Time Loss Due to Oral Health Issues in the Canadian Population

Transcription

1 Time Loss Due to Oral Health Issues in the Canadian Population by Alyssa Nicole Hayes A thesis submitted in conformity with the requirements for the degree of Master of Science in Dental Public Health Graduate Department of Dentistry University of Toronto Copyright by Alyssa Hayes 2012

2 Time Loss Due to Oral Health Issues in the Canadian Population Alyssa Nicole Hayes Master of Science in Dental Public Health Graduate Department of Dentistry University of Toronto 2012 Abstract Objectives: 1. To determine the proportion of people reporting time loss from work, school, or normal activities due to oral health issues. 2. To determine how much time is being lost. 3. To establish predictors of this time loss. 4. To determine the productivity losses. Methods: Data from the Canadian Health Measures Survey ( ) were used. Linear and logistic regressions were employed to determine which variables were predictive of hours lost and reporting time loss. Productivity losses were determined using the lost wages approach. Results: Time loss is more likely among privately insured, high income earners. Experiencing oral pain is the best predictor of reporting time loss. Productivity losses are comparable to those of some musculoskeletal conditions. Conclusions: Participants with higher incomes report time loss more frequently while the total amount of time lost is greatest for those with the lowest incomes. Productivity losses are substantial at the societal level. ii

3 Acknowledgements I would like to acknowledge the support of my supervisor, Dr. Carlos Quiñonez whose exacting standards challenged me to be a better dentist and academic. I would also like to extend my appreciation to my committee members, Dr. Amir Azarpazhooh and Dr. Laura Dempster. Their expertise and support contributed immensely to the successful completion of this research. And to Dr. Vahid Ravaghi, whose statistical support was invaluable. To my parents, Donna and Gordon and my sister Cara and her family, whose unwavering support and understanding has allowed me to achieve my goals. To my grandfather, Sonnie who at 93 is my biggest cheerleader and to my friends, your encouragement has been invaluable. To my colleagues in Dental Public Health, Brandy Thompson, Chantel Ramraj and Dr. Abeer Khalid, who made the past two years more enjoyable. I am honoured to count you all as my friends. Lastly, I would like to extend my appreciation to my funders: The Population Health Improvement Research Network (PHIRN), of the Applied Health Research Network Initiative (AHRNI). iii

4 Table of Contents Abstract... ii Acknowledgements... iii List of Tables... vii List of Figures... ix List of Appendices... x Chapter 1: Introduction The importance of determining time loss due to oral health issues in the Canadian population Using the Canadian Health Measures Survey (CHMS) to assess time loss Rationale for this study Central Research Questions and Objectives Central Research Questions Objectives Summary Statements... 3 Chapter 2: Literature Review Previous discussions on time loss due to oral health issues Summary statements... 7 Chapter 3: Materials and Methods Study Design Sampling strategy Sample size Dwelling and respondent sampling Data collection Household questionnaire Clinical examination Oral health data Examiner calibration Variables used Outcome variables Predisposing factors iv

5 3.5.3 Enabling factors Needs factors Health services use factors Data analysis Weighting of data Statistical tests Estimating potential productivity losses Chapter 4: Results Sample characteristics Predisposing factors Enabling factors Needs factors Health service use factors Proportion and likelihood of reporting time loss Predisposing factors Enabling Factors Needs factors Health service use factors Mean hours lost for those reporting time loss Predisposing factors Enabling factors Needs factors Health service use factors Predictors of reporting time loss Predictors of mean hours lost Potential productivity losses Potential productivity losses at the individual level Potential productivity losses at the societal level Comparisons to other illnesses Chapter 5: Discussion Key findings v

6 5.1.1 Reporting time loss Predicting time loss Amount of time loss Potential productivity losses Quality of time loss Limitations of the study Recommendations Chapter 6: Conclusion Bibliography Appendices vi

7 List of Tables Table 1 Summary of the 1989 NHIS Findings... 6 Table 2 Disability Days Due to Acute Dental Conditions... 8 Table 3 Sample Characteristics by Predisposing Factors Table 4 Sample Characteristics by Enabling Factors Table 5 Sample Characteristics by Needs Factors Table 6 Sample Characteristics by Health Service Use Factors Table 7 Proportion and Likelihood of Reporting Time Loss by Predisposing Factors Table 8 Proportion and Likelihood of Reporting Time Loss by Enabling Factors Table 9 Proportion and Likelihood of Reporting Time Loss by Needs Factors Table 10 Proportion and Likelihood of Reporting Time Loss by Health Service Use Factors Table 11 Mean Hours Lost by Predisposing Factors Table 12 Mean Hours Lost by Enabling Factors Table 13 Mean Hours Lost by Needs Factors Table 14 Mean Hours Lost by Health Service Use Factors Table 15 Multivariate Logistic Regression Predicting Reporting Time Loss Table 16 Multiple Linear Regression Predicting Hours Lost Table 17 Potential Productivity Losses at the Individual Level Table 18 Potential Productivity Losses at the Societal Level vii

8 Table 19 Comparison of Potential Productivity Losses Due to Oral Health Issues to Other Health Conditions viii

9 List of Figures Figure 1 Andersen's Emerging Model of Health Behaviour (taken from Andersen 1995) Figure 2 Modified Health Behaviour Model (adapted from Andersen 1995) ix

