Abstract
This cross-sectional study was conducted to examine tooth loss and associated factors among professional drivers and white-collar workers. The participants were recruited by applying screening procedures to a pool of Japanese registrants in an online database. The participants were asked to complete a self-reported questionnaire. A total of 592 professional drivers and 328 white-collar workers (male, aged 30 to 69 years) were analyzed. A multiple logistic regression analysis was performed to identify differences between professional drivers and white-collar workers. The results showed that professional drivers had fewer teeth than white-collar workers (odds ratio [OR], 1.74; 95% confidence interval [95% CI], 1.150–2.625). Moreover, a second multiple logistic regression analysis revealed that several factors were associated with the number of teeth among professional drivers: diabetes mellitus (OR, 2.68; 95% CI, 1.388–5.173), duration of brushing teeth (OR, 1.66; 95% CI, 1.066–2.572), frequency of eating breakfast (OR, 2.23; 95% CI, 1.416–3.513), frequency of eating out (OR, 1.70; 95% CI, 1.086–2.671) and smoking status (OR, 2.88; 95% CI, 1.388–5.964). These findings suggest that the lifestyles of professional drivers could be related to not only their general health status, but also tooth loss.
Keywords: Professional drivers, Oral conditions, Internet survey, Lifestyle factors, Remaining teeth, Oral health behavior, Male
Introduction
Professional drivers have been reported to constitute a particular disease risk group because of their characteristic working environment1, 2, 3). The main business of professional drivers is driving, and they are exposed to whole-body vibration, noise, and exhaust gas. In addition, when they drive, they are under stress and tend to smoke4). Moreover, professional drivers have been reported to be at risk for diseases such as cardiovascular disease, lower back pain and diabetes mellitus3, 5, 6, 7, 8). Kurosaka et al. reported that drivers with coronary artery disease not only have a high prevalence of various risk factors, but also tend to have three or more risk factors simultaneously9). Therefore, to improve the health status of professional drivers, a multi-angle approach is necessary. Meanwhile, few reports have discussed the oral conditions of professional drivers. However, the factors that have been reported as risk factors for diseases among professional drivers, such as smoking, can also be considered as risk factors for oral diseases10). Therefore, our hypothesis was that professional drivers would have fewer teeth than white-collar workers and the purposes of this study were to reveal tooth loss among professional drivers and to identify factors associated with tooth loss. In this study, we selected white-collar workers as a control group because several studies examining the general health and oral conditions of this group have already been reported11, 12, 13).
Materials and Methods
Subjects
This internet-based survey was conducted in Japan from February 20, 2015, to March 11, 2015. We assumed that 3 weeks would be sufficient to obtain answers from the participants. The Participants were selected from people registered with an online research company called Macromill (http://www.macromill.com/global/index.html). They were aged 30 to 69 years and were fulltime or non-fulltime workers including nurses, cooks and professional drivers. We selected this age range because the proportion of persons with missing teeth increases at ages beyond 30 years, according to the Survey of Dental Diseases in Japan14), and the number of registrants over 70 years of age was too small to analyze. The respondents completed the questionnaire after they had agreed to participate in the survey via a website. As a result, among the respondents who were male, we selected 737 respondents who were professional drivers by occupation and 620 respondents who were teachers, clerks, salespersons, or administrators and were collectively referred to as white-collar workers15).
Moreover, based on the Comprehensive Survey of Living Conditions in Japan16), which is a national survey conducted to study the basic living conditions of subjects, we excluded respondents whose family income was less than 2 million yen or more than 8 million yen to minimize the effect of income on the number of present teeth17). Eventually, 592 male professional drivers and 328 male white-collar workers aged 30 to 69 years were analyzed (total, 920 people). All the subjects were male because there were few female drivers.
Questionnaire
The participants completed a self-reported questionnaire.
