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Original Research: Asthma |

The Preventable Burden of Productivity Loss Due to Suboptimal Asthma ControlProductivity Loss and Clinical Control in Asthma: A Population-Based Study FREE TO VIEW

Mohsen Sadatsafavi, MD, PhD; Roxanne Rousseau, BSc; Wenjia Chen, MSc; Wei Zhang, PhD; Larry Lynd, PhD; J. Mark FitzGerald, MD; the Economic Burden of Asthma Study Team*
Author and Funding Information

From the Institute for Heart and Lung Health (Drs Sadatsafavi and FitzGerald and Ms Rousseau), Department of Medicine; Centre for Clinical Epidemiology and Evaluation (Drs Sadatsafavi and FitzGerald); Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences (Ms Chen and Dr Lynd); and the Centre for Health Evaluation and Outcome Sciences (Drs Zhang and Lynd), the University of British Columbia, Vancouver, BC, Canada.

Correspondence to: Mohsen Sadatsafavi, MD, PhD, Centre for Clinical Epidemiology and Evaluation, 7th Floor, 828 W 10th Ave, Research Pavilion, Vancouver, BC, V5Z 1M9, Canada; e-mail: msafavi@mail.ubc.ca


*A complete list of the study team members can be found in e-Appendix 1.

Funding/Support: This study is funded through the Collaborative Innovative Research Fund, an investigator-initiated, peer-reviewed competition sponsored by GlaxoSmithKline Canada.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2014;145(4):787-793. doi:10.1378/chest.13-1619
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Background:  Productivity loss is an overlooked aspect of the burden of chronic health conditions. While modern guidelines emphasize achieving clinical control in asthma management, few studies have reported on the relationship between asthma control and productivity loss. We calculated the productivity loss that can be avoided by achieving and maintaining clinical control in employed adults with asthma.

Methods:  We prospectively recruited a population-based random sample of adults with asthma in British Columbia, Canada. We measured productivity loss due to absenteeism and presenteeism using validated instruments, and ascertained asthma control according to the GINA (Global Initiative for Asthma) classification. We estimated the average gain in productivity for each individual if the individual’s asthma was controlled in the past week, by fitting two-part regression models associating asthma control and productivity loss, controlling for potential confounding variables.

Results:  The final sample included 300 employed adults (mean age, 47.9 years [SD 12.0]; 67.3% women). Of these, 49 (16.3%) reported absenteeism, and 137 (45.7%) reported presenteeism. Productivity loss due to presenteeism, but not absenteeism, was associated with asthma control. A person with uncontrolled asthma would avoid $184.80 (Canadian dollars [CAD]) in productivity loss by achieving clinical control during a week, CAD$167.50 (90.6%) of which would be due to presenteeism. The corresponding value was CAD$34.20 for partially controlled asthma and was not statistically significant.

Conclusions:  Our results indicate that substantial gain in productivity can be obtained by achieving asthma control. Presenteeism is more responsive than absenteeism to asthma control, and, thus, is a more important source of preventable burden.

Figures in this Article

Productivity loss is the opportunity cost due to foregone labor; it can either be in the form of absenteeism (the withdrawal of labor) or presenteeism (inefficiency of labor due to the impairment).1 Several studies have shown that the indirect costs of asthma surpass the direct costs.216 A landmark early study reported that among the total population of US adults aged ≥ 18 years, asthma-related indirect costs amounted to $1.268 billion, compared with the direct costs of $1.197 billion (in 1990 US dollars).2 Similarly, an early Canadian study reported the indirect costs of asthma to amount to 50% of the total costs of the disease.16 Studies in North America also reported high economic burden due to asthma-related productivity loss.17,18 A recent European study has estimated the indirect costs to constitute 62.5% of total costs of asthma.3

Despite the central role of clinical control in the contemporary management of asthma, only a few studies have reported on the relationship between productivity loss and asthma control.10,19 In addition, a simple reporting of productivity loss across levels of control, though informative on its own, is of limited value to policy makers as it does not infer any causal association. This is because of the impact of confounding factors affecting both asthma control and productivity, and the fact that even patients with controlled asthma might still experience some productivity loss, which means a fraction of productivity loss is unavoidable even when achieving total asthma control. Our research question in the present work was: How much productivity loss, from the societal perspective and considering both absenteeism and presenteeism, can be averted by achieving and maintaining clinical control in individuals with partially controlled or uncontrolled asthma?

