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Connecting national survey data with DSM-IV criteria
JOURNAL OF ADOLESCENT HEALTH 2002;31:475– 481
ORIGINAL ARTICLE
Connecting National Survey Data With DSMIV Criteria MANFRED H. M. van DULMEN, Ph.D., HAROLD D. GROTEVANT, Ph.D., NORA DUNBAR, M.S., BRENT C. MILLER, Ph.D., BRUCE BAYLEY, M.S., MATHEW CHRISTENSEN, M.S., AND XITAO FAN, Ph.D.
Purpose: To show how connections can be made among items in a nationally representative survey of adolescents and criteria for “Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition” (DSM-IV) diagnoses. Methods: Data for this study came from the Wave I in-home interview of the National Longitudinal Study of Adolescent Health (Add Health), a nationwide study of approximately 90,000 adolescents and their parents. Proxy variables were developed for four DSM-IV diagnoses based on Wave I survey questions: conduct disorder, alcohol abuse, cannabis abuse, and major depressive disorder (single episode). Prevalence rates, comorbidity rates, and detailed item analyses of these four constructs are reported. Results: Of the adolescents in the sample under study, 3.4% scored above the threshold for conduct disorder. For the alcohol abuse proxy 4.7% scored above the threshold, compared with 6.3% for the cannabis abuse proxy, and 1% scored above the threshold for major depressive disorder (single episode). Adolescents who scored above the threshold for conduct disorder were three times more likely to receive psychological counseling than adolescents who scored below the threshold for conduct disorder. The rates for alcohol abuse, cannabis abuse, and major depressive disorder (single episode) were 2.0, 3.0, and 5.0, respectively. Conclusions: The prevalence rates for the four constructs in the Add Health data set were generally lower or
From the University of Minnesota, Minneapolis, Minnesota (M.H.M.V., H.D.G., N.D.); Utah State University, Logan, Utah (B.C.M., B.B., M.C.); and University of Virginia, Charlottesville, Virginia (X.F.). Address correspondence to: Manfred H. M. van Dulmen, Ph.D., Institute of Child Development, University of Minnesota, 51 East River Road, Minneapolis, MN 55455. E-mail: [email protected] Manuscript accepted June 26, 2002.
comparable to prevalence rates found in other epidemiological studies in which DSM-IV criteria were applied. The approach described in this study provides a way to identify adolescents who are likely at risk for the development of mental health problems. © Society for Adolescent Medicine, 2002 KEY WORDS:
Add Health Alcohol abuse Conduct disorder Depression DSM-IV Measurement Substance abuse Survey data
The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) [1] provides extensive, broadly accepted guidelines for differentiating between adaptive and problematic behavior of children, adolescents, and adults. Although the DSM-IV has been criticized for its inability to distinguish pathology from normality and its ability to differentiate among mental disorders, it provides the best effort for a common language among researchers and clinicians interested in the diagnosis of mental health disorders [2].
Survey Data One of the recent trends in social science research has been the increase in use of large-scale surveys. Although survey methods are limited in several ways,
© Society for Adolescent Medicine, 2002 Published by Elsevier Science Inc., 360 Park Avenue South, New York, NY 10010
1054-139X/02/$–see front matter PII S1054-139X(02)00453-6
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they have the potential to overcome a major problem of smaller studies, representativeness of sampling. Federal agencies have funded large surveys representative of the United States population, rather than focusing exclusively on small studies with questionable external validity. Many of these national surveys have been made available for public use to maximize the federal research investment. Numerous surveys (such as the National Survey of Families and Households and the National Survey of Children) contain information relevant to family structure, family dynamics, parent-child relationships, and child and adolescent functioning. Researchers interested in these variables must typically construct measures from the items available, because complete scales from prior research are not typically present. For scales measuring individual or family variables, it is difficult to know where to draw the line between responses that indicate adaptive vs. maladaptive functioning, because researchers must develop their own scales and justify their reliability and validity. In the present paper, DSM-IV criteria are applied to provide a benchmark for adaptive vs. maladaptive functioning.
DSM-IV and Survey Data Starting data analysis with a framework for scale development has the benefit of providing a common language among researchers and practitioners; without such a framework communication between clinicians and researchers is problematic. The DSM-IV is the best approximation for the diagnosis of mental disorders [2], and although linking DSM-IV language to existing survey data does not provide diagnoses, it can identify adolescents who would be considered at risk for the development of certain mental health disorders and suggests cut-off points on survey scales that are not arbitrary but based on the DSM-IV framework. An example of this approach is provided by Chen et al. [3], who used the National Household Survey on Drug Abuse [4 – 6] to develop a proxy measure of DSM-IV marijuana dependence. From the NHSDA, Chen et al. were able to assess 5 of the 7 DSM-IV criteria to assess marijuana dependence, however they were not able to validate their findings with clinical assessments. Information from the NHSDA about whether the participants were currently receiving treatment was used as one way to validate their findings.
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Aims The first aim of this paper is to develop proxy variables for selected DSM-IV diagnoses, including a clinical cut-off that approximates the DSM-IV clinical cut-off. The resulting proxy variables are not the same as clinical diagnoses, but they do provide rough guidelines for researchers interested in trying to identify persons who are functioning at a problematic level. Researchers interested in behavioral dimensions can, of course, use continuous scores derived from summing appropriate items. The second aim is to validate the developed proxy variables by analyzing the prevalence rates of proxy variables and their co-occurrence or comorbidity. Furthermore, the validity of the proxy variables is tested by analyzing the relationship between the proxy variables and information on whether or not participants currently receive psychological counseling. The third aim of this paper is to characterize the usefulness of the DSM-IV constructs by conducting detailed item analyses that provide information about which items, or combinations of items, contribute most to the clinical cut-off for different DSM-IV constructs.
