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Childhood circumstances and height among older adults in the United States
Economics and Human Biology 9 (2011) 194–202
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Economics and Human Biology journal homepage: http://www.elsevier.com/locate/ehb
Childhood circumstances and height among older adults in the United States§ Reginald D. Tucker-Seeley a, S.V. Subramanian b,* a
Center for Community Based Research, Dana-Farber Cancer Institute/Department of Society, Human Development and Health, Harvard School of Public Health, USA b Department of Society, Human Development and Health, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
A R T I C L E I N F O
A B S T R A C T
Article history: Received 29 June 2010 Received in revised form 22 November 2010 Accepted 22 November 2010 Available online 29 November 2010
We investigated the association between adult height and three indicators of childhood circumstances: mother’s education, childhood financial hardship, and childhood health in the United States. Cross-sectional analysis of adults aged 50 and older in the 2004 Health and Retirement Study (N = 14,079) was conducted. Gender and gender-race stratified regression models were used to model the association between adult height and childhood circumstances. The gender-stratified results showed a positive gradient association between mother’s education and adult height; those reporting up to grade 8, high school graduate, and greater than high school education for their mother were 4.17 cm (p < 0.001), 4.92 cm (p < 0.001), and 5.83 cm (p < 0.001) taller for men and 2.57 cm (p < 0.001), 3.16 cm (p < 0.001), and 3.85 cm (p < 0.001) taller for women, respectively than those reporting no education for their mother. Childhood health was not statistically significantly associated with adult height, controlling for birth cohort, mother’s education, and childhood financial hardship. Those who did not experience childhood financial hardship were slightly taller than those who did experience such hardship. Gender-race stratified results also showed a positive gradient association between mother’s education and adult height; however, this association was only significant for white men and white women. The study reiterates the importance of childhood circumstances for adult height and for building health stock. ß 2010 Elsevier B.V. All rights reserved.
Keywords: Adult height Childhood health Childhood socioeconomic status United States
1. Introduction Although attained height is largely determined by genetic factors, socioeconomic factors in childhood contribute to the level of height attained in adulthood as well as to its variability (Peck and Lundberg, 1995); and a positive association has been shown between height in adulthood and childhood social class (Kuh and Wads-
§ The study was reviewed by Harvard School of Public Health Institutional Review Board and was considered as exempt from full review as the study was based on an anonymous public use data set with no identifiable information on the survey participants. * Corresponding author. Tel.: +1 617 432 6299; fax: +1 617 432 3123. E-mail addresses: [email protected] (R.D. Tucker-Seeley), [email protected] (S.V. Subramanian).
1570-677X/$ – see front matter ß 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ehb.2010.11.002
worth, 1989; Silventoinen, 2003). As such, research has recently focused on adult height as a marker of circumstances in childhood (Blackwell et al., 2001; Case and Paxson, 2010; Case and Paxson, 2008; Silventoinen, 2003; Subramanian et al., 2009). To further explicate the association between childhood socioeconomic condition and adult height, several indicators of childhood socioeconomic status have been investigated as predictors of adult height. For example, factors such as economic hardship in childhood/adolescence (Peck and Lundberg, 1995), parental education (Webb et al., 2008a), and father’s occupation (Peck and Vagero, 1987) have all been shown to be associated with adult height. The association between socioeconomic condition and height has been investigated in samples from developing countries and developed countries (Meyer and Selmer, 1999; Webb et al., 2008a),
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
including the US; however, few studies have investigated the association of multiple indicators of childhood socioeconomic condition as well as childhood health status with adult height. In addition, whether the association between childhood socioeconomic condition and height in later life is similar between whites and blacks as well as men and women remains an open question. The aim of the present study was to assess the independent association between adult height among older adults and three indicators of childhood circumstances: mother’s education, childhood financial hardship, and childhood health in a nationally representative sample of US adults. We were also interested in the differential association between childhood socioeconomic circumstances and height among older adults across race and gender categories. 2. Methods 2.1. Data We used the Health and Retirement Study (HRS) data for this analysis. The HRS is a biennial national longitudinal study of the economic, health, marital, family status, and public/private support systems of older Americans funded by the National Institute on Aging and the Social Security Administration and conducted by the Institute for Social Research Survey Research Center at the University of Michigan (Health and Retirement Study, 2004). The HRS uses a national multistage area probability sample of households in the U.S., with oversamples of Blacks, Hispaqnics, and residents from the state of Florida. Details of the HRS data collection methods are described elsewhere1 (National Institute on Aging, 2007). In addition to the public use HRS data, we used data prepared by the RAND Center for the Study of Aging2 (RAND HRS Data Version G, 2004). The RAND HRS Data file is an easy to use longitudinal data set based on the HRS data. It was developed at RAND with funding from the National Institute on Aging and the Social Security Administration. For this study we used cross-sectional data from the 2004 wave of the HRS (N = 20,129). Subjects that were not assigned a sampling weight value due to death, being institutionalized or age-ineligible were not included in the analysis (N = 1541). In addition, subjects were excluded from analysis if they were missing values on sociodemographic characteristics (N = 1), mother’s education (N = 1888), childhood health (N = 17), childhood financial hardship (N = 77), and adult SES (N = 32). This analysis focused only on US born whites and blacks; thus non-US born subjects (N = 1644) and subjects who did not identify their race as either black or white (N = 366) were excluded. Lastly, subjects were excluded if born after 1953 (N = 484). The final sample use for analysis was N = 14,079.
1 http://www.nia.nih.gov/ResearchInformation/ExtramuralPrograms/ BehavioralAndSocialResearch/HRS.htm. 2 The RAND HRS Data file is an easy to use longitudinal data set based on the HRS data. It was developed at RAND with funding from the National Institute on Aging and the Social Security Administration. http://www.rand.org/labor/aging/.
195
The HRS began in 1992 and is comprised of several additional cohorts that have been added to ensure it remains representative of adults in the US over age 50 (Health and Retirement Study (HRS), 2008). The baseline HRS sample interviewed in 1992 consisted of respondents born between 1931 and 1941 (N = 12,652). The second sample currently included in the HRS began as a separate study called the Assets and Health Dynamics among the Oldest Old (AHEAD) (N = 8222). The respondents in this sample were born before 1923. The original HRS and the AHEAD were merged into a single study in 1998, subsequently called the HRS. In 1998, two additional samples were added to the HRS: (1) respondents born between the baseline HRS sample and the AHEAD sample and called The Children of the Depression Age sample (1924–1930); and (2) A ‘‘refresher cohort’’ of respondents called the War Baby sample, which consisted of respondents born between 1942 and 1947. Lastly, in 2004, a new sample entered the HRS called the Early Baby Boomer sample, which consisted of respondents born between 1948 and 1953. These combined samples compose the HRS (Health and Retirement Study (HRS), 2008). For analyses here, we defined the cohorts by the birth cohort the HRS used for assigning respondent level sampling weights (Health and Retirement Study, 2004). In so doing, seven cohorts were defined for respondents: (1) born on or before 1913; (2) born 1914–1923; (3) born 1924–1930; (4) born 1931–1936; (5) born 1937–1941; (6) born 1942–1947; (7) born 1948–1953. 2.2. Outcome variable Respondent’s height was self-reported upon their entry into the HRS and recorded in inches. For the present analysis, respondent’s height was converted to centimeters. 2.3. Independent variables Socio-demographic characteristics including race/ethnicity and gender were assessed by self-report in the HRS. We used two categories for race: (1) White and (2) Black/ African American; and gender was self-report (male/ female). We used three indicators of childhood circumstances: mother’s education, childhood health status, and the experience of childhood financial hardship. Mother’s education was used as a proxy for childhood SES and was measured as mother’s educational attainment categorized into four categories: (1) no education; (2) up to grade 8; (3) high school graduate; (4) more than high school education. Childhood health status was measured using one question from the HRS: ‘‘Consider your health while you were growing up, from birth to age 16. Would you say that your health during that time was excellent, very good, good, fair, or poor?’’ The experience of childhood financial hardship was measured using one question from the HRS: ‘‘While you were growing up, before age 16, did financial difficulties ever cause you or your family to move to a different place?’’ 2.4. Statistical analysis Analyses were stratified by gender and race. Bivariate analyses (ANOVA) were used to determine differences in
[()TD$FIG]
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
196
180
170 169
179
168 178
167 166 165
176
164 175
163
174
162 161
173
Women
Men
177
Men White men Black men Women White women Black Women
160 172
159
171
158
Birth Cohort Fig. 1. Average height (cm) across birth cohort categories for white men, black men, white women, and black women.
