Background
It is estimated that oral diseases affected over 95 percent of Canadian adults in 2009 [
1]. Almost 20 percent of Canadian adults had untreated coronal decay, while approximately 20 percent had moderate to severe periodontal disease [
1]. That same year, Canadians spent approximately $12.8 billion for dental care [
1]. Repre- senting about ten percent of overall health care spending in Canada, the direct costs of dentistry are said to rank second only to those for cardiovascular diseases [
1]. Within health economics, direct costs represent the resources consumed to treat a condition and can include health care sector resources, out-of-pocket expenses and sometimes funds from statutory or voluntary bodies [
2]. In contrast, indirect costs represent time (often work time) consumed for treatment and is synonymous with ‘productivity losses’ [
2].
The overall costs of oral disease encompass both direct and indirect costs. The direct costs are attributed to care provided by dental professionals, while the indirect costs are attributed to time loss from work, school or normal activities due to dental problems and treatment. Current estimates on the direct costs of dental care are significant, yet there has been a dearth of estimates regarding the indirect costs. The recently completed Canadian Health Measures Survey (CHMS) provides an opportunity to estimate the indirect costs of dental problems and treatment in Canadian society. Previous research in this area has shown that, as ‘socio-dental indicators,’ the use of time loss from work, school or normal activities allows for dental problems and treatment to be understood in terms of impaired role functioning leading ultimately to potential productivity losses. Time loss is also easily operationalized, and when combined with wage information, can help estimate the economic impacts that oral diseases have on society. Quantifying time loss and the associated potential productivity losses thus allows for policy discussions to focus on the total burden of illness among different diseases and not merely the clinical aspects of any given disease [
3].
Work in this area has been completed in other Organization for Economic Cooperation and Development (OECD) countries (i.e., the United States and Australia). For example, the U.S. Surgeon General (2000), using a question from the 1996 National Health Interview Survey which asked about time loss within a previous 2-week period, reported an estimated 3.7 restricted activity days per 100 persons but did not quantify these losses monetarily [
4]. In comparison, several estimates are available for Australia. The Australian Research Centre for Population Oral Health (ARCPOH) questioned individuals about time loss within a previous 12-month period, and combined this with wage information to estimate potential productivity losses. For example, losses due to oral pain and discomfort were estimated at $836.5 million CDN [
5].
In short, quantifying the impacts of dental problems and treatment monetarily allows governments to more clearly understand the overall burden of illness in the population and allows oral health to be included and compared to other diseases in the broader health policy debate. Thus, the purpose of this study was to quantify time loss due to dental problems and treatment in the Canadian population, to identify factors associated with this time loss, and to provide information regarding the economic impacts of these issues.
Results
The overall participation rate for the CHMS was 51.7 percent, meaning that of the 8,772 households selected, 69.6 percent agreed to participate, and of these, 88.3 percent completed the household questionnaire, and 84.9 percent visited the mobile examination centre [
18]. Table
1 demonstrates that of those surveyed, there was an equal proportion of males (49.9%) and females (50.1%), over 60 percent were between the ages of 20 and 59 years, and 84.7 percent resided in 1 to 4 member households. Almost three quarters of participants reported being educated beyond the high school level (74.4%). Over 62 percent had private dental insurance and 32 percent were uninsured. Almost 80 percent were in the upper middle and highest income brackets with only 5.5 percent being in the lowest income brackets (lower middle and lowest). Almost 85 percent perceived their oral health as good to excellent, and over 88 percent reported having oral pain rarely or never. Almost three quarters visited dental professionals more than once a year, while over 16 percent visited for emergency care only. Of those surveyed, 35.1 percent reported time loss from work, school or normal activities due to dental problems and treatment. Table
2 demonstrates that as income decreased so did the odds of reporting time loss, and as the frequency of experiencing oral pain increased, so did the likelihood of reporting time loss.
