Report 5: Economic Burden of Illness in Canada, 2005–2008 – EBIC value of lost production due to morbidity, 2005–2010
Report 5: EBIC Value of Lost Production due to Morbidity, 2005-2010
The value of lost production due to morbidity is an indirect cost component of EBIC. Morbidity costs are incurred when some form of illness or injury results in time lost from productive activities, whether paid or unpaid. Morbidity costs are also incurred with decreased productivity due to illness or injury (e.g. presenteeism). In this report, morbidity costs were estimated only for the value of lost production due to labour market missed work days (absenteeism). The value of lost production was not estimated for costs associated with presenteeism and non-labour market productive activities. Furthermore, the morbidity costs include only lost production costs associated with an individual's “own” illness and injury; lost production costs due to informal caregiving for the sick and injured were not included.
The human capital method and the friction cost method are the two primary methods for estimating production losses associated with morbidity. The major difference between the two methods is the time period considered for lost production. The human capital method values all lost production from the onset of an illness or injury that results in the inability to work until expected retirement age or life expectancy. It assumes that a worker who becomes unable to work because of illness or injury cannot be replaced, implicitly assuming zero involuntary unemployment. In reality, most economies have pools of unemployed workers willing to fill vacant job positions. For example, the unemployment rate in Canada ranged from 6% to 8% of the labour force in the years 2005-2010 (49,50).Footnote 76
In the 1990s, the friction cost method was developed by a group of Dutch economists (51-54). Unlike the human capital method, this method does not assume full employment; rather, it considers lost production to occur only in the period when the job position is vacant, denoted as the friction period. Specifically, the friction period is considered to start when the individual leaves his or her job, due to illness or injury, and to end when the job vacancy or chain of vacancies are filled.
Traditionally, the human capital method has been used in most studies estimating lost production costs associated with morbidity. It was used in previous published (EBIC 1986, EBIC 1993 and EBIC 1998) and unpublished (EBIC 2000) editions of EBIC (1-4). In preparation for the current edition, the methods for estimating the indirect costs of illness and injury were re-evaluated. Under advisement of economists who attended the 2009 and 2010 EBIC workshops (organized by PHAC), the friction cost method was adopted to estimate indirect costs (55,56).
A prevalence-based approach was used to estimate the EBIC 2005-2010 morbidity costs, as was done in previous editions of EBIC. A prevalence-based approach values all lost production in the year in which it occurred.
In summary, EBIC 2005-2010 morbidity costs associated with labour market missed work days (absenteeism) were estimated using the friction cost method and a prevalence-based approach. The remaining sections of this report describe the data sources and methods used to derive the EBIC 2005-2010 estimates of the value of lost production due to morbidity. Additionally, the 2005-2010 results are presented and discussed along with the data limitations. Although the current edition of EBIC focuses on the years 2005-2008, 2009 and 2010 results are also presented and discussed, as all results (2005-2010) are based on surveyed 2010 missed work days due to illness and injury.Footnote 77
2. Data Sources
Statistics Canada’s 2010 Canadian Community Health Survey (CCHS) Loss of Productivity (LOP) module was used to estimate missed work days due to illness and injury (62-64).Footnote 78,Footnote 79 The CCHS is a cross-sectional survey that collects information related to health status, health determinants and health care utilization for the Canadian population (65). The LOP module was introduced in 2010 as common content (a mandatory module) and will appear again in the 2014 CCHS.Footnote 80 Annual provincial unemployment duration (in weeks), used as a proxy for the friction period, and annual earnings specific to sex, age and province (in constant 2010 dollars), used to value lost production, were obtained from Statistics Canada’s Canadian Socio-economic Information Management (CANSIM) System (57,66).Footnote 81
The value of lost production due to morbidity was estimated for the years 2005-2010 using the friction cost method. The estimates were derived by multiplying the period of lost production by the value of production.
