Funding
Provided
in part by NIOSH
Grant Number:
1 R01 0H03915
Monograph
Lora E Fleming MD PhD, Terry Pitman
BA, William LeBlanc PhD, David Lee PhD,
Study Website: http://www.UMiamiORG.com
Department of Epidemiology and Public Health
University of Miami School of Medicine
Miami, FL 33136
March 2007
TABLE OF CONTENTS
TABLES
The National Health Interview Survey (NHIS) is a multipurpose household survey of the US civilian non-institutionalized population conducted annually since 1957. From 1997-2004, 153,393 US workers age 18 years and older (representing an estimated 126,637,406 US workers annually) participated in a probability sampling of the entire non-institutionalized US population; variables collected included a range of measures of health disparities.
The
objective of this Monograph was to review the health disparities experience for
US workers by occupation using the 1997-2004 NHIS. After adjustment for sample weights and
design effects using SUDAAN, the prevalence rates (with their standard errors)
of a variety of measures of health disparities were examined. For the purposes of this Monograph, health
disparities endpoints included: a mixture of disability (i.e. missed work days,
bed days and self-reported health) and health measures (i.e. the reported
prevalence of hypertension, heart disease, stroke, emphysema, asthma, cancer,
and diabetes). These health disparity measure prevalence rates have been
presented by occupational subgroups, as well as by age, gender, race,
ethnicity, and availability of medical insurance subgroups within each
occupation. Furthermore, the
occupational data are presented by 3 different levels of occupation/industry
groupings: 13 and 41 occupations, and the new NIOSH National Occupational
Research Agenda (NORA) 8 industry groups. Extrapolation to the entire
Understanding the occupational risk factors and improving
the health of
KEY WORDS
Health Disparities, Disability, Race, Ethnicity,
Socio-Economic Class, Education, Health Insurance, Morbidity, Occupation,
Industry, National Health Interview Survey (NHIS), Surveillance, NORA
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 which does not bear any responsibility for the analyses or interpretations presented in this publication This study was funded in part through the National Institute of Occupational Safety and Health (NIOSH) Grant number 1 R01 0H03915-01. Additional information on this study can be found at the Study Website located at: http://www.umiamiorg.com.
INTRODUCTION
It is recognized that a variety of occupational and
environmental risk factors interact to determine the overall health and well
being of the US workforce. Occupational
health surveillance is poised to document these interactions by systematically
collecting, analyzing, and interpreting health data essential to the planning,
implementation and evaluation of public health strategies to maximize workforce
health. These robust surveillance
systems collect and maintain health outcome information for all members of a
temporally and geographically defined population at risk. The Research Group at the
The National Health Interview Survey (NHIS) is a continuous
multipurpose and multistage probability area survey of the US civilian non-institutionalized
population living at addressed dwellings
(NCHS; Kaminski and Spirtas 1980;
Botman and Jack 1995; Botman, Moore et al. 2000)
. Each week a probability sample of households is interviewed by trained
personnel to obtain information about the characteristics of each member of the
household.
(Liao, Cooper et al. 1998)
Data from the 1997-2004 NHIS Surveys included a range of measures of
acute and chronic disability collected in depth for one member of the
household. Recently, the NHIS conducted
a data linkage with the National Death Index resulting in mortality follow up
information including cause of death through 2002 for approximately 97% of the
NHIS survey population from 1986-2000. The NHIS database allows for longitudinal analysis of mortality data as
a retrospective cohort study, as well as for cross-sectional and secular trend
analyses of the aggregate morbidity data. Thus, the NHIS database represents a unique opportunity to explore new
research hypotheses, and to use the data as a surveillance tool to evaluate
time trends and occupational disease and mortality in the US for the past 2 decades in both genders and in a variety of race-ethnic and socio-economic
subpopulations.
Race-ethnicity and socio-economic status health disparities
in occupational health have not been fully investigated.
(Murray 2003; Kilbourne, Switzer et
al. 2006)
Although significantly under-reported, occupational disease, injury, and
mortality represent important health factors for US workers and their
families. The Bureau of Labor Statistics
estimated at least 5,524 traumatic occupational deaths and over 6 million work
related injuries and diseases in the public and private sector of the US work
force in 2002.
(BLS 2006)
However, the risks for occupational mortality and morbidity are not
evenly distributed.
(Murray 2003)
It appears that these risks disproportionately affect US workers from
specific race-ethnicity subpopulations and from lower socio-economic classes.