Methods Participants Data for this study came from the Wave I in-home interviews of the National Longitudinal Survey of Adolescent Health (Add Health), a nationwide study of approximately 90,000 adolescents and their parents. Add Health is a longitudinal survey measuring contextual variables (family, peers, school) that are related to the well-being of adolescents grades 7 through 12 [7]. A school cluster sampling design was used to obtain the sample. At Wave 1, data were collected from parents (questionnaire) and adolescents (in-home interview and school survey). For this study, data from the 90-minute adolescent in-home interview were used. The school survey data were not analyzed because they did not include sufficient questions to develop any DSM-IV construct. We included participants identified by the Add Health team as the “core sample” (see, e.g., 7) in this illustration of our approach. The core sample (n ⫽ 12,103) was selected to be representative of the U.S. adolescent population, correcting for the oversampling of several groups of ethnic minorities and adolescents with disabilities. The mean age of the adolescent core sample was 16.03 years, including
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5779 males and 6324 females. Adolescents’ ethnicity included: 61% white, 19% African-American, 12% Hispanic, 4% Asian-Pacific Islander, and 2% American Indian.
Procedure To link the DSM-IV criteria with the Add Health survey, we identified several mental health-related issues that occur relatively often in adolescents. Items from the Add Health codebooks provided tentative matches with each DSM-IV diagnosis. Several diagnoses were ultimately excluded from consideration because the necessary information was lacking in the Add Health survey. The main reasons diagnoses could not be included were an inability to determine an established pattern (oppositional deviant disorder, mood disorders, personality disorder), or the lack of historical information (attention deficit hyperactivity disorder, anxiety disorders, substance dependence, somatoform disorder, impulse-control disorders, bulimia). The following diagnoses were selected for further attention: conduct disorder, alcohol abuse, major depressive disorder (single episode), anorexia nervosa, cannabis abuse, inhalant abuse, cocaine abuse, and other illicit drug abuse. Next, we constructed the programming code to identify cases for each diagnosis. During this process, anorexia nervosa, inhalant abuse, cocaine abuse, and other illicit drug abuse were eliminated. For anorexia nervosa, it was not possible to determine whether there was an intense fear of gaining weight and whether there was a disturbance in body perception. For inhalant abuse, cocaine abuse, and other illicit drug abuse, it was not possible to establish whether the substance use was related to physically hazardous situations or to tell whether recurrent social or interpersonal problems were caused by using the substance. These are important criteria for DSM-IV diagnoses, but because the needed questions were not asked in the Add Health survey, further development of constructs for these diagnoses was not possible. Finally, we determined that we could develop proxy variables for four DSM-IV diagnoses based on Add Health Wave I survey questions: conduct disorder, alcohol abuse, cannabis abuse, and major depressive disorder (single episode). These four proxy variables are dichotomous, with the threshold for each approximating the conditions necessary to make a positive diagnosis by using the DSM-IV.
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Conduct disorder. Eight Add Health questions were scored for inclusion of the conduct disorder construct. We were able to assess 7 of the 15 criteria given in the DSM-IV diagnosis of conduct disorder; two questions referred to the same DSM-IV criterion. A score that exceeded the DSM-IV criteria on three of the seven symptoms resulted in a score above the threshold for the conduct disorder construct. Add Health did not provide sufficient information to determine clinically impaired functioning and whether criteria were met for antisocial personality disorder [8]. Alcohol abuse. A DSM-IV proxy measure for alcohol abuse was also constructed. First, a variable indicating recurrent alcohol use was created followed by developing the proxy measure for alcohol abuse. Recurrent alcohol use and problems in one of a variety of domains according to the DSM-IV criteria resulted in a score above the threshold for the alcohol abuse construct. Add Health did not provide sufficient information to assess recurrent substancerelated legal problems from the DSM-IV criteria, and whether the symptoms had met the criteria for substance dependence. Substance dependence distinguishes itself from substance abuse in that tolerance, withdrawal, or a pattern of compulsive use is present [1]. These criteria cannot be assessed with the Add Health data. Therefore, it could be that some adolescents who otherwise would have been classified as being alcohol dependent were included in the alcohol abuse category. However, as Harrison et al. [9] have noted, the distinction between alcohol abuse and alcohol dependence may be arbitrary. Cannabis abuse. Similar to the proxy measure for alcohol abuse, a variable that established recurrent cannabis use was created, followed by a construct for cannabis abuse. As with alcohol abuse, recurrent cannabis abuse and problems in one of a variety of domains according to the DSM-IV criteria, resulted in a score above the threshold for cannabis abuse. Again, the Add Health data did not provide sufficient information to determine legal problems related to cannabis abuse, nor was it possible to exclude overlap with cannabis dependence. Major depressive disorder (single episode). Finally, a proxy measure for major depressive disorder (single episode) was created. The Add Health data provided information to assess six of the nine symptoms included under major depressive disorder (single episode) in DSM-IV.