3.1 Period of Lost Production
Non-survey period of lost production estimates were not available for 2005-2010. The 2010 CCHS’s LOP module was used to estimate missed work days due to illness and injury for all years 2005-2010, since survey estimates were not available for 2005-2009.Footnote 82,Footnote 83 It was assumed that after adjustment for sex- and age-specific differences in population across the years, 2010 missed work days due to illness and injury would reflect missed work days for all years. The population adjustments modified the number of employed individuals missing work days and the number of missed work days while assuming the same 2010 CCHS sex- and age-specific employment rate across all years.Footnote 84,Footnote 85,Footnote 86,Footnote 87 The assumptions of the analysis in this report imply that missed work days due to illness and injury increase proportionally with increases in population, since if the population increased and the employment rate remained constant, the number of employed individuals would increase, increasing the number of missed work days.Footnote 88 For example, if there were 100 employed individuals who missed 20 work days due to illness and injury, now with 200 employed individuals in the workforce 40 work days are assumed missed due to illness and injury.
For EBIC 2005-2010 morbidity cost estimates, the period of lost production included missed work days due to chronic conditions, such as arthritis, and acute conditions, such as a cold or the flu.Footnote 89 Specifically, CCHS respondents were asked about missed work days due to illness or injury within the 3 month period prior to the survey.Footnote 90 For the purpose of EBIC analyses, respondents who participated in the 2010 CCHS LOP module were grouped into three categories according to their responses to particular survey questions. The categories were as follows: missed less than 90 days of work due to illness or injury in the past 3 months; missed 90 consecutive days of work due to illness or injury in the past 3 months but had worked in the past 12 months; and excluded from analysis.Footnote 91
For respondents who reported missing less than 90 days due to illness or injury in the past 3 months, the exact number of days missed multiplied by four (to reflect the entire year) was used as the period of lost production. For respondents who reported missing 90 consecutive days of work due to illness or injury in the past three months but had worked in the past 12 months, the average annual provincial unemployment duration (in days), acting as a proxy for the friction period, was used as the period of lost production.Footnote 92,Footnote 93 As these respondents indicated that they had worked in the past 12 twelve months, it was assumed that their friction period fell within the year and that they were replaced after the duration of the friction period (the unemployment duration). All respondents who indicated that they had not worked in the past 12 months were excluded from the analysis, as the friction period and lost production for these individuals would have fallen in another year. In EBIC 1998, morbidity cost estimates were weighted for labour force participation rates; this was not necessary for EBIC 2005-2010 estimates. LOP module respondents were asked about their labour force participation at an earlier point in the CCHS survey, thus their labour force participation status was known. Estimated missed work days from the CCHS were grouped into the EBIC diagnostic categories according to the physical and mental health conditions identified by respondents. Appendix 1 illustrates the relationship between the CCHS disease categories and the EBIC diagnostic categories.
3.2 Value of Production
Average daily earning specific to province, sex and age were used to value production for EBIC 2005-2010. Average annual earnings for these groups, in constant 2010 dollars, were converted to current dollars using the national Consumer Price Index values and then were converted to average daily earnings by dividing average annual earnings by 260 (7).Footnote 94,Footnote 95
Tables 15-22 illustrate the EBIC 2005-2010 national estimates for the value of lost production due to morbidity.Footnote 96 Bootstrapping analysis was performed using the provided CCHS bootstrapping weights; Appendix 2 describes the coefficient of variation (CV) range for each type of estimate.
4.1 Costs by Diagnostic Category
Table 15 illustrates the EBIC 2010 national value of lost production due to morbidity cost estimates by diagnostic category. In 2010, total national morbidity costs were $18.2 billion. The three diagnostic categories with the largest costs were injuries ($3.2 billion, 17.8%), respiratory infections ($2.9 billion, 16.0%) and musculoskeletal diseases ($1.5 billion, 8.4%). The unattributable percentage of morbidity costs refers to costs that could not be attributed to a specific diagnostic category; these costs represented 38.0% ($6.9 billion) of total morbidity costs.
4.2 Costs by Diagnostic Category and Sex
Table 15 illustrates the EBIC 2010 national value of lost production estimates by diagnostic category and sex. Total morbidity costs were higher for males ($9.8 billion, 53.7%) than for females ($8.4 billion, 46.3%). The three diagnostic categories with the highest costs for males were injuries ($2.3 billion), respiratory infections ($1.6 billion) and musculoskeletal diseases ($0.8 billion). The three diagnostic categories with the highest costs for females were respiratory infections ($1.3 billion), injuries ($0.9 billion) and musculoskeletal diseases ($0.8 billion).
