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The impact of comorbidities on productivity loss in asthma Ehteshami-Afshar, Solmaz 2016

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THE IMPACT OF COMORBIDITIES ON PRODUCTIVITY LOSS IN ASTHMA  by  Solmaz Ehteshami-Afshar  MD, Shahid Behehsti University of Medical Sciences, Tehran-Iran, 2014  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   August 2016  © Solmaz Ehteshami-Afshar, 2016   ii Abstract Rationale Health-related productivity loss and the impact of comorbidities on the economic burden of asthma are important, yet overlooked, components. I aimed at revising recent estimates of the costs of asthma worldwide. The empirical research involved evaluating the effect of comorbidities on productivity loss among adult asthma patients.  Methods  A literature review was conducted on studies regarding the costs of asthma (January 2008 to January 2015) and subsequently on the effects of comorbidities on productivity. In parallel data from a prospectively evaluated random sample of employed adults with asthma was used and the prevalence of comorbidities measured using a validated self-administered comorbidity questionnaire (SCQ), range 0 – 39, (the higher the score, the higher the level of comorbidity). Productivity loss, including absenteeism and presenteeism, were also measured using validated instruments in 2010 Canadian dollars ($). I used a two-part regression model to estimate the adjusted difference of productivity loss across levels of comorbidity, controlling for potential confounding variables. Results The review demonstrated that asthma imposes a major economic burden, however there are large discrepancies in the reported estimates. There is also uncertainty about the indirect costs of asthma and the effects of comorbidities on these costs. A random sample of 284 adults with the mean age of 47.8 (SD 11.8) was included (68% women). The mean SCQ score was 2.47 (SD 2.97, range 0-15) and the average productivity loss was $317.5 per week (SD $858.8). Comorbidity was significantly associated with productivity   iii loss. One-unit increase in the SCQ score was associated with a 14% (OR=1.14, 95% CI 1.02-1.28) increase in the odds of reporting productivity loss, and 9.0% (OR=1.09, 95% CI 1.01-1.18) increase in productivity loss among those who reported any loss of productivity. A person with a SCQ score of 15 had  $1,685 per week more productivity loss than a patient with a SCQ of zero.  Conclusion This study demonstrates that comorbidities substantially decrease productivity in working asthma patients. Asthma management strategies must be cognizant of the role of comorbidities and should properly incorporate the effect of comorbidity and productivity loss in estimating the benefit of disease management strategies.    iv Preface Some outputs from chapter 1 has been published as “ Ehteshami-Afshar S, FitzGerald JM, Doyle-Waters MM, Sadatsafavi M. “The global economic burden of asthma and chronic obstructive pulmonary disease.” The International Journal of Tuberculosis and Lung Disease. 2016 Jan 1; 20(1):11-23.” I was responsible for conducting the search, including the relevant articles and writing the first draft of the manuscript. Mimi Doyle-Waters was also involved in searching and finding relevant articles .The review was conducted under the supervision of Drs. FitzGerald and Sadatsafavi and they critically revised the manuscript. They, along with myself, were involved in the acquisition of the data.  A version of Chapter 2 and 3 has been submitted for publication. Data from the Economic Burden of Asthma (EBA) study are used in the present study. University of British Columbia Human Ethics Board approved the EBA study (H10-01542). JM FitzGerald and I proposed the research question. I conceptualized the study design, was the main analyst, and wrote the first draft of the manuscript. Drs. FitzGerald, Sadatsafavi and Carlsten were involved in the acquisition of the data and critically reviewed the manuscript.     v Table of Contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iv Table of Contents .......................................................................................................................... v List of Tables ............................................................................................................................... vii List of Figures ............................................................................................................................. viii List of Abbreviations ................................................................................................................... ix Acknowledgements ....................................................................................................................... x Dedication ..................................................................................................................................... xi Chapter 1: Introduction ............................................................................................................... 1 1.1 Asthma and its epidemiology ....................................................................................................... 1 1.2 What is economic burden? ........................................................................................................... 2 1.2.1 Attributable versus excess costs............................................................................................... 2 1.2.2 Miscellaneous medical and non-medical costs ........................................................................ 3 1.3 Literature review of costs of asthma ........................................................................................... 3 1.3.1 General costs of asthma in the literature .................................................................................. 7 1.3.2 Direct costs ............................................................................................................................ 10 1.3.3 Indirect costs .......................................................................................................................... 11 1.3.4 Costs associated with asthma control status .......................................................................... 12 1.3.5 Costs of asthma exacerbations ............................................................................................... 16 1.4 Literature review of comorbidities and their effect on productivity loss in asthma ............. 17   vi Chapter 2: Body of Thesis .......................................................................................................... 18 2.1 Methods ........................................................................................................................................ 18 2.1.1 EBA study .............................................................................................................................. 18 2.1.2 Inclusion and Exclusion criteria ............................................................................................ 19 2.1.3 Assessments and Procedures in EBA study ........................................................................... 20 2.1.3.1 Baseline visit: ................................................................................................................................ 20 2.1.3.2 Follow-up visits: ............................................................................................................................ 21 2.1.4 Variables ................................................................................................................................ 21 2.1.4.1 Comorbidity ................................................................................................................................... 21 2.1.4.2 Productivity loss ............................................................................................................................ 22 2.1.5 Confounders ........................................................................................................................... 23 2.1.6 Statistical Analysis ................................................................................................................. 24 2.2 Results .......................................................................................................................................... 26 2.2.1 Demographics ........................................................................................................................ 26 2.2.2 Unadjusted analysis ............................................................................................................... 29 2.2.3 Adjusted analysis ................................................................................................................... 32 2.2.4 Marginal effect of comorbidity on productivity loss ............................................................. 35 2.2.5 Sensitivity analyses ................................................................................................................ 37 Chapter 3: Conclusion ................................................................................................................ 41 References .................................................................................................................................... 45 Supplementary Material: Search strategy................................................................................ 51    vii List of Tables   Table 1: Characteristics of included studies ................................................................................... 