measuring the effects of air pollution among persons with severe emphysema
TRANSCRIPT
Measuring the Effects of Air Pollution among Persons with Severe Emphysema: The National Emphysema Treatment Trial (NETT)
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
Mbabazi Maureen Kariisa, B.Sc, MPH
Graduate Program in Public Health
The Ohio State University
2013
Dissertation Committee:
Professor John R. Wilkins III, Advisor
Professor Randi E. Foraker
Professor Michael L. Pennell
Professor Timothy J. Buckley
Copyright by
Mbabazi Maureen Kariisa
2013
ii
Abstract
Background: Emphysema is a chronic obstructive pulmonary disease that affects
approximately 5 million people in the US. For subjects with severe emphysema, few
effective treatment options exist; however, the recent adoption of lung volume reduction
surgery (LVRS) is considered as a promising alternative to traditional therapy. The
National Emphysema Treatment Trial (NETT) was a randomized controlled trial
designed to assess the efficacy of LVRS and medical therapy versus standard medical
management among participants with severe emphysema. Results from the trial
demonstrated less morbidity and mortality among participants receiving LVRS. We
evaluated the effects of air pollution on the health of NETT participants. Methods: Data
from the NETT study (1998-2003) included 1218 subjects, men and women ages 39-84.
We also obtained data from the US Environmental Protection Agency Air Quality
Systems database, which included daily values of fine particulate matter (PM2.5) and
ozone. ZIP code specific exposures were spatially interpolated with the use of log-normal
kriged models. This methodology was employed in order to assign exposures in areas
with few nearby air pollution monitors. We investigated whether there was evidence of
differential air pollution exposure by individual and area-level measures of
socioeconomic status (SES). We assessed both the short- and long-term health effects of
air pollution on the respiratory morbidity of NETT participants using mixed linear and
Poisson models, which accounted for the daily (repeated) measures of air pollution data.
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In addition, we constructed survival models to investigate the impact of air pollutant
exposures on post-LVRS mortality. Results: NETT participants experienced varying
levels of exposure to ozone and PM2.5 and increasing cumulative pollutant exposures
were associated with decreasing values of area-level SES. We found that PM2.5 and
ozone significantly worsened respiratory function in these subjects. There also appeared
to be evidence of a differential effect of ambient air pollutants on pulmonary function and
respiratory symptoms according to their randomization arm. Mean ozone concentrations
were significantly associated with increased post-LVRS mortality risk. The benefits
reported for participants who received LVRS surgery did not persist in the presence of
ambient air pollutants, suggesting that the surgery may not have conferred a protective
survival effect for those participants who underwent the procedure. Conclusion: There
were significant adverse impacts of ambient ozone and PM2.5 on the respiratory health
and survival of NETT participants. LVRS subjects were more likely to experience
worsened air pollution-related lung function outcomes. Implications from our analyses
could lead to the recommendation of lowered acceptable PM2.5 and ozone limits for
individuals with existing respiratory disease.
iv
Dedication
Dedicated to my father, Gabriel Kariisa, who always supported and encouraged me to
strive for academic excellence.
v
Acknowledgments
I would like to thank my advisor, Dr. John Wilkins for all his guidance and support. I am
grateful that he was able to act as my mentor throughout this process and I am truly indebted to
him for all that he has done. I would also like to sincerely thank Dr. Randi Foraker, who was not
only patient, kind and encouraging, but also always ready with advice and feedback when it was
needed most. To the rest of my dissertation committee, Dr. Michael Pennell and Dr. Timothy
Buckley, I extend a heartfelt thank you for all their assistance, instruction and guidance in helping
me craft my dissertation research.
I would also like to recognize the multitude of people at OSU that have aided me in my
research. Zhiguang Zhang and Chris Holloman, at the Statistical Consulting Services, Jyothi
Nagaraja, Dr. Haikady Nagaraja, Dr. Phillip Diaz and Dr. Duanping Liao, at the Pennsylvania
State University were all valuable resources to me throughout this process. To the professors I
had the opportunity to work with at the College of Public Health, I want to express my gratitude
to you in helping me grow and become a better public health professional.
To my dear friends, near and far, with whom I have shared highs and lows, I want to
express my gratitude for always being there, with a kind word and an attentive ear. My progress
over the last few years would not have been possible without you. Last, but not least, I would like
to thank my family. My mother, Agatha, whose advice and unwavering belief in me, was a true
source of strength. To my siblings, Eddie, Kansiime, Ankunda and Jessica, you were all
instrumental in getting me to this point and I am forever grateful to have you as my ever present
cheering section.
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Vita
2003……………………………………………….…..…...B.Sc., Biology Duke University Durham, NC 2005……………………………………………………… MPH, International Health University of Michigan Ann Arbor, MI
2005-2006………………………………………………….Clinical Research Coordinator Mount Sinai Medical Center New York, NY 2006-2008………………………………………………….Clinical Research Coordinator Wexner Medical Center Columbus, OH 2008-2010………………………………………………….Graduate Research Fellow Health Policy Institute of Ohio Columbus, OH 2010 to present……………………………………………..Graduate Teaching Assistant
College of Public Health The Ohio State University
Publications
1. Rajagopalan S, Kariisa M, Dellegrottaglie S, Bard RL, Kehrer C, Matlow S, Daley W, Pitt B, Brook R. (2006) Angiotensin receptor blockade improves vascular compliance in healthy normotensive elderly individuals: results from a randomized double-blind placebo-controlled trial. J Clin Hypertens, 8(11):783-90.
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2. Rajagopalan S, Zannad F, Radauceanu A, Glazer R, Jia Y, Prescott MF, Kariisa M, Pitt B. (2007) Effects of valsartan alone versus valsartan/simvastatin combination on ambulatory blood pressure, C-reactive protein, lipoproteins, and monocyte chemoattractant protein-1 in patients with hyperlipidemia and hypertension. Am J Cardiol, 100(2):222-6. 3. Sanz J, Dellegrottaglie S, Kariisa M, Sulica R, Poon M, O'Donnell TP, Mehta D, Fuster V, Rajagopalan S. (2007) Prevalence and correlates of septal delayed contrast enhancement in patients with pulmonary hypertension. Am J Cardiol,100(4):731-5. 4. Sanz J, Kariisa M, Dellegrottaglie S, Prat-González S, Garcia MJ, Fuster V, Rajagopalan S. (2009) Evaluation of pulmonary artery stiffness in pulmonary hypertension with cardiac magnetic resonance. JACC Cardiovasc Imaging. 2(3):286-95 5. Mihai G, Chung YC, Kariisa M, Raman SV, Simonetti OP, Rajagopalan S. (2009) Initial feasibility of a multi-station high resolution three-dimensional dark blood angiography protocol for the assessment of peripheral arterial disease. J Magn Reson Imaging. 30(4):785-93
Fields of Study
Major: Public Health
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Table of Contents
Abstract ............................................................................................................................... ii Dedication .......................................................................................................................... iv
Acknowledgments................................................................................................................v
Vita ..................................................................................................................................... vi
List of Tables .......................................................................................................................x
List of Figures ................................................................................................................... xii
Chapters
1. Introduction ......................................................................................................................1
2. Background ......................................................................................................................5
2.1 Emphysema ................................................................................................................5
2.1.1 Risk Factors ....................................................................................................9
2.1.2 Diagnosis ......................................................................................................10
2.1.3 Treatment ......................................................................................................12
2.2 Air Pollution .............................................................................................................13
2.2.1 Particulate Matter ..........................................................................................16
2.2.2 Ozone ............................................................................................................18
2.2.3 Environmental Justice ...................................................................................19
3. Methods..........................................................................................................................21
3.1 National Emphysema Treatment Trial .....................................................................21
3.1.1 Study Population ...........................................................................................21
3.1.2 Methods ........................................................................................................21
3.1.3 Results ...........................................................................................................23
3.2 Air Pollution Data ....................................................................................................24
3.3 Exposure Assessment ...............................................................................................26
ix
4. Hypothesis and Specific Aims .......................................................................................30
5. Differential ambient air pollution exposure in a COPD cohort: The role of individual and area-level socioeconomic factors ................................................................................33
5.1 Introduction ..............................................................................................................33
5.2 Methods ....................................................................................................................35
5.3 Results ......................................................................................................................40
5.4 Discussion ................................................................................................................47
6. Short and long-term effects of ambient ozone and fine particulate matter on the respiratory health of COPD subjects..................................................................................56
6.1 Introduction ..............................................................................................................56
6.2 Methods ....................................................................................................................58
6.3 Results ......................................................................................................................65
6.4 Discussion ................................................................................................................81
7. Healthcare utilization and mortality in response to ambient air pollution in a vulnerable population ..........................................................................................................................88
7.1 Introduction ..............................................................................................................88
7.2 Methods ....................................................................................................................90
7.3 Results ......................................................................................................................97
7.4 Discussion ..............................................................................................................105
8. Concluding Remarks ....................................................................................................114
8.1 Statistical Considerations .......................................................................................114
8.2 Exposure Measurement Error.................................................................................117
8.3 Conclusions ............................................................................................................119
Appendix A: Kaplan-Meier Mortality Estimates for All NETT Participants .................123
Appendix B: Spatial Mismatch between ZIP Code Areas and ZCTAs ..........................124
Appendix C: Decline in FEV1 and FVC over Study Duration, by Treatment Arm and Sex..........................................................................................................................................125
Appendix D: Distribution of Total Healthcare Utilization Patterns ...............................128
Appendix E: St. George’s Respiratory Questionnaire .....................................................132
References ........................................................................................................................145
x
List of Tables
Table 1.1. List of Clinical Centers Participating in the National Emphysema
Treatment Trial ...................................................................................................................4 Table 2.1. American Thoracic Society and Global Initiative for Chronic Obstructive Lung Disease (GOLD) Staging Severity for COPD. ..................................................................11 Table 2.2. National Ambient Air Quality Standards .........................................................15 Table 5.1. Summary of NETT Patient Characteristics, by Treatment Arm ......................41 Table 5.2. Bivariate Correlations between Individual and ZIP code-level Covariates .....43
Table 5.3. Regression of Cumulative PM2.5 Exposure (µg/m3) with Individual and ZIP Code Level SES Variables ................................................................................................46
Table 5.4. Regression of Cumulative Ozone Exposure (ppm) with Individual and ZIP Code Level SES Variables ................................................................................................47
Table 6.1. Characteristics of the NETT Study Population, by Treatment Arm ................67 Table 6.2. Mean Ozone and PM2.5 Concentrations by Socio-Demographic Factors ........68
Table 6.3. Regression of Respiratory Function and Symptoms on Mean Pollutant Level in all Participants ...................................................................................................................70
Table 6.4. Regression of Respiratory Function and Symptoms on Cumulative Pollutant
Level in all Participants ....................................................................................................72
Table 6.5. Regression of Respiratory Function and Symptoms on Same-Day Pollutant
Level in all Participants ....................................................................................................74
Table 6.6. Regression of Respiratory Function and Symptoms on Three-Day Lagged
Pollutant Level in all Participants .....................................................................................76
Table 6.7. Regression of Respiratory Function and Symptoms on Mean Pollutant Level
High-Risk Participants ......................................................................................................77
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Table 6.8. Regression of Respiratory Function and Symptoms on Cumulative Pollutant
Level High-Risk Participants ............................................................................................78
Table 6.9. Regression of Respiratory Function and Symptoms on Same-Day Pollutant
Level in High-Risk Participants ........................................................................................79
Table 6.10. Regression of Respiratory Function and Symptoms on Three-Day Lagged
Pollutant Level in High-Risk Participants ........................................................................80
Table 7.1. Total Healthcare Utilization Summary for all NETT Participants, by Treatment
Arm ...................................................................................................................................98
Table 7.2. Poisson Regression Estimates for Total Healthcare Utilization by Mean
Pollutant Level in all Participants .....................................................................................99
Table 7.3. Poisson Regression Estimates for Total Healthcare Utilization by Cumulative
Pollutant Level in all Participants .....................................................................................99
Table 7.4. Poisson Regression Estimates for Total Healthcare Utilization by Cumulative
Pollutant Level in High-Risk Participants ......................................................................100
Table 7.5. Poisson Regression Estimates for Total Healthcare Utilization by Mean
Pollutant Level in High-Risk Participants ......................................................................101
Table 7.6. Cox Regression Models by Mean Pollutant Level and Year in all
Participants ......................................................................................................................101
Table 7.7. Cox Regression Models by Cumulative Pollutant Level and Year in all
Participants ......................................................................................................................102
Table 7.8. Cox Regression Models by Mean Pollutant Level and Year in High-Risk
Participants ......................................................................................................................103
Table 7.9. Cox Regression Models by Cumulative Pollutant Level and Year in High-Risk
Participants ......................................................................................................................104
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List of Figures
Figure 2.1. Lung Structure: Respiratory and Terminal Bronchioles ..................................6
Figures 2.2a, 2.2b and 2.2c. Healthy and Emphysematous Acinar Tissue .........................7
Figure 5.1. Mean Cumulative PM2.5 by Ranked SED index Percentile ...........................44
Figure 5.2. Mean Cumulative Ozone by SED Index Percentile .......................................45
Figure 5.3. Distribution of ZIP Codes for NETT Participants ..........................................48
Figure 5.4. Individual and Area-Level Pathways for Differential Exposure and Susceptibility .....................................................................................................................49
Figure 7.1. Effects of Mean Ozone Exposure on Mortality Risk by Treatment Arm .....102
1
Chapter 1: Introduction
Emphysema is a serious health condition affecting an estimated 4.9 million people in the
US according to the 2009 National Health Interview Survey (1). Due to its chronic and
irreversible nature, emphysema results in the rapid deterioration of lung function, leading
to increased risk of morbidity and mortality. Much has been written on the detrimental
effects of cigarette smoking on the progressive nature of the disease; however, less is
known regarding the possible harmful effects of environmental hazards, such as outdoor
air pollution, on the condition (2).
Air pollution has long been associated with adverse effects on respiratory health.
Epidemiologic studies have reported these adverse effects not only in response to acutely
high air pollutant concentrations but to low ambient levels as well (3). Worsening
respiratory symptoms, increases in hospital admissions and elevated mortality have all
been previously reported as outcomes associated with air pollution exposure (4).
Symptoms of chronic respiratory diseases, such as shortness of breath and wheezing, are
likely exacerbated by exposure to air pollutants (5).
For those afflicted with emphysema, specifically, few good options exist for reducing
overall morbidity and mortality from the disease. Therapies for those with advanced
disease are often palliative and can do little to reverse the damage already caused to the
pulmonary system. Novel treatments such as lung volume reduction surgery have been
2
touted as an alternative therapy. The potential benefits of this surgery are still being
investigated, although several studies have shown improved survival after the
procedure (6).
The National Emphysema Treatment (NETT) trial was a large, multicenter, randomized,
controlled clinical trial designed to test the efficacy of lung volume reduction surgery
(LVRS) among patients with severe emphysema. Findings from the NETT showed that,
in the long term, participants receiving LVRS and medical management fared better, in
terms of survival and morbidity, than those who received only standard medical therapy.
The study, carried out at 17 clinical centers across the US, enrolled a total of 1218
subjects with severe emphysema (Table 1.1). The participants in the study tended to be
older, Caucasian, and male, and had all undergone pulmonary rehabilitation prior to
randomization. Pulmonary rehabilitation consists of exercise training, as well as
nutritional and psychosocial evaluation and therapy. Among COPD subjects, it serves to
optimize quality of life, relieve symptoms, and increase functional capacity (7; 8; 9).
Primary outcomes for the trial included survival and maximum exercise capacity. Quality
of life, cost-effectiveness, lung function and cardiovascular measures were secondary
outcomes of interest to the investigators (10). Air pollution exposure data were collected
from air monitoring databases as part of an independent ancillary study, however, these
data were not investigated as possible confounders and/or effect modifiers in an
assessment of post-treatment pulmonary function, healthcare utilization, and mortality.
3
For this research, we aimed to examine how post-treatment lung function and mortality
were affected as a consequence of exposure to specific air pollutants. Additionally, we
explored how variations in pollutant levels could impact respiratory symptoms and
morbidity. With respect to the proposed analyses, we hypothesized that there would be
differential respiratory effects for LVRS subjects compared to those receiving medical
therapy only. Since the original trial also showed that high-risk NETT participants
received lower survival and respiratory benefits, it stands to reason that the effects of air
pollution would likely be more adverse among high-risk subjects and those who did not
receive the surgery. Assessing the impacts of air pollution on lung function would aid in
elucidating the role of both type and level of air pollutants in the progression of
emphysema.
In order to evaluate the effects of air pollution on the health of NETT participants,
assessing disparities in exposure concentrations was an important preliminary step. We
considered the impacts of area level measures of socioeconomic status (SES) on the
distribution of cumulative exposures and aimed to ascertain whether there was evidence
of differential pollutant exposure across varying levels of SES. Inequalities in exposure
among such a vulnerable population could be indicative of increased susceptibility to
adverse health effects among certain SES groups.
4
Clinical Center
Location
University of Washington Seattle, Washington Cedar-Sinai Medical Center Los Angeles, California University of San Diego San Diego, California National Jewish Medical and Research Center Denver, Colorado Baylor College of Medicine Houston, Texas Mayo Foundation Rochester, Minnesota St. Louis University St. Louis, Missouri University of Michigan Ann Arbor, Michigan The Ohio State University Columbus, Ohio Cleveland Clinic Cleveland, Ohio Duke University Durham, North Carolina University of Pittsburgh Pittsburgh, Pennsylvania Columbia University New York, New York University of Pennsylvania Philadelphia, Pennsylvania Temple University Philadelphia, Pennsylvania University of Maryland at Baltimore Baltimore, Maryland Brigham and Women’s Hospital Boston, Massachusetts
Table 1.1. List of Clinical Centers Participating in the National Emphysema Treatment
Trial.
5
Chapter 2: Background
2.1 Emphysema
Emphysema is a progressive lung disease that along with chronic bronchitis is classified
as a chronic obstructive pulmonary disease (COPD). It had an estimated worldwide
prevalence of 10% in 2007, according to the Burden of Lung Disease (BOLD) study (11).
There exist two primary causes of emphysema: one is primarily caused by cigarette
smoking, while the other can be directly linked to an α1-antitrypsin deficiency (12). In
emphysematous pulmonary tissue, the degeneration of the walls of alveolar sacs leads to
the subsequent collapse of small airways and the trapping of air within the lungs (13; 14),
causing permanent abnormal enlargement of air spaces within the lung tissue. In healthy
lungs, the large surface area of the alveolar walls allows for efficient aeration of blood;
however in emphysematous lungs, the gas exchange of carbon dioxide and oxygen is
severely compromised. The trapping of air within the lungs and increased difficulty in
exhaling leads to the characteristic ‘barrel-chested’ appearance of emphysema patients
(15).
The acinus, or primary functional unit in the lung, is composed of the terminal
bronchiole, respiratory bronchioles, alveolar ducts and alveoli (Fig. 2.1) (14).
Classification of emphysema is determined by the region of the acinus most affected by
the disease.
6
Figure 2.1. Lung Structure: Respiratory and Terminal Bronchioles.
Marieb EN and Hoehn KN (2001). Human Anatomy and Physiology 5thEdition., San Francisco:
Benjamin Cummings.
Two main types of emphysema exist, centriacinar and panacinar emphysema.
Centriacinar or centrilobular emphysema initially affects respiratory bronchioles within
the acinus, leading to an enlargement of the airways and then spreading to the alveoli (Fig
2.2a). It predominantly involves the upper lobes of the lung and is the most common
form of emphysema among smokers. Panacinar emphysema involves the destruction of
the alveoli and is most predominant in the lower lobes of the lungs (Fig 2.2b). It is
typically seen among individuals with an α1-antitrypsin deficiency. Patients with an α1-
antitrypsin deficiency lack the enzyme capable of digesting excess elastase, an enzyme
that primarily digests elastin. In healthy lungs, elastase is involved in maintaining the
7
normal balance of elastin degradation and renewal. Elastin is a protein that provides
elasticity to tissues and organs. The α1-antitrypsin deficiency disrupts this delicate
balance and results in excessive elastin destruction (14; 16).