The questionnaire items were selected after considering the factors associated with the number of present teeth and the characteristics of professional drivers. Dental care utilization patterns18), smoking10, 19, 20), and dental hygiene habits21, 22) have been reported as factors related to the number of present teeth, and BMI3), systemic diseases3, 5, 6, 8, 9, 23), eating habits5, 23), sleeping hours2) have been reported as factors related to professional driving as an occupation. In addition, working environment factors were selected as possible confounders. Height and weight were determined using the questions, “How tall are you?” and “How much do you weigh?” The BMI was then calculated based on the responses and was categorized as <25 or ≥25 kg/m2. Family income was determined using the question, “How much is your annual family income?” The response was then categorized as <4 million yen or ≥4 million yen. Information was also collected on working environment (working hours: “How many hours do you work a day?”, categorized as <8 h or ≥8 h; shift work: “Do you work in shifts?”, categorized as yes or no; duration of employment: “How long have you been working?”, categorized as <10 years or ≥10 years; night work: “Do you have night work?”, categorized as yes or no), systemic diseases that have been reported to be related to professional driving (“Do you have any of the following diseases: diabetes mellitus, hypertension, hypercholesterolemia, cardiovascular disease, lower back pain, or gastrointestinal illnesses?”, categorized as yes or no), dental care utilization patterns (“Have you visited a dental clinic within the past year?”, “Do you visit the same dentist?”, and “Do you have regular dental check-ups?”, categorized as yes or no), lifestyle (frequency of eating breakfast and dinner on weekdays: “How often do you eat breakfast and dinner on weekdays?”, categorized as every day or not every day; frequency of eating out on weekdays: “How often do you eat out on weekdays?”, categorized as time and more per week or never; smoking status: “Do you smoke?”, categorized as smokers or ever smokers and non-smokers; sleeping hours: “How many hours do you sleep at night?”, categorized as <6 h or ≥6 h; eating snacks between meals: “Do you eat snacks between meals?”, categorized as yes or no), and dental hygiene habits (frequency of daily brushing: “How often do you brush your teeth a day?”, categorized as <2 or ≥2; duration of brushing teeth: “How many minutes do you brush your teeth?”, categorized as <3 minutes or ≥3 minutes; timing of brushing teeth: “When do you brush your teeth: before eating breakfast, after eating breakfast, after eating lunch, after eating snack, after eating dinner, or before bed time?”, categorized as yes or no for each option). The number of present teeth was determined by asking “How many teeth do you have?”
Statistical analysis
We divided the subjects into two groups: <20 teeth or ≥20 teeth. First, to compare the number of present teeth between professional drivers and white-collar workers, we selected adjustment factors to adjust for possible confounders: age, family income, and working environment. A chi-squared test (or the Fisher exact test for cases with fewer than five cells in the contingency table) was used for the adjustment factors in professional drivers and white-collar workers to investigate the differences in the distributions of each characteristic. Next, a multiple logistic regression analysis was performed using the number of present teeth as the dependent variable (0=having 20 teeth and more, 1=having fewer than 20 teeth), and the adjustment factors and job category as independent variables.
Furthermore, to examine the effect of each factor on the number of present teeth according to each job category, we performed a chi-squared test and a multiple logistic regression analysis for each job category. The multiple logistic regression analyses were developed using the number of present teeth as the dependent variable (0=having 20 teeth and more, 1=having fewer than 20 teeth), and the adjustment factors and statistically significant factors identified using a chi-squared test were included as independent variables. All the multiple logistic regression analyses were developed using a forced entry method.
The data were analyzed using the computerized statistical package SPSS, version 22.0 (SPSS Japan, Inc. Tokyo. Japan), and a significance level of 5% was used. This study was approved by the ethical committee of Tokyo Dental College (Approval number 602).
Result
Table 1 shows the adjustment factor-related characteristics of the participants comparing job categories. Significant differences in family income (P=0.002), working hours (P=<0.001), shift work (P=<0.001), and duration of employment (P=0.030) were observed.
Table 1. Characteristics of participants related to adjustment factors comparing job categories using the chi-squared test.
| Adjustment factors | Professional drivers | White-collar workers | |||||
|---|---|---|---|---|---|---|---|
| n | % | n | % | P values | |||
| Age group (years) | 30–49 | 297 | 50.2 | 183 | 55.8 | 0.113 | |
| 50–69 | 295 | 49.8 | 145 | 44.2 | |||
| Annual family income | <4 million yen | 232 | 39.2 | 94 | 28.7 | 0.002 | |
| ≥4 million yen | 360 | 60.8 | 234 | 71.3 | |||
| Working hours | <8 h | 118 | 19.9 | 120 | 36.6 | <0.001 | |
| ≥8 h | 474 | 80.1 | 208 | 63.4 | |||
| Shift work | No | 308 | 52.0 | 242 | 73.8 | <0.001 | |
| Yes | 284 | 48.0 | 86 | 26.2 | |||
| Duration of employment | <10 years | 368 | 62.2 | 179 | 54.6 | 0.030 | |
| ≥10 years | 224 | 37.8 | 149 | 45.4 | |||
| Night work | No | 454 | 76.7 | 255 | 77.7 | 0.744 | |
| Yes | 138 | 23.3 | 73 | 22.3 | |||
The results of a multiple logistic regression analysis comparing job categories is shown in Table 2. The dependent variable was the number of present teeth, and the independent variables were the adjustment factors and job category. Job category was significantly associated with the number of present teeth between professional drivers and white-collar workers (odds ratio [OR], 1.74; 95% confidence interval [95% CI], 1.150–2.625).
