Journal of Occupational & Environmental Medicine:
ORIGINAL ARTICLES: CME Available for This Article at ACOEM.org
Cost-Effectiveness and Cost-Benefit of a Lifestyle Intervention for Workers in the Construction Industry at Risk for Cardiovascular Disease
Groeneveld, Iris F. PhD; van Wier, Marieke F. MSc; Proper, Karin I. PhD; Bosmans, Judith E. PhD; van Mechelen, Willem PhD, MD; van der Beek, Allard J. PhD
Continued Medical Education
From the Department of Public and Occupational Health, EMGO Institute for Health and Care Research, VU University Medical Center (Dr Groeneveld, Dr Proper, Prof. dr. van Mechelen, and Prof. dr. van der Beek); Body@Work, Research Center Physical Activity, Work and Health (Dr Groeneveld, Dr Proper, Prof. dr. van Mechelen, and Prof. dr. van der Beek); and Department of Health Economics & Health Technology Assessment, Institute of Health Sciences, Faculty of Earth and Life Sciences, VU University (Drs van Wier and Bosmans), Amsterdam, the Netherlands.
Address correspondence to: Prof. dr. A.J. van der Beek, Department of Public and Occupational Health, EMGO+ Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, the Netherlands. E-mail: firstname.lastname@example.org.
This project was funded by Stichting Arbouw, national organization involved in monitoring and improving working conditions and occupational health of workers in construction industry.
Authors Iris Groeneveld, Marieke van Wier, Karin Proper, Judith Bosmans, Willem van Mechelen, and Allard van der Beek have no commercial interest related to this research.
The JOEM Editorial Board and planners have no financial interest related to this research.
Objective: To investigate the cost-effectiveness and cost–benefit of a lifestyle intervention for construction workers with an elevated risk of cardiovascular disease.
Methods: In this randomized controlled trial, usual care was compared to a 6-month individual-based lifestyle intervention. At 6 and 12 months, weight, absenteeism, health care use, and lifestyle-related expenses were determined. Missing data were imputed. A cost-effectiveness analysis was performed from a societal perspective. Uncertainty around the incremental cost-effectiveness ratio (ICER) was estimated by bootstrapped cost–effect pairs. A cost–benefit analysis was performed from an employer's perspective, subtracting the incremental costs from the incremental benefits.
Results: The ICER was €145/kg weight loss. The difference between intervention and control group in net employer costs was €254 (95% CI: −1070 to 1536).
Conclusion: Implementation of this important and effective intervention depends on the societal and employer's willingness to pay.
* Outline key issues related to the design of the “Health under Construction” study, including important factors affecting the quality of the economic evaluation.
* Discuss the main findings of the trial and economic evaluation, along with the implications for cost-effectiveness of workplace lifestyle interventions.
* Summarize the findings and uncertainties of the cost-effectiveness and cost-benefit analyses.
Worldwide, cardiovascular disease (CVD) is the number one cause of death.1 Important precursors of CVD are overweight and obesity, hypertension, and atherosclerosis. These abnormalities are caused to a large extent by an unhealthy lifestyle, that is, an unhealthy diet2 and insufficient physical activity (PA).3 An unhealthy lifestyle and obesity not only affect health and health care expenditures,4–7 but may also have negative consequences for the employer. Insufficient PA is associated with poor work ability8 and sickness absenteeism.9 Likewise, there is ample evidence that obesity is associated with decreased productivity, due to either presenteeism,10 absenteeism,11 or work disability.12,13 Moreover, among obese workers, the incidence of workplace injuries is higher than among normal weight workers.14,15 Thus, physical inactivity, obesity, and CVD are associated with elevated costs for health care, productivity loss, and work disability. Therefore, the necessity of developing and evaluating lifestyle interventions for workers is beyond doubt. Mixed effects of workplace lifestyle interventions have been achieved on lifestyle behaviors and body weight.16–20 It is difficult for employers or decision makers to decide upon implementation of a lifestyle intervention without insight into its costs and monetary benefits. Therefore, an economic evaluation is an important element of each lifestyle intervention trial.
