95046888741J Occup Environ MedJournal of occupational and environmental medicine / American College of Occupational and Environmental Medicine1076-27521536-594821270653307366110.1097/JOM.0b013e318209915eNIHMS265854ArticleHEALTH INDICATORS AMONG UNEMPLOYED AND EMPLOYED YOUNG ADULTSCaban-MartinezAlberto J.M.P.H.1LeeDavid J.Ph.D.1GoodmanElizabethM.D.2DavilaEvelyn P.Ph.D., M.P.H.1FlemingLora E.M.D., Ph.D., M.P.H., M.Sc.1LeBlancWilliam G.Ph.D.1ArheartKristopher L.Ed.D.1McCollisterKathryn E.Ph.D.1ChristSharon L.Ph.D., M.S., M.A.3ZimmermanFrederick J.Ph.D.4MuntanerCarlesM.D., Ph.D.5HollenbeckJulie AMA1Department of Epidemiology & Public Health, Miller School of Medicine, University of Miami, Miami, FLCenter for Child and Adolescent Health Policy, MassGeneral Hospital for Children, Boston, MACenter for Developmental Science, University of North Carolina at Chapel Hill, Chapel Hill, NCDepartment of Health Services, School of Public Health, UCLA, Los Angeles, CABloomberg Faculty of Nursing and Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadaCenter for Youth & Communities, School for Social Policy & Management, Brandeis University, Waltham, MACorresponding Author: Alberto J. Caban-Martinez Department of Epidemiology & Public Health University of Miami, Miller School of Medicine Clinical Research Building, Room 1073 1120 N.W. 14th Street, 10th Floor (R-669) Miami, Florida 33136 Phone: (305) 243-7565 Fax: (305) 243-5544 acaban@med.miami.edu201201122011122012532196203

Research on the prevalence of health indicators by employment status among young US adults is limited. We analyzed data from a nationally representative sample of young adults aged 18 to 24 years to document the prevalence of five health behaviors (cigarette smoking, risky drinking, leisure-time physical activity, and fruit and French fries consumption) by employment status.

Unemployed young adults reported higher levels of risky drinking and nonengagement in leisure-time physical activity, while employed young adults had higher levels of smoking, French fries consumption, and low fruit/vegetable consumption. Transportation/material-moving young adult workers reported the highest level of risky drinking (13.5%), and precision production/craft/repair workers reported the highest smoking rates (39.7%).

We found an elevated prevalence of risk factors, which places young workers at increased risk for the development of chronic conditions later in life.

F31 AR057687-02 ||ARF31 AR057687-01 ||ARNational Institute of Arthritis and Musculoskeletal and Skin Diseases : NIAMS

Life experiences and circumstances can influence a young person's ability to acquire, maintain, and sustain good health and well-being.1 Research has shown that experiences and exposures across the life course, particularly early on, have long-term implications for health and may be one of the root causes of health inequality in later life.2 Inequalities in socioeconomic status have been shown to be of key importance to the health of young adults as measured by a wide range of indicators, including morbidity, psychosomatic and somatic illness, employment conditions and occupation, perceived health, and mortality.3--5 Nevertheless, there is little research done on the impact of employment status and occupational inequalities in young adults. Early adulthood is a period when young people seek and obtain growing independence from their parents often through employment opportunities.6--9 Thus, during this period, examining health-risk behaviors that can potentially influence a young adult's health state, possibly altering his or her future adult life, is important.

The concept of “health risk” has been used to describe behaviors and health indicators with potentially negative effects on health, such as substance use, risky drinking, and eating disorders.10--14 Studies have shown that young adult workers in the United States are engaging in a range of health behaviors that increase their likelihood of future adverse health outcomes.15--17 Documenting major health indicators relevant to the major fundamental causes of morbidity and mortality, specifically during a life stage when many adult health behavior patterns are forming, is critical to both identifying and preventing potentially deleterious health conditions in the future. For example, a national Canadian study of workers has shown that young men who were obese were more likely to report greater work absenteeism than their older counterparts.18--21