Table 2
Proportion and likelihood of reporting time loss
Age (yrs, N=29,141,400) | | | |
6 to 11 (reference) | 8.7 | | |
12 to 19 | 14.6 | 1.2 (0.9, 1.6) | 0.236 |
20 to 39 | 28.0 | 0.7 (0.5, 0.8) | 0.001 |
40 to 59 | 33.3 | 0.8 (0.6, 1.0) | 0.028 |
60 to 79 | 15.4 | 0.7 (0.5, 0.8) | 0.001 |
Sex (N=29,141.400) | | | |
Male (reference) | 47.4 | | |
Female | 52.6 | 1.2 (0.9, 1.5) | 0.123 |
Educational attainment (N=28,724,760) | | | |
Greater than high school (reference) | 74.3 | | |
Less than high school | 25.7 | 1.0 (0.8, 1.3) | 0.940 |
Career status (N=24,986,201) | | | |
Employed (reference) | 53.7 | | |
Student | 20.0 | 1.4 (1.0, 2.0) | 0.068 |
Unemployed | 26.4 | 0.8 (0.6, 1.1) | 0.093 |
Employment Type (N=20,091,451) | | | |
Part-time (reference) | 20.2 | | |
Full-time | 79.8 | 1.0 (0.8, 1.3) | 0.756 |
Household size (N=29,141,400) | | | |
1 to 2 people (reference) | 42.7 | | |
3 to 4 people | 41.7 | 1.1 (0.9, 1.3) | 0.298 |
5 or more people | 15.6 | 1.1 (0.7, 1.6) | 0.622 |
Aboriginal status (N=29,112,877) | | | |
No (reference) | 96.4 | | |
Yes | 3.6 | 1.3 (0.5, 3.5) | 0.590 |
Immigrant status (N=29,139,278) | | | |
No (reference) | 80.3 | | |
Yes | 19.7 | 0.9 (0.6, 1.4) | 0.565 |
Occupational classification (N=20,182,418) | | 1.0 | |
Management (reference) | 9.4 | | |
Business, finance and administrative | 21.4 | 1.0 (0.6, 1.7) | 0.835 |
Natural and applies sciences and related occupations | 9.5 | 0.8 (0.4, 1.5) | 0.438 |
Health occupations | 5.1 | 0.6 (0.4, 0.9) | 0.023 |
Occupations in social science, education, government service and religion | 10.1 | 0.9 (0.5, 1.4) | 0.491 |
Occupations in art, culture, recreation and sport | 5.6 | 1.0 (0.6, 1.5) | 0.923 |
Sales and service occupations | 23.0 | 0.7 (0.4 ,1.1) | 0.125 |
Trades, transport and equipment operators and related | | | |
Occupations | 11.1 | 0.5 (0.3, 1.0) | 0.040 |
Occupations unique to primary industry | 1.9 | 0.5 (0.2, 1.7) | 0.251 |
Occupations unique to processing, manufacturing and utilities | 2.9 | 0.4 (0.2, 0.9) | 0.037 |
Insurance (N=28,964,287) | | | |
Private insurance (reference) | 71.2 | | |
Public insurance | 5.2 | 0.7 (0.5, 1.0) | 0.045 |
No insurance | 23.6 | 0.5 (0.4, 0.7) | 0.000 |
Income adequacy (N=27,200,795) | | | |
Highest income (reference) | 55.9 | | |
Upper middle income | 29.8 | 0.7 (0.6, 0.8) | 0.001 |
Middle income | 10.6 | 0.5 (0.4, 0.6) | 0.000 |
Lower middle income | 2.8 | 0.5 (0.3, 0.7) | 0.004 |
Lowest income | 1.0 | 0.4 (0.2, 0.9) | 0.037 |
Self-reported oral health (N=29,136,350) | | | |
Good to excellent (reference) | 85.9 | | |
Poor to fair | 14.1 | 0.8 (0.6, 1.2) | 0.277 |
Self-reported oral pain (N=29,133,930) | | | |
Rarely or never (reference) | 84.1 | | |
Sometimes | 12.2 | 1.9 (1.4, 2.4) | 0.001 |
Often | 3.7 | 2.3 (1.1, 4.7) | 0.026 |
Self-reported general health (N=29,137,886) | | | |
Good to excellent (reference) | 93.6 | | |
Poor to fair | 6.4 | 0.5 (0.4, 0.7) | 0.001 |
Self-reported mental health (N=29,137,886) | | | |
