Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment

BACKGROUND

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution.

OBJECTIVE

Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation.

METHODS

We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics.

RESULTS

The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination (R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV-R 2  = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 andR 2  = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV-R 2  = 0.51 (with LCS).

IMPACT

We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Olfactory tract/bulb metal concentration in Manganese-exposed mineworkers

BACKGROUND

Manganese (Mn) is an essential micronutrient as well as a well-established neurotoxicant. Occupational and environmental exposures may bypass homeostatic regulation and lead to increased systemic Mn levels. Translocation of ultrafine ambient airborne particles via nasal neuronal pathway to olfactory bulb and tract may be an important pathway by which Mn enters the central nervous system.

OBJECTIVE

To measure olfactory tract/bulb tissue metal concentrations in Mn-exposed and non-exposed mineworkers.

METHODS

Using inductively coupled plasma-mass spectrometry (ICP-MS), we measured and compared tissue metal concentrations in unilateral olfactory tracts/bulbs of 24 Mn-exposed and 17 non-exposed South African mineworkers. We used linear regression to investigate the association between cumulative Mn exposures and olfactory tract/bulb Mn concentration.

RESULTS

The difference in mean olfactory tract/bulb Mn concentrations between Mn-exposed and non-Mn exposed mineworkers was 0.16 µg/g (95% CI -0.11, 0.42); but decreased to 0.09 µg/g (95% CI 0.004, 0.18) after exclusion of one influential observation. Olfactory tract/bulb metal concentration and cumulative Mn exposure suggested there may be a positive association; for each mg Mn/m3-year there was a 0.05 µg/g (95% CI 0.01, 0.08) greater olfactory tract/bulb Mn concentration overall, but -0.003 (95% CI -0.02, 0.02) when excluding the three influential observations. Recency of Mn exposure was not associated with olfactory tract/bulb Mn concentration.

CONCLUSIONS

Our findings suggest that Mn-exposed mineworkers might have higher olfactory tract/bulb tissue Mn concentrations than non-Mn exposed mineworkers, and that concentrations might depend more on cumulative dose than recency of exposure.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 μg/m3]) compared to the model with all LCM measurements (0.84 [0.9 μg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 μg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 μg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 μg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.

Coarse Particulate Matter and Markers of Inflammation and Coagulation in the Multi-Ethnic Study of Atherosclerosis (MESA) Population: A Repeat Measures Analysis

BACKGROUND

In contrast to fine particles, less is known of the inflammatory and coagulation impacts of coarse particulate matter (PM 10 - 2.5 , particulate matter with aerodynamic diameter ≤ 10 μ m  and > 2.5 μ m ). Toxicological research suggests that these pathways might be important processes by which PM 10 - 2.5 impacts health, but there are relatively few epidemiological studies due to a lack of a national PM 10 - 2.5 monitoring network.

OBJECTIVES

We used new spatiotemporal exposure models to examine associations of both 1-y and 1-month average PM 10 - 2.5 concentrations with markers of inflammation and coagulation.

METHODS

We leveraged data from 7,071 Multi-Ethnic Study of Atherosclerosis and ancillary study participants 45-84 y of age who had repeated plasma measures of inflammatory and coagulation biomarkers. We estimated PM 10 - 2.5 at participant addresses 1 y and 1 month before each of up to four exams (2000-2012) using spatiotemporal models that incorporated satellite, regulatory monitoring, and local geographic data and accounted for spatial correlation. We used random effects models to estimate associations with interleukin-6 (IL-6), C-reactive protein (CRP), fibrinogen, and D-dimer, controlling for potential confounders.

RESULTS

Increases in PM 10 - 2.5 were not associated with greater levels of inflammation or coagulation. A 10 - μ g / m 3 increase in annual average PM 10 - 2.5 was associated with a 2.5% decrease in CRP [95% confidence interval (CI): - 5.5 , 0.6]. We saw no association between annual average PM 10 - 2.5 and the other markers (IL-6: - 0.7 % , 95% CI: - 2.6 , 1.2; fibrinogen: - 0.3 % , 95% CI: - 0.9 , 0.3; D-dimer: - 0.2 % , 95% CI: - 2.6 , 2.4). Associations consistently showed that a 1 0 - μ g / m 3 increase in 1-month average PM 10 - 2.5 was associated with reduced inflammation and coagulation, though none were distinguishable from no association (IL-6: - 1.2 % , 95% CI: - 3.0   , 0.5; CRP: - 2.5 % , 95% CI: - 5.3 , 0.4; fibrinogen: - 0.4 % , 95% CI: - 1.0 , 0.1; D-dimer: - 2.0 % , 95% CI: - 4.3 , 0.3).

DISCUSSION

We found no evidence that PM 10 - 2.5 is associated with higher inflammation or coagulation levels. More research is needed to determine whether the inflammation and coagulation pathways are as important in explaining observed PM 10 - 2.5 health impacts in humans as they have been shown to be in toxicology studies or whether PM 10 - 2.5 might impact human health through alternative biological mechanisms. https://doi.org/10.1289/EHP12972.

Airway Tree Caliber and Susceptibility to Pollution-associated Emphysema: MESA Air and Lung Studies

RATIONALE

Airway tree morphology varies in the general population and may modify the distribution and uptake of inhaled pollutants.

OBJECTIVES

We hypothesized that smaller airway caliber would be associated with emphysema progression and would increase susceptibility to air pollutant-associated emphysema progression.

METHODS

The Multi-Ethnic Study of Atherosclerosis (MESA) is a general population cohort of adults 45-84 years old from six U.S. communities. Airway tree caliber was quantified as the mean of airway lumen diameters measured from baseline cardiac computed tomography (CT) (2000-02). Percent emphysema, defined as percentage of lung pixels below -950 Hounsfield units, was assessed up to 5 times per participant via cardiac CT scan (2000-07) and equivalent regions on lung CT scan (2010-18). Long-term outdoor air pollutant concentrations (PM2.5, NOx, O3) were estimated at residential address with validated spatio-temporal models. Linear mixed models estimated the association between airway tree caliber and emphysema progression; modification of pollutant-associated emphysema progression was assessed using multiplicative interaction terms.

MAIN RESULTS

Among 6,793 participants (mean±SD age: 62±10 years), baseline airway tree caliber was 3.95±1.1 mm and median (interquartile range) of percent emphysema was 2.88 (1.21-5.68). In adjusted analyses, 10-year emphysema progression rate was 0.75 percentage points (95%CI 0.54-0.96%) higher in the smallest compared to largest airway tree caliber quartile. Airway tree caliber also modified air pollutant-associated emphysema progression.

CONCLUSIONS

Smaller airway tree caliber was associated with accelerated emphysema progression and modified air pollutant-associated emphysema progression. A better understanding of mechanisms of airway-alveolar homeostasis and air pollutant deposition are needed.

Traffic-related air pollution and dementia incidence in the Adult Changes in Thought Study

BACKGROUND

While epidemiologic evidence links higher levels of exposure to fine particulate matter (PM2.5) to decreased cognitive function, fewer studies have investigated links with traffic-related air pollution (TRAP), and none have examined ultrafine particles (UFP, ≤100 nm) and late-life dementia incidence.

OBJECTIVE

To evaluate associations between TRAP exposures (UFP, black carbon [BC], and nitrogen dioxide [NO2]) and late-life dementia incidence.

