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Allostatic Load Burden and Racial Disparities in Mortality
O. Kenrik Duru, MD, MSHS, Nina T. Harawa, PhD, Dulcie Kermah, MPH, and Keith C. Norris,
MD
David Geffen School of Medicine, University of California, Los Angeles; Los Angeles (Drs Duru
and Norris); Charles R. Drew University of Medicine and Science, Los Angeles, California (Drs
Harawa and Norris and Ms Kermah)
Abstract
Background—Black-white disparities in mortality persist after adjustment for socioeconomic
status and health behaviors. We examined whether allostatic load, the physiological profile
influenced by repeated or chronic life stressors, is associated with black-white mortality disparities
independent of traditional sociobehavioral risk factors.
Methods—We studied 4515 blacks and whites aged 35 to 64 years from the third National
Health and Nutrition Examination Survey (1988–1994), using the linked mortality file, to
ascertain participant deaths through 2006. We estimated unadjusted sex-specific black-white
disparities in cardiovascular/diabetes-related mortality and noninjury mortality. We constructed
baseline allostatic load scores based on 10 biomarkers and examined attenuation of mortality
disparities in 4 sets of sex-stratified multivariate models, sequentially adding risk factors: (1) age/
clinical conditions, (2) socioeconomic status (SES) variables, (3) health behaviors, and (4)
allostatic load.
Results—Blacks had higher allostatic load scores than whites; for men, 2.5 vs 2.1,
p
< .01; and
women, 2.6 vs 1.9,
p
< .01. For cardiovascular/diabetes-related mortality among women, the
magnitude of the disparity after adjustment for other risk factors (hazard ratio [HR], 1.63; 95%
confidence interval [CI], 0.96–2.75) decreased after adjustment for allostatic load (HR, 1.15; 95%
CI, 0.70–1.88). For noninjury mortality among women, the magnitude of the disparity after
adjustment for other risk factors (HR, 1.43; 95% CI, 1.00–2.04) also decreased after adjustment
for allostatic load (HR, 1.26; 95% CI, 0.90–1.78). For men, disparities were attenuated but
persisted after adjustment for allostatic load.
Conclusions—Allostatic load burden partially explains higher mortality among blacks,
independent of SES and health behaviors. These findings underscore the importance of chronic
physiologic stressors as a negative influence on the health and lifespan of blacks in the United
States.
Keywords
stress; mortality; African Americans
Non-Hispanic blacks in the United States suffer increased all-cause, noninjury, and
cardiovascular-related mortality rates compared to non-Hispanic whites.1–4 These black-
white disparities in mortality are attributed to several chronic conditions among both men
and women, increase progressively from age 20 through 64 years, and decline but remain
Correspondence: Obidiugwu Kenrik Duru, MD, MSHS, Division of General Internal Medicine/Health Services Research, University
of California, Los Angeles, 911 Broxton Plaza, Los Angeles, CA 90095 (kduru@mednet.ucla.edu)..
Disclaimer: The content does not necessarily represent the official views of the NIA or the NIH.
NIH Public Access
Author Manuscript
J Natl Med Assoc
. Author manuscript; available in PMC 2012 August 12.
Published in final edited form as:
J Natl Med Assoc
. 2012 ; 104(1-2): 89–95.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
present through age 85 years.3 Racial differences in life expectancy are typically somewhat
attenuated but persist after statistical adjustment for demographic factors, including
indicators of socioeconomic status (SES) and health insurance.2,5,6
The concept of
allostatic load
, which refers to the accumulation of physiological
perturbations as a result of repeated or chronic stressors in daily life,7–9 could partially
explain the residual black-white disparity in mortality rates. The specific measurement of
allostatic load varies between research studies, but it has generally included levels of
hormones secreted in response to stress (primary, direct mediators) and/or biomarkers that
reflect the effects of these hormones on the body (secondary, indirect mediators).10 This
stress may accumulate from early life through the working years and manifest as cumulative
physiologic dysregulation, leading to an eventual increase in allostatic load as well as an
increase in premature morbidity and mortality from chronic diseases.11
Allostatic load differences by race may explain why black-white disparities in mortality are
observed even among Americans with high SES.5 Although the burden of allostatic load is
greater overall among patients of low SES, Geronimus and colleagues found that black-
white differences in allostatic load are larger for nonpoor vs poor individuals, particularly
among women.12,13 Chronic stressors such as food insecurity, living in substandard housing,
inadequate access to health care, and greater exposure to violence are greater among persons
with low SES, regardless of race. However, both poor and nonpoor blacks may share other
stressors not generally experienced by whites, such as interactions with institutionalized
racism, which could lead to increased allostatic load.13–15
Using longitudinal data from the National Health and Nutrition Examination Survey
(NHANES III) linked mortality file, we investigated whether allostatic load at baseline was
associated with racial differences in subsequent mortality rates among middle-aged adults.
