visible the invisible:
A new tool for US health departments to monitor – and boost
efforts to address – socioeconomic inequalities in health
a pdf version of this page)
problem: scant socioeconomic data in US public health surveillance
Social inequality kills. It unduly deprives individuals and communities
experiencing social deprivation of their health, increases their
burden of disability and disease, and cuts short their lives.1-4
Recognizing the powerful toll of social inequality on health and
well-being, the objectives of Healthy
People 2010 seek “to achieve two overarching goals”5:
Increase quality and years of healthy life
issue are “health disparities among segments of the population,
including differences that occur by gender, race or ethnicity,
education or income, disability, geographic location, or sexual
despite widespread recognition of the toll of economic deprivation
on health, in the US we face a critical problem hampering public
health departments’ ability to mobilize public concern and
resources to eliminate socioeconomic inequalities in health. Why?
problem is a lack of routine community-based data on the magnitude
and trends of socioeconomic inequalities in health, due to the
lack of socioeconomic data in most US public health surveillance
systems, other than birth and death.6-7
Although specialized surveys, such as the National Health Interview
Survey and the Behavioral Risk Factor Surveillance System do collect
socioeconomic data, the vast majority of “disease- and condition-specific
surveillance systems and administrative data systems do not collect
pp.18-19 The net effect is to obscure socioeconomic
gradients in health and the contribution of economic deprivation
to racial/ethnic and gender inequalities in health, at the national,
state, and local level.7-11
invisible, these preventable disparities in health remain hidden
to the view of the public and policy-makers alike. The old adage
applies: “if you don’t ask, you don’t know,
and if you don’t know, you can’t act.”8
Inertia and fatalism flourish, with anecdotal knowledge about
“the poor are always sicker and always with us” unchallenged
by evidence that the patterning of socioeconomic inequalities
in health varies by time and place and hence is not an immutable
or unalterable “fact” beyond the reach of concerted
effort to change.2,3,12-14
absence of state and local public health surveillance data on
socioeconomic inequalities in health has national ramifications.
Reflecting the absence of these data, the federal report Health
United States 2002,15
lacked socioeconomic data in 85% of its 71 tables on “Health
Status and Determinants;” virtually all of these tables,
however, were stratified by “sex, race, and Hispanic origin.”
Similarly, fully 70% of the 467 U.S. Healthy
People 2010 objectives have no socioeconomic targets, given
a lack of baseline data.5
As a nation, we cannot assess whether socioeconomic inequalities
are diminishing or growing over time, or if patterns vary by region
or state, or by racial/ethnic-gender group, within and across
Why does this matter? Because health statistics accurately depicting
the population burden of disease, disability and death, as cogently
stated in the new federal report Shaping
a Health Statistics Vision for the 21st century,7,p.2
“fulfill essential functions for public health, the health
services system, and our society”. They help us understand
“where we stand in terms of health as individuals, as subgroups,
and as a society,” including with regard to “the existence
of health disparities.”7,pp.2-3
Health statistics provide us with the information upon which
we can base important public decisions at the local, state,
and national levels. Once we have made those public decisions,
health statistics make us accountable for the decisions that
we have made. Health statistics thus enable us to evaluate
the impact of health policies and health programs on the public’s
health. In short, health statistics give us the information
we need to improve the population’s health and to reduce
Indeed, the critical importance of documenting the social patterning
of disease and death has been recognized since the rise of the
public health movement in the mid-19th century14
and is of national and global significance.7,16
As Edgar Sydenstricker noted, when establishing the first US population-based
morbidity studies in the 1920s, these data are crucial to “give
glimpses of what the sanitarian has long wanted to see –
a picture of the public-health situation as a whole, drawn in
proper perspective and painted in true colors.”17,
p. 280 It was similarly Sydenstricker’s
profound recognition of the importance of economic deprivation
in shaping population health that led to his conducting, in 1935-1936,
the first national, federally-sponsored 10-city study on the health
impact of the Depression, forerunner to what ultimately became
the National Health Interview Survey.18,
Perhaps the most potent reason why it matters to document and
monitor socioeconomic disparities in health is that this evidence
is vital to boost efforts to reduce these disparities.2,3,8-14,16-18
In 1905, Hermann M. Biggs (1859-1923), internationally renowned
for his work in the New York City Health Department and later
as Commissioner of Health for New York State, roundly declared:20,p.120
Public health is purchasable. Within natural limitations a
community can determine its own death rate.
central point was that societal resources, wisely invested, were
key to improving population health – and that these resources
could only be secured if fundamental data on population health
and its determinants were widely understood and appreciated, by
the general public and policy-makers alike. Absent data on the
public’s health, as Biggs and other public health leaders
of his generation had learned,14,20,21
appeals for resources and regulations to improve the public’s
health – and for collaboration across different government
agencies to develop and implement the necessary policies –
would have no standing or clout.
