Associations between ambient air pollutant mixtures and pediatric asthma emergency department visits in three cities: a classification and regression tree approach

Warm season ambient ozone and children's health in the USA

BACKGROUND

Over 120 million people in the USA live in areas with unsafe ozone (O3) levels. Studies among adults have linked exposure to worse lung function and higher risk of asthma and chronic obstructive pulmonary disease (COPD). However, few studies have examined the effects of O3 in children, and existing studies are limited in terms of their geographic scope or outcomes considered.

METHODS

We leveraged a dataset of encounters at 42 US children's hospitals from 2004-2015. We used a one-stage case-crossover design to quantify the association between daily maximum 8-hour O3 in the county in which the hospital is located and risk of emergency department (ED) visits for any cause and for respiratory disorders, asthma, respiratory infections, allergies and ear disorders.

RESULTS

Approximately 28 million visits were available during this period. Per 10 ppb increase, warm-season (May through September) O3 levels over the past three days were associated with higher risk of ED visits for all causes (risk ratio [RR]: 0.3% [95% confidence interval (CI): 0.2%, 0.4%]), allergies (4.1% [2.5%, 5.7%]), ear disorders (0.8% [0.3%, 1.3%]) and asthma (1.3% [0.8%, 1.9%]). When restricting to levels below the current regulatory standard (70 ppb), O3 was still associated with risk of ED visits for all-cause, allergies, ear disorders and asthma. Stratified analyses suggest that the risk of O3-related all-cause ED visits may be higher in older children.

CONCLUSIONS

Results from this national study extend prior research on the impacts of daily O3 on children's health and reinforce the presence of important adverse health impacts even at levels below the current regulatory standard in the USA.

Acute Effects of Ambient Air Pollution on Asthma Emergency Department Visits in Ten U.S. States

BACKGROUND

Previous studies of short-term ambient air pollution exposure and asthma morbidity in the United States have been limited to a small number of cities and/or pollutants and with limited consideration of effects across ages.

OBJECTIVES

To estimate acute age group-specific effects of fine and coarse particulate matter (PM), major PM components, and gaseous pollutants on emergency department (ED) visits for asthma during 2005-2014 across the United States.

METHODS

We acquired ED visit and air quality data in regions surrounding 53 speciation sites in 10 states. We used quasi-Poisson log-linear time-series models with unconstrained distributed exposure lags to estimate site-specific acute effects of air pollution on asthma ED visits overall and by age group (1-4, 5-17, 18-49, 50-64, and 65+ y), controlling for meteorology, time trends, and influenza activity. We then used a Bayesian hierarchical model to estimate pooled associations from site-specific associations.

RESULTS

Our analysis included 3.19 million asthma ED visits. We observed positive associations for multiday cumulative exposure to all air pollutants examined [e.g., 8-d exposure to PM2.5: rate ratio of 1.016 with 95% credible interval (CI) of (1.008, 1.025) per 6.3-μg/m3 increase, PM10-2.5: 1.014 (95% CI: 1.007, 1.020) per 9.6-μg/m3 increase, organic carbon: 1.016 (95% CI: 1.009, 1.024) per 2.8-μg/m3 increase, and ozone: 1.008 (95% CI: 0.995, 1.022) per 0.02-ppm increase]. PM2.5 and ozone showed stronger effects at shorter lags, whereas associations of traffic-related pollutants (e.g., elemental carbon and oxides of nitrogen) were generally stronger at longer lags. Most pollutants had more pronounced effects on children (<18 y old) than adults; PM2.5 had strong effects on both children and the elderly (>64 y old); and ozone had stronger effects on adults than children.

CONCLUSIONS

We reported positive associations between short-term air pollution exposure and increased rates of asthma ED visits. We found that air pollution exposure posed a higher risk for children and older populations. https://doi.org/10.1289/EHP11661.

