the health-wealth gradient: examining the fetal …
TRANSCRIPT
THE HEALTH-WEALTH GRADIENT: EXAMINING THE
FETAL ORIGINS HYPOTHESIS
A Thesis Submitted to the Faculty of the
Graduate School of Arts and Sciences of Georgetown University
in partial fulfillment of the requirements for the degree of
Master of Public Policy
By
Madeline Hope Otto, B.A.
Washington, DC April 18, 2006
ii
THE HEALTH-WEALTH GRADIENT: EXAMINING THE FETAL ORIGINS HYPOTHESIS
Madeline Hope Otto, B.A.
Thesis Advisor: Harriet Komisar, Ph.D.
ABSTRACT
People with low incomes experience higher mortality and morbidity on average
than the more affluent, even while controlling for health factors such as access to care,
and drinking and smoking. One hypothesis that may explain this difference is the fetal
origins hypothesis, a theory pioneered by David Barker, who argues that poor maternal
health can harm a fetus’ development, compromising the child’s health into adulthood.
Barker suggests that women of low socioeconomic class are less likely to have healthy
pregnancies due to environmental factors and lifestyle behaviors. As a result, children
of low-income women, who are more likely to be low-income themselves, will suffer
worse lifelong health outcomes. To measure how much explanatory power the fetal
origins hypothesis holds for health differentials, I use the 2001 Panel Study of Income
Data to run two separate regressions on adults aged 30 or over. Both have the
dependent variable of health status and independent variables related to socioeconomic
status (SES) and lifestyle characteristics that affect health. The second regression also
has an indicator of healthy fetal development. In comparing the effect of SES on health
across these two regressions, the effect of wealth on health does not change with the
introduction of the fetal development variable, which sheds doubt on the ability of the
fetal origins hypothesis to explain the health-wealth gradient. However, the effect of
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birth weight on adult health is significant, which suggests that improved prenatal care
may improve adult health outcomes over the long term.
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TABLE OF CONTENTS
ABSTRACT………………………………...…………………………………………...ii
INTRODUCTION…………………………...…………………………………………..1
LITERATURE REVIEW………………………..………………………………………2
Allostatic Load………………………...………………………………...………3
Income Inequality…………………………...…………………………………...4
Fetal Origins…………………………...………………………………………...5
CONCEPTUAL FRAMEWORK AND HYPOTHESES…………………………...…..8
DATA AND METHODS…………………………...…………………………………10
RESULTS…………………………...………………………………………………….15
Descriptive Statistics…………………………...………………………………15
Regression Results…………………………...…………………………………19
DISCUSSION…………………………...……………………………………………...22
REFERENCES…………….…………………...………………………………………24
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TABLES AND FIGURES
Figure 1. Conceptual Framework………………………………………………………..9
Table 1. Descriptive Statistics………………………………………………………….17
Table 2. Birth Weight by Self-Rated Health Status……………………………………17
Table 3. Total Family Income in 2000 by Self-Rated Health Status…………………...18
Table 4. LPM and OLS Regression Results……………………………………………21
1
INTRODUCTION
People with low incomes experience higher rates of disease and higher mortality
on average than those who are more affluent. This gradient, running from low income
and poor health to high income and good health, has been documented in the United
States, as well as in other developed countries and underdeveloped countries alike
(Smith 1999). This association is evident in countries with universal health care, such
as Britain, and therefore the discrepancy cannot be completely attributed to healthcare
access. Additionally, since this association persists even when controlling for factors
such as nutrition, exercise, and smoking, these differences cannot be attributed solely to
a greater likelihood among low-income people for certain lifestyle behaviors at the
expense of long-term health.
To try to determine how socioeconomic status (SES) affects health, I consider
the fetal origins hypothesis, primarily researched by David Barker and associates.
