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Disparities in Women's Health Across a Generation: A Mother–Daughter Comparison


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Background: The U.S. Centers for Disease Control and Prevention has set national goals to eliminate health disparities by race, sex, and socioeconomic status. Progress in meeting these goals has been mixed. This paper provides a different view on the evolving health of U.S. women by examining a sample of daughters and their mothers. Methods: The aim was to determine if the health risk profiles of daughters (born 1975-1992) were different from their mothers (born 1957-1964) measured when both were between the ages of 17 and 24 years. The U.S.-based National Longitudinal Survey of Youth 1979 and associated Children and Young Adult Surveys were used. The sample was 2411 non-Hispanic white and African American girls born to 1701 mothers. Outcomes were height, weight, body mass index (BMI), age of menarche, and self-reported health. Results: In both races, daughters were taller but entered adulthood at greater risk for the development of chronic illness than their mothers. Racial differences were greater in the daughters' generation than in the mothers'. Whites in both generations experienced educational differences in health based upon the mother's educational level, with fewer years of maternal education associated with poorer health. African Americans of both generations experienced differences by maternal education in self-reported health. However, when African American daughters were compared with their mothers, daughters born to college educated women gained more weight and had higher BMI and earlier menarche than did daughters born to high school dropouts. Conclusion: Health deterioration across generations in both races suggests that much work is needed to meet Healthy People 2020 goals of health equity.
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Disparities in Women’s Health Across a Generation:
A Mother–Daughter Comparison
Pamela J. Salsberry, RN, PhD, FAAN,
Patricia B. Reagan, PhD,
and Muriel Z. Fang, PhD
Background: The U.S. Centers for Disease Control and Prevention has set national goals to eliminate health dis-
parities by race, sex, and socioeconomic status. Progress in meeting these goals has been mixed. This paper provides
a different view on the evolving health of U.S. women by examining a sample of daughters and their mothers.
Methods: The aim was to determine if the health risk profiles of daughters (born 1975–1992) were different from
their mothers (born 1957–1964) measured when both were between the ages of 17 and 24 years. The U.S.-based
National Longitudinal Survey of Youth 1979 and associated Children and Young Adult Surveys were used. The
sample was 2411 non-Hispanic white and African American girls born to 1701 mothers. Outcomes were height,
weight, body mass index (BMI), age of menarche, and self-reported health.
Results: In both races, daughters were taller but entered adulthood at greater risk for the development of chronic
illness than their mothers. Racial differences were greater in the daughters’ generation than in the mothers’.
Whites in both generations experienced educational differences in health based upon the mother’s educational
level, with fewer years of maternal education associated with poorer health. African Americans of both gen-
erations experienced differences by maternal education in self-reported health. However, when African
American daughters were compared with their mothers, daughters born to college educated women gained
more weight and had higher BMI and earlier menarche than did daughters born to high school dropouts.
Conclusion: Health deterioration across generations in both races suggests that much work is needed to meet
Healthy People 2020 goals of health equity.
The U.S. Centers for Disease Control and Prevention
(CDC) has set national goals to eliminate health disparities
by race, sex, and socioeconomic status, as well to improve
health for all in Healthy People 2020.
This paper provides
evidence regarding progress toward the attainment of these
goals for African American and white women by examining
health risk measures—height, weight, body mass index (BMI),
obesity, overweight, and self-reported health—in a sample of
mothers and daughters. The existing literature, based on cross-
sectional population surveys, has shown that progress toward
meeting these goals has been mixed. The Healthy People 2010
Final Review noted improvements in life expectancy for all
groups, with women gaining an additional year of life over the
although recent work suggests that white women
with the least education (less than a high school degree) are
experiencing a decline in life expectancy.
Race differences in
life expectancy persist.
Furthermore, African American wo-
men experience greater disease burden than their white coun-
as do women with low educational attainment.
Increasing rates of obesity are thought to be a major con-
tributor to these trends.
Obesity rates have risen significantly
over the last three decades in all women. While African
American women weigh more than whites do on average, part
of the BMI difference is explained by height differences.
In studies using birth cohorts from the mid-1940s through
2002, African American females were found to grow faster
throughout childhood and reach their adult height at a youn-
ger age, resulting in a shorter final height.
Recent evidence also
suggests that the stature of African-American females may be
An additional noteworthy trend, thought to be
associated with rising obesity rates, is the declining age of
menarche in all race/ethnic groups.
However, the decline in
age of menarche has been greater in African American girls
than in whites.
Age of menarche is a critical piece of infor-
mation about a woman’s health: the lower the age, the higher
risk of breast cancer, cardiovascular disease, and depression.
Department of Nursing, and
Center for Human Resource Research, Ohio State University, Columbus, Ohio.
Department of Economics, University of Akron, Akron, Ohio.
Volume 22, Number 7, 2013
ªMary Ann Liebert, Inc.
DOI: 10.1089/jwh.2012.4143
These findings present a mixed picture with respect to trends
in African American and white women’s overall health. Al-
though life expectancy has increased, except for the most
disadvantaged white women, there is some evidence that
chronic disease prevalence has increased, especially for those
conditions associated with obesity.
It is unclear from these cross-sectional findings how quickly
the health of African American and white women may be
changing when controlling for genetic and contextual factors.
Furthermore, it is not clear if these patterns are similar across
educational groups within African American and white sub-
groups. This study addresses these questions by comparing
health risk profiles of a cohort of non-Hispanic white and
African American mothers (born 1957–1964) and daughters
(born 1975–1992), age-matched to the same point in the life
cycle when both were young adults (aged 17– 24 years). Health
risk profiles are examined using measures that are highly cor-
related with women’s long-term health. Measures include height,
weight,BMI,obesity,overweight, age of menarche, and self-
reported health status (SRH). The early adult life stage was
chosen because this period of transition into adulthood is seen as
a benchmark for later adult health. The overall aim of this study is
to determine if the health risk profiles of daughters are different
than their mothers and to understand if and how daughter–
mother dyad differences may vary by race and maternal edu-
cation. We hypothesize that in both races the daughter’s health
by race and education will be similar. The study’s strength is that
by comparing biological daughters with their mothers, genetic
and contextual factors are partially controlled for in ways not
possible with large cross-sectional surveys. This study provides
additional evidence to assess progress toward meeting the goals
of improving health and eliminating disparities by race and ed-
The study used data from the U.S.-based National Long-
itudinal Survey of Youth 1979 (NLSY79) and the Children and
Young Adult Surveys of the NLSY79. The NLSY79 enrolled a
nationally representative sample of young people living in
the United States in December 1978, born between 1957 and
1964. Extensive data on these respondents (the mothers in this
study) have been collected annually through 1994 and bien-
nially thereafter. Data on biological children born to these
mothers have been collected biennially beginning 1986.
