Sociodemographic characteristics of blue-collar workers may be attributed to the higher rates of obesity and chronic disease seen among them compared with white-collar workers.
Objectives: To examine the association between occupational type and obesity among adult workers in Hawai`i, using both international and suggested ethnic-specific body mass index (BMI) cut-points.
Study Design: Cross-sectional study.
Methods: The study population included 22,340 adult subscribers of a large health plan who completed the Succeed Health Risk Assessment (Succeed) questionnaire from July 1, 2008, to June 31, 2009. Logistic regression analysis was performed to examine the association between occupational type (blue-collar workers [BCWs] vs white-collar workers [WCWs]) and obesity, controlling for age, gender, and other demographic, health, and behavioral variables known to be related to obesity.
Results: Being a BCW and specific occupational categories were associated with obesity—an effect that remained, although attenuated, after controlling for ethnicity and using ethnic-specific BMI cut-points. After controlling for demographic, health, and behavioral variables, an inverse association was seen between obesity and BCWs and most occupational categories relative to WCWs. Ethnicity, education, smoking status, stress, and depression were found to be associated with obesity. Specific food groups were positively associated with obesity to different degrees, with greater odds ratios seen for the protein and saturated fat groups. A series of regression models, examining the effects of selected variables (ie, age, gender, ethnicity, and education) on the relationship between BCWs and obesity, demonstrated an inverse association only after education was added to the model.
Conclusions: Researchers seeking to reduce obesity among employees should target ethnic groups at highest risk for obesity (eg, Native Hawaiians) and those with lower education levels, rather than occupational type.
Obesity is increasing at an alarming rate and has more than doubled since 1980.1 Worldwide, an estimated 600 million individuals are obese. In the United States alone, more than 2 in 3 adults are considered overweight (body mass index [BMI] = 25-29.9) or obese (BMI ≥30).2 In 2010, overweight and obesity combined contributed to an estimated 3.4 million deaths globally. In Hawai`i, more than 2 in 4 adults are overweight or obese, with differing rates seen between ethnic groups3; for example, among Native Hawaiians, almost 3 in 4 are considered overweight or obese.
Poor diet and physical inactivity have been considered the main causes of obesity, which is also a major risk factor for leading chronic diseases and conditions.4 Considering the relationship of these diseases with obesity, it is not surprising that, compared with the state of Hawai`i, Native Hawaiians specifically experience a high prevalence of these chronic diseases, including coronary heart disease (4.2% vs 2.8%), stroke (4.1% vs 2.9%), diabetes (9.5% vs 7.8%), and hypertension (31.3% vs 28.7%).3
BMI is calculated from one’s weight and height.4 According to the World Health Organization (WHO), the BMI weight categorical cut-points demonstrate a “continuum of increasing risk with increasing BMI,” based on “statistical data from reference populations” examining “excess morbidity and mortality associated with increasing body fat content.”5 Considering that these international BMI cut-points are based on research among individuals who are white and that a variety of research has demonstrated differences in body composition, fat distribution, skeletal dimension, and genetic susceptibility for disease between whites and different Asian and Pacific Island population groups, ethnic-specific BMI cut-points for these groups have been proposed.6-8 However, due to observed variation in health risks reported at different BMI levels, the WHO recommended that the current BMI cut-points should be retained as the international classification pending further review and assessment.
Given that working adults spend approximately 30% of their waking hours at their jobs, worksites are a sensible place to promote healthy lifestyle behaviors aimed at preventing and reducing obesity.9 In planning worksite health programs, consideration must be taken of the higher rates of obesity and chronic diseases in blue-collar workers (BCWs), who make up approximately 61% and 65% of the working population aged under 65 years in the United States and Hawai`i, respectively, compared with white-collar workers (WCWs).10-13
A clear understanding of the factors related to the high prevalence of obesity among BCWs is needed in order to effectively target them in worksite health programs. This is particularly necessary when examining those relationships among Hawai`i’s BCWs, as most obesity-related worksite studies comprised those ethnic groups typified of the United States (ie, whites, blacks, and Hispanics), as opposed to being inclusive of groups largely representative of Hawai`i’s population (ie, Native Hawaiians and Asians).