According to Barker, lifelong health is programmed in utero, since maternal health
affects fetal development, and fetal development affects an individual’s health
throughout life. While Barker acknowledges the potential for his theory to explain the
health-wealth gradient, others have extended his theory more explicitly (Wadsworth and
Kuh 1997). The logic is as follows: if a low-income woman is more likely to have poor
nutrition, to smoke, or to drink during pregnancy, her child will be more likely to have
health problems throughout life. Thus, the health-wealth gradient we currently observe
may actually be an association between the health of an individual and the wealth of his
or her mother. Therefore, the fact that the association between health and wealth
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persists so strongly in the observed generation results from the relatively low variation
in social class between parents and children.
While Barker focuses on how lifelong health is programmed in utero, the main
focus of his research is not on how the fetal origins hypothesis can explain the health-
wealth gradient. Thus, he provides evidence that suggests that fetal programming is
linked to the health-wealth gradient, however he does not demonstrate this link
conclusively, or measure the magnitude of the explanatory power that fetal
programming theory holds for the health-wealth gradient.
In order to further explore the relationship between fetal origins and the health-
wealth gradient, I use the 2001 Panel Study of Income Dynamics, a family-level data set
that was nationally representative when it was created in 1968, and that continues to
follow the original families as they have expanded over the years. Data that describes
an individual’s current health status, current socioeconomic status, and fetal
environment is available, allowing the examination of whether and how much the fetal
programming hypothesis contributes to the observed health-wealth gradient.
Using these data, I test whether controlling for fetal environment reduces the
observed relationship between wealth and health, and therefore whether the fetal origins
hypothesis can account for the observed variation in health between social classes.
LITERATURE REVIEW
Much of the current literature concerning the effect of socio-economic status
(SES) on health is rooted in the field of life course epidemiology (Gorman 2004, Kuh
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and Ben-Shlomo 2004, Smith 1999). The life course approach is predicated on the
assumption that physical and social experiences throughout a person’s lifespan have a
lasting effect on his or her lifelong health. These physical experiences in utero are
hypothesized to impact adult health, and both physical and social experiences
throughout life are hypothesized to affect adult health. Some claim that these
experiences can even have intergenerational health effects. The life course approach is
distinct from traditional epidemiological studies of adult health, which largely focus on
how an adult’s current lifestyle behaviors can affect health (Kuh and Ben-Shlomo 2004,
Hayward and Gorman 2004).
Among literature based on the life course approach, there are three main
hypotheses that attempt to explain the effect of SES on health: income inequality,
allostatic load, and the fetal origins hypothesis (Smith 1999). The income inequality
and allostatic load hypotheses focus primarily how stressors related to SES can affect
health, whereas the fetal origins hypothesis suggests that a mother’s SES can affect her
child’s health.
Allostatic Load
Allostatic load is a term that refers to the “accumulated physiologic toll exacted
on the body over time by efforts to adapt to life experiences” (Seeman et al. 1997).
Thus, the theory of allostatic load suggests that stressors throughout life compound and
affect the functioning of the body later in life. Stressors are hypothesized to affect the
body through adrenalin levels. When an individual confronts a stressful situation, his or
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her adrenalin levels increase, which affects blood pressure, heart rate, and immune
system functioning. If this occurs too frequently, it may be more difficult for the body
to return to a normal, stress-free state. This could result in high blood pressure,
diabetes, and high cholesterol (Seeman et al. 1997). This theory of allostatic load could
explain differences in mortality and morbidity between social classes if it is true that
lower social classes in general experience more situations where it is necessary to adapt
to difficult or troubling circumstances. More research is necessary to establish this link.
Income Inequality
The theory of income inequality posits that health is not affected by the absolute
amount of financial resources available to an individual, but his or her relative position
in society. International comparisons support this theory: in industrialized economies,
average mortality is not related to average income differences between countries, but
average differences within countries. For example, in Sweden and Norway, the
variation of individual incomes is much less than in other industrialized countries such
as Britain and the United States. Average mortality is two to three years later in these
more egalitarian countries, despite the fact that both Britain and the United States have
greater overall financial resources than these countries (Marmot 2001).
