95046888741J Occup Environ MedJ. Occup. Environ. Med.Journal of occupational and environmental medicine / American College of Occupational and Environmental Medicine1076-27521536-594827058472492236310.1097/JOM.0000000000000670HHSPA795606ArticlePrevalence of Injury in Occupation and Industry: Role of Obesity in the National Health Interview Survey 2004 to 2013GuJa K.MSPHCharlesLuenda E.PhD, MPHFekedulegnDestaPhDMaClaudia C.MPHAndrewMichael E.PhDBurchfielCecil M.PhD, MPHBiostatistics and Epidemiology Branch, Health Effects Laboratory Division, National Health Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Morgantown, West VirginiaAddress correspondence to: Ja K. Gu, MSPH, National Institute for Occupational Safety and Health, HELD/BEB, Mailstop L-4050, 1095 Willowdale Rd., Morgantown, WV 26505-2888 (GUM4@CDC.GOV).1662016420162762016584335343Objectives

The aim of this study was to estimate prevalence of injury by occupation and industry and obesity’s role.

Methods

Self-reported injuries were collected annually for US workers during 2004 to 2013. Prevalence ratios (PRs) and 95% confidence intervals (CIs) were obtained from fitted logistic regression models.

Results

Overall weighted injury prevalence during the previous three months was 77 per 10,000 workers. Age-adjusted injury prevalence was greatest for Construction and Extraction workers (169.7/10,000) followed by Production (160.6) among occupations, while workers in the Construction industry sector (147.9) had the highest injury prevalence followed by the Agriculture/Forestry/Fishing/Mining/Utilities sector (122.1). Overweight and obese workers were 26% to 45% more likely to experience injuries than normal-weight workers.

Conclusion

The prevalence of injury, highest for Construction workers, gradually increased as body mass index levels increased in most occupational and industry groups.

Work-site injury is a leading cause of morbidity and mortality worldwide. The International Labor Organization estimated that there are approximately 270 million injuries annually at a cost of $76 billion.1 The Bureau of Labor Statistics (BLS) in the United States (US) reported nearly 3.6 million cases for nonfatal occupational injuries and an incidence rate of 3.5 cases per 100 equivalent full-time workers in 2012.2 Certain occupational groups had a high incidence rate of injuries and illnesses. Among state or local government employees, the protective services (police and sheriff’s patrol officers, correctional officers, and fire fighters) had a rate of 329/10,000 full-time workers, and among private employees, the transportation and material moving occupations had a rate of 278/10,000 full-time workers.3 Agriculture/forestry/fisheries and construction were among the industry sectors reporting the highest injury risks for workers across all age groups.4 These two industries have been reported to be the most dangerous industries with regard to injuries. In the US construction industry, a study found that the total costs of fatal and nonfatal injuries were estimated at $11.5 billion ($27,000/case) in 2002.5 A Finnish study discovered that the incidence rate of injury was 7.4/100 in 1996 and the total insurance cost was 23.5 million euros in 1999.6 Results from a Canadian study showed that workers in the occupation of Manufacturing & Utilities had the highest risk of serious injury, followed by those in Trades, Transport, & Construction.7

Several factors are known to be associated with an increased prevalence of injuries and one of the most modifiable is obesity. Obesity among US adults is a major public health concern and the obesity prevalence among US workers is also alarming. In the US, the prevalence of obesity among workers has dramatically increased over the past three decades, from 10% in 1986 to 28% in 2011.8,9 Employees in the protective services had an obesity prevalence of more than 40% and there was a 36% prevalence in community and social services.10 Schulte et al11 showed that obesity is related to increased health care costs and work-related conditions such as injury, cardiovascular disease, asthma, musculoskeletal disorders, stress, and cancer. Multiple studies have provided evidence that increased obesity is a risk factor for workplace injury.12–15 Obesity was shown to be highly related to musculoskeletal injuries among firefighters.16 Motor vehicle drivers who are obese tend to decrease seatbelt usage. Therefore, obesity may be associated with vehicular collision related injuries.17,18

At least one study has been published on the prevalence of obesity by occupational groups and the association between obesity and occupational injury characteristics (eg, site of injury, type of injury, external causes, treatment places, and missed work days).13 However, studies investigating the prevalence of injury by occupational and industry groups and the relationship between obesity and work-site injury by occupational and industry groups are rare. Therefore, the aims of this study are to (1) estimate the prevalence of injury among US workers by occupational and industry groups, and (2) investigate the association between obesity and injury across occupational and industry groups using the National Health Interview Survey (NHIS) 2004 to 2013 data.

METHODSData

We used data from the NHIS to examine the prevalence of injury among US workers by industry and occupation. The NHIS was developed and administered by the National Center for Health Statistics (NCHS) in the US Centers for Disease Control and Prevention (CDC) to monitor trends in illness and disability and to track health status, health care access, and progress toward achieving national health objectives since 1957. Extensive details about the questionnaire, methodology, data, and documentation are available on the NHIS website.19

Data from the past 10 years 2004 to 2013 of the NHIS core questionnaires (Sample Adults, Family) were pooled for statistical analysis. Our sample included paid workers aged 18 years and older who were “working at a job or business” or “with a job or business but not at work” during the week before their interview. The total number of the combined 2004 to 2013 NHIS adults was 289,187 (an average response rate of 79.8%). From this population, our study included 159,961 working adults, after excluding those who were nonworking, pregnant, or missing the body mass index (BMI) variable.

Occupational Injury

Information on participants’ injuries was collected using the Injury and Poisoning questionnaire in the NHIS Family core questionnaire. For our study, injured workers were defined as those who answered that they were “working at a paid job” to the question, “What activity were you involved in at the time of the injury?” Before 2004, the surveys reported all injuries that occurred within four months of the interview. Beginning in 2004, NCHS decided to retain all injury episodes that reportedly occurred during the three months (91 days) before the date of the injury in question to reduce recall bias that may be associated with less serious injury.19

Occupational and Industry Groups

The NHIS Sample Adult data obtained verbatim responses from each participant regarding his/her occupation and industry. This information was subsequently reviewed by U.S. Census Bureau coding specialists, who assigned appropriate industry and occupation codes. These codes, developed by U.S. Census Bureau staff for use in noneconomic Federal surveys, are four-digit Census codes for industry and occupation consistent with the North American Industry Classification System (NAICS) and Standard Occupational Classification (SOC).19 Although the Sample Adult File available for public use does not include the in-house Census codes, it does include an occupation recode with 23 simple categories, which were derived from the 93 SOC occupation groups. As some of 23 occupational groups did not have enough injured workers, we recategorized into 13 occupational groups for this study. For the industries, we also recategorized the 21 sectors to 11 sectors. We excluded participants who worked in the Military-specific occupational group or the Armed Forces industry group, as there were only a small number of injuries in the military occupation and industry.