Health effects of air pollutant mixtures (volatile organic compounds, particulate matter, sulfur and nitrogen oxides) - a review of the literature

The health risks associated with individual air pollutant exposures have been studied and documented, but in real-life, the population is exposed to a multitude of different substances, designated as mixtures. A body of literature on air pollutants indicated that the next step in air pollution research is investigating pollutant mixtures and their potential impacts on health, as a risk assessment of individual air pollutants may actually underestimate the overall risks. This review aims to synthesize the health effects related to air pollutant mixtures containing selected pollutants such as: volatile organic compounds, particulate matter, sulfur and nitrogen oxides. For this review, the PubMed database was used to search for articles published within the last decade, and we included studies assessing the associations between air pollutant mixtures and health effects. The literature search was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A number of 110 studies were included in the review from which data on pollutant mixtures, health effects, methods used, and primary results were extracted. Our review emphasized that there are a relatively small number of studies addressing the health effects of air pollutants as mixtures and there is a gap in knowledge regarding the health effects associated with these mixtures. Studying the health effects of air pollutant mixtures is challenging due to the complexity of components that mixtures may contain, and the possible interactions these different components may have.

Synergistic or Antagonistic Health Effects of Long- and Short-Term Exposure to Ambient NO2 and PM2.5: A Review

Studies on adverse health effects associated with air pollution mostly focus on individual pollutants. However, the air is a complex medium, and thus epidemiological studies face many challenges and limitations in the multipollutant approach. NO₂ and PM_2.5 have been selected as both originating from combustion processes and are considered to be the main pollutants associated with traffic; moreover, both elicit oxidative stress responses. An answer to the question of whether synergistic or antagonistic health effects of combined pollutants are demonstrated by pollutants monitored in ambient air is not explicit. Among the analyzed studies, only a few revealed statistical significance. Exposure to a single pollutant (PM_2.5 or NO₂) was mostly associated with a small increase in non-accidental mortality (HR:1.01-1.03). PM_2.5 increase of <10 µg/m³ adjusted for NO₂ as well as NO₂ adjusted for PM_2.5 resulted in a slightly lower health risk than a single pollutant. In the case of cardiovascular heart disease, mortality evoked by exposure to PM_2.5 or NO₂ adjusted for NO₂ and PM_2.5, respectively, revealed an antagonistic effect on health risk compared to the single pollutant. Both short- and long-term exposure to PM_2.5 or NO₂ adjusted for NO₂ and PM_2.5, respectively, revealed a synergistic effect appearing as higher mortality from respiratory diseases.

PM2 .5, PM10 and bronchiolitis severity: A cohort study

BACKGROUND

A few studies suggest that particulate matter (PM) exposure might play a role in bronchiolitis. However, available data are mostly focused on the risk of hospitalization and come from retrospective studies that provided conflicting results. This prospective study investigated the association between PM (PM_2.5 and PM₁₀ ) exposure and the severity of bronchiolitis.

METHODS

This prospective cohort study was conducted between November 2019 and February 2020 at the pediatric emergency department of the Fondazione IRCCS Ca' Ospedale Maggiore Policlinico, Milan, Italy. Infants <1 year of age with bronchiolitis were eligible. The bronchiolitis severity score was assessed in each infant and a nasal swab was collected to detect respiratory viruses. The daily PM₁₀ and PM_2.5 exposure in the 29 preceding days were considered. Adjusted regression models were employed to evaluate the association between the severity score and PM₁₀ and PM_2.5 exposure.

RESULTS

A positive association between the PM_2.5 levels and the severity score was found at day-2 (β 0.0214, 95% CI 0.0011-0.0417, p = .0386), day-5 (β 0.0313, 95% CI 0.0054-0.0572, p = .0179), day-14 (β 0.0284, 95% CI 0.0078-0.0490, p = .0069), day-15 (β 0.0496, 95% CI 0.0242-0.0750, p = .0001) and day-16 (β 0.0327, 95% CI 0.0080-0.0574, p = .0093).Similar figures were observed considering the PM₁₀ exposure and limiting the analyses to infants with respiratory syncytial virus.