This Health Disparities Monograph establishes and applies a
methodology to assess prevalence rates of a number of disability and health
conditions associated with health disparities during the 1997-2004 time period
for US workers by occupation. After
adjustment for sample weights and design effects, the prevalence rates were
created in tabular format. These
prevalence rates have been presented by different occupational groups
(including the new NIOSH NORA industry classifications), as well as by age,
gender, race, ethnicity, and insurance availability. These data have also been extrapolated to the
entire
The European countries, particularly
England since 1837 in their Registrar General’s Decennial Supplements for
England and Wales, have had a long history of performing nationwide
occupational studies.
(Drever 1995)
As noted in the 1995 Registrar General’s Report
(Drever 1995)
, these data have provided a
valuable means of generating hypotheses about work-related risks to health as
well as insight in the effectiveness of preventive measures. These studies have provided important
documentation of a socioeconomic gradient of mortality, where those who are of
lower social class have higher rates of death. The
In the
A modest collection of recent
studies have considered the impact of workforce characteristics such as age,
race-ethnicity, gender, and socio-economic class on the
Health Disparities and Worker Subpopulations
Health disparities represent a burgeoning area of research which
recognizes and seeks to eliminate differences in the morbidity and mortality
risks and experiences of women, the elderly, and different race-ethnic and
socio-economic subpopulations in the US.
(House 2002; Keppel, Pearcy et al.
2002)
Health disparities appear to be associated with a wide range of factors
including: the individual’s demographic factors (such as age and gender),
socio-economic class and race-ethnicity, and access to high quality prevention
and healthcare. A key objective of the Healthy People 2010 is, “to eliminate health disparities among
segments of the population including differences that occur by gender, race or
ethnicity, geographic location, or sexual orientation”.
(2000)
As noted by Barbeau et al.
(Barbeau, Krieger et al. 2004)
, this key objective does not
identify occupation as a significant
factor in health disparities in the US.
Occupation is often linked with
socio-economic class (including education and income). However, occupation can
be an important independent health determinant directly in terms of hazardous
exposures, and indirectly in terms of influencing health behavior. For example, Barbeau et al.
(Barbeau, Krieger et al. 2004;
Barbeau, McLellan et al. 2004)
noted that the prevalence of
cigarette smoking was independently associated with occupation, educational
level and income level. There can be
varying occupational morbidity and mortality risks by different gender and
race-ethnic subpopulations. Loomis and
Richardson
(Loomis and Richardson 1998)
found that African American men had
an increased risk of occupational injury mortality, even taking into account
differing employment patterns by occupation. Unequal distribution of risk and explicit
discrimination within occupations, as well as inequalities in access to the
labor market, are potential explanations for these differences in occupational
risk among occupational race-ethnic subpopulations. Nevertheless, as noted by Murray
(Murray 2003)
and others
(Ward, Jemal et al. 2004)
, there has been relatively little
research into this area particularly at the national level. Therefore, the NHIS database with the
extended mortality follow up provides a unique opportunity to explore morbidity
and mortality risks by different genders and race-ethnic subpopulations among
different occupations.
Although women have been the
majority of the civilian non-institutionalized US adult population since 1950
and approximately half the labor force since 1990, as discussed above,
relatively few studies have considered morbidity/mortality issues specific to
women workers.
(Wagener, Walstedt et al. 1997;
O'Campo, Eaton et al. 2004)
In general, women have been assumed to be healthier than men, but this
may not hold true for working women of particular race-ethnic subpopulations
and among women working in traditional male occupations.
(Murray 2003)
It has been shown that women are paid less than men for the same jobs,
even accounting for education, training, and job experience.
(Valian 1998)
In addition to income inequality, a few studies examining female US
workers and their health and disability status found potential gender health
inequalities.
(Peterson and Zwerling 1998; Valian
1998; Khlat, Sermet et al. 2000; Janzen and Muhajarine 2003; O'Campo, Eaton et
al. 2004)
In the US, race-ethnic health
disparities reflect, to a large degree, socio-economic differences that have a
substantial impact on many aspects of health status, especially in terms of
prevention and intervention.
(Murray 2003)
Furthermore, although relatively little occupational research exists,
even within particular occupations or industries, race-ethnic, socio-economic
and female subpopulations appear to experience varying health status and
mortality rates compared to the white male US worker.
(Frumkin and Pransky 1999)
For example, recent studies have found increased risk among certain
race-ethnic US worker subpopulations for occupational cancer, fatal injury,
lower back pain, occupational asthma and asthma mortality, and lead poisoning,
among other diseases.
