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Smoking history in AATD-associated COPD

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Title:
Smoking history in AATD-associated COPD differences in demographic and psychosocial features between never smokers and former smokers
Uncontrolled:
Differences in demographic and psychosocial features between never smokers and former smokers
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Fekri, Shiva ( author )
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English
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1 electronic file (86 pages) : ;

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Lungs -- Diseases, Obstructive ( lcsh )
Lungs -- Diseases, Obstructive ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Chronic Obstructive Pulmonary Disease (COPD) is a category of diseases that are identified by progressive and chronic airflow obstruction. Alpha-1 Antitrypsin Deficiency is a genetic disorder characterized by a deficiency or lack of the antiprotease, alpha-1 antitrypsin, leading to the development of COPD. Data were collected as part of a larger study that was conducted by Dr. Kristen Holm and funded by the Alpha-1 Foundation. Participants were recruited through the Alpha-1 Foundation Research Registry using a mailed questionnaire. This study is the first to look specifically at the psychosocial impact of smoking history in a sample of individuals with AATD-associated COPD. The association between smoking history and the unique health, psychosocial, and perceptual characteristics of patients with AATD-associated COPD was examined. Smoking history consistently predicted 3 out of the 4 outcome variables: characterological self-blame, behavioral self-blame, and perceived family blame among individuals with AATD-associated COPD. Perceived family criticism was not predicted by smoking history.
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Includes bibliographical references.
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System requirements: Adobe Reader.
Statement of Responsibility:
by Shiva Fekri.

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University of Florida
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All applicable rights reserved by the source institution and holding location.
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985116203 ( OCLC )
ocn985116203
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LD1193.L645 2016m F45 ( lcc )

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Full Text
SMOKING HISTORY IN AATD-ASSOCIATED COPD: DIFFERENCES IN
DEMOGRAPHIC AND PSYCHOSOCIAL FEATURES BETWEEN NEVER SMOKERS
AND FORMER SMOKERS By
SHIVA FEKRI
B.A., Yeshiva University, 2009
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Arts Clinical Psychology
2016


2016
SHIVA FEKRI ALL RIGHTS RESERVED
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This thesis for the Master of Arts degree by Shiva Fekri
has been approved for the Clinical Health Psychology Program by
Kristin Kilbourn, Chair Kristen Holm Barbara Walker
Date: July 30, 2016


Fekri, Shiva (M.A. Clinical Psychology)
Smoking History in AATD-Associated COPD Thesis directed by Associate Professor Kristin Kilbourn
ABSTRACT
Chronic Obstructive Pulmonary Disease (COPD) is a category of diseases that are identified by progressive and chronic airflow obstruction. Alpha-1 Antitrypsin Deficiency is a genetic disorder characterized by a deficiency or lack of the antiprotease, alpha-1 antitrypsin, leading to the development of COPD. Data were collected as part of a larger study that was conducted by Dr. Kristen Holm and funded by the Alpha-1 Foundation. Participants were recruited through the Alpha-1 Foundation Research Registry using a mailed questionnaire. This study is the first to look specifically at the psychosocial impact of smoking history in a sample of individuals with AATD-associated COPD. The association between smoking history and the unique health, psychosocial, and perceptual characteristics of patients with AATD-associated COPD was examined. Smoking history consistently predicted 3 out of the 4 outcome variables: characterological self-blame, behavioral selfblame, and perceived family blame among individuals with AATD-associated COPD. Perceived family criticism was not predicted by smoking history.
The form and content of this abstract are approved. I recommend its publication.
Approved: Kristin Kilbourn
IV


DEDICATION
To my Joshua, my everlasting mate in unconditional love and faith. You are my inspiration, my sunlight, and my ocean.
And little Banana, whose snuggles, sighs and licks remind me that life is fundamentally simple and sweet.
v


ACKNOWLEDGEMENTS
Thank you, Kristen Holm, for generously providing me with this wonderful dataset and for sitting with me tirelessly, kindly and patiently, molding this project and molding me as a graduate student. You are a major force in the manifestation of my efforts in this program.
Thank you, Kristin Kilbourn, for advocating for me, being present with me, mentoring me, and always believing in me. My favorite times in the program have been working by your side.
Thank you, Barbara Walker, for introducing me to the biopsychosocial model, for pushing me to be a better writer, and for pushing me to evaluate myself with humility and honesty.
Thank you to my mother, Rachel Giilen, and to Eliyahu for your emotional and financial support during this endeavor. And to my Bijili, Daniel, for accepting that I cannot be close but that Im never far.
Thank you to my Baba, Amir Fekri, for rousing and cultivating my intellect, and for loving me with all of your heart and soul.
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TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION...................................................... 1
Chronic Obstructive Pulmonary Disease.......................... 1
AATD-Associated COPD........................................... 2
Defining Smoking History......................................4
Gender Differences in Smoking Behavior in COPD................. 5
Smoking and AATD-Associated COPD............................... 5
Prognosis and Treatment of AATD-Associated COPD................ 8
Physical Adjustment to AATD....................................13
Psychosocial Adjustment to AATD............................... 14
Self-Blame.................................................... 16
Demographic, Socioeconomic Status, and Health................. 19
Family Support and Health......................................22
Family Criticism, Family Blame, and Health in AATD............ 23
Conclusion.....................................................25
II. AIMS AM) HYPOTHESES.............................................. 27
Aim 1..........................................................27
Aim 2..........................................................27
Aim 3......................................................... 28
III. METHODS.......................................................... 29
Participants and Procedures....................................29
Measures.......................................................31
Demographics & health characteristics................... 31
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Smoking behaviors.........................................32
Self-blame............................................... 32
Family blame and family criticism.........................33
Statistical Analyses...........................................33
Preliminary Analyses..................................... 33
Analyses for Aim 1........................................34
Analyses for Aim 2........................................34
Analyses for Aim 3........................................35
IV. RESULTS........................................................... 37
V. DISCUSSION........................................................ 47
REFERENCES.............................................................. 59
APPENDIX
MegaQuest Questionnaire items used in this project.................. 73
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LIST OF TABLES
TABLE
1. Characteristics of Sample..................................................38
2. Characterological and Behavioral Self-Blame............................... 41
3. Results of logistic regression models to predict characterological and behavioral
self-blame.................................................................42
4. Perceived Family Blame and Perceived Family Criticism...................... 44
5. Results of logistic regression models to predict family blame & family
criticism..................................................................45
IX


LIST OF FIGURES
FIGURE
1. Recruitment flow diagram......................................30
x


LIST OF ABBREVIATIONS
AAT Alphal Antitrypsin
AATD Alphal-Antitrypsin Deficiency
CDC Center for Disease Control and Prevention
COPD Chronic Obstructive Pulmonary Disease
FEV1 Force Expiatory Volume at 1 second
FVC Forced Vital Capacity
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CHAPTER I
INTRODUCTION
Chronic Obstructive Pulmonary Disease
Chronic Obstructive Pulmonary Disease (COPD) is a category of diseases that are characterized by progressive and chronic airflow obstruction. In this category are emphysema, chronic bronchitis, and asthma. The CDC has found that prevalence of COPD in the United States ranges from less than 4% in some states and up to 9% in others (Centers for Disease Control and Prevention [CDC], 2014). It is estimated that 14.2 million people have been diagnosed with COPD in the United States, and it is the 3rd leading cause of mortality in the U.S. with 149,205 deaths from COPD or chronic lower respiratory diseases per year (CDC, 2014c). The total national medical costs attributed to COPD were estimated to be $32.1 billion in 2010, with an additional cost of $3.9 billion in loss due to absenteeism, bringing the total national cost to $36 billion dollars (Ford, Murphy, Khavjou, Giles, Holt, & Croft, 2015). The average age of onset for COPD is 53 (CDC, 2014c).
COPD is characterized by dyspnea (pronounced disp-nee-uh) upon exertion, coughing or wheezing, sputum and mucus production, frequent respiratory infections, and at later stages, fatigue and weight loss. Spirometry is a test that is used to help diagnose COPD. It generates two measures of lung capacity for diagnosis: forced vital capacity (FVC) and forced expiratory volume at one second (FEV1). FVC is the total amount of air that is exhaled in one breath. COPD is confirmed when FVC drops to below 70% (Petty, 2004). FEV1 is the amount one is able to exhale in the first second, and it is used to determine the stage of COPD, of which there are four: 0 At Risk, I Mild COPD, II Moderate COPD, III Severe COPD. Prognosis in COPD is variable depending on the stage of COPD,
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exacerbations of COPD, continued smoking, and status of comorbid medical conditions or other risk factors such as Body Mass Index (BMI) and exercise capacity.
COPD is caused by a combination of factors, but 85% of cases of COPD are attributable to smoking (Holm, LaChance, Bowler, Make, Wamboldt, 2010). Fifteen to twenty percent of smokers develop COPD (Ioachimescu & Stoller, 2005). In combination with and in addition to smoking, factors leading to COPD are exposure to environmental pollutants and genetic predisposition. The most commonly identified genetic risk factor for COPD is alpha-1 antitrypsin deficiency (AATD). Individuals with AATD develop COPD at an earlier age than people who do not have this genetic condition.
AATD-Associated COPD
Alpha-1 Antitrypsin Deficiency is a genetic disorder that was first identified in 1963, and it is characterized by a deficiency or lack of the antiprotease, alpha-1 antitrypsin. An antiprotease is an agent that blocks the activity of a particular protease. A protease is an enzyme that lyses proteins. Neutrophil elastase is a protease that is released by neutrophils (a type of white blood cell that is part of the immune response), which are located in the lower respiratory tract. When neutrophils release neutrophil elastase (NE), it causes the breakdown of proteins in the extracellular matrix that makes up the structure of the lung tissue. The breakdown of these proteins creates an increase in airspaces in the tissue, and over time, the development of emphysema and COPD (McElvaney, Stoller, Buist, Prakash, Brantly, Schluchter, Crystal, Alpha-1 Antitrypsin Deficiency Registry Study Group, 1997).
Alpha-1 antitrypsin (AAT) is coded by the SERPINA1 gene on the autosomal chromosome number 14, making AATD an autosomal codominant disorder. Over 150 alleles of AAT have been identified and they make up four phenotypes or physiologic manifestations of the genotype: normal normal alleles, normal functioning; deficient -
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lower plasma AAT levels; null no AAT detectible in plasma; dysfunctional AAT present but the protein is ineffective. The M allele is most common allele for AAT and is what codes for normal functioning of the SERPINA1 gene. The S allele codes for producing lower levels of the protein, and it causes mild deficiency. The Z allele produces very low levels of the protein and causes severe deficiency. As an autosomal codominant disease, people have two copies of the gene. Patients who have one copy of the Z gene are called carriers, because they may display symptoms later on in life, but they are more likely to become symptomatic if they smoke.
Alpha-1 antitrypsin is produced by liver cells, and in the case of AATD, the alpha-1 antitrypsin that is produced is not adequately released, which causes damage to the lung tissue where it is needed, but also causes damage to the liver tissue where it begins to build up, particularly in individuals with the more severe variants of the AATD alleles. Liver damage can result in neonatal hepatitis and cirrhosis (Buist, 1990). It is estimated that 4-6% of the Caucasian population in the United States carries the allele for AATD (de Serres, 2002).
Many individuals with AATD are initially asymptomatic and are unaware that they may develop a serious medical condition (Seersholm & Jensen, 1998). The initial symptoms of the condition are common pulmonary symptoms such as dyspnea with and without exertion, coughing and mucus/sputum production, which are often misdiagnosed as asthma or allergies (McElvaney, et. al. 1997). Those with AATD are often diagnosed and treated by primary care physicians, allergists, and internists as opposed to pulmonologists, who are more familiar with the condition (McElvaney, et al. 1997; Stoller, Strange, Schwarz, Kallstrom, Chatburn, 2014). Thus, many individuals with AATD are often diagnosed with
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the condition many years after the onset of their initial pulmonary symptoms (Campos, Wanner, Zhang, Sandhaus, 2005; Stoller & Brantly, 2013).
Compared to the average age of COPD onset of 53, individuals with AATD will develop COPD between the ages of 20 and 50 years old. The variety of alleles of AATD in combination with behavioral factors (e.g. smoking, comorbid health conditions) and environmental factors (e.g. other pollutants or insults to lung tissue) are what account for the large variance in age of COPD onset. According to Kelly, Greene, Carroll, McElvaney & ONeill (2010), non-smoking individuals with AATD develop COPD at 50 to 60 years old, whereas smokers with AATD develop COPD between 20 and 40 years of age. A primary determinant of age at which COPD develops is an individuals smoking history.
Defining Smoking History
According to the CDCs Behavioral Risk Factor Surveillance System (CDC, 2014a), smoking 100 cigarettes or more in ones lifetime constitutes a history of smoking. A study on the genetic epidemiology of COPD is the COPDGene study (Reagan et al. 2010.) It has been enrolling smokers and non-smoker controls with and without COPD and studying the genetic factors, phenotypes, disease etiology, and progression of COPD. The largest study of its kind, it has established criteria for clinically significant smoking history. The inclusion criteria specify smoking history as having a minimum of 10-pack-years in their lifetime (Putcha, et al. 2014.) Pack-years is a measure of extent of smoking history and is calculated by multiplying the number of packs of cigarettes smoked per day by the number of years the person smoked.
Gender Differences in Smoking Behavior in COPD
In a publication resulting from the COPDGene study, researchers found that the female sex was significantly associated with early-onset COPD (Foreman et al. 2011).
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Campos et al. (2009) also found that female patients with AATD-associated COPD tend to have more frequent exacerbations. Another study from the COPDGene dataset found that the female sex was the most significant predictor of lung tissue that is more vulnerable (e.g. lower measurements in wall area percentage, lumen area, inner diameter, and wall thickness) to insults and subsequent pulmonary disease (Kim et al. 2011). This evidence suggests that not only are women more likely to develop COPD earlier, but it may take less exposure to smoking to cause serious damage to the lung tissue as compared to the amount of exposure that males can sustain before having similar levels of damage.
Smoking and AA TD-Associated COPD
COPD is often defined as an irreversible disease because, unlike asthma, it is thought to be unresponsive to bronchodilators, which are medications that reduce resistance in airflow by dilating the bronchioles and bronchi. However, patients with COPD often do show a positive response to bronchodilators, as measured by improvement in FEV1, when they stop smoking (Petty, 2004). Research has found that cigarette smoking accelerates the onset of dyspnea by up to 19 years in patients with AATD-associated COPD (Ioachimescu & Stoller, 2005). Therefore, a major barrier to improvement in lung function in COPD and AATD-associated COPD patients is persistence of smoking (Bednarek et al. 2006; Ioachimescu & Stoller, 2005; Jimenez-Ruiz et al. 2001; Tashkin & Murray, 2009). Given the connection between smoking and the acceleration and exacerbation of pulmonary disease in AATD (Petty, 2003), it is important to understand the factors associated with smoking behavior in individuals with AATD-associated COPD and other chronic illness populations.
In studies on smoking cessation in patients with head and neck cancers, several variables were found to contribute in varying degrees to smoking cessation rates: tumor stage, gender, age, marital status, education level, tumor site, treatment modality, and
5


tobacco use history (Ark, DiNardo & Oliver, 1997). In their own study on smoking cessation in head and neck cancer patients, Ark, DiNardo and Oliver (1997) found that persistence in smoking was more common in older adults. They also found that smoking cessation rates were higher in women (82%) than in men (61%). People were more likely to quit when having more intensive treatment modalities for their head and neck cancer, such as surgery, with the highest quit rate in the study being among patients who had a total laryngectomy (95%). Stage of tumor trended but was not statistically significant in impacting quit rates.
Ark and colleagues also found that the median age of onset of smoking was higher in successful quitters, and the median number of cigarettes smoked per day was less in successful quitters. A study on the characteristics of successful quitters (Lee & Kahende, 2007) found that they tended to have no smoking rules at home, were less likely to have switched to lighter options (e.g. light-tar cigarettes), were aged 35 and older, were married or had a domestic partner, were non-Hispanic White, and had a college education.
To date, there are only a limited number of studies examining smoking behavior in those with AATD associated COPD, and there is limited information on smoking rates in this population. A study by Carpenter and colleagues (2007) examined the impact of genetic testing for AATD on smoking cessation. They reported that 59% of individuals with the severe genotype of AATD attempted to quit smoking within the first 24 hours of receiving their genetic test result. In comparison, a 26% quit attempt rate was seen in individuals who tested negative for the AATD genotype, and a 34% quit attempt rate was seen in individuals who were carriers. No significant differences in abstinence rates were found between the three groups at the three-month follow-up. This indicates that while there may be greater motivation for smoking cessation closer to the time of diagnosis, the increased level of motivation to quit smoking that was observed in those diagnosed with AATD was not
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sustained long term, suggesting that other factors such as environment, support from family, and various cognitions are associated with tobacco quit attempts (Carpenter et al., 2007).
In a study on smoking cessation in COPD, Bednarek and colleagues (2006) found that smoking cessation advice had a greater impact on patients who were experiencing more airway obstruction, suggesting that the discomfort and anxiety caused by airway obstruction motivated patients to change their smoking status (Bednarek et al. 2006). Parkes Greenhalgh, Griffin, & Dent (2008) found that notifying patients of their lung age, based on spirometry values and adjusted for gender and height, was associated with better quit rates than only notifying them of their FEV1 value from spirometry. One study that highlights what may be a major underlying problem in smoking cessation in people with COPD found that smokers with COPD had greater nicotine dependence than smokers without COPD (Jimenez-Ruiz et al. 2001). This may be a factor in why the average long-term quit rate among patients with COPD is only 25% (GOLD, 2013; Klinke & Jonsdottir, 2014). Another factor is that comorbid emotional disorders can be a barrier in successful smoking cessation. Wilson (2006) states that 25% of people with severe COPD are also depressed, while 19.6% of people with mild COPD are depressed. According to Tashkin and Murray (2009) psychiatrists state that smoking has antidepressant affects in depressed people. Smoking is also perceived by COPD patients to have a calming effect (Coronini-Cronberg, Heffernan & Robinson, 2011), which is an added barrier to smoking cessation in this medical population.
In a study on family factors impacting smoking status in COPD, researchers found that unsupportive family relationships were associated with high psychological distress, and psychological distress, in turn, was associated with smoking status (Holm, LaChance,
Bowler, Make, Wamboldt, 2010). The researchers concluded that in targeting smoking in
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patients with COPD it is necessary to target both psychological distress and family relationship function.
Prognosis and Treatment of AATD-Associated COPD
An important factor in treatment and management of AATD-associated COPD is accurate and timely diagnosis (Craig, 2015). Studies have shown that approximately 10,000 of the estimated 100,000 individuals with AATD in United States, or 10%, have been diagnosed (Stoller, Snider, Brantly, Fallat, Stockley, 2013), indicating that diagnosis of AATD remains a major challenge (Stoller, et al 2014). Patients with pulmonary symptoms stemming from AATD are typically diagnosed with asthma, treated with multiple courses of antibiotics, and are evaluated for gastroesophageal reflux, sinusitis or post-nasal drip (Izaguirre, Lanza, & Bryd, 2014). The average time from onset of symptoms to diagnosis of AATD is 8.3 +/- 6.9 years (Campos, et al 2005).
Individuals with COPD are extremely susceptible to pulmonary infections, viral and bacterial, due to certain features of the disease. Pulmonary illnesses, including chronic bronchitis, COPD, and AATD-associated COPD are all characterized by sputum production (Hill, Campbell, Hill, Bayley, Stockley, 2000). The respiratory tract has its own bacterial flora, which is kept in balance by the immune system and its inflammatory and antiinflammatory properties (Koby, 2007, p. 447). The bacteria in the sputum of patients with pulmonary disease reflect the health of the respiratory system. Studies have found that 20-40% of patients with COPD have positive sputum cultures for bacteria and viruses, even when they are clinically stable (Sykes, Madia & Johnston, 2007). These pathogens are not necessarily implicated in causing the bacterial or viral illnesses that trigger exacerbations, but they are part of why these patients are more susceptible to contracting the illnesses that lead to exacerbations.
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The colonization of sputum by pathogenic bacteria and viruses is understood to be due to an ineffectiveness of the immune system in the respiratory system to sterilize the airways (Hill, et al. 2000). In the case of a weakened innate or primary immune response, which can be due to chronic or long-term exposure to pathogens, the adaptive or secondary immune response is triggered, which enlists proinflammatory mediators that have a larger systemic impact (Hill, et al. 2000). Increased inflammation resulting from the adaptive immune response leaves a system more reactive and vulnerable to new pathogens. Increased inflammation also upregulates airflow obstruction, further exacerbating the pulmonary disease state. Illness exacerbation is one of the most common complications and contributors to medical and psychological morbidity in COPD (Campos, et al. 2009). An exacerbation causes a sharp decrease in lung functioning that can lead to morbidity, hospital admissions, and death (Wedzicha & Donaldson, 2003). Exacerbations can be caused by a bacterial infection, a cold, other viruses, pollutants, or other lung irritants, and in many cases, the etiology of the exacerbation is unclear. Studies have found that these exacerbations strongly impact health related quality of life and occur 2-3 times per year in patients who are non-smokers. In smokers, exacerbations occur the upwards of 3 to 5 times a year (Campos, Alazemi, Zhang, Wanner, Salathe, Baier & Sandhaus, 2009; Wedzicha & Donaldson, 2003). Campos et al. (2009) found that the patients with AATD-associated COPD who tend to have more frequent exacerbations have the following characteristics: female, younger age, have a smoking history, and are unemployed. These characteristics were also unaffected by whether or not they were receiving augmentation therapy, the common treatment for AATD.
Currently, there are three lines of treatment for AATD associated COPD (Ioachimescu & Stoller, 2005). The first line of treatments, or the less invasive and more commonly used treatments for COPD (AATD-associated and non AATD-associated) are the
9


use of inhaled and systemic anti-inflammatory agents, pulmonary rehabilitation, bronchodilators, and at later stages, oxygen therapy (Ioachimescu & Stoller, 2005; Kaplan & Cosentino, 2010). Patients are also encouraged to obtain yearly preventative vaccinations (e.g. flu and pneumonia vaccinations) and they are strongly advised to quit smoking (Ioachimescu & Stoller, 2005; Kaplan & Cosentino, 2010). These treatments and recommendations are commonly prescribed at the first onset of symptoms and are ideally adhered to for the rest of the patients life as a primary method of symptom management (Izaguirre, et al, 2014; MacDonald & Johnson, 1995).
The second line of treatment, and the only treatment specific for AATD-associated COPD is augmentation therapy. Augmentation therapy is weekly intravenous infusions of pooled human plasma containing alpha-1 antitrypsin. This procedure is done to boost the presence of AAT in the patients system (Campos, Alazemi, Zhang, Wanner, Salathe, Baier, & Sandhaus, 2009; Ioachimescu & Stoller, 2005). Findings are mixed on the effectiveness of augmentation therapy. One study by Seerholm, Wencker, Banik, Viskum, Dirksen, KokJensen, Konietzko (1997) found that in comparison to AATD patients not receiving augmentation therapy, AATD patients who received augmentation therapy had lower decline in FEV1, suggesting clinical benefit. Some studies state that augmentation therapy confers pulmonary anti-inflammatory benefits, which may reduce airflow obstruction and may reduce the number and severity of exacerbations (Ioachimescu & Stoller, 2005).
Other studies state, however, that augmentation therapy does not have significant effects on pulmonary function in AATD-patients and that even when improvement in comparison to placebo is observed, these observations fail to reach statistical significance (Dirksen, Dijkman, Madsen, Stoel, Hutchison, Ulrik, Skovgaard, Kok-Jensen, Rudolphus, Seersholm, Vrooman, Reiber, Hansen, Heckscher, Viskum, &. Stolk, 1999; Stockley, 2014).
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The premise behind augmentation therapy is that biochemically, replacing the deficient agent should prevent further breakdown of lung tissue, promote its preservation and its functionality. But this biochemical phenomenon has not demonstrated consistent clinical efficacy (Stockley, 2014). One argument that is made against studies that find augmentation therapy to be effective is that the range typically used to determine significant impact is FEV1 decline values between 35% and 60%. FEV1 decline is found to be most rapidly changing and declining in this range (Stockley, 2014), so conclusions based on changes in this range of values could be an inflated representation of the true effectiveness of augmentation therapy. Another argument against the effectiveness of augmentation therapy is that it is very expensive and cost-prohibitive. Consequently, it is more likely that individuals who receive this therapy have high socioeconomic status and typically have better health outcomes than people of lower socioeconomic status. Therefore, the existing data on the effectiveness of augmentation therapy are neither statistically compelling nor reflective of outcomes in the larger clinical population due to health disparities.
Another treatment option for AATD is gene therapy, which is introducing the needed genetic material or the effective version of the gene to liver cells. Drug therapy, an additional treatment option, is the administration of certain drugs that aim to reverse the emphesymatous changes to the lung tissue, or the pathological development of spaces in lung tissue causing ineffective gas exchange and susceptibility to lung infections (Ioachimescu & Stoller, 2005).
The third line of treatment for AATD-associated COPD is surgery and can involve lung transplantation or lung volume reduction surgery, which is when the non-functional parts of the lung are removed so that the healthy tissue can function better (Ioachimescu & Stoller, 2005). Liver transplants are also done in patients with AATD, but it is primarily done
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in people with the severe ZZ phenotype, which in addition to pulmonary illness, causes severe liver disease, fibrosis, and cirrhosis of the liver. 2% of infants or children with a ZZ phenotype develop serious liver disease, while about 19% of adults over the age of 50 with the ZZ phenotype will have develop liver damage through fibrosis, or accumulated scarring in liver tissue, leading to cirrhosis of the liver (Fregonese & Stolk, 2008).
AATD-associated COPD is the 4th indication for lung transplantation in the United States and worldwide (Giacoboni, Barrecheguren, Esquinas, Rodriguez, Monforte, Braveo, Pirina, Miravitller, Roman, 2015; Tanash, et al, 2014), and in the United Sates 9% of lung transplants are done for AATD-associated COPD (Stoller, Lacbawan, Aboussouan, 2006; Tanash, et al, 2014). 2,182 lung transplants were done world-wide for patients with AATD-associated COPD between January 1995 and June 2012. This was 5.8% of the total number of lung transplants that were conducted in this time period (Giacoboni, et al, 2015).
Candidates for lung transplantation are required to have 2 years or less of life expectancy, good nutritional status, and a strong and stable psychosocial profile (Tanash, et al 2014). The differences between AATD-associated COPD patients and non-AATD associated COPD patients who received lung transplants were that AATD-associated COPD patients tended to be younger and had less exposure to smoking than the non-AATD group (Giacoboni, 2015). Lung or liver transplantation is an option for patients who may be younger, have more serious progression of the illness and/or are at the end-stage of emphysema or liver disease (Stoller, at al 2006). Studies on patients who have undergone lung transplantation demonstrate improvement in survivorship and health status for 2-9 years post-surgery (Tanash, Riise, Hansson, 2011). Lung volume reduction surgery is less often indicated since the mechanisms of how the surgery may be beneficial are still being studied (Sciurba, 1997).
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Physical Adjustment to AATD
Patients with COPD and patients with AATD-associated COPD share clinical features and differ primarily in age of onset of COPD. As mentioned above, patients with AATD-associated COPD tend to be diagnosed at a younger age than non-AATD COPD patients. The predominant physical symptom of COPD and AATD-associated COPD is dyspnea, or shortness of breath. The American Thoracic Society describes dyspnea as a subjective experience of breathing discomfort... [and] derives from interactions among multiple physiological, psychological, social, and environmental factors, (American Thoracic Society, 1999). The pathophysiology of dyspnea is comprised of numerous mechanisms having to do with conflicting sensory information between the central nervous system and the receptors along the respiratory tract (De Peuter, Diest, Lamaigre, Verleden, Demedts, Van den Bergh, 2004). Research has also found that experience of dyspnea is highly impacted by patient characteristics such as age, gender, airway reactivity, duration of disease, level of airway inflammation, patient affect, and subsequent patient perception of dyspnea (De Peuter, et al. 2004).
In addition to dyspnea, patients with AATD-associated COPD experience fatigue. Fatigue is defined as a global feeling of tiredness and lack of energy. In COPD and AATD-associated COPD, fatigue stems from a lack of oxygen as well as from an increased exertion of the body to attain and utilize what oxygen is available. Fatigue in COPD and AATD-associated COPD is also caused by hypercapnia, or a rise in carbon dioxide levels in the blood due to inefficient and insufficient gas exchange. Having a limited capacity for airflow makes certain levels of physical activity prohibitive, and the level of physical activity typically becomes progressively less as the disease progresses. People with AATD-associated COPD also experience weight loss and loss of muscle mass. Fatigue and inability
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to be physically active often decrease the appetite causing weight loss. Lack of physical activity will also lead to loss of muscle mass.
AATD-associated COPD also causes mucus and sputum production, which then leads to chronic coughing. Chronic coughing is another contributor to the experience of fatigue, and, much like dyspnea, is associated with other physiological, psychological, and social factors. Chronic coughing can lead to complications such as pain, hernias, or other tissue damage resulting from frequent, violent coughing. Over time, chronic coughing can also cause a person to feel anxious and hopeless because it is difficult to control coughing or stop it from occurring (Ludviksdottir, Bjomsson, Janson, Boman, 1996; Morice, 2008). Chronic coughing tends to impact an individuals social life as well. Individuals may begin to feel uncomfortable in social situations or public settings because of how others react or how they perceive that others are reacting to their cough (Morice, 2008). The symptoms of AATD-associated COPD lead to significant changes in functionality and quality of life, and consequently, lead to significant changes in emotional quality of life as well.
Psychosocial Adjustment to AATD
COPD has a high prevalence of concurrent anxiety and depression (Yohannes, Willgoss, Baldwin & Connolly, 2010). Kunik and colleagues, (2005) found that close to 65% of patients with COPD screened positive for anxiety or depression. Dyspnea is also highly correlated with anxiety, as research has found that anxiety disorders are the most prevalent psychiatric disorders among patients experiencing dyspnea (Smoller, Simon, Pollack, Kradin & Stem, 1999). In their 1994 study, Janson, Bjomsson, Hetta, and Boman found a correlation between the reporting of asthma symptoms and depression. However, they found no correlation between anxiety, depression and an objective measure of asthma severity suggesting that the experience of pulmonary distress is potentially worsened by the presence
14