Good to excellent (reference) | 93.6 | | |
Poor to fair | 6.4 | 0.5 (0.4, 0.7) | 0.001 |
Self-reported disability status (N=28,856,199) | | | |
No to mild disability (reference) | 74.1 | | |
Moderate to severe disability | 26.0 | 1.1 (0.9, 1.2) | 0.500 |
dmft prevalence (N=27,629,868) | | | |
dmft=0 (reference) | 94.5 | | |
dmft>0 | 5.5 | 1.4 (1.1, 2.0) | 0.021 |
DMFT prevalence (N=27,629,868) | | | |
DMFT=0 (reference) | 14.7 | | |
DMFT>0 | 85.3 | 0.9 (0.7, 1.2) | 0.584 |
Treatment needs (N=29,133,930) | | | |
No (reference) | 27.1 | | |
Yes | 72.9 | 1.5 (1.3, 1.9) | 0.001 |
Preventive needs (N=29,133,930) | | | |
No (reference) | 90.1 | | |
Yes | 9.9 | 0.6 (0.5, 0.7) | 0.001 |
Frequency of seeing dental professional (N=29,139,733) | | | |
Less than once a year (reference) | 3.5 | | |
One or more times per year | 92.3 | 5.4 (2.9, 9.9) | 0.000 |
Emergency or never | 4.2 | 0.6 (0.3, 1.2) | 0.136 |
Time since last dental visit (N=28,196,229) | | | |
Less than 1 year ago (reference) | 97.1 | | |
More than 1 year ago | 2.9 | 0.04 (0.03, 0.06) | 0.000 |
When looking at the impact that dental problems and treatment had on the sample, a mean of 3.5 hours per participant were lost from work, school, or normal activities due to dental problems and treatment, which equated to a total of 40.36 million hours at the population level. For adults (20–69 yrs), this equated to 4.14 million days lost, and for children (6–19 yrs), 2.27 million days lost. Table
3 shows that there were no significant differences between age groups in terms of the amount of time lost, while there was more than an hour difference between males and females (2.9 hrs vs. 4.2 hrs). Also, as the frequency of experiencing oral pain increased, so did the amount of time lost, with those experiencing frequent oral pain losing more than twice the amount of time than those who rarely or never experienced oral pain (7.5 hrs vs. 3.2 hrs). While not statistically significant, those who only sought professional care in emergency situations tended to lose more time than those who visited frequently (5.1 hrs vs. 3.5 hrs).
Age (yrs) | | |
6 to 11 | 2.4 (2.1, 2.8) | |
12 to 19 | 5.3 (3.3, 7.4) | |
20 to 39 | 3.3 (2.7, 4.0) | |
40 to 59 | 3.3 (2.5, 4.1) | |
60 to 79 | 3.4 (2.9, 3.9) | 0.407 |
Sex | | |
Male | 2.9 (2.3, 3.4) | |
Female | 4.2 (3.5, 4.8) | 0.015 |
Educational attainment | | |
Greater than high school | 3.4 (3.0, 3.8) | |
Less than high school | 4.0 (2.8, 5.2) | 0.352 |
Career status | | |
Employed | 3.4 (2.8, 4.1) | |
Student | 5.2 (3.4, 7.1) | |
Unemployed | 3.0 (2.6, 3.5) | 0.047 |
Employment type | | |
Part-time | 4.3 (2.4, 6.1) | |
Full-time | 3.2 (2.7, 3.7) | 0.286 |
Household size | | |
1 to 2 people | 4.2 (3.6, 4.8) | |
3 to 4 people | 3.2 (2.8, 3.5) | |
5 or more people | 2.8 (2.0, 3.6) | 0.004 |
Aboriginal status | | |
No | 3.5 (3.2, 3.9) | |
Yes | 3.4 (1.7, 5.0) | 0.833 |
Immigrant status | | |
No | 3.6 (3.2, 4.0) | |
Yes | 3.4 (2.4, 4.4) | 0.763 |