Despite the growing evidence that early unhealthy behaviors may lead to negative health conditions,22--27 there have been remarkably few large-population-based US health studies of young adult workers. Describing differences in select major Healthy People 2010 indictors and behaviors among employed and unemployed young adults may shed additional insight into the association between employment status, occupation type, and healthy worker effect. Seeds of occupational health disparities documented by researchers now could take root in these early years when workers adopt unhealthy lifestyles. In the present study, we document the prevalence of select health indicators and behaviors among employed and unemployed young adults, as well as examine the relationship between occupation type and five major health behaviors using a nationally representative sample of young adults. We hypothesized that employed young adults would report lower levels of negative health behaviors than their unemployed counterparts.

METHODSData Source

We pooled data from the 1997 to 2004 National Health Interview Survey (NHIS), an annual cross-sectional population-based survey of the health of civilian noninstitutionalized US residents.28 Interviews were conducted in-person by trained interviewers. In the family-core component, information was collected on sociodemographic characteristics, health status and conditions, and health behaviors for all members of the household. In the sample adult-core component, one adult household member was randomly selected to provide more detailed personal health and employment information. Annual response rates to the 1997 to 2004 adult-core survey ranged from 70% to 80%.29--35

Study Sample

We selected all respondents aged 18 to 24 years during the study period. If employed during the study period, these young adults also reported on their occupation for the week prior to interview and were grouped into 13 major occupational group classifications.36 The final sample, inclusive of employed and unemployed young adults for this study period, was 27,010 representing an estimated annual number of 26,649,129 young US adults.

Health Indicator Variables

Smoker status was assessed by the following two questions: (1) “Have you smoked at least 100 cigarettes in your entire life?”; and (2) “Do you now smoke cigarettes every day, some days, or not at all?” Smoking status was coded as “current smoker” versus “former/never smoker.” Risky drinking was defined as 10 or more drinks per week in men, and 7 or more drinks per week in women, or 5 or more drinks at one sitting, 1 or more times in a year37; young adults meeting this definition were coded in the affirmative as “risky drinkers.” Self-reported height and weight measures were utilized to calculate body mass index. Respondents were coded as “obese” if they had a body mass index value of 30.0 kg/m2 or more.38 We defined leisure-time physical activity (LTPA) levels as those young adults who engaged in either “light-moderate” physical activity [classified as > 30 minutes > 5 times per week), “vigorous” physical activity (>20 minutes > 3 times per week), or young adults who qualified for both activity categories].39

Among the subpopulation of the 2000 NHIS young adults who also participated in the 2000 NHIS cancer supplement, the prevalence of fruit consumption based on US dietary guidelines (2 to 4 servings a day of fruit) was coded into a variable from NHIS question, “How often did you eat FRUIT? Count fresh, frozen, or canned fruit. Do not count juices.” Using the same NHIS cancer supplement, French fries consumption was ascertained by asking participants if they consumed 3 or more times per week, “How often did you eat French fries, home fries, or hash brown potatoes?” (dichotomized into a variable from NHIS question). Both the fruit and French fries consumption variables were evaluated (fruit, n = 2197, and French fries, n = 1823).

Self-rated health and functional limitations were included as additional control measures in multivariable analyses examining predictors of our five health indicators listed earlier. Self-rated health was based on a single item; we dichotomized these responses into fair/poor versus good/very good/excellent. Functional limitations for nine activities were assessed in the NHIS: walking, climbing, standing, sitting, stooping, carrying, pushing, grasping, and reaching. Participants were coded as having physical limitations (vs no limitations) if they indicated any level of difficulty with any of these activities.

Statistical Analysis

First, prevalence estimates for the five health indicators are calculated by demographic characteristics within employment status categories and within occupation type. Unknown values (ie, responses coded as “refused,” “not ascertained,” or “don't know”) were not counted in the denominators when calculating estimates. Next, logistic regression models for smoking, drinking, and nonengagement in LTPA as dependent variables were undertaken (our fruit and French fries consumption measures were not included because of small sample size). For the occupational independent variable, the group with the healthiest prevalence estimate for each model outcome (smoking, drinking, and nonengagement in LTPA) was selected as the reference group. For example, in the logistic regression model, where smoking status was the outcome, young adults employed as professional specialty workers were set as the reference group, given that they had the lowest smoking estimate (15.5%) as compared with other occupational groups. Logistic regression analyses were adjusted for gender, race (black, white, or other), and obesity status (healthy weight vs obese). Obesity was included as a potential confounder, given the evidence of the associations between obesity, smoking, drinking, and physical activity.40--42 We list both the unadjusted and adjusted estimates for each of the three models.