10 List of Appendices Appendix 1 Coding of Variables Used in Analysis Appendix 2 Average Hourly Wages (February 2012, unadjusted data) Appendix 3 National Health Interview Survey (NHIS) Questions Relating to Time Loss Appendix 4 Oral Health Component of Household Questionnaire Appendix 5 Oral Health Component of the Clinic Questionnaire x

11 Chapter 1: Introduction 1.1 The importance of determining time loss due to oral health issues in the Canadian population The medical literature frequently discusses the social impacts of chronic diseases, however, there is relatively little discussion on this issue in the dental literature. Discussions surrounding oral health have historically centred on clinical indices (e.g., Decayed-Missing-Filled-Teeth (DMFT) Index, Gingival Index, Periodontal Screening and Recording (PSR)), which highlight past pathology and completed treatment and offer little insight into the impacts of oral health issues on the completion of daily tasks or productivity. Alternatively, subjective measures of oral health (e.g., Oral Health Impact Profile (OHIP), Geriatric Oral Health Assessment Index (GOHAI)) reflect patient centred definitions of oral health and assess the impact of oral conditions on a person s functioning and well-being (Atchison 1997, Kressin 1997). These measures provide information integral to understanding levels of disease, how individuals perceive their oral health needs and outcomes, and what factors result in seeking professional care, but are unable to provide full insight into the impacts of oral disease at a societal level (Allen 2003). In this respect, time loss, especially from work, has been shown to be useful as a population indicator of oral health status among the employed, since work loss is highly prevalent and is a relatively gross indicator of the more subtle impacts of dental problems (Reisine 1988, p.8). Importantly, an individual s work role also forms a large part of their self-image or self-esteem and is equally important to the employer as reduced productivity impacts their economic viability (Reisine 1981). Dental caries and periodontitis are highly prevalent, but are often viewed as non-life threatening, readily treatable, and of acute origin; therefore the overall burden on individuals is minimized in comparison to other chronic conditions (Reisine 1988). The use of socio-dental indicators (e.g., time loss from work, school or normal activities) allows for oral health issues to be understood in terms of impaired role functioning, and early research has shown that work loss has severe consequences for some individuals and that oral health status, in the broadest sense, is affected by social and structural constraints related to the workplace and to economic and health care 1

12 delivery factors (Reisine 1988, p.7). Data pertaining to time loss is easily collected within surveys and is easily operationalized, and when combined with wage information, time loss from work can help to estimate the economic impacts or indirect costs of oral health issues. Indirect costs represent an individual s loss of productivity and often constitute a large portion of the total costs of any disease (Liljas 1998, Berger, Murray and Pauly 2001). These potential productivity losses can be compared across illnesses and illustrate that outwardly harmless conditions, such as oral health issues, are associated with large losses due to their high prevalence (Mattke, et al. 2007). In this way, quantifying time loss and the associated potential productivity losses allows for program planning and evaluation discussions to focus on the total burden of illness among different diseases and not merely the clinical aspects of any given disease (Reisine 1988). 1.2 Using the Canadian Health Measures Survey (CHMS) to assess time loss The CHMS represents one of Statistics Canada s few surveys to collect direct measurements. Prior to which, data pertaining to oral health was not routinely collected by provincial/territorial or the federal health departments, thus the CHMS aimed to address longstanding limitations [by collecting] key information including data regarding the oral health status of the Canadian population (Health Canada 2010, p.15). The CHMS provides baseline information regarding time loss from work, school or normal activities due to oral health issues and the quantity of time lost. As a result, investigation into the relationship between socioeconomic status (income, education, etc.), occupational classification, and experiencing oral pain and reporting time loss and/or the amount of time lost is now possible. Potential productivity losses can also be estimated using occupation wage data. In sum, there is an opportunity to evaluate time loss at a national level, to show the distribution of the social impacts of oral health issues across the Canadian population, and to compare the overall burden of oral health issues to other illnesses. 1.3 Rationale for this study There is no nationally representative data on time loss from work, school or normal activities due to oral health issues for the Canadian population. This information is integral to demonstrating the economic impacts of oral health issues thereby allowing policy makers to understand how the burden of oral health issues is distributed within the Canadian population, and how it compares to other chronic diseases and/or conditions. Thus when used in combination with clinical 2

13 measures, it can provide evidence for the effective use of existing resources towards the population segments most impacted, and novel information for policy leaders in their efforts to improve access to dental care. 1.4 Central Research Questions and Objectives Central Research Questions 1. What proportion of the Canadian population report time loss from work, school or normal activities due to dental check-ups, treatments or oral health problems? 2. How much time is lost? 3. What are the predictors of this time loss? 4. What are the productivity losses to Canadian society? Objectives 1. To determine the proportion of people reporting time loss from work, school or normal activities due to dental check-ups, treatments or oral health problems. 2. To determine how much time is being lost due to these oral health issues. 3. To establish predictors of this time loss. 4. To determine productivity losses through average wages per occupational classifications of those reporting time loss. 1.5 Summary Statements There is little to no data dealing with the societal impacts of oral health issues in Canada. Dental disease is prevalent in Canadian society and has economic impacts, including time loss from work, school or normal activities. Time loss is easily collected and operationalized and allows oral health issues to be compared to other illnesses. The CHMS provides national data that can be used to determine who lost time, how oral health issues are impacting the lives of Canadians, and what economic impact these issues have on Canada as a whole. 3