METHODS

We ascertained dementia incidence in the Seattle-based Adult Changes in Thought (ACT) prospective cohort study (beginning in 1994) and assessed ten-year average TRAP exposures for each participant based on prediction models derived from an extensive mobile monitoring campaign. We applied Cox proportional hazards models to investigate TRAP exposure and dementia incidence using age as the time axis and further adjusting for sex, self-reported race, calendar year, education, socioeconomic status, PM2.5, and APOE genotype. We ran sensitivity analyses where we did not adjust for PM2.5 and other sensitivity and secondary analyses where we adjusted for multiple pollutants, applied alternative exposure models (including total and size-specific UFP), modified the adjustment covariates, used calendar year as the time axis, assessed different exposure periods, dementia subtypes, and others.

RESULTS

We identified 1,041 incident all-cause dementia cases in 4,283 participants over 37,102 person-years of follow-up. We did not find evidence of a greater hazard of late-life dementia incidence with elevated levels of long-term TRAP exposures. The estimated hazard ratio of all-cause dementia was 0.98 (95 % CI: 0.92-1.05) for every 2000 pt/cm3 increment in UFP, 0.95 (0.89-1.01) for every 100 ng/m3 increment in BC, and 0.96 (0.91-1.02) for every 2 ppb increment in NO2. These findings were consistent across sensitivity and secondary analyses.

DISCUSSION

We did not find evidence of a greater hazard of late-life dementia risk with elevated long-term TRAP exposures in this population-based prospective cohort study.

Comparison of PM2.5 Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS)

BACKGROUND

Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear.

OBJECTIVES

Our objective is to compare particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses.

METHODS

We assigned annual PM 2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM 2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region.

RESULTS

With a few exceptions, relative agreement of approach-specific PM 2.5 exposure estimates was high for PM 2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM 2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM 2.5 . There was no evidence of large differences in health effects associations with PM 2.5 among estimation approaches in analyses adjusted for climate region.

CONCLUSIONS

Different estimation approaches produced similar spatial patterns of PM 2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM 2.5 -health effects associations were similar among estimation approaches. PM 2.5 estimates and PM 2.5 -health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM 2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

The Effect of Pesticide Spray Season and Residential Proximity to Agriculture on Glyphosate Exposure among Pregnant People in Southern Idaho, 2021

BACKGROUND

Glyphosate is one of the most heavily used pesticides in the world, but little is known about sources of glyphosate exposure in pregnant people living in agricultural regions.

OBJECTIVE

Our objective was to evaluate glyphosate exposure during pregnancy in relation to residential proximity to agriculture as well as agricultural spray season.

METHODS

We quantified glyphosate concentrations in 453 urine samples collected biweekly from a cohort of 40 pregnant people in southern Idaho from February through December 2021. We estimated each participant's glyphosate exposure as the geometric mean (GM) of glyphosate concentrations measured in all samples (average n = 11 samples/participant), as well as the GM of samples collected during the pesticide "spray season" (defined as those collected 1 May-15 August; average n = 5 samples/participant) and the "nonspray season" (defined as those collected before 1 May or after 15 August; average n = 6 samples/participant). We defined participants who resided < 0.5 km from an actively cultivated agriculture field to live "near fields" and those residing ≥ 0.5 km from an agricultural field to live "far from fields" (n = 22 and 18, respectively).

RESULTS

Among participants living near fields, urinary glyphosate was detected more frequently and at significantly increased GM concentrations during the spray season in comparison with the nonspray season (81% vs. 55%; 0.228 μ g / L vs. 0.150 μ g / L , p < 0.001 ). In contrast, among participants who lived far from fields, neither glyphosate detection frequency nor GMs differed in the spray vs nonspray season (66% vs. 64%; 0.154 μ g / L vs. 0.165 μ g / L , p = 0.45 ). Concentrations did not differ by residential proximity to fields during the nonspray season (0.154 μ g / L vs. 0.165 μ g / L , for near vs. far, p = 0.53 ).

DISCUSSION

Pregnant people living near agriculture fields had significantly increased urinary glyphosate concentrations during the agricultural spray season than during the nonspray season. They also had significantly higher urinary glyphosate concentrations during the spray season than those who lived far from agricultural fields at any time of year, but concentrations did not differ during the nonspray season. These findings suggest that agricultural glyphosate spray is a source of exposure for people living near fields. https://doi.org/10.1289/EHP12768.

Comparison of Particulate Air Pollution From Different Emission Sources and Incident Dementia in the US

Importance

Emerging evidence indicates that exposure to fine particulate matter (PM2.5) air pollution may increase dementia risk in older adults. Although this evidence suggests opportunities for intervention, little is known about the relative importance of PM2.5 from different emission sources.

Objective

To examine associations of long-term exposure of total and source-specific PM2.5 with incident dementia in older adults.

Design, Setting, and Participants

The Environmental Predictors of Cognitive Health and Aging study used biennial survey data from January 1, 1998, to December 31, 2016, for participants in the Health and Retirement Study, which is a nationally representative, population-based cohort study in the US. The present cohort study included all participants older than 50 years who were without dementia at baseline and had available exposure, outcome, and demographic data between 1998 and 2016 (N = 27 857). Analyses were performed from January 31 to May 1, 2022.

Exposures

The 10-year mean total PM2.5 and PM2.5 from 9 emission sources at participant residences for each month during follow-up using spatiotemporal and chemical transport models.

Main Outcomes and Measures

The main outcome was incident dementia as classified by a validated algorithm incorporating respondent-based cognitive testing and proxy respondent reports. Adjusted hazard ratios (HRs) were estimated for incident dementia per IQR of residential PM2.5 concentrations using time-varying, weighted Cox proportional hazards regression models with adjustment for the individual- and area-level risk factors.

Results

Among 27 857 participants (mean [SD] age, 61 [10] years; 15 747 [56.5%] female), 4105 (15%) developed dementia during a mean (SD) follow-up of 10.2 [5.6] years. Higher concentrations of total PM2.5 were associated with greater rates of incident dementia (HR, 1.08 per IQR; 95% CI, 1.01-1.17). In single pollutant models, PM2.5 from all sources, except dust, were associated with increased rates of dementia, with the strongest associations for agriculture, traffic, coal combustion, and wildfires. After control for PM2.5 from all other sources and copollutants, only PM2.5 from agriculture (HR, 1.13; 95% CI, 1.01-1.27) and wildfires (HR, 1.05; 95% CI, 1.02-1.08) were robustly associated with greater rates of dementia.

Conclusion and Relevance

In this cohort study, higher residential PM2.5 levels, especially from agriculture and wildfires, were associated with higher rates of incident dementia, providing further evidence supporting PM2.5 reduction as a population-based approach to promote healthy cognitive aging. These findings also indicate that intervening on key emission sources might have value, although more research is needed to confirm these findings.

Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.

Urinary Glyphosate Concentrations among Pregnant Participants in a Randomized, Crossover Trial of Organic and Conventional Diets

BACKGROUND

Consumption of an organic diet reduces exposure to a range of agricultural pesticides. Only three studies have examined the effect of an organic diet intervention on exposure to the herbicide glyphosate, the most heavily used agricultural chemical in the world. Despite its widespread use, the primary sources of glyphosate exposure in humans are poorly understood.

OBJECTIVE

Our objective was to examine the effect of an organic diet intervention on urinary glyphosate concentrations among pregnant individuals.

METHODS

We conducted a 2-wk randomized crossover trial in which 39 pregnant participants living near (≤ 0.5 km ) and far (> 0.5 km ) from agricultural fields received a 1-wk supply of conventional groceries and 1 wk of organic groceries, randomized to order. We collected daily first morning void urine samples and analyzed composite samples from each week for glyphosate. We examined differences in urinary glyphosate concentrations between the conventional week and the organic week among all participants and stratified by residential proximity to an agricultural field.

