Young Worker Monograph

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Disability, Morbidity, Health, and Health Behaviors of US Young Workers:
The National Health Interview Survey (NHIS) 2004-2010
Monograph
Manuel A Ocasio, Lora E Fleming MD PhD, Julie Hollenbeck MA, Cristina A Fernandez MSEd, William G
LeBlanc PhD, Jenelle Lin, Henry Olano, Alberto J Caban Martinez DO PhD, Tainya C Clarke MS MPH, Diana
Kachan, Sharon L Christ PhD, Kathryn E McCollister PhD, Kristopher L Arheart EdD, David J Lee PhD
Study Website: http://www.umiamiorg.com
Department of Epidemiology and Public Health
University of Miami School of Medicine
Miami, FL
33136
April 2013
1
Table of Contents
Abstract ......................................................................................................................................................... 3
Keywords ...................................................................................................................................................... 4
Acknowledgements ....................................................................................................................................... 5
Introduction.................................................................................................................................................... 6
Background ................................................................................................................................................... 7
Methods ........................................................................................................................................................ 11
Results .......................................................................................................................................................... 17
Discussion ..................................................................................................................................................... 62
References .................................................................................................................................................... 65
Appendices.................................................................................................................................................... 70
2
Abstract
The National Health Interview Survey (NHIS) is a multipurpose household survey of the US civilian noninstitutionalized population conducted annually since 1957. From 1986-2010, over 768,046 US workers, age
18 years and older, participated in a probability sampling of the entire non-institutionalized US population;
variables collected included demographic characteristics (e.g. employment status and occupation) as well a
range of measures of acute and chronic morbidity and disability. Data on employment status and work
information were also recorded permitting the classification of workers on the basis of both industry and
occupation.
The objective of this Monograph was to provide an overview of current and baseline acute and chronic
disability, morbidity, health, and health behavior data for US young workers age 18-24 years by occupation
using the 2004-2010 NHIS data. After adjustment for sample weights and design effects, several measures of
acute and chronic disability, morbidity, health, and health behaviors were created in tabular format. These
data have been presented by occupation subgroups, as well as by gender, race, ethnicity, health insurance
status, and educational attainment level.
The two Study Websites (http://www.umiamiorg.com and
www.flye.co) contain a repository of interactive data tables which are available in Excel and PDF formats;
additional study and relevant NHIS documentation are also available at the UM NIOSH Research Group
website (http://www.umiamiorg.com).
Understanding the occupational risk factors and improving the health of the US workforce remain paramount to
the public health profession. The surveillance of occupational subgroups using the NHIS dataset allows for the
careful monitoring of young workers and their risk factors in order to prevent and minimize disability and
morbidity, and to maximize health and positive health behaviors in the workplace into the future.
3
Key Words
Young workers, Youth workers, Young Adult Workers, Acute Disability, Youth Occupation, Chronic Disability,
Health Status, Occupation, Morbidity, Wellbeing, National Health Interview Survey (NHIS), Self Reported
Health, Surveillance, Functional Limitation, Health Behaviors
4
Acknowledgements
The data for the National Health Interview Survey (NHIS) were originally collected and prepared by the US
Dept of Health and Human Services and the National Center for Health Statistics. The collector of the original
data bears no responsibility for the analyses or interpretations presented in this publication. This study was
funded in part through the National Institute for Occupational Safety and Health (NIOSH) Grant number R01
0H003915. Funding was also provided to the European Centre for Environment and Human Health at the
University of Exeter Medical School through the European Union Convergence Program (European Regional
Development Fund and European Social Fund). Additional information on this study can be found at the Study
Websites located at: http://www.umiamiorg.com and www.flye.co.
5
Introduction
Occupational health surveillance is the process of collecting, analyzing, and interpreting information concerning
the morbidity, mortality, and health behaviors of US workers over time. These data are essential to the
planning, implementation and evaluation of public health strategies to maximize workforce health. The
University of Miami Occupational Research Group (UMORG) is currently funded by the National Institute for
Occupational Safety and Health (NIOSH) to analyze the National Health Interview Survey (NHIS) dataset
(collected and conducted by the National Center for Health Statistics (NCHS) at the Centers for Disease
Control (CDC)) as part of this occupational surveillance effort.
The National Health Interview Survey (NHIS) is a continuous multipurpose and multistage probability area inperson survey of the US civilian non-institutionalized population living at addressed dwellings.1 Each week a
probability sample of households is interviewed by trained personnel to obtain information about the
characteristics of each member of the household. Data from the NHIS include a range of measures of acute
and chronic disability as well as health behaviors and risk factors collected for all participants. Through data
linkage with the National Death Index (NDI) for the 1986-2004 data, the NHIS has also conducted a Mortality
Follow Up with cause of death currently through 2006. The NHIS database allows for longitudinal analysis of
mortality data as a retrospective cohort study, as well as for cross-sectional and annual trend analysis of the
aggregated data. Thus, the NHIS database represents a unique opportunity to explore new research
hypotheses, and to use more than two decades of data as a surveillance tool to evaluate time trends and
occupational disease and risk factor patterns in the US over the past two and more decades across gender by
and a variety of race-ethnic subpopulations.
This Disability, Morbidity, Health, and Health Behaviors of US Young Workers monograph (focused on young
workers aged 18-24 years) applies an established methodology to assess predictors of acute and chronic
disability morbidity for US workers by nine occupational subgroupings based on the 2000 US Census and the
NCHS using the 2004-2010 NHIS data.2 After adjustment for sample weights and design effects, several
measures of acute and chronic disability and morbidity were created in tabular format. These data have been
presented by the nine occupational subgroups derived from the 2000 US census, as well as by gender, race,
ethnicity, health insurance status, and educational attainment level.
6
Background
The European countries, particularly England since 1837 in their Registrar General’s Decennial Supplements
for England and Wales, have had a long and illustrious history of performing nationwide occupational
studies.3,4 The England and Wales studies are based on surveys conducted through the office formerly known
as the Office of Population Censuses and Surveys, which later became the Office of National Statistics
(http://www.ons.gov.uk/census/index.html).
As noted in the 1995 Registrar General’s Report,3,4 these data have provided a valuable means of generating
hypotheses about work-related risks to health as well as insights into the effectiveness of preventive measures.
The United States has had relatively few studies of equal scope and caliber to evaluate the causes of morbidity
and mortality, and their trends, in US workers.3,5-14 The majority of these studies have focused on special
subsets of data, rather than truly representative national data, and they have focused on mortality rather than
morbidity. Furthermore, all of these previous attempts have been biased by selective reporting and the use of
occupation at time of death as the definition of occupational exposure. Focus has also been primarily based on
traumatic injury, and sampling issues have limited their generalizability to the entire US workforce. As noted in
the 1995 Registrar General’s Report,3,4 mortality data alone cannot describe the nature, scale and impact of all
occupational diseases and injuries since many of them are non-fatal.
Previous NHIS Occupational Morbidity Studies
Previous studies have used the NHIS data to explore a range of occupational issues, including: injury, smoking
characteristics, health characteristics in the longest held occupation and industry, injuries in racial subgroups,
cardiovascular disease and working women, impairments and chronic diseases in farmers, back injury and
disability, workplace accommodations, AIDS knowledge among health care workers, and carpal tunnel. 5,15-31
Kaminski and Spirtas5 analyzed data from the 1969-74 NHIS surveys as Proportional Morbidity Ratios (PMRs)
to examine the morbidity, disability, and reported health care use patterns for 498,580 individuals by industry.
They did not look at trends over this relatively short time period. The highest specific disease conditions were
reported for agriculture, furniture manufacturing, metal fabrication, railroad transport, repair services,
amusement and recreational services, state and local government workers, and new workers; the highest
disabilities were found for forestry and fisheries workers, certain manufacturers, medical and health services
workers, and federal government employees; the greatest use of medical services was among metal industry
workers, specific manufacturers, and railroad workers; the greatest morbidity was reported by private
household service workers, although they had less disability and use of medical services; overall
manufacturing industries had the largest proportion of workers with work injuries and the service industries had
the smallest. The authors pointed out that although some of the results confirmed previous studies, other
results of their study revealed new associations of morbidity with particular industry/occupation of US workers.
These new associations were possible because the NHIS data are not limited to a particular industry,
occupation, or geographic area. Therefore, Kaminski and Spirtas5 suggested that NHIS data can be used as a
surveillance system for occupational disease morbidity and mortality for US workers, and recommended that
its use for this purpose be explored further.
The University of Miami Occupational Research Group (UMORG) has used NHIS data from 1986-2007, with
mortality follow up through 2006 for 1986-2004 NHIS participants, to evaluate various issues of health
disparities among all US workers, particularly among poor and minority worker subpopulations predominantly
by occupational subcategory.32-37 They have also evaluated morbidity, mortality, and quality-adjusted life years
in all US workers by the National Institute for Occupational Safety and Health (NIOSH) National Occupational
Research Agenda (NORA) 8 Industry sectors in a series of 4 Monographs in collaboration with NIOSH.35,36,38,39
In the Monographs and in published peer review papers, they have evaluated occupational health disparities in
terms of health behaviors, health insurance, obesity and exercise, occupational segregation and occupational
prestige, morbidity, and mortality in all US workers, as well as morbidity and mortality within particular
occupations and industry sectors. Overall, they have found that minority and blue collar workers are less likely
to report having health insurance, health screening (such as cancer screenings), and receiving health
prevention information from their health care providers. At the same time, these workers are more likely to be
7
obese, less likely to exercise, more likely to report morbidity, and more likely to report risky drinking and
smoking behaviors (See http://www.umiamiorg.com/ for online monographs and other documentation).
Young Workers
Young workers (<24 years) are a large and relatively unstudied population in the US.40 Yet, by the time they
finish high school, 80% of US youth (approximately 8 million youth) will have worked in some capacity, which is
the highest proportion of young workers in any developed nation.41 Over a third of US high school students
work during the school year, and an even greater proportion work during the summer.42
In the past, agriculture was a significant source of work for young workers (especially for those living on farms).
While young workers are still over-represented in agriculture, the majority of young US workers are now
employed in the Retail (>50%) and Service (25%) Sectors.41 43 Under the 1938 Fair Labor Standards Act
(FLSA), the number of permitted work hours for youth under 16 years is limited, and youth less than 18 years
are restricted from particular hazardous non-agricultural occupations; however, youth of any age may work in
agriculture and/or family-owned businesses. Additionally, state laws vary considerably with some states
allowing up to 50 hours/week of work for youth under 18.40
Research to date suggests that work for youth can provide both benefits and risks, in both the short and long
term. As described below in more detail, often young workers are at increased risk for injury, illness and death
compared to all other workers.40,41,43-45 As their working hours increase (and possibly related to the increased
health risks), young workers are more likely to engage in risky personal behaviors (such as smoking and drug
use with decreased physical activity and sleep), participate less in extracurricular activities, spend less time
with family (unless working in a family business), have trouble at school, and engage in illegal activities.40,41 In
fact, substantially longer working hours during high school has been associated with lower educational
attainment as long as a decade beyond.40
At the same time, work can provide youth with a range of tangible and intangible benefits, particularly now that
the majority of US youth do not work solely to provide income for the family. 40,43 Even part-time work can teach
youth valuable lessons (such as responsibility and independence), as well as provide real work, life skills, and
increased self-esteem.41,43 Furthermore, research has shown that young workers may attain higher
employment rates and better wages as long as a decade after high school graduation.40,46
Mortality and Younger Workers
Each year in the US, over 200,000 youth experience work-related injuries, resulting in the deaths of at least 70
young workers.40,41,43,47 The highest rates have been among male young workers at 91% and among
Occupational groups, those working in the Agricultural Sector accounted for 16% of all job-related fatalities.48
For male young workers, the deaths are associated with motor-vehicle related events, while for female young
workers the deaths are associated with homicide (particularly in the Retail Sector).43
Using data from the 1986-2000 National Health Interview Survey (NHIS) and its public-use mortality follow-up
through 2002 to look at young workers aged 18-24 years, Davila et al49 examined mortality after two- year
follow-up using employment status at baseline and controlling for gender, race, education, season, and survey
design; the study found that having been employed was associated with significantly lower risks of all-cause,
homicide, and “other-cause” mortality (adjusted odds ratios range: 0.51-0.60). This suggests that working may
be a potential factor in the prevention of premature mortality among young adults. Conversely, increasing
unemployment might result in elevated mortality risks among young adults in the future.
Injury, Disability, Morbidity, and Younger Workers
Although highly under-reported, according to the National Research Council,40 young workers overall have
substantially higher injury rates (4.9 per 100 full-time equivalent [FTE]) compared to all other workers (2.8 per
100 FTE). NIOSH found that in 1993, there were 21,620 injuries among workers <18 years reported in a
national sample of employers.47 Among young workers, males have greater numbers and higher rates of
injuries than female adolescents, and the majority of the youth non-fatal work-related injuries occur in the retail
trades, particularly restaurants, although agriculture has a high rate of youth work-related injury.40 In a
8
Canadian study, Breslin et al50 demonstrated that young workers holding manual jobs were 2.65 times more
likely to have a work disability absence compared with young workers with non-manual jobs; and those with
less than a high school education were almost 3 times more likely to have a work disability absence. Kachan
et al51 evaluated workers by age groups using pooled data from the 1997-2009 NHIS: 18-25, 26-54, and 55+
using NIOSH NORA Industry sectors. Workplace injury risk comparisons were made (with the Services sector
as the referent) with adjustment for sample design, gender, education, race/ethnicity, age, and poverty-toincome ratio. The highest risk sectors for workers aged 18-25 were Agriculture/Forestry/Fisheries (odds
ratio=4.32 [95% Confidence Interval 2.03-9.17]), Construction (2.75 [1.62-4.66]), and Transportation/
Communication/Other Public Utilities (2.68 [1.37-5.24]).
These work-related injuries have other consequences for young workers and their families, including school
absences, and potentially short- and long-term disability and morbidity, as well as societal consequences.
Using several large national databases, Miller et al45 estimated that 371,000 youth were injured in the US
workplace, accounting for 4.2% of all occupational injuries in 1993. They estimated that the cost for these
injuries was $5 billion, representing approximately 3% of the total of injury costs involving teenagers. Knight et
al52 reported that among those young workers who visited emergency rooms for their work-related injuries,
over 25% experienced limitations in their normal activities for over 1 week after the injury. Belville et al53 found
that 44% of young workers who received Workers Compensation in New York State suffered permanent
disability, with younger workers aged 14 and 15 at greatest risk. Finally, in a Canadian population-based study
investigating the longer-term health consequences of work-related injuries among youth, Koehoorn et al54
found that persistent use of healthcare services may represent a cumulative burden of morbidity over the life
course as a result of a work-related injury in general among young women and as a result of musculoskeletal
injuries in particular among males.
There is also very little information on the short- and long-term effects of toxic exposures (such as respiratory
irritants and carcinogens, reproductive toxins, and noise) on young workers.40,41 For example, many young
workers on family farms are regularly exposed to pesticides and noise (as well as injury from machines and