14 Chapter 2: Literature Review 2.1 Previous discussions on time loss due to oral health issues The broad definition of health presented by the World Health Organization (WHO) in the 1940 s shifted the concept of disease beyond the mere absence of disease or infirmity to include a person s complete physical, mental and social well-being (World Health Organization 1948, p.100). This allowed the impact of disease to be conceptualized in broader terms and not just by clinical indices. In this way, the burden of dental disease at the societal level, for example, can be represented by time loss from work, school or normal activities due to dental problems. The early work of Miller, Elwood and Swallow (1975) into the incidence of dental pain, found that of 439 people questioned, 109 days of pain were reported in the previous month (Miller, Elwood and Swallow 1975). This represents three days of pain per person per year, and when combined with the knowledge that 86 percent of those reporting pain did not seek treatment, the authors concluded that then existing national estimates of emergency dental treatment needs were an underestimation (Miller, Elwood and Swallow 1975). From a programming standpoint, if resource allocation was based on this underestimation of need, the potential for further access issues to arise was likely. In a later article by Miller (1978), it was reported that most people do not suffer economic impacts from dental disease until pain develops (Miller 1978). He goes on to state that the dental profession, at that point, was unable to quantify how dental disease affects the quality of life [and] as a profession [ ] have been unable to describe the value of preventive measures for dental disease in terms which the public would understand (Miller 1978, p.70). As stated, describing dental problems in terms of time loss from work, school and normal activities allows for comparison to other common illnesses where these impacts are known, which arguably facilitates policy as well as public understanding of the importance of oral health. Looking at work loss associated with dental disease, Reisine (1984) reported that one quarter of participants in one survey (n=2,600) lost time for dental problems or visits, with a mean of 6.2 hours lost (Reisine 1984). Those who lost more hours were female, had poor self-perceived oral health, dental insurance and curative dental treatment (Reisine 1984). Of interest is the finding that 95% of dental related work loss was attributed to preventive or curative visits and not for symptomatic relief (Reisine 1984). Despite preventive visits accounting for the most episodes of 4

15 time lost (62.8%), it also resulted in fewer hours being lost (32.3%) (Reisine 1985). In a subsequent article Reisine stated that time loss rates vary from 15 percent to 33 percent [and] work impact appeared to be greatest for blue collar workers who risk loss of income and fringe benefits, as well as their jobs, for dental visits during working hours (Reisine 1985, p.28). This is consistent with the later finding of Reisine and Miller (1985), who stated that the individuals who are the least affluent have more time lost from work, which is likely the cause of both worse oral health and limited access to care (Reisine and Miller 1985, p.1314). In a survey of Toronto residents (n=1,014) Locker and Grushka (1987) showed that over 39 percent of those surveyed reported oral or facial pain within the previous month (Locker and Grushka 1987). Over 20 percent of respondents reported time-lost from work or normal activities as a result of the pain (Locker and Grushka 1987). The literature consistently reports that at the individual level time loss is small; however, when considered at a population level, the potential economic repercussions are substantial and that lower socioeconomic individuals bear the combined burden of an increased number of lost work and school hours and of restricted activity days (Reisine 1985, Reisine and Miller 1985, Locker and Grushka 1987, Gift, Reisine and Larach 1992). The most significant paper on this issue was by Gift, Reisine and Larach (1992), who examined the 1989 U.S. National Health Interview Study (NHIS, n=109,603) and for the first time reported national data pertaining to time loss from oral health issues (Gift, Reisine and Larach 1992). The authors noted that for employed individuals approximately 164 million hours of work were lost and that those who were female, Blacks, and those aged 17 to 24, without dental insurance, less education and with lower income (below $35,000) had missed the most hours (Gift, Reisine and Larach 1992, p.1665). Additionally, more than 41 million restricted activity days were reported beyond time lost from school or work with those in more disadvantaged socioeconomic groups (those in households with incomes below $20,000, those without dental insurance and those with less than 12 years education) [having] a significantly greater number of restricted activity days than their counterparts. (Gift, Reisine and Larach 1992, p.1666). In terms of school hours lost, more than 51 million hours were reported being missing annually, and increased time loss was experienced by females, older children, ethnic groups (Hispanics), those without dental insurance and those with lower incomes (Gift, Reisine and Larach 1992). Of interest are the authors findings that individuals who experience the greater burden of illness have less job autonomy, 5