(Loomis and Richardson 1998; Prout,
Wesley et al. 2000; Schulz and Loomis 2000; Elmarsafawy, Tsaih et al. 2002;
Keppel, Pearcy et al. 2002; Briggs, Levine et al. 2003; Steenland, Burnett et
al. 2003; Steenland, Halperin et al. 2003; Stellman, Chen et al. 2003)
Although laudable, this research is subject to certain key
limitations including selective reporting, the use of occupation at time of
death as the definition of occupational exposure, focusing purely on traumatic
injury, and/or lack of generalizability to the entire US workforce due to
sampling issues. As noted by Kaminski
and Spirtas
(Kaminski and Spirtas 1980)
, the National Health Interview
Survey (NHIS) is a nationally representative dataset, that can be used as a
surveillance system for occupational disease morbidity and mortality for all US
workers, and they recommended that its use for this purpose be explored
further.
The National Health Interview Survey
(NHIS)
Since 1957, the National Center for
Health Statistics (NCHS) has annually administered the National Health
Interview Survey (NHIS), a continuous multipurpose and multistage area
probability survey of the US civilian non-institutionalized population living
at addressed dwellings.
(Botman and Jack 1995; Botman, Moore
et al. 2000)
The survey was authorized by Congress in order to obtain national
estimates on disease, injury, impairment, morbidity, mortality, health
behaviors, and related issues on a uniform basis for the entire US population.
(Fowler 1996)
The NHIS Survey has evolved over
the years (as described below), with a significant redesign in 1997.
NHIS Annual Survey 1986-1994
During the
1986-1994 period, each week a probability sample of households was interviewed
by trained personnel to obtain information about the characteristics of each
member of the household. The NHIS collected basic health data in four main
areas including: hospitalizations, use of medical services, effects of health
on functioning, and the presence of acute and chronic health conditions.
(Botman and Jack 1995; Fowler 1996;
Botman, Moore et al. 2000)
These questions were relatively
unchanged over the 1986-94 survey period. The NHIS also included periodic supplements on special health topics;
for example, in 1988, NIOSH and the NCHS collaborated on an Occupational Health
Supplement.
(Massey, Moore et al. 1989; Landen
and Hendricks 1992)
Households were selected by a multi-stage probability
sampling strategy involving both clustering and stratification and designed to
provide a representative sample of US adults.
(Massey 1989; Massey, Moore et al.
1989)
Approximately 50,000 households and 120,000 persons were interviewed
annually using a primary household respondent, with all adults in the home
participating in the interview. For the
1986-1994 surveys, some data for individuals were obtained by proxy (i.e., from
the primary household respondent) if not all adults were present in the
household at the time of the interview. For the 1986-1994 surveys, household response rates exceeded 90%, and
the data can be analyzed at the individual or family level.
(Atrostic, Bates et al. 1999)
In addition to a wide range of
self-reported demographic and health data, the NHIS annual surveys contained
substantial information concerning employment. There was considerable exploration as to the current employment status,
as well as assignment of the appropriate industry and occupation codes for each
participant. During this period of time,
the NHIS used current employment status during the 2 weeks prior to interview
for all persons 18 years or older to categorize into occupational groups.
(Brackbill, Frazier et al. 1988)
The Research Group has published extensively using the
1986-1994 NHIS data (including the mortality follow up through 2002); links to
these publications can be found at the Study Website located at:
http://www.UMiamiORG.com.
Due to
major design issues (including occupational coding), the data from the 1995 and
1996 NHIS are not used by the Research Group.
NHIS Annual Survey 1997-2004
Following incremental modifications in the 1995 and
1996 surveys (rendering these 2 years difficult to analyze), the NHIS was
completely redesigned in 1997. The redesigned NHIS collects key health
information from a single randomly selected adult household member (using the
“Adult Questionnaire”). This strategy greatly enhances the reliability of acute
and chronic condition assessment; it includes individual level information for
all participants on functional status, health conditions, physical and social
activity limitations, psychological distress, chronic conditions and recent
injuries, important risk factors (such as tobacco and alcohol use), and access
to care. In addition to better information on health conditions, this new NHIS
information will allow for greater exploration of morbidity and health
behaviors of all US workers. The annual response rates to
the 1997-2004 adult core have ranged from 70% in survey year 1999 to 80% in
survey year 1997.