motor vehicles). Moreover, research has shown that these young workers are often poorly trained and
protected.40 Therefore, there is an urgent need to evaluate the short- and long-term consequences (both
positive and negative) of work on the health and future of young workers.
Other Consequences and Younger Workers
Since random assignment of youth into work settings is an impractical and unethical study design, all existing
research into the risks and benefits of working is subject to selection bias. Nevertheless, a number of well
designed cross-sectional and longitudinal studies have examined the consequences of work for youth in the
US. For example, several studies have looked at the short- and long-term effects of youth work on educational
attainment, controlling for a variety of factors such as SES, race-ethnicity and family background. The negative
effects of youth work appear to be associated with increasing work hours: with lower intensity work hours,
young workers are actually more likely to finish high school and successfully finish college, while young
workers with high intensity work hours are more likely to drop out of high school or not complete college. It is
important to note that these educational consequences appear to persist for at least a decade.46,55
There is also literature investigating the possible negative relationship between youth work and risky health
behaviors such as substance abuse, early sexual activity, and delinquent behavior.56-58 Again, increased
numbers of hours worked by young workers seems to be associated with these negative work consequences.
However, recent analyses have suggested that this association may be due in part to selection bias and
inappropriate statistical analyses of the data to take this bias into account.57
Although there has been very little research, there does seem to be a differential effect of both positive and
negative consequences of youth work across race-ethnic and socio-economic subpopulations. In particular, it
appears that youth of lower socio-economic class, as well as Hispanic and African American youth, are more
likely to experience the negative effects of youth work in the short and long term, even after controlling for work
hour intensity.59
9
Health Disparities and Occupational Studies in Young workers
There has been relatively little research on different race-ethnic and female subpopulations, as well as SES, of
the US workforce as a whole. As noted above, this is particularly true for young workers.40,59 Recent research
in health disparities has shown that race-ethnicity, lower SES, and even some female subpopulations are at
increased risk for occupational disability and mortality compared to their white male counterparts.18,60-74 The
limited studies which have been published indicate that for all ages, minority workers as well as female workers
may be at increased risk for occupational injury and death compared to white male workers. 71-73,75,76
10
Methods
The National Health Interview Survey (NHIS)
Since 1957, the National Center for Health Statistics (NCHS) has administered the NHIS as a continuous
multipurpose and multistage area probability in-person survey of the US civilian non-institutionalized population
living at addressed dwellings.5,77-79 The survey was authorized by Congress in order to obtain national
estimates on disease, injury, impairment, disability, and related issues on a uniform basis for the US
population. The NHIS has evolved over the years, with a significant redesign in 1997.
NHIS Annual Survey 1997+ – The NHIS was completely redesigned in 1997 to collect key health information
from a single randomly selected adult household member. In case the randomly selected household member is
not home when the interviewer goes to the home, then the interviewer returns at a different date to interview
this person. This strategy greatly enhances the reliability of acute and chronic condition assessment and other
data. Data exist in three separate files: the Person, Sample Adult, and Sample Child. These files include both
household and individual level information on various demographic characteristics and aspects of health.
However, the data that exist in each of these files differ. For example, the Sample Adult file contains
information on health conditions, physical and social activity limitations, psychological distress, chronic
conditions, and important risk factors and health behaviors (such as tobacco and alcohol use, and preventive
medicine compliance) among the adult randomly selected to be interviewed, while the Person file contains
information on functional status and access to health care for all NHIS participants. Data on occupation and
industry are only available in the Sample Adult file. For the 1997-2010 NHIS, there were 242,487 adult
participants currently employed at the time of the NHIS interview (see Table 1). Annual response rates to the
2004-2010 adult core ranged from 61% (in 2010) to 73% (in 2004).80
Table 1. Sample sizes for employed adult participants by ethnicity/race within gender subgroups: NHIS
2004-2010
Males
Females
18-24
25-59
>60
18-24
25-59
>60
Ethnicity
Hispanic/Latino
1,654
8,774
549
1,198
7,507
437
Not Hispanic
4,015
33,508
4,497
4,658
34,937
4,709
Latino
Race
White
4,107
30,981
4,089
4,037
29,723
4,078
Black/African
660
5,362
533
1,095
7,634
705
American
Unknown/multiple
902
5,939
424
724
5,087
363
Race
Total
5,669
42,282
5,046
5,856
42,444
5,146
Key 2004-2010 NHIS Measures- In the Table below (Table 2) is the abbreviated listing of the Disability,
Morbidity, and Health Behaviors measures assessed consistently by the NHIS from 2004-2010; in the
Appendix is a complete Matrix of these survey items including technical aspects of the variables, such as their
Monograph definitions, response categories, cut-points used, etc (Appendix 2). These variables include:
demographic information, measures of morbidity and wellbeing in terms of functional health (including physical,
mental and social limitations) and medical health (including chronic health conditions), other measures of
health (including self-rated health and use of health services), and measures of health behavior (including
ethanol and tobacco use, exercise, and use of preventive vaccinations). Many of these measures have also
been included in other Monographs published by the UMORG allowing for comparisons with industry sectors,
older age groups and other pooled years of data.
11
Table 2. Abbreviated matrix of morbidity, disability and healthcare utilization questions
from the NHIS asked consistently across survey years 2004-2010
Tab
Variable
NHIS Question
1
Demographics
1
Gender
Are you male or female?
1
Race
What races do you consider yourself to be?
1
Ethnicity
Do you consider yourself to be Hispanic or Latino? Hispanic includes: Puerto Rican, Cuban,
Dominican, Mexican, Central/South American, other Latin American, other Hispanic
1
Insurance
Are you covered by health insurance or any other health care plan?
1
Education
What is the highest level of education that you have completed?
Morbidity Domain: Functional Health Capabilities
2
Special
Equipment
Do you now have any health problem that requires you to use special equipment, such as a
cane, a wheelchair, a special bed, or a special telephone?
3
Any
functional
Limitations
NHIS recode based on all the 12 NHIS questions on activity limitations
4
Hearing
Impairment
Which statement best describes your hearing (without a hearing aid): good, a little trouble, a lot
of trouble, deaf?
5
Visual
Impairment
Based on two questions:
o
o
Do you have trouble seeing, even when wearing glasses or contact lenses? and
Are you blind or unable to see at all?
Morbidity Domain: Medical Health Conditions
6
Body Mass
Index (cat)
NHIS recode variable based on the questions:
o
How tall are you without shoes?
o
How much do you weigh without shoes?
7
Cancer
Have you EVER been told by a doctor or other health professional that you had cancer or a
malignancy of any kind? (yes/no)
8
Hypertension
Have you EVER been told by a doctor or other health professional that you have had
hypertension, also called high blood pressure?
9
Heart Disease
Have you EVER been told by a doctor or other health professional that you ha/have heart
disease?. Based on NHIS questions of specific diseases:
10
Asthma
o Coronary heart disease
o Angina
o Heart attack
o Any kind of heart condition or heart disease
Have you EVER been told by a doctor or other health professional that you have had asthma?
11
Severe
Psychological
Is the individual depressed? Based on 6 NHIS questions: “During the past 30 days how often
did you feel…? “
12
Distress
o so sad that nothing could cheer you up?
o nervous?
o restless or fidgety?
o hopeless?
o that everything was an effort?
o worthless?
Have you EVER been told by a doctor or other health professional that you have diabetes or
sugar diabetes?
12
Diabetes
13
Chronic
Bronchitis
DURING THE PAST 12 MONTHS, have you been told by a doctor or other health professional
that you had Chronic bronchitis?
14
Sinusitis
15
Hay Fever
DURING THE PAST 12 MONTHS, have you been told by a doctor or other health professional
that you had Sinusitis?
DURING THE PAST 12 MONTHS, have you been told by a doctor or other health professional
that you had:Hay fever?
16
Non-HIV STD
The next questions are about other sexually transmitted diseases or STDs. STDs are also
known as venereal diseases or
VD. Examples of STDs are gonorrhea, Chlamydia (CLUH-MIH-DEE-UH), syphilis, herpes,
and genital warts.
In the past five years, have you had an STD other than HIV or AIDS?
17
Hepatitis
Have you EVER had hepatitis?
Health Domain: Healthcare Utilization
18
Health Last
Year
Compared with 12 MONTHS AGO, would you say your health is (better, worse, or about the
same)?
19
Self Rated
Health
Would you say health in general is excellent, very good, good, fair, or poor?
20
Seen Primary
Health care
Provider
During the past 12 months, have you seen a primary health care provider (any of the
following):
21
Dental
o Ob/GYN
o general doctor
About how long has it been since you last saw or talked to a dentist? Include all types of
dentists, such as orthodontists, oral surgeons, and all other dental specialists, as well as
dental hygienists.
22
Seen Mental
Health
Provider
During the past 12 months, that is, since [12 month reference date], have you seen or talked
to a mental health professional? (A mental health professional such as a psychiatrist,
psychologist, psychiatric nurse, or clinical social worker.)
23
Seen Eye
Doctor
During the past 12 months, that is, since [12 month reference date], have you seen or talked
to an optometrist, ophthalmologist, or eye doctor (someone who prescribes eyeglasses)?
24
Seen
Chiropractor
During the past 12 months, that is, since [12 month reference date], have you seen or talked
to a chiropractor?
25
Surgery
During the PAST 12 MONTHS, have you had SURGERY or other surgical procedures either
as an inpatient or an outpatient? This includes both major surgery and minor procedures such
as setting bones or removing growths.
26
Routine Care
What kind of place do you USUALLY go to when you need routine or preventive care, such as a
physical examination or
check-up? (assuming they have a USUAL place)
13
27
Needed but
DURING THE PAST 12 MONTHS, was there any time when [fill1: you/someone in the family]
couldn’t afford needed medical care/dental care/eyeglasses/prescription medicine/mental health care but did
care
not get it because [fill2: you/the family] couldn't afford it?
28
Delayed
Medical Care
DURING THE PAST 12 MONTHS, [fill: have you delayed seeking medical care/has medical
care been delayed for anyone in the family] because of worry about the cost?
29
Emergency
Room Visit
During the PAST 12 MONTHS, HOW MANY TIMES have you gone to a HOSPITAL
EMERGENCY ROOM for your health?
30
Bed Day (Cat)
During the PAST 12 MONTHS, that is, since [12 month ref date], ABOUT how many days did
illness or injury keep you in bed for more than half of the day? (Include days while an
overnight patient in a hospital).
31
Lost Work
Day (CAT)
During the PAST 12 MONTHS, that is, since [12 month ref date], ABOUT how many days did
you miss work at a job or business because of illness or injury (do not include maternity
leave)?
Behavior Domain: Health Behavior
32
33
34
Smoking
Risky
Drinking
Leisure Time
Physical
Activity
Is the individual a never smoker, former smoker, or current smoker? Based on the NHIS
questions:
o Have you smoked at least 100 cigarettes in your entire life?
o Do you now smoke cigarettes every day, some days, or not at all?
Is the individual a risky drinker? Based on the NHIS questions:
In your entire life, have you had at least 12 drinks of any type of alcoholic beverage?
In the past year, how often did you drink alcoholic beverages?
In the past year, on those days that you drank, on the average, how many drinks did
you have?
Did the individual meet CDC Health People 2010 recommendations for leisure time physical
activity (i.e. engaged or light-moderate activity for >=3- minutes >=5 times/week or “vigorous
activity” >=20 min >=3 times per week or both. (Adams et al 2006). Based on NHIS questions:
o
o
o
o Frequency of light/moderate activity (times per week)?
o Duration of light/moderate activity (in minutes)?
o Frequent vigorous activity (times per week)?
o Duration of vigorous activity (in minutes)?
On average, how many hours of sleep do you get in a 24-hour period? * Enter hours of sleep
in whole numbers, rounding 30 minutes (1/2 hour) or more UP to the next whole hour and
dropping 29 or fewer minutes.
35
Sleep
36
Influenza
Vaccine
During the past 12 months, have you had a flu shot? A flu shot is usually given in the fall and
protects against influenza for the flu season.
37
HIV/AIDS Test
The next questions are about the test for HIV. Have you ever been tested for HIV?
38
AIDS Risk
Tell me if ANY of these statements is true for YOU. Do NOT tell me WHICH Statement or
statements are true for you.
Just IF ANY of them are.
* Read if necessary.
(a) You have hemophilia and have received clotting factor concentrations.
(b) You are a man who has had sex with other men, even just one time.
(c ) You have taken street drugs by needle, even just one time.
(d) You have traded sex for money or drugs, even just one time.
(e) You have tested positive for HIV (the virus that causes AIDS).
(f) You have had sex (even just one time) with someone who would answer "yes" to any of
these statements.
14
39
Perceived HIV
Risk
What are your chances of GETTING HIV (the virus that causes AIDS)?
Would you say high, medium, low, or none?
40
Hepatitis B
Vaccine
Have you EVER received the hepatitis B vaccine?
Employment and Occupation - As of 1997, employment has been defined as having worked during the week
prior to the NCHS survey and asked of all Sample Adult NHIS participants 18 years and older. This definition
includes paid as well as unpaid work. The NHIS employs US Census Occupational and Industrial Codes to
classify workers.81 The NHIS uses 1990 US Census Occupational Codes (SOC Codes) through 2004 NHIS;
from 2004 forward, the NHIS also uses the 2000 US Census SOC Codes to create 93 occupational subgroups
(although the NHIS no longer releases very detailed SOC codes for occupation). Of note, there is no
appropriate crosswalk between the 1990 and the 2000 US Census SOC Codes;82 therefore, it is not possible to
link occupations coded by the 1990 Census with occupations coded by the 2000 Census.
In this Monograph using 2004-2010 NHIS data, we have grouped workers into 9 occupational categories
based on the 9 job categories of the US Census regroupings of the Occupational codes (see Table 3). A
detailed
listing
of
these
occupations
can
be
found
at
the
US
Census
website
(http://www.census.gov/hhes/www/eeoindex/jobgroups.pdf).
We have created a crosswalk between the 93 more detailed NHIS occupational codes and these 9 Census
occupational grouping (see Appendix 3-4).
Table 3. Job Codes based on 2000 US Census SOC Codes
EEO-1
Job Codes
01
02
03
04
05
06
07
08
09
EEO-1 Job Categories and Titles for the
Census 2000 Special EEO File
Officials and Managers
Professionals
Technicians
Sales Workers
Administrative Support Workers
Craft Workers
Operatives
Laborers and Helpers
Service Workers
Statistical Methods
Because of the multi-stage sampling design, all analyses were performed with adjustment for sample weights
and design effects using the SUDAAN 10.0 and SAS 9.3 statistical packages.83 These analyses also took into
account relatively minor sample design modifications implemented in 2006 due to smaller sample size
recruitment targets.84 The sample weights used were those required for the analysis of data from combined
survey years, and were calculated as originally specified by Botman and currently recommended by the
NCHS.77,84 Sample weights are also used to estimate the number of workers in the US with various health
conditions. In some cases, these values will be underestimates due to either: 1) the presence of missing data
for the condition of interest (e.g., respondent did not respond to a health indicator question); or 2) in the case of
stratified analyses, values were missing for the stratification variable (e.g., educational attainment).
The data are presented in tabular format for all US workers, and then for each of the 9 occupational groups.
Within each table, these data are shown for all workers of the particular subpopulation, and then by gender,
race, ethnicity, education, and health insurance status within that subpopulation; each table also gives the
15
NHIS sample size and the estimated US worker population by each of these subcategories. In the Appendices,
additional data are presented, in particular the standard errors for all the prevalence data. All of these unique
data tables have been made available as Excel spreadsheets (file extension: .xls) at the Study Website (URL:
http://www.umiamiorg.com/). The Excel files can be downloaded to a remote computer in order to manipulate
the data locally.
To save an Excel file to a local computer system, the Study Website user can position their computer cursor
over a link and right-click their mouse, at which time a dialog box will appear. Select the “Save target as” option
to save the file from the Study website to the local system. Researchers can utilize these additional data tables
to further explore disability and health reported among this population-based sample and to extrapolate to the
general US workforce. As discussed above, the standard errors (SEs) are presented in the Appendix Tables.
These SEs can be used to generate confidence intervals for variables with dichotomous outcomes, which are
not provided in this document nor in the appendices. For example, for a particular disability measure, the
reader can take (1.96 x SE) ± Prevalence to generate the 95% prevalence estimate range of that particular
measure among US workers.
16
Results
The first section of results presented below summarize the prevalence for the morbidity, disability, healthcare
utilization, and health behavior measures during the study period 2004-2010 for all US workers aged 18-24
years by gender, race, ethnicity, educational attainment, and insurance status. This is followed by summary
reports of key findings for each of the 9 occupational groups. Standard errors for dichotomous outcomes and
95% confidence intervals for outcomes with three or more categories are listed with prevalence estimates.
Tabulated prevalence estimates by gender, race, ethnicity, education, and availability of insurance for all
workers and by the 9 occupational groups can be found in the Excel files. The Excel files have numbered tabs
for each indicator listed below. Within each tab are tables for all workers and for each of the 9 occupational
groups. Refer to the Appendix for a table of the health indicators and the corresponding tab number and name.
Sociodemographic diversity should be taken into consideration when comparing and interpreting health status
across occupations.
To summarize, comparison of sociodemographic indicators across occupations reveals:

Over a twenty-seven-fold variation among the occupations in the prevalence of having less than a high
school education, with the highest prevalence found in Laborer and Helper occupations (41%)

Over a four-fold variation among the occupations in the prevalence of Hispanic workers, with the
highest prevalence found in Laborer and Helper occupations (48%)

Almost five-fold variation among the occupations in the prevalence of black workers, with the largest
prevalence found among workers in Administrative Support occupations (22%)

Almost two-fold variation among the occupations in the prevalence of insured workers across
occupations, with the highest prevalence noted in Professional occupations (83%)

Over a twenty-three-fold variation among the occupations in the prevalence of female workers, with
largest prevalence in Administrative Support occupations (68%)
1. Overall Health Indicators among all US young workers
From 2004-2010, 11,279 US workers aged 18-24 years (representing an estimated 16,909,733 US young
workers annually) participated in a probability sampling of the entire non-institutionalized US population (see
overall demographics, Tab 1). Of the US workers, there were approximately equal numbers of men (49.0%)
and women (50.1%) during this time period. The majority of the US workers self-identified as white (78.3%)
with 15.8% black and 6.0% “other” races, while 24.6% were Hispanic and 75.4% Non-Hispanic. The majority
(56.7%) of US workers had more than a high school education, with 14.7% having less than a high school
education and 28.3% having completed high school. Finally, although 66.9% reported having health insurance,
32.5% did not have health insurance.
1.A. Morbidity Domain: Functional Health
1.A.1. Need Special Equipment
Special equipment utilization was uncommon among US workers aged 18-24 as a whole (0.5±0.1). Among the
occupational groups, Technicians (0.9±0.9) and Operatives (0.9±0.3) experienced the highest overall
prevalence of needing special equipment, while no workers in Laborers and Helpers occupations reported
need for special equipment. In tab 2, the prevalence of all US workers reporting needing special equipment is
presented by gender, race, ethnicity, education, and insurance availability as well as the estimated US
population numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance
17
availability subgroups, insured workers experienced the highest overall prevalence (0.6±0.1), while workers of
“other” races experienced the lowest overall prevalence with 100% of these workers reporting no need for
special equipment.
Note: Laborers and Helpers prevalence = 0 %; Craft workers prevalence = 0.05%
1.A.5. Any Functional Limitations
More than 6% (6.1±0.3) of all US workers aged 18-24 reported functional limitations. Among the occupational
groups, Laborers and Helpers experienced the highest prevalence of any functional limitation (6.9±1.8), while
Craft workers experienced the lowest (4.6±0.8). These functional limitations include: having difficulty walking ¼
mile without special equipment, reaching over without special equipment, attending events without special
equipment, etc (see Appendix for detailed listing of all functional limitations). In tab 3, the prevalence of all US
workers reporting any functional limitation is presented by age, gender, race, ethnicity, education, and
insurance availability as well as the estimated US population numbers and NHIS sample numbers. Among the
age-gender-race-ethnicity-education-insurance availability subgroups, females (8.0±0.5), those with a high
school (7.2±0.6), and less than high school education (7.2±0.8) experienced the highest overall prevalence of
any functional limitation, while males (4.3±0.4) and workers of “other” races (4.3±1.0) experienced the lowest.
18
1.A.6. Hearing Impairment
Among all US workers aged 18-24, less than 6% (5.5±0.3) reported any hearing impairment (defined as a little
trouble, a lot of trouble, or deaf). There was almost a three-fold difference in prevalence of any reported
hearing impairment across the occupational groups. Craft workers (6.7±1.1) and Operatives (6.6±0.8) workers
reported markedly higher prevalence of hearing loss compared to the Technicians (2.9±1.1). In tab 4, the
prevalence of all US workers reporting any hearing impairment is presented by age, gender, race, ethnicity,
education, and insurance availability as well as the estimated US population numbers and NHIS sample
numbers. Among the age-gender-race-ethnicity-education-insurance availability subgroups, males (5.9±0.4)
and the uninsured (6.0±0.5) experienced the highest overall prevalence, while Hispanic workers experienced
the lowest (3.6±0.5).
19
1.A.7. Visual Impairment
There was an overall visual impairment prevalence of 5.5% (±0.3) among all US workers aged 18-24 years.
Among the occupational groups, Sales workers experienced the highest overall prevalence of reporting current
visual impairment (6.7±0.7), while Laborers and Helpers experienced the lowest (3.2±0.9). In tab 5, the
prevalence of all US workers reporting current visual impairment is presented by age, gender, race, ethnicity,
education, and insurance availability as well as the estimated US population numbers and NHIS sample
numbers. Among the age-gender-race-ethnicity-education-insurance availability subgroups, females (6.9±0.4)
and those with less than a high school education (7.1±0.9) experienced the highest overall prevalence, while
males (4.2±0.4) and workers of “other” races experienced the lowest (4.5±1.1).
20
1.B. Morbidity Domain: Medical Health
1.B.1. Body Mass Index (BMI)
Among the occupational groups, Operatives experienced the highest overall prevalence of being obese,
defined as having a body mass index (BMI) greater than or equal to 30 (21.0%; 95% CI 18.0-24.3), while
Officials and Managers experienced the lowest obesity prevalence (10.6%; 8.1-13.8). In tab 6, the prevalence
of all US workers being obese is presented by age, gender, race, ethnicity, education, and insurance
availability as well as the estimated US population numbers and NHIS sample numbers. Among the agegender-race-ethnicity-education-insurance availability subgroups, blacks experienced the highest (23.0%;
20.7-25.5) and workers of “other” races experienced the lowest (12.6%; 9.2-17.0).
21
1.B.2. Cancer
Among all occupational groups, there was a very low prevalence of ever having cancer (0.8±0.1); with Craft
workers experiencing the lowest prevalence (0.2±0.2) and Technicians experiencing the highest overall
prevalence of (1.5±0.7). In tab 7, the prevalence of all US workers reporting ever having a diagnosis of cancer
is presented by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US
population numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance
availability subgroups, females reported the highest lifetime prevalence of cancer (1.3±0.2), while Hispanics
reported the lowest cancer rates (0.2±0.1).
22
1.B.3. Hypertension
Less than 5% (4.8±0.2) of all US workers aged 18-24 reported having a diagnosis of hypertension. Among the
occupational groups, Operatives experienced the highest overall prevalence of hypertension (6.8±0.8), while
Craft workers experienced the lowest (3.0±0.6). In tab 8, the prevalence of all US workers reporting ever
having a diagnosis of hypertension is presented by age, gender, race, ethnicity, education, and insurance
availability as well as the estimated US population numbers and NHIS sample numbers. Among the agegender-race-ethnicity-education-insurance availability subgroups, black workers experienced the highest
overall prevalence (7.0±0.7), while workers with less than a high school education (4.0±0.6) and Hispanics
(4.0±0.5) experienced the lowest.
23
1.B.4. Heart Disease
There was an overall prevalence of 3.3% (±0.2) of ever having a diagnosis of any kind of heart disease among
all US workers aged 18-24. Among the occupational groups, Service workers experienced the highest
prevalence of heart disease (4.1±0.5), while Laborers and Helpers experienced the lowest (1.9±0.8). In tab 9,
the prevalence of all US workers reporting ever having a diagnosis of any kind of heart disease is presented by
age, gender, race, ethnicity, education, and insurance availability as well as the estimated US population
numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance availability
subgroups, workers with more than a high school education (3.6±0.3) and the uninsured (3.6±0.4) experienced
the highest prevalence, while those of “other” races experienced the lowest (1.5±0.5).
24
1.B.5. Asthma
Among the occupational groups, Officials and Managers experienced the highest overall prevalence of ever
having a diagnosis of asthma (16.3±1.9), while Laborers and Helpers experienced the lowest (9.7±2.0). In tab
10, the prevalence of all US workers reporting ever having a diagnosis of asthma is presented by age, gender,
race, ethnicity, education, and insurance availability as well as the estimated US population numbers and NHIS
sample numbers. Among the age-gender-race-ethnicity-education-insurance availability subgroups, blacks
experienced the highest overall prevalence (15.8±1.1), while Hispanic workers experienced the lowest
(10.0±0.8).
25
1.B.6. Severe Psychological Distress
The prevalence of severe psychological distress in the previous 30 days was very low. This estimate is based
on scores of < 13 on the K6 scale, an instrument developed in part by the National Center for Health Statistics
for assessing symptoms associated with serious mental illness and therefore greatest likelihood of being
diagnosed with a mental illness (see also the complete Matrix in the Appendix). Among the occupational
groups, Craft workers experienced the highest overall mean prevalence of severe psychological distress
(0.7±0.4), while workers in Laborer and Helper and Technician occupations did not experience any
psychological distress. In tab 11, the prevalence of severe psychological distress is presented by age, gender,
race, ethnicity, education, and insurance availability as well as the estimated US population numbers and NHIS
sample numbers. Among the age-gender-race-ethnicity-education-insurance availability subgroups, black
(0.6±0.2) and uninsured (0.6±0.2) workers reported the highest prevalence of severe psychological distress,
while “other” race workers experienced the lowest (0.7±0.1).
26
*Labores and Helpers Prevalence = 0 ;
Technicians Prevalence = 0
1.B.7. Diabetes
Among the occupational groups, Operative workers (1.7±0.6) experienced the highest overall prevalence of
reporting ever having a diagnosis of diabetes, while Craft workers experienced the lowest (0.3±0.2). In tab 12,
the prevalence of all US workers reporting ever having a diagnosis of diabetes is presented by age, gender,
race, ethnicity, education, and insurance availability as well as the estimated US population numbers and NHIS
sample numbers. Among the age-gender-race-ethnicity-education-insurance availability subgroups, black
workers experienced the highest overall prevalence (1.5±0.4), while “other” race workers experienced the
lowest (0.7±0.3).
27
1.B.8. Chronic Bronchitis
Among the occupational groups, Professionals experienced the highest prevalence of chronic bronchitis
(3.4±0.7), while Technicians experienced the lowest prevalence (0.5±0.4). In tab 13, the prevalence of chronic
bronchitis is presented by age, gender, race, ethnicity, education, and insurance availability as well as the
estimated US population numbers and NHIS sample numbers. Among the age-gender-race-ethnicityeducation-insurance availability subgroups, females (3.2±0.3) reported the highest prevalence of chronic
bronchitis while workers of “other” races experienced the lowest (1.3±0.5).
28
1.B.9. Sinusitis
Among the occupational groups, Technicians experienced the highest prevalence of sinusitis (12.7±3.3), while
Craft workers experienced the lowest prevalence (3.4±0.9). In tab 14, the prevalence of sinusitis is presented
by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US population
numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance availability
subgroups, females (9.6±0.5) reported the highest prevalence of sinusitis while Hispanic workers experienced
the lowest (4.1±0.5).
29
1.B.10. Hay Fever
Among the occupational groups, Technicians experienced the highest prevalence of hay fever (7.0±2.9), while
Craft workers experienced the lowest prevalence (2.9±0.8). In tab 15, the prevalence of hay fever is presented
by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US population
numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance availability
subgroups, females (4.7±0.4) reported the highest prevalence of hay fever while Hispanic workers experienced
the lowest (2.8±0.4).
30
1.B.11. Non-HIV STD
Among the occupational groups, Officials and Managers experienced the highest prevalence of having a nonHIV STD (5.8±1.7), while Laborers and Helpers experienced the lowest prevalence (2.4±1.1). In tab 16, the
prevalence of a non-HIV STD is presented by age, gender, race, ethnicity, education, and insurance availability
as well as the estimated US population numbers and NHIS sample numbers. Among the age-gender-raceethnicity-education-insurance availability subgroups, blacks (7.5±0.7) reported the highest prevalence of
having a non-HIV STD while Hispanic workers experienced the lowest (3.1±0.4).
31
1.B.12. Hepatitis
Among the occupational groups, Laborers and Helpers experienced the highest prevalence of Hepatitis
(1.4±0.8), while Officials and Managers experienced the lowest prevalence (0.4±0.2). In tab 17, the prevalence
of hepatitis is presented by age, gender, race, ethnicity, education, and insurance availability as well as the
estimated US population numbers and NHIS sample numbers. Among the age-gender-race-ethnicityeducation-insurance availability subgroups, workers of “other” races reported the highest prevalence of having
hepatitis (1.7±0.6) while non-Hispanic workers experienced the lowest (0.7±0.1).
32
1.C. Health Domain: Healthcare Utilization
1.C.1. Health Last Year
Among the occupational groups, Service workers experienced the highest prevalence of reporting worse health
last year compared to the present (4.8%; 95% CI 3.94-5.89), while Laborers and Helpers experienced the
lowest prevalence (2.5%; 1.36-4.50). In tab 18, the prevalence of reporting worse health is presented by age,
gender, race, ethnicity, education, and insurance availability as well as the estimated US population numbers
and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance availability subgroups,
females reported the highest prevalence of reporting worse health (4.5%; 3.90-5.12) while males experienced
the lowest (3.3%; 2.79-3.80).
33
1.C.2. Self-Rated Health
Among the occupational groups, Laborers and Helpers experienced the highest overall prevalence of reporting
fair/poor health (4.6±1.3), while Professionals experienced the lowest (1.0±0.3). In tab 19, the prevalence of all
US workers reporting fair/poor health is presented by age, gender, race, ethnicity, education, and insurance
availability as well as the estimated US population numbers and NHIS sample numbers. Among the agegender-race-ethnicity-education-insurance availability subgroups, workers with less than a high school
education experienced the highest overall prevalence of self-reported fair/poor health (5.4±0.6), while those
with more than a high school high school education experienced the lowest (1.5±0.2).
34
1.C.3. Seen or talked to primary healthcare provider in past year
Among the occupational groups, Professional workers experienced the highest overall prevalence of seeing or
talking to a primary care provider in the past year (69.7±1.7), while Craft workers experienced the lowest
(36.0±2.1). In tab 20, the prevalence of all US workers reporting seeing or talking to a primary care provider in
the last 12 months is presented by age, gender, race, ethnicity, education, and insurance availability as well as
the estimated US population numbers and NHIS sample numbers. Among the age-gender-race-ethnicityeducation-insurance availability subgroups, females experienced the highest overall prevalence (76.4±0.7).
Uninsured workers had the lowest prevalence of seeing or talking to a primary care provider in the past 12
months (40.7±1.1).
35
1.C.4. Seen or talked to Dentist over a year ago or never having seen a Dentist.
Among the occupational groups, Professionals experienced the highest overall prevalence of seeing or talking
to a dentist more than a year ago or never having seen a dentist (66.8±1.8), Craft workers experienced the
lowest (41.7±2.1). In tab 21, the prevalence of all US workers reporting not having seen a dentist in the past 12
months is presented by age, gender, race, ethnicity, education, and insurance availability as well as the
estimated US population numbers and NHIS sample numbers. Among the age-gender-race-ethnicityeducation-insurance availability subgroups, uninsured workers experienced the highest overall prevalence of
seeing or talking to a dentist more than a year ago or never having seen a dentist when compared to within a
year ago (63.9±1.0), while insured workers experienced the lowest (31.9±0.7).
36
1.C.5. Seen or talked to mental health provider in past year
Among the occupational groups, Officials and Managers experienced the highest prevalence of seeing or
talking to a mental health care provider in the past 12 months (8.5±1.9), while Craft workers experienced the
lowest (2.0±0.5). In tab 22, the prevalence of all US workers reporting seeing or talking to a mental health care
provider in the past 12 months is presented by age, gender, race, ethnicity, education, and insurance
availability as well as the estimated US population numbers and NHIS sample numbers. Among the agegender-race-ethnicity-education-insurance availability subgroups, females experienced the highest prevalence
seeing or talking to a mental health care provider in the past 12 months (7.4±0.5), while Hispanics experienced
the lowest (3.3±0.4).
37
1.C.6. Seen or talked to eye doctor in past year
Among the occupational groups, Officials and Managers experienced the highest prevalence seeing or talking
to an eye doctor in the past 12 months (36.1±2.5), while Craft workers experienced the lowest (13.9±1.5). In
tab 23, the prevalence of all US workers reporting seeing or talking to an eye doctor in the past 12 months is
presented by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US
population numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance
availability subgroups, females experienced the highest prevalence seeing or talking to an eye doctor in the
past 12 months (33.9±0.8), while those with less than a high school education experienced the lowest
(14.9±1.3).
38
1.C.7. Seen or talked to chiropractor
Among the occupational groups, Technicians experienced the highest prevalence of seeing or talking to a
chiropractor in the past 12 months (9.3±2.5), while Officials and Managers experienced the lowest (5.1±1.1). In
tab 24, the prevalence of all US workers reporting seeing or talking to a chiropractor in the past 12 months is
presented by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US
population numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance
availability subgroups, insured workers experienced the highest prevalence seeing or talking to a chiropractor
in the past 12 months (7.7±0.4), while black workers experienced the lowest (3.0±0.5).
39
1.C.8. Surgery in past year
Among the occupational groups, Officials and Managers experienced the highest overall prevalence of
reporting having had surgery (9.9±1.5), while Craft workers experienced the lowest (6.9±1.0). In tab 25, the
prevalence of all US workers reporting having had surgery in the last 12 months is presented by age, gender,
race, ethnicity, education, and insurance availability as well as the estimated US population numbers and NHIS
sample numbers. Among the age-gender-race-ethnicity-education-insurance availability subgroups, females
experienced the highest overall prevalence (10.7±0.5), while workers of “other” races experienced the lowest
(4.8±0.9).
40
1.C.9. Routine or preventive care
Among the occupational groups, Administrative Support workers experienced the highest prevalence of
seeking routine or preventive care at a health center, doctor’s office, HMO or hospital outpatient facility (71.6%;
95% CI 68.98-74.11), while Craft workers experienced the lowest prevalence (50.8%; 46.44-55.15). In tab 26,
the prevalence of all US workers reporting seeking routine at a health center, doctor’s office, HMO or hospital
outpatient facility is presented by age, gender, race, ethnicity, education, and insurance availability as well as
the estimated US population numbers and NHIS sample numbers. Among the age-gender-race-ethnicityeducation-insurance availability subgroups, insured workers reported the highest prevalence of seeking routine
or preventive care at a health center, doctor’s office, HMO or hospital outpatient facility (78.0%; 76.82-79.20)
while uninsured workers experienced the lowest (36.4%; 34.10-38.69).
41
1.C.10. “Can’t Afford Care”
Among the occupational groups, Service workers experienced the highest prevalence of not getting medical
care because of cost in the past 12 months (23.8±1.0), while Professionals experienced the lowest (16.0±1.2).
In tab 27, the prevalence of all US workers reporting not getting care (including medical care, dental care,
eyeglasses, prescription medicine and/or mental health care) in the past 12 months because of cost is
presented by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US
population numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance
availability subgroups, uninsured workers experienced the highest prevalence of not getting medical care due
to cost (39.2±1.1), while insured workers experienced the lowest (13.1±0.5).
42
1.C.11. Delayed medical care because of cost
Among the occupational groups, Service workers experienced the highest prevalence of delaying medical care
because of cost in the past 12 months (11.1±0.7), while Laborers and Helpers experienced the lowest
(6.0±1.4). In tab 28, the prevalence of all US workers reporting delaying seeking medical care because of cost
in the past 12 months is presented by age, gender, race, ethnicity, education, and insurance availability as well
as the estimated US population numbers and NHIS sample numbers. Among the age-gender-race-ethnicityeducation-insurance availability subgroups, uninsured workers experienced the highest prevalence of delaying
seeking medical care due to cost (20.4±0.8), while insured workers experienced the lowest (4.2±0.3).
43
1.C.12. Emergency room visits in past 12 months
Among the occupational groups, Service workers experienced the highest overall prevalence of reporting
having had at least one emergency room visit (27.4±1.1) in the past 12 months, while Professionals
experienced the lowest (17.5±1.3). In tab 29, the prevalence of all US workers reporting having had at least
one emergency room visit in the last 12 months is presented by age, gender, race, ethnicity, education, and
insurance availability as well as the estimated US population numbers and NHIS sample numbers. Among the
age-gender-race-ethnicity-education-insurance availability subgroups, blacks experienced the highest overall
prevalence (28.3±1.3), while workers of “other” races experienced the lowest (15.4±1.8).
44
1.C.13. Bed disability days in past 12 months
Among the occupational groups, Professionals experienced the highest prevalence of two or more bed
disability days due to injury or illness in the last 12 months, that is days in bed for half a day or longer because
of illness or injury (29.2%; 95% CI 26.3-32.3), while Craft workers experienced the lowest (19.6%; 16.4-23.3).
In tab 30, all US workers reporting the mean number of bed disability days in the past 12 months is presented
by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US population
numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance availability
subgroups, female workers experienced the highest prevalence of two or more bed disability days (31.5%;
29.9-33.1), while Hispanic workers experienced the lowest (18.3%; 16.6-20.3).
45
1.C.14. Work days lost in past 12 months
Among the occupational groups, Craft workers experienced the highest prevalence of six or more work days
lost due to injury or illness in the past 12 months (10.4%; 95% CI 8.1-13.3), while Laborers and Helpers
experienced the lowest (7.4%; 4.9-11.1). In tab 31, all US workers reporting the prevalence of six or more
work loss days in the past 12 months is presented by age, gender, race, ethnicity, education, and insurance
availability as well as the estimated US population numbers and NHIS sample numbers. Among the agegender-race-ethnicity-education-insurance availability subgroups, females experienced the highest prevalence
of six or more work days lost 9.9%; 95% CI 9.0-10.9), while Hispanics experienced the lowest (6.5%; 5.3-7.9).
46
1.D. Behavior Domain
1.D.1. Cigarette Smoking
Among the occupational groups, Craft workers experienced the highest overall smoking prevalence (36.4%;
95% CI 32.2-40.74), while Professionals reported the lowest (12.3%; 10.29-14.54). In tab 32, the prevalence of
all US workers reporting being current smokers is presented by age, gender, race, ethnicity, education, and
insurance availability as well as the estimated US population numbers and NHIS sample numbers. Among the
age-gender-race-ethnicity-education-insurance availability subgroups, those with less than a high school
education experienced the highest overall prevalence (34.8%; 31.4-38.4), while Hispanics experienced the
lowest (16.0%; 14.2-18.0).
47
1.D.2. Risky Drinking
Among the occupational groups, Officials and Managers experienced the highest overall prevalence of
reporting having met the definition of risky alcohol drinker (44.9±2.7), while Administrative Support workers
experienced the lowest (30.7±1.3). In tab 33, the prevalence of all US workers reporting having met the
definition of risky drinkers is presented by age, gender, race, ethnicity, education, and insurance availability as
well as the estimated US population numbers and NHIS sample numbers. Among the age-gender-raceethnicity-education-insurance availability subgroups, male workers experienced the highest overall prevalence
(43.5±1.0), while blacks experienced the lowest (17.0±1.3).
48
1.D.3. Leisure Time Physical Activity
Among the occupational groups, Professionals experienced the highest overall prevalence of reporting having
met the CDC-recommended definition of healthy leisure time physical activity (50.7±1.9), while Operatives
experienced the lowest (32.3±1.7). In tab 34, the prevalence of all US workers reporting having met the CDC
definition of healthy leisure time physical activity is presented by age, gender, race, ethnicity, education, and
insurance availability as well as the estimated US population numbers and NHIS sample numbers. Among the
age-gender-race-ethnicity-education-insurance availability subgroups, workers with more than a high school
education experienced the highest overall prevalence (44.0±0.9), while workers with less than a high school
education experienced the lowest (29.9±1.6).
49
1.D.4. Mean hours of sleep per night
Among the occupational groups, Laborers and Helpers experienced the highest overall mean hours of sleep
within a 24-hour period (7.4±0.1), while Technicians experienced the lowest (7.0±0.1). In tab 35, the mean
number of hours of sleep is presented by age, gender, race, ethnicity, education, and insurance availability as
well as the estimated US population numbers and NHIS sample numbers. Among the age-gender-raceethnicity-education-insurance availability subgroups, Hispanics and those with less than a high school
education experienced the highest mean (7.4±0.03 and 7.4±0.1), while black workers experienced the lowest
(7.1±0.1).
50
1.D.5. Influenza shot in the past 12 months
Among the occupational groups, Technicians experienced the highest overall prevalence of workers reporting
having received a flu shot in the past 12 months (30.6±4.2), while Laborers and Helpers experienced the
lowest (5.8±1.5). In tab 36, the prevalence of all US workers reporting having received a flu shot in the past 12
months is presented by age, gender, race, ethnicity, education, and insurance availability as well as the
estimated US population numbers and NHIS sample numbers. Among the age-gender-race-ethnicityeducation-insurance availability subgroups, females experienced the highest overall prevalence (16.2±0.7),
while uninsured workers experienced the lowest (9.0±0.7).
51
1.D.6. Lifetime HIV test
Among the occupational groups, Officials and Managers experienced the highest overall prevalence of
reporting having ever had testing for HIV (36.6±2.5), while Laborers and Helpers experienced the lowest
(22.4.±2.8). In tab 37, the prevalence of all US workers reporting having ever had testing for HIV is presented
by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US population
numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance availability
subgroups, blacks experienced the highest overall prevalence (54.0±1.6), while males experienced the lowest
(24.7±0.7).
52
1.D.7. Risk factor for AIDS
Among the occupational groups, Sales workers (4.6±0.7) and Craft workers (4.6±1.0) experienced the highest
overall prevalence of having at least one risk factor for AIDS, while Laborers and Helpers experienced the
lowest (1.1±0.8). In tab 38, the prevalence of all US workers reporting having at least one risk factor for AIDS is
presented by age, gender, race, ethnicity, education, and insurance availability as well as the estimated US
population numbers and NHIS sample numbers. Among the age-gender-race-ethnicity-education-insurance
availability subgroups, black (4.7±0.9) and uninsured (4.7±0.5) workers experienced the highest overall
prevalence while females experienced the lowest (2.4±0.2).
53
1.D.8. Perceived chance of getting HIV
Among the occupational groups, Administrative Support workers experienced the highest overall prevalence of
reporting that they believed to have no chance of getting HIV (71.1%; 95% CI 68.5-73.7), while Technicians
experienced the lowest (62.1%; 53.6-69.8). In tab 39, the prevalence of all US workers reporting their
perceived chance of getting HIV is presented by age, gender, race, ethnicity, education, and insurance
availability as well as the estimated US population numbers and NHIS sample numbers. Among the agegender-race-ethnicity-education-insurance availability subgroups, workers of “other” races experienced the
highest overall prevalence (72.1%; 67.2-76.5), while males experienced the lowest (63.5%; 61.9-65.2).
54
1.D.9. Hepatitis B vaccine
Among the occupational groups, Technicians experienced the highest overall prevalence of ever having
received a Hepatitis B vaccine (71.5±3.6), while Craft workers experienced the lowest (35.6±2.3). In tab 40, the
prevalence of all US workers reporting having ever received a pneumonia shot is presented by age, gender,
race, ethnicity, education, and insurance availability as well as the estimated US population numbers and NHIS
sample numbers. Among the age-gender-race-ethnicity-education-insurance availability subgroups, those with
more than a high school education experienced the highest overall prevalence (63.4±0.9), while those with less
than a high school education experienced the lowest (39.2±1.8).
55
Health Indicators by Occupational Groups
The following provides prevalence estimate summaries for each Occupational group noting health indicators
for which each group was either at the highest or lowest extreme relative to the other Occupational groups.
2. Occupation-Specific Summary Findings for the US Workforce
2. A. Officials and Managers:

Had the highest prevalence of:
o
Asthma (16.3% versus 9.7%-16.0%) with blacks having the highest prevalence (24.5%) and
Hispanics having the lowest prevalence (11.9%)
o
Risky drinking (44.9% versus 30.7%-44.8%), with males having the highest prevalence (55.6%)
and those with a high school education having the lowest prevalence (21.8%)
o
Ever receiving an HIV test (36.6% versus 22.3%-35.4%), with blacks having the highest
prevalence (58.1%) and males having the lowest prevalence (28.8%)
o
Having a non-HIV STD (5.8% versus 2.4%-4.4%), with females having the highest prevalence
(7.6%) and those with a high school education having the lowest prevalence (0.2%)
o
Having surgery or any other surgical procedures either as an inpatient or outpatient in the past
12 months (9.9% versus 6.9%-9.2%), with those with less than a high school education having
the highest prevalence (18.8%) and those of other races having the lowest prevalence (4.2%)
56

o
Seeing or talking to an optometrist, ophthalmologist, or eye doctor in the past 12 months (36.1%
versus 13.9%-34.9%), with those of other races having the highest prevalence (46.2%) and the
uninsured having the lowest prevalence (21.3%)
o
Seeing or talking to a mental health provider in the past 12 months (8.5% versus 2.0%-7.8%),
with those with less than a high school education having the highest prevalence (9.4%) and
those of other races having the lowest prevalence (5.5%)
Had the lowest prevalence of:
o
Seeing or talking to a chiropractor in the past 12 months (5.1% versus 5.7%-9.3%), with those
with less than a high school education having the highest prevalence (13.7%) and blacks with
the lowest prevalence (1.0%)
o
Perceiving High chances of getting AIDS/already having AIDS (0.3% versus 0.4%-2.1%), with
females and those with less than a high school education having the highest prevalence (69.8%
and 88.5% respectively)
o
Being obese (10.6% versus 11.5%-21.0%), with those with less than a high school having the
highest prevalence (30.4%) and those of other races having the lowest prevalence (1.9%)
2.B Professional Workers:


Had the highest prevalence of:
o
Chronic Bronchitis (3.4% versus 0.5%-2.8%), in which those with a high school education had
the highest prevalence (8.7%) and those with less than a high school high school education
having no reports of chronic bronchitis (0.0%).
o
Two or more bed days caused by illness or injury (29.2% versus 19.6%-28.6%), with females
having the highest prevalence of 2+ bed days (34.9%) and those with less than a high school
high school education having the lowest prevalence (17.2%)
o
Seeing or talking to a primary health care provider in the past 12 months (69.7% versus 36.0%67.6%), with those who are insured having the highest prevalence (72.4%) and males having
the lowest prevalence (50.7%)
o
Meeting the CDC Healthy People 2010 Recommendations for leisure time physical activity
(50.7% versus 32.2%-45.3%), with those with a high school education having the highest
prevalence (56.6%) and those with less than a high school education having the lowest
prevalence (33.6%)
Had the lowest prevalence of:
o
Fair/Poor self-rated health (1.0% versus 1.3%-4.6%), with those with less than a high school
education having the highest prevalence (4.4%) and those of other races not reporting fair/poor
self-rated health (0.0%)
o
Seeing or talking to a dentist more than 12 months ago or never having never seen a dentist
(66.8% versus 41.7%-63.8%), with the uninsured having the highest prevalence (54.9%) and
the insured having the lowest prevalence (28.9%)
o
Not receiving medical care (medical care/dental care/eyeglasses/prescription medicine or
mental health care) because of cost during the past 12 months (16.0% versus 19.2%-23.8%)
with the uninsured having the highest prevalence (39.1%) and males having the lowest
prevalence (10.2%)
57
o
Going to an emergency room (1+ visits) in the past 12 months (17.5% versus 18.8%-27.4%),
with those with a high school education having the lowest prevalence (75.9%) and males having
the highest prevalence (86.3%)
2.C Technicians:


Had the highest prevalence of:
o
Cancer (1.5% versus 0.2%-1.3%), with the uninsured having the highest prevalence (5.4%) and
those of other races, Hispanics, with less than a high school education, and with a high school
education having no reports of cancer (0.0%)
o
Sinusitis (12.7% versus 3.8%-8.9%), with females having the highest prevalence (15.0%) and
those of other races and with less than a high school education having no reports of sinusitis
(0.0%)
o
Hay fever (7.0% versus 2.9%-5.3%), with the uninsured having the highest prevalence (12.9%)
and those of other races and with less than a high school education having no reports of hay
fever (0.0%)
o
Seeing or talking to a chiropractor in the past 12 months (9.3% versus 5.1%-7.5%), with those
of other races having no reports of seeing or talking to a chiropractor (0.0%)
o
Perceiving no chances of getting AIDS/already having AIDS (2.1% versus 0.3%-1.1%), with
those with more than a high school education having the highest prevalence (2.7%) and those
of other races, blacks, those with less than a high school education, those with a high school
education, and the uninsured having no reports of perceived risk (0.0%)
o
Receiving an influenza shot in the past 12 months (30.6% versus 5.8%-18.5%), with females
having the highest prevalence (36.7%) and those with less than a high school education having
no reports of receiving an influenza shot (0.0%)
o
Receiving the Hepatitis B vaccine (71.5% versus 35.6%-68.3%), with those with less than a high
school education having the highest prevalence (96.0%) and the uninsured having the lowest
prevalence (42.7%)
Had the lowest prevalence of:
o
Hearing impairment (2.9% versus 4.3%-6.7%), with males having the highest prevalence (6.0%)
and those of other races, less than high school education, and the uninsured having no reports
of hearing impairment (0.0%)
o
Chronic bronchitis (0.5% versus 1.4%-3.4%), with the uninsured having the highest prevalence
(2.6%) and those of other races, blacks, those with less than a high school education, and those
with a high school education having no reports of chronic bronchitis (0.0%)
o
Having a non-HIV STD (2.4% versus 2.6%-4.4%), with blacks having the highest prevalence
(7.1%) and Hispanics having the lowest prevalence (0.7%)
o
Mean hours of sleep in a 24-hour period (7.0 versus 7.1-7.3), with those with less than a high
school education having the highest amount of mean hours (7.9) and those with more than a
high school education having the lowest amount of mean hours (6.9)
2.D Sales Workers
58

Had the highest prevalence of:
o
Visual impairment (6.7% versus 3.2%-6.2%), with those with less than a high school education
having the highest prevalence (11.2%) and those of other races having the lowest prevalence
(3.6%)
o
Having at least one risk factor for AIDS (4.6% versus 1.1%-3.9%), with blacks having the
highest prevalence (8.3%) and those of other races having the lowest prevalence (3.0%)
2.E Administrative Support Workers:

Had the highest prevalence of:
o

Seeking routine or preventive care at a clinic, health center, doctor’s office, HMO or hospital
outpatient facility (71.6% versus 50.8-70.8%), with females having the highest prevalence
(76.0%) and uninsured workers having the lowest prevalence (41.8%)
Had the lowest prevalence of:
o
Risky drinking (30.7% versus 33.6%-44.9%), with males having the highest prevalence (36.5%)
and blacks having the lowest prevalence (13.7%)
2.F Craft Workers:


Had the highest prevalence of:
o
Severe psychological distress (0.7% versus 0.0%-0.6%), with those with a high school
education having the highest prevalence (2.3%) and females and those of other races having no
reports of severe psychological distress (0.0%)
o
Six or more work-days lost due to illness or injury, not including maternity leave (10.4% versus
7.4%-10.0%), with those of other races having the highest prevalence (37.3%) and Hispanics
having the lowest prevalence (7.6%)
o
At least 1 risk factor for AIDS (4.6% versus 1.1%-3.9%), with those with more than a high school
education having the highest prevalence (6.4%) and females having the lowest prevalence
(1.6%)
Had the lowest prevalence of:
o
Receiving the Hepatitis B vaccine (35.6% versus 43.1%-71.5%), with more than a high school
education having the highest prevalence (49.3%) and Hispanics having the lowest prevalence
(21.7%)
o
Any functional limitations (4.6% versus 4.8%-6.9%), with females having the highest prevalence
(5.3%) and those of other races having no reports of functional limitations (0.0%)
o
Hypertension (3.0% versus 3.5%-6.8%), with those of other races having the highest prevalence
(4.8%) and those with less than a high school education having the lowest prevalence (1.8%)
o
Diabetes (0.3% versus 0.6%-1.7%), with Hispanics and the insured having the highest
prevalence (both 0.5%) and females, blacks, and those of other races having no reports of
diabetes (0.0%)
59
o
Sinusitis (3.4% versus 3.8%-12.7%), with those with more than a high school education having
the highest prevalence (6.3%) and Hispanics having the lowest prevalence (0.7%)
o
Hay fever (2.9% versus 3.3%-7.0%), with females having the highest prevalence (5.9%) and
Blacks and those of other races having no reports of hay fever (0.0%)
o
Seeing or talking to a primary health care provider in the past 12 months (36.0% versus 46.5%69.7%), with females having the highest prevalence (60.4%) and those of other races having
the lowest prevalence (18.4%)
o
Seeking routine or preventive care at a clinic, health center, doctor’s office, HMO or hospital
outpatient facility (50.8% versus 50.8-71.6%), with females having the highest prevalence
(76.0%) and uninsured workers having the lowest prevalence (41.8%)
o
Seeing or talking to a dentist more than 12 months ago or never having seen a dentist (41.7%
versus 47.0%-66.8%), with females having the highest prevalence (79.3%) and insured workers
having the lowest prevalence (41.7%)
o
Seeing or talking to a mental health provider in the past 12 months (2.0% versus 3.2%-8.5%),
with females having the highest prevalence (5.1%) and those of other races reporting not seeing
or talking to a mental health provider (0.0%)
o
Seeing or talking to an ophthalmologist, optometrist, or eye doctor in the past 12 months (13.9%
versus 15.1%-36.1%), with those of other races having the highest prevalence (28.8%) and
uninsured workers having the lowest prevalence (6.1%)
o
Having surgery or any other surgical procedure either as an outpatient or inpatient in the past 12
months (6.9% versus 7.9%-9.9%), with insured workers having the highest prevalence (10.0%)
and those of other races having the lowest prevalence (1.1%)
o
Two or more bed days caused by illness or injury (19.6% versus 20.2%-29.2%), with those of
other races having the highest prevalence (39.0%) and blacks having the lowest prevalence
(11.8%)
o
Never having smoked (36.4% versus 12.3%-31.9%), with uninsured workers having the highest
prevalence (39.5%) and Hispanics having the lowest prevalence (19.6%)
2.G Operatives:

Had the highest prevalence of:
o
Any health problem that requires the use of special equipment, such as a cane, a wheelchair, a
special bed, or a special telephone (0.9% versus 0.0%-0.9%), with Hispanics having the highest
prevalence (1.3%) and those of other races reporting no use of special equipment (0.0%)
o
Hypertension (6.8% versus 3.0%-5.3%), with those of other races having the highest prevalence
(9.5%) and Hispanics having the lowest prevalence (4.2%)
o
Diabetes (1.7% versus 0.3%-1.1%), with those with less than a high school education having
the highest prevalence (3.2%) and those with more than a high school education having the
lowest prevalence (0.6%)
o
Being obese (21.0% versus 10.6%-17.9%), with blacks having the highest prevalence of being
obese (26.7%) and Hispanics having the lowest prevalence of being obese (18.5%)
60
o
Not meeting the CDC Healthy People 2010 recommendations for leisure time physical activity
(67.8% versus 49.3%-67.5%), with females having the highest prevalence (78.3%) and those
with more than a high school education having the lowest prevalence (61.8%)
2. H Laborers and Helpers:


Had the highest prevalence of:
o
Any functional limitations (6.9% versus 4.6%-6.6%), with blacks having the highest prevalence
(14.0%) and those of other races not reporting any functional limitations (0.0%)
o
Fair/poor self rated health (4.6% versus 1.0%-3.2%), with blacks having the highest prevalence
(20.1%) and those of other races not reporting fair/poor self rated health (0.0%)
o
Mean hours of sleep in a 24-hour period (7.4 versus 7.0-7.3), with females having the highest
mean hours of sleep (8.0) and males and the uninsured having the lowest mean hours of sleep
(both 7.3)
Had the lowest prevalence of:
o
Visual impairment (3.2% versus 4.1%-6.7%), with blacks having the highest prevalence (10.9%)
and those of other races having no reports of visual impairment (0.0%)
o
Heart disease (1.9% versus 2.4%-4.1%), with Hispanics having the highest prevalence (2.3%)
and blacks and those of other races having no reports of heart disease (0.0%)
o
Asthma (9.7% versus 11.3%-16.3%), with those of other races having the highest prevalence
(14.7%) and Hispanics having the lowest prevalence (4.3%)
o
Receiving an influenza shot in the past 12 months (5.8% versus 9.3%-30.6%), with insured
workers having the highest prevalence (8.9%) and blacks and those of other races having no
reports of receiving an influenza shot (0.0%)
o
Six or more work-days lost because of injury or illness, not including maternity leave (7.4%
versus 7.7%-10.4%), with those of other races having the highest prevalence (22.8%) and those
with more than a high school education having the lowest prevalence (4.0%)
o
Ever being tested for HIV/AIDS (22.3% versus 27.6%-36.6%), with those of other races having
the highest prevalence (60.3%) and insured workers having the lowest prevalence (16.5%)
o
At least one risk factor for HIV/AIDS (1.1% versus 1.7%-4.6%), with those with a high school
education reporting the highest prevalence (2.1%) and blacks and those of other races reporting
no risk factors for AIDS (both 0.0%)
2.I Service Workers:

Had the highest prevalence of:
o
Heart disease (4.1% versus 1.9%-3.6%), with those with more than a high school education
having the highest prevalence (4.9%) and those of other races having the lowest prevalence
(1.8%)
o
Needing care (medical care/dental care/eyeglasses/prescription medicine or mental health care)
but did not get because couldn’t afford in the past 12 months (23.8% versus 16.0%-22.8%), with
the uninsured having the highest prevalence (43.1%) and the insured having the lowest
prevalence (12.4%)
61
o