This study is a quantitative, exploratory study examining secondary data of 22,340 adult subscribers of one of Hawai`i’s largest medical insurers, who also completed the Succeed Health Risk Assessment (Succeed) questionnaire, from July 1, 2008, to June 31, 2009. The Succeed questionnaire was developed by HealthMedia, Inc, and is used by health plans, employers, and pharmaceutical firms nationally to evaluate health behavior and history of participants.14 About 85% of HealthMedia Succeed participants rated the questionnaire as “excellent,” “very good,” or “good.”
The Succeed questionnaire was available at no cost to statewide subscribers of eligible plans, who were 18 years or older.15 Subscribers were encouraged to participate in this self-administered survey through the insurer’s website, their employer groups, and via mail-outs. A limited dataset was provided by the health insurer, with all direct identifiers removed for use in this study. This study was reviewed and approved by the University of Hawai`i (UH) Committee on Human Studies, the Institutional Review Board for the UH system.
Ethnicity. Survey respondents were asked to select 1 ethnic group with whom they identified, from choices pre-determined by HealthMedia. (see eAppendix A [eAppendices available at www.ajmc.com] for Succeed questions used).
Obesity status. For this study, only the validated BMIs (ie, those obtained through office visits vs self-reports) were used. Obesity associations were estimated using both the international BMI definition for obesity (BMI ≥30), as well as ethnic-specific BMI cut-points for obesity suggested for Asians (BMI ≥25) and Pacific Islanders (BMI ≥32).5 Analysis examining sociodemographic and behavioral factors of obesity used international BMI cut-points only.
Occupational types and categories. Occupational categories were classified as BCW or WCW, using definitions by the US Department of Labor for each. BCWs are those individuals working in production, maintenance, cleaning, and other manual labor occupations who are paid by the hour or on an incentive basis.16 WCWs are those who work in office, clerical, administrative, sales, professional, and technical occupations. Those participants “not currently working outside the home” were omitted from the study population.
Physical activity level. The activity levels of each participant were categorized into 1 of 3 levels: sedentary (<150 minutes of physical activity per week), moderately active (150-300 minutes per week), and active (>300 minutes per week).17
Nutritional habits. Each participant’s intake was compared with nutrition recommendations based on a 2000-calorie reference diet (adopted as such by the FDA to provide a general assessment on whether recommended food intake patterns were being met or not).18 In this paper, intake of different foods (total grains, protein, oils) is reported as units of measurement versus number of servings. An individual was determined to meet the sodium intake recommendation if they “rarely” consumed salt/sodium foods, the whole-grain intake recommendation if at least half or more of their total grain servings were whole grains, and the saturated/trans fats intake recommendation if they ate 0 servings.
Smoking status. Consistent with Prochaska’s Stages of Change model, responses were placed into 1 of 3 categories for this study: never smoked, current smoker (pre-contemplation, contemplation, and preparation), and past smoker (action and maintenance).19
Other health behaviors. Responses from questions regarding strength exercises, stress, depression, and sleeping hours were also examined in this study.
Descriptive statistics were used to characterize the study population. Logistic regression modeling was employed to estimate age- and gender-adjusted associations (odds ratios [ORs] and 95% CIs) between occupational type and categories with obesity, using international BMI and ethnic-specific BMI cut-points. Models with (n = 12,101) and without (n = 9503) nutritional habits were specified, due to possible inaccuracies in estimating portion sizes.20 A series of 4 regression models were specified, in which, selected variables (age, gender, ethnicity, and education) were chosen—based on their positive associations seen with obesity—and individually added into a model to determine which may be potential confounders affecting the relationship between being a BCW and obesity. Sets of 1 or more binary (dummy) variables were used to model the effects of all predictors, with the exception of age, which was modeled as a continuous predictor. Observations with missing variables were omitted from analysis. Those who were pregnant were excluded from the analysis. Data were analyzed using SAS version 9.2 (SAS Institute, Cary, North Carolina).
The descriptive characteristics of participants are listed in eAppendices B and C. Approximately 18.5% of respondents were BCWs, the majority being male, Asian, and with less than a college degree. Most WCWs were female, Asian, and college graduates. Twice as many Native Hawaiians (10% vs 5.9%) and Pacific Islanders (5.8% vs 3.2%) were BCWs than WCWs.