1911, the motto “Public Health is Purchasable” became
the official slogan of the Monthly Bulletin of the NYC Health
Department, with the rationale solidly explained in an editorial
by Biggs, reflecting the era’s language of social reform:20,p.320
is a largely removable evil. It continues to afflict humanity,
not only because of incomplete knowledge of its causes and
lack of individual and public hygiene, but also because it
is extensively fostered by harsh economic and industrial conditions
and by wretched housing in congested communities. These conditions
and consequently the disease which spring from them can be
removed by better social organization. No duty of society,
acting through its government agencies, is paramount to this
obligation to attack the removable cause of disease. The duty
of leading this attack and bringing home to public opinion
the fact that the community can buy its own health protection
is laid upon all health officers, organization and individuals
interested in public health movements. For the provision of
more and better facilities for the protection of the public
health must come in the last analysis through the education
of public opinion so that the community shall vividly realize
both its needs and its powers. The modern spirit of social
religion, dealing with the concrete facts of life, demands
the reduction of the death rate as the first result of its
activity. The reduction of the death rate is the principal
statistical expression and index of human and social progress.
It means the saving and lengthening of the lives of thousands
of citizens, the extension of the vigorous working period
into old age, and the prevention of inefficiency, misery,
and suffering. These advances can be made by organized social
reform. Public health is purchasable.
as suggested by the population health model articulated in Shaping
a Health Statistics Vision for the 21st century7,p.9
(Figure 1 below), it is obvious that the field of public health
cannot, by itself, improve health and prevent disease; a societal
effort is required. As part of this effort, however, it is our
singular task—and fundamental responsibility--to provide
the data on population distributions of health, disease, disability
and death, and social disparities in these outcomes. Or, as stated
in Healthy People 2010:5
People 2010 recognizes that communities, States, and national
organizations will need to take a multidisciplinary approach
to achieving health equity—an approach that involves
improving health, education, housing, labor, justice, transportation,
agriculture, and the environment, as well as data collection
itself. In fact, current data collection methods make it impossible
to assess accurately the health status for some populations,
particularly relatively small ones.
in US health over the course of the 20th century, and especially
the decline in childhood infectious disease, demonstrate the salience
of Biggs’ words. So too does a burgeoning European and Canadian
literature on the vital necessity of documenting social inequalities
in health as an essential component of what policy makers need
to take up these disparities a matter of key importance requiring
intersectoral work (see Box 1 below).
Examples of population health reports emphasizing social inequalities
in health that galvanized policy initiatives to address these
disparities: Canada and the United Kingdom
Population health reports emphasizing social inequalities in health
Canada. Achieving Health for All: A Framework for Health Promotion
(1986). Accessed on March 2, 2004 at: http://www.hc-sc.gc.ca/english/care/achieving_health.html
Canada. Population health/Santé de la Population. Accessed
on March 2, 2004 at: http://www.hc-sc.gc.ca/hppb/phdd/
Canada. Toward a Healthy Future - Second Report on the Health of Canadians
(1999). Accessed on March 2, 2004 at: http://www.hc-sc.gc.ca/hppb/phdd/report/toward/index.html
policy initiatives galvanized by these reports
Canada. Population Health Mobilization: A Regional Strategy - June
1999. Accessed on March 2, 2004 at:
Canada. Strategies for Population Health: Investing in the Health
of Canadians. Prepared by the Federal, Provincial and Territorial
Advisory Committee on Population Health for the Meeting of Ministers
of Health, Halifax, Nova Scotia, Sept 14-15, 1994. Accessed on March
2, 2004 at: http://www.hc-sc.gc.ca/hppb/phdd/pdf/e_strateg.pdf
health reports emphasizing social inequalities in health
(Department of Health and Society Security). Inequalities in Health:
Report of a Working Group. London: DHHS, 1980. (“The Black Report”);
see also:Townsend P, Davidson N (eds). Inequalities in Health: The
Black Report (3rd ed); Whitehead M. The Health Divide. London: Penguin
F, Whitehead M (eds). Health Inequalities: Decennial Supplement. London:
The Stationary Office, 1997.
