ORIGINAL ARTICLE
Workers’ Knowledge and Beliefs About Cardiometabolic Health Risk Olga C. Damman, PhD, Allard J. van der Beek, PhD, and Danielle R.M. Timmermans, PhD
Objective: Investigate workers’ knowledge and beliefs about cardiometabo lic risk. Methods: A survey on the risks of diabetes, cardiovascular disease, and chronic kidney disease was disseminated among Dutch construction workers and employees from the general working population. Results: We had 482 respondents (26.8%) among construction workers and 738 respondents (65.1%) among the general working population. Employees showed reasonable basic knowledge, especially about cardiovascular disease risk factors and risk reduction. Nevertheless, they also had knowledge gaps (eg, specific dietary intake) and showed misconceptions of what elevated risk entails. Employees having lower education, being male, and having lower health literacy demonstrated less adequate knowledge and beliefs. Conclusion: To improve the potential effect of health risk assessments in the occupational setting, physicians should explain what it means to be at elevated cardiometabolic risk and target their messages to employee subgroups.
T
he cardiometabolic diseases diabetes, cardiovascular disease (CVD), and chronic kidney disease are a major health problem worldwide.1 These diseases have common lifestyle-related risk factors2,3 and a lot can be done to reduce cardiometabolic risk, the most important strategies being smoking cessation, becoming more physically active, and adopting a healthy diet.4–7 Nevertheless, to accomplish such lifestyle changes, public awareness and knowledge seem required, because accurate risk knowledge and beliefs are a precondition for several cognitions influencing risk-reducing behavior.8–12 In primary care as well as occupational health services, health risk assessments are increasingly applied to determine people’s health risks.13,14 Disease risk prediction models are used to calculate people’s risk, to educate them about their risk, and to motivate those at elevated risk to modify their lifestyle. Especially in occupational health care, it is feasible to reach large numbers of (relatively healthy) people with different lifestyle and socioeconomic status.15 Other advantages of the occupational setting are that companies are usually able to offer lifestyle interventions to large populations and that they also have an interest in such initiatives (ie, healthy workers and less sick leave). Despite an increase in such strategies, many people in industrialized countries such as the Netherlands continue to smoke, eat unhealthily, and display sedentary behavior.16–18 Effective health education and especially supporting people in acquiring a correct risk understanding has proved to be difficult.19–21 It may be that the epidemiological approach typically endorsed is not the most effective way to get the message across to employees. In this From the Department of Public and Occupational Health and the EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands. This study received financial support from the “Live a Healthy and Long Life” program of the Dutch Diabetes Research Foundation, the Dutch Heart Foundation, and the Dutch Kidney Foundation. The authors declare no conflicts of interest. Address correspondence to: Olga C. Damman, PhD, Department of Public and Occupational Health and the EMGO+ Institute for Health Care and Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, the Netherlands ([email protected]). C 2013 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0000000000000041
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respect, it seems that employees’ own perspective could be more explicitly taken into account in risk education.22 Furthermore, for welltargeted messages, it is important to identify differences in knowledge and beliefs between employee subgroups. People from lower socioeconomic status backgrounds will probably have less correct knowledge and beliefs, because of a cycle of scant formal education, inadequate reading and arithmetic skills, and poor conceptual health knowledge and vocabulary. In addition, the general culture toward health promotion might differ between companies and branches of industry (eg, the construction industry vs the financial sector), which could be related to employees’ knowledge and beliefs. If branch of industry, sociodemographic, or other individual characteristics indeed have a profound effect on specific knowledge and beliefs, this could provide clinicians with suggestions for well-targeted risk education. Although several studies investigated people’s knowledge and beliefs about diabetes and CVD8,23–29 this research has tended to focus on individual diseases, rather than on disease risk as a consequence of unhealthy lifestyle. Because more and more emphasis is placed on the shared risk factors of cardiometabolic diseases as well as the interplay of detrimental physiological processes,30 it is important to assess how workers themselves think about cardiometabolic risk. To achieve better results from cardiometabolic health risk assessments in the occupational setting, we conducted this study, in which the knowledge and beliefs regarding cardiometabolic risk were examined in different Dutch employee subgroups. In addition, we compared employees from the construction industry with employees from the general working population, to be able to look at differences between branches of industry. By investigating potential knowledge gaps and misconceptions, we looked for focal points for better and well-targeted risk education by occupational physicians.
METHODS We used a survey on the risks of diabetes, CVD, and chronic kidney disease; this survey was based on a qualitative study in which concepts important to experts and lay people were identified.31,22 Because a large number of concepts needed to be captured, we divided the survey into two versions.
Participants and Procedure We approached two samples: 1800 construction workers eligible for their periodic health check and 1133 people from the general working population. We chose these samples to be able to look at differences between branches of industry; employees in the construction industry were selected because of their relatively low educational level and socioeconomic status as well as a high prevalence of overweight and obesity, a group of workers for whom lifestyle changes are likely to be highly effective.32 Construction workers might have less correct knowledge and beliefs than those from the general working population because of inadequate conceptual health vocabulary and relatively low health literacy levels, and because of the relatively negative culture toward health promotion in the construction industry.33 Construction workers aged 16 to 65 years were invited via a letter sent by Arbouw. Arbouw is the Dutch national institute coordinating occupational health care for all workers in the construction industry. Construction workers could choose to fill out the questionnaire either on-line or using pencil and paper. JOEM r Volume 56, Number 1, January 2014
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Workers’ Beliefs About Cardiometabolic Risk
Employees from the general working population were drawn from an on-line access panel (FlyCatcher Internet Research, 20,000 panel members in total, ISO 20252 and ISO 26362 certified); panel members aged 16 to 65 years with paid employment were invited through e-mail. We oversampled (60%) people aged between 40 and 65 years because of typical higher cardiometabolic risks in older people.
2 by 374 (65.7%), which made 738 respondents (65.1%). In total, we had 1220 respondents, resulting in a response rate of 41.6% (ie, the total number of respondents divided by the total number of people invited for participation). Table 1 displays respondents’ characteristics. No differences between the two versions were found for the characteristics listed.
Measures
Employees’ Knowledge
Both survey versions consisted of five parts capturing knowledge and beliefs about (1) causes and risk factors, (2) physiological processes, (3) disease symptoms, (4) disease consequences, and (5) risk-reduction strategies. These categories were based on our previously developed expert model of cardiometabolic risk31 and on typical lay representations of diseases.34,35 First, concepts for which it was clear that they were either correct or incorrect were included: expert concepts (eg, genetic predisposition), lay concepts (eg, poverty), and trick questions (eg, infections). Second, we posed questions about concepts whose accuracy was less definite. These predominantly concerned pronounced lay beliefs (eg, having had another disease) and concepts important to both lay people and experts but with experts’ notion that little evidence is available so far (eg, the muscular system). All items were formulated for the three diseases in a matrix format with response categories “true,” “false,” and “don’t know.” Third, we posed questions about people’s representation of the cardiometabolic risk concept, such as multifactorial influence of factors and the diseases’ interconnectedness. These items were measured using a scale from 1 (true) to 5 (false). The survey further consisted of items assessing population risk estimates of the diseases (“Of every 100 people in the Netherlands, I think that . . . . will develop diabetes/CVD/chronic kidney disease during their lifetime,” and “Of every 100 people in the Netherlands, I think that . . . . will develop diabetes/CVD/chronic kidney disease before the age of 60”), sociodemographic background (age, gender, and educational level), health (self-rated general health status, self-rated general lifestyle, having diabetes, CVD, or kidney disease, and having experienced cardiovascular problems), having a family history of diabetes and CVD, and subjective health literacy.36 The questions were carefully worded to avoid medical language and pretested among three construction workers.
The answers to all items are displayed in the Appendix. Table 2 presents the average percentages of correct answers to the knowledge composites as well as differences between employee subgroups in these answers. Employees were most correct regarding concepts related to CVD, followed by diabetes and chronic kidney disease. They were particularly able to identify CVD risk-reduction strategies. Although only few misconceptions were found (eg, sweet food/sugar causes diabetes and drinking too little water causes kidney disease; see the Appendix), employees did show several knowledge gaps. Especially specific risk factors (eg, physical inactivity, ethnicity, and specific dietary intake such as not eating enough grain and fibers), and physiological processes (eg, disturbed metabolism and inflammation processes) were rather unknown to workers (see the Appendix). We found a number of significant differences in knowledge between employee subgroups. Higher-educated workers had better knowledge than lower-educated workers (13 of the 15 composites). Furthermore, women consistently seemed to have more knowledge than men (9 of the 15 composites), especially concerning diabetes risk. Employees with low and high health literacy also differed on a substantial number (5) of composites, with workers with higher health literacy reporting better knowledge.
Statistical Analyses We conducted descriptive analyses to assess the prevalence of knowledge and beliefs. For all items, we calculated average percentages for reported answers. Composite knowledge scores representing knowledge across items were constructed for the five survey parts; only items being either correct or incorrect were included, with “don’t know” being considered incorrect. Concerning items assessing people’s representation of the cardiometabolic risk concept, mean values were calculated and reverse-coded: a higher score indicated a higher average agreement. We used t tests and multiple linear regression analyses to evaluate which individual characteristics affected knowledge and beliefs. In the regression analyses, the following characteristics were considered: age, gender, educational level, subjective health literacy, having a family history of cardiometabolic diseases, having a cardiometabolic disease, and general health status. Furthermore, we assessed whether adding a branch of industry variable (construction workers vs general working population) had an additional influence.
