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RESEARCH AND PRACTICE |
Alberto J. Caban is with the Department of Epidemiology and Public Health at the University of Miami School of Medicine and with Nova Southeastern University College of Osteopathic Medicine, Florida. Lora E. Fleming, David J. Lee, Orlando Gómez-Marín, William LeBlanc, and Terry M. Pitman are all with the University of Miami School of Medicine, Florida.
Correspondence: Requests for reprints should be sent to Lora E. Fleming, MD, PhD, MPH, MS, Department of Epidemiology and Public Health, University of Miami School of Medicine, 1801 NW 9th Ave., Highland Professional Building, Suite 200, Miami, FL 33136 (e-mail: lfleming{at}med.miami.edu).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Objectives. Obesity has emerged as one of the most important public health issues in the United States. We assessed obesity prevalence rates and their trends among major US occupational groups.
Methods. Self-reported weight and height were collected annually on US workers, aged 18 years or older, from the 1986 to 1995 and the 1997 to 2002 National Health Interview Surveys. Overall, occupation-, race-, and gender-specific rates of obesity (defined as a body mass index>30.0 kg/m2) were calculated with data pooled from both study periods (n>600000). Annual occupation-specific prevalence rates were also calculated, and their time trends were assessed.
Results. Obesity rates increased significantly over time among employed workers, irrespective of race and gender. The average yearly change increased from 0.61% (±.04) during the period from 1986 to 1995 to 0.95% (±.11) during the period from 1997 to 2002. Average obesity prevalence rates and corresponding trends varied considerably across occupational groups; pooled obesity prevalence rates were highest in motor vehicle operators (31.7% in men; 31.0% in women).
Conclusions. Weight loss intervention programs targeting workers employed in occupational groups with high or increasing rates of obesity are urgently needed.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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The relationship between obesity and occupation has not been fully investigated. Work-related factors, such as job and position, job stress, and extended work (including overtime night work and sedentary work) may promote weight gain and abdominal fat accumulation.11–14 One of the national Healthy People 2010 Objectives is to reduce the prevalence rate of obesity among adults to less than 15%,15 therefore, because treatment often fails, research efforts focused on prevention are required. Weight loss intervention and education programs targeting workers employed in various occupational groups are urgently needed, but, unfortunately, nationally representative data identifying occupational groups with the highest obesity rates are not presently available.16,17 It is also not known which occupational groups are experiencing large increases in obesity rates. Our research objective was to evaluate overall, gender- and race-specific obesity rates and their 17-year trends, including the past decade, within 41 occupational groups using nationally representative samples of the US worker population.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Body mass index (BMI) is commonly used to define obesity and has been found to closely correlate with the level of body fat.23 BMI was calculated by dividing weight in kilograms by height in meters squared. Respondents were classified as obese if their BMI was greater than 30.0 kg/m2.24 From 1986 to 1995, the NHIS reported weight and height values for all participants. Data from the 1996 survey year are not presented because, for that year, the National Center for Health Statistics (NCHS) reported data only for participants with a weight between 98 and 289 pounds and a height between 59 and 76 inches; BMI for the 1996 participants outside of these weight and height ranges were not made available by NCHS. Starting in 1997, the NHIS was redesigned and the NCHS made available the BMI values for all participants, even those with weight and height outside the above ranges.25 Because of these differences in the reporting, and because of the major redesign of the sampling and interview format, we analyzed data separately for NHIS survey periods 1986 to 1995 and 1997 to 2002.
In the 1986 to 1995 NHIS, employment information was collected on all subjects aged 18 years or older who reported working during the 2 weeks prior to the survey26,27; starting in 1997, NCHS collected employment information from adults who stated that they were working during the week before the NHIS survey. Both of these definitions included paid and unpaid work. Forty-one standardized occupational codes derived from more detailed US Census occupational codes were provided in the NHIS database from 1986 to 1995 and from 1997 to 2002.28,29 We grouped survey participants in the trend data analysis into White, Black, or "other race" category. "Other race" included other, Aleutian Eskimo/American Indian, Asian/Pacific Islander, and unknown/multiple races.