Figures 2.2a, 2.2b and 2.2c. Healthy and Emphysematous Acinar Tissue.
Kumar V, Cotran RS and Robbins, SL (2003).Robbins Basic Pathology, 7th Edition.
Elsevier Science.
8
The prevailing hypothesis for the pathogenesis of smoking-induced emphysema is based
on a protease/anti-protease imbalance. Proteases are responsible for the breakdown of
peptide bonds in proteins. This theory gained popularity as a result of evidence
associating α1-antitrypsin deficiencies with emphysema. It posits that exposure to
oxidants in cigarette smoke provokes the release of inflammatory cells such as
macrophages in the lungs. These macrophages subsequently release oxidants and
proteases that injure and digest the extra cellular matrix (ECM) in the alveolar walls and
respiratory bronchioles. Additional proteases are then released by the pulmonary cells
themselves, causing further harm to the walls of the alveolar sac. The inactivation of
proteinase inhibitors by inflammatory cells facilitates damage to the ECM. Destruction of
the ECM, a structural network of elastic fibers and collagen primarily composed of
elastin, leads to reduced elasticity of the alveolar sacs and results in impaired lung
function (16; 17; 18).
The progression of emphysema is typically slow, with a heavy smoking patient
presenting with symptoms in their fifties and sixties (19). The most common signs and
symptoms of emphysema are shortness of breath, wheezing, coughing and hyperinflation
of the lung. Subjects often experience increasing dyspnea with worsening airflow
obstruction leading to increased impairment of lung function (7; 20). Disability among
emphysema subjects worsens with disease progression, as the elasticity of the lungs
decreases and regular daily activities become more taxing. Additionally, mental well-
being is impacted by emphysema. Studies have reported that increasing symptoms of
depression and anxiety are correlated with disease severity (21; 22).
9
2.1.1 Risk Factors
Known risk factors for emphysema include smoking, age, sex, certain genetic conditions,
occupational exposures and air pollution (23; 24). Although an important primary cause
of emphysema, smoking alone will not always lead to emphysema. Intensity and duration
of smoking likely affect the risk of developing the disease (14). Prospective cohort
studies in Britain and China have shown a 2 to 7 fold increase in the risk of COPD
mortality among smokers compared to non-smokers. When the results were further
stratified by smoking intensity, the risk of dying from COPD rose dramatically for heavy
versus moderate smokers (23). Additionally, multiple studies have shown decreases in
COPD-related deaths among individuals who stopped smoking. A prospective study of
US veterans reported that COPD mortality rates among former smokers were almost 4
times lower than those of current smokers. Persons who had stopped smoking for more
than 10 years received the most benefit; however, their mortality rates were still 2 to 3
times greater than those who had never smoked (25).
Various occupational studies have shown the increased risk of occupational exposure and
COPD development and progression. The highest risk has been observed among miners,
most notably, coal miners (26). Cockroft et al. demonstrated that severity of emphysema
among coal miners was associated with dust content as well as cumulative lifetime
exposure to coal dust (27). Coal dust inactivation of α1-antitrypsin has been shown in in
vitro studies, increasing the risk among those exposed of developing emphysema (26).
Other occupational agents associated with an increased risk of COPD include cement,
silica, cadmium, vanadium, vinyl chloride, polycyclic aromatic hydrocarbons, cotton,
10
grains and wood (28). Worsening lung function, as measured by declines in FEV1 (forced
expiratory volume in 1 second) and FVC (forced vital capacity) among workers exposed
to toluene diisocyanates, revealed that chronic low levels of exposure could significantly
impact respiratory health, independent of smoking status (28). Emphysema caused by
occupational exposures is indistinguishable from smoking-induced emphysema and it is
common for workers to be simultaneously exposed to cigarette smoke and workplace
irritants (27).
Airborne pollutants have long been associated with respiratory diseases. Specifically,
observational studies have shown a higher occurrence of COPD and emphysema
symptoms in areas of high air pollution. Aggravation of existing COPD by environmental
irritants such as tobacco smoke, dust and gases has been suggested to result from
increased inflammation and possible oxidative injury (2). Although the role of outdoor air
pollution in the progression of emphysema is less clear, it is hypothesized that it acts in a
similar fashion as tobacco smoke exposure (3; 13).
2.1.2 Diagnosis
Diagnosis of emphysema is usually confirmed by histopathology and computed
tomography (CT) scans (29). Lung function is assessed with spirometry tests, which
measure pulmonary factors such as FEV1, FVC, vital capacity (VC), FEV1 % predicted,
forced expiratory flow (FEF), peak expiratory flow rate (PEFR), and maximal voluntary
ventilation (MVV) (14). FEV1 and FVC values are used most commonly in determining
11
degree of lung function impairment as well as designating the stage of COPD in subjects
(Table 2.1).
Stage Description Spirometry Measures I Mild FEV1/FVC ratio < 70% and
% Predicted FEV1 ≥ 80% II Moderate FEV1/FVC< 70% and
% Predicted FEV1≥ 50% and < 80%
III Severe FEV1/FVC< 70% and % Predicted FEV1≥ 30% and < 50%
IV Very severe FEV1/FVC< 70% and % Predicted FEV1<30%
Table 2.1. American Thoracic Society and Global Initiative for Chronic Obstructive Lung
Disease (GOLD) Staging Severity for COPD.
Although spirometry measures aid in quantifying the degree of airflow limitation, they
are unable to differentiate between bronchitis predominant and emphysema predominant
COPD. Additionally, clinical tests may fail to accurately characterize the extent to which
pulmonary tissues are emphysematous. Thus the use of CT scans is essential; not only
can they quantify the percentage of emphysema in the lungs but they can also help
distinguish the pattern and distribution of alveolar destruction in pulmonary tissue (e.g.,
homogenous versus heterogeneous, upper lobe versus lower lobe, etc.) (30).
12
2.1.3 Treatment
As of yet, the only successful method to reduce the progression of emphysema has been
smoking cessation (31). Other treatments currently administered to emphysematous
patients include antibiotics, steroids and bronchodilators. Bronchodilators have been
shown to improve pulmonary function, exercise capacity and quality of life among those
with emphysema (10). In addition, pulmonary rehabilitation, which includes exercise,
improved nutrition, education, and psychosocial support, can also result in improved
exercise capacity and may reduce the number of emphysema-related hospitalizations (8).
Meanwhile, oxygen therapy has been shown as the most effective treatment in reducing
mortality (7). For the most serious cases, lung transplants have been used as a last resort.
As subjects with emphysema often develop enlarged lungs as a result of the disease,
LVRS has been touted as a novel treatment that may help reduce symptoms of
emphysema (31; 32). It was initially developed in the 1950s by Otto Brantigan, with the
aim of excising 20-30% of the most affected and diseased portions of the lung. Viewed as
harmful to subjects, this surgical procedure was mostly ignored until renewed interest by
Cooper et al. in the 1990s. Subsequent improvements in the original surgery allowed for
the simultaneous operation of both lungs with increased pain management post-surgery
(6). Early trials showed a decrease in mortality rates and improvement in overall lung
function by spirometry after subjects received LVRS (33). To assess the efficacy of this
surgical procedure on a large scale, the Health Care Financing Administration and the
National Heart, Lung and Blood Institute co-sponsored the NETT trial to evaluate health
outcomes and cost-effectiveness of LVRS (6).
13
2.2 Air Pollution
The effect of air pollution exposure on human health has become an ever-growing
concern worldwide. Prior to the 1960s, few regulatory policies for air pollution existed in
the US. The few measures in place were aimed mostly at controlling smoke from
industrial processes; however, these regulations were largely ineffective due to poor
enforcement. Legislation enacted in 1955 declared that air pollution control was the
responsibility of local and state agencies. This was followed by the implementation of the
Clean Air Act (CAA) in 1963, which called for a larger governmental role as well as the
“improvement of state and local air pollution control efforts.” In spite of these measures,
progress remained slow. It was not until the 1970s that amendments to the CAA resulted
in the establishment of uniform National Ambient Air Quality Standards (NAAQS). The
standards delineated limits on acceptable levels of key or ‘criteria’ pollutants. Significant
efforts in the control of air pollution continued, namely through the increasing interaction
between federal, state and local agencies (15; 34).
Currently, air quality monitoring in the US is performed by both state and local agencies
and is comprised of 3 different categories: State and Local Air Monitoring Agencies
(SLAMS), National Air Monitoring Stations (NAMS) and Special Purpose Monitoring
Stations (SPMS). Air quality measures capture 6 ‘criteria’ pollutants: particulate matter
(PM), ozone, nitrogen oxides, carbon monoxide (CO), lead and sulfur dioxide (Table 2.2)
(35). These criteria pollutants have been implicated in the development of numerous
adverse health outcomes. The goal of regulatory pollution standards is to prevent the
occurrence of chronic diseases and conditions as a result of ambient pollutant exposures
14
(15). Levels shown in table 2.2 refer to the primary standards, limits set to protect the
health of the population, including sensitive subgroups such as asthmatics, children and
the elderly. Air from pollution monitors is continuously sampled and the data are
typically averaged over multiple time frames. These time frames vary and can be based
on 1-hour, 8-hour, 24-hour, monthly, and annual average concentrations (36).
15
Pollutant Level Averaging Time Carbon Monoxide 9 ppm 8 hours (10 mg/m3) 35 ppm 1 hour (40 mg/m3) Lead
0.15 µg/m3 3 Months
Nitrogen Dioxide 53 ppb Annual 100 ppb 1 hour Particulate Matter (PM10)
150 µg/m3 24-hours
Particulate Matter (PM2.5)
15.0 µg/m3 Annual
35 µg/m3 24 hours Ozone 0.075 ppm 8 hours
0.08 ppm 8 hours 0.12 ppm 1 hour
Sulfur Dioxide 0.03 ppm Annual 0.14 ppm 24 hours 75 ppb 1 hour
Table 2.2. National Ambient Air Quality Standards. United States Environmental Protection
Agency (2010). National Ambient Air Quality Standards.
Multiple studies have examined the effect of air pollution on several different health
conditions. Many of these studies have focused on respiratory conditions such as asthma
and COPD (3; 37; 38). Among individuals with COPD, specific air pollutants have been
shown to be associated with an aggravation of symptoms as well as an increased risk in
mortality (13; 23; 39). These air pollutants include PM measuring less than 10 microns
and 2.5 microns (PM10 and PM2.5, respectively), ozone, nitrogen oxides, and sulfur
16
dioxide. In addition to playing a role in COPD development and progression, the adverse
effects of these pollutants have also been investigated in both short and long-term
epidemiologic studies (2).
2.2.1 Particulate Matter (PM)
Particulate matter is a broad term used to refer to substances or particles that remain
suspended in the atmosphere. A major source of particulates originates from soil or
combustion of fossil fuels from cars and industrial processes (3). Particulates can also
include natural particles such as dust and pollen (38). PM is typically categorized by its
aerodynamic size, which determines the length of time it remains suspended in the
atmosphere. Particulates with an aerodynamic size less than 20 microns are of particular
health concern as they can persist longer in the air and are considered breathing hazards.
Particulates measuring less than 10 microns are considered ‘inhalable’ as they can be
inspired and deposited into the respiratory tract. Particulates measuring less than 2.5
microns are defined as ‘respirable,’ as they are able to travel deeper into lung tissue and
are considered more toxic (40). Since the rate of clearance of respirable particles is
considerably slower than the rate for inhalable particles, respirable particles are
substantially more damaging as they can remain in contact with more sensitive tissue for
a longer period of time (15). The current EPA 24-hour limits for PM10 and PM2.5 are 150
µg/m3 and 35 µg/m3, respectively.
Epidemiologic studies investigating the effects of ambient PM levels on health outcomes
have shown that particulates have adverse effects on morbidity and mortality. The
17
Harvard Six Cities study by Dockery et al., reported that PM2.5 and sulfate particles were
correlated with increased mortality risk (41). The Air Pollution and Health: A European
Approach (APHEA) study reported similar findings, showing that an increase of 10μg/m3
in PM10 levels was associated with a daily mortality increase of about 0.6% (42). In
addition to an increased mortality risk, chronic PM exposures have been linked with
increased incidence of cardiovascular disease, diabetes, and lung cancer, in addition to
COPD and other respiratory conditions (41). Data among a cohort of Seventh Day
Adventists revealed a strong adverse relationship between particulates and respiratory
symptoms (43).
The effects of ambient PM exposure is of significant concern for vulnerable or high-risk
populations (e.g., individuals with existing respiratory disease), with limited data
suggesting that PM exposure is capable of aggravating COPD (44). Mechanistically, PM
is believed to worsen lung function among COPD subjects by constricting already narrow
airways. Deposition of PM2.5 in the alveoli can also trigger an inflammatory response
resulting in additional damage to the tissue (44). Exposure to PM10 poses a risk to COPD
subjects, as the mucus produced as a result of PM10 inhalation can result in increased
airway resistance and may block off smaller secondary airways (45). Additionally,
particle deposition in the lung for those with existing COPD may be characteristically
different due to higher ventilation rates and the presence of previously inflamed airways
(44; 45; 46).
2.2.2 Ozone
18
Ozone is a colorless and odorless gas that plays an important protective role in the
stratosphere by blocking ultraviolet rays from the sun; however, at ground level, it is
toxic to humans (47). Ozone is a primary component of smog and is produced from
photochemical reactions that include volatile organics, nitrogen oxides, and sunlight (48).
The highest ozone levels can be expected in the summer months and in areas with high
levels of nitric oxides and volatile organic compounds (15). The current EPA standards
for ozone at 1 and 8-hour averaging times are 0.12 ppm and 0.075 ppm, respectively.
From a human health perspective, elevated concentrations of ozone have been associated
with increased overall mortality and higher prevalence of respiratory symptoms (37).
Ozone toxicity and associated lung impairment has also been demonstrated in several
animal and epidemiologic studies. Tager et al. reported that young adults briefly exposed
to ozone levels greater than 0.18 ppm had worsened pulmonary function, as measured by
FEV1 and FVC (49). Numerous studies have investigated the effects of ozone exposure
on asthmatics, specifically examining the association between asthma-related hospital
admissions in response to ambient pollution levels (15; 50). Gent et al. found that low
ambient ozone levels were associated with increased asthma symptoms in children (51).
The effects of ozone on other vulnerable subpopulations have also been investigated in
various epidemiologic studies. Delfino et al. reported spikes in levels of ozone as being
associated with increased respiratory-related hospital visits among elderly patients (52),
while Sunyer et al. reported an association with long-term ambient ozone concentrations
and increases in emergency room visits among individuals with COPD (39).
19
The role of ozone in the exacerbation of asthma and other respiratory conditions is
believed to occur through injury to the lung tissue and airway inflammation via oxidative
stress (45). Experiments in primate animal models have revealed that chronic exposure to
ozone may result in physiological changes to bronchioles. Similar effects were observed
in studies involving humans (49).
2.2.3 Environmental Justice
The disproportional burden of air pollution exposure among the poor and minorities has
been a significant concern since the 1970s and the release of the influential report by
Freeman et al in Distribution of Environmental Quality (53). The results of which
demonstrated that poorer individuals and minorities residing in Kansas City, St. Louis
and Washington, DC were more likely to have higher exposures to sulfates and total
suspended particulates compared to wealthier individuals and Caucasians (53). Much of
the succeeding literature has focused on the unequal burden in environmental hazards
experienced by disadvantaged minorities. These studies have shown that among such
groups, there is a higher likelihood of residing close to point sources of pollution, such as
industrial plants and waste sites (54; 55). The term ‘environmental racism’ was coined as
a direct result of one of these studies as a way to describe the apparent racial inequity in
the location of hazardous waste sites (56). In addition to race, environmental inequality
by socioeconomic status (SES) has been researched in a number of studies. Although the
level of disparity in exposures has been stronger along racial/ethnic lines, there is still
evidence of an exposure differential with respect to poverty. A recent national study by
20
Miranda et al. found that low income communities tended to experience higher levels of
ambient air pollution (57).
Differential susceptibility to pollutants, in addition to differential exposure, has also been
reported in poorer individuals and minorities (58) (59). In these groups, increased
susceptibility to pollution may be a result of crowding, inadequate nutrition, and SES-
related stressors (60).
Conclusions
Given that air pollution data were collected for many of the areas in which the
participants resided, we proposed to investigate how lung function, healthcare utilization,
morbidity and mortality were affected as a result of ozone and PM exposures. We also
aimed to examine whether exposure concentrations were distributed unequally among
NETT participants by SES status. This proposed research aimed to assess the relationship
between differential air pollution exposure and overall lung function, healthcare usage,
and mortality in the NETT population.
21
Chapter 3: Methods
3.1 National Emphysema Treatment Trial
3.1.1 Study Population
The National Emphysema Treatment Trial (NETT) was a multicenter, randomized
controlled trial with the objective of assessing whether LVRS in conjunction with
medical therapy was a better long-term treatment for subjects with severe emphysema
when compared to medical therapy alone. Severe emphysema was defined as the
presence of severe airflow obstruction, as outlined by the American Thoracic Society
guidelines, and hyperinflation. The major outcomes of interest in the study were post-
treatment mortality and maximal exercise capacity. Secondary outcomes of interest
included lung function, as determined by spirometry, distance walked in six minutes and
quality of life. During the period 1998-2002, a total of 1218 subjects were enrolled in the
study, of which 608 were randomized to LVRS and medical management; 610 were
randomized to traditional medical therapy alone, such as steroids and bronchodilators.
3.1.2 Methods
Radiologists characterized emphysema distribution and magnitude in the lungs with chest
computed-tomography (CT) scans and a visual scoring scale. Emphysema distribution
was classified as either heterogeneous or homogeneous as well as by location of the most
22
affected lobes (e.g. upper-lobe predominant). All participants were required to have
stopped smoking at least four months before the start of the trial. All subjects also
underwent pulmonary rehabilitation prior to and after randomization. For those
randomized to surgery, subjects received either a “bilateral stapled resection via median
sternotomy or video assisted thoracic surgery.” The aim of the LVRS was to remove 25%
to 35% of the lung, with a particular focus on the most affected areas (12; 61).
Participants assigned to medical therapy alone were treated with bronchodilators,
provided with oxygen therapy and immunizations, and were enrolled in smoking
cessation programs in the event that they resumed smoking during the trial. Medical
histories of all participants were collected at 6, 12, 24, 36, 48 and 60 months after
baseline. Participants were followed for an average of 29.2 months (10; 12; 61).
Mortality was assessed by reports from the participating clinical centers as well as from
the Social Security Death Master File. Maximal exercise capacity was measured by cycle
ergometry, which consists of a stationary bicycle attached to a monitor designed to
capture an individual’s work output. Lung function was measured at baseline as well as at
each follow-up visit. Participants were given spirometry tests to obtain total lung capacity
(TLC), MVV, slow vital capacity (SVC), residual volume (RV), FEV1 and FVC values.
Functional residual capacity (FRC), a measure of the volume of residual air in the lung at
the end of expiration, was evaluated with a plethysmograph. Additional lung measures
included single breath diffusing capacity, arterial blood analysis and the maximal
expiratory and inspiratory pressures. Data on hospitalization rates for the trial participants
23
were obtained through Medicare claims data, which provided information on the number
of inpatient, outpatient and emergency room visits.