The three diagnostic categories with the largest difference in the cost distribution across the sexes were diabetes mellitus (88.6% male, 11.4% female), genitourinary diseases (81.7% male, 18.3% female) and malignant neoplasms (76.1% male, 23.9% female).
4.3 Costs by Diagnostic Category and Age
Table 15 illustrates the EBIC 2010 national value of lost production estimates by diagnostic category and age group.Footnote 97 Total morbidity costs were higher for individuals aged 15-54 years ($14.9 billion, 81.8%) than for individuals aged 55-75 years ($3.3 billion, 18.2%). The three diagnostic categories with the highest costs for individuals aged 15-54 year were respiratory infections ($2.5 billion), injuries ($2.5 billion) and musculoskeletal diseases ($1.2 billion). The three diagnostic categories with the highest costs for individuals aged 55-75 years were injuries ($0.8 billion), respiratory infections ($0.4 billion) and musculoskeletal diseases ($0.3 billion).
The three diagnostic categories with the largest difference in the cost distribution across the age groups were genitourinary diseases (95.0% 15-54 years, 5.0% 55-75 years), neuropsychiatric conditions (89.6% 15-54 years, 10.4% 55-75 years) and certain infectious and parasitic diseases (87.2% 15-54 years, 12.8% 55-75 years).
Table 21 shows total 2010 (and 2009-2005) national morbidity cost estimates by more disaggregated age groups. Given the guidelines limiting the release of costs based on small cell counts, costs by diagnostic category could not be released for these age groups. In 2010, individuals aged 35-54 years accounted for 59.0% of total morbidity costs.
4.4 Costs Across the Years 2005-2010
Table 22 illustrates the EBIC 2005-2010 national total value of lost production due to morbidity estimates in 2010 constant dollars.Footnote 98 The value of lost production increased in each year of analysis, with an overall increase of 15.3% from 2005 to 2010.
5.1 Value of 2005-2010 Annual Morbidity Cost Estimates
There were large benefits to estimating the value of lost production due to morbidity for each year 2005-2010, even if surveyed estimates for missed work days due to illness and injury were available only for 2010. The value of lost production due to morbidity is one of several cost components for the EBIC project; producing annual morbidity cost estimates means that estimates can be added to those of other EBIC cost components to obtain total Canadian economic burden of illness and injury estimates for each year of analysis. Three adjustments were made to 2010 missed work days to reflect missed work days in the years 2005-2009. First, the year-specific cost estimates for years other than 2010 were adjusted for sex- and age-specific differences in population. Second, the year-specific cost estimates were adjusted to reflect differences in the unemployment duration across the years of analysis. Between the years 2005 and 2010, it was common for provincial unemployment duration to increase or decrease by a magnitude of 25%-50% over the time span of a year or two. For example, Alberta’s average unemployment duration was 2, 3 and 4 months in 2008, 2009 and 2010 respectively (66). Third, the year-specific cost estimates were adjusted to reflect differences in sex-, age- and province-specific earnings across the years of analysis.
5.2 Sex Differences in the Value of Lost Production Due to Morbidity
EBIC 2005-2010 morbidity cost differences between the sexes were influenced by both missed work days due to illness and injury and the sex-specific earnings. The CCHS estimates for 2010 missed work days showed that males and females missed approximately 52,967,900 and 64,678,000 work days respectively.Footnote 99 Therefore, females reported 22.1% more missed work days due to illness or injury than males. The difference in missed work days is not explained by the difference in the number employed, since in 2010 the number of males employed was 9.6% higher than the number of females (69). Therefore, working women have a higher rate of missed work days due to morbidity per employed person than working men. The higher rate of missed work days could be influenced by unknown factors, such as differences in the prevalence of illness and injury between working men and women and/or working men being more likely to go to work ill or injured.
In Canada, across the years 2005-2010, national male earnings were, on average, 52% higher than national female earnings (57). As sex-specific earnings were used to value the period of lost production, morbidity costs for males would be higher than for females given the same number of missed work days.
5.3 Age Group Differences in the Value of Lost Production Due to Morbidity
EBIC 2005-2010 morbidity cost differences between the age groups were influenced by both missed work days due to illness and injury and the age-specific earnings. Estimates for 2010 missed work days showed that individuals aged 15-54 years reported 4.1 times more missed work days than individuals aged 55-75 years; this difference could partly be explained by the difference in the number employed between the two age groups. In 2010, the number of employed individuals aged 15-54 years was 4.8 times higher than those aged 55 years and older (69).Footnote 100 Additionally, across all years 2005-2010, earnings were highest for individuals aged 35-54 years, which may have also contributed to higher costs.