of psychosocial distress. Hayen, Herigstad, and Pattinson (2013) suggest that biopsychological factors such as mood changes, hormonal changes, and psychological comorbidities, may lead to the exacerbation of dyspnea, intensify the symptoms, and decrease quality of life. They argue that effective treatment of dyspnea requires direct focus on the biopsychological contributors of the clinical presentation. It is estimated that as much as 50% of variance in dyspnea is attributed to psychological distress and symptoms of depression and anxiety (Bestall, Paul, Garrod, Garnham, Jones & Wedzicha, 1999; Holm, Wamboldt, Ford, Sandhaus, Strand, Strange & Hoth, 2013).
In a study looking at the impact of age on outcomes among COPD patients, researchers found that anxiety and health-related quality of life were both associated with age and that younger people had worse outcomes (Holm, Plaufcan, Ford, Sandhaus, Strand, Strange, & Wamboldt, 2013). The study also found that age and relationship status interacted significantly when predicting breathlessness, depression and health-related quality of life. Younger people who were single perceived themselves as having more breathlessness, endorsed more symptoms of depression and reported worse health-related quality of life than did patients who were older and coupled. This suggests that AATD-associated COPD bears an added psychosocial burden for the affected individual due to the earlier age of onset. The age range that individuals with AATD may start to become symptomatic coincides with the developmental stage of building careers, developing long-term romantic relationships, and starting families. Adjustment to illness can be difficult in COPD given the functional limitations the condition imposes. Illness adjustment becomes more challenging in AATD-associated COPD when most same aged peers are very active and substantially less limited than the individual with the disorder (Campos, et al 2009).
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Self-Blame
COPD is an irreversible disease that has an immense impact on health related quality of life, functionality, and emotional well-being. An added aspect of COPD is that in developed countries, the main cause of COPD is smoking behavior in the individual (Mayo Clinic Staff, 2015), and in a qualitative study done by Oliver (2001), patients with COPD often felt that their illness was self-inflicted. When one is has a critical illness like COPD, in order to be able to adjust to or cope with the reality of the illness, the individual needs to be able to organize the events that have occurred and assign attributions, or explanations for the causes of the events. Attributions that are made can be adaptive, helpful for the illness adjustment process, or maladaptive, leading to more distress. A type of attribution that can be made is self-blame. Blame is placing responsibility or fault on someone for a mistake or something bad that has happened (Merriam Webster, 2015), and self-blame is when that responsibility or fault is placed on the self.
Literature on self-blame in patients with chronic disease indicates that in the process of adjustment to illness, patients often blame themselves for actions or behaviors that may have led to their disease state (Malcarne, Compas, Epping-Jordan, Howell, 1995). However, in a study looking at coping in patients who have had a freak accident, self-blame emerged as a predictor of better coping (Bulman & Wortman, 1977). The researchers explained that self-blame was an adaptive psychological mechanism because it lead to making an attribution and a belief of personal control over the events in ones life. This suggests that there may be aspects of attributions of self-blame that are maladaptive and other aspects that are adaptive.
Voth and Sirois (2009) looked at the effect of different types of attributions in adjustment to inflammatory bowel disease. They found that believing the disease was somehow brought on intentionally or that it was due to internal and stable factors in the
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person was associated with avoidant coping and poorer psychological adjustment (Voth & Sirois, 2009). In comparison, taking responsibility for the disease and personal behaviors led to decreased avoidance and improved psychological adjustment to the disease.
Janoff-Bulman, in her 1979 article on self-blame in rape and depression, conceptualized self-blame as being made up of two parts: behavioral self-blame and characterological self-blame. These parts comprise the adaptive and maladaptive aspects of self-blame. Janoff-Bulman discusses how in the case of rape, it may help the victim gain a sense of control and a way of processing her experience if she can attribute the event to certain actions of hers. Janoff-Bulman then contrasts that example with Becks (1967) argument regarding depressed patients and their maladaptive tendency to attribute causality for any negative event to her or himself.
According to Janoff-Bulman, the adaptive orientation of self-blame, or behavioral self-blame, is a control-oriented response whereby the person takes on blame or responsibility by believing that his or her behavior played a role in the events that transpired. When one attributes the cause of a situation to his or her own behaviors, one can develop a sense of control over future outcomes through possibly different, future behaviors. This attribution instills a sense of hope as well as an internal sense of control. The maladaptive orientation of self-blame, or characterological self-blame, is a self-deprecating response to negative events or illness whereby the person becomes focused on the past and attributes a sense of (self) deservingness of past negative events. In this type of blame, a person identifies something intrinsically wrong about him or herself that made and will always make him or her deserving of a negative outcome (Janoff-Bulman, 1977). This attribution lends itself to feelings of helplessness and other depressive cognitions.
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A study looking at adjustment to cancer through attributions of self-blame found that behavioral self-blame and characterological self-blame impact psychological adjustment not only independently but also in interaction with one another. Malcame and colleagues (1995) found that behavioral self-blame at the time of diagnosis was associated with increased psychological distress when characterological self-blame was also present. Furthermore, they found that attributions of characterological self-blame and the co-existence of characterological self-blame and behavioral self-blame near the time of diagnosis were predictive of poor psychological adjustment 4 months post-diagnosis. Behavioral self-blame alone did not have an association with psychological distress.
In a study on self-blame and adjustment to breast cancer in women, Glinder & Compas (1999) found that when the variables of characterological self-blame and behavioral self-blame were entered into a regression analysis simultaneously, behavioral self-blame predicted psychological distress more consistently cross-sectionally, and characterological self-blame more consistently predicted psychological distress prospectively. This indicates that characterological self-blame was associated with long-term psychological adjustment, which in this study was up to one year post-diagnosis.
In their 2012 study on behavioral and characterological self-blame in COPD, Plaufcan, Wamboldt and Holm found that behavioral self-blame in this sample of COPD patients was associated with tobacco exposure (measured in pack-years). Current smoking status, however, was associated with characterological self-blame. In this sample, perception of family blame for the individuals COPD was associated with both behavioral and characterological self-blame. But scores of general family functioning (with higher scores indicating better perceived family functioning) were associated positively with behavioral self-blame. Characterological self-blame associated positively with depressive symptoms,
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and individuals who had the highest score for behavioral self-blame reported fewer symptoms of depression and lower impairment in health related quality of life. In the case of individuals with AATD-associated COPD, studies indicate that smokers in this population also harbor self-blame for their smoking behavior that has lead to their illness (Plaufcan, Wamboldt, Holm, 2011). Behavioral and characterological self-blame have not yet been examined in a sample of AATD-associated COPD and is one of the primary objectives of this project.
Demographics, Socioeconomic Status, and Health
Socioeconomic status (SES) is defined as a measure of ones social and economic standing, is usually gleaned from considering ones income, education, and occupation, and it has been found to be positively associated with and predictive of better health outcomes (Baker, 2014). Demographics and SES play an important role in COPD. In 2011 the CDC conducted a survey to identify the risk factors that were driving up the prevalence of COPD and found that prevalence was strikingly different across different states and across different levels of SES (Siegel, 2013). 6.8% of COPD patients did not have a high school diploma whereas 4.6% had a diploma or some college education. 20.9% of COPD patients were not working whereas 3.8% were employed fulltime (CDC, 2014).
Despite significant findings of a predictive relationship between SES and health, the precise mechanism by which SES contributes to health status, health behaviors and health outcomes remains somewhat unclear in the literature. Adler and colleagues (1995) argued that a main problem in understanding the impact SES on health is that often SES is relegated as a control variable. Also, many studies compare the health of people at the very lowest level of poverty with those above poverty and those at the very top level of the hierarchy. Focusing only on the extremes in the hierarchy underestimates the impact of SES on
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biological outcomes. Adler and colleagues argue that health is impacted by socioeconomic status at every level of the social hierarchy. There are finely stratified differences running across the entire hierarchy, and what accounts for health outcomes at lower levels are not the same as what accounts for changes at higher levels (Adler et al. 1995).
In a 1995 article on the links between education and health, Ross and Wu found that higher education level was associated with better self-reported health, more positive health behaviors (e.g. reduced drinking and smoking), better physical functionality, and a greater sense of control over life and personal health. Ross and Wu hypothesized that education provides greater and more satisfying job opportunities, fewer financial stressors, better quality of life and a greater sense of control in life. In another article on education and health, Schnittker (2004) stated that the association be income and health varies in strength and shape by the level of education. This is to say that education improves health, and this improvement is greater at lower levels of income. Studies have found that higher education level is associated with better utilization of medical care, better adherence to medical intervention, better health behaviors, and less engagement in behaviors that insult the health (e.g. smoking, poor eating behaviors, hygiene, substance abuse) (Rose, Chassin, Presson, 1996; Ross, Wu, 1995; Adler, et al., 1994).
In a study done by Eisner and colleagues in 2011 looking at SES, race and COPD outcomes, they found that greater COPD severity scores were consistently associated with lower educational attainment, and this was true even after controlling for race and ethnicity. They found that black race was associated with greater severity of COPD symptoms, but these findings were not maintained after controlling for SES variables and various covariates such as comorbidities, smoking, BMI, and occupational exposure. Greater risk for COPD exacerbations were associated with lower education and lower income independently.
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Omachi and colleagues (2013) make a distinction in understanding the impact of the education component of SES on COPD outcomes. They state that education attainment is an important predictor of COPD outcomes, but they argue that health literacy, while a product of education, is separate from education. Therefore, poor health literacy was associated with greater COPD severity, greater sense of helplessness, lower health related quality of life, and great utilization of emergency services for COPD-related issues. This association remained after controlling for other SES factors like education level and income.
In a study looking at differences in adjustment between people with AATD-associated COPD and those with non-AATD COPD (Holm et al. 2013), researchers found that patients with AATD-associated COPD differed from non-AATD COPD patients in being more likely to be coupled and also in having greater educational attainment. The researchers concluded that the lower rates of anxiety and depression in AATD-associated COPD patients could be associated with the positive impact of the relationship status (social support) and with the higher education level. Overall, the sample in the study indicated that lower education was associated with more symptoms of anxiety and depression, more dyspnea, and impairment in health related quality of life.
In another study of patients with AATD-associated COPD looking at demographic factors, emergency room visits and alcohol use, problem drinking (alcohol) predicted more ER visits (Hoth, Ford, Sandhaus, Strange, Wamboldt, Holm, 2012). But the problem drinking was also associated with symptoms of anxiety, greater lifetime tobacco exposure and higher education level. This is an example of the complexity of SES in predicting health behavior. Typically, higher education level is associated with positive health behaviors, but these findings are supported by other research that have also found an association between alcohol abuse, higher social status and educational achievement (Hoth, et al, 2012; Stutske, 2005).
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These findings highlight the complexity of the impact of demographics and SES on chronic illness, and on specifically AATD-associated COPD.
Family Support and Health
Social support is defined by Uchino (2004, pp. 9-10) as being both the social contexts within which a person exists (e.g. family, group of friends, membership in a community) and also the functions that those relationships serve (e.g. informational, tangible or emotional support). Social support has received a great deal of research within the field of health psychology (Franks, Campbell & Shield, 1992; Uchino, 2005). The strength of the association between social relationships (meaningful connectedness with others) and health behaviors is comparable to the association between measures of health (e.g. blood pressure or obesity) and health outcomes (House, Landis, Umberson, 1988; Holt-Lundstad, smith, Layton, 2010).
Uchino (2004) talks about the inconsistencies in literature on the impact of social support on health. He argues that the inconsistencies arise partly due to the dynamic and bidirectional association between social support and health. It also has to do with the variety of contexts and forms of social support that can be experienced. The variety of emotions that can be shared and exchanged in a social relationship (e.g. positive emotion, negative emotion, stressful interactions) also alters outcomes. Keicolt-Glaser (1997) discusses in her article on marital conflict and endocrine and immunologic changes that while social support has typically been conceptualized as a protective factor in health, there are aspects of it that can be a threat to health, such a poor relational skills in a marriage leading to unhealthy changes in endocrine and immunologic responses.
Sarason, Sarason, Shearin and Pierce (1987) argue that part of what makes social support so vital, impactful and compelling is that they are continuations of our attachment
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patterns from infancy and childhood into our adulthood. This finding is validated by Franks, Campbell and Shields (1992) when they stated that variables of family interactions have a more power association with health behaviors than variables of social support alone. They researchers argued that emotional valence in family relationships is higher and therefore has a greater impact on disease management in chronic illness than does emotional support from other (non-family members).
COPD and other chronic illnesses such a diabetes frequently rely on family support due to the level of impairment, its progressiveness, and the need for greater lifestyle changes (Kara Ka§ikqi, M., Albert, 2006; Martire et al. 2004; Weihs, Fisher, Baird, 2002). Kleinman, Esenberg & Good (1978) explain that family has a shared reality, and this reality is related to health, and the family environment is where disease management takes place.
In a study on social support and self-efficacy in older COPD patients, Marino, Sirey, Raue & Alexopoulos (2008) found that social support and self-efficacy were independently associated with better overall functioning, even in the presence of severe mental illness and depression. Another study of COPD patients in Turkey found similar results, that there was a moderately strong association between family relationship and self-efficacy in self-care (Kara Ka§ikqi & Alberto, 2006).
Family Criticism, Family Blame, and Health inAATD
Social support alone, however, may not be sufficient in terms of influencing health behaviors and illness adjustment. Factors associated with family interactions are found to have more powerful and direct associations with health behaviors than non-familial social support (Franks, et al. 1992). This influence can go both ways: positively and negatively. A negative aspect of family interactions is the exposure to family criticism. Campbell and Fiscella (1999) concluded that perceived family criticism leads to poor health behaviors, and
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this association is mediated by negative affect (e.g. depression and hostility), which can occur in reaction to perceived family criticism. Perceived criticism is understood as being comprised of two pieces: first, a distorting, negative filter of perceptions about the self and other, and second, actual negative family interactions, particularly expressed negative emotions (Fiscella, Franks, Shield, 1997).
Perceived family criticism is a measure of negative emotion in a family dynamic. It is measured by the amount of criticism an individual feels he or she receives from his or her family members (Plaufcan et al, 2012). In study on family factors associated with psychological distress and smoking factors in COPD, Holm and colleagues (2010) found that perceived family criticism lead to increased psychological distress, which then impacted smoking status (e.g. amount currently smoked). In 2013, Holm and colleagues looked at perceived criticism and dyspnea in AATD and found that perceived criticism from family is associated with dyspnea in individuals with elevated levels of psychological distress. The researchers theorized that this may be due to a parallel neural mechanism that has to do with appraisal of criticism and appraisal of dyspnea, which literature argues is a highly subjective experience largely impacted by psychological symptoms (De Peuter et al. 2004).
Family blame is a perception that ones family is blaming him or her for the illness, in this case, COPD (Plaufcan et al, 2012). An individuals perception of family blame for COPD associated positively with both characterological self-blame and behavioral selfblame, the combination of which indicates increased psychological distress (Plaufcan et al, 2012). But when patients perceived their family as being healthier, as measured by score on a 12-item Family Assessment Device, family support served as a protective factor such that COPD patients endorsed the more adaptive behavioral self-blame.
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Perceived family criticism and perceived family blame, therefore, may serve as separate and unique predictors of disease adjustment, health behavior change, and potentially, health outcomes.
Conclusion
Given the findings reviewed above, one could conclude that there are a number of variables that may impact smoking behavior in those with AATD-associated COPD. Although SES is strongly associated with various health and psychosocial outcomes, there has been very little research examining the relationship between SES and smoking in those with AATD-associated COPD. Research to date has found that SES is complex construct and in order to have a more nuanced understanding of the characteristics of a sample, it is important to consider the multidimensional contributions of demographic factors. Particularly in the case of patients with AATD-associated COPD, investigating the relative contributions of the components of SES could help shed light on certain characteristics of smokers versus non-smokers. Self-blame has also been investigated in various health populations, including in samples of patients with COPD. An important continuation of this research is to understand if and how characterological self-blame and behavioral self-blame differ specifically between patients with AATD-associated COPD who are never smokers versus those who are past smokers. Furthermore, given the importance of family support in health status and health outcome in patients with AATD-associated COPD, it is also important to further this understanding of how smoking history impacts perceived family criticism and perceived family blame. Gaining more insight into how AATD-associated COPD patients with a history of smoking may differ from those who are lifetime non-smokers on socioeconomic, attributional and perceptual variables can potentially inform both medical
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and psychological therapeutic interventions. This insight may also help make medical and psychological therapeutic interventions more effective and accessible to this population.
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CHAPTER II
AIMS AND HYPOTHESES
Aiml
Examine the association between smoking history and demographic, socioeconomic and health characteristics in patients with AATD-associated COPD.
Hypothesis la: Smoking history will be significantly associated with lower socioeconomic status, as measured by income and education variables, in patients with AATD-associated COPD.
Hypothesis lb: Smoking history will be significantly associated with a greater number of medical co-morbidities in patients with AATD-associated COPD.
Aim 2
Examine how smoking history is associated with the level of characterological and behavioral self-blame experienced by patients with AATD-associated COPD while controlling for relevant demographic, educational and health characteristics.
Hypothesis 2a: Presence of smoking history will not be predictive of characterological self-blame among patients with AATD-associated COPD. Hypothesis 2b: Presence of smoking history is predictive of higher levels of behavioral self-blame among patients with AATD-associated COPD.
Aim 2
Examine the extent to which smoking history is predictive of perception of family criticism and perception of family blame among patients with AATD-associated COPD. Hypothesis 3a: Smoking history will be predictive of perceived blame from family members in patients with AATD-associated COPD.
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Hypothesis 3b: Smoking history will be predictive of perceived criticism from family members in patients with AATD-associated COPD.
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CHAPTER III
METHODS
Participants and Procedures
Data were collected as part of a larger study that was conducted by Dr. Kristen Holm and funded by the Alpha-1 Foundation. The study was conducted at National Jewish Health in Denver, Colorado and at the Medical University of South Carolina in Charleston, South Carolina. The studys aims were to examine social and perceptual factors that affect adjustment in AATD-associated COPD. All data were collected via a self-report questionnaire.
Participants were recruited through the Alpha-1 Foundation Research Registry using a mailed questionnaire. The National Jewish Health Institutional Review board and the Medical University of South Carolina Institutional Review Board approved this study. The study was granted a waiver of documentation of informed consent because data were collected via questionnaires that posed no more than minimal risk to participants. The questionnaire is included in Appendix A with the relevant sections of the questionnaire highlighted.
The questionnaire was mailed to 1727 potential participants, and 621 returned the questionnaire for a response rate of 36%. Of the 621 questionnaires, 22 respondents were excluded because they did not endorse having COPD. Of the remaining participants, 37 were excluded from the analyses because they indicated that they are currently smoking. Their experiences are likely to be distinctly different from never smokers and former smokers, but the small number of current smokers precludes examining their experiences in the current study. In addition, 71 participants were excluded due to having smoked more than 100 cigarettes but having less than a 10-pack-year history. The decision to exclude these
29


individuals is consistent with prior research in which 10-pack-years is considered the definition of a clinically significant smoking history (Plaufcan, Wamboldt, Holm, 2012; Putcha, et al. 2014; Regan, et al. 2010.) An additional 28 participants were removed due to missing data. To be included in analyses, respondents needed to have adequate data to determine smoking history (including number of pack-years smoked) and have provided a response for at least one of the variables that is being examined as a correlate of smoking
history in Aim 1. Hence, the final sample size was 463 (see Fig. 1).
Never & Former
Smokers
N = S99
Sample with Full
Data
N = 534
r Not 1
leturne< >
L Y = not J
r Current smoker
L N = 37 J
Final Sample
N = 463
Fig. 1. Recruitment flow diagram.
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Measures
Demographics & health characteristics.
Age, age at COPD diagnosis, gender, relationship status, education level, income, and race and ethnicity were measured via the questionnaire. In addition, dyspnea was measured by the Modifed Medical Research Council Dyspnea Scale (Fletcher, Elmes, Fairbairn & Wood, 1959), a single-item scale with response options that range from 1 to 5. Higher scores indicate more dyspnea. This scale predicts 5-year survival among patients with COPD (Nishimura, Izumi, Tsukino & Oga, 2002).
Rating of health was measured using the following item from the Behavioral Risk Factor Surveillance System Survey Questionnaire (CDC, 2004a): Would you say that in general your health is. The item has five response options that range from poor (scored as a 1) to excellent (scored as a 5). As such, a higher score indicates better health.
Total number of comorbidities was measured by summing the number of medical conditions participants endorsed from a list that included the following conditions: liver disease, heart disease/heart surgery, hypertension/high blood pressure, diabetes/blood sugar problems, bone problems (osteoporosis or fracture), and cancer. Participants scores could range from 0 to 6 with a higher number indicating more medical comorbidities.
Body Mass Index (BMI) was calculated using the self-reported values for height and weight. BMI values were separated into the following 4 categories: underweight (BMI < 18.5), normal weight (BMI between 18.5 and 24.9), overweight (BMI between 25.0 and 29.9), obese (BMI > 30.0).
The following three health characteristics were examined as indicators of illness severity: AATD genotype, oxygen use, and history of augmentation therapy. Genotype was categorized as severely deficient (e.g. SS, SZ, and ZZ), not severely deficient (e.g. MZ, MS,
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and SS) and unknown. Oxygen use was assessed using a single-item yes/no question that asked, Are you using oxygen for your COPD? Augmentation therapy was also assessed using a single-item yes/no question, Have you EVER undergone augmentation therapy (Prolastin, Aralast, or Zemaira)? Individuals who are using oxygen or who have undergone augmentation therapy are considered to have more severe COPD.
Smoking behaviors.
Smoking behaviors were assessed via questions from the National Health Interview Survey (NHIS) (CDC, 2004b) a subset of which are also in the Behavioral Risk Factor Surveillance Survey (BFRSS) (CDC, 2004a) Participants were asked whether they have smoked more than 100 cigarettes in their lifetime. Participants who indicated that they had not smoked more than 100 cigarettes were considered never smokers. People who indicated that they had smoked more than 100 cigarettes were considered to have a history of smoking. Participants who indicated that they had smoked more than 100 cigarettes were instructed to provide information regarding the number of packs smoked per day as well as the number of years smoked. Extent of lifetime tobacco exposure (i.e., pack-years) was then calculated by multiplying the average number of cigarette packs per day by the number of years smoked. Participants were also asked whether they are still smoking currently. Current smokers were excluded from analyses.
Self-blame.
Characterological self-blame was assessed via the self-blame subscale of the Internal Locus of Control scale (Marshall, 1991). The self-blame subscale is comprised of three items that assess a tendency toward self-blame in regards to negative health outcomes. These items ask for the extent of agreement with the following statements: 1) whatever goes wrong with my health is my own fault; 2) when I get sick, I am to blame; and 3) when I feel ill, I know it
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is because I have not been taking care of myself properly. The response for each item ranges from 1 (strongly disagree) to 5 (strongly agree). Total scores for this subscale range from 3 to 15 with higher scores indicating greater characterological self-blame. Cronbachs alpha in this sample was 0.82.
Behavioral self-blame was assessed by a single item that was adapted from a study of self-blame in head and neck cancer (Malcarne, Compas, Epping-Jordan, Howell, 1995). The item reads as follows: How much do you blame yourself for any behavior that led to your COPD? Responses for this item range from 1 (not at all) to 5 (completely), with a higher score indicating greater behavioral self-blame.
Family blame andfamily criticism.
Perception of family criticism was assessed by the PCM. The PCM is a single-item measure that is worded as follows: On average, how critical do you think your family is of you with response options that range from 1 (not at all critical) to 10 (very critical) (Hooley & Teasdale, 1989). A higher score indicates greater perceived family criticism.
Perception of family blame was assessed by a single item that was modeled after the Perceived Criticism Measure (PCM). The item is worded as follows: To what extent do you think your family blames you for having COPD with response options that range from 1 (not at all) to 10 (completely). A higher score indicates greater perceived family blame.
Statistical Analyses
Data were analyzed using IBM SPSS Statistics version 23 (SPSS Inc., 2015). Preliminary analyses.
All variables used in analyses were examined using means and standard deviations for continuous variables and number and percentage of participants for categorical variables. Individuals who were excluded from analyses due to missing data were compared to
33


individuals who were included in analyses via t-tests for continuous variables and chi-square tests for categorical variables.
Analyses for aim 1.
To examine the association of smoking history with demographic, socioeconomic and health characteristics, never smokers were compared to former smokers. The following variables are continuous and therefore were examined via t-tests: age, age at COPD diagnosis, dyspnea, rating of health, comorbidities, and pack years. The following variables are categorical and therefore were examined using chi-square tests: gender, relationship status, education, income, race/ethnicity, BMI, genotype, oxygen use, and augmentation therapy.
Analyses for aim 2.
Analyses for Aim 2 were done in three parts. First, the distributions of the scores for characterological self-blame and behavioral self-blame were examined via frequencies, skewness, and kurtosis to determine whether these variables were normally distributed. Both variables, characterological self-blame and behavioral self-blame, were skewed, and data transformation did not make these variables normally distributed. As such, both variables were recomputed into dichotomous variables using the median split. For characterological self-blame, scores of 3 to 5 comprised 40.2% of the sample and were coded as low characterological self blame. Scores from 6 to 15 comprised 59.8% of the sample and were coded as high characterological self-blame. For behavioral self-blame, scores of 1 and 2 comprised 54.3% of the sample and were coded as low behavioral self-blame. Scores from 3 to 5 comprised 45.7% of the sample and were coded as high behavioral self-blame.
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Next, the bivariate associaton of smoking history with characterological and behavioral self-blame was examined by conducting t-tests using the original, continuous, non-computed scores and chi square tests tests for the computed, categorical scores.
Then, the multivariate association was examined via logistic regression to examine the extent to which smoking history is associated with characterological and behavioral self-blame after adjusting for relevant demographic and health covariates. The dichotomous variables were used as dependent variables in logistic regression models.
Two logistic regression models were calculated. Characterological self-blame was the dependent variable in the first model, and behavioral self-blame was the dependent variable in the second model. Smoking history was the primary predictor of interest. Variables that were examined in Aim 1 that had a statistically significant association with smoking history were included as covariates in both models. Both models included the same set of predictors; the dependent variable is the only thing that differed between the models.
Analyses for aim 3.
The analyses for Aim 3 were also done in three parts. First, the distributions of the scores for perceived family blame and perceived family criticism were examined via frequencies, skewness, and kurtosis to determine whether these variables were normally distributed. Both variables, perceived family blame and perceived family criticism, were skewed, and data transformation did not make these variables normally distributed. As such, both variables were categorized into none versus any since the lowest possible value had such a high percentage of the responses for each variable. For perceived family blame, scores of 1, which made up 44.6% of the sample, were coded as no family blame. Scores of 2 to 10, which made up 55.6% of the sample, were coded as endorsing family blame. For perceived family criticism, scores of 0, which made up 75.9% of the sample, were coded as no family
35