The primary sample inclusion criterion for this study was a
daughter born to an African American or non-Hispanic white
woman in the NLSY79 between the years 1975 and 1992. The
eligible sample consisted of 2925 girls (white n=1619; African-
American n=1306) born to 2035 mothers (white n=1177; Af-
rican American n=858). There was no missing data on mater-
nal education and race. The study sample required mothers
and daughters to have observations on height and weight at
17–24 yearsof age. This resulted in a studysample of 2411 girls
(82% of the eligible sample) with 1311 white girls (81% of eli-
gible) and 1100 African American girls (84% of eligible) born to
956 white and 745 African American mothers. The eligible and
study sample were compared on race-specific distributions of
maternal education and no differences were found. The sample
sizes for analyses of age of menarche were smaller due to
missing data: for whites, sample size for daughters was 1300
and for mothers it was 946; for African Americans, sample size
for daughters was 1088 and for mothers it was 740. Sample size
for daughter–mother dyad differences in age of menarche was
1290 for whites and 1082 for African Americans. Analysis of
maternal self-reported health was based on 911 white mothers
(95% of study sample) and 710 African American mothers (95%
of study sample). Sample size for daughters’ self-reported
health at 17–24 years of age was the same as the study sample.
Race. Maternal race was recorded during the initial inter-
view. This value has been cross-checked against a question asked
in subsequent surveys allowing women to self-identify race/
ethnicity. Of women who were classified as African American
during the initial survey, 99% self-identified as African Ameri-
can. Of women in the NLSY who were classified as non-African
American, non-Hispanic, and non-Asian during the initial in-
terview, 97% self-identified as white. Daughters were assigned
the race of their mothers but this too has been cross-checked in
subsequent surveys by asking daughters to self-identify race/
ethnicity. For those who answered questions (approximately
60% of the daughters) about ethnicity and race, over 96% of girls
Height and weight. For mothers and daughters, height in
inches and weight in pounds were self-reported and taken
from the first interview in which the respondent was between
17 and 24 years old.
Age of menarche. Age of menarche for mothers was col-
lected in 1984 and 1985 when these women were between 19 to
28 years of age. About 75% of the mothers reported age in years
and 25% reported age in months. Menarche age reported in
months was converted to years. Mothers of daughters, who were
at least 8 years of age and less than 14 years of age, were asked
whether or not the daughter had reached menarche. If so, the
mother reported the year and month of menarche. Girls 14 and
older were asked these questions directly. This information was
combined with the girls’ year and month of birth to determine
their age of menarche in months and then converted to years.
Maternal education. Highest education level attained after
the age of 25 years.
Self-rated health status. Health status of mothers was
collected in the interview following their 40th birthday. In the
daughters, health status was taken from the first interview in
which the girl was aged 17 to 24 years. Both questions asked
the respondents to rate their health as excellent, very good,
good, fair, or poor. This was collapsed to an indicator taking a
value of 1 for fair or poor health and a value of zero for ex-
cellent, very good, or good health.
Body mass index. Relationship of height to weight; for-
mula is weight in pounds times 703 divided by height in in-
ches squared.
Overweight and obesity. Obesity is defined as a BMI >30;
overweight is defined as a BMI >25 and <30.
Dyad differences. Height, weight, BMI, and menarche
age differences were calculated as daughter value minus
mother value.
The analysis began by estimating means and 95% confi-
dence interval for height, weight, BMI, and age of menarche
for mothers and daughters separately by race. Since some
mothers had multiple daughters in the sample, the means and
confidence intervals for daughters were derived from mixed
model regression that accounted for clustering within fami-
lies. To control for this correlation within families, we ran
mixed models clustering on mother identification code using
the xtmixed command in Stata.
Frequencies were reported
for categorical variables: overweight, obese, maternal educa-
tion categories, and SRH. The analysis proceeded with cross-
sectional estimates of health gradients by maternal education
for each generation by race. These estimates were used to test
for mean differences in health outcomes by maternal educa-
tion. The gradients for mothers were estimated by ordinary
least squares for continuous outcomes with indicators for each
education category, while the gradients for daughters were
estimated from mixed model regressions. Since SRH was di-
chotomous, odds ratios were estimated from logistic regres-
sion for mothers and random effects logistic regression for
daughters. To assess whether there was a race difference in
how daughters fare relative to their mothers, dyad differences
in height, weight, BMI, and age of menarche were estimated
using mixed model regressions. Pvalues for a two-sided test
of the null hypothesis that no race differences in the dyads
were reported. We also analyzed the race-specific daughter–
mother dyad differences by maternal educational attainment.
Since the literature suggests that declines in health have been
greater for white women with low education,
we conducted
one-sided tests ( pvalues reported) of the null hypothesis that
the absolute value of the dyad difference in the maternal
dropout group was greater than or equal to the absolute value
of the dyad differences in the maternal college graduate
Table 1 displays the sample characteristics by race and
generation. Among both whites and African Americans,
daughters had earlier menarche, weighed more, and had
higher BMI than their mothers, suggesting deterioration in
average health across the generations. The only evidence of an
improvement in health was found in height, with daughters
in both races being slightly taller than their mothers.
Table 2 reports cross-sectional estimates of generation 1
education gradients in health by race. There was no gradient
in height, weight, or age of menarche for either white or Af-
rican American women in generation 1. There were gradients
in BMI and SRH at age 40. The BMIs of whites with some
college or a college degree were lower than BMIs of dropouts,
and the BMIs of African Americans with a college degree were
lower than for dropouts. White mothers with some college or
a college degree were less likely to rate their health at age 40 as
poor/fair compared with those who dropped out of high
school. African American mothers with a GED or greater
education were less likely to report health at 40 as poor/fair
than were high school dropouts. On balance, the health gra-
dients by educational attainment are similar for white and
African American mothers, with threshold effects in BMI and
SRH at 40 for both groups.
Table 3 reports mixed model estimates of generation 2
health gradients as a function of maternal education. Like
their mothers, there was no gradient in height for either race.
However, daughters of white mothers who had dropped out
of high school were heavier than those whose mothers had
graduated from college, with a difference of about 14.5
pounds. The BMI of daughters of white dropouts was over 2
BMI points greater than daughters of white college graduates.
Daughters born to white high school dropouts had a lower
age of menarche than daughters born to white college edu-
cated women. For whites, SRH at age 17–24 was more likely to
be reported as fair/poor for daughters of dropouts than those
born to mothers with at least some college. Thus, the gradients
for white daughters in BMI and SRH showed a similar pattern
to their mothers. There was no gradient in weight, BMI, or age
of menarche for African American daughters. The threshold
effect of education for BMI in mothers, that is, a higher BMI for
dropouts than college graduates, disappeared in the daugh-
ters. However, there was a gradient in SRH at age 17–24 years
for African Americans. Daughters of African American
dropouts were more likely to report fair/poor health than
were daughters of any of the other maternal education
Table 4 reports mixed model estimates of daughter–mother
dyad differences in health by race for the continuous vari-
ables. Chi-square tests of the null hypothesis of no race dif-
ference indicated that height dyad differences for whites and
African Americans were not significantly different from each
other. However, there were statistically significant race dif-
ferences in the daughter–mother dyad measures in weight,
BMI, and age of menarche. The daughter–mother differences
in weight and BMI were about 50% greater for African
Americans compared with whites. African American girls
were heavier than their mothers by 21.35 pounds, whereas
white girls were 14.14 pounds heavier than their mothers.