RESULTS Participant Characteristics Among construction workers, version 1 was filled out by 235 workers (26.1%) and version 2 by 247 workers (27.4%), which made 482 respondents (26.8%). Among employees from the general working population, version 1 was filled out by 364 (64.0%) and version
Employees’ Representations of Cardiometabolic Risk Table 3 displays mean values on the items about the cardiometabolic risk concept. Employees were quite familiar with the meaning of high cholesterol and high blood pressure, but high blood glucose levels were poorly comprehended. Employees knew well that there are usually multifactorial causes of cardiometabolic diseases. They were less informed about the diseases’ interconnectedness. Although it seemed that employees were slightly aware of some connection, they also thought that the three diseases had different causes. Respondents were able to define an “elevated risk” as a higher-thanaverage disease risk, but the fact that there may already be physical damage before occurrence of the disease was relatively unknown. Subgroups as defined by educational level differed significantly from each other on 9 of the 18 items representing the cardiometabolic risk concept, with higher-educated employees being more correct than lower-educated employees on eight concepts. Notably, for one item (elevated risk means that there is physical damage [true]), lower-educated people were more correct. We also found that women had more correct representations than men on five items. Employees with low and high health literacy differed on four items, with people with higher health literacy having more correct representations.
Employees’ Risk Estimates Highest estimates were given for the population risks of developing CVD (mean lifetime risk = 33.9%, mean risk before the age of 60 years = 24.0%). For diabetes, the mean lifetime risk was rated as 27.6% and the mean risk before the age of 60 years as 19.0%. Concerning kidney disease, the mean lifetime risk was estimated as 17.5% and the mean risk before the age of 60 years as 12.5%. Consistent differences in employee subgroups were found for educational level (with higher-educated people reporting lower risk estimates) and having a family history of diabetes and/or CVD
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TABLE 1. Characteristics of Respondents Participating in the Study (n = 1120)
Variable Age, y 16–29 30–39 40–49 50–59 60–69 Gender Male Female General health status (self-rated) Excellent Very good Good Fair Poor General lifestyle (self-rated) Very healthy Healthy Not particularly healthy or unhealthy Unhealthy Very unhealthy Education* Low Medium High Health literacy† Inadequate Adequate Having one of the diseases Diabetes (yes) CVD (yes) Chronic kidney disease (yes) Family history of diseases‡ Diabetes (yes) CVD (yes)
General Working Population (n = 738), n (%)
Total, n (%)
Construction Workers (n = 482), n (%)
139 (11.6) 177 (14.8) 357 (29.8) 434 (36.3) 89 (7.4)
24 (5.2) 42 (9.2) 113 (24.7) 237 (51.7) 42 (9.2)
115 (15.6) 135 (18.3) 244 (33.1) 197 (26.7) 47 (6.4)
842 (70.2) 358 (29.8)
441 (95.5) 21 (4.5)
401 (54.3) 337 (45.7)
77 (6.6) 212 (18.1) 717 (61.1) 153 (13.0) 14 (1.2)
28 (6.1) 82 (17.8) 296 (64.2) 50 (10.8) 5 (1.1)
49 (6.9) 130 (18.3) 421 (59.1) 103 (14.5) 9 (1.3)
91 (7.8) 531 (45.3) 458 (39.1)
24 (5.2) 220 (47.8) 195 (42.4)
67 (9.4) 311 (43.7) 263 (36.9)
83 (7.1) 9 (0.8)
21 (4.6) ...
62 (8.7) 9 (1.3)
486 (42.2) 365 (31.7) 301 (26.1)
295 (66.9) 108 (24.5) 38 (8.6)
191 (26.9) 257 (36.1) 263 (37.0)
255 (21.7) 918 (78.3)
182 (39.5) 279 (60.5)
73 (10.3) 639 (89.7)
61 (5.2) 91 (7.8) 13 (1.1)
23 (5.0) 50 (10.9) 5 (1.1)
38 (5.3) 41 (5.8) 8 (1.1)
175 (16.1) 370 (34.1)
66 (14.3) 167 (36.4)
109 (17.4) 203 (32.4)
* Low: primary school, lower level of secondary school, or lower vocational training. Medium: higher level of secondary school or intermediate vocational training. High: higher vocational training or university. †On the basis of an 1-item measurement (“How confident are you filling out forms by yourself?”) tested by Chew et al.36 ‡On the basis of the following questions derived from the Dutch national cardiometabolic health risk check: “Does your father, mother, brother, or sister have type 2 diabetes?” and “Did your mother, father, brother, or sister have a cardiovascular disease before the age of 65?” CVD, cardiovascular disease.
(with people with a family history reporting higher estimates on all three risks).
Multiple Regression Analyses In the multiple regression analyses, the same pattern of subgroup differences appeared as demonstrated by t tests. Educational level and gender were most predictive of knowledge and beliefs. Overall, a higher education was associated with better knowledge and more correct beliefs, as well as with lower risk estimates (β value ranging from 0.08 to 0.26). The effect that lower-educated employees knew better that there can be already physical damage in the case of an elevated risk continued to exist while adjusting for 94
other variables (β = 0.10; P = 0.01). Female gender was generally related to better knowledge and beliefs (β value ranging from 0.06 to 0.17), except for two concepts (high blood glucose levels means too much of the hormone insulin in your blood (β = 0.08; P = 0.10) and the representation that an elevated risk means there is nothing serious (β = 0.06; P = 0.05). Female gender was also associated with higher risk estimates (β value ranging from 0.08 to 0.14). Adding the branch of industry did not result overall in more variance explained. We found this variable to be a significant predictor leading to more variance explained for only a few concepts, namely for knowledge about CVD physiological processes (β = −0.08; P = 0.04), representations of abdominal obesity (mainly fat
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TABLE 2. Mean Percentages of Correct Answers Regarding the Knowledge Composites* (n = 1120) % Correct Answers Across Items (Range Across Items) Diabetes Causes and risk factors Physiological processes Symptoms Consequences Risk-reducing strategies Cardiovascular diseases Causes and risk factors Physiological processes Symptoms Consequences Risk-reducing strategies Chronic kidney disease Causes and risk factors Physiological processes Symptoms Consequences Risk-reducing strategies
45.9 (16.1–87.2)†‡§ 48.8 (19.1–93.4)†‡ 51.1 (12.9–81.6)†‡|| 54.5 (33.5–82.6)†‡|| 53.9 (12.7–86.8)†§# 63.0 (11.5–96.7)‡ 43.8 (20.0–94.7)‡ 60.3 (37.0–94.7)‡|| 49.7 (18.5–90.2)‡ 73.4 (25.4–96.3)†‡** 41.1 (12.7–66.0)‡** 45.6 (21.0–66.1)‡ 36.1 (8.6–66.7)†‡|| 56.8 (24.5–83.8)†‡|| 48.6 (13.2–72.2)†
*All t tests were performed with significance level of 0.01. †Significant difference between male and female employees, with women having better knowledge than men. ‡Significant difference between lower- and higher-educated employees, with higher-educated people having better knowledge than lower-educated people §Significant differences between employees with good general health and employees with bad general health, with people in bad health having better knowledge than people in good health. ||Significant difference between employees with inadequate health literacy and employees with adequate health literacy, with higher-literate people having better knowledge than lower-literate people. #Significant difference between employees having one of the cardiometabolic diseases and “healthy” employees, with people having a disease being more knowledgeable than people without a disease, and people without a disease being more knowledgeable than people with a disease. **Significant difference between younger and older employees, with younger people having better knowledge than older people, and older people having better knowledge than younger people.
on your belly is a risk: β = −0.12; P = 0.03), elevated risk representations (elevated risk means that you have certain complaints: β = 0.11; P = 0.00), and representations of the diseases’ interconnectedness (same type of diseases: β = −0.12; P = 0.00; different causes: β = 0.09; P = 0.02). For all these concepts, people from the general working population showed more correct responses. Adding the variable also resulted in more explained variance of CVD risk estimates (lifetime risk: β = 0.08; P = 0.04; risk before the age of 60 years: β = 0.10; P = 0.01) and kidney disease (lifetime risk: β = 0.14; P = 0.00; risk before the age of 60 years: β = 0.12; P = 0.00), with construction workers reporting higher risk percentages.