Because of the complex sample survey design, analyses were completed with the SUDAAN package to take into account sample weights and design effects.30 For pooled prevalence estimates, sample weights were adjusted to account for the aggregation of data over multiple survey years by dividing the original weight by 10 (the number of years combined in survey years 1986 through 1995) and by 6 (the number of years combined in survey years 1997 through 2002).18 To assess obesity trends within each survey period, a weighted linear regression model was fitted to the annual design-adjusted rates within occupational groups. The weight used for each annual rate was the inverse of its variance.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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The average yearly change (±SE) in obesity rates increased from 0.61% (±.04) in the 1986 to 1995 period to 0.95% (±.11) in the 1996 to 2002 period. Annual obesity rates increased significantly among all gender-race groups in the survey periods 1986 to 1995 and 1997 to 2002 (Figure 1
). In all survey years, annual obesity rates were highest in Black workers (particularly women) and lowest among those in the "other race" category.
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and 2 show: the sample size; the percentage of Black workers for each occupational group (given that Black workers had the highest rates of obesity); the pooled and annual prevalence rates of obesity; and the slope (i.e., yearly change in obesity rate) of the weighted linear regression of rate of obesity over time, its standard error, and the corresponding P value. Slopes were not calculated for a particular occupational group when the sample size for any given survey year was below 46. Pooled and annual obesity rates preceded by an asterisk have a relative standard error [defined as 100 x SE (rate)/rate] of greater than 30% and, following the practice of the NCHS, should be considered imprecise estimates.31
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In the period from 1997 to 2002 (Table 2
), the highest pooled obesity rates were observed for male workers employed as motor vehicle operators (31.7%), police and firefighters (29.8%), other transportation except motor vehicle moving operators (28.7%), and material-moving equipment operators (28.2%); and, for female workers, those employed as motor vehicle operators (31.0%), other protective service workers (30.5%), material-moving equipment operators (29.5%), and cleaning and building service workers (25.3%). In contrast to the earlier survey period, there were no occupational groups among the men with an obesity rate below 11%. Among women, only those employed in the health-diagnosing occupations (10.3%), as architects and surveyors (7.3%), and in the construction and extractive trades (6.9%) had obesity rates below 11%. There were no significant downward trends in obesity rates for any occupational group during the survey period from 1997 to 2002. Obesity rates among male workers employed as police or firefighters had an annual increase of 2.1% (±.8); female workers with annual increases above 2% included motor vehicle operators (5.7 ±1.1); health service workers (2.4 ±.5); other professional specialty occupation employees (2.1 ±.7); and fabricators, assemblers, inspectors, and samplers (2.1 ±.9).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Obesity and its related health conditions directly damage the health and well-being of the current workforce and significantly contribute to long-term chronic disability.32–36 Additionally, the significant increase in the prevalence of obesity among children and adolescents indicates an even greater problem that employers will likely confront within the future workforce.37 Short-term disability claims attributed to obesity have increased 10-fold over the past decade, according to an UnumProvident study that analyzed its extensive disability database.38 Obesity-related disabilities cost employers an average of $8720 per employee every year.38 Designing and implementing worksite weight-loss programs that educate and help employees to achieve and maintain weight loss could substantially reduce the costly health burden on both employers and workers. This effort will not only prevent work-related illness, injury, and disability but also promote healthy lifestyles, which, in turn, will prevent and reduce chronic disease in working-age Americans, many of whom spend 8 to 12 hours per day at work.
Limitations
The NHIS data are cross-sectional data that permit only inferences of association of obesity in the 41 occupations analyzed. However, findings from this study are similar to those of others,33,34 in which the prevalence of obesity has been found to vary according to occupation. Consistent with the present findings, previous research has shown that race/ethnicity, social class, age, and/or sedentary jobs can contribute to an increase in obesity.32,33,37 Furthermore, it is possible that among obese people there exists bias by self-selection of occupation.