More BCWs (41.9% vs 23.3%) and WCWs (32.9% vs 16.2%) were classified as obese when the ethnic-specific cut-points were applied versus international ones. BCWs were more likely to be classified as physically active; however, they were also more likely to smoke and be depressed at least some of the time compared with WCWs, although WCWs were more likely to be stressed at least some of the time. Compared with WCWs, more BCWs exceeded the calcium/dairy, protein, oils, and saturated/trans fat recommendations,
After adjusting for age and gender only and using the international BMI cut-points, obesity was found to be associated with being a BCW (OR, 1.4; 95% CI, 1.3-1.5); this positive association remained, although attenuated, when ethnicity was controlled for (Table 1). When ethnic-specific BMI cut-points were used, obesity continued to be associated with being a BCW (OR, 1.2; 95% CI, 1.1-1.3), with odds unchanged before and after adjustment for ethnicity, and similar to the ethnicity-adjusted model using international BMI cut-points.
After controlling for all sociodemographic and behavioral characteristics, obesity was found to be inversely associated with being a BCW (OR, 0.8; 95% CI, 0.7-1.0) and either inversely or not associated with most occupational categories (Table 2). Native Hawaiians (OR, 3.3; 95% CI, 2.6-4.1) and those with less than a college education were most likely to be obese compared with their referents. Being a current or former smoker and/or having stress or depression often or most of the time was associated with obesity; higher levels of physical activity, strength exercises, and sleep were associated with decreased odds of obesity.
Positive associations between obesity and whole grain intake were seen in those who had less than half of their total grain intake as whole grains, and considerably greater for those consuming at least 6.5 ounces of protein (compared with the referent of 5.5 ounces) (Table 3). A dose-response relationship was demonstrated between obesity and calcium/dairy foods and saturated/trans fats, with increasing amounts of each associated with increasing odds of obesity.
Table 4 lists odds of obesity among BCWs using 4 different models. Obesity was positively associated with being a BCW (OR, 1.6; 95% CI, 1.4-1.7) in the unadjusted model; however, the odds of obesity steadily decreased as additional variables were included (ie, age/gender, ethnicity). An inverse relationship was seen only after education was included.
The large sample size and range of ethnicities and occupational categories represented in our study population are unique, as most research done with this purpose is typically done among specific worksites, a limited range of occupational categories, and/or with those ethnic groups that comprise the majority of the country’s population. Due to the distinctive characteristics of our study population, it was necessary to explore a plethora of variables to determine which factors may play a role in the occupational type—obesity relationship, with ethnicity, education levels, and health behaviors demonstrating the greatest effect.
In terms of ethnicity, Native Hawaiians were found to have the greatest odds of obesity: 230% greater than whites (referent), which is consistent with research elsewhere.3,21-23 Ethnicity has been characterized as a “sense of group belonging, based on ideas of common origins, history, culture, language, experience and values.”24 Those who regard ethnicity from a primordial perspective, in that it is a natural result of biological differences or a long historical process, may agree with research suggesting that Native Hawaiians have genetic predispositions to obesity, as ethnic admixtures that included Native Hawaiian ancestry were found to have a higher prevalence of obesity compared with those from most other ethnic combinations.23,25 Further, Native Hawaiians may have higher percentages of fat-free mass, bone mineral content, and appendicular skeletal mass compared with Asians and whites. Studies demonstrate the same among Pacific Islanders and Maori adults, although with higher body fat percentages and BMI.26,27 This theory may be supported by results from this study, which shows odds of obesity unchanged before and after adjustment for ethnicity (when using ethnic-specific cut-points) and similar to the ethnicity-adjusted model (when using international BMI cut-points).
From a constructivist perspective, ethnicity, although a cultural endowment, is also based on one’s identity, which can be fluid and reshaped over time by decisions or external processes taken by those outside their group.24 However, using a socially constructed variable as an explanation for obesity prevalence is problematic when employing a quantitative data inquiry. To reconcile these differences in perspective, ethnicity has been proposed as an indicator of disparity and social diversity versus an intrinsic measure. Considering the disproportionate burden of disease experienced by Native Hawaiians, ethnicity as a measure of, health disparities, in this case, is understandable. This thereby lends additional weight to the argument for ethnic-specific BMI cut-points, as each ethnic group is an embodiment of disparities in health and social differences, along with differences in body composition.
In this study, those who completed less than a college education were more likely to be obese than those who were college graduates. This effect was the same, regardless of if they were WCWs or BCWs (supplementary analysis not shown in this paper). Among the occupational categories, the likelihood of obesity was highest among operator/laborers and lowest among technical support workers compared with professional workers. Entry-level requirements for machine operators include a preference for those with a high school education, whereas construction laborer jobs have no education requirements.16 Technical support workers generally require a bachelor’s degree or higher.