3.1.3 NETT Results
Important findings from this trial showed that those undergoing LVRS had better exercise
capability, pulmonary function, and quality of life as compared to those on medical
therapy alone. Post-operative mortality at 90 days was higher in the surgical group
compared to the medical management group, but as the trial progressed there was a
significant decrease in the overall mortality for the LVRS treatment arm. Subgroup
analyses were performed for high-risk and non-high-risk subjects. High-risk subjects
were classified as having an FEV1 < 20% of predicted, a high perfusion ratio and
homogeneous emphysema. The overall mortality rate in this group was almost two times
greater for subjects receiving LVRS compared to those on medical therapy (RR=1.82,
95% CI=1.2-2.7 ) (10). Participants were further stratified into four different groups
based on the pattern of emphysema distribution and the maximum exercise test score
(high/low). Those with predominantly upper-lobe emphysema and low exercise capacity
experienced lower mortality and a higher likelihood of improvement in symptoms if they
had received LVRS compared to receiving medical management alone (RR=0.89, 95%
CI=0.7-1.1).Those with mostly lower-lobe emphysema and high exercise capacity had
increased mortality after receiving LVRS (RR=2.06, 95% CI=1.1-3.7). When overall
mortality for all subjects was examined over the long-term, (i.e., median of 43 months),
the LVRS arm was found to have a significant survival benefit when compared to the
medical therapy group (RR= 0.85, P=0.02) (Appendix A) (10).
24
3.2 Air Pollution Data
Although air pollution data were not collected as part of the NETT per se, concentration
levels of three pollutants by patient ZIP code were obtained from monitoring stations
through the Air Quality Systems (AQS) database (62). Pollution data reported to the AQS
are typically required by law. As of 2002, there were over 5,900 active monitors at 5,557
sites across the US, with different sampling processes based on their location and
pollutant type. Ozone sampling is typically performed continuously, with 1-hour averages
reported to the AQS. PM10 monitors, on the other hand, have both filter-based and
continuous samplers, with more than 80% of the monitors being filter-based. 24-hour
values are reported to the AQS from the filter-based samplers, whereas the continuous or
automated samplers report only 1-hour averages. The sampling frequency for filter-based
monitors varies, from daily sampling to every 6th day. Like PM10, the PM2.5 monitoring
network contains both filter-based and continuous samplers, with the latter being required
in large metropolitan areas. Filter-based sampling sites report 24-hour PM2.5 values to the
AQS daily, every 3rd day or every 6th day, while continuous sampling sites report 1-hour
values to the AQS (63).
For this research, data were restricted to average daily ozone levels, obtained for 1997-
2003, and average daily PM2.5 values, collected from 1999-2003. Averages were
calculated using ≥ 18 hourly measurements. For ZIP codes with more than one
monitoring station, a ZIP-code-specific average was calculated from values from all
monitoring stations within that site. The ZIP-code-specific air pollution data were then
estimated with log-normal kriging, a technique that allows for the estimation of pollutant
25
values in areas that may not be monitored, and in which data from nearby monitoring
stations are weighted based on distance and number of monitored locations. These
parameters help define the spatial autocorrelation structure necessary to run the spatial
models (62; 64).
The technique is further described by Liao et al. who evaluated this kriging methodology
by geocoding participant and clinic site addresses from the Women’s Health Initiative
(WHI) study and calculating pollutant concentrations. Three different spatial models
were employed (spherical, exponential and Gaussian) and cross-validated, by removing
monitoring stations one at a time and predicting the concentration of pollutants from each
omitted station using known values from other monitors. The differences between the
actual and predicted values obtained were evaluated with prediction errors (mean of the
difference) (PE), standardized prediction errors (SPE), root mean square standardized
error (RMSS), and standard errors (SE). Both regular and log-normal kriging models
were employed in their analyses. Because kriging works best with normally distributed
data, log transforming the pollution values can be beneficial as it normalizes the data and
limits the influence of large outliers. In their findings, Liao et al. reported that the
spherical spatial model delivered slightly better results than either the exponential or
Gaussian models (62), with the PE and SPE values being closest to zero. Liao et al. also
compared regional and national-scale kriging and found that they performed similarly
(62).
26
3.3 Exposure Assessment
Summary Measures
In epidemiologic studies, exposure is often limited to surrogate exposure measures such
as ambient concentrations, proximity to pollutant emitting sources or duration of
exposure (e.g. occupational history) (65). Summary exposure measures such as
cumulative and mean concentrations have been used widely in studies of the association
between air pollutants and health. Although the use of ambient concentrations as proxies
for individual exposures is less than ideal, the availability of pollutant monitoring data
makes its use appealing in certain studies. The choice of an appropriate and specific
summary measure, however, is an issue that is less discussed in the literature. For
instance, selecting a cumulative exposure concentration in lieu of a mean concentration
has important biological implications. In the subsequent sections, we compare different
summary measures and discuss their potential biological significance, with respect to
emphysema.
Cumulative exposure
In our study, cumulative exposure concentration refers to the total summed daily ambient
concentrations in a given period of time (e.g. year). This commonly used measure can
also be defined as the ‘time integral of exposure intensity (66).’ According to Smith and
Kriebel, cumulative exposures are typically associated with cumulative and irreversible
effects (65). In the case of emphysema, where damage to the lung is irreversible, the
27
cumulative ambient exposure would be proportional to the total pollutant concentration
that reaches the lung tissue, and thus proportional to the risk of injury.
Increased ventilation rates in individuals with emphysema leads to higher PM deposition
in the lung (67). Research by Ling et al. showed that PM primarily deposited in the
bronchial and alveolar epithelial cells as well as in the airway and alveolar macrophages
in subjects with COPD. They also found that total PM burden increased with COPD
disease severity and that cumulative burden of PM was associated with a decline in the
FEV1/FVC ratio (68). In our study, a cumulative concentration of PM would be a useful
exposure index, where higher total concentrations could be indicative of greater PM
burden in the lung.
As a gaseous highly reactive pollutant, ozone is capable of inducing oxidative stress in
lung tissue. Typically, ozone travels to the lower respiratory tract, where it dissolves in
the thin layer of epithelial lining fluid, disrupting this protective barrier and facilitating
the penetration of ‘serum proteins and fluid into air spaces (69; 70).’ Studies have shown
that among adolescents, lifetime exposure to ozone can lead to physiological
abnormalities in the airways (71). It has been posited that exposure early in childhood
could have a lasting effect on pulmonary physiology and function (72). Among subjects
with COPD, increased ozone concentrations have been hypothesized to lead to further
disease progression via increased inflammation and airway narrowing. This damage is
more likely to occur at elevated concentrations of ozone (73). Employing summary
measures like cumulative exposure concentrations would help capture these instances of
28
peak or elevated exposure and allow us to assess how cumulative ozone concentrations
impact lung function in subjects with severe emphysema.
Mean exposure
The mean ambient concentration refers to the average ambient exposure concentrations in
a given period of time. Mean concentrations, like cumulative measures, are proportional
to the total concentration that reaches the target tissue (65). However, unlike cumulative
exposures, mean concentrations can be viewed in terms of repeated or recurrent short-
term exposure concentrations (i.e. daily mean concentration) and are usually associated
with partially reversible effects such as changes in FEV1 (66).
Exposure to recurrent short-term ozone and PM concentrations in subjects with COPD is
hypothesized to lead to persistent inflammation and increased airway hyperreactivity (73;
67). However, higher mean concentrations could lead to characteristically different
responses than higher cumulative concentrations. For instance, mean ozone
concentrations have been linked with attenuated pulmonary responses in asthmatics (74).
These findings corroborate results observed in controlled exposure studies, where
repeated ozone exposures were associated with progressively reduced effects over time
(75; 76). Increased tolerance to criteria ambient pollutants could result from repeated
exposures to PM and ozone among those with COPD. To date, few studies have
considered the possible attenuation effect of these exposures on lung function and
respiratory morbidity. Utilizing mean concentrations of PM and ozone could help us
29
gauge how tolerant or intolerant these subjects may be to chronic ambient pollutant
concentrations.
Conclusions
The use of summary measures in occupational and environmental health studies often
requires the assumption of increasing risk as a result of increasing dose. The use of
different exposure metrics can lead to different classifications for subjects, based on the
nature of the exposure index. This multiple measures approach has been advocated by
Blair et al. who stated that in the presence of a true association, the employment of
differing measures of exposure would decrease the likelihood of selecting a summary
measure that correlates poorly with target organ dosage. Conversely, in the absence of a
true association, multiple measures of exposure would reduce the likelihood of a lack of
or an inverse association being due to exposure misclassification (77). In our study,
utilizing both cumulative and mean concentrations represents two different ways to
assess the nature of the relationship between air pollution exposure and respiratory
morbidity and mortality in subjects with severe emphysema.
30
Chapter 4: Hypothesis and Specific Aims
Due to the serious nature of severe emphysema, few options exist to reduce the
progression of disease among subjects. Recent clinical trials such as the NETT have
sought to investigate whether surgical procedures (e.g., LVRS) could benefit emphysema
subjects. Even though the use of novel methods such as LVRS is not meant to be
curative, it could serve as a means to improve overall quality of life and increase survival.
Despite the fact that the NETT trial controlled for a host of established risk factors (e.g.,
smoking history, age, socioeconomic status, etc.), the analyses did not account for
ambient air pollution. In this vulnerable population, air pollution may be an important
factor to consider, as mortality, lung function, and health care utilization could be
significantly impacted as a result of exposure to air pollution. Because individuals in this
cohort are more susceptible due to severe respiratory illness and poorer overall health, it
stands to reason that exposure to ambient air pollutants would lead to increased morbidity
and mortality. Investigating this relationship would enable us to evaluate the effect of
variations in the levels of air pollutants on the exacerbation of emphysema in this
population.
Utilizing the air pollution data, the proposed research aimed to 1) examine differential
levels of outdoor air pollution exposure experienced among those enrolled in NETT, 2)
study the relationship between both short and long-term air pollution exposure
31
concentrations and post-treatment lung function, and 3) study the relationship between
long-term air pollution exposure concentrations, healthcare utilization patterns, and
mortality.
We hypothesized that ambient air pollution would modulate progression of disease
among severe emphysema subjects. The association was posited to persist among those
who received either LVRS or only medical therapies. Whether LVRS helped attenuate
the effects of air pollution exposure among this cohort is less clear, however, our analyses
would aim to better explain this relationship.
Specific Aims:
1) Characterize differential patterns in air pollution levels for ozone and PM2.5
over varying geographic locations and socioeconomic characteristics.
In order to better understand the potential influence of ambient pollutant exposures on the
health of NETT participants, an actual representation of their pollution exposures was
important. Using log-normally kriged pollution data estimated by Liao et al, we evaluated
trends and associations of cumulative ozone and PM2.5 levels with area-level SES
characteristics.
32
2) Assess whether short and long-term temporal variations in air pollution
exposures were associated with impairment in lung function and respiratory
morbidity among participants enrolled in the NETT
Few studies have been able to demonstrate both the long- and short-term effects of air
pollution exposures on persons with COPD. Employing several of the spirometric lung
function measures collected as part of NETT, longitudinal models evaluated the
relationship between cumulative long-term ozone and PM2.5 exposures and spirometric
change in lung function and respiratory morbidity. Mixed effects models also tested the
associations between lagged ambient ozone and PM2.5, concentrations and lung function,
as well as respiratory morbidity, controlling for demographic, geographic, and treatment
variables. Additional models investigated the effect of short-term pollutant exposures on
changes in lung function and respiratory morbidity. Like the previous models, they took
into account demographic, geographic, and treatment differences.
3) Examine whether air pollution exposures were associated with increased
healthcare utilization and mortality.
As mortality and morbidity were important endpoints in the original NETT, it was
necessary to test how these measures changed as a consequence of air pollutant
exposures. Using the mean and cumulative yearly values for ozone and PM2.5, changes in
mortality risk and healthcare usage were assessed with Cox regression and Poisson mixed
effects models, respectively. These models took into account demographic, geographic
and treatment variables.
33
Chapter 5: Differential Ambient Air Pollution Exposure in a COPD cohort: The Role of
Individual and Area-Level Socioeconomic Factors
5.1 Introduction
Social inequalities in air pollution exposures have been investigated in several
epidemiologic studies. One of the earliest of these investigations was performed by
Freeman et al in Distribution of Environmental Quality. The results demonstrated that
poorer individuals and minorities residing in Kansas City, St. Louis and Washington, DC
were more likely to have higher exposures to sulfates and total suspended particulates
compared to wealthier individuals and Caucasians (53). Much of the succeeding literature
has focused on the unequal burden in environmental hazards experienced by
disadvantaged minorities. These studies have suggested that among such groups, there is
a higher likelihood of residing close to point sources of pollution, such as industrial
plants and waste sites (54; 55). The term ‘environmental racism’ was coined as a direct
result of one of these studies as a way to describe the apparent racial inequity in the
location of hazardous waste sites (56). In addition to race, socioeconomic status (SES) is
an important social determinant in environmental justice. Brochu et al. studied the
association between particulate matter (PM) and area-level-SES in the Northeastern US.
They reported that individuals with lower SES tended to be exposed to higher levels of
34
PM when compared to higher SES individuals (78). A recent nationwide study, published
by Miranda et al, reported similar findings (57).
Despite the expansive nature of the published literature, there is no clear methodological
consensus with respect to assessing differential air pollution exposures by socio-
demographic characteristics. Several challenges in comparing research findings on
systematic racial or socioeconomic disparities in air pollution exposure include the use of
different geographical units of analyses (e.g. regional, census tract and ZIP code level),
different analytic methodologies, difficulties in disentangling race and SES, and different
measures of exposure (79; 80; 81). A 2001 review of environmental justice-related
studies by Bowen cited inconsistencies in statistical methodologies and a lack of
‘empirical certainty’ as reasons why “little can be said with scientific authority regarding
the existence of geographical patterns of disproportionate distributions and their health
effects on minority, low-income and other disadvantaged communities” (82).
To date, much of the extant literature on environmental justice has been based on group
level variables in the general population (80). Less is known on how area-level SES
predictors relate to ambient air pollution exposures in an already vulnerable subgroup.
This is particularly important in studies that consider the effects of air pollution on
respiratory health outcomes since ambient air pollution concentrations and pre-existing
health conditions may be modified by area-level SES characteristics.
This paper aimed to characterize and quantify the burden of ambient air pollution
exposures experienced by a vulnerable subgroup of subjects with chronic obstructive
35
pulmonary disease (COPD). We used both USEPA Air Quality Systems (AQS) pollution
data and results from the National Emphysema Treatment Trial (NETT) to detect
differences in ambient air pollution exposure with area-level SES factors. Identifying
whether there exists evidence of differential exposure in air pollution concentrations
based area-level SES factors would contribute to a greater understanding of the potential
health risk faced in an already susceptible population.
5.2 Methods
Study Population
The NETT was a multicenter, randomized controlled trial with the objective of assessing
whether lung volume reduction surgery (LVRS) in conjunction with medical therapy was
a better long-term treatment for subjects with severe emphysema when compared to
medical therapy alone (12; 61). The major outcomes of interest in the study were post-
treatment mortality and maximal exercise capacity. Additional outcomes of interest
included lung function, as determined by spirometry, distance walked in six minutes and
quality of life. During the period 1998-2002, a total of 1218 participants were enrolled in
the study, of which 608 were randomized to LVRS and medical management; 610 were
randomized to traditional medical therapy alone, such as steroids and bronchodilators
(10).
Radiologists characterized emphysema distribution and magnitude in the lungs with chest
computed tomography (CT) scans and a visual scoring scale. The aim of the LVRS was
to remove 25% to 35% of the lung, with a particular focus on the most affected areas
36
(12). Medical histories of all participants were collected at 6, 12, 24, 36, 48 and 60
months after baseline. Participants were followed for an average of 29.2 months (10; 12).
Air Pollution Data
We obtained pollution data from the US Environmental Protection Agency (USEPA) Air
Quality Systems (AQS) database (63). We restricted these data to average daily ozone
levels (1997-2003), and average daily PM2.5 values (1999-2003). Because exact
addresses were not available for NETT participants, we used ZIP codes to define
participant place of residence. Participants were assigned residence at the centroid of
their ZIP code, as determined by the 2000 census. Values from monitors within specific
ZIP codes were used to interpolate pollutant concentrations at the centroid of the ZIP
code, and were then merged with the NETT data. Only resident ZIP codes corresponding
with kriged ZIP code specific pollution levels, were considered for the analysis, per Liao
et al. (62). Air pollution data were estimated using log-normal kriging, a technique that
allows for the estimation of pollutant values in areas that may not be monitored, in which
data from nearby monitoring stations are weighted based on distance and number of
monitored locations. Participants with the same ZIP code were assumed to share the same
level of exposure. In these analyses, daily pollutant concentrations were averaged as a
cumulative measure over the duration of the study.
37
Geographical Units of Analysis
Aggregate SES data were obtained from the 2000 US Census Summary File 3 (SF3) (83).
Variables were obtained at the ZIP code tabulation area (ZCTA) level. Although ZCTA
codes are similar to ZIP codes in that they both share the same designation, ZIP codes
were developed as a means to expedite postal delivery, whereas ZCTA codes were
developed based on characteristics of Census blocks. The ZCTA code is determined by
the US Census by calculating the total number of addresses contained within the Census
block boundaries. The Census bureau then assigns the most frequently occurring ZIP
code to the Census block tabulation area. In the case where no ZIP code data are
available, values from a contiguous block are employed to assign the ZCTA code. Only
one ZIP code is assigned to each Census block tabulation area regardless of its size (84;
85).
Socio-demographic Variables
Measures of area-level socioeconomic deprivation (SED) have been common and
effective ways to examine the effect of socioeconomic disparities on health. Indices such
as the Carstairs score and the Townsend SED index have been utilized to capture and
summarize multiple aspects of SES (86; 87). In our study, we measured area-level SES
with the use of the Townsend SED Index, which more readily allowed for the inclusion
of Census data. This index was constructed with the following variables: percent of
unemployed individuals aged 16 and older, percent renters, percent overcrowding
(defined as more than one occupant per room), and percent of individuals that do not own
38
a car. Values of all variables were obtained at the ZCTA level. Percent unemployed and
percent overcrowding were log-transformed before being standardized. Percent renters
and percent without a car were also standardized before being included in the overall
score. Standardization was performed by subtracting the mean value from the variable of
interest and dividing it by its standard deviation. These individual z-scores were then
summed to yield the overall SED index value. The SED index value was then stratified
and ranked by deciles. Additional ZCTA-level variables considered in the analyses
included total population, median income, mean earnings, percent urban residents, and
percent of individuals living below 200% of the federal poverty level (FPL).
The following baseline demographic variables were obtained from the NETT dataset:
age, sex, income (categorized as <$15,000, $15,000-$29,999, $30,000-$49,999, and
$50,000 or more), education (categorized as less than high school, high school, some
college, and bachelor’s degree or higher), and region of residence (defined as Northeast,
Midwest, South, and West).
Statistical Analysis
Air pollution, Census, and NETT data were merged based on participant ZIP code.
Bivariate analyses were performed to assess how ZIP code level variables correlated with
cumulative ozone and PM2.5. ZIP code level variables that were significantly correlated
(r >0.3, p-value <0.05) with pollutant concentrations were included in the linear models.
Area-level SES variables that were highly correlated with the SED index were not
included in the linear models in order to minimize problems associated with
39
multicollinearity. To model cumulative pollutant exposure, models were initially
constructed with ZCTA-level SES factors alone, and then with interactions of ZCTA-
level SES characteristics. Effect modification was evaluated by including interaction
terms in the models and examining them for statistical significance (p<0.05). Because we
were interested in evaluation the association between area level SES variables with ZIP
code level pollutant exposures, we employed linear regression models as our primary
analysis. The unit of analysis in the linear regression models was the participant ZIP code
rather than the individual subject. Although not a concern for our analyses, clustering by
ZIP code was relatively minimal; only 91 out of 1204 subjects shared a ZIP code and the
largest cluster size contained only 3 subjects. We compared nested linear regression
models using a likelihood ratio test or the likelihood-based Akaike’s information criterion
(AIC), with a lower AIC indicates a better fitting model (88). The final linear models
included the following covariates: region of residence, SED index deciles, total number
of people residing in the ZIP code, median income, and percent of people living below
the poverty level in the ZIP code.