African-American were 3.22 BMI points heavier than their
mothers compared with white girls, who were 2.07 BMI
points heavier. African American girls on average reached
menarche earlier than their mothers by 0.81 years (about 9.72
months earlier), which was significantly earlier than white
girls, who on average reached menarche earlier than their
mothers by 0.29 years (about 3.5 months).
Table 5 reports mixed model estimates of race-specific ed-
ucational gradients in the daughter–mother dyad differences
in health. In whites, the daughters of mothers who dropped
out of high school were over 16 pounds heavier than their
mothers, with a BMI difference of more than 2 points, and had
an earlier age of menarche by approximately 4 months. The
daughters of college educated white women were almost 11
pounds heavier than their mothers, with a BMI difference of
1.65 and an earlier age of menarche by approximately 2.5
months. One-tailed tests did not reject the null hypothesis that
generational declines in health among white maternal drop-
outs were at least as great as those observed among white
college graduates. Among African Americans, the daughters
of mothers who dropped out of high school were over 14
pounds heavier than their mothers, with a BMI difference of
more than 2 points, and had an earlier age of menarche by
approximately 3 months. However, the daughters of college
educated African American women were almost 25 pounds
heavier than their mothers, with a BMI difference of close to 4
points, and had an earlier age of menarche by approximately 9
months. For African Americans, one-sided tests rejected the
null hypothesis that generational declines in age of menarche
and increases in weight were greater for dropouts than for the
college educated. This suggests that the daughters of college
educated African American women were doing worse rela-
tive to their mothers than daughters born to African American
mothers who dropped out of high school.
Table 1. Sample Characteristics (Mean, Confidence Interval, Number, Percentage) by Race and Generation
Whites African Americans
Gen 1: Mothers Gen 2: Daughters Gen 1: Mothers Gen 2: Daughters
Mean age of menarche in years
[95% CI]
[12.73, 12.93]
[12.45, 12.59]
[12.68, 12.94]
[11.92, 12.11]
Mean height in inches as young adult
[95% CI]
[64.54, 64.85]
[64.98, 65.32]
[64.09, 64.49]
[64.53, 64.91]
Mean weight in pounds as young adult
[95% CI]
[128.93, 131.82]
[142.56, 146.43]
[135.42, 139.39]
[157.22, 162.40]
Mean BMI as young adult
[95% CI]
[21.63, 22.14]
[23.62, 24.22]
[23.09, 23.67]
[26.40, 27.23]
Number (%) overweight as young adult 110 (12%) 247 (19%) 130 (17%) 288 (26%)
Number (%) obese as young adult 31 (3%) 151 (12%) 67 (9%) 288 (26%)
Number (%) maternal education categories
Dropouts 67 (7%) 90 (7%) 75 (10%) 130 (12%)
GED 89 (9%) 129 (10%) 76 (10%) 147 (13%)
HS Diploma 356 (37%) 493 (38%) 258 (35%) 353 (32%)
Some college 251 (26%) 334 (25%) 238 (32%) 344 (31%)
College graduate 193 (20%) 265 (20%) 98 (13%) 126 (12%)
Number (%) health at 40: rated as fair/poor
109 (12%) 149 (20%)
Number (%) YA health: rated as fair/poor
118 (9%) 154 (14%)
Sample size 956 1311 745 1100
Means and confidence intervals based on robust standard errors that account for clustering in generation 2.
Sample size for whites, Gen 1, n=946; whites, Gen 2, n=1300; AA, Gen 1, n=740; AA, Gen 2, n=1088.
Sample size for Gen 1 whites, n=911; sample size for Gen 1 AA, n=710.
BMI, body mass index; CI, confidence interval; Gen, generation; HS, high school; GED, General Education Test; YA, young adult.
Table 2. Estimates of Health Gradients by Education Categories for Generation 1by Race
Generation 1: Whites
Height (inches)
Weight (pounds)
Age at menarche (years)
Health at 40 fair/poor
Dropout 64.17
[63.57, 64.78]
[129.14, 140.24]
[22.14, 23.87]
[12.29, 13.06]
GED 64.14
[63.63, 64.66]
[126.96, 136.43]
[21.72, 23.19]
[12.18, 12.82]
[0.38, 2.45]
HS diploma 64.72
[64.46, 64.98]
[128.87, 133.58]
[21.64, 22.37]
[12.81, 13.13]
[0.27, 1.34]
Some college 64.75
[64.44, 65.06]
[126.26, 131.94]
[21.19, 22.08]
[12.55, 12.94]
[0.15, 0.90]
College grad 65.01
[64.66, 65.35]
[125.19, 131.59]
[20.85, 21.85]
[12.67, 13.11]
[0.15, 0.94]
No. of mothers 956 956 956 946 911
Generation 1: African Americans
Dropout 64.01
[63.37, 64.64]
[136.88, 149.35]
[23.50, 25.53]
[11.98, 12.78]
GED 64.23
[63.59, 64.86]
[130.67, 143.14]
[22.38, 24.41]
[12.41, 13.21]
[0.18, 0.97]
HS diploma 63.84
[63.50, 64.18]
[134.19, 140.90]
[23.24, 24.34]
[12.68, 13.11]
[0.16, 0.61]
Some college 64.77
[64.41, 65.12]
[134.31, 141.31]
[22.48, 23.62]
[12.68, 13.13]
[0.14, 0.54]
College graduate 64.61
[64.05, 65.17]
[126.36, 137.44]
[21.30, 23.11]
[12.32, 13.03]
[0.08, 0.54]
No. of mothers 745 745 745 740 710
Ordinary least square coefficients (group means) and 95% confidence intervals (in brackets).
Odds ratios and 95% confidence intervals (in brackets) from a logistic regression.
Comparisons across the generations suggest that the
daughters’ overall health risk profiles, assessed when both
were young adults, were generally worse than those of their
mothers. The one exception was that both white and African
American daughters were slightly taller than their mothers.
There was no evidence that the height of African American
women in this sample was decreasing, as Komlos
using the NHANES. Daughters arrived at adulthood heavier
than their mothers did, with a significant portion at weights
that place them in high risk categories associated with chronic
health conditions. This is not surprising, given that the rates of
overweight and obesity have increased significantly over the
last 30 years. In our sample, 15% of white and 31% of African
American mothers were either overweight or obese; 26% of
white and 52% of African American daughters were either
overweight or obese. What is noteworthy here is the magni-
tude of the change within a generation and within families,
which allows for some control for genetic predisposition and
an obesogenic environment. At comparable ages, daughters
are heavier than their mothers among both African Americans
and whites, and no education group was immune from this
change. The increases in BMI in generation 2 have possible
long-term effects, as these young women were just entering
their child-bearing years. Research over the past decade has
established significant risk associated with maternal obesity
for both the mother and fetus.
In this study, 12% of the
whites and 26% of the African Americans daughters were
obese, thus placing their own and their future children’s
health at risk.