Because of the complex sample survey design, weighted analyses were completed with adjustments for the design effects using SAS and SUDAAN.43 To enhance the precision of estimates, data were pooled over the years 1997 to 2004. For pooled estimates, sample weights were adjusted to account for the aggregation of data over multiple survey years by dividing the original weight by 8 (the number of years combined in NHIS years 1997 through 2008).44 The study protocol was approved by the institutional review board of the University of Miami.

RESULTS

There were a total of 10,102 unemployed young adults (representing an estimated annual 9,749,452) and 16,908 employed young adults (representing an estimated annual 16,899,677 young workers) participating in the 1997 to 2004 NHIS. Overall, 61.2% unemployed versus 60.3% employed did not engage in LPTA; 24.2% unemployed versus 28.2% employed currently smoked; 9.1% unemployed versus 8.2% employed engaged in risky drinking; 88.6% unemployed versus 90.1% employed were not eating the recommended two or more servings of fresh fruit per day; and 36.1% unemployed versus 39.1% employed consumed three or more servings of French fries per week (Table 1).

Young workers employed in transportation and material-moving occupations reported greater risky drinking (13.5%), while those in protective-service occupations reported the least risky drinking (3.7%). Smoking rates were highest among precision production, craft, and repair workers (39.7%) but lowest among professional specialty workers (15.5%). Young adults employed as machine operators, assemblers, and inspectors reported the highest levels of nonengagement in LPTA (68.7%) compared with protective-service occupation workers (37.3%) who reported the lowest. Young adult workers employed in transportation and material-moving occupations (95.6%) were more likely to fail to meet the US dietary guideline criteria for fruit consumption than those employed as technicians and related support occupations (85.4%), while Hispanic technicians and related occupations (85.4%) and black protective-services youth workers (91.2%) were most likely to consume three or more servings of French fries per week.

Occupational Results for the Unadjusted Logistic Regression Models

In the univariate logistic regression model (Table 2), young adults employed in precision production, craft, and repair occupations were most likely to report being a current smoker than those young adults employed in professional specialty jobs (odds ratio [OR]: 3.59; 95% confidence interval [CI]: 2.90 to 4.43). Nevertheless, in relation to professional specialty workers, the odds of smoking were significantly elevated for all other worker groups except technicians and related support workers (ORs: 1.59 to 3.44). Young adults employed in transportation and material-moving jobs had the highest odds of risky drinking relative to protective-service workers (OR: 3.47; 95% CI: 2.63 to 4.57); blue-collar, sales, and service workers also had significantly elevated rates of risky drinking (ORs: 2.33 to 3.44). Machine operators, assemblers, and inspectors had the highest rates of nonengagement of LPTA relative to protective-service workers (OR: 3.70; 95% CI: 2.60 to 5.27). In addition, all other worker groups had rates on nonengagement of LPTA, which were significantly higher than that of protective-service workers (ORs: 1.81 to 2.78).

Smoking Status Model

In the multivariable model with adjustment for gender, race/ethnicity, obesity status, self-rated health, and function-limitations status (Table 2), the odds of smoking was highest among young adults employed as machine operators, assemblers, inspectors as compared with young adults employed in professional specialty occupations (adjusted odds ratio [AOR]; 3.90; 95% CI: 3.09 to 4.93). Similar patterns of significantly elevated smoking rates in all other occupational groups noted in the univariate analyses were also found to be significantly elevated in the adjusted analysis (range of AORs: 1.76 to 3.72). Young adults of black, other, and Hispanic race/ethnicity versus white young adults were significantly less likely to report being a current smoker (OR: 0.42, 95% CI: 0.37 to 0.49]; OR: 0.73, 95% CI: 0.58 to 0.93; and OR: 0.45, 95% CI: 0.40 to 0.51), respectively. Young male adults (OR: 1.17, 95% CI: 1.06 to 1.29) versus female adults, those reporting fair to poor health (OR: 2.00; 95% CI: 1.60 to 2.50) versus those reporting excellent to good, and those with one or two functional limitations (OR: 1.52; 95% CI: 1.35 to 1.70) versus no limitations were significantly more likely to report being a current smoker.