The Whitehall studies are a pair of studies that show how this inequality
principle operates on an individual level. Both these studies looked at the incidence of
coronary disease among a cohort of British civil servants. What is striking about these
studies is that the health-wealth differential persisted within the group, even though all
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of the participants were white-collar civil servants with adequate income. The first
study, done in 1967, looked only at men and found a high positive correlation between
occupation status and health (as measured by morbidity and mortality). Whitehall II
(1985-1988) looked at both men and women. The study showed that despite overall
increases in health outcomes, the differences in health outcomes between people of
different occupational status remained (Hemingway et al. 1997).
So how does income inequality actually cause the health-wealth gradient?
Explanations are scarce, and scientific evidence for these explanations is even scarcer.
One popular theory is that psycho-social stress is increased among those in society with
a lower relative position, and this increase in stress affects the functioning of the
endocrine and immunological systems of these individuals, resulting in poorer health
outcomes among the lower classes (Hemingway et al. 1997).
Both the income inequality theory and the allostatic load theory are difficult to
investigate because of the definition of “stress.” What constitutes a stressor? Is it the
same for every person? These theories are predicated on the assumption that stress is
higher on average in low-income populations than in their more affluent counterparts.
While this is a plausible assumption, more evidence is needed to determine how health-
affecting stressors vary across social classes.
Fetal Origins
Proponents of the theory of the impact of early childhood hypothesize that
exposure to negative health factors (e.g., poor nutrition, smoking) can have a lifelong
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negative impact on health, especially if exposure happens at critical periods in a child’s
fetal development. These negative health factors are also more prevalent among people
with low socioeconomic status, resulting in poorer health for children from low
socioeconomic backgrounds (Smith 1999).
This explanation for the health-wealth gradient was sparked by work done by
David Barker and associates, researchers in the United Kingdom. According to Barker
(1997, 1998), the developing fetus is especially susceptible to negative effects
stemming from the scarcity of nutrients or oxygen. An embryo develops through the
rapid division of cells, and when there is a lack of oxygen or nutrients, cell division
slows, resulting in the production of fewer new cells. Due to this scarcity of resources,
the embryo favors the production of cells that are necessary to sustain early life at the
expense of those necessary to sustain later life. Thus, since the brain is the most
important organ for early survival, much growth occurs in this area, while organs within
the body are underdeveloped. These underdeveloped organs place the individual at
higher risk for chronic diseases such as heart disease, stroke, diabetes, and hypertension
later in life.
A study by Wadsworth and Kuh (1997) provides some empirical support for this
theory and makes the link between this theory and the health-wealth gradient more
explicit. This study looked at a cohort of subjects sampled from the United Kingdom,
all born within one week in 1946. These 5,362 respondents were visited 8 weeks after
birth, and follow up interviews were done 22 times, with the last interview when the
respondents were aged 51. In addition to initial information such as birth weight (a
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proxy for fetal development), information on growth and health was collected
throughout the years, and occupational and family information was collected at
adulthood.
The study showed that low birth weight was associated with a number of
negative health outcomes in respondents. In addition, children from low socioeconomic
classes were more likely to experience poor health as adults, controlling for other
factors relevant to health, including birth weight. Birth weight was negatively
associated with blood pressure in adults—by age 36, individuals who had been born
with low birth weight were more likely to have high blood pressure than those born
with normal birth weight. This association persisted after controlling for various factors
such as current weight, family history of heart disease, smoking, and educational
achievement. Low birth weight was also correlated with respiratory function. Low
birth weight was found to be associated with decreased lung capacity at age 36, even
after controlling for factors such as cigarette smoking, education level, and
socioeconomic characteristics in adulthood.
A recent study by Johnson and Schoeni (2005) looks at the correlation between
health and economic status in early life, and health, education, and labor market
outcomes in later life using the Panel Study of Income Dynamics, a nationally
representative longitudinal data set. This study provides additional support for Barker’s
thesis as it shows a positive and significant correlation between birth weight and health
in later life among men, and is the first to do so using data from the United States.