Although BMI has been shown, traditionally, to correlate with fat distribution, it must be noted that it does not take into account individuals who may have a large muscular habitus, nor does it directly measure percent body fat. However, most health organizations and scientists support the use of BMI to define overweight and obesity, particularly when direct measures of fat distribution are not available.39–41 Using a 2 or 1 week reference period prior to the NHIS interview to characterize occupational status might lead to misclassification of individuals with respect to their usual occupation. However, ongoing analyses of the NHIS data by the present team of investigators indicate a substantial concordance between self-reported current occupation and longest-held job.42
The present analysis suffers from many of the limitations seen in large population-based studies. Weight and height were collected in a self-reported or proxy fashion, which could have led to less precision in the calculation of the BMI.43,44 For example, previous research has suggested that people tend to underreport their weight and overreport their height, leading to the underestimation of BMI; additionally, the degree of under- and overreporting varies as a function of age, gender, race, ethnicity, and social class.45–47
The 1986 to 1995 NHIS employed proxy information when adults were not available for household interview. Proxy reports of weight and height may also be subject to bias. To reduce this potential bias, we reanalyzed our 1986 to 1995 data in the 61% of NHIS participants who directly reported weight and height during the interview. Results indicate that, for most occupations, the self-reported BMIs would be even higher than the combined proxy and self-reported BMIs. Examining the BMIs for all workers from 1986 to 1995, we found the average annual difference in the percentage of obesity between the nonproxy (self-reported) BMIs and the combined proxy and self-reported BMIs was 0.73%.
Finally, the change in the survey design methodology in 1996 prevented trend comparisons over the total 17-year time period. Moreover, small sample sizes could lead to less reliable estimates of obesity rates and trends in some worker subpopulations (e.g., private household occupations among men, and architects and surveyors among women).
Strengths
Despite the limitations presented, the use of large sample sizes, the nationally representative nature of the sample, oversampling of select subgroups (e.g., Blacks), and the annual assessment useful for assessing trends in prevalence of obesity within occupations allows this study to be favorably compared to other evaluations of the US obesity epidemic.
Irrespective of gender, individuals employed as motor vehicle operators were found to have the highest prevalence of obesity in both time periods. Among men, these pooled prevalence rates increased from 19.8% in the 1986 to 1995 survey period to 31.7% in the 1997 to 2002 survey period; corresponding rates for women were 22.6% and 31.0%, respectively. Developing weight-loss programs designed to take into account the job demands, physical demands, and even the socioeconomic and cultural backgrounds of motor vehicle operators could potentially help reduce this detrimental increase in obesity within this occupational group. Furthermore, examining occupations with a lower prevalence of obesity (such as female architects and surveyors or men employed in the health-diagnosing professions) could help researchers elucidate the relationship between occupation and optimal body weight.
Conclusions
The behavioral effects of physical activity on health are well established.48–51 Although the most promising weight-loss interventions focus on increasing physical activity in addition to implementing dietary changes, the increasing trend towards automation and other labor-saving strategies found at many work-sites will not foster physical activity conducive to weight loss. Primary and secondary prevention of obesity in occupational settings must therefore take into account the many societal and occupational factors that influence energy imbalance via multifaceted interventions (e.g., accountability of healthy food choices and food quantity, exercise programs). Such comprehensive, worksite-based interventions are urgently needed in order to slow the growing epidemic of obesity in the United States.
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Acknowledgments |
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Findings from this study were presented at the Annual Education Conference in 2004 of the Florida Public Health Association, Orlando, Florida, July 28, 2004; the Centers for Disease Control (CDC)/NIOSH conference "Steps to a Healthier US Workforce," October 26–28, 2004; Washington, DC, Cafritz Conference Center; and at the 132nd Annual Meeting of the American Public Health Association in Washington, DC, November 8, 2004.
The data used in this article were made available in part by the Inter-University Consortium for Political and Social Research. The data for the National Health Interview Survey (NHIS) were originally collected and prepared by the US Department of Health and Human Services and the National Center for Health Statistics.
Note. Neither the collector of the original data nor the Inter-University Consortium for Political and Social Research bears any responsibility for the analyses or interpretations presented in this article.
Human Participant Protection
Because this study utilized anonymous data from a publicly available database, the protocol was reviewed and approved for exemption by the institutional review board of the University of Miami School of Medicine.
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Footnotes |
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Contributors
A. J. Caban, L. E. Fleming, and D. J. Lee originated the study and led the writing of this article. W. LeBlanc and O. Gómez-Marín managed the data and performed statistical analyses. T. Pitman provided study support and data management. All authors helped conceptualize ideas, interpret findings, and provide critical review of the article.
Accepted for publication October 8, 2004.
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References |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