Heavy Drinking Model

Report of heavy drinking was significantly associated with young adults employed as transportation and material-moving workers (OR: 3.55, 95% CI: 1.53 to 8.25) versus those in protective-service occupations even after adjusting for confounders. Once again, a pattern of results noted in the univariate analyses for the other occupational groups was also found in the multivariable analyses. Young male adults (OR: 1.75, 95% CI: 1.49 to 2.07) versus female adults and those reporting one or two functional limitations (OR: 1.32, 95% CI: 1.05 to 1.67) versus no limitations were also significantly more likely to report being a heavy drinker. Nevertheless, young adults of black (OR: 0.32, 95% CI: 0.21 to 0.48) and Hispanic (OR: 0.69, 95% CI: 0.54 to 0.89) race/ethnicity versus young white adults were significantly less likely to report being a heavy drinker.

Nonengagement in LPTA Model

After adjusting for confounders, nonengagement in LPTA guidelines was significantly associated with young adults employed as machine operators, assemblers, and inspectors (OR: 3.91, 95% CI: 2.71 to 5.64) as compared with young adult protective-service workers. In comparison with protective-service workers, all other occupations except private household workers had elevated rates of nonengagement in LPTA (range of AORs: 1.78 to 3.21). Young black (OR: 1.53, 95% CI: 1.35 to 1.75), other (OR: 1.48, 95% CI: 1.19 to 1.82), and Hispanic (OR: 1.87, 95% CI: 1.66 to 2.10) adults versus young white adults, those who are obese (OR: 1.26, 95% CI: 1.10 to 1.43) versus those having healthy weight, and those reporting fair to poor health (OR: 2.03, 95% CI: 1.57 to 2.63) versus excellent to good health were significantly more likely not to meet engagement guidelines for LPTA. Finally, male versus female workers were significantly more likely to meet prescribed guidelines for LPTA levels (OR: 0.58, 95% CI: 0.54 to 0.63).

DISCUSSION

In 2000, the top-three risk behaviors related to premature mortality in the United States were (1) tobacco, (2) unhealthy alcohol consumption patterns, and (3) poor diet and physical inactivity.45 To the best of our knowledge, this is the first study to present nationally representative data on these leading health indicators among both employed and unemployed young adults in the United States. We found an elevated prevalence of these three leading contributors to mortality in the United States between several young adult worker occupational groups.

Risky alcohol consumption patterns remain a major risk factor for occupational injuries.46 Over the past 20 years, surveys that focus on occupational health and drinking consistently show higher rates of drinking among employed young adults than among their older employed counterparts.46 We found that employed black young adults engaged in less risky drinking than did unemployed black young adults. Sloan et al47 found racial differences in the relationship between alcohol use during early adulthood and employment status at midlife. Specifically, blacks who were very heavy drinkers as young adults were 4 times as likely as blacks who were occasional drinkers to be unemployed after 15 years of follow-up. We also found that young adult workers employed in transportation and material moving were 4 times more likely to report being a heavy drinker than young adults in protective-service occupations, while young adults employed as machine operators, assemblers, inspectors; precision production, craft, and repair; and farming, forestry, and fishing occupations were 3 times more likely to report being a heavy drinker also. Developing workplace-specific alcohol abuse prevention interventions among young adults worker groups identified in this study as at risk could assist in thwarting injuries due to risky alcohol consumption patterns.

