Abstract
Objectives: To assess the relationships among work stress, oral health and oral health-related quality of life (OHRQoL) in information technology (IT) professionals in south India. Methods: The study population consisted of 134 IT industry workers in four mid-size IT companies in south India. A self-administered questionnaire consisting of the eight-item Oral Impact on Daily Performance (OIDP) scale and a 25-item modified version of the original 167-item Work Stress Questionnaire was given, following which an oral examination was carried out. Results: Mean ± standard deviation scores on the Work Stress Questionnaire, the decayed, missing and filled teeth (DMFT) index and the Community Periodontal Index of Treatment Needs (CPITN) for the sample population were 53.82 ± 15.07, 4.23 ± 3.47 and 1.81 ± 0.58, respectively. A comparison of clinical oral health status data against respondents’ work stress and OIDP scores showed that mean DMFT and CPITN scores were significantly greater among those who reported oral impact on their daily performance. However, although participants who reported oral problems had consistently higher work stress scores, the differences were statistically significant only for gingival bleeding and sensitive teeth. Multivariate analysis after controlling for age and sex showed that higher levels of work stress and periodontal disease were significant predictors for poor OHRQoL in the sample population. Conclusions: Work stress may be an important predictor for poor OHRQoL and hence requires to be studied in greater detail.
Key words: Work stress, oral health-related quality of life, information technology, India
INTRODUCTION
Workplace stress is the harmful physical and emotional response that occurs when job demands are poorly matched with the capabilities, resources or needs of the worker1. Stress-related disorders encompass a broad array of conditions, including psychological disorders, maladaptive behaviours and cognitive impairment. In turn, these conditions may lead to poor work performance or even injury2. Job stress is also associated with various biological reactions that may lead ultimately to compromised health, causing conditions such as cardiovascular disease or, in extreme cases, death3., 4.. Previous research has shown a relationship between work stress and oral health in which those with higher work stress had poorer oral health5., 6.. Marcenes and Sheiham5 and Green et al.6 reported that increased work stress was associated with poorer oral health status and particularly with poorer periodontal status. Previous research has shown that stress can also impact on quality of life (QoL)7., 8., 9.. A previous study by one of the present authors showed that high stress was related to poor oral health-related QoL (OHRQoL) in a sample of dental patients10.
The information technology (IT) sector is usually considered one of the fastest growing and most competitive of industries worldwide. This is even more so in developing countries in which IT firms compete to win outsourcing contracts from big multinational corporations. India is known as the global hub of outsourcing, where thousands of IT companies employ millions of employees who perform back office work for large companies worldwide. Although competition is high, it is particularly severe among the small and medium-size software companies that make up the bottom of the IT industry pyramid (http://www.livemint.com/2010/02/.../small-midsize-bpo-firms-seek.html). This competition leads to small profit margins and consequently to greater employee workload, which can increase levels of stress.
Working with frequent deadlines has been found to be associated with work-related musculoskeletal disorders11, occupational stress and work exhaustion in IT professionals12. It is possible that manifestations of work stress among IT professionals may impact on oral health and OHRQoL. A search of the literature revealed no studies on the impact of work stress on oral health and OHRQoL among IT professionals. Hence, the objectives of this study were to assess the relationships among work stress, oral health and OHRQoL among IT professionals in south India.