(Blackwell, Collins et al. 2002;
Pleis and Coles 2002; Pleis, Benson et al. 2003; Pleis and Coles 2003;
Lethbridge-Cejku, Schiller et al. 2004; Lucas, Schiller et al. 2004)
In the NHIS, “disability days” were defined as missing work because of illness or
injury (not including maternity leave) over the last 12 months; “bed days” were
defined as the number of days in which an illness or injury kept the
participant in bed for more than half of the day (including days spent
overnight as a patient in a hospital). For the present analyses, the overall prevalence (with standard error)
as well as the prevalence for 0, 1-2, and 3+ disability and bed days were
presented (see Tables 2-5; 7-8).
In addition, a general question was asked about current self-rated health status (i.e. excellent, very good, good, fair,
and poor), as well as the reported lifetime prevalence of ever having been told by a doctor
that the participant suffered from hypertension (or high blood pressure), heart
disease, stroke, emphysema, asthma, cancer, and diabetes.
Of note, for the purposes of this study,
“self rated health” was defined as either good (ie. excellent, very good or
good) or poor (i.e. fair or poor), and “heart disease” was defined as the
combination of any affirmative answer to ever having been told by a doctor that
the participant suffered from: coronary heart disease, angina pectoris, heart
attack (also called myocardial infarction), or any kind of heart condition or
heart disease. For each of the disability and health variables, analyses were
performed for specific occupations (13 and 41), for the 8 NORA industry
categories, and by age, gender, race, ethnicity, and availability of
insurance.
During the 1997-2004 interview
period, the NHIS used current employment status (paid and unpaid) during the 1
week prior to interview for all persons 18 years or older.
(Kaminski and Spirtas 1980;
Brackbill, Frazier et al. 1988; Zwerling, Whitten et al. 1997)
Standardized Occupational Codes (really US Census codes) were provided in the NHIS database
Table 1 (See Appendix) presents the number of workers who participated in the NHIS survey pooled over the 1997-2004 period (“sample size”) and the estimated/extrapolated number of workers these participants represented in the US worker population during this time period (“estimated US worker population”) by age, gender, race, ethnicity, and insurance availability subpopulations. Of note, tables for the 13 and 41 occupational and NORA groups are available at the Study Website (www.UMiamiORG.com).
Statistical Methods
Because of the multi-stage sampling design, all analyses
were performed with adjustment for sample weights and design effects using the
SUDAAN statistical package.
(RTI 2004)
The sample weights used were those required for the analysis of data
from combined survey years and were calculated as specified by Botman and Jack
(Botman and Jack 1995; Botman, Moore et al. 2000)
Data Tables and Use
The Health Disparity data are presented in Tables 2-8 for
each of the 2 levels of major
For pooled prevalence estimates, sample weights were
adjusted to account for the aggregation of data over multiple survey years by
dividing the original weight by 7 (the number of years combined in survey years
1997 through 2004).
(Fowler 1996)
It should be noted that prevalence
rates with less than 25-45 individuals/occupational subpopulation can result in
very unstable point estimates and corresponding standard errors. Furthermore,
because of the sample weighting of the NHIS, in some instances a very small
standard error, including a value of "0.0" might result.
Therefore, analyses based on small numbers of workers should be interpreted
with caution.
These unique data tables have been made available in two
file formats: Portable Document Format (PDF) and Excel Spread Sheets (Excel) at
the Study Website (URL: http://www.UMiamiORG.com).
The PDF format allows researchers to quickly
view and print the current table layouts, while the Excel format can be
utilized to download the files to a remote computer and manipulate the data
table locally.
To save an Excel file to a local computer system, the Study
Website user can right click with their mouse over a link, 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. A file viewer for PDF files is
readily and freely available at the following website http://www.adobe.com/products/acrobat/readstep2.html.
Researchers can utilize these additional data
tables to further explore the health disparities experienced among this
population-based sample and extrapolate to the general US
workforce. As discussed above, the
Standard Errors (SEs) presented in the Tables for the prevalence rates are
presented. These SEs can be used to
generate 95% confidence intervals (CI) of the prevalence estimates (i.e., 1.96
x ± SE). Multiplying the population
estimates for a given worker population by the 95% CI will provide the lower
and upper estimate of the number of workers for a given condition.
From 1997-2004, 153,393 US workers age 18 years and older (representing an estimated 126,637,406 US workers annually) participated in a probability sampling of the entire non-institutionalized US population (Table 1). The results presented below summarize the prevalence for the health disparities measures (± their corresponding standard errors [SE]) during the study period 1997-2004 for each of the occupational groupings (i.e. 13 and 41) and the 8 NORA industry groups. For each occupation and NORA industry group, all tables report the data by age, gender, race, ethnicity, and availability of insurance as well as for total populations (See Appendix for Tables 1-8).