Obesity

BMI is commonly used as an indicator of adiposity and it is highly correlated with body fat.20 BMI was calculated as weight in kilograms divided by height in meters squared (kg/m2). In the Sample Adults questionnaire, participants were asked their height in inches (“How tall are you without shoes?”) and their weight in pounds (“How much do you weigh without shoes?”) and the values were converted to meters and kilograms, respectively. We used BMI as both a continuous variable and a categorical variable (BMI: 18.5 to 24.9 kg/m2 for normal weight, 25 to 29.9 kg/m2 for overweight, 30+ kg/m2 for obese). We excluded persons who were underweight (BMI <18.5 kg/m2) because the number of injuries in that group was too small.

Covariates

The NHIS questionnaire collected information from participants on demographics (sex, age, race/ethnicity, marital status, education), lifestyle characteristics (smoking status, alcohol intake, physical activity, sleeping), and job characteristics (length of employment, work hours, number of employees at work, second job). As several demographic variables may be related to injury and obesity, they were treated as potential confounders: age, sex, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, and all others), marital status (single, married, divorced), education (less than high school graduate, high school graduate or GED, some college or associate degree, and 4-year college or graduate). Sub-sequent models also included lifestyle characteristics and job characteristics, which are associated with both injury and BMI. Smoking status was categorized as never, former, and current. Alcohol intake was classified as never/former, current intake less of three drinks or less per week, and current intake of four or more drinks per week. Sleep duration was dichotomized as inadequate sleep (1 to 6 hours/day) and adequate sleep (7+ hours/day), based on the National Institute of Health recommendation.21 Physical activity had three levels, inactive, insufficiently active, and sufficiently active. Inactive was defined as participating in no leisure-time aerobic activity that lasted at least 10 minutes; insufficiently active, as participating in moderate-intensity leisure-time aerobic activities for 10 minutes or more but less than 150 minutes per week; and sufficiently active, as participating in moderate-intensity leisure-time aerobic activity for 150 minutes or more per week, or in vigorous-intensity leisure-time aerobic activity 75 minutes or more per week, or an equivalent combination.22 The number of employees at work was categorized as 1 to 9, 10 to 49, 50 to 249, and more than 250.

Data Analysis

All prevalence estimates (per 10,000 workers) were weighted using the NHIS individual sample adult record weight. Point estimates of injury with the relative standard error larger than 30% were considered unreliable and were identified by a symbol (†) in the tables.23 The standard errors with complex multistage designs were estimated using Taylor series linearization. SUDAAN v11 was used in all data analysis to account for the complex sample survey design.24 Age-adjusted prevalence estimates were based on the 2010 U.S. workers standard population by BLS, using five age groups: 18 to 34, 35 to 44, 45 to 54, 55 to 64, more than 65 years.

Associations were assessed for covariates with both injury and BMI using Chi-square and analysis of variance, respectively. Prevalence ratios (PRs), adjusted for covariates, were obtained from average marginal predictions in the fitted logistic regression models.25 Adjustments were made for the following potential confounders: sex, age, race/ethnicity, marital status, education, smoking status, alcohol intake, sleep duration, physical activity, and number of employee at work. Within occupation and industry groups, PRs were calculated for associations between injury and BMI (using both continuous BMI and categorical BMI). The injury prevalence among overweight or obese workers was compared with that among the normal-weight group. The PRs between occupational and industry groups were also calculated using as the referent group “Professional” (for occupational group) and “Services” (for industry group). We also calculated mean values of BMI by demographic factors, lifestyle characteristics, and job characteristics. Effect modification (ie, interaction) was assessed for all of these variables in the association between obesity and injury but none were found to be significant (P > 0.05). Multivariable-adjusted PRs and 95% confidence intervals (95% CIs) for the main associations were calculated. All reported P values were two-sided and a P value of less than 0.05 was considered statistically significant.

RESULTS

Table 1 summarizes the prevalence of injury and mean values of BMI by demographic, lifestyle, and job characteristics. In this sample of US workers, 54.9% was male and 14.1% was of Hispanic ethnicity. The mean (±SD) age of the sample was 41.4 (±0.07) years. Half of the workers earned less than $35K per year. Approximately 20% of the workers were current smokers. Thirty percent of the workers reported that they had less than 6 hours sleep a day and about two-thirds were overweight or obese. One-fifth of employees worked 50 hours or more per week and 8.8% reported that they had a second job.

The overall weighted prevalence of injury during the past three months was 77.0 per 10,000 workers. The highest prevalence of injury occurred in blacks (87.9/10,000), followed by whites and Hispanics, and the lowest prevalence was in “other” race/ethnicity (46.7). The prevalence of injury declined with age. Several measures of adverse lifestyle behaviors (current smoking, higher alcohol consumption, physical inactivity, short sleep duration, and obesity) were associated with an increased prevalence of work injury. Current smokers had the highest prevalence of injury (118.8), which was almost double that of the never smokers (59.7). Workers who were employed less than a year, worked long hours, or had a second job tended to have a higher prevalence of injury. The prevalence of injury (63.2/10,000) among workers in large industries (≥250 employees) was lower than that (96.4/10,000) in the medium size industries (50 to 249 employees).

The overall mean value of BMI for US workers was 27.6 kg/m2. Male workers had a significantly higher mean BMI than female workers. Non-Hispanic black workers had the highest average BMI (29.2) of the racial/ethnic groups. Workers who reported more than 7 hours of sleep per day and sufficient physical activity had significantly lower mean BMI than the workers who reported shorter sleep duration and less physical activity. Participants who worked long hours (ie, more than 50 hours/week) had the highest mean BMI (28.3 kg/m2) compared with those in the other groups.