Table 2. Results of a multiple logistic regression analysis comparing job categories n=920.
| Independent variables | OR | 95% CI | P values | |
|---|---|---|---|---|
| Job category | White-collar workers | 1.00 | ||
| Professional drivers | 1.74 | 1.150–2.625 | 0.009 | |
| Age group (years) | 30–49 | 1.00 | ||
| 50–69 | 2.30 | 1.600–3.301 | <0.001 | |
| Annual family income |
≥4 million yen | 1.00 | ||
| <4 million yen | 1.45 | 1.016–2.066 | 0.040 | |
| Working hours | <8 h | 1.00 | ||
| ≥8 h | 0.76 | 0.513–1.137 | 0.185 | |
| Shift work | No | 1.00 | ||
| Yes | 1.34 | 0.925–1.940 | 0.122 | |
| Duration of employment | <10 years | 1.00 | ||
| ≥10 years | 0.78 | 0.538–1.126 | 0.184 | |
| Night work | No | 1.00 | ||
| Yes | 1.04 | 0.683–1.585 | 0.853 |
Table 3 shows a comparison of professional drivers and white-collar workers for factors associated with the number of present teeth. Among professional drivers, significant differences in age group (P<0.001), working hours (P=0.022), diabetes mellitus (P<0.001), hypertension (P=0.004), hypercholesterolemia (P=0.013), frequency of eating breakfast (P<0.001), frequency of eating out (P=0.005), smoking status (P<0.001), frequency of daily brushing (P=0.024), duration of brushing teeth (P=0.003), brushing after breakfast (P=0.042), and brushing after eating dinner (P=0.006) were observed. Meanwhile, among white-collar workers, significant differences in age group (P=0.017), smoking status (P=0.007) were observed.
Table 3. Comparison of factors associated with having fewer than 20 teeth between professional drivers and white-collar workers using the chi-squared test.
| Professional drivers | White-collar workers | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Having fewer than 20 teeth | Having fewer than 20 teeth | ||||||||||
| Factors | n1 | n2 | % | P values | n1 | n2 | % | P values | |||
| Characteristics | Age group (years) | 30–49 | 297 | 41 | 13.8 | <0.001 | 183 | 15 | 8.2 | 0.017 | |
| 50–69 | 295 | 82 | 27.8 | 145 | 25 | 17.2 | |||||
| BMI | <25 | 396 | 78 | 19.7 | 0.389 | 228 | 28 | 12.3 | 1.000 | ||
| ≥25 | 196 | 45 | 23.0 | 100 | 12 | 12.0 | |||||
| Annual family income | <4 million yen | 232 | 57 | 24.6 | 0.078 | 94 | 16 | 17.0 | 0.096 | ||
| ≥4 million yen | 360 | 66 | 18.3 | 234 | 24 | 10.3 | |||||
| Working environment | Working hours | <8 h | 118 | 34 | 28.8 | 0.022 | 120 | 15 | 12.5 | 1.000 | |
| ≥8 h | 474 | 89 | 18.8 | 208 | 25 | 12.0 | |||||
| Shift work | Yes | 284 | 69 | 24.3 | 0.054 | 86 | 11 | 12.8 | 0.849 | ||
| No | 308 | 54 | 17.5 | 242 | 29 | 12.0 | |||||
| Duration of employment | <10 years | 368 | 80 | 21.7 | 0.531 | 179 | 27 | 15.1 | 0.091 | ||
| ≥10 years | 224 | 43 | 19.2 | 149 | 13 | 8.7 | |||||
| Night work | Yes | 138 | 33 | 23.9 | 0.338 | 73 | 10 | 13.7 | 0.686 | ||
| No | 454 | 90 | 19.8 | 255 | 30 | 11.8 | |||||
| Systemic diseases | Diabetes mellitus | Yes | 54 | 23 | 42.6 | <0.001 | 29 | 5 | 17.2 | 0.374 | |
| No | 538 | 100 | 18.6 | 299 | 35 | 11.7 | |||||
