Childhood Socioeconomic Position and Disability in Later Life: Results of the Health and Retirement Study

Article (PDF Available)inAmerican Journal of Public Health 100 Suppl 1(S1):S197-203 · September 2009with79 Reads
DOI: 10.2105/AJPH.2009.160986 · Source: PubMed
Abstract
We used a life course approach to assess the ways in which childhood socioeconomic position may be associated with disability in later life. We used longitudinal data from the nationally representative Health and Retirement Study (1998-2006) to examine associations between parental education, paternal occupation, and disabilities relating to activities of daily living (ADLs) and instrumental activities of daily living (IADLs). Respondents whose fathers had low levels of education and those whose fathers were absent or had died while they were growing up were at increased risk of disability in later life, net of social, behavioral, and pathological health risks in adulthood. Social mobility and health behaviors were also important factors in the association between low childhood socioeconomic position and ADL and IADL disabilities. Our findings highlight the need for policies and programs aimed at improving the well-being of both children and families. A renewed commitment to such initiatives may help reduce health care costs and the need for people to use health and social services in later life.
Childhood Socioeconomic Position and Disability in
Later Life: Results of the Health and Retirement Study
Mary Elizabeth Bowen, PhD, and Hector M. Gonza
´
lez, PhD
The disablement process model has been used
to show that disability is a long-term process
varying largely according to disease type and
severity but also according to social status and
health behaviors.
1
For example, the health status
of people with few socioeconomic resources is
consistently worse than that of their better-off
counterparts.
2,3
Poor health behaviors such as
smoking and physical inactivity also vary
according to socioeconomic position (SEP), and
thesebehaviorsareassociatedwithanincreased
risk for a variety of disabling conditions and
other adverse health outcomes in adulthood.
4
As
such, most disability studies have focused on
health processes occurring during the midlife to
later life period,
5–7
andlessisknownabout
earlier life course processes that may be associ-
ated with accumulated risk.
Life course researchers have increasingly
suggested that exposure to adverse health risks
in childhood may have long-term effects.
8,9
According to the life course approach, disability
and other poor health outcomes may be the
product of a range of adverse social and behav-
ioral health risks incurred across the life course
and stemming from early life conditions and
experiences. For example, the Whitehall studies
of British civil servants reported relationships
between low childhood SEP and cardiovascular
disease, morbidity, and mortality.
10,11
Although
these relationships have been contested,
12 ,13
other
life course studies have similarly suggested re-
lationships of morbidity and mortality with poor
maternal and fetal nutrition,
14
low parental edu-
cation, nativity,
15,16
and manual paternal occupa-
tion.
17
The reason may be that children frompoor
families are at risk for low birthweight and poor
nutrition and are exposed to more adverse health
risks (e.g., toxins, pollution, lead, secondhand
smoke) than their peers.
18
As a result, respiratory
disorders, infectious diseases, chronic conditions,
and functional limitations are typically more
common among children from such families.
19
The adverse effects of low childhood SEP
may increase the risk of disability in several
ways. Children from poor families have fewer
opportunities for socioeconomic achievement
in adulthood than do their counterparts from
more advantaged families.
11,15
As such, low
childhood SEP may begin a ‘‘chain of risk’’
8
leading to further disadvantages across the life
course. This idea is similar to cumulative disad-
vantage
20
(the accrual of adverse social, behav-
ioral, and other health risks over time) and
allostatic load
21
(the cumulative negative effects
incurred when the body adapts to various
challenges and adverse environments). Low
childhood SEP may also initiate a pattern of poor
health behaviors.
22
Children from disadvantaged
families have poorer nutrition and lower physi-
cal activity levels than their counterparts,
23
and
this situation may continue into adulthood,
adversely affecting health and functioning.
24
There is additional evidence that some of the
adverse effects of low childhood SEP on health
may be independent of these social and be-
havioral pathways. For example, low childhood
SEP has been associated with adult morbidity,
poor health, and mortality independent of
socioeconomic achievement and health be-
haviors.
25–28
These findings are consistent with
the notion of biological embedding,
29
the process
through which health risks interact to create
systematic differences in host resilience, and the
fetal origins hypothesis,
30
according to which
childhood is a critical period of development in
which exposure to adverse health risks has long-
term and enduring effects on adult health out-
comes.
We used a life course approach to explore
the relationship between low childhood SEP
(assessed via parental education and father’s
occupation) and severe disabilities in later life
self-care (e.g., bathing, eating) and moderate
disabilities in other areas of functioning (e.g.,
taking medications, preparing meals). We also
examined the ways in which factors such as
social mobility (education, income, and wealth)
and behavioral transitions (smoking, drinking,
exercising, and body weight) in adulthood may
alter this relationship over time. We used a
nationally representative sample of older
Americans and accounted for predisposing
characteristics (age, gender, and race) and
intraindividual changes in pathology (heart
problems, diabetes, stroke, hypertension, and
lung disease) over a 9-year period. We hy-
pothesized that low childhood SEP would
increase disability risk in later life through
Objectives. We used a life course approach to assess the ways in which
childhood socioeconomic position may be associated with disability in later life.
Methods. We used longitudinal data from the nationally representative Health
and Retirement Study (1998–2006) to examine associations between parental
education, paternal occupation, and disabilities relating to activities of daily
living (ADLs) and instrumental activities of daily living (IADLs).
Results. Respondents whose fathers had low levels of education and those
whose fathers were absent or had died while they were growing up were at
increased risk of disability in later life, net of social, behavioral, and pathological
health risks in adulthood. Social mobility and health behaviors were also important
factors in the association between low childhood socioeconomic position and ADL
and IADL disabilities.