16 fewer transport options and greater barriers to accessing dental care (second jobs, limited child care options) and are more likely to use home remedies and self-care to avoid incurring the cost of professional care (Gift, Reisine and Larach 1992). This supports the understanding that losing time from work or school is associated with demographic variables (age, sex), and socioeconomic variables, with the lower socioeconomic classes experiencing the most time loss from work, school and normal activities (Gift, Reisine and Larach 1992). Table 1 summarizes the authors findings for the 1 percent of the sample who reported time lost, it should be clarified that the 1 percent represents the unweighted prevalence of reporting time loss. Table 1 Summary of the 1989 NHIS Findings Total Hours (in thousands) Mean Hours lost Work Hours (own problem only) 164, Work Hours (own problem and for 189, assisting family) School Hours 51, Restricted Activity Days 41,406.0 * adopted from the Gift, Reisine and Larach Finally, in their pilot study Quiñonez et al. (2010) looking at Canadians reported that 3.3 percent of participants (n=1,005) experienced a disability day related to dental problems in the previous 14 days, where disability days were defined as days spent in bed (bed days) and days in which normal activity was restricted (cut-down days) (Quiñonez, Figueiredo and Locker 2010, p.1). The authors were unable to estimate a total burden of disability days attributable to dental problems due to the low prevalence of positive responses. They also suggested that the presence of more robust national data could elucidate whether productivity losses are profound or not (Quiñonez, Figueiredo and Locker 2010). Importantly, research into the impact of dental problems on school aged children is limited; however, what is available suggests that dental problems in children affect the child themselves, the parents or guardians and society as a whole (Shepherd, Nadanovsky and Sheiham 1999). Dental problems are associated with difficulties in concentration, learning and school attendance (Pourat and Nicholson 2009, Jackson, et al. 2011). Interestingly Jackson et al. (2011) reported that school absences caused by dental pain or infection were significantly related to parents 6

17 reports of poor school performance, whereas school absences for routine dental care were not [which provides] further evidence that children experiencing pain or infection may have a diminished educational experience (Jackson, et al. 2011, p.e4). While the current literature discusses the impact of oral health issues in terms of time loss from work or school, it scarcely discusses the impact of these issues on normal activities. Reisine (1985) briefly touched on this when she reported that full-time housewives experienced 1.8 million disability days (2.7 days per housewife) due to both acute and chronic oral conditions (Reisine 1985). The following year Locker and Grushka (1987) reported that the most common impacts of dental pain were worry and concern (regarding personal oral health), taking medication and sleep disturbance (Locker and Grushka 1987). These findings, while limited show the impact oral health issues have on daily living. Also missing from the literature is the general ability to differentiate where time was lost (e.g., is it lost from work, school or normal activities), which has different implications in the valuing of the indirect costs of illness. Time lost from work has proven to be a valuable population measure but is only valid for the employed portion of a population. Also, the lack of detail surrounding the underlying causes of time loss (e.g., toothache vs. preventive visits) limits the understanding of the patterns of time loss due to oral health issues in certain population or employment sectors (Reisine 1985, Gift, Reisine and Larach 1992). Finally, as previously mentioned, medical conditions are often compared in terms of societal costs (including potential productivity losses) yet this is rarely quantified for oral health issues. The collection of both labour force information (occupational classification) and data pertaining to time loss in the CHMS allows for the calculation of potential productivity losses. Again, this is important if oral health is to be included and compared to other diseases in the broader health policy debate. 2.2 Summary statements Time loss due from work, school or normal activities due to oral health issues at the individual level is small but at the societal level losses are substantial. There is little known about the nature and type of oral health issues that result in time loss from work, school or normal activities. 7

18 The impacts of time loss from work represent potential productivity losses that can be quantified using wage data. The U.S. Surgeon General (2000) highlighted the findings of the 1996 NHIS on dental disability days which are presented in Table 2. Table 2 Disability Days Due to Acute Dental Conditions Dental Conditions Total days (in thousands) Days Per 100 Persons Work days lost 2, School days lost 1, Bed days 4, Restricted activity 9, * adopted from the U.S. Department of Health and Human Resources

19 Chapter 3: Materials and Methods 3.1 Study Design Statistics Canada designed the CHMS to be a voluntary, nationally representative cross-sectional survey which aimed to collect direct health measures (Tremblay, Wolfson and Connor Gerber 2007). The availability of national baseline data pertaining to several health indicators including obesity, infectious disease exposure and oral health were expected to address knowledge gaps in Canada s health information system (Tremblay, Wolfson and Connor Gerber 2007). Data collection occurred during 2007 to 2009 and comprised a household interview and a visit to a mobile examination centre (Giroux 2007). Canadians aged 6 to 79 years who resided in private households were targeted while those living in institutions, on crown land or Indian reserves, in remote regions and full-time members of the Canadian Forces were excluded (Giroux 2007). This study was secondary analysis of the CHMS which required accessing the data files through Statistics Canada s Research Data Centre (RDC). The RDC operates in partnership with the Social Sciences and Humanities Research Council (SSHRC), the Canadian Institutes of Health Research (CIHR) and the Canada Foundation for Innovation (CFI) to allow secure access to the confidential data files pertaining to the CHMS. 3.2 Sampling strategy Sample size Data were collected from approximately 5,600 Canadians, of which 5,586 were clinically examined, statistically representing 97 percent of Canadians aged 6 to 79 years. This strategy provided national estimates for each of the 5 age groups, which were equally distributed for age and sex (6 to 11, 12 to 18, 20 to 39, 40 to 59, and 60 to 79) for a total of 10 groups (Giroux 2007). To minimize non-response bias and to adjust for out-of-scope dwelling the sample sizes were inflated (Giroux 2007). National estimates for conditions with a prevalence of 10 percent or higher and a coefficient of variation (CV) of 16.5 percent were obtained, where the CV represents the estimated standard error percentage of the survey estimate (Statistics Canada 2010). 9

