Measurement Error Correction for Predicted Spatiotemporal Air Pollution Exposures

Understanding the Cardiovascular and Metabolic Health Effects of Air Pollution in the Context of Cumulative Exposomic Impacts

Poor air quality accounts for more than 9 million deaths a year globally according to recent estimates. A large portion of these deaths are attributable to cardiovascular causes, with evidence indicating that air pollution may also play an important role in the genesis of key cardiometabolic risk factors. Air pollution is not experienced in isolation but is part of a complex system, influenced by a host of other external environmental exposures, and interacting with intrinsic biologic factors and susceptibility to ultimately determine cardiovascular and metabolic outcomes. Given that the same fossil fuel emission sources that cause climate change also result in air pollution, there is a need for robust approaches that can not only limit climate change but also eliminate air pollution health effects, with an emphasis of protecting the most susceptible but also targeting interventions at the most vulnerable populations. In this review, we summarize the current state of epidemiologic and mechanistic evidence underpinning the association of air pollution with cardiometabolic disease and how complex interactions with other exposures and individual characteristics may modify these associations. We identify gaps in the current literature and suggest emerging approaches for policy makers to holistically approach cardiometabolic health risk and impact assessment.

Ambient traffic related air pollution in relation to ovarian reserve and oocyte quality in young, healthy oocyte donors

Studies in mice and older, subfertile women have found that air pollution exposure may compromise female reproduction. Our objective was to evaluate the effects of air pollution on ovarian reserve and outcomes of ovarian stimulation among young, healthy females. We included 472 oocyte donors who underwent 781 ovarian stimulation cycles at a fertility clinic in Atlanta, Georgia, USA (2008-2019). Antral follicle count (AFC) was assessed with transvaginal ultrasonography and total and mature oocyte count was assessed following oocyte retrieval. Ovarian sensitivity index (OSI) was calculated as the total number of oocytes divided by total gonadotrophin dose × 1000. Daily ambient exposure to nitric oxide (NOx), carbon monoxide (CO), and particulate matter ≤ 2.5 (PM2.5) was estimated using a fused regional + line-source model for near-surface releases at a 250 m resolution based on residential address. Generalized estimating equations were used to evaluate the associations of an interquartile range (IQR) increase in pollutant exposure with outcomes adjusted for donor characteristics, census-level poverty, and meteorological factors. The median (IQR) age among oocyte donors was 25.0 (5.0) years, and 31% of the donors were racial/ethnic minorities. The median (IQR) exposure to NOx, CO, and PM2.5 in the 3 months prior to stimulation was 37.7 (32.0) ppb, 612 (317) ppb, and 9.8 (2.9) µg/m3, respectively. Ambient air pollution exposure in the 3 months before AFC was not associated with AFC. An IQR increase in PM2.5 in the 3 months before AFC and during stimulation was associated with -7.5% (95% CI -14.1, -0.4) and -6.4% (95% CI -11.0, -1.6) fewer mature oocytes, and a -1.9 (95% CI -3.2, -0.5) and -1.0 (95% CI -1.8, -0.2) lower OSI, respectively. Our results suggest that lowering the current 24-h PM2.5 standard in the US to 25 µg/m3 may still not adequately protect against the reprotoxic effects of short-term PM2.5 exposure.

Prediction and model evaluation for space-time data

Evaluation metrics for prediction error, model selection and model averaging on space-time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space-time prediction problems. Motivated by air pollution data collected during California wildfires in 2008, this manuscript attempts a formalization of the true prediction error associated with spatial interpolation. We investigate a variety of cross-validation (CV) procedures employing both simulations and case studies to provide insight into the nature of the estimand targeted by alternative data partition strategies. Consistent with recent best practice, we find that location-based cross-validation is appropriate for estimating spatial interpolation error as in our analysis of the California wildfire data. Interestingly, commonly held notions of bias-variance trade-off of CV fold size do not trivially apply to dependent data, and we recommend leave-one-location-out (LOLO) CV as the preferred prediction error metric for spatial interpolation.