Conclusions. Our findings highlight the need for policies and programs aimed
at improving the well-being of both children and families. A renewed commit-
ment to such initiatives may help reduce health care costs and the need for
people to use health and social services in later life. (Am J Public Health. 2010;
100:S197–S203. doi:10.2105/AJPH.2009.160986)
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lez | Peer Reviewed | Research and Practice | S197
a variety of adverse socioeconomic and be-
havioral factors.
METHODS
Our data were derived from the Health and
Retirement Study (HRS), a prospective cohort
study conducted by the University of Michigan
with support from the National Institute on
Aging. In 1992, a probability cohort sample of
individuals aged 51 to 61 years took part in the
first wave of this multistage nationally repre-
sentative study; this first-wave cohort sample
was merged with the Asset and Health Dy-
namics of the Oldest Old Study cohort (born
1890–1923) in 1998. Two other cohorts, the
Children of the Depression (born 1924–1930)
and War Babies (born 1942–1947) cohorts,
were added in 1998 to fill in between-group
age and cohort gaps, resulting in a sample
design nationally representative of US residents
aged older than 50 years in 1998. Further
details on the HRS design and methods have
been previously published.
31
We used 5 waves of data (1998–2006) from
the HRS combined with data prepared by the
RAND Center for the Study of Aging (RAND–
HRS). The benefits of using the RAND–HRS
data included the availability of detailed health,
behavioral, and socioeconomic information
and the use of bracketing methods to minimize
nonresponse for these data.
32
Data were
weighted via respondent-level sampling weights
to account for the HRS sample design.
31
After
exclusion of respondents with sampling weights
of zero, indicating that they had been unable to
answer survey questions at baseline, were in-
stitutionalized, or had died, our final sample
consisted of 18465 individuals. By the final
survey year included in our study (2006),
approximately 22.8% of respondents had died
and 8.2% had been lost to follow-up. HRS
attrition rates are comparable to those of other
panel surveys,
33
and sample attrition has not
significantly influenced the representativeness of
the remaining sample.
34
Measures of Interest
We used scales assessing respondents’ abil-
ities with respect to activities of daily living
(ADLs) and instrumental activities of daily
living (IADLs) to predict disabilities in self-care
and other forms of functioning.
1
In the case of
ADLs, respondents were asked whether they had
severe difficulty walking across a room, bathing,
eating, dressing, and getting in and out of bed.
For IADLs, respondents were asked whether
they had moderate difficulty using the phone,
managing money, taking medications, shopping
for groceries, and preparing meals. Each scale
ranged from zero to 5, with 5 indicating the
highest level of difficulty.
Predictor Variables
Respondents’ reports of parental educational
levels (both mother and father, each ranging
from zero to 17 years) and father’s occupation
(main occupation when the respondent was
aged 16 years) were used to assess childhood
SEP. Although retrospective reports of child-
hood SEP may underestimate childhood dis-
advantage,
35
there is evidence that retrospective
reports of father’s education and other familial
characteristics are reliable.
36
Paternal occupa-
tional categories were as follows: professional
(manager or administrator; reference category),
craftsman, farmer or farm manager, clerical or
sales worker, operative (e.g., machine or trans-
port worker), and service worker or laborer.
Respondents who reported that their father
was disabled or had never worked, or whose
father was absent while they were growing up
or had died during this period were grouped
into 2 categories (father disabled or never
worked and father absent or deceased). Re-
spondents with missing data on mother’s
(10.0%) and father’s (14.4%) educational level
and father’s occupation (4.8%) were ex-
cluded from our analyses. These respondents
were more likely to be older, female, and
Black and to have completed fewer years of
education.
We also examined social mobility and be-
havioral transitions in adulthood to determine
potentially mediating relationships between
these variables and childhood SEP and dis-
ability.
11,15 , 2 4
Social mobility was assessed in
terms of education (zero to 17 years), income
(log-transformed total household income), and
wealth (log-transformed value of assets). The
health behaviors examined were smoking (cur-
rent, never, former smoker), alcohol use (yes or
no), and exercise; body mass index (BMI; defined
as weight in kilograms divided by height in
meters squared) was also assessed. The exercise
category included activities such as physical
labor on the job, heavy housework, aerobics,
bicycling, running or jogging, and swimming 3 or
more times per week; we used this measure
because it was used consistently over the differ-
ent waves of the HRS.
To account for pathology, the leading cause
of self-care and functioning difficulties,
1
re-
spondents were asked whether a doctor had ever
told them that they had heart problems (in-
cluding coronary heart disease, heart attack,
congestive heart failure, and heart surgery), di-
abetes (or high blood sugar), lung disease (ex-
cluding asthma, chronic bronchitis, and emphy-
sema), or hypertension (or high blood pressure)
and whether they had had a stroke (or a transient
ischemic attack). Self-reported health conditions
have shown substantial agreement with both
survey and medical record reports.
37
In applicable instances, variables were mea-
sured at baseline (1998) and subsequently
every 2 years over the course of the study. As
such, age, social mobility (income, wealth),
health behaviors, pathology, and disability
varied over the course of time, allowing for an
examination of intra-individual changes over
the 9-year study period. Gender, race/ethnicity
(White versus non-White), educational level,
and childhood SEP variables were fixed.
Statistical Analysis
In conducting our statistical analyses, we
used generalized linear latent and mixed-model
commands
38
availablefortheanalysisofcom-
plex sample survey data in Stata version 9.2.
39
All of the statistical analyses were design based,
accounting for the complex HRS sampling design
and the subset analyzed. Our multilevel models
incorporated information from 5 waves (or 9
years;1998–2006) of data simultaneously in the
same model.
We used 2-level generalized latent and
mixed models. The first level examined intra-
individual changes in disability related to social
mobility, health behaviors, and pathology.