RESULTS

Median specific gravity-adjusted glyphosate concentrations were 0.19 μ g / L and 0.16 μ g / L during the conventional and organic weeks, respectively. We observed modest decreases in urinary glyphosate concentrations from the conventional to organic week among far-field participants, but no difference among near-field participants. In secondary analyses excluding participants who did not meet a priori criteria of compliance with the intervention, we observed significant decreases in urinary glyphosate concentrations, particularly among far-field participants (p < 0.01 - 0.02 , depending on exclusion criteria).

DISCUSSION

This trial is the first to examine the effect of an organic diet intervention on glyphosate among people living near and far from agricultural fields. Our results suggest that diet is an important contributor to glyphosate exposure in people living > 0.5 km from agricultural fields; for people living near crops, agriculture may be a dominant exposure source during the pesticide spray season. https://doi.org/10.1289/EHP12155.

Exposure Disparities by Income, Race and Ethnicity, and Historic Redlining Grade in the Greater Seattle Area for Ultrafine Particles and Other Air Pollutants

BACKGROUND

Growing evidence shows ultrafine particles (UFPs) are detrimental to cardiovascular, cerebrovascular, and respiratory health. Historically, racialized and low-income communities are exposed to higher concentrations of air pollution.

OBJECTIVES

Our aim was to conduct a descriptive analysis of present-day air pollution exposure disparities in the greater Seattle, Washington, area by income, race, ethnicity, and historical redlining grade. We focused on UFPs (particle number count) and compared with black carbon, nitrogen dioxide, and fine particulate matter (PM 2.5 ) levels.

METHODS

We obtained race and ethnicity data from the 2010 U.S. Census, median household income data from the 2006-2010 American Community Survey, and Home Owners' Loan Corporation (HOLC) redlining data from the University of Richmond's Mapping Inequality. We predicted pollutant concentrations at block centroids from 2019 mobile monitoring data. The study region encompassed much of urban Seattle, with redlining analyses restricted to a smaller region. To analyze disparities, we calculated population-weighted mean exposures and regression analyses using a generalized estimating equation model to account for spatial correlation.

RESULTS

Pollutant concentrations and disparities were largest for blocks with median household income of < $ 20,000 , Black residents, HOLC Grade D, and ungraded industrial areas. UFP concentrations were 4% lower than average for non-Hispanic White residents and higher than average for racialized groups (Asian, 3%; Black, 15%; Hispanic, 6%; Native American, 8%; Pacific Islander, 11%). For blocks with median household incomes of < $ 20,000 , UFP concentrations were 40% higher than average, whereas blocks with incomes of > $ 110,000 had UFP concentrations 16% lower than average. UFP concentrations were 28% higher for Grade D and 49% higher for ungraded industrial areas compared with Grade A. Disparities were highest for UFPs and lowest for PM 2.5 exposure levels.

DISCUSSION

Our study is one of the first to highlight large disparities with UFP exposures compared with multiple pollutants. Higher exposures to multiple air pollutants and their cumulative effects disproportionately impact historically marginalized groups. https://doi.org/10.1289/EHP11662.

Wildfire smoke exposure and emergency department visits in Washington State

Wildfires are increasing in prevalence in western North America due to changing climate conditions. A growing number of studies examine the impact of wildfire smoke on morbidity; however, few evaluate these impacts using syndromic surveillance data that cover many emergency departments (EDs). We used syndromic surveillance data to explore the effect of wildfire smoke exposure on all-cause respiratory and cardiovascular ED visits in Washington state. Using a time-stratified case crossover design, we observed an increased odds of asthma visits immediately after and in all five days following initial exposure (lag 0 OR: 1.13; 95% CI: 1.10, 1.17; lag 1-5 ORs all 1.05 or greater with a lower CI of 1.02 or higher), and an increased odds of respiratory visits in all five days following initial exposure (lag 1 OR: 1.02; 95% CI: 1.00, 1.03; lag 2-5 ORs and lower CIs were all at least as large) comparing wildfire smoke to non-wildfire smoke days. We observed mixed results for cardiovascular visits, with evidence of increased odds emerging only several days following initial exposure. We also found increased odds across all visit categories for a 10 μg m^-3 increase in smoke-impacted PM_2.5. In stratified analyses, we observed elevated odds for respiratory visits among ages 19-64, for asthma visits among ages 5-64, and mixed risk estimates for cardiovascular visits by age group. This study provides evidence of an increased risk of respiratory ED visits immediately following initial wildfire smoke exposure, and increased risk of cardiovascular ED visits several days following initial exposure. These increased risks are seen particularly among children and younger to middle-aged adults.

Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment

BACKGROUND

Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment.

OBJECTIVE

We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces.

METHODS

We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model.

RESULTS

The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results.

SIGNIFICANCE

A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts.

IMPACT STATEMENT

Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs

BACKGROUND

Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms.

OBJECTIVES

We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies.

METHODS

We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs.

RESULTS

Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility.

DISCUSSION

Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework

BACKGROUND

Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.

OBJECTIVES

To develop a daily, high-resolution ambient CO exposure prediction model at the city scale.

METHODS

We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations.

RESULTS

The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads.

DISCUSSION

The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 μg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.

Environmental manganese exposure and cognitive control in a South African population

OBJECTIVE

To characterize the association between environmental (residential air) manganese (Mn) exposure and cognitive performance, focusing on cognitive control, in a Black African population.

METHODS

We administered the Go-No-Go, Digit Span, and Matrix Reasoning tests to population-based samples age ≥40 from a high Mn (smelter) exposed community, Meyerton (N = 629), and a demographically comparable low (background levels) non-exposed community, Ethembalethu, (N = 96) in Gauteng province, South Africa. We investigated the associations between community and performance on the cognitive tests, using linear regression. We adjusted a priori for age and sex, and examined the effect of adjustment for education, nonverbal IQ, smoking, and alcohol consumption. We measured airborne PM_2.5-Mn to confirm community exposure differences.

RESULTS

Compared to Ethembalethu residents, Meyerton residents' test scores were lower (poorer) for all tests: 0.55 (95 % confidence interval [CI] 0.08, 1.03) lower scores for Matrix Reasoning, 0.34 (95 % CI -0.07, 0.75) lower for Digit Span, and 0.15 (95 % CI 0.09, 0.21) lower for Go-No-Go (high frequency discriminability index [probability]). The latter represented the most marked difference in terms of z-scores (0.50, 95 % CI 0.30, 0.71 standard deviations lower). The mean of the z-score of each of the three tests was also lower (0.34, 95 % CI 0.18, 0.50 standard deviations lower). These associations were similar in men and women, but attenuated with adjustment for education. Differences for Matrix Reasoning and Digit Span between the two communities were observed only among those who had lived in Meyerton ≥10 years, whereas for Go-No-Go, differences were also apparent among those who had lived in Meyerton <10 years. Mean PM_2.5-Mn at a long-term fixed site in Meyerton was 203 ng/m³ and 10 ng/m³ in Ethembalethu.

CONCLUSION

Residence in a community near a high Mn emission source is associated with cognitive dysfunction, including aspects of cognitive control as assessed by the Go-No-Go test.

Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM_2.5 in epidemiological studies. Using sparse regulatory PM_2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM_2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM_2.5 measurements from a publicly available commercial low-cost PM_2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R² and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m³ to 0.92 and 1.63 μg/m³, respectively. The external spatial validation R² and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m³ to 0.79 and 0.88 μg/m³, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010

BACKGROUND

Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time.