5.4 Increasing Morbidity Costs Over Time
The value of lost production due to morbidity (in constant dollars) increased across each year of analysis, with a total increase of 15.3% from 2005 to 2010. On average, national unemployment duration remained fairly constant across the years 2005-2010 (66).Footnote 101 Therefore, a combination of population and labour productivity (earnings) changes are likely responsible for the increasing costs. As 2010 missed work days due to illness and injury were adjusted to reflect differences in the population, which accounted for population growth, higher costs in later years may be in part due to larger populations. Specifically, the Canadian population increased by 7% from 2005 to 2010 (36-39,67,68). Similarly, average national annual earnings (in constant 2010 dollars) increased 4% across the same years (male and female earnings increased 2% and 9% respectively) (57). Comparison of total morbidity costs across the years 2005-2010 provides an estimate of the magnitude difference; however, there are limitations, as 2010 adjusted missed work days were used for all years.
5.5 Indirect Cost Methods and the Value of Lost Production Due to Morbidity
In previous editions of EBIC, the human capital method was used to estimate indirect costs, whereas the friction cost method was used in the current edition. In EBIC 1998 and EBIC 2008, morbidity costs were 55.6% and 97.3% of indirect costs respectively.Footnote 102 As premature mortality costs are the remainder of indirect costs, morbidity became considerably more costly relative to premature mortality when the friction cost method was adopted. One reason for this difference is that with the adoption of the friction cost method the period of lost production for premature mortality costs only equalled the length of the unemployment duration instead of the time of death until life expectancy. Therefore, in each year of analysis, the period of lost production for premature mortality became more comparable to that of morbidity, and since more individuals contributed to the value of lost production due to morbidity than to premature mortality (approximately 105 times, in 2008), morbidity costs became a considerably larger percentage of indirect costs.Footnote 103
6.1 Comparison Across Diagnostic Categories, Cost Components and Editions of EBIC
As a result of the change in methods used to derive the value of lost production due to morbidity, comparison of morbidity cost estimates between the current and previous editions of EBIC is not recommended. The incomparability of estimates is evidenced by several published studies comparing the costs obtained using both the methods. Specifically, these studies found the human capital method to produce estimates that ranged from 2 to 30 times higher than those resulting from the friction cost method (51,53,60,70-74).Footnote 104 The differences between the estimates found using each method vary with the number and age of individuals affected, illness(es) being studied, cost components considered, length of the friction period and the use of an elasticity. Furthermore, the estimates are not comparable to previous editions as a different survey was used to estimates missed work days and unpaid labour costs are excluded from the current edition's estimates.
EBIC 2005-2010 estimates of the value of lost production due to morbidity are not available by ICD codes or all EBIC diagnostic categories/subcategories. Since the CCHS is a survey, missed work days could be estimated only by very aggregated diagnostic categories/subcategories. The guidelines that restrict the release of data based on small cell counts also limited the release of certain diagnostic categories/subcategories. Additionally, these guidelines restricted the release of morbidity cost estimates by diagnostic category for the EBIC age groups. Instead, morbidity cost estimates were released by diagnostic category for very aggregated age groups (15-54 years and 55-75 years).Footnote 105,Footnote 106 Provincial and territorial morbidity cost estimates by diagnostic category could not be released either because of the set guidelines. Therefore, a complete economic burden of illness and injury, from the summation of all EBIC costs components, can be found only for very aggregated EBIC categories.
As mentioned earlier, Appendix 1 shows the mapping of the CCHS disease categories to the EBIC diagnostic categories; unfortunately, they do not map directly. In the CCHS LOP module, spina bifida is included in the chronic condition category ‘neurological diseases’, while in EBIC it is included in the diagnostic category ‘congenital anomalies’.Footnote 107 Therefore, costs for spina bifida are included in a different diagnostic category for the morbidity cost component than for the other EBIC cost components. Similarly, a chronic condition category in the CCHS LOP module comprised fibromyalgia, chronic fatigue syndrome and multiple chemical sensitivities; these conditions are included in separate EBIC diagnostic categories (based on ICD coding). As only one EBIC diagnostic category could have been selected to assign costs from fibromyalgia, chronic fatigue syndrome and multiple chemical sensitivities, it was assumed that fibromyalgia was associated with the highest number of missed work days, and all costs were assigned to the EBIC diagnostic category ‘musculoskeletal diseases’ (the EBIC diagnostic category for fibromyalgia).