The PROC REG procedure was used for the linear regression models. All analyses were
performed with SAS 9.2 (SAS Institute Inc, Cary, NC).
Ethics
Approval for this study was received from the Institutional Review Board at The Ohio
State University. Because this was a secondary data analysis, no patient contact or
consent was required.
40
5.3 Results
Table 5.1 summarizes the personal and ZIP code-level SES characteristics for the NETT
subjects. Of the original 1218 participants, there were 1204 subjects for whom there was
complete ozone and PM2.5 data and whose ZIP codes were also designated in the US
Census SF3 database. There were a total of 1128 unique ZIP codes.
41
Variable LVRS (n=601)
Non-LVRS (n=603)
Overall (n=1204)
Age (mean (SD)) 66.2(6.30)a 66.4(5.92) 66.4 (6.11) Sex Female (n (%)) 248 (41.3%)b 216 (35.8%) 464 (38.5%) Mean Cumulative Ozone, ppm
87.32 (8.44) 87.31 (8.88) 87.32 (8.66)
Mean Cumulative PM2.5, µg/m3
24,161.01 (5,164.27)
24,013.12
(5,244.94) 24,086.97 (5203.19)
Education (n(%)) < High School 117 (19.5%) 129 (21.4%) 246 (20.4%) High School 213 (35.4%) 166 (27.5%) 379 (31.5%) < College 193 (32.1%) 207 (34.3%) 400 (33.2%) College+ 78 (12.9%) 101 (16.8%) 179 (14.9%) Income <$15,000 112 (18.6%) 113 (19.2%) 225 (18.9%) $15,000-$29,999 201 (33.4%) 209 (35.4%) 410 (34.4%) $30,000-$49,999 176 (29.2%) 162 (27.5%) 338 (28.4%) $50,000 or more 113 (18.8%) 106 (17.9%) 219 (18.4%) Employed 40 (6.7%) 54 (8.9%) 94 (7.8%) Percent Urbana 75.0% (34.2) 77.1% (32.6) 76.1% (33.43) Percent below 200% FPLa
6.84% (7.53) 7.15% (8.39) 6.9% (7.97)
Total Populationa 10,306.7 (7,808)
10,262.8 (6,786) 10,284.68 (7,311)
SED Indexa -0.0912 0.0702 -0.0104 (1.83) Median Incomea $45,879.1
($15,322.1) $45,015.5 ($15,470)
$56,391.5 ($20,383.43)
Table 5.1. Summary of Individual and ZIP Code-Level NETT Patient Characteristics,
by Treatment Arm
FPL= Federal Poverty Level; a Variable obtained at the ZIP Code Level
42
Results of our analyses show evidence of an association between SES measures and air
pollution. Specifically, individuals residing in ZIP codes that were in the lowest
percentile of the SED index had increased PM2.5 exposures compared to those residing in
the highest percentiles (Figure 5.1). Although there was more variability seen with
cumulative ozone levels, it is still indicative of the trend line of increased exposure
among those with a higher SED index (Figure 5.2). In the bivariate analyses, cumulative
ozone was significantly correlated with median income (r=-0.22, p<0.01) and percent
below 200% FPL (r=0.11, p<0.01). It was not however correlated with SED index
(r=0.04, p=0.14) but it was significantly correlated with the ranked SED index (r=-0.07,
p<0.05). Cumulative PM2.5 was significantly correlated with SED index (r=0.12, p<0.01),
ranked SED index (r=-0.11, p<0.01), median income (r=0.11, p<0.01) and percent below
200% FPL (r=-0.1, p<0.01) (Table 5.2).
43
Variable Name
SED No car
Unemployed (log)
Renter Crowding (log)
Median Income
Percent < 200%
FPL
Cumulative Ozone
Cumulative PM2.5
Percent Urban
SED 1 0.56** 0.52** 0.32** 0.45** 0.13** -0.04 0.04 0.12** 0.30**
No Car 0.56** 1 -0.22** -0.36** 0.62** 0.59** -0.21** -0.14** 0.09** 0.45**
Unemployed (log)
0.52** -0.22**
1 0.47** -0.29** -0.45** 0.26** 0.21** 0.05 -0.14**
Renter 0.32** -0.36**
0.47** 1 -0.51** -0.71** 0.13** 0.13** -0.02 -0.06
Crowding (log)
0.45** 0.62** -0.29** -0.51** 1 0.8** -0.15** -0.13** 0.1** 0.28**
Med Income 0.13** 0.59** -0.45** -0.71** 0.8** 1 -0.11** -0.22** 0.11** 0.21**
% Poverty -0.04 -0.21**
0.26** 0.13** -0.15** -0.11** 1 0.11** -0.1** -0.54**
Cumulative Ozone
0.04 -0.14**
0.21** 0.13** -0.13** -0.22** 0.11** 1 0.21** -0.07**
Cumulative PM2.5
0.12** 0.09** 0.05 -0.02 0.1** 0.11** -0.1** 0.21** 1 0.11**
Percent Urban
0.30** 0.45** -0.14** -0.06 0.28** 0.21** -0.54** -0.07** 0.11** 1
Table 5.2. Bivariate Spearman Correlations between Individual and ZIP code-level Covariates; SED: Socioeconomic Deprivation Index;
FPL: Federal Poverty Level; ** p-value <0.05
44
Figure 5.1. Mean Cumulative PM2.5 by Ranked SED Index Percentile
Mean cumulative PM2.5 were calculated and plotted against each ranked SED percentile. Error
bars represent ± 1 standard error of the mean.
In the cumulative pollutant regression models adjusted for region of residence, SED
index deciles, total number of people residing in the ZIP code, median income, and
percent of people living below the poverty level in the ZIP code, ranked SED index was
significantly associated with cumulative PM2.5 (Table 5.3). Increasing SED index decile
was associated with a decrease of 155.09 µg/m3 (SE = 58.11, p <0.01) in cumulative
PM2.5. Interestingly, median income was significantly associated with increased
cumulative PM2.5 exposure, although the effect was relatively small (β=0.03, SE= 0.01, p
45
<0.01). Individual level age and education were not considered as covariates in the
cumulative PM2.5 model as the model was focused solely on ZIP code-level variables.
Figure 5.2. Mean Cumulative Ozone by SED Index Percentile
Mean cumulative ozone concentrations were calculated and plotted against each ranked SED
percentile. Error bars represent ± 1 standard error of the mean.
For the cumulative ozone model, there was a significant negative effect of the ranked
SED index (β=-0.32, p=0.02); however this occurred in the presence of a significant
46
interaction between the ranked SED index and total population (Table 5.4). The only
other significant covariates in the model were median income, region of residence and
percent living below 200% FPL. Large effect estimates were seen for region of residence
in both pollutant models.
Variables Estimate SE p-value Intercept 19509 679.34 <0.01 Region Midwest 2715.66 453.13 Northeast 3392.08 432.33 <0.01 South 3420.76 443.35 <0.01 West (Referent) - - <0.01 Median Income 0.030 0.01 <0.01 Ranked SED Index -155.09 58.11 <0.01 Total Population 0.128 0.022 <0.01
Table 5.3. Regression of Cumulative PM2.5 Exposure (µg/m3) with ZIP code-level SES Variables
47
Variables Estimate SE p-value Intercept 93.9 1.26 <0.01 Region Midwest -3.29 0.69 <0.01 Northeast -3.59 0.68 <0.01 South 4.93 0.71 <0.01 West (Referent) - - - Median Income -0.0001 0 <0.01 Ranked SED Index -0.32 0.14 0.02 Total Population -0.0001 0 0.02 Ranked SED Index*Total Population
0.000 0.00 0.02
Percent Below Poverty Level
0.069 0.034 0.05
Table 5.4. Regression of Cumulative Ozone Exposure (ppm) with ZIP Code-Level SES Variables
5.4 Discussion
Our findings suggest there is a significant differential exposure to ambient ozone and
PM2.5 exposures among persons with lower area-level measures of SES. As a measure of
area-level SES, a higher SED index was clearly and strongly associated with increasing
levels of ambient ozone and PM2.5. Utilizing pollution data from over 1000 ZIP codes,
the NETT study allowed us to assess differential ambient exposures on a national scale
(Figure 5.3). Unlike many previous studies that examined air pollution disparities in the
United States, our study is one of the few to consider the effects of Census-based area-
level measures of SES on differential pollutant exposures. To date, it is also the only
study to employ data from a cohort of individuals with severe respiratory disease.
Although our study does not explicitly focus on the health effects related to differential
48
exposures, it provides a preliminary estimate of the influence of SES factors on health in
the context of a ubiquitous environmental risk factor, air pollution. In a group of already
susceptible subjects, this differential exposure may be of particular concern in the
evaluation of health outcomes.
Figure 5.3. Distribution of ZIP Codes for NETT Participantsa
a Number of ZIP codes = 1128
Environmental justice is usually defined in terms of differential exposure and differential
susceptibility. As we studied an already vulnerable population, we could not assess
differential susceptibility in our study. However, using the pathways illustrated in Figure
49
5.4, we can see how the presence of differential exposure in our study could relate to
differential susceptibility in a healthy but otherwise similar population. Social
epidemiologists have postulated that area-level factors exert independent effects on health
outcomes (89). Area-level indicators of SES may represent environmental factors such as
safety, crowding, availability of healthy food sources and a sense of community. One or
both of these factors could influence differential air pollution exposure risk and
differential susceptibility. Those with a lower area-level SES may also be more likely to
experience disproportionately higher levels of ambient air pollution exposures due to
closer residential proximity to high traffic roadways and industrial sites (53; 55; 79).
Figure 5.4. Area-Level SES Pathways for Differential Exposure and Susceptibility
50
Comparing our findings to other studies, we found similarities in terms of overall
conclusions. Studies by Marshall et al. and Su et al. reported a disproportionate burden of
air pollution exposure among lower-income groups (90; 91). A study by Young et al.
described differential hazardous air pollution (HAP) exposures with respect to
urbanization and neighborhood-level SED, reporting increasing exposure hazards with
increasing levels of SED (59). Despite similar findings, several of the existing studies
have focused on the relationship between SES and proximity to hazardous sites and
facilities rather than with summary measures of exposure (79). In addition, these studies
have tended to focus on relatively small geographic areas and disparate group level
variables (e.g. census tract, census block group, county), thereby making direct
comparisons problematic.
Strengths
Our measure of air pollution exposure was particularly well-suited for this type of study.
Typically, spatial autocorrelation is a limitation that is often present in other
environmental studies. Air pollution data are particularly subject to this limitation since
measures of air quality in nearby communities are not independent of each other. The
term spatial autocorrelation in the context of air pollution can be aptly described with the
statement “everything is related to everything else, but near things are more related than
distant things (92).” Failure to account for this spatial autocorrelation would bias the
results by assuming independence in air pollution values when in fact there is not. Our
study, which relies on kriged air pollution data, is not subject to this limitation. The
kriging methodology is highly dependent on the spatial autocorrelation structure in the
51
data for its prediction or interpolation of air pollution values (93; 62; 92). Secondly, we
chose to evaluate air pollution exposure with a cumulative rather than a mean value.
Using a cumulative measure allowed us to take into account all the daily exposure data as
well as capture ranges of low to high exposure concentrations. Relying strictly on a mean
value would mask some of the extreme values especially since we are dealing with
several years’ worth of pollution data. Lastly, the large geographic area covered by the
NETT study participants also allows for the generalizability of our findings to
populations across the US.
Limitations
Although we observed an association between area-level SES and air pollution exposure,
we could not assess a similar relationship between individual measures of SES and
differential air pollution exposure. Since our exposure was obtained at the ecological
level, we could not include individual level measures of SES in our regression analyses.
However, since ambient pollutant concentrations were kriged to the centroid of the ZIP
code, assessing the relationship between individual-level SES and ZIP code-level
concentrations could have been subject to considerable bias. The largely small cluster
sizes, ranging from 1 to 3, may have not been representative of the individuals residing
within that ZIP code. Since our sample population tended to be largely Caucasian, older
and male, it is likely that their individual-level SES characteristics could have differed
considerably from others in their ZIP codes.
52
As stated previously, much of the literature on environmental justice has been based on
ecological or aggregate data. Dependence on ecological data to assess evidence of
differential exposure or susceptibility limits one’s ability to extrapolate results to the
individual level. Control for confounding is also difficult when dealing with purely
ecological data, since adjusting for confounders in group level data can be subject to bias
or misspecification (94). Confounders at the group-level may not be confounders when
examined at the individual-level and the examination of individual-level variables as
confounders or effect modifiers cannot be evaluated in strictly ecological studies (95).
Employing two separate measures of SES in our study would have permitted the
multilevel specification of individual and contextual effects on differential exposures.
One important limitation associated with using ZCTAs to evaluate ZIP code-specific SES
characteristics for NETT participants is that although they share the same designation,
there is no clear spatial correlation between ZCTAs and ZIP codes (Appendix B) (85). In
addition, there are several pitfalls in utilizing ZIP codes as geographical units of analyses.
ZIP code boundaries can overlap, be included within other ZIP codes or may be
discontinued or newly created over time (96). They can also encompass vast geographical
areas and have an average population of approximately 30,000. The development of
ZCTA codes as Census groupings arose as a result of the increased use of ZIP codes by
public health researchers and investigators (84; 96). ZCTA codes, unlike ZIP codes, are
restricted to Census block groups (average population: 1,000) (96). In terms of our study,
there is potentially significant spatial mismatch in the individuals selected and their
representation within the given ZIP code. It is possible that they may reside in an area
53
assigned one ZCTA code but may actually have a different ZIP code. Area-level SES
designations may then be inaccurate for these individuals. There is unfortunately no way
in which to verify how much spatial mismatch may be present, but the inclusion of
individual-level SES measures is a way to capture some aspect of their overall SES level,
even if their area-level SES values are misspecified.
The lack of racial/ethnic diversity in our sample did not allow us to assess racial
disparities in exposure. Although important, racial and SES inequalities are not strictly
functions of one another. Much of the literature on social disparities emphasizes the
importance of distinguishing between SES- and racial-related health disparities (97). It
has been demonstrated that racial minorities are often at increased risk of adverse health
outcomes and higher ambient exposures (98; 99; 100; 101; 102; 103; 104). They are also
often more likely to have a lower SES (99). However, studies have shown that SES and
race have independent effects on health outcomes and employing one as a proxy for
another may be an inadequate analytic choice (97).
Although not the aim of our analyses, we could not infer a causal relationship between
SES and differential air pollution exposure. It is often difficult to determine which
preceded which – environmental hazards or vulnerable populations. Since region of
residence is a non-random selection and land values are typically lower in areas with a
greater preponderance of manufacturing plants and waste treatment facilities, it is
possible that those from a lower SES would live in areas that contain more of these
environmental hazards (98). It is also possible that noxious facilities may be driven away
from affluent neighborhoods into poorer areas (56).
54
Another limitation of our study is the lack of information on personal exposures. For this
research, we relied on kriged ambient air pollutant concentrations as proxies for personal
exposure. Spatial heterogeneity in exposure could have impacted how closely these
ambient concentrations correlated with personal exposures. In our study however, this
limitation may not be as significant since the heterogeneity in the distribution of ambient
pollutant exposure varies greatly by pollutant type. Specifically, PM2.5 has been shown to
have a rather uniform distribution in urban areas due to the predominant influence of
small, long-range transport particles (105). This is particularly applicable to our study as
our sample was largely urban (Table 5.1). Like PM2.5, ozone has also been shown to have
a homogenous distribution over large areas (15). Thus, this limitation may be relatively
minor within our sample population.
Lastly, our analyses did not consider the potential multiplicative or additive effects of
multiple pollutants. Bivariate correlations revealed a moderate association between ozone
and PM2.5. However, simultaneous consideration of both pollutants in our model would
have been inappropriate, as the availability of pollution data was not equal for both
pollutants. Ozone data were available from 1998-2003, whereas PM2.5 were only
available from 1999-2003. The disparity in the duration of exposure between pollutants
would have complicated controlling for possible interactive effects.
55
Conclusion
The implications of our study are multifold. We found evidence of an environmental
justice equity issue in a vulnerable population. This differential pollutant exposure was
shown to be strongly associated with area-level SES but not individual-level SES. Our
study which utilized both individual and area-level SES descriptors, over a large
geographical area, may enable researchers to more readily compare findings and results.
Investigators may also consider applying a similar air pollution kriging methodology in
the secondary analysis of health studies with large geographical cohorts. The availability
of air pollution data from monitoring databases readily allows for the assessment of
differential ambient air pollution in geographically diverse study populations.
Specifically, our results speak more directly in support of environmental inequality rather
than environmental justice. Characterizing whether this inequality is socially unjust is
less clear and subject to further research.
56
Chapter 6: Short and Long-term Effects of Ambient Ozone and Fine Particulate Matter
on the Respiratory Health of COPD Subjects
6.1 Introduction
Numerous epidemiologic studies have shown that fine particulates (PM2.5) and ozone
have adverse effects on respiratory morbidity. The effects of ambient PM exposure is of
significant concern for vulnerable or high-risk populations (e.g., individuals with existing
respiratory disease), with limited data suggesting that PM exposure is capable of
aggravating chronic obstructive pulmonary disease (COPD) (44). Mechanistically, PM is
believed to worsen lung function among COPD subjects by constricting already narrow
airways. Deposition of PM2.5 in the alveoli can trigger an inflammatory response
resulting in additional damage to the lung tissue (44). Additionally, particle deposition in
the lung for those with existing COPD may be characteristically different due to higher
ventilation rates and the presence of previously inflamed airways. Higher ventilation rates
in these subjects could increase the penetration depth of pollutants in the lung. (44; 45;
46).
The effects of ozone on vulnerable subpopulations have also been investigated in several
epidemiologic studies. Delfino et al. reported acute exposures of ozone as associated with
increased respiratory-related hospital visits among elderly patients, while Sunyer et al.
57
reported an association between chronic ambient ozone concentrations and increases in
emergency room visits among individuals with COPD (39; 52).
Although there has been much literature published on the effects of ambient air pollutants
among those with existing respiratory disease, few studies have examined both the short-
and long-term effects of these exposures on subjects with COPD. Many of the studies
investigating the effects of air pollution exposure on COPD subjects have been limited in
both duration and geographic scope. In addition, the concentration levels at which
pollutants increase symptoms and worsen pulmonary function is less understood. To date,
no previous study has examined the effects of ambient ozone and PM2.5 in subjects with
severe emphysema who were also candidates for lung volume reduction surgery (LVRS).
Due to the serious nature of severe emphysema, few options exist to reduce the
progression of disease among subjects. Recent clinical trials such as the NETT have
sought to investigate whether surgical procedures (e.g., LVRS) could benefit severely
emphysematous subjects. Even though the use of novel methods such as LVRS was not
meant to be curative, it served as a means to improve overall quality of life and increase
survival. Despite the fact that the NETT trial controlled for a host of established risk
factors (e.g. smoking history, age, socioeconomic status, etc.), the analyses did not
account for ambient air pollution. Because individuals in this cohort are more susceptible
due to severe respiratory illness and poorer overall health, it stands to reason that
exposure to ambient air pollutants could lead to increased respiratory morbidity.
Therefore, we investigated the relationship between ambient air pollution and pulmonary
58
health among NETT participants by evaluating the effect of short- and long-term ambient
exposures on lung function outcomes and respiratory symptoms.