Low education levels have been associated with decreased perception of health risks or benefits of healthful behaviors, resulting in less healthful practices being initiated or sustained.28 Individuals with more education may have more access to health information, such as the health risks associated with obesity, what constitutes a healthy diet, and benefits of being more physically active.29 With about 42.4% of Native Hawaiians being high school graduates and 15.6% holding a bachelor’s degree or higher, versus the state percentage of 90.4% and 30.1%, respectively, the true effects of education level and ethnicity with obesity are difficult to ascertain.30,31
BCWs were found to participate in several health behaviors that are shown to increase their likelihood of being obese. Compared with WCWs, BCWs were more likely to be short sleepers (sleep ≤6 hours per day), depressed (most of the time), stressed (fairly to very often), current smokers, and exceed saturated/trans fats, oils, dairy/calcium, and protein food recommendations. However, BCW were also found to be more physically active and meet the whole grain recommendations.
The risk behaviors characterizing the BCW in our study may be related to the nature of a blue-collar job in itself. BCWs commonly reported shorter sleep duration, which, along with obesity, can contribute to heart disease and diabetes.32 Being obese further exacerbates the problem, as it can cause sleep-related breathing disorders, such as sleep apnea, which contribute to sleep disturbance.33 Factors related to depression among workers include working in jobs of low social status or of low recognition, those physically demanding or dangerous, job insecurity, and low social support at work—attributes common with blue-collar jobs.34 This, combined with domestic responsibilities and possible hardship from working in a low-wage job, among other factors, could intensify feelings of depression.35,36 Blue-collar work is also often characterized by higher job strain (eg, when job demands are higher and worker control is minimal) and effort—reward discrepancy (eg, putting in increased effort in an insecure job with little recognition).37,38 Stress, along with depression, may create substantial psychological strain among BCWs, which, in turn, may contribute to the likelihood of practicing unhealthful behaviors and eating practices, thus making obesity prevention interventions more complex. BCWs are more likely to smoke and smoke more heavily than WCWs.39 Job stress and short sleep have both been positively correlated with current smoking status, which has previously been discussed as factors related to obesity.40,41
The nature of blue-collar jobs may also encourage positive health attributes, as well, as the function of many of these jobs requires high physical demands.42 Native Hawaiians have also been found to have high levels of physical activity (second only to whites), with research showing increased levels of physical activity with increasing percentage of Native Hawaiian ancestry.23,25 It may be that although Native Hawaiians, like BCWs, are more active, they are consuming high amounts of caloric-dense foods, therefore attributing to their high prevalence of obesity despite the physically active lifestyle.22,43
Few studies examining the food intake of BCWs have been done, despite the importance this information can have in targeting specific behaviors among this population. As in other studies, protein, dairy/calcium foods, saturated/trans fats, and oils were found to be consumed in excess by BCW and were all associated with obesity. One study among Hawai`i’s population found that protein or meat consumption predicted a higher BMI, with meats eaten predominantly among Native Hawaiians; similar results were found in other populations, as well.22,44 Although calcium intake has been shown to be associated with a decreased likelihood of obesity, it may be that results from this study are due to the source of calcium (particularly high-fat dairy choices), rather than the calcium itself, since energy intake was not adjusted for.45 Saturated/trans fats and oil (specifically unhealthy ones) are, as commonly known, associated with obesity.46,47
Study findings are not generalizable to other places or representative of the state of Hawai`i, as the study population constituted approximately 3.2% of total subscribers of insured individuals at a single health plan in Hawai`i and were self-selected.
Not having data on energy intake and income to adjust for is another limitation, as both are individually associated with specific behaviors and health outcomes.48,49 The exclusion of common cultural food (eg, poi, tofu) and activity (eg, surfing, karate) examples may have caused respondents not to account for them in their diet and activity estimates. Other potential limitations include varying sample sizes being used due to missing data, small sample sizes of certain ethnic groups, participant response error due to self-reporting, and measurement error due to the possibility that individuals may have multiple ethnicities represented by multiple categories, thus causing the wrong BMI cut-point to be selected in classifying their weight.
As this is a cross-sectional study, it was difficult to determine whether those who are obese have a tendency to gain employment in blue-collar work or engage in certain behaviors, or whether the jobs and/or behaviors lead to obesity.