criticism. Scores from 2 to 10, which made up 24.1% of the sample, were coded as endorsing family criticism.
Next, the bivariate associaton of smoking history with perceived family blame and perceived family criticism was examined by conducting t-tests using the original, continuous, non-computed scores and chi square tests tests for the computed, categorical scores.
Then, the multivariate association was examined via logistic regression to examine the extent to which smoking history is associated with perceived family blame and perceived family criticism after adjusting for relevant demographic and health covariates. The categorical variables were used as dependent variables in logistic regression models. Perceived family blame was the dependent variable in the first model, and perceived family criticism was the dependent variable in the second model. Smoking history was the primary predictor of interest. Variables that were examined in Aim 1 (identified in Table 1) that had a statistically significant association with smoking history were included as covariates in both models.
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CHAPTER IV
RESULTS
Characteristics of Participants
Individuals who were removed due to missing data were compared to those who were included in analyses. The two groups were compared on every variable listed in Table 1. The two groups did not differ with regard to any of these variables (p > 0.05).
Characteristics of the sample are in Table 1. On average, never smokers, M(SD) = 64.49(10.70), were older than former smokers, 57.92(9.00); t(461)=6.83, p<0.001. Never smokers were also older at COPD diagnosis, 53.34(11.63), than former smokers, 44.69(8.47); t(451)=8.88, p<0.001. Gender was equal across the sample with 49.9% females and 50.1% males. A larger percentage of the never smokers were female (64.1%) while a larger percentage of the former smokers were male (56.4%). Among former smokers, the average number of pack years was 29.33 (21.78). The majority of the combined sample was coupled (77.3%) as opposed to single (22.7%). Also, never smokers had higher education (51.1% completed college or more) than former smokers.
The sample examined in this project is characterized as having more severe disease as 82.7% of the sample had the more severe genotypes of AATD. Also, half of the sample, 51.8%, used oxygen, and 79.7% of the sample had augmentation therapy.
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Table 1 Characteristics of Sample (N = 463)
Combined Sample (n= 463) Never Smokers (n= 142) Former smokers (n= 321) p-value for comparison
Variable Mean (SD) Mean (SD) Mean (SD)
Age 59.94 (10.01) 64.49 (10.70) 57.92 (9.00) <0.001
Age at Diagnosis 47.33 (10.33) 53.34 (11.63) 44.69 (8.47) <0.001
Dyspnea 2.90 (1.16) 2.58 (1.14) 3.04 (1.14) <0.001
Rating of Health 2.67 (0.97) 2.85 (1.00) 2.60 (0.95) 0.009
Comorbidities 0.98 (0.97) 1.09 (0.98) 0.93(0.97) 0.128
Pack-Years NA NA 29.33 (21.78) NA
Variable N (%) N (%) N (%)
Gender
Female 231 (49.9) 91 (64.1) 140 (43.6) <0.001
Male 232(50.1) 51 (35.9) 181 (56.4)
Relationship Status
Single 105 (22.7) 25 (17.6) 80 (24.9) 0.083
Coupled 358 (77.3) 117(82.4) 241 (75.1)
Education
Grade 12/GED, or less 133 (29.4) 31 (22.3) 102 (32.5) <0.001
College 1-3 years 164 (36.2) 37(26.6) 127 (40.4)
College Grad and above 156(34.4) 71 (51.1) 85 (27.1)
Income
<35,000 138 (31.7) 37 (28.0) 101 (33.3) 0.181
35,001 -75,000 156 (35.9) 44(33.3) 112(37.0)
75,001< 141 (32.4) 51 (38.6) 90 (29.7)
Race & Ethnicity
Caucasian, non-Hispanic 451 (98.3) 139(99.3) 312(97.8) 0.608
Black, Non-Hispanic 2 (0.4) 0 (0.0) 2 (0.6)
Hispanic 4 (0.9) 1 (0.7) 3 (0.9)
Other 2 (0.4) 0 (0.0) 2 (0.6)
BMI
Underweight 22 (4.9) 10 (7.2) 12(3.8) 0.247
Normal Weight 211 (46.8) 67 (48.6) 144 (46.0)
Overweight 142 (31.5) 43 (31.2) 99 (31.6)
Obese 76(16.9) 18 (13.0) 58 (18.5)
Genotype
Severely Deficient 383 (82.7) 113 (79.6) 270 (84.1) 0.085
Not Severely Deficient 39 (8.4) 18 (12.7) 21 (6.5)
Unknown 41 (8.9) 11 (7.7) 30(9.3)
Oxygen Use
Yes 239(51.8) 50 (35.7) 189 (58.9) <0.001
No 222 (48.2) 90 (64.3) 132(41.1)
Augmentation Therapy
Yes 366 (79.7) 96 (68.6) 270 (84.6) <0.001
No 93 (20.3) 44 (31.4) 49(15.4)
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Results for Aim 1
Hypothesis la.
Hypothesis la stated that smoking history will be significantly associated with lower socio-economic status, as measured by income and education variables. This hypothesis was partially supported. Education was significantly associated with smoking status (p<0.001) but income was not. Fifty-one percent of those who reported that they never smoked had a college education or higher whereas only 27.1% of former smokers had completed college or more, indicating that the participants who never smoked had achieved higher levels of education compared to former smokers.
Hypothesis lb.
Hypothesis lb stated that smoking history will be significantly associated with a greater number of medical comorbidity. This hypothesis was not supported. Number of comorbid conditions did not differ significantly between never smokers and former smokers. Additional findings regarding differences between never andformer smokers.
While never smokers did not differ from former smokers with regard to number of comorbid conditions, these groups did differ with regard to dyspnea and rating of health. Never smokers had an average dyspnea score of M(SD) = 2.58(1.14) while former smokers had an average score of 3.04(1.14); t(454)=-3.98, p<0.001. These results indicated greater dyspnea among former smokers. Never smokers also reported a higher average score for rating of health, 2.85(1.00), indicating a better rating of health among never smokers. Former smokers had a significantly lower average score of 2.60(0.95); t(447)=2.61, p=0.009. Both of these findings suggest that former smokers experienced worse health outcomes in comparison to never smokers.
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Augmentation therapy and oxygen use also differed significantly between never smokers and former smokers (both p<0.001). Among former smokers, 58.9% used oxygen compared to 35.7% of never smokers. Among former smokers, 84.6% had a history of augmentation therapy compared to 68.6% of never smokers. The fact that former smokers were more likely to use oxygen and augmentation therapy also suggests that former smokers experienced worse health outcomes than never smokers.
Results for Aim 2
Bivariate analyses.
Aim 2 examines the association of smoking history with characterological and behavioral self-blame. First, the bivariate association of smoking history with these variables was examined (see Table 2). As seen in Table 2, smoking history had a statistically significant bivariate association with both the continuous variable for characterological selfblame (p = 0.035) and the dichotomous variable (p = 0.011). Also, smoking history had a significant bivariate association with the continuous variable for behavioral self-blame (p < 0.001) and with the categorical variable for behavioral self-blame (p < 0.001). Hence, a higher percentage of individuals reported high blame in the group of former smokes than in the group of never smokers. These results are presented in Table 2.
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Table 2 Characterological and Behavioral Self-Blame
Total Never Former
sample Smokers smokers
Continuous Variable Mean (SD) Mean (SD) Mean (SD) p-value for comparison
Characterological Self -Blame (N=445) 5.95 (2.45) 5.57 (2.24) 6.11 (2.52) 0.035
Behavioral Self -Blame (N=405) 2.37(1.28) 1.31 (0.63) 2.73 (1.24) <0.001
Categorical Variable N (%) N (%) N (%) p-value for comparison
Characterological Self -Blame Low Characterological 179 (40.2) 66 (49.3) 113 (36.3) 0.011
Self-blame (s5) High Characterological Self-blame (s6) 266 (59.8) 68 (50.7) 198 (63.7)
Behavioral Self-Blame Low Behavioral Self- 220 (54.3) 94 (93.1) 126 (41.4) <0.001
blame (*s2) High Behavioral Selfblame (^3) 185 (45.7) 7(6.9) 178 (58.6)
Multivariate analyses.
Next, the association of smoking history with self-blame was examined via multivariate logistic regression. The regression models included as covariates all variables that had a significant bivariate association at p < 0.05 with smoking history, as reported in Table 1. The variables that met this criterion were the following: age, age at COPD diagnosis, dyspnea, rating of health, gender, education, oxygen use, and augmentation therapy. Table 3 provides the results of the logistic regression models.
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Table 3. Results of logistic regression models to predict self-blame
Covariates Characterological Self-Blame (N= 411) Behavioral Self-Blame (N=374)
OR (95% Cl for OR) p OR (95% Cl for OR) p
Smoking History
Never smokers Reference Reference
Former Smokers 1.72 (1.03 2.86) 0.038 25.13 (9.38-67.3) <0.001
Age 0.99 (0.96- 1.03)0.680 0.98 (0.95 1.02)0.328
Age at Diagnosis 1.01 (0.98- 1.04)0.473 1.02 (0.98- 1.05)0.382
Dyspnea 0.75 (0.60 0.94) 0.014 1.02 (0.80- 1.31)0.866
Rating of Health 0.88 (0.69- 1.13)0.311 0.93 (0.71 1.23)0.611
Gender
Male Reference Reference
Female 0.61 (0.40 0.94) 0.026 1.25 (0.77-2.04)0.368
Education
Grade 12/GED or less 1.06 (0.63 1.80)0.817 1.10(0.60-2.03)0.757
College 1-3 years 1.68 (1.01-2.81) 0.047 1.20 (0.67-2.15)0.545
College Graduate or more Reference Reference
Oxygen Use
No Reference Reference
Yes 1.21 (0.73 -2.00)0.460 1.45 (0.83 -2.53)0.191
Augmentation Therapy
No Reference Reference
Yes 0.71 (0.41 1.24)0.229 0.74 (0.38- 1.45)0.382
Hypothesis 2a.
Hypothesis 2a stated that smoking history would not be predictive of higher levels of characterological self-blame. This hypothesis was not supported (see Table 3), as presence of smoking history is predictive of higher characterological self-blame (OR for former smokers = 1.72, 95% Cl = 1.03 2.86, p = 0.038). Dyspnea also was found to be predictive of higher characterological self-blame (OR = 0.75, 95% Cl = 0.60-0.94, p = 0.014.) This indicates that people who have more dyspnea have lower characterological self-blame. Gender was also predictive of high characterological self-blame (OR = 0.61, 95% Cl = 0.40-0.94, p = 0.026) indicating that males tended to have higher characterological self-blame. Finally, education was predictive of characterological self-blame (OR = 1.68, 95% Cl = 1.01-2.81), p = 0.047),
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with people who had 1-3 years of college being more likely to have higher characterological self-blame compared to the reference group which was people who had completed college or more.
Hypothesis 2b.
Hypothesis 2b stated that smoking history is predictive of higher levels of behavioral self-blame. This hypothesis was supported. Former smokers were significantly more likely (OR = 25.13, 95% Cl 9.37 67.3, p <0.001) to have high behavioral self-blame. The confidence interval here, however, was very broad. Also, none of the covariates were significant in this model for behavioral self-blame.
Results for Aim 2
Bivariate analyses.
Aim 3 examines the association of smoking history with perceived family blame and perceived family criticism. First, the bivariate association of smoking history with these variables was examined (see Table 4). As seen in Table 4, smoking history had a statistically significant bivariate association with both the continuous and categorical variables for perceived family blame (p <0.001), but smoking history did not have a significant bivariate association with either the continuous or the categorical variable for perceived family criticism.
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Table 4 Perceived Family Blame and Perceived Family Criticism
Total sample Never Smokers Former smokers
Continuous Variable Mean (SD) Mean (SD) Mean (SD) p-value for comparison
Perceived Family Blame (N=452) 1.73 (1.76) 1.24 (1.06) 1.94 (1.94) <0.001
Perceived Family Criticism (N=451) 2.47 (2.21) 2.36 (2.03) 2.51 (2.29) 0.523
Categorical Variable N (%) N (%) N (%) p-value for comparison
Perceived Family Blame Low Perceived Family Blame (=1) High Perceived Family Blame (s*2) 343 (75.9) 109 (24.1) 125 (91.9) 11(8.1) 218 (69.0) 98 (31.0) <0.001
Perceived Family Criticism Low Perceived Family Criticism (=1) High Perceived Family Criticism (s*2) 201 (44.6) 250(55.4) 55 (40.1) 82 (59.9) 146 (46.5) 168 (53.5) 0.212
Multivariate analyses.
Next, the association of smoking history with perceived family blame and perceived family criticism was examined via multivariate logistic regression. The regression models included as covariates all variables that had a significant bivariate association at p < 0.05 with smoking history, as reported in Table 1. The variables that met this criterion were the following: age, age at COPD diagnosis, dyspnea, rating of health, gender, education, oxygen use, and augmentation therapy. Table 5 provides the results of the logistic regression models.
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Table 5. Results of logistic regression models to predict family blame & family criticism.
Covariates Perceived Family Blame (N= 414) Perceived Family Criticism (N= 413)
OR (95% Cl for OR) p OR (95% Cl for OR) p
Smoking History
Never smokers Reference Reference
Former Smokers 5.96 (2.60 13.68) <0.001 0.74 (0.45- 1.24)0.253
Age 0.96 (0.93 1.00) 0.047 0.96 (0.93 0.99) 0.012
Age at Diagnosis 1.00 (0.96- 1.03)0.796 1.02 (0.99- 1.05)0.302
Dyspnea 0.93 (0.72- 1.21)0.590 1.02 (0.82- 1.27)0.830
Rating of Health 0.82 (0.61 1.09)0.167 0.92 (0.72- 1.16)0.469
Gender
Male Reference Reference
Female 1.09 (0.66- 1.81)0.730 0.76 (0.49- 1.15)0.194
Education
Grade 12/GED or less 0.61 (0.32-1.18)0.142 0.53 (0.31 0.90) 0.019
College 1-3 years 0.90 (0.50- 1.63)0.725 0.78 (0.48- 1.28)0.321
College Graduate or more Reference Reference
Oxygen Use
No Reference Reference
Yes 1.33 (0.73 -2.40)0.348 0.88 (0.54- 1.44)0.617
Augmentation Therapy
No Reference Reference
Yes 0.43 (0.23 0.81) 0.009 0.63 (0.37- 1.07)0.087
Hypothesis 3a.
Hypothesis 3a stated that smoking history will be predictive of level of perceived family blame. This hypothesis was supported (see Table 5) with former smokers having higher perceived family blame (OR = 5.96, 95% Cl = 2.60 13.68, p<0.001) than never-smokers. Age was also a significant predictor of perceived family blame in that participants who were older reported lower perceived family blame (OR = 0.96, 95% Cl = 0.93 1.00, p = 0.047.) Augmentation therapy was also a significant predictor of perceived family blame (OR = 0.43, 95% Cl 0.23 0.81, p = 0.009). Participants who had augmentation therapy were likely to report less perceived family blame than participants who did not have augmentation therapy.
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Hypothesis 3b.
Hypothesis 3b stated that smoking history will be predictive of level of perceived family criticism. This hypothesis was not supported by the regression model. However, age was predictive of perceived family criticism (OR = 0.96, 95% Cl = 0.93 0.99, p = 0.012) with older participants reporting less family criticism. Also, education was predictive of perceived family blame (OR = 0.53, 95% Cl = 0.31 0.90, p = 0.019). Participants with 12 years of education/GED or less reported less family blame than participants with higher levels of education.
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CHAPTER V
DISCUSSION
This study is the first to look specifically at the psychosocial impact of smoking history in a sample of individuals with AATD-associated COPD. The objective was to evaluate four psychosocial constructs that were found in past literature to negatively impact various health populations: behavioral self-blame, characterological self-blame, perceived family blame, and perceived family criticism. Three out of four of these constructs were found to be significantly associated with smoking history in this sample of patients with AATD-associated COPD; all were associated except for perceived family criticism. This is a unique finding for this population and sets the stage for future research that can expand on the particular psychosocial burdens in this population. Additionally, these findings identify possible points of entry for behavioral health interventions for this population.
Findings of Aim 1
Several demographic and health characteristics emerged as differing between never smokers and former smokers with AATD-associated COPD. While socioeconomic status, as a whole, was not associated with smoking history, education was statistically different between the groups. This finding is consistent with prior literature that higher educational attainment is associated with better health behaviors (Rose, Chassin, Presson, 1996; Ross, Wu, 1995; Adler, et al., 1994.) The statistically significant difference in level of education but lack of significance in income is consistent with Schnittkers argument in his 2004 article stating that the association between income and health varies in strength and shape by the level of education, suggesting that education tends to be robust as a demographic characteristic associated with health.
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The statistically significant differences between never smokers and former smokers in age at time of study participation and their age at diagnosis are consistent with literature on AATD-associated COPD that reports that smoking accelerates the onset of COPD, which may lead to earlier diagnosis in smokers (Ioachimescu & Stoller, 2005; Kaplan & Cosentino, 2010.) In addition, former smokers had characteristics of more severe disease (e.g. more severe genotypes, more oxygen use, and more participants who have had augmentation therapy).
As discussed earlier, dyspnea is highly influenced by patient characteristics such as age, gender, airway reactivity, duration of disease, level of airway inflammation, patient affect (De Peuter, et al. 2004) and is even defined as a subjective experience by the American Thoracic Society, (1999). Rating of health is also fundamentally subjective as it is a self-report, single item measure. Comorbidity, on the other hand, is a more objective measure, as patients are asked to indicate if they have additional medical conditions. Both dyspnea (p<0.001), and rating of health (p=0.009) differed significantly between never smokers and former smokers, while comorbidity did not. Therefore, the number of additional medical conditions does not differ between these groups, but subjective measures such as perceived health and dyspnea are worse in former smokers. This suggests that the subjective experience of COPD and the appraisal of the symptoms may be worse among former smokers.
Findings of Aim 2
Both characterological self-blame and behavioral self-blame were higher in participants who were former smokers. Literature on these two types of blame indicate that characterological self-blame is the maladaptive form of blame because it has a greater burden
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on self-concept, a vital psychological construct, and can be generalized into the persons perceptions about future events, fostering more hopelessness (Janoff-Bulman, 1977.)
In the regression model for characterological self-blame, gender was a significant covariate. Females were found to have less characterological self-blame than males. In this sample, more males were former smokers than females. In a study on smoking behavior and blame in non-AATD COPD, Plaufcan, Wamboldt, and Holm (2013) found that the more an individual had smoked, the more self-blame they attributed to the development of their COPD. Plaufcan and colleagues argued that if smoking identity is part of how an individual labels him or herself, it is logical that they would experience characterological self-blame for their smoking behavior. In this current sample, since more males were former smokers, more males experienced characterological self-blame than females. Within the scope of this study, it is not possible to argue that this gender difference is meaningful beyond the fact that there were more males who smoked and consequently had characterological self-blame than females.
Dyspnea is a subjective experience that can have a significant impact on quality of life (Hayen, Herigstad, Pattinson, 2013; Holm, Bowler, Make, Wamboldt, 2009). In this sample, dyspnea was a significant covariate in the regression model for characterological self-blame. Experiencing dyspnea was associated with less characterological self-blame. A possible explanation for this is that experiencing certain symptoms allows a person to feel less personally responsible and more at the mercy of their illness. Perhaps participants who endorsed more dyspnea, a consequence of COPD, may attribute more blame for their quality of life (or lack of it) to the nature of the illness, rather than attributing it to themselves or feeling personally responsible for their quality of life.
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As mentioned above, in the regression model for behavioral self-blame, smoking history was the only significant predictor of the level of behavioral self-blame, indicating that former smokers had higher behavioral self-blame than never smokers. No other covariates were found to be significant in this model.
An important consideration in evaluating the model for behavioral self-blame is that as a variable, behavioral self-blame (N = 373) had more missing data than characterological self-blame (N = 411). This can be seen in its confidence interval that ranges from 9.38-67.3. Behavioral self-blame was a single item self-report measure that was located on the bottom of page 4 of the mega questionnaire (see Appendix A.) It was the last question of this page, and the question prior to it asks If your COPD was caused by smoking, how much do you blame yourself for smoking? It is possible that people who did not have a history of smoking or people who did not feel their COPD was cause by smoking could have skipped the behavioral self-blame item, assuming that it was a continuation of the previous item and also related to smoking. This would explain some of the missing data but also suggests, that if this reasoning were accurate as to why people missed this question, data on behavioral self-blame could possibly be skewed with more answers from the people who answered the previous question and thus smoked and blamed themselves for developing COPD. The results for behavioral self-blame, therefore, while statistically significant, need to be considered with discretion.
Findings of Aim 3
Former smokers were found to endorse perceived family blame, which suggests that an important aspect of the experience of having AATD-associated COPD has to do with patients experiences with their families. Age is a significant covariate in the regression model for perceived family blame. Participants who were older were less likely to endorse
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family blame. In this sample, the never smokers were, on average, older than the former smokers. This could explain why older participants denied family blame, as younger participants tended to be former smokers and could have more reason to be blamed or to perceive blame from their families. An additional consideration is that as people age, societal expectations about health change so that having health issues is normative in older people. Therefore, patients with AATD-associated COPD may be less likely to be blamed or to perceive themselves as being blamed by their families for being ill. Additionally, as people age, their role and status in a family may change so that blame is less likely to be placed or to be received by the individual. An important consideration that future studies can look into is whether people are considering their families of origin or the families that they have established. Responses to questions for each of these (family of origin versus family that was established) may differ, and the questionnaire used in this study did not make this distinction in the questions that were asked (see Appendix A.)
Augmentation therapy was also a significant covariate within the perceived family blame regression model. Participants who had received augmentation therapy were less likely to have perceived family blame. Engagement in some form of intervention for their condition may attenuate the patients perceptions of being blamed by their family because receiving treatment may be seen as being proactive and engaged in the management of their illness.
Perceived family criticism was the only outcome that was not predicted by smoking history. A possible explanation for this is that family criticism is not an experience unique to formers smokers, which may be the reason it could not be predicted by smoking history. Age, however, was a significant covariate in this model. Similar to perceived family blame, participants who were older tended to have no perceived family criticism. This may, once again, have to do with never smokers being, on average, older than their former smoker
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counterparts. People who are older may not impacted by family criticism the way younger people are due to their stage of life, due to whether the person is more involved with their family origin or their own established family, or due to societal norms where older people may be less likely to be criticized or effected by criticism. In a study looking at the impact of age on psychological adjustment in AATD-associated COPD, Holm and colleagues (2013) found that younger patients more often had symptoms of anxiety and lower health-related quality of life. They also found that age was associated with relationship status, and people who were younger and uncoupled had increased symptoms of depression and dyspnea. This finding validates the finding in the current study that being younger increased the odds of experiencing perceived family criticism, which can be seen as a negative psychosocial outcome.
Having a high school education/GED or less was also found to be a significant covariate associated with perceived family criticism. This level of education was associated with not having perceived family criticism, and there are several ways to understand this. Education is a component of socioeconomic status and this status is often shared among family members, primarily those living in the same home (Conley & Gauber, 2005). It has been found that people with less education have less access to and ability to utilize health information (Rose, Chassin, Presson, 1996; Ross, Wu, 1995; Adler, et al., 1994). Literature on socioeconomic status and COPD found that lower educational attainment was also consistently associated with more severe COPD (Eisner et al., 2011.) Perceived family criticism may be less in people who this lower level of educational attainment because of a combination of the findings above. Having lower educational attainment is often shared within a family, and if as a family there is less health literacy about COPD, patients with COPD in this family environment may be receiving less family criticism.
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Alternatively, the perceived family criticism item differs from the perceived family blame item in that the blame item asks specifically about blame placed for the individuals COPD. The family criticism item asks only about perceived criticism in general, not specifying a target or subject of criticism. Because of the lack of specificity in the perceived family criticism question, the scores for this item may be more directly getting at specific issues in the family dynamic as a whole rather than getting at something solely in the individual. Hoth and colleagues (2014) looked at the family environment and its impact on illness uncertainty and found that there is complex relationship between family interactions (e.g. perceived family criticism, shared illness history, etc) and illness uncertainty in patients with AATD-associated COPD.
Limitations
When analyzing data from a self-report questionnaire, there are limits to the generalizability of the data to the larger population being examined. Typically, there are differences between people who participate and send back the questionnaire and those who do not or send the questionnaire back incomplete. The people in the study are self-selected and may represent those in the population who are interested in participating in research, who do not have major barriers to participating, and/or who are able to follow and complete a questionnaire independently of researcher facilitation. Therefore, it is important to be conservative in generalizing findings from such a sample to the larger population.
An additional limitation in this sample is that it is cross-sectional, and having data from a single time-point is another reason that findings are not wholly generalizable. A cross-sectional study also makes it difficult to make strong conclusions or make causal assumptions. Results from multiple time-points tend to lend more validity to findings and may minimize certain confounds that have to do with a single time-point. On the other hand,
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participants who would be able to successfully participate in multiple time-points longitudinally are also somewhat self-selecting and therefore not entirely generalizable to the population, which includes people who have significant barriers to participating in a study longitudinally.
In a study where a questionnaire is mailed out to participants, significant faith is placed in the participants that they will not only decide to complete the questionnaire, but they will complete it carefully and correctly without the facilitation of researcher. Consequently, a significant portion of the data that comes in has to be excluded, and this can be seen in Figure 1 in the method section of this project. The benefit of a mailed questionnaire, however, is that researchers can have an expanded pool of potential participants and potentially, a larger sample size.
A difficulty with a questionnaire that is mailed out is that without the facilitation of a researcher, participants are more susceptible to potential weaknesses in questionnaire lay-out which may result in incomplete or inaccurate data from participants. This was, unfortunately the case for certain variables examined in this project. One example is the item for behavioral self-blame. Behavior self-blame had the greatest amount of missing data among the four predictors examined in this project (see Table 2.) The item for behavioral self-blame was placed after a series of questions on smoking or other behaviors that may have contributed to COPD (see Appendix A for items). The sample size for the behavioral self-blame items and the placement of these items in the questionnaire suggest that never smokers who read through the prior questions about smoking behaviors may have skipped the last item on the page (the item for behavioral self-blame) assuming that it does not apply to them. The smaller sample size and the cofounded response pattern were confirmed by the very large confidence interval in the regression model for behavioral self-blame and smoking history.
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Consequently, findings about behavioral self-blame are evaluated more conservatively. It would be particularly important to conduct a study where behavioral self-blame can be reexamined without these issues to determine whether the findings in this study remain.
A similar issue exists for the item on comorbid medical conditions. This item was placed immediately after questions about oxygen use. This placement made it more likely that a participant who did not endorse oxygen use would overlook this question. Consequently, the data appear to be more representative of the more severely ill members of the population, both never smokers and former smokers. This could account for why hypothesis 2b was not supported and why never smokers and former smokers appeared to be so similar in this characteristic.
Another weakness in the data was identified when participants who were included in the analyses were compared to participants who were excluded due to missing data. Missing data from participants can be an indicator of worse outcomes either in terms of health or socioeconomic status (McKnight, McKnight, Sidani, Figueredo, 2007), and the lack of difference between the two groups was notable. One possible explanation for the lack of statistically significant difference between the groups could be that since this project is studying cross-sectional data it does not capture differences that could exist if this data were longitudinal. With multiple time points it is more likely that disparities and unique burdens will become obstacles in completing participation in a study such as this.
An important aspect of AATD-associated COPD that was not examined in this project is the impact of this genetic condition on family interactions. This aspect of the experience of this disease is a vital one, but was unfortunately not part of the scope of this project.
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Implications for Future Research
This project highlights many areas that can be looked into further to better understand the population of patients with AATD-associated COPD. In future studies it will be important to arrange the questionnaire in a way that ensures that critical items are less likely to be overlooked or misunderstood. In the interest of having a larger sample size, it may be helpful to offer an incentive to participants to motivate them to complete their questionnaires. This way, the benefit of a large mailing list may also result in greater response rates.
Having a longitudinal dataset would also provide greater strength and validity to possible findings. A longitudinal dataset will most likely have a smaller sample size, but the quality of the data which is longitudinal would make up for what may be lost in sample size.
An important finding to re-examine future studies is that greater dyspnea leads to lower characterological self-blame. It would be meaningful to determine whether this finding is replicable and what mechanism may be causing this association. To understand the mechanism, additional variables such as health-related quality of life and disability may be considered, as well as other mood or personality factors.
Another potential area of future research is looking further at the impact of age on AATD-associated COPD. Given the consistent association between age, health-related quality of life, psychological adjustment (in previous literature) and measures of family blame and criticism (found in this study), an important future step would be to examine how family relationships, both in families of origin and families established with a spouse, are impacted by the age of the patients with AATD-associated COPD. Understanding this can shed more light on factors that help patients have better quality of life, psychological resilience, and well-being through the various stages of life.
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Additionally, there was an association between lower educational attainment and perceived family criticism, but there was not sufficient evidence in this project to make a stronger statement about the nature of the association between education and perceived criticism. It would therefore be an important next step to examine educational attainment, health literacy and perceived family blame and perceived family criticism in patients with AATD-associated COPD.
Finally, looking at family blame and family criticism in this population, another important aspect to consider is the impact of AATD, a genetic condition, causing COPD. Having a genetic condition engages the family members of a patient in unique ways in comparison to non-AATD associated COPD. This is because a genetic condition implies that other family members either had, may develop in the future, or are carriers for the same illness. This aspect of family factors and interaction was not covered in the scope of this project. Given the results found in the project for family blame and family criticism, it is clear that looking further into how the genetic component of this illness impacts family interactions pertaining to the illness would be an important and meaningful investigation.
Implications for Intervention
It is most important to be able to first replicate the findings of this project. In the events that they are replicable, clinicians and medical providers will have several points of entry for providing interventions. Clinicians can focus on the impact of certain symptoms of the illness, such as dyspnea, on a patients health-related quality of life. Providing psychoeducation on the subjectivity of the dyspnea or providing biofeedback could empower a patient and help them develop some control over their perceptions of their symptoms.
Another point of entry is in providing health education to the patients and their family collectively. This would be particularly helpful for families that may have more limited
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access to health information and/or for families who are concerned about carriers in their family or multiple members with the illness. Given the association between access to health information and health outcomes, this intervention could empower patients and their families and facilitate communication between family members facing this illness.
Clinicians can also target the risk factors and protective factors for patients with AATD-associated COPD, for example, age. Younger patients may benefit from more interventions that focus on social skills building, communication skills building, development of self-confidence, and cognitive behavioral techniques that can help them navigate feelings of blame, criticism and loneliness. These interventions would clearly benefit any patient with AATD-associated COPD, but it would important to find ways to engage younger patients for whom these interventions may be particularly needed.
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REFERENCES
Adler, N. E., Boyce, T., Chesney, M. A., Cohen, S., Folkman, S., Kahn, R. L., & Syme, S.L. (1994). Socioeconomic status and health. The challenge of the gradient. American Psychologist, 49(1), 15-24.
Adler, N. E., Boyce, W. T., Chesney, M. A., Folkman, S., & Syme, S. L. (1993).
Socioeconomic inequalities in health. No easy solution. Journal of the American Medical Association, 269(24), 3140-3145.
Ali, A., Toner, B. B., Stuckless, N., Gallop, R., Diamant., N. E., Gould, M. I., & Vidins, E. I. (2000). Emotional abuse, self-blame, and self-silencing in women with irritable bowel syndrome.
Psychosomatic Medicine, 62(1), 76-82.
American Thoracic Society. (1999). Dyspnea. Mechanisms, assessment, and management: a consensus statement. American Journal of Respiratory Critical Care Medicine,
159(1), 321-340.
Beck, A. T. (1967) Depression: Clinical, experimental, and theoretical aspects. New York, NY: Harper & Row.
Baker, E. H. (2014). Socioeconomic Status, Definition. The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society, 2210-2214.
Bednarek, M., Gorecka, D., Wielgomas, J., Czajkowska-Malinowska, M., Regula, J.,
Mieszko-Filipczyk, G., Jasionowicz, M., Bijata-Bronisz, R., Lempicka-Jastrzebska, M., Czaikowski, M., Przybylski, G. & Zielinski, J. (2006). Smokers with airway obstruction are more likely to quit smoking. Thorax, 61, 869-873.
Bestall, J.C., Paul, E.A., Garrod, R., Garnham, R., Jones, P.W., & Wedzicha, J.A. (1999).
Usefulness of the Medical Research Council (MRC) dyspnoea scale as a measure of
59