adult height across the categories of birth cohort and childhood circumstances variables. We used OLS regression to test the association among adult height, childhood socioeconomic circumstances, and childhood health in gender and gender and race-stratified samples. We used a step-wise modeling strategy. The first model included only the birth cohort variable. Next, to determine the incremental influence of the childhood related variables, childhood health was entered and then mother’s education and childhood financial hardship were entered into the [()TD$FIG]
models. All models were weighted for differential sampling rates and non-response. SAS1 9.2 was used for all analyses. 3. Results The average age of the sample was 68 years old (ranging from 51 to 108). Average height across birth cohort categories for men and women and also stratified by gender and race is shown in Fig. 1; and average height
185
180
175
170 No educaon 165
Up to grade 8 High school graduate
160
More than high school
155
150
145 Men
Women
White men Black men
White women
Black Women
Fig. 2. Average height (cm) across mother’s education categories for white men, black men, white women, and black women.
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197
Table 1 Height (cm) by birth cohorts, childhood health and childhood socioeconomic categories by gender. Men N Birth cohort 1913 1914–1923 1924–1930 1931–1936 1937–1941 1942–1947 1948–1953 Mother’s education (mother’s educational attainment) No education Up to grade 8 High school graduate More than high school Childhood Health Excellent Very good Good Fair Poor Childhood financial hardship No hardship present Hardship present
Women Mean adult height
95% CL
p-Value
N
Mean adult height
95% CL
p-Value
p < 001 83 665 932 1284 1316 859 1003
176.15 176.61 177.60 177.98 178.68 178.99 178.94
174.75 175.99 177.03 177.59 178.32 178.47 178.45
177.57 177.22 178.16 178.37 179.03 179.50 179.44
p < 0.001 157 1126 1159 1554 1609 1363 964
160.06 161.96 162.42 163.55 163.76 163.85 163.49
159.21 161.52 162.01 163.15 163.36 163.45 162.97
160.92 162.40 162.83 163.94 164.16 164.25 164.02
p < 001 99 2046 3263 734
173.36 177.57 178.60 179.72
171.33 177.17 178.27 179.14
175.38 177.97 178.94 180.30
3299 1577 953 242 71
178.72 178.38 177.38 177.96 177.15
178.41 178.06 176.79 177.03 174.05
179.04 178.70 177.97 178.90 180.26
4937 1205
178.56 177.70
178.30 177.25
178.82 178.16
p < 001 121 2967 3917 927
160.28 162.68 163.37 164.32
158.52 162.38 163.11 163.69
162.05 162.97 163.63 164.95
4104 2012 1321 384 111
163.40 162.98 162.99 163.45 163.15
163.13 162.63 162.58 162.64 161.85
163.68 163.33 163.40 164.26 164.45
6579 1353
163.29 162.93
163.08 162.58
163.51 163.28
p < 0.001
p = 0.09
p < 0.001
p = 0.04
Averages are weighted by sample weights.
across categories of mother’s education is shown in Fig. 2. Average height across birth cohort categories and all the childhood circumstances variables is shown in tabular form for men and women in Table 1 and stratified by gender and race in Table 2. Average height for men and women across the categories of mother’s education showed a positive gradient. Height also progressively increased across the birth cohorts, except for the youngest cohort where men were 0.05 cm and women were 0.36 cm shorter than the previous cohort; and those who experienced childhood financial hardship were 0.86 cm for men and 0.36 cm for women taller than those who did not. However, the gradient was not noted across the childhood health categories in the gender-stratified sample (Table 1). In the gender-race stratified sample, the white samples more closely resembled the gender-stratified sample than the black samples in average height across the birth cohort categories (Fig. 1). Across the childhood circumstances variables, the average height gradient patterns in the white male sample were also consistent with the genderstratified sample. However, the black male sample differed in that a positive gradient across the birth cohort categories was not noted, but there was a gradient in average height across the childhood health categories for black men. Across the childhood circumstances variables for women, the white female sample closely resembled the gender-stratified sample; however, a surprising result for the black female sample showed that women who experienced childhood financial hardship were slightly taller (0.34 cm) than those who did not experience hardship (Table 2). Overall, the results here suggest a significant height advantage for those with higher mother’s education and those who did not experience childhood financial hardship.
Gender stratified bivariate analyses revealed statistically significant differences in average height across the birth cohort categories and the categories for each of the childhood circumstances variables for men; yet, for women statistically significant differences were noted in all the variables except childhood health (Table 2). Genderrace stratified bivariate analyses revealed statistically significant differences across the birth cohort categories for white men (p < 0.001), black men (p < 0.001), white women (p < 0.001), and black women (p < 0.01). Across the categories of mother’s education, statistically significant differences were only noted for white men (p < 0.001) and white women (p < 0.001), and not for black men (p = 0.08) or black women (p = 0.45). Similarly, for childhood health, statistically significant differences were again only noted for white men (p < 0.01) and white women (p < 0.01), but not for black men (p = 0.31) or black women (p = 0.85). Lastly, statistically significant differences in childhood financial hardship were only noted for white men (p < 0.001) and white women (p = 0.02), but not for black men (p = 0.94) or black women (p = 0.91). The gender stratified regression models including birth cohort and all the childhood circumstances variables in the model revealed a gradient in average adult height across the birth cohort categories, with the exception of the cohort born between 1948 and 1953, which was shorter than the previous cohort born 1942–1947 for both men and women (Table 3). Comparing the mother’s education category of no education to the up to grade 8, high school graduate, and greater than high school categories also showed a statistically significant association for men and women. For example, when controlling for birth cohort, childhood health, and childhood financial hardship men whose mother had greater than a high school education were 5.83 cm (p < 0.001) taller than those whose mother
198
Table 2 Height (cm) by birth cohorts, childhood health and childhood socioeconomic categories by gender and race.
Birth cohort 1913 1914–1923 1924–1930 1931–1936 1937–1941 1942-1947 1948–1953 Mother’s education No education Up to grade 8 High school graduate More than high school Childhood health Excellent Very good Good Fair Poor Childhood financial hardship No Hardship Present Hardship present z
Black men z
N
Mean adult height
74 603 858 1101 1141 756 848
176.05 176.75 177.64 177.94 178.77 179.04 178.84
174.58 176.12 177.06 177.55 178.41 178.47 178.34
177.52 177.37 178.21 178.33 179.14 179.61 179.34
80 1690 2938 675
172.94 177.56 178.58 179.73
2929 1387 802 203 62
178.71 178.41 177.33 178.03 177.28
4337 178.56 1046 177.66
Averages are weighted by sample weights.