METHODS
The study population consisted of employees working in software companies involved in outsourcing in the coastal area of the state of Karnataka. A total of 20 companies were found, four of which satisfied the criteria for a mid-size company. After obtaining permission, surveys were scheduled and employees who consented to participate in the study were given a self-administered questionnaire consisting of the eight-item Oral Impact on Daily Performance (OIDP) scale13 and a 25-item modified version of the original 167-item Work Stress Questionnaire14. Participants who completed the questionnaires then underwent an oral examination. The English-language versions of the questionnaires were used as an excellent knowledge of English is an essential prerequisite for employment in this industry. Responses on the Work Stress Questionnaire items were graded from 1 (Not at all) to 5 (Very frequent). Total scores ranged from 25 to 125 and higher scores indicated greater work stress. Total scores on the OIDP ranged from 8 to 40 and higher scores indicated greater impact of oral health on daily performance. The objective assessment of oral health status consisted of a caries examination using the decayed, missing and filled teeth (DMFT) index15 and an examination of periodontal health status using the Community Periodontal Index of Treatment Needs (CPITN)16. The examiner was trained and calibrated in the use of the indices by an expert over a period of 2 days. Twenty employees were re-examined after 1 week to test intra-examiner reliability. Kappa scores for the clinical indices ranged from 0.60 to 0.84. Self-reported oral health status was also assessed using questions on gingival bleeding, loose teeth, toothache, decay, food lodging and sensitive teeth. These questions were formulated by the investigators for the present study. This study was conducted in full accordance with the World Medical Association Declaration of Helsinki and permission was obtained from the ethics board of Manipal College of Dental Sciences, Manipal University. Written consent from the participants was obtained prior to the study.
The median work stress score (53) was used to dichotomise the population into those with (scores > 53) and without (scores ≤ 53) work stress. Student’s test and the Mann–Whitney U-test (for non-parametric distribution) were used for comparisons between means. Variables that were found to be significantly associated (relaxed statistical criteria of P ≤ 0.2) with OIDP scores in the bivariate analysis were included in the multiple logistic regression analysis. This analysis was used to assess the impact of different factors on OHRQoL. A P-value of < 0.05 was considered to indicate statistical significance. All statistical analyses were performed using spss Version 16.0 (SPSS, Inc., Chicago, IL, USA).
RESULTS
A total of four mid-size companies (of 200–500 employees) were approached and asked to participate. Two companies agreed. The two participating companies were similar with respect to employee numbers, revenue and domain of expertise. In these two companies, a total of 215 employees were invited to participate; of these, 134 agreed, giving a participation rate of 62.3%. The mean ± standard deviation (SD) age of the participants was 25.97 ± 4.68 years; 71.6% of the sample were male and 80.6% were single. The mean ± SD length of work experience of the sample was 2.65 ± 2.15 years (range: < 1 year to 15 years). The mean ± SD daily working time was 8.85 ± 1.13 hours (range: 8–15 hours). Only nine employees (6.7%) reported a smoking habit.
Mean ± SD work stress, DMFT and CPITN scores for the sample population were 53.82 ± 15.07, 4.23 ± 3.47 and 1.81 ± 0.58, respectively. The mean ± SD OIDP score was 2.7 ± 3.2. A comparison of self-reported oral health status against work stress and OIDP scores showed that although participants who reported oral problems had consistently higher work stress scores, the differences were statistically significant only for gingival bleeding and sensitive teeth. Mean ± SD work stress scores for those who did and did not report bleeding gums were 63.16 ± 16.00 and 51.68 ± 14.08, respectively. Mean ± SD work stress scores for those who did and did not report sensitive teeth were 59.23 ± 15.99 and 52.26 ± 14.51, respectively. However, except for scores on ‘tooth decay’, OIDP scores were significantly higher for those who reported oral problems (Table 1).
Table 1.
Comparisons of scores on the Work Stress Questionnaire and the Oral Impact on Daily Performance (OIDP) scale against self-reported oral health status
| Work stress score | OIDP score | |||||
|---|---|---|---|---|---|---|
| Mean | SD | P-value* | Mean | SD | P-value* | |
| Bleeding gums | ||||||
| No (n = 109) | 51.68 | 14.08 | < 0.001 | 2.38 | 2.94 | 0.003 |
| Yes (n = 25) | 63.16 | 16.00 | 4.48 | 3.72 | ||
| Loose teeth | ||||||
| No (n = 125) | 53.38 | 15.07 | 0.212 | 2.58 | 3.14 | 0.012 |
| Yes (n = 9) | 59.89 | 14.63 | 5.33 | 3.04 | ||
| Toothache | ||||||
| No (n = 113) | 53.62 | 15.05 | 0.721 | 2.56 | 3.18 | 0.076 |
| Yes (n = 21) | 54.90 | 15.53 | 3.90 | 3.08 | ||
| Decay | ||||||
| No (n = 73) | 52.48 | 14.40 | 0.261 | 1.81 | 2.38 | < 0.001 |
| Yes (n = 61) | 55.43 | 15.81 | 3.92 | 3.66 | ||
| Smoking | ||||||
| No (n = 125) | 53.62 | 14.89 | 0.560 | 2.66 | 3.14 | 0.158 |
| Yes (n = 9) | 56.67 | 18.15 | 4.22 | 3.80 | ||
| Food lodging | ||||||
| No (n = 68) | 51.85 | 13.84 | 0.126 | 2.13 | 3.02 | 0.019 |
| Yes (n = 66) | 55.85 | 16.10 | 3.42 | 3.26 | ||
| Sensitive teeth | ||||||
| No (n = 104) | 52.26 | 14.51 | 0.025 | 2.45 | 2.92 | 0.071 |
| Yes (n = 30) | 59.23 | 15.99 | 3.87 | 3.86 | ||
P-values in bold are significant at P ≤ 0.05.