6.2 Methods
Study Population
The NETT was a multicenter, randomized controlled trial with the objective of assessing
whether LVRS in conjunction with medical therapy was a better long-term treatment for
subjects with severe emphysema when compared to medical therapy alone (12; 61). The
major outcomes of interest in the study were post-treatment mortality and maximal
exercise capacity. Additional outcomes of interest included lung function, as determined
by spirometry, distance walked in six minutes and quality of life. During the period 1998-
2002, a total of 1218 subjects were enrolled in the study, of which 608 were randomized
to LVRS and medical management; 610 were randomized to traditional medical therapy
alone, such as steroids and bronchodilators (10).
Radiologists characterized emphysema distribution and magnitude in the lungs with chest
CT scans and a visual scoring scale. The aim of the LVRS was to remove 25% to 35% of
the lung, with a particular focus on the most affected areas (12). Medical histories of all
subjects were collected at 6, 12, 24, 36, 48 and 60 months after baseline. Participants
were followed for an average of 29.2 months (10; 12).
59
Air Pollution Data
We obtained pollution data from the US Environmental Protection Agency (USEPA) Air
Quality Systems (AQS) database (63). We restricted these data to average daily ozone
levels (1997-2003), and average daily PM2.5 values (1999-2003). Exact addresses were
not available for NETT participants, thus, we used ZIP codes to define participant place
of residence. Participants were assigned residence at the centroid of their ZIP code, as
determined by the 2000 census. Values from monitors within specific ZIP codes were
used to interpolate pollutant concentrations at the centroid of the ZIP code, and were then
merged with the NETT data. Only resident ZIP codes corresponding with kriged ZIP
code specific pollution levels, were considered for the analysis, per Liao et al. (62). Air
pollution data were estimated using log-normal kriging, a technique that allows for the
estimation of pollutant values in areas that may not be monitored, in which data from
nearby monitoring stations are weighted based on distance and number of monitored
locations. Participants with the same ZIP code were assumed to share the same level of
exposure.
Lung Function Measurements
The lung function of the NETT participants was assessed initially at baseline and then at
each of the following time points: 6, 12, 24, 36, 48, and 60 months. Although multiple
endpoints were obtained with these tests, analyses evaluating impairment in lung function
focused on the forced expiratory volume in one second (FEV1), the forced vital capacity
(FVC), the FEV1/FVC ratio, the post-bronchodilator predicted FEV1%, and the post-
60
bronchodilator predicted FVC%. These are robust, reliable and established spirometric
measures used to classify airflow limitation and serve as bases for grading COPD
severity (39). The spirometry outcomes were treated as continuous variables with a unit
decrease corresponding to a decline in overall pulmonary function.
Respiratory Outcomes
Respiratory morbidity was evaluated at baseline and at subsequent follow-up visits. It
was measured via the St. George’s respiratory questionnaire, a validated and reliable 58
item self-administered questionnaire designed for individuals with chronic airflow
limitation. The questionnaire consists of three components. The symptom component
asks for information on the type, frequency, and duration of respiratory symptoms. The
activity component asks about activities that limit or cause shortness of breath. The last
component focuses on the personal impacts felt due to the individual’s respiratory
condition. It asks about effects on employment, perceived stigmatization, need for
medication, feelings of panic and disturbances to daily life. Results from the
questionnaire are scored on a scale from 1 to 100, with increasing values on the scoring
index representing increasing health impairment (106; 107).
Covariates and Potential Confounders
Primary exposure variables were specific pollutant concentrations (PM2.5 and ozone).
Potential confounders included age, sex (coded as male=0, female=1), educational level
(categorized as less than high school, high school, some college, and bachelor’s degree or
higher), race/ethnicity (defined as White non-Hispanic, Black non-Hispanic, Hispanic,
61
Asian/Pacific Islander, American Indian/Alaskan Native, and other) and smoking history
defined as ever smoked (yes/no) and number of pack-years. Other covariates of interest
were treatment group (LVRS or medical management), body mass index (BMI, kg/m2),
baseline pulmonary function (FEV1, FVC, FEV1%, FVC%, and FEV1/FVC ratio at
baseline), season (winter, spring, summer, fall), year of study, region (West, Northeast,
Midwest, South), and clinical center. Covariates were considered as confounders if their
addition to the model resulted in a change of 10% or more in the regression coefficient
estimates. Effect modification of the exposure-outcome associations by region and season
was assessed with interaction terms. Wald and likelihood ratio tests, where appropriate,
were used to determine the statistical significance (p<0.05) of effect modifiers.
Model Selection
Descriptive statistics employed simple line plots to examine change in lung function over
the course of the study by treatment arm and sex (Appendix C). The high-risk participant
group was defined as having an FEV1 < 20% of predicted. Simple bivariate analyses
evaluated the associations between mean PM2.5 and ozone levels with mean respiratory
index scores, from the St. George’s respiratory questionnaire. In order to examine the
effects of air pollutants on lung function, the model was specified as follows:
Yijt= β0+ βCXijt+b0j + εijt
where Yijt refers to the lung function response for individual i for the jth cluster (ZIP
code) at time t, β0 is the fixed intercept, Xijt is the air pollutant concentration at time t for
62
person i the jth cluster, and b0j is the random effect for the jth cluster. βc corresponds to the
parameter estimate and εi is the individual error term.
When selecting models for our analysis, a comparison of fit was necessary. Using the
information criteria (i.e. Akaike or Schwarz) helped determine which models were
preferable. Models with smaller Akaike Information Criterion (AIC) or Bayesian
Information Criterion (BIC) values were preferred. Selection of the appropriate random
effects structure was critical and was ascertained with likelihood ratio tests using
restricted maximum likelihood (REML). Our model tested for the inclusion of no random
effects, a random intercept effect for subject, and a random intercept for ZIP code.
Selection of fixed effects into the models was determined using likelihood ratio tests,
which were implemented using Maximum Likelihood (ML) estimation. Covariates
entered into the final model included both fixed and random effects. Random effects
accounted for the clustering within ZIP codes. Longitudinal models accounted for the
correlation within subjects using a spatial power covariance structure. Model diagnostics
using residuals were employed to assess fit. Because longitudinal data are often
correlated and not always homoscedastic (constant variance), usual residual diagnostic
methods were inappropriate. The transformation of the residuals, to remove correlations,
was necessary to perform diagnostic tests. Once transformed, the residuals were then be
used in standard diagnostic procedures (108). In this analysis, these included scatterplots
to check for constant variance and normal quantile plots to check for normality of the
data.
63
Long-Term Models
As the pollutants of interest are typically correlated, single pollutant models were
constructed for the effects of ozone and PM2.5 separately. Each pollutant-specific model
examined the relationship between lung function and pollutant concentration using FEV1,
FVC, or the FEV1/FVC ratio as outcomes, in both short- and long-term models. For the
long-term exposure models, long-term exposures were defined as both the average and
the cumulative exposure levels experienced by the patient by time t. Averages were
calculated based on time intervals between visits. Cumulative concentration levels were
averaged over the time intervals between visits. Because season and region are important
modifiers of air pollution exposure, their inclusion was necessary in all of the statistical
models. Both temporal models controlled for confounders and other covariates of
interest, such as age, sex, race, height, BMI, region, education, randomization arm,
clinical center, baseline pulmonary function, and year of study. Both height and BMI
have been shown to be significant predictors of COPD risk in a number of epidemiologic
studies (109; 110). The ozone models controlled for ozone season, defined as lasting
from April-October and categorized as a dichotomous yes/no variable, whereas season in
the PM2.5 models was included as a four level categorical variable (winter, spring,
summer, and fall). Data were assumed to be missing at random (MAR).
Short-Term Exposure Models
The effects of short-term air pollution exposure on lung function were evaluated with
lagged exposure models. Pollutant values taken at lag 0 (same day of the follow-up visit)
64
and lag 3 days (3 days prior to the follow-up visit) were used to examine the possible
delayed exposure effects on pulmonary function impairment. Previous epidemiologic
studies have shown that air pollution exposure could have adverse effects on lung
function parameters up to three days after exposure in subjects with COPD (111). Lag
distributed models were specified for each pollutant and controlled for confounders and
other covariates of interest, such as age, sex, height, race, BMI, season, education, type of
treatment received, baseline pulmonary function, and year of study. The ozone models
controlled for ozone season, defined as lasting through April-October and categorized as
a dichotomous yes/no variable, whereas season in the PM2.5 models was included as a
categorical variable (winter, spring, summer and fall). Data were assumed to be MAR.
Respiratory Morbidity Analyses
Descriptive statistics examined change in respiratory symptoms over the course of the
study by treatment arm. Bivariate analyses evaluated the associations between mean
PM2.5 and ozone levels with mean respiratory index scores. Similar to previous analyses,
mixed linear models examining the effect of fixed (pollution) and random (ZIP code)
effects were assessed. Models controlled for age, sex, height, BMI, educational level,
season, type of treatment received, race, baseline respiratory score, clinic, and year of
study. As air pollution levels are known to vary by geography and time, effect
modification of the exposure-outcome association by region and season was assessed
with the inclusion of interaction terms. Confounding was defined as a 10% change or
more in the original coefficient estimates. Model selection was based on a comparison of
65
AIC and BIC values. Models having smaller values were considered as better fitting the
data.
Subgroup Analyses
The final set of models focused on high-risk NETT participants as described previously.
Methodologically, these analyses are similar to the ones described above. They focused
on examining the effects of acute and chronic ambient exposures on lung function and
respiratory symptoms. Data were again assumed to be MAR.
All data were analyzed using SAS 9.2 (SAS Institute Inc, Cary, NC).
Ethics
Approval for this study was obtained from the Institutional Review Board at The Ohio
State University. Since this was a secondary data analysis, no patient contact or consent
was required.
6.3 Results
Out of 1218 participants, there was complete ozone and PM2.5 data on 1212. Four
subjects were excluded as their ZIP codes were no longer in existence, two additional
subjects were excluded from the analysis as their ZIP codes corresponded to post office
boxes rather than physical addresses.
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Patient and Air Pollution Characteristics
Table 6.1 provides a description of the characteristics of the study population. The mean
age for study subjects was 66.4 years. 61% of the participants were male and the majority
of all participants (94.9%) were Caucasian. In terms of education, there was a relatively
even distribution of educational attainment. Mean lung function values and respiratory
scores are also presented in Table 1. Overall, study participants exhibited severe to very
severe lung function impairment as defined by the American Thoracic Society and Global
Initiative for Chronic Obstructive Lung Disease (GOLD) staging severity for COPD
(Table 2.2). Post-bronchodilator (BD) FEV1% predicted was quite poor, averaging 29%
in all participants and 26.8% in LVRS participants.
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LVRS (N=605) Non-LVRS (N=607) Overall (N=1212) Age 66.3 (6.3) 66.5 (5.9) 66.4 (6.1) Sex Male 354 (58.5) 389 (64.1) 743 (61%) Female 251 (41.5) 218 (35.9) 469 (39%) Race White 578 (95.5%) 572 (94.2%) 1150 (94.9%) Black 19 (3.1%) 23 (3.8%) 42 (3.5%) Hispanic 2 (0.3%) 5 (0.8%) 5 (0.41%) Other 6 (1.0%) 4 (0.7%) 15 (1.24%) Education < High School 129 (21.3) 118 (19.5) 247 (20.4%) High School 167 (27.5) 214 (35.4) 381 (31.4%) < College 209 (34.4) 194 (32.1) 403(33.2%) College+ 102 (16.8) 79 (13.1) 181(15%) Baseline FEV1 (L) 2.89(0.65) 2.95(0.63) 2.88(0.64) Baseline FVC (L) 3.72(0.85) 3.80(0.83) 3.72 (0.84) Baseline FEV1/FVC 0.78(0.02) 0.78(0.02) 0.78 (0.02) Baseline Post BD FEV1% predicted
26.8(7.3) 26.6(7.1) 29.4 (10.1)
Baseline Post BD FVC% predicted
66.9(15.8) 67.1(15.2) 70.61 (17.2)
Baseline SGRQf 53.6(12.7) 52.4(12.7) 50.13 (16.1)
Table 6.1. Characteristics of the NETT Study Population, by Treatment Arm
Data are presented as mean (standard deviation) or N (%); FEV1=forced expiratory volume in 1
sec; FVC= forced vital capacity; BD = Bronchodilator; SGRQ = St. George’s Respiratory
Questionnaire score
Mean pollutant levels for ozone and PM2.5 are presented in Table 6.2. In general, the
mean pollutant concentrations were below the USEPA National Ambient Air Quality
Standards (Table 2.2). There was little observable difference when these mean values
were compared across sex, race, education, or region of residence.
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Mean Ozone (ppm)
SDa Mean PM2.5 (µg/m3) SD
Sex Male 0.0402 0.0052 13.43 3.11 Female 0.0394 0.0049 13.16 3.03 Race White 0.0399 0.0050 13.27 3.07 Black 0.0393 0.0059 14.52 2.20 Hispanic 0.0411 0.0047 12.74 5.40 Asian 0.0372 0.0053 14.05 3.98 Other 0.0430 0.0057 9.74 1.71 Education < High School 0.0398 0.0049 13.39 2.92 High School 0.0399 0.0045 13.36 2.91 < College 0.0401 0.0055 13.25 3.23 College+ 0.0395 0.0054 13.32 3.27 Region West 0.0401 0.0069 12.14 4.39 South 0.0429 0.0041 13.28 2.84 Northeast 0.0384 0.0042 13.90 1.64 Midwest 0.0389 0.0035 13.81 2.64
Table 6.2. Mean Ozone and PM2.5 Concentrations by Socio-Demographic Factors. aSD = Standard deviation.
Long-Term Exposure Models
Average ozone was significantly associated with increased pulmonary function
impairment as measured by FEV1 (mL) (Table 6.3). There was a significant interaction
between mean ozone levels and ozone season in the FEV1 models. The effect of mean
ozone was weaker during the ozone season compared to the non-ozone season (β=0.741
ml, p-value=0.02). Although the effect of average PM2.5 on FEV1 (mL) differed by
region, many of the estimates were small and not associated with worsened lung function.
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The relationship between average ozone and FVC was significantly modified by ozone
season, with a greater positive effect occurring during the non-ozone season (β=0.59, p
<0.01). The FEV1/FVC ratio was significantly associated with mean PM2.5, but not
ozone. However, the parameter estimate for PM2.5 indicated a positive relationship
between mean PM2.5 and FEV1/FVC, which is contradictory. There was a significant
interaction between average PM2.5 and region for post-BD FVC%. These stratum-
specific effects for region and PM2.5 were largest in the Midwest region (β=-8.51, p-
value<0.01). There was no effect of mean ozone on post-BD FEV1%. Average PM2.5 was
significantly associated with worsened post-BD FEV1%, (-0.1514, p<0.01). The St.
George’s respiratory score was not associated with either mean ozone or mean PM2.5.
None of the average ozone outcome models showed evidence of effect modification by
randomization arm.
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Variable Estimate SE p-value FEV1 (mL) PM2.5 (West) * 0.82 1.31 <.0001† PM2.5 (Midwest) 6.63 2.03 0.016 PM2.5 (Northeast) 3.89 2.69 0.31 PM2.5 (South) 6.53 2.04 0.02 Ozone (Non Ozone Season)*
-0.34 0.398 0.39
Ozone (Ozone Season) 0.741 0.30 0.02 FVC (mL) PM2.5 1.64 1.03 0.002 Ozone (Non Ozone Season)*
-0.57 0.34 0.095
Ozone (Ozone Season) 0.59 0.26 0.004 FEV1/FVC PM2.5 0.000035 0.000014 0.013 Ozone 0.000031 0.000038 0.41 Post-BD FEV1% PM2.5 -1.51 0.54 0.005 Ozone -.227 .159 0.16 Post-BD FVC % PM2.5 (West) * 0.0715 0.1258 0.0003† PM2.5 (Midwest) -8.51 2.00 0.0003 PM2.5 (Northeast) -4.29 2.68 0.1346 PM2.5 (South) -2.66 2.01 0.2487 Ozone -.499 0.27 0.068 St. George’s Score PM2.5 1.25 0.95 0.19 Ozone -.346 0.263 0.19 Table 6.3. Regression of Respiratory Function and Symptoms on Mean Pollutant Level
in All Participants
†Main effect pollutant estimate in a model including a significant interaction with region.
ǂMain effect for ozone in a significant interaction with ozone season.
Stratum specific estimates are presented. Post-BD: post bronchodilator aPM2.5 units are in increments of 10 µg/m3. bOzone units are in increments of 10 ppb.
*Referent category.
71
Cumulative exposures for PM2.5 were significantly associated with worsened lung
function for most pulmonary function outcomes and increased respiratory morbidity
(Table 6.4). Like the mean models, the parameter estimates were relatively small but
statistically significant. There was no evidence of interactions between cumulative PM2.5
and either region or season for any of the respiratory function models. Cumulative
exposures for ozone were significantly associated with worsened post-BD FVC% and
worsened respiratory score. The cumulative PM2.5 models demonstrated a significant
increase in predicted post-BD FEV1% and FVC% for participants randomized to the
LVRS arm compared to those on medical management. The St. George’s respiratory
questionnaire score was also significantly lower for those randomized to LVRS. In terms
of cumulative ozone exposure, subjects receiving LVRS had a significantly higher post-
BD FEV1% compared to those on medical management alone. There was also a
significant difference by randomization arm for FVC% and respiratory score but these
values showed a minimal but worsened effect of LVRS on pulmonary and respiratory
outcomes in response to cumulative ozone exposures.
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Variable Estimate SE p-value FEV1 (µL) PM2.5 0.795 0 <.0001 Ozone 0.0975 0 0.1243 FVC (µL) PM2.5 -0.818 0 0.0034 Ozone -0.099 0 0.16 FEV1/FVC PM2.5 1.59e-8 0 0.72 Ozone 1.36e-8 0 0.25 Post-BD FEV1% PM2.5 -0.00198 0.00016 <.0001 Ozone -0.00065 0.000045 <.0001 Post-BD FVC % PM2.5 -0.00011 0.000052 0.0376 Ozone -0.00063 0.000082 <0.001 St. George’s Score PM2.5 -0.00041 0.00048 0.399 Ozone 0.000945 0.00014 <0.001
z
Table 6.4. Regression of Respiratory Function and Symptoms on Cumulative Pollutant
Level in all Participants
Post-BD: post bronchodilator aPM2.5 units are in increments of 10 µg/m3. bOzone units are in increments of 10 ppb.
Lagged Exposure Models
As stated previously, two primary lag models were examined: same day exposure (lag 0)
on visit date and ambient exposures three days (lag 3) prior to the scheduled visit date.
There were several significant effects of same day PM2.5 exposure on several lung
function outcomes but not on the respiratory score (Table 6.5). Same day ozone was
73
significantly associated with FEV1(mL) and the FEV1/FVC ratio and marginally
significant with FVC(L). There was a signification effect modification of the association
between same day ozone exposure and the FEV1/FVC ratio by region. However these
effects were all positive. Same day PM2.5 concentrations were negatively associated with
post-BD FVC% (β=-0.002, p-value<0.01). Same day ozone and PM2.5 concentrations did
not appear to be significantly associated with the overall respiratory symptoms score.
74
Variable Estimate SE p-value FEV1 (mL) PM2.5 0.12 0.22 0.61 Ozone 63.8 23.8 0.032 FVC (mL) PM2.5 0.11 0.19 0.57 Ozone 51.1 20.6 0.091 FEV1/FVC PM2.5 -8.6e-6 0.000034 0.80 Ozone (West) * 0.01221 0.003834 0.0007† Ozone (Midwest) 0.00025 0.00217 0.0067 Ozone (Northeast) 0.00461 0.00244 0.0731 Ozone (South) 0.00702 0.00307 0.2643 Post-BD FEV1% PM2.5 -0.090 0.0126 0.47 Ozone 4.94 6.56 0.45 Post-BD FVC % PM2.5 -0.00174 0.00029 <0.0001 Ozone 7.20 11.29 0.52 St. George’s Score PM2.5 0.2000 0.2224 0.3687 Ozone 0.054 0.11 0.628 Table 6.5. Regression of Respiratory Function and Symptoms on Same-Day Pollutant
Level in all Participants
†Main effect pollutant estimate in a model including a significant interaction with region.