Table 2 presents results of the prevalence of injury during the past three months across several occupational and industry groups. Among the occupational groups, the age-adjusted prevalence of injury was greatest for Construction and Extraction (166.0/10,000), followed by Production (162.6), Protective Services (135.9), and Installation/Maintenance/Repair (133.5). The occupations showing the lowest prevalence were Professional (28.3) and Office and Administrative Support (43.2). Among the industry categories, workers in the Construction sector (147.9) had the highest injury prevalence, followed by the Agriculture/Forestry/Fishery/Mining sector (122.1) and the Manufacturing sector (111.2). These results represent an estimated 142,000 workers in Construction, 32,000 workers in the Agriculture/Forestry/Fishery/Mining, and 150,000 workers in Manufacturing. The industries showing the lowest prevalence were Services (42.0) and Education (56.2).

Employees with the highest age-adjusted mean BMI were in Protective Service (29.1 kg/m2) and in Transportation and Material Moving (28.8 kg/m2) among occupation groups. Workers in the Transportation/Warehousing (28.7 kg/m2) and the Public Administration (28.5 kg/m2) had the highest age-adjusted BMI among industry groups. Those having the lowest mean BMI were in Education/Training/Library (26.8 kg/m2) among occupation groups and in Education (27.0 kg/m2) among industries.

Figures 1 and 2 show the scatter plots of the age-adjusted mean BMI and the prevalence of injury by occupations and industries, respectively. The employees in Protective Service (“Prtc” in Fig. 1) and Transportation and Material Moving (“Tran”) had high mean values of BMI and relatively high injury prevalence. The workers in Construction (“Cnst” in Fig. 2) and in Agriculture/Forestry/Fishery/Mining (“Agrc”) had a high prevalence of injury and moderately high mean BMI, while the workers in Transportation and Warehousing (‘Tran’) and in Public Administration (“Pblc”) had high mean BMI and relatively high prevalence of injury. The Education and Services industry sectors both had low mean values of BMI and low prevalence of injury.

The age-adjusted prevalence of injury during the previous three months is reported by occupational and industry groups and BMI category in Table 3. Among occupational groups, the highest injury prevalence occurred among obese employees in Production (200.8/10,000), followed by Construction and Extraction (179.3) and Transportation and Material Moving (178.9). Among industry groups, obese workers in Agriculture/Forestry/Fishery/Mining (170.4/10,000) had the highest injury prevalence, followed by Construction (159.3) and Manufacturing (152.4). The prevalence of injury in most occupational and industry groups gradually increased as obesity levels increased. For example, the prevalence of injury in Management was 20.8 cases per 10,000 workers among those with normal weight, 43.5 among the overweight, and 77.3 among the obese. In the industry categories, the prevalence of injury in the Manufacturing sector was 80.5/10,000 among those with normal weight, 112.5 among the overweight, and 152.4 among the obese.

Table 4 summarizes the association between BMI and the prevalence of occupational injuries after controlling for sex, age, race/ethnicity, marital status, education, smoking status, alcohol intake, sleep duration, physical activity, and number of employees at work. Compared with normal-weight workers, those who were overweight or obese had significantly elevated prevalence of all occupational injuries combined; PR = 1.25 (95% CI 1.05 to 1.51) for overweight and PR = 1.45 (95% CI 1.20 to 1.74) for obese workers. For every one unit increase in BMI, the prevalence of injury increased by 2% (PR = 1.02, 95% CI 1.01 to 1.03). Among the occupational groups, BMI was significantly associated with occupational injury among those who worked in Management (PR = 1.05, 95% CI 1.02 to 1.09), Health care/Personal care/Community (PR = 1.05, 95% CI 1.02 to 1.07), and Transportation and Material Moving (PR = 1.05, 95% CI 1.01 to 1.08). Workers in the obese category in the previous three occupational groups had a prevalence of injury more than two times higher than those in the normal BMI category (PR = 2.70, 95% CI 1.22 to 6.00 for Management; PR = 2.34, 95% CI 1.42 to 3.84 for Health care/Personal Care/Community; PR = 2.51, 95% CI 1.21 to 5.21for Transportation and Material Moving). Among the industry groups, BMI was significantly related to injury among workers in Education (PR = 1.05, 95% CI 1.01 to 1.09). Compared with workers with a normal BMI, workers in Education and Health Care/Social Assistance who were obese had a higher prevalence of injury; PR = 2.18, 95% CI 1.03 to 4.61 and PR = 1.77, 95% CI 1.08 to 2.90, respectively.

In addition, we investigated injury prevalence among occupational groups (referent is Professional) and among industry groups (referent is Services). Compared with workers in the Professional group, workers in the Construction and Extraction were four and a half times more likely to have had an injury (PR = 4.51, 95% CI 2.92 to 6.96). This was followed by Protective Service (PR = 3.91, 95% CI 2.42 to 6.31), Production (PR = 3.88, 95% CI 2.54 to 5.87), and Installation/Maintenance/Repair (PR = 3.27, 95% CI 2.01 to 5.31). Among industry groups, workers in Construction were more than two and a half times as likely to have an injury compared with workers in Services (PR = 2.60, 95% CI 1.93 to 3.50). This was followed by Agriculture/Forestry/Fishing/Mining/Utilities (PR = 2.10, 95% CI 1.36 to 3.25), Public Administration (PR = 1.97, 95% CI 1.39 to 2.80), and Manufacturing (PR = 1.91, 95% CI 1.40 to 2.60).

DISCUSSION

In this study of the US adult working population, we used pooled data across the 10 most recent years of the NHIS survey (2004 to 2013) and investigated the prevalence of work-site injury by occupational and industrial groups and the association between BMI and injury across occupation and industry. The primary findings of this study were that the highest prevalence of injury occurred in the Construction and Extraction and Production (among the occupational groups) and in the Construction sector followed by the Agriculture/Forestry/Fishing/Mining/Utilities sector and Manufacturing sector (among the industry groups). Our findings are consistent with those of previous studies in the US and other countries. The US BLS reported that workers in the Protective Services, Building and Grounds Cleaning and Maintenance, and Transportation and Material Moving had the highest incidence rate for nonfatal occupational injuries.3 The UN International Labor Organization also reported that the industrial sectors of Agriculture, Fishing, Mining, and Construction had the leading risk of injury, caused by machinery, falls, crashes, electrical hazards, chemicals, etc.1 Our study showed that the majority (34%) of work-site injuries were in Construction and Extraction, Production, and Installation/Maintenance/Repair.