Daughters’ age of menarche was earlier than their moth-
ers’, again not surprising given the population trends. In the
1963–1979 <Please expand abbreviation NHESNHES, the age
of menarche for whites was 12.8 and for African American it
was 12.5; in the 1988–1994 NHANES 3, the age of menarche
for white females was 12.6 and for African American females
it was 12.1.
Our results were similar to these reports for
whites, though the age of menarche in African American
mothers was later at 12.85 years in our sample. In our sample,
the daughters’ average age of menarche was about 1 month
earlier than the population reports for each race. The decrease
in age of menarche across one generation in the daughter–
mother dyads was striking. For African American women
there was an approximately 9-month drop and for white
women, a 3-month drop. An earlier age of menarche may
Table 3. Estimates of Health Gradients by Maternal Education Categories for Generation 2by Race
Generation 2: Whites
Height (inches)
Weight (pounds)
Age at menarche (years)
Health at 17–24 fair/poor
Dropout 65.24
[64.62, 65.87]
[146.77, 160.98]
[24.23, 26.44]
[11.91, 12.46]
GED 64.48
[63.95, 65.01]
[141.16, 153.12]
[23.98, 25.84]
[12.11, 12.57]
[0.39, 2.00]
HS diploma 65.11
[64.84, 65.38]
[141.76, 147.84]
[23.55, 24.50]
[12.43, 12.66]
[0.24, 1.00]
Some college 65.34
[65.02, 65.67]
[141.17, 148.48]
[23.22, 24.36]
[12.41, 12.69]
[0.19, 0.90]
College graduate 65.26
[64.89, 65.62]
[135.07, 143.34]
[22.32, 23.61]
[12.47, 12.78]
[0.16, 0.80]
No. daughters
No. mothers
Generation 2: African-Americans
(inches) Weight
(pounds) BMI
Age Menarche years
Health at 17–24 fair/poor
Dropout 64.32
[63.76, 64.87]
[149.34, 164.69]
[25.46, 27.91]
[11.93, 12.46]
GED 64.38
[63.84, 64.91]
[152.62, 167.40]
[25.95, 28.30]
[11.63, 12.14]
[0.29, 0.95]
HS diploma 64.57
[64.25, 64.89]
[155.84, 164.66]
[26.34, 27.75]
[11.83, 12.14]
[0.23, 0.65]
Some college 65.14
[64.82, 65.47]
[156.11, 165.18]
[25.85, 27.30]
[11.89, 12.20]
[0.23, 0.66]
College graduate 64.87
[64.35, 65.40]
[149.61, 164.23]
[25.05, 27.39]
[11.72, 12.22]
[0.11, 0.52]
No. daughters
No. mothers
95% confidence intervals (shown in brackets) based on robust standard errors accounting for clustering within families.
Mixed model coefficients and 95% confidence intervals.
Odds ratios and 95% confidence intervals from random effects logistic regression.
Note these are the number of mothers associated with the daughters who had an age of menarche in the data set; no requirement that
mother also have age of menarche.
place these women at increased risk for a range of problems, a
change that portends possible increased chronic disease in
this cohort of daughters.
There were education gradients in health measured by BMI,
age of menarche, and SRH among both generations of whites.
When the daughter–mother dyad health differences were ex-
amined by maternal education, there was no evidence of
change in the magnitude or direction of the education gradi-
ents. Thus, the educational gradient for whites, measured
using mothers’ education, appears to be stable across gener-
ations. This is in contrast to what was happening among Af-
rican Americans. The education gradient in BMI found in
mothers did not exist among daughters, although both gen-
erations showed an education gradient in SRH. A flattening of
the education gradient might be viewed in a positive light as a
reduction over time in education disparities in health among
African American women. However, the reason for the flat-
tening across African American generations in the education
gradient for health was revealed by evidence from dyad dif-
ferences, which showed that increases in weight and declines
in age of menarche were greater among the college educated
than among dropouts. This is not the route to reducing educa-
tion disparities in health envisioned by Healthy People 2020.
Despite significant efforts over the past 20 years to reduce
health differences by race and economic status progress has
been slow. The Healthy People 2010 Final Review
noted that
there was no change in the racial health disparities in 69% of
the objectives, and there was no change in health disparity by
education level for 81% of these objectives. Our results are in
line with these findings and suggest that in addition to the
within race generation findings, the racial differences in
generation 2 may be widening. For example, in generation 1,
the African American mothers were approximately 7 pounds
heavier than their white counterparts, and there was no dif-
ference in the age of menarche. In generation 2, the African
American daughters were approximately 15 pounds heavier
than their white counterparts, and there was a 6-month dif-
ference in the daughters’ age of menarche. Mothers of dif-
ferent education attainment differ in SRH; this education
gradient extended to their daughters’ SRH. In generation 1
Table 4. Estimates of Daughter-Mother Dyad Differences in Mean Health Outcomes by Race
Whites African Americans pvalue of Chi-square test of no race difference
Height difference (inches) 0.42
[0.25, 0.58]
[0.28, 0.67]
Weight difference (pounds) 14.14
[12.29, 15.99]
[18.92, 23.78]
BMI difference 2.07
[1.77, 2.36]
[2.82, 3.63]
Menarche difference
(years) -0.29
[-0.39, -0.18]
[-0.95, -0.67]
No. of daughters
No. of mothers
Confidence intervals (shown in brackets) based on standard errors accounting for clustering within families.
Based on 1290 white dyads and 1082 African American dyads.
Table 5. Estimates of Daughter–Mother Dyad Differences in Mean Health Outcomes
by Race and Education Category
Whites African Americans
(pounds) BMI
Age at menarch
(pounds) BMI
Age at menarch
Dropout 16.88
[9.82, 23.95]
[1.00, 3.23]
[-0.76, 0.08]
[7.57, 22.12]
[1.10, 3.52]
[-0.69, 0.17]
GED 16.52
[10.57, 22.46]
[1.71, 3.58]
[-0.48, 0.22]
[15.06, 29.02]
[2.35, 4.68]
[-1.50, -0.67]
Diploma 13.49
[10.47, 16.51]
[1.55, 2.50]
[-0.59, -0.24]
[16.96, 25.36]
[2.33, 3.73]
[-1.10, -0.63]
Some college 16.01
[12.38, 19.64]
[1.65, 2.80]
[-0.42, 0.01]
[17.88, 26.50]
[2.64, 4.07]
[-1.10, -0.61]
College graduate 10.90
[6.79, 15.00]
[1.00, 2.29]
[-0.45, 0.03]
[18.00, 31.93]
[2.78, 5.09]
[-1.18, -0.41]
pvalue 1-tailed test jdropj
0.075 0.237 0.300 0.024 0.058 0.035
n1311 1311 1290 1100 1100 1082
Confidence intervals (in brackets) based on random effects regression with clustering on families.
Mixed model coefficients (group mean dyad differences) and 95% confidence intervals.
1-tailed test that absolute value of dyad difference in maternal dropout group absolute value of dyad difference in maternal college
educated group.
n, no. of dyads.
there is a very wide gap in SRH at age 40 between the most
advantaged women, college educated white women, and
most disadvantaged women, African American dropouts.