95% CL
White women z
N
Mean adult height
9 63 74 183 173 103 155
177.76 174.16 176.89 178.41 177.41 178.35 179.82
173.42 172.19 174.20 177.16 176.29 176.73 178.41
182.09 176.14 179.59 179.67 178.54 179.96 181.22
170.61 177.09 178.25 179.11
175.27 19 175.61 178.02 356 177.71 178.91 326 178.93 180.34 59 179.62
178.39 178.07 176.69 177.00 174.25
179.03 371 178.90 178.75 190 177.99 177.96 151 177.87 179.07 39 177.43 180.30 9 175.14
178.30 178.83 601 178.49 177.18 178.15 159 178.06
95% CL
Black women z
N
Mean adult heightz 95% CL
17 125 103 267 289 208 207
158.40 162.90 163.80 163.88 163.66 164.94 163.22
153.78 161.46 162.33 162.97 162.80 164.12 162.00
163.03 164.35 165.27 164.78 164.52 165.80 164.44
158.04 162.21 163.01 163.67
160.91 36 161.85 162.86 585 163.57 163.59 508 164.16 164.97 87 164.28
158.13 162.90 163.35 162.82
165.58 164.23 164.97 165.75
163.07 162.48 162.40 162.63 161.76
163.66 548 163.78 163.25 303 164.11 163.31 263 163.94 164.47 75 162.89 164.65 27 162.86
163.20 163.34 162.78 161.16 159.94
164.36 164.88 165.11 164.62 165.79
N
Mean adult height
140 1001 1056 1287 1321 1155 757
160.17 161.91 162.30 163.51 163.77 163.72 163.54
159.31 161.45 161.86 163.10 163.33 163.31 162.93
161.03 162.37 162.73 163.94 164.21 164.13 164.15
172.50 176.71 177.70 177.47
178.72 85 159.48 178.70 2382 162.54 180.15 3409 163.30 181.77 841 164.32
177.81 176.61 176.00 175.64 166.26
179.99 3556 163.37 179.38 1709 162.87 179.75 1058 162.85 179.21 309 163.55 184.01 84 163.20
177.52 179.46 5580 163.24 176.85 179.28 1136 162.80
95% CL
163.04 163.45 999 163.75 162.39 163.21 217 164.09
163.19 164.31 163.04 165.14
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
White men
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
199
Table 3 Regression estimates of height (cm) as dependent variable by gender*. Men, N = 6142
Intercept Birth cohort 1913 1914–1923 1924–1930 1931–1936 1937–1941 1942–1947 1948–1953 Childhood health Excellent Very good Good Fair Poor Mother’s education No education Up to grade 8 High school grad >High school Childhood financial hardship Yes No
Women, N = 7932
Estimate
95% CI
170.90
166.85, 174.95
Reference 0.28 1.26 1.59 2.18 2.38 2.15
1.20, 1.76 .35, 2.87 0.14, 3.04 0.69, 3.66 0.86, 3.88 0.63, 3.67
0.71 0.53 0.25 0.26 Reference
2.33, 2.54, 3.47, 2.75,
p-Value
3.74 3.60 2.96 3.26
0.71 0.12 0.03 0.005 0.003 0.006 0.64 0.73 0.87 0.87
Estimate
95% CI
157.08
154.78, 159.37
Reference 1.84 2.29 3.39 3.53 3.53 3.08
0.75, 1.27, 2.43, 2.65, 2.59, 2.17,
0.03 0.19 0.09 0.28 Reference
p-Value
2.94 3.31 4.34 4.41 4.47 3.99
1.37, 1.67, 1.51, 1.37,
1.44 1.29 1.33 1.89
Reference 4.17 4.92 5.83
2.16, 6.26 2.86, 6.99 3.67, 7.99
<0.001 <0.001 <0.001
Reference 2.57 3.16 3.85
0.77, 4.37 1.45, 4.88 2.10, 5.61
Reference 0.45
0.03, 0.93
0.06
Reference 0.21
0.17, 0.60
0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.96 0.80 0.90 0.73
0.01 <0.001 <0.001
0.28
*
Regression models were weighted to account for non-response and unequal selection probability; adjusted R2 for men = 0.03; adjusted R2 for women = 0.02.
had no education and for women the difference was 3.85 cm (p < 0.001). Those who did not experience childhood financial hardship were slightly taller than those who did not; although the association between adult height and childhood financial hardship was not statistically significant. The association comparing poor childhood health to the other categories of childhood health on adult height was also not statistically significant (Table 3). The results of gender-race stratified models including birth cohort and all the childhood circumstances variables revealed a statistically significant association between birth cohort and adult height for all groups except black men. Mother’s education also showed a positive gradient with adult height across the groups; however, this association was only statistically significant for white men and white women and not for black men and black women. Those who experienced childhood financial hardship were slightly taller than those who did not (except for black women), but this association was not statistically significant (Table 4). Similar to the genderstratified models, these results suggest significant height advantage for those with higher mother’s education, even after controlling for childhood health, childhood financial hardship, and birth cohort; however, only for white men and women. Although this pattern was also present in black women and men, mother’s education was not statistically significant for those groups. 4. Discussion Consistent with other studies suggesting a secular trend in adult height in Western countries (Cole, 2000; Cole, 2003; Komlos and Lauderdale, 2007; Ogden et al., 2004), the findings from this study revealed a positive trend in
adult height across birth cohorts in the Health and Retirement Study. This trend is consistent with the increasing average heights noted in Komlos (2010) using gender-race stratified data from NHANES. Although Komlos (2010) showed mean heights for white men and white women consistently higher than black men and black women across birth years (Komlos, 2010), our results did not reveal this same pattern. Our results also revealed attenuation in the association between birth cohort and adult height when mother’s education and childhood health were entered in the models especially for men (but less so for women). Thus, the variability in adult height by birth cohort can at least be partially explained by differences in childhood circumstances. Surprisingly, childhood health was not associated with adult height when variables such as mother’s education and childhood financial hardship were included in the models. Overall, our results suggest that adult height is more sensitive to mother’s education than childhood health; and that in particular for white men and white women, childhood socioeconomic factors (i.e., mother’s education) may exert a significant influence on adult height. The importance of mother’s education on subsequent health outcomes has also been shown in relation to obesity (Baum and Ruhm, 2009). In particular, using data from the National Longitudinal Survey of Youth Baum and Ruhm (2009) showed that an extra year of maternal education reduces estimated obesity by 1.20 percentage points. The mechanisms through which mother’s education impacts children’s health could be through the association between the mother’s higher educational attainment and improved prenatal care, better health behaviors, and an increased likelihood that she is married (Currie and Moretti, 2003). The lack of an association for
200
Table 4 Regression estimates of height (cm) as dependent variable by race and gender*.
Intercept Birth cohort 1913 1914–1923 1924–1930 1931–1936 1937–1941 1942–1947 1948–1953 Childhood health Excellent Very good Good Fair Poor Mother’s education No education Up to grade 8 High school grad >high school Childhood financial hardship Yes No *
Black men, N = 760
Estimate
95% CI
170.44
166.32, 174.56
Reference 0.51 1.36 1.64 2.35 2.51 2.09
1.05, 2.06 0.29, 3.01 0.16, 3.11 0.82, 3.87 0.96, 4.06 0.56, 3.63
0.61 0.46 0.43 0.23 Reference
2.38, 2.52, 3.61, 2.73,
p-Value
3.60 3.44 2.76 3.20
0.51 0.10 0.03 0.003 0.002 0.01 0.69 0.76 0.79 0.87
White women, N = 6716
Estimate
95% CI
173.70
163.26, 184.14
Reference 3.87 0.77 0.31 0.67 0.07 1.54
8.69, 5.90, 4.48, 5.56, 4.51, 3.11,
0.96 4.35 5.11 4.22 4.66 6.18
0.11 0.76 0.90 0.78 0.97 0.51
6.00, 6.69, 6.48, 7.03,
10.55 9.94 10.09 9.22
0.58 0.70 0.66 0.79
2.27 1.63 1.80 1.09 Reference
p-Value
Estimate
95% CI
156.63
154.30, 158.96
Reference 1.70 2.07 3.24 3.43 3.24 2.97
0.57, 1.01, 2.27, 2.52, 2.27, 2.02,
0.07 0.35 0.28 0.30 Reference
p-Value
2.82 3.13 4.21 4.33 4.21 3.92
1.60, 2.02, 1.85, 1.39,
Black women, N = 1216
1.46 1.32 1.29 1.99
0.004 <0.001 <0.001 <0.001 <0.001 <0.001 0.93 0.68 0.72 0.72
Estimate
95% CI
155.38
148.85, 161.91
Reference 4.54 5.70 5.64 5.45 6.90 4.84
0.32, 9.41 1.10, 10.30 0.92, 10.35 0.56, 10.35 2.29, 11.51 0.18, 9.85
0.68 1.18 1.09 0.04 Reference
2.60, 1.55, 2.03, 3.77,
p-Value
0.07 0.02 0.02 0.03 0.004 0.06
3.96 3.92 4.20 3.70
0.68 0.39 0.49 0.98
Reference 4.59 5.35 6.30
2.34, 6.85 3.01, 7.69 3.86, 8.75
<0.001 <0.001 <0.001
Reference 1.83 2.52 2.73
1.59, 5.24 1.20, 6.24 1.24, 6.69
0.29 0.18 0.17
Reference 3.14 3.79 4.55
1.66, 4.62 2.38, 5.19 3.00, 6.09
<0.001 <0.001 <0.001
Reference 2.30 2.98 3.01
1.77, 6.37 0.93, 6.89 1.09, 7.12
0.26 0.13 0.15
Reference 0.50
0.005, 1.00
0.05
Reference 0.41
1.09, 1.90
0.59
Reference 0.28
0.13, 0.69
0.17
Reference 0.64
1.65, 0.38
0.21
Regression models were weighted to account for non-response and unequal selection probability; adjusted R2 for white men = 0.03; black men = 0.02; white women = 0.02; black women = 0.01.