CONCLUSION

This study shows for the first time a direct association between PM_2.5 and PM₁₀ levels and the severity of bronchiolitis.

Joint association between ambient air pollutant mixture and pediatric asthma exacerbations

Exposure to air pollutants is known to exacerbate asthma, with prior studies focused on associations between single pollutant exposure and asthma exacerbations. As air pollutants often exist as a complex mixture, there is a gap in understanding the association between complex air pollutant mixtures and asthma exacerbations. We evaluated the association between the air pollutant mixture (52 pollutants) and pediatric asthma exacerbations.

Method

This study focused on children (age ≤ 19 years) who lived in Douglas County, Nebraska, during 2016-2019. A seasonal-scale joint association between the outdoor air pollutant mixture adjusting for potential confounders (temperature, precipitation, wind speed, and wind direction) in relation to pediatric asthma exacerbation-related emergency department (ED) visits was evaluated using the generalized weighted quantile sum (qWQS) regression with repeated holdout validation.

Results

We observed associations between air pollutant mixture and pediatric asthma exacerbations during spring (lagged by 5 days), summer (lag 0-5 days), and fall (lag 1-3 days) seasons. The estimate of the joint outdoor air pollutant mixture effect was higher during the summer season (adjusted-β_WQS = 1.11, 95% confidence interval [CI]: 0.66, 1.55), followed by spring (adjusted-β_WQS = 0.40, 95% CI: 0.16, 0.62) and fall (adjusted-β_WQS = 0.20, 95% CI: 0.06, 0.33) seasons. Among the air pollutants, PM_2.5, pollen, and mold contributed higher weight to the air pollutant mixture.

Conclusion

There were associations between outdoor air pollutant mixture and pediatric asthma exacerbations during the spring, summer, and fall seasons. Among the 52 outdoor air pollutant metrics investigated, PM_2.5, pollen (sycamore, grass, cedar), and mold (Helminthosporium, Peronospora, and Erysiphe) contributed the highest weight to the air pollutant mixture.

Bayesian Information Criterion for Fitting the Optimum Order of Markov Chain Models: Methodology and Application to Air Pollution Data

The analysis of air pollution behavior is becoming crucial, where information on air pollution behavior is vital for managing air quality events. Many studies have described the stochastic behavior of air pollution based on the Markov chain (MC) models. Fitting the optimum order of MC models is essential for describing the stochastic process. However, uncertainty remains concerning the optimum order of such models for representing and characterizing air pollution index (API) data. In this study, the optimum order of the MC models for hourly and daily API sequences from seven stations in the central region of Peninsular Malaysia is identified, based on the Bayesian information criteria (BIC), contributing to exploring an adequate explanation of the probabilistic dependence of air pollution. A summary of the statistics for the API was calculated prior to the analysis. The Markov property and the divergence for the empirically estimated transition matrix of an MC sequence are also investigated. It is found from the analysis that the optimum order varies from one station to another. At most stations, for both observed and simulated API data, the second and third orders of the MC models are found to be optimum for hourly API occurrences, while the first-order MC is found to be most fitting for describing the dynamics of the daily API. Overall, fitting the optimum order of the MC model for the API data sequence captured the delay effect of air pollution. Accordingly, we concluded that the air quality standard lies within controllable limits, except for some infrequent occurrences of API values exceeding the unhealthy level.