Only 5% of college educated white women reported fair/poor
health, while 40% of the African American dropouts reported
fair/poor health. In generation 2 there was a gap in SRH at
age 17–24, with 6% of whites born to college graduates re-
porting fair/poor health compared to 26% of African Amer-
icans born to dropouts. When one considers the goal in
Healthy People 2020 is to raise everyone to the healthiest
group, the gap is wide and begins at an early age. This sug-
gests that considerable work will need to be done to achieve
the Healthy People 2020 goal of health equity.
There are limitations to this study. The mothers were a
representative sample of their birth cohorts and the daughters
were a representative sample of the daughters born to those
women during the study years, but not a representative
sample of their birth cohort. The health measures were
self-reported, not clinician evaluated. The accuracy of self-
reported heights and weights in other studies have been
shown to depend upon sample characteristics, with differ-
ences noted by age and gender,
but not by race.
ages were less accurate in reporting their heights and
weights, and women generally underreport weight and
overreport height, thus leading to a lower BMI.
the study criteria limited the sample to only females, within
the ages of 17 and 24, this likely limits the bias introduced by
self-report, though it is likely that the overall BMI values
may be on the low side. Menarche data were collected
without the long time lags that occur in many studies. In the
daughters, the question was asked of the mother or of the
young women every 2 years until menarche had occurred. In
the mother’s generation the question was asked while she
was still a young woman, thus reducing the time between
menarche and the reporting.
This measure of SRH status has been used widely as an
indicator of an individual’s overall perception of their own
health. For adults, there is a large body of literature that has
shown SRH to be a robust predictor of adult health
and in
prospective studies SRH was a significant predictor for later
morbidity, mortality, and the use of health services.
Based upon data from the Behavioral Risk Factor Surveillance
Survey (BRFSS), the CDC found that 11.34% of whites and
18.4% of African American females aged 35 to 44 reported
fair/poor health.
Our results were similar with 11% of
whites and 21% of African Americans mothers reporting fair/
poor health at age 40 years. In adolescents, the literature also
supports the use of SRH as a general health assessment
measure. The SRH question has been shown to correspond
closely with objective clinical assessments by health profes-
and SRH has been shown to be a stable measure
from adolescent to young adulthood.
Using data from the
BRFSS, the CDC reported fair/poor health in the 18- to 24-
year-old group to be 8.7%. We found a similar overall value
for whites in this study, 8% reported fair/poor health, with a
greater percentage of African-Americans reporting fair/poor
health (14%).
There are four key results: (1) In both races, daughters were
taller but entered adulthood at greater risk for the develop-
ment of chronic illness than their mothers. (2) Racial differ-
ences were greater in the daughters’ generation than in the
mothers’ generation. (3) Whites in both generations experi-
enced educational differences in health based upon the
mother’s educational level, with fewer years of maternal ed-
ucation associated with poorer health. (4) African Americans
of both generations experienced differences by maternal ed-
ucation in self-reported health; however, when African
American daughters were compared with their mothers,
daughters born to college educated women gained more
weight and had earlier menarche than daughters born to high
school dropouts. This led to a generational flattening in the
education gradient in health among African American wo-
men, but it is not the desired path to health equity laid out in
Healthy People 2020. These results, in combination, indicate
that there is much work to do to improve the health and
reduce disparities between African American and white
This study was supported by an award from the National
Institute of Nursing Research, National Institutes of Health
(R01 NR009384). The content is solely the responsibility of the
authors and does not necessarily represent the official views
of the National Institute of Nursing Research, of the National
Institutes of Health.
Disclosure Statement
No competing financial interests exist.
1. National Center for Health Statistics. Healthy People 2020.
Hyattsville, MD: National Center for Health Statistics,
2. National Center for Health Statistics. Healthy People 2010
Final Review. Hyattsville, MD: National Center for Health
Statistics, 2011.
3. Olshansky SJ, Antonucci T, Berkman L, et al. Differences in
life expectancy due to race and educational differences are
widening, and many may not catch up. Health Aff( Millwood).
4. National Center for Health Statistics. Health, United States,
2011: With special feature on socioeconomic status and
health. Hyattsville, MD: National Center for Health Statis-
tics, 2012.
5. Ogden CL, Carroll MD, Flegal KM. Epidemiologic trends in
overweight and obesity. Endocrinol Metab Clin North Am
2003;32:741–760, vii.
6. Patterson RE, Frank LL, Kristal AR, White E. A compre-
hensive examination of health conditions associated with
obesity in older adults. Am J Prev Med 2004;27:385–390.
7. Komlos J, Breitfelder A. Height of U.S.-born non-Hispanic
children and adolescents ages 2–19, born 1942–2002 in the
NHANES samples. Am J Hum Biol 2008;20:66–71.
8. Komlos J. The recent decline in the height of African-
American women. Econ Hum Biol 2010;8:58–66.
9. Biro FM, Khoury P, Morrison JA. Influence of obesity on
timing of puberty. Int J Androl. 2006;29:272–277, 286–290.
10. Salsberry PJ, Reagan PB, Pajer K. Growth differences by age
of menarche in African American and White girls. Nurs Res
2009 2009;58:382–390.
11. Lee JM, Appugliese D, Kaciroti N, Corwyn RF, Bradley RH,
Lumeng JC. Weight status in young girls and the onset of
puberty. Pediatrics 2007;119:e624–e630.
12. Huang B, Biro FM, Dorn LD. Determination of relative
timing of pubertal maturation through ordinal logistic
modeling: evaluation of growth and timing parameters.
J Adolesc Health 2009;45:383–388.
13. Coleman L, Coleman J. The measurement of puberty: A re-
view. J Adolesc 2002;25:535–550.
14. De Stavola BL, dos Santos Silva I, McCormack V, Hardy RJ,
Kuh DJ, Wadsworth ME. Childhood growth and breast
cancer. Am J Epidemiol 2004;159:671–682.
15. Remsberg KE, Demerath EW, Schubert CM, Chumlea WC,
Sun SS, Siervogel RM. Early menarche and the development
of cardiovascular disease risk factors in adolescent girls: the
Fels Longitudinal Study. J Clin Endocrinol Metab 2005;
16. Kaltiala-Heino R, Kosunen E, Rimpela M. Pubertal timing,
sexual behaviour and self-reported depression in middle
adolescence. J Adolesc 2003;26:531–545.
17. Corp S. Stata statistical software: Release 12. College Station,
TX: StataCorp LP, 2011.
18. Kristensen J, Vestergaard M, Wisborg K, Kesmodel U, Se-
cher NJ. Pre-pregnancy weight and the risk of stillbirth and
neonatal death. BJOG 2005;112:403–408.
19. Shaikh H, Robinson S, Teoh TG. Management of maternal
obesity prior to and during pregnancy. Semin Fetal Neonatal
Med 2010;15:77–82.
20. Anderson SE, Dallal GE, Must A. Relative weight and race
influence average age at menarche: results from two na-
tionally representative surveys of U.S. girls studied 25 years
apart. Pediatrics 2003;111:844–850.