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
White men, N = 5382
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mother’s education in black men and black women in our study is worth further exploration in larger black samples. Previous research has shown that the association between childhood SES and adult health outcomes in later life do not vary substantially by race (Luo and Waite, 2005); thus a finding of a differential association between mother’s education and height by race in our study warrants additional investigation. Possible explanations for the lack of an association between childhood health and childhood financial hardship and adult height could be measurement error as these were both assessed with only one question from the HRS. There are several limitations to this study. First, the mother’s education and childhood health were retrospective reports made by the respondents and all other variables (including present height) were self-report as well; thus, the measures are subject to recall bias. Height was asked of HRS respondents in the baseline study or when the respondents initially enter the study (Jenkins et al., 2008). Overestimation of height has been shown to increase with age and decrease with measured height (Bostrom and Diderichsen, 1997; Kuczmarski et al., 2001; Merrill and Richardson, 2009). However, Gunnell et al. (2000) showed that the correlation between self-report and measured height was over 0.90 in older adults with shorter subjects more likely to over-report. Investigations of the effects of over-reporting on socioeconomic differences in height in Sweden resulted in an underestimation of SES differentials in height (Bostrom and Diderichsen, 1997). The over-reporting in our sample could be due to shrinkage, with older adults recalling their height from earlier in their lives; thus such over-reporting might be closer to an estimation of achieved height. We did not account for the potential height loss (shrinkage) that occurs as individuals age (Sorkin et al., 1999); however, the potential for shrinkage was not expected to be patterned by the socioeconomic variables used in this study. We also acknowledge the limitation of using one question to measure each of the childhood socioeconomic circumstances, and understand that the multidimensionality of these constructs may not necessarily be captured by the single question; however, we contend that using these measures together in a single model may indeed tap into components of childhood socioeconomic circumstances better than using only one of these questions in a model. Additionally, it is not possible to tease apart whether the lack of chronic health conditions in childhood, which may not necessarily be captured by self-reported childhood health, lead to increased height or a confounder of later life height and childhood health lead to increased height (Case and Paxson, 2008). Lastly, potentially confounding genetic factors and parental height were not included in this study and the possible correlation between parental height, mother’s education, and childhood height could introduce bias in the parameter estimates; yet the potential accuracy of such information as parental height would be difficult to substantiate (Webb et al., 2008a,b), especially in samples of older adults. Our results are consistent with other studies on the association between childhood socioeconomic circumstances and adult height (Kuh and Wadsworth, 1989; Peck and Vagero, 1987; Peck
201
and Lundberg, 1995; Webb et al., 2008a,b). Our results for white men and white women are also consistent with the Case and Paxson (2008) results that reported bivariate associations between childhood health (dichotomized) and adult height; but, when we controlled for birth cohort, mother’s education, and childhood financial hardship the association in our results between childhood health and adult height became non-significant. Differences that could help explain the divergent results between our study and the Case and Paxson (2008) study include that they dichotomized childhood health and in the present study where we used all five categories of childhood health, our results are stratified by gender and race, and our study includes additional control variables such as mother’s education and childhood financial hardship. To our knowledge no study has simultaneously assessed the association between the three indicators of childhood circumstances and adult height assessed in this study in a US sample. Our results reveal the relative importance of various childhood circumstances to adult height in US adults. In particular, our results suggest that the socioeconomic circumstances in childhood as captured by measures of mother’s education and childhood financial hardship may be more important than health in childhood for adult height. Yet, it should be noted that childhood socioeconomic circumstances such as mother’s education and financial hardship may substantially influence childhood health; and the lack of a statistically significant association in our fully controlled models for childhood health may suggest that childhood health is on the causal pathway between these childhood socioeconomic circumstances and adult height, especially for white men and white women. Adult height has been shown to be associated with morbidity and mortality (Batty et al., 2009), with a negative association with cardiovascular disease (Davey Smith et al., 2000) and a positive association with some cancers (Gunnell et al., 2009). Steckel (2009) suggests that to better understand how average height can be used as a useful measure of human welfare, we need to know the various socioeconomic factors that can affect it and the impact of those factors (Steckel, 2009). It has been hypothesized that adult height is a good marker of socioeconomic experiences in childhood (Case and Paxson, 2010; Case and Paxson, 2008), with the current evidence in the literature suggesting that adult height is indeed a good marker of post-natal ‘‘diet, psychosocial stress, chronic illness, and social circumstances’’ (Batty et al., 2009). In summary, this study provides evidence for an association between childhood socioeconomic conditions and adult height substantiating the importance of early childhood circumstances for later health and well being. Acknowledgement No direct funding was available for this study. SV Subramanian is supported by the Robert Wood Johnson Investigator Award in Health Policy Research, and the National Institutes of Health Career Development Award (NHLBI K25 HL081275).
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Komlos, J., Lauderdale, B.E., 2007. The mysterious trend in American heights in the 20th century. Annals of Human Biology 34 (2), 206–215. Kuczmarski, M.F., Kuczmarski, R.J., Najjar, M., 2001. Effects of age on validity of self-reported height, weight, and body mass index: findings from the third National Health and Nutrition Examination Survey, 1988–1994. Journal of the American Dietetic Association 101 (1), 28–34. Kuh, D, Wadsworth, M., 1989. Parental height: childhood environment and subsequent adult height in a National Birth Cohort. International Journal of Epidemiology 18 (3), 663–668. Luo, Y., Waite, L.J., 2005. The impace of childhood and adult SES on physical mental, and cognitive well-being in later life. Journal of gerontology: Social Sciences 60B (2), S93–S101. Merrill, R.M., Richardson, J.S., 2009. Validity of self-reported height, weight, and body mass index: findings from the National Health and Nutrition Examination Survey, 2001–2006. Preventing Chronic Disease 6. (4). Meyer, H.E., Selmer, R., 1999. Income, educational level and body height. Annals of Human Biology 26 (3), 219–227. National Institute on Aging, 2007. Growing Old in America: The Health and Retirement Study. . Ogden, C.L., Fryar, C.D., Carroll, M.D., Flegal, K.M., 2004. Mean body weight, height, and body mass index, United States 1960–2002. Advance Data from Vital and Health Statistics Number 347. Peck, A.M., Vagero, D.H., 1987. Adult body height and childhood socioeconomic group in the Swedish population. Journal of Epidemiology and Community Health 41, 333–337. Peck, M.N., Lundberg, O., 1995. Short stature as an effect of economic and social conditions in childhood. Social Science & Medicine 41 (5), 733– 738. RAND HRS Data Version G, 2004. Produced by the RAND Center for the Study of Aging,with funding from the National Institute on Aging and the Social Security Administration. . Silventoinen, K., 2003. Determinants of variation in adult body height. Journal of Biosocial Science 35, 263–285. Sorkin, J.D., Muller, D.C., Andres, R., 1999. Longitudinal change in height of men and women: implications for interpretation of the body mass index. American Journal of Epidemiology 150 (9), 969–977. Steckel, R.H., 2009. Heights and human welfare: recent developments and new directions. Explorations in Economic History 46 (1), 1–23. Subramanian, S.V., Ackerson, L.K., Davey Smith, G., John, N.A., 2009. Association of maternal height with child mortality, anhropometric failure, and anemia in India. JAMA 301 (16), 1691–1701. Webb, E., Kuh, D., Peasey, A., Pajak, A., Malyutina, S., Kubinova, R., ToporMadry, R., Denisova, D., Capkova, N., Marmot, M., Bobak, M., 2008a. Childhood socioeconomic circumstances and adult height and leg length in central and eastern Europe. Journal of Epidemiology and Community Health 62, 351–357. Webb, E.A., Kuh, D., Pajak, A., Kubinova, R., Malyutina, S., Bobak, M., 2008b. Estimation of secular trends in adult height, and childhood socioeconomic circumstances in three Eastern European populations. Economics and Human Biology 6, 228–236.