DISCUSSION In this study, we demonstrated that Dutch employees had reasonable levels of knowledge about the principal concepts related to cardiometabolic risk, especially concerning CVD risk. At the same time, they seemed to have only vague knowledge about the interconnectedness of cardiometabolic diseases, and misconceptions of what elevated risk entails. An important finding is that, compared with CVD, people had less knowledge about diabetes and chronic kidney disease. It may be
Workers’ Beliefs About Cardiometabolic Risk
TABLE 3. Mean Values Regarding Lay Representations of the Cardiometabolic Risk Concept (n = 1120)* Lay Belief Biological risk factors High cholesterol means too much sugar in your blood High cholesterol means too much fat in your blood Mainly fat on your belly is a risk Mainly fat on your hips is a risk High blood pressure means too much fat in your blood High blood pressure means excessive pressures in the walls of your arteries High blood glucose levels means too much sugar in your blood High blood glucose levels means too much of the hormone insulin in your blood Multifactorial influence Usually one single cause of the diseases Usually various causes of the diseases Elevated risk concept Elevated risk means having certain complaints Elevated risk means higher-than-average chance of developing disease Elevated risk means there is nothing serious Elevated risk means being ill Elevated risk means there is physical damage Interconnectedness diseases The three diseases are interrelated The three diseases are the same type of diseases The three diseases have different causes
M (SD)
1.83 (1.23)†‡§|| 4.39 (1.06)||¶ 4.08 (1.19)†‡ 2.50 (1.24) 2.32 (1.29)‡ 4.37 (0.99) 3.69 (1.44) 3.11 (1.51)
2.20 (1.37)†‡§¶ 4.45 (0.82) 2.35 (1.58)†‡§¶ 4.73 (0.65)†‡§ 2.94 (1.47) 1.76 (1.17)‡ 2.41 (1.43)‡¶ 3.18 (1.35)‡ 2.12 (1.16) 4.22 (0.95)
*A higher mean value indicates a higher agreement with the statement. All t tests were performed with significance level of 0.01. †Significant difference between male and female employees, with women having better knowledge than men. ‡Significant difference between lower- and higher-educated employees, with higher-educated people having better knowledge than lower-educated people, and lower-educated people having better knowledge than higher-educated people. §Significant difference between employees with inadequate health literacy and employees with adequate health literacy, with higher-literate people having better knowledge than lower-literate people. ||Significant difference between employees having one of the cardiometabolic diseases and “healthy” employees, with people having a disease being more knowledgeable than people without a disease, and people without a disease being more knowledgeable than people with a disease. ¶Significant difference between younger and older employees, with younger people having better knowledge than older people, and older people having better knowledge than younger people.
that the public has adequately learned about CVD risk through intensive educational messages, while coverage of diabetes and kidney disease risk has been more rare. This pattern would correspond to UK findings that CVD receives more media coverage than diabetes.37 In this respect, it is encouraging that a substantial percentage of employees did identify overweight as a diabetes risk factor, a factor that steadily receives more media attention.38 An important knowledge gap, however, concerned physical inactivity as a risk factor for diabetes. Although these figures have been worse (eg, only 32% of Dutch respondents linked physical inactivity to diabetes in 2007,39 a more prominent focus on physical inactivity in risk education seems warranted (see also Refs. 40 and 41). Health risk assessments at work may be the ideal setting to achieve this; increasing numbers of
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people have sedentary occupations and several effective workplace interventions have been developed.42 Although the entire survey concentrated on the three diseases and the link between them may have become apparent, employees failed to answer correctly about this relation. This may nevertheless not sound surprising, as the relation between diseases is complex and not broadly reported on.37 Perhaps physicians are reluctant to discuss such details and address it only when people are already affected by one of the diseases. Although such reluctance on the part of physicians seems justifiable, the downside is that people at risk will not focus on the combination of factors that brings about a set of detrimental cardiometabolic processes, and that their knowledge remains fragmentary. Because more than three quarters of respondents also explicitly stated that the three diseases have different causes, we would urge for more effective education about this aspect. In health risk assessments, occupational physicians could explicitly address cardiometabolic risk and explain it in relation to the three diseases, instead of focusing on either diabetes, CVD, or chronic kidney disease. Especially for construction workers, such an approach may be worthwhile, as they showed relatively less correct knowledge of the diseases’ interconnectedness. A final finding to reflect on is that, although employees were able to think of an elevated cardiometabolic risk as a “higher-thanaverage chance of disease,” this seemed to be an abstract concept without sufficient meaning. Importantly, employees did not know that an elevated risk can mean that there is physiological damage already, and notably higher-educated employees seemed to be even less knowledgeable than the lower-educated employees. Many employees believed that “there is nothing serious” when you have an elevated risk, and especially lower-educated employees and construction workers thought that an elevated risk means that you have certain complaints. Hence, while the lower-educated employees seem to have trouble distinguishing an elevated risk from actually having a disease, the higher-educated employees seem to be insufficiently aware of the physical damage that can be present during early stage diseases. These findings are particularly interesting in light of research indicating that people usually have difficulty in deriving meaning from numerical risk information.43,44 Verbal labels such as “elevated” are often used in combination with numerical risk information to support people in deriving meaning from risk information. An important question is how we should use such verbal labels if people have misconceptions about the label itself.
LIMITATIONS We had a relatively low response rate among construction workers (26.8%, compared with 65.1% in the general working population). More differences between the branches of industry might have been demonstrated, if we had had a higher response rate among construction workers. It seems plausible that respondents (compared with nonrespondents) among construction workers were more similar to the people from the general working population (because they were people motivated to complete surveys). Another limitation might be that we had only the construction industry as a separate branch of industry to compare to the general working population. Because of the financial crisis, other branches of industry that were initially interested in participation unfortunately declined to participate. Further research could focus on employees’ knowledge and beliefs about the risks communicated in risk assessments in different branches of industry. Another limitation might be that we did not test the questionnaire among a large group of people. Nevertheless, pretesting has been done by collecting information from three interviews, where the third interview did not reveal new insights in addition to the two earlier interviews. Nevertheless, it should be mentioned that, in our pretest, we focused on interpretation problems with specific concepts, and we did not evaluate the complete experience of filling out the questionnaire, nor did we ask respondents 96
for suggestions for improvements. A final limitation is that we had only self-reported data on general health and lifestyle, and no information on employees’ actual cardiometabolic risk. Future research could focus on the relation between the extent of people’s actual risk and their knowledge and beliefs.
PRACTICE IMPLICATIONS To achieve better results from cardiometabolic risk assessments at work, occupational physicians should (1) explain the relation between cardiometabolic diseases and the overlap in risk factors to the employee. Especially physicians working for the construction industry should focus on explaining the role of abdominal obesity and the accompanying physiological processes as key factors for the three diseases, because it seemed that construction workers were particularly ignorant on these issues compared with the general working population; (2) check what an elevated cardiometabolic risk means for the employee (eg, let them explain the risk in their own words) and help produce a meaningful risk understanding. For example, physicians could compare an employee’s risk with that of an average employee of their age in their branch of industry, or with an average person their age in the general working population. It seems that such support should especially be targeted at employee subgroups as defined by educational level, gender, and health literacy, and to some extent also to the employee’s branch of industry. Further tests are needed to assess whether such specific support as noted earlier will actually motivate lifestyle changes, and to investigate whether providing the support can be integrated in occupational health guidelines.45
ACKNOWLEDGMENT The authors thank Cor van Duijvenbooden (Arbouw) for his help in organizing the data collection in the construction industry.
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13. Freedman AN, Seminara D, Gail MH, et al. Cancer risk prediction models: a workshop on development, evaluation, and application. J Natl Cancer Inst. 2005;97:715–723. 14. Nielen MM, Assendelft WJ, Drenthen AJ, van den Hombergh P, van Dis I, Schellevis FG. Primary prevention of cardio-metabolic diseases in general practice: a Dutch survey of attitudes and working methods of general practitioners. Eur J Gen Pract. 2010;16:139–142. 15. Lusk SL, Raymond DM III. Impacting health through the worksite. Nurs Clin North Am. 2002;37:247–256. 16. Ooijendijk WTM, Hildebrandt VH, Hopman-Rock M. Bewegen in Nederland. 2000-2005. Trendrapport Bewegen en Gezondheid 2004/2005. Hoofddorp/Leiden, the Netherlands: TNO; 2007. 17. Ramsey F, Ussery-Hall A, Garcia D, et al. Prevalence of selected risk behaviors and chronic diseases—Behavioral Risk Factor Surveillance System (BRFSS), 39 steps communities, United States, 2005. MMWR Surveill Summ. 2005;57:1–20. 18. Rijksinstituut voor Volksgezondheid en Milieu. Nationaal Kompas Volksgezondheid: Volksgezondheid Toekomstverkenningen. Bilthoven, the Netherlands: Rijksinstituut voor Volksgezondheid en Milieu; 2010. 19. Edwards AG, Evans R, Dundon J, Haigh S, Hood K, Elwyn GJ. Personalised risk communication for informed decision making about taking screening tests. Cochrane Database Syst Rev. 2006;4:CD001865. 20. Hanlon P, McEwen J, Carey L, et al. Health checks and coronary risk: further evidence from a randomized controlled trial. BMJ. 1995;311: 1609–1613. 21. Sheridan SL, Viera AJ, Krantz MJ, Ice CL, Steinman LE, Peters KE. The effect of giving global coronary risk information to adults. Arch Intern Med. 2010;170:230–239. 22. Morgan MG, Fischhoff B, Bostrom A, Atman CJ. Risk Communication. A Mental Models Approach. New York, NY: Cambridge University Press; 2002 23. Furze G, Bull P, Lewin R, Thompson DR. Development of the York Angina Beliefs Questionnaire. J Health Psychol. 2003;8:307–316. 24. Furze G, Lewin RJP, Murberg T, Bull P, Thomson DR. Does it matter what patients think? The relationship between changes in patients’ beliefs about angina and their psychological and functional status. J Psychosom Res. 2005;59:323–329. 25. Jafary FH, Aslam F, Mahmud H, et al. Cardiovascular health knowledge and behavior in patient attendants at four tertiary care hospitals in Pakistan—a cause for concern. BMC Public Health. 2005;5:124. 26. Kayaniyil S, Ardern C, Winstanley J, et al. Degree and correlates of cardiac knowledge and awareness among cardiac inpatients. Patient Educ Couns. 2009;75:99–107. 27. Momtahan K, Berkman J, Sellick J, Kearns SA, Lauzon N. Patients’ understanding of cardiac risk factors: a point-prevalence study. J Cardiovasc Nurs. 2004;19:13–20. 28. Petrie KJ, Weinman J, Sharpe N, Buckley J. Role of patients’ view of their illness in predicting return to work and functioning after myocardial infarction: longitudinal study. BMJ. 1996;312:1191–1194.