21. Herman-Giddens ME, Slora EJ, Wasserman RC, et al. Sec-
ondary sexual characteristics and menses in young girls seen
in office practice: A study from the Pediatric Research in
Office Settings network. Pediatrics 1997;99:505–512.
22. Gorber SC, Tremblay M, Moher D, Gorber B. A comparison
of direct vs. self-report measures for assessing height, weight
and body mass index: A systematic review. Obes Rev
23. Sherry B, Jefferds ME, Grummer-Strawn LM. Accuracy of
adolescent self-report of height and weight in assessing
overweight status: a literature review. Arch Pediatr Adolesc
Med 2007;161:1154–1161.
24. Field AE, Aneja P, Rosner B. The validity of self-reported
weight change among adolescents and young adults. Obe-
sity (Silver Spring) 2007;15:2357–2364.
25. Krul AJ, Daanen HA, Choi H. Self-reported and measured
weight, height and body mass index (BMI) in Italy, the
Netherlands and North America. Eur J Public Health 2011;
26. Kuczmarski MF, Kuczmarski RJ, Najjar M. Effects of age on
validity of self-reported height, weight, and body mass in-
dex: findings from the Third National Health and Nutrition
Examination Survey, 1988–1994. J Am Diet Assoc 2001;101:
28–34; 35–26.
27. Bailis DS, Segall A, Chipperfield JG. Two views of self-rated
general health status. Soc Sci Med 2003;56:203–217.
28. Breidablik HJ, Meland E, Lydersen S. Self-rated health during
adolescence: stability and predictors of change (Young-
HUNT study, Norway). Eur J Public Health 2009;19:73–78.
29. Idler EL, Benyamini Y. Self-rated health and mortality: a
review of twenty-seven community studies. J Health Soc
Behav 1997;38:21–37.
30. Manor O, Matthews S, Power C. Self-rated health and lim-
iting longstanding illness: Inter-relationships with morbidity
in early adulthood. Int J Epidemiol 2001;30:600–607.
31. Manderbacka K, Lahelma E, Martikainen P. Examining the
continuity of self-rated health. Int J Epidemiol 1998;27:208–
32. Lundberg O, Manderbacka K. Assessing reliability of a mea-
sure of self-rated health. Scand J Soc Med 1996;24:218–224.
33. DeSalvo KB, Bloser N, Reynolds K, He J, Muntner P. Mor-
tality prediction with a single general self-rated health ques-
tion. A meta-analysis. J Gen Intern Med 2006;21:267–275.
34. Jylha M. What is self-rated health and why does it predict
mortality? Towards a unified conceptual model. Soc Sci Med
35. Centers for Disease Control and Prevention. Nationwide
trends in self-rated health by age, gender, and race, 2010.
Available at
36. Robert Wood Johnson Foundation. America’s health starts
with health children: How do states compare? Princeton, NJ:
Robert Wood Johnson Foundation, 2008.
37. Fosse NE, Haas SA. Validity and stability of self-reported
health among adolescents in a longitudinal, nationally rep-
resentative survey. Pediatrics 2009;123:e496–e501.
38. Boardman JD. Self-rated health among U.S. adolescents.
J Adolesc Health 2006;38:401–408.
Address correspondence to:
Pamela J. Salsberry, RN, PhD, FAAN
College of Nursing
The Ohio State University
1585 Neil Avenue
Columbus, OH 43210
... Despite well-documented gender differences in depression in the US [7], only a subset of existing studies examining the effects of early life SD on adulthood depression have assessed these associations among women, specifically [9,12,16,22,24,25,27,28]. In addition, while researchers have posited that the effects of education on health may change over the lifecourse [30], and have changed more broadly across birth cohorts [31], studies assessing such effects have to date primarily focused on adulthood educational attainment and physical health outcomes [32]. Taken together, there is a need to better understand the intergenerational effects of childhood SD on later life depression onset among women, over the lifecourse and across those born into different birth cohorts. ...
Full-text available
Understanding of the role of objective versus subjective childhood socioeconomic disadvantage (SD) in depression onset in adulthood among women, independent of later life SD, and across birth cohorts, is limited. We examined the association between objective (i.e., household education level) and subjective (i.e., rank of family income and report of not enough food to eat) SD during childhood and diagnosis of clinical depression after age 30 among 47,055 women in the Sister Study. We used Cox proportional hazard models adjusting for women’s race/ethnicity, childhood household composition, mother’s age at her birth adulthood educational attainment, and calendar year of birth. Analyses were repeated stratified by 10-year birth group. A total of 8036 (17.1%) women were diagnosed with clinical depression over a mean follow-up of 24.0 (± 9.9) years. Those reporting being poor (versus well-off) or not having enough food to eat in childhood had a 1.28 (95% confidence interval (CI) 1.13, 1.44) and 1.30 (95% CI 1.21, 1.41) times higher rate of depression diagnosis, respectively, with consistent associations observed across birth year groups. An inverse association between low household education level and incident depression was observed at baseline (i.e., age 30) becoming positive over time in the total sample but only among women born between 1935–1954 in analyses stratified by 10-year birth group. Our findings suggest that subjective SD in childhood is a largely consistent predictor of depression onset among women in adulthood whereas the effects of household education level in childhood may vary across women born into different birth cohorts, and for some, across the lifecourse.
... Second, we added a covariate that captures the respondent's mother's level of education, which reflects childhood socioeconomic conditions that may be associated with health outcomes. 28 Third, to control for assaults subsequent to sexual initiation, we added a covariate in the main model for later sexual assault. Findings were considered significant at an α level of .05. ...
Importance The #MeToo movement has highlighted how frequently women experience sexual violence. However, to date, no recent studies have assessed the prevalence of forced sex during girls' and women’s first sexual encounter or its health consequences. Objective To estimate the prevalence of forced sexual initiation among US women and its association with subsequent reproductive, gynecologic, and general health outcomes. Design, Setting, and Participants A cross-sectional analysis of the 2011-2017 National Survey of Family Growth was conducted, including a population-based sample of 13 310 US women. The study was conducted from September 2011 to September 2017. Exposures Self-reported forced vs voluntary first sexual intercourse. Main Outcomes and Measures Prevalence of forced sexual initiation, age of woman and partner/assailant at first sexual encounter, and odds ratios (ORs) (adjusted for sociodemographic characteristics) for having an unwanted first pregnancy or abortion, development of painful pelvic conditions, and other reproductive and general health measures. Results A total of 13 310 women between the ages of 18 and 44 years were included in the study. After survey weights were applied, 6.5% (95% CI, 5.9%-7.1%) of respondents reported experiencing forced sexual initiation, equivalent to 3 351 733 women in this age group nationwide. Age at forced sexual initiation averaged 15.6 (95% CI, 15.3-16.0) years vs 17.4 (95% CI, 17.3-17.5) years for voluntary sexual initiation (P < .001). The mean age of the partner/assailant at first sexual encounter was 6 years older for women with forced vs voluntary sexual initiation (27.0; 95% CI, 24.8-29.2 years vs 21.0; 95% CI, 20.6-21.3 years). Compared with women with voluntary sexual initiation, women with forced sexual initiation were more likely to experience an unwanted first pregnancy (30.1% vs 18.9%; adjusted OR [aOR], 1.9; 95% CI, 1.5-2.4) or an abortion (24.1% vs 17.3%; aOR, 1.5; 95% CI, 1.2-2.0), endometriosis (10.4% vs 6.5%; aOR, 1.6; 95% CI, 1.1-2.3), pelvic inflammatory disease (8.1% vs 3.4%; aOR, 2.2; 95% CI, 1.5-3.4), and problems with ovulation or menstruation (27.0% vs 17.1%; aOR, 1.8; 95% CI, 1.4-2.3). Survivors of forced sexual initiation more frequently reported illicit drug use (2.6% vs 0.7%; aOR, 3.6; 95% CI, 1.8-7.0), fair or poor health (15.5% vs 7.5%; aOR, 2.0; 95% CI, 1.5-2.7), and difficulty completing tasks owing to a physical or mental health condition (9.0% vs 3.2%; aOR, 2.8; 95% CI, 2.0-3.9). Conclusions and Relevance Forced sexual initiation in women appears to be common and associated with multiple adverse reproductive and general health outcomes. These findings highlight the possible need for public health measures and sociocultural changes to prevent sexual violence, particularly forced sexual initiation.