Disability
Lost Work Days
13 Occupations: In Table 2, the prevalence for at least one lost work day by the different
occupations is presented by age, gender, race, ethnicity, and insurance
availability as well as by the US population overall. Among the
13 occupations, Administrative support occupations, including clerical
workers, experienced the highest overall prevalence of lost work days (54.98±0.39),
while Farming, forestry, fishing workers experienced the lowest (30.01±0.94).
Among the age-gender-race-ethnicity-insurance availability subgroups, Female
Technicians/related support workers experienced the highest overall prevalence
(57.59±1.11), while Male Private household workers experienced the lowest
(16.98±7.59).
41 Occupations: In Table 2, the prevalence for at least one lost work day by the 41 different
occupational categories is presented by age, gender, race, ethnicity, and
insurance availability as well as by the US population overall. Among the
41 occupations, Officials and public administration workers experienced the
highest overall prevalence of lost work days (58.60±1.77), while Farm operators
and managers workers experienced the lowest (24.15±1.35). Among the age-gender-race-ethnicity-insurance
availability subgroups, other race Other transportation except motor vehicles
workers, experienced the highest overall prevalence (78.20±22.71), while 65
years and older Computer equipment operators, and 65 years and older Other
transportation except motor vehicles workers experienced the lowest (0.00±0.00
based on an n of 1 and 2, respectively).
8 NORA Industry Groups:
In Table 2, the prevalence for at least one lost work day by the different
occupations is presented by age, gender, race, ethnicity, and insurance
availability as well as the estimated US population numbers. Among the
NORA Sectors, Healthcare and social assistance workers experienced the highest
overall prevalence of at least one lost work day (51.84±0.44), while
Agriculture, forestry, fishing workers experienced the lowest
(30.53±0.95). Among the age-gender-race-ethnicity-insurance availability subgroups, female Mining
workers experienced the highest overall prevalence (57.68±6.94), while other
race Agriculture, forestry, fishing workers experienced the lowest
(20.01±3.79).
In addition, in Tables 4 and 7, the prevalences for lost work days divided by different categories (ie. 0, 1-2,
3+ days) are presented for each occupation and NORA industry group, all tables
report the data by age, gender, race, ethnicity, and availability of insurance
as well as for the total populations.
Bed Days
13 Occupations: In Table 2, the prevalence for at least one bed day by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as the estimated US population numbers. Among the 13 occupations, Administrative support occupations including clerical workers experienced the highest overall prevalence of at least one bed day
(43.29±0.42), while Farming, forestry, fishing workers experienced the lowest
(24.52±0.91). Among the age-gender-race-ethnicity-insurance availability
subgroups, female Professional specialty workers experienced the highest
overall prevalence (47.03±0.49), while Hispanic Farming, forestry, fishing
workers experienced the lowest .
41 Occupations: In Table 2, the prevalence for at least one bed day by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as the estimated USpopulation numbers. Among the 41 occupations,
Other professional specialty Occupations workers experienced
the highest overall prevalence of at least one bed day (45.55±1.01), while Farm
operators and managers experienced the lowest (21.22±1.39). Among the
age-gender-race-ethnicity-insurance availability subgroups, other race
Architects and surveyors workers experienced the highest overall prevalence
(64.00±13.89), while 65 year and older Computer equipment operators and other
race Other transportation except motor vehicles workers experienced the lowest
(0.00±0.00 based on an n of 1 and 2, respectively).
8 NORA Industry Groups: In Table 2,
the prevalence rate for at least one bed day by the different
occupations is presented by age, gender, race, ethnicity, and insurance
availability as well as for the US population overall. Among the NORA Sectors, Healthcare and social assistance workers experienced the highest
overall prevalence (42.09 ±0.42), while Agriculture, forestry, fishing workers
experienced the lowest (25.15 ±0.88). Among the
age-gender-race-ethnicity-insurance availability subgroups, other race Mining
workers experienced the highest overall prevalence (58.27± 13.61), while other
race Agriculture, forestry, fishing workers experienced the lowest (14.26 ±
2.67).
In addition, in Table 5 and 8,
the prevalences for bed days divided by different categories (ie. 0, 1-2, 3+
days) are presented for each occupation and NORA industry group, all tables
report the data by age, gender, race, ethnicity, and availability of insurance
as well as for the total populations.