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Table 1. Proportion of Respondents Reporting Each Criterion by Number of Criteria for DSM-IV Constructs Number of Criteria
Criteria for conduct disorder Damage property Runaway Stealing Forced to have sex Pulled weapon/shoot/stabbed someone Steal something worth ⬎$50 Used weapon to threat Criteria for alcohol abuse Physically hazardous Trouble with parents Problems with friends Problems with dating partner Physical fighting Problems with school work Criteria for cannabis abuse Physically hazardous Problems with school work Criteria for major depressive disorder (single episode) Feeling blue Feel life not worth living Poor appetite Feel too tired Depressed mood Suicidal thoughts
1
2
3
4
5
6
7
67.7 3.9 6.2 1.9 9.4 4.0 2.5
71.2 9.3 32.1 3.3 30.4 24.3 9.0
79.5 13.5 52.9 5.3 47.5 46.7 18.0
89.3 20.4 73.8 4.9 60.2 72.8 22.3
94.1 31.4 92.2 7.8 92.2 90.2 62.7
100.0 88.2 100.0 11.8 100.0 100.0 88.2
100.0 100.0 100.0 100.0 100.0 100.0 100.0
75.7 8.6 2.3 3.9 6.5 2.9
81.1 38.7 15.3 28.8 23.4 12.6
74.5 47.1 39.2 52.9 52.9 33.3
80.0 66.7 66.7 80.0 46.7 60.0
75.0 75.0 75.0 100.0 75.0 100.0
0.0 0.0 0.0 0.0 0.0 0.0
24.1 75.9
100.0 100.0
32.2 6.7 15.8 28.0 10.3 6.9
56.4 14.3 33.6 45.1 39.5 11.1
77.9 31.7 46.9 55.0 73.2 15.4
90.7 59.1 56.1 73.4 92.4 28.3
95.9 83.7 82.7 90.8 94.9 52.0
100.0 100.0 100.0 100.0 100.0 100.0
Analysis Plan Analyses were conducted in SPSS 10.0. The percentage of participants scoring above the threshold for each proxy variable was calculated. Next, descriptive analyses were conducted to receive information on how often a criterion that was part of a proxy variable was present, depending on the level of the proxy variable. For example, to receive information on which criterion was most often included for those participants who only had one conduct disorder criterion, all participants with only one conduct disorder criterion were selected and percentages were calculated for each conduct disorder criterion. In other words, information was derived on how often a particular conduct disorder criterion was present given that participants only met the threshold for one criterion. The co-occurrence, or comorbidity, of proxy variables was analyzed by calculating the percentage overlap among the proxy variables. Odds ratios were calculated to test whether participants who received a score above the threshold for the proxy variables were more likely to have received psychological counseling in the past year than participants receiving a score below the threshold for the proxy vari-
ables. Odds ratios were also calculated to test whether participants who received a score above the threshold for alcohol abuse or cannabis abuse were more likely to have attended a substance abuse program in the past year than participants receiving a score below the threshold for alcohol abuse or cannabis abuse.
Results Descriptive frequencies and percentages of the four DSM proxy variables with the Add Health core sample were tabulated. Of the adolescents in the core sample, 3.4% scored above the threshold for conduct disorder. For the alcohol abuse proxy measure, 4.7% scored above the threshold, compared with 6.3% for the cannabis abuse proxy measure. The results for the major depressive disorder (single episode) proxy measure showed that 1.0% scored above the threshold. Table 1 shows the proportion of respondents affirming each criterion by number of criteria for the DSM-IV constructs. For example, with regard to the construct “conduct disorder,” 67.7% of the adolescents who only met one criterion did so because they
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scored over the threshold for ‘damage property.’ To score above the threshold on the DSM proxy measure for conduct disorder, adolescents must have met at least three of the seven criteria. For adolescents who met the minimum criterion, the following components were most commonly included (see column 3): damage property (79.5%), steal something worth ⬎ $50 (52.9%), and pulled a weapon or shot someone (47.5%). To be scored above the threshold for alcohol abuse, the criteria for at least one of the six components had to be met. In 75.7% of the cases, adolescents scored above the threshold because of meeting the criteria for physically hazardous (recurrent alcohol use and having been driving while drunk). To be scored above the threshold for cannabis abuse, the criteria for both components must be met. However, of the adolescents who met at least one criterion, 75.9% met the criteria for problems with school work compared with 24.1% for physically hazardous. None of the adolescents met all six criteria and therefore the percentages in this column are zero. To be scored above the threshold for major depressive disorder (single episode), five of the six possible criteria must be met. Among adolescents who met the minimum threshold, the following criteria were indicated: feeling blue (95.9%), depressed mood (94.9%), feel too tired (90.8%), feel life is not worth living (83.7%), and poor appetite (82.7%). Comorbidity rates among the four DSM constructs were also obtained. The comorbidity rate between conduct disorder and major depressive disorder (single episode) was 5.3% in this study. The comorbidity rates among other constructs included: 11.7% between conduct disorder and alcohol abuse, 15.5% between conduct disorder and drug abuse, and 19.4% between cannabis abuse and alcohol abuse. To further validate the usefulness of the DSM-IV constructs and to see whether or not they would distinguish among adolescents, odds ratios were calculated linking the DSM-IV proxies to a question about psychological counseling (“In the past year, have you received psychological or emotional counseling?”). Odds ratios showed that receiving psychological counseling was significantly related to receiving a score above the threshold for each of the four DSM-IV proxies (Table 2). Adolescents who scored above the threshold for conduct disorder were 3.0 times more likely to receive psychological counseling than adolescents who scored below the threshold for conduct disorder. The rates for alcohol abuse, canna-
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Table 2. Odds Ratio Calculations DSM-IV Constructs, by Receiving Psychological Counseling and Attendance Substance Abuse Program in the Past Year
Conduct disorder Alcohol abuse Cannabis abuse Major depressive disorder (single episode)
Psychological Counseling OR (CI)
Attend Substance Abuse Program OR (CI)
3.01* (2.42–3.74) 2.04* (1.66 –2.49) 2.97* (2.51–3.51) 5.02* (3.49 –7.22)
— 5.91* (4.47–7.81) 6.00* (4.65–7.76) —
* p ⬍ .01. OR ⫽ Odds Ratio; CI ⫽ 95% Confidence Interval Odds Ratio.
bis abuse, and major depressive disorder (single episode) were 2.0, 3.0, and 5.0, respectively. The proxy measures for substance abuse were further linked with receiving substance abuse counseling (“In the past year, have you attended a drug abuse or alcohol abuse treatment program?”). Adolescents who scored above the threshold for alcohol abuse were 5.9 times more likely to have received substance abuse counseling than adolescents who scored below the threshold for alcohol abuse. Adolescents who scored above the threshold for cannabis abuse were 6.0 times more likely to have received substance abuse counseling than adolescents who scored below the threshold for cannabis abuse.