We used the prospectively collected cross-sectional data of the Economic Burden of Asthma (EBA) study, a longitudinal study with the ultimate aim of measuring the burden of asthma in Canada (University of British Columbia Human Ethics No. H10-01542). The study’s catchment areas were composed of two census subdivisions in British Columbia, Canada: the Vancouver subdivision and Central Okanagan subdivision, with a 2011 population of 603,502 and 179,830, respectively.20 The rationale for the selection of the two areas was to increase the generalizability of findings by including both urban and rural populations. Individuals aged 1 to 85 years with a self-reported diagnosis of asthma by a physician were identified using random-digit dialing (including both landline and cellular numbers). Eligibility criteria also included having had at least one encounter with the health-care system (eg, visit to a doctor, ED, or hospital, or receiving an inhaler medication) in the past 5 years. Eligible individuals were invited to the study centers, and for those who gave written informed consent, a series of questionnaires was administered. The data collected in the baseline visit included demographics, socioeconomic status, asthma-related symptoms, employment status (and if employed, job title and description), quality of life, mental health status, and productivity loss. In addition, a lung function test was administered by a trained technician. The sample for the present study included adults who were employed at the time of recruitment.

We matched the stated job titles and descriptions to the National Occupation Classification codes.21 This was performed independently by two investigators, and disagreements were resolved by a third investigator. The hourly wage for each individual was estimated from the sex- and age-specific values for the corresponding National Occupation Classification code for the year 2010 from Statistics Canada.22 All costs were, therefore, in 2010 Canadian dollars (CAD).

The effect of a patient’s asthma on their work was assessed using the Work Productivity and Activity Impairment (WPAI)23 and the Valuation of Lost Productivity (VOLP)24 questionnaires. The WPAI questionnaire captures the work time lost due to absenteeism and presenteeism, with a recall period of 1 week.23 Specifically, for absenteeism, it asks the participant about how many hours they missed from work in the past 7 days because of their health, asking the respondent to consider hours missed on sick days and the time they went in late or left early. For presenteeism, the questionnaire asks about the extent to which individual’s health affected their productivity while the person was working, asking the respondent to think about days they were limited in the amount or kind of work they could do, days they accomplished less than they would like, or days they could not do their work as carefully as usual. The VOLP questionnaire captures aspects of the current work environment, such as the contribution of the individual to team productivity, availability of a replacement, and time sensitivity of the job.24 Such information is used to produce multipliers, separately for absenteeism and presenteeism, that convert the hourly wage to marginal value of productivity for an hour of work, considering the impact of individual’s productivity loss in the work environment.25

Assessing Asthma Control

We applied the Global Initiative for Asthma (GINA) definition of asthma control.26 In this definition, asthma is categorized as controlled, partially controlled, or uncontrolled, based on measures of perceived impairment as well as the ratio of FEV1, obtained through spirometry, to its predicted value. We used the Third National Health and Nutrition Examination Survey reference standards for calculating the predicted FEV1.27

Statistical Analysis

All analyses were performed using SAS, version 9.3 (SAS Institute Inc). Two-tailed P values at .05 were considered statistically significant. We performed descriptive analysis of baseline variables according to the level of asthma control, using analysis of variance for continuous variables and the χ2 test for categorical variables to compare their distribution across asthma control levels.

Productivity loss at the individual level in this study is the product of three terms: the weekly hours of work lost, the individual’s hourly wage, and the VOLP multiplier converting wage to productivity rate. Unadjusted analysis included reporting the percentages of individuals with lost work due to presenteeism/absenteeism, and weekly hours of lost work across levels of asthma control.

Adjusted Analysis: Estimating the Preventable Productivity Loss:

We fitted regression models with hours of productivity loss (separately for absenteeism and presenteeism) as the dependent variable, and asthma control and potential confounding variables as independent variables. Asthma control entered the models as two dummy variables representing partially controlled and uncontrolled asthma (with the reference being controlled asthma). The potential confounding variables, selected from a larger set of candidates based on exploratory analysis of crude associations, consisted of 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).