Using Bayesian time-stratified case-crossover models to examine associations between air pollution and "asthma seasons" in a low air pollution environment

Many areas of the United States have air pollution levels typically below Environmental Protection Agency (EPA) regulatory limits. Most health effects studies of air pollution use meteorological (e.g., warm/cool) or astronomical (e.g., solstice/equinox) definitions of seasons despite evidence suggesting temporally-misaligned intra-annual periods of relative asthma burden (i.e., “asthma seasons”). We introduce asthma seasons to elucidate whether air pollutants are associated with seasonal differences in asthma emergency department (ED) visits in a low air pollution environment. Within a Bayesian time-stratified case-crossover framework, we quantify seasonal associations between highly resolved estimates of six criteria air pollutants, two weather variables, and asthma ED visits among 66,092 children ages 5–19 living in South Carolina (SC) census tracts from 2005 to 2014. Results show that coarse particulates (particulate matter <10 μm and >2.5 μm: PM_10-2.5) and nitrogen oxides (NO_x) may contribute to asthma ED visits across years, but are particularly implicated in the highest-burden fall asthma season. Fine particulate matter (<2.5 μm: PM_2.5) is only associated in the lowest-burden summer asthma season. Relatively cool and dry conditions in the summer asthma season and increased temperatures in the spring and fall asthma seasons are associated with increased ED visit odds. Few significant associations in the medium-burden winter and medium-high-burden spring asthma seasons suggest other ED visit drivers (e.g., viral infections) for each, respectively. Across rural and urban areas characterized by generally low air pollution levels, there are acute health effects associated with particulate matter, but only in the summer and fall asthma seasons and differing by PM size.

Robust empirical Bayes approach for Markov chain modeling of air pollution index

Air pollution is a matter of concern among the public, especially for those living in urban and industrial areas. Markov chain modeling is often used to model the underlying dynamics of air pollution, which involves describing the transition probability of going from one air pollution state to another. Thus, estimating the transition probability matrix for the data of the air pollution index (API) is an essential process in the modeling. However, one may observe many zero probabilities in the transition probability matrix, especially when faced with a small sample, interpreting the results with respect to the climate condition less realistic. This study proposes a robust empirical Bayes method, which incorporates a method of smoothing the zero frequencies in the count matrix, contributing to an improved estimation of the transition probability matrix. The robustness of the empirical Bayesian estimation is investigated based on Bayes risk. The transition probability matrices estimated based on the robust empirical Bayes method for the hourly API data collected from seven monitoring stations in Malaysia for the period 2012 to 2014 are used for determining the air pollution characteristics such as the mean residence time, the steady-state probability and the mean recurrence time. Furthermore, the proposed method has been evaluated by Monte Carlo simulations. Results suggest that it is quite effective in producing non-zero transition probability estimates, and superior to the maximum likelihood method in terms of minimizing the mean squared error for individual and entire transition probabilities. Therefore, the robust empirical Bayes method proves to be an improved approach to the estimation of the Markov chain. When applied to API data, it could provide important information on air pollution dynamics that may help guiding the development of proper strategies for managing the impact of air quality.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-020-00607-4.

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

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

The Respiratory Risks of Ambient/Outdoor Air Pollution

Globally, exposure to ambient air pollutants is responsible for premature mortality and is implicated in the development and exacerbation of several acute and chronic lung disease across all ages. In this article, we discuss the source apportionment of ambient pollutants and the respiratory health effects in humans. We specifically discuss the evidence supporting ambient pollution in the development of asthma and chronic obstructive pulmonary disease and acute exacerbations of each condition. Practical advice is given to health care providers in how to promote a healthy environment and advise patients with chronic conditions to avoid unsafe air quality.

A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation

Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) has been linked to various adverse health outcomes. PM_2.5 arises from both natural and anthropogenic sources, and PM_2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM_2.5 measurements, potentially limiting the accuracy of PM_2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM_2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM_2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM_2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R². The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.

Triggering of cardiovascular hospital admissions by source specific fine particle concentrations in urban centers of New York State

BACKGROUND

Previous work reported increased rates of acute cardiovascular hospitalizations associated with increased PM2.5 concentrations in the previous few days across urban centers in New York State from 2005 to 2016. These relative rates were higher after air quality policies and economic changes resulted in decreased PM2.5 concentrations and changes in PM composition (e.g. increased secondary organic carbon), compared to before and during these changes. Changes in PM composition and sources may explain this difference.