Discussion We have presented an approach for constructing DSM-IV proxy measures from survey data for four commonly diagnosed adolescent behavior problems. The main strength of this approach is that it links survey items to a benchmark diagnostic system that is widely accepted in the mental health community. Other than the work by Chen et al. [3], we believe this is the only study that has attempted to relate items and scales in existing surveys to thresholds for DSM-IV diagnoses. In four important mental health-related areas (conduct disorder, alcohol abuse, cannabis abuse, major depressive disorder [single episode]), DSM-IV criteria could be used as a guiding tool in creating scales of adolescent problem behavior in Add Health data. Because it was not possible to find survey questions for all aspects of these diagnoses, the constructs developed from the DSM-IV criteria should not be seen as clinical diagnoses. However, the variables do identify individuals whose probability of receiving a DSM-IV diagnosis would be higher rather than lower. Furthermore, in support of
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the validity of the proxy measures results showed that adolescents who received a score above the threshold for one of the DSM-IV proxies were several times more likely to receive psychological counseling than adolescents who received a score below the threshold for each DSM-IV construct. In any case, use of the proxy measures helped us develop a fuller picture of adolescents’ mental health problems and their relationship with other individual and contextual phenomena. Prevalence Rates The prevalence rates for the four constructs in the Add Health data set were generally lower or comparable to prevalence rates found in other epidemiological studies in which DSM-IV criteria were applied. The prevalence rate found for conduct disorder (3.4%) was similar to the 1.5% to 3.4% prevalence rates for conduct disorder in the general population of children and adolescents [8]. The prevalence rates for alcohol abuse (4.7%) and cannabis abuse (6.7%) were comparable to what has been found in the population. Rates for alcohol abuse, based on recent general population studies, range from 3.5% [10] to 5– 6% [11]. For cannabis abuse, prevalence rates range from 5% to 7% [11]. There are no data available to determine population prevalence rates among adolescents based on the DSM-IV criteria [12]. A prevalence rate of 1% was found for major depressive disorder (single episode). In a representative sample of high school students, prevalence rates of 2.9% and 3.1% [13] were reported for major depressive disorder. The fact that the prevalence rates in this study generally were comparable to or lower than prevalence rates found in other epidemiological studies can in part be explained by the fact that information was not available for each sub-component. It is important for researchers developing large-scale surveys, such as Add Health, to take into consideration all the criteria that are used to assess problem behaviors in a classification system such as the DSM-IV. This enables researchers to develop problem behavior constructs with meaningful thresholds of severe problem behavior. Alternatively, the somewhat low prevalence rates might also be owing to the Add Health design. The Add Health study is a schoolbased design and therefore adolescents who were not currently in school were not included in the study. This is an important limitation of the Add Health study. We do not know whether the prevalence rates would have been different if adolescents
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who are not in school had been included in the Add Health study. The lower prevalence rate for major depressive disorder (single episode) can also be explained by the fact that the Add Health design did not provide information on some of criteria for major depressive disorder (single episode). Specifically, the in-home interview did not provide information on the following DSM-IV criteria for major depressive disorder (single episode): insomnia or hypersomnia nearly every day, psychomotor agitation or retardation nearly every day, and diminished ability to think or concentrate, or indecisiveness, nearly every day.
Comorbidity Rates The comorbidity rates found in this study were also comparable to what has been found in the general population. The comorbidity between conduct disorder/oppositional defiant disorder and depression ranging from 2.9% to 8.2% in general population studies using DSM-IV criteria [14], is consistent with the 5.3% found in this study for the comorbidity between conduct disorder and major depressive disorder (single episode). As described in the results section we found the following comorbidity rates for other constructs: conduct disorder and alcohol abuse was 11.7%, conduct disorder and drug abuse was 15.5%, cannabis abuse and alcohol abuse was 19.4%. Rohde et al. found point prevalence rates for cannabis abuse and problem drinking (meeting at least one criterion of DSM-IV substance abuse/dependence but no diagnosis) at 15.5% and for cannabis abuse and alcohol abuse/dependence 57.4%. Note that the alcohol abuse/dependence group in the study by Rohde et al. consisted mostly of adolescents who had received an alcohol dependence diagnosis. Comorbidity between substance abuse and major depressive disorder (single episode) was about 3.4% (cannabis abuse) and 3.6% (alcohol abuse) in this same study [15].
Limitations Using a categorical classification system (such as the DSM-IV) in research has limitations. Because the proxy variables are not diagnoses, researchers must be careful when they interpret their data. Furthermore, use of the proxies implies acceptance of the DSM-IV system and the implicit theory behind it.