The second level examined individual varia-
tions in these factors across groups categorized
according to characteristics such as gender,
race/ethnicity, and childhood SEP. Many
health and functioning distributions are non-
normal, reflecting the higher frequency of in-
tact functioning among the general, commu-
nity-dwelling older adult population. As such,
nonlinear models that modeled Poisson
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lez American Journal of Public Health | Supplement 1, 2010, Vol 100, No. S1
distributions of the ADL and IADL data fit
better than the linear models. We present the
results of these nonlinear analyses.
To examine the cumulative effects of low
childhood SEP on disability through tempo-
rally ordered social and behavioral health risks
across the life course, we entered childhood
SEP (model 1), social mobility (model 2), and
health behaviors (model 3) sequentially into
multilevel models. All models were adjusted for
predisposing characteristics (age, gender, race/
ethnicity), and model 4 accounted for pathol-
ogy in addition to these characteristics.
RESULTS
Consistent with a community-dwelling sam-
ple of older adults, our respondents reported
relatively few ADL and IADL disabilities at
baseline (Table 1). On average, mothers’ edu-
cational levels were higher than fathers’ educa-
tional levels, and most respondents reported
their father’s main occupation as craftsman,
farmer or farm manager, or operative.
Activities of Daily Living
As can be seen in model 1 of Table 2, which
accounted for age, gender, and race/ethnicity,
each additional year of parental education was
associated with a decreased risk of ADL dis-
abilities. For example, respondents whose par-
ents had completed high school (12 years of
education) had a lower risk of ADL disabilities
than did respondents whose parents had com-
pleted only secondary school (8 years of
education). Relative to respondents whose
fathers were professionals, respondents whose
fathers were farmers or farm managers, oper-
atives, or service workers also were at in-
creased risk for ADL disabilities. Respondents
whose fathers had never worked or were
disabled, or were absent or had died were at
increased risk as well.
The addition of social mobility variables to
the model weakened the effects of parental
education on ADL disabilities and statistically
explained much of the relationship between
father’s (manual) occupation and such disabil-
ities. Having a father who was an operative,
who had never worked, or who was disabled
was no longer associated with increased risk for
ADL disabilities in this model. In addition,
having a father who was a farmer or farm
manager was associated with a decreased risk
for ADL disabilities.
Additional analyses (data not shown) were
conducted to further examine the pathways
linking childhood SEP, social mobility, and
ADL disabilities. An examination of childhood
SEP and social mobility interactions showed
that education mediated the relationship be-
tween ADL disabilities and having a father who
was an operative. Also, income lowered ADL
disability risk among respondents with fathers
who were farmers or farm managers or oper-
atives and respondents whose fathers were
absent or had died while they were growing up.
As can be seen in model 3 of Table 2, the
addition of behavioral variables did not affect
the relationship between parental education
and ADL disabilities. However, the strength of
the relationship between father’s occupation
and ADL disabilities was reduced for respon-
dents whose fathers were service workers or
laborers and respondents whose fathers were
absent or had died while they were growing up.
Finally, the addition of pathology variables
statistically explained the relationship between
mother’s education and ADL disabilities and
reduced the strength of the relationship be-
tween father’s education and ADL disabilities.
Also in this model, having a father who was
a farmer or farm manager or an operative was
associated with a decreased risk for ADL
disabilities, and having a father who was absent
or had died was associated with an increased
risk for such disabilities. The additions of social
mobility, health behaviors, and pathology
accounted for some of the between-person
variability in initial status, meaning that these
adult health risks accounted for a portion of the
between-person variability in ADL disabilities
(Table 2).
Instrumental Activities of Daily Living
Model 1 in Table 3 shows that, after control
for age, gender, and race/ethnicity, each addi-
tional year of parental education was associ-
ated with a reduced risk for IADL disabilities.
Also, relative to respondents whose fathers
were professionals, respondents whose fathers
were farmers or farm managers were at in-
creased risk for IADL disabilities, and respon-
dents whose fathers were clerical or sales
workers had a decreased risk for IADL dis-
abilities.
TABLE 1—Characteristics of
Respondents: Health and Retirement
Study, United States, 1998–2006
Characteristic
Sample
(n =18465)
Demographic characteristics
Age, y, mean (SE) 64.3 (0.15)
Female, % (SE) 57 (0.01)
White, % (SE) 87 (0.00)
Childhood socioeconomic position
Mother’s education, y, mean (SE) 9.46 (0.03)
Father’s education, y, mean (SE) 9.17 (0.03)
Father’s primary occupation, % (SE)
Professional (manager or
administrator)
15 (0.00)
Craftsman 21 (0.00)
Farmer/farm manager 21 (0.00)
Clerical/sales worker 11 (0.00)
Operative (machine or transport
worker)
20 (0.00)
Service worker/laborer 4 (0.00)
Father never worked/disabled <1 (0.00)
Father absent/deceased 1 (0.00)
Adult characteristics
Disability score, mean (SE)
ADLs 0.26 (0.00)
IADLs 0.18 (0.01)
Social mobility, mean (SE)
Education, y 12.4 (0.02)
Income (log transformed) 4.48 (0.00)
Wealth (log transformed) 4.65 (0.02)
Health behaviors, % (SE)
Current smoker 18 (0.00)
No history of smoking 40 (0.00)
Former smoker 42 (0.00)
Drinks alcohol 52 (0.01)
Exercises 3 or more times/wk 45 (0.00)
BMI, mean (SE) 25 (0.05)
Health conditions, % (SE)
Heart problems 19 (0.00)
Diabetes 12 (0.00)
Stroke 6 (0.00)
Hypertension 42 (0.00)
Lung disease 7 (0.00)
Note. ADL = activities of daily living; BMI = body mass
index (defined as weight in kilograms divided by height
in meters squared); IADL = instrumental activities of
daily living. Data are weighted. Values were calculated
at baseline (1998).