As we expected, all blue-collar workers—in Food Preparation and Serving, Building and Grounds Cleaning and Maintenance, Construction and Extraction, Installation/Maintenance/Repair, Production, Transportation and Material Moving—were more likely to have injuries than Professional workers. Workers in the Protective Service group were nearly four times more likely to experience injuries than workers in the Professional group. Protective services employees, mostly law enforcement officers and firefighters, work under dangerous conditions for the public’s safety. Many law enforcement officers are frequently in a sedentary working environment. They experience high weight gain and frequently drive in severe weather and at high speeds.26 Thirty-six percent of the law enforcement injuries were caused by motor-vehicle related incidents such as vehicle crashes or being struck by a vehicle while outside their patrol car,26 and 45% of the firefighter injuries occurred during fire-fighting operations.27

With respect to the association between BMI and injury, in general, overweight and obese workers were more likely to experience injuries than normal-weight workers. The prevalence of injury in most occupational and industry groups tended to increase as BMI levels increased. However, only three occupations and one industry were found wherein obesity was associated with the prevalence of workplace injury: Management, Health Care/Personal Care/Community Service, Transportation and Material Moving, and the Education sector. Two studies of hospital employees found that obesity was associated with a higher risk of work-site injury.13,28 In many US studies, vehicle drivers who are obese had a greater likelihood of a crash and fatality from traffic collision related injuries, and were less likely to wear seatbelts than nonobese drivers.17,18,29,30 We observed that those in the Services and Education sector had a relatively lower prevalence of workplace injury 42.0/10,000 and 56.2/10,000, respectively, and lower mean BMI. The prevalence of workplace injury in the Management and Education sector was higher in the obese than in the normal-weight workers. On the contrary, the occupations with relatively high mean BMI and a high prevalence of injury—Protective Service, Production, Construction, and Installation/Maintenance/Repair—did not have a significant relationship between BMI and injury.

Although our study did not find an association between BMI and work-site injury in the Construction industry sector, Dong et al31 found that obesity and overweight significantly increased the risk of work-related injury among construction workers. The differing results from the two studies are probably due to the sample sizes and the injury recall periods. Our study had a small number of injuries in the Construction sector with a relatively short recall period—only three months (166 injured workers among 10,800 total construction workers for 10 years), while the study by Dong et al31 had a relatively large number of injuries with a long recall period—two years (545 injured among 5287 total construction workers for 13 years).

Limitations

There are some limitations in this study that should be considered when interpreting its findings.

First, this study takes into account acute injuries only that were self-reported and does not include fatal injuries; this may have led to an underestimation of injury. Small sample sizes prevented us from estimating the prevalence of work-site injury and the PR in some occupational and industrial groups by BMI level.

An additional limitation of this study is that our results are from a cross-sectional study, and the association between BMI and injury across occupational and industrial groups cannot provide evidence of causality or temporal sequence whether increase in BMI precedes the injury, or vice versa. BMI measurements in our study may have been underestimated because the NHIS survey used self-reported weight and height rather than objective measures for weight and height taken by trained research staff members using a standardized protocol. Shift work has been shown to be associated with an increased risk of workplace injury and workers’ obesity.32–36 Unfortunately, we were not able to control for shift work in our analysis because the variable was only available in the 2010 NHIS Occupational Health Supplement.

Strengths

Despite the limitations presented, the current study has a large sample size. Information was obtained from a nationally representative dataset that enabled estimates of occupational injury prevalence by demographic, lifestyle characteristics, job characteristics, and also by occupations and industries. Availability of a variety of relevant variables allowed adjustment for important confounders in the analysis of the association between BMI and injury.

In addition, the current study that reported injuries during the past three-month period may have minimized recall bias that could have decreased the potential for underestimating injury prevalence. There are many previous studies that have investigated injures in specific occupations or injuries by external causes and anatomical sites.12,13,15,37 This study is unique in that we investigated the prevalence of injury by occupation and industry groups among U.S. workers and also the association between obesity and injury in these workers.

CONCLUSION

This study examined the prevalence of workplace injuries across occupations and industries during a 10-year period. The highest prevalence of injury was in Construction and Extraction. Compared with workers in the Professional group, workers in Construction and Extraction were four times more likely to report an injury. Overall, overweight and obese workers were 26% and 45%, respectively, more likely to experience injuries than normal-weight workers. Future prospective studies are warranted to determine risk of injury associated with elevated BMI and potential benefits that may result from reductions in BMI.

There is no funding for this study.

Authors Gu, Charles, Fekedulegn, Ma, Andrew, and Burchfiel have no relationships/conditions/circumstances that present potential conflict of interest.

The JOEM editorial board and planners have no financial interest related to this research.