disability in patients with chronic obstructive pulmonary disease. Thorax, 54, 581-586.
Bishop, D. S., Epstein, N. B., Keitner, G. I., Miller, I. W., & Srinivasan, S. V., (1986).
Stroke: morale, family functioning, health status, and functional capacity. Archives of Physical Medicine & Rehabilitation, 67, 84-87.
Bomhorst, J. A., Greene, D. N., Ashwood, E. R., Grenache, D. G. (2013). A1-Antitrypsin phenotypes and associated serum protein concentrations in a large clinical population. Chest, 143(4), 1000-1008.
Brantly, M. L., Paul, L. D., Miller, B. H., Falk, R. T., Wu, M. & Crystal, R. G. (1988).
Clinical features and history of the destructive lung disease associated with alpha-1-antitrypsin deficiency of adults with pulmonary symptoms. American Review of Respiratory Disorders, 138, 327-336.
Buist, A. S. (1990). Alpha 1-antitrypsin deficiencydiagnosis, treatment, and control: identification of patients. Lung, 168, 543-551.
Campos M. A., Alazemi, S., Zhang, G., Wanner, A., Salathe, M., Baier, EL, Sandhaus, R. A. (2009). Exacerbations in subjects with alpha-1 antitrypsin deficiency receiving augmentation therapy. Respiratory Medicine, 103(10), 1532-1539.
Campos, M.A., Alazemi, S., Zhang, G., Wanner, A., Sandhaus, R. A. (2009). Effects of a disease management program in individuals with alpha-1 antitrypsin deficiency. COId), 6(1), 31-40.
Campos, M. A., Wanner, A., Zhang, G., & Sandhaus, R.A. (2005). Trends in the diagnosis of symptomatic patients with alphal-antitrypsin deficiency between 1968 and 2003. Chest, 128(3), 1179-86.
60


Centers for Disease Control and Prevention (CDC), Behavioral Risk Factor Surveillance System Survey Questionnaire. 2004a, Atlanta, Georgia: U.S. Department of Health and Human Services.
Centers for Disease Control and Prevention (CDC), National Health Interview Survey Questionnaire. 2004b, Atlanta, Georgia: U.S. Department of Health and Human Services.
Center for Disease Control and Prevention. (2014c). Data and statistics. COPD Homepage. Retrieved from http://www.cdc.gov/copd/data.htm on November 6, 2015.
Conley, D. & Gauber, R. (2005). Sibling similarity and difference in socioeconomic status: life course and family resource effects. The National Bureau of Economic Research, 11320.
Coronini-Cronberg, S., Heffernan, C., & Robinson, M. (2011). Effective smoking cessation interventions for COPD patients: a review of the evidence. Journal of Royal Society of Medicine Short Reports, 2(10), 78.
Craig, T.J. (2015). Suspecting and testing for alpha-1 antitrypsin deficiency an allergists and/or immunologists perspective. Journal of Allergy and Clinical Immunology Practice, 3(4), 506-511.
Dani, J. A., Harris, R. A. (2005). Nicotine addiction and comorbidity with alcohol abuse and mental illness. Nature & Neuroscience, 8(11), 1465-1470.
De Serres, F. J. (2002). Worldwide racial and ethnic distribution of alpha 1-antitrypsin deficiency: summary of an analysis of published epidemiologic surveys. Chest, 122(5), 1818-1829.
Dirksen, A., Dijkman, J. H., Madsen, F., Stoel, B., Hutchison, D. C., Ulrik, C. S., Skovgaard, L. T., Kok-Jensen, A., Rudolphus, A., Seersholm, N., Vrooman, H. A., Reiber, J. H.,
61


Hansen, N. C., Heckscher, T., Viskum, K., & Stolk, J. (1999). A randomized clinical trial of alpha(l)-antitrypsin augmentation therapy. American Journal of Respiratory Critical Care Medicine, 160, 1468-1472.
Eisner, M. D., Blanc, P. D., Omachi, T. A., Yelin, E. H., Sidney, S., Katz, P. P. Ackerson, L. M., Sanchez, G., Tolstykh, I., & Iribarren, C. (2011) Socioeconomic status, race, and COPD health outcomes. Journal of Epidemiological Community Health, 65(1), 26-34.
Epstein, N. B., Baldwin, L. M., & Bishop, D. S. (1983). The McMaster Family Assessment Device. Journal of Marital and Family Therapy, 9, 171-180.
Fiscella, K., Franks, P., & Shields, C. G. (1997). Perceived family criticism and primary care utilization: psychosocial and biomedical pathways. Family Process, 36, 25-41.
Fiscella, K. & Campbell, T. L. (1999). Association of perceived family criticism with health behaviors. Journal of Family Practice, 48, 128-134.
Fletcher, C. M., Elmes, P. C., Fairbrain, A. S., & Wood, C. H. (1959). The significance of respiratory symptoms and the diagnosis of chronic bronchitis in a working population. British Medical Journal, 2, 257-266.
Ford, E.S., Murphy, L.B., Khavjou, O., Giles, W.H., Holt, J.B., & Croft, J.B. (2015) Total
and state-specific medical absenteeism costs of COPD among adults >18 years in the United States for 2010 and projections through 2020. Chest, 147(1), 31-45.
Foreman, M. G., Zhang, L., Murphy, J., Hansel, N. N., Make, B., Hokanson, J. E., Washko, G., Regan, E. A., Crapo, J. D., Silverman, E. K., Demeo, D. L., & the COPDGene Investigators. (2011). Early-onset chronic obstructive pulmonary disease Is associated with female sex, maternal factors, and african american race in the COPDGene study. American Journal of Respiratory and Critical Care Medicine, 184(4), 414-420.
62


Franks, P., Campbell, T. L & Shields, C. G. (1992). Social relationships and health: the
relative roles of family functioning and social support. Social Sciences Medicine, 34, 779-788.
Fregonese, L & Stol, J. (2008). Hereditary alpha-1-antitrypsin deficiency and its clinical consequences. Orphanet Journal of Rare Diseases, 3, 16.
Giacoboni, D., Barrecheguren, M., Esquinas, C., Rodrigues, E., Berastegui, C., Lopez-Memeguer, M., Monforte, V., Bravo, C., Pirna, P., Miravitlles, M. & Roman, A. (2015) Characteristics of candidates for lung transplantation due to chronic obstructive pulmonary disease and alpha-1 antitrypsin deficiency emphysema. Archivos de Bronconeumologia, 51(8), 379-383.
Global Initiative for Chronic Obstructive Lung Disease (GOLD). (2013). Global strategy for the diagnosis, management and prevention of COPD. Global Initiative for Chronic Obstructive Lung Disease. Retrieved from www.goldcopd.org/guidelines-global-strategv-for-diagnosis-management.html on November 27, 2015.
Gunzerath, L., Connelly, B., Albert, P., & Knebel, A. (2001). Relationship of personality traits and coping strategies to quality of life in patients with alpha-1 antitrypsin deficiency. Psychology, Health and Medicine. 6, 335-341.
Hajiro, T., Nishimura, K., Tsukino, M., Ikeda, A., & Oga, T. (2000). Stages of disease
severity and factors that affect the health status of patients with chronic obstructive pulmonary disease. Respiratory Medicine, 94, 841-846.
Hamilton, J.G., Lobel, M., & Moyer, A. (2009). Emotional distress following genetic testing for hereditary breast and ovarian cancer: a meta-analytic review. Health Psychology, 28(4), 510-518.
63


Hayen, A., Herigstad, M., & Pattinson, K.T.S. (2013). Understanding dyspnea as a complex individual experience. Maturitas, 76, 45-50.
Hill, A.T., Campbell, E.J., Hill, S.L., Bayley, D.L., & Stockley, R.A. (2000). Association between airway bacterial load and markers of airway inflammation in patients with stable chronic bronchitis. The American Journal of Medicine. 109, 288-295.
Holm, K.E., Borson, S., Sandhaus, R. A., Ford, D. W., Strange, C., Bowler, R. P., Make, B.
J. , & Wamboldt, F. S. (2013). Differences in adjustment between individuals with alpha-1 antitrypsin deficiency (AATD)-associated COPD and non-AATD COPD. Journal of Chronic Obstructive Pulmonary Disease, 10, 226-234.
Holm, K. E., Bowler, R. P., Make, B. J., & Wamboldt, F. S. (2009). Family relationship quality is associated with psychological distress, dyspnea, and quality of life in COPD. COPD, 6(5), 359-368.
Holm, K.E., LaChance, H.R., Bowler, R.P., Make, B.J., & Wamboldt, F.S. (2010). Family factors are associated with psychological distress and smoking status in chronic obstructive pulmonary disease. General Hospital Psychiatry, 32(5), 492-498.
Holm, K.E., Wamboldt, F.S., Ford, D.W., Sandhaus, R.A., Strand, R.A., Strange, C. & Hoth,
K. (2013) The prospective association of perceived criticism with dyspnea in chronic lung disease. Journal of Psychosomatic Research. 74(5), 450-453.
Holt-Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: a meta-analytic review. PLoS medicine, 7(7), 859.
Hooley, J.M. & Teasdale, J.D. (1989). Predictors of relapse in unipolar depressives:
Expressed emotion, marital distress, and perceived criticism. Journal of Abnormal Psychology. 98(3), 229-235.
Hoth, K. F., Wamboldt, F .S., Strand, M., Ford, D. W., Sandhaus, R. A., Strange, C.,
64


Bekelman, D. B., & Holm, K. E. (2013). Prospective impact of illness uncertainty on outcomes in chronic lung disease. Health Psychology, 32(11), 1170-1174.
House, J. S., Landis, K. R., & Umberson, D. (1988). Social relationships and health. Science, 241(4865), 540-545.
Ioachimescu, O. C. & Stoller, J. K. (2005). A review of alpha-1 antitrypsin deficiency. COPD, 2, 263-275.
Janoff-Bulman, R. (1979). Characterological versus behavioral self-blame: inquiries into depression and rape. Journal of Personality and Social Psychology. 37(10), 1798-809.
Janson, C., Bjomsson, E., Hetta, J., & Biman, G. (1994). Anxiety and depression in relation to respiratory symptoms in asthma. American Journal of Respiratory and Critical Care Medicine, 149, 930-934.
Jimenez-Ruiz, C. A., Masa, F., Miravitlles, M., Gabriel, R., Viejo, J. L., Villasante, C.,
Sobradillo, V., & the IBERPOC Study Investigators. (2001). Smoking characteristics: differences in attitudes and dependence between healthy smokers and smokers with COPD. Chest, 119, 1365-1370.
John, U., Meyer, C., Rumpf, H. J., Schumann, A., Thyrian, J. R., & Hapke, U. (2003).
Strength of the relationship between tobacco smoking, nicotine dependence and the severity of alcohol dependence syndrome criteria in a population-based sample. Alcohol, 38(6), 606-612.
Kaplan, A. & Cosentino, L. (2010) al-antitrypsin deficiency. Canadian Family Physician,
56, 19-24.
Kelly, E., Greene, C. M., Carroll, T. P., McElvaney, N. G., ONeill, S. J. (2010). Alpha-1 antitrypsin deficiency. Respiratory Medicine, 104(6), 763-772.
65


Ketelaars, C. A., Schlosser, M. A., Mostert, R., Huyer Abu-Saad, H., Halfens, R. J., &
Wouters, E. F. (1996). Determinants of health-related quality of life in patients with chronic obstructive pulmonary disease. Thorax, 51, 39-43.
Kiecolt-Glaser, J. K., Glaser, R., Cacioppo, J. T., MacCallum, R. C., Syndersmith, M., Kim, C., & Malarkey, W. M. (1997). Marital conflict in older adults: Endocrinological and immunological correlates. Psychosomatic Medicine, 59, 339-349.
Kim, Y.I., Schroeder, J., Lynch, D., Newell, J., Make, B., Friedlander, A., Estepar, R. S. J., Hanania, N. A., Washko, G., Murphy, J., A., Wilson, C., Hokanson, J. E., Zach, J., Butterfield, K., Bowler, R. P., & COPDGene Investigators. (2011). Gender differences of airway dimensions in anatomically matched sites on CT in smokers. COPD, 5(4), 285-292.
Kindt, T. J., Goldsby, R. A., & Osborne, B. A. (2007). Immunology. New York, NY: W.H. Freeman and Company.
Kleinman, A., Eisenberg, L., & Good, B. (1978). Culture, illness, and care: Clinical lessons from anthropologic and cross-cultural research. Annals of Internal Medicine, 88(2), 251-258.
Kunik, M. E., Roundy, K., Veazey, C., Souchek, J. Richardson, P. Wray, N. P. & Stanley, M. A. (2005). Surprisingly high prevalence of anxiety and depression in chronic breathing disorders. Chest, 127, 1205-1211.
Ludviksdottir, D., Bjomsson, E., Janson, C., & Boman, G. (1996). Habitual coughing and its associations with asthma, anxiety, and gastroesophageal reflux. Chest, 109(5), 1262-1268.
66


MacDonald, J. L. & Johnson, C. E. (1995). Pathophysiology and treatment of alpha sub-1-antitrypsin deficiency. American Journal of Health-System Pharmacy, 52(5), 481 -489.
Mahler, D. A., Harver, A., Lentine, T., Scott, J. A., Beck, K., & Schwartzstein, R.M. (1996). Descriptors of breathlessness in cardiorespiratory diseases. American Journal of Respiratory and Critical Care Medicine, 154, 1357-1363.
Malcarne, V. L., Compas, B. E., Epping-Jordan, J. E., & Howell, D. C. (1995). Cognitive
factors in adjustment to cancer: attributions of self-blame and perceptions of control. Journal of Behavioral Medicine. 18(5), 401-417.
Marino, P., Sirey, J. A., Raue, P.J., & Alexopoulos, G. S. (2008). Impact of social support and self-efficacy on functioning in depressed older adults with chronic obstructive pulmonary disease. International Journal of Chronic Obstructive Pulmonary Disease, 3(4), 713-718.
Marshall, G. N. (1991). A multidimensional analysis of internal health locus of control beliefs: Separating the wheat from the chaff. Journal of Personality and Social Psychology, 61(3), 483-491.
Martire, L. M., Lustig, A. P., Schulz, R., Miller, G. E., Helgeson, V. S. (2004). Is it beneficial to involve a family member? A meta-analysis of psychosocial interventions for chronic illness. Health Psychology, 23(6), 599-611.
Mayo Clinic Staff. (2015, July 21). Diseases and Conditions: COPD. Mayo Clinic. Retrieved from http://www.mavoclinic.org/diseases-conditions/COPD/basics/causes/con-20032017 on November 29, 2015.
McElvaney, N. G., Stoller, J. K., Buist, A. S., Prakash, El. B., Brantly, M. L., Schluchter, M. D., Crystal, R. D., & the a 1-Antitrypsin Deficiency Registry Study Group. (1997).
67


Baseline characteristics of enrollees in the National Heart, Lung and Blood Institute Registry of alpha 1-antitrypsin deficiency. Chest, 111(1), 394-403.
McKnight, P. E., McKnight, K. M., Sidani, S., Figueredo, A. J. (2007). Missing data: A gentle introduction. New York: The Guilford Press.
"Blamq." Merriam-Webster.com. 2015. http://www.merriam-webster.com (28 November 2015).
Mishel, M. H. & C. J. Braden. (1987). Uncertainty, a mediator between support and adjustment. Western Journal Nursing Research, 9, 43-57.
Molfino, N. A. (2004). Genetics of COPD. Chest, 125, 1929-1940.
Morice, A. H. (2008). Chronic cough: epidemiology. Chronic Respiratory Disease, 5, 43-47.
Mullins, L. L., Chaney, J. M., Pace, T. M., & Hartman, V. L. (1997). Illness uncertainty, attributional style, and psychological adjustment in older adolescents and young adults with asthma. Journal of Pediatric Psychology. 22, 871-880.
Nishimura, K., Izumi, T., Tsukino, M., & Olga, T. (2002). Dyspnea is a better predictor of 5-year survival than airway obstruction in patients with COPD. Chest, 121, 1434-1440.
Oliver, S. M. (2001). Living with failing lungs: the doctor-patient relationship. Family Practice. 18(4), 430-439.
Omachi, T. A., Sarkar, Urmimala, S., Yelin, E. H., Blanc, P. D., & Katz, P. P. (2013). Lower health literacy in associated with poorer health status and outcomes in chronic obstructive pulmonary disease. Journal of General Internal Medicine, 28(1), 74-81.
Parkes, G., Greenhalgh, T., Griffin, M., & Dent, R. (2008). Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised controlled trial. British Medical Journal (BMJ), 336, 598-600.
68


Petty, T. L. (2004) Definition, epidemiology, course, and prognosis of COPD. Clinical Cornerstone, 5(1), 1-10.
Pollock, S. E., Christian, B. J., & Sands, D. (1990). Responses to chronic illness: analysis of psychological and physiological adaptation. Nursing Research, 39(5), 300-304. Regan, E. A., Hokanson, J. E., Murphy, J. R., Make, B., Lynch, D. A., Beaty, T. EL, Curran-Everett, D., Silverman, E.K., Crapo, J. D. (2010). Genetic epidemiology of COPD (COPDGene) study design. COPD, 7(1), 32-43. http://doi.org/10.3109/15412550903499522 Ross, C. E. & Wu, C.L. (1995). The links between education and health. American Sociological Review, 60, 719-745.
Ryan, C. E., Epstein, N. B., Keitner, G. I., Miller, I. W., & Bishop, D. S. Evaluating and treating
families: the McMaster approach. New York: Routledge.
Sarason, I. G., Sarason, B. R., Shearin, E. N., & Pierce, G. R. (1987). A brief measure of social support: practical and theoretical implications. Journal of Social & Personal Relationships, 4, 497-510.
Scharloo, M., Kaptein, A.A., Weinman, J. A., Willems, L. N., & Rooijmans, H. G. (2000). Physical and psychological correlates of functioning in patients with chronic obstructive pulmonary disease. Journal of Asthma, 37, 17-29.
Schnittker, J. (2004). Education and the changing shape of the income gradient in health.
Journal of Health & Social Behavior, 45(3), 286-305.
Shadel, W. G., Mermelstein, R., & Borrelli, B. (1996). Self-concept changes over time in cognitive-behavioral treatment for smoking cessation. Addictive Behaviors, 21(5), 659-663.
69


Shields, C. G., Franks, P., Harp, J. J., McDaniel, S. H., & Campbell, T. L. (1992).
Development of the Family Emotional Involvement and Criticism Scale (FEICS): a self-report scale to measure expressed emotion. Journal of Marital & Family Therapy, 18, 395-407.
Shields CG, Franks P, Harp JJ, Campbell TL, McDaniel SH. (1994). Family Emotional
Involvement and Criticism Scale (FEICS): II. Reliability and validity studies. Family Systems Medicine, 12, 361-377.
Skelvington, S. M, Pilaar, M., Routh, D., & MacLeod, R. D. (1997). On the language of breathlessness. Psychology and Health, 12, 677-689.
Smith, G. D., & Egger, M. (1992). Socioeconomic differences in mortality in Britain and The U.S. American Journal of Public Health, 82, 1079-1080.
Smoller, J.W., Simon, B.M., Pollack, M.H., Kradin, R., & Stern, T. (1999). Anxiety in
patients with pulmonary disease: comorbidity and treatment. Seminars in Clinical Neuropsychiatry, 4(2), 84-97.
Stoller, J. K. & Brantly, M. (2013). The challenge of detecting alpha-1 antitrypsin deficiency. COPD, 1, 26-34.
Stoller, J. K., Lacbawan, F. L., & Aboussouan, L. S. (2006 Oct 27 [Updated 2014 May 1]). Alpha-1 Antitrypsin Deficiency. In: Pagon, R. A., Adam, M. P., & Ardinger, H. H., editors. GeneReviews [Internet], Seattle (WA): University of Washington, Seattle; 1993-2015.
Stoller, J. K., Smith, P., Yang, P., & Spray, J. (1994). Physical and social impact of Alphal-antitrypsin deficiency: results of a survey. Cleveland Clinic Journal of Medicine, 61, 461-467.
70


Stoller J. K., Snider G. L., Brantly M. L., Fallat, R. J., & Stockley, R. A. for the Alpha-1 Antitrypsin Deficiency Task Force of the American Thoracic Society /European Respiratory Society. (2003). Standards for the diagnosis and management of patients with alpha-1 antitrypsin deficiency. American Journal of Respiratory Critical Care Medicine. 168(7), 816-900.
Stoller, J. K., Strange, C., Schwarz, L., Kallstron, T. J., & Chatburn, R. L. (2014). Detection of alpha-1 antitrypsin deficiency by respiratory therapists: experience with an educational program. Respiratory Care, 59(5), 667-672.
Sykes, A., Mallia, P., & Johnston, S. L. (2007). Diagnosis of pathogens in exacerbations of chronic obstructive pulmonary disease. Proceedings of the American Thoracic Society, 4, 642-646.
Tanash, H. A., Riise, G. C., Hansson, L., Nilsson, P. M., & Piitulainen, E. (2011). Survival benefit of lung transplantation in individuals with severe al-anti-trypsin deficiency (PiZZ) and emphysema. The Journal of Heart and Lung Transplantation, 20(12), 1342-1347.
Uchino, B. N. (2004). Social support and physical health: Understanding the health
consequences of relationships (Current perspectives in psychology). New Haven, Connecticut: Yale University Press.
Uchino, B. N. (2005). Social support and physical health: Understanding the health
consequences of relationships. American Journal of Epidemiology, 161(3), 158.
van den Putte B, Yzer M, Willemsen MC, & de Bruijn GJ. (2009). The effects of smoking self identity and quitting: self-identity on attempts to quit smoking. Health Psychology, 28(5), 535-544.
Vangeli, E., Stapleton, J., & West, R. (2010). Residual attraction to smoking and smoker
71


identity following smoking cessation. Nicotine & Tobacco Research, 12(8),
865-869.
Voth, J. & Sirois, F. M. (2009). The role of self-blame and responsibility in adjustment to inflammatory bowel disease. Rehabilitation Psychology, 54(1), 99-108.
Weihs, K., Fisher, L., & Baird, M. (2002). Families, health, and behavior: A section of the commissioned report by the committee on health and behavior: Research, practice, and policy Family Systems & Health, 20, 7-46.
Wilson, F (2006). Depression in the patient with COPD. International Journal of Chronic Obstructive Pulmonary Disease, 1, 61-64.
Yohannes, A. M., Willgoss, T. G., Baldwin, R. C., & Connolly, M. J. (2010). Depression and anxiety in chronic heart failure and chronic obstructive pulmonary disease; prevalence, relevance, clinical implications and management principles. International Journal of Geriatric Psychiatry. 25, 1209-1221.
72


APPENDIX
Mega Quest Questionnaire is source of all variables for proposed analyses: Below are questions for demographic data.
Wfl iiSEN6.2KNl
RJCHT WRONG
O <30 1
Mega Quest
Instructions
Tharfc, you for taking ihe lime 10 complete this queaoonnwre We value tha information you ar* giving us
Please read each question care My, but do not spend loo much tme thinking about your response Your immediate reaction to each question s Mcety to be more accurate than a long, thought-out response Pesse respond to each Question, even If it is very similar to other questions in this questionnaire Again, thank you for your help *Mh this study
Date of Birth
General Information About My Health and Mo
Month Day
Year
Offic* Use Only
Your ethnic background:
Hispanic or Latno Not Hispanic or Latino
Which one or more of Fie Mowing *txiC yCM say is your rsca?
'Atube Asian
Amercen idiar. Aaska Native
Black or Afrcan American
Native Hawaiian or Other Pacific islander
Dont KnowiMol Sure
Which ONE of the following beet represents your race?
While
Asian
American Indian, Alaska NaFve Black ix African-American Natr* Hawaiian or Other Pacific Islander Other. _____________
Don't Knorts'Not Sure ____
F

feat inches
weghi
Your Gender
Milt
Female
Which statement below best describes your fcving situation?
living alone
living with famJy members {such as spouse^partner children!
other {please describe.________________________________________I
_L
| What is the highest grade or year of school you completed?
grade 8 or less (elementary or less)
grades 9-11 (some high school)
grade 12 or uEO |hi$yi school p-adua'.el collogo 1 year to 3 years {some co*e&9 or technical schoci| collage 4 years or more (ooilege graduate)
* What >s your annual housebote Trjome from all sources7 Less than $15,000
Between $15,001 and Between 525,001 and Between $36.001 and Between $50,001 and $75,001 or more
525.000
535.000 550, OCO
575.000
In M-jt stale do you Irvel
A

At what age did your lung
* symptoms begin?
At what age was your alpha-1 antitrypsin dsftooncy diagnosed7
o
1 I
2
3!
4
5
6
sL
How many afferent doctors did youvisu balare being diagnosed?
Are you currently married
d vorcedi'separaled widowed never narried
a member of an unmarried couple Do you hava doperdent children at borne7
Yes No
Would you say mat in general your healb e
Excellent Very good Good Fair Poor
Which type 22
SZ
MZ
unknown other _____
Bubble ol thal deserbe yotr empfcrymenl status homemaker working full none working part lime jiiempoyedidckng lor work retired reached retirement age retired due to king disease
Yes/Nc Do you participate in Apha-f support group or educational day meetings? If yes how many tines in the past 12 months have you participated?
I 1
Yqs/No Do any Of your family memcors hava alpha* 1 anblrypsin Cfirierc>'?
YesfNo Do any of your family rrambars have COPD. empiysema chronc bronchitis, Of asthma?
3
Megal 1st
73