Those with increased work stress reported a higher prevalence of bleeding gums. Oral health-related quality of life was poorer among those with poorer self-reported oral health status.
SD, standard deviation.
A comparison of clinical oral health status against respondents’ work stress and OIDP scores showed that mean DMFT and CPITN scores were significantly greater among those who reported oral impact on their daily performance. Mean ± SD DMFT and CPITN scores for those who reported an impact of oral health were 7.10 ± 4.43 and 2.30 ± 0.67, respectively, compared with 1.56 ± 1.98 and 1.77 ± 0.55, respectively, for those who did not report any such impact. Although similar differences were observed for work stress, they were not statistically significant (Table 2). Scores on the OIDP scale were significantly higher among those with above-average work stress scores. Mean ± SD OIDP scores were 2.06 ± 2.57 and 3.46 ± 3.59 for participants who did and did not report work stress, respectively. This difference was statistically significant (P = 0.01).
Table 2.
Comparisons of scores on the Work Stress Questionnaire and the Oral Impact on Daily Performance (OIDP) scale against clinical oral health status
| Variable | DT | MT | FT | DMFT | CPI | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | SD | Mean | SD | Mean | SD | Mean | SD | Mean | SD | ||
| Work stress | |||||||||||
| No stress | 66 | 1.45 | 1.95 | 0.27 | 0.62 | 1.94 | 2.70 | 3.67 | 3.25 | 1.74 | 0.56 |
| Stressed | 68 | 2.07 | 2.50 | 0.32 | 0.80 | 2.43 | 2.66 | 4.78 | 3.61 | 1.88 | 0.59 |
| T-test P-value | 0.112 | 0.683 | 0.294 | 0.063 | 0.162 | ||||||
| Mann–Whitney U-test P-value | 0.206 | 0.987 | 0.246 | 0.065 | 0.209 | ||||||
| OIDP scale | |||||||||||
| No impact | 124 | 1.56 | 1.98 | 0.30 | 0.70 | 2.16 | 2.65 | 4.00 | 3.30 | 1.77 | 0.55 |
| Impact | 10 | 4.30 | 3.77 | 0.30 | 0.95 | 2.50 | 3.10 | 7.10 | 4.43 | 2.30 | 0.67 |
| T-test P-value | 0.048 | 0.995 | 0.702 | 0.006 | 0.005 | ||||||
| Mann–Whitney U-test P-value | 0.023 | 0.519 | 0.958 | 0.023 | 0.009 | ||||||
P-values in bold are significant at P ≤ 0.05.
There was no relationship between work stress and clinical oral health status. Oral health was poorer among those who reported impact on daily performance.
DT, decayed teeth; MT, missing teeth; FT, filled teeth; DMFT, decayed, missing and filled teeth; CPI, Community Periodontal Index; SD, standard deviation.