D, Barker D, Chambers J, Graham H, Marmot M, Whitehead M. The Report
of the Independent Inquiry into Health Inequalities. London: The Stationary
Office, 1998. (“The Acheson Report”) Accessed on March
2, 2004 at: http://www.archive.official-documents.co.uk/document/doh/ih/contents.htm
policy initiatives galvanized by these reports
Department of Health. Saving Lives: Our Healthier Nation. London:
The Stationary Office, 1999.
of Health. Reducing Health Inequalities: An Action Report. London:
Department of Health, 1999.
Department of Health. Our Healthier Nation. Accessed on March 2, 2004
“Influences on the population’s health”
(from Shaping a Vision of Health Statistics for the 21st Century.7)
these methods to enable monitoring of current socioeconomic inequalities
in health and their contribution to racial/ethnic and gender disparities
in health remains one of the important challenges we face in fulfilling
our public health responsibilities.
we enter the 21st century, with greater awareness that socioeconomic
inequalities in health extend across the entire spectrum, deeply
affecting but not restricted to the most destitute, it is all
the more vital that our health statistics systems reflect these
realities to the public and our policy makers. Only by making
this invisible problem visible will we be able to generate the
necessary evidence to move beyond fatalism and instead galvanize
public concern and debate over how we, as a nation, can achieve
the profoundly vital goal of eliminating social disparities in
back to top
solution: geocoding and using area-based socioeconomic measures
key findings of The Public Health Disparities Geocoding Project
one potential and relatively inexpensive solution to the problem
of absent or limited socioeconomic data in US public health surveillance
systems is provided by the methodology of geocoding residential
addresses and using area-based socioeconomic measures (ABSMs).22-26
In this approach, which draws on multilevel frameworks and area-based
measures, both cases (numerators) and the catchment population
(denominators) are classified by the socioeconomic characteristics
of their residential area, thereby permitting calculation of rates
stratified by the ABSMs.
although this approach has been employed in US health research
for over 75 years (see Box 2 below),27-30
to date there exists no consensus or standard as to which ABSMs,
at which level of geography, are best suited for monitoring US
socioeconomic inequalities in health, whether within the total
population or within diverse racial/ethnic-gender groups.22,31
Instead, published research has exhibited a remarkable eclecticism
regarding choice of geographic level and types of ABSM used, both
single-variable and composite.22-26
Although such a plurality of measures may be useful for etiologic
research, in the case of monitoring, such heterogeneity impedes
comparing results across studies, across outcomes, regions, and
A brief history of the early use of census tract data in US public
health research to document socioeconomic inequalities in health
utility of linking public health data to US census-based socioeconomic
data to assess socioeconomic inequalities in health was first
recognized in the 1920s and 1930s, in pathbreaking studies supported
by the National Tuberculosis Association, following establishment
of the first census tracts in New York City in 1906. These investigations,
listed below, assessed people’s risk of TB and later other
health outcomes in relationship to socioeconomic conditions of
their census tracts, which initially were also termed “sanitary
areas” because of their utility for public health planning.
WB. Health conditions in North Harlem 1923-1927. New York: National
Tuberculosis Association, 1932.
HW. Tuberculosis and economic strata, Cleveland’s Five-City
Area, 1928-1931. Cleveland, OH: Anti-Tuberculosis League, 1932.
HW. The use of census tracts in analyzing the population of
a metropolitan community. J Am Stat Assoc 1933; 28:147-153.
M. Relation of economic status to tuberculosis mortality by
age and sex. Am J Public Health 1948; 38:1061-70.
additional discussion of early use of census tract data in public
health analyses, see:
RJ. Introduction. In: Watkins RJ, Swift AL Jr, Green HW, Eckler
AR. Golden Anniversary of Census Tracts, 1956. Washington, DC:
American Statistical Association; 1956:1-2.
EJ, Guralnick L. Analysis of vital statistics by census tract.
J Am Stat Assoc 1959;54:730-40.
accordingly launched the Public Health Disparities Geocoding Project
to ascertain which ABSMs, at which geographic level (census block
group [BG], census tract [CT], or ZIP Code [ZC]), would be most
apt for monitoring US socioeconomic inequalities in the health.