OBJECTIVE

The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010.

METHODS

We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter with aerodynamic diameter ≤2.5μm (PM2.5; excluding year-1990), particulate matter with aerodynamic diameter ≤10μm (PM10), and sulfur dioxide (SO2). We used census data for demographic information and a national empirical model for ambient air pollution levels.

RESULTS

For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ethnic group with the highest vs. lowest national-average exposure was largest for NO2 [54% (4.6 ppb)], smallest for O3 [3.6% (1.6 ppb)], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for PM2.5 in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial/ethnic exposure disparities declined by between 35% (0.66μg/m3; PM2.5) and 88% (0.35 ppm; CO); relative disparities declined to between 0.99× (PM2.5; i.e., nearly zero change) and 0.71× (CO; i.e., a ∼29% reduction).

DISCUSSION

As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/EHP8584.

Reanalysis of the association between reduction in long-term PM2.5 concentrations and improved life expectancy

BACKGROUND

Much of the current evidence of associations between long-term PM_2.5 and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in the late 1990s. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 μg/m³ reduction in PM_2.5 was associated with an average 0.61 year (standard error (SE) = 0.20) longer life expectancy. That analysis used 1979-1983 averages of PM_2.5 across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 130 monitoring sites. Our reanalysis re-examines this association using modeled PM_2.5 in order to assess population- or spatially-representative exposure. We hypothesized that modeled PM_2.5 with finer spatial resolution provides more accurate health effect estimates compared to limited monitoring data.

METHODS

We used the same data for life expectancy and confounders, as well as the same analysis models, and investigated the same 211 continental U.S. counties, as Pope et al. (2009). For modeled PM_2.5, we relied on a previously-developed point prediction model based on regulatory monitoring data for 1999-2015 and back-extrapolation to 1979. Using this model, we predicted annual average concentrations at centroids of all 72,271 census tracts and 12,501 25-km national grid cells covering the contiguous U.S., to represent population and space, respectively. We averaged these predictions to the county for the two time periods (1979-1983 and 1999-2000), whereas the original analysis used MSA averages given limited monitoring data. Finally, we estimated regression coefficients for PM_2.5 reduction on life expectancy improvement over the two periods, adjusting for area-level confounders.

RESULTS

A 10 μg/m³ decrease in modeled PM_2.5 based on census tract and national grid predictions was associated with 0.69 (standard error (SE) = 0.31) and 0.81 (0.29) -year increases in life expectancy. These estimates are higher than the estimate of Pope et al. (2009); they also have larger SEs likely because of smaller variability in exposure predictions, a standard property of regression. Two sets of effect estimates, however, had overlapping confidence intervals.

CONCLUSIONS

Our approach for estimating population- and spatially-representative PM_2.5 concentrations based on census tract and national grid predictions, respectively, provided generally consistent findings to the original findings using limited monitoring data. This finding lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.

Principal Component Analysis of Striatal and Extrastriatal D2 Dopamine Receptor Positron Emission Tomography in Manganese-Exposed Workers

The relationships between the neurotoxicant manganese (Mn), dopaminergic pathology, and parkinsonism remain unclear. Therefore, we used [11C](N-methyl)benperidol (NMB) positron emission tomography to investigate the associations between Mn exposure, striatal and extrastriatal D2 dopamine receptors (D2R), and motor function in 54 workers with a range of Mn exposure. Cumulative Mn exposure was estimated from work histories, and all workers were examined by a movement specialist and completed a Grooved Pegboard test (GPT). NMB D2R nondisplaceable binding potentials (BPND) were calculated for brain regions of interest. We identified 2 principal components (PCs) in a PC analysis which explained 66.8% of the regional NMB BPND variance (PC1 = 55.4%; PC2 = 11.4%). PC1 was positively correlated with NMB binding in all regions and inversely correlated with age. PC2 was driven by NMB binding in 7 brain regions (all p < .05), positively in the substantia nigra, thalamus, amygdala, and medial orbital frontal gyrus and negatively in the nucleus accumbens, anterior putamen, and caudate. PC2 was associated with both Mn exposure status and exposure duration (years). In addition, PC2 was associated with higher Unified Parkinson's Disease Rating Scale motor subsection 3 (UPDRS3) scores and slower GPT performance. We conclude Mn exposure is associated with both striatal and extrastriatal D2R binding. Multifocal alterations in D2R expression are also associated with motor dysfunction as measured by both the GPT and UPDRS3, demonstrating a link between Mn exposure, striatal and extrastriatal D2R expression, and clinical neurotoxicity.

Fine Particulate Matter and Dementia Incidence in the Adult Changes in Thought Study

BACKGROUND

Air pollution may be associated with elevated dementia risk. Prior research has limitations that may affect reliability, and no studies have evaluated this question in a population-based cohort of men and women in the United States.

OBJECTIVES

We evaluated the association between time-varying, 10-y average fine particulate matter (PM2.5) exposure and hazard of all-cause dementia. An additional goal was to understand how to adequately control for age and calendar-time-related confounding through choice of the time axis and covariate adjustment.

METHODS

Using the Adult Changes in Thought (ACT) population-based prospective cohort study in Seattle, we linked spatiotemporal model-based PM2.5 exposures to participant addresses from 1978 to 2018. Dementia diagnoses were made using high-quality, standardized, consensus-based protocols at biennial follow-ups. We conducted multivariable Cox proportional hazards regression to evaluate the association between time-varying, 10-y average PM2.5 exposure and time to event in a model with age as the time axis, stratified by apolipoprotein E (APOE) genotype, and adjusted for sex, education, race, neighborhood median household income, and calendar time. Alternative models used calendar time as the time axis.

RESULTS

We report 1,136 cases of incident dementia among 4,166 individuals with nonmissing APOE status. Mean [mean ± standard deviation (SD)] 10-y average PM2.5 was 10.1 (±2.9) μg/m3. Each 1-μg/m3 increase in the moving average of 10-y PM2.5 was associated with a 16% greater hazard of all-cause dementia [1.16 (95% confidence interval: 1.03, 1.31)]. Results using calendar time as the time axis were similar.

DISCUSSION

In this prospective cohort study with extensive exposure data and consensus-based outcome ascertainment, elevated long-term exposure to PM2.5 was associated with increased hazard of all-cause dementia. We found that optimal control of age and time confounding could be achieved through use of either age or calendar time as the time axis in our study. Our results strengthen evidence on the neurodegenerative effects of PM2.5. https://doi.org/10.1289/EHP9018.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO₂; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O₃) and NO₂. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R² measures (CO: RMSE = 18 ppb, R² = 0.97; NO: RMSE = 2 ppb, R² = 0.97). Performance measures for NO₂ and O₃ were somewhat lower (NO₂: RMSE = 3 ppb, R² = 0.79; O₃: RMSE = 4 ppb, R² = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Depression and anxiety in a manganese-exposed community

OBJECTIVE

To characterize the association between residential environmental manganese (Mn) exposure and depression and anxiety, given prior associations among occupationally-exposed workers.

METHODS

We administered the Beck Depression Inventory (BDI) and the State-Trait Anxiety Inventory (STAI) to 697 study participants in their preferred languages. These participants represented a population-based sample of residents aged ≥40 from two predominantly Black African communities in Gauteng province, South Africa: 605 in Meyerton, adjacent to a large Mn smelter, and 92 in Ethembalethu, a comparable non-exposed community. We investigated the associations between community (Meyerton vs. Ethembalethu) and severity of depression and anxiety, using linear regression, adjusting for age and sex. To document community-level differences in Mn exposure, we measured airborne PM_2.5-Mn.