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The addition of social mobility variables to
the model statistically explained the relation-
ship between mother’s education and IADL
disabilities and weakened the effects of father’s
education on IADL disabilities. With these
additions, having a father who was a farmer or
farm manager or a clerical or sales worker was
associated with a decreased risk for IADL
disabilities. As such, social mobility in adult-
hood may mediate the adverse effects of having
a father who was a farmer or farm manager on
IADL disabilities. As with the ADL model,
additional analyses (data not shown) were
conducted to further examine the pathways
linking childhood SEP, social mobility, and
IADL disabilities. In these analyses, income
mediated the relationship between IADL dis-
abilities and having a father who was a farmer
or farm manager.
Itcanbeseeninmodel3ofTable3that
the addition of health behavior variables did
not affect the relationship between parental
education and IADL disabilities, although it
did statistically explain the relationship be-
tween having a father who was a clerical or
sales worker and IADL disabilities. The
addition of pathology variables in the final
model reduced the strength of the relation-
ship between IADL disabilities and father’s
education as well as the relationship between
IADL disabilities and having a father who
was a farmer or farm manager. Also, each
additional year of father’s education was
associated with a reduced risk for IADL
disabilities net of the other adult health risks
considered.
The addition to the model of social mobility,
health behaviors, and pathology accounted for
some of the between-person variability in
initial status, and the addition of health be-
haviors and pathology accounted for some of
the between-person variability in the rate of
change in disability across the survey period.
These findings suggest that unhealthy behav-
iors and pathology in adulthood result in
TABLE 2—Results of Multilevel Models Examining the Relationship Between Activities of Daily Living
Disabilities and Childhood Socioeconomic Position: Health and Retirement Study, United States, 1998–2006
Model 1, b (SE) Model 2, b (SE) Model 3, b (SE) Model 4, b (SE)
Childhood socioeconomic position
Mother’s education, y –0.04*** (0.00) –0.01* (0.00) –0.01** (0.00) –0.02 (0.00)
Father’s education, y –0.06*** (0.01) –0.04*** (0.01) –0.04** (0.01) –0.03** (0.01)
Father’s primary occupation
Professional (manager or administrator; Ref) 1.00 1.00 1.00 1.00
Craftsman 0.14 (0.12) 0.03 (0.12) –0.02 (0.11) –0.07 (0.08)
Farmer/farm manager 0.16*** (0.01) –0.05*** (0.04) –0.05*** (0.01) –0.03* (0.01)
Clerical/sales worker 0.02 (0.07) –0.00 (0.09) –0.02 (0.06) 0.02 (0.03)
Operative (machine or transport worker) 0.18* (0.09) 0.01 (0.10) 0.08 (0.07) –0.12* (0.06)
Service worker/laborer 0.25*** (0.01) 0.17*** (0.04) 0.11*** (0.03) 0.03 (0.02)
Father never worked/disabled 0.72* (0.33) 0.51 (0.34) 0.24 (0.41) 0.19 (0.36)
Father absent/deceased 0.43*** (0.10) 0.21** (0.12) 0.19** (0.07) 0.16* (0.07)
Adult characteristics
Social mobility
Education, y –0.09*** (0.01) –0.07*** (0.01) –0.06*** (0.01)
Income (log transformed) –0.17*** (0.04) –0.20*** (0.03) –0.19*** (0.02)
Wealth (log transformed) –0.07*** (0.00) –0.08*** (0.00) –0.07*** (0.00)
Health behaviors
No history of smoking (vs current smoker) –0.30*** (0.00) –0.22*** (0.00)
Former smoker 0.19*** (0.00) 0.16*** (0.00)
Drinks alcohol –0.22*** (0.00) –0.19*** (0.00)
Exercises 3 or more times/wk –0.30*** (0.00) –0.29*** (0.00)
BMI –0.04*** (0.00) –0.09*** (0.00)
Random effect estimates
Within person 0.18 0.18 0.18 0.19
Rate of change 0.07 0.07 0.07 0.06
Initial status 13.71 13.23 11.93 10.49
Note. BMI = body mass index (defined as weight in kilograms divided by height in meters squared). The sample size was n = 18 465. Data are weighted. Respondents were interviewed at baseline
(1998) and at 2-year intervals thereafter. All models adjusted for age, gender, and race, and model 4 also adjusted for pathology (heart problems, diabetes, lung disease, hypertension, and stroke).
*P < .05; **P < .01; ***P < .001.
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variations in levels of IADL disability risk over
time (Table 3).
DISCUSSION
In this nationally representative prospec-
tive study of community-dwelling older
Americans, low childhood SEP was associ-
ated with both severe ADL disabilities and
moderate IADL disabilities, although the
childhood indicators and pathways predict-
ing these disability levels varied over time.
Social mobility and health behaviors in
adulthood mediated much of the impact of
low childhood SEP on severe and moderate
disability risk; however, low paternal educa-
tional level and having a father who was
absent or had died during one’s childhood (in
the case of ADL disabilities only) remained
a significant predictor of disability net of
these adult health risk factors.
As such, low paternal educational level and
absence of one’s father during childhood may
have long-term health consequences. Policies
aimed at reducing these childhood risks may
help decrease the high health care costs asso-
ciated with severe disablement in old age. Our
findings are particularly strong given that we
accounted for temporally ordered social and
behavioral risks and intra-individual changes in
pathology and disability during a period in the
life course in which pathology and disability
are manifest.
Consistent with the results of traditional
disability studies, we found that social mobility,
health behaviors, and pathology were impor-
tant factors in the disablement process, leading
to variations in the severity of disability in later
life.