5 Table 2: Average direct and indirect costs of asthma (per person-year) and its components (2014 USD) ............................................................................................................................................... 8 Table 3: Average costs of asthma across asthma control levels per patient-year (2014 USD) .... 14 Table 4 Characteristics of study sample ....................................................................................... 27 Table 5 unadjusted regression analyses ........................................................................................ 30 Table 6 Results of the adjusted regression analysis of productivity loss on SCQ score .............. 33 Table 7: Incremental costs of productivity loss based on SCQ score (2010 CAD) ...................... 36 Table 8: Results of alternative model specification (with main model also reported for comparison) for total productivity loss on SCQ ........................................................................... 38 Table 9: Costs of productivity loss per week (CAD)  , comparing the main model with the alternative model adding control status as confounder ................................................................. 40       viii List of Figures Figure 1: Flow chart of study population…………………………………………………...…………26 Figure 2: Incremental costs of productivity loss based on comorbidity score .............................. 35    ix List of Abbreviations BC: British Columbia  CAD: Canadian dollars  EBA study: Economic Burden of Asthma study ED: Emergency Department  GINA: Global Initiative for Asthma  NOC: National Occupation Classification  OR: odds ratio  PDC: proportion of days covered (by any asthma controller medication) PY: per person-year RR: relative rate  SCQ: Self-administered Comorbidity Questionnaire  SD: standard deviation  USD: United States dollar VOLP: Valuation of Lost Productivity. WPAI: Work Productivity and Activity Impairment    x Acknowledgements First and foremost I would like to thank my supervisor, Dr. J Mark FitzGerald for giving me the prodigious opportunity and privilege to study and work under his guidance. I would especially like to thank Dr. Mohsen Sadatsafavi for his constant support, patience, and exceptional research advice.  I also want to thank Dr. Christopher Carlsten for his guidance and scientific input.  I further acknowledge Dr.Hamid Tavakoli for his assistance in conducting statistical analysis and other members of Economic Burden of Asthma study (EBA) for their assistance in coordinating this study. I am extremely grateful for the unconditional love and support of my parents, without whom I could not pursue my dreams and reach my goals.    xi Dedication This thesis is dedicated to my parents for their love, endless support and encouragement.     1 Chapter 1: Introduction  1.1 Asthma and its epidemiology Asthma is a heterogeneous disease defined by symptoms including wheezing, chest tightness, shortness of breath, and variable airflow obstruction that is typically associated with airway hyper responsiveness and airway inflammation.  Asthma affects about 300 million people worldwide and causes an estimated 250,000 deaths annually 2. The prevalence of asthma varies significantly throughout the world; WHO has estimated that the number of asthma patients will increase by 100 million by 2025 4. Not only is the prevalence increasing but also evidence suggests that per-patient costs are increasing 5, resulting in a significant upward trend in the overall burden of asthma. The management of chronic diseases is a challenge in countries with both established and emerging economies 6. The burden of non-communicable diseases (NCDs) is increasing worldwide and chronic respiratory diseases play a major role in this growth 7. Asthma is among the main drivers of medical costs 8 and because it is more prevalent among younger working age groups, it has major economic impact by its effect on productivity loss 9.  On the other hand asthma is associated with several comorbidities; however, the prevalence varies across studies 10–13 . In a study from the United States, 26% and 10% of asthma patients had at least one or ≥ 3 comorbidities, respectively 10. In a study from Germany, 26% of asthma patients had at least one other comorbidity while 17% had 2 or more 14. In a Canadian study, almost 60% of asthma patients had at least one comorbidity 11, while in another study 12.5% of   2 adult asthma patients reported having three or more comorbidities, increasing to 20% for adults 55 years and older 11,12.   1.2 What is economic burden? In general, the quantifiable economic burden of a disease is classified in terms of its direct and indirect costs 15. Direct costs are the costs associated with the utilization of health care resources for diagnosis and treatment of disease such as outpatient clinic visits, emergency department visits, inpatient care, medications, and diagnostic tests. Non-medical direct costs include expenditure on transportation and hoteling for medical care use and also the health services at home. Indirect costs refer to the costs incurred from the cessation or reduction of work productivity because of the illness 16. They can be quantified as school days loss and productivity loss for patients and their caregivers. This can be further classified as absenteeism (the withdrawal of labor – the individual does not attend work due to illness, resulting in loss of productivity for the individual and potentially co-workers in the context of teamwork) or presenteeism (inefficiency of labor due to the impairment – the individual continues to work despite illness but underperforms with an associated significant loss of productivity) 17.  1.2.1 Attributable versus excess costs Cost of illness studies typically measure either excess or attributable costs 18. Excess costs are the differences between the costs of the disease and the costs of a comparison population who do not have the disease. Attributable costs are costs that can be attributed directly to the disease (e.g., hospitalization costs if the main reason for hospital admission has been asthma). Excess costs properly take into account the burden of comorbid conditions but need careful analysis that   3 controls for the confounding differences between the diseased and control population (e.g., sex, comorbidity before disease onset). The calculation of attributable costs is less affected by such confounding factors but requires an inevitably subjective process of designating a particular costs component as being due to the disease or not 18.   1.2.2 Miscellaneous medical and non-medical costs There are some medical expenses that cannot be classified into any of the major components of costs (inpatient, outpatient, medication), which are sometimes referred to as other direct medical costs and there is no comprehensive definition of this group. However, it is acknowledged that the magnitude of these expenses can be large and difficult to ignore. For instance a study reported home health care as the second largest proportion of medical expenditure 19. These costs are usually overlooked.   1.3 Literature review of costs of asthma Several reviews have been published regarding the costs of asthma 20–22. However, very few studies are recent or have highlighted the variation across geographical regions and health care jurisdictions. The evolution of risk factors and disease incidence, emergence of new health technologies and disease management strategies, and updates in guidelines all create a dynamic landscape demanding constant monitoring of the changing burden so that policy, practice, and research priorities are kept up-to-date. The purpose of this literature review is to highlight some of the noticeable variations and geographical and temporal trends in the economic burden of asthma. I focused on more recent studies (published between January 2008 and January 2015) as the older studies have been reviewed in detail previously, and their estimates might not be   4 relevant to contemporary understanding of the burden of asthma given the ever-changing pattern of the asthma burden 22,23. The detailed characteristics of all included studies are shown in the Table 1 and the keywords used in the search are demonstrated in supplementary material in appendices. I have summarized the findings in terms of direct costs and their major components, as well as indirect costs. Only studies that were based on a well-defined study samples (with or without a comparison [control] group) were included. Studies that estimated costs from secondary sources, from model-based predictions, or purely from expert opinion were excluded from this report. The focus was on the costs per person-year (PY). National-level costs were not reported in this study as they are a direct function of the country’s population size and thus are not informative in cross-jurisdiction comparisons. As there is no comprehensive definition of miscellaneous medical and non-medical costs, they cannot be compared across different studies and are hence excluded from consideration in this thesis. Also, non-medical direct costs, such as cost of transportation and hoteling are only selectively reported in my thesis (mainly to ensure consistency across total costs and cost components) due to the paucity of data. I also compared costs across groups defined by accepted disease classification algorithms. To facilitate comparison of reported figures, I converted all costs to United State (US) dollars for year 2014 ($). This was performed in two steps: first, local currencies were converted into $US for the same year based on historical purchasing power parities 24; next, this value was then converted to year 2014 values using the historical US consumer price index 25.  