We hypothesized that after adjusting for SES measures, health insurance status, and health
behaviors, further adjustment for a 10-component secondary measure of allostatic load
would substantially reduce the magnitude of subsequent black-white mortality disparities.
METHODS
Survey Design and Data Collection
The NHANES is conducted by the National Center for Health Statistics, using a stratified
multistage probability design to obtain a representative sample of the civilian,
noninstitutionalized US population. Details on the sampling strategy and weighting methods
are available in electronic form.16 The NHANES includes household interviews that collect
sociodemographic and clinical information; standardized physical examinations, including
height, weight, and blood pressure; and collection of blood samples in special mobile
examination centers. As the NHANES data are publicly available and subjects can never be
identified, these analyses are not considered human subjects research and are exempt from
institutional board review at the institutions of the coauthors.
We used data from NHANES III (1988–1994), which included a sample of approximately
40 000 persons from 89 randomly selected locations throughout the United States. Using a
longitudinal study design, we examined the association between allostatic load at baseline
and subsequent mortality as well as black-white disparities in mortality rates. For this
analysis, we included participants aged 35 to 64 years at the time of interview who self-
identified as either non-Hispanic black or non-Hispanic white (n = 5478). Pregnant women
and patients who were interviewed but not examined were excluded. We focused on the age
range of 35 to 64 years in order to include participants who were old enough to have
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developed physiologic dysregulation yet had variable health insurance coverage (ie, not yet
eligible for Medicare).
Variable Definitions
Based on prior literature, we constructed a summed allostatic load score based on values for
10 secondary biomarkers, available in the NHANES data set, that represent physiologic
dysregulation.13,17 These biomarkers included metabolic markers (waist to hip ratio,
glycated hemoglobin), cardiovascular markers (systolic blood pressure, diastolic blood
pressure, total cholesterol, triglycerides, homocysteine), inflammatory markers (albumin, C-
reactive protein), and a marker of organ dysfunction (estimated glomerular filtration rate
[eGFR]). These 10 biomarkers are not a comprehensive measure of physiologic regulation
and are unlikely to capture alterations in immune function, inflammatory responses, or in
neuroendocrine systems such as the hypothalamic-pituitary axis.
For each biomarker in our study, we stratified the sample by gender and identified the
participants with values in the highest-risk quartile (<25th percentile for eGFR and albumin,
>75th percentile for all others). Participants received 1 point toward their allostatic load
score for each value in the highest-risk quartile, with a maximum score of 10. As there is
little difference in the predictive ability of simple count scores compared to more complex
weighted measures, we used the former approach for ease of interpretation.10 We excluded
participants who were missing data for 2 or more components of the score (n = 963).
However, we imputed data for participants missing only a single value (n = 2504), based on
the mean value for their age, gender, and race.
We used the NHANES III Linked Mortality File to calculate race-specific death rates for
NHANES III participants through 2006, up to 18 years later. Since we hypothesized that
elevated allostatic load would lead to increased mortality through biologic mechanisms, we
specifically focused on noninjury mortality, excluding accidents, suicide, and homicide. We
also examined cardiovascular- and diabetes-related mortality, combining deaths from heart
disease, cardiovascular disease, and diabetes, in a separate analysis. In the analysis
examining cardiovascular- and diabetes-related mortality, we controlled for multiple self-
reported noncardiovascular comorbidities, including chronic obstructive pulmonary disease
(COPD), cancers (other than skin cancer), thyroid disease, rheumatoid arthritis, systemic
lupus erythematosus, and asthma. We controlled for additional covariates in both sets of
models, including education (<9 years, 9–12 years, >12 years), health insurance (yes/no),
and poverty to income ratio (PIR) at the time of NHANES III.