An economic evaluation can comprise various types of analyses, such as a cost-effectiveness analysis, in which the incremental costs associated with an incremental change in a health outcome are determined, or a cost–benefit analysis, in which the incremental costs are subtracted from incremental monetary benefits.21 These analyses can be done from different perspectives, depending on the stakeholder responsible for the costs. In general, workplace lifestyle interventions are equally or more effective in improving health outcomes than usual care, but the costs borne by employers are usually higher.16,22 Several workplace lifestyle interventions were shown to result in a net monetary gain for the employer, since they provided a positive return on investment through a reduction of productivity, and, for U.S. employers, of health care costs.23 Thorough economic evaluations alongside high quality randomized controlled trials (RCTs) of workplace lifestyle interventions aimed at CVD risk reduction are scarce.
In the health under construction study, the effectiveness of an individual-based lifestyle intervention for workers in the construction industry with an elevated risk of CVD was investigated. A significant and sustained decrease in body weight was found.24 The current economic evaluation consisted of a cost-effectiveness analysis from the societal perspective, in which body weight was the outcome of interest. Also, a cost–benefit analysis from the employer's perspective was performed. The time horizon of the economic evaluation was 12 months.
Study Design and Participants
Male workers in the construction industry aged 18 to 65 with an elevated risk of CVD, based on a screening instrument adapted from the Framingham risk score25 that was applied to the results of their periodical health screening (PHS), were invited to participate in the Health under Construction study. The workers who consented to participate were prestratified for work type (blue-collar workers performing the construction work vs. white-collar workers involved in administration and supervision), and individually randomized into the control group or the intervention group by using Random Allocation Software (Version 1.0., Esfāhān, Iran). The persons involved in data collection and data analysis were blinded to group allocation, whereas the participants and intervention providers were not. The design of the study and the characteristics of the study participants were published previously.26,27 The Medical Ethics Committee of the VU University Medical Center approved the study protocol. In the baseline questionnaire, all participants had to indicate whether they preferred to improve PA or diet, or to quit smoking. Since the outcome of interest in the cost-effectiveness study was body weight, only workers who chose to aim at PA or diet were included in the analyses, that is, two-thirds of all participants. The participants in the control group received usual care, consisting of brief information from the occupational physician about their CVD risk profile. Participants in the intervention group received a 6-month intervention aimed at improving lifestyle.
The intervention consisted of three 45 to 60 minute face-to-face counseling sessions at the nearest occupational health service (OHS), and four 15 to 30 minute telephone counseling sessions with an occupational physician or occupational nurse. All counselors received a 2- or 3-day training in motivational interviewing (MI). In the first counseling session, certain predefined items were discussed, including the participant's CVD risk profile, the advantages and disadvantages of behavior change, his willingness, readiness, and perceived confidence in his ability to change, and personal short- and long-term goals. In subsequent sessions, progress, and barriers were discussed and goals could be adjusted. All participants, in the intervention and the control group, received brochures on healthy lifestyle and CVD. Information on the process of the intervention, such as counselors' compliance, has been described elsewhere.28
Body weight was measured at baseline, 6 and 12 months by doctors' assistants at the OHS. Body weight was measured using a digital balance with participants wearing no shoes and no jacket.
To provide insight into all resources used, the number of counseling sessions for each participant was administrated by the counselors between baseline and 6 months. At 6 and 12 months, the number of visits to providers of primary care, specialist care, paramedical care, and alternative care, the use of blood pressure or cholesterol-lowering medication, and the purchases of all products associated with improving diet and physical activity, for example, sports equipment, were reported by the participants by means of a questionnaire. Moreover, every 2 months, at 2, 4, 6, 8, 10, and 12 months, the participants filled in the number of days absent from work in the previous 2 months, a question that was adapted from the questionnaire of Koopmanschap et al.29 The time frame of 2 months was chosen to minimize the risk of recall bias while limiting participant burden.30 At 6 months, the participants reported the number of hours taken off for visits to counseling sessions at the OHS.