Ethnicity and education were more strongly associated with obesity than occupational type; however, this does not mean that targeting BCWs would be ineffective, considering their sociodemographic make-up. Strategies developed must be comprehensive and mindful of differing cultural interests and preferences in order to develop programs more effectively and applicable to these individuals. Research examining obesity between occupational types is still expanding and is relatively unexplored among Hawai`i’s multiethnic population. Findings from this study contribute to this growing field, helping to build a good foundation upon which future worksite-related research can be based, and helping to direct planning of worksite obesity prevention efforts.
The authors would like to extend their gratitude to HMSA for their provision of and help with the Succeed dataset. They would also like to recognize and give thanks to Drs Kathryn Braun, Noreen Mokuau, and Andrew Grandinetti for their technical guidance and assistance, and to the University of Hawai`i at MÄnoa, Department of Public Health Sciences, which provided partial funding for this research study. The findings and conclusions of this study do not necessarily represent the views of HMSA.
Author Affiliations: Department of Human Nutrition, Food, and Animal Sciences (JHL, RN) and Department of Public Health Sciences (ELH), University of Hawai`i at MÄnoa, Honolulu, HI; Daniel K. Inouye College of Pharmacy, University of Hawai`i at Hilo (DT), Honolulu, HI.
Source of Funding: Partial funding was provided by the University of Hawai`i at MÄnoa, Department of Public Health Sciences.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (DT, JHL); acquisition of data (DT, JHL); analysis and interpretation of data (ELH, DT, JHL, RN); drafting of the manuscript (JHL); critical revision of the manuscript for important intellectual content (ELH, DT, JHL, RN); statistical analysis (ELH); obtaining funding (JHL); administrative, technical, or logistic support (JHL); and supervision (JHL).
Send Correspondence to: Jodi H. Leslie, DrPH, RDN, LDN, Department of Human Nutrition, Food, and Animal Sciences, University of Hawai`i at MÄnoa, 1955 East West Rd, AgSci 229, Honolulu, HI 96822. E-mail: email@example.com.
1. Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766-781. doi: 10.1016/S0140-6736(14)60460-8.
2. BRFSS prevalence & trends data. CDC website. http://www.cdc.gov/brfss/brfssprevalence/. Updated August 18, 2016. Accessed September 2016.
3. Nguyen D-H, Salvail FR. The Hawaii Behavioral Risk Factor Surveillance System: 2014 results. Hawai‘i Department of Health website. http://health.hawaii.gov/brfss/files/2015/08/HBRFSS_2014_results.pdf. Accessed September 2016.
4. Adult obesity causes & consequences. CDC website. http://www.cdc.gov/obesity/adult/causes/index.html. Updated August 15, 2016. Accessed December 2014.
5. World Health Organization—Western Pacific Region; International Association for the Study of Obesity; International Obesity Task Force. The Asia-Pacific perspective: redefining obesity and its treatment. World Health Organization website. http://www.wpro.who.int/nutrition/documents/docs/Redefiningobesity.pdf. Accessed September 2016.
6. BMI classification. World Health Organization website. http://apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed April 19, 2010.
7. Erber E, Hopping BN, Grandinetti A, Park SY, Kolonel LN, Maskarinec G. Dietary patterns and risk for diabetes: the multiethnic cohort. Diabetes Care. 2010;33(3):532-538. doi: 10.2337/dc09-1621.
8. Novotny R, Nabokov V, Derauf C, Grove J, Vijayadeva V. BMI and waist circumference as indicators of health among Samoan women. Obesity (Silver Spring). 2007;15(8):1913-1917.
9. Lusk SL, Raymond DM 3rd. Impacting health through the worksite. Nurs Clin North Am. 2002;37(2):247-256.
10. Workers by occupational category. Kaiser Family Foundation website. http://kff.org/other/state-indicator/blue-and-white-collar-workers/. Accessed November 2014.
11. Bentley R, Kavanagh AM, Subramanian SV, Turrell G. Area disadvantage, individual socio-economic position, and premature cancer mortality in Australia 1998 to 2000: a multilevel analysis. Cancer Causes Control. 2008;19(2):183-193.
12. Clarke TC, Soler-Vila H, Lee DJ, et al. Working with cancer: health and disability disparities among employed cancer survivors in the U.S. Prev Med. 2011;53(4-5):331-334. doi: 10.1016/j.ypmed.2011.07.022.
