Results

Table 1 presents the demographic characteristics of the study population. The current smoker group had a greater proportion of males and full time (versus part time) employees than the other two groups. Former smokers tended to be slightly older than the other groups, while never smokers included a greater proportion of minority individuals. However, the differences among the three smoking status groups were not significant for any of these variables.

Table 2 presents absenteeism results by smoking status. This table presents the mean, standard deviation, and range of absenteeism values for each smoking status group. Statistics were performed to evaluate differences across the three groups. Current smokers had greater absenteeism than did never smokers during the study period, as measured by either absenteeism days or attendance interruptions. Former smokers had intermediate absenteeism values except for number of attendance interruptions caused by sickness during the previous year and total study days lost because of work injury, where former smokers had the lowest values. Results were similar for the year before the study period: current smokers had the greatest rate of absenteeism, never smokers had the lowest rate, and former smokers were intermediate. Differences in absenteeism were significant. As presented in the last row of table 2, former smokers had the lowest rate of study days lost because of work injuries. However, all work injury related absenteeism values were small, and these differences were not significant.

Table 3 presents regression analysis results evaluating the impact of time since smoking cessation on absenteeism rates. Multivariate ordinary least squares regression was performed on current and former smokers, controlling for age, sex, full time/part time employment status, and time since smoking cessation (set to zero for current smokers). Using data from the study period, former smokers experienced a decrease in 0.1 absenteeism days and 0.017 attendance interruptions for every year of smoking cessation. Similar results were observed for regression analysis of data from the year before the study: former smokers experienced significant decreases of 0.15 absenteeism days and 0.045 attendance interruptions for each year since cessation. The impact of years of cessation was significant for all of these absenteeism measures with p < 0.05.

Objective productivity measures by smoking status were also examined. As presented in the Methods section, data were provided on 10 productivity categories: five absolute measures for each study participant and five relative measures, comparing each study participant's productivity to that of the relevant co-workers (the DSA). Compared to current smokers, former smokers were more productive for 7 of the 10 categories. The difference in productivity for former smokers (as compared to current smokers) ranged from improvement of 19% to worsening of approximately 6%. The average across all five absolute measures indicated a higher productivity level of over 7% for former smokers compared to current smokers. The percentage of difference in productivity in the relative measures was smaller than that observed in the absolute measures, but the average across these five measures indicated improved productivity of former smokers by 1.72%. Across all 10 objective measures, the mean percentage increase for former smokers compared to current smokers was approximately 4.5%. None of the productivity differences between current and former smokers or current and never smokers reached significance.

As discussed in the Methods section, the summary productivity measure is a derived performance point total. We evaluated total performance points by smoking status. The mean performance points for never smokers (36.0) was greater than that for current smokers (34.6), with higher values reflecting greater objective productivity. Former smokers had a mean total performance points of 37.5, greater than that for either never or current smokers. We also evaluated the impact of time since smoking cessation on former smokers' performance score. Among former smokers who have quit smoking for less than one year, the mean total performance points (24.1) is lower than that for current smokers. However, by one to five years following cessation, the former smoker mean performance value (40.7) is higher than those of current smokers, reflecting greater objective productivity. This value remains fairly consistent; following five or more years of cessation, the mean total performance points for former smokers is 39.8. Differences in total performance points between current and former smokers or current and never smokers did not reach significance.

Table 4 present results from subjective productivity assessment using the HWQ. It presents results from the ANOVA of HWQ subscales by smoking status. Probability values provided in this table represent statistical differences comparing subscale scores across all three smoking status groups; there are two significant differences. First, assessment of productivity by others was significant (p = 0.026), with highest assessment scores among never smokers, lowest among current smokers, and intermediate scores for former smokers. A similar pattern was also seen in the personal life satisfaction subscale (p = 0.015). Differences in the overall productivity subscale scores, which include productivity assessment both by self and by others, approached significance (p = 0.065). Pairwise comparisons of current and former smokers did not yield any significant differences.

As significant differences were observed in the productivity assessment-by-others subscale, we examined differences between current and former smokers for individual items comprising this subscale. Figure 1 presents results from the six items in the productivity assessment by others subscale. These six items capture self reported productivity assessment in three dimensions (efficiency, quality, and amount of work) from two external perspectives (supervisors and co-workers). While none of the pairwise comparisons produced significant differences, former smokers indicated higher productivity levels (as assessed by others) as compared to current smokers for all six items. Differences in the items dealing with quality reported by supervisors or by co-workers approached significance (p = 0.10 and 0.09, respectively).