RESULTS

Meyerton residents had BDI scores 5.63 points (95 % CI 3.07, 8.20) higher than Ethembalethu residents, with all questions contributing to this significant difference. STAI-state scores were marginally higher in Meyerton than Ethembalethu residents [2.12 (95 % CI -0.17, 4.41)], whereas STAI-trait scores were more similar between the communities [1.26 (95 % CI -0.82, 3.35)]. Mean PM_2.5-Mn concentration was 203 ng/m³ at a long-term fixed site in Meyerton and 10 ng/m³ in Ethembalethu.

CONCLUSION

Residence near Mn emission sources may be associated with greater depression symptomatology, and possibly current, but not lifetime, anxiety.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

PURPOSE OF REVIEW

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.

RECENT FINDINGS

Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Associations of Household Income with Health-Related Quality of Life Following a Colorectal Cancer Diagnosis Varies With Neighborhood Socioeconomic Status

BACKGROUND

Existing evidence indicates household income as a predictor of health-related quality of life (HRQoL) following a colorectal cancer diagnosis. This association likely varies with neighborhood socioeconomic status (nSES), but evidence is limited.

METHODS

We included data from 1,355 colorectal cancer survivors participating in the population-based Puget Sound Colorectal Cancer Cohort (PSCCC). Survivors reported current annual household income; we measured HRQoL via the Functional Assessment of Cancer Therapy - Colorectal (FACT-C) tool. Using neighborhood data summarized within a 1-km radial buffer of Census block group centroids, we constructed a multidimensional nSES index measure. We employed survivors' geocoded residential addresses to append nSES score for Census block group of residence. With linear generalized estimating equations clustered on survivor location, we evaluated associations of household income with differences in FACT-C mean score, overall and stratified by nSES. We used separate models to explore relationships for wellbeing subscales.

RESULTS

We found lower household income to be associated with clinically meaningful differences in overall FACT-C scores [<$30K: -13.6; 95% confidence interval (CI): -16.8 to -10.4] and subscale wellbeing after a recent colorectal cancer diagnosis. Relationships were slightly greater in magnitude for survivors living in lower SES neighborhoods.

CONCLUSIONS

Our findings suggest that recently diagnosed lower income colorectal cancer survivors are likely to report lower HRQoL, and modestly more so in lower SES neighborhoods.

IMPACT

The findings from this work will aid future investigators' ability to further consider the contexts in which the income of survivors can be leveraged as a means of improving HRQoL.

Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions

Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005 to 2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single 2-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO₂ and NO_x concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs and only use mobile data collected within 300 m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R² changing from 0.40 to 0.82) but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases, additional spatial information from mobile monitoring and personal sampling is potentially cost-efficient inexpensive way of improving exposure predictions at both 2-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign.

Severity of parkinsonism associated with environmental manganese exposure

BACKGROUND

Exposure to occupational manganese (Mn) is associated with neurotoxic brain injury, manifesting primarily as parkinsonism. The association between environmental Mn exposure and parkinsonism is unclear. To characterize the association between environmental Mn exposure and parkinsonism, we performed population-based sampling of residents older than 40 in Meyerton, South Africa (N = 621) in residential settlements adjacent to a large Mn smelter and in a comparable non-exposed settlement in Ethembalethu, South Africa (N = 95) in 2016-2020.

METHODS

A movement disorders specialist examined all participants using the Unified Parkinson Disease Rating Scale motor subsection part 3 (UPDRS3). Participants also completed an accelerometry-based kinematic test and a grooved pegboard test. We compared performance on the UPDRS3, grooved pegboard, and the accelerometry-based kinematic test between the settlements using linear regression, adjusting for covariates. We also measured airborne PM_2.5-Mn in the study settlements.

RESULTS

Mean PM_2.5-Mn concentration at a long-term fixed site in Meyerton was 203 ng/m³ in 2016-2017 - approximately double that measured at two other neighborhoods in Meyerton. The mean Mn concentration in Ethembalethu was ~ 20 times lower than that of the long-term Meyerton site. UPDRS3 scores were 6.6 (CI 5.2, 7.9) points higher in Meyerton than Ethembalethu residents. Mean angular velocity for finger-tapping on the accelerometry-based kinematic test was slower in Meyerton than Ethembalethu residents [dominant hand 74.9 (CI 48.7, 101.2) and non-dominant hand 82.6 (CI 55.2, 110.1) degrees/second slower]. Similarly, Meyerton residents took longer to complete the grooved pegboard, especially for the non-dominant hand (6.9, CI -2.6, 16.3 s longer).

CONCLUSIONS

Environmental airborne Mn exposures at levels substantially lower than current occupational exposure thresholds in the United States may be associated with clinical parkinsonism.

Fine Particulate Matter and Markers of Alzheimer's Disease Neuropathology at Autopsy in a Community-Based Cohort

BACKGROUND

Evidence links fine particulate matter (PM2.5) to Alzheimer's disease (AD), but no community-based prospective cohort studies in older adults have evaluated the association between long-term exposure to PM2.5 and markers of AD neuropathology at autopsy.

OBJECTIVE

Using a well-established autopsy cohort and new spatiotemporal predictions of air pollution, we evaluated associations of 10-year PM2.5 exposure prior to death with Braak stage, Consortium to Establish a Registry for AD (CERAD) score, and combined AD neuropathologic change (ABC score).

METHODS

We used autopsy specimens (N = 832) from the Adult Changes in Thought (ACT) study, with enrollment ongoing since 1994. We assigned long-term exposure at residential address based on two-week average concentrations from a newly developed spatiotemporal model. To account for potential selection bias, we conducted inverse probability weighting. Adjusting for covariates with tiered models, we performed ordinal regression for Braak and CERAD and logistic regression for dichotomized ABC score.

RESULTS

10-year average (SD) PM2.5 from death across the autopsy cohort was 8.2 (1.9) μg/m3. Average age (SD) at death was 89 (7) years. Each 1μg/m3 increase in 10-year average PM2.5 prior to death was associated with a suggestive increase in the odds of worse neuropathology as indicated by CERAD score (OR: 1.35 (0.90, 1.90)) but a suggestive decreased odds of neuropathology as defined by the ABC score (OR: 0.79 (0.49, 1.19)). There was no association with Braak stage (OR: 0.99 (0.64, 1.47)).

CONCLUSION

We report inconclusive associations between PM2.5 and AD neuropathology at autopsy among a cohort where 94% of individuals experienced 10-year exposures below the current EPA standard. Prior studies of AD risk factors and AD neuropathology are similarly inconclusive, suggesting alternative mechanistic pathways for disease or residual confounding.

Fine Particulate Matter Exposure and Cerebrospinal Fluid Markers of Vascular Injury

BACKGROUND

Cerebrovascular diseases play an important role in dementia. Air pollution is associated with cardiovascular disease, with growing links to neurodegeneration. Prior studies demonstrate associations between fine particulate matter (PM2.5) and biomarkers of endothelial injury in the blood; however, no studies have evaluated these biomarkers in cerebrospinal fluid (CSF).

OBJECTIVE

We evaluate associations between short-term and long-term PM2.5 exposure with CSF vascular cell adhesion molecule-1 (VCAM-1) and e-selectin in cognitively normal and mild cognitive impairment (MCI)/Alzheimer's disease (AD) individuals.

METHODS

We collected CSF from 133 community volunteers at VA Puget Sound between 2001-2012. We assigned short-term PM2.5 from central monitors and long-term PM2.5 based on annual average exposure predictions linked to participant addresses. We performed analyses stratified by cognitive status and adjusted for key covariates with tiered models. Our primary exposure windows for the short-term and long-term analyses were 7-day and 1-year averages, respectively.