PIR
is an income-to-needs
variable measuring the ratio of household income to the US poverty threshold for each
respondent's family size and composition. We also controlled for health behaviors, namely
the Healthy Eating Index,18 which is scored from 0 to 100,smoking status (current, former,
never), physical activity (any vs none), and alcohol use (nondrinker, 1–30 drinks/month, >30
drinks/month).
Statistical Analyses
We calculated allostatic load scores by gender and race. We also calculated mean values for
the demographic and clinical covariates of interest for black men, black women, white men,
and white women. All estimates were weighted to adjust for the differential probabilities of
sampling and nonresponse to represent the total civilian, noninstitutionalized US population.
Estimates derived from a sample size smaller than the recommended lower limit in the
NHANES analytic guidelines were considered unreliable.16
We constructed several sets of logistic regressions, comparing the black-white risks of
noninjury and cardiovascular- and diabetes-related mortality separately for men and for
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women. The regressions comparing noninjury mortality included 4 models, the first
adjusting for age; the second, adding education, PIR, and health insurance status; the third,
adding health behaviors; and the fourth, adding allostatic load score. The regressions
comparing cardiovascular- and diabetes-related mortality also included 4 models: first,
adjusting for age together with several noncardiac clinical conditions and then adding
education, PIR, health insurance status, health behaviors, and allostatic load. Of note,
because of low disease prevalence, we did not include systemic lupus erythematosus in the
regression models predicting cardiovascular-and diabetes-related mortality among men.
Results are expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). All
analyses were performed with the use of SUDAAN (Research Triangle Park, North
Carolina), a statistical package that adjusts all estimates for the complex NHANES survey
design. Because the observations contributed by each participant in the sample are weighted
for the differential probabilities of selection and nonresponse, actual sample sizes are not
reported along with percentages.
In order to assess whether medication use affected our results by lowering blood pressure
and/or cholesterol levels, in sensitivity analyses, we assigned 1 point toward the allostatic
load score if a participant was taking antihypertensives but had well-controlled blood
pressure below the 75th percentile threshold. We also assigned 1 point toward the allostatic
load score if a participant was taking cholesterol-lowering medications but had a total
cholesterol value lower than the 75th percentile threshold. As the results from these
sensitivity analyses did not appreciably alter our findings, we report only the results from the
main analyses.
RESULTS
The final analytic sample included 4515 NHANES participants between 35 and 64 years of
age. Within the included sample, black men and women had fewer years of education, were
less likely to have health insurance, and were more likely to have a high PIR compared to
white men and women (Table 1). Black men and women also had higher mean allostatic
load scores compared to white men (2.5 vs 2.1,
p
< .01) and women (2.6 vs 1.9,
p
< .01).
As expected, we observed statistically significant black-white differences in cardiovascular-
and diabetes-related and noninjury mortality among both women and men (Tables 2 and 3).
The disparity in cardiovascular- and diabetes-related disease mortality for women (HR, 2.00;
95% CI, 1.31–3.06) was slightly less than the corresponding disparity for men (HR, 2.24;
95% CI, 1.59–3.16) after adjustment for age and comorbid conditions. As shown in Table 2,
after sequential adjustment for baseline education and poverty status and health behaviors,
the mortality disparity for women was somewhat attenuated and no longer statistically
significant (HR, 1.63; 95% CI, 0.96–2.75). The magnitude of the disparity declined much
further after adjustment for allostatic load (HR, 1.15; 95% CI, 0.70–1.88). The disparity in
men was also somewhat attenuated but persisted after adjustment (HR, 1.55; 95% CI, 1.04–
2.32).
We observed a similar pattern for noninjury mortality. As shown in Table 3, after
adjustment for both baseline education and poverty status variables and baseline allostatic
load scores, the black-white disparity in mortality at follow-up for women was attenuated
and no longer statistically significant (HR, 1.26; 95% CI, 0.90–1.78), while the mortality
disparity for men remained marginally significant (HR, 1.39; 95% CI, 1.00–1.92).
In both sexes, each 1-point increase in allostatic load score was associated with increased
mortality at follow-up in all models, ranging from an HR of 1.22 (95% CI, 1.13–1.30) for
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noninjury mortality among men, to an HR of 1.65 (95% CI, 1.44–1.90) for cardiovascular-
and diabetes-related mortality among women (Tables 2 and 3).