... The present study builds on this prior literature in two ways. First, our study includes an assessment of mother's SRH that allows us to directly assess whether race and SES disparities in one generation (mothers) are also evident in the next (offspring) (see also Salsberry et al., 2013). Second, we use prospectively collected information concerning emerging health, cognitive abilities and family socioeconomic circumstances at multiple points during the life course that either could not have been ascertained in a retrospective study or would have been subject to possible recall biases. ...
Extensive evidence leads us to expect that health disparities by race and socioeconomic status found in one generation might be reproduced in the next. To the extent that this occurs it is important to assess life course processes responsible for the reproduction. Prospective evidence concerning such life course processes is hard to come by as it requires long-term follow-up of individuals from childhood through adult life. We present data from the Child Health and Development Disparities study that provides evidence relevant to this issue with respect to self-rated health. Mothers and offspring recruited in California's Bay Area between 1959 and 1967 were assessed during pregnancy with follow-up exams of offspring along with in-person interviews with mothers (at offspring ages 5, 9–11, 15–17) and offspring (at ages 15–17, ∼50). Available data allow us to assess the importance of three potential life course pathways in the reproduction of inequalities in self-rated health – socioeconomic pathways, cognitive pathways and pathways involving emerging health itself. As expected we found that race and SES disparities in SRH are reproduced across generations. They are evident in mothers, not strong or significant in offspring at 15–17, but present once again in offspring at age ∼50. Concerning potential pathways, we found that indicators of child health were related to adult SRH and played some role in accounting for race but not SES disparities in adult SRH. Cognitive abilities were unrelated to adult SRH with childhood SES controlled. Childhood SES was associated with adult SRH independent of other childhood factors and is reduced to non-significance only when offspring college attainment is controlled. Race and SES disparities in self-reported health in one generation are re-expressed in the next with strongest support for SES pathways in this transmission.
The relationship between mothers’ educational attainment and their daughters’ fast food intake and the moderating effect of race/ethnicity on the relationship was examined. The National Longitudinal Survey of Youth 1979 (NLSY79) and the National Longitudinal Survey of Youth 1979 for Children and Young Adults (NLSY79 CY) were used. Young women with mothers who received higher education were less likely to eat fast food. Race/ethnicity moderated the relationship between mothers’ educational attainment and their young adult daughters’ fast food intake. Through this study, we seek to understand the intergenerational relationship between mother and daughter and the effect of mothers’ education on their young adult children’s fast food consumption. Providing more opportunities for mothers to increase their educational attainment should be considered to reduce their children’s fast food intake. Mothers’ educational attainment should be focused on more closely for non-Hispanic Whites as a factor to reduce young women’s fast food intake, and other economic factors should be considered to understand the role of mothers’ educational attainment among African Americans and Hispanic/Latinas.
Despite the lifecourse focus of nursing clinical care, nursing research largely remains cross-sectional or process-oriented within silos determined by patient characteristics such as age, acuity, or disease process. Incorporating interdisciplinary lifecourse theory into pediatric nursing research provides the opportunity to expand nursing theories and research beyond practice, age, and disease silos. One such theory is the Lifecourse Health Development (LCHD) framework. LCHD takes a more expansive view of health development from preconception through old age based on the premise that health is a consequence of transactions between genetic, biological, behavioral, social, and economic contexts that change as a child develops over time (Halfon & Hochstein, 2002). LCHD also explains how intergenerational influences and prevention during early life help predict health development and disease over the lifespan. The preventive and lifecourse focus of LCHD is well-aligned with the lifespan wellness foci of pediatric nurses. The purpose of this article is to introduce pediatric nurse researchers to LCHD and discuss proposed augmentations and implications related to expanding LCHD into pediatric nursing research.
Full-text available
BACKGROUND: The study examines whether self-rated health forms a continuum from poor through average to good health in terms of two groups of health-related variables. METHODS: The data come from the 1994 Finnish Survey on Living Conditions, a representative sample of Finnish men and women aged 25 years or older (n = 7290). Logistic regression analysis was used with two dependent variables: (1) average versus good/excellent self-rated health and (2) poor versus good/excellent self-rated health. Two groups of independent variables were used: (1) risk factors and (2) ill-health indicators. Separate analyses were made for men and women controlling for sociodemographic background variables. RESULTS: Of the risk factors, BMI and physical exercise were associated both with average and poor self-rated health whereas frequency of drinking was only associated with poor health. All used indicators of ill health were strongly associated with average as well as poor self-rated health. With the exception of BMI, the associations of both risk factors and ill health were stronger with poor than with average health. CONCLUSIONS: The study suggests that self-rated health forms a continuum from poor to good health when risk factors and indicators of ill health are considered, and that there are only minor differences in the continuity of self-rated health between men and women.
Full-text available
It has long been known that despite well-documented improvements in longevity for most Americans, alarming disparities persist among racial groups and between the well-educated and those with less education. In this article we update estimates of the impact of race and education on past and present life expectancy, examine trends in disparities from 1990 through 2008, and place observed disparities in the context of a rapidly aging society that is emerging at a time of optimism about the next revolution in longevity. We found that in 2008 US adult men and women with fewer than twelve years of education had life expectancies not much better than those of all adults in the 1950s and 1960s. When race and education are combined, the disparity is even more striking. In 2008 white US men and women with 16 years or more of schooling had life expectancies far greater than black Americans with fewer than 12 years of education-14.2 years more for white men than black men, and 10.3 years more for white women than black women. These gaps have widened over time and have led to at least two "Americas," if not multiple others, in terms of life expectancy, demarcated by level of education and racial-group membership. The message for policy makers is clear: implement educational enhancements at young, middle, and older ages for people of all races, to reduce the large gap in health and longevity that persists today.