20 3.2.2 Dwelling and respondent sampling Dwellings were selected using the 2006 Census household composition data. Within each CHMS collection site (defined as a geographic area with a population of at least 10,000 and a maximum respondent travelling distance of 100km), dwellings with known compositions were stratified by age resulting in 5 strata from which random sampling occurred. Selected dwellings were contacted to provide a list of the current household members, from which survey participants were selected (Giroux 2007). Non-coverage bias was reduced using supplementary methods (e.g., address register, labour force survey) to capture missing or new dwellings during the survey time frame (Giroux 2007). Within each dwelling stratum different selection probabilities were used to ensure that sampling targets were met for each age range (n=1,000 per age category) (Giroux 2007). Random sampling of the participating dwellings was used to select individuals for clinical examination (Giroux 2007). 3.3 Data collection Data collection comprised two stages, a household questionnaire and clinical examinations. Collection sites were selected using the Labour Force Survey (LFS) area frame to ensure minimal travel time for participants (Giroux 2007). A collection site was defined as a geographic area with a population of at least 10,000 and a maximum respondent travel distance of 100 kilometres (km). The resulting 15 sites represented 96.3 percent of the Canadian population (Giroux 2007). Data were collected from March 2007 to March Household questionnaire The household questionnaire consisted of 47 modules (722 questions) focusing on health status, nutrition and food, medication use, health behaviours, environmental factors and socio-economic information (Tremblay, Wolfson and Connor Gerber 2007). The computer-aided questionnaire was conducted by a Statistics Canada interviewer. Within six weeks those selected reported to the mobile clinic for the collection of physical measures, including oral health data (Tremblay, Wolfson and Connor Gerber 2007). 10

21 3.3.2 Clinical examination The second stage of data collection was conducted in a mobile examination clinic (MEC) where physical measures (cardiovascular fitness, oral health exam, anthropometry, muscle strength and flexibility), blood, and urine samples were gathered (Tremblay, Wolfson and Connor Gerber 2007). Use of MECs was patterned after the U.S. National Health and Nutrition Examination Survey (NHANES) and clinics remained at each site for 6 to 7 weeks (Bryan, St-Denis and Wojtas 2007). Mobile clinics operated 7 days a week to accommodate the approximately 350 exams per collection site, with each visit lasting on average 2.5 hours (Bryan, St-Denis and Wojtas 2007). To ensure maximal participation home visits were offered to those with barriers to travel and a modified examination was conducted (i.e., oral health component only completed if dentist available) (Bryan, St-Denis and Wojtas 2007) Oral health data Oral health data were collected in both the household questionnaire and the clinical examination. Within the questionnaire there were 34 oral health related questions pertaining to oral health satisfaction, dental care habits, oral symptoms and disability days (Health Canada 2010). The oral health exam began with a further 18 questions regarding symptoms (bleeding, pain, xerostomia etc.) followed by a clinical exam (Health Canada 2010). The average examination times were 13 minutes if dentate, 3 mins if edentate and 7 to 8 minutes for children (Health Canada 2010). 3.4 Examiner calibration Oral examinations were conducted by two groups of Canadian Forces dentists (5 to 7 dentists per group) who were calibrated to World Health Organization (WHO) standards (Health Canada 2010). Calibration was completed prior to data collection and again at each new site for all oral health measures, with further calibration pertaining to fluorosis (against reference photographs) completed at the middle and end of each site visit (Health Canada 2010). High agreement for all examiners was achieved at the start of each visit (Cohen s Kappa 0.6) (Health Canada 2010). 11

22 3.5 Variables used Selection of variables was based on the information collected in the CHMS and categorized according to Andersen s health behaviour model (see Figure 1) (Andersen 1995). The model was used as a way to conceptualize and group variables only. The intent was not to formally test the model for its predictive capabilities. For a complete listing of all the variables used in this study and how they were coded please refer to Appendix A. Environment Population Characteristics Health Behaviour Outcomes Health Care System External Environment Predisposing Characteristics Enabling Resources Need Personal Health Practices Use of Health Services Perceived Health Status Evaluated Health Status Consumer Satisfaction Figure 1 Andersen's Emerging Model of Health Behaviour (taken from Andersen 1995) Initially, Andersen s model was designed to show that an individual s use of health services was a function of their predisposition to use services, factors which enable or impede use, and their need for care (Andersen 1995). Within predisposing characteristics, demographic factors (age, sex) were posited to represent biological imperatives suggesting the likelihood that people will need health services while factors pertaining to social structure (education, occupation, ethnicity) represented a person s status within the community and their ability to cope with presenting problems and commanding resources to deal with these problems (Andersen 1995). 12

23 Enabling resources were those required to be present for use to occur and typically include income and insurance (Andersen 1995). Andersen defined need factors as both how individuals viewed their own health (perceived need) and normative need for care (evaluated need) (Andersen 1995). For the purposes of this study, Anderson s emerging model was modified to understand the factors that could influence reporting time loss due to oral health issues, rather than predicting healthcare use (see Figure 2). Environment Population Characteristics Health Behaviour Outcome Dental Care System External Environment Predisposing Characteristics Enabling Resource Need (perceived and evaluated) evaluated) Use of Dental Services Reporting Time Loss Amount of Time Loss Figure 2 Modified Health Behaviour Model (adapted from Andersen 1995) Outcome variables The CHMS collected data pertaining to time loss when it asked participants the following questions: In the past 12 months, have you taken time away from work, school or your normal activities due to check-ups, treatments or because of problems with your mouth? (yes/no) In the past 12 months, how many hours were you away from your normal activities? (to the nearest 0.5 hour) 13