Three cost categories were measured: direct health care costs, direct nonhealth care costs, and indirect nonhealth care costs. The direct health care costs included costs for the intervention and costs for other health care consumed. Intervention costs consisted of the costs for the trainer, for printing manuals, and for the face-to-face and telephone counseling sessions, according to prices paid. Also, a €15 reimbursement for the participants for each visit to a counseling session was included, as well as costs for the person who coordinated the counseling sessions, using the number of hours involved, valued as the gross salary including additional employers' costs such as premiums and holiday payments. Costs for visits to care providers were valued using Dutch standard costs.31 For the valuation of visits to care providers for whom standard cost prices were not available, prices according to professional organizations were used. Medication use was valued according to unit prices provided by the Royal Dutch Society for Pharmacy.32 All prices were adjusted to the year 2008, using consumer price indices.33 The direct nonhealth care costs, that is, out-of-pocket costs for purchases associated with improving diet and PA, were self-reported by the participants. Indirect nonhealth care costs were costs associated with absenteeism. Data on the average gross year salaries for blue- and white-collar workers, including employer's costs, were provided by the Dutch Economic Institute for the construction industry. The costs associated with one working day of absenteeism were calculated by dividing the average gross year salary including additional employers' costs by the total amount of working days per year, that is, 260. In the societal perspective, according to the equity principle, costs per working day were considered independent of work type and amounted to €229 per day. When calculating the average gross salary the proportions of white- and blue-collar workers in the study population, that is, 0.32 versus 0.68, were taken into account. However, for the cost–benefit analysis, which is conducted from an employer's perspective, the valuation of an absenteeism day was done according to work type, that is, the costs for a working day of a white-collar worker were €264, and those for a working day of a blue-collar worker were €216.
Multiple Imputations of Missing Values
All missing data on body weight, absenteeism, direct health care costs, direct nonhealth care costs, and indirect nonhealth care costs, were imputed using multiple imputations. Five different data sets were created in SPSS (version 17.0, Chicago, IL) using fully conditional specification and predictive mean matching procedures. All available data on body weight at baseline, 6 and 12 months, group allocation, age, smoking status, absenteeism, and costs, were included in the imputation model. Thereafter the multiple datasets were analyzed as described further, using R (version 2.10.1).34 Pooled estimates were computed following the rules as described by Rubin.35
The main analyses were based on group allocation, regardless of adherence to the intervention. The intervention effect on body weight at 12 months was analyzed in SPSS (version 17.0, Chicago, IL), using linear regression analysis, adjusted for baseline body weight. Outcomes with a P value of < 0.05 were considered statistically significant. The mean cost differences between intervention and control group were calculated for direct health care costs, direct nonhealth care costs, indirect nonhealth care costs, and total costs. The 95% confidence intervals of the cost differences were estimated with approximate bootstrap confidence (ABC) intervals.36 For the cost-effectiveness analysis, an incremental cost-effectiveness ratio (ICER) was estimated by dividing the difference in total costs between the intervention and control groups by the difference in effect on body weight. To graphically present uncertainty around the ratio, bootstrapped cost/effect pairs, using 5000 replications, were plotted in cost-effectiveness planes.37 A cost-effectiveness acceptability curve (CEAC) was estimated to provide insight into the probability that the intervention was cost-effective (y-axis) for a range of potential maximum amounts (x-axis) that a decision maker is willing to pay.38 At a probability of 0.95 or higher, the intervention can be regarded as cost-effective. For the cost–benefit analysis, the incremental costs of the intervention itself were subtracted from the incremental benefits associated with absenteeism. Confidence intervals of the cost differences were estimated by ABC intervals.
To test the robustness of the results, and to evaluate the cost-effectiveness and cost–benefit of the intervention considering different scenarios, several sensitivity analyses were performed: (1) A cost-effectiveness and a cost–benefit analysis in which all absenteeism costs were multiplied by 0.8. An elasticity of 0.8 is frequently used, implying that a 100% loss of work time corresponds to an 80% reduction in productivity;39 (2) A cost-effectiveness and a cost–benefit analysis using a dataset in which all costs for occupational physicians were replaced by costs for occupational nurses. These analyses were done since we found that an intervention performed by an occupational nurse was equally effective in lowering body weight as an intervention performed by a—more expensive—occupational physician; (3). A cost-effectiveness and cost–benefit analysis including the complete cases only. The 95% confidence intervals around the mean cost differences were estimated by using a bootstrap method with 5000 replications.