To provide a robust evaluation, guided by ecosocial theory,32,33
we designed the study to encompass a wide variety of health outcomes,
hypothesizing that some ABSMs and geographic levels might be more
sensitive to socioeconomic gradients for some health outcomes
than others. Drawing on 1990 census data and public health surveillance
systems of 2 New England states, Massachusetts and Rhode Island,
we included 7 types of outcomes: mortality (all cause and cause-specific),
cancer incidence (all-sites and site-specific), low birth weight,
childhood lead poisoning, sexually transmitted infections, tuberculosis,
and non-fatal weapons-related injuries.31,34-37
We likewise hypothesized that some socioeconomic measures might
be more sensitive than others to socioeconomic gradients in health,
and so analyzed socioeconomic gradients in relation to 18 ABSMs:
11 single-variable and 7 composite (Table 1 below). Pertinent
a priori considerations to decide which measure(s) at which geographic
level(s) would be best suited for monitoring socioeconomic gradients
in health across diverse outcomes and within diverse racial/ethnic-gender
groups were derived in part from Rossi and Gilmartin’s criteria
for valid and useful social indicators,38
and included: (a) external validity (do the measures find gradients
in the direction reported in the literature, i.e., positive, negative,
or none, and across the full range of the distribution?), (b)
robustness (do the measures detect expected gradients across a
wide range of outcomes?), (c) completeness (is the measure relatively
unaffected by missing data?), and (d) user-friendliness (how easy
is the measure to understand and explain?).
Area-based socioeconomic measures: constructs and operational definitions,
using 1990 US census data
of persons employed in predominantly working class occupations, i.e.,
as non-supervisory employees, operationalized as % of persons employed
in the following 8 of 13 census-based occupational groups: administrative
support; sales; private household service; other service (except protective);
precision production, craft, repair; machine operators, assemblers,
inspectors; transportation and material moving; handlers, equipment
of persons age 16 and older in the labor force who are unemployed
(and actively seeking work)
Median household income
household income in year prior to the decennial census (for US in
1989 = $30,056)
of households with income < 50% of US median household income (i.e.,
of households with incomes > 400% of the US median household income
(i.e., > $150,000)
measure of income inequality, regarding the share of income distribution
across the population, calculated using the standard algorithm employed
by the US Bureau of Census to extrapolate the lower and upper ends
of the income distribution
of persons below the federally-defined poverty line, a threshold which
varies by size and age composition of the household; in 1989, it equaled
$12,647 for a family of 4.
of owner-occupied homes worth > $300,000 (400% of the median value
of owned homes: 1989)
Low: < high school
of persons, age 25 and older, with less than a 12th grade education
High: > 4 yrs college
of persons, age 25 and older, with at least 4 years of college
of households with > 1 person per room
deprivation measure consisting of a standardized z score combining
data on percent crowding, percent unemployment, percent no car ownership,
and percent renters
P71, H37, H4, H8
deprivation measure consisting of a standardized z score combining
data on percent crowding, percent male unemployment, percent no car
ownership, and percent low social class (US census categories for:
transportation and material moving; handlers, equipment cleaners,
and laborers; household service)
P71, H37, P78
Index of Local Economic Resources
“summary index” based on: “white collar employment,
unemployment, and family income”
composite categorical variable based on percent < poverty, working
class, and expensive homes
composite categorical variable based on percent < poverty, working
class, and high income
factor pertaining to economic resources; highly correlated with poverty,
median household income, home ownership, and car ownership
factor pertaining to occupation and education; highly correlated with
percent working class, < high school, and > 4 yrs college
summary deprivation measure consisting of a standardized z score combining
data on percent working class, unemployed, < poverty, < high
school, expensive homes, and median household income†
variables used in the factor analysis: percent working class, unemployed,
< poverty, home ownership, car ownership, no telephone, expensive
homes, < high school education, > four years of college education,
household crowding, households with only one room, no kitchen, no
private plumbing, and also median household income and proportion
of total income in the area derived from interest, dividends, and
for “expensive homes” and “median household income”
were reversed before computing the z score so that a higher score
on the SEP index would correspond to a higher degree of deprivation.