5 Table 1: Characteristics of included studies  Author Published year Enrolled year Study design Number of participants Age of participants Inclusion criteria regarding disease status Country Adjusted value Type of costs Accordini  et al 26  2013 1999-2002 Prevalent based, Prospective survey  462 30-54 years Persistent asthma 11 European countries 2010 euro Direct and indirect Alzaabi et al 27  2014 2011 Prevalent based, Retrospective administrative health  139092 All ages Asthma Abu Dhabi, united Arab emirates 2011 Dirhams Direct Barnett et al 28  2010 2002-2007 Prevalent based, Retrospective administrative health  8719 All ages Asthma  US 2009 USD Direct and indirect Bavbek et al 29  2010 2008 Prevalent based, Retrospective survey 294 ≥18 years Asthma exacerbation Turkey 2008-2009 euro Direct Bedouch et al 30  2012 2002-2007 Prevalent based, Retrospective administrative health  398235 All ages Narrow definition of asthma BC, Canada 2008 CAD Direct Doz et al 31  2013 2010 Prevalent based, Prospective survey  2671 ≥18 years Asthma for at least 12 month Spain and France 2010 euro Direct and indirect Gold et al 32  2013 2009 Prevalent based, Retrospective survey  2493 ≥12 years Persistent asthma US USD* Direct Gold et al 33  2014 2011 Prevalent based, Retrospective survey  Argentina=436 Brazil=399 Mexico=532 Puerto- Ricco =401 Venezuela=400 ≥12 years Asthma Latin America 2013 USD Direct and indirect Ivanova et al 34  2012 1999-2007 Inception cohort, Retrospective survey based 61118 12-64 years Exacerbation of Moderate to severe asthma US 2007 USD Direct Jang et al 35  2013 2005-2009 Prevalent based, Retrospective survey  31.4 M All age groups Asthma US 2009 USD Direct Kambel et al 19  2009 2004 Prevalent based, Retrospective administrative health  1616  >18 years Asthma US 2007 USD Direct    6 Author Published year Enrolled year Study design Number of participants Age of participants Inclusion criteria regarding disease status Country Adjusted value Type of costs Kim et al 36  2011 2003 Prevalent based, Prospective administrative health  NA All ages Asthma Korea 2005 USD Direct Kim et al 37  2012 2009-2010 Prevalent based, Retrospective survey  314 >14 years Persistent asthma Korea 2009 USD Direct and indirect Lee et al 38  2011 2008 Prevalent based, Retrospective administrative health  2,273,290  All ages Asthma Korea 2008 USD Direct+ indirect Miguel Diez  et al 39  2014 2002-2010 Inception cohort, Retrospective survey  2792  16-45 years Asthma exacerbation Spain Euro* Direct Sadatsafavi  et al 40  2010 1996-2000 Prevalent based, Retrospective administrative health  158516 5-55 years Asthma BC Canada 2006 CAD Direct Sadatsafavi  et al 41  2014 2011-2012 Prevalent based, Prospective survey  300 Employed adults >=18 years Asthma BC, Canada 2010CAD Indirect Sun et al 42  2008 2002 Prevalent based, Retrospective administrative health  1726 18-55 years Asthma Taiwan New Taiwan dollar * Direct  * Costs were assumed to be adjusted by the last year of the study period    USD = United States dollar   CAD= Canadian dollar   7 1.3.1 General costs of asthma in the literature I included 18 studies published since 2008, four from Europe26,29,31,43 , five from the US 19,28,32,34,35 , three from Canada 30,40,41, one from the Middle-East 27,44, four from South East Asia 36–38,42 and one from Latin America 33. Eleven studies reported on costs of asthma 19,26–28,30,35–38,40,42, while six studies reported costs across control levels 26,31–33,37,41, and three provided information on costs of asthma exacerbations 29,34,43.  Table 2 presents the included studies on the cost of asthma. As expected, estimated costs and their components varied between different regions of the world, which represents the combined variation in the burden of asthma, variations in the health delivery models and the different methodologies used to estimate its burden. An obvious source of difference can be the demographic characteristics of the study population, as some studies included patients from all age groups 27,28,30,35,36,38 while others applied age cut-offs 19,26,37,40,42,44. Other differences in study design are, however, more likely to have generated the difference in estimates. Some studies were survey-based 19,26,28,35, while others used administrative health data 27,30,37,38,40,42,44. In general, well-designed survey-based studies, such as those based on the US Medical Expenditure Panel Survey 19,28,35, are likely to have a high degree of representativeness and external validity. However, survey-based methods can also suffer from recall bias, specifically if this bias is preferential (e.g., if individuals with self-report of a disease also have a higher rate of recall of resource utilization) although it was previously demonstrated that there is good agreement between self-report and administrative data regarding direct costs but not for absenteeism 45. On the other hand, administrative health data routinely record patient encounters and resource use as they occur, but are limited in scope in terms of cost items.   8 Table 2: Average direct and indirect costs of asthma (per person-year) and its components (2014 USD) Region Study (country, study period) Type of costs Source  of data Target  population Total direct costs Medication Outpatient Inpatient ED visits Other direct medical/non-medical costs Indirect costs  North America  Kamble  et al19 (US) (2004) Excess costs (Adjusted*) National population-based survey ≥18 years with asthma-related medical events or self-reported asthma 2309 + 715  510  364 61  672  Barnett  et al28  (US) (2002-2007) Excess costs (Adjusted†) National population-based survey All ages with asthma-related medical events or self-reported 3210 1817  792  482 119   426 Jang et al35  (US) (2005-2009) § Excess costs (Adjusted*) National population-based survey All ages with asthma-related medical events or self-reported 2164  952  411 584 65 152  Bedouch  et al30 (BC,Canada) (2002-2007) Attributable costs Administrative health data All ages with asthma-related medical events  140 96 22 22   Sadatsafavi et al40 (BC,Canada) (1996-2000) Attributable costs Administrative health data 5-55 years with asthma-related medical events  312 198 57 57    Europe Accordini et al26 (Europe‡) (1999-2002) Attributable costs European Community Respiratory Health Survey 30-54 years with physician diagnosed asthma 693 368 113 141 31  1154   9 Region Study (country, study period) Type of costs Source  of data Target  population Total direct costs Medication Outpatient Inpatient ED visits Other direct medical/non-medical costs Indirect costs Middle East Alzaabi  et al27 (Abu Dhabi, United Arab Emirate)  (2011) Attributable costs Administrative health data  All ages with asthma-related medical events  149 46 68 18 6   South East Asia Kim et al 37 (Korea) (2009-2010)  Attributable costs Cohort study in tertiary hospitals >14 years with physician diagnosis and asthma-related medical events  1432      1274 Kim et al36 (Korea) (2004) Attributable costs National administrative health data  All ages with physician diagnosis 547       Lee et al38 (Korea) (2008) Attributable costs National administrative health data All ages with asthma-related medical events  247 69 149 29   142 Sun et al42 (Taiwan) (2002)  Excess costs National administrative health data 18-55 years with asthma-related medical events  592   291 278 23   *Adjusted for age, gender, race, ethnicity, education, marital status, insurance, region, and Charlson Comorbidity Index  †Adjusted for age groups, married status, minority race, region, level of education, female sex, poverty, uninsured status, and Charlson comorbidity  ‡ Belgium, Estonia, France, Germany, Iceland, Italy, Norway, Spain, Sweden, Switzerland, UK.  §These study reported costs separately within each year; only the last year of data was used. Shared of cost components, however, was reported across the entire study period.  10 1.3.2 Direct costs Annual costs varied from less than $150/PY (Abu Dhabi, United Arab Emirates) to more than $3,000/PY (US). In general, studies that measured attributable costs 26,27,30,36–38,40,42,44 estimated lower values than those estimating the excess costs 19,28,35; this potentially indicates the role of comorbid conditions that are not fully captured in the former method 46. Another source of difference between the two types of costs estimates could be the inclusion criteria: survey-based studies often relied on a self-report of asthma diagnosis, but studies based on administrative health data applied case definitions based on asthma resource use. An additional confounder relates to the fact that a recent record of asthma resource use, often used in studies based on administrative health data, can indicate active asthma that is consuming higher amounts of health care resources.  In terms of cost components, in North America and Europe, medication was the biggest component of direct medical costs, varying from 68% of total direct costs in Canada to 51% of total direct costs in US 28,30. During 1999-2002, in 11 European countries the costs of medications ranged from 45% (Spain) to 84% (Germany) of total direct costs 26. Some of the recent findings suggest that the costs of medications in the US and Canada are increasing over time. There has been a 1.5% increase in medication costs per PY between 1996-2000 40 which increased to 2.4% growth in 2002-2007 30. Likewise in the US there was a 49% increase in the costs of medications between 2000 to 2009 35. On the other hand, in the Middle-East and South East Asia, outpatient costs 27,38,42 were responsible for a greater share of total costs. This could be due to the differences in the unit price of medication and health services across jurisdictions. Another important factor to consider is the lack of access to asthma medications, especially the   11 more expensive combination inhalers, which can result in higher rates of poorly controlled asthma causing frequent visits to care providers.  