Stratum specific estimates are presented. aPM2.5 units are in increments of 10 µg/m3. bOzone units are in increments of 10 ppb.
Post-BD: post bronchodilator
*Referent category.
Similarly to the same day exposure models, several of the three-day lagged models
showed marginally significant associations between ambient PM2.5 and the post-BD
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FEV1% and FVC% outcomes (Table 6.6). Three-day lagged ozone was significantly
associated with FEV1 and the FEV1/FVC ratio, but these parameter estimates were
positively associated with increased ozone concentrations. There were no apparent
interactions between three-day lagged ozone and PM2.5 concentrations with region,
season or ozone season. Similar to the same day exposures, the St. George’s respiratory
score was not associated with either the three-day lagged ozone or PM2.5 concentrations.
Changes in respiratory function measures were small, biologically, but statistically
significant. The effects of the lagged pollutant exposures did not differ by treatment arm.
76
Variable Estimate SE p-value FEV1 (mL) PM2.5 0.307 0.22 0.17 Ozone 21.9 10.2 0.032 FVC (mL) PM2.5 0.23 0.19 0.22 Ozone 15.9 8.82 0.071 FEV1/FVC PM2.5 0.000042 0.000034 0.21 Ozone 0.00388 0.00169 0.022 Post-BD FEV1% PM2.5 -0.29 0.128 0.088 Ozone -9.27 6.62 0.17 Post-BD FVC % PM2.5 -0.4052 0.2205 0.066 Ozone -5.75 11.40 0.61 St. George’s Score PM2.5 -0.0091 0.2195 0.9669 Ozone 0.021 0.11 0.85 Table 6.6. Regression of Respiratory Function and Symptoms on Three-Day Lagged
Pollutant Level in all Participants aPM2.5 units are in increments of 10 µg/m3. bOzone units are in increments of 10 ppb.
Post-BD: post bronchodilator
77
Variable Estimate SE p-value FEV1 (mL) PM2.5
0.83 2.41 0.73 Ozone -0.69 0.74 0.36 FVC (mL) PM2.5 1.25 1.91 0.51 Ozone -0.77 0.63 0.23 FEV1/FVC PM2.5 0.00019 0.00026 0.48 Ozone -0.00003 0.000065 0.67 Post-BD FEV1% PM2.5 -0.85 0.93 0.36 Ozone -0.12 0.23 0.61 Post-BD FVC% PM2.5 1.81 2.49 0.47 Ozone -0.61 0.56 0.28 St. George’s Score PM2.5 1.96 1.77 0.27 Ozone -.23 0.476 0.6307 Table 6.7. Regression of Respiratory Function and Symptoms on Mean Pollutant
Level in High-Risk Participants aPM2.5 units are in increments of 10 µg/m3. bOzone units are in increments of 10 ppb.
Post-BD: post bronchodilator
High-Risk Groups
There were a total 328 participants who were classified as high-risk, with 145 of them
randomized to the LVRS arm and the other 183 randomized to the traditional medical
management arm. Within the high-risk group, there was no evidence of an effect of
average PM2.5 or average ozone exposure on FEV1, FVC, post-BD FEV1% and FVC%
(Table 6.7). The St. George’s respiratory score was also not significantly associated with
78
either average exposure. In this cohort of subjects, differences by randomization arm
were less evident. None of the average exposure models showed a significant difference
in lung function measures for either those receiving LVRS or those on medical
management alone. Unlike previous results, region did not appear to be an important
effect modifier of average PM2.5 exposure in any of the lung function models.
Variable Estimate SE p-value FEV1 (µL) PM2.5 -1.17 0 0.0067 Ozone 0.31 0 0.052 FVC (µL) PM2.5 -0.98 0 0.073 Ozone -0.24 0 0.079 FEV1/FVC PM2.5 -4.17e-8 0 0.61 Ozone -7.92e-9 0 0.71 Post-BD FEV1% PM2.5 -0.00110 0.000289 0.0002 Ozone -0.00031 0.000074 <0.001 Post-BD FVC % PM2.5 -0.0020 0.00071 0.0049 Ozone -0.00061 0.00018 0.0006 St. George’s Score PM2.5 -0.00054 0.00092 0.56 Ozone 0.00066 0.00015 <0.001 Table 6.8. Regression of Respiratory Function and Symptoms on Cumulative Pollutant \
Level in High-Risk Participants aPM2.5 units are in increments of 10 µg/m3. bOzone units are in increments of 10 ppb.
79
Among high-risk participants, cumulative PM2.5 exposures were negatively associated
with all pulmonary function outcomes as well as with the respiratory score; however
many of these effects were very small in magnitude (Table 6.8). Cumulative ozone, on
the other hand, was only significantly associated with the respiratory score (β=0.076,
p=0.0086) and post-BD FEV1% and FVC%. Randomization arm was not an important
effect modifier of the relationship between cumulative exposure and
pulmonary/respiratory outcomes.
Variable Estimate SE p-value FEV1 (mL) PM2.5 0.15 0.41 0.77 Ozone 7.85 4.13 0.123 FVC (mL) PM2.5 -0.05 0.35 0.88 Ozone 8.10 3.52 0.22 FEV1/FVC PM2.5 0.000076 0.000058 0.187 Ozone 0.00699 0.0062 0.048 FEV1%* PM2.5 -0.25 0.21 0.23 Ozone -19.46 10.5 0.064 FVC %* PM2.5 -0.098 0.46 0.83 Ozone 3.51 23.18 0.88 St. George’s Score PM2.5 0.76 0.43 0.0798 Ozone 7.8749 20.5196 0.7012 Table 6.9. Regression of Respiratory Function and Symptoms on Same Day Pollutant
Level in High-Risk Participants
Ozone units are in increments of 10 ppb.
PM2.5 units are in increments of 10 µg/m3.
80
For the lagged models, we observed that same day exposures correlated poorly with lung
function outcomes and respiratory scores in high-risk participants. This was true for both
ozone and PM2.5 concentrations (Table 6.9). The same day PM2.5 exposure in the
respiratory score model showed a marginally significant positive effect of air pollution
(β=0.075, p=0.079). Same day ozone concentrations were marginally associated with
decreased post-BD FEV1% (β=-19.5, p=0.064). Like same day exposures, the three-day
lagged concentrations did not correlate strongly with most of the lung function outcomes
(Table 6.10). Three-day lagged ozone appeared to be related to FEV1, the FEV1/FVC
ratio and post-BD FEV1%, though the relationships were all positive. Only three-day
lagged PM2.5 correlated negatively with post-BD FEV1% among high-risk subjects.
Randomization arm was not an important effect modifier of the relationship between
lagged exposure and pulmonary/respiratory outcomes in this subgroup.
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Variable Estimate SE p-value FEV1 (mL) PM2.5 0.41 0.42 0.33 Ozone 4.62 2.02 0.022 FVC (mL) PM2.5 0.34 0.36 0.35 Ozone 2.96 1.73 0.087 FEV1/FVC PM2.5 0.00011 0.000060 0.066 Ozone 0.0092 0.00317 0.0032 Post-BD FEV1%* PM2.5 -0.53 0.21 0.014 Ozone -28.86 11.01 0.0089 Post-BD FVC %* PM2.5 -0.93 0.60 0.12 Ozone 7.32 24.38 0.76 St. George’s Score PM2.5 -0.6464 0.4123 0.1173 Ozone -2.7612 20.9676 0.8953 Table 6.10. Regression of Respiratory Function and Symptoms on Three-Day Pollutant
Level in High-Risk Participants
Ozone units are in increments of 10 ppb.
PM2.5 units are in increments of 10 µg/m3.
Post-BD: post bronchodilator
6.4 Discussion
In spite of multiple studies investigating the health risks associated with ambient air
pollution, less has been written on the short and long-term effects of air pollution on
those with severe respiratory disease (5) (46) (2). In our study, we found that increases in
cumulative PM2.5 concentrations were associated with statistically significant decreases
in most lung function outcome models. Results of previous studies support these findings,
showing that fine particulate matter can worsen lung function among those with existing
82
respiratory disease (44) (43). However, these studies mostly considered the short-term
effects of ambient PM2.5 on outcomes such as hospital admissions and mortality. An
epidemiologic study by Logario et al. is one of the few studies to examine the effects of
cumulative pollutant exposure on the decline in lung function. Similar to our study,
exposure to PM2.5 was associated with an increased risk of worsened lung function.
However, this study, undertaken in 1999, did not examine the specific effects of fine
particulate matter or ozone on the risk of COPD symptoms or lung function decline
(111). In our study, cumulative ozone concentrations did not correlate with many of the
lung function outcomes, but it was associated with worsened respiratory index score. The
relatively low levels of ambient ozone levels may have affected our ability to detect long-
term pulmonary function effects. As no studies have examined the long-term pulmonary
health effects of ozone exposure among individuals with COPD, it is difficult to interpret
our findings with respect to the existing literature. However, it is likely that ozone may
not increase inflammation and bronchial responsiveness of the lung tissue in COPD
subjects in the same fashion as in healthy adults.
Unlike our cumulative PM2.5 models, results from the average PM2.5 models were less
indicative of an adverse effect on pulmonary function. Although the FEV1/FVC ratio and
post-BD FEV1% were significantly worsened in response to mean PM2.5; the effects
were relatively small. Pope et al. described physiologically small but statistically
significant decrements in pulmonary function (112) in response to ambient air pollutants.
These small biological changes in lung function outcomes may not be very surprising
given that we are dealing with a susceptible population with severely impaired lung
83
function. In our analyses, mean ambient ozone was not significantly associated with any
of the lung function outcomes or the respiratory score. One issue to consider when
dealing with ‘average’ exposure is that these mean measures do not account for peak
exposures and can mask pollution effects, due to the summary nature of the mean
concentration. In addition, since subjects were only required to have clinic visits at
baseline, 6, 12, 24, 36, 48 and 60 months, there was a large degree of daily exposure
information that had to be captured in one average measure, thereby losing a considerable
amount of variability in the pollutant data. Restricting the exposure window to the ozone
season may have yielded different results.
With respect to the lagged models, the same day exposure concentrations failed to show
much evidence of worsening respiratory function or symptoms in either the PM2.5 or
ozone-specific models. It is likely that the exposures experienced on the same days as the
visits may not have been sufficient in duration to cause any significant changes in either
lung function values or in respiratory symptoms. Another possible explanation could be
that effects may not manifest over such a small time frame, except possibly in the
presence of above normal exposure levels. It is unclear how many subjects were exposed
to levels above the NAAQS limits for at least one of their scheduled follow-up visits.
Additional analyses might examine a subset of participants who experienced at least two
same day exposures that were in exceedance of NAAQS limits. Since most of the follow-
up visits were up to one year apart, if subjects had visits during the non-ozone season, the
risk of increased ozone exposure would be relatively minimal.
84
Three-day lagged exposure concentrations showed little to no effect of PM2.5 on overall
lung function and respiratory symptoms. Three-day lagged ozone did impact FEV1
adversely although there was no significant evidence of adverse effects of lagged ozone
on any other respiratory function outcomes. Various investigators have demonstrated the
short-term effects of PM2.5 on the respiratory health of individuals with COPD. Many of
these studies used time-series analyses and focused on examining increases in emergency
room visits in response to changes in daily PM2.5 levels (113). Our study presented a
unique opportunity to investigate the effects of air pollutants in not only subjects with
severe emphysema, but also among those who had been treated with LVRS. In many of
the lagged models, there did not appear to be a modulation of the effect of PM2.5 or ozone
on respiratory function by LVRS. Previous studies have shown that lagged ozone
exposure is capable of inducing exacerbations among COPD subjects and increased
hospitalizations (44) (52) (45).
Sub-analyses among high-risk participants did not reveal much evidence of a mean
ambient pollution exposure effect on pulmonary health. Cumulative ozone appeared to be
significantly associated with worsened pulmonary function, but the changes were
relatively small. There was some indication that lagged ozone worsened FEV1 for high-
risk subjects, although this was limited to the FEV1/FVC ratio. Lack of variability in lung
function measurements among this subgroup of participants and a considerably smaller
sample size could have impacted our ability to observe any pollution-specific effects.
Restricting our analyses to this subgroup was an attempt to identify if there was increased
susceptibility to air pollutants among those with the poorest lung function outcomes. In
85
the original trial, high-risk participants were classified as having an FEV1 < 20% of
predicted, a high perfusion ratio and homogeneous emphysema. The overall mortality
rate in this group was almost two times greater for participants receiving LVRS
compared to those on medical therapy (RR=1.82, 95% CI=1.2-2.7) (10). The absence of
increased impairment in pulmonary function among these very susceptible individuals
could be due to the fact that decreases in lung functions would be relatively minimal
given that that their lung function was already so poor. The degree of change in
individuals with such advanced disease may be relatively imperceptible.
Progression of emphysema remains poorly understood but is theorized to occur in a
similar fashion to disease onset in subjects with an α1-antitrypsin deficiency (18). In our
study, we observed several contradictory exposure-outcome associations. It appeared that
in several instances, increased air pollution exposure was associated with improved lung
function. Although several limitations may have contributed to our observed results,
much remains unclear on the rate of decline in lung function among those with severe
COPD. In healthy adults, there is a 25 -30% age-related loss in pulmonary function over
their lifetime, however this decline does not result in compromised lung function (114).
Among those with severe COPD, the rate of pulmonary function decline is often larger
and greater than that observed among healthy subjects (24). However, even among
emphysematous subjects, there is considerable variability in the rates of pulmonary
function decline. Research by Nishimura et al. found that among a cohort of
emphysematous subjects, there were three basic sub-groups for lung function decline:
rapid decliners, slow decliners and sustainers. In these subjects, the percentage of
86
emphysematous lung tissue at baseline was strongly predictive of the rate of lung
function decline (115). In performing our analyses, we did not consider how the percent
of emphysema distribution at baseline could have affected the decline in pulmonary
function. Although lung function was severely compromised in all subjects, distribution
of emphysema in the lung was variable. It is possible that the incongruity in some of our
results could been explain by heterogeneous patterns of emphysema in these subjects.
Pulmonary susceptibility to air pollution exposure may have been dependent on the
distribution patterns of emphysema for these participants.
Limitations
Limitations of our study included lack of information on indoor exposures, long time
gaps between follow-up visits, no information on duration of exposure, and a focus on
single pollutant models. Because our study focused on a cohort of older individuals in
poor respiratory health, it is unclear how much of a role indoor pollutant concentrations
could have played in the worsening of lung function in these subjects. Ambient exposure
levels may not serve as adequate proxies for indoor exposures, which can vary
considerably. Given the fact that indoor exposures likely constitute the main exposures
for our cohort and much of the general population, it is difficult to definitively state that
how much of a risk ambient exposures pose to these subjects. Accounting for the amount
of time spent indoors would be an important variable to consider in future analyses. True
exposure characterization is complex and hard to accurately capture for these subjects
without the use of personal air monitors. A study by Ebelt et al. characterized the
relationship between personal and ambient air pollution exposures. They found that
87
personal particle sulfate exposures correlated strongly with ambient levels yet personal
PM2.5 levels were not as clearly associated with ambient levels (116).
As our models were restricted to single pollutants, they were only reflective of one
component of the total risk posed by air pollutants. Accounting for multiple pollutants, in
addition to ozone and PM2.5, may have been a better way to accurately capture the
complex interactive effects of actual ambient exposures experienced by these individuals.
Conclusion
Findings from our analyses have considerable impacts on the original results obtained
from the NETT trial. We found that low ambient level fine particulate matter and ozone
can significantly affect respiratory function in COPD subjects. The benefits reported for
subjects who received LVRS appeared to persist in the presence of ambient air pollutants,
suggesting that the surgery may have conferred a protective effect among those
participants who underwent the procedure. Implications from our analyses could lead to
the recommendation of lowered acceptable PM2.5 and ozone limits for individuals with
existing respiratory disease.
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Chapter 7: Healthcare utilization and mortality in response to ambient air pollution in a
vulnerable population
7.1 Introduction
Excess morbidity and premature mortality have long been associated with air pollution
exposure (42). Substantial evidence has suggested that exposure to criteria pollutants
such as ozone and particulate matter is associated with increased pulmonary morbidity
and mortality risk (41; 47). These effects have been examined both in the short- and long-
term and have revealed a higher likelihood of cardiovascular- and respiratory-related
morbidity and mortality risk as a result of increased air pollution concentrations (38).
Healthcare utilization has been a common way to assess cardiopulmonary-related
morbidity in many pollution studies, most notably by examining fluctuations in hospital
admissions and emergency department (ED) visits in response to ambient air pollution
exposure concentrations. Many of these studies have employed a time-series study
design, utilizing both daily pollution and daily hospital admissions data (117; 118). In
general, results from these investigations show daily increases in hospital admissions and
ED visits as a consequence of increased daily pollutant exposure. Notable effects have
been seen with sulfates and fine particulate matter (PM2.5), and to a lesser extent, with
ozone concentrations (44). Among susceptible groups, epidemiologic studies have
reported increased pulmonary and cardiovascular-related hospitalizations as a result of
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elevated ambient air pollutant exposures. The long-term effects of ambient pollutant
concentrations on healthcare utilization patterns have been less studied, however,
especially among subjects with existing respiratory disease. There is evidence suggesting
that short-term pollutant-related exacerbations are more likely to occur among susceptible
patient populations; however, the results vary and depend on pollutant type (119).
The association between mortality and long-term air pollutant exposure has also been
intensively investigated. The Harvard Six Cities study and the American Cancer Society
study of particulate air pollution and mortality are two hallmark research investigations
that showed an increased risk of mortality in response to elevated concentrations of
particulate matter (120; 121). These were the primary studies employed by the US
Environmental Protection Agency (USEPA) to “support new National Ambient Air
Quality Standards (NAAQS) for PM2.5 and to maintain the standards for coarse
particulate matter (PM10) that were already in effect (122).” Although findings have
supported the link between particulate matter and increased mortality risk, the association
between ozone concentrations and risk of death has been less clear (123). Some of the
reasons for the lack of conclusiveness in the results include limited mortality data and the
primary examination of short-term exposures (124). Among subjects with chronic
obstructive pulmonary disease (COPD), the data have been mainly restricted to short-
term ozone or PM2.5 concentrations and small geographic areas (125).
Morbidity and mortality in response to air pollution exposure have not been previously
studied among National Emphysema Treatment Trial (NETT) participants. Results from
the original trial showed improvements in survival and respiratory symptoms for
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participants receiving lung volume reduction surgery (LVRS) compared to traditional
medical therapy. Because survival was a primary endpoint, it is important to test how
mortality was affected by long-term exposure to pollution across the different treatment
arms. In addition, it is likely that healthcare utilization patterns differed between study
arms. Individuals with COPD may increasingly seek out medical assistance in response
to worsening respiratory symptoms, exacerbated by exposure to PM2.5 and ozone.
Specific healthcare utilization patterns (i.e., hospitalizations, doctor and ED visits) are
useful proxies for capturing increasing morbidity. Analysis of these data would aid in
understanding the additional risk for increased morbidity and mortality posed by PM2.5
and ozone exposures among COPD subjects.
7.2 Methods
Study Population
The NETT was a multicenter, randomized controlled trial with the objective of assessing
whether LVRS in conjunction with medical therapy was a better long-term treatment for
subjects with severe emphysema when compared to medical therapy alone (12; 61). The
major outcomes of interest in the study were post-treatment mortality and maximal
exercise capacity. Additional outcomes of interest included lung function, as determined
by spirometry, distance walked in six minutes and quality of life. During the period 1998-
2002, a total of 1218 subjects were enrolled in the study, of which 608 were randomized
to LVRS and medical management; 610 were randomized to traditional medical therapy
alone, such as steroids and bronchodilators (10).