Health
Poor self rated health
13 Occupations: In Table 2,
the prevalence rate for poor self-rated health by the different
occupations is presented by age, gender, race, ethnicity, and insurance
availability as well as for US population overall. Among the 13
occupations, Private household workers experienced the highest overall
prevalence of poor self-rated health (40.48 ±1.81), while Professional
specialty workers experienced the lowest (18.68±0.29). Among the
age-gender-race-ethnicity-insurance availability subgroups, 65 year and older
Transportation/ material moving workers experienced the highest overall
prevalence (54.54±3.25), while male Professional specialty workers experienced
the lowest (17.19±0.40).
41 Occupations: In Table 2, the prevalence of poor self-rated health by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as for the US population overall. Among the 41
occupations, Health service workers experienced the highest overall
prevalence of poor self-rated health (41.86±0.99), while Health diagnosing
occupation workers experienced the lowest (9.91±0.94). Among the
age-gender-race-ethnicity-insurance availability subgroups, 65 year and older
Forestry and fishing workers experienced the highest overall prevalence (64.77±16.96),
while 65 year and older Computer equipment operators and 65 year and older
Other transportation except motor vehicles workers experienced the lowest (0.00±0.00
based on an n of 1 and 2, respectively).
8 NORA Industry Groups: In Table 2,
the prevalence of poor self-rated health by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as the estimated US population numbers. Among the
NORA Sectors, Agriculture, forestry, fishing workers experienced the highest
overall prevalence of poor self-rated health (31.90±0.83), while Service
workers experienced the lowest (24.82±0.24). Among the
age-gender-race-ethnicity-insurance availability subgroups, 65 year and older
Mining workers experienced the highest overall prevalence (59.64±11.70), while
other race Mining workers experienced the lowest (9.94±6.16).
In addition, in Table 6,
the prevalences for each self-reported health category (i.e. poor, fair, good,
very good, and excellent) are presented for each occupation and NORA industry
group, all tables report the data by age, gender, race, ethnicity, and
availability of insurance as well as for the total populations.
Hypertension
13 Occupations: In Table 3, the prevalence for hypertension by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as the estimated US population numbers. Among the 13 occupations,
Transportation/ material moving workers experienced the
highest overall prevalence (21.71±0.62), while Handlers, equipment cleaners,
helpers, laborers workers experienced the lowest (13.66 ± 0.52). Among the age-gender-race-ethnicity-insurance
availability subgroups, 65 year and older Protective service workers
experienced the highest overall prevalence (55.01±5.20), while Hispanic
Handlers, equipment cleaners, helpers, laborers workers experienced the lowest
(7.91±0.70).
41 Occupations: In Table
3, the prevalence of hypertension by the different occupations is presented
by age, gender, race, ethnicity, and insurance availability as well as the
estimated
8 NORA Industry Groups: In Table
3, the prevalence of hypertension by the different occupations is presented
by age, gender, race, ethnicity, and insurance availability as well as the
estimated
Heart disease
13 Occupations: In Table 3,
the prevalence of heart disease by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as the estimated
41 Occupations: In Table
3, the prevalence for heart disease by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as the estimated
8 NORA Industry Groups: In Table 3, the prevalence for heart disease by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as the estimated
Stroke
13 Occupations: In Table
3, the prevalence of stroke by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
41
Occupations: In Table
3, the prevalence rate of stroke by the different occupations is presented
by age, gender, race, ethnicity, and insurance availability as well as the
estimated
8 NORA Industry Groups: In Table
3, the prevalence of stroke by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
Emphysema
13 Occupations: In Table
3, the prevalence rate for emphysema by the different occupations is
presented by age, gender, race, ethnicity, and insurance availability as well
as the estimated
41 Occupations: In Table
3, the prevalence of emphysema by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
8 NORA
Industry Groups: In Table
3, the prevalence of emphysema by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
Asthma
13 Occupations: In Table
3, the prevalence of asthma by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
41 Occupations: In Table
3, the prevalence of asthma by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
8 NORA Industry Groups: In Table
3, the prevalence of asthma by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
Cancer
13 Occupations: In Table
3, the prevalence of cancer by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
41 Occupations: In Table
3, the prevalence of cancer by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
8 NORA Industry Groups: In Table
3, the prevalence of cancer by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
Diabetes
13 Occupations: In Table
3, the prevalence of diabetes by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
41 Occupations: In Table
3, the prevalence of diabetes by the different occupations is presented by
age, gender, race, ethnicity, and insurance availability as well as the
estimated
8 NORA Industry Groups: In Table
3, the prevalence of diabetes by the different occupations is presented by age,
gender, race, ethnicity, and insurance availability as well as the estimated
This Occupations and Health Disparities Monograph of all
currently employed adults 18 years or older from the 1997-2004 NHIS surveys
demonstrated health disparities as measured by disability (lost work days and
self rated health) and health conditions among certain occupational groups
compared to all others. This Monograph confirmed some possible associations
between particular occupations (and their exposures) and certain
age-gender-race-ethnicity-insurance availability subpopulations already
recognized in the literature. This Occupation and Health Disparities Monograph
also demonstrated possible new associations between occupation and these
subpopulations with respect to their health. In general, these new associations were detectable due to the large
representative sample size of the US workforce provided by the NHIS
surveys. Furthermore, these possible new associations can be considered
“hypothesis generating” and worthy of future investigation and research.