DISCUSSION
Our findings indicate that baseline racial differences in indicators of physiologic
dysregulation, as measured by secondary markers of allostatic load, help to explain black-
white disparities in mortality among middle-aged adults followed up to 18 years later. This
effect is additive to that of health behaviors and basic measures of SES, including education,
poverty, and health insurance status. Our work expands on prior studies that linked allostatic
load with mortality among adults 70 years of age and older12,13,19 and raises the possibility
that decreasing allostatic load burdens among black persons earlier in life may reduce racial
disparities in mortality—particularly cardiovascular- and diabetes-related mortality—in later
years.
The relations among stress, increased allostatic load, and disease are multifactorial and
complex. McEwen conceptualizes the development of allostatic load as the relationship of
an individual to their particular environmental stressors, which is modified by person-level
differences (genetic variation, life experiences), different perception of environmental
stressors, and different behavioral responses (including variation in both positive health
behaviors such as physical exercise as well as negative health behaviors such as tobacco
use) to these environmental stressors.8 Elevated allostatic load and organ dysfunction can
result from more frequent environmental stressors, an inability to adapt to constant or
repeated stressors over time, and both anticipation of stressors (eg, worry about a stressful
event in the future that may or may not take place) and memories of stressors that took place
in the past (eg, posttraumatic stress disorder).8 Cohen and colleagues describe a similar
mechanism—namely, that stress in the environment results in negative emotional states and
psychological distress as well as the adoption of unhealthy behaviors as a coping
mechanism. These psychological and behavioral responses ultimately result in long-term
physiologic changes that increase allostatic load and lead to organ dysfunction.20 Recent
empirical evidence is supportive of these concepts, showing that environmental stressors,
particularly financial strain and relationship stressors, are more common among blacks as
compared to whites and are also strongly linked to poor health.21
While racial differences in allostatic load may be influenced to some extent by genetic
differences between racially designated groups, this is unlikely to be the sole or predominant
explanatory factor for observed black/ white disparities in the United States. Adults in sub-
Saharan Africa have much lower rates of hypertension, diabetes, and obesity than do blacks
in the United States.22–24 All of these conditions are likely to involve many genes, each of
which has multiple possible variants. In addition, genomic studies indicate that as few as 3
to 5 common haplotypes include the bulk of allelic variation at any specific location in the
genome. These haplotypes are well represented in the populations of all continents, so any
specific “susceptibility” alleles that exist must be shared across all racial groups.24
Of note, interactions between genes and the environment may still contribute to black-white
disparities, if blacks and whites have the same high-risk genes but have varying levels of
environmental exposures that differentially affect expression of these genes. The National
Institutes of Health recently funded an Epigenomics Program to conduct research on the
influence of gene promoters, gene suppressors, and other key determinants of gene
expression.25 Ongoing work in this area may provide interesting and important evidence
supporting the concept that differing patterns of social exposures produce changes in gene
regulators that ultimately contribute to racial disparities in health, as described by Williams
et al.26
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In addition to person-level genetic variation and different behavioral responses to
environmental stressors, psychological stressors that disproportionately affect blacks may
help explain racial differences in allostatic load as well as racial differences in mortality. As
an example, perceived racial discrimination as experienced in interpersonal interactions or
as a result of institutional racism could potentially result in elevation of primary (eg,
cortisol) and secondary (eg, systolic blood pressure) biomarkers in this population, leading
in turn to subclinical disease, overt disease, and, ultimately, death from a variety of
conditions. Furthermore, internalized racism and the acceptance of negative societal beliefs
about oneself may lead to similar outcomes.27
Several recently published studies have examined the association between perceived racism
and individual biomarkers among blacks, with the majority finding a positive link. This
literature includes both overall and health care–specific perceptions of race-specific
discrimination, which have been linked to higher systolic and diastolic blood pressure and
glycated hemoglobin, as well as to an increase over time in waist to hip ratio (or waist
circumference).27–33 Each of these physiologic measures independently predicts mortality
and is included in the allostatic load score operationalized in our study. Additional studies
examining the link between perceived racism and other inflammatory/organ dysfunction
markers (eg, albumin, eGFR), would provide further information about the possible
relationship between race-related stress and other biomarkers that predict morbidity and
mortality.