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Conclusion Although a direct diagnosis might be greatly preferred by clinicians, our approach offers researchers a way to identify adolescents who are likely to be at risk. Most survey data sets like Add Health do not include complete measures of maladaptation and therefore researchers have to develop their own scales. Even if these survey data sets would include complete scales, researchers still have to make decisions about where to draw the line between adaptive and maladaptive functioning. These decisions are generally not guided by prior theory but based on a percentage cut-off point such as the top 10% or 15%. The approach illustrated in this paper identifies adolescents at risk based on the DSM-IV. Therefore, the decision where to draw the line between maladaptive and adaptive functioning is not arbitrary but based on an existing classification system and can be applied to other data sets that measure maladaptive functioning. We hope that this work will stimulate researchers to think about how survey data can be most useful in studies of mental health-related issues using existing data. A benchmarking approach such as we have presented might be useful with other types of data as well. Time efficiency, cost effectiveness, availability, and generalizability are among the advantages we see in working with survey data. We encourage researchers to consider maximizing use of these valuable resources by building further crosswalks between survey data and other theoretically or conceptually important constructs, and also by studying the relationship between these constructs and the developmental context in which the adolescent is situated. We gratefully acknowledge support provided by grant HD 36479 from the National Institute of Child Health and Human Development, to Brent C. Miller, P.I. We especially thank DenYelle Baete, Blaine Kirkpatrick, Matthew May, Reed Reichwald, and Sarah Scott who made key contributions to this paper. This research is based on data from the Add Health project, a program designed by J. Richard Udry (P.I.) and Peter Bearman, and funded by grant P01-HD31921 from the National Institute of Child Health and Human Development to the Carolina Population Center, University of North Carolina at Chapel Hill, with cooperative funding participation by the National Center Institute; the National Institute of Alcohol Abuse and Alcoholism; the National Institute on Deafness and Other Communication Disorders; the National Institute on Drug Abuse; the National Institute of General Medical Sciences; the National Institute of Mental Health; the National Institute of Nursing Research; the Office of AIDS research, NIH; the Office of Research on Women’s Health, NIH; the Office of Population Affairs, DHHS; the National Center for Health Statistics, Centers for Disease Control and Prevention, DHHS, the Office
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of the Assistant Secretary for Planning and Evaluation, DHHS; and the National Science Foundation. Persons interested in obtaining data files from the National Longitudinal Study of Adolescent Health should contact Add Health, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 27516-3997 (E-mail [email protected]).
References 1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Washington, DC: American Psychiatric Association, 1994. 2. Widiger TA, Clark LA. Toward DSM-V and the classification of psychopathology. Psychol Bull 2000;126:946 –63. 3. Chen K, Kandel DB, Davies M. Relationships between frequency and quantity of marijuana use and last year proxy dependence among adolescents and adults in the United States. Drug Alcohol Depend 1997;46:53–67. 4. Substance Abuse and Mental Health Service Administration (SAMSHA). National Household Survey on Drug Abuse: Main Findings 1991. Rockville, MD: U.S. Department of Health and Human Services, 1993. 5. Substance Abuse and Mental Health Service Administration (SAMSHA). National Household Survey on Drug Abuse: Main Findings 1992. Rockville, MD: U.S. Department of Health and Human Services, 1995. 6. Substance Abuse and Mental Health Service Administration (SAMSHA). National Household Survey on Drug Abuse: Main Findings 1993. Rockville, MD: U.S. Department of Health and Human Services, 1995. 7. Resnick MD, Bearman PS, Blum RW, et al. Protecting adolescents from harm: Findings from the National Study on Adolescent Health. JAMA 1997;278:823–32. 8. American Academy of Child and Adolescent Psychiatry. Practice parameters for the assessment and treatment of children and adolescents with conduct disorder. J Am Acad Child Adolesc Psychiatry 1997;36(Suppl):122–39. 9. Harrison PA, Fulkerson JA, Beebe TJ. DSM-IV substance use disorder criteria for adolescents: A critical examination based on a statewide school survey. Am J Psychiatry 1998;155:486 – 92. 10. Warren LA, Kessler RC, Hughes M, et al. Prevalence and correlates of drug use and dependence in the United States. Arch Gen Psychiatry 1995;52:219 –29. 11. Lewinsohn PM, Hops H, Roberts RE, et al. Adolescent psychopathology: I. Prevalence and incidence of depression and other DSM-III-R disorders in high school students. J Abnorm Psychol 1993;102:133–44. 12. Newcomb MD. Identifying high-risk youth: Prevalence and patterns of adolescent drug abuse. In: Rahdert E, Czechowicz D (eds). Adolescent Drug Abuse: Clinical Assessment and Therapeutic Interventions. Rockville, MD: U.S. Dept of Health and Human Services, 1995:7–38. 13. Lewinsohn PM, Rohde P, Seeley JR. Major depressive disorder in older adolescents: Prevalence, risk factors, and clinical implications. Clin Psychol Rev 1998;18:765–94. 14. Angold A, Costello EJ, Erkanli A. Comorbidity. J Child Psychol Psychiatry 1999;40:57–87. 15. Rohde P, Lewinsohn PM, Seeley JR. Psychiatric comorbidity with problematic alcohol use in high school students. J Am Acad Child Adolesc Psychiatry 1996;35:101–9.
ORIGINAL ARTICLE
Connecting National Survey Data With DSMIV Criteria MANFRED H. M. van DULMEN, Ph.D., HAROLD D. GROTEVANT, Ph.D., NORA DUNBAR, M.S., BRENT C. MILLER, Ph.D., BRUCE BAYLEY, M.S., MATHEW CHRISTENSEN, M.S., AND XITAO FAN, Ph.D.
Purpose: To show how connections can be made among items in a nationally representative survey of adolescents and criteria for “Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition” (DSM-IV) diagnoses. Methods: Data for this study came from the Wave I in-home interview of the National Longitudinal Study of Adolescent Health (Add Health), a nationwide study of approximately 90,000 adolescents and their parents. Proxy variables were developed for four DSM-IV diagnoses based on Wave I survey questions: conduct disorder, alcohol abuse, cannabis abuse, and major depressive disorder (single episode). Prevalence rates, comorbidity rates, and detailed item analyses of these four constructs are reported. Results: Of the adolescents in the sample under study, 3.4% scored above the threshold for conduct disorder. For the alcohol abuse proxy 4.7% scored above the threshold, compared with 6.3% for the cannabis abuse proxy, and 1% scored above the threshold for major depressive disorder (single episode). Adolescents who scored above the threshold for conduct disorder were three times more likely to receive psychological counseling than adolescents who scored below the threshold for conduct disorder. The rates for alcohol abuse, cannabis abuse, and major depressive disorder (single episode) were 2.0, 3.0, and 5.0, respectively. Conclusions: The prevalence rates for the four constructs in the Add Health data set were generally lower or
From the University of Minnesota, Minneapolis, Minnesota (M.H.M.V., H.D.G., N.D.); Utah State University, Logan, Utah (B.C.M., B.B., M.C.); and University of Virginia, Charlottesville, Virginia (X.F.). Address correspondence to: Manfred H. M. van Dulmen, Ph.D., Institute of Child Development, University of Minnesota, 51 East River Road, Minneapolis, MN 55455. E-mail: [email protected] Manuscript accepted June 26, 2002.