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29. Valerio MA, Kanjirath PP, Klausner CP, Peters MC. A qualitative examination of patient awareness and understanding of type 2 diabetes and oral health care needs. Diabetes Res Clin Pract. 2011;93:159–165. 30. Cardiometabolic Risk Working Group. Cardiometabolic risk in Canada: a detailed analysis and position paper by the Cardiometabolic Risk Working Group. Can J Cardiol. 2011;17:e1–e33. 31. Damman OC, Timmermans DRM. Educating health consumers about cardiometabolic health risk: what can we learn from lay mental models of risk? Patient Educ Couns. 2012;89:300–308. 32. Groeneveld IF, Proper KI, van der Beek AJ, van Mechelen W. Sustained body weight reduction by an individual-based lifestyle intervention for workers in the construction industry at risk for cardiovascular disease: results of a randomized controlled trial. Prev Med. 2010;51:240–246. 33. Groeneveld IF, Proper KI, van der Beek AJ, Hildebrand VH, van Mechelen W. Factors associated with non-participation and drop-out in a lifestyle intervention study for workers with an elevated risk of cardiovascular disease. Int J Behav Nutr Phys Act. 2009;6:80. 34. Leventhal H, Benyamini Y, Brownlee S, et al. Illness representations: theoretical foundations. In: Petrie KJ, Weinman JA, eds. Perceptions of Health and Illness. Amsterdam, the Netherlands: Harwood Academic Publishers; 1997:19–45. 35. Hagger M, Orbell S. A meta-analytic review of the common-sense model of illness representations. Psychol Health. 2003;18:141–184. 36. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients with inadequate health literacy. Fam Med. 2004;36:588–594. 37. Hellyer NE, Haddock-Fraser J. Reporting diet-related health issues through newspapers: portrayal of cardiovascular disease and type 2 diabetes. Health Educ Res. 2010;26:13–25. 38. Gollust SE, Lantz P. Communicating population health: print news media coverage of type 2 diabetes. Soc Sci Med. 2009;69:1091–1098. 39. Dutch Diabetes Research Foundation. Onderzoek Preventie Diabetes. Tussenmeting 2010. Amersfoort, the Netherlands: Diabetes Research Foundation/GfK; 2010. 40. Reiner Z, Sonicki Z, Tedeschi-Reiner E. Public perceptions of cardiovascular risk factors in Croatia: the PERCRO survey. Prev Med. 2010;51:491–496. 41. Khan KM. Prescribing exercise in primary care. BMJ. 2011;343:d4141. 42. Freak-Poli R, Wolfe R, Backholer K, de Courten M, Peeters A. Change in risk factors for cardiovascular disease and diabetes amongst participants in a fourmonth pedometer-based workplace health program. Prev Med. 2011;53:162– 171. 43. Weinstein ND. What does it mean to understand a risk? Evaluating risk comprehension. J Natl Cancer Inst Monogr. 1999;25:15–20. 44. Fagerlin A, Zikmund-Fisher BJ, Ubel PA. Helping patients decide: ten steps to better risk communication. J Natl Cancer Inst. 2011;103:1436–1443. 45. Verweij LM, Proper KI, Weel AN, Hulshof CT, Van Mechelen W. The application of an occupational health guideline reduces sedentary behavior and increases fruit intake at work: results from an RCT. Occup Environ Med. 2012;69:500–507.
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APPENDIX Survey Items and Average Percentages of People Stating That the Item Is True (n = 1120) % True†
Section of the Survey Causes and risk factors version 1
Causes and risk factors version 2
Physiological processes version 1
Physiological processes version 2
Disease symptoms version 1
Disease symptoms version 2
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Item Genetic predisposition Sweet food, food with much sugar Poverty, having a low income Salty food Sex (being a woman) Drinking too little water Alcohol consumption High cholesterol levels Overweight Eating too little whole grain and fiber A virus An irregular sleeping pattern/sleep disturbance Lack of physical activity Fat food Ethnicity Smoking Having had another disease Stress Ageing Pollution A high blood pressure Family members having the disease or having had it Infections Eating too few vegetables and fruit Disturbed metabolism Veins that sludge Lung processes Glucose levels in the blood The muscular system The bronchial system Hormones Vasoconstriction Inflammation processes Proteins in urine Fluid retention Frequent urinating Fever Pain in the legs when walking Nausea and vomiting Muscular pain Fatigue/lack of energy Being thirsty Decreased appetite Chest pain Fainting Oppressed feeling
Type of Item*
Diabetes
CVD
Chronic Kidney Disease
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
62.6 58.1 21.5 14.9 8.0 23.0 38.3 38.6 87.2 20.3 9.0 17.9 50.1 63.3 16.1 32.5 16.6 26.2 81.4 11.0 39.0 81.9
75.8 45.7 30.3 69.8 5.8 17.2 64.8 90.4 92.7 27.2 23.9 40.4 89.0 96.7 11.5 95.9 25.8 91.4 81.9 30.8 94.0 83.4
35.9 21.1 12.7 61.9 2.2 65.8 66.0 28.7 49.1 15.7 27.4 12.2 15.3 40.3 6.8 32.0 25.2 23.0 61.3 28.2 42.9 59.7
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
18.2 46.3 59.5 19.1 6.5 93.4 24.6 6.5 44.7 21.3 31.1 37.1 28.1 46.4 5.5 29.7 12.9 16.3 66.6 78.6 23.2 2.7 81.6 12.7
31.1 57.0 29.0 94.7 53.7 31.0 52.5 54.1 27.3 94.7 42.3 16.3 42.5 7.4 10.2 52.9 16.4 32.8 79.6 3.9 13.5 94.7 59.3 92.5
50.7 33.8 55.8 21.0 9.4 32.5 19.5 10.0 31.3 33.2 57.3 66.1 66.7 47.6 19.7 13.6 21.3 13.4 51.7 41.0 20.8 1.5 21.7 8.6 (continues)
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Workers’ Beliefs About Cardiometabolic Risk
APPENDIX (Continued) % True*
Section of the Survey Disease consequences version 1
Disease consequences version 2
Disease consequences (both versions)
Risk reduction strategies version 1
Risk reduction strategies version 2
Section of the Survey Multifactorial influence version 1 Multifactorial influence version 2 The elevated risk concept (both versions)
Section of the Survey Genetic predisposition version 1
Chronic Kidney Disease
Type of Item†
Diabetes
Complications/infections Psychosocial problems Dialysis Physical constraints Amputations of limbs Eye disorders Premature death Paralysis Changed daily life Organ transplantation Developing diabetes
1 1 1 1 1 1 1 1 1 1 1
56.9 41.4 16.6 62.4 71.1 82.6 63.2 13.6 79.1 12.5 —
56.2 48.4 6.9 82.2 18.5 11.4 90.2 42.6 79.0 69.5 23.9
64.8 38.5 80.6 74.6 6.1 12.6 69.7 10.5 75.1 80.8 25.6
Developing CVD Developing chronic kidney disease Eating fewer sweet foods and sugars Moving to the country Losing weight Eating more fruit and vegetables Using medication Not smoking/smoking cessation Drinking clean water Walking and cycling Regular examinations by physicians Eating less fat food Relaxing/avoiding stress Eating less salt Eating more whole grain and fiber Surgery and medical treatment Eating less food Abstaining from alcohol Having regular physical activity/going to a fitness center Administering insulin
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
37.2 34.2 76.9 3.8 68.8 69.1 54.3 61.2 47.0 74.4 79.1 67.0 50.9 42.4 54.2 31.3 45.3 56.4 60.6
— 22.1 56.6 17.2% 90.6 80.1 61.4 91.3 42.8 92.6 80.1 96.3 95.5 87.3 67.1 67.2 51.0 65.9 87.8
23.9 — 32.8 4.3 47.7 59.4 52.9 56.7 68.1 55.6 76.6 50.4 41.6 72.2 45.6 58.8 24.0 67.6 42.4
1
77.7
2.7
5.3
Item
Item Usually one single cause of the diseases Usually various causes of the diseases Elevated risk means having certain complaints
Type of Item† 2 2 2
CVD
% True* Cardiometabolic Diseases in General 22.3 88.8 33.0
Elevated risk means higher-than-average chance of developing disease Elevated risk means there is nothing serious Elevated risk means being ill Elevated risk means there is physical damage
2
94.2
2 2 2
46.6 13.4 31.9
Item
Type of Item† 2
Genetic predisposition means that disease runs in your family Genetic predisposition means a congenital disease Genetic predisposition means a disease in your genes
2 2
% True* Cardiometabolic Diseases in General 90.0 56.2 92.4 (continues)
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APPENDIX (Continued) Section of the Survey
Item
Cholesterol version 1
High cholesterol means too much sugar in your blood High cholesterol means too much fat in your blood Mainly fat on your belly is a risk Mainly fat on your hips is a risk The risk increases with ageing The risk decreases after reaching 65 years High blood pressure means too much fat in your blood High blood pressure means excessive pressure in the walls of your arteries High blood glucose levels mean too much sugar in your blood High blood glucose levels mean too much of the hormone insulin in your blood The three diseases are interrelated
Abdominal obesity version 1 Ageing version 2 Hypertension version 2
Blood glucose levels version 2
Interconnectedness diseases (both versions)
Types of diabetes (version 1)
Types of diabetes (version 2)
The three diseases are the same type of diseases The three diseases have different causes Late onset diabetes is non–insulin-dependent diabetes Type 2 diabetes only apparent in older persons Type 2 diabetes means no insulin supply by the body Type 1 diabetes is innate
Type of Item† 2
% True* Cardiometabolic Diseases in General 12.0
2
83.2
2 2 2 2 2
70.1 20.1 95.4 5.6 18.3
2
80.7
2
61.6
2
42.4
2
47.8
2
12.1
2 2
79.7 36.0
2 2
15.6 35.9
2
36.6
*1, true, false, do not know; 2, 5-point scale (true/false). †For the items on cardiometabolic diseases in general with response category on a 5-point scale, the percentages of “true” and “maybe true” were totaled.