| Hypertension | Yes | 141 | 42 | 29.8 | 0.004 | 59 | 9 | 15.3 | 0.509 | ||
| No | 451 | 81 | 18.0 | 269 | 31 | 11.5 | |||||
| Hypercholesterolemia | Yes | 74 | 24 | 32.4 | 0.013 | 32 | 5 | 15.6 | 0.567 | ||
| No | 518 | 99 | 19.1 | 296 | 35 | 11.8 | |||||
| Cardiovascular disease | Yes | 6 | 1 | 16.7 | 1.000 | 6 | 0 | 0.0 | 1.000 | ||
| No | 586 | 122 | 20.8 | 322 | 40 | 12.4 | |||||
| Lower back pain | Yes | 96 | 18 | 18.8 | 0.681 | 43 | 6 | 14.0 | 0.626 | ||
| No | 496 | 105 | 21.2 | 285 | 37 | 11.9 | |||||
| Gastrointestinal disease | Yes | 18 | 6 | 4.9 | 0.233 | 9 | 2 | 22.2 | 0.302 | ||
| No | 574 | 117 | 20.4 | 319 | 38 | 11.9 | |||||
| Dental care utilization | Dental visits in past year | Yes | 299 | 67 | 22.9 | 0.225 | 149 | 20 | 13.4 | 0.612 | |
| No | 293 | 56 | 18.7 | 179 | 20 | 11.2 | |||||
| Visiting same dentist | Yes | 351 | 78 | 22.2 | 0.305 | 182 | 23 | 12.6 | 0.866 | ||
| No | 241 | 45 | 18.7 | 146 | 17 | 11.6 | |||||
| Regular dental check-ups | Yes | 244 | 55 | 22.5 | 0.411 | 126 | 19 | 15.1 | 0.227 | ||
| No | 348 | 58 | 19.5 | 202 | 21 | 10.4 | |||||
| Life style | Frequency of eating breakfast on weekdays | Every day | 378 | 60 | 15.9 | <0.001 | 244 | 30 | 12.3 | 1.000 | |
| Not every day | 214 | 63 | 29.4 | 84 | 10 | 11.9 | |||||
| Frequency of eating dinner on weekdays | Every day | 516 | 101 | 19.6 | 0.069 | 295 | 36 | 12.2 | 1.000 | ||
| Not every day | 76 | 22 | 28.9 | 33 | 4 | 12.1 | |||||
| Frequency of eating out on weekdays | 1 time and more per week | 191 | 53 | 27.7 | 0.005 | 111 | 16 | 14.4 | 0.378 | ||
| Never | 401 | 70 | 17.5 | 217 | 24 | 11.1 | |||||
| Smoking status | Smokers or ever smokers | 471 | 113 | 24.0 | <0.001 | 218 | 34 | 15.6 | 0.007 | ||
| Non-smokers | 121 | 10 | 8.3 | 110 | 6 | 5.5 | |||||
| Sleeping hours | <7 h | 339 | 76 | 22.4 | 0.262 | 180 | 23 | 12.8 | 0.738 | ||
| ≥7 h | 253 | 47 | 18.6 | 148 | 17 | 11.5 | |||||
| Eating snacks between meals | Yes | 451 | 93 | 20.6 | 0.905 | 241 | 32 | 13.3 | 0.559 | ||
| No | 141 | 30 | 21.3 | 81 | 8 | 9.9 | |||||
| Dental hygiene habits | Frequency of daily brushing | <2 | 254 | 64 | 25.2 | 0.024 | 110 | 11 | 10.0 | 0.476 | |
| ≥2 | 338 | 59 | 17.5 | 218 | 29 | 13.3 | |||||
| Duration of brushing teeth | <3 minutes | 286 | 74 | 25.9 | 0.003 | 163 | 24 | 14.7 | 0.180 | ||
| ≥3 minutes | 306 | 49 | 16.0 | 165 | 16 | 9.7 | |||||
| Timing of brushing teeth | Before eating breakfast | Yes | 227 | 54 | 23.8 | 0.176 | 105 | 16 | 15.2 | 0.279 | |
| No | 365 | 69 | 18.9 | 223 | 24 | 10.8 | |||||
| After eating breakfast | Yes | 265 | 45 | 17.0 | 0.042 | 196 | 19 | 9.7 | 0.121 | ||
| No | 327 | 78 | 23.9 | 132 | 21 | 15.9 | |||||
| After eating lunch | Yes | 73 | 18 | 24.7 | 0.441 | 66 | 5 | 7.6 | 0.291 | ||
| No | 519 | 105 | 20.2 | 262 | 35 | 13.4 | |||||
| After eating snack | Yes | 14 | 6 | 42.9 | 0.050 | 6 | 0 | 0.0 | 1.000 | ||
| No | 578 | 117 | 20.2 | 322 | 40 | 12.4 | |||||
| After eating dinner | Yes | 137 | 17 | 12.4 | 0.006 | 74 | 10 | 13.5 | 0.689 | ||