1
For example, health behaviors significantly
altered the disability trajectories of our partici-
pants, accounting for some of the between-
person variability in initial status (in terms of both
ADL and IADL disabilities) and rate of change (in
terms of IADL disabilities only) over the course
of the 9-year study period. As such, interventions
TABLE 3—Results of Multilevel Models Examining the Relationship Between Instrumental Activities
of Daily Living Disabilities and Childhood Socioeconomic Position: Health and Retirement Study, United States, 1998–2006
Model 1, b (SE) Model 2, b (SE) Model 3, b (SE) Model 4, b (SE)
Childhood socioeconomic position
Mother’s education, y –0.04*** (0.00) –0.01 (0.01) –0.01 (0.01) –0.01 (0.02)
Father’s education, y –0.06*** (0.01) –0.04*** (0.00) –0.04*** (0.00) –0.03*** (0.01)
Father’s primary occupation
Professional (manager or administrator; Ref) 1.00 1.00 1.00 1.00
Craftsman 0.08 (0.17) –0.06 (0.17) –0.09 (0.18) –0.13 (0.15)
Farmer/farm manager 0.10*** (0.02) –0.13* (0.06) –0.12* (0.06) –0.09** (0.03)
Clerical/sales worker –0.24* (0.10) –0.26* (0.10) –0.23 (0.12) –0.17 (0.11)
Operative (machine or transit worker) 0.04 (0.16) –0.13 (0.17) –0.19 (0.13) –0.22 (0.12)
Service worker/laborer 0.12 (0.16) 0.03 (0.18) –0.02 (0.11) –0.08 (0.10)
Father never worked/disabled 0.44 (0.53) 0.23 (0.51) 0.19 (0.46) 0.11 (0.44)
Father absent/deceased 0.18 (0.17) –0.05 (0.20) –0.09 (0.10) –0.11 (0.11)
Adult characteristics
Social mobility
Education, y –0.09*** (0.02) –0.08*** (0.02) –0.08** (0.02)
Income (log transformed) –0.19*** (0.06) –0.23*** (0.06) –0.22*** (0.04)
Wealth (log transformed) –0.07*** (0.01) –0.08*** (0.01) –0.07*** (0.01)
Health behaviors
No history of smoking (vs current smoker) –0.01 (0.05) 0.03 (0.05)
Former smoker 0.32*** (0.05) 0.26*** (0.05)
Drinks alcohol –0.14*** (0.02) –0.46*** (0.02)
Exercises 3 or more times/wk –0.34*** (0.00) –0.70*** (0.03)
BMI 0.13*** (0.02) –0.00 (0.01)
Random effect estimates
Within person 0.18 0.18 0.18 0.19
Rate of change 0.13 0.13 0.11 0.09
Initial status 24.85 24.32 21.4 17.26
Note. BMI = body mass index (defined as weight in kilograms divided by height in meters squared). The sample size was n = 18 465. Data are weighted. Respondents were interviewed at baseline
(1998) and at 2-year intervals thereafter. All models adjusted for age, gender, and race, and model 4 also adjusted for pathology (heart problems, diabetes, lung disease, hypertension, and stroke).
*P < .05; **P < .01; ***P < .001.
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aimed at improving health behaviors in adult-
hood may reduce the prevalence of disability
in elderly populations. Most important for this
and other life course research, however, our
findings suggest that adverse socioeconomic
conditions in early life may have long-term
effects on disability and that a life course ap-
proach may provide additional insight into dis-
ablement processes over time.
8,9
Although
the findings of life course studies have been
mixed,
12 ,13
most of these studies have reported
both direct and indirect pathways between
childhood social conditions and adult health
outcomes.
11,15 ,2 5
Consistent with the fetal origins hypothesis
and biological embedding, individuals whose
fathers were absent during their childhood or
died during that period may be at risk for
severe disability years later.
29,30
The reason
may be that childhood is a critical period of
development in which access to material and
socialresourcesisimportantforlaterlife
health outcomes. Although such hypotheses
have largely focused on the role of childhood
health, it is likely that childhood health and
SEP effects are interwoven.
40
As mentioned,
poverty exposes children to a variety of harmful
health risks, ranging from environmental pollut-
ants and toxins to poor nutrition and health
problems.
19 ,41
Low childhood SEP may also initiate a cu-
mulative ‘‘chain of risk,’’ predisposing at-risk
children to further socioeconomic, behavioral,
and pathological disadvantages across the life
course.
11,15, 42
In this study, social mobility medi-
ated much of the relationship between low
childhood SEP and severe disability and was
a particularly important factor among respon-
dents whose fathers had worked in manual
occupations, had never worked, or had been
disabled while they were growing up.
In addition, low childhood SEP may increase
the risk for severe disability by initiating a
pattern of poor health behaviors. This rela-
tionship was most evident among respondents
whose fathers were service workers or la-
borers and respondents whose fathers were
absent or had died while they were growing up,
possibly because children from these families
have restricted access to the information nec-
essary to promote good health.
23,43
As de-
scribed earlier, poor health behaviors such as
physical inactivity and smoking are associated
with a number of debilitating conditions in later
life, including cardiovascular and lung disease.
4
Some childhood factors may also have pro-
tective effects. For example, we found that each
additional year of father’s education was asso-
ciated with a reduced risk for severe and
moderate disabilities. Although some studies
have reported similar benefits from mother’s
education,
44
we found that the relationship
between mother’s education and severe and
moderate disabilities was mediated largely by
social mobility. Our findings might differ because
we accounted for multiple family structures (e.g.,
absent and deceased fathers) and temporally
ordered social mobility indicators across the life
course in attempting to capture socioeconomic
potential (e.g., education) and the ability to
purchase the goods and services necessary for
good health (e.g., income and wealth).
We also found that having a father who was
a farmer or farm manager or an operative (in
the case of ADL disabilities only) was associ-
ated with a decreased risk for disabilities. The
initial disadvantages associated with these
manual backgrounds were mediated by edu-
cational level and income in adulthood, sug-
gesting that social mobility may be particularly
important in reducing disability risk among
these groups. Previous life course research has
similarly reported benefits of growing up in
a rural area or having a father in a farmer or
farm manager position.