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Workers’ Knowledge and Beliefs About Cardiometabolic Health Risk Olga C. Damman, PhD, Allard J. van der Beek, PhD, and Danielle R.M. Timmermans, PhD
Objective: Investigate workers’ knowledge and beliefs about cardiometabo lic risk. Methods: A survey on the risks of diabetes, cardiovascular disease, and chronic kidney disease was disseminated among Dutch construction workers and employees from the general working population. Results: We had 482 respondents (26.8%) among construction workers and 738 respondents (65.1%) among the general working population. Employees showed reasonable basic knowledge, especially about cardiovascular disease risk factors and risk reduction. Nevertheless, they also had knowledge gaps (eg, specific dietary intake) and showed misconceptions of what elevated risk entails. Employees having lower education, being male, and having lower health literacy demonstrated less adequate knowledge and beliefs. Conclusion: To improve the potential effect of health risk assessments in the occupational setting, physicians should explain what it means to be at elevated cardiometabolic risk and target their messages to employee subgroups.
T
he cardiometabolic diseases diabetes, cardiovascular disease (CVD), and chronic kidney disease are a major health problem worldwide.1 These diseases have common lifestyle-related risk factors2,3 and a lot can be done to reduce cardiometabolic risk, the most important strategies being smoking cessation, becoming more physically active, and adopting a healthy diet.4–7 Nevertheless, to accomplish such lifestyle changes, public awareness and knowledge seem required, because accurate risk knowledge and beliefs are a precondition for several cognitions influencing risk-reducing behavior.8–12 In primary care as well as occupational health services, health risk assessments are increasingly applied to determine people’s health risks.13,14 Disease risk prediction models are used to calculate people’s risk, to educate them about their risk, and to motivate those at elevated risk to modify their lifestyle. Especially in occupational health care, it is feasible to reach large numbers of (relatively healthy) people with different lifestyle and socioeconomic status.15 Other advantages of the occupational setting are that companies are usually able to offer lifestyle interventions to large populations and that they also have an interest in such initiatives (ie, healthy workers and less sick leave). Despite an increase in such strategies, many people in industrialized countries such as the Netherlands continue to smoke, eat unhealthily, and display sedentary behavior.16–18 Effective health education and especially supporting people in acquiring a correct risk understanding has proved to be difficult.19–21 It may be that the epidemiological approach typically endorsed is not the most effective way to get the message across to employees. In this From the Department of Public and Occupational Health and the EMGO+ Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands. This study received financial support from the “Live a Healthy and Long Life” program of the Dutch Diabetes Research Foundation, the Dutch Heart Foundation, and the Dutch Kidney Foundation. The authors declare no conflicts of interest. Address correspondence to: Olga C. Damman, PhD, Department of Public and Occupational Health and the EMGO+ Institute for Health Care and Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, the Netherlands ([email protected]). C 2013 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0000000000000041
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respect, it seems that employees’ own perspective could be more explicitly taken into account in risk education.22 Furthermore, for welltargeted messages, it is important to identify differences in knowledge and beliefs between employee subgroups. People from lower socioeconomic status backgrounds will probably have less correct knowledge and beliefs, because of a cycle of scant formal education, inadequate reading and arithmetic skills, and poor conceptual health knowledge and vocabulary. In addition, the general culture toward health promotion might differ between companies and branches of industry (eg, the construction industry vs the financial sector), which could be related to employees’ knowledge and beliefs. If branch of industry, sociodemographic, or other individual characteristics indeed have a profound effect on specific knowledge and beliefs, this could provide clinicians with suggestions for well-targeted risk education. Although several studies investigated people’s knowledge and beliefs about diabetes and CVD8,23–29 this research has tended to focus on individual diseases, rather than on disease risk as a consequence of unhealthy lifestyle. Because more and more emphasis is placed on the shared risk factors of cardiometabolic diseases as well as the interplay of detrimental physiological processes,30 it is important to assess how workers themselves think about cardiometabolic risk. To achieve better results from cardiometabolic health risk assessments in the occupational setting, we conducted this study, in which the knowledge and beliefs regarding cardiometabolic risk were examined in different Dutch employee subgroups. In addition, we compared employees from the construction industry with employees from the general working population, to be able to look at differences between branches of industry. By investigating potential knowledge gaps and misconceptions, we looked for focal points for better and well-targeted risk education by occupational physicians.
METHODS We used a survey on the risks of diabetes, CVD, and chronic kidney disease; this survey was based on a qualitative study in which concepts important to experts and lay people were identified.31,22 Because a large number of concepts needed to be captured, we divided the survey into two versions.
Participants and Procedure We approached two samples: 1800 construction workers eligible for their periodic health check and 1133 people from the general working population. We chose these samples to be able to look at differences between branches of industry; employees in the construction industry were selected because of their relatively low educational level and socioeconomic status as well as a high prevalence of overweight and obesity, a group of workers for whom lifestyle changes are likely to be highly effective.32 Construction workers might have less correct knowledge and beliefs than those from the general working population because of inadequate conceptual health vocabulary and relatively low health literacy levels, and because of the relatively negative culture toward health promotion in the construction industry.33 Construction workers aged 16 to 65 years were invited via a letter sent by Arbouw. Arbouw is the Dutch national institute coordinating occupational health care for all workers in the construction industry. Construction workers could choose to fill out the questionnaire either on-line or using pencil and paper. JOEM r Volume 56, Number 1, January 2014
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Workers’ Beliefs About Cardiometabolic Risk
Employees from the general working population were drawn from an on-line access panel (FlyCatcher Internet Research, 20,000 panel members in total, ISO 20252 and ISO 26362 certified); panel members aged 16 to 65 years with paid employment were invited through e-mail. We oversampled (60%) people aged between 40 and 65 years because of typical higher cardiometabolic risks in older people.
2 by 374 (65.7%), which made 738 respondents (65.1%). In total, we had 1220 respondents, resulting in a response rate of 41.6% (ie, the total number of respondents divided by the total number of people invited for participation). Table 1 displays respondents’ characteristics. No differences between the two versions were found for the characteristics listed.
Measures
Employees’ Knowledge
Both survey versions consisted of five parts capturing knowledge and beliefs about (1) causes and risk factors, (2) physiological processes, (3) disease symptoms, (4) disease consequences, and (5) risk-reduction strategies. These categories were based on our previously developed expert model of cardiometabolic risk31 and on typical lay representations of diseases.34,35 First, concepts for which it was clear that they were either correct or incorrect were included: expert concepts (eg, genetic predisposition), lay concepts (eg, poverty), and trick questions (eg, infections). Second, we posed questions about concepts whose accuracy was less definite. These predominantly concerned pronounced lay beliefs (eg, having had another disease) and concepts important to both lay people and experts but with experts’ notion that little evidence is available so far (eg, the muscular system). All items were formulated for the three diseases in a matrix format with response categories “true,” “false,” and “don’t know.” Third, we posed questions about people’s representation of the cardiometabolic risk concept, such as multifactorial influence of factors and the diseases’ interconnectedness. These items were measured using a scale from 1 (true) to 5 (false). The survey further consisted of items assessing population risk estimates of the diseases (“Of every 100 people in the Netherlands, I think that . . . . will develop diabetes/CVD/chronic kidney disease during their lifetime,” and “Of every 100 people in the Netherlands, I think that . . . . will develop diabetes/CVD/chronic kidney disease before the age of 60”), sociodemographic background (age, gender, and educational level), health (self-rated general health status, self-rated general lifestyle, having diabetes, CVD, or kidney disease, and having experienced cardiovascular problems), having a family history of diabetes and CVD, and subjective health literacy.36 The questions were carefully worded to avoid medical language and pretested among three construction workers.