Occupational classification | | |
Management | 2.9 (2.1, 3.7) | |
Business, finance and administrative | 3.8 (2.6, 5.1) | |
Natural and applies sciences and related occupations | 2.9 (2.1, 3.7) | |
Health occupations | 3.6 (1.1, 6.1) | |
Occupations in social science, education, government service and | | |
Religion | 3.7 (2.6, 4.8) | |
Occupations in art, culture, recreation and sport | 3.9 (1.9, 5.9) | |
Sales and service occupations | 3.7 (2.6, 4.9) | |
Trades, transport and equipment operators and related | | |
Occupations | 2.8 (2.0, 3.5) | |
Occupations unique to primary industry | 3.3 (2.4, 4.2) | |
Occupations unique to processing, manufacturing and utilities | 2.2 (1.7, 2.6) | 0.450 |
Insurance | | |
Private insurance | 3.6 (3.2, 4.1) | |
Public insurance | 2.8 (1.4, 4.1) | |
No insurance | 3.4 (2.8, 4.1) | 0.498 |
Income adequacy | | |
Highest income | 3.5 (2.9, 4.1) | |
Upper middle income | 3.9 (2.6, 5.1) | |
Middle income | 3.4 (2.9, 3.9) | |
Lower middle income | 3.9 (0.9, 7.0) | |
Lowest income | 2.7 (1.9, 3.5) | 0.483 |
Self-reported oral health | | |
Good to excellent | 3.4 (3.0, 3.7) | |
Poor to fair | 4.6 (3.2, 6.0) | 0.105 |
Self-reported oral pain | | |
Rarely or never | 3.2 (2.8, 3.6) | |
Sometimes | 4.9 (3.2, 6.6) | |
Often | 7.5 (4.7, 10.3) | 0.022 |
Self-reported general health | | |
Good to excellent | 3.6 (3.2, 3.9) | |
Poor to fair | 3.0 (2.3, 3.7) | 0.114 |
Self-reported mental health | | |
Good to excellent | 3.6 (3.2, 3.9) | |
Poor to fair | 3.0 (2.3, 3.7) | 0.114 |
Self-reported disability status | | |
No to mild disability | 3.4 (3.0, 3.8) | |
Moderate to severe disability | 4.0 (3.2, 4.8) | 0.170 |
dmft prevalence | | |
dmft=0 | 3.6 (3.2, 3.9) | |
dmft>0 | 2.8 (2.3, 3.4) | 0.045 |
DMFT prevalence | | |
DMFT=0 | 4.0 (1.8, 6.2) | |
DMFT>0 | 3.4 (3.0, 3.8) | 0.614 |
Treatment needs | | |
Yes | 3.6 (3.1, 4.1) | |
No | 3.4 (2.8, 4.1) | 0.759 |
Preventive needs | | |
Yes | 3.7 (2.6, 4.9) | |
No | 3.5 (3.1, 3.9) | 0.753 |
Frequency of seeing dental professional | | |
Less than once a year | 3.9 (2.2, 5.7) | |
One or more times per year | 3.5 (3.1, 3.8) | |
Emergency or never | 5.1 (2.0, 8.3) | 0.448 |
Time since last dental visit | | |
Less than 1 year ago | 3.6 (3.2, 3.9) | |
More than 1 year ago | 2.2 (1.1, 3.2) | 0.019 |
Table
4 shows that those who reported experiencing oral pain often were almost 5.0 times more likely to report time lost compared to their counterparts, making oral pain the strongest predictor of reporting time loss. Table
5 shows that in terms of hours lost, being female was associated with a 1.3 hour increase in the amount of time lost when compared to males. While not statistically significant, being a student (at the university level) equated to 1.6 more hours lost when compared to employed individuals. Here again, as the frequency of experiencing oral pain increased so did the amount of time lost, with frequent oral pain being associated with an almost 4.0 hour increase in the amount of time lost.