The majority of adult smokers in the United States initiate the habit before the age of 20 years, while few adults take up smoking after the age of 24 years.48 We found that employed young adults, specifically non-Hispanic white male adults, reported the highest rates of smoking as compared with nonworkers. Relative to young adults employed in professional specialty occupations, almost all occupations had 2 times the rate of reporting being a current smoker. It may be possible that young adults seeking full-time employment early in life (rather than higher education) are more likely to come from lower socioeconomic backgrounds where such settings favor the initiation of smoking.49 Preventing tobacco use among young adults has been shown to affect both duration and intensity of total use of tobacco, potentially reducing long-term health consequences significantly. While the majority of US workplaces have prohibited smoking on-site,50 occupational health professionals could develop workplace-sensitive smoking cessation programs that target these worker groups demonstrating disproportionate levels of smoking.

We found that young workers employed in the “protective-service occupations” and young Hispanics employed as “technicians and related support” occupations reported the highest rates of French fries consumption. Findings from the present study are consistent with those from the Pitt County study and others reporting young American adults consuming few portions of fresh fruit per week.51--53 Eating behaviors adopted during this period are likely to be maintained through adulthood, underscoring the importance of encouraging healthy eating strategies and resources at the workplace. Increasing the provision and range of healthy, affordable snacks and meals in the workplace may enable them to exercise their choice of healthier, tasty options and improve their diet.

Young adulthood is characterized by important changes in physical activity, weight, and other factors linked with hypertension.54,55 The Coronary Artery Risk Development in Young Adults study demonstrated a 17% risk reduction in incident hypertension among young adults who were more physically active than those who were less physically active.55 We found that more than 60% of both employed and unemployed young adults reported not engaging in adequate LPTA levels. Even after adjusting for confounders, young adults in almost all occupations had a 2-fold risk in reporting that they did not meet recommended LPTA guidelines (young machine operators, assemblers, and inspectors were the highest) relative to protective-service workers who may be more likely to engage in physical activity in order to meet and maintain job-related physical fitness requirements. Employer-sponsored initiatives such as physical activity workplace promotion programs that educate young adults at the job site on the importance of regular physical activity could assist in addressing the long-term sequelae of hypertension as well as weight management.

Limitations

The cross-sectional design and potential selection effects prevented us from making causal inference. In this study, employment and occupation were based on employment in the 1-week period prior to the survey. Also, self-reported data may be subject to recall bias. It was not possible to control in the models for other important occupational factors such as the number of hours worked (part-time/full-time), permanent versus temporary or contractual worker, unionized versus nonunionized as these indicators were not collected as part of the NHIS. Despite these limitations, the major advantage of the NHIS is that it is a large, multistage nationally representative probability sample of the US population, with a high participation rate and minimal selection bias.36,44

CONCLUSIONS

Although in general, because of the “healthy worker effect,” workers are healthier on average than the general population or unemployed individuals,22,23 we observed significantly elevated and variable rates of engagement in negative health indicators and behaviors among all young adults. Unemployed youth showed the highest levels of risky drinking and nonengagement in LTPA, while employed youth had the highest levels of smoking, consumption of French fries, and lowest levels of fruit and vegetable consumption. In addition, we found significant variation in the prevalence of health behaviors by specific occupational groups, even with adjustment for sociodemographic indicators and health status. Given that work sites provide ready access to 65% of the population older than 18 years,57 developing job-specific interventions is both critical and logical for implementing tailored health promotion activities. Continued surveillance of these health indicators among employed young adults is critical to monitoring changes in the rates of these negative health indicators.

The passage of the new health care reform bill (HB 3962), which supports the evaluation of workplace health promotion programs that lower health costs and boost productivity among all workers, may serve as a springboard to harness the financial and policy elements needed to implement these necessary workplace promotion programs.58 Occupational health professionals and employers should be cognizant of the resource and best practices supported by the health care reform bill to address the disproportionate burden of these negative health indicators among high-risk young adult worker groups identified in this study.