Contents lists available at ScienceDirect
Economics and Human Biology journal homepage: http://www.elsevier.com/locate/ehb
Childhood circumstances and height among older adults in the United States§ Reginald D. Tucker-Seeley a, S.V. Subramanian b,* a
Center for Community Based Research, Dana-Farber Cancer Institute/Department of Society, Human Development and Health, Harvard School of Public Health, USA b Department of Society, Human Development and Health, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA
A R T I C L E I N F O
A B S T R A C T
Article history: Received 29 June 2010 Received in revised form 22 November 2010 Accepted 22 November 2010 Available online 29 November 2010
We investigated the association between adult height and three indicators of childhood circumstances: mother’s education, childhood financial hardship, and childhood health in the United States. Cross-sectional analysis of adults aged 50 and older in the 2004 Health and Retirement Study (N = 14,079) was conducted. Gender and gender-race stratified regression models were used to model the association between adult height and childhood circumstances. The gender-stratified results showed a positive gradient association between mother’s education and adult height; those reporting up to grade 8, high school graduate, and greater than high school education for their mother were 4.17 cm (p < 0.001), 4.92 cm (p < 0.001), and 5.83 cm (p < 0.001) taller for men and 2.57 cm (p < 0.001), 3.16 cm (p < 0.001), and 3.85 cm (p < 0.001) taller for women, respectively than those reporting no education for their mother. Childhood health was not statistically significantly associated with adult height, controlling for birth cohort, mother’s education, and childhood financial hardship. Those who did not experience childhood financial hardship were slightly taller than those who did experience such hardship. Gender-race stratified results also showed a positive gradient association between mother’s education and adult height; however, this association was only significant for white men and white women. The study reiterates the importance of childhood circumstances for adult height and for building health stock. ß 2010 Elsevier B.V. All rights reserved.
Keywords: Adult height Childhood health Childhood socioeconomic status United States
1. Introduction Although attained height is largely determined by genetic factors, socioeconomic factors in childhood contribute to the level of height attained in adulthood as well as to its variability (Peck and Lundberg, 1995); and a positive association has been shown between height in adulthood and childhood social class (Kuh and Wads-
§ The study was reviewed by Harvard School of Public Health Institutional Review Board and was considered as exempt from full review as the study was based on an anonymous public use data set with no identifiable information on the survey participants. * Corresponding author. Tel.: +1 617 432 6299; fax: +1 617 432 3123. E-mail addresses: [email protected] (R.D. Tucker-Seeley), [email protected] (S.V. Subramanian).
1570-677X/$ – see front matter ß 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ehb.2010.11.002
worth, 1989; Silventoinen, 2003). As such, research has recently focused on adult height as a marker of circumstances in childhood (Blackwell et al., 2001; Case and Paxson, 2010; Case and Paxson, 2008; Silventoinen, 2003; Subramanian et al., 2009). To further explicate the association between childhood socioeconomic condition and adult height, several indicators of childhood socioeconomic status have been investigated as predictors of adult height. For example, factors such as economic hardship in childhood/adolescence (Peck and Lundberg, 1995), parental education (Webb et al., 2008a), and father’s occupation (Peck and Vagero, 1987) have all been shown to be associated with adult height. The association between socioeconomic condition and height has been investigated in samples from developing countries and developed countries (Meyer and Selmer, 1999; Webb et al., 2008a),
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
including the US; however, few studies have investigated the association of multiple indicators of childhood socioeconomic condition as well as childhood health status with adult height. In addition, whether the association between childhood socioeconomic condition and height in later life is similar between whites and blacks as well as men and women remains an open question. The aim of the present study was to assess the independent association between adult height among older adults and three indicators of childhood circumstances: mother’s education, childhood financial hardship, and childhood health in a nationally representative sample of US adults. We were also interested in the differential association between childhood socioeconomic circumstances and height among older adults across race and gender categories. 2. Methods 2.1. Data We used the Health and Retirement Study (HRS) data for this analysis. The HRS is a biennial national longitudinal study of the economic, health, marital, family status, and public/private support systems of older Americans funded by the National Institute on Aging and the Social Security Administration and conducted by the Institute for Social Research Survey Research Center at the University of Michigan (Health and Retirement Study, 2004). The HRS uses a national multistage area probability sample of households in the U.S., with oversamples of Blacks, Hispaqnics, and residents from the state of Florida. Details of the HRS data collection methods are described elsewhere1 (National Institute on Aging, 2007). In addition to the public use HRS data, we used data prepared by the RAND Center for the Study of Aging2 (RAND HRS Data Version G, 2004). The RAND HRS Data file is an easy to use longitudinal data set based on the HRS data. It was developed at RAND with funding from the National Institute on Aging and the Social Security Administration. For this study we used cross-sectional data from the 2004 wave of the HRS (N = 20,129). Subjects that were not assigned a sampling weight value due to death, being institutionalized or age-ineligible were not included in the analysis (N = 1541). In addition, subjects were excluded from analysis if they were missing values on sociodemographic characteristics (N = 1), mother’s education (N = 1888), childhood health (N = 17), childhood financial hardship (N = 77), and adult SES (N = 32). This analysis focused only on US born whites and blacks; thus non-US born subjects (N = 1644) and subjects who did not identify their race as either black or white (N = 366) were excluded. Lastly, subjects were excluded if born after 1953 (N = 484). The final sample use for analysis was N = 14,079.
1 http://www.nia.nih.gov/ResearchInformation/ExtramuralPrograms/ BehavioralAndSocialResearch/HRS.htm. 2 The RAND HRS Data file is an easy to use longitudinal data set based on the HRS data. It was developed at RAND with funding from the National Institute on Aging and the Social Security Administration. http://www.rand.org/labor/aging/.
