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ADOLESCENT HEALTH |
J. Richard Udry and Janet Hendrickson-Smith are with the Carolina Population Center, University of North Carolina at Chapel Hill. Rose Maria Li is with Analytical Sciences, Inc.
Correspondence: Requests for reprints should be sent to: J. Richard Udry, PhD, Carolina Population Center, CB# 8120, 123 West Franklin Street, Chapel Hill, NC 275162524 (e-mail: udry{at}unc.edu).
| ABSTRACT |
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Objectives. This study compared the health and risk status of adolescents who identify with 1 race with those identifying with more than 1 race.
Methods. Data are derived from self-reports of race, using the National Longitudinal Study of Adolescent Health (Add Health), which provides a large representative national sample of adolescents in grades 7 through 12. Respondents could report more than 1 race.
Results. Mixed-race adolescents showed higher risk when compared with single-race adolescents on general health questions, school experience, smoking and drinking, and other risk variables.
Conclusions. Adolescents who self-identify as more than 1 race are at higher health and behavior risks. The findings are compatible with interpreting the elevated risk of mixed race as associated with stress.
| INTRODUCTION |
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Most studies are based on clinical reports or reports of mixed-race samples without comparison to single-race groups. It is not surprising that such samples lead to the conclusion of emotional and behavior problems, as clinical samples are self-selected for problems. No national data on adolescents have been reported, except from the sample we used.
In 2000, the Bureau of the Census introduced a new system of reporting race, providing a list of races and asking respondents to check all that apply. Because an adult in the household filled out the census, children and adolescents had their race reported by a household adult. The National Health Interview Survey (NHIS) has been using a check-all-that-apply race classification for data collection for 20 years, but data on the health of those reporting mixed race is only recently being reported.10 In the NHIS, race for adolescents and children is reported by a household adult.
These 2 national sources will provide new data on mixed-race adults and children. However, such data are not suitable for examining the racial identity of adolescents, as their race is reported by another person in the household.
We test the prevailing view of the literature that mixed-race adolescents are at higher health and behavior risk than single-race individuals because of stress associated with mixed racial identity. An alternative and simpler hypothesis is that mixed-race adolescents are affected by the cultural experience of both races and will have risk status in between their 2 component races. We test the hypothesis that mixed-race adolescents are within the boundary values for the nonrisk individual and family attributes of the 2 single-race groups that constitute their identities.
| METHODS |
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About a year later, a probability subsample of school respondents (plus those on the school roster but not present at school administration) were interviewed at home, using a laptop questionnaire (questionnaire recorded in a laptop computer with answers electronically recorded) that was administered by an interviewer. Sensitive questions were self-administered using earphones to listen to the questions read from the computer while shown on the screen. The home interview collected a broader range of data than the school questionnaire. A parent or guardian was also interviewed for most respondents. The same race question was asked on the school, home, and parental surveys.
Add Health respondents were asked to identify their race answering the following question: "What is your race? You may give more than 1 answer: White, Black or African American, American Indian or Native American, Asian or Pacific Islander, Other."
Racial reporting of respondents was based on self-identification in self-administered school questionnaires and interviewer-administered home interviews. Add Health used the check-all-that-apply technique, allowing respondents to choose as many races as they wished.
Cooney and Radina11 exploited another Add Health possibility for multiple race classification from this same data source. Cooney and Radina used the small, public use subset of Add Health cases and further limited their analysis to adolescents living with both biological parents, 1 of whom had provided a parental interview. Because only slightly more than half of Add Health respondents lived with both biological parents, this analytic strategy resulted in a much reduced sample size, consisting only of adolescents in biologically intact families. If the parent self-identified as 1 race and identified the other parent as another, Cooney and Radina classified the child as mixed race. This strategy does not provide the adolescents racial self-concepts. Parker and Lucas12 found that parents who reported a spouse of a different race did not necessarily report that their child was of more than 1 race.
This article uses the childs report of his own race without reference to parents races. It should not be assumed that the child reported what parents or coresident adults would have reported for the child, nor that the parents would report themselves as the same race combination (if any) as the child self-reported. This self-identification assures us that the adolescent racial self-concept is what we are working with.
Measurement of Dependent Variables
Variables to be correlated with race were derived from both the self-administered school questionnaire and the home laptop interview. They fall into 3 general categories: risk variables (school questionnaire), risk variables (home interview), and nonrisk attributes (school and home surveys).
Risk variables (school questionnaire). General health: Self-reported healthfair or poor (vs excellent or good); wake up feeling tired often or every day in last month; have skin problems such as itching or pimples often or every day last month; have headache often or every day in last month; have aches, pains, or soreness in your muscles or joints every day last month; have trouble falling asleep or staying asleep often or every day in last month; feel depressed or blue often or every day in last month.
Substance use: Smoked cigarettes at least 2 days/month during last 12 months; drink beer, wine, or liquor at least 2 days/month during last 12 months; get drunk at least 2 days/month during the last 12 months.
Risk variables (home interview). Access to guns: Guns easily available in the home.
Nonrisk attributes (school and home surveys). Vocabulary score/picture vocabulary test (PVT): Add Health short version of Peabody PVT,13 percentage in category that are above the overall 75th percentile (home interview).
School questionnaires with sampling weights were completed by 83 135 respondents, and home interviews by 18 924 adolescents. Analysis is computed in Stata (Stata Corp, College Station, Tex) to adjust for differential probabilities of selection and clustering of the sample. Weighted analyses provide estimates that are representative of the adolescent U.S. population.
| RESULTS |
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In the 1997 NHIS, unpublished data collected on a representative sample of Americans indicate that only 1.4% selected more than 1 race, a figure stable over the last 20 years.10 The 2 sources from which data on mixed-race children have been estimated (census and NHIS) use reports of child race given by a household adult.