| No | 455 | 106 | 23.3 | 254 | 30 | 11.8 | |||||
| Before bed time | Yes | 307 | 65 | 21.2 | 0.840 | 179 | 24 | 13.4 | 0.501 | ||
| No | 285 | 58 | 20.4 | 149 | 16 | 10.7 | |||||
n1: total number of participants for each item, n2: the number of participants who had fewer than 20 teeth
The results of a multiple logistic regression analysis for factors associated with the number of present teeth in professional drivers and white-collar workers is presented in Table 4. The independent variables were adjustment factors and factors that were significantly different according to a chi-squared test. The dependent variable was the number of present teeth. Among professional drivers, the highest OR was observed for the smoking status (OR, 2.88; 95% CI, 1.388–5.964), followed by diabetes mellitus (OR, 2.68; 95% CI, 1.388–5.173), frequency of eating breakfast (OR, 2.23; 95% CI, 1.416–3.513), frequency of eating out (OR, 1.70; 95% CI, 1.086–2.671), and duration of brushing teeth (OR, 1.66; 95% CI, 1.066–2.572). Meanwhile, among white-collar workers, the smoking status (OR, 2.81; 95% CI, 1.083–7.300) and brushing teeth after eating breakfast (OR, 2.43; 95% CI, 1.054–5.617) were significantly different.
Table 4. Factors associated with having fewer than 20 teeth among professional drivers and white-collar workers using the multiple logistic regression analysis.
| Professional drivers | n=592 | White-collar workers | n=328 | |||||
|---|---|---|---|---|---|---|---|---|
| OR | 95% CI | P values | OR | 95% CI | P values | |||
| Diabetes mellitus | No | 1.00 | 1.00 | |||||
| Yes | 2.68 | 1.388–5.173 | 0.003 | 0.97 | 0.300–3.106 | 0.966 | ||
| Hypertension | No | 1.00 | 1.00 | |||||
| Yes | 1.31 | 0.790–2.160 | 0.298 | 0.93 | 0.359–2.419 | 0.884 | ||
| Hypercholesterolemia | No | 1.00 | 1.00 | |||||
| Yes | 0.93 | 0.468–1.857 | 0.842 | 1.37 | 0.411–4.553 | 0.610 | ||
| Frequency of eating breakfast on weekdays | Every day | 1.00 | 1.00 | |||||
| Not every day | 2.23 | 1.416–3.513 | 0.001 | 0.93 | 0.391–2.228 | 0.877 | ||
| Frequency of eating out on weekdays | Never | 1.00 | 1.00 | |||||
| 1 time and more per week | 1.70 | 1.086–2.671 | 0.020 | 1.78 | 0.842–3.790 | 0.131 | ||
| Smoking status | Non-smokers | 1.00 | 1.00 | |||||
| Smokers+ever smokers | 2.88 | 1.388–5.964 | 0.004 | 2.81 | 1.083–7.300 | 0.034 | ||
| Frequency of daily brushing | <2 | 1.00 | 1.00 | |||||
| ≥2 | 1.00 | 0.619–1.626 | 0.991 | 2.30 | 0.935–5.640 | 0.070 | ||
| Duration of brushing teeth | ≥3 minutes | 1.00 | 1.00 | |||||
| <3 minutes | 1.66 | 1.066–2.572 | 0.025 | 0.51 | 0.248–1.057 | 0.070 | ||
| Brushing teeth after eating breakfast | Yes | 1.00 | 1.00 | |||||
| No | 1.34 | 0.832–2.152 | 0.229 | 2.43 | 1.054–5.617 | 0.037 | ||
| Brushing teeth after eating dinner | Yes | 1.00 | 1.00 | |||||
| No | 1.58 | 0.840–2.955 | 0.156 | 0.65 | 0.259–1.646 | 0.366 | ||
Age, annual family income, working hours, shift work, duration of employment, and night shift were included as adjustment factors in the model.