Given the zero-inflated and right-skewed nature of the productivity loss data, we used two-part regression models with logistic and Gamma (generalized linear model with logarithmic link function and Gamma distribution) components.28 The logistic component calculated an OR for the association between asthma control and presence of productivity loss, and the Gamma component generated a rate ratio (RR) for the association of asthma control with the hours of lost work in the subset of individuals who reported productivity loss. The two measures were combined to generate an overall incremental effect of uncontrolled or partially controlled asthma on hours of lost work, with controlled asthma as the reference category. This incremental value was then converted to monetary value of lost productivity using the individual wage and VOLP multipliers. CIs and P values were estimated using parametric bootstrapping by repeating the entire process 100 times.28

Unlike the unadjusted analysis, this analysis took into account the impact of potential confounders, and by taking individuals with controlled asthma as the reference group, it removed the impact of any non-asthma-related productivity loss as well as the residual asthma-related productivity loss that even patients with controlled asthma can experience. As such, the coefficients of productivity loss associated with partially controlled and uncontrolled asthma in this framework can have a causal interpretation, answering our research question.

Missing Values:

A small percentage of covariates were missing. To optimally analyze the available data yet properly account for the uncertainty around the missing values, we performed multiple imputation of such missing data by imputing the missing data within each of the 100 bootstraps.29

Sensitivity Analyses:

Sensitivity analyses included using a regression model with the dependent variable being the monetary value of productivity loss (instead of the hours of lost work), repeating the analysis after removing observations with outlying values of VOLP multiplier, and alternative handling of equivocal responses to asthma-symptoms questionnaires.

Figure 1 illustrates the flowchart of number of individuals according to their asthma control and productivity loss status. The final sample consisted of 300 individuals recruited between January 2011 and July 2012.

Figure Jump LinkFigure 1. Flow chart of the sample selection.Grahic Jump Location

In 59 (19.7%), 119 (39.7%), and 122 (40.7%) individuals, asthma was classified respectively as controlled, partially controlled, and uncontrolled. Baseline characteristics of the study subjects are presented in Table 1.30,31

Table Graphic Jump Location
Table 1 —Demographic Characteristics of the Study Sample, According to the Level of Control

Data given as % unless otherwise indicated. CAD = Canadian dollars.

a 

Cutoff of CAD$60K was chosen because it is close to the median household income in Canada.30

b 

High education was defined as having obtained a 4-y college or university degree or higher.

c 

Rural areas were defined as postal codes with < 400 people/km2.31

Unadjusted Analysis

Table 2 presents productivity loss overall and across asthma control levels. In total, 146 individuals (48.7%) reported productivity loss due to asthma. Presenteeism was more common than absenteeism, with 137 reporting presenteeism (45.7%) and 49 reporting absenteeism (16.3%).

Table Graphic Jump Location
Table 2 —Productivity Loss by Type (Absenteeism or Presenteeism) and by Level of Asthma Control

Data given as No. (%) unless otherwise indicated.

a 

Significant at the .05 level.

On average, an employed adult with asthma lost 7.6 h per week for health-related productivity loss, equal to 20.9% of the time they worked; 5.5 h (72.4%) of the lost productivity was due to presenteeism. The number of hours of work lost per week was 5.1 for controlled, 6.2 for partially controlled, and 10.1 for uncontrolled asthma (P = .03). The unadjusted time loss was significantly different across control levels for presenteeism (P < .01) but not for absenteeism (P = .40).

Adjusted Analysis

The results of the regression analysis associating asthma control and productivity loss, controlling for the effect of potential confounders, are presented in Table 3. Compared with controlled asthma, neither partially controlled nor uncontrolled asthma was associated with absenteeism, whereas partially controlled asthma (OR = 2.03; 95% CI, 1.02-4.05) and uncontrolled asthma (OR = 3.41; 95% CI, 1.72-6.76) were associated with presenteeism.

Table Graphic Jump Location
Table 3 —Results of the Regression Analysis of Productivity Loss on Control Level

Data in parentheses are 95% CIs; those in brackets are P values. Monetary values are given in 2010 CAD. See Table 1 legend for expansion of abbreviation.

a 

Adjusted incremental effects for overall productivity loss were estimated by summing the individual-level predicted values for absenteeism and presenteeism. The OR of reporting productivity loss and the ratio of productivity loss among those who reported productivity loss were estimated from fitting a two-part model with the overall productivity loss as the dependent variable.

b 

From the first part of the two-part model: a logistic regression with the occurrence of productivity loss as the dependent variable.

c 

Significant at the .05 level.

d 

From the second part of the two-part model: a generalized linear model with hours of work lost as the dependent variable, with Gamma distribution and logarithmic link function.