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models

BACKGROUND

Machine-learning algorithms are becoming popular techniques to predict ambient air PM_2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM_2.5 concentrations (mean PM_2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM_2.5).

OBJECTIVE

Our goal was to develop a machine-learning model to predict mean PM_2.5 and max PM_2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019.

METHODS

We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM_2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM_2.5 and heat, compliance with local air-quality standards, and the relationship of PM_2.5 exposure with social marginalization.

RESULTS

Our models for mean and max PM_2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 μg/m³, respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m³. In 2010, everybody in the study region was exposed to unhealthy levels of PM_2.5. Hotter days had greater PM_2.5 concentrations. Finally, we found similar exposure to PM_2.5 across levels of social marginalization.

SIGNIFICANCE

Machine learning algorithms can be used to predict highly spatiotemporally resolved PM_2.5 concentrations even in regions with sparse monitoring.

IMPACT

Our PM_2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

Maternal exposure to ambient PM2.5 and term birth weight: A systematic review and meta-analysis of effect estimates

Effect estimates of prenatal exposure to ambient PM_2.5 on change in grams (β) of birth weight among term births (≥37 weeks of gestation; term birth weight, TBW) vary widely across studies. We present the first systematic review and meta-analysis of evidence regarding these associations. Sixty-two studies met the eligibility criteria for this review, and 31 studies were included in the meta-analysis. Random-effects meta-analysis was used to assess the quantitative relationships. Subgroup analyses were performed to gain insight into heterogeneity derived from exposure assessment methods (grouped by land use regression [LUR]-models, aerosol optical depth [AOD]-based models, interpolation/dispersion/Bayesian models, and data from monitoring stations), study regions, and concentrations of PM_2.5 exposure. The overall pooled estimate involving 23,925,941 newborns showed that TBW was negatively associated with PM_2.5 exposure (per 10 μg/m³ increment) during the entire pregnancy (β = -16.54 g), but with high heterogeneity (I² = 95.6%). The effect estimate in the LUR-models subgroup (β = -16.77 g) was the closest to the overall estimate and with less heterogeneity (I² = 18.3%) than in the other subgroups of AOD-based models (β = -41.58 g; I² = 95.6%), interpolation/dispersion models (β = -10.78 g; I² = 86.6%), and data from monitoring stations (β = -11.53 g; I² = 97.3%). Even PM_2.5 exposure levels of lower than 10 μg/m³ (the WHO air quality guideline value) had adverse effects on TBW. The LUR-models subgroup was the only subgroup that obtained similar significant of negative associations during the three trimesters as the overall trimester-specific analyses. In conclusion, TBW was negatively associated with maternal PM_2.5 exposures during the entire pregnancy and each trimester. More studies based on relatively standardized exposure assessment methods need to be conducted to further understand the precise susceptible exposure time windows and potential mechanisms.

An examination of dose uncertainty and dose distribution effects on community noise attitudinal survey outcomes

Social survey data sets of large numbers of individual respondents' opinions are generally viewed as supporting reliable inferences of relationships between the prevalence of noise-induced annoyance and noise exposure levels. The current analyses identify conditions under which noise dose distributions and acoustic measurement uncertainty lead to appreciable mis-estimation of the slopes of empirical dose-response relationships with respect to those of true slopes in exposure ranges of interest. These findings were revealed by Monte Carlo methods for creating simulated data sets with varying exposure ranges and degrees of dose uncertainty. These simulated data sets support quantitative comparisons of dose-response relationships between empirical outcomes and known (assumed) relationships. The effect of noise dose uncertainty is appreciable for dose uncertainties with standard deviations greater than about 2 decibels. Limited dose ranges as well as haystack-shaped (non-uniform) dose distributions magnify the biasing effect of dose uncertainty on the slopes of observed relationships. Narrow exposure ranges can also create a false asymptotic behavior in the relationship. These phenomena are well documented in the non-acoustic literature.

