean (GM) of creatinine-adjusted EDE, EDM, and EDAP in this population were 81.0, 71.8 and 168.0 nmol/g, respectively. Increase in uLTE4 levels was consistently associated with increased exposures to DAPs (interquartile range in µg/g): ßEDE: 8.7 (95%CI: 2.8, 14.6); ßEDM: 1.1 (0.5, 1.7); ßEDAP: 4.1 (0.7, 7.5).Conclusion: This study suggests that short-term OP exposure is associated with a higher risk of asthma morbidity, as indicated by increased uLTE4 levels in this cohort of children with asthma in an agricultural community. Additional studies are required to confirm these adverse effects, and explore the mechanisms underlying this relationship.

Characterizing the joint effects of pesticide exposure and criteria ambient air pollutants on pediatric asthma morbidity in an agricultural community.

BACKGROUND: Environmental contributions to pediatric asthma morbidity have been studied extensively in urban settings; exposures characteristic of agricultural and rural communities have received less attention despite a comparable burden of morbidity. METHODS: We obtained repeated urine samples (n = 139) from 16 school-age children with asthma in the Yakima Valley of Washington State between July and October 2012. Biomarkers of organophosphate (OP) pesticide exposure (dialkyl phosphates [DAPs]) and asthma exacerbation (leukotriene E4 [LTE4]) were analyzed in samples. Corresponding 24-hour average particulate matter <2.5 µg (PM2.5) and maximum 8-hour ozone concentration data for the study period were available from local monitoring stations. We evaluated the independent and multi-pollutant associations between LTE4 and exposure to ambient air pollutants and DAPs using generalized estimating equations. For multi-domain and multi-pollutant models, we created categorized pollution combination levels and estimated the relative health impact of exposure to pollutant mixtures. RESULTS: In single-pollutant models, an interquartile range increase in exposures to DAPs was associated with increase in LTE4 levels (ß: 4.1 [0.6-7.6] pg/mg). PM2.5 and ozone were also associated with increase in LTE4, though confidence intervals contained the null value. Increase in LTE4 levels was consistently associated with increase in median-dichotomized multi-pollutant combination exposures; the highest effect estimates were observed with joint highest (vs. the lowest) category of the three-pollutant exposure (PM2.5, ozone, and OP; ß: 53.5, 95% confidence interval = 24.2, 82.8 pg/mg). CONCLUSION: Concurrent short-term exposure to criteria air pollutants and OPs in an agricultural community was associated with an increase in a marker of asthma morbidity.

Optimizing community-level surveillance data for pediatric asthma management.

Community-level approaches for pediatric asthma management rely on locally collected information derived primarily from two sources: claims records and school-based surveys. We combined claims and school-based surveillance data, and examined the asthma-related risk patterns among adolescent students. Symptom data collected from school-based asthma surveys conducted in Oakland, CA were used for case identification and determination of severity levels for students (high and low). Survey data were matched to Medicaid claims data for all asthma-related health care encounters for the year prior to the survey. We then employed recursive partitioning to develop classification trees that identified patterns of demographics and healthcare utilization associated with severity. A total of 561 students had complete matched data; 86.1% were classified as high-severity, and 13.9% as low-severity asthma. The classification tree consisted of eight subsets: three indicating high severity and five indicating low severity. The risk subsets highlighted varying combinations of non-specific demographic and socioeconomic predictors of asthma prevalence, morbidity and severity. For example, the subset with the highest class-prior probability (92.1%) predicted high-severity asthma and consisted of students without prescribed rescue medication, but with at least one in-clinic nebulizer treatment. The predictive accuracy of the tree-based model was approximately 66.7%, with an estimated 91.1% of high-severity cases and 42.3% of low-severity cases correctly predicted. Our analysis draws on the strengths of two complementary datasets to provide community-level information on children with asthma, and demonstrates the utility of recursive partitioning methods to explore a combination of features that convey asthma severity.