24 3.5.2 Predisposing factors Variables included here represented those believed to predispose individuals to reporting time loss. These included demographic factors such as sex (male/female), age (categorized as: 6- to 11, 12 to 19, 20 to 39, 40 to 59 and 60 to 79 years), immigrant status (yes/no), Aboriginal status (yes/no). Variables relating to social structure included educational attainment (greater than high school/less than high school), career status (employed/student (at university level)/unemployed) and occupational classification (categorized as: management; business, finance and administrative; natural and applied sciences and related occupations; health occupations; occupations in social science, education, government service and religion; occupations in art, culture, recreation and sport; sales and service occupations, trades, transport and equipment operators and related occupations, occupations unique to primary industry and occupations unique to processing, manufacturing and utilities), household size (categorized as: 1 to 2 people, 3 to 4 people, 5 or more people), and employment type (full-time/part-time) Enabling factors The variables included here represented those required for time loss to occur, such as insurance (private/public/no insurance), income adequacy (highest income/upper middle income/middle income/lower middle income/lowest income) Needs factors Variables included within this category represented those that were self-perceived needs and clinically determined needs, which warranted time loss. Self-perceived variables included oral health (good to excellent/poor to fair), general health (good to excellent/poor to fair), mental health (good to excellent/poor to fair), disability status (no to mild/moderate to severe), and oral pain (rarely or never/sometimes/often). Clinically determined needs included treatment needs (yes/no), preventive needs (yes/no), DMFT prevalence (DMFT=0/DMFT>0), and dmft prevalence (dmft=0/dmft>0) Health services use factors The variables listed here reflected how individuals used current oral health services and the role this has on both reporting time loss and the amount of time lost. Both the frequency of seeing a 14

25 dental professional (less than once a year/one or more times per year/emergency or never) and time since last dental visit (less than one year ago/more than one year ago) were included. 3.6 Data analysis Weighting of data The CHMS employed a multi-stage sampling method that incorporated both dwelling and physical data collection locations. For each respondent a sample weight was applied to ensure the data were nationally representative (Health Canada 2010). To account for the complex sampling strategy employed by the CHMS bootstrap weights were required. Bootstrapping is a form of resampling employed with complex survey designs to determine the quality of the estimate and to calculate the coefficient of variation (CV) which otherwise cannot be easily accomplished (Statistics Canada 2011). The CHMS required that 500 bootstrap weights be used over the 10 age-sex groups (Statistics Canada 2010). Both the sample and bootstrap weights were applied in Stata v12.0 (StataCorp LP 2012) prior to statistical analysis Statistical tests Descriptive statistics (frequencies, means) were used to estimate the sample characteristics and to determine the proportion of the sample who reported time loss by selected predisposing, enabling, needs and health services use factors. Bivariate logistic regression was conducted to produce unadjusted odds ratios (OR) for reporting time loss. Multivariate logistic regression was conducted to determine which variables were dominant predictors of reporting time loss. Odds ratios below 1 were inverted for ease of understanding (e.g., OR=0.3; inverted OR=1/0.3=3.3). Lastly a multivariate linear regression was employed for the continuous outcome of hours lost to see which variables were predictive of this outcome. It is important to note that only variables with low multicollinearity, as represented by a variance inflation factor (VIF) of less than 3 and p-values less than 0.25 in the bivariate analyses were entered into the multivariate logistic and linear analyses. It is important to note that the VIF, which is the inverse of tolerance, represents the extent to which variances are inflated or increased due to collinearity (O'Brien 2007). All analyses were conducted using Stata v12.0 (StataCorp LP 2012). 15

26 3.7 Estimating potential productivity losses Potential productivity losses were monetized using the human capital or lost wages method. This method was chosen due to its simplicity where wages equate to the revenue a person produces and when lost, represents their forgone earnings or opportunity cost (Liljas 1998, Berger, Murray and Pauly 2001). The CHMS collected labour force information pertaining to occupation classification, which corresponded to data collected in the Labour Force Survey (LFS) (Statistics Canada 2012). Average hourly wages per occupation classification (accurate as of February 2012) were used to estimate individual losses (i.e., Individual losses = mean hours lost x average hourly wages). Societal losses were estimated using individual losses attributed across the total number of employees per occupation, which were also accurate as of February 2012 (i.e., Societal losses = individual losses x number of employees). Finally the potential productivity losses due to oral health issues were compared to similar losses (short and long-term disability losses) for other illnesses (musculoskeletal, respiratory diseases, alcohol- and tobaccoattributable diseases and cancer) (Coyte, et al. 1998, Patra, et al. 2007). All monetary values were inflated to 2012 Canadian dollars using the Bank of Canada inflation calculator (Bank of Canada 2012) to allow for accurate comparisons. 16