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Individual Health Indicators by Youth 18–24 years of age in 13 major occupational groups and those unemployed: NHIS 1997–2004

13-Level Major Occupational GroupsPopulation Estimate‡Sample N‡% Non-Engagement in Leisure-Time Physical Activity% Smokers% Risky Drinkers% not eating ≥ 2 Servings of Fruit/Day*% >3 Servings of French Fries per week*
Unemployed Youth 9,749,45210,10261.224.29.188.636.1
 Male4,454,2614,02952.326.611.890.246.3
 Female5,295,1916,07368.822.16.187.226.6
 White6,948,2307,05459.126.910.088.034.8
 Black1,845,4801,94565.916.84.990.143.9
 Other955,7431,10367.118.47.089.630.4
 Non-Hispanic8,096,7657,45159.226.29.589.237.7
 Hispanic1,652,6882,65171.214.26.085.527.3
Total for all employed Youth 16,899,67716,90860.328.28.290.139.1
 Male8,877,3008,22155.530.810.291.348.4
 Female8,022,3778,68765.725.35.788.929.8
 White13,774,88013,31358.930.38.890.238.7
 Black1,966,6312,23567.516.93.090.137.2
 Other1,158,1661,36065.222.77.789.145.6
 Non-Hispanic14,343,08113,00258.429.88.489.939.2
 Hispanic2,556,5963,90671.119.47.191.238.4
Specific Occupation Type
Administrative support occupations including clerical2,646,7742,81062.322.56.291.130.3
 Male734,86571653.824.28.489.046.0
 Female1,911,9082,09465.621.95.392.023.7
 White2,098,0052,14162.125.67.190.729.0
 Black376,93644564.69.42.094.236.6
 Other171,83322459.613.81.787.328.0†
 Non-Hispanic2,290,1152,19960.823.86.790.831.2
 Hispanic356,65961171.814.62.793.923.3
Professional specialty1,476,2411,58151.815.55.688.527.5
 Male625,30263447.216.58.191.852.0
 Female850,94094755.214.83.886.818.6
 White1,256,1391,29350.216.26.088.425.9
 Black116,24615659.37.92.085.3†37.0†
 Other103,85613262.915.93.492.1†42.0†
 Non-Hispanic1,374,8791,41551.015.95.588.526.9
 Hispanic101,36316663.29.57.488.4†35.9†
Executive, administrative managerial1,043,8241,12856.026.87.293.537.2
 Male463,31445649.725.17.798.951.7
 Female580,51067261.128.16.790.629.3
 White860,05288454.629.27.393.138.4
 Black107,63214563.39.73.092.2†23.5†
 Other76,1409961.623.910.297.7†44.2†
 Non-Hispanic933,23394954.927.97.193.634.6
 Hispanic110,59117965.117.48.192.4†63.5
Sales2,716,5232,66662.324.58.187.642.6
 Male1,131,97499550.525.710.192.949.9
 Female1,584,5491,67170.823.76.383.637.7
 White2,098,3912,00159.526.89.086.341.5
 Black390,05643472.814.22.393.131.8
 Other228,07623171.021.25.190.3†57.0
 Non-Hispanic2,337,2582,08561.425.68.187.443.2
 Hispanic379,26558168.517.67.588.3†38.5
Precision production, craft, repair1,583,9341,47661.439.711.689.446.4
 Male1,475,7131,34660.039.212.288.547.0
 Female108,22113080.146.31.497.5†42.0†
 White1,390,5331,26560.241.511.991.247.6
 Black108,7439970.521.62.879.125.2†
 Other84,65811269.732.116.284.4†53.5