16 , 45
Limitations
There are several limitations to consider
when interpreting our results. First, studies
comparing childhood records with reports of
childhood SEP in adulthood suggest that ret-
rospective reports may be more favorable.
35
Although we used multiple measures of child-
hood SEP, our findings may represent an un-
derestimate of the relationship between child-
hood SEP and disability. Second, poor childhood
health may affect a variety of health out-
comes,
14,46,47
but the HRS includes only limited
measures of childhood health, and we were
unable to examine its effects on disability risk.
Third, attrition is a persistent problem in
longitudinal studies.
33
Attrition has not signifi-
cantly influenced the representativeness of the
HRS sample in terms of demographic, economic,
andhealthmeasures.
34
Nevertheless, given
that the healthiest adults are more likely to be
selectedintoandremaininthestudyover
time, this study’s ndings may err toward an
underestimation of the relationship between
childhood SEP and disability.
Conclusions
In terms of public policy, programs such as
Medicaid and the State Health Insurance Pro-
gram may provide needed health benefits to
children and their families and promote good
health across the life course. Unfortunately,
however, these programs are threatened by
rising health care costs, unemployment rates,
and poverty rates and declining tax revenues.
48
Even with these programs in place, millions of
poor children and adolescents have restricted
access to the goods and services needed to
maintain and promote health.
49
Programs aiming to increase access to higher
education and promote healthy behaviors
across the life course are also needed. Educa-
tional loans have helped young adults from
poor families afford college tuition. However,
these loan programs are also threatened. As
such, short-sighted health policies—focused on
end-of-life care or treatment of disease—are not
likely to be as effective as preventative health
care measures geared toward long-term health
goals. Reducing SEP differences in health will
require policy initiatives that address early-,
middle-, and later-life components of socio-
economic status (income, education, and occu-
pation) as well as the pathways through which
these factors affect health.
j
About the Authors
Mary Elizabeth Bowen is with the Institute of Gerontology,
Wayne State University, Detroit, MI. Hector M. Gonza
´
lez is
with the Institute of Gerontology and the Department of
Family Medicine and Public Health Sciences, Wayne State
University, Detroit.
Correspondence should be sent to Mary Elizabeth Bowen,
PhD, Institute of Gerontology, Wayne State University, 87
E Ferry St, 226 Knapp Building, Detroit, MI 48202
(e-mail: mbowen@wayne.edu). Reprints can be ordered at
http://www.ajph.org by clicking the ‘‘Reprints/Eprints’’ link.
This article was accepted February 20, 2009.
Contributors
M. E. Bowen was responsible for the study design,
analysis, and interpretation of findings. She also pre-
pared the article for publication. H. M. Gonza
´
lez assisted
with preparing the article for publication.
Acknowledgments
Mary Elizabeth Bowen is supported by a National In-
stitutes of Health postdoctoral training grant (HS
RESEARCH AND PRACTICE
S202 | Research and Practice | Peer Reviewed | Bowen and Gonza
´
lez American Journal of Public Health | Supplement 1, 2010, Vol 100, No. S1
013819). Hector M. Gonza
´
lez is supported by a Career
Development Award from the National Institute of
Mental Health (K08 MH67726). The Health and Re-
tirement Study is sponsored by the National Institute on
Aging (grant U01AG009740).
The data used in this article were made available by
the Inter-University Consortium for Political and Social
Research at the University of Michigan. We acknowledge
the anonymous reviewers for their valuable editorial
comments.
Human Participant Protection
No protocol approval was needed for this study.
References
1. Verbrugge LM, Jette AM. The disablement process.
Soc Sci Med. 1994;38:1–14.
2. Link BG, Phelan J. Social conditions as fundamental
causes of disease. J Health Soc Behav. 1995;35(extra
issue):80–94.
3. Adler NE, Boyce T, Chesney MA, et al. Socioeco-
nomic status and health: the challenge of the gradient.
Am Psychol. 1994;49:15–24.
4. McGinnis JM, Foege WH. Actual causes of death in
the United States. JAMA. 1993;270:2207–2212.
5. Anderson RT, James MK, Miller ME, Worley AS,
Longino CF Jr. The timing of change: patterns in transi-
tions in functional status among elderly persons.
J Gerontol B Psychol Sci Soc Sci. 1998;53:S17–S27.
6. Fried LP, Guralnik JM. Disability in older adults:
evidence regarding significance, etiology, and risk. JAm
Geriatr Soc. 1997;45:92–100.
7. Marmot MG, Davey Smith G, Stansfeld S, et al.
Health inequalities among British civil servants: the
Whitehall II Study. Lancet. 1991;337:1387–1393.
8. Ben-Shlomo Y, Kuh D. A life course approach to
chronic disease epidemiology: conceptual models, em-
pirical challenges and interdisciplinary perspectives. Int
J Epidemiol. 2002;31:285–293.
9. Lynch J, Smith GD. A life course approach to
chronic disease epidemiology. Annu Rev Public Health.
2005;26:1–35.
10. Brunner E, Shipley MJ, Blane D, Smith GD, Marmot
MG. When does cardiovascular risk start? Past and
present socioeconomic circumstances and risk factors in
adulthood. J Epidemiol Community Health. 1999;53:
757–764.
11. Marmot M, Shipley M, Brunner E, Hemingway H.
Relative contribution of early life and adult socioeco-
nomic factors to adult morbidity in the Whitehall II
Study. J Epidemiol Community Health. 2001;55:
301–307.
12. Schwartz JE, Friedman HS, Tucker JS, Tomlinson-
Keasey C, Wingard DL, Criqui MH. Sociodemographic
and psychosocial factors in childhood as predictors
of adult mortality. Am J Public Health. 1995;85:
1237–1245.
13. Frankel S, Smith GD, Gunnell D. Childhood socio-
economic position and adult cardiovascular mortality:
the Boyd Orr cohort. Am J Epidemiol. 1999;150:
1081–1084.