comparable to prevalence rates found in other epidemiological studies in which DSM-IV criteria were applied. The approach described in this study provides a way to identify adolescents who are likely at risk for the development of mental health problems. © Society for Adolescent Medicine, 2002 KEY WORDS:
Add Health Alcohol abuse Conduct disorder Depression DSM-IV Measurement Substance abuse Survey data
The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) [1] provides extensive, broadly accepted guidelines for differentiating between adaptive and problematic behavior of children, adolescents, and adults. Although the DSM-IV has been criticized for its inability to distinguish pathology from normality and its ability to differentiate among mental disorders, it provides the best effort for a common language among researchers and clinicians interested in the diagnosis of mental health disorders [2].
Survey Data One of the recent trends in social science research has been the increase in use of large-scale surveys. Although survey methods are limited in several ways,
© Society for Adolescent Medicine, 2002 Published by Elsevier Science Inc., 360 Park Avenue South, New York, NY 10010
1054-139X/02/$–see front matter PII S1054-139X(02)00453-6
476
van DULMEN ET AL.
they have the potential to overcome a major problem of smaller studies, representativeness of sampling. Federal agencies have funded large surveys representative of the United States population, rather than focusing exclusively on small studies with questionable external validity. Many of these national surveys have been made available for public use to maximize the federal research investment. Numerous surveys (such as the National Survey of Families and Households and the National Survey of Children) contain information relevant to family structure, family dynamics, parent-child relationships, and child and adolescent functioning. Researchers interested in these variables must typically construct measures from the items available, because complete scales from prior research are not typically present. For scales measuring individual or family variables, it is difficult to know where to draw the line between responses that indicate adaptive vs. maladaptive functioning, because researchers must develop their own scales and justify their reliability and validity. In the present paper, DSM-IV criteria are applied to provide a benchmark for adaptive vs. maladaptive functioning.
DSM-IV and Survey Data Starting data analysis with a framework for scale development has the benefit of providing a common language among researchers and practitioners; without such a framework communication between clinicians and researchers is problematic. The DSM-IV is the best approximation for the diagnosis of mental disorders [2], and although linking DSM-IV language to existing survey data does not provide diagnoses, it can identify adolescents who would be considered at risk for the development of certain mental health disorders and suggests cut-off points on survey scales that are not arbitrary but based on the DSM-IV framework. An example of this approach is provided by Chen et al. [3], who used the National Household Survey on Drug Abuse [4 – 6] to develop a proxy measure of DSM-IV marijuana dependence. From the NHSDA, Chen et al. were able to assess 5 of the 7 DSM-IV criteria to assess marijuana dependence, however they were not able to validate their findings with clinical assessments. Information from the NHSDA about whether the participants were currently receiving treatment was used as one way to validate their findings.
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Aims The first aim of this paper is to develop proxy variables for selected DSM-IV diagnoses, including a clinical cut-off that approximates the DSM-IV clinical cut-off. The resulting proxy variables are not the same as clinical diagnoses, but they do provide rough guidelines for researchers interested in trying to identify persons who are functioning at a problematic level. Researchers interested in behavioral dimensions can, of course, use continuous scores derived from summing appropriate items. The second aim is to validate the developed proxy variables by analyzing the prevalence rates of proxy variables and their co-occurrence or comorbidity. Furthermore, the validity of the proxy variables is tested by analyzing the relationship between the proxy variables and information on whether or not participants currently receive psychological counseling. The third aim of this paper is to characterize the usefulness of the DSM-IV constructs by conducting detailed item analyses that provide information about which items, or combinations of items, contribute most to the clinical cut-off for different DSM-IV constructs.
Methods Participants Data for this study came from the Wave I in-home interviews of the National Longitudinal Survey of Adolescent Health (Add Health), a nationwide study of approximately 90,000 adolescents and their parents. Add Health is a longitudinal survey measuring contextual variables (family, peers, school) that are related to the well-being of adolescents grades 7 through 12 [7]. A school cluster sampling design was used to obtain the sample. At Wave 1, data were collected from parents (questionnaire) and adolescents (in-home interview and school survey). For this study, data from the 90-minute adolescent in-home interview were used. The school survey data were not analyzed because they did not include sufficient questions to develop any DSM-IV construct. We included participants identified by the Add Health team as the “core sample” (see, e.g., 7) in this illustration of our approach. The core sample (n ⫽ 12,103) was selected to be representative of the U.S. adolescent population, correcting for the oversampling of several groups of ethnic minorities and adolescents with disabilities. The mean age of the adolescent core sample was 16.03 years, including
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5779 males and 6324 females. Adolescents’ ethnicity included: 61% white, 19% African-American, 12% Hispanic, 4% Asian-Pacific Islander, and 2% American Indian.