Mega Quest 4
cT
We are interested m what you consider may have been the cause of your COPD As people are very different. there e no correct answer tor this question We are most interested in your own views about the (actors that caused your COPD rather than what others (including doctors and lam*y) may have suggested to you. Below is a list c possible causes for your COPD Please indicate how much you agree or disagree that they were causes (or you by bubbling the appropriate circle when: 1 = Strongly Disagree; 2 Disagree. 3 Neither Agree nor disagree; 4 Agree; 5 = Strongly Agree
1
Stressor worry
Hereditary it runs in my tamy. A germ or virus Diet or eabrg habts Exercise haMsactnity patterns.
Chanoe or bad luck Poor medical care in my past Pollution in Ihe envinonmcntiworkplace My own behavior
My menial attitude log. thinking about life negatively).
Smoking
Accident or njury,
My personality Altered Immunity, Infection or pneumonia
Family problems or worries caused my COPO.
Overwork
My emotional stale (eg feeing down, lonely aneous. empty I Aging.
Akxihol.
People often think about Ihe reasons why things happen Id them lAtien you consider why you got COPD. whal do you usually thnk the reasons are? Please answer Ihe fblkiwng usng this scale; f = Afever thin* this is e reason.
2 = Rarefy f/ivt* this is a reason. 3 Sometimes think this is a reason. 4 Often think Uhls is a reason.
12 3 4
That you are an unlucky person.
That II was just a matter of bad luck That you smoked.
That you are a careless person That you arc an ignorant person
That you arc the type of person Ihs kind of ftiing happens to
If your COPO was caused by smoking, now much do you blame yowsetf for smoking?
Not at all A little Some Mostly Completely
How much do you blame yourself for any behavior that led to your COPD?
Not at all A little Some
Mostly
Completely
Bubble here 4 your COPD was HOT caused by smoking
Items for characterological self-blame are under the second block of writing, the 4-point rating scale. The questions below those are for behavioral self-blame, but only the second question will be used given the proposed dichotomization of sample into past smokers and never smokers.
74


First seven questions are the items to be used for Perceived Family Criticism. The last of the three questions following Perceive Family Criticism is the item that will be used for Perceived Family Blame.
Mega Quasi Page 8
My Family
Please answer the following Describe your family.
1 Almost newer; 2 Once in a whale. 3 Sometimes; 4 Often; 5 Almost always
1 2 3 4 5
My family approves of most everything I do.
My family finds Ml wtdi my friends.
My family complains about the way I hanrSe money.
My family approves of my friends My family complans about what I do for fun My family is always frying lo gel me to change t have to be careful of what I do or my family wd put me down For the loNavrmg throe questions, piease bubble the number on the scale that corresponds lo your i On average, how critical do you think your family is of you?
Not at all critical Very critical
How critical do you thxik your spouse1 partner of you?
Not at all critical Very crvticsl
0
1
2
3
4
5
6
7
8 9
Official Used Only
iiffl
To what extent do you think your famay blames you (or havng COPD9 Not M B Opmpleiely
For the following questions. 1 Strongly agree; 2=Agree; 3=Otsagree. 4Strongly Disagree
12 3 4
Planning family activities is difficult because we misunderstand each other
In times cf crisis we can turn to each other for support
We cannot talk to each other about the sadness wo loci
Individuals are accepted for what they are
We avoid discussing our fears and concerns
We can express feelirgs to each other
There are lots of bad fadings i the family
We feel accepted for what we are
Making decisions is a problem for our family
We are able to make decisions about now to solve problems.
We don't get along we together We confide in each other
Social Support
The following questions ask about people who provide you with help or support Each question has two pans
The first part asks how satisfied you are with support in this area Bubble the circle that best desribes how satisfied you are with support in this area
The second part asks about sources of support For this pert, bubble the circle next to each person who provides this type of support Select all dial apply,
If you have no support for a question, bubble next to 'No one. hut atilt rate your level of satisfaction when 1Very satlefied; 2>Fairty Satisfied; 3A little Satisfied; 4=A little Dissatisfied; $=Falrly Dissatisfied. 6Very Dissatisfied
Who can you really count on to be dependable when you need help?
Spouse Partner Other Family Other People Friends No one
Who can you count on to help you feel more relaxed when you are under pressure or tense?
SpousefPartner Other Family Other People Friends No one
Who accepts you totally. including both your worst and your best points?
SpousefPartner Other Family Other People Friends No one
Who can you really count on to care about you. regards ss of what is happening to you?
Other Family Other People Friends No one
to help you when you are feeling generally down-tn-the-diarpe?
Other Family Other People Friends No one
to ccnsole you when you are very upset?
Other Family Other People c> Friends c No one
SpouseiPartner
Who can you count on SpousefPartner Who can you count on SpousefPartner
75
lllllllllllllllllllll 111111111111 l I I 111111111II l


Full Text

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SMOKING HISTORY IN A ATD ASSOCIATED COPD: DIF FERENCES IN DEMOGRAPHIC AND PSYC HOSOCIAL FEATURES BE TWEEN NEVER SMOKERS AND FORMER SMOKERS By SHIVA FEKRI B.A. Yeshiva University, 2009 A thesis submitted to the Faculty of the Graduate S chool of the U niversity of Colorado in partial fulfillment o f the requirements for the degree of Master of Arts Clinical Psychology 2016

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! "" 2016 SHIVA FEKRI ALL RIGHTS RESERVED

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! """ This thesis for the Master of Arts degree by Shiva Fekri has been approved for the Clinical Health Psychology Program by Kristin Kilbourn, Chair Kristen Holm Barbara Walker Date: July 30, 2016

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! "# Fekri, Shiva (M.A. Clinical Psychology) Smoking History in AATD Associated COPD Thesis di rected by Ass ociate Professor Kristin Kilbourn ABSTRACT Chronic Obstructive Pulmonary Disease (COPD) is a category of diseases that are identified by progressive and chronic airflow obstruction. Alpha 1 Antitrypsin Deficiency is a genetic disorder chara cterized by a deficiency or lack of the antiprotease, alpha 1 antitrypsin, leading to the development of COPD. Data were collected as part of a larger study that was conducted by Dr. Kristen Holm and funded by the Alpha 1 Foundation. Participants were recr uited through the Alpha 1 Foundation Research Registry using a mailed questionnaire. This study is the first to look specifically at the psychosocial impact of smoking history in a sample of individuals with AATD associated COPD. The association between sm oking history and the unique health, psychosocial, and perceptual characteristics of patients with AATD associated COPD was examined S moking history consistently predicted 3 out of the 4 outcome variables: characterological self blame, behavioral self bla me, and perceived family blame among individuals with AATD associated COPD. Perceived family criticism was not predicted by smoking history. The form and content of this abstract are approved. I recommend its publication. Approved: Krist in Kilbourn

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! # DEDICATION To my Joshua, my everlasting mate in unconditional love and faith. You are my inspiration, my sunlight, and my ocean. And little Banana, whose snuggles, sighs and licks remind me that life is fundamentally simple and sweet.

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! #" ACKNOWLEDGEMENTS Thank you, Kristen Holm, for generously providing me with this wonderful dataset and for sitting with me tirelessly, kindly and patiently, molding this project and molding me as a graduate student. You are a major force in the manifest ation of my efforts in this program. Thank you, Kristin Kilbourn, for advocating for me, being present with me, mentoring me, and always believing in me. My favorite times in the program have been working by your side. Thank you, Barbara Walker, for int roducing me to the biopsychosocial model, for pushing me to be a better writer, and for pushing me to evaluate myself with humility and honesty. Thank you to my mother Rachel GŸlen, and to Eliyahu for your emotional and financial support during this ende avor. And to my B ijili, Daniel, for accepting that I cannot be close but that I'm never far. Thank you to my Baba, Amir Fekri, for rousing and cultivating my intellect, and for loving me with all of your heart and soul.

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! #"" TABLE OF CONTENTS CHAPTER I. INTRO D UCTION 1 Chronic Obstructive Pulmonary Disease 1 AATD Associated COPD 2 Defining "S moking History" 4 Gender Differences i n Smoking Behavior in COPD 5 Smo king and AATD Associated COPD. 5 Prognosis and Treatment of AATD Associated COPD 8 Physical Adjustment to AATD 13 Psychosocial Adjustment to AATD ... 14 Self Blame 16 Demographic, Socioeconomic Status, and Health 19 Family Support and Health 22 Family Criticism, F amily Blame, and Health in AATD 23 Conclusion .. 25 II. AIMS AND HYPOTHESES 27 Aim 1 .. 27 Aim 2 .. 27 Aim 3 28 III. METHODS 29 Participants and Procedures 29 Measures 31 Demogr aphics & health characteristics 31

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! #""" Smoking behaviors 32 Self blame 32 Fa mily blame and family c riticism 33 Statistical Analyses ... 33 Preliminary Analyses 33 Analyses for Aim 1 34 Analyses for Aim 2 34 Analyses for Aim 3 35 IV. RESULTS 37 V. DISCUSSION 47 REFERENCES 59 APPENDIX MegaQ uest Questionnaire items used in this project 73

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! "$ LIST OF TABLES TABLE 1. Characteristics of Sample 38 2. Characterological and Behavioral Self Blame 41 3. Results of logistic regression models to predict characterological and behavioral self blame .. 42 4. Perceived Family Blame and Perceived Family Criticism.. 44 5. Results of logistic regression models to predict family blam e & family criticism 45

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! $ LIST OF FIGURES FIGURE 1. Recruitment flow diagram. 30

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! $" LIST OF ABBREVIATIONS AAT Alpha1 Antitrypsin AATD Alpha1 Antitrypsin Deficiency CDC Center for Disease Control and Pr evention COPD Chronic Obstructive Pulmonary Disease FEV1 Force Expiatory Volume at 1 second FVC Forced Vital Capacity

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! % CHAPTER I INTRODUCTION Chronic Obstructive Pulmonary Disease Chronic Obstructive Pulmonary Disease (COPD) is a category of diseas es that are characterized by progressive and chronic airflow obstruction. In this category are emphysema, chronic bronchitis, and asthma. The CDC has found that prevalence of COPD in the United States ranges from less than 4% in some states and up to 9% in others (Centers for Disease Control and Prevention [CDC], 2014). It is estimated that 14.2 million people have been diagnosed with COPD in the United States, and it is the 3 rd leading cause of mortality in the U.S. with 149,205 deaths from COPD or chronic lower respiratory diseases per year (CDC, 2014 c ). The total national medical costs attributed to COPD were estimated to be $32.1 billion in 2010, with an additional cost of $3.9 billion in loss due to absenteeism, bringing the total national cost to $36 b illion dollars (Ford, Murphy, Khavjou, Giles, Holt, & Croft, 2015). The average age of onset for COPD is 53 (CDC, 2014 c ). COPD is characterized by dyspnea ( pronounced disp nee uh ) upon exertion, coughing or wheezing, sputum and mucus production, frequen t respiratory infections, and at later stages, fatigue and weight loss. Spirometry is a test that is used to help diagnose COPD. It generates two measures of lung capacity for diagnosis: forced vital capacity (FVC) and forced expiratory volume at one secon d (FEV1). FVC is the total amount of air that is exhaled in one breath. COPD is confirmed when FVC drops to below 70% (Petty, 2004). FEV1 is the amount one is able to exhale in the first second, and it is used to determine the stage of COPD, of which there are four: 0 At Risk, I Mild COPD, II Moderate COPD, III Severe COPD. Prognosis in COPD is variable depending on the stage of COPD,

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! & exacerbations of COPD, continued smoking, and status of comorbid medical conditions or other risk factors such as Bo dy Mass Index (BMI) and exercise capacity. COPD is caused by a combination of factors, but 85% of cases of COPD are attributable to smoking (Holm, LaChance, Bowler, Make, Wamboldt, 2010). Fifteen to twenty percent of smokers develop COPD (Ioachimescu & St oller, 2005). In combination with and in addition to smoking, factors leading to COPD are exposure to environmental pollutants and genetic predisposition. The most commonly identified genetic risk factor for COPD is alpha 1 antitrypsin deficiency (AATD). I ndividuals with AATD develop COPD at an earlier age than people who do not have this genetic condition. A ATD A ssociated COPD Alpha 1 Antitrypsin Deficiency is a genetic disorder that was first identified in 1963, and it is characterized by a deficiency o r lack of the antiprotease, alpha 1 antitrypsin. An antiprotease is an agent that blocks the activity of a particular protease. A protease is an enzyme that lyses proteins. Neutrophil elastase is a protease that is released by neutrophils (a type of white blood cell that is part of the immune response), which are located in the lower respiratory tract. When neutrophils release neutrophil elastase (NE), it causes the breakdown of proteins in the extracellular matrix that makes up the structure of the lung ti ssue. The breakdown of these proteins creates an increase in airspaces in the tissue, and over time, the development of emphysema and COPD (McElvaney, Stoller, Buist, Prakash, Brantly, Schluchter, Crystal, Alpha 1 Antitrypsin Deficiency Registry Study Grou p, 1997). Alpha 1 antitrypsin (AAT) is coded by the SERPINA1 gene on the autosomal chromosome number 14, making AATD an autosomal codominant disorder. Over 150 alleles of AAT have been identified and they make up four phenotypes or physiologic manifestat ions of the genotype: normal normal alleles, normal functioning; deficient

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! lower plasma AAT levels; null no AAT detectible in plasma; dysfunctional AAT present but the protein is ineffective. The "M" allele is most common allele for AAT and is what codes for normal functioning of the SERPINA1 gene. The "S" allele codes for producing low er levels of the protein, and it causes mild deficiency. The "Z" allele produces very low levels of the protein and causes severe deficiency. As an autosomal codomina nt disease, people have two copies of the gene. Patients who have one copy of the "Z" gene are called carriers, because they may display symptoms later on in life, but they are more likely to become symptomatic if they smoke. Alpha 1 antitrypsin is produc ed by liver cells, and in the case of AATD, the alpha 1 antitrypsin that is produced is not adequately released, which causes damage to the lung tissue where it is needed, but also causes damage to the liver tissue where it begins to build up, particularly in individuals with the more severe variants of the AATD alleles. Liver damage can result in neonatal hepatitis and cirrhosis (Buist, 1990). It is estimated that 4 6% of the Caucasian population in the United States carries the allele for AATD (de Serres, 2002). Many individuals with AATD are initially asymptomatic and are unaware that they may develop a serious medical condition (Seersholm & Jensen, 1998). The initial symptoms of the condition are common pulmonary symptoms such as dyspnea with and without exertion, coughing and mucus/sputum production which are often misdiagnosed as asthma or allergies (McElvaney, et. al. 1997). Th ose with AATD are often diagnosed and treated by primary care physicians allergists, and internists as opposed to pulmonologi sts, who are more familiar with the condition ( McElvaney, et al. 1997; Stoller, Strange, Sc hwarz, Kallstrom, Chatburn, 2014 ). Thus, many individuals with AATD are often diagnosed with

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! ( the condition many years after the onset of their initial pulmonary symp toms (Campos, Wanner, Zhang, Sandhaus, 2005; Stoller & Brantly, 2013) Compared to the average age of COPD onset of 53, individuals with AATD will develop COPD between the ages of 20 and 50 years old. The variety of alleles of AATD in combination with beha vioral factors (e.g. smoking, comorbid health conditions) and environmental factors (e.g. other pollutants or insults to lung tissue) are what account for the large variance in age of COPD onset. According to Kelly, Greene, Carroll, McElvaney & O'Neill (20 10), non smoking individuals with AATD develop COPD at 50 to 60 years old, whereas smokers with AATD develop COPD between 20 and 40 years of age. A primary determinant of age at which COPD develops is an individual's smoking history. Defining Smoking Hist ory According to the CDC's Behavioral Risk Factor Surveillance System (CDC, 2014a), smoking 100 cigarettes or more in one's lifetime constitutes a history of smoking. A study on the genetic epidemiology of COPD is the COPDG ene study (Reagan et al. 2010.) It has been enrolling smokers and non smoker controls with and without COPD and studying the genetic factors, phenotypes, disease etiology, and progression of COPD. The largest study of its kind, it has established criteria for clinically significant "smo king history." The inclusion criteria specify smoking history as having a minimum of 10 pack years in their lifetime ( Putcha, et al. 2014.) Pack years is a measure of extent of smoking history and is calculated by multiplying the number of packs of cigaret tes smoked per day by the num ber of years the person smoked. Gender Differences in Smoking Behavior in COPD In a publication resulting from the COPDGene study, researchers found that the female sex was significantly associated with early onset COPD (Fore man et al. 2011).

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! ) Campos et al. (2009) also found that female patients with AATD associated COPD tend to have more frequent exacerbations. Another study from the COPDGene dataset found that the female sex was th e most significant predictor of lung tissue that is more vulnerable (e.g. lower measurements in wall area percentage, lumen area, inner diameter, and wall thickness) to insults and subsequent pulmonary disease (Kim et al. 2011). This evidence suggests that not only are women more likely to develop C OPD earlier, but it may take less exposure to smoking to cause serious damage to the lung tissue as compared to the amount of exposure that males can sustain before having similar levels of damage. Smoking and AATD A ssociated COPD COPD is often defined as an irreversible disease because, unlike asthma, it is thought to be unresponsive to bronchodilators, which are medications that reduce resistance in airflow by dilating the bronchioles and bronchi. However, patients with COPD often do show a positive resp onse to bronchodilators, as measured by improvement in FEV1, when they stop smoking (Petty, 2004). Research has found that cigarette smoking accelerates the onset of dyspnea by up to 19 years in patients with AATD associated COPD (Ioachimescu & Stoller, 20 05). Therefore, a major barrier to improvement in lung function in COPD and AATD associated COPD patients is persistence of smoking (Bednarek et al. 2006; Ioachimescu & Stoller, 2005; Jimenez Ruiz et al. 2001; Tashkin & Murray, 2009). Given the connection between smoking and the acceleration and exacerbation of pulmonary disease in AATD ( Petty, 2003 ), it is important to understand the factors associated with smoking behavior in individuals with AATD associated COPD and other chronic illness populations. In studies on smoking cessation in patients with head and neck cancers, several variables were found to contribute in varying degrees to smoking cessation rates: tumor stage, gender, age, marital status, education level, tumor site, treatment modality, and

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! to bacco use history (Ark, DiNardo & Ol i ver, 1997). In their own study on smoking cessation in head and neck cancer patients, Ark, DiNardo and Oliver (1997) found that persistence in smoking was more common in older adults. They also found that smoking cessat ion rates were higher in women (82%) than in men (61%). People were more likely to quit when having more intensive treatment modalities for their head and neck cancer, such as surgery, with the highest quit rate in the study being among patients who had a total laryngectomy (95%). Stage of tumor trended but was not statistically significant in impacting quit rates. Ark and colleagues also found that the median age of onset of smoking was higher in successful quitters, and the median number of cigarettes smo ked per day was less in successful quitters. A study on the characteristics of successful quitters (Lee & Kahende, 2007) found that they tended to have no smoking rules at home, were less likely to have switched to "lighter" options (e.g. light tar cigar ettes), were aged 35 and older, were married or had a domestic partner, were non Hispanic White, and had a college education. To date, there are only a limited number of studies examining smoking behavior in those with AATD associated COPD, and there is l imited information on smoking rates in this population. A study by Carpenter and colleagues (2007) examined the impact of genetic testing for AATD on smoking cessation. They reported that 59% of individuals with the severe genotype of AATD attempted to q uit smoking within the first 24 hours of receiving their genetic test result. In comparison, a 26% quit attempt rate was seen in individuals who tested negative for the AATD genotype, and a 34% quit attempt rate was seen in individuals who were carriers. N o significant differences in abstinence rates were found between the three groups at the three month follow up. This indicates that while there may be greater motivation for smoking cessation closer to the time of diagnosis, the increased level of motivati on to quit smoking that was observed in those diagnosed with AATD was not

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! + sustained long term, suggesting that other factors such as environment, support from family, and various cognitions are associated with tobacco quit attempts (Carpenter et al., 2007) In a study on smoking cessation in COPD, Bednarek and colleagues (2006) found that smoking cessation advice had a greater impact on patients who were experiencing more airway obstruction, suggesting that the discomfort and anxiety caused by airway obstr uction motivated patients to change their smoking status (Bednarek et al. 2006). Parkes Greenhalgh, Griffin, & Dent (2008) found that notifying patients of their "lung age," based on spirometry values and adjusted for gender and height, was associated wit h better quit rates than only notifying them of their FEV1 value from spirometry. One study that highlights what may be a major underlying problem in smoking cessation in people with COPD found that smokers with COPD had greater nicotine dependence than sm okers without COPD (Jimenez Ruiz et al. 2001). This may be a factor in why the average long term quit rate among patients with COPD is only 25% (GOLD, 2013; Klinke & Jonsdottir, 2014). Another factor is that comorbid emotional disorders can be a barrier in successful smoking cessation. Wilson (2006) states that 25% of people with severe COPD are also depressed, while 19.6% of people with mild COPD are depressed. According to Tashkin and Murray (2009) psychiatrists state that smoking has antidepressant affec ts in depressed people. Smoking is also perceived by COPD patients to have a calming effect (Coronini Cronberg, Heffernan & Robinson, 2011), which is an added barrier to smoking cessation in this medical population. In a study on family factors impacting s moking status in COPD, researchers found that unsupportive family relationships were associated with high psychological distress, and psychological distress, in turn, was associated with smoking status (Holm, LaChance, Bowler, Make, Wamboldt, 2010). The re searchers concluded that in targeting smoking in

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! patients with COPD it is necessary to target both psychological distress and family relationship function. Prognosis and Treatment of AATD Associated COPD An important factor in treatment and management of AATD associated COPD is accurate and timely diagnosis (Craig, 2015). Studies have shown that approximately 10,000 of the estimated 100,000 individuals with AATD in United States, or 10%, have been diagnosed (Stoller, Snider, Brantly, Fallat, Stockley, 201 3), indicating that diagnosis of AATD remains a major challenge (Stoller, et al 2014). Patients with pulmonary symptoms stemming from AATD are typically diagnosed with asthma, treated with multiple courses of antibiotics, and are evaluated for gastroesopha geal reflux, sinusitis or post nasal drip (Izaguirre, Lanza, & Bryd, 2014). The average time from onset of symptoms to diagnosis of AATD is 8.3 +/ 6.9 years (Campos, et al 2005). Individuals with COPD are extremely susceptible to pulmonary infections, vi ral and bacterial, due to certain features of the disease. Pulmonary illnesses, including chronic bronchitis, COPD, and AATD associated COPD are all characterized by sputum production (Hill, Campbell, Hill, Bayley, Stockley, 2000). The respiratory tract ha s its own bacterial flora, which is kept in balance by the immune system and its inflammatory and anti inflammatory properties (Koby, 2007, p. 447). The bacteria in the sputum of patients with pulmonary disease reflect the health of the respiratory system. Studies have found that 20 40% of patients with COPD have positive sputum cultures for bacteria and viruses, even when they are clinically stable (Sykes, Mallia & Johnston, 2007). These pathogens are not necessarily implicated in causing the bacterial or viral illnesses that trigger exacerbations, but they are part of why these patients are more susceptible to contracting the illnesses that lead to exacerbations.

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! The colonization of sputum by pathogenic bacteria and viruses is understood to be due to an i neffectiveness of the immune system in the respiratory system to sterilize the airways (Hill, et al. 2000). In the case of a weakened innate or primary immune response, which can be due to chronic or long term exposure to pathogens, the adaptive or seconda ry immune response is triggered, which enlists proinflammatory mediators that have a larger systemic impact (Hill, et al. 2000). Increased inflammation resulting from the adaptive immune response leaves a system more reactive and vulnerable to new pathogen s. Increased inflammation also upregulates airflow obstruction, further exacerbating the pulmonary disease state. Illness exacerbation is one of the most common complications and contributors to medical and psychological morbidity in COPD (Campos, et al. 2 009). An exacerbation causes a sharp decrease in lung functioning that can lead to morbidity, hospital admissions, and death (Wedzicha & Donaldson, 2003). Exacerbations can be caused by a bacterial infection, a cold, other viruses, pollutants, or other lun g irritants, and in many cases, the etiology of the exacerbation is unclear. Studies have found that these exacerbations strongly impact health related quality of life and occur 2 3 times per year in patients who are non smokers. In smokers, exacerbations occur the upwards of 3 to 5 times a year (Campos, Alazemi, Zhang, Wanner, Salathe, Baier & Sandhaus, 2009; Wedzicha & Donaldson, 2003). Campos et al. (2009) found that the patients with AATD associated COPD who tend to have more frequent exacerbations have the following characteristics: female, younger age, have a smoking history, and are unemployed. These characteristics were also unaffected by whether or not they were receiving augmentation therapy, the common treatment for AATD. Currently, there are thre e lines of treatment for AATD associated COPD (Ioachimescu & Stoller, 2005). The first line of treatments, or the less invasive and more commonly used treatments for COPD (AATD associated and non AATD associated) are the

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! %. use of inhaled and systemic anti in flammatory agents, pulmonary rehabilitation, bronchodilators, and at later stages, oxygen therapy (Ioachimescu & Stoller, 2005; Kaplan & Cosentino, 2010). Patients are also encouraged to obtain yearly preventative vaccinations (e.g. flu and pneumonia vacc inations) and they are strongly advised to quit smoking (Ioachimescu & Stoller, 2005; Kaplan & Cosentino, 2010). These treatments and recommendations are commonly prescribed at the first onset of symptoms and are ideally adhered to for the rest of the pa tient's life as a primary method of symptom management (Izaguirre, et al, 2014; MacDonald & Johnson, 1995). The second line of treatment, and the only treatment specific for AATD associated COPD is augmentation therapy. Augmentation therapy is weekly intr avenous infusions of pooled human plasma containing alpha 1 antitrypsin. This procedure is done to boost the presence of AAT in the patient's system (Campos, Alazemi, Zhang, Wanner, Salathe, Baier, & Sandhaus, 2009; Ioachimescu & Stoller, 2005). Findings a re mixed on the effectiveness of augmentation therapy. One study by Seerholm, Wencker, Banik, Viskum, Dirksen, KokJensen, Konietzko (1997) found that in comparison to AATD patients not receiving augmentation therapy, AATD patients who received augmentation therapy had lower decline in FEV1, suggesting clinical benefit. Some studies state that augmentation therapy confers pulmonary anti inflammatory benefits, which may reduce airflow obstruction and may reduce the number and severity of exacerbations (Ioachi mescu & Stoller, 2005). Other studies state, however, that augmentation therapy does not have significant effects on pulmonary function in AATD patients and that even when improvement in comparison to placebo is observed, these observations fail to reach statistical significance (Dirksen, Dijkman, Madsen, Stoel, Hutchison, Ulrik, Skovgaard, Kok Jensen, Rudolphus, Seersholm, Vrooman, Reiber, Hansen, Heckscher, Viskum, &. Stolk, 1999; Stockley, 2014).

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! %% The premise behind augmentation therapy is that biochemic ally, replacing the deficient agent should prevent further breakdown of lung tissue, promote its preservation and its functionality. But this biochemical phenomenon has not demonstrated consistent clinical efficacy (Stockley, 2014). One argument that is ma de against studies that find augmentation therapy to be effective is that the range typically used to determine significant impact is FEV1 decline values between 35% and 60%. FEV1 decline is found to be most rapidly changing and declining in this range (St ockley, 2014), so conclusions based on changes in this range of values could be an inflated re presentation of the true effectiveness of augmentation therapy. Another argument against the effectiveness of augmentation therapy is that it is very expensive an d cost prohibitive. Consequently, it is more likely that individuals who receive this therapy have high socioeconomic status and typically have better health outcomes than people of lower socioeconomic status Therefore, the existing data on the effectiven ess of augmentation therapy are neither statistically compelling nor reflective of outcomes in the larger clinical population due to health disparities. Another treatment option for AATD is gene therapy, which is introducing the needed genetic material o r the effective version of the gene to liver cells. Drug therapy an additional treatment option, is the administration of certain drugs that aim to reverse the emphesymatous changes to the lung tissue, or the pathological development of spaces in lung tis sue causing ineffective gas exchange and susceptibility to lung infections (Ioachimescu & Stoller, 2005). The third line of treatment for AATD associated COPD is surgery and can involve lung transplantation or lung volume reduction surgery, which is when the non functional parts of the lung are removed so that the healthy tissue can function better (Ioachimescu & Stoller, 2005). Liver transplants are also done in patients with AATD, but it is primarily done

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! %& in people with the severe ZZ phenotype, which in addition to pulmonary illness, causes severe liver disease, fibrosis, and cirrhosis of the liver. 2% of infants or children with a ZZ phenotype develop serious liver disease, while about 19% of adults over the age of 50 with the ZZ phenotype will have deve lop liver damage through fibrosis, or accumulated scarring in liver tissue, leading to cirrhosis of the liver (Fregonese & Stolk, 2008). AATD associated COPD is the 4 th indication for lung transplantation in the United States and worldwide (Giacoboni, Bar recheguren, Esquinas, Rodriguez, Monforte, Braveo, Pirina, Miravitller, Roman, 2015; Tanash, et al, 2014), and in the United Sates 9% of lung transplants are done for AATD associated COPD (Stoller, Lacbawan, Aboussouan, 2006; Tanash, et al, 2014). 2,182 lu ng transplants were done world wide for patients with AATD associated COPD between January 1995 and June 2012. This was 5.8% of the total number of lung transplants that were conducted in this time period (Giacoboni, et al, 2015). Candidates for lung trans plantation are required to have 2 years or less of life expectancy, good nutritional status, and a strong and stable psychosocial profile (Tanash, et al 2014). The differences between AATD associated COPD patients and non AATD associated COPD patients who received lung transplants were that AATD associated COPD patients tended to be younger and had less exposure to smoking than the non AATD group (Giacoboni, 2015). Lung or liver transplantation is an option for patients who may be younger, have more seriou s progression of the illness and/or are at the end stage of emphysema or liver disease (Stoller, at al 2006). Studies on patients who have undergone lung transplantation demonstrate improvement in survivorship and health status for 2 9 years post surgery ( Tanash, Riise, Hansson, 2011). Lung volume reduction surgery is less often indicated since the mechanisms of how the surgery may be beneficial are still b eing studied (Sciurba, 1997).