Delay in seeking medical care in the past year because of worrying about the cost (11.1%
versus 5.9%-9.6%), with the uninsured having the highest prevalence (23.3%) and the insured
having the lowest prevalence (4.1%)
Had the lowest prevalence of:
o
One or more emergency room visits during the past 12 months (72.6% versus 74.5%-82.5%),
with Hispanics having the highest prevalence (79.9%) and females having the lowest
prevalence (69.4%)
Discussion
Summary Findings for the US Workforce: The Importance of Sociodemographic Status
With regard to US youth worker morbidity, health, healthcare utilization, and health behavior, it is important to
characterize the workforce in terms of sociodemographic status since indicators such as race/ethnicity and
education can be strongly associated with health status, health behaviors and health care utilization. Although
this Monograph is based on pooled cross-sectional data, it is also important to note macro-level changes in
workforce composition, which will also have implications for the future health status of the US workforce.
Overall, young US workers are relatively healthy with fairly low burdens of acute and chronic diseases (e.g.
cancer, heart diseases) and current functional disability compared to older workers and the general US
population.32-35,85,86 However, there are a number of indicators of potential future disability and disease. For
example, young workers have high rates of risky behaviors (e.g. risky drinking and smoking), and there are
already high rates of obesity. They also receive relatively little preventive health (e.g. hepatitis B and influenza
vaccines, HIV testing, and regular doctor/dentist visits). Furthermore, these indicators are not evenly
distributed in the young worker population; young workers who are black, with less than a high school
education, without health insurance, and in blue collar occupations (e.g. service workers, operatives) are more
likely to have risky behaviors and/or be obese, and be less likely to receive preventive health. These factors all
are harbingers of future chronic disease and disability with decreased quality of life and increased expensive
health care utilization. Intervention programs delivered in the workplace can provide education and resources
for consequences of risky behaviors and health conditions, as well as better access to preventive health (e.g.,
influenza vaccines); these programs can be targeted at the higher risk subpopulations identified in this
Monograph among the larger population of young workers.
The US workforce overall, including the young workforce, is becoming increasing diverse in terms of race and
ethnicity. Currently, approximately 20% of the US workforce reports their race as being ‘black” or another nonwhite race and 24.6% report being Hispanic. Despite a recession-related spike in the unemployment rate,
Hispanics will continue to comprise an increasing proportion of the US workforce in the coming years.
Hispanics reported consistently better health status, with the exception of slightly higher reports of fair/poor
health status (3.9% versus 2.3%) compared to non-Hispanics. However, in the literature, Hispanics report
worse health care access relative to non-Hispanics, at least partially associated with their poor access to
health care insurance.87 Furthermore, in this Monograph, Hispanic workers reported a lower prevalence of
having seen a primary health care provider in the previous 12 months (45.1% versus 63.1%). There are also
some notable differences in the health status of black versus white workers. Relative to whites, black workers
reported higher prevalence of diabetes (0.9% versus 1.5%), obesity (15.7% versus 23.0%), and fair/poor
health status (2.4% versus 3.8%). As with Hispanic workers, this may indicate an increasing disease burden
among black workers in the future.
In the years 2004-2010, 32.4% of US young workers were uninsured. Uninsured workers are more likely to
report fair/poor health status relative to insured workers (3.9% versus 1.9%), less likely to report a health care
provider visit in the previous 12 months (40.7% versus 68.7%), and less likely to receive a flu shot in the
previous 12 months (8.9% versus 15.6%). Prior research has indicated that uninsured workers are less likely to
pursue other forms of preventive medical care, such as cancer screening.88 Therefore, uninsured workers may
62
represent an increasingly at risk worker subpopulation. Relationships between gender and health outcomes
are complex.89,90 For example, the prevalence of hearing impairments is higher in male versus female workers
(5.9% versus 5.0%) while the opposite finding is seen for visual impairment (4.2% versus 6.9%). Female young
workers are far more likely to report a primary health care provider visit relative to male workers (76.4% versus
44.9%), but they also were more likely to report having 2 or more bed days due to illness or injury in the
previous 12 months (31.5% versus 20.8%).
Finally, there are strong inverse correlations between educational attainment and health status and health care
utilization, as well as risky health behaviors such as smoking.65,91,92 Relative to those with more than a high
school education, the prevalence of diabetes was higher among workers reporting less than a high school
education (0.8% versus 1.4%). In addition, those with less than a high school education reported the highest
prevalence of fair/poor health status relative to any other sociodemographic subgroup (5.4% versus 1.5% 3.96%). Relative to those with more than a high school education, workers with less than a high school
education were less likely to report a primary health provider visit in the previous 12 months (66.6% versus
44.3%), less likely to engage in leisure time physical activity (44.0% versus 29.9%), and more likely to be a
smoker (18.1% versus 34.8%). Of note, smoking is the single most important cause of premature mortality in
the US,93 and the widening disparities in health outcomes in the US have been attributed by some researchers
to a concentration of continued smoking in the lower socioeconomic status segments of the US population.94
Limitations
The cross-sectional survey design of the NHIS does not allow for causal inference. Nevertheless, the NHIS
collects annually a representative sample of the US civilian population making it a powerful surveillance tool to
pool data and observe trends of a range of factors among all US civilian workers.
Due to the self-reporting nature of the NHIS, there is considerable potential for biases of both under- and overreporting. For example, weight and height were collected in a self-reported or proxy fashion, which could have
led to less precision in the calculation of body mass index (BMI). Previous research has suggested that people
tend to under-report their weight and over-report their height; this would lead to an underestimation. It is also
important to note that the degree to which these values are under- and over-reported can vary considerably by
a number of sociodemographic characteristics, such as age, gender, race, ethnicity, and social class.95-98
Despite the ability to pool data across years, small sample sizes are of concern in certain cases. Some of the
Occupational groups, particularly Technicians and Laborers and Helpers have relatively few workers (n=263
and n=353, respectively); therefore, it may be inappropriate to draw conclusions, particularly when stratified by
the different demographic subpopulations. In addition, young individuals tend to be healthy and certain health
conditions are rare in this age group (i.e., cancer) and likely to produce unreliable estimates.
Summary/Recommendations
This Monograph employed nationally-representative data from the 2004-2010 NHIS and demonstrated varying
degrees of disability and morbidity across nine Occupational groups and key sociodemographic subgroups for
workers aged 18-24. Thirty-nine self-reported health indicator measures were categorized into five overarching
health domains: health behavior, healthcare utilization, medical health conditions and functional health
capabilities. Not surprisingly, workers in white-collar occupations reported higher preventive healthcare
utilization (such as Professionals being more likely to have seen or talked to a primary care physician in the
past 12 months). Furthermore, presumably due to access to health care issues, uninsured workers reported
less use of healthcare services (such as seeing or talking to a dentist). Finally, persons with less than a high
school education report a higher prevalence in poor health behaviors (such as more current smoking).
It is clear that differential distribution of sociodemographic factors are associated with increased disability and
morbidity risks across Occupational groups. For example, as noted above, persons with less than a high
school education and/or without medical insurance may be more likely found in certain groups such as in Craft
and Operative Occupations. Therefore, more likely due to increased access to care and insurance,
Technicians reported a higher prevalence of healthcare utilization and medical conditions (such as seeing or
talking to a primary care provider and being diagnosed with heart disease), while Occupational groups with
63
less insurance and access to medical care (such as workers in Craft and Laborer and Helper Occupations)
reported both less healthcare utilization and less diagnosis of medical conditions.
The NHIS data are unique in providing reported functional capabilities, medical conditions, healthcare
utilization, and health behaviors among certain demographic subpopulations and among Occupational groups
for the entire US population. Although these data do not directly reflect occupational exposures, these
conditions and behaviors form the backdrop to the health of US workers and their families, as well as providing
important information on evolving trends. As the NIOSH Total Worker HealthTM initiative aims to improve
worker health via integration of health promotion and improvement in workplace safety,99 results from this
Monograph may provide additional insight for targeting efforts among the US young worker population.
Prior research has demonstrated that the numbers of uninsured workers (and their families) are growing. This
is important because the NHIS data demonstrate that these workers are reporting lower prevalence of seeking
preventive health care, which will lead inevitably to increased healthcare burden and costs to these workers
and society as a whole in the future. With regards to these particular Occupational groups, the NHIS data
would indicate that Craft workers and Laborers and Helpers are particularly at increased risk for illness in the
future due to lack of access to health care and poor current health behaviors. Finally, these NHIS data
suggest that the introduction of health prevention programs in the workplace targeted at particular worker and
industry subpopulations might be efficacious in preventing future morbidity.
Improved occupational surveillance systems are needed in order to better assess the health status of all
workers and to advance our knowledge as to how the workplace affects health. Examples of needed data not
routinely collected by the National Health Interview Survey include: assessment of work exposures, better
documentation of workplace injury and illnesses, and better documentation of work characteristics (such as the
number of hours worked, shift-work, and work-related strain at work). More comprehensive assessment of
work histories, as opposed to the type of work done in the week or two prior to the interview, would provide
valuable information on potential lifetime occupational exposures. Although some of these data are available at
the population level (such as via the Bureau of Labor Statistics), these surveys lack information on worker
health status needed in order to perform etiologic-focused analyses. In addition, data on health promotion
programs and workplace accommodations at the population level, and as reported by the employee, would be
imperative to determine if the needs of those workers in worse health are being met, and consequently which
occupations or industries are in greatest risk of injuries, morbidity, and mortality. Ultimately, the information
from studies such as in this Monograph, as well as future research studies with more comprehensive
assessments of worker exposures, are vital for the development of targeted worker health promotion programs
and for the development of evidence-based workplace policies designed to maximize worker health and
productivity.
64
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
About the National Health Interview Survey: Sample Design. National Health Interview Survey 2012;
http://www.cdc.gov/nchs/nhis/about_nhis.htm - sample_design. Accessed June 8, 2012, 2012.
Occupational Crosswalk to EEO Occupational Groups and EEO-1 Job Categories. In: Division HaHES, ed: US
Census Bureau.
Drever F. The Registrar General's Decennial Supplement for England & Wales. Vol 10. London: Office of
Population Censuses and Surveys Health and Safety Executive, Series DS; 1995.
Boffetta P, Kogevinas M. Occupational cancer in europe. Environ Health Perspect. May 1999;107 Suppl 2:227.
Kaminski R, Spirtas R. Industrial Characteristics of Persons Reporting Morbidity during the Health Interview
Surveys Conducted in 1969-1974. Cincinnati, OH. NIOSH. 1980.
Milham S. Occupational Mortality in Washington State 1950-71. Cincinnati, OH: NIOSH, Dept HEW #76-175a;
1976.
Milham S. Occupational Mortality in Washington State 1950-79. Cincinnati, OH: NIOSH, Dept HEW #83-116;
1983.
NIOSH. Mortality by Occupation, Industry and Cause of Death: 24 Reporting States (1984-88). Cincinnati, OH:
NIOSH #1997-114; 1997.
Guralnick L. Mortality by Industry and Cause of of Death Among Men 20-64 Years of Age. Vol 53(4). Washigton,
DC: National Center for Health Statistics; 1963.
Guralnick L. Mortality by occupation and causes of death among men 20-64 years of Age: US, 1950. Vol 53(3).
Washington, DC: NCHS; 1963.
Guralnick L. Mortality by occupation and industry among men 20-64 years of Age: US, 1950. Vol 53(2).
Washington, DC: NCHS; 1962.
Gallagher R. Occupational Mortality in British Columbia 1950-1984. Cancer Control Agency of British Columbia.
1989.
NIOSH. Fatal Injuries to Workers in the US, 1980-89. Cincinnati, OH: NIOSH, #93-108; 1993.
Murphy PL, Sorock GS, Courtney TK, Webster BS, Leamon TB. Injury and illness in the American workplace: a
comparison of data sources. American journal of industrial medicine. Aug 1996;30(2):130-141.
La Rosa JH. Women, work, and health: employment as a risk factor for coronary heart disease. Am J Obstet
Gynecol. Jun 1988;158(6 Pt 2):1597-1602.
Wagener DK, Winn DW. Injuries in working populations: black-white differences. Am J Public Health. Nov
1991;81(11):1408-1414.
Zwerling C, Whitten PS, Davis CS, Sprince NL. Occupational injuries among workers with disabilities: the National
Health Interview Survey, 1985-1994. Jama. Dec 24-31 1997;278(24):2163-2166.
Zwerling C, Whitten PS, Davis CS, Sprince NL. Occupational injuries among older workers with visual, auditory,
and other impairments. A validation study. J Occup Environ Med. Aug 1998;40(8):720-723.
Zwerling C, Whitten PS, Sprince NL, et al. Workplace accommodations for people with disabilities: National
Health Interview Survey Disability Supplement, 1994-1995. J Occup Environ Med. May 2003;45(5):517-525.
Brackbill R, Frazier T, Shilling S. Smoking characteristics of US workers, 1978-1980. American journal of industrial
medicine. 1988;13(1):5-41.
Brackbill RM, Cameron LL, Behrens V. Prevalence of chronic diseases and impairments among US farmers, 19861990. Am J Epidemiol. Jun 1 1994;139(11):1055-1065.
Nelson DE, Emont SL, Brackbill RM, Cameron LL, Peddicord J, Fiore MC. Cigarette smoking prevalence by
occupation in the United States. A comparison between 1978 to 1980 and 1987 to 1990. J Occup Med. May
1994;36(5):516-525.
Nelson DE, Giovino GA, Emont SL, et al. Trends in cigarette smoking among US physicians and nurses. Jama. Apr
27 1994;271(16):1273-1275.
Sterling T, Weinkam J. The confounding of occupation and smoking and its consequences. Soc Sci Med.
1990;30(4):457-467.
Sterling TD, Weinkam JJ. Comparison of smoking-related risk factors among black and white males. American
journal of industrial medicine. 1989;15(3):319-333.
65
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
Cooper SP, Buffler PA, Lee ES, Cooper CJ. Health characteristics by longest held occupation and industry of
employment: United States, 1980. American journal of industrial medicine. Jul 1993;24(1):25-39.
Hurwitz EL, Morgenstern H. Correlates of back problems and back-related disability in the United States. J Clin
Epidemiol. Jun 1997;50(6):669-681.
Behrens V, Seligman P, Cameron L, Mathias CG, Fine L. The prevalence of back pain, hand discomfort, and
dermatitis in the US working population. Am J Public Health. Nov 1994;84(11):1780-1785.
Biddlecom AE, LeClere FB, Hardy AM, Hendershot GE. National study of knowledge of AIDS, testing patterns, and
self-assessed risk among health care workers. J Acquir Immune Defic Syndr. 1992;5(11):1131-1136.
Tanaka S, Wild DK, Seligman PJ, Halperin WE, Behrens VJ, Putz-Anderson V. Prevalence and work-relatedness of
self-reported carpal tunnel syndrome among U.S. workers: analysis of the Occupational Health Supplement data
of 1988 National Health Interview Survey. American journal of industrial medicine. Apr 1995;27(4):451-470.
Guo H, Tanaka S, Halperin W, Cameron L. Back pain prevalence in US industry and estimates of lost workdays.
Am J Public Health. July 1, 1999 1999;89(7):1029-1035.
Fleming LE. Interactive Monograph of Occupation, Disability, and Self-reported Health: The National Health
Interview Survey (NHIS) 1986-1994. http://www.rsmas.miami.edu/groups/niehs/niosh/monographs.html
(accessed April 28, 2007).
Fleming LE. Interactive Monograph of Occupations and Health Disparities: The National Health Interview Survey
1997-2004. http://www.rsmas.miami.edu/groups/niehs/niosh/monographs.html (accessed April 28, 2007).
Fleming LE. Interactive Monograph of Occupation and Mortality: The National Health Interview Survey (NHIS)
1986-1994. http://www.rsmas.miami.edu/groups/niehs/niosh/monographs.html (accessed April 28, 2007).
Davila E LD, LeBlanc W, Fleming LE, Caban-Martinez AJ, Christ SL, Clarke T, McCollister K, Arheart K, Sestito J.
NORA morbidity and disability monograph: The National Health Interview Survey (NHIS) 1986-1996. National
Institute of Occupational Safety and Health (NIOSH) Conference.; 2011; Cincinatto, OH.
Fleming LE OM, LeBlanc W, Davila E, Lee DJ, Caban Martinez A, McCollister K, Arheart K, Sestito J NORA
Mortality Monograph: The National Health Interview Survey (NHIS) 1986-2004. . National Institute of
Occupational Safety and Health (NIOSH). 2011; Cincinnati, OH.
Occupational Crosswalk to EEO Occupational Groups and EEO-1 Job Categories In: Bureau UC, ed2006.
Lee DJ DE, LeBlanc W, Caban Martinez A, Fleming LE, Christ SL, McCollister K, Arheart K, Sestito J. NORA
Morbidity and Disability: The National Health Interview Survey (NHIS) 1997-2007. 2009.
http://www.umiamiorg.com.
McCollister KE MP, Davila E, Lee DJ, LeBlanc W, Fleming LE, Caban Martinez A, Ocasio MA, Clarke T, Arheart K,
Sestito J. . Health-Adjusted Life Years and Burden of Disease by NORA Sectors: The National Health Interview
Survey (NHIS) 1986-1996. . National Institute of Occupational Safety and Health (NIOSH). 2011; Cincinnati, OH. .
NRC. Protecting Youth at Work: Health, Safety and Development of Working Children and Adolescents in the US. .
1999.
Wegman DH, Davis LK. Protecting youth at work. American journal of industrial medicine. Nov 1999;36(5):579583.
Rubenstein H, Sternbach MR, Pollack SH. Protecting the health and safety of working teenagers. Am Fam
Physician. Aug 1999;60(2):575-580, 587-578.
Castillo DN, Davis L, Wegman DH. Young workers. Occup Med. Jul-Sep 1999;14(3):519-536.
Cohen LR, Potter LB. Injuries and violence: risk factors and opportunities for prevention during adolescence.
Adolesc Med. Feb 1999;10(1):125-135, vi.
Miller TR, Waehrer GM. Costs of occupational injuries to teenagers, United States. Inj Prev. Sep 1998;4(3):211217.
Carr RV, Wright JD, Brody CJ. Effects of high school work experience a decade later: evidence from the NLYS.
Sociology Education 1996;69:66-81.
NIOSH. Analysis of the Survey of Occupational Illnesses and Injuries. Washington, DC: US Department of Health
and Human Services. 1996.
Derstine B. Job related fatalities involving youths, 1992-1995. Compensation and Working Conditions December
1996:1-3.
66
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
Davila EP, Christ SL, Caban-Martinez AJ, et al. Young adults, mortality, and employment. J Occup Environ Med.
May;52(5):501-504.
Breslin FC PJ, Tompa E, Amick BC, Smith P, Johnson SH. . Antecedents of Work Disability Absence Among Young
People: A Prospective Study. Annals of Epidemiology. 2007;17(10):814-820.
Kachan D, Fleming LE, LeBlanc WG, et al. Worker populations at risk for work-related injuries across the life
course. American journal of industrial medicine. Apr 2012;55(4):361-366.
Knight EB, Castillo DN, Layne LA. A detailed analysis of work-related injury among youth treated in emergency
departments. American journal of industrial medicine. Jun 1995;27(6):793-805.
Belville R, Pollack SH, Godbold JH, Landrigan PJ. Occupational injuries among working adolescents in New York
State. Jama. Jun 2 1993;269(21):2754-2759.
Koehoorn M BF, Xu F. . Investigating the longer-term health consequences of work-related injuries among youth.
. Journal of adolescent health. 2008;43(5):466-473.
Ruhm CJ. Is high school employment consumption or investment? . J Labor Ecnomics 1997;15(4):735-776.
1997;15(4):735-776.
Bozick R. Precocious behaviors in early adolescence - Employment and the transition to first sexual intercourse. J
Early Adolescence 2006;26(1):60-86.
Paternoster R, Bushway S, Brame R, Apel R. The effect of teenage employment on delinquency and problem
behaviors. . Social Forces 2003;82(1):297-335.
Warren JR, Lee JC. The impact of adolescent employment on high school dropout: Differences by individual and
labor-market characteristics. Soc Sci Research 2003;32(1):98-128.
Gardecki RM. Racial differences in youth employment. Monthly Labor Review 2001;124(8):51-67.
Zwerling C, Sprince NL, Wallace RB, Davis CS, Whitten PS, Heeringa SG. Risk factors for occupational injuries
among older workers: an analysis of the health and retirement study. Am J Public Health. Sep 1996;86(9):13061309.