Another emerging pattern is a trend towards decreasing rates and costs of inpatient care for asthma in North America 30,35,40. In Canada the number of hospitalizations decreased from 1.7 to 1.2 per 100 patients from 1996 to 2000 40, a pattern that remains largely the same in an updated analysis by the same investigators 30. The costs of physician visits also decreased by 9.7% PY in Canada between 2002 and 2007 30. Between 2000 and 2009 in the US the costs of inpatient care decreased by 2.3% but still accounted for 23% of the total direct costs 35. In contrast, in the same study the costs of outpatient visits and emergency department (ED) visits increased by 3% and 8%, respectively.  1.3.3 Indirect costs A recent review demonstrated that in general, the loss of work/school days accounted for the greatest proportion of the asthma costs 22. Since the publication of this review, very few additional studies have reported indirect costs of asthma (Table 2). Among those studies, the results varied widely across jurisdictions, with both the lowest and highest estimates coming from the same country (South Korea) 37,38. This might partly be due to different definitions and methods of measurement across the studies. In the study with the highest estimated indirect costs ($1274/PY), work/school days lost, early retirement and work absenteeism were all included in the calculation of productivity loss. However this study only included patients from tertiary hospitals which creates further potential for a upwardly biased estimate caused by asthma severity 37.    12  An important, and often overlooked, component of indirect costs is “presenteeism” 47. Studies that have calculated the indirect costs based on reported time off-work due to asthma fail to include the impact of presenteeism 26,28,38. Measuring presenteeism is challenging but there are validated instruments for this purpose. Likewise, only a few studies calculated the effect of premature death on the indirect costs of asthma. In a US study, this component was higher than the measurable loss of productivity (8% vs. 12% total annual costs for work/school days loss and death, respectively) 28.   1.3.4 Costs associated with asthma control status While asthma is not a curable disease, it is considered a largely controllable condition 48. The conventional wisdom is that with proper management, asthma can be symptomatically and clinically controlled in the majority of patients 48.The potential reduction in the costs of asthma when an individual moves from the uncontrolled to the controlled state can therefore be considered a preventable source of burden. Studies that report on this value can therefore be more informative for policymaking regarding asthma management than the asthma costing studies. Nevertheless, care should be taken in reporting and interpreting such results. A simple difference in the costs of asthma across control levels does not imply causality due to the effect of potential confounding variables such as asthma severity and socio-economic status. Studies that rigorously adjust for such variables can produce results that approximate the causal association between asthma control and costs. Unfortunately, such studies are rare. Six studies reported costs across different levels of asthma control (Table 3)26,31–33,37,41, in only three of which regression-based methods were used to derive an adjusted estimate of the impact of   13 control on costs 31,32,41. For the most part, asthma control was based on the Global Initiatives for Asthma (GINA) 26,31–33,41 control criteria. One study used the asthma control categories defined in the asthma control test (ACT) 37.   There was one Canadian study which evaluated only indirect costs and demonstrated that a patient with uncontrolled asthma would avoid $7768/PY in productivity loss, of which 90.6% was due to presenteeism, if the disease were to become controlled 41. The other study that reported adjusted values assessed direct costs in Spain and France which reported $122/PY and $129/PY, respectively, in the uncontrolled group in comparison to the controlled group 31. The indirect costs also had a reverse association with the level of control 26,31,33,37,41. In several studies, the indirect costs become greater than direct costs in partially controlled and uncontrolled asthma26,31,37.   14 Table 3: Average costs of asthma across asthma control levels per patient-year (2014 USD) Study Country Definition of asthma control Target population Type of costs Mean annual costs per patient Controlled Partially controlled Uncontrolled Accordini  et al 26 11 European countries  GINA definition 2002 30-54 years with physician diagnosed asthma Direct + indirect (Unadjusted) 594  819  2663 Direct (unadjusted) 594 328 959 Indirect (unadjusted) - 491 1704 Doz et al 31   Spain GINA definition 2009 ≥18 years with asthma-related medical events Direct +indirect (unadjusted) 178 281 650 Direct (unadjusted) 173 268 507 Direct (adjusted*)  Ref 2 122 Indirect  (unadjusted) 4 64 680 France Direct +indirect (unadjusted) 100 366 628 Direct  (unadjusted) 95 136 263 Direct (adjusted*)  Ref 17 129 Indirect (unadjusted) 5 230 365 Gold et al 33   Argentina GINA definition 2010 ≥12 years with asthma  Direct +indirect (unadjusted) 193 2439 5488 Direct (unadjusted) 181 1219 2846 Indirect (unadjusted) 12 1220 2642 Brazil Direct +indirect (unadjusted) 71 386 935 Direct  (unadjusted) 54 338 772 Indirect (unadjusted) 17 48 163 Mexico Direct + indirect (unadjusted) 112 315 681 Direct (unadjusted) 85 262 549 Indirect (unadjusted) 27 53 132 Puerto-Ricco Direct +indirect (unadjusted) 630 2205 4878 Direct (unadjusted) 346 1087 3049 Indirect (unadjusted) 284 1118 1829 Venezuela     Direct +indirect (unadjusted) 620 681 1321 Direct (unadjusted) 617 663 1270 Indirect (unadjusted) 3 18 51   15  * Adjusted for sex, age, episodes of exacerbation of asthma, prescription of a controller treatment and follow-up by a lung specialist  † Adjusted for age, gender, whether someone in the household smoked, race, education, and insurance ‡Adjusted for sex, age at baseline visit, socioeconomic status, education, the type of residence (urban or rural), and the possession of drug insurance (no coverage, partial coverage, and full coverage for medications) §Adjusted values are calculated by subtracting the costs of uncontrolled and partially controlled from controlled groupRegion Study (country, study period) Type of costs Source  of data Target  population Total direct costs Medication Outpatient Gold et al 32 US  GINA definition 2010 ≥12 years self-reported asthma or with asthma-related medical Direct (unadjusted) 855 1633 5669 Direct (adjusted†) Ref 660 3667§ Kim et al 37 Korea  Korean version of asthma control test (ACT) >14 years with physician diagnosis and asthma-related medical events Direct +indirect (unadjusted) 1641 2948 7583 Direct (unadjusted) 1211 1331 2639 Indirect (unadjusted) 430 1617 4944 Sadatsafavi  et al 41 British Columbia, Canada GINA definition 2013  1-85 years with self-reported asthma Indirect (adjusted‡) Ref 1438 7768   16  1.3.5 Costs of asthma exacerbations  Asthma exacerbations are associated with increased consumption of resources 29,34,43. Interestingly, the level of asthma control prior to an exacerbation seems to have a role in health care resource utilization and expenditure as the costs were higher in uncontrolled patients 29. In a study in Turkey, total direct costs per exacerbation were higher in severe compared to mild and moderate exacerbations, ranging from $152 for a mild exacerbation to $1,939 for life threatening episodes 29. However, the factors driving such increased costs are different across jurisdictions. In the US, for example, outpatient and inpatient costs were the major component of the total costs of exacerbations 34 whereas in Turkey medications were responsible for the highest share of costs 29.  In a Spanish study, the costs of hospitalization per exacerbation increased from $3,033 in 2002 to $3,562 in 2010. In the same period the length of hospital stay decreased from 5.36 days to 5.16 days 43. This may represent a shift towards more aggressive but shorter episodes of inpatient care. The authors of the previous study attributed the observation to the concurrent increase in the usage of non-invasive ventilation 43. Studies also compared costs among patients with and without a history of asthma exacerbations. In a US study, individuals with a history of moderate/severe exacerbations had $4,681 and $992 higher total health care and asthma-related costs, respectively, than those without a history of exacerbations 34.       17 1.4 Literature review of comorbidities and their effect on productivity loss in asthma Clinically, comorbidities are relevant given their potential effect on the index disease in terms of diagnosis, prognosis, and management 49. Also, comorbid conditions increase the need for medication, risk of adverse effects and drug interactions, and reduce adherence to treatments, quality of life and functional status 11,12,50. Patients with multiple comorbidities tend to use more medical services and impose a greater burden on the health-care system 11,51.  