REFERENCESSafety in Numbers2003International Labor Organization, United NationsGeneva, SwitzerlandAvailable at: http://www.ilo.org/public/english/region/eurpro/moscow/areas/safety/docs/safety_in_numbers_en.pdf. Accessed October 9, 2014Employer-reported Workplace Injuries and Illnesses: 2012Bureau of Labor Statistics, News Release, USDL-13-2119, US Department of Labor2013Available at: http://www.bls.gov/news.release/pdf/osh.pdf. Accessed October 13, 2014Nonfatal Occupational Injuries and Illnesses Requiring Days Away From Work, 2012News Release, USDL-13-2257. Bureau of Labor Statistics, US Department of Labor2013Available at: http://www.bls.gov/news.release/pdf/osh2.pdf. Accessed October 9, 2014KachanDFlemingLELeBlancWGWorker populations at risk for work-related injuries across the life courseAm J Ind Med20125536136622170632WaehrerGMDongXSMillerTCosts of occupational injuries in construction in the United StatesAccid Anal Prev2007391258126617920850RautiainenRHOhsfeldtRSprinceNLCost of compensated injuries and occupational diseases in agriculture in FinlandJ Agromed2005102129FanJMcLeodCBKoehoornMDescriptive epidemiology of serious work-related injuries in British Columbia, CanadaPLoS One20127e3875022723884CabanAJLeeDJFlemingLEObesity in US workers: the National Health Interview Survey, 1986 to 2002Am J Public Health2005951614162216051934GuJKCharlesLEBangKMPrevalence of obesity by occupation among US workers: the national health interview survey 2004–2011J Occup Environ Med20145651652824682108LuckhauptSECohenMALiJPrevalence of obesity among U.S. workers and associations with occupational factorsAm J Prev Med20144623724824512862SchultePAWagnerGROstryAWork, obesity, and occupational safety and healthAm J Public Health20079742843617267711JanssenIBaconEPickettWObesity and its relationship with occupational injury in the Canadian workforceJ Obes2011201153140321773008KouvonenAKivimakiMOksanenTObesity and occupational injury: a prospective cohort study of 69,515 public sector employeesPLoS One20138e7717824146966LombardiDAWirtzAWillettsJLIndependent effects of sleep duration and body mass index on the risk of a work-related injury: evidence from the US National Health Interview Survey (2004–2010)Chronobiol Int20122955656422621351PollackKMSorockGSSladeMDAssociation between body mass index and acute traumatic workplace injury in hourly manufacturing employeesAm J Epidemiol200716620421117485732JahnkeSAPostonWSHaddockCKObesity and incident injury among career firefighters in the central United StatesObesity (Silver Spring)2013211505150823512940BehzadBKingDMJacobsonSHSeatbelt usage: is there an association with obesity?Public Health201412879980325183564RiceTMZhuMDriver obesity and the risk of fatal injury during traffic collisionsEmerg Med J20143191223337422National Center for Health Statistics (NCHS)Center for Disease Control and Prevention, U.S. Department of Health and Human ServicesNational Health Interview Survey Public Use Data Release NHIS Survey DescriptionAvailable at: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_ Documentation/NHIS/2013/srvydesc.pdf. Accessed October 17, 2014AdamsTDHeathEMLaMonteMJThe relationship between body mass index and per cent body fat in the severely obeseDiabetes Obes Metab2007949850517587392National Heart, Lung, and Blood Institute, National Institutes of HealthHow Much Sleep is Enough?2014Available at: http://www.nhlbi.nih.gov/health/health-topics/topics/sdd/howmuch.html. Accessed November 17, 2014BlackwellDLClarksTCSummary health statistics for US adults: National Health Interview Survey, 2012Vital Health Stat201410260LJKleinRJProctorSEBoudreaultMATurczynKMHealthy people 2010 criteria for data suppressionHealthy People 2010 Stat Notes200224Research Triangle InstituteSUDAAN Language Manual, Volumes 1 and 2, Release 112012Research Triangle InstituteResearch Triangle Park, NCBielerGSBrownGGWilliamsRLEstimating model-adjusted risks, risk differences, and risk ratios from complex survey dataAm J Epidemiol201017161862320133516TiesmanHMMotor Vehicle Safety and Law Enforcement OfficersNIOSH Science Blog2014Available at: http://blogs.cdc.gov/niosh-science-blog/2014/11/18/leo-motor-vehicle-safety/. Accessed May 04, 2015KarterMKUS Firefigher Injuries 2013National Fire Protection Association2014Available at: http://www.nfpa.org/research/reports-and-statistics/the-fire-service/fatalities-and-injuries/firefighter-injuries-in-the-united-states. Accessed May 4, 2015BrownNDThomasNIExploring variables among medical center employees with injuries: developing interventions and strategiesAAOHN J20031147048114651387JehleDDoshiCKaragianisJObesity and seatbelt use: a fatal relationshipAm J Emerg Med20143275676024816062AndersonJEGovadaMSteffenTKObesity is associated with the future risk of heavy truck crashes among newly recruited commercial driversAccid Anal Prev20124937838423036416DongXSWangXLargayJAOccupational and non-occupational factors associated with work-related injuries among construction workers in the USAInt J Occup Environ Health20153714215025816923WongISMcLeodCBDemersPAShift work trends and risk of work injury among Canadian workersScand J Work Environ Health201137546120890587BussJAssociations between obesity and stress and shift work among nursesWorkplace Health Saf201260453458quiz 45923054165KirshVCotterchioMMcGlynnNThe association between shift work and obesity in Canada: a cross-sectional study using a novel exposure assessment toolOccup Environ Med201471Suppl 1A88ZhaoIBogossianFTurnerCThe effects of shift work and interaction between shift work and overweight/obesity on low back pain in nurses: results from a longitudinal studyJ Occup Environ Med20125482082522796926ItaniOKaneitaYMurataAAssociation of onset of obesity with sleep duration and shift work among Japanese adultsSleep Med20111234134521377926RenJWaclawczykAHartfieldDAnalysis of fall injuries by body mass indexSouth Med J201410729430024937728

Age-adjusted BMI and prevalence of injury by occupation. Adms, Office and Administrative Support; Bldg, Building and Grounds Clean & Maintenance; Cnst, Construction and Extraction; Educ, Educations, Training, Library; Food: Food Preparation & Serving; Hlth, Health care, Personal care, Community; Mngm, Management; Prod, Production; Prof, Professional; Prtc, Protective Service; Repr, Installation, Maintenance, and Repair; Sale, Sales and Related; Tran, Transportation and Material Moving.

Age-adjusted BMI and prevalence of injury by industry. Adms, Adm., Support, Waste Management; Agrc, Agr., Forestry, Fishing, Mining, Utilities; Cnst, Construction; Educ, Education; Food, Accommodation, Food; Hlth, Health Care, Social Assistance; Mnfc, Manufacturing; Pblc, Public Administration; Serv, Services; Trad, Trade (wholesale, retail); Tran, Transportation, Warehousing.