RESULTS

Among cognitively normal individuals, a 5 μg/m3 increase in 7-day and 1-year average PM2.5 was associated with elevated VCAM-1 (7-day: 35.4 (9.7, 61.1) ng/ml; 1-year: 51.8 (6.5, 97.1) ng/ml). A 5 μg/m3 increase in 1-year average PM2.5, but not 7-day average, was associated with elevated e-selectin (53.3 (11.0, 95.5) pg/ml). We found no consistent associations among MCI/AD individuals.

CONCLUSIONS

We report associations between short-term and long term PM2.5 and CSF biomarkers of vascular damage in cognitively normal adults. These results are aligned with prior research linking PM2.5 to vascular damage in other biofluids as well as emerging evidence of the role of PM2.5 in neurodegeneration.

Flawed analysis of an intentional human dosing study and its impact on chlorpyrifos risk assessments

In March 1972, Frederick Coulston and colleagues at the Albany Medical College reported results of an intentional chlorpyrifos dosing study to the study's sponsor, Dow Chemical Company. Their report concluded that 0.03 mg/kg-day was the chronic no-observed-adverse-effect-level (NOAEL) for chlorpyrifos in humans. We demonstrate here that a proper analysis by the original statistical method should have found a lower NOAEL (0.014 mg/kg-day), and that use of statistical methods first available in 1982 would have shown that even the lowest dose in the study had a significant treatment effect. The original analysis, conducted by Dow-employed statisticians, did not undergo formal peer review; nevertheless, EPA cited the Coulston study as credible research and kept its reported NOAEL as a point of departure for risk assessments throughout much of the 1980's and 1990's. During that period, EPA allowed chlorpyrifos to be registered for multiple residential uses that were later cancelled to reduce potential health impacts to children and infants. Had appropriate analyses been employed in the evaluation of this study, it is likely that many of those registered uses of chlorpyrifos would not have been authorized by EPA. This work demonstrates that reliance by pesticide regulators on research results that have not been properly peer-reviewed may needlessly endanger the public.

Transcriptomic profiling of PBDE-exposed HepaRG cells unveils critical lncRNA- PCG pairs involved in intermediary metabolism

Polybrominated diphenyl ethers (PBDEs) were formally used as flame-retardants and are chemically stable, lipophlic persistent organic pollutants which are known to bioaccumulate in humans. Although its toxicities are well characterized, little is known about the changes in transcriptional regulation caused by PBDE exposure. Long non-coding RNAs (lncRNAs) are increasingly recognized as key regulators of transcriptional and translational processes. It is hypothesized that lncRNAs can regulate nearby protein-coding genes (PCGs) and changes in the transcription of lncRNAs may act in cis to perturb gene expression of its neighboring PCGs. The goals of this study were to 1) characterize PCGs and lncRNAs that are differentially regulated from exposure to PBDEs; 2) identify PCG-lncRNA pairs through genome annotation and predictive binding tools; and 3) determine enriched canonical pathways caused by differentially expressed lncRNA-PCGs pairs. HepaRG cells, which are human-derived hepatic cells that accurately represent gene expression profiles of human liver tissue, were exposed to BDE-47 and BDE-99 at a dose of 25 μM for 24 hours. Differentially expressed lncRNA-PCG pairs were identified through DESeq2 and HOMER; significant canonical pathways were determined through Ingenuity Pathway Analysis (IPA). LncTar was used to predict the binding of 19 lncRNA-PCG pairs with known roles in drug-processing pathways. Genome annotation revealed that the majority of the differentially expressed lncRNAs map to PCG introns. PBDEs regulated overlapping pathways with PXR and CAR such as protein ubiqutination pathway and peroxisome proliferator-activated receptor alpha-retinoid X receptor alpha (PPARα-RXRα) activation but also regulate distinctive pathways involved in intermediary metabolism. PBDEs uniquely down-regulated GDP-L-fucose biosynthesis, suggesting its role in modifying important pathways involved in intermediary metabolism such as carbohydrate and lipid metabolism. In conclusion, we provide strong evidence that PBDEs regulate both PCGs and lncRNAs in a PXR/CAR ligand-dependent and independent manner.

Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression

National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM₁₀, PM_2.5, NO₂, SO₂, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R² conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.

Mortality associated with wildfire smoke exposure in Washington state, 2006-2017: a case-crossover study

BACKGROUND

Wildfire events are increasing in prevalence in the western United States. Research has found mixed results on the degree to which exposure to wildfire smoke is associated with an increased risk of mortality.

METHODS

We tested for an association between exposure to wildfire smoke and non-traumatic mortality in Washington State, USA. We characterized wildfire smoke days as binary for grid cells based on daily average PM2.5 concentrations, from June 1 through September 30, 2006-2017. Wildfire smoke days were defined as all days with assigned monitor concentration above a PM2.5 value of 20.4 μg/m3, with an additional set of criteria applied to days between 9 and 20.4 μg/m3. We employed a case-crossover study design using conditional logistic regression and time-stratified referent sampling, controlling for humidex.

RESULTS

The odds of all-ages non-traumatic mortality with same-day exposure was 1.0% (95% CI: - 1.0 - 4.0%) greater on wildfire smoke days compared to non-wildfire smoke days, and the previous day's exposure was associated with a 2.0% (95% CI: 0.0-5.0%) increase. When stratified by cause of mortality, odds of same-day respiratory mortality increased by 9.0% (95% CI: 0.0-18.0%), while the odds of same-day COPD mortality increased by 14.0% (95% CI: 2.0-26.0%). In subgroup analyses, we observed a 35.0% (95% CI: 9.0-67.0%) increase in the odds of same-day respiratory mortality for adults ages 45-64.

CONCLUSIONS

This study suggests increased odds of mortality in the first few days following wildfire smoke exposure. It is the first to examine this relationship in Washington State and will help inform local and state risk communication efforts and decision-making during future wildfire smoke events.

Calibration of low-cost particulate matter sensors: Model development for a multi-city epidemiological study

Low-cost air monitoring sensors are an appealing tool for assessing pollutants in environmental studies. Portable low-cost sensors hold promise to expand temporal and spatial coverage of air quality information. However, researchers have reported challenges in these sensors' operational quality. We evaluated the performance characteristics of two widely used sensors, the Plantower PMS A003 and Shinyei PPD42NS, for measuring fine particulate matter compared to reference methods, and developed regional calibration models for the Los Angeles, Chicago, New York, Baltimore, Minneapolis-St. Paul, Winston-Salem and Seattle metropolitan areas. Duplicate Plantower PMS A003 sensors demonstrated a high level of precision (averaged Pearson's r = 0.99), and compared with regulatory instruments, showed good accuracy (cross-validated R² = 0.96, RMSE = 1.15 µg/m³ for daily averaged PM_2.5 estimates in the Seattle region). Shinyei PPD42NS sensor results had lower precision (Pearson's r = 0.84) and accuracy (cross-validated R² = 0.40, RMSE = 4.49 µg/m³). Region-specific Plantower PMS A003 models, calibrated with regulatory instruments and adjusted for temperature and relative humidity, demonstrated acceptable performance metrics for daily average measurements in the other six regions (R² = 0.74-0.95, RMSE = 2.46-0.84 µg/m³). Applying the Seattle model to the other regions resulted in decreased performance (R² = 0.67-0.84, RMSE = 3.41-1.67 µg/m³), likely due to differences in meteorological conditions and particle sources. We describean approach to metropolitan region-specific calibration models for low-cost sensors that can be used with cautionfor exposure measurement in epidemiological studies.