27 Chapter 4: Results 4.1 Sample characteristics The CHMS surveyed 5,600 Canadians of which 5,586 were clinically examined. While not specified, it is likely that those excluded from the clinical examination were done so on the grounds of co-morbid medical conditions or an inability to travel to the mobile examination clinic. When weighted those examined represent almost 97% of the Canadians aged 6 to 79 years (Health Canada 2010). What follows are general descriptions of the sample based on predisposing, enabling, needs, and health service use factors Predisposing factors Table 3 shows that of those surveyed over 60 percent were between the ages of 20 and 59 years of age, with equal sex distribution (males 49.9%; females 50.1%), with 84.7 percent residing in 1 to 4 member households. In terms of education almost three quarters of participants reported being educated beyond the high school level (74.4%). Just over half of those surveyed reported being employed (53%) and 32 percent being unemployed with the remainder being students at the university level (16.2%). In terms of employed individuals almost 80 percent worked in a full-time capacity. Business, Finance, Administrative Occupations and the Sales and Service Occupations were the two largest employment sectors in the survey. The majority of those surveyed were not of Aboriginal status (96.9%) or recent immigrants to the country (79%), which makes sense since neither Aboriginal Canadians nor recent immigrants were specifically sampled in this survey (Giroux 2007). 17

28 Table 3 Sample Characteristics by Predisposing Factors Variable (N) % Age (yrs, N=29,157,460) 6 to to to to to 79 Sex (N=29,157,460) Male Female Educational Attainment (N=28,740,821) Greater than high school Less than high school Career Status (N=25,001,490) Employed Student Unemployed Employment Type (N=0.8626) Full-time Part-time Household Size (N=29,157,460) 1 to 2 people 3 to 4 people 5 or more people Aboriginal Status (N=29,128,937) Yes No Immigrant Status (N=29,155,338) Yes No Occupational Classification (N=20,193,946) Management Business, Finance and Administrative Natural and Applies Sciences and Related Occupations Health Occupations Occupations in Social Science, Education, Government Service and Religion Occupations in Art, Culture, Recreation and Sport Sales and Service Occupations Trades, Transport and Equipment Operators and Related Occupations Occupations Unique to Primary Industry Occupations Unique to Processing, Manufacturing and Utilities

29 4.1.2 Enabling factors Table 4 illustrates that over 62 percent of participants had private dental insurance while almost 32 percent remained uninsured. These values mirror those present in the previous section relating to career status, since dental insurance in Canada is largely an employment benefit. In terms of income almost 80 percent of participants were in the upper middle and highest income brackets with only 5.5 percent being the lowest income brackets (lower middle and lowest). Table 4 Sample Characteristics by Enabling Factors Variable (N) % Insurance (N=28,976,586) Private Insurance Public Insurance No Insurance Income Adequacy (N=27,216,855) Highest Income Upper Middle Income Middle Income Lower Middle Income Lowest Income Needs factors Table 5 shows that of those Canadians surveyed almost 85 percent perceived their oral health to be good to excellent and over 88 percent reported having oral pain rarely or never. Over 90 percent of those surveyed perceived both their general and mental health to be good to excellent while three quarters reported having no or mild disabilities (74.7%). In terms of clinically determined needs over 95 percent of children and 14 percent of adults had no decay experience. Over 65 percent of all Canadians required treatment of some form with 14 percent having preventive needs. 19

30 Table 5 Sample Characteristics by Needs Factors Variable (N) % Self-reported Oral Health (N=29,152,410) Good to Excellent Poor to Fair Self-reported Oral Pain (N=29,149,758) Rarely or Never Sometimes Often Self-reported General Health (N=29,153,947) Good to Excellent Poor to Fair Self-reported Mental Health (N=29,153,947) Good to Excellent Poor to Fair Self-reported Disability Status (N=28,868,498) No to Mild Disability Moderate to Severe Disability dmft Prevalence (N=27,642,167) dmft=0 dmft>0 DMFT Prevalence (N=27,642,167) DMFT=0 DMFT>0 Treatment Needs (N=29,149,991) Yes No Preventive Needs (N=29,149,991) Yes No Health service use factors Table 6 shows that almost three quarters of those surveyed visited a dental professional more than once a year while over 16 percent restricted their visits for emergency care only. Of those surveyed a quarter reported that their last dental visit was over one year ago. 20

31 Table 6 Sample Characteristics by Health Service Use Factors Variable (N) % Frequency of seeing Dental Professional (N=29,152,032) Less Than Once a Year One or More Times Per Year Emergency or Never Time Since Last Dental Visit (N=28,208,528) Less than 1 year ago More than 1 year ago Proportion and likelihood of reporting time loss Of those surveyed 35.1 percent reported time loss due from work, school or normal activities due to oral health issues. Bivariate logistic regression analyses were completed to produce unadjusted odds ratios (OR) for reporting time loss, and are presented below Predisposing factors Table 7 shows the proportion and the unadjusted odds ratios for reporting time loss by predisposing factors. Sex, educational attainment, Aboriginal status, immigrant status, career status, employment type, and household size were not statistically significant predictors of reporting time loss. Canadians aged 20 to 39 years (OR=1.4, 95% CI= , P=0.001), 40 to 59 years (OR=1.25, 95% CI= , P=0.028) and 60 to 79 years (OR=1.4, 95% CI= , P=0.001) were less likely to report time loss than those aged 6 to 11 years. Those in the health Occupations (OR=1.7, 95% CI= , P=0.023) and those employed in Trades, Transport and Equipment Operators and Related Occupations (OR=2.0, 95% CI= , P=0.040) were 1.7 to 2.0 times less likely than those in Management to report time loss. Table 7 Proportion and Likelihood of Reporting Time Loss by Predisposing Factors Variable (N) % Unadjusted OR (95% CI) Age (yrs, N=29,141,400) 6 to 11 (reference) to (0.9, 1.6) 20 to (2.0, 1.3)* 40 to (1.7, 1.1)* 60 to (2.0, 1.3)* P-value