Table 4
Multivariable logistic regression of what predicts reporting time loss (En Bloc model)
Age (yrs) | | |
6 to 19 (reference) | | |
20 to 39 | 0.9 (0.5, 1.6) | 0.726 |
40 to 59 | 0.9 (0.4, 1.7) | 0.666 |
60 to 79 | 1.3 (0.7, 2.2) | 0.326 |
Sex | | |
Male (reference) | | |
Female | 1.1 (0.8, 1.4) | 0.638 |
Career Status | | |
Employed (reference) | | |
Student | 1.3(0.7, 2.4) | 0.402 |
Unemployed | 0.8 (0.6, 1.2) | 0.221 |
Occupational classification | | |
Management (reference) | | |
Business, finance and administrative | 1.0 (0.6, 1.6) | 0.932 |
Natural and applies sciences and related occupations | 0.7 (0.4, 1.4) | 0.261 |
Health occupations | 0.5 (0.3, 0.9) | 0.022 |
Occupations in social science, education, government service and | | |
Religion | 0.6 (0.4, 1.0) | 0.054 |
Occupations in art, culture, recreation and sport | 0.6 (0.3, 1.4) | 0.211 |
Sales and service occupations | 0.6 (0.4, 0.9) | 0.025 |
Trades, transport and equipment operators and related occupations | 0.6 (0.3, 1.1) | 0.098 |
Occupations unique to primary industry | 1.0 (0.2, 3.9) | 0.973 |
Occupation unique to processing, manufacturing and utilities | 0.3 (0.1, 0.9) | 0.04 |
Insurance | | |
Private insurance (reference) | | |
Public insurance | 0.5 (0.1, 1.6) | 0.205 |
No insurance | 1.1 (0.7, 1.5) | 0.758 |
Income adequacy | | |
Highest income (reference) | | |
Upper middle income | 0.9 (0.7, 1.3) | 0.704 |
Middle income | 0.9 (0.5, 1.4) | 0.531 |
Lower middle income | 0.5 (0.2,1.2) | 0.093 |
Lowest income | 0.6 (0.1, 3.2) | 0.055 |
Self-reported oral pain | | |
Rarely or never (reference) | | |
Sometimes | 2.8 (1.6, 4.8) | 0.001 |
Often | 4.8 (2.2, 10.4) | 0.001 |
Self-reported general health | | |
Good to excellent (reference) | | |
Poor to fair | 0.5 (0.3, 0.8) | 0.004 |
Treatment needs | | |
No (reference) | | |
Yes | 1.1 (0.8, 1.5) | 0.558 |
Preventive needs | | |
No (reference) | | |
Yes | 1.4 (0.8, 2.5) | 0.217 |
Frequency of seeing dental professional | | |
Less than once a year (reference) | | |
One or more times per year | 1.2 (0.6, 2.4) | 0.493 |
Emergency or never | 1.2 (0.6, 2.7) | 0.551 |
Time since last dental visit | | |
Less than 1 year ago (reference) | | |
More than 1 year ago | 0.04 (0.02, 0.08) | 0.001 |
Table 5
Multivariable linear regression of what predicts the amount of time lost (En Bloc model)
Predisposing factors
| | |
Sex | | |
Male (reference) | | |
Female | 1.28 (0.2, 2.4) | 0.03 |
Career Status | | |
Employed (reference) | | |
Student | 1.6 (−0.6, 3.8) | 0.137 |
Unemployed | −0.5 (−1.2, 0.3) | 0.211 |
Needs factors
| | |
Self-reported oral health | | |
Good to excellent (reference) | | |
Poor to fair | 0.76 (−0.6, 2.1) | 0.235 |
Self-reported oral pain | | |
Rarely or never (reference) | | |
Sometimes | 1.27 (−1.0, 3.5) | 0.241 |
Often | 3.88 (0.8, 7.0) | 0.019 |
Self-reported general health | | |
Good to excellent (reference) | | |
Poor to fair | −0.45 (−1.4, 0.5) | 0.322 |
Health service use factors
| | |
Time since last dental visit | | |
Less than 1 year ago (reference) | | |
More than 1 year ago | −1.5 (−2.3, -0.3) | 0.021 |
R2
| 0.0399 |
F | F (8, 4) 2.60 |
Prob>F | 0.186 |
Table
6 illustrates the potential productivity losses by occupation classification at both the individual and societal level. Individual losses are arguably minimal, ranging from almost $43 for those employed in processing, manufacturing and utilities, to over $110 for those employed in social science, education, government service and religion. When these losses are translated to the entire job sector, these losses become more substantial, with those employed in business, finance and administrative occupations, for example, having potential losses of over $230 million.