Association between work in deforested, compared to forested, areas and human heat strain: An experimental study in a rural tropical environment

BACKGROUND

With climate change, adverse human health effects caused by heat exposure are of increasing public health concern. Forests provide beneficial ecosystem services for human health, including local cooling. Few studies have assessed the relationship between deforestation and heat-related health effects in tropical, rural populations. We sought to determine whether deforested compared to forested landscapes are associated with increased physiological heat strain in a rural, tropical environment.

METHODS

We analyzed data from 363 healthy adult participants from ten villages who participated in a two-by-two factorial, randomized study in East Kalimantan, Indonesia from 10/1/17 to 11/6/17. Using simple randomization, field staff allocated participants equally to different conditions to conduct a 90-minute outdoor activity, representative of typical work. Core body temperature was estimated at each minute during the activity using a validated algorithm from baseline oral temperatures and sequential heart rate data, measured using chest band monitors. We used linear regression models, clustered by village and with a sandwich variance estimator, to assess the association between deforested versus forested conditions and the number of minutes each participant spent above an estimated core body temperature threshold of 38.5°C.

RESULTS

Compared to those in the forested condition (n=172), participants in the deforested condition (n=159) spent an average of 3.08 (95% CI 0.57, 5.60) additional minutes with an estimated core body temperature exceeding 38.5°C, after adjustment for age, sex, body mass index, and experiment start time, with a larger difference among those who began the experiment after 12 noon (5.17 [95% CI 2.20, 8.15]).

CONCLUSIONS

In this experimental study in a tropical, rural setting, activity in a deforested versus a forested setting was associated with increased objectively measured heat strain. Longer durations of hyperthermia can increase the risk of serious health outcomes. Land use decisions should consider the implications of deforestation on local heat exposure and health as well as on forest services, including carbon storage functions that impact climate change mitigation.

Exposure to glyphosate-based herbicides and risk for non-Hodgkin lymphoma: A meta-analysis and supporting evidence

Glyphosate is the most widely used broad-spectrum systemic herbicide in the world. Recent evaluations of the carcinogenic potential of glyphosate-based herbicides (GBHs) by various regional, national, and international agencies have engendered controversy. We investigated whether there was an association between high cumulative exposures to GBHs and increased risk of non-Hodgkin lymphoma (NHL) in humans. We conducted a new meta-analysis that includes the most recent update of the Agricultural Health Study (AHS) cohort published in 2018 along with five case-control studies. Using the highest exposure groups when available in each study, we report the overall meta-relative risk (meta-RR) of NHL in GBH-exposed individuals was increased by 41% (meta-RR = 1.41, 95% confidence interval, CI: 1.13-1.75). For comparison, we also performed a secondary meta-analysis using high-exposure groups with the earlier AHS (2005), and we calculated a meta-RR for NHL of 1.45 (95% CI: 1.11-1.91), which was higher than the meta-RRs reported previously. Multiple sensitivity tests conducted to assess the validity of our findings did not reveal meaningful differences from our primary estimated meta-RR. To contextualize our findings of an increased NHL risk in individuals with high GBH exposure, we reviewed publicly available animal and mechanistic studies related to lymphoma. We documented further support from studies of malignant lymphoma incidence in mice treated with pure glyphosate, as well as potential links between glyphosate / GBH exposure and immunosuppression, endocrine disruption, and genetic alterations that are commonly associated with NHL or lymphomagenesis. Overall, in accordance with findings from experimental animal and mechanistic studies, our current meta-analysis of human epidemiological studies suggests a compelling link between exposures to GBHs and increased risk for NHL.

Evaluation of 1-Nitropyrene as a Surrogate Measure for Diesel Exhaust

We investigated the viability of particle bound 1-nitropyrene (1-NP) air concentration measurements as a surrogate of diesel exhaust (DE) exposure, as compared with industry-standard elemental carbon (EC) and total carbon (TC) measurements. Personal exposures are reported for 18 employees at a large underground metal mine during four different monitoring campaigns. Full-shift personal air exposure sampling was conducted using a Mine Safety and Health Administration (MSHA) compliant diesel particulate matter (DPM) impactor cassette downstream of a GS-1 cyclone pre-selector. Each DPM filter element was analyzed for EC and organic carbon (OC) using NIOSH Method 5040. After EC and OC analysis, the remaining portion of each DPM filter was analyzed for 1-NP using liquid chromatography tandem mass spectrometry (LC/MS/MS). We observed high correlations between the quantiles of 1-NP and EC exposures across 10 different work shift task groups (r = 0.87 to 0.96), and a linear relationship with a slope between 6.0 to 6.9 pg 1-NP per µg EC. However, correlation between 1-NP and EC was weak (r =0.34) for the 91 individual sample pairs due to low EC concentrations and possible heterogeneity of DE composition. While both 1-NP and EC differentiated between high and low exposure groups categorized by job location, measurements of 1-NP, but not EC further differentiated between specific job activities. Repeated measurements on individual subjects verified the relationship between 1-NP and EC and demonstrated substantial within-subject variability in exposure. The detection limit of TC air concentration ranged between 18 and 28 µg m-3 and was limited by OC contamination of the quartz filters in the MSHA compliant DPM samplers.

Ambient Air Pollution Exposure and Fecundability in Women Undergoing In Vitro Fertilization

BACKGROUND

Limited research suggests ambient air pollution impairs fecundity but groups most susceptible have not been identified. We studied whether long-term ambient air pollution exposure prior to an in vitro fertilization (IVF) cycle was associated with successful livebirth, and whether associations were modified by underlying infertility diagnosis.

METHODS

Data on women initiating their 1st autologous IVF cycle in 2012-13 were obtained from four U.S. clinics. Outcomes included pregnancy, pregnancy loss, and livebirth. Annual average exposure to fine particulate matter (PM2.5), PM10, and nitrogen dioxide (NO2) prior to IVF start were estimated at residential address using a validated national spatial model incorporating land-use regression and universal kriging. We also assessed residential distance to major roadway. We calculated risk ratios (RR) using modified Poisson regression and evaluated effect modification (EM) by infertility diagnosis on additive and multiplicative scales.

RESULTS

Among 7,463 eligible participants, 36% had a livebirth. There was a non-significant indication of an association between PM2.5 or NO2 and decreased livebirth and increased pregnancy loss. Near roadway residence was associated with decreased livebirth (RR: 0.96, 95% CI: 0.82, 0.99. There was evidence for EM between high exposure to air pollutants and a diagnosis of diminished ovarian reserve (DOR) or male infertility and decreased livebirth.

CONCLUSIONS

Despite suggestive but uncertain findings for the overall effect of air pollution on fecundity, we found a suggestive indication that there may be synergistic effects of air pollution and DOR or male infertility diagnosis on livebirth. This suggests two possible targets for future research and intervention.

Maternal urinary phthalate metabolites in relation to gestational diabetes and glucose intolerance during pregnancy

BACKGROUND

Phthalates are common plasticizer chemicals that have been linked to glucose intolerance in the general population, but there is only limited research on their association with gestational diabetes (GDM).

OBJECTIVE

We evaluated the association between 11 urinary phthalate metabolites and GDM, impaired glucose tolerance (IGT), and continuous blood glucose concentration during pregnancy in The Infant Development and Environment Study (TIDES). Based on prior study results, our primary analyses focused on monoethyl phthalate (MEP) in relation to our outcomes of interest.