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! %' Physical Adjustment to AATD Patients with COPD and patients with AATD as sociated COPD share clinical f e atures and differ primarily in age of onset of COPD. As mentioned above, p atients with AATD associated COPD tend to be diagnosed at a younger age than non AATD COPD patients. The predominant physical symptom of COPD and AATD associated COPD is dyspnea or shortness of breath The American Thoracic Society describes dyspnea a s "a subjective experience of breathing discomfo rt [and] derives from interactions among multiple physiological, psychological, social, and environmental factors," (American Thoracic Society, 1999). The pathophysiology of dyspnea is comprised of numerous mechanisms having to do with conflicting sensory information between the central nervous system and the receptor s along the respiratory tract ( De Peuter, D iest, Lamaigre, Verleden, Demedts, Van den Bergh, 2004). Research has also found that experience of dyspnea is highly impacted by patient characteristics such as age, gender, airway reactivity, duration of disease, level of airway inflammation patient aff ect, and subsequent patient perception of dyspnea (De Peuter, et al. 2004). In addition to dyspnea, patients with AATD associated COPD experience fatigue Fatigue is defined as a global feeling of tiredness and lack of energy. In COPD and AATD associated C OPD, fatigue stems from a lack of oxygen as well as from an increased exertion of the body to attain and utilize what oxygen is available Fatigue in COPD and AATD associated COPD is also caused by hypercapnia, or a rise in carbon dioxide levels in the blo od due to inefficient and insufficient gas exchange. Having a limited capacity for airflow makes certain levels of physical activity prohibitive, and the level of physical activity typically becomes progressively less as the disease progresses. People with AATD associated COPD also experience weight loss and loss of muscle mass. Fatigue and inability

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! %( to be p hysically active often decrease the appetite causing weight loss Lack of physical activity will also lead to loss of muscle mass. AATD associated COPD also cau ses mucus and sputum production which then leads to chronic coughing. Chronic coughing is another contributor to the experience of fatigue, and, much like dyspnea, is associated with other physiological, psychological, and social factors. Chronic coughing can lead to complications such as pain, hernias or other tissue damage resulting from frequent, violent coughing. Over time, chronic coughing can also cause a person to feel anxious and hopeless because it is difficult to control coughing or sto p it from occurring (Lœdv’ksd—ttir, Bj! rnsson, Janson, Boman, 1996 ; Morice, 2008 ) Chronic coughing tends to impact an individual's social life as well. Individuals may begin to feel uncomfortable in social situations or public settings because of how othe rs react or how they perceive that others are reacting to their cough (Morice, 2008) The symptoms of AATD associated COPD lead to significant changes in functionality and quality of life, and consequently, lead to significant changes in emotional quality of life as well. Psychosocial Adjustment to AATD COPD has a high prevalence of concurrent anxiety and depression (Yohannes, Willgoss, Baldwin & Connolly, 2010). Kunik and colleagues (2005) found that close to 65% of patients with COPD screened positive f or anxiety or depression. Dyspnea is also highly correlated with anxiety, as research has found that anxiety disorders are the most prevalent psychiatric disorders among patients experiencing dyspnea (Smoller, Simon, Pollack, Kradin & Stern, 1999). In thei r 1994 study, Janson, Bjšrnsson, Hetta, and Boman found a correlation between the reporting of asthma symptoms and depression. However, they found no correlation between anxiety, depression and an objective measure of asthma severity suggesting that the ex perience of pulmonary distress is potentially worsened by the presence

PAGE 26

! %) of psychosocial distress Hayen, Herigstad, and Pattinson (2013) suggest that biopsychological factors such as mood changes, hormonal changes, and psychological comorbidities, may lead to the exacerbation of dyspnea, intensify the symptoms, and decrease quality of life. They argue that effective treatment of dyspnea requires direct focus on the biopsychological contributors of the clinical presentation. It is estimated that as much as 50 % of variance in dyspnea is attributed to psychological distress and symptoms of depression and anxiety (Bestall, Paul, Garrod, Garnham, Jones & Wedzicha, 1999; Holm, Wamboldt, Ford, Sandhaus, Strand, Strange & Hoth, 2013). In a study looking at the impac t of age o n outcomes among COPD patients, researchers found that anxiety and health related quality of life were both associated with age and that younger people had worse outcomes (Holm, Plaufcan, Ford, Sandhaus, Strand, Strange, & Wamboldt, 2013). The st udy also found that age and relationship status interacted significantly when predicting breathlessness, depression and health related quality of life. Younger people who were single perceived themselves as having more breathlessness, endorsed more symptom s of depression and reported worse health related quality of life than did patients who were older and coupled. This suggests that AATD associated COPD bears an added psychosocial burden for the affected individual due to the earlier age of onset. The age range that individuals with AATD may start to become symptomatic coincides with the developmental stage of building careers, developing long term romantic relationships and starting families. Adjustment to illness can be difficult in COPD given the functi onal limitations the condition imposes. Illness adjustment becomes more challenging in AATD associated COPD when most same aged peers are very active and substantially less limited than the individual with the disorder (Campos, et al 2009)

PAGE 27

! %* Self Blame COP D is an irreversible disease that has an immense impact on health related quality of life, functionality, and emotio nal well being. An added aspect of COPD is that in developed countries, the main cause of COPD is smoking behavior in the individual (Mayo C linic Staff, 2015), and in a qualitativ e study done by Oliver (2001), patients with COPD often felt that their illness was self inflicted. When one is has a critical illness like COPD in order to be able to adjust to or cope with the reality of the illnes s, the individual needs to be able to organize the events that have occurred and assign attributions, or explanation s for the cause s of the events Attributions that are made can be adaptive, helpful for the illness adjustment process, or maladaptive, lead ing to more distress. A type of attribution that can be made is self blame. Blame is placing responsibility or fault on someone for a mistake or something bad that has happened (Merriam Webster 2015) and self blame is when that responsibility or fault is placed on the self. Literature on self blame in patients with chronic disease indicates that in the process of adjustment to illness, patients often blame themselves for actions or behaviors that may have led to their disease state (Malcarne, Compas, Epp ing Jordan, Howell, 1995). However, i n a study looking a t coping in patients who have had a "freak accident", self blame emerged as a predictor of better coping ( Bulman & Wortman 1977) The researchers explained that self blame was an adaptive psychologic al mechanism because it lead to making an attribution and a belief of personal control over the events in one's life. This suggests that there may be aspects of attributions of self blame that are maladaptive and other aspects that are adaptive. Voth and S irois (2009) looked at the effect of different types of attributions in adjustment to inflammatory bowel disease They found that believing the disease was somehow brought on intentionally or that it was due to internal and stable factors in the

PAGE 28

! %+ person was associated with avoidant coping and poorer psychological adjustment (Voth & Sirois, 2009). In comparison, taking responsibility for the disease and personal behaviors led to decreased avoidance and improved psychological adjustment to the disease. Janoff Bulman, in her 1979 article on self blame in rape and depression, conceptualized self blame as being made up of two parts: behavioral self blame and characterological self blame These parts comprise the adaptive and maladaptive aspects of self blame. Jano ff Bulman discusses how in the case of rape, it may help the victim gain a sense of control and a way of processing her experience if she can attribute the event to certain actions of hers. Janoff Bulman then con trasts that example with Beck's (1967) argum ent regarding depressed patients and their maladaptive tendency to attribute causality for any negative event to her or himself According to Janoff Bulman, t he adaptive orientation of self blame or behavioral self blame, is a control oriented response w hereby the person takes on blame or responsibility by believing that his or her behavior played a role in the events that transpired. When one attributes the cause of a situation to his or her own behaviors, one can develop a sense of control o ver future o utcomes through possibly different, future behaviors. This attribution instills a sense of hope as well as an internal sense of control. The maladaptive orientation of self blame or characterological self blame, is a self deprecating response to negative events or illness whereby the person becomes focused on the past and attributes a sense of (self) deservingness of past negative events. In this type of blame a person identifies something intrinsically wrong about him or herself that made and will always make him or her deserving of a negative outcome (Janoff Bulman, 1977). This attribution lends itself to feelings of helplessness and other depressive cognitions.

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! %, A study looking at adjustment to cancer through attributions of self blame found that behavi oral self blame and characterological self blame impact psychological adjustment not only independently but also in interaction with one another. Malcarne and colleagues (1995) found that behavioral self blame at the time of diagnosis was associated with i ncreased psychological distress when cha racterological self blame was also present. Furthermore, th ey found that attributions of characterological self blame and the co existence of characterological self blame and behavioral self blame near the time of di agnosis w ere predict ive of poor psychological adjustment 4 months post diagnosis. Behavioral self blame alone did not have an association with psychological distress. In a study on self blame and adjustment to breast cancer in women, Glinder & Compas (199 9) found that when the variables of ch aracterological self blame and behavioral self blame were entered into a regression analysis simultaneously, behavioral self blame predicted psychological distress more consistently cross sectionally, and characterolog ical self blame more consistently predicted psychological distress prospectively. This indicates that characterological self blame was associated with long term psychological adjustment which in this study was up to one year post diagnosis In their 2012 study on behavioral and characterological self blame in COPD, Plaufcan, Wamboldt and Holm found that behavioral self blame in this sample of COPD patients was associated with tobacco exposure ( measured in pack years). C urrent smoking status, however, was a ssociated with characterological self blame In this sample, p erception of family blame for the individual's COPD was associated with both behavioral and characterological self blame But s cores of general family functioning (with higher scores indicating better perceived family functioning) were associated positively with behavioral self blame Characterological self blame associated positively with depressive symptoms,

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! %! and individuals who had the highest score for behavioral self blame reported fewer symp toms of depression and lower impairment in health related quality of life. In the case of individuals with AATD associated COPD, studies indicate that smokers in this population also harbor self blame for their smoking behavior that has lead to their illne ss (Plaufcan, Wamboldt, Holm, 2011). Behavioral and characterological self blame have not yet been examined in a sample of AATD associated COPD and is one of the primary objectives of this project. Demographics, Socioeconomic Status and Health Socioecon omic status (SES) is defined as a measure of one's social and economic standing, is usually gleaned from considering one's income, education, and occupation and it has been found to be positively associated with and predictive of better health outcomes (B aker, 2014) Demographics and SES play an important role in COPD. In 2011 the CDC conducted a survey to identify the risk factors that were driving up the prevalence of COPD and found that prevalence was strikingly different across different states and acr oss different levels of SES (Siegel, 2013). 6.8% of COPD patients did not have a high school diploma whereas 4.6% had a diploma or some college education. 20.9% of COPD patients were not working whereas 3.8% were employed fulltime (CDC, 2014). Despite sig nificant findings of a predictive relationship between SES and health, the precise mechan ism by which SES contributes to health status, health behaviors and health outcomes remains somewhat unclear in the literature Adler and colleagues (1995) argued that a main problem in understanding the impact SES on health is that often SES is relegated as a control variable. Also, many studies compare the health of people at the very lowest level of poverty with those above poverty a nd those at the very top level of the hierarchy Focusing only on the extremes in the hierarchy underestimates the impact of SES o n

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! &. biological outcomes. Adler and colleagues argue that health is impacted by socioeconomic status at every level of the social hierarchy. There are finely strat ified difference s running across the entire hierarchy, and what accounts for health outcomes at lower levels are not the same as what accounts for changes at higher levels (Adler et al. 1995) In a 1995 article on the links between education and health, Ross and Wu found that higher education level was associated with better self reported health, more positive health behaviors (e.g. reduced drinking and smoking), better physical functionality, and a greater sense of control over life and personal health. Ross and Wu hypothesized that education provides greater and more satisfying job opportunities, fewer financial stressors, better quality of life and a greater sense of control in life. In another article on education and health, Schnittker (2004) stated t hat the association be income and health varies in strength and shape by the level of education. This is to say that education improves health, and this improvement is greater at lower levels of income. Studies have found that higher education level is ass ociated with better utilization of medical care, better adherence to medical intervention, better health behaviors, and less engagement in behaviors that insult the health (e.g. smoking, poor eating behaviors, hygiene, substance abuse) (Rose, Chassin, Pres son, 1996; Ross, Wu, 1995; Adler, et al., 1994). In a study done by Eisner and colleagues in 2011 looking at SES, race and COPD outcomes, they found that greater COPD severity scores w ere consistently associated with lower educational attainment, and th is was true even after controlling for race and ethnicity. They found that black race was associated with greater severity of COPD symptoms, but these findings were not maintained after controlling for SES variables and various covariates such as comorbidi ties, smoking, BMI, and occupational exposure Greater risk for COPD exacerbations were associated with lower education and lower income independently.

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! &% O machi and colleagues (2013) make a distinction in understanding the impact of the education component of SES on COPD outcomes. They state that education attainment is an important predictor of COPD outcomes, but they argue that health literacy, while a product of education, is separate from education. Therefore, poor health literacy was associated with gre ater COPD severity, greater sense of helplessness, lower health related quality of life, and great utilization of emergency services for COPD related issues. This association remained after controlling for other SES factors like education level and income. In a study looking at differences in adjustment between people with AATD associated COPD and those with non AATD COPD (Holm et al. 2013), researchers found that patients with AATD associated COPD differed from non AATD COPD patients in being more likely to be coupled and also in having greater educational attainment. The researchers concluded that the lower rates of anxiety and depression in AATD associated COPD patients could be associated with the positive impact of the relationship status (social supp ort) and with the higher education level. Overall, the sample in the study indicated that lower education was associated with more symptoms of anxiety and depression, more dyspnea, and impairment in health related quality of life. In another study of pati ents with AATD associated COPD looking at demographic factors, emergency room visits and alcohol use, "problem drinking" (alcohol) predicted more ER visits (Hoth, Ford, Sandhaus, Strange, Wamboldt, Holm, 2012) But the problem drinking was also associated with symptoms of anxiety, greater lifetime tobacco exposure and higher education level. This is an example of the complexity of SES in predicting health behavior. Typically, higher education level is associated with positive health behaviors, but these fin dings are supported by other research that have also found an association between alcohol abuse, higher social statu s and educational achievement ( Hoth, et al, 2012; Stutske, 2005).

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! && These findings highlight the complexity of the impact of demographics and SES on chronic illness, and on specifically AATD associated COPD. Family Support and Health Social support is defined by Uchino (2004, pp. 9 10) as being both the social contexts within which a person exists (e.g. family, group of friends, membership in a community) and also the functions that those relationships serve (e.g. informational, tangible or emotional support). Social support has received a great deal of research within the field of health psychology ( Franks, Campbell & Shield, 1992; Uchino 2 005 ). The strength of the association between s ocial relationships (meaningful connectedness with others) and health behaviors is comparable to the association between measure s of health (e.g. blood pressure or obesity ) and health outcomes (House, Landis, Umberson, 1988; Holt Lundstad, smith, Layton, 2010). Uchino (2004) talks about the inconsistencies in literature on the impact of social support on health. He argues that the inconsistencies arise partly due to the dynamic and bidirectional association be tween social support and health. It also has to do with th e variet y of contexts and forms of social support that can be experienced The variet y of emotions that can be shared and exchanged in a social rela tionship (e.g. positive emotion, negative emotion stressful interactions ) also alters outcomes. Keicolt Glaser (1997) discusses in her article on marital conflict and endocrine and immunologic changes that while social support has typically been conceptualized as a protective factor in health, there are aspects of it that can be a threat to health, such a poor relational skills in a marriage leading to unhealthy changes in endocrine and immunologic responses. Sarason, Sarason, Shearin and Pierce (1987) argue that part of what makes social support so vita l, impactful and compelling is that they are continuations of our attachment

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! &' patterns from infancy and childhood into our adulthood. This finding is validated by Franks, Campbell and Shields (1992) when they stated that variables of family interactions hav e a more power association with health behaviors than variables of social support alone. The y researchers argued that emotional valence in family relationships is higher and therefore has a greater impact on disease management in chronic illness than does emotional support from other (non fami ly members). COPD and other chronic illnesses such a diabetes frequently rely on family support due to the level of impairment, its progressiveness, and the need for greater lifestyle changes ( Kara Ka"k, M., Albert, 2006; Martire et al. 2004; Weihs, Fisher, Baird, 2002). Kleinman, Esenberg & Good (1978) explain that family has a shared reality, and this reality is related to health, and the family environment is where disease management takes place In a s tudy on social support and self efficacy in older COPD patients, Marino, Sirey, Raue & Alexopoulos (2008) found that social support and self efficacy were independently associated with better overall functioning, even in the presence of seve re mental illness and depression. Another study of COPD patients in Turkey found similar results, that there was a moderately strong association between family relationship and self efficacy in self care (Kara Ka"k & Alberto, 2006). Family Criticism, Family Blame and Health in AATD Social support alone, however, may not be sufficient in terms of influencing health behaviors and illness adjustm ent. Factors associated with family interactions are found to have more powerful and direct associations with health behaviors than non familial social support (Franks, et al. 1992). This influence can go both ways: positively and negatively. A negative a spect of family interactions is the exposure to family criticism. Campbell and Fiscella (1999) concluded that perceived family criticism leads to poor health behaviors, and

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! &( this association is mediated by negative affect (e.g. depression and hostility) wh ich can occur in reaction to perceived family criticism. Perceived criticism is understood as being comprised of two pieces: first, a distorting, negative filter of perceptions about the self and other, and second, actual negative family interactions, part icularly expressed negative emotions (Fiscella, Franks, Shield, 1997). Perceived family criticism is a measure of negative emotion in a family dynamic. It is measured by the amount of criticism an individual feels he or she receives from his or her family members (Plaufcan et al, 2012). In study on family factors associated with psychological distress and smoking factors in COPD, Holm and colleagues (2010) found that perceive d family criticism lead to increased psychological distress, which then impacted sm oking status (e.g. amount currently smoked). In 2013, Holm and colleagues looked at perceived criticism and dyspnea in AATD and found that perceived criticism from family is associated with dyspnea in individuals with elevated levels of psychological distr ess. The researchers theorized that this may be due to a parallel neural mechanism that has to do with appraisal of criticism and appraisal of dyspnea, which literature argues is a highly subjective experience largely impacted by psychological symptoms (De Peuter et al. 2004). Family blame is a perception that one's family is blaming him or her for the illness, in this case, COPD (Plaufcan et al, 2012). An individual's perception of family blame for COPD associated positively with both characterological se lf blame and behavioral self blame, the combination of which indicates increased psychological distress (Plaufcan et al, 2012). But when patients perceived their family as being healthier, as measured by score on a 12 item Family Assessment Device, family support served as a protective factor such that COPD patients endorsed the more adaptive behavioral self blame.

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! &) Perceived family criticism and perceived family blame, therefore, may serve as separate and unique predictors of disease adjustment, health beh avior change, and potentially, health outcomes. Conclusion Given the findings reviewed above, one could conclude that there are a number of variables that may impact smoking behavior in those with AATD associated COPD Although SES is strongly associated with various health and psychosocial outcomes, there has been very little research examining the relationship between SES and smoking in those with AATD associated COPD. Research to date has found that SES is complex construct and in order to have a more nuanced understanding of the characteristics of a sample, it is important to consider the multidimensional contributions of demographic factors. Particularly in the case of patients with AATD associated COPD, investigating the relative contributions of th e components of SES could help shed light on certain characteristics of smokers versus non smokers. Self blame has also been investigated in various health populations, including in samples of patients with COPD. An important continuation of this research is to understand if and how characterological self blame and behavioral self blame differ specifically between patients with AATD associated COPD who are never smokers versus those who are past smokers. Furthermore, given the importance of family support i n health status and health outcome in patients with AATD associated COPD, it is also important to further this understanding of how smoking history impacts perceived family criticism and perceived family blame. Gaining more insight into how AATD associated COPD patients with a history of smoking may differ from those who are lifetime non smokers on socioeconomic attributional and perceptual variables can potentially inform both medical

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! &* and psychological therapeutic interventions. This insight may also help make medical and psychological therapeutic interventions more effective and accessible to this population.

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! &+ CHAPTER II AIMS AND HYPOTHESES Aim1 Examine the association between smoking history and demographic, socioeconomic and health characte ristics in patients with AATD associate d COPD. Hypothesis 1a: Smoking history will be significantly associated with lower socioeconomic status as measured by income and education variables, in patients with AATD associated COPD. Hypothesis 1b: Smoking his tory will be significantly associated with a greater number of medical co morbidities in patients with AATD associated COPD. Aim 2 Examine how smoking history is associated with the level of cha ra cterological and behavioral self blame experienced by pat ients with AATD associated COPD while controlling for relevant demographic, educational and health characteristics Hypothesis 2a: Presence of smoking history will not be predictive of characterological self blame among patients with AATD associated COPD. Hypothesis 2b: Presence of smoking history is predictive of higher levels of behavioral self blame among patients with AATD associated COPD. Aim 3 Examine the extent to which smoking history is predictive of perception of family criticism and perception of family blame among patients with AATD associated COPD Hypothesis 3a: Smoking history will be predictive of perceived blame from family members in patients with AATD associated COPD.

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! &, Hypothesis 3b: Smoking history will be predictive of perceived crit icism from family members in patients with AATD associated COPD.

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! &! CHAPTER III METHODS Participants and Procedures Data were collected as part of a larger study that was conducted by Dr. Kristen Holm and funded by the Alpha 1 Foundation. The study was c onducted at National Jewish Health in Denver, Colorado and at the Medical University of South Carolina in Charleston, South Carolina. The study's aims were to examine social and perceptual factors that affect adjustment in AATD associated COPD All data we re collected via a self report questionnaire. Participants were recruited through the Alpha 1 Foundation Research Registry using a mail ed questionnaire. The National Jewish Health Institutional Review board and the Medical University of South Carolina Inst itutional Review Board approved this study. Th e study was granted a waiver of documentation of informed consent because data were collected via questionnaires that posed no more than minimal risk to participants. The questionnaire is included in Appendix A with the relevant sections of the questionnaire highlighted. The questionnaire was mailed to 1727 potential participants, and 621 returned the questionnaire for a re sponse rate of 36%. Of the 621 questionnaires, 22 respondents were excluded because they d id not endorse having COPD. Of the remaining participants, 37 w ere excluded from the analyses because they indicated that they are currently smoking. Their experiences are likely to be distinctly different from never smokers and former smokers, but the sma ll number of current smokers precludes examining their experiences in the current study In addition 71 participants were excluded due to having smoked more than 100 cigarettes but having less than a 10 pack year history. The decision to exclude these

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! '. ind ividuals is consistent with prior research in which 10 pack years is considered the definition of a clinically significant smoking history (Plaufcan, Wamboldt, Holm, 2012; Putcha, et al. 2014 ; Regan, et al. 2010 ) An additional 28 parti c ipants were removed due to missing data. To be included in analyses, respondents needed to have adequate data to determine smoking history (including number of pack years smoked) and have provided a response for at least one of the variables that is being examined as a corre late of smoking history in Aim 1. Hence, the final sample size was 463 (see Fig. 1). Fig. 1. Recruitment flow diagram.

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! '% Measures Demographics & health c haracteristics A ge, age at COPD diagnosis gender, relationship status, education level, income, and r ace and ethnicity were measured via the questionnaire In addition, d yspnea w as measured by the Modifed Medical Research Council Dyspnea Scale (Fletcher, Elmes, Fairbairn & Wood, 1959) a single item scale with response options that range from 1 to 5 H igh er scores indicat e more dyspnea. T his scale predicts 5 year survival among patients with COPD (Nishimura, Izumi, Tsukino & Oga, 2002) Rating of health was measured using the following item from the Behavioral Risk Factor Surveillance System Survey Questi onnaire (CDC, 2004 a ) : "Would you say that in general your health is Th e item has five response options that rang e from poor (scored as a 1) to excellent (scored as a 5) A s such, a higher score i ndicates better health. Total number of comorbidities wa s measured by summing the number of medical conditions participants endorsed from a list that included t he following conditions : liver disease, heart disease/heart surgery, hypertension/high blood pressure, diabetes/blood sugar problems, bone problems (osteo porosis or fracture), and cancer. Participants' scores could range from 0 to 6 with a high er number indicating more medical comorbidities. Body Mass Index ( BMI ) was calculated using the self reported values for height and weight. BMI values were separate d into the following 4 categories: underweight (BMI < 18.5), normal weight (BMI between 18.5 and 2 4.9 ) overweight ( BMI between 25 .0 and 29.9 ) obese (BMI > 30 .0 ) The following three health characteristics were examined as indicators of illness severity: AATD genotype, oxygen use, and history of augmentation therapy. G enotype was categorized as severely deficient (e.g. SS, SZ, and ZZ ), not severely deficient (e.g. MZ, MS,

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! '& and SS) and unknown. Oxygen use was assessed using a single item yes/no question that asked, "Are you using oxygen for your COPD?" Augmentation therapy was also assessed using a single item yes/no question, "Have you EVER undergone augmentation therapy (Prolastin, Aralast, or Zemaira)?" Individuals who are using oxygen or who have undergon e augmentation therapy are considered to have more severe COPD. Smoking b ehaviors Smoking behaviors were assessed via questions from the National Health Interview Survey (NHIS) ( CDC, 2004b ) a subset of which are also in the Behavioral Risk Factor Surveill ance Survey (BFRSS) ( CDC, 2004a) Pa rticipants were asked whether they have smoked more than 100 cigarettes in their lifetime. Pa rticipants who indicated that they had not smoke d more than 100 cigarettes were considered "never smokers." People who indicated that they had smoked more than 100 cigarettes were considered to have a history of smoking. P articipants who indicated that they had smoked more than 100 cigarettes were instructed to provide information regarding the number of packs smoked per day as wel l as the number of years smoked. Extent of lifetime tobacco exposure (i.e., pack years) was then calculated by multiplying the average number of cigarette packs per day by the number of years smoked Participants were also ask ed whether the y are still smok ing currently. Current smokers were excluded from analys e s. Self b lame Characterological s e l f b lame was assessed via the self blame subscale of the Internal Locus of Control scale (Marshall, 1991). The self blame subscale is comprised of three items that assess a tendency toward self blame in regards to negative health outcomes. These items ask for the extent of agreement with the following statemen ts: 1) w hatever goes wrong with my health is my own fault; 2) when I get sick, I am to blame; and 3) when I f eel ill, I know it

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! '' is because I have not been taking care of myself properly. The r esponse for each item range s from 1 (strongly disagree) to 5 (strongly agree). Total scores for this subscale range from 3 to 15 with higher scores indicating greater charac terological self blame. Cronbac h 's alpha in this sample was 0 .82 Behavioral s elf b lame was assessed by a single item that was adapted from a study of self blame in head and neck cancer (Malcarne, Compas, Epping Jordan, Howell, 1995). The item reads as fo llows: How much do you blame yourself for any behavior that led to your COPD? Responses for this item range from 1 (not at all) to 5 (completely), with a higher score indicating greater behavioral self blame. Family blame and family criticism Perceptio n of family criticism was assessed by the PCM. The PCM is a single item measure that is worded as follows: "On average, how critical do you think your family is of you" with response options that range from 1 (not at all critical) to 10 (very critical) (Ho oley & Teasdale, 1989). A higher score indicates greater perceived family criticism. Perception of family blame was assessed by a single item that was modeled after the Perceived Criticism Measure (PCM) The item is worded as follows: "To what extent do yo u think your family blames you for having COPD" with response options that range from 1 (not at all) to 10 (completely). A higher score indicates greater perceived family blame. Statistical Analyses D ata were analyzed usin g IBM SPSS Statistics version 23 ( SPSS Inc., 2015 ). Preliminary a nalyses All variables used in analyses were examined using means and standard deviations for continuous variables and number and percentage of participants for categorical variables. Individuals who were excluded from analy ses due to missing data were compared to

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! '( individuals who were included in analyses via t tests for continuous variables and chi square t ests for categorical variables. Analyse s for a im 1 To examine the association of smoking history with demographic, soc ioeconomic and health characteristics, never smokers were compared to former smokers. The following variables are c ontinuous and therefore were examined via t tests: age, age at COPD diagnosis, dyspnea, rating of health, comorbidities, and pack years. The following variables are c ategorical and therefore were examined using chi square tests: gender, relationship status, education, income, race / ethnicity, BMI, genotype, oxygen use, and augmentation therapy. Analyses for a im 2 Analyses for Aim 2 were done in three parts. First, the distributions of the scores for characterological self blame and behavioral self blame were examined via frequencies, skewness, and kurtosis to determine whether these variables were normally distributed. Both variables, characte rological self blame and behavioral self blame, were skewed, and data transformation did not make these variables normally distributed. As such, both variables were recomputed into dichotomous variables using the median split. For characterological self bl ame, scores of 3 to 5 comprised 40.2% of the sample and were coded as low characterological self blame. Scores from 6 to 15 comprised 59.8% of the sample and were coded as high characterological self blame. For behavioral self blame, scores of 1 and 2 comp rised 54.3% of the sample and were coded as low behavioral self blame. Scores from 3 to 5 comprised 45.7% of the sample and were coded as high behavioral self blame.