Maternal Ambient Exposure to Atmospheric Pollutants during Pregnancy and Offspring Term Birth Weight in the Nationwide ELFE Cohort

Background: Studies have reported associations between maternal exposure to atmospheric pollution and lower birth weight. However, the evidence is not consistent and uncertainties remain. We used advanced statistical approaches to robustly estimate the association of atmospheric pollutant exposure during specific pregnancy time windows with term birth weight (TBW) in a nationwide study. Methods: Among 13,334 women from the French Longitudinal Study of Children (ELFE) cohort, exposures to PM_2.5, PM₁₀ (particles < 2.5 µm and <10 µm) and NO₂ (nitrogen dioxide) were estimated using a fine spatio-temporal exposure model. We used inverse probability scores and doubly robust methods in generalized additive models accounting for spatial autocorrelation to study the association of such exposures with TBW. Results: First trimester exposures were associated with an increased TBW. Second trimester exposures were associated with a decreased TBW by 17.1 g (95% CI, −26.8, −7.3) and by 18.0 g (−26.6, −9.4) for each 5 µg/m³ increase in PM_2.5 and PM₁₀, respectively, and by 15.9 g (−27.6, −4.2) for each 10 µg/m³ increase in NO₂. Third trimester exposures (truncated at 37 gestational weeks) were associated with a decreased TBW by 48.1 g (−58.1, −38.0) for PM_2.5, 38.1 g (−46.7, −29.6) for PM₁₀ and 14.7 g (−25.3, −4.0) for NO₂. Effects of pollutants on TBW were larger in rural areas. Conclusions: Our results support an adverse effect of air pollutant exposure on TBW. We highlighted a larger effect of air pollutants on TBW among women living in rural areas compared to women living in urban areas.

A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R² of 0.83 and a root-mean-square error of 0.15 μg/m³ for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.

Application of the navigation guide systematic review methodology to evaluate prenatal exposure to particulate matter air pollution and infant birth weight

Low birth weight is an important risk factor for many co-morbidities both in early life as well as in adulthood. Numerous studies report associations between prenatal exposure to particulate matter (PM) air pollution and low birth weight. Previous systematic reviews and meta-analyses report varying effect sizes and significant heterogeneity between studies, but did not systematically evaluate the quality of individual studies or the overall body of evidence. We conducted a new systematic review to determine how prenatal exposure to PM2.5, PM10, and coarse PM (PM2.5-10) by trimester and across pregnancy affects infant birth weight. Using the Navigation Guide methodology, we developed and applied a systematic review protocol [CRD42017058805] that included a comprehensive search of the epidemiological literature, risk of bias (ROB) determination, meta-analysis, and evidence evaluation, all using pre-established criteria. In total, 53 studies met our inclusion criteria, which included evaluation of birth weight as a continuous variable. For PM2.5 and PM10, we restricted meta-analyses to studies determined overall as "low" or "probably low" ROB; none of the studies evaluating coarse PM were rated as "low" or "probably low" risk of bias, so all studies were used. For PM2.5, we observed that for every 10 µg/m3 increase in exposure to PM2.5 in the 2nd or 3rd trimester, respectively, there was an associated 5.69 g decrease (I2: 68%, 95% CI: -10.58, -0.79) or 10.67 g decrease in birth weight (I2: 84%, 95% CI: -20.91, -0.43). Over the entire pregnancy, for every 10 µg/m3 increase in PM2.5 exposure, there was an associated 27.55 g decrease in birth weight (I2: 94%, 95% CI: -48.45, -6.65). However, the quality of evidence for PM2.5 was rated as "low" due to imprecision and/or unexplained heterogeneity among different studies. For PM10, we observed that for every 10 µg/m3 increase in exposure in the 3rd trimester or the entire pregnancy, there was a 6.57 g decrease (I2: 0%, 95% CI: -10.66, -2.48) or 8.65 g decrease in birth weight (I2: 84%, 95% CI: -16.83, -0.48), respectively. The quality of evidence for PM10 was rated as "moderate," as heterogeneity was either absent or could be explained. The quality of evidence for coarse PM was rated as very low/low (for risk of bias and imprecision). Overall, while evidence for PM2.5 and course PM was inadequate primarily due to heterogeneity and risk of bias, respectively, our results support the existence of an inverse association between prenatal PM10 exposure and low birth weight.