The concept of allostatic load as a lifelong cumulative measure of physiologic dysfunction
resulting from stress suggests that efforts within the health care system to reduce secondary
biomarkers (eg lower blood pressure and glycated hemoglobin) with medications and diet is
one approach to reduce racial disparities in mortality. Numerous studies have shown that
interactions between patients and physicians are complicated. Unintentional
misinterpretation by physicians of patient wishes and patient-related information may lead to
lower rates of potentially beneficial therapeutic interventions for minorities.5 Systemic
influences such as differences in health care accessibility and adverse financial incentives
may further exacerbate disparities in health care and in outcomes.5 Broad-based efforts to
improve the equity of health care delivery may help to attenuate racial disparities in
allostatic load and, ultimately, in mortality.
Addressing some of the systemic disparities in areas other than health care delivery that
contribute to increased stressors among black adolescents and young adults is another
potential approach. Examples of these systemic racial disparities include disproportionately
punitive treatment from the justice system for black adolescents vs white adolescents34,35 or
racial discrimination in the apartment rental and home mortgage markets.36 Eliminating
discriminatory policies and practices has been shown to result in improved health among
blacks; several studies have demonstrated reductions in black-white disparities in life
expectancy and infant mortality after passage of the 1964 Civil Rights Act.37 Yet another
approach could be trying to alter the response to environmental stressors among young
racial/ethnic minorities (eg, by increasing psychosocial reserve capacity to cope with stress),
which could also potentially reduce allostatic load. Small feasibility studies of this approach
could be an important and viable first step in efforts to reduce persistent and unacceptable
racial disparities in mortality.38
Our study had several limitations. First, the NHANES does not include information on
primary hormonal mediators of stress (eg, markers of the hypothalamic-pituitary axis), and
we were therefore unable to include them in our measure of allostatic load. Second, our
measures of SES were limited to basic compositional measures (years of education, poverty
status), and we were unable to adjust for either detailed individual SES measures or
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contextual measures such as neighborhood- and community-level SES indicators. Third,
although we used a simple summary score, it is likely that some biomarkers contributed
more than others to the overall allostatic load measure. However, comparisons of a simple
count with more complex weighted measures have not found major differences in predictive
ability, and simpler measures are more easily defined and interpreted across populations.10
Finally, as with any observational study, we cannot definitively infer causality, although our
longitudinal study design greatly minimizes the effect of time-dependent confounding and
reverse causality.
In summary, a composite measure of allostatic load partially explains black-white disparities
in mortality, particularly cardiovascular- and diabetes-related mortality, after adjustment for
education, poverty status, and health insurance status. These findings underscore the
potential importance of chronic physiologic stressors as a negative influence on the health
and lifespan of blacks in the United States. Eliminating black/white disparities in mortality
across different conditions will require additional efforts to understand and mitigate the
stress-induced physiologic deterioration experienced by black Americans.
Acknowledgments
Funding/Support: Support was provided in part by National Institutes of Health (NIH) grants RR026138 and
MD000182. Dr Duru received support from the University of California, Los Angeles, Resource Centers for
Minority Aging Research, Center for Health Improvement of Minority Elderly under NIH/National Institute on
Aging (NIA) grant P30-AG021684.
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Table 1