32 Variable (N) % Unadjusted OR (95% CI) Sex (N=29, ) Male (reference) Female Educational Attainment (N=28,724,760) Greater than high school (reference) Less than high school Career Status (N=24,986,201) Employed (reference) Student Unemployed Employment Type (N=20,091,451) Part-time (reference) Full-time Household Size (N=29,141,400) 1 to 2 people (reference) 3 to 4 people 5 or more people Aboriginal Status (N=29,112,877) No (reference) Yes Immigrant Status (N=29,139,278) No (reference) Yes Occupational Classification (N=20,182,418) Management (reference) Business, Finance and Administrative Natural and Applies Sciences and Related Occupations Health Occupations Occupations in Social Science, Education, Government Service and Religion Occupations in Art, Culture, Recreation and Sport Sales and Service Occupations Trades, Transport and Equipment Operators and Related Occupations Occupations Unique to Primary Industry Occupations Unique to Processing, Manufacturing and Utilities * denotes inverted odds ratio and 95% CI P-value (0.9, 1.5) (0.8, 1.3) (1.0, 2.0) 1.25 (1.7, 0.9)* (0.8, 1.3) (0.9, 1.3) 1.1 (0.7, 1.6) (0.5, 3.5) (1.7, 0.7)* (0.6, 1.7) 1.25 (2.5, 0.7)* 1.7 (2.5, 1.1)* 1.1 (2.0, 0.7)* 1.0 (0.6, 1.5) 1.4 (2.5,0.9)* 2.0 (3.3, 1.0)* 2.0 (5.0,.6)* 2.5 (5.0, 1.1)*

33 4.2.2 Enabling Factors Table 8 shows that there is a linear trend for income and reporting time loss, with those in the lowest income bracket being 2.5 times less likely to report time loss compared to their higher income counterparts (OR=2.5, 95% CI= , P=0.037). Compared to privately insured Canadians, those with public insurance were 1.4 times less likely to report time loss (OR=1.4, 95% CI= , P=0.045) and those without dental insurance were 2.0 times less likely to report time loss (OR=2.0, 95% CI= , P=0.000). Table 8 Proportion and Likelihood of Reporting Time Loss by Enabling Factors Variable (N) % Unadjusted OR (95% CI) Insurance (N=28,964,287) Private Insurance (reference) 71.2 Public Insurance (2.0, 1.0)* No Insurance (2.5, 1.4)* Income Adequacy (N=27,200,795) Highest Income (reference) Upper Middle Income Middle Income Lower Middle Income Lowest Income * denotes inverted odds ratio and 95% CI (1.7, 1.3)* 2.0 (2.5, 1.7)* 2.0 (3.3, 1.4)* 2.5 (5.0, 1.1)* P-value Needs factors Table 9 shows that self-ratings of oral health (poor to fair) do not appear to be a statistically significant predictor of reporting time loss (OR=1.25, 95% CI= , P=0.277). Yet individuals who reported having poor to fair general and mental health were 2.0 times less likely to report time loss compared to their counterparts (OR=2.0, 95% CI= , P=0.001). As the frequency of experiencing oral pain increased so did the likelihood of reporting time loss. Of interest is the finding that those with preventive needs were almost 2.0 times less likely to report time loss (OR=1.7, 95% CI= , P=0.001). 23

34 Table 9 Proportion and Likelihood of Reporting Time Loss by Needs Factors Variable (N) % Unadjusted OR (95% CI) P-value Self-reported Oral Health (N=29,136,350) Good to Excellent (reference) Poor to Fair (1.7, 0.8)* Self-reported Oral Pain (N=29,133,930) Rarely or Never (reference) Sometimes Often Self-reported General Health (N=29,137,886) Good to Excellent (reference) Poor to Fair Self-reported Mental Health (N=29,137,886) Good to Excellent (reference) Poor to Fair Self-reported Disability Status (N=28,856,199) No to Mild Disability (reference) Moderate to Severe Disability dmft Prevalence (N=27,629,868) dmft=0 (reference) dmft>0 DMFT Prevalence (N=27,629,868) DMFT=0 (reference) DMFT>0 Treatment Needs (N=29,133,930) No (reference) Yes Preventive Needs (N=29,133,930) No (reference) Yes * denotes inverted odds ratio and 95% CI (1.4, 2.4) 2.3 (1.1, 4.7) (2.5, 1.4)* (2.5, 1.4)* (1.1, 0.8)* (1.1, 2.0) (1.4, 0.8)* (1.3, 1.9) (2.0, 1.4)* Health service use factors Table 10 shows how the current use of health care services influences the likelihood of reporting time loss. Individuals who visited a dental professional one or more times a year were over 5.0 times more likely to report time loss than those who visited less than once a year (OR=5.4, 95% CI= , P=0.001). Those who had not visited a dentist for more than a year were 25.0 times less likely to report time loss (OR=25, 95% CI=33-17, P=0.001). 24