14. Barker D. Fetal and infant origins of adult disease.
Monatsschr Kinderheilkd. 2001;149(suppl 1):S2–S6.
15. Hayward MD, Gorman BK. The long arm of child-
hood: the influence of early-life social conditions on
men’s mortality. Demography. 2004;41:87–107.
16. Preston SH, Hill ME, Drevenstedt GL. Childhood
conditions that predict survival to advanced ages among
African-Americans. Soc Sci Med. 1998;47:1231–1246.
17. Smith GD, Hart C, Blane D, Hole D. Adverse
socioeconomic conditions in childhood and cause specific
adult mortality: prospective observational study. BMJ.
1998;316:1631–1635.
18. Chen E, Matthews KA, Boyce WT. Socioeconomic
differences in children’s health: how and why do these
relationships change with age? Psychol Bull. 2002;128:
295–329.
19. Pamuk E, Makuc D, Heck K, Reuben C, Lochner K.
Socioeconomic Status and Health Chartbook. Hyattsville,
MD: National Center for Health Statistics; 1998.
20. O’Rand AM. The precious and the precocious:
understanding cumulative disadvantage over the life
course. Gerontologist. 1996;36:230–238.
21. McEwen BS. Stress, adaptation, and disease: allo-
stasis and allostatic load. Ann N Y Acad Sci. 1998;840:
33–44.
22. Wray LA, Alwin DF, McCammon RJ. Social status
and risky health behaviors: results from the Health and
Retirement Study. J Gerontol B Psychol Sci Soc Sci. 2005;
60(extra issue):85–92.
23. Lynch JW, Kaplan GA, Salonen JT. Why do poor
people behave poorly? Variation in adult health behav-
iours and psychosocial characteristics by stages of the
socioeconomic lifecourse. Soc Sci Med . 1997;44:
809–819.
24. van de Mheen H, Stronks K, Looman CW,
Mackenbach JP. Does childhood socioeconomic status
influence adult health through behavioural factors? Int
J Epidemiol. 1998;27:431–437.
25. Guralnik JM, Butterworth S, Wadsworth ME, Kuh D.
Childhood socioeconomic status predicts physical func-
tioning a half century later. J Gerontol A Biol Sci Med Sci.
2006;61:694–701.
26. Wannamethee SG, Whincup PH, Shaper G, Walker
M. Influence of fathers’ social class on cardiovascular
disease in middle-aged men. Lancet. 1996;348:
1259–1263.
27. Hart CL, Smith GD, Blane D. Social mobility and 21
year mortality in a cohort of Scottish men. Soc Sci Med.
1998;47:1121–1130.
28. Poulton R, Caspi A, Milne BJ, et al. Association
between children’s experience of socioeconomic disad-
vantage and adult health: a life-course study. Lancet.
2002;360:1640–1645.
29. Hertzman C. The biological embedding of early
experience and its effects on health in adulthood. Ann
N Y Acad Sci. 1999;896:85–95.
30. Barker DJ, Eriksson JG, Forsen T, Osmond C. Fetal
origins of adult disease: strength of effects and biological
basis. Int J Epidemiol. 2002;31:1235–1239.
31. Heeringa SG, Connor J. Technical Description of the
Health and Retirement Study Sample Design. Ann Arbor,
MI: Survey Research Center, Institute for Social Re-
search; 1995.
32. HRS Data Documentation, Version H: National
Institute on Aging and the Social Security Administration.
Santa Monica, CA: RAND Corp; 2008.
33. Groves R, Couper M. Nonresponse in Household
Surveys. New York, NY: John Wiley & Sons Inc; 1998.
34. Cao H, Hill DH. Active Versus Passive Sample
Attrition: The Health and Retirement Study. Ann Arbor,
MI: University of Michigan; 2005.
35. Batty GD, Lawlor DA, Macintyre S, Clark H, Leon
DA. Accuracy of adults’ recall of childhood social class:
findings from the Aberdeen Children of the 1950s Study.
J Epidemiol Community Health. 2005;59:898–903.
36. Krieger N, Okamoto A, Selby JV. Adult female twins’
recall of childhood social class and father’s education:
a validation study for public health research. Am J
Epidemiol. 1998;147:704–708.
37. Bush TL, Miller SR, Golden AL, Hale WE. Self-
report and medical record report agreement of selected
medical conditions in the elderly. Am J Public Health.
1989;79:1554–1556.
38. Rabe-Hesketh S, Skrondal A. Multilevel and Longi-
tudinal Modeling Using Stata. 2nd ed. College Station, TX:
Stata Press; 2008.
39. Stata [computer program]. Version 9.0. College
Station, TX: StataCorp LP; 2005.
40. Haas SA. Health selection and the process of social
stratification: the effect of childhood health on socioeco-
nomic attainment. J Health Soc Behav. 2006;47:
339–354.
41. Chen E, Martin AD, Matthews KA. Understanding
health disparities: the role of race and socioeconomic
status in children’s health. Am J Public Health. 2006;96:
702–708.
42. Kahn JR, Fazio EM. Economic status over the life
course and racial disparities in health. J Gerontol B Psychol
Sci Soc Sci. 2005;60(extra issue):76–84.
43. Harkonmaki K, Korkeila K, Vahtera J, et al. Child-
hood adversities as a predictor of disability retirement.
J Epidemiol Community Health. 2007;61:479–484.
44. Luo Y, Waite LJ. The impact of childhood and
adult SES on physical, mental, and cognitive well-being
in later life. J Gerontol B Psychol Sci Soc Sci. 2005;60:
S93–S101.
45. Warner DF, Hayward MD. Early-life origins of the
race gap in men’s mortality. J Health Soc Behav. 2006;
47:209–226.