STUDY DESIGN

We used multi-variable logistic regression to examine the odds of GDM and IGT in relation to an interquartile-range (IQR) increase in natural log (ln)-transformed, specific gravity (SG)-adjusted first trimester (T1) and average of T1 and third trimester (T3) ("T1T3avg") phthalate metabolite concentrations. We fit linear regression models to examine the percent change in blood glucose per IQR increase in ln-transformed, SG-adjusted T1 and T1T3avg phthalates. In sensitivity analyses, we examined interactions between exposure and race. We adjusted for maternal age, maternal body mass index, study center, race/ethnicity, parity, and gestational age at glucose testing.

RESULTS

In our sample of 705 pregnant women, we observed 60 cases of GDM, 90 cases of IGT, and an average GLT blood glucose of 113.6 ± 27.7 mg/dL. In our primary analysis, T1T3avg MEP was positively associated with GDM ([OR (95% CI) per IQR increase] T1T3avg MEP: 1.61 (1.10, 2.36)). In secondary analyses, most other phthalates were not found to be related to study outcomes, though some associations were noted. Sensitivity analyses indicated possible strong race-specific associations in Asians, though these results are based on a small sample size (n = 35).

CONCLUSION

In alignment with our a priori selection, we documented an association between T1T3avg MEP and GDM. Additional phthalate metabolites were also found to be linked to glucose intolerance, with possible stronger associations in certain racial/ethnic subgroups. Given the prevalence of phthalate exposures and the growing evidence of associations with metabolic outcomes, future studies should continue to examine this question in diverse cohorts of pregnant women, particularly in those who may be at higher risk for GDM and IGT.

The evidence of human exposure to glyphosate: a review

BACKGROUND

Despite the growing and widespread use of glyphosate, a broad-spectrum herbicide and desiccant, very few studies have evaluated the extent and amount of human exposure.

OBJECTIVE

We review documented levels of human exposure among workers in occupational settings and the general population.

METHODS

We conducted a review of scientific publications on glyphosate levels in humans; 19 studies were identified, of which five investigated occupational exposure to glyphosate, 11 documented the exposure in general populations, and three reported on both.

RESULTS

Eight studies reported urinary levels in 423 occupationally and para-occupationally exposed subjects; 14 studies reported glyphosate levels in various biofluids on 3298 subjects from the general population. Average urinary levels in occupationally exposed subjects varied from 0.26 to 73.5 μg/L; environmental exposure urinary levels ranged from 0.16 to 7.6 μg/L. Only two studies measured temporal trends in exposure, both of which show increasing proportions of individuals with detectable levels of glyphosate in their urine over time.

CONCLUSIONS

The current review highlights the paucity of data on glyphosate levels among individuals exposed occupationally, para-occupationally, or environmentally to the herbicide. As such, it is challenging to fully understand the extent of exposure overall and in vulnerable populations such as children. We recommend further work to evaluate exposure across populations and geographic regions, apportion the exposure sources (e.g., occupational, household use, food residues), and understand temporal trends.

Selective D2 receptor PET in manganese-exposed workers

OBJECTIVE

To investigate the associations between manganese (Mn) exposure, D2 dopamine receptors (D2Rs), and parkinsonism using [11C](N-methyl)benperidol (NMB) PET.

METHODS

We used NMB PET to evaluate 50 workers with a range of Mn exposure: 22 Mn-exposed welders, 15 Mn-exposed workers, and 13 nonexposed workers. Cumulative Mn exposure was estimated from work histories, and movement disorder specialists examined all workers. We calculated NMB D2R nondisplaceable binding potential (BPND) for the striatum, globus pallidus, thalamus, and substantia nigra (SN). Multivariate analysis of covariance with post hoc descriptive discriminate analysis identified regional differences by exposure group. We used linear regression to examine the association among Mn exposure, Unified Parkinson's Disease Rating Scale motor subsection 3 (UPDRS3) score, and regional D2R BPND.

RESULTS

D2R BPND in the SN had the greatest discriminant power among exposure groups (p < 0.01). Age-adjusted SN D2R BPND was 0.073 (95% confidence interval [CI] 0.022-0.124) greater in Mn-exposed welders and 0.068 (95% CI 0.013-0.124) greater in Mn-exposed workers compared to nonexposed workers. After adjustment for age, SN D2R BPND was 0.0021 (95% CI 0.0005-0.0042) higher for each year of Mn exposure. Each 0.10 increase in SN D2R BPND was associated with a 2.65 (95% CI 0.56-4.75) increase in UPDRS3 score.

CONCLUSIONS AND RELEVANCE

Nigral D2R BPND increased with Mn exposure and clinical parkinsonism, indicating dose-dependent dopaminergic dysfunction of the SN in Mn neurotoxicity.

Association between Precipitation and Diarrheal Disease in Mozambique

Diarrheal diseases are a leading cause of morbidity and mortality in Africa. Although research documents the magnitude and pattern of diarrheal diseases are associated with weather in particular locations, there is limited quantification of this association in sub-Saharan Africa and no studies conducted in Mozambique. Our study aimed to determine whether variation in diarrheal disease was associated with precipitation in Mozambique. In secondary analyses we investigated the associations between temperature and diarrheal disease. We obtained weekly time series data for weather and diarrheal disease aggregated at the administrative district level for 1997–2014. Weather data include modeled estimates of precipitation and temperature. Diarrheal disease counts are confirmed clinical episodes reported to the Mozambique Ministry of Health (n = 7,315,738). We estimated the association between disease counts and precipitation, defined as the number of wet days (precipitation > 1 mm) per week, for the entire country and for Mozambique’s four regions. We conducted time series regression analyses using an unconstrained distributed lag Poisson model adjusted for time, maximum temperature, and district. Temperature was similarly estimated with adjusted covariates. Using a four-week lag, chosen a priori, precipitation was associated with diarrheal disease. One additional wet day per week was associated with a 1.86% (95% CI: 1.05–2.67%), 1.37% (95% CI: 0.70–2.04%), 2.09% (95% CI: 1.01–3.18%), and 0.63% (95% CI: 0.11–1.14%) increase in diarrheal disease in Mozambique’s northern, central, southern, and coastal regions, respectively. Our study indicates a strong association between diarrheal disease and precipitation. Diarrheal disease prevention efforts should target areas forecast to experience increased rainfall. The burden of diarrheal disease may increase with increased precipitation associated with climate change, unless additional health system interventions are undertaken.

The use of cost-utility analysis for the evaluation of caries prevention: an exploratory case study of two community-based public health interventions in a high-risk population in the UK

BACKGROUND

Economic evaluations are important tools for decision makers to determine the best allocation of resources in a healthcare system. This study explored the use of economic evaluation in oral health promotion.

METHODS

A literature review identified oral health promotion programmes that measured both the health impact and costs of oral health interventions. A decision analysis model was constructed to examine the cost utility of preventing dental caries in 5 and 12-year-old children via tooth brushing schemes and fluoride varnish programmes. The costs per child that would be justified according to the National Institute for Health and Care Excellence's threshold of £20,000 per QALY were calculated.

RESULTS

The analysis showed that NICE would consider that the expenditure of £55 per child on supervised tooth brushing, or £100 per child on fluoride varnish application would give sufficient health benefits to be justified according to their threshold.

CONCLUSIONS

Greater attention needs to be paid to the collection of robust data on costs for oral health promotion. Dental researchers also urgently need to collect outcome data in a form that can be translated into a Quality of Life measure, so that the true cost effectiveness and value for money achieved through the prevention of dental disease can be recognised and compared to other allocations of resource.