Procedure To link the DSM-IV criteria with the Add Health survey, we identified several mental health-related issues that occur relatively often in adolescents. Items from the Add Health codebooks provided tentative matches with each DSM-IV diagnosis. Several diagnoses were ultimately excluded from consideration because the necessary information was lacking in the Add Health survey. The main reasons diagnoses could not be included were an inability to determine an established pattern (oppositional deviant disorder, mood disorders, personality disorder), or the lack of historical information (attention deficit hyperactivity disorder, anxiety disorders, substance dependence, somatoform disorder, impulse-control disorders, bulimia). The following diagnoses were selected for further attention: conduct disorder, alcohol abuse, major depressive disorder (single episode), anorexia nervosa, cannabis abuse, inhalant abuse, cocaine abuse, and other illicit drug abuse. Next, we constructed the programming code to identify cases for each diagnosis. During this process, anorexia nervosa, inhalant abuse, cocaine abuse, and other illicit drug abuse were eliminated. For anorexia nervosa, it was not possible to determine whether there was an intense fear of gaining weight and whether there was a disturbance in body perception. For inhalant abuse, cocaine abuse, and other illicit drug abuse, it was not possible to establish whether the substance use was related to physically hazardous situations or to tell whether recurrent social or interpersonal problems were caused by using the substance. These are important criteria for DSM-IV diagnoses, but because the needed questions were not asked in the Add Health survey, further development of constructs for these diagnoses was not possible. Finally, we determined that we could develop proxy variables for four DSM-IV diagnoses based on Add Health Wave I survey questions: conduct disorder, alcohol abuse, cannabis abuse, and major depressive disorder (single episode). These four proxy variables are dichotomous, with the threshold for each approximating the conditions necessary to make a positive diagnosis by using the DSM-IV.
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Conduct disorder. Eight Add Health questions were scored for inclusion of the conduct disorder construct. We were able to assess 7 of the 15 criteria given in the DSM-IV diagnosis of conduct disorder; two questions referred to the same DSM-IV criterion. A score that exceeded the DSM-IV criteria on three of the seven symptoms resulted in a score above the threshold for the conduct disorder construct. Add Health did not provide sufficient information to determine clinically impaired functioning and whether criteria were met for antisocial personality disorder [8]. Alcohol abuse. A DSM-IV proxy measure for alcohol abuse was also constructed. First, a variable indicating recurrent alcohol use was created followed by developing the proxy measure for alcohol abuse. Recurrent alcohol use and problems in one of a variety of domains according to the DSM-IV criteria resulted in a score above the threshold for the alcohol abuse construct. Add Health did not provide sufficient information to assess recurrent substancerelated legal problems from the DSM-IV criteria, and whether the symptoms had met the criteria for substance dependence. Substance dependence distinguishes itself from substance abuse in that tolerance, withdrawal, or a pattern of compulsive use is present [1]. These criteria cannot be assessed with the Add Health data. Therefore, it could be that some adolescents who otherwise would have been classified as being alcohol dependent were included in the alcohol abuse category. However, as Harrison et al. [9] have noted, the distinction between alcohol abuse and alcohol dependence may be arbitrary. Cannabis abuse. Similar to the proxy measure for alcohol abuse, a variable that established recurrent cannabis use was created, followed by a construct for cannabis abuse. As with alcohol abuse, recurrent cannabis abuse and problems in one of a variety of domains according to the DSM-IV criteria, resulted in a score above the threshold for cannabis abuse. Again, the Add Health data did not provide sufficient information to determine legal problems related to cannabis abuse, nor was it possible to exclude overlap with cannabis dependence. Major depressive disorder (single episode). Finally, a proxy measure for major depressive disorder (single episode) was created. The Add Health data provided information to assess six of the nine symptoms included under major depressive disorder (single episode) in DSM-IV.
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Table 1. Proportion of Respondents Reporting Each Criterion by Number of Criteria for DSM-IV Constructs Number of Criteria
Criteria for conduct disorder Damage property Runaway Stealing Forced to have sex Pulled weapon/shoot/stabbed someone Steal something worth ⬎$50 Used weapon to threat Criteria for alcohol abuse Physically hazardous Trouble with parents Problems with friends Problems with dating partner Physical fighting Problems with school work Criteria for cannabis abuse Physically hazardous Problems with school work Criteria for major depressive disorder (single episode) Feeling blue Feel life not worth living Poor appetite Feel too tired Depressed mood Suicidal thoughts
1
2
3
4
5
6
7
67.7 3.9 6.2 1.9 9.4 4.0 2.5
71.2 9.3 32.1 3.3 30.4 24.3 9.0
79.5 13.5 52.9 5.3 47.5 46.7 18.0
89.3 20.4 73.8 4.9 60.2 72.8 22.3
94.1 31.4 92.2 7.8 92.2 90.2 62.7
100.0 88.2 100.0 11.8 100.0 100.0 88.2
100.0 100.0 100.0 100.0 100.0 100.0 100.0
75.7 8.6 2.3 3.9 6.5 2.9
81.1 38.7 15.3 28.8 23.4 12.6
74.5 47.1 39.2 52.9 52.9 33.3
80.0 66.7 66.7 80.0 46.7 60.0
75.0 75.0 75.0 100.0 75.0 100.0
0.0 0.0 0.0 0.0 0.0 0.0
24.1 75.9
100.0 100.0
32.2 6.7 15.8 28.0 10.3 6.9
56.4 14.3 33.6 45.1 39.5 11.1
77.9 31.7 46.9 55.0 73.2 15.4
90.7 59.1 56.1 73.4 92.4 28.3
95.9 83.7 82.7 90.8 94.9 52.0
100.0 100.0 100.0 100.0 100.0 100.0
Analysis Plan Analyses were conducted in SPSS 10.0. The percentage of participants scoring above the threshold for each proxy variable was calculated. Next, descriptive analyses were conducted to receive information on how often a criterion that was part of a proxy variable was present, depending on the level of the proxy variable. For example, to receive information on which criterion was most often included for those participants who only had one conduct disorder criterion, all participants with only one conduct disorder criterion were selected and percentages were calculated for each conduct disorder criterion. In other words, information was derived on how often a particular conduct disorder criterion was present given that participants only met the threshold for one criterion. The co-occurrence, or comorbidity, of proxy variables was analyzed by calculating the percentage overlap among the proxy variables. Odds ratios were calculated to test whether participants who received a score above the threshold for the proxy variables were more likely to have received psychological counseling in the past year than participants receiving a score below the threshold for the proxy vari-
ables. Odds ratios were also calculated to test whether participants who received a score above the threshold for alcohol abuse or cannabis abuse were more likely to have attended a substance abuse program in the past year than participants receiving a score below the threshold for alcohol abuse or cannabis abuse.