Of the 816 participants included in the Health under Construction study, 573 participants chose to aim at diet or PA, of whom 293 had been allocated to the intervention group and 280 to the control group. For 285 participants, at any time point, one or more variables were missing, which were mainly cost data. For those cases, the missing data were imputed. A participant flow is presented in Figure 1. In Table 1, the baseline characteristics for the intervention and control group are presented as well as the differences between completers and noncompleters, as derived from the original, unimputed database. Also, the differences between completers and noncompleters in body weight at 12 months are presented. Noncompleters were on average 3 years younger than completers. No significant differences were found between noncompleters and completers in baseline body weight and body weight at 12 months.
Linear regression analysis on the imputed datasets showed a statistically significant effect of the intervention on body weight (β −2.0, 95% CI: −2.9 to −1.2). From baseline to 12-month follow-up, the control group gained on average 1.0 kg, whereas the intervention group lost on average 1.1 kg of body weight.
For the participants in the intervention group, the average number of counseling sessions was 5 (SD 2.3). As missing data on health care use were not imputed, the following data were derived from the unimputed dataset. Participants in the intervention group had on average 5.7 (SD 8.8) visits to a care provider, as opposed to 7.1 (SD 11.1) in the control group. This difference was mainly caused by the number of visits to the physiotherapist; 3.7 (SD 9.8) in the control group and 1.8 (SD 5.7) in the intervention group. In the intervention group, 29% used cholesterol or blood pressure lowering medication at some period during the 12 months, whereas in the intervention group this was 30%. In the intervention group, 69% bought one or more diet- or PA-related products, as compared to 64% in the control group. Of the complete cases, participants in the intervention and control group reported on average 12.3 (SD 29.7) and 9.1 (SD 24.0) days of absenteeism, respectively, and 7.5% versus 5.8% were absent from work for more than 3 months in total. In contrast, based on the imputed dataset, the average number of days absent from work was 14.4 in the intervention group, as opposed to 15.7 in the control group.
All average costs and cost differences are presented in Table 2. The mean intervention costs were €605 (SD 230) per intervention group participant. The direct health care costs, excluding the intervention costs, were significantly lower in the intervention group than in the control group. The total costs, that is, direct health care, direct nonhealth care, and indirect nonhealth care costs, were higher in the intervention group than in the control group.
The ICER was €145/kg weight loss, implying that for one additional kg of body weight lost, the additional societal costs were €145. The uncertainty around this ICER was large, as shown in Figure 2. In this cost-effectiveness plane, 66.6% of the cost–effect pairs were in the northeast quadrant; in these cases, the intervention was more effective and more expensive than usual care, and 33.4% of the cost–effect pairs were in the southeast quadrant. In these cases, the intervention was more effective and less expensive than usual care. According to the CEAC, as shown in Figure 3, if society is not willing to have extra expenses to attain 1 kg extra weight loss, there is a probability of 0.33 that the intervention can be regarded as cost-effective. If society would be willing to pay €2000, the probability would be higher than 0.95, and the intervention could be regarded as cost-effective.
On the basis of the imputed datasets, the employer had a net loss of €254 (95% CI: 1070 to 1536) as a result of the intervention. Again, the uncertainty around this amount was large, as indicated by the 95% confidence interval. The intervention can therefore not be regarded as cost saving.
All cost and effect differences and the ICERs of the main and sensitivity analyses are presented in Table 3. When applying an elasticity of 0.8 in the cost-effectiveness analysis, the ICER was €175/kg weight loss. The cost–benefit analysis resulted in a monetary loss of €324 (95% CI: −741 to 1354) per employee. In case all occupational physicians would be replaced by occupational nurses, the ICER was €128/kg weight loss, and the employer had a monetary loss of €219 (95% CI: −1097 to 1501) per employee. When only analyzing the complete cases, an ICER of €658/kg weight loss was found, which means that the society would have to invest €658 for 1 kg of additional body weight loss. For the employer, the complete cases scenario resulted in a monetary loss of € 1132 (95% CI: −328 to 2601).