13. Luckhaupt SE, Calvert GM; CDC. Prevalence of coronary heart disease or stroke among workers aged <55 years—United States, 2008-2012. MMWR Morb Mortal Wkly Rep. 2014;63(30):645-649.
14. Participation & engagement: HealthMedia SUCCEED Health Risk Assessment. Johnson & Johnson Health and Wellness Solutions Inc website. http://www.wellnessandpreventioninc.com/solutions/healthplans/engagement-hra. Accessed August 2016.
15. Hawaii Medical Service Association. The Health Project website. http://thehealthproject.com/winner/hawaii-medical-service-association-hmsa-healthpass/. Accessed September 2016.
16. Herman AM, Abraham KG. Glossary of compensation terms. Bureau of Labor Statistics website. http://stats.bls.gov/ocs/sp/ncbl0062.pdf. Published August 1998. Accessed October 2008.
17. US Department of Agriculture; US Department of Health and Human Services. Dietary guidelines for Americans: 2010. 7th edition. Office of Disease Prevention and Health Promotion website. https://health.gov/dietaryguidelines/dga2010/dietaryguidelines2010.pdf. Published December 2010. Accessed September 2016.
18. How to understand and use the nutrition facts label. FDA website. http://www.fda.gov/Food/IngredientsPackagingLabeling/LabelingNutrition/ucm274593.htm#overview. Updated May 25, 2016. Accessed September 2016.
19. Prochaska JO, DiClemente CC. Self change processes, self efficacy and decisional balance across five stages of smoking cessation. Prog Clin Biol Res. 1984;156:131-140.
20. Natarajan L, Pu M, Fan J, et al. Measurement error of dietary self-report in intervention trials. Am J Epidemiol. 2010;172(7):819-827. doi: 10.1093/aje/kwq216
21. Johnson DB, Oyama N, LeMarchand L, Wilkens L. Native Hawaiians mortality, morbidity, and lifestyle: comparing data from 1982, 1990, and 2000. Pac Health Dialog. 2004;11(2):120-130.
22. Maskarinec G, Takata Y, Pagano I, et al. Trends and dietary determinants of overweight and obesity in a multiethnic population. Obesity (Silver Spring). 2006;14(4):717-726.
23. Mau MK, Wong KN, Efird J, West M, Saito EP, Maddock J. Environmental factors of obesity in communities with native Hawaiians. Hawaii Med J. 2008;67(9):233-236.
24. Brown GK, Langer A. Conceptualizing and measuring ethnicity. Oxford Development Studies. 2010;38(4):411-436.
25. Grandinetti A, Keawe’aimoku Kaholokula J, Chang HK, et al. Relationship between plasma glucose concentrations and Native Hawaiian ancestry: the Native Hawaiian Health Research Project. Int J Obes Relat Metab Disord. 2002;26(6):778-782.
26. Rush EC, Freitas I, Plank LD. Body size, body composition and fat distribution: comparative analysis of European, Maori, Pacific Island and Asian Indian adults. Brit J Nutr. 2009;102(4):632-641. doi: 10.1017/S0007114508207221.
27. Rush EC, Goedecke JH, Jennings C, et al. BMI, fat and muscle differences in urban women of five ethnicities from two countries. Int J Obes (Lond). 2007;31(8):1232-1239.
28. Huisman M, Van Lenthe F, Avendano M, Mackenbach J. The contribution of job characteristics to socioeconomic inequalities in incidence of myocardial infarction. Soc Sci Med. 2008;66(11):2240-2252. doi: 10.1016/j.socscimed.2008.01.049
29. Brown DE, Hampson SE, Dubanoski JP, Murai AS, Hillier TA. Effects of ethnicity and socioeconomic status on body composition in an admixed, multiethnic population in Hawaii. Am J Hum Biol. 2009;21(3):383-388. doi: 10.1002/ajhb.20889.
30. 2013 State of Hawai’i data book. Hawai‘i Department of Business, Economic Development and Tourism website. http://dbedt.hawaii.gov/economic/databook/db2013/. Accessed November 2014.
31. Native Hawaiian data book 2013. Office of Hawaiian Affairs website. http://www.ohadatabook.com/DB2013.html. Updated March 28, 2013. Accessed November 2014.
32. Luckhaupt SE, Tak S, Calvert GM. The prevalence of short sleep duration by industry and occupation in the National Health Interview Survey. Sleep. 2010;33(2):149-159.