King TK, Borrelli B, Black C, Pinto BM, Marcus BH. Minority women and tobacco: implications for smoking
cessation interventions. Ann Behav Med. Summer 1997;19(3):301-313.
Bollini P, Siem H. No real progress towards equity: health of migrants and ethnic minorities on the eve of the
year 2000. Soc Sci Med. Sep 1995;41(6):819-828.
Murray LR. Sick and tired of being sick and tired: scientific evidence, methods, and research implications for
racial and ethnic disparities in occupational health. Am J Public Health. Feb 2003;93(2):221-226.
Boffetta P, Kogevinas M, Westerholm P, Saracci R. Exposure to occupational carcinogens and social class
differences in cancer occurrence. IARC Sci Publ. 1997(138):331-341.
Barbeau EM, Krieger N, Soobader MJ. Working class matters: socioeconomic disadvantage, race/ethnicity,
gender, and smoking in NHIS 2000. Am J Public Health. Feb 2004;94(2):269-278.
Steenland K, Burnett C, Lalich N, Ward E, Hurrell J. Dying for work: The magnitude of US mortality from selected
causes of death associated with occupation. American journal of industrial medicine. May 2003;43(5):461-482.
Steenland K, Halperin W, Hu S, Walker JT. Deaths due to injuries among employed adults: the effects of
socioeconomic class. Epidemiology. Jan 2003;14(1):74-79.
O'Campo P, Eaton WW, Muntaner C. Labor market experience, work organization, gender inequalities and
health status: results from a prospective analysis of US employed women. Soc Sci Med. Feb 2004;58(3):585-594.
Janzen BL, Muhajarine N. Social role occupancy, gender, income adequacy, life stage and health: a longitudinal
study of employed Canadian men and women. Soc Sci Med. Oct 2003;57(8):1491-1503.
Khlat M, Sermet C, Le Pape A. Women's health in relation with their family and work roles: France in the early
1990s. Soc Sci Med. Jun 2000;50(12):1807-1825.
Gómez-Marín O. Acute and Chronic Disability among US Farmers and Pesticide Applicators: The National Health
Interview Survey (NHIS). J Agricultural Safety Health. in press.
Wegman DH, McGee JP. Health and Safety Needs of Older Workers. Washington, D.C.: The National Academies
Press; 2004.
Santiago AM, Muschkin CG. Disentangling the effects of disability status and gender on the labor supply of
Anglo, black, and Latino older workers. Gerontologist. Jun 1996;36(3):299-310.
67
74.
Pollan M, Gustavsson P. High-risk occupations for breast cancer in the Swedish female working population. Am J
Public Health. Jun 1999;89(6):875-881.
75.
Fernandez ME, Mutran EJ, Reitzes DC, Sudha S. Ethnicity, gender, and depressive symptoms in older workers.
Gerontologist. Feb 1998;38(1):71-79.
76.
Loomis D, Richardson D. Race and the risk of fatal injury at work. Am J Public Health. Jan 1998;88(1):40-44.
77.
Botman SL, Jack SS. Combining National Health Interview Survey Datasets: issues and approaches. Stat Med. Mar
15-Apr 15 1995;14(5-7):669-677.
78.
Botman SL, Moore TF, Moriarty CL, Parsons VL. Design and estimation for the National Health Interview Survey,
1995-2004. Vital Health Stat 2. Jun 2000(130):1-31.
79.
Massey JT, Moore TF, Parsons VL, Tadros W. Design and estimation for the National Health Interview Survey,
1985-94. Vital Health Stat 2. 1989:1-33.
80.
NHIS Survey Description. In: Statistics DoHI, ed. Hyattsville, Maryland: National Center for Health Statistics;
2011:90-97.
81.
NCHS. Industry and Occupation Coding for Death Certificates 1993. Hyattsville, MD: Public Health Service; 1992.
82.
Bureau UC. The relationship between the 1990 census and 2000 census industry and occupational
classifications. Technical paper #65.: Prepared by Thomas S. Scopp Under Contract with the U.S. Census Bureau.
NUMBER 43-YA-BC-265775. ; 2003.
83.
RTI. Software for Survey Data Analysis (SUDAAN) Version 8.0.2. 2004.
84.
NCHS. Variance Estimation and Other Analytic Issues, NHIS 2007. 2007 National Health Interview Survey (NHIS)
Public Use Data Release. NHIS Survey Description Division of Health Interview Statistics National Center for
Health Statistics
ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2007/srvydesc.pdf (Accessed
September 5, 2009). Hyattsville2007:97-108.
85.
Fleming LE, LeBlanc W, Caban A, Lee DJ, Gómez-Marín O. Monograph: Occupation, Disability and Self Reported
Health in the National Health: 1986-1994. 2004. http://www.rsmas.miami.edu/groups/niehs/niosh/. 2004.
86.
Fleming LE OM, LeBlanc WG, Davila EP, Lee DJ, Caban-Martinez AJ, McCollister KE, Arheart KL, Fernandez CA,
Sestito JP. NORA Mortality Monograph: The National Health Interview Survey (NHIS) 1986-2004
. Not yet published2010.
87.
McCollister KE, Arheart KL, Lee DJ, et al. Declining Health Insurance Access Among US Hispanic Workers: Not all
Jobs are Created Equal. American Journal of Industrial Medicine 2009;In press
88.
Vidal L, LeBlanc WG, McCollister KE, et al. Cancer Screening in US Workers. AMerican Journal of Public Health
2009;99:59-65.
89.
Kuh D, Hardy R. A life course approach to women's health New York: Oxford 2002.
90.
Wagener DK, Walstedt J, Jenkins L, Burnett C, Lalich N, Fingerhut M. Women: work and health. Vital Health Stat
3. Dec 1997(31):1-91.
91.
House JS. Understanding social factors and inequalities in health: 20th century progress and 21st century
prospects. J Health Soc Behav. Jun 2002;43(2):125-142.
92.
IOM. Unequal Treatment: Confronting Racial and Ethnic Disparities in Healthcare. Washington D.C.: National
Academy Press; 2003.
93.
Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. Jama. Mar 10
2004;291(10):1238-1245.
94.
Singh GK, Siahpush M. Widening socioeconomic inequalities in US life expectancy, 1980-2000. International
journal of epidemiology. Aug 2006;35(4):969-979.
95.
Caban AJ, Lee DJ, Fleming LE, Gomez-Marin O, LeBlanc W, Pitman T. Obesity in US workers: The National Health
Interview Survey, 1986 to 2002. Am J Public Health. Sep 2005;95(9):1614-1622.
96.
Kuczmarski MF, Kuczmarski RJ, Najjar M. Effects of age on validity of self-reported height, weight, and body
mass index: findings from the Third National Health and Nutrition Examination Survey, 1988-1994. J Am Diet
Assoc. Jan 2001;101(1):28-34; quiz 35-26.
97.
Engstrom JL, Paterson SA, Doherty A, Trabulsi M, Speer KL. Accuracy of self-reported height and weight in
women: an integrative review of the literature. Journal of midwifery & women's health. Sep-Oct 2003;48(5):338345.
68
98.
99.
100.
Spencer EA, Appleby PN, Davey GK, Key TJ. Validity of self-reported height and weight in 4808 EPIC-Oxford
participants. Public health nutrition. Aug 2002;5(4):561-565.
TOTAL WORKER HEALTH™. http://www.cdc.gov/niosh/TWH/totalhealth.html, 2013.
Pratt LAD, N.A.; Cohen, A.J. Characteristics of Adults with Serious Psychological Distress as Measured by the K6
Scale: United States, 2001–04. March 30, 2007.
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Appendices
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
Appendix 1: Publications of the University of Miami Occupational Research Group
Appendix 2: Detailed Matrix of Morbidity, Disability and Healthcare Utilization questions from
the NHIS asked consistently 2004-2010
Appendix 3: Census 2000 Special EEO Tabulation: Occupational Crosswalk To 14 EEO
Occupational groups and 9 EEO-1 Job Categories
Appendix 4: Occupational Codes from the 2004-2010 NHIS Crosswalked to EEO-1 2000 Census
Job Categories
Appendix 5: Tabulated prevalence estimates by gender, race, ethnicity, education, and
availability of insurance for all workers and by the 9 occupational groups in accompanied Excel
file.
70
71
Appendix 1. Publications of the University of Miami Occupational Research Group
1. Fleming LE, Gómez-Marín O, Zheng D, Ma F, Lee D. National Health Interview Survey (NHIS) Mortality
among US Farmers and Pesticide Applicators. Am J Ind Med, 2003;43(2):227-233.
http://www.ncbi.nlm.nih.gov/pubmed/12541279
2. Lee DJ, LeBlanc WG, Fleming LE, Gómez-Marín O, Pitman T. Trends in US Smoking Rates in
Occupational Groups: The National Health Interview Survey 1987-1994. J Occ Env Med 2004;46:538548.
http://www.ncbi.nlm.nih.gov/pubmed/15213515
3. Lee D, Fleming L, Gomez O, Leblanc WG. Risk of Hospitalization among Firefighters: The National
Health Interview Survey 1986-1994. Am J Pub Health 2004;94:1938-39.
http://www.ncbi.nlm.nih.gov/pubmed/15514232
4. Gómez-Marín O, Fleming LE, Lee DJ, LeBlanc WG, Zheng D, Ma F, Jane D, Pitman T, Caban A. Acute
and Chronic Disability among US Farmers and Pesticide Applicators: The National Health Interview
Survey (NHIS). J Agricultural Safety Health 2004;10:285-293.
http://www.ncbi.nlm.nih.gov/pubmed/15603226
5. Gómez-Marín O, Fleming LE, Caban A, LeBlanc WG, Lee D, Pitman T. Longest Held Job in US
Occupational Groups: The National Health Interview Survey. J Occ Env Med 2005;47:79-90.
http://www.ncbi.nlm.nih.gov/pubmed/15643162
6. Caban A, Lee D, Fleming LE, Gómez-Marín O, LeBlanc WG, Pitman T. Obesity in U.S. Workers: The
National Health Interview Survey 1986 – 2002 Am J Public Health 2005;95:1614-1622.
http://www.ncbi.nlm.nih.gov/pubmed/16051934
7. Lee DJ, Fleming LE, Gomez Marin O, LeBlanc WG, Arheart K, Caban AJ, Christ SL, Chung-Bridges K,
Pitman T. Morbidity ranking of US workers employed in 206 occupations: The National Health
Interview Survey (NHIS) 1986-1994. JOEM 2006;48:117-134.
http://www.ncbi.nlm.nih.gov/pubmed/16474261
8. Lee DJ, Fleming LE, LeBlanc WG, Arheart KL, Chung Bridges K, Christ SL, Caban AJ, Pitman T.
Occupation and Lung Cancer Mortality in a Nationally Representative US Cohort: The National
Health Interview Survey (NHIS). JOEM 2006;48:823-32.
http://www.ncbi.nlm.nih.gov/pubmed/16902375
9. Lee DJ, Fleming LE, Arheart KL, LeBlanc WG, Caban AJ, Chung Bridges K, Christ SL, McCollister K,
Pitman J. Smoking rate trends in US occupational groups: The 1987-2004 National Health Interview
Survey (NHIS). JOEM 2007;49(1):75-81.
http://www.ncbi.nlm.nih.gov/pubmed/17215716
10. Fleming LE, Lee DJ, Martinez AJ, LeBlanc WG, McCollister K, Bridges Chung K, Christ SL, Arheart KL,
Pitman T. The Health Behaviors of the Older US Worker: The National Health Interview Survey. Am J
Ind Med 2007;50(6):427-437.
http://www.ncbi.nlm.nih.gov/pubmed/17503458
71
72
11. Caban-Martinez AJ, Lee DJ, Fleming LE, LeBlanc WG, Arheart K, Chung-Bridges K, Christ SL, McCollister
KE, Pitman T. Leisure-time physical activity levels of the US workforce. Prev Med May 2007;
44(5):432-6.
http://www.ncbi.nlm.nih.gov/pubmed/17321584
12. Caban-Martinez AJ, Lee DJ, Fleming LE, Arheart KL, LeBlanc WG, Chung-Bridges K, Christ SL, Pitman T.
Dental care access and unmet dental needs among US workers: The National Health Interview
Survey 1997 to 2003. J American Dental Association 2007;138(2):227-230.
http://www.ncbi.nlm.nih.gov/pubmed/17272379
13. Christ SL, Lee D, Fleming LE, LeBlanc WG, Arheart K, Chung-Bridges K, Caban A, McCollister KE.
Employment and Occupation effects on depressive symptoms in older Americans: does working
past age 65 protect against depression? J Gerontology: Social Sciences 2007;62(6):S399-403.
http://www.ncbi.nlm.nih.gov/pubmed/18079428
14. Lee DJ, Fleming LE, McCollister KE, Caban AJ, Arheart K, LeBlanc WG, Chung-Bridges K, Christ SL,
Dietz N, Clark JD 3rd. Healthcare provider smoking cessation advice among US worker groups.
Tobacco Control 2007;16:325-328.
http://www.ncbi.nlm.nih.gov/pubmed/17897991
15. Chung Bridges K, Fleming LE. WHO/ILO Employment Conditions Knowledge Network: United States.
In: Employment Conditions and Health Inequalities for the WHO/ILO. Benach J, Muntaner C, Santana
V, Editors. Employment Conditions and Knowledge Network.
http://www.who.int/social_determinants/resources/articles/emconet_who_report.pdf
16. LeBlanc WG, Vidal L, Kirsner RS, Lee DJ, Caban-Martinez AJ, McCollister KE, Arheart KL, Chung-Bridges
K, Christ S, Clark J 3rd, Lewis JE, Davila EP, Rouhani P, Fleming LE. Reported Skin Cancer Screening
of US Adult Workers. Journal American Academy of Dermatology 2008;59:55-63.
http://www.ncbi.nlm.nih.gov/pubmed/18436338
17. Arheart KL, Lee DJ, Dietz NA, Wilkinson JD, Clark JD 3rd, LeBlanc WG, Serdar B, Fleming LE. Declining
Trends in Serum Cotinine Levels in US Worker Groups: The Power of Policy. JOEM 2008;50(1):5763.
http://www.ncbi.nlm.nih.gov/pubmed/18188082
18. Chung-Bridges K, Muntaner C, Fleming LE, Lee DJ, Arheart KL, LeBlanc WG, Christ SL, McCollister KE,
Caban AJ, Davila EP. Occupational segregation as a determinant of US worker health. Am J Ind
Medicine. 2008;51(8):555-567.
http://www.ncbi.nlm.nih.gov/pubmed/18553362
19. Clark JD 3rd, Wilkinson JD, LeBlanc WG, Dietz NA, Arheart KL, Fleming LE, Lee DJ. Inflammatory
markers and secondhand tobacco smoke exposure among US workers. Am J Ind Med
2008;51(8):626-632.
http://www.ncbi.nlm.nih.gov/pubmed/18481260
20. Fleming LE, Levis S, LeBlanc WG, Dietz NA, Arheart KL, Wilkinson JD, Clark J, Serdar B, Davila EP, Lee
DJ. Earlier Age at Menopause, Work and Tobacco Smoke Exposure Menopause 2008 ;15(6):1103-8.
http://www.ncbi.nlm.nih.gov/pubmed/18626414
72
73
21. Arheart KL, Lee DJ, Fleming LE, LeBlanc WG, Dietz NA, McCollister KE, Wilkinson JD, Lewis JE, Clark JD
3rd, Davila EP, Bandiera FC, Erard MJ. Accuracy of self-reported smoking and secondhand smoke
exposure in the US workforce: the National Health and Nutrition Examination Surveys. JOEM
2008;50(12):1414-20.
http://www.ncbi.nlm.nih.gov/pubmed/19092497
22. Vidal L, LeBlanc WG, McCollister KE, Arheart KL, Chung-Bridges K, Christ S, Caban-Martinez AJ, Lewis
JE, Lee DJ, Clark J 3rd, Davila EP, Fleming LE. Cancer Screening in US Workers. Am J Pub Health
2009;99:59-65.
http://www.ncbi.nlm.nih.gov/pubmed/19008502
23. Caban-Martinez A, Lee DJ, Fleming LE, Loubriel L, Ahmed SM, Alicea-Clark A, Clark JD, Davila EP.
Cancer Health Education Preferences among Miami-Dade County Construction Workers. Florida
Pub Health Rev 2009; 6, 58-61.
http://hsc.usf.edu/NR/rdonlyres/AA07BD97-7607-45AF-825631A2B8885793/0/2009pp058061FPHRCabanMartinezetal.pdf
24. Davila EP, Caban-Martinez AJ, Muennig P, Lee DJ, Fleming LE, Ferraro KF, LeBlanc WG, Lam BL,
Arheart KL, McCollister KE, Zheng D, Christ SL. Sensory impairment in older US workers. APHA
2009;99:1378–1385.
http://www.ncbi.nlm.nih.gov/pubmed/19542042
25. Lewis JE, Arheart KL, LeBlanc WG, Fleming LE, Lee DJ, Davila EP, Caban-Martinez AJ, Dietz NA,
McCollister KE, Bandiera FC, Clark JD Jr. Food label use and awareness of nutritional information and
recommendations among persons with chronic disease. American Journal of Clinical Nutrition
2009;90: 1351-7.
http://www.ncbi.nlm.nih.gov/pubmed/19776144
26. Caban-Martinez AJ, Lee DJ, Davila EP, LeBlanc WG, Arheart KL, McCollister KE, Christ SL, Clarke T,
Fleming LE. Sustained Low Influenza Vaccination Rates in US Healthcare Workers. Preventive
Medicine 2010:50:210–212.
http://www.ncbi.nlm.nih.gov/pubmed/20079761
27. Kim IH, Muntaner C, Chung H, Benach J, Burstrom B, Jodar P, Chung-Bridges K, Fleming LE, Vivas S,
Martinez ME, Alvarado CH, Armada F, Schuld L, Guerra Salazar R, Li Y, Diala C, Rodriguez Fazzone M,
Gonnet Wainmayer M, Sanhueza Cid D. The Role of Employment Relations in Reducing Health
Inequalities: Case studies on employment related health inequalities in countries representing
different types of labor markets. International Journal of Health Services 2010;40(2):255-267.
http://www.ncbi.nlm.nih.gov/pubmed/20440969
28. Bandiera FC, Caban-Martinez AJ, Arheart KL, Davila EP, Fleming LE, Dietz NA, Lewis JE, Fabry D, Lee
DJ. Secondhand Smoke Policy and the Risk of Depression. Annals Behav Med 2010:39:198–203.
http://www.ncbi.nlm.nih.gov/pubmed/20354832
29. Bandiera FC, Arheart KL, Caban-Martinez AJ, Fleming LE, McCollister, KE, Dietz NA, LeBlanc WG, Davila
EP, Lewis JE, Serdar B, Lee, DJ. Secondhand smoke exposure and depressive symptoms.
Psychosomatic Medicine 2010;72:68-72.
http://www.ncbi.nlm.nih.gov/pubmed/19949159
73
74
30. Davila EP, Christ SL, Caban-Martinez A, Lee DJ, Arheart KL, LeBlanc WG, McCollister KE, Clarke T,
Zimmerman FJ, Goodman E, Muntaner C, Fleming LE. Young Adults, Mortality, and Employment.
JOEM 2010;52(5):501-504.
http://www.ncbi.nlm.nih.gov/pubmed/20431416
31. Muntaner C, Sridharan S, Chung H, Solar O, Quinlan M, Vergara M, Benach J, , Burstrom B, Jodar P,
Chung-Bridges K, Fleming LE, Vivas S, Martinez ME, Alvarado CH, Armada F, Schuld L, Guerra Salazar
R, Li Y, Diala C, Rodriguez Fazzone M, Gonnet Wainmayer M, Sanhueza Cid D. The solution space:
developing research and policy agendas to eliminate employment-related health inequalities.
International Journal of Health Services, Volume 40, Number 2, Pages 309–314, 2010.
http://www.ncbi.nlm.nih.gov/pubmed/20440973
32. Davila EP, Florez H, Fleming LE, Lee DJ, Goodman E, LeBlanc WG, Caban-Martinez AJ, Arheart KL,
McCollister KE, Christ SL, Clark J, Clarke T. Prevalence of the Metabolic Syndrome among US
Workers. Diabetes Care 2010;33 (11):2390-2395.
http://www.ncbi.nlm.nih.gov/pubmed/20585004
33. McCollister KE, Arheart KL, Lee DJ, Fleming LE, Davila EP, LeBlanc WG, Christ SL, Caban-Martinez AJ,
West JP, Clark JE 3rd, Erard MJ. Declining Health Insurance Access among US Hispanic Workers:
Not all Jobs are Created Equal. AJIM 2010;53(2):163-170.
http://www.ncbi.nlm.nih.gov/pubmed/19565629
34. Chung-Bridges, K and Fleming, LE. Estados Unidos. in Empleo Trabajo y Desigualdades en salud:
una Vision Global. Benach J and Muntaner C, eds. Barcelona: Icaria Editorial 2010:77-79.
35. Clarke TC, Arheart KL, Muennig P, Fleming LE, Caban-Martinez AJ, Dietz N, Lee DJ. Healthcare Access
and Utilization among Children of Single Working and nonworking Mothers in the United States.
International Journal of Health Services 2011;41(1):11-26.
http://www.ncbi.nlm.nih.gov/pubmed/21319718
36. Kachan D, Lewis JE, Davila EP, Arheart KL, LeBlanc WG, Fleming LE, Caban-Martinez AJ, Lee DJ..
Nutrient intake and adherence to dietary recommendations among US workers. J Occup Environ
Med. 2012 Jan;54(1):101-5.
http://www.ncbi.nlm.nih.gov/pubmed/22193114
37. Kachan D, Fleming LE, LeBlanc WG, Goodman E, Arheart KL, Caban-Martinez AJ, Clarke TC, Ocasio MA,
Christ S, Lee DJ. Worker populations at risk for work-related injuries across the life course. Am J
Ind Med. 2011 Dec 13.
http://www.ncbi.nlm.nih.gov/pubmed/22170632
38. Caban Martinez, Lee DJ, Clarke TC, Davila E, Clark JD, Ocasio M, Fleming L. Self-Reported Joint and
Back Pain among Construction Workers: A Pilot Workplace Musculoskeletal Assessment. JMR.
2010;13(2): 49-55.
http://www.worldscientific.com/doi/abs/10.1142/S0218957710002508
39. Fabry DA, Davila EP, Arheart KL, Serdar B, Dietz NA, Bandiera FC, Lee DJ. Secondhand Smoke
Exposure and the Risk of Hearing Loss. Tob Control. 2011 Jan;20(1):82-5.
http://www.ncbi.nlm.nih.gov/pubmed/21081307
74
75
40. Caban-Martinez AJ, Lee DJ, Goodman E, Davila EP, Fleming LE, LeBlanc WG, Arheart KL, McCollister
KE, Christ SL, Zimmerman FJ, Muntaner C, Hollenbeck JA. Health Indicators among Unemployed and
Employed Young Adults. J Occup Environ Med. 2011 Feb;53(2):196-203.
http://www.ncbi.nlm.nih.gov/pubmed/21270653
41. Davila EP, Florez H, Trepka MJ, Fleming LE, Niyonsenga T, Lee DJ, Parkash J. Long work hours is
associated with suboptimal glycemic control among US workers with diabetes. Am J Ind Med. 2011
May;54(5):375-83.
http://www.ncbi.nlm.nih.gov/pubmed/21246586
42. Caban Martinez A, Christ SL, Lee D, Fleming LE, Arheart K, McCollister KE, Muennig P. The Effects of
Occupation and Health Behaviors on Functional Limitations among US Workers with Arthritis: A
Structural Equation Modeling Approach. J Occup Environ Med. 2011 February; 53(2): 196–203.
43. Caban-Martinez AJ, Lee DJ, Fleming LE, Tancredi DJ, Arheart KL, LeBlanc WG, McCollister KE, Christ SL,
Louie GH, Muennig PA. Arthritis, Occupational Class, and the Aging US Workforce. Am J Public
Health. 2011 Sep;101(9):1729-34.
http://www.ncbi.nlm.nih.gov/pubmed/21778483
44. Davila EP, Florez H, Trepka MJ, Fleming LE, Niyonsenga T, Lee DJ, Parkash J. Strict Glycemic Control
and Mortality Risk Among US adults with Type 2 Diabetes. J Diabetes Complications. 2011 SepOct;25(5):289-91.
http://www.ncbi.nlm.nih.gov/pubmed/21658973
45. Arheart KL, Fleming LE, Lee DJ, LeBlanc WG, Caban-Martinez AJ, Ocasio MA, McCollister KE, Christ SL,
Clarke T, Kachan D, Davila EP, Fernandez CA. Occupational vs Industry Sector Classification of the
US workforce: Which approach is more strongly associated with worker health outcomes? Am J Ind
Med. 2011 Oct;54(10):748-57.
http://www.ncbi.nlm.nih.gov/pubmed/21671459
46. Dietz NA, Lee DJ, Fleming LE, LeBlanc WG, McCollister KE, Arheart KL, Davila EP, Caban-Martinez AJ.
Trends in Smokeless Tobacco Use in the US Workforce: 1987-2005. Tob Induc Dis. 2011 Jun 1;9(1):6.
http://www.ncbi.nlm.nih.gov/pubmed/21631951
47. Lewis JE, Clark JD 3rd, LeBlanc WG, Fleming LE, Caban-Martinez AJ, Arheart KL, Tannenbaum SL,
Ocasio MA, Davila EP, Kachan D, McCollister KE, Dietz N, Bandiera FC, Clarke TC, Lee DJ.
Cardiovascular Fitness Levels among American Workers. J Occup Environ Med. 2011
Oct;53(10):1115-21.
http://www.ncbi.nlm.nih.gov/pubmed/21915067
48. Clarke TC, Soler-Vila H, Lee DJ, Arheart KL, Ocasio MA, LeBlanc WG, Fleming LE. Working with
Cancer: Health and disability disparities among employed cancer survivors in the U.S. Prev Med.
2011 Oct;53(4-5):331-4.
http://www.ncbi.nlm.nih.gov/pubmed/21884724
49. Lee DJ, Fleming LE, LeBlanc WG, Arheart KL, Ferraro KF, Pitt-Catsouphes M, Muntaner C, Fernandez
CA, Caban-Martinez AJ, Davila EP, Bandiera FC, Lewis JE, Kachan D. Health Status and Risk Indicator
Trends of the Aging U.S. Healthcare Workforce. J Occup Environ Med. 2012 Mar 22.
http://www.ncbi.nlm.nih.gov/pubmed/22446575
75
76
50. Koru-Sengul T, Clark III JD, Ocasio MA, Wanner A, Fleming LE, Lee DJ. (2011) Utilization of the
National Health and Nutrition Examination (NHANES) Survey for Symptoms, Tests, and Diagnosis
of Chronic Respiratory Diseases and Assessment of Second hand Smoke Exposure. Epidemiol
1:104. doi:10.4172/2161-1165.1000104
http://www.omicsonline.org/2161-1165/2161-1165-1-104.php
51. Christ, SL, Fleming, LE, Lee DJ, Muntaner C, Muennig, PA, Caban-Martinez, AJ. (2012). The effects of a
psychosocial dimension of socioeconomic position on survival: occupational prestige and
mortality among US working adults. Sociology of Health & Illness. doi: 10.1111/j.14679566.2012.01456.x
http://www.ncbi.nlm.nih.gov/pubmed/22443309
52. Caban-Martinez AJ, Clarke TC, Davila EP, Fleming LE, Lee DJ. Application of Handheld Devices to
Field Research among Underserved Construction Worker Populations. Environ Health. 2011 Apr
1;10:27.
http://www.ncbi.nlm.nih.gov/pubmed/21453552
53. McCollister KE, Zheng DD, Fernandez CA, Lee DJ, Lam BL, Arheart KL, Galor A, Ocasio MA, Muennig P.
Racial Disparities in quality-adjusted life-years associated with diabetes and visual impairment.
Diabetes Care. 2012 Aug;35(8):1692-4.
http://www.ncbi.nlm.nih.gov/pubmed/22751960