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The HRS began in 1992 and is comprised of several additional cohorts that have been added to ensure it remains representative of adults in the US over age 50 (Health and Retirement Study (HRS), 2008). The baseline HRS sample interviewed in 1992 consisted of respondents born between 1931 and 1941 (N = 12,652). The second sample currently included in the HRS began as a separate study called the Assets and Health Dynamics among the Oldest Old (AHEAD) (N = 8222). The respondents in this sample were born before 1923. The original HRS and the AHEAD were merged into a single study in 1998, subsequently called the HRS. In 1998, two additional samples were added to the HRS: (1) respondents born between the baseline HRS sample and the AHEAD sample and called The Children of the Depression Age sample (1924–1930); and (2) A ‘‘refresher cohort’’ of respondents called the War Baby sample, which consisted of respondents born between 1942 and 1947. Lastly, in 2004, a new sample entered the HRS called the Early Baby Boomer sample, which consisted of respondents born between 1948 and 1953. These combined samples compose the HRS (Health and Retirement Study (HRS), 2008). For analyses here, we defined the cohorts by the birth cohort the HRS used for assigning respondent level sampling weights (Health and Retirement Study, 2004). In so doing, seven cohorts were defined for respondents: (1) born on or before 1913; (2) born 1914–1923; (3) born 1924–1930; (4) born 1931–1936; (5) born 1937–1941; (6) born 1942–1947; (7) born 1948–1953. 2.2. Outcome variable Respondent’s height was self-reported upon their entry into the HRS and recorded in inches. For the present analysis, respondent’s height was converted to centimeters. 2.3. Independent variables Socio-demographic characteristics including race/ethnicity and gender were assessed by self-report in the HRS. We used two categories for race: (1) White and (2) Black/ African American; and gender was self-report (male/ female). We used three indicators of childhood circumstances: mother’s education, childhood health status, and the experience of childhood financial hardship. Mother’s education was used as a proxy for childhood SES and was measured as mother’s educational attainment categorized into four categories: (1) no education; (2) up to grade 8; (3) high school graduate; (4) more than high school education. Childhood health status was measured using one question from the HRS: ‘‘Consider your health while you were growing up, from birth to age 16. Would you say that your health during that time was excellent, very good, good, fair, or poor?’’ The experience of childhood financial hardship was measured using one question from the HRS: ‘‘While you were growing up, before age 16, did financial difficulties ever cause you or your family to move to a different place?’’ 2.4. Statistical analysis Analyses were stratified by gender and race. Bivariate analyses (ANOVA) were used to determine differences in
[()TD$FIG]
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
196
180
170 169
179
168 178
167 166 165
176
164 175
163
174
162 161
173
Women
Men
177
Men White men Black men Women White women Black Women
160 172
159
171
158
Birth Cohort Fig. 1. Average height (cm) across birth cohort categories for white men, black men, white women, and black women.
adult height across the categories of birth cohort and childhood circumstances variables. We used OLS regression to test the association among adult height, childhood socioeconomic circumstances, and childhood health in gender and gender and race-stratified samples. We used a step-wise modeling strategy. The first model included only the birth cohort variable. Next, to determine the incremental influence of the childhood related variables, childhood health was entered and then mother’s education and childhood financial hardship were entered into the [()TD$FIG]
models. All models were weighted for differential sampling rates and non-response. SAS1 9.2 was used for all analyses. 3. Results The average age of the sample was 68 years old (ranging from 51 to 108). Average height across birth cohort categories for men and women and also stratified by gender and race is shown in Fig. 1; and average height
185
180
175
170 No educaon 165
Up to grade 8 High school graduate
160
More than high school
155
150
145 Men
Women
White men Black men
White women
Black Women
Fig. 2. Average height (cm) across mother’s education categories for white men, black men, white women, and black women.
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Table 1 Height (cm) by birth cohorts, childhood health and childhood socioeconomic categories by gender. Men N Birth cohort 1913 1914–1923 1924–1930 1931–1936 1937–1941 1942–1947 1948–1953 Mother’s education (mother’s educational attainment) No education Up to grade 8 High school graduate More than high school Childhood Health Excellent Very good Good Fair Poor Childhood financial hardship No hardship present Hardship present
Women Mean adult height
95% CL
p-Value
N
Mean adult height
95% CL
p-Value
p < 001 83 665 932 1284 1316 859 1003
176.15 176.61 177.60 177.98 178.68 178.99 178.94
174.75 175.99 177.03 177.59 178.32 178.47 178.45
177.57 177.22 178.16 178.37 179.03 179.50 179.44
p < 0.001 157 1126 1159 1554 1609 1363 964
160.06 161.96 162.42 163.55 163.76 163.85 163.49
159.21 161.52 162.01 163.15 163.36 163.45 162.97
160.92 162.40 162.83 163.94 164.16 164.25 164.02
p < 001 99 2046 3263 734
173.36 177.57 178.60 179.72
171.33 177.17 178.27 179.14
175.38 177.97 178.94 180.30
3299 1577 953 242 71
178.72 178.38 177.38 177.96 177.15
178.41 178.06 176.79 177.03 174.05
179.04 178.70 177.97 178.90 180.26
4937 1205
178.56 177.70
178.30 177.25
178.82 178.16
p < 001 121 2967 3917 927
160.28 162.68 163.37 164.32
158.52 162.38 163.11 163.69
162.05 162.97 163.63 164.95
4104 2012 1321 384 111
163.40 162.98 162.99 163.45 163.15
163.13 162.63 162.58 162.64 161.85
163.68 163.33 163.40 164.26 164.45
6579 1353
163.29 162.93
163.08 162.58
163.51 163.28
p < 0.001
p = 0.09
p < 0.001
p = 0.04
Averages are weighted by sample weights.
across categories of mother’s education is shown in Fig. 2. Average height across birth cohort categories and all the childhood circumstances variables is shown in tabular form for men and women in Table 1 and stratified by gender and race in Table 2. Average height for men and women across the categories of mother’s education showed a positive gradient. Height also progressively increased across the birth cohorts, except for the youngest cohort where men were 0.05 cm and women were 0.36 cm shorter than the previous cohort; and those who experienced childhood financial hardship were 0.86 cm for men and 0.36 cm for women taller than those who did not. However, the gradient was not noted across the childhood health categories in the gender-stratified sample (Table 1). In the gender-race stratified sample, the white samples more closely resembled the gender-stratified sample than the black samples in average height across the birth cohort categories (Fig. 1). Across the childhood circumstances variables, the average height gradient patterns in the white male sample were also consistent with the genderstratified sample. However, the black male sample differed in that a positive gradient across the birth cohort categories was not noted, but there was a gradient in average height across the childhood health categories for black men. Across the childhood circumstances variables for women, the white female sample closely resembled the gender-stratified sample; however, a surprising result for the black female sample showed that women who experienced childhood financial hardship were slightly taller (0.34 cm) than those who did not experience hardship (Table 2). Overall, the results here suggest a significant height advantage for those with higher mother’s education and those who did not experience childhood financial hardship.
Gender stratified bivariate analyses revealed statistically significant differences in average height across the birth cohort categories and the categories for each of the childhood circumstances variables for men; yet, for women statistically significant differences were noted in all the variables except childhood health (Table 2). Genderrace stratified bivariate analyses revealed statistically significant differences across the birth cohort categories for white men (p < 0.001), black men (p < 0.001), white women (p < 0.001), and black women (p < 0.01). Across the categories of mother’s education, statistically significant differences were only noted for white men (p < 0.001) and white women (p < 0.001), and not for black men (p = 0.08) or black women (p = 0.45). Similarly, for childhood health, statistically significant differences were again only noted for white men (p < 0.01) and white women (p < 0.01), but not for black men (p = 0.31) or black women (p = 0.85). Lastly, statistically significant differences in childhood financial hardship were only noted for white men (p < 0.001) and white women (p = 0.02), but not for black men (p = 0.94) or black women (p = 0.91). The gender stratified regression models including birth cohort and all the childhood circumstances variables in the model revealed a gradient in average adult height across the birth cohort categories, with the exception of the cohort born between 1948 and 1953, which was shorter than the previous cohort born 1942–1947 for both men and women (Table 3). Comparing the mother’s education category of no education to the up to grade 8, high school graduate, and greater than high school categories also showed a statistically significant association for men and women. For example, when controlling for birth cohort, childhood health, and childhood financial hardship men whose mother had greater than a high school education were 5.83 cm (p < 0.001) taller than those whose mother
198
Table 2 Height (cm) by birth cohorts, childhood health and childhood socioeconomic categories by gender and race.