Injury Prevalence During the Past Three Months and Mean BMI by Demographic, Lifestyle, and Job Characteristics

All Workers
Injured Workers
BMI (kg/m2) for All Workers
No. of SampleEst. Pop. %* No. of Injured WorkersEst. No. of Injured WorkersPrev*±SE P † Mean*±SE P †
All154,179126,676,222100.01258974,92677.0±2.727.6±0.02
Demographic characteristics
 Sex
 Men18,31269,531,29754.9746662,50395.3±4.5<0.00128.0±0.03<0.001
  Women75,86757,144,92545.1512312,42854.7±2.827.2±0.04
 Race/ethnicity
  White, non-Hispanic94,02187,684,73569.2784686,25178.3±3.60.00127.4±0.03<0.001
  Black, non-Hispanic22,31114,200,926 17,843,67611.2197124,77187.9±7.529.2±0.06
  Hispanic28,1246,946,88614.1222131,47873.7±5.528.1±0.05
  All other, non-Hispanic9,7235.55532,42646.7±7.825.4±0.08
 Age (yrs)
  18–3451,33143,610,10834.4445385,68288.4±5.4<0.00126.7±0.03<0.001
  35–4436,89929,492,36423.4311235,96380.0±5.628.1±0.04
  40–4935,34930,406,15524.0286220,51072.5±5.128.2±0.04
  50–5923,19618,142,84614.3173107,24159.1±5.328.2±0.05
  60+7,4045,024,7494.04325,53050.8±9.527.5±0.06
  Mean±SE41.4±0.07
 Marital status
  Single45,84634,024,27126.9429316,36992.9±6.1<0.00126.9±0.4<0.001
  Married73,26572,487,70157.4467445,61261.5±3.327.8±0.3
  Divorced34,68319,894,77515.7361210,529105.8±6.828.0±0.5
 Education
  <12 yrs16,93612,404,8289.8177138,779105.2±9.2<0.00128.0±0.06<0.001
  High Grad/GED38,22032,331,93025.7375312,43892.7±5.928.2±0.04
  Some coll./Assoc. degr.48,71739,997,67631.8490392,10397.0±5.628.0±0.04
  Bachelor+49,36741,209,44532.7207155,15736.6±3.126.7±0.03
 Annual income
  <$35K65,24650,679,61450.3633488,78196.4±5.4<0.00127.6±0.04<0.001
  $35–65K37,86930,972,71730.7318258,25083.4±5.728.0±0.04
  $65K+21,57519,120,49819.010972,49437.9±4.327.5±0.05
Lifestyle characteristics
 Smoking status
  Never93,41476,144,98260.2596454,91459.7±3.1<0.00127.5±0.03<0.001
  Former29,16924,602,98519.5272214,43687.2±6.728.3±0.04
  Current31,34225,716,63720.3390305,576118.8±8.027.2±0.04
 Alcohol
  Never/Former43,39934,232,02527.4337246,58772.0±4.80.00127.9±0.04<0.001
  Current (≤3 drks/wk)73,25561,076,83048.8557424,14469.4±3.627.8±0.03
  Current (4+ drks/wk)35,49429,737,04723.8344285,67796.1±6.627.0±0.04
 Physical activity‡
  Inactive46,18736,317,17228.9390300,33582.7±5.10.14128.2±0.04<0.001
  Insufficiently active30,34125,233,68520.1254205,07681.3±6.328.2±0.05
  Sufficiently active76,61764,302,93251.0595455,22270.8±3.927.1±0.03
 Sleep duration
  Inadequate (1–6 hrs/day)48,32538,911,47530.9531405,262104.1±5.6<0.00128.3±0.04<0.001
  Adequate (7+ hrs/day)105,09987,204,24669.1722562,80864.5±3.027.3±0.03
  Mean±SE7.0±0.01
 BMI (kg/m2)
  Normal (18.5–25)55,61745,692,26336.1359267,40158.5±3.9<0.001N/AN/A
  Overweight (25–30)56,36146,628,56736.8466371,93279.8±4.9
  Obese (30+)42,20134,355,39227.1433335,59297.7±5.5
Job characteristics
 Length of employment (yrs)
  <124,84220,637,77416.4285231,540112.2±9.0<0.00127.0±0.050.001
  1–449,50840,464,58132.1396326,59580.7±5.027.3±0.04
  5–931,43325,385,06020.1235169,96467.0±5.227.8±0.04
  10+47,60639,609,93731.4339244,81261.8±4.128.2±0.03
  Mean±SE7.9±0.04
 Work hours (hrs/week)
  <3022,02218,170,06114.6141109,72060.4±6.40.10826.9±0.05<0.001
  30 to <50100,37281,512,44365.7779603,41874.0±3.527.7±0.03
 50+28,92524,474,86519.7254198,87579.2±6.328.0±0.04
  Mean±SE40.3±0.05
 No. of employees at work
  1–938,57931,581,70227.1270204,61564.8±4.7<0.00127.3±0.04<0.001
  10–4938,97932,475,00527.9335264,65581.5±5.727.5±0.04
  50–24934,61328,666,70024.6339276,35496.4±6.827.9±0.04
  250+29,37123,821,89620.4220150,57163.2±5.827.8±0.05
 Second job
  Yes13,54211,161,2438.812497,82888.7±9.90.08927.6±0.070.692
  No140,452115,358,61591.21,040804,23669.7±2.827.6±0.02

Weighted value of column percent, and prevalence of injury weighted per 10,000 (=[estimated injury/estimated population]×10,000), and weighted mean of BMI.

The levels (Inactive, Insufficiently active, Sufficiently active) of physical activity reflect the 2008 Physical Activity Guidelines for Americans (available at http:// www.health.gov/PAGuidelines/).

P value from Chi-square for injury, and P value from ANOVA for BMI.

“Inactive” is participating in no leisure-time aerobic activity that lasted at least 10 minutes.

“Insufficiently active” is participating in aerobic activities for 10 minutes or more but less than 150 minutes per week.

“Sufficiently active” is participating in moderate-intensity leisure-time physical activity 150 minutes or more per week, or in vigorous-intensity leisure-time physical activity 75 minutes or more per week, or an equivalent combination.

Reproduced from 22.