Table 6
Potential productivity losses due to dental problems and treatment at the individual and societal level
Management | 2.9 | 108.16 | 104,287,872 |
Business, finance and administrative | 3.8 | 85.15 | 239,109,715 |
Natural and applies sciences and related occupations | 2.9 | 95.17 | 103,278,484 |
Health occupations | 3.6 | 97.44 | 97,790,784 |
Occupations in social science, education, government service and religion | 3.7 | 112.51 | 165,333,445 |
Occupations in art, culture, recreation and sport | 3.9 | 91.67 | 33,212,041 |
Sales and service occupations | 3.7 | 58.72 | 220,857,664 |
Trades, transport and equipment operators and related occupations | 2.8 | 64.51 | 131,064,967 |
Occupations unique to primary industry | 3.3 | 76.39 | 16,439,128 |
Occupations unique to processing, manufacturing and utilities | 2.2 | 42.96 | 32,232,888 |
Discussion
This is the first study to use nationally representative data on reported time loss due to dental problems and treatment in the Canadian population. These findings are integral to understanding the impact of dental problems and treatment at the societal level, and to the inclusion of oral health in broader health policy debates, especially because of a renewed interest in the economic implications of illness [
19]. This study found that 39 percent of participants, representing over 13 million Canadians, reported time loss from work, school or normal activities due to dental problems and treatment. Among participants who reported time loss, the majority were from middle- to high-income groups. This is consistent with the findings of Reisine and Miller (1985), who reported that in a sample of Americans, those with greater financial resources were more likely to miss work for dental visits [
20]. Also, given the structure of American and Canadian oral health care systems (i.e. almost wholly financed and delivered privately on a fee for service basis), this finding is not surprising considering that utilizing and accessing dental care is largely determined by an individual’s ability to pay [
21]. In this regard, the current study found that 71 percent of those with private insurance reported time loss compared to only 23 percent of those without dental insurance. When amount of time loss was quantified, the mean number of hours lost per participant was arguably inconsequential at 3.5 hours, however the total number of hours lost at the societal level was estimated at over 40 million, arguably representing a substantial impact. This study also found that experiencing oral pain often was associated with a 4-hour increase in time lost, which is consistent with the literature, which states that pain is often associated with reporting time loss and losing more time as the treatments required are more extensive [
6]. Oral pain is the strongest predictor of reporting time loss and is associated with more lost time. As Ramraj (2012) reported those within the lowest income brackets and those without insurance were over 2 times more likely to have surgical treatment needs, which is often preceded by pain [
22]. This is consistent with the finding of Quiñonez et al. (2010) who reported that those in the lowest income brackets, without dental insurance, that experienced oral pain, and that had visited a hospital emergency department in the past due to a dental problem, were all more likely to report a disability day due to a dental problem [
23].
This study’s finding also highlights the importance of “good” and “bad” time loss (e.g., time lost for check-ups and preventive care vs. for major restorative or surgical care). This concept becomes important in the realm of policy and insurance decisions. For example, from an employer’s viewpoint, investments in prevention and accessible care for all employees would likely mitigate both “bad” time loss and potential productivity losses due to dental problems and treatment. In terms of insurance decisions, amending coverage to include prevention may reduce overall costs by reducing the need for more complex and costly treatments. This brings to the surface the idea that by raising the financial eligibility level for current public programs, this would also allow those segments of the population not currently covered increased access to care (e.g., the working poor), further mitigating “bad” time loss. At the policy level this is consistent with prevention strategies for other conditions (i.e., back injuries where protocols are used to prevent or minimize the risk of injury at work), and provides another opportunity for dentistry to be discussed within broader health policy.
As mentioned in the introduction, despite discussion surrounding the indirect costs of medical conditions, there are very few examples of this in the dental literature [
3,
6,
20,
23]. These discussions have focused on the differences in time loss (i.e., days lost from work or disability days) by occupational class (e.g., blue versus white collar jobs) and by job autonomy, such that those with white collar jobs or greater autonomy tended to lose more time, albeit for different reasons than those with blue collar jobs or less autonomy [
3,
6]. The availability of labour force data in the CHMS, which corresponded to the wage data of the Canadian Labour Force Survey (LFS) allowed for further examination of this relationship and to monetize these potential losses. This study found that the potential productivity losses attributed to time lost from work were over $1 billion. This is likely an underestimation of the overall productivity losses as non-market losses were not valued (time from school or normal activities). Yet by quantifying these losses, this study arguably provides a starting point for discussions on the economic importance of oral health, while providing policymakers with a better understanding of the true cost of dental problems and treatment in the Canadian population.
Comparison between productivity losses due to dental problems and treatment and other illnesses were undertaken, in an attempt to bridge a key knowledge gap in the understanding of the burden of dental problems and treatment and its magnitude compared to other illnesses. The potential losses attributed to dental problems and treatment ($1,143,606,988) were found to be most comparable to those resulting from musculoskeletal strains and sprains ($1,250,947,561), and bone disorders ($1,482,048,535) [
24]. Nonetheless, strong caveats accompany this comparison. First, a complete economic analysis was not undertaken here and only similar costs (i.e., lost wages and morbidity) were compared.