It has been well demonstrated that comorbidities are associated with poor outcomes in asthma patients 52. Asthma patients with comorbidities experience more asthma exacerbations 11,53–55 and there is a significant relationship between asthma control and the presence of comorbidities 31,49,56. The reason behind this fact is unclear. It could be because the patient places a higher priority on other health conditions, which influences the adherence to asthma treatment. Also the nature of comorbidities like depression may cause the patient to pay less attention to their general health status and care less 10. In British Columbia (BC), 25% of asthma patients have depression 12. However, to the best of our knowledge, there is no other study assessing the general impact of comorbidities on productivity loss in asthma patients including both absenteeism and presenteeism and transforming the productivity loss into its monetary value.     18 Chapter 2: Body of Thesis It has been demonstrated that indirect costs of asthma accounted for the greater proportion of costs of asthma than direct costs, however most of the studies overlooked this amount 10. Also despite the documented burden of comorbidities in asthma, their effect on productivity loss has been overlooked in the past. One potential reason for this is that asthma patients are relatively young and historically maybe assumed to be free of comorbidities 56. The increase in life expectancy and the increase in the retirement age will inevitably result in more and more working asthma subjects. The aim of my thesis was to evaluate the effect of comorbidities on productivity loss in a population-based sample of adult asthma patients.  2.1 Methods 2.1.1 EBA study The Economic Burden of Asthma (EBA) was a 1-year prospective cohort study with the specific aim of estimating the economic and humanistic burden of asthma (University of British Columbia Human Ethics Board H10-01542). Residents of two census subdivisions of BC, with a self-reported physician diagnosis of asthma, were randomly chosen by landline and cell phone-based random digit dialing (RDD). The study’s catchment areas were Vancouver and Central Okanagan census areas, the latter being in the interior of the BC Province with a large fraction of the population residing in rural areas. According to 2006 census profile, the population of these two sub-divisions was, respectively, 578,041 and 162,276.   RDD samples have been shown to be more representative than population samples drawn using alternative methods such as telephone directories or electronic white pages 57. RDD sampling is   19 subject to no-telephone bias 58, households without telephones, which are probably differing in socioeconomic status than overall population, are out of reach by RDD. Inclusion of cell phones was an attempt to increase the fraction of population covered like younger and educated groups. To exclude the overlap only those who do not have landline were interviewed by cellphone.   Respondents were asked if there is a member of their household between ages of 1 to 85 who has had asthma ever diagnosed by a physician. If the response was positive the study assistant asked for direct conversation with the asthma patient or their parents in case of children. Verbal consent is received from eligible subjects after informing them about objectives of the study. Eligible patients were asked to come to the study laboratory to receive a detailed explanation of the study and the study consent form at that time. Patients who signed consent form then proceeded to spirometry and data collection. After the baseline visit, participants enter a 12-months period of follow-up with visits at months 3, 6, 9, and 12.    2.1.2 Inclusion and Exclusion criteria   Inclusion criteria: 1) Patient must state that they have had a diagnosis of asthma by a physician. In addition, the patient must have had a self-reported health care interaction related to asthma (physician visit, ED visit, hospitalization) in the past 5 years. 2) Patient must be ≥19 years old, which were employed at the baseline visit.     20  Exclusion Criteria: 1) Patients unable to provide informed consent due to language difficulties or cognitive impairment. 2) Patients who had a greater than 10 pack-year smoking history (this will exclude patients with possible COPD). 3) Patients who know that they will be moving out of the province within 12 months of study entry. 4) Patients in whom methacholine challenge test was contraindicated for non-asthma-related reasons: patients with a recent history of stroke or heart attack, or cerebral aneurysm, and pregnant and breastfeeding participants.  2.1.3 Assessments and Procedures in EBA study   2.1.3.1 Baseline visit:  At the first visit after informed consent the following information was collected; age, gender, ethnicity (including country of origin, if born in Canada parents country of origin as well as primary language spoken at home) asthma medication use (type of medication used and duration of use), height, weight, body-mass index, date of onset of respiratory symptoms, date of asthma diagnosis, name of the physician who diagnosed asthma and his/her specialty, and co-morbid disease history. Exposure to cigarette smoke, occupational and allergen related exposures (such as pets, molds, etc.) were also documented. History of any ED visits, or hospitalizations for exacerbations of asthma within the previous 12 months were also was recorded.     21 Alternative diagnoses that could be mistaken for asthma were recorded including: symptoms of gastro-esophageal reflux (assessed using the validated “Reflux Symptom Questionnaire‟), history of confirmed gastro-esophageal reflux, and history of anemia, vocal cord dysfunction, congestive heart failure, bronchiectasis, COPD, or lung cancer. Baseline spirometry was performed to identify the level of airflow obstruction.  2.1.3.2 Follow-up visits: Each participant was scheduled for four follow-up visits at month 3, 6, 9, and 12. Visits took place at one of the two study sites. During the follow-up visits participants were asked to report exposure to cigarette smoke, as well as occupational and allergen related exposures since the last visit. The follow-up questionnaires to capture the productivity loss consisted of the Valuation of Lost Productivity Questionnaire (VOLP), and Work Productivity and Activity Impairment Questionnaire (WAPI) were administered. Also Self-administered Comorbidity Questionnaire (SCQ) for evaluation of comorbidity status was also administered in the last follow-up visit.  2.1.4 Variables  2.1.4.1 Comorbidity A comorbidity score was calculated based on the Self-administered Comorbidity Questionnaire (SCQ) administered in the last visit 59. The recall period of the questionnaire is 12 months and thus I assumed comorbidity score was constant across the study period 59. The SCQ score not only considers the number of the comorbidities but also their severity. Each included comorbid condition can get a maximum of three points based on the presence of disease, whether receiving   22 treatment, and any functional limitation due to the condition. This questionnaire has been validated and has a moderately strong correlation with the Charlson comorbidity index a chart review-based comorbidity instrument 59. The original SCQ included 13 common comorbidities, but in this study the questions related to pulmonary disorders were excluded (given that all patients had asthma), leaving the questionnaire with a maximum of 36 scores, 3 points for each of the 12 questions. Comorbidities that were included in the questionnaire were:  cardiac diseases, hypertension, diabetes, ulcer or stomach disease, kidney diseases, liver diseases, back pain, rheumatoid arthritis, osteoarthritis or degenerative arthritis, depression, cancer, anemia or other blood disease.  2.1.4.2 Productivity loss Productivity loss was measured at baseline by two validated questionnaires: the Work Productivity and Activity Impairment (WPAI) 60, and the Valuation of Lost Productivity (VOLP) 61. The WPAI records patients’ absenteeism (missing work due to health conditions) and presenteeism (attending work but not being fully functional) in the last 7 days by asking about the hours they missed from work because of sick days or the times they went in late or left early due to health status and times they were not functional with limited accomplishment and unable to concentrate on their tasks due to the health status respectively 61. The validity of the WPAI has been established for asthma 60. The VOLP questionnaire collects information about the work environment such as time sensitivity of the job, team work, and availability of replacement, to calculate a coefficient that measures the contribution of individual to the work place (a coefficient of X indicates that each hour of a person’s absence is equivalent of X hours of work loss) 61,62.    23  The monetary value of productivity loss was the product of three terms: amount of work time (hours) lost, the VOLP coefficient, and the hourly wage of the individual. Job titles were matched to the National Occupation Classification (NOC) codes 63 to estimate the hourly wage based on sex and age for each NOC from Statistics Canada for year 2010 41. The reported costs are therefore in 2010 Canadian dollars (CAD).  2.1.5 Confounders Socio-demographic data collected at the baseline visit were included in the statistical models as potential confounders (factors that can affect both comorbid level and productivity but are not on the causal pathway). They included: sex, age, household income levels (low v. high at cut-off of CAD$60,000 per year), education (low v. high at cut-off of 4-year college/university degree), type of residence (urban v. rural), place of birth (Canada v. abroad), drug insurance (having no insurance, being partially insured, or being fully insured), and the proportion of days covered (PDC) by any asthma controller medication in past 12 months as an indicator of adherence (cut-off values <50%, 50-80%, ≥80%).   