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The fetal origins hypothesis has two main advantages over the stress-related
hypotheses: it is based on a more explicitly defined underlying mechanism, and
therefore, it is easier to test. These advantages notwithstanding, the fetal origins
hypothesis is not necessarily more correct than the stress-related hypotheses. All three
of these hypotheses could potentially play a role in shaping the health-wealth gradient.
Although much work has been done on the study of the fetal origins hypothesis
and the explication of the physical processes by which it functions, relatively little work
has been done to link this hypothesis with the health-wealth gradient. Therefore, this
study attempts to establish the existence and measure the magnitude of the fetal
programming effect on the health-wealth gradient. By comparing the effect of income
on health with and without the addition of a fetal environment variable, I attempt to
identify the explanatory power that the fetal origins hypothesis holds for the health-
wealth gradient.
CONCEPTUAL FRAMEWORK AND HYPOTHESES
For the fetal origins hypothesis to explain some or all of the difference in health
outcomes by SES that are not explained by lifestyle behaviors, many underlying
relationships must hold (see Figure 1). As Barker (1997, 1998) has shown, a mother’s
health is positively associated with healthy fetal development, which is associated with
the adult health of her child. Barker also demonstrates that SES at birth is correlated
with SES later in life. For this theory to explain the health-wealth gradient, maternal
health must then be positively correlated with her SES. It is fair to assume that
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maternal health is correlated with SES since her health is correlated with SES
throughout the rest of her life, and there is no indication that this relationship should not
hold during pregnancy. If these relationships hold, it would suggest that low-income
mothers (who are less likely to have adequate nutrition, more likely to drink or smoke
during pregnancy, and are therefore more likely to be in poor health) are more likely to
have children whose lifelong health has been compromised in utero (Barker 1997,
Wadsworth and Kuh 1997). Therefore, these children will be more likely to experience
poor health in adulthood, and will also be more likely to have low socio-economic
status, since SES is correlated in generations of the same family (Barker 1997,
Wadsworth and Kuh 1997).
Figure 1. Conceptual Framework
Based on this model, I attempt to test whether and how much the fetal origins
hypothesis contributes to the health-wealth gradient. To test this theory, I estimate the
impact of socioeconomic status on health, controlling for lifestyle factors. I then
Maternal Health
Mother’s SES
Fetal Development Adult Health
Adult Child’s SES
Lifestyle Characteristics
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compare this to a second estimate of the effect of socioeconomic status on health that
takes into account lifestyle behaviors and a measure of fetal development. If the effect
of socioeconomic status on adult health decreases with the additional consideration of
fetal environment, this would provide strong evidence that the fetal origins hypothesis
does in fact contribute to the observed health-wealth gradient. That is, if the magnitude
of the effect of income on health changes with the addition of a fetal development
variable, it stands to reason that fetal development explains at least some of the impact
of wealth on health.
DATA AND METHODS
To test these questions, I use the 2001 Panel Study of Income Dynamics (PSID),
the most recent PSID data available. This study is funded through both federal and
private sources. Researchers began collecting data for the PSID in 1968, when they
selected a cross-sectional nationally-representative group of roughly 3,000 families, as
well as an additional sub-sample of about 2,000 low-income families. Information on
health, income, and other characteristics was collected annually through 1979 on all
members of these families, and on additional individuals who were born into or married
into the families. Therefore, the number of individuals in the data set grew annually
until 1979, when the core sample was cut from over 8,000 families to approximately
6,000 families, and data collection was reduced to every other year. At this time, 441
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additional families were added to represent immigrant populations that had grown
substantially since 1968. Attrition in this data set is relatively low.1
These data are suited to address the impact of the fetal origins hypothesis on the
health-wealth gradient because they have extensive information on the lifelong health
and wealth of individuals. Unfortunately, some information is not as rich as it could be.