Full-text available
Self-reported values of height and weight are used increasingly despite warnings that these data-and derived body mass index (BMI) values-might be biased. The present study investigates whether differences between self-reported and measured values are the same for populations from different regions, and the influences of gender and age. Differences between self-reported and measured weights, heights and resulting BMIs are compared for representative samples of the adult population of Italy, the Netherlands and North America. We observed that weight is under-reported (1.1 ± 2.6 kg for females and 0.4 ± 3.1 kg for males) and height over-reported (1.1 ± 2.2 cm for females and 1.7 ± 2.1 cm for males), in accordance with the literature. This leads to an overall underestimation of BMI values (0.7 ± 1.2 kg/m(2) or 2.8% for females and 0.6 ± 1.1 kg/m(2) or 2.3% for males). When BMI values are assigned to four categories (from 'underweight' to 'obesity'), 11.2% of the females and 12.0% of the males are categorized too low when self-reported weights and heights are used, with an extreme of 17.2% for Italian females. Older people tend to relatively over-report height and under-report weight, but the magnitude differs between countries and gender. We conclude that, apart from a general overestimation of height and underestimation of weight resulting in an underestimation of BMI, substantial differences are observed between countries, between females and males and between age groups.
Height trends since World War II are analyzed using the NHANES surveys for US-born individuals stratified by gender, ethnicity and income. After stagnating or declining for nearly a generation, the height of adult white men and women began to increase among the birth cohorts of ca. 1975-1986, who reached adulthood between 1995 and 2006. The increase in their height overcame the prior downturn that lasted between ca. 1965 and 1974. The height gap between white and black men has increased by 0.43cm (0.17in.) during past decade compared to the previous quarter century, to reach 1.0cm (0.39in.). In contrast to the three other groups examined, the height of black women has been actually declining by some 1.42cm (0.56in.). Consequently, a very considerable wedge has developed between black and white women's height of 1.95cm (0.77in.). In addition, black women in the age range 20-39 weigh some 9.5kg (21.0lb) more than their white counterparts. Two hypotheses are worth considering, namely, (a) that the decline in their height is related to the obesity epidemic and to inadequate dietary balance, and (b) that their future health will be subject to a double jeopardy in the sense that both their increasing weight and decreasing physical stature are likely associated with negative health consequences.
The prevalence of obesity is high and rising worldwide. The greatest prevalence of obesity is found in the western world and in urban developing countries. There is an increased maternal mortality associated with maternal obesity. There are increased risks of most maternal complications in pregnancy including pre-eclampsia, gestational and pre-existing type 2 diabetes mellitus and thromboembolic disorders. There is an increased perinatal mortality associated with maternal obesity; there are increased risks of congenital malformation, fetal macrosomia and indeed risks for the fetus as a child and adult in the years to come. There are increased risks of complications of pregnancy including caesarean section, traumatic delivery and a reduced chance of breastfeeding. Maternal obesity in pregnancy predicts long-term risks for that mother. The management includes increased surveillance for these risks and lifestyle modulation during pregnancy. This includes dietary measures and encouraging modest increase in exercise. Ideally, the mother should achieve closer to an ideal body mass index prior to pregnancy using lifestyle intervention but possibly with pharmacological therapy or bariatric surgery. The ideal weight gain for an obese mother is less than the ideal weight gain for a lean mother.
(1) To propose a new method using statistical modeling to determine relative timing of pubertal maturation; (2) to validate the new method by evaluating its relationship with pubertal growth and timing parameters, including age at menarche, age onset of areolar maturation, age of peak height velocity, age at attainment of adult height, adult height, peak height velocity, body mass index, and percent body fat; and (3) to contrast the new method with relative timing of menarche on these pubertal parameters. The timing of puberty has a well-known impact on anthropometric and psychosocial outcomes. Multiple methods have been used to determine pubertal timing, but all with limitations. A uniformly applicable method is needed for different study designs and study populations. Using the National Heart Lung and Blood Institute Growth and Health Study data, an ordinal logistic modeling was used to assess relative timing of pubertal maturation. The proposed method demonstrated good reliability and strong associations with all pubertal timing parameters, also body mass index and percent body fat. Timing was not significantly associated with adult height and peak height velocity. The proposed method is highly feasible, easy to implement, and valid. The study demonstrated important differences between the relationships of relative timing of secondary sexual characteristics and the timing of menarche on pubertal parameters. The study also demonstrates that individuals with early or late timing at one point of time are likely to maintain the same relative timing throughout puberty.
Because of the rapid increases in childhood obesity coupled with decreases in the median age of menarche, there is interest in how growth (body mass index [BMI] and height) in childhood may be associated with timing of menarche. Two research questions were addressed in this article: (a) Within each race, at what ages were BMI and height differences evident among the early-, the mid-, and the late-onset groups? And (b) within each timing group, at what ages were BMI and height differences evident between White and African American girls? The mother/child files of the National Longitudinal Survey of Youth were used for this study. Menarcheal timing groups were identified using the 25th and the 75th percentile of the age distribution for each race. Longitudinal statistical techniques were used to estimate BMI and height as polynomial functions of age and age relative to menarche for African American and White girls. Significant differences in BMI by timing group were found. By 3 years of age, significant differences were found between early- and mid-onset African American girls, by 5 years of age between mid- and late-onset African American girls, and by 6 years of age among the three timing groups of White girls. Significant height differences were evident by 5 years of age when comparing early- to mid-onset and mid- to late-onset girls in both race groups. Comparing across race and within timing group, BMI and height differences were evident. African American girls were more likely than White girls to experience accelerated growth and earlier menarche. This is one of the few longitudinal studies of differences in growth by timing of menarche that includes data on girls younger than 5 years with large samples of both African American and White girls. Understanding when differences are first apparent is critical in establishing the critical period for prevention of these high-risk growth patterns.
The association of self-rated health with mortality is well established but poorly understood. This paper provides new insights into self-rated health that help integrate information from different disciplines, both social and biological, into one unified conceptual framework. It proposes, first, a model describing the health assessment process to show how self-rated health can reflect the states of the human body and mind. Here, an analytic distinction is made between the different types of information on which people base their health assessments and the contextual frameworks in which this information is evaluated and summarized. The model helps us understand why self-ratings of health may be modified by age or culture, but still be a valid measure of health status. Second, based on the proposed model, the paper examines the association of self-rated health with mortality. The key question is, what do people know and how do they know what they know that makes self-rated health such an inclusive and universal predictor of the most absolute biological event, death. The focus is on the social and biological pathways that mediate information from the human organism to individual consciousness, thus incorporating that information into self-ratings of health. A unique source of information is provided by the bodily sensations that are directly available only to the individual him- or herself. According to recent findings in human biology, these sensations may reflect important physiological dysregulations, such as inflammatory processes. Third, the paper discusses the advantages and limitations of self-rated health as a measure of health in research and clinical practice. Future research should investigate both the logics that govern people's reasoning about their health and the physiological processes that underlie bodily feelings and sensations. Self-rated health lies at the cross-roads of culture and biology, therefore a collaborative effort between different disciplines can only improve our understanding of this key measure of health status.