Contribution by A Caban Martinez to article on Obesity in US Workers in People Magazine, Jan 12, 2009
(ongoing series) pg 83-90.
Fleming LE, Pitman T, LeBlanc WG, Lee D, Gómez-Marín O. Interactive Monograph of Occupation,
Disability, and Self-reported Health in the National Health Interview Survey (1986-1994) available at
www.umiamiorg.com)
Fleming LE, Pitman T, LeBlanc WG, Lee D, Caban A, Gómez-Marín O Interactive Monograph of
Occupation and Mortality in the National Health Interview Survey (1986-1994) available at Study
Website (www.umiamiorg.com).
Fleming LE, Pitman T, LeBlanc WG, Lee D, Caban A, Chung Bridges K, Christ SL, Arheart K, McCollister
K, Ferraro K. Interactive Monograph of Occupation and Health Disparities in the National Health
Interview Survey (1997-2004) available at Study Website (www.umiamiorg.com).
Lee DJ, Davila E, LeBlanc WG, Caban Martinez A, Fleming LE, Christ SL, McCollister K, Arherart K,
Sestito J NORA Morbidity and Disability Monograph: The National Health Interview Survey (NHIS)
1997-2007. National Institute of Occupational Safety and Health (NIOSH), Cincinnati, OH. 2009. To be
available at NIOSH Website and Study Website in future
Davila E, Lee DJ, LeBlanc WG, Fleming LE, Caban Martinez A, Christ SL, Clarke T, McCollister K, Arheart
K, Sestito J NORA Morbidity and Disability Monograph: The National Health Interview Survey (NHIS)
1986-1996. National Institute of Occupational Safety and Health (NIOSH), Cincinnati, OH. 2010. To be
available at NIOSH Website and Study Website in future
Fleming LE, Ocasio MA, LeBlanc WG, Davila E, Lee DJ, Caban Martinez A, McCollister K, Arheart K,
Sestito J NORA Mortality Monograph: The National Health Interview Survey (NHIS) 1986-2004.
National Institute of Occupational Safety and Health (NIOSH), Cincinnati, OH. 2010. To be available at
NIOSH Website and Study Website in future
McCollister KE, Muennig P, Davila E, Lee DJ, LeBlanc WG, Fleming LE, Caban Martinez A, Ocasio MA,
Clarke T, Arheart K, Sestito J. Health-Adjusted Life Years and Burden of Disease by NORA Sectors:
76
77