OBJECTIVES

To estimate the rate of acute cardiovascular hospitalizations associated with increases in source specific PM2.5 concentrations.

METHODS

Using source apportioned PM2.5 concentrations at the same NYS urban sites, a time-stratified case-crossover design, and conditional logistic regression models adjusting for ambient temperature and relative humidity, we estimated the rate of these acute cardiovascular hospitalizations associated with increases in mean source specific PM2.5 concentrations in the previous 1, 4, and 7 days.

RESULTS

Interquartile range (IQR) increases in spark-ignition emissions (GAS) concentrations were associated with increased excess rates of cardiac arrhythmia hospitalizations (2.3%; 95% CI = 0.4%, 4.2%; IQR = 2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI = 1.1%, 6.4%; 2. 73 μg/m3) over the next day. IQR increases in diesel (DIE) concentrations were associated with increased rates of congestive heart failure hospitalizations (0.7%; 95% CI = 0.2% 1.3%; 0.51 μg/m3) and ischemic heart disease hospitalizations (0.8%; 95% CI = 0.3%, 1.3%; 0.60 μg/m3) over the next day, as hypothesized. However, secondary sulfate PM2.5 (SS) was not. Increased acute cardiovascular hospitalization rates were also associated with IQR increases in concentrations of road dust (RD), residual oil (RO), and secondary nitrate (SN) over the previous 1, 4, and 7 days, but not other sources.

CONCLUSIONS

These findings suggest a role of several sources of PM2.5 in New York State (i.e. traffic emissions, non-traffic emissions such as brake and tire wear, residual oil, and nitrate that may also reflect traffic emissions) in the triggering of acute cardiovascular events.

Associations between multipollutant day types and select cardiorespiratory outcomes in Columbia, South Carolina, 2002 to 2013

Background

Health studies of air pollution are increasingly aiming to study associations between air pollutant mixtures and health.

Objective

Estimate associations between observed combinations of ambient air pollutants and select cardiorespiratory outcomes in Columbia, SC during 2002 to 2013.

Methods

We estimate associations using a two-stage approach. First, we identified a collection of observed pollutant combinations, which we define as multipollutant day types (MDTs), by applying a self-organizing map (SOM) to daily measures of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 microns (PM_2.5). Then, overdispersed Poisson time-series models were used to estimate associations between MDTs and each outcome using a 'clean' MDT referent and controlling for long-term, seasonal, and day-of-the-week trends and meteorology. Outcomes included daily emergency department visits for asthma and upper respiratory infection (URI), and hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD).

Results

We found that a number of MDTs were significantly and positively associated (point estimates ranged from~2-5%) with cardiorespiratory outcomes in Columbia when compared to days with low pollution. Estimated associations revealed that outcomes for asthma, URIs, and IHD increased 2-4% on warm, dry days experiencing elevated levels of O₃ and PM_2.5. We also found that cooler days with higher NO₂ pollution associated with increased asthma, CHF, and IHD outcomes (2-5%).

Conclusion

Our analysis continues support for using self-organizing maps to develop multipollutant exposure metrics and further illustrates how such metrics can be applied to explore associations between pertinent pollutant combinations and health.

Ambient air pollution and emergency department visits for asthma in Erie County, New York 2007-2012

PURPOSE

8% of the US population has asthma. Air pollution is linked to exacerbation in susceptible individuals. The objective was to identify air pollutants that increased the risk of asthma emergency department visits during a time wherein a polluting factory was criminally convicted, changing local air pollutant levels.

METHODS

An ecological time-series design used a daily count of asthma emergency visits from 2007 to 2012 as the dependent variable. Independent variables air pollutants (NO₂, PM_2.5 CO, and O₃), controlling for meteorological conditions, were analyzed using time-series and Poisson GLM models.