Birth cohort 1913 1914–1923 1924–1930 1931–1936 1937–1941 1942-1947 1948–1953 Mother’s education No education Up to grade 8 High school graduate More than high school Childhood health Excellent Very good Good Fair Poor Childhood financial hardship No Hardship Present Hardship present z
Black men z
N
Mean adult height
74 603 858 1101 1141 756 848
176.05 176.75 177.64 177.94 178.77 179.04 178.84
174.58 176.12 177.06 177.55 178.41 178.47 178.34
177.52 177.37 178.21 178.33 179.14 179.61 179.34
80 1690 2938 675
172.94 177.56 178.58 179.73
2929 1387 802 203 62
178.71 178.41 177.33 178.03 177.28
4337 178.56 1046 177.66
Averages are weighted by sample weights.
95% CL
White women z
N
Mean adult height
9 63 74 183 173 103 155
177.76 174.16 176.89 178.41 177.41 178.35 179.82
173.42 172.19 174.20 177.16 176.29 176.73 178.41
182.09 176.14 179.59 179.67 178.54 179.96 181.22
170.61 177.09 178.25 179.11
175.27 19 175.61 178.02 356 177.71 178.91 326 178.93 180.34 59 179.62
178.39 178.07 176.69 177.00 174.25
179.03 371 178.90 178.75 190 177.99 177.96 151 177.87 179.07 39 177.43 180.30 9 175.14
178.30 178.83 601 178.49 177.18 178.15 159 178.06
95% CL
Black women z
N
Mean adult heightz 95% CL
17 125 103 267 289 208 207
158.40 162.90 163.80 163.88 163.66 164.94 163.22
153.78 161.46 162.33 162.97 162.80 164.12 162.00
163.03 164.35 165.27 164.78 164.52 165.80 164.44
158.04 162.21 163.01 163.67
160.91 36 161.85 162.86 585 163.57 163.59 508 164.16 164.97 87 164.28
158.13 162.90 163.35 162.82
165.58 164.23 164.97 165.75
163.07 162.48 162.40 162.63 161.76
163.66 548 163.78 163.25 303 164.11 163.31 263 163.94 164.47 75 162.89 164.65 27 162.86
163.20 163.34 162.78 161.16 159.94
164.36 164.88 165.11 164.62 165.79
N
Mean adult height
140 1001 1056 1287 1321 1155 757
160.17 161.91 162.30 163.51 163.77 163.72 163.54
159.31 161.45 161.86 163.10 163.33 163.31 162.93
161.03 162.37 162.73 163.94 164.21 164.13 164.15
172.50 176.71 177.70 177.47
178.72 85 159.48 178.70 2382 162.54 180.15 3409 163.30 181.77 841 164.32
177.81 176.61 176.00 175.64 166.26
179.99 3556 163.37 179.38 1709 162.87 179.75 1058 162.85 179.21 309 163.55 184.01 84 163.20
177.52 179.46 5580 163.24 176.85 179.28 1136 162.80
95% CL
163.04 163.45 999 163.75 162.39 163.21 217 164.09
163.19 164.31 163.04 165.14
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White men
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199
Table 3 Regression estimates of height (cm) as dependent variable by gender*. Men, N = 6142
Intercept Birth cohort 1913 1914–1923 1924–1930 1931–1936 1937–1941 1942–1947 1948–1953 Childhood health Excellent Very good Good Fair Poor Mother’s education No education Up to grade 8 High school grad >High school Childhood financial hardship Yes No
Women, N = 7932
Estimate
95% CI
170.90
166.85, 174.95
Reference 0.28 1.26 1.59 2.18 2.38 2.15
1.20, 1.76 .35, 2.87 0.14, 3.04 0.69, 3.66 0.86, 3.88 0.63, 3.67
0.71 0.53 0.25 0.26 Reference
2.33, 2.54, 3.47, 2.75,
p-Value
3.74 3.60 2.96 3.26
0.71 0.12 0.03 0.005 0.003 0.006 0.64 0.73 0.87 0.87
Estimate
95% CI
157.08
154.78, 159.37
Reference 1.84 2.29 3.39 3.53 3.53 3.08
0.75, 1.27, 2.43, 2.65, 2.59, 2.17,
0.03 0.19 0.09 0.28 Reference
p-Value
2.94 3.31 4.34 4.41 4.47 3.99
1.37, 1.67, 1.51, 1.37,
1.44 1.29 1.33 1.89
Reference 4.17 4.92 5.83
2.16, 6.26 2.86, 6.99 3.67, 7.99
<0.001 <0.001 <0.001
Reference 2.57 3.16 3.85
0.77, 4.37 1.45, 4.88 2.10, 5.61
Reference 0.45
0.03, 0.93
0.06
Reference 0.21
0.17, 0.60
0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.96 0.80 0.90 0.73
0.01 <0.001 <0.001
0.28
*
Regression models were weighted to account for non-response and unequal selection probability; adjusted R2 for men = 0.03; adjusted R2 for women = 0.02.
had no education and for women the difference was 3.85 cm (p < 0.001). Those who did not experience childhood financial hardship were slightly taller than those who did not; although the association between adult height and childhood financial hardship was not statistically significant. The association comparing poor childhood health to the other categories of childhood health on adult height was also not statistically significant (Table 3). The results of gender-race stratified models including birth cohort and all the childhood circumstances variables revealed a statistically significant association between birth cohort and adult height for all groups except black men. Mother’s education also showed a positive gradient with adult height across the groups; however, this association was only statistically significant for white men and white women and not for black men and black women. Those who experienced childhood financial hardship were slightly taller than those who did not (except for black women), but this association was not statistically significant (Table 4). Similar to the genderstratified models, these results suggest significant height advantage for those with higher mother’s education, even after controlling for childhood health, childhood financial hardship, and birth cohort; however, only for white men and women. Although this pattern was also present in black women and men, mother’s education was not statistically significant for those groups. 4. Discussion Consistent with other studies suggesting a secular trend in adult height in Western countries (Cole, 2000; Cole, 2003; Komlos and Lauderdale, 2007; Ogden et al., 2004), the findings from this study revealed a positive trend in
adult height across birth cohorts in the Health and Retirement Study. This trend is consistent with the increasing average heights noted in Komlos (2010) using gender-race stratified data from NHANES. Although Komlos (2010) showed mean heights for white men and white women consistently higher than black men and black women across birth years (Komlos, 2010), our results did not reveal this same pattern. Our results also revealed attenuation in the association between birth cohort and adult height when mother’s education and childhood health were entered in the models especially for men (but less so for women). Thus, the variability in adult height by birth cohort can at least be partially explained by differences in childhood circumstances. Surprisingly, childhood health was not associated with adult height when variables such as mother’s education and childhood financial hardship were included in the models. Overall, our results suggest that adult height is more sensitive to mother’s education than childhood health; and that in particular for white men and white women, childhood socioeconomic factors (i.e., mother’s education) may exert a significant influence on adult height. The importance of mother’s education on subsequent health outcomes has also been shown in relation to obesity (Baum and Ruhm, 2009). In particular, using data from the National Longitudinal Survey of Youth Baum and Ruhm (2009) showed that an extra year of maternal education reduces estimated obesity by 1.20 percentage points. The mechanisms through which mother’s education impacts children’s health could be through the association between the mother’s higher educational attainment and improved prenatal care, better health behaviors, and an increased likelihood that she is married (Currie and Moretti, 2003). The lack of an association for
200
Table 4 Regression estimates of height (cm) as dependent variable by race and gender*.