In general, among the occupations, more skilled “white
collar” workers were more likely to report increased measures of disability
(ie. lost work days and bed days), but less likely to report poor health or
negative health conditions (with the exception of cancer).
(Fleming; Lee, Fleming et al. 2006)
This may be due to the increased
likelihood of both paid sick days and health insurance among these more skilled
workers, allowing them to take days off from work, while “blue collar” workers
and their families might be less likely to have such work benefits.
(Arheart, Lee et al. 2006)
It is also interesting to speculate about the possible under-reporting
of health conditions among the “blue collar” workers due to their lack of
access to medical care and thus possible under-diagnosis of these conditions,
as may be reflected in their increased self-reporting of poor health.
Nevertheless, within particular occupations, Hispanic, black and “other race”
workers were often more likely to experience increased prevalence of negative
health conditions. Of note, these same
worker subgroups were more likely to report acute and chronic disabilities,
poor self-rated health, and more negative health conditions relative to all
workers. (Fleming 2004) It is also interesting to consider the pattern of
increased (and possibly underreported) negative health conditions among workers
65 years and older since they represent a rapidly growing segment of the US
workforce.
(Wegman 1999; Fleming, Lee et al. (in press))
In a separate analysis, the investigators have modeled acute
and chronic disability adjusting for age, gender, race/ethnicity, and education
in the entire NHIS worker database, as well as comparing disability among
farmers and pesticide applicators with all other US workers.
(Fleming; Gomez-Marin, Fleming et al. 2004)
Among all NHIS workers, being of a younger age was a significant risk
factor for reporting acute disability; older age was a significant risk factor
in a dose response fashion for reporting chronic disability and poor health
status. Of note, using the same NHIS
database, Zwerling et al (Zwerling, Sprince et al. 1995;
Zwerling, Sprince et al. 1996; Zwerling, Sprince et al. 1996; Daltroy, Iversen
et al. 1997; Zwerling, Whitten et al. 1997; Peterson and Zwerling 1998;
Zwerling, Sprince et al. 1998; Zwerling, Whitten et al. 1998; Zwerling, Whitten
et al. 2003) found a significantly increased
injury risk among older and disabled US workers. As with other investigators, Gomez Marin et
al (Gomez-Marin, Fleming et al. 2004) also found that older female
uneducated workers were at significantly higher risk of chronic disability and
experienced poor health status compared to all other US workers. Less educated older female workers are more
likely to have poorly paying jobs with significant physical labor and longer
hours.
Several health conditions among those examined in this
Monograph have been traditionally associated with occupational exposures
including Asthma, Emphysema, and Cancer (Nathell, Malmberg et al. 2000;
Karjalainen, Kurppa et al. 2002; Arif 2003; Trupin, Earnest et al. 2003;
Le Moual 2004; MacDonald, Dixon et al. 2004;
Rosengren 2004; Fleming 2005; McDonald2005; Rosenstock 2005)
Although cancer mortality has been associated with a few specific
occupations (such as farmers and pesticides), in this study, it was the higher
socio-economic class workers (e.g. Professional specialty workers) with the
increased risk of cancer prevalence. Again, this might be explained by
“under-diagnosis” in lower class workers who may have less access to healthcare.
Asthma, emphysema and respiratory diseases have been reported in a variety of
occupations associated with a range of dust and chemical exposures ((NIOSH) 2003)
. Transportation workers and Health
service workers seemed to be particularly at risk for reported prevalence of
asthma; Transportation workers can be exposed to diesel fumes while Health
service workers are not only more likely to be exposed to infectious respiratory
agents repeatedly but also to respiratory allergens such as latex and
gluteraldehyde.