on our methodologic research (see our appended published papers),
our key methodologic finding was that the ABSM most apt for monitoring
socioeconomic inequalities in health was the census tract (CT)
Specifically, we demonstrated that:
CT poverty measure:
consistently detected expected socioeconomic gradients in health
across a wide range of health outcomes, among both the total
population and diverse racial/ethnic-gender groups;
maximal geocoding and linkage to area-based socioeconomic data
(compared to BG and ZC data), and
readily interpretable to and could feasibly be used by state
health department staff
fully 98% of our records could be geocoded to CT level, and data
on poverty was missing for only 0.7% of the catchment area’s
CTs. We also demonstrated that:
accuracy of geocoding, not just completeness, matters;39
ZIP Code data should not be used, because of biases introduced
by the spatiotemporal mismatch of ZIP Code and US Census data;40
some socioeconomic measures (e.g., pertaining to wealth and
to income inequality) were particularly insensitive to the expected
socioeconomic gradients observed with the poverty measure and
other ABSMs designed to measure economic deprivation.
on these considerations, we arrived at our recommendation that
the CT level measure of “percent of persons below poverty”
would be most apt for monitoring US socioeconomic inequalities
Figure 2, we show
what socioeconomic gradients in health would look like, across
our varied outcomes, if routinely monitored using the CT poverty
measure. According to the US Census Bureau, CTs are “small,
relatively permanent statistical subdivision of a county ... designed
to be relatively homogeneous with respect to population characteristics,
economic status, and living conditions” and on average contain
4,000 persons. 41,
pp. G-10,G-11 For 1990 census data, the poverty
line (which varies by household size and age composition) equaled
$12,647 for a family of 2 adults and 2 children.42
In this Figure, we employ the following a priori cut points for
the CT measure “percent of persons below poverty,”
based on our prior analyses34-37:
0-4.9%, 5.0-9.9%, 10.0-19.9%, and >=20%, the federal definition
of a “poverty area.”43
this measure, we were able to provide evidence of powerful socioeconomic
gradients not only for mortality and low birthweight, as has been
but also for myriad other outcomes for which socioeconomic data
in the US are not routinely available: sexually transmitted infections,
tuberculosis, violence, cancer incidence, and childhood lead poisoning.
Additionally advantages were that:
We were able to assess socioeconomic gradients in health, within
the total population and diverse racial/ethnic-gender groups
using a consistent socioeconomic measure across all outcomes,
from birth to death, thereby avoiding well-known problems with
individual-level measures of education and occupation (e.g.,
how to classify children and others who have not completed their
education or who are not in the paid labor force).22
We could show that adjusting solely for CT poverty substantially
reduced excess risk observed in the black and Hispanic compared
to white population.
(3) We likewise could generate what to our knowledge is the
first statewide data on the population attributable fraction
in relation to poverty, whereby we found that for half the outcomes
over 50% of cases overall would have been averted if everyone’s
risk equaled that of persons in the least impoverished CT, the
only group that consistently achieved Healthy People 2000 goals
a decade ahead of time.
the approach we employed permitted documenting the temporal
persistence—and worsening status of—a previously
identified “zone of excess mortality.”
salient, our method relied solely on appending nationally-available
and widely-accessible US census data to the relevant public health
records, thereby generating state-level data that could be aggregated
up to national-level data, to monitor national trends in socioeconomic
inequalities in health. Indeed, a recently issued monograph from
the National Cancer Institute, on Area Socioeconomic Variations
in U.S. Cancer,44
does just this: following the recommendation of our project, it
employed the census-derived poverty measure at the tract level,
where feasible, or otherwise at the county level, to document
socioeconomic inequalities in cancer incidence, stage, treatment,
survival, and mortality.