The aim of this economic evaluation was to assess the cost-effectiveness and cost–benefit of an individual-based lifestyle intervention for workers in the construction industry with an elevated risk of CVD. The 6-month intervention had a significantly positive effect on body weight at 12-months follow-up. The intervention was more effective, but also more expensive than usual care. To attain 1 kg of body weight loss, as compared to usual care, would cost society €145. If society would be willing to pay €2000, the intervention could be regarded as cost-effective. The cost–benefit analysis showed a monetary loss for the employer of €254 (95% CI: −1070 to 1536) per employee. Hence, the intervention did not appear cost saving.
The intervention effect on body weight of 2 kg was larger than the effect of 1.2 (−0.7 to 1.6) found by Verweij et al20 in a meta-analysis of nine workplace physical activity and dietary behavior intervention studies.20 The relatively large effect in our study may be related to the individual and client-centered approach. Not unexpectedly, due to this individual approach, and the number and duration of sessions, the intervention was relatively expensive. The costs for visits to other care providers and medication costs were significantly lower in the intervention group than in the control group, but did by far not offset intervention costs. When looking more closely to the data, we found that the direct health care costs were lower in the intervention group than in the control group only in the first 6 months of the evaluation period, that is, during the intervention. However, between 6 and 12 months, there was no difference between groups in direct health care use anymore. In other studies, lasting reductions in health care use costs resulting from workplace lifestyle-related interventions have been described.40,41 In those studies, the type of intervention and the health care resources included may have differed from our study. Furthermore, standard costs for health care differ considerably between countries, impairing comparability of studies.42 In our study, there was a nonsignificant reduction in absenteeism costs of €302 during the entire period, between baseline and 12 months. In general, absenteeism costs are skewed and therefore extremely large sample sizes are required to reach significance. Our study was underpowered to be certain about the observed change in absenteeism costs we found.
With respect to cost-effectiveness, most other workplace lifestyle cost-effectiveness studies only took the intervention costs into account.43,44 If we would do so, 1 kg of body weight loss would cost €300, which is 10 times as much as in several other studies. An intervention of Rasu et al,44 consisting of tailored Internet messages and two telephone calls for overweight workers, was equally effective but far less expensive than ours; 1 kg of body weight loss after 6 months could be achieved at the cost of U.S. $25.92 (∼€19.38) per participant.44 Whether this effect was sustained at the long term was not reported. In the British counterweight study, on the effects of group and individual-based sessions among 642 overweight adults, the intervention led to a significant weight loss at 12 months of 3 kg, at the cost of UK £60 (∼€45) per participant.43 In comparison to those studies, additional costs were included in our analyses to obtain a more realistic estimation of the overall societal costs associated with this intervention. One of the few comparable studies in which the cost-effectiveness for weight loss from a societal perspective was investigated was the study of Van Wier et al (Van Wier et al, oral communication, June 1, 2010). From their individual-based lifestyle intervention delivered by Internet, aimed at weight loss of Dutch overweight office workers, an ICER of €16/kg was concluded. This ICER seems more favorable than the one found in our study. Nonetheless, according to Van Wier et al the probability that the Internet intervention would be cost-effective at a willingness to pay of €1000 was 0.8, whereas in our study this probability would be 0.9. Thus, not only the ICER but also the probability of cost-effectiveness should be considered in interpreting the results of an economic evaluation. With respect to cost–benefit studies, we found one study in which a comparable approach, a 9-month individual counseling intervention for Dutch civil servants, was investigated. In that study, the intervention costs were €430 per participant and the absenteeism-related monetary benefit was €125 per participant.22 These results are in line with our study; both workplace lifestyle intervention studies with a moderately long follow-up period resulted in a monetary loss for the employer, as the positive trend toward lower absenteeism costs in the intervention group did not outweigh the intervention costs. Aldana and Pronk45 concluded from the scientific literature that workplace health promotion intervention-induced decreases in absenteeism were usually modest, and that absenteeism reductions did not appear until the end of the evaluation period, after 1–4 years.