Intercept Birth cohort 1913 1914–1923 1924–1930 1931–1936 1937–1941 1942–1947 1948–1953 Childhood health Excellent Very good Good Fair Poor Mother’s education No education Up to grade 8 High school grad >high school Childhood financial hardship Yes No *
Black men, N = 760
Estimate
95% CI
170.44
166.32, 174.56
Reference 0.51 1.36 1.64 2.35 2.51 2.09
1.05, 2.06 0.29, 3.01 0.16, 3.11 0.82, 3.87 0.96, 4.06 0.56, 3.63
0.61 0.46 0.43 0.23 Reference
2.38, 2.52, 3.61, 2.73,
p-Value
3.60 3.44 2.76 3.20
0.51 0.10 0.03 0.003 0.002 0.01 0.69 0.76 0.79 0.87
White women, N = 6716
Estimate
95% CI
173.70
163.26, 184.14
Reference 3.87 0.77 0.31 0.67 0.07 1.54
8.69, 5.90, 4.48, 5.56, 4.51, 3.11,
0.96 4.35 5.11 4.22 4.66 6.18
0.11 0.76 0.90 0.78 0.97 0.51
6.00, 6.69, 6.48, 7.03,
10.55 9.94 10.09 9.22
0.58 0.70 0.66 0.79
2.27 1.63 1.80 1.09 Reference
p-Value
Estimate
95% CI
156.63
154.30, 158.96
Reference 1.70 2.07 3.24 3.43 3.24 2.97
0.57, 1.01, 2.27, 2.52, 2.27, 2.02,
0.07 0.35 0.28 0.30 Reference
p-Value
2.82 3.13 4.21 4.33 4.21 3.92
1.60, 2.02, 1.85, 1.39,
Black women, N = 1216
1.46 1.32 1.29 1.99
0.004 <0.001 <0.001 <0.001 <0.001 <0.001 0.93 0.68 0.72 0.72
Estimate
95% CI
155.38
148.85, 161.91
Reference 4.54 5.70 5.64 5.45 6.90 4.84
0.32, 9.41 1.10, 10.30 0.92, 10.35 0.56, 10.35 2.29, 11.51 0.18, 9.85
0.68 1.18 1.09 0.04 Reference
2.60, 1.55, 2.03, 3.77,
p-Value
0.07 0.02 0.02 0.03 0.004 0.06
3.96 3.92 4.20 3.70
0.68 0.39 0.49 0.98
Reference 4.59 5.35 6.30
2.34, 6.85 3.01, 7.69 3.86, 8.75
<0.001 <0.001 <0.001
Reference 1.83 2.52 2.73
1.59, 5.24 1.20, 6.24 1.24, 6.69
0.29 0.18 0.17
Reference 3.14 3.79 4.55
1.66, 4.62 2.38, 5.19 3.00, 6.09
<0.001 <0.001 <0.001
Reference 2.30 2.98 3.01
1.77, 6.37 0.93, 6.89 1.09, 7.12
0.26 0.13 0.15
Reference 0.50
0.005, 1.00
0.05
Reference 0.41
1.09, 1.90
0.59
Reference 0.28
0.13, 0.69
0.17
Reference 0.64
1.65, 0.38
0.21
Regression models were weighted to account for non-response and unequal selection probability; adjusted R2 for white men = 0.03; black men = 0.02; white women = 0.02; black women = 0.01.
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
White men, N = 5382
R.D. Tucker-Seeley, S.V. Subramanian / Economics and Human Biology 9 (2011) 194–202
mother’s education in black men and black women in our study is worth further exploration in larger black samples. Previous research has shown that the association between childhood SES and adult health outcomes in later life do not vary substantially by race (Luo and Waite, 2005); thus a finding of a differential association between mother’s education and height by race in our study warrants additional investigation. Possible explanations for the lack of an association between childhood health and childhood financial hardship and adult height could be measurement error as these were both assessed with only one question from the HRS. There are several limitations to this study. First, the mother’s education and childhood health were retrospective reports made by the respondents and all other variables (including present height) were self-report as well; thus, the measures are subject to recall bias. Height was asked of HRS respondents in the baseline study or when the respondents initially enter the study (Jenkins et al., 2008). Overestimation of height has been shown to increase with age and decrease with measured height (Bostrom and Diderichsen, 1997; Kuczmarski et al., 2001; Merrill and Richardson, 2009). However, Gunnell et al. (2000) showed that the correlation between self-report and measured height was over 0.90 in older adults with shorter subjects more likely to over-report. Investigations of the effects of over-reporting on socioeconomic differences in height in Sweden resulted in an underestimation of SES differentials in height (Bostrom and Diderichsen, 1997). The over-reporting in our sample could be due to shrinkage, with older adults recalling their height from earlier in their lives; thus such over-reporting might be closer to an estimation of achieved height. We did not account for the potential height loss (shrinkage) that occurs as individuals age (Sorkin et al., 1999); however, the potential for shrinkage was not expected to be patterned by the socioeconomic variables used in this study. We also acknowledge the limitation of using one question to measure each of the childhood socioeconomic circumstances, and understand that the multidimensionality of these constructs may not necessarily be captured by the single question; however, we contend that using these measures together in a single model may indeed tap into components of childhood socioeconomic circumstances better than using only one of these questions in a model. Additionally, it is not possible to tease apart whether the lack of chronic health conditions in childhood, which may not necessarily be captured by self-reported childhood health, lead to increased height or a confounder of later life height and childhood health lead to increased height (Case and Paxson, 2008). Lastly, potentially confounding genetic factors and parental height were not included in this study and the possible correlation between parental height, mother’s education, and childhood height could introduce bias in the parameter estimates; yet the potential accuracy of such information as parental height would be difficult to substantiate (Webb et al., 2008a,b), especially in samples of older adults. Our results are consistent with other studies on the association between childhood socioeconomic circumstances and adult height (Kuh and Wadsworth, 1989; Peck and Vagero, 1987; Peck
201
and Lundberg, 1995; Webb et al., 2008a,b). Our results for white men and white women are also consistent with the Case and Paxson (2008) results that reported bivariate associations between childhood health (dichotomized) and adult height; but, when we controlled for birth cohort, mother’s education, and childhood financial hardship the association in our results between childhood health and adult height became non-significant. Differences that could help explain the divergent results between our study and the Case and Paxson (2008) study include that they dichotomized childhood health and in the present study where we used all five categories of childhood health, our results are stratified by gender and race, and our study includes additional control variables such as mother’s education and childhood financial hardship. To our knowledge no study has simultaneously assessed the association between the three indicators of childhood circumstances and adult height assessed in this study in a US sample. Our results reveal the relative importance of various childhood circumstances to adult height in US adults. In particular, our results suggest that the socioeconomic circumstances in childhood as captured by measures of mother’s education and childhood financial hardship may be more important than health in childhood for adult height. Yet, it should be noted that childhood socioeconomic circumstances such as mother’s education and financial hardship may substantially influence childhood health; and the lack of a statistically significant association in our fully controlled models for childhood health may suggest that childhood health is on the causal pathway between these childhood socioeconomic circumstances and adult height, especially for white men and white women. Adult height has been shown to be associated with morbidity and mortality (Batty et al., 2009), with a negative association with cardiovascular disease (Davey Smith et al., 2000) and a positive association with some cancers (Gunnell et al., 2009). Steckel (2009) suggests that to better understand how average height can be used as a useful measure of human welfare, we need to know the various socioeconomic factors that can affect it and the impact of those factors (Steckel, 2009). It has been hypothesized that adult height is a good marker of socioeconomic experiences in childhood (Case and Paxson, 2010; Case and Paxson, 2008), with the current evidence in the literature suggesting that adult height is indeed a good marker of post-natal ‘‘diet, psychosocial stress, chronic illness, and social circumstances’’ (Batty et al., 2009). In summary, this study provides evidence for an association between childhood socioeconomic conditions and adult height substantiating the importance of early childhood circumstances for later health and well being. Acknowledgement No direct funding was available for this study. SV Subramanian is supported by the Robert Wood Johnson Investigator Award in Health Policy Research, and the National Institutes of Health Career Development Award (NHLBI K25 HL081275).
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