The percentage of morbidity costs unattributable by diagnostic category was significant. In 2010, unattributable EBIC morbidity costs were 38.0% ($6.9 billion) of total morbidity costs. Approximately 24.7% of CCHS LOP respondents who had missed 3 consecutive months of work due to a chronic physical or mental health condition identified a condition that fell in the ‘other’ category (from a set list of chronic conditions) as the chronic condition responsible for the highest number of missed work days during the 3-month period.Footnote 108 Respondents who answered ‘other’ were asked to specify their chronic condition; however, these ‘other’ conditions were not coded for in the CCHS dataset. Had these conditions been coded for, the unattributable percentage of morbidity costs would have been lower. In the future, the addition of other chronic condition categories such as chronic infectious diseases (e.g. HIV/AIDS, hepatitis C) and sense organ diseases (e.g. glaucoma) may also help to decrease the number of individuals identifying ‘other’ chronic conditions. Additionally, since respondents were asked to identify the chronic condition responsible for the highest number of missed work days, morbidity costs for ‘secondary’ chronic conditions contributing to missed work days may be underestimated. Finally, survey questions that ask about missed work days due to any ‘other reason related to physical or mental health’ could be split into two questions, one question to ask about other reasons related to mental health and the second to ask about physical health. With two separate questions, the appropriate costs could be attributed directly to mental health, reducing unattributable morbidity costs.
6.2 Period of Lost Production
Unfortunately, non-survey data on missed work days due to illness and injury were not available for 2005-2010; instead, survey data were used. There are limitations to estimates from surveys, as participants’ responses may not reflect true population values, especially if missed work days due to illness and injury are highly variable. Survey estimates of missed work days due to illness and injury were only available for 2010. It was assumed that after adjustment for sex- and age-specific differences in the population, 2010 missed work days would appropriately reflect missed work days for all years. However, even after adjustment for population differences, 2010 missed work day estimates may not have accurately reflected missed work days in 2005-2009, since the prevalence of certain diseases, resulting in missed work days, within specific sex-age cohorts may vary from year to year. Additionally, even if prevalence remains the same, the number of missed work days may be highly variable, resulting in differences in missed work days from year to year. Considerable variations in missed work days may even occur for a single respondent within a given year. For example, a respondent could have missed 1 day of work because of a cold in the 3-month period surveyed but 4 days of work for the same reason in a different 3-month period in the same year, a 300% difference. Although, these limitations exist, asking respondents to recall missed work days for a period of longer than 3 months could have presented difficulties with recall accuracy, potentially resulting in even larger negative impacts on the accuracy of survey responses. Furthermore, the 3-month period acts as an appropriate proxy for the friction period. Given the limitations outlined, EBIC 2005-2010 morbidity cost estimates by category should not be compared across years, since 2010 adjusted missed work days were used for all years. Results from the 2014 CCHS LOP module may provide insight as to whether adjusted missed work days accurately represent those in other years.Footnote 109
Koopmanschap & van Ineveld and Koopmanschap et al. used Dutch vacancy duration data to estimate the friction period for the Netherlands (51,53).Footnote 110,Footnote 111 Vacancy duration data may have provided a better estimate of the friction period for Canada, but these data were unavailable. Therefore, annual provincial average unemployment duration was used as a proxy for the friction period.Footnote 112,Footnote 113,Footnote 114 Many labour market factors can affect how accurately the unemployment duration reflects the friction period: the number of unemployed individuals, the number of job vacancies, and how well the skills of the unemployed match the skills required for the vacant job position. Three possible relationships may exist between the unemployment duration (UD) and the friction period (FP): UD>FP, UD<FP or UD=FP. If UD>FP it may be that the number of unemployed individuals is very large relative to the number of job vacancies. Similarly, if UD<FP it may be that there are very few unemployed people relative to the number of vacant job positions. Skill-to-job match also plays an important role in both the length of the unemployment duration and the friction period, with poor skill-to-job match increasing the length of both (perhaps with different magnitudes given other labour market factors). In addition, the unemployment duration and friction period can be affected differently by various elements of the labour market. For example, given the same number of vacant job positions, an increase in the unemployment rate will likely increase the unemployment duration and decrease the friction period, as more people are unemployed and employers have a larger (and likely more diverse) pool of workers from which to select employees. For the reasons above, it is reasonable to assume that there is some number of unemployed workers and job vacancies, as well as a certain skill-to-job match in the unemployment pool of workers, that results in UD=FP. How closely the unemployment duration reflects the friction period in the years 2005-2010 is unknown. However, if an initial job vacancy is filled by an employed individual rather than an unemployed individual, a chain of vacancies will occur until the vacancy at the end of the chain is filled by the unemployed individual. If chain vacancies are occurring the majority of the time, the unemployment duration may be a reasonable estimate for the period of lost production; nevertheless, many complex labour market factors (e.g. number of unemployed individuals) will affect the representativeness of the unemployment duration.