33. What is sleep apnea? National Heart, Lung, and Blood Institute website. http://www.nhlbi.nih.gov/health/health-topics/topics/sleepapnea/. Updated July 10, 2012. Accessed October 24, 2011.
34. Lipscomb HJ, Dement JM, Epling CA, Gaynes BN, McDonald MA, Schoenfisch AL. Depressive symptoms among working women in rural North Carolina: a comparison of women in poultry processing and other low-wage jobs. Int J Law Psychiat. 2007;30(4-5):284-298.
35. Alterman T, Luckhaupt SE, Dahlhamer JM, Ward BW, Calvert GM. Job insecurity, work-family imbalance, and hostile work environment: prevalence data from the 2010 National Health Interview Survey. Am J Ind Med. 2013;56(6):660-669. doi: 10.1002/ajim.22123.
36. Spitzer DL. Engendering health disparities. Can J Public Health. 2005;96(suppl 2):S78-S96.
37. Nieuwenhuijsen K, Bruinvels D, Frings-Dresen M. Psychosocial work environment and stress-related disorders, a systematic review. Occup Med (Lond). 2010;60(4):277-286. doi: 10.1093/occmed/kqq081.
38. Kivimaki M, Nyberg ST, Batty GD, et al; IPD-Work Consortium. Job strain as a risk factor for coronary heart disease: a collaborative meta-analysis of individual participant data. Lancet. 2012;380(9852):1491-1497. doi: 10.1016/S0140-6736(12)60994-5.
39. Lee DJ, Fleming LE, Arheart KL, et al. Smoking rate trends in U.S. occupational groups: the 1987 to 2004 National Health Interview Survey. J Occup Environ Med. 2007;49(1):75-81.
40. Léger D, Roscoat E, Bayon V, Guignard R, Pâquereau J, Beck F. Short sleep in young adults: insomnia or sleep debt? prevalence and clinical description of short sleep in a representative sample of 1004 young adults from France. Sleep Med. 2011;12(5):454-462. doi: 10.1016/j.sleep.2010.12.012.
41. Radi S, Ostry A, Lamontagne AD. Job stress and other working conditions: relationships with smoking behaviors in a representative sample of working Australians. Am J Ind Med. 2007;50(8):584-596.
42. Vandelanotte C, Duncan MJ, Short C, et al. Associations between occupational indicators and total, work-based and leisure-time sitting: a cross-sectional study. BMC Public Health. 2013;13:1110. doi: 10.1186/1471-2458-13-1110.
43. Kachan D, Lewis JE, Davila EP, et al. Nutrient intake and adherence to dietary recommendations among US workers. J Occup Environ Med. 2012;54(1):101-105. doi: 10.1097/JOM.0b013e31823ccafa.
44. Rouhani MH, Salehi-Abargouei A, Surkan PJ, Azadbakht L. Is there a relationship between red or processed meat intake and obesity? a systematic review and meta-analysis of observational studies. Obes Rev. 2014;15(9):740-748. doi: 10.1111/obr.12172.
45. Rodriguez-Rodriguez E, Perea JM, López-Sobaler AM, Ortega RM; Research Group: 920030. An adequate calcium intake could help achieve weight loss in overweight/obese women following hypocaloric diets. Ann Nutr Metab. 2010;57(2):95-102. doi: 10.1159/000319374.
46. Moussavi N, Gavino V, Receveur O. Could the quality of dietary fat, and not just its quantity, be related to risk of obesity? Obesity (Silver Spring). 2008;16(1):7-15. doi: 10.1038/oby.2007.14.
47. Bjermo H, Iggman D, Kullberg J, et al. Effects of n-6 PUFAs compared with SFAs on liver fat, lipoproteins, and inflammation in abdominal obesity: a randomized controlled trial. Am J Clin Nutr. 2012;95(5):1003-1012. doi: 10.3945/ajcn.111.030114.
48. Faeh D, Braun J, Bopp M. Prevalence of obesity in Switzerland 1992-2007: the impact of education, income and occupational class. Obes Rev. 2011;12(3):151-166. doi: 10.1111/j.1467-789X.2010.00793.x.
49. Shay CM, Van Horn L, Stamler J, et al; INTERMAP Research Group. Food and nutrient intakes and their associations with lower BMI in middle-aged US adults: the International Study of Macro-/Micronutrients and Blood Pressure (INTERMAP). Am J Clin Nutr. 2012;96(3):483-491.