The answers to all items are displayed in the Appendix. Table 2 presents the average percentages of correct answers to the knowledge composites as well as differences between employee subgroups in these answers. Employees were most correct regarding concepts related to CVD, followed by diabetes and chronic kidney disease. They were particularly able to identify CVD risk-reduction strategies. Although only few misconceptions were found (eg, sweet food/sugar causes diabetes and drinking too little water causes kidney disease; see the Appendix), employees did show several knowledge gaps. Especially specific risk factors (eg, physical inactivity, ethnicity, and specific dietary intake such as not eating enough grain and fibers), and physiological processes (eg, disturbed metabolism and inflammation processes) were rather unknown to workers (see the Appendix). We found a number of significant differences in knowledge between employee subgroups. Higher-educated workers had better knowledge than lower-educated workers (13 of the 15 composites). Furthermore, women consistently seemed to have more knowledge than men (9 of the 15 composites), especially concerning diabetes risk. Employees with low and high health literacy also differed on a substantial number (5) of composites, with workers with higher health literacy reporting better knowledge.
Statistical Analyses We conducted descriptive analyses to assess the prevalence of knowledge and beliefs. For all items, we calculated average percentages for reported answers. Composite knowledge scores representing knowledge across items were constructed for the five survey parts; only items being either correct or incorrect were included, with “don’t know” being considered incorrect. Concerning items assessing people’s representation of the cardiometabolic risk concept, mean values were calculated and reverse-coded: a higher score indicated a higher average agreement. We used t tests and multiple linear regression analyses to evaluate which individual characteristics affected knowledge and beliefs. In the regression analyses, the following characteristics were considered: age, gender, educational level, subjective health literacy, having a family history of cardiometabolic diseases, having a cardiometabolic disease, and general health status. Furthermore, we assessed whether adding a branch of industry variable (construction workers vs general working population) had an additional influence.
RESULTS Participant Characteristics Among construction workers, version 1 was filled out by 235 workers (26.1%) and version 2 by 247 workers (27.4%), which made 482 respondents (26.8%). Among employees from the general working population, version 1 was filled out by 364 (64.0%) and version
Employees’ Representations of Cardiometabolic Risk Table 3 displays mean values on the items about the cardiometabolic risk concept. Employees were quite familiar with the meaning of high cholesterol and high blood pressure, but high blood glucose levels were poorly comprehended. Employees knew well that there are usually multifactorial causes of cardiometabolic diseases. They were less informed about the diseases’ interconnectedness. Although it seemed that employees were slightly aware of some connection, they also thought that the three diseases had different causes. Respondents were able to define an “elevated risk” as a higher-thanaverage disease risk, but the fact that there may already be physical damage before occurrence of the disease was relatively unknown. Subgroups as defined by educational level differed significantly from each other on 9 of the 18 items representing the cardiometabolic risk concept, with higher-educated employees being more correct than lower-educated employees on eight concepts. Notably, for one item (elevated risk means that there is physical damage [true]), lower-educated people were more correct. We also found that women had more correct representations than men on five items. Employees with low and high health literacy differed on four items, with people with higher health literacy having more correct representations.
Employees’ Risk Estimates Highest estimates were given for the population risks of developing CVD (mean lifetime risk = 33.9%, mean risk before the age of 60 years = 24.0%). For diabetes, the mean lifetime risk was rated as 27.6% and the mean risk before the age of 60 years as 19.0%. Concerning kidney disease, the mean lifetime risk was estimated as 17.5% and the mean risk before the age of 60 years as 12.5%. Consistent differences in employee subgroups were found for educational level (with higher-educated people reporting lower risk estimates) and having a family history of diabetes and/or CVD
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TABLE 1. Characteristics of Respondents Participating in the Study (n = 1120)
Variable Age, y 16–29 30–39 40–49 50–59 60–69 Gender Male Female General health status (self-rated) Excellent Very good Good Fair Poor General lifestyle (self-rated) Very healthy Healthy Not particularly healthy or unhealthy Unhealthy Very unhealthy Education* Low Medium High Health literacy† Inadequate Adequate Having one of the diseases Diabetes (yes) CVD (yes) Chronic kidney disease (yes) Family history of diseases‡ Diabetes (yes) CVD (yes)
General Working Population (n = 738), n (%)
Total, n (%)
Construction Workers (n = 482), n (%)
139 (11.6) 177 (14.8) 357 (29.8) 434 (36.3) 89 (7.4)
24 (5.2) 42 (9.2) 113 (24.7) 237 (51.7) 42 (9.2)
115 (15.6) 135 (18.3) 244 (33.1) 197 (26.7) 47 (6.4)
842 (70.2) 358 (29.8)
441 (95.5) 21 (4.5)
401 (54.3) 337 (45.7)
77 (6.6) 212 (18.1) 717 (61.1) 153 (13.0) 14 (1.2)
28 (6.1) 82 (17.8) 296 (64.2) 50 (10.8) 5 (1.1)
49 (6.9) 130 (18.3) 421 (59.1) 103 (14.5) 9 (1.3)
91 (7.8) 531 (45.3) 458 (39.1)
24 (5.2) 220 (47.8) 195 (42.4)
67 (9.4) 311 (43.7) 263 (36.9)
83 (7.1) 9 (0.8)
21 (4.6) ...
62 (8.7) 9 (1.3)
486 (42.2) 365 (31.7) 301 (26.1)
295 (66.9) 108 (24.5) 38 (8.6)
191 (26.9) 257 (36.1) 263 (37.0)
255 (21.7) 918 (78.3)
182 (39.5) 279 (60.5)
73 (10.3) 639 (89.7)
61 (5.2) 91 (7.8) 13 (1.1)
23 (5.0) 50 (10.9) 5 (1.1)
38 (5.3) 41 (5.8) 8 (1.1)
175 (16.1) 370 (34.1)
66 (14.3) 167 (36.4)
109 (17.4) 203 (32.4)
* Low: primary school, lower level of secondary school, or lower vocational training. Medium: higher level of secondary school or intermediate vocational training. High: higher vocational training or university. †On the basis of an 1-item measurement (“How confident are you filling out forms by yourself?”) tested by Chew et al.36 ‡On the basis of the following questions derived from the Dutch national cardiometabolic health risk check: “Does your father, mother, brother, or sister have type 2 diabetes?” and “Did your mother, father, brother, or sister have a cardiovascular disease before the age of 65?” CVD, cardiovascular disease.
(with people with a family history reporting higher estimates on all three risks).
Multiple Regression Analyses In the multiple regression analyses, the same pattern of subgroup differences appeared as demonstrated by t tests. Educational level and gender were most predictive of knowledge and beliefs. Overall, a higher education was associated with better knowledge and more correct beliefs, as well as with lower risk estimates (β value ranging from 0.08 to 0.26). The effect that lower-educated employees knew better that there can be already physical damage in the case of an elevated risk continued to exist while adjusting for 94
other variables (β = 0.10; P = 0.01). Female gender was generally related to better knowledge and beliefs (β value ranging from 0.06 to 0.17), except for two concepts (high blood glucose levels means too much of the hormone insulin in your blood (β = 0.08; P = 0.10) and the representation that an elevated risk means there is nothing serious (β = 0.06; P = 0.05). Female gender was also associated with higher risk estimates (β value ranging from 0.08 to 0.14). Adding the branch of industry did not result overall in more variance explained. We found this variable to be a significant predictor leading to more variance explained for only a few concepts, namely for knowledge about CVD physiological processes (β = −0.08; P = 0.04), representations of abdominal obesity (mainly fat
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TABLE 2. Mean Percentages of Correct Answers Regarding the Knowledge Composites* (n = 1120) % Correct Answers Across Items (Range Across Items) Diabetes Causes and risk factors Physiological processes Symptoms Consequences Risk-reducing strategies Cardiovascular diseases Causes and risk factors Physiological processes Symptoms Consequences Risk-reducing strategies Chronic kidney disease Causes and risk factors Physiological processes Symptoms Consequences Risk-reducing strategies
45.9 (16.1–87.2)†‡§ 48.8 (19.1–93.4)†‡ 51.1 (12.9–81.6)†‡|| 54.5 (33.5–82.6)†‡|| 53.9 (12.7–86.8)†§# 63.0 (11.5–96.7)‡ 43.8 (20.0–94.7)‡ 60.3 (37.0–94.7)‡|| 49.7 (18.5–90.2)‡ 73.4 (25.4–96.3)†‡** 41.1 (12.7–66.0)‡** 45.6 (21.0–66.1)‡ 36.1 (8.6–66.7)†‡|| 56.8 (24.5–83.8)†‡|| 48.6 (13.2–72.2)†
*All t tests were performed with significance level of 0.01. †Significant difference between male and female employees, with women having better knowledge than men. ‡Significant difference between lower- and higher-educated employees, with higher-educated people having better knowledge than lower-educated people §Significant differences between employees with good general health and employees with bad general health, with people in bad health having better knowledge than people in good health. ||Significant difference between employees with inadequate health literacy and employees with adequate health literacy, with higher-literate people having better knowledge than lower-literate people. #Significant difference between employees having one of the cardiometabolic diseases and “healthy” employees, with people having a disease being more knowledgeable than people without a disease, and people without a disease being more knowledgeable than people with a disease. **Significant difference between younger and older employees, with younger people having better knowledge than older people, and older people having better knowledge than younger people.
on your belly is a risk: β = −0.12; P = 0.03), elevated risk representations (elevated risk means that you have certain complaints: β = 0.11; P = 0.00), and representations of the diseases’ interconnectedness (same type of diseases: β = −0.12; P = 0.00; different causes: β = 0.09; P = 0.02). For all these concepts, people from the general working population showed more correct responses. Adding the variable also resulted in more explained variance of CVD risk estimates (lifetime risk: β = 0.08; P = 0.04; risk before the age of 60 years: β = 0.10; P = 0.01) and kidney disease (lifetime risk: β = 0.14; P = 0.00; risk before the age of 60 years: β = 0.12; P = 0.00), with construction workers reporting higher risk percentages.