RESULTS

76,651 emergency asthma visits were included with an average of 35 visits per day (SD = 9.2, range 11-80) in a stationary time series. Increased visit volume in fall and spring had no associations to the air pollutants. Associations between individual air pollutants occurred in otherwise low-volume months for asthma emergency visits. The strongest relationship was an 11.6% increase in the asthma emergency visit rate during the month of June. In monthly groupings that removed most of the autumn and spring months, O₃, PM_2.5, CO, and NO₂ were associated with 5, 4, 2, and 2% increases in asthma emergency visits, respectively. CO was the only pollutant with a negative association with asthma emergency visits, occurring in the month of April.

CONCLUSIONS

Pollutants NO₂, PM_2.5 CO, and O₃ were associated with increased emergency asthma visits in some, but not all months of the year. Air pollution's impact on asthma emergencies may be masked by other, more influential seasonal triggers, such as infections or allergies.

Current approaches used in epidemiologic studies to examine short-term multipollutant air pollution exposures

PURPOSE

Air pollution epidemiology traditionally focuses on the relationship between individual air pollutants and health outcomes (e.g., mortality). To account for potential copollutant confounding, individual pollutant associations are often estimated by adjusting or controlling for other pollutants in the mixture. Recently, the need to characterize the relationship between health outcomes and the larger multipollutant mixture has been emphasized in an attempt to better protect public health and inform more sustainable air quality management decisions.

METHODS

New and innovative statistical methods to examine multipollutant exposures were identified through a broad literature search, with a specific focus on those statistical approaches currently used in epidemiologic studies of short-term exposures to criteria air pollutants (i.e., particulate matter, carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone).

RESULTS

Five broad classes of statistical approaches were identified for examining associations between short-term multipollutant exposures and health outcomes, specifically additive main effects, effect measure modification, unsupervised dimension reduction, supervised dimension reduction, and nonparametric methods. These approaches are characterized including advantages and limitations in different epidemiologic scenarios.

DISCUSSION

By highlighting the characteristics of various studies in which multipollutant statistical methods have been used, this review provides epidemiologists and biostatisticians with a resource to aid in the selection of the most optimal statistical method to use when examining multipollutant exposures.

Prognostic factors analysis in EGFR mutation-positive non-small cell lung cancer with brain metastases treated with whole brain-radiotherapy and EGFR-tyrosine kinase inhibitors

The survival time of non-small cell lung cancer (NSCLC) patients with brain metastases has been previously reported to be 6.5-10.0 months, even with systematic treatment. Patients that possess a certain epidermal growth factor receptor (EGFR) mutation alongside NSCLC with brain metastases also have a short survival rate, and a reliable prognostic model for these patients demonstrates a strong correlation between the outcome and treatment recommendations. The Cox proportional hazards regression and classification tree models were used to explore the prognostic factors in EGFR mutation-positive NSCLC patients with brain metastases following whole-brain radiation therapy (WBRT) and EGFR-tyrosine kinase inhibitor (EGFR-TKI) treatment. A total of 66 EGFR mutation-positive NSCLC patients with brain metastases were retrospectively reviewed. Univariate and multivariate analyses by Cox proportional hazards regression were then performed. The classification tree model was applied in order to identify prognostic groups of the patients. In the survival analysis, age, carcinoembryonic antigen (CEA) and status of the primary tumor were prognostic factors for progression free survival (P=0.006, 0.014 and 0.005, respectively) and overall survival (P=0.009, 0.013 and 0.009, respectively). The classification tree model was subsequently applied, which revealed 3 patient groups with significantly different survival times: Group I, age <65 years and CEA ≤10 µg/ml; Group II, age <65 years and CEA >10 µg/ml or age ≥65 years and CEA ≤10 µg/ml; and Group III, age ≥65 years and CEA >10 µg/ml. The major prognostic predictors for EGFR mutation-positive NSCLC patients with brain metastases following WBRT and EGFR-TKI were age and CEA. In addition, primary tumor control may be important for predicting survival.