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Radiologists characterized emphysema distribution and magnitude in the lungs with chest
CT scans and a visual scoring scale. The aim of the LVRS was to remove 25% to 35% of
the lung, with a particular focus on the most affected areas (12). Medical histories of all
subjects were collected at 6, 12, 24, 36, 48 and 60 months after baseline. Participants
were followed for an average of 29.2 months (10; 12).
Air Pollution Data
We obtained pollution data from the USEPA Air Quality Systems (AQS) database (63).
We restricted these data to average daily ozone levels (1997-2003), and average daily
PM2.5 values (1999-2003). Exact addresses were not available for NETT participants,
thus, we used ZIP codes to define participant place of residence. Participants were
assigned residence at the centroid of their ZIP code, as determined by the 2000 census.
Values from monitors within specific ZIP codes were used to interpolate pollutant
concentrations at the centroid of the ZIP code, and were then merged with the NETT
data. Only resident ZIP codes corresponding with kriged ZIP code-specific pollution
levels, were considered for the analysis, per Liao et al. (62). Air pollution data were
estimated using log-normal kriging, a technique that allows for the estimation of pollutant
values in areas that may not be monitored, in which data from nearby monitoring stations
are weighted based on distance and number of monitored locations. Participants with the
same ZIP code were assumed to share the same level of exposure.
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Mortality Outcomes
Mortality estimates were obtained from both the clinical centers and reviews of the
December 2002 Death Master File from the Social Security Administration. Time to
death was assessed from the randomization date. All-cause mortality was the primary
endpoint of interest, as our focus was on the long-term effects of air pollutant exposure
on mortality.
Health Care Utilization Outcomes
The number of ED and doctor visits as well as hospital admissions were abstracted from
the Medicare claims database. Total visit counts between follow-up periods were
summed and entered in participants’ files at the next follow-up visit. Hospital length of
stay was defined as the number of nights spent in a hospital. The healthcare utilization
models also examined total counts of ED and doctor visits and hospital admissions, for
each subject, over the duration of the study.
Survival Analysis
Results from the NETT trial showed improved survival for participants receiving LVRS
compared to those on traditional medical management (31). Our analyses characterized
mortality for all participants and high-risk individuals, defined as having a baseline
forced expiratory volume in 1 second (FEV1) of less than 20%. We fit separate models
with long-term cumulative and average exposures for ozone and PM2.5 to estimate hazard
ratios and 95% confidence intervals (HR, 95% CI). Short-term and lag exposures were
not considered part of this analysis since the focus was on all-cause mortality, however,
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models were stratified by one-year intervals in order to minimize autocorrelation and to
better characterize the changing risk of death over the course of the study. These
extended Cox regression analyses estimated the risk of death at time t based on the
concentration of the pollutant at time t. Because we were interested in long-term rather
than short-term effects, we created a single cumulative or average time-dependent
pollutant concentration. We considered cumulative and average pollution exposure as
time-varying covariates even though they were not modeled explicitly as such (i.e. an
interaction term with time). Cumulative exposure by year was defined as the total
exposure experienced by the participant from their date of randomization until their death
or December 31st. We defined average exposure by year as the mean air pollution
exposure from the date of randomization until their death or December 31st. If a
participant survived to the second year, their cumulative exposure from the first year was
added to the second year exposure and their average exposure was calculated as a
function of total pollution exposure divided by the total number of days in the study.
Participants alive at the end of the study (December 31st, 2003) were censored. Only
participants with complete ozone and PM2.5 data were included in the analysis.
Because it is likely that participants residing within the same ZIP code would be more
similar than individuals from different ZIP codes, a standard Cox regression model,
which assumes independence in the response over space and time, would not be
appropriate. In the survival analysis, we considered a generalized estimating equations
(GEE) approach with a repeated (clustering) effect for ZIP code and a robust sandwich
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variance estimate to compute standard errors (126; 127). The general marginal Cox
model was expressed as follows:
λijs(t) = λ0j(t)exp(βCXijs(t)),
where λijs(t) is the hazard of death for the ith subject in jth year for the sth cluster (ZIP
code) at time t, given a baseline hazard of λ0j in the jth year, and a cumulative or average
history of pollutant exposure of Xijs(t) for the ith subject in the jth year for the sth cluster at
time t.
We compared nested models using the Akaike’s Information Criterion (AIC) and a
Schwartz Bayesian Information Criterion (BIC), with a lower value indicating a better
fitting model (128). The final Cox models included the following covariates: age, sex,
education, marital status (categorized as single, separated/divorced, widowed, or
married), region of residence (Northeast, South, Midwest or West), Caucasian (yes/no),
treatment group (LVRS or medical management), body mass index (BMI), baseline
pulmonary function (FEV1% and St. George’s respiratory score at baseline), and clinical
center.
The model specified an unstructured correlation matrix. Although selecting the
appropriate correlation structure is important, a GEE analysis using a sandwich estimator
is relatively robust against a misspecified correlation matrix, especially with large
samples. (129) We used the PROC PHREG procedure in SAS 9.2 (SAS Institute Inc,
Cary, NC) to model the survival data. Data were assumed to be missing completely at
random (MCAR).
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Healthcare Utilization Analyses
Univariate analyses for healthcare utilization included histograms of ED, doctor and
hospital stays (Appendix D). We modeled the total number of hospital admissions, doctor
and ED visits in response to pollution effects with Poisson and negative binomial GEE
models fit using GEE to account for clustering by ZIP code. These models considered
cumulative and mean exposures to PM2.5 and ozone. Histograms for the total number of
ED visits, reported at each follow-up visit, revealed a preponderance of zero counts.
There was no evidence of excess zero counts for the total number of doctor visits or
hospital stays (Appendix D). When dealing with count data such as ED visits, there is a
likelihood of an excessive number of ‘zero’ counts. This excessive number of zeroes can
be handled with either a zero-inflated Poisson (ZIP) model or a zero inflated negative
binomial model (ZINB). The ZIP models assume that the data are a mixture of two
different distributions: one distribution consists of patients who will theoretically never
see the doctor and the other distribution consists of patients whose number of doctor
visits follows a Poisson distribution. An issue to be aware of, when dealing with count
data, is the likelihood of overdispersion. Overdispersion refers to more variation in the
data than expected under the distribution assumption (130). With respect to the Poisson
distribution, the variance should equal the mean; when this does not occur, the data are
considered overdispersed. In SAS, a scale parameter of 1 indicates overdispersion in the
data. The scale parameter is calculated by dividing the deviance, a measure of goodness
of fit, by its degrees of freedom. If the scale parameter is close to 1, there is no evidence
to suggest overdispersion in the data (131; 130; 132).
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In the case of excessive zero count data, it is also likely that there may be unobserved
heterogeneity and that the Poisson distribution assumption in the mixture distribution
would be too restrictive for the model (133). In that case, a ZINB model that includes a
dispersion parameter, α, would be preferable. When there is no overdispersion in the
model, α reduces to 0 and the second distribution assumes a normal Poisson distribution.
In our analyses, ED visit outcomes were first estimated with a ZINB model. If the ZINB
model failed to converge, we assumed that it was indicative of insufficient overdispersion
in the data. The data were then fit with the simpler ZIP model. Since there is no simple
way to test for evidence of overdispersion with clustered data, all healthcare utilization
outcomes were modeled with negative binomial Poisson regression models. The NBREG,
ZIP, and ZINB procedures in STATA 12.1 (STATA Corp LP, College Station, TX) were
used to model the data (134). Data were assumed to be missing at random (MCAR).
Missingness in the data was relatively low and mostly restricted to ZIP codes that were
no longer in existence.
Because our GEE models were not likelihood based, we could not compare nested
models using the likelihood-based AIC. Instead, variables were selected into model a
priori and assessed for statistical significance. The final regression models included the
following covariates: age, sex, education, region of residence, Caucasian race, treatment
group, BMI, and clinical center. The zero rate was modeled with the following
covariates: age, sex, education, region of residence, Caucasian race, treatment group,
BMI, and clinical center. Coefficients were not included in the results as our analyses
were focused on modeling the rate of non-zero events.
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Ethics
Approval for this study was received from the Institutional Review Board at The Ohio
State University. As this was a secondary data analysis, no patient contact or consent was
required.
7.3 Results
Healthcare Utilization
Summary data for total healthcare utilization are shown in Table 7.1. Overall, ED visits
and hospital admissions were relatively rare. Doctor visits, in contrast, occurred more
regularly over the course of the study. Among all subjects, there did not appear to be a
significantly increased likelihood of healthcare usage in relation to either mean PM2.5 or
ozone exposure levels (Table 7.2). The exception was mean ozone which was
significantly associated with hospitalizations. Specifically, a 1 ppb increase in mean
ozone exposure increased the rate of hospitalizations by a factor of 2.373, among those
with the same length of follow-up. Cumulative concentrations of ozone and PM2.5 were
significantly associated with healthcare utilization outcomes; although the association
was positive, the incidence rate ratios were very close to 1 (Table 7.3). Generally, there
were differences in healthcare usage by randomization arm, adjusting for mean exposure
and other covariates of interest. Specifically, subjects who were randomized to LVRS
were less likely to have doctor visits compared non-LVRS subjects.
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LVRS (N=604)
Non-LVRS (N=607)
Overall (N=1211)
Minimum (Overall)
Maximum (Overall)
Emergency department visits
0.55 (0.99)a 0.60 (1.04)
0.57 (1.02) 0 7
Doctor visits 7.27 (7.10) 6.72(6.34) 6.99 (6.73) 0 62 Hospitalizations 1.91 (6.09) 1.99
(4.91) 1.95 (5.53) 0 79
Table 7.1. Total Healthcare Utilization Summary for All NETT Participants, by Treatment Arm aMean (Standard Deviation)
Among high-risk participants, findings were similar to those observed in all subjects.
Total healthcare utilization in relation to mean exposures was, for the most part, not
significantly associated with either pollutant type (Table 7.4). Hospitalization rates
appeared to be inversely related with mean ozone concentrations; however this
relationship was only marginally significant. Cumulative concentrations of ozone and
PM2.5 were significantly associated with healthcare utilization outcomes; although the
association was positive, the incidence rate ratios were very close to 1 (Table 7.5).
Among high-risk participants, those randomized to LVRS had a lower rate ED visits after
adjusting for mean and cumulative exposures. However, in this subgroup of subjects, the
rate of hospital admissions was greater for those who underwent LVRS compared to non-
LVRS participants.
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Variable IRRa Robust SE of IRR
p-value
Emergency department visitsb
PM2.5 0.996 0.038 0.916 Ozone 1.362 0.402 0.296 Doctor visitsc PM2.5 1.0280 0.024 0.230 Ozone 1.0006 0.149 0.997 Hospitalizationsc PM2.5 1.040 0.089 0.647 Ozone 2.373 0.897 0.022 Table 7.2. Poisson Regression Estimates for Total Healthcare Utilization by
Mean Pollutant Level in All Participants aIncidence rate ratio. SE: Standard Error bOutcomes were modeled with ZIP models.
cOutcomes were modeled with negative binomial models.
Variable IRRa Robust SE of IRR
p-value
Emergency department visitsb
PM2.5 0.999 0.0004 < 0.01 Ozone 0.994 0.001 < 0.01 Doctor visits PM2.5
b 0.996 0.0005 < 0.01
Ozonec 0.996 0.0014 < 0.01 Hospitalizations PM2.5
b 0.992 0.0013 < 0.01
Ozonec 0.997 0.0004 < 0.01 Table 7.3. Poisson Regression Estimates for Total Healthcare Utilization by Cumulative
Pollutant Level in All Participants aIncidence rate ratio. SE: Standard Error bOutcomes were modeled with ZIP models.
cOutcomes were modeled with negative binomial models.
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Variable IRRa Robust SE of IRR
p-value
Emergency department visitsb
PM2.5 0.996 0.0016 < 0.01 Ozone 0.998 0.0005 < 0.01 Doctor visitsc PM2.5 0.992 0.003 0.020 Ozone 0.996 0.0011 < 0.01 Hospitalizationsc PM2.5 0.997 0.002 0.167 Ozone 0.999 0.0008 0.311 Table 7.4. Poisson Regression Estimates for Total Healthcare Utilization by Cumulative
Pollutant Level in High-Risk Participants aIncidence rate ratio. SE: Standard Error bOutcomes were modeled with ZIP models.
cOutcomes were modeled with negative binomial models.
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Variable IRRa Robust SE of IRR
p-value
Emergency department visitsb
PM2.5 1.049 0.056 0.363 Ozone 1.775 0.880 0.247 Doctor visitsc PM2.5 1.008 0.073 0.908 Ozone 0.556 0.527 0.535 Hospitalizationsc PM2.5 1.153 0.111 0.137 Ozone 0.331 0.213 0.085 Table 7.5. Poisson Regression Estimates for Total Healthcare Utilization by Mean
Pollutant Level in High-Risk Participants aIncidence rate ratio. SE: Standard Error bOutcomes were modeled with ZIP models.
cOutcomes were modeled with negative binomial models.
Pollutant Hazard Ratio 95% CI p-value Mean Ozone 1998 1.405 0.993-1.988 0.0552 1999 1.448 1.174-1.788 0.0006 2000 3.159 2.288-4.361 0.0001 2001 2.959 2.075-4.220 0.0001 2002 4.812 3.080-7.520 0.0001 2003 5.882 3.024-11.44 0.0001 Mean PM2.5 1999 0.927 0.835-1.029 0.1533 2000 0.908 0.723-1.140 0.4058 2001 0.773 0.576-1.036 0.0852 2002 0.532 0.324-0.874 0.0126
2003 0.238 0.170-0.334 0.0001 Table 7.6. Cox Regression Models by Mean Pollutant Level and Year in All Participants
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Pollutant Hazard Ratio 95% CI p-value Cumulative Ozone 1998 0.289 * 1.0000 1999 0.994 0.991-0.996 0.0001 2000 0.998 0.997-0.998 0.0001 2001 0.998 0.997-0.998 0.0001 2002 0.998 0.998-0.988 0.0001 2003 0.998 0.998-0.998 0.0001 Cumulative PM2.5 1999 0.998 0.998-0.999 0.0001 2000 0.999 0.999-0.999 0.0001 2001 0.999 0.999-1.000 0.0001 2002 1.000 1.000-1.000 0.0001 2003 1.000 1.000-1.000 0.0001 Table 7.7. Cox Regression Models by Cumulative Pollutant Level in All Participants
*Model was unstable, parameter estimates are questionable.
Figure 7.1. Effects of Mean Ozone Exposure on Mortality Risk by Treatment Arm
0
2
4
6
8
10
1998 1999 2000 2001 2002 2003 2004
Haza
rd R
atio
Year
Effects of Mean Ozone Exposure on Mortality Risk by Treatment Arm
Non-LVRS
LVRS
103
Pollutant Hazard Ratio† 95% CI p-value Mean Ozone 1998 54757.94 32474-
92332 0.0001*
1999 1.513 0.847-2.703 0.1617 2000 3.829 1.634-8.975 0.0020 2001 5.251 2.546-10.82 0.0001 2002 1.715 0.478-6.155 0.4078 2003 9.607 2.444-37.76 0.0012 Mean PM2.5 1999 0.869 0.645-1.170 0.3540 2000 0.852 0.438-1.657 0.6367 2001 0.809 0.464-1.412 0.4556 2002 0.291 0.154-0.550 0.0001 2003 0.161 0.077-0.336 0.0001 Table 7.8. Cox Regression Models by Mean Pollutant Level and Year in High-Risk
Participants
*Model was unstable and parameter estimates are questionable. There were very few deaths
occurring in the first year of the trial.
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Pollutant Unit Hazard Ratio 95% CI p-value Cumulative Ozone 1998 10 ppb 0.667 0.658-
0.676 0.0001
1999 10 ppb 0.996 0.994-0.998
0.0001
2000 10 ppb 0.998 0.997-0.999
0.0001
2001 10 ppb 0.998 0.997-0.999
0.0001
2002 10 ppb 0.998 0.997-0.998
0.0001
2003 10 ppb 0.998 0.997-0.999
0.0001
Cumulative PM2.5 1999 1
µg/m3 0.913 - *
2000 1 µg/m3
0.999 0.998-0.999
0.0001
2001 1 µg/m3
0.999 0.999-1.000
0.0001
2002 1 µg/m3
1.000 0.999-1.000
0.0001
2003 1 µg/m3
1.000 0.999-1.000
0.0001
Table 7.9 Cox Regression Models by Cumulative Pollutant Level and Year in High-Risk
Participants
*Model was unstable, parameter estimates are questionable.
Survival Analysis
Mortality risk was variable across the years of the study. Kaplan-Meier curves showed an
increased risk of death in non-LVRS participants in the first year of the study however,
by the fifth year, the risk profile had changed, with LVRS participants at a decreased risk
of death compared to non-LVRS participants (Appendix A). Extended Cox regression
models revealed that mean ozone concentrations were consistently and significantly
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associated with an increased risk of death among all participants (Table 7.6). The effect
of zone exposure was greatest in 2003 (HR=5.88, 95% CI= 3.02-11.44, p-value<0.01).
For the most part, the risk of death was not significantly different with respect to
treatment arm (Figure 7.1). Mean PM2.5 concentrations tended to be associated with a
decreased mortality risk in both treatment arms, although this effect was not significant
over the entire course of the study (Table 7.6). Cumulative exposures of both ozone and
PM2.5 were associated with a slight, but statistically significant, decrease in the risk of
death for all participants (Table 7.7).
Among high-risk participants, we observed similar results as in all NETT participants.
HRs tended to be greater than those observed in all participants with respect to mean
ozone concentrations (Table 7.8); however, these values were not significant for each
year of the study. The risk of death in 1998 was considerably larger than any other year
(HR=54,757.94, 95% CI= 32,474-92,332, p-value<0.01). However model stability was
questionable and the estimates observed may be unreliable. Mean PM2.5 concentrations
were associated with a lower risk of death; however, this effect was only significant in
the later part of the study. Cumulative levels of ozone and PM2.5 were also inversely
associated with mortality risk; however these HRs were relatively close to 1 (Table 7.9).
7.4 Discussion
Healthcare Utilization Patterns
We found little evidence of increased healthcare utilization in response to increasing
levels of air pollution exposure. Increased ED visits were not significantly associated
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with either mean or cumulative exposure levels, indicating that ambient pollutants may
be poor predictors of acute episodes among severe emphysematous subjects.
Hospitalizations, on the other hand, did show some positive association with ozone
exposures, but the results were somewhat inconsistent. Increasing mean concentrations of
ozone were shown to be more likely to result in an increase in the rate of hospitalizations.
However, cumulative exposures for PM2.5 and ozone were associated with a slightly
decreased rate of doctor visits, hospitalizations and ED visits. Restricting our sample to
high-risk participants produced similar results to those noted among all participants.
There were several differences by treatment arm in both groups of participants,
specifically with relation to hospitalizations and ED visits.
Our inability to observe any significant findings, with respect to ED visits, was possibly
impacted by the relatively low number of events. Although sensitive measures of
morbidity and disease severity, health measures such as ED visits tended to occur at a
much lower frequency than doctor visits (4). Analyzing such rare events makes it likelier
to observe few or spurious associations since too few events results in decreased
statistical power for the analyses. Possible ways to deal with this limitation would be to
increase the sample size and/or the duration of follow-up. Although our sample was
relatively large, previous studies evaluating change in healthcare utilization patterns in
response to air pollution exposure have been considerably larger, relying on city and
national hospital admissions databases (135; 44). Despite few events, we were able to
detect significant differences in the rates of healthcare usage by treatment arm.