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

Exposure Measurement Error in Air Pollution Studies: The Impact of Shared, Multiplicative Measurement Error on Epidemiological Health Risk Estimates

Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage, ensemble learning based nitrogen oxides (NOx) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NOx exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NOx model. We then determined the impact of SMME on the variance of the health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NOx. With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR=1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential "worst case scenario" of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

Bootstrap Analysis of the Production Processes Capability Assessment

The high customer requirements for appropriate product quality pose a challenge for manufacturers and suppliers and also cause them many problems related to ensuring a sufficiently high product quality throughout the entire production cycle. For the above reasons, it is so important to assess the capability of monitored processes, and shaping, analyzing and controlling the capability of processes is an important aspect of managing an organization that uses a process approach to management. The use of an appropriate method to analyze the course of production processes is a necessity imposed by quality standards, e.g., ISO 9001: 2015. That is why it is so important to propose a quick and low-cost method of assessing production processes. For this purpose, a method of assessing the capability of the manufacturing process using bootstrap analysis was used. The article presents the analysis of inherent properties of the production process based on the results of measurements of the characteristic features of the process or the characteristics of the manufactured products (process variables) for the shafts with grooves. The main goals of the work are to develop a procedure for determining process capability based on the bootstrap method, including criteria for the classification of production process capability; to develop the criterion values for confidence intervals of production process capability; as well as to demonstrate the practical application of bootstrap analysis in manufacturing. Moreover, comparative analyses of process capabilities using bootstrap and classic methods were carried out. They confirm both the narrowing of the confidence interval when using the bootstrap method and the possibility of determining a better estimator of the lower limit of this range compared to the results obtained using the classic method. The tests carried out for the unit production of shafts with grooves showed that the analysis of the process capability for measuring tests n = 10 is possible. Finally, new criterion values for the assessment of process capability for the bootstrap method were proposed. The model for assessing the capability of production processes presented in the paper was implemented in low-volume production in the defense industry.

Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS

In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM_2.5) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtained from the Mississippi State Department of Health (MSDH) Asthma Surveillance System. Patient geolocations were aggregated into a grid with 0.1° × 0.1° resolution within the Jackson Metropolitan Statistical Area. Daily PM_2.5 concentrations were estimated via machine-learning algorithms with remotely sensed aerosol optical depth and other associated parameters as inputs. Controlling for long-term temporal trends and meteorology, we estimated a 7.2% (95% confidence interval 1.7-13.1%) increase in daily all-age asthma emergency room admissions per 10 μg/m³ increase in the 3-day average of PM_2.5 levels (current day and two prior days). Stratified analyses reveal significant associations between asthma and 3-day average PM_2.5 for males and blacks. Our results contribute to the current epidemiologic evidence on the association between acute ambient air pollution exposure and asthma morbidity, even in an area characterized by relatively good air quality.

Applying the E Value to Assess the Robustness of Epidemiologic Fields of Inquiry to Unmeasured Confounding

We explored the use of the E value to gauge the robustness of fields of epidemiologic inquiry to unmeasured confounding. We surveyed nutritional and air pollution studies that found statistically significant associations between exposures and incident outcomes. For 100 studies in each field, we extracted adjusted relative effect estimates and associated confidence intervals. We inverted estimates where necessary so that all effects were greater than 1. We calculated E values for both the effect estimate and the lower limit of the 95% confidence interval. Nutritional studies were smaller than air pollution studies (median participants per study, 40,652 vs. 72,460). More than 90% of nutritional studies categorized the exposure, whereas 89% of air pollution studies analyzed the exposure as a continuous variable. The median relative effect was 1.33 in nutrition and 1.16 in air pollution. The corresponding median E values for the estimates were 2.00 and 1.59, respectively. E values for the 95% confidence intervals had median values of 1.39 and 1.26, respectively. Little to moderate unmeasured confounding could explain away most observed associations. The E value is necessarily larger for smaller studies that reach statistical significance, making cross-field comparison difficult. The E value for the 95% confidence interval might be a more useful measure in reports of epidemiologic observational studies.

Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases

The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.

Impacts of gestational age uncertainty in estimating associations between preterm birth and ambient air pollution

Supplemental Digital Content is available in the text.

Background:

Previous epidemiologic studies utilizing birth records have shown heterogeneous associations between air pollution exposure during pregnancy and the risk of preterm birth (PTB, gestational age <37 weeks). Uncertainty in gestational age at birth may contribute to this heterogeneity.

Methods:

We first examined disagreement between clinical and last menstrual period-based (LMP) determination of PTB from individual-level birth certificate data for the 20-county Atlanta metropolitan area during 2002 to 2006. We then estimated associations between five trimester-averaged pollutant exposures and PTB, defined using various methods based on the clinical or LMP gestational age. Finally, using a multiple imputation approach, we incorporated uncertainty in gestational age to quantify the impact of this variability on associations between pollutant exposures and PTB.

Results:

Odds ratios (OR) were most elevated when a more stringent definition of PTB was used. For example, defining PTB only when LMP and clinical diagnoses agree yielded an OR of 1.09 (95% confidence interval [CI] = 1.04, 1.14) per interquartile range increase in first trimester carbon monoxide exposure versus an OR of 1.04 (95% CI = 1.01, 1.08) when PTB was defined as either an LMP or clinical diagnosis. Accounting for outcome uncertainty resulted in wider CIs—between 7.4% and 43.8% wider than those assuming the PTB outcome is without error.

Conclusions:

Despite discrepancies in PTB derived using either the clinical or LMP gestational age estimates, our analyses demonstrated robust positive associations between PTB and ambient air pollution exposures even when gestational age uncertainty is present.

Long-Term Coarse Particulate Matter Exposure Is Associated with Asthma among Children in Medicaid

RATIONALE

Short- and long-term fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter [PM2.5]) pollution is associated with asthma development and morbidity, but there are few data on the effects of long-term exposure to coarse PM (PM10-2.5) on respiratory health.

OBJECTIVES

To understand the relationship between long-term fine and coarse PM exposure and asthma prevalence and morbidity among children.

METHODS

A semiparametric regression model that incorporated PM2.5 and PM10 monitor data and geographic characteristics was developed to predict 2-year average PM2.5 and PM10-2.5 exposure during the period 2009 to 2010 at the zip-code tabulation area level. Data from 7,810,025 children aged 5 to 20 years enrolled in Medicaid from 2009 to 2010 were used in a log-linear regression model with predicted PM levels to estimate the association between PM exposure and asthma prevalence and morbidity, adjusting for race/ethnicity, sex, age, area-level urbanicity, poverty, education, and unmeasured spatial confounding.

MEASUREMENTS AND MAIN RESULTS

Exposure to coarse PM was associated with increased asthma diagnosis prevalence (rate ratio [RR] for 1-μg/m3 increase in coarse PM level, 1.006; 95% confidence interval [CI], 1.001-1.011), hospitalizations (RR, 1.023; 95% CI, 1.003-1.042), and emergency department visits (RR, 1.017; 95% CI, 1.001-1.033) when adjusting for fine PM. Fine PM exposure was more strongly associated with increased asthma prevalence and morbidity than coarse PM. The estimates remained elevated across different levels of spatial confounding adjustment.

CONCLUSIONS

Among children enrolled in Medicaid, exposure to higher average coarse PM levels is associated with increased asthma prevalence and morbidity. These results suggest the need for direct monitoring of coarse PM and reconsideration of limits on long-term average coarse PM pollution levels.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses

PURPOSE OF REVIEW

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data.

RECENT FINDINGS

We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.