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Figure 1

HWQ items in assessment by self and assessment by others productivity subscales by smoking status. None of the pairwise comparisons produced significant differences. Differences in the items dealing with quality reported by supervisors or by co-workers approached significance (p = 0.10 and 0.09, respectively).

Discussion

This analysis has evaluated differences in absenteeism, objectively assessed productivity, and subjectively assessed productivity by smoking status in a defined workplace cohort. The small size of the study population limits our ability to assess significant differences. However, changes in absenteeism did differ significantly in comparing never, former, and current smokers. The impact of time since smoking cessation on absenteeism was also significant when comparing only former and current smokers, suggesting a “dose-response” relationship between years of cessation and absenteeism. Objective productivity measures did not significantly differ across the smoking status groups, but trends were observed indicating greater productivity among former versus current smokers and increased productivity with time since smoking cessation. Subjective productivity measured showed a small number of significant differences or trends with smoking status. It is of particular interest that differences by smoking status were not observed when respondents were asked to rate their own productivity; rather, significant differences in productivity subscales were seen only when respondents were asked to specify how others (co-workers or supervisors) would rate their productivity.

There are a number of limitations for this study, mainly related to the study population. In order to capture complete absenteeism and productivity information, we chose a cohort of reservation agents working for a major US airline. The reservation agents are younger than the mean US workforce age, and a larger proportion is female. These workers are highly monitored during the course of the duties, and are aware of the impact of their productivity on remuneration. As such, their productivity values may not correspond to that of workers in other fields or work environments, similar to the Hawthorne effect observed for health behaviours of individuals in health outcomes studies. Further, the tasks for reservation agents involve a defined and specific set of activities. Because of these factors, generalisation of the findings in this study to other types of workers or work environments must be performed with care. However, the results present internally consistent information on the effect of smoking status on absenteeism and productivity. Also, as noted above in Methods, this study was initially planned as a pilot/feasibility study for a more comprehensive workplace based smoking cessation intervention; as such, sample sizes were not established to achieve statistical power.

Previous assessments of productivity losses among smokers have primarily been based upon assumptions. In a model of worksite smoking cessation programmes, Warner and colleagues11 assumed a decrease in productivity of 1% (five minutes per eight hour day for smokers) as a base case. Kristein8 made a similar assumption as a “reasonable underestimate” for productivity losses by smokers, while Weis12 assumed 35 minutes per day due to smoking breaks. A number of studies have provided indirect assessment of the productivity impact of smoking. Robin and Weir13reported that smoking by dentists was associated with decreased motor performance and dexterity and increased fatigue, while Barad14 reported that over 50% of non-smokers experienced difficulty working near a smoker. The present study is the first published attempt to quantify the productivity impact of smoking using both subjective and objective data.

Other studies have shown that the cost of absenteeism and decreased productivity caused by medical conditions can be substantial. For example, Greenberg and colleagues15 estimated the societal costs of absenteeism and decreased productivity for depressed employees were each approximately $12 billion (in 1990). Similarly, Stoller16 estimated that the 1990 societal productivity loss caused by insomnia exceeded $40 billion. The results of our study show that former smokers and never smokers may indeed be more productive than current smokers; this may result in even greater societal costs being attributable to smoking.

In future research, it will be important to assess the generalisability of these results by assessing productivity by smoking status among diverse types of workers. Objective measures constitute a “gold standard” for workplace productivity assessment. However, as many (or most) occupations do not lend themselves to objective evaluation of productivity, use of a subjective instrument that has undergone at least preliminary evaluation will provide a basis for quantitative assessments. We do not believe that the HWQ can completely substitute for objective productivity measures, and this instrument has not yet been validated. However, the differences seen by smoking status among this small study population suggest that the HWQ may be useful for projecting certain aspects of productivity, especially when objective measures cannot be assessed. With growing concerns regarding the cost of medical care and the “value for money” of new preventive and therapeutic interventions, the ability to access the full cost of health care conditions, including their impact on workplace productivity, will become increasingly important.