Multiple logistic regression was performed to study the roles of various factors in predicting self-perceived OHRQoL. The predictor variables were periodontal disease (CPITN score of 3 or 4), caries experience (DMFT score of > 0), work stress (score of > 53) self-reported oral health status and smoking prevalence. After adjusting for potential confounders such as age and sex, we found that work stress [odds ratio (OR) = 8.9, 95% confidence interval (CI) 1.0–79.5; P = 0.05] and periodontal disease (OR = 10.05, 95% CI 1.92–52.4; P = 0.006) were significant predictors for poor OHRQoL (Table 3).
Table 3.
Multiple logistic regression to study the effects of different variables on scores on the Oral Impact on Daily Performance (OIDP) scale
| Crude OR | Adjusted OR | P-value | 95% CI | |
|---|---|---|---|---|
| Sex | 0.530 | 1.700 | 0.59 | 0.241–12.000 |
| Age | 0.100 | 1.105 | 0.9 | 0.217–5.644 |
| Caries experience | −1.201 | 0.301 | 0.42 | 0.016–5.803 |
| Periodontitis | 2.850 | 17.289 | 0.05 | 0.941–317.655 |
| Work stress | 2.999 | 20.057 | 0.03 | 1.313–306.358 |
| Gingival bleeding* | −1.550 | 0.212 | 0.31 | 0.011–4.272 |
| Loose teeth* | 0.838 | 2.313 | 0.53 | 0.162–32.967 |
| Tooth decay* | 2.085 | 8.045 | 0.08 | 0.765–84.597 |
| Smoking* | 1.827 | 6.217 | 0.17 | 0.455–84.929 |
| Tooth sensitivity* | −0.103 | 0.902 | 0.92 | 0.118–6.902 |
| Toothache* | −0.836 | 0.434 | 0.55 | 0.028–6.777 |
| Food lodging* | −0.110 | 0.896 | 0.90 | 0.146–5.486 |
Self-reported oral health status. P ≤ 0.05 indicates statistical significance.
Periodontal status and work stress were significantly associated with oral health-related quality of life.
Outcome: presence or absence of oral impact on daily performance; outcome adjusted for age and sex.
OR, odds ratio; 95% CI, 95% confidence interval.
DISCUSSION
The objectives of this study were to assess the relationships among work stress, oral health and OHRQoL among IT professionals in south India. We found that periodontal disease and work stress had significant impacts on perceived OHRQoL. Although a search of the literature revealed reports on the association between work stress and QoL17, no reports on the relationship between work stress and OHRQoL were found. We found that those who reported bleeding gums, loose teeth, decayed teeth, food lodging and sensitive teeth had significantly poorer OHRQoL. However, we also found that work stress was greater only among those who reported bleeding gums and sensitive teeth.
In our study, clinical oral health status was poorer among those who reported an impact of oral health on daily performance. No such difference in oral health status was observed between the stressed and non-stressed groups. This study also showed that those with greater work stress had poorer self-reported OHRQoL. Multivariate analysis after controlling for age and sex showed that higher levels of work stress and periodontal disease were significant predictors for poor OHRQoL in the sample population. Those with work stress and periodontal disease were more likely to report an adverse impact of poor oral health on their QoL than those without.
The study did not confirm the previously reported finding that clinical oral health status was associated with work stress5., 6.. One of the reasons for this may refer to the relatively young age (mean age: 25.92 years) of the study population, which may have had a bearing on the results. Smoking as a habit was not found to be associated with either work stress or OHRQoL. The low prevalence of smoking may explain these findings. This is in agreement with the findings of a previous study, which demonstrated a low prevalence of smoking among the well-educated population in the state of Karnataka18. A key limitation of this study concerns its design (i.e. cross-sectional), which precludes any discussion of causality. However, this study is an exploratory work and more detailed studies with larger sample sizes drawn from companies at all levels of the industry pyramid are required.
In conclusion, work stress may be an important predictor for poor OHRQoL. The IT industry needs to consider ways of reducing employees’ work stress as doing so would improve the oral health status of the workforce and lessen the impact of oral health issues on work productivity.
Acknowledgement
The authors would like to express their gratitude to Professor Ray Croucher, Professor of Community Oral Health, School of Dentistry, Queen Mary University, London, UK, for his constant encouragement and guidance during the preparation of this manuscript.
Conflicts of interest
None declared.
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