DISCUSSION In this study, we demonstrated that Dutch employees had reasonable levels of knowledge about the principal concepts related to cardiometabolic risk, especially concerning CVD risk. At the same time, they seemed to have only vague knowledge about the interconnectedness of cardiometabolic diseases, and misconceptions of what elevated risk entails. An important finding is that, compared with CVD, people had less knowledge about diabetes and chronic kidney disease. It may be
Workers’ Beliefs About Cardiometabolic Risk
TABLE 3. Mean Values Regarding Lay Representations of the Cardiometabolic Risk Concept (n = 1120)* Lay Belief Biological risk factors High cholesterol means too much sugar in your blood High cholesterol means too much fat in your blood Mainly fat on your belly is a risk Mainly fat on your hips is a risk High blood pressure means too much fat in your blood High blood pressure means excessive pressures in the walls of your arteries High blood glucose levels means too much sugar in your blood High blood glucose levels means too much of the hormone insulin in your blood Multifactorial influence Usually one single cause of the diseases Usually various causes of the diseases Elevated risk concept Elevated risk means having certain complaints Elevated risk means higher-than-average chance of developing disease Elevated risk means there is nothing serious Elevated risk means being ill Elevated risk means there is physical damage Interconnectedness diseases The three diseases are interrelated The three diseases are the same type of diseases The three diseases have different causes
M (SD)
1.83 (1.23)†‡§|| 4.39 (1.06)||¶ 4.08 (1.19)†‡ 2.50 (1.24) 2.32 (1.29)‡ 4.37 (0.99) 3.69 (1.44) 3.11 (1.51)
2.20 (1.37)†‡§¶ 4.45 (0.82) 2.35 (1.58)†‡§¶ 4.73 (0.65)†‡§ 2.94 (1.47) 1.76 (1.17)‡ 2.41 (1.43)‡¶ 3.18 (1.35)‡ 2.12 (1.16) 4.22 (0.95)
*A higher mean value indicates a higher agreement with the statement. All t tests were performed with significance level of 0.01. †Significant difference between male and female employees, with women having better knowledge than men. ‡Significant difference between lower- and higher-educated employees, with higher-educated people having better knowledge than lower-educated people, and lower-educated people having better knowledge than higher-educated people. §Significant difference between employees with inadequate health literacy and employees with adequate health literacy, with higher-literate people having better knowledge than lower-literate people. ||Significant difference between employees having one of the cardiometabolic diseases and “healthy” employees, with people having a disease being more knowledgeable than people without a disease, and people without a disease being more knowledgeable than people with a disease. ¶Significant difference between younger and older employees, with younger people having better knowledge than older people, and older people having better knowledge than younger people.
that the public has adequately learned about CVD risk through intensive educational messages, while coverage of diabetes and kidney disease risk has been more rare. This pattern would correspond to UK findings that CVD receives more media coverage than diabetes.37 In this respect, it is encouraging that a substantial percentage of employees did identify overweight as a diabetes risk factor, a factor that steadily receives more media attention.38 An important knowledge gap, however, concerned physical inactivity as a risk factor for diabetes. Although these figures have been worse (eg, only 32% of Dutch respondents linked physical inactivity to diabetes in 2007,39 a more prominent focus on physical inactivity in risk education seems warranted (see also Refs. 40 and 41). Health risk assessments at work may be the ideal setting to achieve this; increasing numbers of
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people have sedentary occupations and several effective workplace interventions have been developed.42 Although the entire survey concentrated on the three diseases and the link between them may have become apparent, employees failed to answer correctly about this relation. This may nevertheless not sound surprising, as the relation between diseases is complex and not broadly reported on.37 Perhaps physicians are reluctant to discuss such details and address it only when people are already affected by one of the diseases. Although such reluctance on the part of physicians seems justifiable, the downside is that people at risk will not focus on the combination of factors that brings about a set of detrimental cardiometabolic processes, and that their knowledge remains fragmentary. Because more than three quarters of respondents also explicitly stated that the three diseases have different causes, we would urge for more effective education about this aspect. In health risk assessments, occupational physicians could explicitly address cardiometabolic risk and explain it in relation to the three diseases, instead of focusing on either diabetes, CVD, or chronic kidney disease. Especially for construction workers, such an approach may be worthwhile, as they showed relatively less correct knowledge of the diseases’ interconnectedness. A final finding to reflect on is that, although employees were able to think of an elevated cardiometabolic risk as a “higher-thanaverage chance of disease,” this seemed to be an abstract concept without sufficient meaning. Importantly, employees did not know that an elevated risk can mean that there is physiological damage already, and notably higher-educated employees seemed to be even less knowledgeable than the lower-educated employees. Many employees believed that “there is nothing serious” when you have an elevated risk, and especially lower-educated employees and construction workers thought that an elevated risk means that you have certain complaints. Hence, while the lower-educated employees seem to have trouble distinguishing an elevated risk from actually having a disease, the higher-educated employees seem to be insufficiently aware of the physical damage that can be present during early stage diseases. These findings are particularly interesting in light of research indicating that people usually have difficulty in deriving meaning from numerical risk information.43,44 Verbal labels such as “elevated” are often used in combination with numerical risk information to support people in deriving meaning from risk information. An important question is how we should use such verbal labels if people have misconceptions about the label itself.
LIMITATIONS We had a relatively low response rate among construction workers (26.8%, compared with 65.1% in the general working population). More differences between the branches of industry might have been demonstrated, if we had had a higher response rate among construction workers. It seems plausible that respondents (compared with nonrespondents) among construction workers were more similar to the people from the general working population (because they were people motivated to complete surveys). Another limitation might be that we had only the construction industry as a separate branch of industry to compare to the general working population. Because of the financial crisis, other branches of industry that were initially interested in participation unfortunately declined to participate. Further research could focus on employees’ knowledge and beliefs about the risks communicated in risk assessments in different branches of industry. Another limitation might be that we did not test the questionnaire among a large group of people. Nevertheless, pretesting has been done by collecting information from three interviews, where the third interview did not reveal new insights in addition to the two earlier interviews. Nevertheless, it should be mentioned that, in our pretest, we focused on interpretation problems with specific concepts, and we did not evaluate the complete experience of filling out the questionnaire, nor did we ask respondents 96
for suggestions for improvements. A final limitation is that we had only self-reported data on general health and lifestyle, and no information on employees’ actual cardiometabolic risk. Future research could focus on the relation between the extent of people’s actual risk and their knowledge and beliefs.
PRACTICE IMPLICATIONS To achieve better results from cardiometabolic risk assessments at work, occupational physicians should (1) explain the relation between cardiometabolic diseases and the overlap in risk factors to the employee. Especially physicians working for the construction industry should focus on explaining the role of abdominal obesity and the accompanying physiological processes as key factors for the three diseases, because it seemed that construction workers were particularly ignorant on these issues compared with the general working population; (2) check what an elevated cardiometabolic risk means for the employee (eg, let them explain the risk in their own words) and help produce a meaningful risk understanding. For example, physicians could compare an employee’s risk with that of an average employee of their age in their branch of industry, or with an average person their age in the general working population. It seems that such support should especially be targeted at employee subgroups as defined by educational level, gender, and health literacy, and to some extent also to the employee’s branch of industry. Further tests are needed to assess whether such specific support as noted earlier will actually motivate lifestyle changes, and to investigate whether providing the support can be integrated in occupational health guidelines.45
ACKNOWLEDGMENT The authors thank Cor van Duijvenbooden (Arbouw) for his help in organizing the data collection in the construction industry.