For example, information on fetal environment and fetal health is scarce – information
on a mother’s nutrition and whether or not she smoked during pregnancy is not
available for any respondents older than 35. Even if the parents of individuals over 35
are included in the survey, the survey was not begun until 1968, after they would have
been born and therefore information about the mother’s health when she was pregnant
would not be available. In this study, an individual’s birth weight is used as a proxy for
his or her fetal health.
From this data set, I was able to obtain a total of 10,400 observations for adults
age 30 and older. The sample is limited to adults because, according to the fetal origins
hypothesis, adverse health outcomes that were programmed in utero tend to manifest
later in life. However, 7268 of these observations were unusable because they didn’t
have valid information on birth weight. Regressing health status on income and control
variables for both the entire sample of over 30-year-olds with birth weight information
and the sub-sample of adults without birth weight information yields similar results.
Therefore, I infer that the sub-sample is not statistically different from the sample. An
1 Exact numbers for the rate of attrition are difficult to obtain because families may leave the study for a period, and then return. (For additional information, see “An Overview of the Panel Study of Income Dynamics.”)
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additional 19 observations lacked information on the dependent variable, self-rated
health status. These observations represent a very small portion of the sample and were
therefore excluded. An additional 57 observations lacked appropriate information on
total family income, and exclusion of these observations is discussed below. Therefore,
only 3,056 of these observations were used.
Using this group of 3,056 observations, I test whether the fetal origins
hypothesis explains the health-wealth gradient by running two separate Ordinary Least
Squares (OLS) regressions and two separate Linear Probability Model (LPM)
regressions on self-described health status. I run both an OLS model and an LPM
model to compare results for consistency. Health status is obtained by asking the
respondent to rate his or her health in general, and possible responses are: excellent,
very good, good, fair, or poor. In the OLS model, the dependant variable is self-defined
health status, where 1 = poor, 2 = fair, 3 = good, 4 = very good, and 5 = excellent. For
the LPM model, the dependant variable is equal to 1 when health is described as
excellent or very good, and equal to 0 when health is described as good, fair or poor.
Although self-rated health status may be skewed, Johnson and Schoeni (2005) find that
the results from running OLS and LPM models like these is comparable to the results
obtained when using outside data to convert health to a 100-point scale to reduce
heteroskedasticity.
The independent variables fall into three main categories: SES variables, the
fetal environment variable, and other lifestyle factors that contribute to health.
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To estimate socioeconomic status, I include total family income in the year
2000. I expect this variable to be positively correlated with health. Total family
income in 2000 ranges between -$59,948 and $2,112,300. A negative value indicates
that the family experienced a net loss for the year, however this could be misleading
since a family with large assets and little or no income from work may post a loss for a
year but still in fact be relatively affluent. While this could be the case for any of the
positive observations as well, it seems more likely to be so for these observations since
a family must have some assets in order to post a loss. Therefore, I dropped the 16
observations with negative family income, as well as the 41 outlier observations who
had a total family income in 2003 of more than $800,000.2
Fetal environment is measured using birth weight as a proxy, as discussed
above. I expect birth weight to be positively correlated with adult health. For
individuals in this study, birth weight is a binary variable, and an individual is coded as
being either 5.5 pounds or above (normal birth weight), or below 5.5 pounds (low birth
weight). While this certainly limits variation, it does not preclude me from finding
statistically significant results.
Lifestyle characteristics that influence a person’s health are numerous, and they
include whether one drinks alcoholic beverages, smokes, or exercises, and one’s access
to health care. The variables used for drinking include information on how many
alcoholic beverages an individual consumes per day, on average, as well as a squared
2 The cutoff of $800,000 was chosen because there were few observations around $800,000, and the vast majority of the observations were below $800,000.
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term to reflect that drinking in moderation may have a positive effect on health, but
drinking excessively harms health. Therefore, the initial drinking variable should have
a positive correlation with health, and the squared term should have a negative
correlation with health. The variable used for smoking is the average number of
cigarettes smoked per day, and is expected to show a negative correlation with health.