The cost of productivity loss due to presenteeism, but not absenteeism, was associated with asthma control (Table 3). When absenteeism- and presenteeism-related productivity loss estimates were combined, the preventable burden of uncontrolled asthma was estimated to be 4.10 (95% CI, 0.83-7.37) work hours per week, equivalent to CAD$184.80 (95% CI, 42.2-327.4). The estimated preventable burden of 0.22 h (P = .68) or CAD$34.20 (P = .60) for partially controlled asthma was not statistically significant. Coefficients for covariates obtained from the regression model are presented in the e-Tables 1-3.

Sensitivity Analyses:

Results of the sensitivity analyses are provided in e-Table 4. In none of the analyses did asthma control become associated with absenteeism. On the other hand, asthma control remained significantly associated with presenteeism in all the analyses.

In this prospective cross-sectional study, we studied the association of productivity loss with asthma control. In our study, presenteeism was not only the larger component of productivity loss, it was more responsive to asthma control than absenteeism. Because the contemporary belief is that control is achievable in the majority of individuals with asthma, the adjusted weekly productivity loss of CAD$184.80 among individuals with uncontrolled asthma is a preventable source of burden. To put this value in context, patients with current asthma were estimated to cost the health-care system of British Columbia, on average, CAD$461 (in 2010 CAD) per year in billed medical goods and services,32 equal to 2.5 weeks of preventable productivity loss in patients with uncontrolled asthma. This simple comparison suggests that by solely relying on billed services and ignoring indirect costs, policy makers severely underestimate the burden of the disease and risk misallocating resources.

We believe there are plausible mechanisms explaining the observed association between asthma control and presenteeism and lack of such association with absenteeism. Individuals with asthma might use all their allowed sick leave and so be forced to be present at work despite asthma impairment. This will cause a ceiling effect for absenteeism, weakening its association with asthma control. Unfortunately, we did not have data on whether individuals had used up their sick leave to test this hypothesis. In addition, absenteeism is most likely due to an episode of exacerbation, while presenteeism is likely due to the presence of daily asthma impairment. The current methods of assessing asthma control are heavily based on measures of impairment, and, as such, achieving clinical control according to such instruments will most likely result in minimal impairment, making presenteeism appear to be more responsive to asthma control than absenteeism.

To our knowledge, our study is the first in asthma that has used productivity rate instead of wage rate to estimate the cost of lost work time, and has measured the preventable loss of productivity. Some of the previous studies that measured productivity loss have used nonvalidated instruments.9,16 For example, one relied on descriptive self-report of days absent from work or days of work with limited activity due to asthma.16 Other studies have used patients’ disability claims to estimate indirect costs.8 Further, many studies have not measured presenteeism,6,8 which, as our results indicate, is the most important source of indirect costs. Some studies have only reported unadjusted levels of productivity loss across levels of asthma control or severity, but such results do not represent the preventable burden due to the confounding effect of other variables, as well as inevitability of residual productivity loss in patients with well-controlled asthma. Finally, estimates of productivity loss are sensitive to the instrument used. The WPAI questionnaire used to estimate the hours of lost work in this study tends to give a higher estimate of presenteeism compared with other measurement methods.33 On the other hand, the proportion of our sample who reported productivity loss and the magnitude of the loss is in-line with other reports in other respiratory conditions,34 as well as the productivity loss in the general North American population.35,36

The limitations of our study should be acknowledged. None of the adults in the EBA study reported being unemployed because of asthma; thus, the potential contribution of this aspect of the burden of asthma was not captured in our sample. Cost data often have large variations. As such, even with a sample size of 300 and prospectively collected data, our study might have been underpowered to capture rare events such as exacerbations (and, thus, absenteeism). Individuals’ asthma-related impairment might vary even within a given control level, and this residual variation might act as a confounding factor in relation with productivity loss. Finally, we have estimated the burden of productivity loss associated with suboptimal asthma control, and our results can be used to evaluate the benefits of policies aimed at improving asthma management. However, lack of a reference nonasthma group has precluded us from assessing the burden of productivity loss due to asthma itself; as such, our results cannot be used to inform preventive policies (eg, controlling environmental risk factors) aimed at reducing the incidence of asthma.