the methodology we employed does not treat CT-level measures as
a “proxy” for individual-level measures. Rather, it
posits that ABSMs capture a mix of individual- and/or area-based
socioeconomic effects, if extant. Likely at issue are a complex
combination of 3 factors: (1) composition (people in poor areas
have poor health because poor people, as individuals, have poor
health), (2) context (people in poor areas also have poor health
because concentration of poverty creates or exacerbates harmful
social interactions), and (3) location of public goods or environmental
conditions (poor areas are less likely to have good supermarkets
and are more likely to be situated next to industrial plants,
thereby harming health of their residents).45,46
Were the relevant data available, these complex interactions could
be analyzed using multilevel methods.45-47
Even absent these more detailed data, however, using only ABSMs
we could still detect marked—yet typically undocumented--socioeconomic
gradients in health within diverse racial/ethnic-gender groups
plus provide conservative estimates of their contribution to racial/ethnic
so, caution is required regarding interpretation of our data in
relation to race/ethnicity. This is because our estimates of the
magnitude of socioeconomic inequalities in health, within and
across diverse racial/ethnic groups, necessarily are subject to
concerns about racial/ethnic misclassification and the census
By itself, the method of geocoding and employing area-based socioeconomic
measures cannot directly address these two problems, which affect
all population-based analyses reliant on public health surveillance
and census data.44,48,49
Recent analyses, however, suggest that these problems result in
estimates of US death rates among the white and black population
being overstated in official publications by only 1% and 5%, respectively,
and being understated, by a similar degree, for Hispanics (by
2%), but by a much larger degree for American Indians (by 21%)
and Asian or Pacific Islanders (by 11%).48
Similar patterns have been reported for cancer registry data44,50
and likely would affect the other outcomes (i.e., STI, TB, and
injuries) also reliant on census denominators and total or partial
use of non-self-report data on race/ethnicity. Such errors would
result in a tendency to overestimate, compared to the white population,
an excess risk among the black population and a reduced risk among
the Hispanic population. Analyses of low birth weight and childhood
lead poisoning, by contrast, would not be affected by the census
undercount, since the denominators were, respectively, the births
themselves and the children screened; moreover, racial/ethnic
misclassification was minimized by use of self-report racial/ethnic
data in these surveillance systems.
additional caveat pertains to our use of the US poverty line as
an indicator of socioeconomic deprivation. Although debates exist
over how best to measure poverty in the US, 51-54
precisely because of its significance for policies and for resource
evidence indicates the CT poverty measure, especially in excess
of 20% (the federal definition of a “poverty area”43),
does provide a reasonable decennial indicator of neighborhood
economic deprivation, as assessed in relation to housing deterioration,
refuse, crime, and other social indicators (e.g., unemployment,
low earnings, low education).43,44,52,54
underscoring the robustness of the CT poverty measure as a useful
economic indicator, we found similar results34-37
analyses utilizing data on the percent of persons below 50% of
the US poverty line, above 200% of the US poverty line, and below
50% of the US median household income (an alternative measure
of poverty employed in many European countries53).
all of these analyses, the magnitude of the socioeconomic gradients
detected were on par with available estimates reported in the
The net implication is use of the CT poverty measure is unlikely
to overestimate either the extent of socioeconomic gradients or
their contribution to racial/ethnic disparities in health, and
instead provides a useful metric that reveals the widespread and
often profound extent to which socioeconomic deprivation adversely
shapes population health, from infancy to death.
conclusion, results of our study highlight the importance—and
feasibility--of routinely monitoring US socioeconomic inequalities
in health, overall and stratified by race/ethnicity and gender,
thereby painting a truer picture of the “public-health situation
as a whole,” as long urged by Sydenstricker and other public
Addressing gaps in policy-relevant knowledge,1-3,7-14,16-18,56,57
the evidence generated by our approach could be used to
set health objectives, guide resource allocation, and track progress—and
setbacks--in reducing social disparities in both health and health
care, at the national, state, and local level. Relying on widely-available
data, the proposed methodology not only is cost-efficient but
also permits comparisons within and across health outcomes throughout
the US, over time, based on a common metric for socioeconomic
position derived from US census data. Timeliness of CT data, moreover,
will be improved, starting in 2008, when the American Community
Survey starts releasing annual CT estimates, based on 5-year rolling
Were data on US socioeconomic inequalities in health readily available,
and reported upon yearly, for both the total population and diverse
racial/ethnic-gender groups, efforts to track—and improve
accountability for addressing—social disparities in health
would be greatly enhanced. We suggest this can be accomplished
by geocoding US public health surveillance data and using the
CT-level measure “percent of persons below poverty.”
the rest of this monograph, we explain our methods to facilitate
their use by others. Specific sections focus on:
we geocoded our data;
we constructed the ABSMs;
we tested these measures across diverse health outcomes at different
we generated our figures; and
guided exercise, using a sample data file, to facilitate trying
out our approach, with steps clearly delineated and answers
provided to check accuracy of implementation.
hope you will find this monograph useful in improving efforts
to monitor socioeconomic inequalities in health, both within
the total population and diverse racial/ethnic-gender groups,
thereby making a vital contribution to identifying and galvanizing
action to address social disparities in health.
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