Age-Adjusted Prevalence of Injury During the Past Three Months and Mean of BMI by Occupational and Industry groups

Injury
BMI (kg/m2)
No. in SampleEstimated PopulationNo. of Injured WorkersEstimated Injured WorkersUnadjusted Prevalence* of InjuryAge-adjusted Prevalence* of InjuryUnadjusted Mean of BMIAge-adjusted Mean of BMI
154,179126,676,2221258974,92677.0±2.777.1±2.727.6±0.0227.6±0.02
Occupational groups†
 Management14,00012,185,7277457,25647.0±6.644.4±6.727.7±0.0627.5±0.06
 Professional‡21,01517,328,2987449,63328.1±4.228.3±4.327.0±0.0527.0±0.05
 Education, Training, Library98048,077,5776552,30764.8±9.866.0±10.026.9±0.0826.8±0.08
 Health care, Personal care, Community20,92615,953,681159101,22563.4±6.463.6±6.527.5±0.0627.5±0.06
 Protective Service33312,722,6064937,551137.9±22.6135.9±22.529.1±0.1329.1±0.13
 Food Preparation and Serving82376,552,7146954,13982.5±13.586.7±13.426.8±0.0927.4±0.10
 Building and Grounds Clean & Maintenance70485,199,2977356,897109.3±16.1111.7±16.927.8±0.1027.8±0.10
 Sales and Related15,71013,389,5878369,90752.1±6.652.3±6.527.3±0.0627.4±0.06
 Office and Administrative Support20,90116,558,58010471,89743.1±5.343.2±5.127.8±0.0627.9±0.06
 Construction and Extraction88567,653,431150129,843169.7±17.6166.0±17.627.8±0.0727.8±0.07
 Installation, Maintenance, and Repair54004,914,9557564,934132.1±19.9133.5±20.528.2±0.0928.1±0.09
 Production10,3238,418,384166135,174160.6±16.2162.6±16.428.2±0.0728.2±0.07
 Transportation and Material Moving92027,721,38511795,474123.6±14.3126.1±14.828.8±0.0828.8±0.08
Industry groups§
 Agr., Forestry, Fishing, Mining, Utilities32042,711,5194331,913117.7±23.3122.1±25.328.3±0.1128.1±0.11
 Construction10,8009,496,826166142,594150.4±14.2147.9±14.027.7±0.0627.7±0.06
 Manufacturing16,36614,046,321181150,299107.0±10.2111.2±10.828.1±0.0528.0±0.06
 Trade (wholesale, retail)19,96417,033,471153128,83075.6±7.774.2±7.427.5±0.0527.6±0.05
 Transportation, Warehousing66515,542,1046756,742102.4±15.3106.4±16.428.8±0.0828.7±0.09
 Services∥35,64429,524,507162122,58741.7±4.042.0±4.127.2±0.0427.1±0.04
 Adm., Support, Waste Management70255,413,0656854,391100.5±15.597.9±15.228.0±0.0928.1±0.09
 Education14,93812,132,8369466,23654.6±7.256.2±7.627.1±0.0627.0±0.06
 Health Care, Social Assistance21,64816,523,899175109,38966.2±6.166.7±6.327.9±0.0627.8±0.06
 Accommodation, Food99277,872,4077055,69470.7±11.269.5±10.926.6±0.0827.1±0.08
 Public Administration81856,511,6357956,25186.6±11.687.9±11.928.6±0.0828.5±0.09

Prevalence of injury weighted per 10,000 [=(estimated injured workers/estimated population) × 10,000]. Age-adjusted prevalence estimates were based on the 2010 U.S. workers standard population by Bureau of Labor Statistics, using five groups: 18-34, 35-44, 45-54, 55-64, 65+.

Occupational groups are simplified to 13 groups from 23 major groups, which are derived from the Standard Occupation Classification (SOC) Occupation Groups as determined by the US Census Bureau and the Bureau of Labor Statistics.

Professional includes business, financial, computer, mathematical, architecture, engineering, life/physical/social science, legal, arts/design/entertainment/sports/media.

Industry groups are simplified to 11 groups from 21 major groups, which are derived from the North American Industrial Classification System (NAICS) Industry Sectors as identified by the US Census Bureau and the Bureau of Labor Statistics.

Services include information, finance/insurance, real estate/rental/leasing, professional/scientific/technical services, management of companies/enterprises, arts/entertainment/ recreation, other services except public administration.

Age-Adjusted Prevalence* of Injury During the Past Three Months by Occupation and Industry Groups and BMI Category

Normal (BMI: 18.5–24.9)
Overweight (BMI: 25.0–29.9)
Obese (BMI: ≥ 30.0)
nPrev±SEnPrev±SE n Prev±SE
35957.7±3.846680.6±5.143399.6±5.8
Occupational groups
 Management1320.8±6.53143.5±9.73077.3±20.8
 Professional†2826.2±5.82428.6±7.92228.7±8.2
 Education, Training, Library2144.1±11.32380.0±21.62195.3±25.8
 Health care, Personal care, Community4540.8±7.24557.8±11.569105.6±17.5
 Protective Service10125.8±48.022154.6±37.817127.8±35.9
 Food Preparation and Serving2698.0±27.92378.1±19.62076.1±17.1
 Building and Grounds Clean and Maintenance2197.9±26.528107.2±29.624127.4±29.8
 Sales and Related2739.9±8.32855.6±12.82870.3±16.2
 Office and Administrative Support3128.6±6.43754.0±10.13649.2±9.7
 Construction and Extraction41138.8±29.366180.2±29.143179.3±33.2
 Installation, Maintenance, and Repair24150.3±37.631152.5±34.82094.0±25.3
 Production46162.3±32.465139.6±21.155200.8±33.9
 Transportation and Material Moving2687.5±21.743116.6±23.548178.9±34.7
Industry groups
 Agr., Forestry, Fishing, Mining, Utilities869.6±28.017123.6±43.0§18170.4±65.8§
 Construction46130.2±25.576159.7±22.944159.3±27.6
 Manufacturing4480.5±15.073112.5±19.464152.4±24.3
 Trade (wholesale, retail)5561.2± 11.04975.8±13.84993.2±16.8
 Transportation, Warehousing854.4±21.429115.5±33.130126.9±28.7
 Services‡6342.9±6.65338.4±6.54645.9±8.1
 Adm., Support, Waste Management2078.6±23.030126.4±28.61878.0±20.8
 Education2832.6±7.33266.2±15.63497.7±23.6
 Health Care, Social Assistance4647.0±7.95663.0±10.87398.2±15.1
 Accommodation, Food2870.8±21.22159.3±16.32184.3±20.2
 Public Administration1347.2±14.33088.1±20.036123.6±26.0

Age-adjusted prevalence estimates were based on the 2010 U.S. workers standard population by Bureau of Labor Statistics, using five groups: 18-34, 35-44, 45-54, 55-64, 65+.