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! ') Next, the bivariate associaton of smoking history with characterological and behavioral self blame was examined by conducting t tests using the original, continuous, non computed scores and chi square tests tests for the computed, categorical scores. Then, the multivariate association was examined via logistic regression to examine the ext ent to which smoking history is associated with characterological and behavioral self blame after adjusting for relevant demographic and health covariates The dichotomous variables were used as dependent variables in logistic regression models Two logis tic regression models were calculated. Characterological self blame was the dependent variable in the first model and behavioral self blame was the dependent variable in the second model. Smoking history was the primary predictor of interest. Variables th at were examined in Aim 1 that had a statistically significant association with smoking history were included as covariates in both models. Both models included the same set of predictors; the dependent variable is the only thing that differed between the models. Analyses for a im 3 The analyses for Aim 3 were also done in three parts. First, the distributions of the scores for perceived family blame and perceived family criticism were examined via frequencies, skewness, and kurtosis to determine whether t hese variables were normally distributed. Both variables, perceived family blame and perceived family criticism, were skewed, and data transformation did not make these variables normally distributed. As such, both variables were categorized into none ve rsus any since the lowest possible value had such a high percentage of the responses for each vari a ble. For perceived family blame, scores of 1, which made up 44.6% of the sample, were coded as no family blame. Scores of 2 to 10, which made up 55.6% of t he sample, were coded as endorsing family blame. For perceived family criticism, scores of 0, which made up 75.9% of the sample, were coded as no family

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! '* criticism. Scores from 2 to 10, which made up 24.1% of the sample, were coded as endorsing family criti cism. Next, the bivariate associaton of smoking history with perceived family blame and perceived family criticism was examined by conducting t tests using the original, continuous, non computed scores and chi square tests tests for the computed, catego rical scores. Then, the multivariate association was examined via logistic regression to examine the extent to which smoking history is associated with perceived family blame and perceived family criticism after adjusting for relevant demographic and hea lth covariates. The categorical variables were used as dependent variables in logistic regression models. Perceived family blame was the dependent variable in the first model, and perceived family criticism was the dependent variable in the second model. S moking history was the primary predictor of interest. Variables that were examined in Aim 1 (identified in Table 1) that had a statistically significant association with smoking history were included as covariates in both models.

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! '+ CHAPTER IV RESULTS Ch aracteristics of Participants Individuals who were removed due to missing data were compared to those who were included in analyses The two groups were compared on every variable listed in Table 1. The two groups did not differ with regard to any of these variables (p > 0.05). Characteristics of the sample are in Table 1. On average, never smokers, M(SD) = 64.49(10.70), were older than former smokers, 57.92(9.00); t(461)=6.83, p<0.001. Never smokers were also older at COPD diagnosis, 53.34(11.63), than fo rmer smokers, 44.69(8.47); t(451)=8.88, p<0.001. Gender was equal across the sample with 49.9% females and 50.1% males. A larger percentage of the never smokers were female (64.1%) while a larger percentage of the former smokers were male (56.4%). Among fo rmer smokers, the average number of pack years was 29.33 (21.78). The majority of the combined sample was coupled (77.3%) as opposed to single (22.7%). Also, never smokers had higher education (51.1% completed college or more ) than former smokers. The sam ple examined in this project is characterized as having m ore severe disease as 82.7% of the sample ha d the more severe genotype s of AATD. Also, half of the sample, 51.8%, used oxygen, and 79.7% of the sample had augmentation therapy.

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! ', Table 1 Charact eristics of Sample (N = 463) Combined Sample (n= 463) Never Smokers (n= 142) Former smokers (n= 321) p value for comparison Variable Mean (SD) Mean (SD) Mean (SD) Age 59.94 (10.01) 64.49 (10.70) 57.92 (9.00) <0.001 Age at Diagnosis 47.33 (10.33) 5 3.34 (11.63) 44.69 (8.47) <0.001 Dyspnea 2.90 (1.16) 2.58 (1.14) 3.04 (1.14) <0.001 Rating of Health 2.67 (0.97) 2.85 (1.00) 2.60 (0.95) 0.009 Comorbidities 0.98 (0.97) 1.09 (0.98) 0.93(0.97) 0.128 Pack Years NA NA 29.33 (21.78) NA Variable N (%) N ( %) N (%) Gender Female 231 (49.9) 91 (64.1) 140 (43.6) <0.001 Male 232 (50.1) 51 (35.9) 181 (56.4) Relationship Status Single 105 (22.7) 25 (17.6) 80 (24.9) 0.083 Coupled 358 (77.3) 117 (82.4) 241 (75.1) Education Grade 12/GED, or l ess 133 (29.4) 31 (22.3) 102 (32.5) <0.001 College 1 3 years 164 (36.2) 37(26.6) 127 (40.4) College Grad and above 156 (34.4) 71 (51.1) 85 (27.1) Income <35,000 138 (31.7) 37 (28.0) 101 (33.3) 0.181 35,001 75,000 156 (35.9) 44(33.3) 112 (37.0) 75,001< 141 (32.4) 51 (38.6) 90 (29.7) Race & Ethnicity Caucasian, non Hispanic 451 (98.3) 139 (99.3) 312 (97.8) 0.608 Black, Non Hispanic 2 (0.4) 0 (0.0) 2 (0.6) Hispanic 4 (0.9) 1 (0.7) 3 (0.9) Other 2 (0.4) 0 (0.0) 2 (0.6) BMI Unde rweight 22 (4.9) 10 (7.2) 12 (3.8) 0.247 Normal Weight 211 (46.8) 67 (48.6) 144 (46.0) Overweight 142 (31.5) 43 (31.2) 99 (31.6) Obese 76 (16.9) 18 (13.0) 58 (18.5) Genotype Severely Deficient 383 (82.7) 113 (79.6) 270 (84.1) 0.085 Not Severel y Deficient 39 (8.4) 18 (12.7) 21 (6.5) Unknown 41 (8.9) 11 (7.7) 30 (9.3) Oxygen Use Yes 239 (51.8) 50 (35.7) 189 (58.9) <0.001 No 222 (48.2) 90 (64.3) 132 (41.1) Augmentation Therapy Yes 366 (79.7) 96 (68.6) 270 (84.6) <0.001 No 93 (20 .3) 44 (31.4) 49 (15.4)

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! '! Results for Aim 1 Hypothesis 1a Hypothesis 1a stated that smoking history will be significantly associated with lower socio economic status, as measured by income and education variables. This hypothesis was partially supported E ducation was significantly associated with smoking status (p<0.001) but income was not. Fifty one percent of those who reported that they never smoke d had a college education or higher whereas only 27.1% of former smokers had completed colleg e or more, in dicating that the participants who never smoke d had achieved high er levels of education compared to former smokers. Hypothesis 1b Hypothesis 1b stated that smoking history will be significantly associated with a greater number of medical comorbidity. Thi s hypothesis was not supported Number of comorbid conditions did not differ significantly between never smokers and former smokers. Additional findings regarding differences between never and former s mokers While never smokers did not differ from former smokers with regard to number of comorbid conditions, these groups did differ with regard to dyspnea and rating of health. Never smokers ha d an average dyspnea score of M(SD) = 2.58(1.14) while former smokers had an average score of 3.04(1.14) ; t(454)= 3 .98, p<0.001. These results indicat ed great er dyspnea among former smokers Never smokers also reported a higher ave rage score for rating of health, 2.85(1.00) indicating a better rating of health among never smokers. Fo rmer smokers ha d a significantly lo wer average score of 2.60(0.95) ; t(447)=2.61, p=0.009. Both of these findings suggest that former smokers experienced worse health outcomes in comparison to never smokers.

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! (. Augmentation therapy and oxygen use also differed significantly between never smoke rs and former smokers (both p<0.001). Among former smokers, 58.9% used oxygen compared to 35.7% of never smokers. Among former smokers, 84.6% had a history of augmentation therapy compared to 68.6% of never smokers. The fact that former smokers were more l ikely to use oxygen and augmentation therapy also suggests that former smokers experienced worse health outcomes than never smokers. Results for Aim 2 Bi variate a nalyses Aim 2 examines the association of smoking history with characterological and behavio ral self blame. First, the bivariate association of smoking history with these variables was examined (see Table 2). As seen in Table 2, smoking history had a statistically significant bivariate association with both the continuous variable for characterol ogical self blame (p = 0.035) and the dichotomous variable (p = 0.011) Also, smoking history had a significant bivariate association with the continuous variable for be havioral self blame (p < 0.001) and with the categorical variable for behavioral self b lame (p < 0.001). Hence, a higher per centage of individuals reported high blame in the group of former smokes than in the group of never smokers. These results are presented in Table 2.

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! (% Table 2 Characterological and Behavioral Self Blame Total sa mple Never Smokers Former smokers Continuous Variable Mean (SD) Mean (SD) Mean (SD) p value for comparison Characterological Self Blame (N=445) 5.95 (2.45) 5.57 (2.24) 6.11 (2.52) 0.035 Behavioral Self Blame (N=405) 2.37(1.28) 1.31 (0.63) 2.73 (1.24) <0.001 Categorical Variable N (%) N (%) N (%) p value for comparison Characterological Self Blame Low Characterological Self blame ( 5) 179 (40.2) 66 (49.3) 113 (36.3) 0.011 High Characterological Self blame ( 6) 266 (59.8) 68 (50.7) 198 (63.7) Behavioral Self Blame Low Behavioral Self blame ( 2) 220 (54.3) 94 (93.1) 126 (41.4) <0.001 High Behavioral Self blame ( 3) 185 (45.7) 7 (6.9) 178 (58.6) Multivariate a nalyses Next, the association of smok ing history with self blame was exami ned via multivariate logistic regression The regression models included as covariates all variables that had a significant bivariate association at p < 0.05 with smoking history, as reported in Table 1 The variables that met this criterion were the follo wing: age, age at COPD diagnosis, dyspnea, rating of health, gender, education, oxygen use, and augmentation therapy. Table 3 provides the results of the logistic regression models

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! (& Table 3 Results of logistic regression models to predict self blame Covariates Characterological Self Blame (N= 411) Behavioral Self Blame (N=374) OR (95% CI for OR) p OR (95% CI for OR) p Smoking History Never smokers Reference Reference Former Smokers 1.72 (1.03 2.86) 0.038 25.13 (9.38 67.3) <0.001 Age 0 .99 (0.96 1.03) 0.680 0.98 (0.95 1.02) 0.328 Age at Diagnosis 1.01 (0.98 1.04) 0.473 1.02 (0.98 1.05) 0.382 Dyspnea 0.75 (0.60 0.94) 0.014 1.02 (0.80 1.31) 0.866 Rating of Health 0.88 (0.69 1.13) 0.311 0.93 (0.71 1.23) 0.611 Gender M ale Reference Reference Female 0.61 (0.40 0.94) 0 .026 1.25 (0.77 2.04) 0.368 Education Grade 12/GED or less 1.06 (0.63 1.80) 0.817 1.10 (0.60 2.03) 0.757 College 1 3 years 1.68 (1.01 2.81) 0.047 1.20 (0.67 2.15) 0.545 College Graduate o r more Reference Reference Oxygen Use No Reference Reference Yes 1.21 (0.73 2.00) 0.460 1.45 (0.83 2.53) 0.191 Augmentation Therapy No Reference Reference Yes 0.71 (0.41 1.24) 0.229 0.74 (0.38 1.45) 0.382 Hypothesis 2a Hypothesis 2a st ated that smoking history would not be predictive of higher levels of characterologic al self blame. This hypothesis w as not supported (see Table 3), as presence of smoking history is predictive of higher characterological self blame (OR for former smokers = 1.72, 95% CI = 1.03 2.86, p = 0.038). Dyspnea also was found to be predictive of higher characterological self blame (OR = 0.75, 95% CI = 0.60 0.94, p = 0.014.) This indicates that people who have more dyspnea have lower characterological self blame. G ender was also predictive of high characterological self blame (OR = 0.61, 95% CI = 0.40 0.94, p = 0.026) indicating that males tended to have higher characterological self blame. Finally, education was predictive of characterological self blame (OR = 1.68 95% CI = 1.01 2.81), p = 0.047),

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! (' with people who had 1 3 years of college being more likely to have higher characterological self blame compared to the reference group which was people who had completed college or more Hypothesis 2b Hypothesis 2b stat ed that smoking history is predictive of higher levels of behavioral self blame. This hypothesis was supported Former smokers were significantly more likely (OR = 25.13, 95% CI 9.37 67.3, p <0.001) to have high behavioral self blame. The confidence in terval here, however, was very broad. Also, n one of the covariates were significant in this model for behavioral self blame. Results for Aim 3 B ivariate a nalyses Aim 3 examines the association of smoking history with perceived family blame and perceived f amily criticism. First, the bivariate association of smoking history with these variables was examined (see Table 4 ). As seen in Table 4 smoking history had a statistically significant bivariate association with both the continuous and categorical variabl es for perceived family blame (p <0.001 ) but smoking history did not have a significant bivariate association with either the continuous or the categorical variable for perceived family criticism.

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! (( Table 4 Perceived Family Blame and Perceived Fa mily Criticism Total sample Never Smokers Former smokers Continuous Variable Mean (SD) Mean (SD) Mean (SD) p value for comparison Perceived Family Blame (N=452) 1.73 (1.76) 1.24 (1.06) 1.94 (1.94) <0.001 Perceived Family Criticism (N=451) 2.47 (2.21) 2.3 6 (2.03) 2.51 (2.29) 0.523 Categorical Variable N (%) N (%) N (%) p value for comparison Perceived Family Blame Low Perceived Family Blame (=1) 343 (75.9) 125 (91.9) 218 (69.0) <0.001 High Perceived Family Blame ( 2) 109 (24.1) 11 (8.1) 98 (31.0) Perceived Family Criticism 0.212 Low Perceived Family Criticism (=1) 201 (44.6) 55 (40.1) 146 (46.5) High Perceived Family Criticism ( 2) 250 (55.4) 82 (59.9) 168 (53.5) Multivariate a nalyses Next, the association of smoking history with per ceived family blame and perceived family criticism was examined via multivariate logistic regression. The regression models included as covariates all variables that had a significant bivariate association at p < 0.05 with smoking history, as reported in T able 1. The variables that met this criterion were the following: age, age at COPD diagnosis, dyspnea, rating of health, gender, education, oxygen use, and augmentation therapy. Table 5 provides the results of the logistic regression models

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! () Table 5 Results of logistic regression models to predict family blame & family criticism. Covariates Perceived Family Blame (N= 414) Perceived Family Criticism (N= 413) OR (95% CI for OR) p OR (95% CI for OR) p Smoking History Never smokers Reference Ref erence Former Smokers 5.96 (2.60 13.68) <0.001 0.74 (0.45 1.24) 0.253 Age 0.96 (0.93 1.00) 0.047 0.96 (0.93 0.99) 0.012 Age at Diagnosis 1.00 (0.96 1.03) 0.796 1.02 (0.99 1.05) 0.302 Dyspnea 0.93 (0.72 1.21) 0.590 1.02 (0.82 1.27) 0.830 Rating of Health 0.82 (0.61 1.09) 0.167 0.92 (0.72 1.16) 0.469 Gender Male Reference Reference Female 1.09 (0.66 1.81) 0.730 0.76 (0.49 1.15) 0.194 Education Grade 12/GED or less 0.61 (0.32 1.18) 0.142 0.53 (0.31 0.90) 0.019 Colleg e 1 3 years 0.90 (0.50 1.63) 0.725 0.78 (0.48 1.28) 0.321 College Graduate or more Reference Reference Oxygen Use No Reference Reference Yes 1.33 (0.73 2.40) 0.348 0.88 (0.54 1.44) 0.617 Augmentation Therapy No Reference Reference Yes 0. 43 (0.23 0.81) 0.009 0.63 (0.37 1.07) 0.087 Hypothesis 3a Hypothesis 3a stated that smoking history will be predictive of level of perceived family blame. This hypothesis was supported (see Table 5) with former smokers having higher perceived family blame (OR = 5.96, 95% CI = 2.60 13.68, p<0.001) than never smokers. Age was also a significant predictor of perceived family blame in that participants who were older reported lower perceived family blame (OR = 0.96, 95% CI = 0.93 1.00, p = 0.047.) Au gmentation therapy was also a significant predictor of perceived family blame (OR = 0.43, 95% CI 0.23 0.81, p = 0.009). Participants who had augmentation therapy were likely to report less perceived family blame than participants who did not have augment ation therapy.

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! (* Hypothesis 3b Hypothesis 3b stated that smoking history will be predictive of level of perceived family criticism. This hypothesis was not sup ported by the regression model However, age was predictive of perceived family criticism (OR = 0. 96, 95% CI = 0.93 0.99, p = 0.012) with older participants reporting less family criticism. Also, education was predictive of perceived family blame (OR = 0.53, 95% CI = 0.31 0.90, p = 0.019). Participants with 12 years of education/GED or less reporte d less family blame than participants with higher levels of education.

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! (+ CHAPTER V DISCUSSION This study is the first to look specifically at the psychosocial impact of smoking history in a sample of individuals with AATD associated COPD. The objective was to evaluate four psychosocial constructs that were found in past li terature to negatively impact various health population s: b ehavioral self blame, characterological self blame, perceived family blame, and perceived family criticism Three out of four of these constructs were found to be significantly associated with smoking history in this sample of patients with AATD associated COPD ; all were associated except for perceived family criticism This is a unique finding for this population and sets the s tage for future research that can expand on the particular psychosocial burdens in this population. Additionally, these findings identify possible points of entry for behavioral health interventions for this population. Findings of Aim 1 Several demograp hic and health characteristics emerged as differing between never smokers and former smokers with AATD associated COPD. While socioeconomic status as a whole was not associated with smoking history, education was statistically different between the group s. This finding is consistent with prior literature that higher educational attainment is associated with better health behaviors ( Rose, Chassin, Presson, 1996; Ross, Wu, 1995; Adler, et al., 1994 .) The statistically significant difference in level of educ ation but lack of significance in income is consistent with Schnittker's argument in his 2004 article stating that t he association be tween income and health varies in strength and shape by the level of education suggesting that education tends to be robus t as a demographic characteristic associated with health.

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! (, The statisti cally significant differences between never smokers and former smokers in age at time of study participa t ion and their age at diagnosis are consistent with literature on AATD associated COPD that reports that smoking accelerates the onset of COPD which may lead to earlier diagnosis in smoker s (Ioachimescu & Stoller, 2005; Kaplan & Cosentino, 2010 ) In addition, former smokers had characteristics of more severe disease (e.g. more severe genotypes, more oxygen use, and more participants who have had augmentation therapy ). As discussed earlier, dyspnea is highly influenced by patient characteristics such as age, gender, airway reactivity, duration of disease, level of airway inflammation, patient affect (De Peuter, et al. 2004) and is even defined as a "subjective experience" by the American Thoracic Society, (1999). Rating of health is also fundamentally subjective as it is a self report, single item measure. Comorbidity, on the other han d, is a more objective measure, as patients are asked to indicate if they have additional medical conditions Both dyspnea (p<0.001), and rating of health (p=0.009) differed significantly between never smokers and former smokers, while comorbidity did not. Therefore, the number of additional medical conditions does not differ between these groups, but subjective measures such as perceived health and dyspnea are worse in former smokers This suggests that the subjective experience of COPD and the appraisal o f the symptoms may be worse among former smokers. Findings of Aim 2 Both characterological self blame and behavioral self blame were higher in participants who were former smokers. Literature on these two types of blame indicate that characterological se lf blame is the maladaptive form of blame because it has a greater burden

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! (! on self concept a vital psychological construct, and can be generalized into the person's perceptions about future events, fostering more hopelessness (Janoff Bulman, 1977.) In th e regression model for characterological self blame, gender was a significant covariate F emales were found to have less characterological self blame than males. In this sample, more males were former smokers than females. I n a study on smoking behavior an d blame in non AATD COPD, Plaufcan, Wamboldt, and Holm (2013) found that the more an individual had smoked the more self blame they attributed to the development of their COPD. Plaufcan and colleagues argued that if smoking identity is part of how an ind ividual labels him or herself, it is logical that they would experience characterological self blame for their smoking behavior. In this current sample since m ore males were former smokers, more males experienced characterologi cal self blame than females. Within the scope of this study, it is not possible to argue that this gender difference is meaningful beyond the fact that there were more males who smoked and consequently had characterological self blame than females. Dyspnea is a subjective experienc e that can have a significant impact on quality of life (Hayen, Herigstad, Pattinson, 2013; Holm, Bowler, Make, Wamboldt, 2009) In this sample, dyspnea was a significant covariate in the regression model for characterological self blame. Experiencing dysp nea was associated with less characterological self blame. A possible explanation for this is that experiencing certain symptoms allows a person to feel less personally responsible and more at the mercy of their illness. Perhaps p articipants who endorse d m ore dyspnea a consequence of COPD, may attribute more blame for their quality of life (or lack of it) to the nature of the illness rather than attributing it to themselves or feeling personally responsible for their quality of life

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! ). As mentioned above, i n the regression model for behavioral self blame, smoking history was the only significant predictor of the level of behavioral self blame, indicating that former smokers had higher behavioral self blame than never smokers. No other covariates were found to be significant in this model. An important consideration in evaluating the model for behavioral self blame is that as a variable, behavioral self blame (N = 373) had more missing data tha n characterological self blame (N = 411) Th is can be seen in it s confidence interval that ranges from 9.38 67.3. Behavioral self blame was a single item self report measure that was located on the bottom of page 4 of the mega questionnaire (see Appendix A.) It was the last question of this page, and the qu estion prior to it asks "If your COPD was caused by smoking, how much do you blame yourself for smoking?" It is possible that people who did not have a history of smoking or people who did not feel their COPD was cause by smoking could have skipped the behavioral self blame item, assuming that it was a continuation of the previous item and also related to smoking. This would explain some of the missing data but also suggests, that if this reasoning were accurate as to why people missed this question, data on behavioral self blame could possibly be skewed with more answers from the people who answered the previous question and thus smoked and blamed themselves for developing COPD. The results for behavioral self blame, therefore, while statistically significant, need to be considered with discretion. Findings of Aim 3 Former smokers were found to endorse perceived family blame, which suggests that an important aspect of the experience of having AATD assoc iated COPD has to do with patients' experiences with their famili es Age is a significant covariate in the regression model for perceived family blame. Participants who were older were less likely to endorse

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! )% family blame. In this sample, the never smokers were, on average, older than the former smokers. This could expla in why older participants denied family blame, as younger participants tended to be former smokers and could have more reason to be blamed or to perceive blame from their families. An additional consideration is that a s people age, societal expectations ab out health change so that having health issues is normative in older people Therefore, patients with AATD associated COPD may be less likely to be blamed or to perceive themselves as being blamed by their families for being ill Additionally, as people ag e, their role and status in a family may change so that blame is less likely to be placed or to be received by the individual. An important consideration that future studies can look into is whether people are considering their families of origin or the fa milies that they have established. Responses to questions for each of these (family of origin versus family that was established) may differ, and the questionnaire used in this study did not make this distinction in the questions that were asked (see Appen dix A.) Augmentation therapy was also a significant covariate with in the perceived family blame regression model Participants who had received augmentation therapy were less likely to have perceived fami ly blame. Engagement in some for m of intervention f or their condition may attenuate the patient s perception s o f being blamed by their family because receiving treatment may be seen as being proactive and engaged in the management of their illness Perceived family criticism was the only outcome that wa s not predicted by smoking history. A possible explanation for this is that family criticism is not an exper ience unique to formers smokers which may be the reason it could not be predicted by smoking history. Age, however, was a significant covariate in this model. Similar to perceived family blame, participants who were older tended to have no perceived family criticism. This may, once again, have to do with never smokers being, on average, older than their former smoker

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! )& counterparts. People who are olde r may not impacted by fam ily criticism the way younger people are due to their stage of life, due to whether the person is more involved with their family origin or their own established family, or due to societal norms where older people may be less likel y to be criticized or effected by criticism. In a study looking at the impact of age on psychological adjustment in AATD associated COPD, Holm and colleagues (2013) found that younger patients more often had symptoms of anxiety and lower health related qua lity of life. They also found that age was associated with relationship status, and people who were younger and uncoupled had increased symptoms of depression and dyspnea. This finding validates the finding in the current study that being younger increased the odds of experiencing perceived family criticism, which can be seen as a negative psychosocial outcome. Having a high school education/GED or less was also found to be a significant covariate associated with perceived family criticism. This level of education was associated with not having perceived family criticism, and there are several ways to understand this. Education is a component of socioeconomic status and this status is often shared among family members primarily those living in the same ho me ( Conley & Gauber, 2005 ) It has been found that people with less education have less access to and ability to utilize health information (Rose, Chassin, Presson, 1996; Ross, Wu, 1995; Adler, et al., 1994). Literature on socioeconomic status and COPD fou nd that lower educational attainment was also consistently associated with more severe COPD (Eisner et al., 2011.) Perceived family criticism may be less in people who this lower level of educational attainment because of a combination of the findings abov e. Having lower educational attainment is often shared within a family, and if as a family there is less health literacy about COPD, patients with COPD in this family environment may be receiving less family criticism.

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! )' Alternatively, the perceived family criticism item differs from the perceived family blame item in that the blame item asks specifically about blame placed for the individual's COPD. The family criticism item asks only about perceived criticism in general, not specifying a target or subject of criticism. Because of the lack of specificity in the perceived family criticism question, the scores for t his item may be more directly getting at specific issues in the family dynamic as a whole rather than getting at something solely in the individua l. Hoth and colleagues (2014) looked at the family environment and its impact on illness uncertainty and found that there is complex relationship between family interactions (e.g. perceived family criticism, shared illness history, etc) and illness uncerta inty in patients with AATD associated COPD. Limitations When analyzing data from a self report questionnaire there are limits to the generalizability of the data to the larger population being examined. Typically, there are differences between people wh o participate and send back the questionnaire and those who do not or send the questionnaire back incomplete. The people in the study are self selected and may represent those in the population who are interested in participating in research, who do not ha ve major barriers to participating, and/or who are able to follow and complete a questionnaire independently of researcher facilitation Therefore, it is important to be conservative in generalizing findings from such a sample to the larger population. A n additional limitation in this sample is that it is cross sectional, and having data from a single time point is another reason that findings are not wholly generalizable. A cross sectional study also makes it difficult to make strong conclusions or make causal assumptions. Results from multiple time points tend to lend more validity to findings and may minimize certain confounds tha t have to do with a single time point On the other hand,

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! )( participants who would be able to successful ly participate in multi ple time points longitudinally are also somewhat self selecting and therefore not entirely generalizable to the population, which includes people who have significant barriers to participating in a study longitudinally. In a study where a questionnair e is mailed out to participants, significant faith is placed in the participants that they will not only decide to complete the questionnaire, but they will complete it carefully and correctly without the facilitation of researcher. Consequently, a signifi cant portion of the data that comes in has to be excluded, and this can be seen in Figure 1 in the method section of this project The benefit of a mailed questionnaire, however, is that researchers can have an expanded pool of potential participants and p otentially, a larger sample size. A difficulty with a questionnaire that is mailed out is that without the facilitation of a researcher participants are more susceptible to potential weaknesses in questionnaire lay out which may result in incomplete or inaccurate data from participants. This was, unfortunately the case for certain variables examined in this project. One example is the item for behaviora l self blame. Behavior self blame had the greatest amount of missing data among the four predictors exa mined in this project (see Table 2.) The item for behavioral self blame was placed after a series of questions on smoking or other behaviors that may have contributed to COPD (s ee Appendix A for items). The sample size for the behavioral self blame items a nd the placement of these items in the questionnaire suggest that never smokers who read through the prior questions about smoking behaviors may have skipped the last item on the page (the item for behavioral self blame) assuming that it does not apply to them. The smaller sample size and the cofounded response pattern were confirmed by the very large confidence interval in the regression model for behavioral self blame and smoking history.