Direct comparison to the United States is difficult due to the vastly different recall periods of the U.S. National Health Interview Study (1996, NHIS) and the CHMS (2 weeks vs. 12 months). The NHIS survey queried time loss for personal dental care, accompanying a family member for dental care and additional impacts on normal activities. Comparatively the CHMS merely asked if time was lost from any aspect of daily living in the previous 12 months. More comparable are the findings of the recent Australian National Dental Telephone Interview Survey (2010, NDTIS), which asked participants to report time loss from work/school and restricted activity separately, in the previous 12 months due to dental problems. The study found that those who reported their oral health as poor to fair reported losing more time than those with good to excellent oral health [
5]. The authors stated that for a segment of the population the cycle of poor oral health and problem oriented visiting warranted further attention [
5]. The authors also estimated $836.5 million (CDN) in losses due to missed work and restricted activity due to oral health problems, a finding similar to the losses estimated in this study [
5]. Ultimately, policy changes aimed to relieve pressures faced by those burdened with the most time loss (the poor), such as raising the financial threshold for public programs, improving the quality of insurance offered to more junior employees, or subsidizing dental treatment in some form, may also reduce the impact of dental problems and treatment on society through concomitant productivity losses.
It is important to understand the limitations of this study. Most significant is the limited way in which the question regarding time loss was structured. For example, this study was unable to discern which aspect of daily living was impacted (work, school, or normal activities) and what the underlying reasons for seeking care were; both of which are required to better understand the dynamics and quality of time lost in the population. This inability to discriminate between time loss for treatment, which could mostly be due to preventive reasons, and that due to pain or problems may explain why those in higher income brackets reported more time loss. This may also explain why individuals with a DMFT=0 lost more time than those with a DMFT>0 as seen in Table
3. Thus, it is possible that the majority of time loss reported in this study was due to check-ups or preventive services and that those with significant dental problems did not report losing as much time due to dental issues, perhaps due to barriers to care. This reasoning is consistent with what is known about social gradients in oral health, the use of dental care, and access to dental care, in North America. In this light, it was assumed that for every adult reporting time loss, the loss was from work and that every school-age individual reporting lost time did so from school.
The overall participation in the CHMS was low (51.7%) and as such multiple biases are likely. First, with the voluntary nature of the CHMS, those who participated may be inherently different from those who chose not to participate, thus reducing the generalizability of the data. Second, the combined length of the household questionnaire (722 questions) and the subsequent clinic visit may have acted as a deterrent for participation. Further to this, for those who participated, social desirability bias may have also played a role. Here, participants could have responded with answers they believed could show them in a good light, or allowed them to complete the questionnaire more quickly.
For the calculation of potential productivity losses (at the societal level) it was assumed that each and all job sector employees visited a dental professional in the last 12 months. Also a complete economic analysis was not completed, thus the estimate of productivity losses reflects only those that were assumed to be attributable to work loss (i.e. time lost for employed persons only). In light of these limitations it is suggested that future cycles of the CHMS include separate questions for time loss from work, school or normal activities. Also, questions that clarify the underlying reasons for seeking professional care would allow for better policy information. For example, collection of data regarding time loss from medical and/or other issues (as the NHIS does) may yield common reasons for time loss (e.g., pain), may strengthen an understanding of the relationship between oral health and general health, and may identify factors which are amenable to policy change. It may be that investments outside of the health care sector yield the largest returns for improving both general and oral health (e.g., increased access to quality employment or child care), thereby eliminating competing economic stressors and increasing the uptake of dental services (more discretionary income and time, which has been associated with increased utilization of dental services, for example). Finally, a complete economic analysis of the impacts of dental problems and treatment on Canadian society would allow for ease of comparison to medical conditions and would remove some of the caveats associated with this study’s estimate of potential productivity losses. A complete economic analysis would also provide more detailed information regarding the total burden of dental problems and treatment and could be used as justification for increased funding or more efficient use of dental care dollars by directing investments towards programs and infrastructure that would mitigate these losses.
Competing interests
The authors declare they have no competing interests.
Authors’ contributions
AH, CQ, LD, and AA contributed to the study conception and design. AH was granted access to the RDC for data analysis and interpretation. VR provided assistance with the analysis and interpretation. CQ revised the manuscript critically for intellectual content. All authors reviewed, read and approved the final manuscript.