 Non-Hispanic1,252,5701,01257.743.412.087.651.3
 Hispanic331,36446474.425.79.395.429.0
Service occs, except protective/household3,260,7413,22663.233.18.289.741.8
 Male1,310,7241,19256.935.110.290.347.6
 Female1,950,0172,03467.431.86.889.237.5
 White2,616,6542,50061.335.48.589.742.8
 Black411,95147070.021.94.589.560.6
 Other232,13625672.127.38.388.849.4
 Non-Hispanic2,770,7902,50461.635.28.689.340.3
 Hispanic489,95172271.921.25.191.951.2
Machine operators, assemblers, inspectors898,23586868.738.711.692.345.6
 Male643,93157863.644.413.092.055.0
 Female254,30529081.724.25.993.025.2
 White726,24666567.242.011.895.944.0
 Black113,59913276.225.33.585.2†59.4
 Other58,3907173.723.622.8†73.6†40.1†
 Non-Hispanic708,80760066.943.510.694.746.3
 Hispanic189,42826875.620.616.377.7†41.7†
Transportation/ material moving476,29143659.138.313.595.656.3
 Male436,79438858.638.013.895.358.6
 Female39,4974864.6†41.08.8100.0† †
 White405,29536058.940.414.495.254.0
 Black44,3924864.5†29.23.9100.0†100.0†
 Other26,6042853.0†21.2†14.6100.0†83.14
 Non-Hispanic397,42433159.341.513.894.955.4
 Hispanic78,86710557.922.212.0100.0†64.2†
Handlers, equipment cleaners, helpers, laborers1,374,8901,22357.029.39.190.140.6
 Male1,192,5261,03156.328.29.490.742.6
 Female182,36319261.935.97.486.7†29.9†
 White1,113,95198256.531.19.889.240.5
 Black178,33515262.421.25.091.3†48.5†
 Other82,6048953.222.58.095.5†31.5†
 Non-Hispanic1,099,64283053.131.29.790.540.6
 Hispanic275,24839372.421.56.988.040.7
Technicians/related support564,23459658.917.85.585.434.1
 Male268,78026558.716.96.088.347.6
 Female295,45533159.118.55.182.722.2†
 White472,35148058.119.05.786.034.0
 Black54,0957565.512.53.266.6†31.0†
 Other37,7894158.7†9.9†7.2†100.0†40.6†
 Non-Hispanic510,52750257.617.55.084.230.2
 Hispanic53,7089470.720.410.7100.0†85.4
Farming, forestry, fishing484,72751561.730.210.791.650.8
 Male398,36442461.029.612.091.453.0
 Female86,3639164.633.13.793.2†37.1†
 White444,99445560.429.010.992.949.7
 Black17,1132576.6†34.1† † 69.8†66.1†
 Other22,6213574.6†52.7†12.6†83.6†67.6†
 Non-Hispanic352,46928554.134.312.591.5†73.7
 Hispanic132,25923080.119.43.091.821.0†
Protective service261,99827037.328.93.795.134.6
 Male191,07319133.529.33.798.839.5†
 Female70,9257947.127.73.683.3†15.6†
 White198,45119635.126.94.495.926.0†
 Black41,6004743.5†30.8 † 91.2†91.2
 Other21,9472745.7†42.8†3.1†91.6†48.6†
 Non-Hispanic225,49820634.730.74.198.1†37.8†
 Hispanic36,5006452.117.71.082.9†24.0
Private household111,26611355.327.66.695.5†20.4
 Male3,941560.7†54.9†14.1†28.5†28.5†
 Female107,32610855.126.66.1100.0†19.5†
 White93,8199156.926.06.794.7†26.0†
 Black5,934778.6†51.7† † † †
 Other11,5121530.2†28.2†9.4†100.0† †
 Non-Hispanic89,8708452.832.48.492.8† †
 Hispanic21,3962965.7†7.4† † 100.0†43.4†