Information regarding an individual’s level of exercise is available in this data set;
however, it was not used since this variable has strong feedback effects with health—
that is, an individual’s level of exercise may affect his or her health, but an individual’s
health also affects his or her ability to exercise. Access to health care is difficult to
specify, but I attempt to approximate it using a binary variable that describes whether a
person was insured at any time during the past year. This relies on the assumption that
recent insurance status is an indicator of history of insurance status, and therefore of
access to health care. This variable should be positively correlated with health (i.e.,
increased access should result in better health).
In addition, I control for individual characteristics such as gender and age at
time of interview (since age is negatively correlated with health).
As discussed above, I run two separate OLS regressions of health status on SES
and lifestyle characteristics: one which includes the birth weight variable and the other
which does not. Therefore, the first regressions of each pair are of the form:
uagefemaleinsuredalcoholalcoholcigarettestbirthweighincomehealth +++++++++= 8762
543210 ααααααααα
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The second regressions of each pair are of the form:
vagefemaleinsuredalcoholalcoholcigarettesincomehealth ++++++++= 7652
43210 ββββββββ
Comparing α1 and β1 from these two regressions yields an estimate for the magnitude of
the explanatory power of the fetal origins hypothesis.
RESULTS
Descriptive Statistics
The total number of weighted observations for this study is 81,242 (see Table 1).
When health status is measured with the five-point scale, the mean is 3.75. Roughly
63% of the population reports excellent or very good health, while the remaining 37%
report good, fair or poor health. About 7% of the individuals had low birth weight.
The study population is roughly half male and half female. About 40% of the
population is between the ages of 30 and 39, and an additional group of roughly 40% is
between the ages of 40 and 49. About 15% of the study population is between the ages
of 50 and 59, and a relatively small fraction is aged 60 or older. The mean yearly
household income in 2000 for the sample is $81,690, with a standard deviation of
$74,830.
Table 2 shows the relationship between birth weight and self-rated health status
for this sample. Individuals classified as low birth weight are 5.1 percentage-points
more likely to identify themselves as currently being in poor health than those who were
not low birth weight. These low birth weight individuals are 3.1 percentage-points less
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likely than those who were not low birth weight to identify themselves as currently
being in excellent health status
Table 3 shows the health-wealth gradient, specifically, the relationship between
total family income in 2000 and self-rated health status. High-income individuals are
more likely to be in excellent or very good health than low-income individuals, and
low-income individuals are more likely to be in poor or fair health than high-income
individuals. For example, in the lowest income bracket of $0 to $9,999, 11.1% of
individuals describe their health as poor, compared to only 0.5% of those in the highest
income bracket, $100,000 or more. Only 8.8% of those in the lowest income bracket
describe their health as excellent, compared to 37.7% of those in the highest income
bracket.
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Table 1. Descriptive Statistics
Unweighted Weighted
Standard Standard Mean Deviation Mean Deviation Variable
Dependent Variables: Health Statusa 3.62 1.04 3.75 0.99 Very Good or Excellent Health 0.57 0.50 0.63 0.48 Independent Variables: Total Family Income 2000, in Thousands of $ 68.80 65.53 81.69 74.83 Low Birth Weight 0.07 0.26 0.07 0.25 Number of Cigarettes per Day 3.68 8.01 3.69 8.36 Alcoholic Beverages per Dayb 0.80 0.81 0.88 0.79 Whether Insured at all in Past Year 0.86 0.34 0.90 0.30 Female 0.54 0.50 0.49 0.50 Age 41.67 7.38 41.81 7.39 NOTE: Sample consists of individuals aged 30 and over, with information available on self-rated health status, birth weight, and total family income in 2000. a. Where 1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent. b. Where more than four alcoholic beverages per day is coded as 5. Source: Author's analysis of data from the 2001 Panel Study of Income Dynamics. n = 3,056; weighted n = 81,090. Table 2. Birth Weight by Self-Rated Health Status
Distribution (in %)
Number Excellent Very Good Good Fair Poor
Study population 81,090 24.9% 38.1% 26.8% 7.8% 2.4% Birthweight at 5.5 pounds or above 75,518 25.1% 38.9% 26.3% 7.6% 2.1% Birthweight below 5.5 pounds 5,573 22.0% 27.2% 33.9% 9.8% 7.2%
Source: Author's analysis of data from the 2001 Panel Study of Income Dynamics.