Professional include business, financial, computer, mathematical, architecture, engineering, life/physical/social science, legal, arts/design/entertainment/sports/media.

Services include information, finance/insurance, real estate/rental/leasing, professional/scientific/technical services, management of companies/enterprises, arts/entertainment/ recreation, other services except public administration.

The estimate of the prevalence is unreliable because the relative standard error of the estimate of injury prevalence in the referent level is larger than 30% (Klein et al23). Relative standard error is calculated by dividing the standard error of the estimate by the estimate itself, then multiplying that result by 100.

Age-Adjusted Prevalence Ratio for Injury by BMI Levels and Occupational/Industry Groups

BMI Levels
BMI ContinuousNormal (BMI: 18.5–24.9)Overweight (BMI: 25.0–29.9)Obese (BMI: ≥ 30.0)
PR* (95% CI)PR* (referent)PR* (95% CI)PR* (95% CI)Overall
1.02 (1.01–1.03)1 (referent)1.26 (1.05–1.51)1.45 (1.20–1.74)PR* (95% CI)
Occupational groups
 Management1.05 (1.01–1.09)1 (referent)1.96 (0.92–4.16)2.70 (1.22–6.00)1.51 (0.98–2.31)
 Professional†1.00 (0.96–1.04)1 (referent)1.07 (0.50–2.27)1.19 (0.60–2.35)1 (referent)
 Education, Training, Library1.01 (0.97–1.06)1 (referent)1.54 (0.73–3.24)1.51 (0.67–3.43)2.78 (1.73–4.46)
 Health care, Personal care, Community1.05 (1.02–1.07)1 (referent)1.24 (0.71–2.14)2.34 (1.42–3.84)2.15 (1.44–3.20)
 Protective Service1.00 (0.96–1.04)1 (referent)1.07 (0.61–1.86)§1.06 (0.54–2.07)§3.91 (2.42–6.31)
 Food Preparation and Serving0.98 (0.93–1.03)1 (referent)0.87 (0.41–1.84)0.68 (0.35–1.34)1.87 (1.15–3.06)
 Building and Grounds Clean & Maintenance1.01 (0.95–1.07)1 (referent)0.76 (0.35–1.64)1.07 (0.53–2.16)3.18 (2.00–5.03)
 Sales and Related1.00 (0.98–1.02)1 (referent)1.08 (0.74–1.57)1.04 (0.72–1.51)1.47 (0.96–2.23)
 Office and Administrative Support1.01 (0.98–1.04)1 (referent)2.11 (1.16–3.87)1.60 (0.88–2.90)1.17 (0.77–1.78)
 Construction and Extraction1.02 (0.98–1.06)1 (referent)1.55 (0.92–2.60)1.66 (0.94–2.90)4.51 (2.92–6.96)
 Installation, Maintenance, and Repair0.99 (0.94–1.04)1 (referent)1.02 (0.62–1.69)0.81 (0.46–1.41)3.27 (2.01–5.31)
 Production1.04 (1.00–1.08)1 (referent)0.70 (0.42–1.18)1.02 (0.59–1.76)3.88 (2.57–5.87)
 Transportation and Material Moving1.05 (1.01–1.08)1 (referent)1.50 (0.75–2.99)2.51 (1.21–5.21)2.69 (1.72–4.20)
Industry groups
 Agr., Forestry, Fishing, Mining, Utilities1.02 (0.96–1.09)1 (referent)1.80 (0.63–5.15)§1.89 (0.66–5.45)§2.10 (1.36–3.25)
 Construction1.02 (0.98–1.07)1 (referent)1.38 (0.85–2.24)1.41 (0.84–2.36)2.60 (1.93–3.50)
 Manufacturing1.04 (1.00–1.08)1 (referent)1.04 (0.64–1.71)1.49 (0.90–2.47)1.91 (1.40–2.60)
 Trade (wholesale, retail)1.01 (0.98–1.04)1 (referent)1.44 (0.86–2.41)1.46 (0.84–2.53)1.31 (0.97–1.76)
 Transportation, Warehousing1.03 (0.98–1.07)1 (referent)2.45 (0.85–7.07)§2.03 (0.68–6.09)§1.54 (1.03–2.31)
 Services‡1.00 (0.97–1.04)1 (referent)0.81 (0.49–1.34)0.99 (0.60–1.62)1 (referent)
 Adm., Support, Waste Management1.00 (0.95–1.06)1 (referent)1.31 (0.56–3.02)1.06 (0.51–2.20)1.70 (1.16–2.48)
 Education1.05 (1.01–1.09)1 (referent)1.82 (0.93–3.57)2.18 (1.03–4.61)1.69 (1.20–2.39)
 Health Care, Social Assistance1.03 (1.00–1.06)1 (referent)1.25 (0.75–2.07)1.77 (1.08–2.90)1.61 (1.21–2.12)
 Accommodation, Food0.99 (0.94–1.04)1 (referent)0.83 (0.38–1.81)0.92 (0.45–1.89)1.04 (0.70–1.55)
 Public Administration1.00 (0.98–1.03)1 (referent)1.03 (0.65–1.66)1.12 (0.70–1.79)1.97 (1.39–2.80)

PR: prevalence ratios with 95% confidence interval are adjusted for sex, age, race/ethnicity, marital status, education, smoking status, alcohol intake status, sleep duration, physical activity, and number of employee at work. Prevalence ratios are obtained from marginal predictions in the fitted logistic regression model (Bieler et al25 ).

Professional include business, financial, computer, mathematical, architecture, engineering, life/physical/social science, legal, arts/design/entertainment/sports/media.

Services include information, finance/insurance, real estate/rental/leasing, professional/scientific/technical services, management of companies/enterprises, arts/entertainment/ recreation, other services except public administration.

The estimate of the prevalence ratio is unreliable because the relative standard error of the estimate of injury prevalence in the referent level is larger than 30% (Klein et al23).

Learning Objectives

Discuss previous evidence suggesting obesity as an important modifiable risk factor for occupational injuries.

Summarize the new findings on the association of overweight/obesity and injury across US occupational and industry groups.

Discuss the implications for efforts addressing body weight to reduce the rate of injuries in specific occupations and industries.