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13. Freedman AN, Seminara D, Gail MH, et al. Cancer risk prediction models: a workshop on development, evaluation, and application. J Natl Cancer Inst. 2005;97:715–723. 14. Nielen MM, Assendelft WJ, Drenthen AJ, van den Hombergh P, van Dis I, Schellevis FG. Primary prevention of cardio-metabolic diseases in general practice: a Dutch survey of attitudes and working methods of general practitioners. Eur J Gen Pract. 2010;16:139–142. 15. Lusk SL, Raymond DM III. Impacting health through the worksite. Nurs Clin North Am. 2002;37:247–256. 16. Ooijendijk WTM, Hildebrandt VH, Hopman-Rock M. Bewegen in Nederland. 2000-2005. Trendrapport Bewegen en Gezondheid 2004/2005. Hoofddorp/Leiden, the Netherlands: TNO; 2007. 17. Ramsey F, Ussery-Hall A, Garcia D, et al. Prevalence of selected risk behaviors and chronic diseases—Behavioral Risk Factor Surveillance System (BRFSS), 39 steps communities, United States, 2005. MMWR Surveill Summ. 2005;57:1–20. 18. Rijksinstituut voor Volksgezondheid en Milieu. Nationaal Kompas Volksgezondheid: Volksgezondheid Toekomstverkenningen. Bilthoven, the Netherlands: Rijksinstituut voor Volksgezondheid en Milieu; 2010. 19. Edwards AG, Evans R, Dundon J, Haigh S, Hood K, Elwyn GJ. Personalised risk communication for informed decision making about taking screening tests. Cochrane Database Syst Rev. 2006;4:CD001865. 20. Hanlon P, McEwen J, Carey L, et al. Health checks and coronary risk: further evidence from a randomized controlled trial. BMJ. 1995;311: 1609–1613. 21. Sheridan SL, Viera AJ, Krantz MJ, Ice CL, Steinman LE, Peters KE. The effect of giving global coronary risk information to adults. Arch Intern Med. 2010;170:230–239. 22. Morgan MG, Fischhoff B, Bostrom A, Atman CJ. Risk Communication. A Mental Models Approach. New York, NY: Cambridge University Press; 2002 23. Furze G, Bull P, Lewin R, Thompson DR. Development of the York Angina Beliefs Questionnaire. J Health Psychol. 2003;8:307–316. 24. Furze G, Lewin RJP, Murberg T, Bull P, Thomson DR. Does it matter what patients think? The relationship between changes in patients’ beliefs about angina and their psychological and functional status. J Psychosom Res. 2005;59:323–329. 25. Jafary FH, Aslam F, Mahmud H, et al. Cardiovascular health knowledge and behavior in patient attendants at four tertiary care hospitals in Pakistan—a cause for concern. BMC Public Health. 2005;5:124. 26. Kayaniyil S, Ardern C, Winstanley J, et al. Degree and correlates of cardiac knowledge and awareness among cardiac inpatients. Patient Educ Couns. 2009;75:99–107. 27. Momtahan K, Berkman J, Sellick J, Kearns SA, Lauzon N. Patients’ understanding of cardiac risk factors: a point-prevalence study. J Cardiovasc Nurs. 2004;19:13–20. 28. Petrie KJ, Weinman J, Sharpe N, Buckley J. Role of patients’ view of their illness in predicting return to work and functioning after myocardial infarction: longitudinal study. BMJ. 1996;312:1191–1194.
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APPENDIX Survey Items and Average Percentages of People Stating That the Item Is True (n = 1120) % True†
Section of the Survey Causes and risk factors version 1
Causes and risk factors version 2
Physiological processes version 1
Physiological processes version 2
Disease symptoms version 1
Disease symptoms version 2
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Item Genetic predisposition Sweet food, food with much sugar Poverty, having a low income Salty food Sex (being a woman) Drinking too little water Alcohol consumption High cholesterol levels Overweight Eating too little whole grain and fiber A virus An irregular sleeping pattern/sleep disturbance Lack of physical activity Fat food Ethnicity Smoking Having had another disease Stress Ageing Pollution A high blood pressure Family members having the disease or having had it Infections Eating too few vegetables and fruit Disturbed metabolism Veins that sludge Lung processes Glucose levels in the blood The muscular system The bronchial system Hormones Vasoconstriction Inflammation processes Proteins in urine Fluid retention Frequent urinating Fever Pain in the legs when walking Nausea and vomiting Muscular pain Fatigue/lack of energy Being thirsty Decreased appetite Chest pain Fainting Oppressed feeling
Type of Item*
Diabetes
CVD
Chronic Kidney Disease
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
62.6 58.1 21.5 14.9 8.0 23.0 38.3 38.6 87.2 20.3 9.0 17.9 50.1 63.3 16.1 32.5 16.6 26.2 81.4 11.0 39.0 81.9
75.8 45.7 30.3 69.8 5.8 17.2 64.8 90.4 92.7 27.2 23.9 40.4 89.0 96.7 11.5 95.9 25.8 91.4 81.9 30.8 94.0 83.4
35.9 21.1 12.7 61.9 2.2 65.8 66.0 28.7 49.1 15.7 27.4 12.2 15.3 40.3 6.8 32.0 25.2 23.0 61.3 28.2 42.9 59.7
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
18.2 46.3 59.5 19.1 6.5 93.4 24.6 6.5 44.7 21.3 31.1 37.1 28.1 46.4 5.5 29.7 12.9 16.3 66.6 78.6 23.2 2.7 81.6 12.7
31.1 57.0 29.0 94.7 53.7 31.0 52.5 54.1 27.3 94.7 42.3 16.3 42.5 7.4 10.2 52.9 16.4 32.8 79.6 3.9 13.5 94.7 59.3 92.5
50.7 33.8 55.8 21.0 9.4 32.5 19.5 10.0 31.3 33.2 57.3 66.1 66.7 47.6 19.7 13.6 21.3 13.4 51.7 41.0 20.8 1.5 21.7 8.6 (continues)
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APPENDIX (Continued) % True*
Section of the Survey Disease consequences version 1
Disease consequences version 2
Disease consequences (both versions)
Risk reduction strategies version 1
Risk reduction strategies version 2
Section of the Survey Multifactorial influence version 1 Multifactorial influence version 2 The elevated risk concept (both versions)
Section of the Survey Genetic predisposition version 1
Chronic Kidney Disease
Type of Item†
Diabetes
Complications/infections Psychosocial problems Dialysis Physical constraints Amputations of limbs Eye disorders Premature death Paralysis Changed daily life Organ transplantation Developing diabetes
1 1 1 1 1 1 1 1 1 1 1
56.9 41.4 16.6 62.4 71.1 82.6 63.2 13.6 79.1 12.5 —
56.2 48.4 6.9 82.2 18.5 11.4 90.2 42.6 79.0 69.5 23.9
64.8 38.5 80.6 74.6 6.1 12.6 69.7 10.5 75.1 80.8 25.6
Developing CVD Developing chronic kidney disease Eating fewer sweet foods and sugars Moving to the country Losing weight Eating more fruit and vegetables Using medication Not smoking/smoking cessation Drinking clean water Walking and cycling Regular examinations by physicians Eating less fat food Relaxing/avoiding stress Eating less salt Eating more whole grain and fiber Surgery and medical treatment Eating less food Abstaining from alcohol Having regular physical activity/going to a fitness center Administering insulin
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
37.2 34.2 76.9 3.8 68.8 69.1 54.3 61.2 47.0 74.4 79.1 67.0 50.9 42.4 54.2 31.3 45.3 56.4 60.6
— 22.1 56.6 17.2% 90.6 80.1 61.4 91.3 42.8 92.6 80.1 96.3 95.5 87.3 67.1 67.2 51.0 65.9 87.8
23.9 — 32.8 4.3 47.7 59.4 52.9 56.7 68.1 55.6 76.6 50.4 41.6 72.2 45.6 58.8 24.0 67.6 42.4
1
77.7
2.7
5.3
Item
Item Usually one single cause of the diseases Usually various causes of the diseases Elevated risk means having certain complaints
Type of Item† 2 2 2
CVD
% True* Cardiometabolic Diseases in General 22.3 88.8 33.0
Elevated risk means higher-than-average chance of developing disease Elevated risk means there is nothing serious Elevated risk means being ill Elevated risk means there is physical damage
2
94.2
2 2 2
46.6 13.4 31.9
Item
Type of Item† 2
Genetic predisposition means that disease runs in your family Genetic predisposition means a congenital disease Genetic predisposition means a disease in your genes
2 2
% True* Cardiometabolic Diseases in General 90.0 56.2 92.4 (continues)
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APPENDIX (Continued) Section of the Survey
Item
Cholesterol version 1
High cholesterol means too much sugar in your blood High cholesterol means too much fat in your blood Mainly fat on your belly is a risk Mainly fat on your hips is a risk The risk increases with ageing The risk decreases after reaching 65 years High blood pressure means too much fat in your blood High blood pressure means excessive pressure in the walls of your arteries High blood glucose levels mean too much sugar in your blood High blood glucose levels mean too much of the hormone insulin in your blood The three diseases are interrelated
Abdominal obesity version 1 Ageing version 2 Hypertension version 2
Blood glucose levels version 2
Interconnectedness diseases (both versions)
Types of diabetes (version 1)
Types of diabetes (version 2)
The three diseases are the same type of diseases The three diseases have different causes Late onset diabetes is non–insulin-dependent diabetes Type 2 diabetes only apparent in older persons Type 2 diabetes means no insulin supply by the body Type 1 diabetes is innate
Type of Item† 2
% True* Cardiometabolic Diseases in General 12.0
2
83.2
2 2 2 2 2
70.1 20.1 95.4 5.6 18.3
2
80.7
2
61.6
2
42.4
2
47.8
2
12.1
2 2
79.7 36.0
2 2
15.6 35.9
2
36.6
*1, true, false, do not know; 2, 5-point scale (true/false). †For the items on cardiometabolic diseases in general with response category on a 5-point scale, the percentages of “true” and “maybe true” were totaled.
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