46. Barker DJ, Osmond C. Infant mortality, childhood
nutrition, and ischemic heart disease in England and
Wales. Lancet. 1986;327:1077–1081.
47. Blackwell DL, Hayward MD, Crimmins EM. Does
childhood health affect chronic morbidity in later life?
Soc Sci Med. 2001;52:1269–1284.
48. Morreale MC, English A. Eligibility and enrollment
of adolescents in Medicaid and SCHIP: recent progress,
current challenges. J Adolesc Health. 2003;32(suppl 6):
25–39.
49. Bloom B, Cohen RA, Vickerie JL, Wondimu EA.
Summary health statistics for U.S. children: National
Health Interview Survey, 2001. Vital Health Stat 10.
2003;No. 216:1–54.
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Reproducedwithpermissionofthecopyrightowner.Furtherreproductionprohibitedwithoutpermission.
    • "We used demographic, socioeconomic, lifestyle and healthrelated characteristics as explanatory factors in our study. Some earlier life course studies have suggested that adverse socioeconomic conditions in early life may have long-term effects on disability (Bowen and Gonzalez 2010; Case et al. 2005). However, the data on socioeconomic conditions in early life were not available in our study. "
    [Show abstract] [Hide abstract] ABSTRACT: Purpose Occupations during adult life may have long-term effects and subsequently increase the risk of disability in old age. We investigated the associations between job profile groups in midlife and disability in old age for women and men. Methods This prospective 28-year follow-up study (1981–2009) examined 2998 municipal employees (1892 women and 1106 men) aged 44–58 years at baseline. A detailed analysis of the demands of 88 occupations based on interviews and observations at the work places was made at baseline. Thirteen job profile clusters emerged. Questionnaire information on health, lifestyle and socio-demographic factors was collected at baseline. In 2009, five Activities of Daily Living and seven Instrumental Activities of Daily Living tasks were assessed. A sum score of ‘0–12’ was calculated using 12 dichotomous tasks where ‘0’ indicates no difficulties in any tasks and ‘1–12’ indicates increasing disability. Negative binomial regression was used to calculate rate ratios (RR) and their 95 % confidence intervals (CIs) for disability due to midlife job profiles. Results After adjusting for age, socioeconomic, lifestyle and health-related characteristics, women in auxiliary (RR 2.1, 95 % CI 1.4–3.2), home care (2.1, 1.4–3.2), kitchen supervision (2.0, 1.1–3.6) and office (1.6, 1.1–2.4) job profiles had a higher risk of disability in later life than those in administrative jobs. Auxiliary (1.5, 1.1–2.9) and technical supervision (1.7, 1.1–2.7) job profiles carried an increased risk among men. Conclusion Midlife job profiles mainly linked with physically heavy work were strong predictors of disability in later life. In women, office work also increased the risk of disability.
    Article · May 2016
    • "Although the association between active life expectancy and educational attainment is well established, a life course epidemiological perspective impels us to consider the possibility that active life expectancy reflects experiences throughout the life course. Indeed, many childhood and adult experiences independently predict the prevalence of functional limitations (Alvarado et al. 2007; Guralnik et al. 2006; Haas 2008; Haas and Rohlfsen 2010; Luo and Waite 2005; Turrell et al. 2007) and disability (Bowen and Gonzalez 2010; Freedman et al. 2008; Haas 2008) as well as mortality risk (Barker 1997; Davey Smith et al. 1998; Finch and Crimmins 2004; Hayward and Gorman 2004; Kuh et al. 2002; Montez and Hayward 2011; Turrell et al. 2007; Warner and Hayward 2006). The next step, which we address here, is to assess how early-life experiences shape transitions in functional ability across the disablement process (Verbrugge and Jette 1994) and thereby shape disparities in active life expectancy. "
    Full-text · Article · Jan 2014
    • "Although the association between active life expectancy and educational attainment is well established, a life course epidemiological perspective impels us to consider the possibility that active life expectancy reflects experiences throughout the life course. Indeed, many childhood and adult experiences independently predict the prevalence of functional limitations (Alvarado et al. 2007; Guralnik et al. 2006; Haas 2008; Haas and Rohlfsen 2010; Luo and Waite 2005; Turrell et al. 2007) and disability (Bowen and Gonzalez 2010; Freedman et al. 2008; Haas 2008) as well as mortality risk (Barker 1997; Davey Smith et al. 1998; Finch and Crimmins 2004; Hayward and Gorman 2004; Kuh et al. 2002; Montez and Hayward 2011; Turrell et al. 2007; Warner and Hayward 2006). The next step, which we address here, is to assess how early-life experiences shape transitions in functional ability across the disablement process (Verbrugge and Jette 1994) and thereby shape disparities in active life expectancy. "
    [Show abstract] [Hide abstract] ABSTRACT: Studies of the early-life origins of adult physical functioning and mortality have found that childhood health and socioeconomic context are important predictors, often irrespective of adult experiences. However, these studies have generally assessed functioning and mortality as distinct processes and used cross-sectional prevalence estimates that neglect the interplay of disability incidence, recovery, and mortality. Here, we examine whether early-life disadvantages both shorten lives and increase the number and fraction of years lived with functional impairment. We also examine the degree to which educational attainment mediates and moderates the health consequences of early-life disadvantages. Using the 1998-2008 Health and Retirement Study, we examine these questions for non-Hispanic whites and blacks aged 50-100 years using multistate life tables. Within levels of educational attainment, adults from disadvantaged childhoods lived fewer total and active years, and spent a greater portion of life impaired compared with adults from advantaged childhoods. Higher levels of education did not ameliorate the health consequences of disadvantaged childhoods. However, because education had a larger impact on health than did childhood socioeconomic context, adults from disadvantaged childhoods who achieved high education levels often had total and active life expectancies that were similar to or better than those of adults from advantaged childhoods who achieved low education levels.
    Full-text · Article · Nov 2013
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