The main analysis did not adjust for asthma control, as it cannot be a confounder; rather, it is potentially being on the causal pathway (that is, part of the impact of comorbidity on productivity might be due to the comorbid conditions’ affecting the likelihood of achieving asthma control). It is also very unlikely for the current asthma control status to have an effect on comorbidities (thus being a confounding factor). But a sensitivity analysis was performed to assess the effect of adjusting for control status on the outcomes. I defined control status based on   24 Global Initiative for Asthma (GINA) 2012 definition, which included the presence of symptoms and impairment in lung function (all measured at baseline visit).  2.1.6 Statistical Analysis All analyses were performed using Stata (version 14; StataCorp, College Station, TX, USA). Two-tailed p-values at 0.05 were considered statistically significant. Descriptive analysis was performed on the baseline variables. We reported the hours and costs of both components of productivity loss (absenteeism and presenteeism), as well as total productivity loss across different levels of SCQ score.   As the productivity loss data were zero-inflated, I used two-part models for statistical inference 64. The first part was a logistic component and the second part was a generalized linear model with logarithmic link function and gamma distribution. The first part generates odds ratio (OR) associating covariates with any loss of productivity, and the second component produces relative rate (RR) associating covariates with the magnitude of productivity loss among those with any loss of productivity. For both components the dependent variable was the monetary value of productivity loss and the independent variables were the SCQ score and other covariates as previously mentioned. As there were missing values among some of the covariates, multiple imputations were first performed, creating 5 imputed datasets without missing variables; results of separate analyses on the imputed datasets were combined. To estimate the marginal effect of SCQ on productivity loss (that is, the weekly loss of productivity associated with any level of SCQ score), the OR and RR from the two components were combined, and p-values and confidence intervals were estimated using bootstrapping (500 times) as described elsewhere 65.   25 The procedure was conducted separately with absenteeism, presenteeism, and total productivity loss as the dependent variables.  26  2.2 Results 2.2.1 Demographics Figure 1 shows the flowchart of the subject recruitment. The respond rate to RDD sampling was 75% and from that population the final sample consisted of 284 individuals whose baseline characteristics are shown in Table 4 were included in this study. The sample was 68% female with a mean age of 47.8 ± 11.8 with generally high levels of education and household income. Most of the subjects (63%) had at least one comorbid condition and the overall SCQ score was 2.47 ± 2.97, with a minimum of 0 and a maximum of 15. Only 48% of patients reported any productivity loss, with 36% of them reporting absenteeism and 64% reporting presenteeism. Mean weekly hours and costs of productivity loss were 16 ± 17.6 hours and $317.49 ± $858.83 respectively.             618 participants  Aged > 18 years (Baseline examination 2011-2012)  Unemployed were excluded  (n=316) Participants with missing data of spirometry were excluded  (n=2) Participants with missing data on comorbidities were excluded  (n=16) 284 eligible participants included Figure 1: Flow chart of study population 27  Table 4: Characteristics of study sample  Study population =284 Age, mean ± SD*   47.8 ± 11.8 Sex (%)        Women 193(68)      Men 91(32) Household income (%)       High (>60,000 CAD) 178(62.7)      Low 96(33.8) Educational level (%)       High 229(80.6)      Low 55(19.4) Place of birth (%)       Canada 207(72.9)      Outside Canada 77(27.1) Ethnicity (%)       Caucasian 231(81.3)      Asian 18(6.3)      Other 35(12.4) Residence type (%)       Urban 260(91.5)      Rural 24(8.5) Asthma medication adherence (%)       PDC †<50% 171(60.2)      50%≤PDC<80% 31(10.9)      PDC≥80% 81(28.5) Asthma control level (%)       Controlled  55(19.4)      Partially Controlled 113(39.8)      Uncontrolled 115(40.5)  Productivity Loss (%) 136(48)      Absenteeism (%) 49(36)      Presenteeism (%)   127(64)      Hours of overall productivity loss, mean ± SD 16 ± 17.6      Costs of overall productivity loss, mean ± SD $317.49 ± $858.83‡ Comorbidities       Overall SCQ§ score, mean± SD  2.47 ± 2.97      Heart disease (%) 15(5.3)      Hypertension (%) 35(12.3)      Diabetes (%) 9(3.2)      Ulcer or Stomach Disease (%) 37(13)      Kidney disease (%)   3(1.1)  28       Liver disease (%) 2(0.7)      Anemia or other blood disease (%) 21(7.4)      Cancer (%) 6(2.1)      Depression (%) 40(14.1)      Osteoarthritis, degenerative arthritis (%) 62(21.8)      Back pain (%)  99(34.9)      Rheumatoid arthritis (%) 2(0.7) * Standard deviation, † proportions of days covered, ‡ 2010 Canadian dollars, §self-administered comorbidity questionnaire                                          29  2.2.2 Unadjusted analysis Table 5 shows the results of the unadjusted analysis. The hours of absenteeism increased from 1.26 to 7.14 hours as the SCQ increased from 0 to 15, and for presenteeism it rose from 3.97 to 12.59 hours. The costs of absenteeism increased from $50/week for a SCQ of 0 to almost $300/week for a SCQ of 15, while the corresponding values for presenteeism was $140/week and $734/week respectively. Similar increases were seen for the total productivity loss, from $190/per week to $1036/per week. 30  Table 5: Unadjusted regression analyses SCQ score Hours of Absenteeism Costs of Absenteeism* Hours of Presenteeism Costs of Presenteeism* Hours of total productivity loss Costs of  total productivity loss* 0 1.26 (0.54-1.98)  50.35 (19.88-80.81)  3.97 (2.73-5.22)  140.20 (65.27-215.13)  5.22 (3.49-6.96)  190.18 (97.22-283.14)  1 1.65 (0.99-2.32)  66.91 (40.19-93.63)  4.55 (3.48-5.62)  179.81 (118.75-240.87)  6.20 (4.70-7.70)  246.60 (169.16-324.03)  2 2.04 (1.22-2.87)  83.47 (49.80-117.14)  5.12 (4.00-6.25)  219.41 (145.85-292.98)  7.17 (5.52-8.83)  303.01 (208.30-397.72)  3 2.44 (1.33-3.54)  100.03 (53.27-146.79)  5.70 (4.31-7.08)  259.02 (155.75-362.28)  8.15 (6.04-10.26)  359.43 (226.90-491.95)  4 2.83 (1.38-4.27)  116.59 (54.35-178.83)  6.27 (4.51-8.03)  298.62 (159.03-438.21)  9.13 (6.41-11.85)  415.84 (237.59-594.10)  5 3.22 (1.41-5.03)  133.15 (54.45-211.86)  6.84 (4.65-9.04)  338.22 (159.70-516.76)  10.10 (6.70-13.50)  472.26 (245.10-699.42)  6 3.61 (1.43-5.79)  149.71 (54.06-245-36)  7.42 (4.76-10.08)  377.83 (159.12-596.53)  11.08 (6.96-15.20)  528.67 (251.09-806.25)  7 4.00 (1.44-6.56)  166.27 (53.42-279.13)  7.99 (4.85-11.13)  417.43 (157.90-676.97)  12.05 (7.19-16.91)  585.09 (256.28-913.90)  8 4.39 (1.45-7.34)  182.83 (52.61-313.06)  8.57 (4.94-12.20)  457.04 (156.28-757.79)  13.03 (7.42-18.64)  641.50 (260.97-1022.03)  9 4.79 (1.45-8.12)   199.39 (51.70-347.09)  9.14 (5.01-13.27)  496.64 (154.41-838.87)  14.01 (7.63-20.38)  697.92 (265.36-1130.48)  31  SCQ score Hours of Absenteeism Costs of Absenteeism* Hours of Presenteeism Costs of Presenteeism* Hours of total productivity loss Costs of  total productivity loss* 10 5.18 (1.46-8.90)  215.96 (50.72-381.19)  9.72 (5.09-14.34)  536.24 (152.38-920.11)  14.98 (7.84-22.12)  754.33 (269.53-1239.14)  11 5.57 (1.46-9.68)  232.52 (49.69-415.35)  10.29 (5.16-15.42)  575.85 (150.23-1001.47)  15.96 (8.05-23.86)  810.75 (273.56-1347.94)  12 5.96 (1.46-10.46)  249.08 (48.62-449.54)  10.86 (5.23-16.50)  615.45 (147.98-1082.92)  16.93 (8.26-25.61)  867.17 (277.48-1456.86)  13 6.35 (1.46-11.25)  265.64 (47.52-483.75)  11.44 (5.29-17.58)  655.06 (145.68-1164.43)  17.91 (8.46-27.36)  923.58 (281.31-1565.85)  14 6.74 (1.45-12.03)  282.20 (46.41-517.99)  12.01 (5.36-18.67)  694.66 (143.32-1246)  18.88 (8.66-29.11)  979.99 (285.08-1674.91)  15 7.14 (1.45-12.82)  298.76 (45.27-552.25)  12.59 (5.42-19.75)  734.26 (140.93-1327.60)  19.86 (8.86-30.86)  1036.41 (288.80-1784.03)   All the p-values <0.05 *2010 CAD 32  2.2.3 Adjusted analysis The results of the two-part regression model are demonstrated in Table 6. In the logistic part of the analysis, SCQ was significantly associated with higher odds of reporting absenteeism, presenteeism and total productivity loss. However, in the second part of the regression, among patients with productivity loss, SCQ was only significantly associated with the total productivity loss (RR=1.09, CI=1.01-1.18, P=0.02) and not presenteeism or absenteeism separately. The other covariates were not significantly associated with productivity loss in either parts of the model.  