Baseline Characteristics of NHANES III Population, Stratified by Race and Gender
Black Men (n = 832) White Men (n = 1226) P Value Black Women (n = 1056) White Women (n = 1401) p Value
Demographics
Mean age (SD) 46 (0.3) 47 (0.3) <.01 46 (0.3) 47 (0.3) .01
Education, y
<9 (%) 14.9 6.7 <.01 11.1 5.8 <.01
9–12 (%) 52.8 38.1 56.6 47.4
>12 (%) 32.3 55.3 32.3 46.8
Poor (poverty to income ratio <2), % 49.8 17.0 <.01 54.9 20.7 <.01
Has health insurance, % 85.9 92.9 .01 85.0 91.8 <.01
Comorbidities (noncardiovascular-related)
Lung disease, % 3.9
a
6.0 .04 9.2
a
10.0 .62
Cancer, % 1.1
a
2.1
a
.10 3.8
a
5.7
a
.05
Thyroid disease, % 0.7
a
1.3
a
.2 6.4
a
11.4 <.01
Rheumatoid arthritis, % 3.1
a
2.6
a
.6 6.2
a
5.1
a
.36
Systemic lupus erythematosus, % 0.2
a
0.4
a
.5 0.2
a
0.5
a
.18
Asthma, % 6.8
a
7.7 .5 9.9 9.1 .54
Health behaviors
Current smokers, % 47.7 30.7 30.4 25.0
Former smokers, % 24.7 38.8 <.01 16.4 26.0 <.01
Never smokers, % 27.6 30.5 53.2 49.1
Physically active, % 73.9 86.9 <.01 56.4 77.8 <.01
Nondrinkers, % 35.5 34.5 61.1 49.4
1–30 alcoholic drinks/mo, % 51.5 53.3 .69 36.5 46.1 <.01
>30 alcoholic drinks/mo, % 13.0 12.2 2.5 4.5
Healthy Eating Index score 57.8 (0.6) 63.0 (0.6) <.01 60.9 (0.7) 65.1 (0.5) <.01
Allostatic Load Components (% of each subgroup with “high-risk” values)
b
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Black Men (n = 832) White Men (n = 1226) P Value Black Women (n = 1056) White Women (n = 1401) p Value
Systolic blood pressure 28.0 17.8 <.01 26.2 13.2 <.01
Diastolic blood pressure 38.8 30.2 <.01 42.4 26.6 <.01
Glycated hemoglobin 49.9 19.6 <.01 48.0 20.7 <.01
Glomerular filtration rate 10.8 19.0 <.01 8.0
a
18.5 <.01
Albumin 27.5 13.6 <.01 28.5 12.0 <.01
Triglycerides 16.2 26.4 <.01 13.1 22.4 <.01
C-reactive protein 33.7 22.6 <.01 37.0 23.1 <.01
Homocysteine 12.1 9.2
a
.11 9.1
a
8.8
a
.85
Total cholesterol 20.6 26.5 .02 19.0 21.9 .01
Waist to hip ratio 14.2 24.4 <.01 31.9 22.4 .08
Mean (SE) allostatic load score, range 0–10 2.5 (0.1) 2.1 (0.1) <.01 2.6 (0.1) 1.9 (0.1) <.01
Deaths
Cardiovascular-/diabetes-specific mortality rate (per 100 patient-
years) 0.63 0.33 0.36 0.21
All-cause mortality rate (per 100 patient-years) 1.42 0.79 0.91 0.63
a
Estimate is unreliable, as the sample size was smaller than that recommended in the National Health and Nutrition Examination Survey analytic guidelines for the design effect and estimated proportion.
bHigh-risk values
were defined as <25th percentile by gender for estimated glomerular filtration rate and albumin, and >75th percentile by gender for all other allostatic load components.
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Table 2
Black-White Disparities in Cardiovascular- and Diabetes-Related Mortality, by Sex
a
Model 1 Model 2 Model 3 Model 4
Women
Black race 2.00 (1.31–3.06) 1.93 (1.18–3.17) 1.63 (0.96–2.75) 1.15 (0.70–1.88)
Allostatic load (per point) – – 1.65 (1.44–1.90)
Men
Black race 2.24 (1.59–3.16) 2.18 (1.43–3.34) 1.93 (1.27–2.92) 1.55 (1.04–2.32)
Allostatic load (per point) 1.50 (1.34–1.68)
a
Reference group is white race. Model 2 adjusts for education, health insurance, and poverty to income ratio. Model 3 adds smoking status,
physical activity, and alcohol use to the covariates in model 2. Model 4 adds allostatic load to the covariates in model 3. All models are age-
adjusted and also adjust for the Healthy Eating Index score, asthma, chronic obstructive pulmonary disease, nonskin cancer, thyroid disease, and
rheumatoid arthritis. Regression models for women, but not men, also adjust for systemic lupus erythematosus.
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Table 3
Black-White Disparities in Noninjury Mortality, by Sex
a
Model 1 Model 2 Model 3 Model 4
Women
Black race 1.66 (1.27–2.17) 1.50 (1.07–2.11) 1.43 (1.00–2.04) 1.26 (0.90–1.78)
Allostatic load (per point) 1.23 (1.14–1.32)
Men
Black race 2.11 (1.61–2.76) 1.73 (1.07–2.78) 1.54 (1.10–2.17) 1.39 (1.00–1.92)
Allostatic load (per point) 1.22 (1.13–1.30)
a
Reference group is white race. Model 2 adjusts for education, health insurance, and poverty to income ratio. Model 3 adds smoking status,
physical activity, and alcohol use to the covariates in model 2. Model 4 adds allostatic load to the covariates in model 3. All models are age-
adjusted.
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