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Table 3. Total Family Income in 2000 by Self-Rated Health Status
Distribution (in %)
Number Excellent Very Good Good Fair Poor
Study Population 81,242 24.9% 38.1% 26.8% 7.8% 2.4% $0 - $9,999 2,847 8.8% 23.8% 33.9% 22.5% 11.1% $10,000 - $19,999 4,832 7.9% 26.0% 37.0% 20.0% 9.0% $20,000 - $29,999 6,272 13.5% 33.2% 35.7% 14.1% 3.5% $30,000 - $39,999 6,705 17.8% 32.3% 36.6% 10.3% 3.1% $40,000 - $49,999 8,102 18.9% 41.8% 26.9% 7.9% 4.5% $50,000 - $59,999 7,970 19.4% 40.9% 30.7% 7.0% 2.1% $60,000 - $69,999 7,497 24.3% 46.1% 20.4% 8.9% 0.4% $70,000 - $79,999 6,188 28.2% 36.4% 29.5% 4.0% 2.0% $80,000 - $89,999 4,867 32.9% 40.4% 23.9% 2.9% 0.0% $90,000 - $99,999 4,703 28.2% 36.8% 32.7% 2.3% 0.0% $100,000 or more 21,110 37.7% 41.1% 17.1% 3.6% 0.5%
Source: Author's analysis of data from the 2001 Panel Study of Income Dynamics.
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Regression Results
A total of four regressions were run: two LPM regressions measuring health as a
dichotomous variable where “good health” includes those who self-identify as having
excellent or very good health and two OLS regressions measuring health as an interval
between one (poor) and five (excellent). For each formulation of the dependent
variable, one regression was run with the variable birth weight included, and one
without (see Table 4).
Most of the variables included in the model were highly significant, and the sign
and significance of these variables was comparable across all four regressions. The
coefficient on low birth weight in the LPM model suggests that an individual with low
birth weight is 7.9% less likely to be in very good or excellent health, all other relevant
factors held constant. This is comparable to the effect on health of an additional 6.3
years of life. In the OLS model, the coefficient suggests that on average a person who
is low birth weight will be 0.17 points lower on the health scale from 1 to 5, holding all
relevant factors constant. This is also comparable to the effect on health of an
additional 6.3 years of life. These estimates are comparable to those obtained by
Johnson and Schoeni (2005) in their study that considers birth weight, adult health, and
adult labor market outcomes.
Total family income in 2000 is also significant, but the magnitude is relatively
small. The LPM model suggests that for every additional $10,000 of yearly family
income, and individual is 1.5% more likely to be in excellent or very good health,
holding all other relevant factors constant. The OLS model predicts that with an
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additional $10,000 of annual family income, an individual will be 0.03 points higher on
the health scale from 1 to 5, all other relevant factors held constant.
Despite the high significance of both the low birth weight variable and the total
family income in 2000 variable, the coefficient on family income does not vary at all
when low birth weight is removed from the LPM regression or from the OLS
regression. This suggests that birth weight does not explain any of the impact of total
annual family income on self-rated health status. More broadly, it may suggest that
fetal environment does not explain the effect of wealth on health—that the fetal origins
hypothesis may not explain the health-wealth gradient.