33  Table 6: Results of the adjusted regression analysis of productivity loss on SCQ* score   Absenteeism Presenteeism Total Productivity Loss  Female v. male  Adjusted OR for reporting productivity loss  1.72 (0.53-2.57)   1.08 (0.61-2.35)   0.99 (0.56-1.76)   Adjusted ratio of productivity loss among those who reported productivity loss 0.42 (0.15-1.18)  0.88 (0.52-2.18)  0.79 (0.46-1.37)  Age (per 1 year increase) Adjusted OR for reporting productivity loss 0.98 (0.95-1.01)  0.98 (0.96-1.04)  0.98 (0.96-1.01)   Adjusted ratio of productivity loss among those who reported productivity loss 0.98 (0.94-1.03)  0.99  (0.96-1.04)  0.98 (0.96-1.01)  High v. urban education Adjusted OR for reporting productivity loss 1.16 (0.41-3.27)  0.68 (0.34-2.84)  0.52 (0.25-1.1)   Adjusted ratio of productivity loss among those who reported productivity loss 0.92 (0.29-2.96)  1.50 (0.84-2.40)  1.44 (0.76-2.72)  Rural residence Adjusted OR for reporting productivity loss 0.47 (0.13-1.75)   0.38 (0.11-6.17)  0.41 (0.13-1.35)   Adjusted ratio of productivity loss among those who reported productivity loss 0.99 (0.05-20.88)  0.42 (0.21-2.88)  0.44 (0.16-1.15)  Foreign Born v. Canadian-born Adjusted OR for reporting productivity loss 0.95 (0.41-2.19) 1.00 (0.56-2.42) 1.21 (0.67-2.17)   Adjusted ratio of productivity loss among those who reported productivity loss   0.41 (0.12-1.39) 0.84 (0.52-2.08) 0.72 (0.43-1.19) 34  *Self-administered comorbidity questionnaire, † proportion of days covered by medication, ‡ significant P-value   Absenteeism Presenteeism Total Productivity Loss PDC† Level  (Reference: PDC<50%)     50-80% Adjusted OR for reporting productivity loss 2.75 (0.98-7.73) 1.27 (0.54-3.66)  1.34 (0.58-3.10)  Adjusted ratio of productivity loss among those who reported productivity loss 1.49 (0.33-6.64)  2.50 (0.70-6.77)  2.72 (0.91-8.10)  >80% Adjusted OR for reporting productivity loss 1.11 (0.48-2.59) 0.86 (0.47-2.50) 0.97 (0.52-1.80)  Adjusted ratio of productivity loss among those who reported productivity loss 0.95 (0.40-2.29) 1.13 (0.69-2.08) 1.13 (0.68-1.86) Drug Insurance (Reference: full insurance)     Partial Adjusted OR for reporting productivity loss 1.36 (0.41-4.49)   1.03 (0.45-3.44)  1.11 (0.47-2.63)   Adjusted ratio of productivity loss among those who reported productivity loss 0.84 (0.20-3.53)  0.60 (0.24-3.99)  0.73 (0.30-1.80)  None Adjusted OR for reporting productivity loss 0.82 (0.20-3.43)  0.91 (0.36-4.07)  0.86 (0.33-2.25)   Adjusted ratio of productivity loss among those who reported productivity loss 1.09 (0.16-7.33)  0.66 (0.24-4.58)  0.76 (0.28-2.11)  High v. low income Adjusted OR for reporting productivity loss 0.84 (0.37-1.88)  0.87 (0.48-2.47)  0.88 (0.47-1.66)   Adjusted ratio of productivity loss among those who reported productivity loss 1.47 (0.55-3.93)  1.27 (0.74-2.25)  1.27 (0.69-2.32)  SCQ (per 1 unit increase) Adjusted OR for reporting productivity loss 1.17 (1.04-1.32) ‡ 1.14 (1.03-1.17)‡ 1.14 (1.02-1.28)‡  Adjusted ratio of productivity loss among those who reported productivity loss 1.04 (0.90-1.21) 1.05 (0.98-1.11) 1.09 (1.01-1.18)‡ 35  2.2.4 Marginal effect of comorbidity on productivity loss The marginal effect of each level of SCQ score on total productivity loss is demonstrated in Figure 2. In patients without any comorbidity, the productivity loss was $205/week. Total productivity loss was $1,685 higher with a SCQ score of 15 in comparison to a SCQ score of zero. The margins were significant at all the levels, except for a SCQ score of 15 for total productivity loss (P value=0.06). Table 7 demonstrates the incremental productivity loss for absenteeism and presenteeism and total productivity loss separately.   Figure 2: Incremental costs of productivity loss based on comorbidity score 05001000150020002500300035000 2 4 6 8 10 12 14 16Cost of Productivity Loss per week (2010 CAD)self-administered comorbidity questionnaire (SCQ) score 36  Table 7: Incremental costs of productivity loss based on SCQ score (2010 CAD) SCQ score Absenteeism Presenteeism Total Productivity Loss 0 61.87  ± 23.07 160.92  ± 32.57 205.12 ± 42.09  1 73.86  ± 24.93 185.23  ± 34.80 244.42  ± 45.46  2 87.97  ± 27.59 212.33 ± 39.22  290.07  ± 51.73 3 104.48  ± 31.72 242.41 ± 46.58  342.81  ± 62.54 4 123.73  ± 38.12 275.59  ± 57.36  403.47  ± 79.29 5 146.05  ± 47.63 312.03  ± 71.76  472.90  ±103.02 6 171.80  ± 61.03 351.82  ± 89.95  552.02  ± 134.70 7 201.34  ± 79.05 395.09  ± 112.11  641.82  ± 175.39 8 235.03  ± 102.44 441.93  ± 138.47  743.35  ± 226.39 9 273.22  ± 132.04 492.42  ± 169.34  857.74  ± 289.24 10 316.23  ± 168.77* 546.66  ± 205.09  986.20  ± 365.75 11 364.37  ± 213.63* 604.73  ± 246.13  1130.04  ± 458.03 12 417.88  ± 267.76* 666.72  ± 292.92  1290.72  ± 568.50  13 476.99  ± 332.34* 732.74  ± 345.96  1469.79  ± 699.91 14 541.87  ± 408.66* 802.89  ± 405.79  1669.0  ±855.37 15 612.61  ± 498.06* 877.33  ± 473.00*  1890.27  ± 1038.37*  * P value is not significant   37  2.2.5 Sensitivity analyses Sensitivity analyses revealed that the OR for reporting productivity loss and adjusted ratio for productivity loss among those reporting it did not change by adding control status in the model. However, the adjusted RR in the second part for SCQ was no longer significant. OR and RR of other covariates also did not change after adding control status to the model. (Table 8) Adding control status to the model did not have a significant impact on the estimates of the marginal loss of productivity in different levels of SCQ. (Table 9)   38  Table 8: Results of alternative model specification (with main model also reported for comparison) for total productivity loss on SCQ   Main model Alternative model Female  Adjusted OR for reporting productivity loss 0.99 (0.56-1.76) 0.99 (0.52-1.87)  Adjusted ratio of productivity loss among those who reported productivity loss 0.79 (0.46-1.37) 0.64 (0.23-1.82) Age  Adjusted OR for reporting productivity loss 0.98 (0.96-1.01) 0.98 (0.96-1.01)  Adjusted ratio of productivity loss among those who reported productivity loss 0.98 (0.96-1.01) 0.98 (0.95-1.02) High education Adjusted OR for reporting productivity loss 0.52 (0.25-1.1) 0.63 (0.19-2.10)  Adjusted ratio of productivity loss among those who reported productivity loss 1.44 (0.76-2.72) 1.27 (0.48-3.37) Rural residence Adjusted OR for reporting productivity loss 0.41 (0.13-1.35) 0.41 (0.12-1.42)  Adjusted ratio of productivity loss among those who reported productivity loss 0.44 (0.16-1.15) 0.50 (0.07-3.53) Foreign Born Adjusted OR for reporting productivity loss 1.21 (0.67-2.17) 1.11 (0.54-2.29)  Adjusted ratio of productivity loss among those who reported productivity loss 0.72 (0.43-1.19) 0.60 (0.21-1.70) Control Status     Partially controlled Adjusted OR for reporting productivity loss  - 1.32 (0.34-5.05)   Adjusted ratio of productivity loss among those who reported productivity loss  - 0.85 (0.26-2.71)  Uncontrolled Adjusted OR for reporting productivity loss  - 2.47 (0.71-8.56)  39  * Self-administered comorbidity questionnaire, † proportion of days covered by medication, ‡ significant P-value   Main model Alternative model   Adjusted ratio of productivity loss among those who reported productivity loss  - 1.13 (0.38-3.39) PDC Level    50-80% Adjusted OR for reporting productivity loss 1.34 (0.58-3.10) 1.07 (0.31-3.74)  Adjusted ratio of productivity loss among those who reported productivity loss 2.72 (0.91-8.10) 2.15 (0.56-8.25) >80% Adjusted OR for reporting productivity loss 0.97 (0.52-1.80) 0.76 (0.35-1.66)  Adjusted ratio of productivity loss among those who reported productivity loss 1.13 (0.68-1.86) 1.05 (0.52-2.11) Drug Insurance    Partial Adjusted OR for reporting productivity loss 1.11 (0.47-2.63) 1.12 (0.43-2.92)  Adjusted ratio of productivity loss among those who reported productivity loss 0.73 (0.30-1.80) 0.76 (0.25-2.26) None Adjusted OR for reporting productivity loss 0.86 (0.33-2.25) 0.81 (0.27-2.41)  Adjusted ratio of productivity loss among those who reported productivity loss 0.76 (0.28-2.11) 0.82 (0.22-2.98) High income Adjusted OR for reporting productivity loss 0.88 (0.47-1.66) 0.90 (0.46-1.75)  Adjusted ratio of productivity loss among those who reported productivity loss 1.27 (0.69-2.32) 1.37 (0.62-3.04) SCQ Adjusted OR for reporting productivity loss 1.14 (1.02-1.28)‡ 1.15 (1.02-1.29)‡  Adjusted ratio of productivity loss among those who reported productivity loss 1.09 (1.01-1.18)‡ 1.09 (0.98-1.21) 40  Table 9: Costs of productivity loss per week (CAD) ‡, comparing the main model with the alternative model adding control status as confounder  SCQ score Main Model Alternative model† 0 205.12 ± 42.09  215.05  ± 50.51 1 244.42  ± 45.46  253.10  ± 53.85 2 290.07  ± 51.73 296.62 ± 59.65  3 342.81  ± 62.54 346.31 ± 69.40  4 403.47  ± 79.29 402.74  ±84.45  5 472.90  ±103.02 466.56  ± 105.91  6 552.02  ± 134.70 538.45  ± 134.69  7 641.82  ± 175.39 619.12  ± 171.69  8 743.35  ± 226.39 709.36  ±217.94  9 857.74  ± 289.24 809.96  ±274.69  10 986.20  ± 365.75 921.82  ± 343.39  11 1130.04  ± 458.03 1045.86  ± 425.72  12 1290.72  ± 568.50  1183.12  ± 523.60  13 1469.79  ± 699.91 1334.70  ± 639.22  14 1669.0  ±855.37 1501.83  ± 775.04*  15 1890.27  ± 1038.37* 1685.85  ± 933.81*   * Non-significant p-values †  Alternative model: two part model regression analysis on the main model after adding control status ‡ 2010 Canadian dollars  41  Chapter 3: Conclusion In this thesis the literature review of recent studies on the burden of asthma has demonstrated that this condition continues to impose major economic burden in many countries. However, this has also demonstrated some great discrepancies and variations in the reported estimates that cannot plausibly be attributed to differences across jurisdictions or sampling variations. This, unfortunately, undermines the utility of such studies in providing a clear and consistent picture on the burden of asthma. In addition, there is a clear lack of studies from many regions of the world (e.g., South America, Africa) to complete the picture of the global burden of asthma.  Despite such discrepancies and insufficient data, some consistent patterns can still be gleaned. One is the finding that in the developed countries medication costs are the biggest driver of direct costs of asthma19,26,28,30,35,40, while outpatient and inpatient care seem to be the largest component in developing regions 27,38,42. This can reflect better access to asthma controller medications in richer countries, with consequent improvement in asthma control and reduction in encounters with the health care system. Evidence also suggests that asthma medication costs are generally increasing, a trend that seems to parallel a reduction in the costs and number of hospitalizations and physician visits 30,35,40. While this association is ecological, it can be seen as a hypothesis-generating observation on the role of increased use of evidence-based asthma therapies in the declining rate of asthma outcomes. However, alternative factors such as change in pattern of exposure to risk factors in earlier years due to improved environmental settings might be responsible for this trend 66. Studies have shown low levels of asthma control as well as disappointingly low adherence rates to controller medications in jurisdictions in which such trends have been observed. In all studies total costs increased as the control status decreased, 42  although there were discrepancies regarding the changes in components of the costs 26,31–34,41. These findings cast doubt whether the observed reduction in adverse asthma outcomes can be entirely explained by higher rates of evidence-based treatment.   