Discussion
The results of our study revealed several factors that are associated with tooth loss among professional drivers. The first multiple logistic regression analysis showed that, compared with white-collar workers, professional drivers had fewer teeth than white-collar workers (see Table 2). Furthermore, the results of a second multiple logistic regression analysis showed differences in factors associated with tooth loss between professional drivers and white-collar workers (see Table 4). Among professional drivers, smoking status, diabetes mellitus, frequency of eating breakfast, frequency of eating out, and duration of brushing teeth were associated with the number of teeth, though only smoking status and brushing teeth after eating breakfast were associated with tooth loss among white-collar workers. This result shows that compared with white-collar workers, professional drivers have different factors that are associated with tooth loss.
Income could be a confounder in analyses of this data. Income has been reported to be associated with tooth loss and dental care utilization24, 25, 26). We adjusted for the effect of income on tooth loss because the purpose of our study was to reveal the factors associated with tooth loss among professional drivers.
Some previous studies have discussed the factors that were identified as being associated with the number of teeth in the present study. For example, the relationship between the number of teeth and the smoking status has been reported in past studies10, 19, 20). In our study, the same relationship was observed for both professional drivers and white-collar workers. Nitin et al. has reported high smoking rates among truck drivers4). Hence, a smoking cessation program for professional drivers might decrease tooth loss.
A relationship between professional driving and diabetes mellitus has also been reported3, 7). In particular, professional drivers reportedly have a high prevalence of undiagnosed diabetes mellitus23). Moreover, an association between diabetes mellitus and periodontal diseases has also been reported27, 28). Aida et al. showed that approximately 40% of all tooth extractions in Japan are caused by periodontal disease29). The results of the present study suggest that improvements in diabetes mellitus might decrease tooth loss of professional drivers.
A relationship between eating habits and systemic diseases has been supported by several studies. Siu et al. found that eating out 6 times and more per week can increase the risk of undiagnosed diabetes mellitus23). Moreover, Kurosaka et al. pointed out that irregular eating habits can cause obesity and diabetes mellitus9). In addition, Raanaas et al. reported a relationship between eating habits and neck and lower back pain among Norwegian taxi drivers. They also pointed out the possibility of a lack of spare time to spend on eating because of the busyness of their work5). Meanwhile, regarding tooth loss, Yoshida et al. reported that among approximately 2,000 employees of a large petroleum chemical plant, irregular eating habits might have been a cause of greater tooth loss because the frequency of eating was an indicator of healthy food habits, and not maintaining a proper rhythm in daily life could lead to tooth loss30). These reports indicate that inadequate eating habits among professional drivers may affect not only their general health status, but also tooth loss. The present study revealed a similar relationship between the frequency of eating breakfast and the number of teeth. Therefore, improvements in lifestyle, including dietary counseling, might contribute to a decrease in tooth loss.
Although several reports have shown that the frequency of tooth brushing is related to the number of remaining teeth21, 22), few reports reporting the duration of brushing are available. In the present study, Table 3 shows significant differences in the frequency of daily brushing and the duration of brushing teeth, although a multiple logistic regression analysis of professional drivers showed that only the duration of brushing teeth was significantly different (see Table 4). As stated previously, professional drivers may not be able to maintain a proper rhythm in their daily life or to find a place for tooth brushing while working. Therefore, they may not have sufficient time for tooth brushing. The present results indicate the necessity of tooth brushing instruction to enable professional drivers to brush their teeth effectively in a limited amount of time.
This study was a large-scale, self-reported survey conducted via the Internet. As for the self-reported data, the validities of the number of present teeth31, 32), the presence of chronic conditions33, 34), and the BMI35, 36) data have been previously reported. However, the other items might contain incorrect information because of the use of a self-reported questionnaire.
Internet surveys can be a source of selection bias. Moreover, we were unable to control for factors such as education, the amount of sugar consumption, medication, the control of systemic diseases, or the status of periodontal disease. So, these factors could be additional confounders. In addition, the number of participants was selected by the Internet research company. Therefore, we could not take the results of a sample size estimation into account. Finally, this study was a cross-sectional study; therefore, further research is required to demonstrate a causal relationship.
In conclusion, we revealed that professional drivers, compared with white-collar workers, had a higher risk of tooth loss. Moreover, lifestyle was strongly associated with tooth loss among professional drivers. These findings suggest that the lifestyles of professional drivers could be related to not only their general health status, but also tooth loss.
Acknowledgments
This study was supported by the“Research Fund of Clinical Study for Industrial Accident and Disease” (14020101–02) from the Japanese Ministry of Health, Labour and Welfare.