The National Health Interview Survey (NHIS) 1986-1996 . National Institute of Occupational Safety and
Health (NIOSH), Cincinnati, OH. 2010. To be available at NIOSH Website and Study Website in future
Study Website: www.umiamiorg.com.
77
78
Appendix 2. Detailed matrix of Morbidity, Disability and Healthcare Utilization questions from the NHIS
asked consistently across survey years 2004-2010
Tab
Variable
1
Demographics
1
Gender
1
1
1
1
Race
Ethnicity
Insurance
Education
NHIS Question
NHIS Possible
Responses
NHIS Variable
Name
(Internal
Name)
Study Definition
Missing
Data
Male, Female
SEX
1=Male
None
(Sm_sex)
2= Female
NHIS recode variable:
white, black, other. Other
includes other race than
white or black such as
Indian American, Alaska
native, native Hawaiian,
other Pacific islander,
Asian, Indian, Chinese,
Filipino, other race,
multiple races.
RACE
1=White
(Sm_race)
2=Black
Do you consider yourself to
be Hispanic or Latino?
Hispanic includes: Puerto
Rican, Cuban, Dominican,
Mexican, Central/South
American, other Latin
American, other Hispanic
Hispanic, non-Hispanic
HISPAN_I
Are you covered by health
insurance or any other
health care plan?
Yes, No
What is the highest level of
education that you have
completed?
1st, 2nd, 3rd, 4th, 5th, 6th,
7th, 8th, 9th, 10th, 11th, 12th
no diploma; GED, high
school diploma; some
college (no degree),
associate degree,
bachelors, masters,
doctorate degree
Are you male or female?
What races do you consider
yourself to be?
None
3=Other
1=Non-Hispanic
None
2=Hispanic
(Sm_hisp)
NOTCOV
1=Insured
(Insured)
2=Not insured
EDUC
1=Less than high
school
(Sm_educ)
69
36
2=High school or
equivalent
3=Some college or
more
Morbidity Domain: Functional Health Capabilities
2
Special
Equipment
Do you now have any health Yes, No
problem that requires you to
use special equipment, such
as a cane, a wheelchair, a
special bed, or a special
SPECEQ
1= Yes
16
2= No
78
79
telephone?
3
4
Any
Functional
Limitations
NHIS recode based on all
the 12 NHIS questions on
activity limitations
Yes: if any functional
limitation exists (e.g. did
not answer “not at all”);
No: if not limited in any
way (e.g. answered “not
at all”)
Hearing
Impairment
Which statement best
Good, A little trouble, A
describes your hearing
lot of trouble, Deaf
(without a hearing aid): good,
a little trouble, a lot of trouble,
deaf?
FlA1AR
1=Functional limitations
29
0= No functional
limitations
AHEARST
HEARAID
1=Hearing impaired: if
A little or A lot of trouble
hearing, or Deaf.
HRAIDEV
0=Not hearing impaired
2
(Hi)
5
Visual
Impairment
Based on two questions:
o
o
Yes, No
AVISION
Do you have
trouble seeing,
even when wearing
glasses or contact
lenses? and
Are you blind or
unable to see at
all?
ABLIND
1=Visual impaired if
answered “yes” to any
of the two questions
(vi)
0=Not visually impaired
BMI
1=Underweight (
BMI<18.5)
5
Morbidity Domain: Medical Health Conditions
6
7
8
9
Body Mass
Index (cat)
Cancer
Hypertension
Heart Disease
NHIS recode variable based
on the questions:
o
How tall are you
without shoes?
o
How much do you
weigh without
shoes?
NHIS Recoded variable
based on NHIS variables:
o
o
Self-reported
weight without
shoes (pounds)
(AWEIGHTP)
Self-reported
total height in
inches
(AHEIGHT)
2=healthy weight (BMI
=18.5-24.9)
3=overweight ( BMI >=
25.0)
4=obese (BMI >=30)
Have you ever been told by a Yes, No
doctor or other health
professional that you had
cancer or a malignancy of
any kind? (yes/no)
CANEV
Have you ever been told by
a doctor or other health
professional that you have
had hypertension, also
called high blood pressure?
HYPEV
Yes, No
Have you ever been told by a Each of the 4 questions
doctor or other health
have responses of Yes or
290
1=Yes
none
0=No
1=Yes
9
0=No
CHDEV
1= Yes: Answered yes
to having been told had,
none
79
80
professional that you
No
had/have heart disease?
Based on NHIS questions of
specific diseases:
Coronary heart
disease
o Angina
o Heart attack
o Any kind of heart
condition or heart
disease
Have you ever been told by
a doctor or other health
professional that you have
had asthma?
o
10
11
Asthma
Severe
Psychological
Distress
Is the individual depressed?
Based on 6 NHIS
questions: “During the past
30 days how often did you
feel…? “
so sad that
nothing could
cheer you up?
o nervous?
o restless or
fidgety?
o hopeless?
o that everything
was an effort?
o worthless?
Have you ever been told by
a doctor or other health
professional that you have
diabetes or sugar diabetes?
o
12
13
14
15
Diabetes
MIEV
HRTEV
(heartprob)
Yes, No
AASMEV
coronary heart disease,
angina, heart attack, or
any other heart
condition.
0= No: otherwise.
1=Yes
2
0=No
For each of the 6 NHIS
variables, responses are:
1=All of the time, 2=Most
of the time, 3=Some of
the time, 4=A little of the
time, 5=None of the time
SAD
NERVOUS
RESTLESS
HOPELESS
EFFORT
WORTHHLS
(distress)
Yes, No
DIBEV
Score 0-24 based on
sum of the 6 depression
questions of number of
days with symptoms
(e.g. need cheering up,
nervous,
restless/fidgety,
hopeless, too much
effort, worthless, in the
past 30 days. A cutoff
of <13 will be used to
define severe
psychological
distress.100
64
1=Yes
34
0=No
Chronic
Bronchitis
During the past 12 months,
have you been told by a
doctor or other health
professional that you had
Chronic bronchitis?
Yes, No
Sinusitis
During the past 12 months,
have you been told by a
doctor or other health
professional that you had
Sinusitis?
During the past 12 months,
have you been told by a
doctor or other health
professional that you had
Hay fever?
Yes, No
Hay Fever
ANGEV
CBRCHYR
1=Yes
2
2=No
SINYR
1=Yes
3
2=No
Yes, No
AHAYFYR
1=Yes
3
2=No
80
81
16
17
Non-HIV STD
Hepatitis
The next questions are
about other sexually
transmitted diseases or
STDs. STDs are also
known as venereal
diseases or
VD. Examples of STDs are
gonorrhea, Chlamydia ),
syphilis, herpes, and genital
warts.
In the past five years, have
you had an STD other than
HIV or AIDS?
Yes, No
Have you ever had
hepatitis?
Yes, No
STD
1=Yes
297
2=No
AHEP
1=Yes
172
2=No
Health Domain: Healthcare Utilization
18
19
20
21
22
Health Last
Year
Self Rated
Health
Seen Primary
Health care
Provider
Dental
Seen Mental
Health
Provider
Compared with 12 months
ago, would you say your
health is (better, worse, or
about the same)?
Better, Worse, about the
same
Would you say health in
general is excellent, very
good, good, fair, or poor?
Excellent, very good,
goodFair or poor
During the past 12 months,
have you seen a primary
health care provider (any of
the following):
Seen/talked to a Ob/GYN
Seen/talked to a general
doctor
o Ob/GYN
o general doctor
About how long has it been
since you last saw or talked
to a dentist? Include all
types of dentists, such as
orthodontists, oral
surgeons, and all other
dental specialists, as well
as dental hygienists.
During the past 12 months,
that is, since [12 month
reference date], have you
seen or talked to a mental
health professional? (A
mental health professional
such as a psychiatrist,
psychologist, psychiatric
AHSTATYR
1= Better
19
2= Worse
3= about the same
HEALTH
0=Excellent, very good,
good
2
1=Fair or poor
AHCSYR7,
AHCSYR9
(seendrprime)
< 6 months, 6 months –
1 yr, >1 yr but no more
than 2 years, > 2 years
but no more than 5
years, >5 years, Never
ADENLONG2
Yes, No
0=Seen Ob/GYN and/or
general doctor
none
1=Otherwise
163
(dentist)
0=Within the past year
1=Greater than 1 year/
Never
AHCSYR1
1=Yes
116
2=No
81
82
nurse, or clinical social
worker.)
23
24
25
Seen Eye
Doctor
During the past 12 months,
that is, since [12 month
reference date], have you
seen or talked to an
optometrist,
ophthalmologist, or eye
doctor (someone who
prescribes eyeglasses)?
Yes, No
Seen
Chiropractor
During the past 12 months,
that is, since [12 month
reference date], have you
seen or talked to a
chiropractor?
Yes, No
Surgery
During the past 12 months,
have you had surgery or
other surgical procedures
either as an inpatient or an
outpatient? This includes
both major surgery and
minor procedures such as
setting bones or removing
growths.
Yes, No
What kind of place do you
usually go to when you need
routine or preventive care,
such as a physical
examination or
check-up? (assuming they
have a usual place)
Doesn't get preventive
care anywhere, Clinic or
health center, Doctor's
office or HMO, Hospital
emergency room
,Hospital outpatient
department , Some other
place , Doesn't go to one
place most often
26
Routine Care
27
28
AHCSYR2
1=Yes
114
2=No
AHCSYR4
1=Yes
111
2=No
ASRGYR
1=Yes
127
0=No
APLKIND
0=Doesn't get
preventive care
anywhere
1=Clinic or health
center
2=Doctor's office or
HMO
3=Hospital emergency
room
4=Hospital outpatient
department
5=Some other place
6=Doesn't go to one
place most often
3695
Needed but
During the past 12 months, Yes, No
couldn’t afford was there any time when [
care
you/someone in the family
needed medical care/dental
care/eyeglasses/prescription
medicine/mental health care
but did not get it because:
you/the family couldn't afford
it?
(CantAfford)
1=Yes
none
Ahcafyr_1
2=No
Delayed
PDMED12M
During the past 12 months,
have you delayed seeking
Yes, No
Ahcafyr_2
Ahcafyr_3
Ahcafyr_4
1=Yes
5
82
83
Medical Care
29
30
31
medical care/has medical
care been delayed for
anyone in the family because
of worry about the cost?
2=No
(DelayMed)
Emergency
Room Visit
During the past 12 months
how many times have you
gone to a hospital
emergency room for your
health?
Number of times
Bed Day (Cat)
During the past 12 months,
that is, since [12 month ref
date], about how many
days did illness or injury
keep you in bed for more
than half of the day?
(Include days while an
overnight patient in a
hospital).
Number of times
During the past 12 months,
that is, since [12 month ref
date], about how many
days did you miss work at a
job or business because of
illness or injury (do not
include maternity leave)?
Number of times
Lost Work
Day (CAT)
AHERNOY2
1=1 or more visits
none
0=No visits
BEDDAYR
1=0 days
72
2=1 day
3=2 or more days
WKDAYR
1=0 days
67
2=1 day
3=2-5 days
4=6 or more days
Behavior Domain: Health Behavior
32
Smoking
Is the individual a never
smoker, former smoker, or
current smoker? Based on
the NHIS questions:
o
o
o
o
Yes, No
Everyday,
some days, not
at all
(Smoke status)
Have you smoked
at least 100
cigarettes in your
entire life?
Do you now
smoke cigarettes
every day, some
days, or not at
all?
1=Never smoker, if the
person said no to the
question of ever smoker
“ Have you smoked at
least 100 cigarettes in
your entire life?
63
2=Former smoker, if
answered yes to “ever
smoker” but no to the
question “ Do you now
smoke cigarettes every
day, some days or not at
all?
3=Current smoker, if the
person classified as
ever smoker and said
smoke cigarettes every
day or some days
33
Risky
Drinking
Is the individual a risky
drinker? Based on the
o
o
Yes, No
Number of
times
ALCCAMT
1= Yes if an individual
reported: 1) an average
consumption of >14
Alcohol
status 207
83
84
NHIS questions:
In your entire life,
have you had at
least 12 drinks of
any type of
alcoholic
beverage?
o In the past year,
how often did you
drink alcoholic
beverages?
o In the past year,
on those days
that you drank, on
the average, how
many drinks did
you have?
Did the individual meet
CDC Health People 2010
recommendations for
leisure time physical activity
(i.e. engaged or lightmoderate activity for >=3minutes >=5 times/week or
“vigorous activity” >=20 min
>=3 times per week or both.
(Adams et al 2006). Based
on NHIS questions:
o
o
34
Leisure Time
Physical
Activity
Frequency of
light/moderate
activity (times per
week)?
Duration of
light/moderate activity
(in minutes)?
o Freq vigorous
activity (times per
week)?
Duration of vigorous
activity (in minutes)?
On average, how many
hours of sleep do you get in
a 24-hour period? * Enter
hours of sleep in whole
numbers, rounding 30
minutes (1/2 hour) or more
UP to the next whole hour
and dropping 29 or fewer
minutes.
Number of
drinks
(Alcohol status)
Alc5upno
alcoholic beverages for
men or > 9 alcoholic
beverages for women
and 2) reported >12
binge drinking
episodes; a binge
episode is 5+ drinks in
one episode.(Coups et
al., 2004) These are
based on a combination
of questions regarding
frequency and amount
of alcohol consumption
in the past 12 months.
Alcamt
3613
Alc5upno
3694
2=No, otherwise
o
o
Times per
week &
Minutes per
session
Times per
week &
Minutes per
session
(Sm_hp_22)
1=Yes, did meet
recommendations
222
0=No, did not meet
recommendations
o
35
Sleep
36
Influenza
Vaccine
During the past 12 months,
have you had a flu shot? A
flu shot is usually given in
01-24 hours
SLEEP
Mean hours and
standard deviation
133
Yes, No
SHTFLUYR
1=Yes
784
84
85
the fall and protects against
influenza for the flu season.
37
38
HIV/AIDS Test
AIDS Risk
0=No
The next questions are
about the test for HIV. Have
you ever been tested for
HIV?
Yes, No
Tell me if ANY of these
statements is true for you.
Do not tell me which
statement or statements
are true for you.
Just if any of them are.
* Read if necessary.
(a) You have hemophilia
and have received clotting
factor concentrations.
(b) You are a man who has
had sex with other men,
even just one time.
(c ) You have taken street
drugs by needle, even just
one time.
(d) You have traded sex for
money or drugs, even just
one time.
(e) You have tested positive
for HIV (the virus that
causes AIDS).
(f) You have had sex (even
just one time) with
someone who would
answer "yes" to any of
these statements.
Yes, No
HIVTST
1=Yes
293
0=No
STMTRU
1=Yes, at least one
statement is true
379
2=No, none of these
statements are true
39
Perceived HIV
Risk
What are your chances of
getting HIV (the virus that
causes AIDS)?
Would you say high,
medium, low, or none?
High,
Medium, Low, None,
Already have HIV or
AIDS
CHNSADSP
1=High / Already have
HIV or AIDS
2=Medium
3=Low
4=None
313
40
Hepatitis B
Vaccine
Have you ever received the
hepatitis B vaccine?
Yes, No
SHTHEPB
1=Yes
735
2=No
85
86
Appendix 3. Census 2000 Special EEO Tabulation: Occupational Crosswalk To 14 EEO
Occupational groups and 9 EEO-1 Job Categories37
The Census 2000 Special EEO Tabulation occupational crosswalk began with 509 detailed categories from the
Census 2000 classification system and was adjusted as follows: (a) categories with fewer than 10,000 workers
coded nationwide were aggregated into larger categories, so that all occupational categories shown on the
Special EEO Tabulation will be greater than 10,000; (b) four military specific occupations were excluded;
and (c) one category was added for the unemployed with no work experience since 1995. This process
resulted in the 472 occupational categories.
The 472 Census 2000 codes were matched to equivalent 2000 SOC codes and used the following notational
rules for SOC codes. In general, when a census code matche a 2000 SOC detailed occupation, the detailed
SOC code was cited, e.g., 11-1011, 11-9199, and 13-1022. However, if a census code combined all of several
2000 SOC detailed occupations within a broad occupation or minor group, the SOC broad occupation or minor
group code was cited, e.g., 11-2020 and 25-1000. Furthermore, if the census code aggregated two or more
SOC categories in a way that did not have a single SOC equivalent code, the SOC code on the Special EEO
Tabulation contained alpha characters, e.g., 13-11XX and 47-50YY.
A list of codes and titles for the 14 EEO Occupational groups and the 9 EEO-1 Job Categories for the Special
EEO Tabulation are presented below. The Special EEO Tabulation does not contain sub-categories within the
EEO-1 Officials and Managers Job Category, because these levels must account for additional factors, such
as industry and earnings.
EEO Occupational groups and Titles for the Census 2000 Special EEO Tabulation
EEO Occupational Codes EEO Occupational Group Titles for the Census 2000 Special EEO File
01 Management, Business and Financial Workers
02 Science, Engineering and Computer Professionals
03 Healthcare Practitioner Professionals
04 Other Professional Workers
05 Technicians
06 Sales Workers
07 Administrative Support Workers
08 Construction and Extractive Craft Workers
09 Installation, Maintenance and Repair Craft Workers
10 Production Operative Workers
11 Transportation and Material Moving Operative Workers
12 Laborers and Helpers
13 Protective Service Workers
14 Service Workers, except Protective
EEO-1 Job Categories and Titles for the Census 2000 Special EEO File
EEO-1 Job Codes EEO-1 Job Category Titles for the Special EEO File
01 Officials and Managers
02 Professionals
03 Technicians
04 Sales Workers
05 Administrative Support Workers
06 Craft Workers
07 Operatives
86
87
08 Laborers and Helpers
09 Service Workers
87
88
Appendix 4. Occupational Codes from the 2004-2010 NHIS Crosswalked to EEO-1 2000 Census
Job Categories
NHIS Detailed
Occ Code
1
EEOJob1
NHIS Detailed Occ Label
EEOJob1 Label
1
2
1
3
1
= 'Chief executives; general and operations managers;
legislators'
= 'Advertising, marketing, promotions, public relations,
and sales managers'
= 'Operations specialties managers'
4
1
= 'Other management occupations'
5
1
= 'Business operations specialists'
6
1
= 'Financial specialists'
7
8
9
10
11
12
13
14
15
16
2
2
2
2
3
2
2
2
3
2
17
18
19
2
2
5
= 'Computer specialists'
= 'Mathematical science occupations'
= 'Architects, surveyors, and cartographers'
= 'Engineers'
= 'Drafters, engineering, and mapping technicians'
= 'Life scientists'
= 'Physical scientists'
= 'Social scientists and related workers'
= 'Life, physical, and social science technicians'
= 'Counselors, social workers, and other community and
social service specialists'
= 'Religious workers'
= 'Lawyers, judges, and related workers'
= 'Legal support workers'
Officials and
Managers
Officials and
Managers
Officials and
Managers
Officials and
Managers
Officials and
Managers
Officials and
Managers
Professionals
Professionals
Professionals
Professionals
Tecnicians
Professionals
Professionals
Professionals
Tecnicians
Professionals
20
21
2
2
22
23
24
25
26
2
2
2
2
2
27
28
29
2
2
2
= 'Postsecondary teachers'
= 'Primary, secondary, and special education school
teachers'
= 'Other teachers and instructors'
= 'Librarians, curators, and archivists'
= 'Other education, training, and library occupations'
= 'Art and design workers'
= 'Entertainers and performers, sports and related
workers'
= 'Media and communication workers'
= 'Media and communication equipment workers'
= 'Health diagnosing and treating practitioners'
Professionals
Professionals
Administrative
Support Workers
Professionals
Professionals
Professionals
Professionals
Professionals
Professionals
Professionals
Professionals
Professionals
Professionals
88
89
30
31
3
3
= 'Health technologists and technicians'
= 'Other healthcare practitioners and technical
occupations'
= 'Nursing, psychiatric, and home health aides'
= 'Occupational and physical therapist assistants and
aides'
= 'Other healthcare support occupations'
= 'First-line supervisors/managers, protective service
workers'
= 'Fire fighting and prevention workers'
= 'Law enforcement workers'
= 'Other protective service workers'
= 'Supervisors, food preparation, and serving workers'
= 'Cooks and food preparation workers'
= 'Food and beverage serving working'
= 'Other food preparation and serving related workers'
= 'Supervisors, building and grounds cleaning and
maintenance workers'
= 'Building cleaning and pest control workers'
= 'Grounds maintenance workers'
= 'Supervisors, personal care and service workers'
= 'Animal care and service workers'
32
33
9
9
34
35
9
9
36
37
38
39
40
41
42
43
9
9
9
9
9
9
9
8
44
45
46
47
9
9
9
8
48
49
50
51
52
53
54
55
56
57
58
9
9
9
9
9
4
4
4
4
4
5
59
5
= 'Entertainment attendants and related workers'
= 'Funeral service workers'
= 'Personal appearance workers'
= 'Transportation, tourism, and lodging attendants'
= 'Other personal care and service workers'
= 'Supervisors, sales workers'
= 'Retail sales workers'
= 'Sales representatives, services'
= 'Sales representatives, wholesale and manufacturing'
= 'Other sales and related workers'
= 'Supervisors, office and administrative support
workers'
= 'Communications equipment operators'
60
5
= 'Financial clerks'
61
5
= 'Information and record clerks'
62
5
= 'Material recording, scheduling, dispatching, and
distributing workers'
Tecnicians
Tecnicians
Service Workers
Service Workers
Service Workers
Service Workers
Service Workers
Service Workers
Service Workers
Service Workers
Service Workers
Service Workers
Service Workers
Laborers and
Helpers
Service Workers
Service Workers
Service Workers
Laborers and
Helpers
Service Workers
Service Workers
Service Workers
Service Workers
Service Workers
Sales Workers
Sales Workers
Sales Workers
Sales Workers
Sales Workers
Administrative
Support Workers
Administrative
Support Workers
Administrative
Support Workers
Administrative
Support Workers
Administrative
Support Workers
89
90
63
5
= 'Secretaries and administrative assistants'
64
5
= 'Other office and administrative support workers'
65
8
= 'Supervisors, farming, fishing, and forestry workers'
66
8
= 'Agricultural workers'
67
8
= 'Fishing and hunting workers'
68
8
= 'Forest, conservation, and logging workers'
69
70
6
8
= 'Supervisors, construction and extraction workers'
= 'Construction trades workers'
71
8
= 'Helpers, construction trades'
72
8
= 'Other construction and related workers'
73
74
6
6
75
6
76
6
77
6
78
79
80
81
82
83
84
85
86
87
7
7
7
7
7
7
6
7
7
7
88
89
90
91
92
93
7
7
7
7
7
7
= 'Extraction workers'
= 'Supervisors of installation, maintenance, and repair
workers'
= 'Electrical and electronic equipment mechanics,
installers, and repairers'
= 'Vehicle and mobile equipment mechanics, installers,
and repairers'
= 'Other installation, maintenance, and repair
occupations'
= 'Supervisors, production workers'
= 'Assemblers and fabricators'
= 'Food processing workers'
= 'Metal workers and plastic workers'
= 'Printing workers'
= 'Textile, apparel, and furnishings workers'
= 'Woodworkers'
= 'Plant and system operators'
= 'Other production occupations'
= 'Supervisors, transportation and material moving
workers'
= 'Air transportation workers'
= 'Motor vehicle operators'
= 'Rail transportation workers'
= 'Water transportation workers'
= 'Other transportation workers'
= 'Material moving workers'
Administrative
Support Workers
Administrative
Support Workers
Laborers and
Helpers
Laborers and
Helpers
Laborers and
Helpers
Laborers and
Helpers
Craft Workers
Laborers and
Helpers
Laborers and
Helpers
Laborers and
Helpers
Craft Workers
Craft Workers
Craft Workers
Craft Workers
Craft Workers
Operatives
Operatives
Operatives
Operatives
Operatives
Operatives
Craft Workers
Operatives
Operatives
Operatives
Operatives
Operatives
Operatives
Operatives
Operatives
Operatives
90
91
Contact Dr Bill LeBlanc ([email protected])
91
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