Productivity loss is an underappreciated source of economic loss. In other chronic diseases, consideration of productivity loss in policy analyses has resulted in profound changes in the choice of therapies.37,38 Even when studies consider productivity loss, the emphasis has been based on absenteeism.39 As our results suggest, presenteeism can be more responsive to change in the level of control than absenteeism, thus being a more important source of preventable burden. Future cost-effectiveness and burden-of-disease studies should put more emphasis on productivity loss, and future studies evaluating productivity loss should use more rigorous and standardized methodology.

Author contributions: Drs Sadatdafavi and FitzGerald had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Dr Sadatsafavi: contributed to the study concept, design, and protocol and the statistical analysis; wrote the first draft of the manuscript; approved the manuscript; and served as principal author.

Ms Rousseau: contributed to study design and approved the manuscript.

Ms Chen: contributed to the statistical analysis and approved the manuscript.

Dr Zhang: contributed to approval of the manuscript.

Dr Lynd: contributed to the study protocol and approved the manuscript.

Dr FitzGerald: contributed to the study concept, design, and protocol and approved the manuscript.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Sadatsafavi receives salary support from the National Sanitarium Association. Dr Lynd has received other money from the Canadian Institutes of Health Research, BC Pharmacy Association, and Canadian Foundation for Pharmacy; unrestricted research grants from Genzyme Canada Inc and AbbVie Inc; and has provided methodology consultation to Pfizer Canada Inc and Sanofi Aventis Canada. Dr FitzGerald has served on advisory boards for GlaxoSmithKline plc, AstraZeneca plc, Novartis AG, Pfizer Inc, Boehringer-Ingelheim GmbH, Altana AG, Merck & Co Inc, and Topigen Pharmaceutiques Inc; has been a member of speakers’ bureaus for GlaxoSmithKline plc, AstraZeneca plc, Boehringer-Ingelheim GmbH, Pfizer Inc, and Merck & Co Inc; and has received research funding paid directly to the University of British Columbia from the Canadian Institutes of Health Research, AstraZeneca plc, GlaxoSmithKline plc, Boehringer-Ingelheim GmbH, Merck & Co Inc, Pfizer Canada, Bayer Schering Pharma, Genentech Inc, and Topigen Pharmaceutiques Inc. The remaining authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors: The sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.

Additional information: The e-Appendix and e-Tables can be found in the “Supplemental Materials” area of the online article.

CAD

Canadian dollars

VOLP

Valuation of Lost Productivity

WPAI

Work Productivity and Activity Impairment

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Figures

Figure Jump LinkFigure 1. Flow chart of the sample selection.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Demographic Characteristics of the Study Sample, According to the Level of Control

Data given as % unless otherwise indicated. CAD = Canadian dollars.

a 

Cutoff of CAD$60K was chosen because it is close to the median household income in Canada.30

b 

High education was defined as having obtained a 4-y college or university degree or higher.

c 

Rural areas were defined as postal codes with < 400 people/km2.31

Table Graphic Jump Location
Table 2 —Productivity Loss by Type (Absenteeism or Presenteeism) and by Level of Asthma Control

Data given as No. (%) unless otherwise indicated.

a 

Significant at the .05 level.

Table Graphic Jump Location
Table 3 —Results of the Regression Analysis of Productivity Loss on Control Level

Data in parentheses are 95% CIs; those in brackets are P values. Monetary values are given in 2010 CAD. See Table 1 legend for expansion of abbreviation.

a 

Adjusted incremental effects for overall productivity loss were estimated by summing the individual-level predicted values for absenteeism and presenteeism. The OR of reporting productivity loss and the ratio of productivity loss among those who reported productivity loss were estimated from fitting a two-part model with the overall productivity loss as the dependent variable.

b 

From the first part of the two-part model: a logistic regression with the occurrence of productivity loss as the dependent variable.

c 

Significant at the .05 level.

d 

From the second part of the two-part model: a generalized linear model with hours of work lost as the dependent variable, with Gamma distribution and logarithmic link function.

References

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