The estimate presented does not meet the NCHS standard of reliability or precision because the sample size is less than 30 and/or the sample size is less than 30 and has a relative standard error of 30 percent or more.

The population estimate and sample N represent values for the non-engagement in leisure-time physical activity, smokers, and risky drinkers columns, and does not reflect the sample size of population estimate for Fruit and French fry consumption.

Fruit (n= 2,197) and French fry (n= 1,823) consumption assessed in the 2000 NHIS only

Predictors of health behaviors (smoking, drinking, and leisure-time physical activity) among young adult workers 18–24 years of age participating in the 1997–2004 National Health Interview Survey

Smokinga OR [95% CI]Drinkingb OR [95% CI]Non-Engagement in Leisure-Time Physical Activityc OR [95% CI]
Occupational CategoryUORAORUORAORUORAOR
 Administrative support occupations, including clerical 1.59 [1.31–1.92] 1.76 [1.45–2.14] 1.73 [0.80–3.73]2.12 [0.97–4.61] 2.78 [2.01–3.84] 2.38 [1.71–3.32]
 Executive, administrative managerial 2.00 [1.61–2.47] 2.10 [1.68–2.62] 2.02 [0.90–4.52]2.19 [0.98–4.92] 2.14 [1.53–3.00] 2.05 [1.45–2.90]
 Farming, forestry, fishing 2.36 [1.77–3.17] 2.45 [1.81–3.30] 3.14 [1.28–7.73] 2.74 [1.11–6.73] 2.70 [1.84–3.98] 2.96 [2.02–4.36]
 Handlers, equipment cleaners, helpers, laborers 2.26 [1.79–2.84] 2.46 [1.93–3.13] 2.63 [1.14–6.10] 2.44 [1.06–5.66] 2.23 [1.59–2.14] 2.53 [1.78–3.60]
 Machine operators, assemblers, inspectors 3.44 [2.74–4.31] 3.90 [3.09–4.93] 3.44 [1.48–7.98] 3.27 [1.41–7.54] 3.70 [2.60–5.27] 3.91 [2.71–5.64]
 Precision production, craft, repair 3.59 [2.90–4.43] 3.72 [2.96–4.66] 3.42 [1.54–7.59] 2.93 [1.32–6.50] 2.67 [1.90–3.76] 3.21 [2.26–4.55]
 Private household 2.08 [1.19–3.63] 2.29 [1.31–4.00] 1.84 [0.55–6.12]2.64 [0.79–8.84] 2.08 [1.23–3.52] 1.48 [0.89–2.48]
 Professional specialty1.001.001.55 [0.68–3.53]1.66 [0.72–3.80] 1.81 [1.30–2.52] 1.78 [1.27–2.51]
 Protective service 2.21 [1.58–3.11] 2.43 [1.71–3.46] 1.001.001.001.00
 Sales 1.77 [1.47–2.14] 1.97 [1.62–2.40] 2.29 [1.05–5.02] 2.54 [1.16–5.58] 2.78 [2.01–3.86] 2.56 [1.83–3.57]
 Service occupations, except protective/household 2.70 [2.24–3.25] 2.99 [2.47–3.63] 2.33 [1.11–4.90] 2.64 [1.25–5.57] 2.89 [2.10–3.97] 2.61 [1.89–3.61]
 Technicians/related support1.18 [0.87–1.60]1.19 [0.87–1.63]1.53 [0.64–3.69]1.62 [0.67–3.92] 2.41 [1.67–3.47] 2.36 [1.61–3.44]
 Transportation/ material moving 3.38 [2.59–4.41] 3.47 [2.63–4.57] 4.10 [1.78–9.47] 3.55 [1.53–8.25] 2.43 [1.63–3.63] 2.93 [1.93–4.44]
Gender (ref = Females)1.001.001.00
 Males 1.17 [1.06–1.29] 1.75 [1.49–2.07] 0.58 [0.54–0.63]
Race / Ethnicity (ref = White)1.001.001.00
 Black 0.42 [0.37–0.49] 0.32 [0.21–0.48] 1.53 [1.35–1.75]
 Other 0.73 [0.58–0.93] 0.81 [0.51–1.29] 1.48 [1.19–1.82]
 Hispanic 0.45 [0.40–0.51] 0.69 [0.54–0.89] 1.87 [1.66–2.10]
Obesity Status (ref= Healthy weight)1.001.001.00
 Obese0.90 [0.78–1.03]1.00 [0.78–1.28] 1.26 [1.10–1.43]
Self-Rated Health (ref = Excellent to good)1.001.001.00
 Fair to poor health 2.00 [1.60–2.50] 1.20 [0.79–1.81] 2.03 [1.57–2.63]
Functional Limitations (ref = no limitations)1.001.001.00
 One to two limitations 1.52 [1.35–1.70] 1.32 [1.05–1.67] 0.96 [0.85–1.09]

For occupational category, Professional Specialty is set at the reference group.

For occupational category, Protective service is set at the reference group.

For occupational category, Protective service is set at the reference group.