References
- 1.Gill PE, Wijk K (2004) Case study of a healthy eating intervention for Swedish lorry drivers. Health Educ Res 19, 306–15. [DOI] [PubMed] [Google Scholar]
- 2.van der Beek AJ. (2012) World at work: truck drivers. Occup Environ Med 69, 291–5. [DOI] [PubMed] [Google Scholar]
- 3.Marcinkiewicz A, Szosland D (2010) Selected risk factors of diabetes mellitus among road transport drivers. Int J Occup Med Environ Health 23, 175–80. [DOI] [PubMed] [Google Scholar]
- 4.Jain NB, Hart JE, Smith TJ, Garshick E, Laden F (2006) Smoking behavior in trucking industry workers. Am J Ind Med 49, 1013–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Raanaas RK, Anderson DA (2008) questionnaire survey of Norwegian taxi drivers’ musculoskeletal health, and work-related risk factors INT J IND ERGONOM 38, 280–290.
- 6.Miyamoto M, Konno S, Gembun Y, Liu X, Minami K, Ito H (2008) Epidemiological study of low back pain and occupational risk factors among taxi drivers. Ind Health 46, 112–7. [DOI] [PubMed] [Google Scholar]
- 7.Whitfield Jacobson PJ, Prawitz AD, Lukaszuk JM (2007) Long-haul truck drivers want healthful meal options at truck-stop restaurants. J Am Diet Assoc 107, 2125–9. [DOI] [PubMed] [Google Scholar]
- 8.Winkleby MA, Ragland DR, Fisher JM, Syme SL (1988) Excess risk of sickness and disease in bus drivers: a review and synthesis of epidemiological studies. Int J Epidemiol 17, 255–62. [DOI] [PubMed] [Google Scholar]
- 9.Kurosaka K, Daida H, Muto T, Watanabe Y, Kawai S, Yamaguchi H (2000) Characteristics of coronary heart disease in Japanese taxi drivers as determined by coronary angiographic analyses. Ind Health 38, 15–23. [DOI] [PubMed] [Google Scholar]
- 10.Yoshino K, Osada H, Matsukubo T, Takaesu Y (2006) Percentile curves for present teeth in smokers and non-smokers in an adult male population. Bull Tokyo Dent Coll 47, 51–5. [DOI] [PubMed] [Google Scholar]
- 11.Avlund K, Holm-Pedersen P, Morse DE, Viitanen M, Winblad B (2005) The strength of two indicators of social position on oral health among persons over the age of 80 years. J Public Health Dent 65, 231–9. [DOI] [PubMed] [Google Scholar]
- 12.Fernández-de-las-Peñas C, Gröbli C, Ortega-Santiago R, Fischer CS, Boesch D, Froidevaux P, Stocker L, Weissmann R, González-Iglesias J (2012) Referred pain from myofascial trigger points in head, neck, shoulder, and arm muscles reproduces pain symptoms in blue-collar (manual) and white-collar (office) workers. Clin J Pain 28, 511–8. [DOI] [PubMed] [Google Scholar]
- 13.Andersen LL, Mortensen OS, Hansen JV, Burr H (2011) A prospective cohort study on severe pain as a risk factor for long-term sickness absence in blue- and white-collar workers. Occup Environ Med 68, 590–2. [DOI] [PubMed] [Google Scholar]
- 14.Japan Ministry of Health, Labour and Welfare. Survey of Dental Diseases 2011. http://www.mhlw.go.jp/toukei/list/dl/62-17c23-1.pdf Accessed September 24, 2015
- 15.Sanders AE, Spencer AJ (2004) Job characteristics and the subjective oral health of Australian workers. Aust N Z J Public Health 28, 259–66. [DOI] [PubMed] [Google Scholar]
- 16.Japan Ministry of Health, Labour and Welfare. Comprehensive Survey of Living Conditions 2011. http://www.mhlw.go.jp/english/database/db-hss/dl/report_gaikyo_2011.pdf. Accessed September 24, 2015
- 17.Seerig LM, Nascimento GG, Peres MA, Horta BL, Demarco FF (2015) Tooth loss in adults and income: Systematic review and meta-analysis. J Dent 43, 1051–9. [DOI] [PubMed] [Google Scholar]
- 18.Thomson WM, Williams SM, Broadbent JM, Poulton R, Locker D (2010) Long-term dental visiting patterns and adult oral health. J Dent Res 89, 307–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Axelsson P, Paulander J, Lindhe J (1998) Relationship between smoking and dental status in 35-, 50-, 65-, and 75-year-old individuals. J Clin Periodontol 25, 297–305. [DOI] [PubMed] [Google Scholar]