To measure the differences in health and behavior risks between single-race and mixed-race respondents, we used 2 different methods of race comparison. In the first method, we compared respondents who reported that a specific race alone with those who reported race plus any other race by computing the odds ratio between the mixed-race group and the single-race group. The ratio is more than 1.0 when the mixed-race group is at greater risk and less than 1.0 when the mixed-race group is at lower risk. These ratios are shown in Table 2
. If single-race and mixed-race groups have the same risk, their odds ratio will not differ from 1.0 (evaluated here at the .05 level, 2-tailed test). Home-interview risk variables are based on a sample only one fourth as large as variables from the school questionnaire.
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In the analysis in Table 2
, various race combinations are subsumed in the mixed-race comparisons. For example, the unweighted mixed-race computation for Blacks from the school questionnaire puts in the same category the 294 Black/Asian, 590 Black/American Indian, 416 Black/White, 151 Black/Other, and 474 from 3 or more race combinations. If particular race combinations for Blacks have different risks, the differences are obscured in the table.
To examine this issue, we prepared a race-specific analysis in which each single-race group is compared with a specific combination with that race. We eliminated those who chose "Other" either alone or with another race because of a perceived ambiguity in meaning. These data are displayed in Table 3
. The sole purpose of Table 3
is to show whether the patterns of Table 2
are maintained when the mixed-race comparison is a specific 2-race category.
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We have interpreted our data with no controls. These analyses answer the question of whether mixed-race-identified adolescents are at higher risk than single-race-identified adolescents. Given that mixed-race-identified adolescents may differ from single-race-identified adolescents on other important attributes, would they be at higher risk if these differences were statistically controlled? We repeated the analyses presented in Tables 2
and 3
with statistical controls for age, sex, PVT, GPA, family structure, and family education (data available from the authors upon request). Although for many individual risk items the odds ratios change in identification of statistical significance, very few entries change odds ratios from above to below 1.0. Therefore, the overall inference from the controlled analysis is that the differences between single-race and mixed-race identifiers on the control variables we introduced are not the source of the higher risk encountered by mixed-race adolescents.
Previous analyses in this article have been limited to health and behavior risk variables. We examined a set of variables measuring individual and family characteristics that are not in and of themselves health and behavior risk variables, i.e., they are not attitudes, voluntary behaviors, or health symptoms. We offer 4 comparisons (family education, family structure, GPA, and PVT), each of them correlated with socioeconomic status. GPA is a measure of success in school. The PVT is highly correlated with success in school and is correlated with general intelligence tests. Because single-race respondents differ by race on these variables, it is possible that single-race respondents also differ from mixed-race respondents on the same variables. These measures are all considered here as not caused by personal motivations, decisions, or actions with respect to family structure, family education, and PVT, and at least partially for GPA. They represent cultural opportunities and cultural handicaps. Mixed-race adolescents may be supposed to have had exposure to the cultural fates of 2 racial groups. They may therefore be hypothesized to be between the 2 racial groups with which they identify on such attributes.
Table 4
compares the GPA, PVT, family education, and family structure for mixed-race and single-race adolescents by specific race combinations. These variables were all measured from the school questionnaire except for PVT, which was taken from the home interview. Comparisons are evaluated by direction of differences only.
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PVT
In no case is the mixed-race group value beyond the values of its constituent races. The Black/Asian group and the Asian/American Indian group are omitted because they have cell sizes too small.
Family Structure
In only 1 case (White/Asian) is the mixed-race group outside the range of its constituent races.
Family Education
In only 2 cases (Black/Asian and Black/American Indian) was the mixed-race group outside the range of its constituent rates.
The general pattern of these nonrisk attributes is that the mixed-race groups have values that are between the values of the 2 constituent races. This lends support to the hypothesis that the mixed-race adolescents have been influenced by both racial groups and, therefore, have an experience that is between those who report the single constituent races.
| DISCUSSION |
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Because risk among mixed-race adolescents is higher for all race combinations, some across-the-board explanation must be inferred. The most common explanation in the literature is stress associated with identity conflict. We cannot test this hypothesis directly. Many of the school variables tested for mixed-race risk are possible consequences of stress (e.g., most of the general health items, considered suicide, and drinking). Stress, then, is a possible explanation of mixed-race high risk because our risk assessment is based on possible stress symptoms. Whether the stress is associated with identity conflict is beyond our resources to test. Gibbs3 warns against jumping to the conclusion without direct evidence that the stresses of mixed-race adolescents are a consequence of race identity problems.
The findings of this study are subject to the limitations of respondent reporting and cell sizes. Adolescents did not always report their race. Of those who were in both the school and the home survey, 16% gave different answers to the 2 surveys. For a sociological interpretation of the inconsistency in race responses of Add Health respondents, see Harris and Sim.14 Previous studies have found that answers to race questions vary by nativity (foreign or native born), parental ethnicity, national origin, and school racial composition.1518
| CONCLUSION |
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| Acknowledgments |
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We thank Christine A. Bachrach and Stephanie Ventura for their careful review of earlier manuscript drafts, Kathleen Mullan Harris for sharing her insights, Kim Chantala for computational consultation and assistance, and Maryann Belanger for library research assistance. Persons interested in obtaining Add Health data for analysis should contact Francesca Florey, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 275162524 (e-mail: fflorey{at}unc.edu).
Human Participant Protection
Add Health is under continuous monitoring and approval of the institutional review board for the protection of human subjects of the School of Public Health, University of North Carolina at Chapel Hill.
| Footnotes |
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Accepted for publication August 23, 2003.
| References |
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