For the lowest and highest education levels, Koopmanschap et al. estimated the friction period to range from 2.2 to 3.8 months (72% difference) and 2.8 to 3.5 months (25% difference) in 1988 and 1990 respectively (53). Unemployment duration specific to industry or education level may have provided more accurate Canadian friction period estimates; however, these were not available. Had unemployment duration specific to education level been available it could have been matched to CCHS respondents, although it would have been difficult to match industry-specific unemployment duration to each CCHS respondent.
Average provincial unemployment duration by sex and age group was available but was not used; more aggregated levels of unemployment duration were deemed more appropriate since it was unclear how closely the unemployment duration reflected the friction period in the years 2005-2010. There was not a considerable difference between the unemployment duration of each sex and age group, except for those aged 15-24 years. The unemployment duration for individuals aged 15-24 years was lower than for those in other age groups; this was probably because of the high turnover of jobs for younger individuals.Footnote 115 The unemployment duration was used as a proxy for the friction period only when an individual missed 3 consecutive months of work because of illness or injury; in most cases (78%) this was due to a chronic condition. Therefore, using an average unemployment duration for all ages was expected to have little effect on the period of lost production estimates, as individuals aged 15-24 years represented only 3.8% of individuals contributing 3 consecutive months of missed work due to a chronic illness or injury.Footnote 116
Although there are limitations to the value of lost production estimates when the friction cost method is used, the magnitude effects of these limitations on the morbidity cost estimates are negligible compared with the alternative of using the human capital method. As discussed earlier, the human capital method produces considerably larger estimates for the value of lost production, which may not reflect the true burden of lost production to society.
6.3 Missing Components of Lost Production
The value of lost production in this report was estimated for labour market missed work days due to an individual’s ‘own’ morbidity (absenteeism); however, the inclusion of additional components of lost production would have more accurately reflected the true economic burden of illness and injury. First, although the value of lost production from absenteeism was included in this report, lost production from presenteeism was not included. Individuals may attend work while sick or injured; as a result they are less productive and lost production occurs. Second, the value of lost production for non-labour market productive activities (e.g. housework) should be considered; this may be especially important for certain segments of the population. Finally, informal caregiving costs should be considered. Healthy individuals may spend time caring for the sick and injured, which would result in time away from labour market and non-labour market productive activities. Although the above-mentioned components of lost production due to morbidity should be included in an economic burden of illness and injury study, data sources to measure these components across all diagnostic categories were not available.
The 2005-2010 value of lost production due to morbidity was estimated using a prevalence-based approach for the lost production incurred from labour market missed work days due to illness and injury. The friction cost method was adopted to estimate 2005-2010 morbidity costs. Morbidity cost estimates from previous EBIC editions were estimated using the human capital method and thus cannot be compared with the 2005-2010 estimates.
In 2010, total national morbidity costs were $18.2 billion; 62.0% of these costs were attributable by diagnostic category. As adjusted 2010 missed work days were used to estimate the period of lost production for all years 2005-2010, trending morbidity cost estimates by category (e.g. diagnostic category) is not recommended. The value of lost production due to morbidity associated with presenteeism, non-labour market activities and informal caregiving should be considered in future EBIC publications in order to capture the burden of illness and injury to society for these components.
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