(Arif 2003; McDonald 2005) Traditionally, occupation has been evaluated as a socio-economic risk
factor for cardiovascular disease (including hypertension, stroke, heart
disease, and even diabetes). (Mikuni 1983; Steenland, Johnson et
al. 1997; Leigh 1998; Pickering 1999; Hart 2000). It is interesting to note that particularly in the NORA Industry Sectors, there is a consistent risk of
all types of cardiovascular disease for miners – a finding that is consistent
with previous research by Costello et al. (Costello, Ortmeyer et al. 1975)
Finally, when examining the data by NORA Sectors, the Mining
and the Healthcare and Social Assistance Sectors reported the highest
prevalence of negative health conditions: hypertension and stroke for the
Mining Sector workers, and heart disease, asthma, cancer, and diabetes for the
Healthcare and Social Assistance Sector workers, respectively. Of note, a
ranking of US workers based on analysis of 1986-1994 NHIS participants listed
social workers, psychologists, nursing aids, and licensed practical nurses
among the top ten occupational groups with the worst morbidity among the 206
examined worker groups (Lee et al, 2006). When looking at the age-gender-race-ethnicity-insurance availability
subcategories among the NORA Sectors, 65 years and older Mining workers
reported an increased prevalence of hypertension, heart disease, stroke,
emphysema, and cancer, while female Mining workers reported the highest
prevalence of asthma and Black Mining workers the highest prevalence of
diabetes.
Limitations
These analyses are subject to data limitations evident in
previous epidemiologic studies with similar data sets. One important limitation in interpreting the
Morbidity Rates is the small sample size in some subgroups, particularly those
in the 41 Occupational Subgroups further divided by age, gender, race,
ethnicity, and insurance availability. Therefore,
as noted above, any increased rates must be viewed as hypothesis-generating
only. Other limitations included: the self-report and cross sectional nature of
the initial data which might lead to underestimation of the true health
situation since really sick people leave the work force (part of the healthy
worker effect); lack of individual exposure measures; and occupational
misclassification in general due to the use of “current job” as a surrogate for
“longest held job,” as well as misclassification of occupation related
exposures. With regards to the latter
issue, two occupational health related supplements were administered to NHIS
participants, one in 1986 and the second in 1988. We used previously published Kappa values
from these two supplements to estimate the extent to which “current job”
reflected “longest held job” within each occupational category.
(Gomez-Marin, Fleming et al. 2005)
The use of the US worker population as the major comparison population
is appropriate for controlling for the healthy worker effect and other biases.(Monson 1990; Cooper, Buffler et al.
1993; Checkoway 2004)
Previous work using the NHIS database has shown that certain
occupational groups, such as farmers, smoke and drink less than many other
worker groups (15-29% prevalence) which may explain some of the study findings.(Sterling and Weinkam 1976;
Brackbill, Frazier et al. 1988; Sterling and Weinkam 1989; Sterling and Weinkam
1990; Brackbill, Cameron et al. 1994; Nelson, Emont et al. 1994; Lee, LeBlanc
et al. 2004; Lee, Fleming et al. 2006)
Confounding data such as tobacco use and obesity are available for
subpopulations of the NHIS database but were not used in this study; however,
tobacco use data are available for use by researchers at the Study Website (www.UMiamiORG.com).
(Lee, LeBlanc et al. 2004; Caban, Lee et al. 2005; Lee, Fleming et al. 2006)
The NHIS data (including occupation) were based largely on self-report
(by the individual or proxy) without objective confirmation. The limitations of these data are an important
limitation on any conclusions that can be drawn from these analyses.
Ultimately, this analysis of the NHIS data strongly suggested
that selected occupational groups andgender
and race-ethnicity subpopulations should be important target populations for
occupational illness/disability prevention and intervention.(Partanen, Johansson et al. 2002)
Researchers in occupational health are urged to seek out this important
and publicly available resource. The
NHIS database and mortality follow-up represent a probability sample of the
entire US population, with the ability to compare both morbidity and mortality among US
workers. Furthermore, as noted by NIOSH (NIOSH 1997) and the English Registrar’s
Decennial Reports (Drever 1995)
, databases such as the NHIS surveys
and mortality follow-up can be used not only to target studies of work-related
conditions and to add to the body of evidence generated from epidemiologic
studies, but also to provide surveillance data for establishing priorities, and
for tracking progress towards the elimination of preventable diseases and
mortality.
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