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Table 4. LPM and OLS Regression Results
Model 1 – with Birth Weight Model 2 - without Birth Weight Variable Estimate p-value Estimate p-value
LPM Model (Binary Outcome) Intercept 0.8647 0.0001*** 0.8588 0.0001*** Family Income 2001, in thousands 0.0015 0.0001*** 0.0015 0.0001*** Low Birthweight (<5.5 pounds) -0.0785 0.0157* . . Cigarettes per Day -0.0048 0.0001*** -0.0049 0.0001*** Alcoholic Beverages per Day 0.0874 0.0002*** 0.0887 0.0001*** Alcoholic Beverages per Day Squared -0.0187 0.0204* -0.0193 0.0172* Insured in Past Year 0.1502 0.0001*** 0.1527 0.0001*** Female -0.0527 0.0026** -0.0553 0.0015** Age of Individual -0.0125 0.0001*** -0.0125 0.0001*** Adjusted R2 0.1415 0.1415 OLS Model (Outcome Scaled from 1 to 5) Intercept 4.1798 0.0001*** 4.1670 0.0001*** Family Income 2001, in thousands 0.0033 0.0001*** 0.0033 0.0001*** Low Birthweight (<5.5 pounds) -0.1695 0.0119* . . Cigarettes per Day -0.0118 0.0001*** -0.0119 0.0001*** Alcoholic Beverages per Day 0.2544 0.0001*** 0.2572 0.0001*** Alcoholic Beverages per Day Squared -0.0540 0.0013** -0.0551 0.0010*** Insured in Past Year 0.3516 0.0001*** 0.3569 0.0001*** Female -0.1010 0.0053** -0.1065 0.0033** Age of Individual -0.0267 0.0001*** -0.0267 0.0001*** Adjusted R2 0.1827 0.1818
Source: Author's analysis of data from 3,056 individuals in the 2003 Panel Study of Income Dynamics. ***Significant at the .001 level, **Significant at the .01 level, *Significant at the .05 level
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DISCUSSION
All variables in all four regressions were significant, and the direction and
magnitude of the relationships were as expected. However, the coefficient on income
did not change in either regression with the addition of the birth weight variable, as was
expected. In fact, the estimates for income were exactly the same to four decimal points
in each pair of regressions. This finding sheds some doubt on the fetal origins
hypothesis, which suggests that some of the difference in health outcomes by income
can be explained by fetal development.
However, there are possible measurement problems that could mask a
relationship between birth weight, adult health, and adult income. One main problem is
the limited ability to measure fetal environment. Birth weight is measured as a binary
variable, and the information is gathered by asking adults if they know what their birth
weight was. This eliminates an amount of valuable information that could have been
collected for birth weight. In addition, as Barker (1997) points out, birth weight is not a
perfect proxy for fetal health and development. Some babies may be small but well
proportioned, suggesting even and unstressed fetal development, whereas others may
weigh more but have a disproportionately large head, suggesting poor fetal development
towards the end of gestation. Other measurements such as body proportion ratios may
have been more helpful in measuring fetal development. More direct measures of fetal
environment could be obtained from the mother—maternal behaviors such as smoking
and diet could prove particularly relevant. In addition, the average age of the sample,
roughly 42 years old, may be too young to identify the chronic illnesses that the fetal
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origins hypothesis predicts. Revisiting these same subjects in 30 years could provide
different results.
In addition to problems with the data, there may be shortcomings in the research
design. In particular, wealth was assumed to affect health, and the effect of health on
wealth was assumed to be negligible. Although the effects are difficult to disentangle,
most researchers believe that the dominant direction of causality runs from wealth to
health (Smith 1999). However, this may not be the case—people in poor health may
work less and therefore earn less, and if this trend is substantial enough it would bias
the results. Further research in this area is necessary to more definitively rule out the
role of the fetal origins hypothesis in explaining the health-wealth gradient.
While this study did not confirm the effect of fetal development on the adult
health-wealth gradient, it did lend support to the fetal origins hypothesis generally,
which suggests that adult health is affected by fetal development. These findings
emphasize the need for comprehensive prenatal care as a measure to improve the
lifelong health of an individual. While improving prenatal care may not prove to be a
viable solution to decreasing the health-wealth gradient, it does offer promise for
improving health outcomes in general across the population.
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