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! )) Consequently, findings about behavioral self blame are evaluated mo re conservatively. It would be particularly important to conduct a study where behavioral self blame can be re examined without these issues to determine whether the findin g s in this study remain. A similar issue exists for the item on comorbi d medical con ditions. This item was placed immediately after questions about oxygen use. This placement made it more likely that a participant who did not endorse oxygen use would overlook this qu estion. Consequently, the data appear to be more representative of the mo re severely ill members of the population, both never smokers and former smokers. This could account for why hypothesis 2b was not supported and why never smokers and former smokers appeared to be so similar in this characteristic. Another weakness i n the data was identified when p articipants who were included in the analyses were compared to partici p ants who were excl uded due to missing data. M issing data from participants can be an indicator of worse outcomes either in terms of health or socioeconomic st atus ( McKnight, McKnight, Sidani, Figueredo, 2007 ) and the lack of difference between the two groups was notable. One possible explanation for the lack of statistically significant difference between the groups could be that since this project is studying cross sectional data it does not capture differences that could exist if this data were longitudinal. With multiple time points it is more likely that disparities and unique burdens will become obstacles in completing participation in a study such as this An important aspect of AATD associated COPD that was not examined in this project is the impact of this genetic condition on family interactions. This aspect of the experience of this disease is a vital one, but was unfortunately not part of the scope o f this project.

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! )* Implications for Future Research This project highlights many areas that can be looked into further to better understand the population of patients with AATD associated COPD. In future studies it will be important to arrange the questionn aire in a way that ensures that critical items are less likely to be overlooked or misunderstood. In the interest of having a larger sample size, it may be helpful to offer an incentive to participants to motivate them to complete their questionnaires. Th is way, the benefit of a large mailing list may also result in greater response rates. Having a longitudinal dataset would also provide greater strength and validity to possible findings. A longitudinal dataset will most likely have a smaller sample size but the quality of the data which is longitudinal would make up for what may be lost in sample size. An important finding to re examine future studies is that greater dyspnea lead s to lower characterological self blame. It would be meaningful to determ ine whether this finding is replicable and what mechanism may be causing this association To understand the mechanism, additio nal variables such as health related quality of life and disability may be considered, as well as other mood or personality facto rs. Another potential area of future research is looking further at the impact of age on AATD associated COPD. Given the consistent association between age, health related quality of life, psychological adjustment ( in previous literature ) and measures of family blame and criticism ( found in this study ) an important future step would be to examine how fami ly relationships, both in families of origin and families established with a spouse, are impacted by the age of the patients with AATD associated COPD. U nderstanding this can shed more light on factors that help patie nts have better quality of life, psychological resilience and well being through the various stages of life.

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! )+ Additionally, there was an association between lower educational attainment and p ercei v e d family criticism, but there was not sufficient evidence in this project to make a stronger statement about the nature of the association between education and perceived criticism. It would therefore be an important next step to examine educational attainment, health literacy and perceived family blame and perceived family criticism in patients with AATD associated COPD. Finally, looking at family blame and family criticism in this population, another important aspect to consider is the impact of AATD, a genetic condition, causing COPD. Having a genetic condition engages the family members of a patient in unique ways in comparison to non AATD associated COPD. This is because a genetic condition implies that other family members either had, may deve lop in the future, or are carriers for the same illness. This aspect of family factors and interaction was not covered in the scope of this project. Given the results found in the project for family blame and family criticism, it is clear that looking furt her into how the genetic component of this illness impacts family interactions pertaining to the illness would be an important and meaningful investigation. Implications for Intervention It is most important to be able to first replicate the findings of this project. In the events that they are replicable, clinicians and medical providers will have several points of entry for providing interventions. Clinicians can focus on the impact of certain symptoms of the illness, such as dyspnea, on a patient's hea lth related quality of life. Pr oviding psychoeducation on the subjectivity of the dyspnea or providing biofeedback could empower a patient and help them develop some control over their perce p tions of their symptoms. Another point of entry is in providing health education to the patients and their family collectively. This would be particularly helpful for families that may have more limited

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! ), access to health information and/or for families who are concerned about carriers in their family or multiple member s with the illness Given the association between access to health information and health outcomes, this intervention could empower patients and their families and facilitate communication between family members facing this illness. Clinicians can also t arget the risk factors and protective factors for patients with AATD associated COPD for example, age. Younger patients may benefit from more interventions that focus on social skills building, communication skills building, developme n t of self confidence and cognitive behavioral techniques that can help them navigate feelings of blame, criticism and loneliness. These interventions would clearly benefit any patient with AATD associated COPD, but it would important to find ways to engage younger patients f or whom these interventions may be particularly needed

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! )! REFERENCES Adler, N. E., Boyce, T., Chesney, M. A., Cohen, S., Folkman, S., Kahn, R. L., & Syme, S.L. (1994). Socioeconomic status and health. The challenge of the gradient. American Psycholog ist 49(1), 15 24. Adler, N. E., Boyce, W. T., Chesney, M. A., Folkman, S., & Syme, S. L. (1993). Socioeconomic inequalities in health. No easy solution. Journal of the American Medical Association 269(24), 3140 3145. Ali, A., Toner, B. B., Stuckless, N., Gallop, R., Diamant., N. E., Gould, M. I., & Vidins, E. I. (2000). Emotional abuse, self blame, and self silencing in women with irritable bowel syndrome. Psychosomatic Med icine, 62(1), 76 82. American Thoracic Society. (1999). Dyspnea. Mechanisms, asses sment, and management: a consensus statement. American Journal of Respiratory Critical Care Medicine, 159(1), 321 340. Beck, A. T. (1967) Depression: Clinical, experimental, and theoretical aspects. New York, NY: Harper & Row. Baker, E. H. (2014). Socioeco nomic Status, Definition. The Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society 2210 2214. Bednarek, M., Gorecka, D., Wielgomas, J., Czajkowska Malinowska, M., Regula, J., Mieszko Filipczyk, G., Jasionowicz, M., Bijata Bronisz, R., Le mpicka Jastrzebska, M., Czaikowski, M., Przybylski, G. & Zielinski, J. (2006). Smokers with airway obstruction are more likely to quit smoking. Thorax, 61, 869 873. Bestall, J.C., Paul, E.A., Garrod, R., Garnham, R., Jones, P.W., & Wedzicha, J.A. (1999). U sefulness of the Medical Research Council (MRC) dyspnoea scale as a measure of

PAGE 71

! *. disability in patients with chronic obstructive pulmonary disease. Thorax, 54, 581 586. Bishop, D. S., Epstein, N. B., Keitner, G. I., Miller, I. W., & Srinivasan, S. V., (1986 ). Stroke: morale, family functioning, health status, and functional capacity. Archives of Physical Medicine & Rehabilitation, 67, 84 87. Bornhorst, J. A., Greene, D. N., Ashwood, E. R., Grenache, D. G. (2013). A1 Antitrypsin phenotypes and associated seru m protein concentrations in a large clinical population. Chest 143(4), 1000 1008. Brantly, M. L., Paul, L. D., Miller, B. H., Falk, R. T., Wu, M. & Crystal, R. G. (1988). Clinical features and history of the destructive lung disease associated with alpha 1 antitrypsin deficiency of adults with pulmonary symptoms. American Review of Respiratory Disorders 138, 327 336. Buist, A. S. (1990). Alpha 1 antitrypsin deficiency -diagnosis, treatment, and control: identification of patients. Lung, 168, 543 551. Camp os M. A., Alazemi, S., Zhang, G., Wanner, A., Salathe, M., Baier, H., Sandhaus, R. A. (2009). Exacerbations in subjects with alpha 1 antitrypsin deficiency receiving augmentation therapy. Respiratory Medicine, 103(10), 1532 1539. Campos, M.A., Alazemi, S., Zhang, G., Wanner, A., Sandhaus, R. A. (2009). Effects of a disease management program in individuals with alpha 1 antitrypsin deficiency. COPD, 6(1), 31 40. Campos, M. A., Wanner, A., Zhang, G., & Sandhaus, R.A. (2005). Trends in the diagnosis of symptom atic patients with alpha1 antitrypsin deficiency between 1968 and 2003. Chest 128(3), 1179 86.

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! *% Centers for Disease Control and Prevention (CDC), Behavioral Risk Factor Surveillance System Survey Questionnaire. 2004 a Atlanta, Georgia: U.S. Department of H ealth and Human Services. Centers for Disease Control and Prevention (CDC), National Health Interview Survey Questionnaire 2004b, Atlanta, Georgia: U.S. Department of Health and Human Services. Center for Disease Control and Prevention. (2014 c ). Data and statistics. COPD Homepage. Retrieved from http://www.cdc.gov/copd/data.htm on November 6, 2015. Conley, D. & Gauber, R. (2005). Sibling similarity and difference in socioeconomic status: life course and fami ly resource effects. The National Bureau of Economic Research, 11320. Coronini Cronberg, S., Heffernan, C., & Robinson, M. (2011). Effective smoking cessation interventions for COPD patients: a review of the evidence. Journal of Royal Society of Medicine Short Reports 2 (10), 78. Craig, T.J. (2015). Suspecting and testing for alpha 1 antitrypsin deficiency an allergist's and/or immunologist's perspective. Journal of Allergy and Clinical Immunology Practice, 3(4), 506 511. Dani, J. A., Harris, R. A. (200 5). Nicotine addiction and comorbidity with alcohol abuse and mental illness. Nature & Neuroscience, 8(11), 1465 1470. De Serres, F. J. (2002). Worldwide racial and ethnic distribution of alpha1 antitrypsin deficiency: summary of an analysis of published e pidemiologic surveys. Chest, 122(5), 1818 1829. Dirksen, A., Dijkman, J. H., Madsen, F., Stoel, B., Hutchison, D. C., Ulrik, C. S., Skovgaard, L. T., Kok Jensen, A., Rudolphus, A., Seersholm, N., Vrooman, H. A., Reiber, J. H.,

PAGE 73

! *& Hansen, N. C., Heckscher, T., Viskum, K., & Stolk, J. (1999). A randomized clinical trial of alpha(1) antitrypsin augmentation therapy. American Journal of Respiratory Critical Care Medicine, 160, 1468 1472. Eisner, M. D., Blanc, P. D., Omachi, T. A., Yelin, E. H., Sidney, S., Katz, P P. Ackerson, L. M., Sanchez, G., Tolstykh, I., & Iribarren, C. (2011) Socioeconomic status, race, and COPD health outcomes. Journal of Epidemiological Community Health, 65(1), 26 34. Epstein, N. B., Baldwin, L. M., & Bishop, D. S. (1983). The McMaster F amily Assessment Device. Journal of Marital and Family Therapy, 9, 171 180. Fiscella, K., Franks, P., & Shields, C. G. (1997). Perceived family criticism and primary care utilization: psychosocial and biomedical pathways. Family Process, 36, 25 41. Fiscell a, K. & Campbell, T. L. (1999). Association of perceived family criticism with health behaviors. Journal of Family Practice, 48, 128 134. Fletcher, C. M., Elmes, P. C., Fairbrain, A. S., & Wood, C. H. (1959). The significance of respiratory symptoms and th e diagnosis of chronic bronchitis in a working population. British Medical Journal, 2, 257 266. Ford, E.S., Murphy, L.B., Khavjou, O., Giles, W.H., Holt, J.B., & Croft, J.B. (2015) Total and state specific medical absenteeism costs of COPD among adults # 18 years in the United States for 2010 and projections through 2020. Chest, 147(1) 31 45. Foreman, M. G., Zhang, L., Murph y, J., Hansel, N. N., Make, B., Hokanson, J. E., Washko, G., Regan, E. A., Crapo, J. D., Silverman, E. K., Demeo, D. L., & the COPDGene Investigators. (2011). Early onset chronic obstructive pulmonary disease Is associated with female sex, maternal factors and african american race in the COPDGene study. American Journal of Respiratory and Critical Care Medicine 184 (4), 414 420.

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! *' Franks, P., Campbell, T. L & Shields, C. G. (1992). Social relationships and health: the relative roles of family functioning a nd social support. Social Sciences Medicine, 34 779 788. Fregonese, L & Stol, J. (2008). Hereditary alpha 1 antitrypsin deficiency and its clinical consequences. Orphanet Journal of Rare Diseases, 3, 16. Giacoboni, D., Barrecheguren, M., Esquinas, C., Rod rigues, E., Berastegui, C., Lopez Memeguer, M., Monforte, V., Bravo, C., Pirna, P., Miravitlles, M. & Rom an, A. (2015) Characteristics of candidates for lung transplantation due to chronic obstructive pulmonary disease and alpha 1 antitrypsin deficiency em physema. Archivos de Bronconeumologia 51(8) 379 383. Global Initiative for Chronic Obstructive Lung Disease (GOLD). (2013). Global strategy for the diagnosis, management and prevention of COPD. Global Initiative for Chronic Obstructive Lung Disease Retr ieved from www.goldcopd.org/guidelines global strategy for diagnosis management.html on November 27, 2015. Gunzerath, L., Connelly, B., Albert, P., & Knebel, A (2001). Relationship of personality traits and coping strategies to quality of life in patients with alpha 1 antitrypsin deficiency. Psychology, Health and Medicine. 6, 335 341. Hajiro, T., Nishimura, K., Tsukino, M., Ikeda, A., & Oga, T. (2000). Stages of disease severity and factors that affect the health status of patients with chronic obstructive pulmonary disease. Respiratory Medicine, 94, 841 846. Hamilton, J.G., Lobel, M., & Moyer, A. (2009). Emotional distress following genetic testing for heredit ary breast and ovarian cancer: a meta analytic review. Health Psychology, 28(4) 510 518.

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! *( Hayen, A., Herigstad, M., & Pattinson, K.T.S. (2013). Understanding dyspnea as a complex individual experience. Maturitas, 76 45 50. Hill, A.T., Campbell, E.J., Hill S.L., Bayley, D.L., & Stockley, R.A. (2000). Association between airway bacterial load and markers of airway inflammation in patients with stable chronic bronchitis. The American Journal of Medicine 109, 288 295. Holm, K.E., Borson, S., Sandhaus, R. A., Ford, D. W., Strange, C., Bowler, R. P., Make, B. J., & Wamboldt, F. S. (2013). Differences in adjustment between individuals with alpha 1 antitrypsin deficiency (AATD) associated COPD and non AATD COPD. Journal of Chronic Obstructive Pulmonary Disease, 1 0, 226 234. Holm, K. E., Bowler, R. P., Make, B. J., & Wamboldt, F. S. (2009). Family relationship quality is associated with psychological distress, dyspnea, and quality of life in COPD. COPD 6(5), 359 368. Holm, K.E., LaChance, H.R., Bowler, R.P., Make, B.J., & Wamboldt, F.S. (2010). Family factors are associated with psychological distress and smoking status in chronic obstructive pulmonary disease. General Hospital Psychiatry, 32(5), 492 498. Holm, K.E., Wamboldt, F.S., Ford, D.W., Sandhaus, R.A., Stra nd, R.A., Strange, C. & Hoth, K. (2013) The prospective association of perceived criticism with dyspnea in chronic lung disease. Journal of Psychosomatic Research 74(5), 450 453. Holt Lunstad, J., Smith, T. B., & Layton, J. B. (2010). Social relationships and mortality risk: a meta analytic review. PLoS medicine 7 (7), 859. Hooley, J.M. & Teasdale, J.D. (1989). Predictors of relapse in unipolar depressives: Expressed emotion, marital distress, and perceived criticism. Journal of Abnormal Psychology 98(3), 229 235. Hoth, K. F., Wamboldt, F .S., Strand, M., Ford, D. W., Sandhaus, R. A., Strange, C.,

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! *) Bekelman, D. B., & Holm, K. E. (2013). Prospective impact of illness uncertainty on outcomes in chronic lung disease. Health Psychology 32(11), 1170 1174. House J. S., Landis, K. R., & Umberson, D. (1988). Social relationships and health. Science 241(4865), 540 545. Ioachimescu, O. C. & Stoller, J. K. (2005). A review of alpha 1 antitrypsin deficiency. COPD, 2, 263 275. Janoff Bulman, R. (1979). Characterologic al versus behavioral self blame: inquiries into depression and rape. Journal of Personality and Social Psychology 37(10), 1798 809. Janson, C., Bjšrnsson, E., Hetta, J., & Biman, G. (1994). Anxiety and depression in relation to respiratory symptoms in ast hma. American Journal of Respiratory and Critical Care Medicine, 149, 930 934. Jimenez Ruiz, C. A., Masa, F., Miravitlles, M., Gabriel, R., Viejo, J. L., Villasante, C., Sobradillo, V., & the IBERPOC Study Investigators. ( 2001). Smoking characteristics: differences in attitudes and dependence between healthy smokers and smokers with COPD. Chest, 119, 1365 1370. John, U., Meyer, C., Rumpf, H. J., Schumann, A., Thyrian, J. R., & Hapke, U. (2003). Strength of the relationship between tobacco smoking, nicotin e dependence and the severity of alcohol dependence syndrome criteria in a population based sample Alcohol, 38(6), 606 612. Kaplan, A. & Cosentino, L. (2010) $1 antitrypsin deficiency. Canadian Family Physician, 56, 19 24. Kelly, E., Greene, C. M., Carrol l, T. P., McElvaney, N. G., O'Neill, S. J. (2010). Alpha 1 antitrypsin deficiency. Respiratory Medicine, 104(6), 763 772.

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! ** Ketelaars, C. A., Schlosser, M. A., Mostert, R., Huyer Abu Saad, H., Halfens, R. J., & Wouters, E. F. (1996). Determinants of health related quality of life in patients with chronic obstructive pulmonary disease. Thorax, 51, 39 43. Kiecolt Glaser, J. K., Glaser, R., Cacioppo, J. T., MacCallum, R. C., Syndersmith, M., Kim, C., & Malarkey, W. M. (1997). Marital conflict in older adults: E ndocrinological and immunological correlates. Psychosomatic Medicine, 59, 339 349. Kim, Y.I., Schroeder, J., Lynch, D., Newell, J., Make, B., Friedlander, A., Estepar, R. S. J., Hanania, N. A., Washko, G., Murphy, J., A., Wilson, C., Hokanson, J. E., Zach J., Butterfield, K., Bowler, R. P., & COPDGene¨ Investigators. (2011). Gender differences of airway dimensions in anatomically matched sites on CT in smokers. COPD 8 (4), 285 292. Kindt, T. J., Goldsby, R. A., & Osborne, B. A. (2007). Immunology New Yor k, NY: W.H. Freeman and Company. Kleinman, A., Eisenberg, L., & Good, B. (1978). Culture, illness, and care: Clinical lessons from anthropologic and cross cultural research. Annals of Internal Medicine, 88(2), 251 258. Kunik, M. E., Roundy, K., Veazey, C., Souchek, J. Richardson, P. Wray, N. P. & Stanley, M. A. (2005). Surprisingly high prevalence of anxiety and depression in chronic breathing disorders. Chest, 127, 1205 1211. Lœdv’ksd—ttir, D., Bj! rnsson, E., Janson, C., & Boman, G. (1996). Habitual coughing and its associations with asthma, anxiety, and gastroesophageal reflux. Chest, 109(5), 1262 1268.

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! *+ MacDonald, J. L. & Johnson, C. E. (1995). Pathophysiology and treatment of alpha sub 1 antitrypsin deficiency. American Journal of Health System Pharmacy 52(5), 481 489. Mahler, D. A., Harver, A., Lentine, T., Scott, J. A., Beck, K., & Schwartzstein, R.M. (1996). Descriptors of breathlessness in cardiorespiratory diseases. Amer ican Journal of Respiratory and Critical Care Medicine, 154, 1357 1363. Malcarne, V. L., Compas, B. E., Epping Jordan, J. E., & Howell, D. C. (1995). Cognitive factors in adjustment to cancer: attributions of self blame and perceptions of control. Journal of Behavioral Medicine 18(5), 401 417. Marino, P., Sirey, J. A., Raue, P.J., & Alexopoulos, G. S. (2008). Impact of social support and self efficacy on functioning in depressed older adults with chronic obstructive pulmonary disease. International Journal of Chronic Obstructive Pulmonary Disease, 3(4), 713 718. Marshall, G. N. (1991). A multidimensional analysis of internal health locus of control beliefs: Separating the wheat from the chaff. Journal of Personality and Social Psychology 61(3), 483 491. Ma rtire, L. M., Lustig, A. P., Schulz, R., Miller, G. E., Helgeson, V. S. (2004). Is it beneficial to involve a family member? A meta analysis of psychosocial interventions for chronic illness. Health Psychology, 23(6) 599 611. Mayo Clinic Staff. (2015, Jul y 21). Diseases and Conditions: COPD. Mayo Clinic. Retrieved from http://www.mayoclinic.org/diseases conditions/COPD/basics/causes/con 20032017 on November 29, 20 15. McElvaney, N. G., Stoller, J. K., Buist, A. S., Prakash, U. B., Brantly, M. L., Schluchter, M. D., Crystal, R. D., & the $1 Antitrypsin Deficiency Registry Study Group. (1997).

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! *, Baseline characteristics of enrollees in the National Heart, Lung and Blood Institute Registry of alpha 1 antitrypsin deficiency. Chest, 111(1), 394 403. McKnight, P. E., McKnight, K. M., Sidani, S., Figueredo, A. J. (2007). Missing data: A gentle introduction. New York: The Guilford Press. "Blame." Merriam Webster.com 2015. htt p://www.merriam webster.com (28 November 2015). Mishel, M. H. & C. J. Braden. (1987). Uncertainty. a mediator between support and adjustment. Western Journal Nursing Research, 9, 43 57. Molfino, N. A. (2004). Genetics of COPD. Chest, 125, 1929 1940. Morice A. H. (2008). Chronic cough: epidemiology. Chronic Respiratory Disease, 5, 43 47. Mullins, L. L., Chaney, J. M., Pace, T. M., & Hartman, V. L. (1997). Illness uncertainty, attributional style, and psychological adjustment in older adolescents and young a dults with asthma. Journal of Pediatric Psychology. 22, 871 880. Nishimura, K., Izumi, T., Tsukino, M., & Olga, T. (2002). Dyspnea is a better predictor of 5 year survival than airway obstruction in patients with COPD. Chest, 121, 1434 1440. Oliver, S. M. (2001). Living with failing lungs: the doctor patient relationship. Family Practice. 18(4), 430 439. Omachi, T. A., Sarkar, Urmimala, S., Yelin, E. H., Blanc, P. D., & Katz, P. P. (2013). Lower health literacy in associated with poorer health status and o utcomes in chronic obstructive pulmonary disease. Journal of General Internal Medicine, 28(1), 74 81. Parkes, G., Greenhalgh, T., Griffin, M., & Dent, R. (2008). Effect on smoking quit rate of telling patients their lung age: the Step2quit randomised contr olled trial. British Medical Journal (BMJ), 336, 598 600.

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! *! Petty, T. L. (2004) Definition, epidemiology, course, and prognosis of COPD. Clinical Cornerstone 5(1), 1 10. Pollock, S. E., Christian, B. J., & Sands, D. (1990). Responses to chronic illness: ana lysis of psychological and physiological adaptation. Nursing Research 39(5), 300 304. Regan, E. A., Hokanson, J. E., Murphy, J. R., Make, B., Lynch, D. A., Beaty, T. H., Curran Everett, D., Silverman, E.K., Crapo, J. D. (2010). Genetic epidemiology of COP D (COPDGene) study d esign. COPD 7 (1), 32 43. http://doi.org/10.3109/15412550903499522 Ross, C. E. & Wu, C.L. (1995). The links between education and health. American Sociological Review, 60, 719 745. Ryan, C. E., Epstein, N. B., Keitner, G. I., Miller, I W., & Bishop, D. S. Evaluating and treating families: the McMaster approach. New York: Routledge. Sarason, I. G., Sarason, B. R., Shearin, E. N., & Pierce, G. R. (1987). A brief measure of social support: practical and theoretical implications. Journal o f Social & Personal Relationships, 4, 497 510. Scharloo, M., Kaptein, A.A., Weinman, J. A., Willems, L. N., & Rooijmans, H. G. (2000). Physical and psychological correlates of functioning in patients with chronic obstructive pulmonary disease. Journal of A sthma, 37, 17 29. Schnittker, J. (2004). Education and the changing shape of the income gradient in health. Journal of Health & Social Behavior 45(3), 286 305. Shadel, W. G., Mermelstein, R., & Borrelli, B. (1996). Self concept changes over time in cognit ive behavioral treatment for smoking cessation. Addictive Behaviors, 21(5) 659 663.

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! +. Shields, C. G., Franks, P., Harp, J. J., McDaniel, S. H., & Campbell, T. L. (1992). Development of the Family Emotional Involvement and Criticism Scale (FEICS): a self rep ort scale to measure expressed emotion. Journal of Marital & Family Therapy, 18, 395 407. Shields CG, Franks P, Harp JJ, Campbell TL, McDaniel SH. (1994). Family Emotional Involvement and Criticism Scale (FEICS): II. Reliability and validity studies. Famil y Systems Medicine, 12, 361 377. Skelvington, S. M, Pilaar, M., Routh, D., & MacLeod, R. D. (1997). On the language of breathlessness. Psychology and Health, 12, 677 689. Smith, G. D., & Egger, M. (1992). Socioeconomic differences in mortality in Britain and The U.S. American Journal of Public Health, 82, 1079 1080. Smoller, J.W., Simon, B.M., Pollack, M.H., Kradin, R., & Stern, T. (1999). Anxiety in patients with pulmonary disease: comorbidity and treatment. Seminars in Clinical Neuropsychiatry, 4 (2), 84 97. Stoller, J. K. & Brantly, M. (2013). The challenge of detecting alpha 1 antitrypsin deficiency. COPD 1, 26 34. Stoller, J. K., Lacbawan, F. L., & Aboussouan, L. S. (2006 Oct 27 [Updated 2014 May 1]). Alpha 1 Antitrypsin Deficiency. In: Pagon, R. A. Adam, M. P., & Ardinger, H. H., editors. GeneReviews¨ [Internet]. Seattle (WA): University of Washington, Seattle; 1993 2015. Stoller, J. K., Smith, P., Yang, P., & Spray, J. (1994). Physical and social impact of Alpha1 antitrypsin deficiency: results of a survey. Cleveland Clinic Journal of Medicine 61, 461 467.

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! +% Stoller J. K., Snider G. L., Brantly M. L., Fallat, R. J., & Stockley, R.A. for the Alpha 1 Antitrypsin Deficiency Task Force of the American Thoracic Society/European Respiratory Society. (200 3). Standards for the diagnosis and management of patients with alpha 1 antitrypsin deficiency. American Journal of Respiratory Critical Care Medicine 168(7), 816 900. Stoller, J. K., Strange, C., Schwarz, L., Kallstron, T. J., & Chatburn, R. L. (2014). Detection of alpha 1 antitrypsin deficiency by respiratory therapists: experience with an educational program. Respiratory Care, 59(5), 667 672. Sykes, A., Mallia, P., & Johnston, S. L. (2007). Diagnosis of pathogens in exacerbations of chronic obstructive pulmonary disease. Proceedings of the American Thoracic Society 4, 642 646. Tanash, H. A ., Riise, G. C., Hansson, L., Nilsson, P. M., & Piitulainen, E. (2011). Survival benefit of lung transplantation in individuals with severe $1 anti trypsin deficiency (PiZZ) and emphysema. The Journal of Heart and Lung Transplantation, 20(12), 1342 1347. U chino, B. N. (2004). Social support and physical health: Understanding the health consequences of relationships (Current perspectives in psychology). New Haven, Connecticut: Yale University Press. Uchino, B. N. (2005). Social support and physical health: U nderstanding the health consequences of relationships. American Journal of Epidemiology, 161(3) 158. van den Putte B, Yzer M, Willemsen MC, & de Bruijn GJ. (2009). The effects of smoking self identity and quitting: self identity on attempts to quit smokin g. Health Psychology 28(5), 535 544. Vangeli, E., Stapleton, J., & West, R. (2010). Residual attraction to smoking and smoker

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! +& identity following smoking cessation. Nicotine & Tobacco Research 12(8), 865 869. Voth, J. & Sirois, F. M. (2009). The role of self blame and responsibility in adjustment to inflammatory bowel disease. Rehabilitation Psychology, 54(1), 99 108. Weihs, K., Fisher, L., & Baird, M. (2002). Families, health, and behavior: A section of the commissioned report by the committee on health and behavior: Research, practice, and policy Family Systems & Health, 20, 7 46. Wilson, I. (2006). Depression in the patient with COPD. International Journal of Chronic Obstructive Pulmonary Disease, 1, 61 64. Yohannes, A. M., Willgoss, T. G., Baldwin, R. C., & Connolly, M. J. (2010). Depression and anxiety in chronic heart failure and chronic obstructive pulmonary disease; prevalence, relevance, clinical implications and management principles. International Journal of Geriatric Psychiatry. 25, 1209 122 1.

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! +' APPENDIX Mega Quest Questionnaire is source of all variables for proposed analyses: Below are questions for demographic data.

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! +( Items for characterological self blame are under the second block of writing, the 4 point rating scale. The questions b elow those are for behavio ral self blame but only the second question will be used given the proposed dichotomization of sample into past smokers and never smokers.

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! +) First seven questions are the items to be used for Perceived Family Criticism The last o f the three questions follo wing Perceive Family Criticism is the item that will be used for Perceived Family Blame