Randomization to LVRS appeared to confer a benefit for subjects with respect to doctor
107
visits, and in high-risk participants, ED visits. These results are suggestive of fewer
exacerbations for LVRS participants in response to similar levels of air pollution. Among
high-risk LVRS subjects, however, there was an increase in the rate of hospitalizations
after adjusting for ambient pollutant concentrations. This may indicate that exacerbations
in this subgroup of participants, although fewer in number, may be more serious in nature
and require lengthier hospital stays.
With respect to hospitalizations, we were able to detect a positive association with
increasing mean ozone exposures. Cumulative pollutant concentrations had a very slight
inverse association, suggesting the possibility of confounding or effect modification by
unmeasured variables. COPD subjects have been estimated to spend 70% to 90% of their
time indoors, and as such may be at lower risk from ambient air pollutant exposures,
particularly with respect to ozone (116; 136). However, PM2.5 is capable of penetrating
indoors and indoor levels have been shown to be more highly correlated with outdoor
levels (137). Previous studies have described the effect modification of air pollutants and
hospital admissions by air conditioning (AC) use. These studies have reported a lower
indoor PM2.5 concentration in areas with more prevalent AC use (138). As our study
enrolled participants from across the US, AC prevalence is likely variable and an
interesting factor to consider in the health effects of ambient air pollutants in this
population.
Despite the expansive literature on healthcare usage and air pollution exposure, our
results are not directly comparable to other findings. Differing analytic strategies and
alternate study designs impede straightforward comparisons to other studies. However,
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results from other studies show clear evidence of increased healthcare utilization as a
consequence of ambient air pollutant exposures. Specifically, studies by Ko et al. and
Medina-Ramon et al., demonstrated that ambient ozone and PM concentrations were
associated with increased respiratory and COPD admissions (139; 140; 117). This
relationship was even present in studies examining the effects of low ambient ozone
levels among the elderly (141). The key difference between those studies and ours is that
they relied on primarily on daily admissions as well as daily pollution data, focusing on
the short-term effects of air pollutants. This allowed them to examine daily fluctuations in
admission rates, controlling for weather and day of the week, among other factors.
Variability in our results can be explained in part by the fact that our study did not
employ such detailed healthcare data. It also only considered the long-term effects of
ambient pollutants on total healthcare usage.
Strengths
To our knowledge, our study is the only one to examine the long-term effects of ambient
ozone and PM2.5 in individuals with severe emphysema. It utilized a nationally
distributed sample of subjects with varying levels of exposure. It also allows for the
inclusion of several subject-specific variables, such as age, sex, race, educational level,
and BMI. Many of the previous time-series studies examining the relationship between
hospital admissions and air pollutants have not been able to incorporate individual-level
demographic variables in their models, limiting their ability to control for potential
confounders and/or effect modifiers. It is well established that meteorological factors
have a large influence on pollutant concentrations (15). Unlike time-series studies, our
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research is less subject to the effect of season and other meteorological factors, which are
not a concern when considering long-term exposures. Our study considered both mean
and cumulative exposures in our analyses, allowing for the characterization of long-term
exposure in two different ways. Our utilization of ZIP and negative binomial modeling
techniques enabled us to assess the effects of pollutants on hospitalization data with
highly skewed distributions.
Limitations
There were several limitations in our study. As mentioned previously, the small number
of events in our sample probably may have made it difficult to identify significant
findings. Lack of information on the date of admission as well as the reason for
admission was another weakness of our study. Missing data on admission dates limited
our ability to investigate the short-term effects of ambient concentrations. Specifically,
same-day and lagged exposures could not be included as predictors in our analyses. In
addition, lack of information on the reason for the hospital admission or ED visit did not
allow us to consider respiratory-specific healthcare measures. The non-specificity of the
healthcare utilization indices makes it harder to conclusively state that the effects that
were observed corresponded with COPD exacerbations. However, it is important to note
that admissions diagnoses are not always reliable and can be subject to misclassification
(44). Doctor visits, which are by nature non-urgent, may be a result of routine care rather
than worsening of symptoms or increase in respiratory morbidity. This may make
healthcare utilization less likely to be pollutant-related. Lastly, dependent censoring is a
significant limitation in our healthcare usage analyses. Individuals who dropped out of
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the study may have had worse health outcomes and consequently higher healthcare
utilization rates than those remaining in the trial.
Survival Analysis
In terms of all-cause mortality, mean ozone was most strongly associated with increased
risk of death among NETT participants. LVRS assignment did not appear to confer a
protective effect for either pollutant. Mortality risk in high-risk subjects was greater than
in all subjects; however, increased mortality risk was only significant in response to mean
ozone exposure. Cumulative levels of ozone and PM2.5 were not associated with
increased risk of death. There was a slight but significant inverse association between
cumulative pollutant exposure and mortality risk. Our failure to observe any positive
effect between PM2.5 and risk of death could be due to confounding by variables such as
mean temperature, AC use, or level of industrial activity. The use of four delineated
regions may not have been detailed enough in controlling for these potential effects.
Previous epidemiologic research has shown increased mortality risks in response to
chronic PM2.5 exposures, but these studies have been limited to the elderly and the
general population (142; 120; 143). Our study is the only one to examine the effects of
long-term PM2.5 exposure in a cohort with severe COPD. Although we did not observe
any increased risk of death with respect to mean or cumulative PM2.5 concentrations, it is
an indication that further research is warranted and that the characterization of particulate
pollutant-related mortality in COPD subjects may be more complex than previously
anticipated. Mortality resulting from long-term ozone exposure in COPD subjects has
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only been reported in one study to date. Although in agreement, our mortality risk
estimates for mean ozone are markedly higher than those noted by Zanobetti et al. In their
assessment of mortality risk in response to mean ozone in susceptible cohorts, they
reported an increase of 7% for subjects diagnosed with COPD (144). The degree of
disease severity in our cohort could help explain why our estimates are so much larger
than those observed by Zanobetti et al. Lastly, the lack of survival benefit for the LVRS
arm was surprising but in agreement with findings from the original trial. Although
survival was improved for some NETT participants (i.e. upper-lobe predominant
emphysema and low baseline exercise capacity), there was no overall LVRS-related
survival benefit when the analyses were extended to all participants (10).
Strengths
There were several key strengths in our study. First, we were able to examine mortality
risk by year of study. This is particularly important since the mortality risk profile for
these participants was variable from year to year, as evidenced by the overall Kaplan-
Meier curve (Appendix A). Constructing an overall survival analysis model for the entire
duration of the study would not have allowed us to see how much mortality risk could
fluctuate in this cohort, specifically with respect to mean ozone concentrations. Secondly,
we were able to include several important individual-level covariates in our models, such
as BMI, which has been categorized as a significant risk factor in this type of study (142).
Lastly, the nature of our data allowed us to examine the long-term effects of PM2.5 and
ozone across a large geographical area.
112
Limitations
The presence of residual confounding is an important limitation in our study. Although
our models included a number of important covariates, factors such as temperature, level
of industrial activity, oxygen usage and prevalence of AC use could have been important
additions to our models. PM2.5 concentrations are greatly influenced by factors such as
traffic level and industrial processes; although these particles are highly penetrable,
indoor levels can vary depending on AC usage (145). In addition, our study only
considered mortality over the course of the study. It would be interesting to see if
mortality rates change over a longer follow-up period. Although we observed a
significant association between mean ozone and mortality risk, we did not limit ozone
exposure to ozone seasons. Future analyses may want to consider mean warm season
ozone levels as the primary pollutant since participants are more likely to experience
elevated levels during this time frame. Stratification by year allowed us to look at
changing mortality risk by year of study, but it sometimes resulted in thin strata and
unreliable mortality estimates, particularly with respect to analyses in the high-risk
subgroup. Lastly, we did not consider the effect of multiple pollutants in our analyses.
Future directions for this research may include an assessment of the effect of co-exposure
to ozone and PM2.5 on mortality risk in this cohort.
Conclusions
In terms of healthcare utilization patterns, further research is needed to assess the effects
of ambient pollutants on healthcare usage indices. We found some evidence to suggest
113
that increasing cumulative exposures could increase the likelihood of doctor visits;
however, these results were not conclusive. In terms of mortality risk, increasing mean
levels of ambient ozone appear to significantly impact survival among individuals with
existing respiratory disease. These results suggest that in such susceptible populations,
elevated mortality risk may be present even at concentrations below the accepted
National Ambient Air Quality Standards.
114
Chapter 8: Concluding Remarks
8.1 Statistical Considerations
Clustering
In our analyses, we accounted for clustering at the ZIP code level through either the
inclusion of a random effect in our mixed effects models (Chapter 6), or a nuisance
factor, in our GEE analyses (Chapter 7). In our study, we had 1218 subjects and 1128 ZIP
codes. 90 subjects were found to have shared ZIP codes. Of these ninety ZIP code
clusters, the largest cluster size was 3. Consequently, there were a total of 1128
participants with unique ZIP codes in our dataset with their cluster size set at 1. Thus, the
sparseness in the data was relatively high.
There are several approaches to consider when dealing with sparsely clustered data. One
option is to ignore the clustering and treat each individual independently. In doing so, the
potential correlation between subjects is not considered as an important factor. However,
ignoring correlation when present results in smaller standard errors, narrower confidence
intervals and lower p-values. Lower p-values and narrower confidence intervals increase
the likelihood of Type I errors (146). A second option is to treat the correlation as a
nuisance parameter with the use of a robust variance estimator. This method provides
similar parameter estimates as the first option, which treats each individual
115
independently, although it corrects the standard errors, confidence intervals and p-values
for the clustering effect. A disadvantage of this method is that the between cluster
variability is not factored into the parameter estimate (147). The third option specifies a
random effect for the cluster, which accounts for both the correlation within clusters as
well as the variability between clusters. This option is the most optimal, as it provides
parameter estimates that factor in the within cluster correlation as well as estimates for
the between cluster variability (148). Unfortunately, this approach can be
computationally intensive. It is also sensitive to variance-covariance assumptions. Thus,
the selection of an appropriate method is dependent on several considerations. If we are
primarily interested in modeling cluster-specific effects, selecting a mixed effects
approach is optimal. If the cluster variability is not an important consideration in the
analyses, but we still want to correct our estimates for its effect, then a GEE modeling
approach is preferable (149).
There is no clear consensus in the literature regarding as when to account for clustering.
Some researchers have advocated disregarding clustering if there are fewer than 5
subjects per cluster (150). Others suggest that at least 15 to 30 subjects per cluster are
needed in order to consider clustering statistically important (151). In general, there
appears to be more emphasis on the number of clusters needed rather than on the size of
the groups (150). As we had over 1000 ZIP codes in our data, a sufficient number of
clusters was not a concern. Although clustering was minimal, we chose to account for it,
either explicitly and implicitly, inour analytical models.
116
Statistical Limitations
In the present study, accounting for the clustering resulted in several complications in our
analyses. In the first aim, we employed a GEE multilevel modeling approach to examine
the associations between ambient pollutants and individual and area level SES factors.
The initial model was specified as a random effects mixed model, with a random effect
specified for the ZIP code. However, this model failed to converge numerous times and a
GEE estimation method was chosen as an alternative approach. Because our primary
interest was on the marginal, rather than the cluster-specific effects, opting for a GEE
approach was a suitable alternative. For the second aim, we were interested in modeling
the association between lung function and respiratory symptoms with pollutant
concentrations. The original mixed effects model was designed to include random effects
for the subject as well as the ZIP code. However, this option was too computationally
intensive to model efficiently in SAS. The final models specified a random effect for the
ZIP code and within patient correlations were accounted for by modeling the variance-
covariance of the residual errors. In the third aim, we had initially planned to model
mortality risk with the use of a frailty model – which allows the inclusion of a random
effect for the cluster. Instead, we chose to model survival probability with a GEE
approach and treat the clustering at the ZIP code level as a nuisance parameter. We also
examined healthcare utilization patterns in the third aim. These were modeled with ZIP
Poisson and negative binomial GEE models. Both models did not allow for the
specification of random effects. Consequently, we chose to account for the potential
117
correlation within subjects residing in the same ZIP code with a nuisance parameter,
rather than explicitly model the variability by cluster.
Statistical procedures with respect to clustered data involve a variety of approaches.
Flexibility in the selection of statistical approaches is necessary in order to effectively
model outcomes of interest. Opting for a random effects or GEE estimation approach
would depend factors such as computational intensiveness, model reliability, particularly
with respect to variance-covariance assumptions, and the purpose of the analysis.
8.2 Exposure Measurement Error
Throughout this dissertation, we referred to exposure in terms of ambient air pollutant
concentrations. There are several sources of measurement error associated with using
ambient concentrations of PM2.5 and ozone as ‘exposures’. First, actual exposure
intensity is a factor of both concentration and duration of exposure (65). Because
information on time-activity patterns for the NETT population was not available, the true
exposure intensity and subsequent health risk could have varied greatly from individual
to individual. Second, the use of summary measures such as cumulative and mean
concentrations were approximations of individual level exposure but may not have been
adequate proxies for the actual exposure. Lastly, pollutant composition, particularly with
respect to PM2.5, could have varied considerably, based on factors like traffic density and
number of point sources of pollutants.
118
Time Activity Patterns
Variables such as amount of time spent indoors are important to consider in exposure
estimation. It is well known that indoor exposure concentrations can differ considerably
from ambient levels (137). Because disease severity among this group of subjects was
quite pronounced, it is likely that a significant portion of their day would have been spent
indoors. However, even in an indoor setting, the presence of different microenvironments
(e.g. bedroom, car, hospital) could also factor into aptly assessing true exposure. Because
data were not available, our analyses did not attempt to quantify percent of time spent
outdoors. Instead our models are based on the assumption that ambient exposures closely
approximated cumulative exposures (outdoor and indoor exposures).
Summary Measures of Exposure
In our selection of summary exposure measures, we made the key assumption that
exposure estimates were proportional to health risk. We relied primarily on mean and
cumulative concentrations to test for associations between exposure levels and a variety
of outcomes. In section 3.3, we discussed how biological mechanisms are an important
consideration in deciding on the appropriate type of summary measure to employ. Some
researchers have advocated the use of dosimetric modeling as an alternative to summary
exposure measures. Dose metrics rely on detailed exposure histories, and variables
related to biological processes (e.g. clearance rates) to construct likely exposure-response
profiles for subjects (65; 66). This is particularly useful in instances where risk is variable
119
over time and not directly proportional to dose. Employing such a model may be an
important way to increase the sensitivity of air pollution models in epidemiologic studies.
Particulate Composition
Much of the extant literature on health effects and PM exposure emphasize the total mass
particle rather than the composition of the particulate. PM components such as sulfate,
nitrate, silicon and organic carbon matter can translate to differential toxicities between
particles and likely, differential risk (152). Differential toxicities of PM components have
been demonstrated in a number of toxicological studies, although these have been less
well investigated in epidemiologic studies (50). Findings from epidemiologic studies
have revealed that constituents of PM can lead to increased hospital admissions and
respiratory morbidity, particularly for subjects with existing respiratory disease (50; 152).
Our study assumed that similar PM2.5 concentrations between individuals translated to
similar health-related exposure risks. The considerable heterogeneity in particulates
complicates the true characterization of exposure concentrations experienced by
individuals. Furthermore, the high correlation between several of these components
makes it difficult to assess their independent effects on health outcomes (153).
8.3 Conclusions
This dissertation presented several important findings on the effects of air pollution
exposure among subjects with severe emphysema, namely with respect to environmental
justice, respiratory morbidity and mortality.
120
Differential Exposure by Socioeconomic Status
There was evidence of differential air pollution exposure by area-level measures of SES.
This finding adds to the literature on environmental justice and indicates that SES-related
inequalities in air pollution exposure could lead to increased adverse health outcomes,
particularly among individuals with existing respiratory disease.
Pulmonary Function and Respiratory Symptoms
We found that lung function outcomes were impacted by relatively low levels of ambient
air pollutants. Although the effects could be characterized as small to modest, they were
still statistically significant. It remains unclear if the decline in pulmonary function was a
direct result of ambient pollutants or just characteristic of the natural worsening of
pulmonary function in these subjects. Among individuals with emphysema, this decline
over time is larger and faster than in healthy subjects; even among those with COPD,
there is considerable heterogeneity in the rate of decline (114). As indicated in Tables
6.4-6.10, there was little evidence of worsened symptoms in response to air pollutants.
The magnitude of change in respiratory morbidity may have been easier to evaluate with
outcomes such as FEV1 and FVC than with the SGRQ score. Modest changes in
spirometric measures may not translate to noticeable worsening of symptoms, given the
presence of existing severe respiratory morbidity. Lung surgery did appear to confer a
protective effect against air pollution exposure to participants with respect to certain
spirometric measures.
121
Healthcare Utilization Patterns
There was little impact of air pollution exposure on healthcare usage among NETT
participants. The rarity of events such as ED visits may have made it difficult to examine
associations effectively. However, our study was the first to consider the chronic effects
of ambient air pollutants on healthcare utilization patterns in patients with severe
emphysema. In epidemiologic studies with larger cohorts, investigators may want to
consider how long-term air pollution exposures could affect hospitalization rates in
individuals with and without existing respiratory disease. Differences in healthcare
utilization rates were indicative of protective effects from LVRS treatment.
Mortality
Increased mortality risk was significantly associated with mean ozone but not PM2.5
concentrations (see Tables 7.6 and 7.8). Our results suggest that in such susceptible
populations, elevated mortality risk may be present event at concentrations below the
accepted US EPA National Ambient Air Quality Standards. LVRS assignment did not
appear to confer a survival benefit in either all participants or high-risk subjects.
Public Health Implications and Future Considerations
This dissertation research has both clinical and public health implications. Although
exposure was estimated at the group level (i.e. ZIP code), the use of individual level
variables in our analytical models increased the inferential validity of the research. We
assessed the impact of air pollution on lung function, elucidating the role of both type and
level of air pollutants in the progression of emphysema. We also found that mortality risk
122
among emphysematous subjects was impacted by mean ozone exposure. We expect this
research to bring increased attention to a largely preventable disease which results in the
premature death of millions of people worldwide. The ubiquity of air pollution reinforces
the need for more research on its effects on the development and exacerbation of
emphysema.
The inclusion of air pollution data from environmental databases is a promising and
important consideration for many types of studies. The application of a similar
methodological approach could help assess the impacts of ambient air pollution exposure
on not only individuals with respiratory disease, but on a wide range of other health
conditions.
123
Appendix A. Kaplan-Meier Mortality Estimates for All NETT Participants
124
Appendix B: Spatial Mismatch between ZIP Code Areas and ZCTAs.
a) Postal ZIP code boundaries b) b) ZCTA boundaries from the 2000 US Census c) c) overlay of postal ZIP code and the ZCTA boundaries in “On the use of ZIP codes and
ZIP code tabulation areas (ZCTAs) for the spatial analysis of epidemiological data” by Grubesic, TH and Matisziw, TC, 2006, International Journal of Health Geographics, 5(58), p. 13.
125
Appendix C. Decline in FEV1 and FVC over Study Duration, by Treatment Arm and Sex
126
127
128
Appendix D. Distribution of Total Healthcare Utilization Patterns
129
Distribution of Emergency Room Visits
0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 0
10
20
30
40
50
60
70
Total Number of Emergency Room Visits
Perc
ent
130
Distribution of Hospital Admissions
2.5 7.5 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 0
20
40
60
80
100
Total Number of Hospital Admissions
Perc
ent
131
Distribution of Doctor Visits
2 6 10 14 18 22 26 30 34 38 42 46 50 54 58 62 0
5
10
15
20
25
30
35
Total Number of Doctor Visits
Perc
ent
132
Appendix E: St. George’s Respiratory Questionnaire
133
134
135
136
137
138
139
140
141
142
143
144
145
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