As said, the intervention was relatively expensive. Therefore, a cost-effectiveness analysis and a cost–benefit analysis were done assuming a strategy in which only the relatively inexpensive but equally competent occupational health professionals were deployed. Because of all 27 counselors, only three were occupational physicians and thus replaced for the analyses, the difference in ICERs between the main (€145/kg) and the sensitivity (€128/kg) analysis was only modest. Still, the results of these sensitivity analyses indicate the advantages of deploying less expensive care providers. Another sensitivity analysis concerned the valuation of absenteeism. When assuming an elasticity of 0.8, the societal costs associated with the intervention became higher because the absolute difference in absenteeism costs between intervention and control group decreased. If a lower elasticity than 0.8 would be applied, the difference in absenteeism costs would become even lower. It should be noted that for the Dutch construction industry, no data are available on elasticity. In fact, in each job type, a different elasticity might apply. To estimate the cost–benefit and costeffectiveness of an intervention as accurate as possible, the elasticity that is applicable to the working population under study should be determined first.
Strikingly, there were substantial differences in mean absenteeism between the complete cases and the cases for which data were imputed. Among the complete cases, absenteeism was highest in the intervention group, whereas after multiple imputations, total absenteeism was highest in the control group. Thus, the participants in the control group who had incomplete data at certain time points were likely to have a high absenteeism, or predictors of absenteeism, at one or more other time points. On the reasons for the loss to follow-up of participants with high absenteeism in the control group, we can only speculate. A reason for this selective loss to follow-up might have been related to body weight gain during the study (as can be seen in Table 1), implying that control group participants with high absenteeism at baseline might not have appeared at the follow-up measurements because they were dissatisfied with their body weight gain. Another reason might have been that the control group participants who were lost to follow-up were already frequently contacted by the OHS, because of their absenteeism. This underestimation of absenteeism in the control group may have been (partly) corrected for in the imputed datasets. When applying multiple imputations, it is assumed that data are missing at random; an assumption that may not necessarily hold true. Especially when large amounts of data are missing, multiple imputations may lead to flawed results. In our study, for half of all participants, one or more missing values had to be imputed. To make an accurate estimation of the cost-effectiveness of a study, noncompletion should thus be prevented.
Strengths & Limitations
Several strengths of the study need to be mentioned. First, a full economic evaluation was performed within a study with a strong design, that is, a randomized controlled trial. Second, the cost-effectiveness of the intervention was not assessed until 6 months after the intervention had ended. Third, sensitivity analyses were done to indicate the impact of a change in assumptions. All three strengths are proposed by Tompa et al46 because quality indicators for economic evaluations of workplace interventions.46 Last, this trial can be regarded as pragmatic instead of exploratory, since the intervention took place within the OHS and was delivered by OHS professionals. A pragmatic trial is the preferred option for an economic evaluation, as it reflects the costs and effects that will be generated in “real life.”47
A first limitation concerns the fact that the economic evaluation was underpowered to detect differences in costs, as the sample size calculation was based on a clinical outcome. This is problematic since cost data usually follow a highly skewed distribution, implying a need for larger sample sizes in cost-effectiveness studies as compared to effectiveness studies.48 In the design of a trial, the sample size needed for the economic evaluation should already be determined, and sufficient time for recruitment should be planned. Another weakness may be the lack of data on presenteeism. We chose not to measure presenteeism, since it relies on self-report of a difficult concept,49 and there is no golden standard for measuring presenteeism yet.50 A major limitation is that noncompletion was relatively large and partly selective. Hence, many imputations had to be done, part of which were probably not accurate because the missing may not have been completely at random.
In the health under construction study, the intervention was more effective but also more expensive than usual care, resulting in an ICER of €145/kg weight loss. Although the absenteeism costs were slightly lower in the intervention group, the intervention costs were high, resulting in an average monetary loss for the employer of €254. Implementation of the intervention depends, among others, on the societal and employer's willingness to pay for an extra kg of body weight loss. By measuring absenteeism on the longer term, insight could be gained in possible future monetary benefits. As the results we found appeared after the imputation of missing data, these conclusions should be interpreted with caution.
We thank Stichting Arbouw for covering the study costs.
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