- 20.Fardal Ø, Johannessen AC, Linden GJ (2004) Tooth loss during maintenance following periodontal treatment in a periodontal practice in Norway. J Clin Periodontol 31, 550–5. [DOI] [PubMed] [Google Scholar]
- 21.Vysniauskaité S, Kammona N, Vehkalahti MM (2005) Number of teeth in relation to oral health behaviour in dentate elderly patients in Lithuania. Gerodontology 22, 44–51. [DOI] [PubMed] [Google Scholar]
- 22.Kressin NR, Boehmer U, Nunn ME, Spiro A 3rd (2003) Increased preventive practices lead to greater tooth retention. J Dent Res 82, 223–7. [DOI] [PubMed] [Google Scholar]
- 23.Siu SC, Wong KW, Lee KF, Lo YY, Wong CK, Chan AK, Fong DY, Lam CL (2012) Prevalence of undiagnosed diabetes mellitus and cardiovascular risk factors in Hong Kong professional drivers. Diabetes Res Clin Pract 96, 60–7. [DOI] [PubMed] [Google Scholar]
- 24.Vehkalahti M, Paunio I (1989) Remaining teeth in Finnish adults related to the frequency of tooth-brushing. Acta Odontol Scand 47, 375–81. [DOI] [PubMed] [Google Scholar]
- 25.Mroczek B, Sitko Z, Augustyniuk K, Pierzak-Sominka J, Wróblewska I, Kurpas D (2015) Socioeconomic Indicators Shaping Quality of Life and Illness Acceptance in Patients with Chronic Obstructive Pulmonary Disease. Adv Exp Med Biol 861, 19–30. [DOI] [PubMed] [Google Scholar]
- 26.Muirhead VE, Quiñonez C, Figueiredo R, Locker D (2009) Predictors of dental care utilization among working poor Canadians. Community Dent Oral Epidemiol 37, 199–208. [DOI] [PubMed] [Google Scholar]
- 27.Kuo LC, Polson AM, Kang T (2008) Associations between periodontal diseases and systemic diseases: a review of the inter-relationships and interactions with diabetes, respiratory diseases, cardiovascular diseases and osteoporosis. Public Health 122, 417–33. [DOI] [PubMed] [Google Scholar]
- 28.Casanova L, Hughes FJ, Preshaw PM (2014) Diabetes and periodontal disease: a two-way relationship. Br Dent J 217, 433–7. [DOI] [PubMed] [Google Scholar]
- 29.Aida J, Ando Y, Akhter R, Aoyama H, Masui M, Morita M (2006) Reasons for permanent tooth extractions in Japan. J Epidemiol 16, 214–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Yoshida Y, Hatanaka Y, Imaki M, Ogawa Y, Miyatani S, Tanada S (2001) Epidemiological study on improving the QOL and oral conditions of the aged—Part 2: Relationship between tooth loss and lifestyle factors for adults men. J Physiol Anthropol Appl Human Sci 20, 369–73. [DOI] [PubMed] [Google Scholar]
- 31.Pitiphat W, Garcia RI, Douglass CW, Joshipura KJ (2002) Validation of self-reported oral health measures. J Public Health Dent 62, 122–8. [DOI] [PubMed] [Google Scholar]
- 32.Ueno M, Zaitsu T, Shinada K, Ohara S, Kawaguchi Y (2010) Validity of the self-reported number of natural teeth in Japanese adults. J Investig Clin Dent 1, 79–84. [DOI] [PubMed] [Google Scholar]
- 33.Martin LM, Leff M, Calonge N, Garrett C, Nelson DE (2000) Validation of self-reported chronic conditions and health services in a managed care population. Am J Prev Med 18, 215–8. [DOI] [PubMed] [Google Scholar]
- 34.Wada K, Yatsuya H, Ouyang P, Otsuka R, Mitsuhashi H, Takefuji S, Matsushita K, Sugiura K, Hotta Y, Toyoshima H, Tamakoshi K (2009) Self-reported medical history was generally accurate among Japanese workplace population. J Clin Epidemiol 62, 306–13. [DOI] [PubMed] [Google Scholar]
- 35.Ekström S, Kull I, Nilsson S, Bergström A (2015) Web-based self-reported height, weight, and body mass index among Swedish adolescents: a validation study. J Med Internet Res 17, e73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Yoshitake N, Okuda M, Sasaki S, Kunitsugu I, Hobara T (2012) Validity of self-reported body mass index of Japanese children and adolescents. Pediatr Int 54, 397–401. [DOI] [PubMed] [Google Scholar]