Few studies attempted to evaluate the indirect costs of asthma, and the results varied widely. Presenteeism, which means attending work but not being fully functional due to illness, is a non-negligible component of the indirect costs of asthma, and most studies fail to consider its contribution to the overall indirect costs of asthma. Given that asthma also affects any younger individuals, the contribution of work/school day’s loss can also be significant. Indeed a recent systematic review demonstrated that this component is the largest component of the indirect costs of asthma 22.   Previous studies assessing the impact of comorbidities on asthma patients mostly focused on direct costs or health services use 10,54,56,67. For example, they have demonstrated that the rate of hospitalization due to asthma and Emergency Department (ED) visits in asthma patients increased in the presence of comorbidities 10,54,56,67. It has also been shown that the presence of some comorbidities increase the risk of mortality 52,67. The relationship between comorbidities and asthma exacerbations has also been demonstrated 11. A study conducted in Finland showed that the presence of one and more than two comorbidities increased the risk of work disability with hazard ratios of 2.2 and 4.5, respectively 68. In that study, work disability was defined as long-term sickness absence (≥90 days) and receiving a disability pension. The results of the current study are consistent with our previous study that demonstrated the presence of comorbid psychological conditions in asthma patients significantly increases productivity loss 69.  43  To the best of our knowledge, there is no other study assessing the general impact of comorbidities on productivity loss in asthma patients including both absenteeism and presenteeism and transforming the productivity loss into its monetary value. The use of validated instruments in this thesis enabled me to transform productivity loss time to its monetary value, incorporating the impact of the affected individual on team productivity, and the use of a robust statistical method enabled me to properly handle statistical issues around zero-inflated and skewed costs data.  In summary I analyzed longitudinal data including 284 individuals with asthma and demonstrated that as the SCQ score, a validated quantitative measure of the burden of comorbidity, increased, the hours of absenteeism and presenteeism increased significantly to almost 20 hours per week. This caused almost $1,685/week higher productivity loss in patients with a score of 15, the maximum score observed in our sample, in comparison to those with a zero score. The average SCQ score in the sample was 2.47 ± 2.97. At this level, productivity loss was almost 1.5 times higher than in individuals without any comorbidity (SCQ=0). In the full two-part regression, SCQ increased the odds of reporting productivity loss, absenteeism and or presenteeism by 14-17%. In addition, among those with productivity loss, one-unit increase in SCQ increased productivity loss by 9%. Overall, our results demonstrate the substantial effect of comorbidity on productivity loss in patients with asthma.  This thesis has several limitations worth mentioning. First, the final sample size (284) might have underpowered the results. Second, our sample only included employed asthma patients. None of the participants in the original study reported being unemployed because of asthma. As 44  such, I could not incorporate the loss of productivity for asthma patients who lose their job due to the asthma-related or comorbidity-related impairment. Third, self-reported physician diagnosis of asthma and self-reported comorbidities and productivity loss might reduce the accuracy of the data I used. Fourth, the percentage of patients with higher scores of SCQ was limited such that the results for the patients with SCQ scores of ≥ 10 should be interpreted cautiously. Also as this study was a prevalent based cohort, in comparison to inception cohort, those patients with more severe asthma or other comorbidities might not be included due to mortality.   Taking these limitations into account, the thesis has highlighted the important association of comorbidities with productivity loss in working asthma patients. Productivity loss has often been a disregarded aspect of the economic burden of asthma 70. Thus the thesis is a reminder for health care providers to pay greater attention to comorbidities in the management of asthma in order to reduce the burden of this common disease that disproportionately affects individuals in their productive years of life.         45  References 1.  Gauthier M, Ray A, Wenzel SE. Evolving Concepts of Asthma. Am J Respir Crit Care Med. 2015;192(6):660–8.  2.  Olin JT, Wechsler ME. Asthma: pathogenesis and novel drugs for treatment. BMJ. 2014; 349:g5517.  3.  Lenaeus MJ, Hirschmann J. Primary Care of the Patient with Asthma. Med Clin North Am. 2015; 99(5): 953–67.  4.  Masoli M, Fabian D, Holt S, Beasley R, Global Initiative for Asthma (GINA) Program. 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The global economic burden of asthma and chronic obstructive pulmonary disease. Int J Tuberc Lung Dis. 2016; 20(1):11–23.   51  Supplementary Material: Search strategy  Asthma searches were run in MEDLINE (Ovid), Embase (Ovid), NHS Economic Evaluation Database (NHSEED), Cumulative Index to Nursing and Allied Health Literature (CINAHL) and EconLit  Asthma MEDLINE (Ovid) Search  Database: Ovid MEDLINE(R) In-Process & Other Non-Indexed Citations and Ovid MEDLINE(R) <1946 to Present> Search Strategy: ----------------------------------------------------------------1     asthma/ (107912) 2     asthma, aspirin-induced/ or asthma, exercise-induced/ or asthma, occupational/ or status asthmaticus/ (3570) 3     asthma$.mp. (146803) 4     wheez$.mp. (10638) 5     reactive airway disease.mp. (282) 6     (allergic adj5 respirator$).mp. (2120) 7     (chronic adj5 respirator$).mp. (10879) 8     allergens/ (33092) 9     (airway$ adj3 inflam$).mp. (14837) 10     (whistl$ adj10 chest).mp. (45) 11     (short$ adj10 breath).mp. (5710) 12     (tight$ adj10 chest).mp. (1133) 13     Dyspnea/ (15931) 14     Respiratory Sounds/ (7197) 15     Bronchial Diseases/ (7219) 16     Bronchial Hyperreactivity/ (7051) 17     bronchial hyperactivity.mp. (52) 18     Respiratory Tract Diseases/ (19072) 19     Bronchitis/ (19784) 20     bronchitis.mp. (29469) 21     hypersensitivity/ (38373) 22     respiratory hypersensitivity/ (8837) 23     hypersensitivity, immediate/ (11886) 24     hypersensitivity, delayed/ (18605) 25     hypersensitivity.mp. (138643) 26     (respiratory adj3 (hypersen$ or inflam$ or obstruct$)).mp. (12622) 27     Bronchoconstriction/ (3777) 28     Bronchoconstrict$.mp. (10672) 29     (bronch$ adj3 constrict$).mp. (627) 30     excessive airway narrowing.mp. (47) 31     (antiasthma$ or anti-asthma$).mp. (10599) 52  32     ((Bronchial$ or respiratory or airway? or lung?) adj4 (Hypersensittiv$ or hyperreactiv$ or allerg$ or insufficiency)).mp. (52378) 33     Bronchial Spasm/ (4146) 34     bronchospas$.mp. (4936) 35     (bronch$ adj3 spasm$).mp. (4356) 36     or/1-35 [Asthma] (394204)  37     economics/ (27435) 38     exp "costs and cost analysis"/ (190833) 39     exp "economics, hospital"/ (20284) 40     economics, medical/ (8895) 41     economics, nursing/ (4026) 42     economics, pharmaceutical/ (2645) 43     (economic$ or cost or costs or costly or costing or price or prices or pricing or pharmacoeconomic$).ti,ab. (501294) 44     (expenditure$ not energy).ti,ab. (20137) 45     value for money.ti,ab. (1048) 46     budget$.ti,ab. (20096) 47     or/37-46 [NHS EED Filter] (631703)  48     36 and 47 (8546) 49     asthma/ and ec.fs. [Economics] (1860) 50     or/48-49 (9007)  51     comment/ or letter/ or news/ (1317846) 52     50 not 51 (8735) 53     limit 52 to yr="2008 -2014" (3298) 54     limit 53 to English language (3215)  55     Direct costs.kw. (20) 56     Direct cost.kw. (18) 57     Direct Medical Cost.kw. (4) 58     Economic Cost.kw. (10) 59     Employer Health Costs/ (1069) 60     ((direct or indirect) adj5 cost?).tw. (11069) 61     (productivity adj3 (loss or cost?)).tw. (2424) 62     ((burden or cost?) adj3 societ$).tw. (3993) 63     (Economic adj3 (impact or burden or cost?)).tw. (15404) 64     caregiver$ burden.tw. (1977) 65     (caregiver adj3 cost?).tw. (130) 66     ((lost or work) adj7 productivity).tw. (2793) 67     ((work$ or employ$) adj5 disability).tw. (4591) 68     or/55-67 (37268) 69     36 and 68 (1184)  53  70     asthma, aspirin-induced/ or asthma, exercise-induced/ or asthma, occupational/ or status asthmaticus/ (3514) 71     ec.fs. [economics] (340387) 72     70 and 71 (27) 73     69 or 72 (1206) 74     comment/ or letter/ or news/ (1270738) 75     73 not 74 (1196) 76     limit 75 to yr="2008 -2014" (512) 77     limit 76 to English language (477)  Search Results 54     limit 53 to English language (3215) 77     limit 76 to English language (477)   

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