Results Descriptive frequencies and percentages of the four DSM proxy variables with the Add Health core sample were tabulated. Of the adolescents in the core sample, 3.4% scored above the threshold for conduct disorder. For the alcohol abuse proxy measure, 4.7% scored above the threshold, compared with 6.3% for the cannabis abuse proxy measure. The results for the major depressive disorder (single episode) proxy measure showed that 1.0% scored above the threshold. Table 1 shows the proportion of respondents affirming each criterion by number of criteria for the DSM-IV constructs. For example, with regard to the construct “conduct disorder,” 67.7% of the adolescents who only met one criterion did so because they
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scored over the threshold for ‘damage property.’ To score above the threshold on the DSM proxy measure for conduct disorder, adolescents must have met at least three of the seven criteria. For adolescents who met the minimum criterion, the following components were most commonly included (see column 3): damage property (79.5%), steal something worth ⬎ $50 (52.9%), and pulled a weapon or shot someone (47.5%). To be scored above the threshold for alcohol abuse, the criteria for at least one of the six components had to be met. In 75.7% of the cases, adolescents scored above the threshold because of meeting the criteria for physically hazardous (recurrent alcohol use and having been driving while drunk). To be scored above the threshold for cannabis abuse, the criteria for both components must be met. However, of the adolescents who met at least one criterion, 75.9% met the criteria for problems with school work compared with 24.1% for physically hazardous. None of the adolescents met all six criteria and therefore the percentages in this column are zero. To be scored above the threshold for major depressive disorder (single episode), five of the six possible criteria must be met. Among adolescents who met the minimum threshold, the following criteria were indicated: feeling blue (95.9%), depressed mood (94.9%), feel too tired (90.8%), feel life is not worth living (83.7%), and poor appetite (82.7%). Comorbidity rates among the four DSM constructs were also obtained. The comorbidity rate between conduct disorder and major depressive disorder (single episode) was 5.3% in this study. The comorbidity rates among other constructs included: 11.7% between conduct disorder and alcohol abuse, 15.5% between conduct disorder and drug abuse, and 19.4% between cannabis abuse and alcohol abuse. To further validate the usefulness of the DSM-IV constructs and to see whether or not they would distinguish among adolescents, odds ratios were calculated linking the DSM-IV proxies to a question about psychological counseling (“In the past year, have you received psychological or emotional counseling?”). Odds ratios showed that receiving psychological counseling was significantly related to receiving a score above the threshold for each of the four DSM-IV proxies (Table 2). Adolescents who scored above the threshold for conduct disorder were 3.0 times more likely to receive psychological counseling than adolescents who scored below the threshold for conduct disorder. The rates for alcohol abuse, canna-
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Table 2. Odds Ratio Calculations DSM-IV Constructs, by Receiving Psychological Counseling and Attendance Substance Abuse Program in the Past Year
Conduct disorder Alcohol abuse Cannabis abuse Major depressive disorder (single episode)
Psychological Counseling OR (CI)
Attend Substance Abuse Program OR (CI)
3.01* (2.42–3.74) 2.04* (1.66 –2.49) 2.97* (2.51–3.51) 5.02* (3.49 –7.22)
— 5.91* (4.47–7.81) 6.00* (4.65–7.76) —
* p ⬍ .01. OR ⫽ Odds Ratio; CI ⫽ 95% Confidence Interval Odds Ratio.
bis abuse, and major depressive disorder (single episode) were 2.0, 3.0, and 5.0, respectively. The proxy measures for substance abuse were further linked with receiving substance abuse counseling (“In the past year, have you attended a drug abuse or alcohol abuse treatment program?”). Adolescents who scored above the threshold for alcohol abuse were 5.9 times more likely to have received substance abuse counseling than adolescents who scored below the threshold for alcohol abuse. Adolescents who scored above the threshold for cannabis abuse were 6.0 times more likely to have received substance abuse counseling than adolescents who scored below the threshold for cannabis abuse.
Discussion We have presented an approach for constructing DSM-IV proxy measures from survey data for four commonly diagnosed adolescent behavior problems. The main strength of this approach is that it links survey items to a benchmark diagnostic system that is widely accepted in the mental health community. Other than the work by Chen et al. [3], we believe this is the only study that has attempted to relate items and scales in existing surveys to thresholds for DSM-IV diagnoses. In four important mental health-related areas (conduct disorder, alcohol abuse, cannabis abuse, major depressive disorder [single episode]), DSM-IV criteria could be used as a guiding tool in creating scales of adolescent problem behavior in Add Health data. Because it was not possible to find survey questions for all aspects of these diagnoses, the constructs developed from the DSM-IV criteria should not be seen as clinical diagnoses. However, the variables do identify individuals whose probability of receiving a DSM-IV diagnosis would be higher rather than lower. Furthermore, in support of
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the validity of the proxy measures results showed that adolescents who received a score above the threshold for one of the DSM-IV proxies were several times more likely to receive psychological counseling than adolescents who received a score below the threshold for each DSM-IV construct. In any case, use of the proxy measures helped us develop a fuller picture of adolescents’ mental health problems and their relationship with other individual and contextual phenomena. Prevalence Rates The prevalence rates for the four constructs in the Add Health data set were generally lower or comparable to prevalence rates found in other epidemiological studies in which DSM-IV criteria were applied. The prevalence rate found for conduct disorder (3.4%) was similar to the 1.5% to 3.4% prevalence rates for conduct disorder in the general population of children and adolescents [8]. The prevalence rates for alcohol abuse (4.7%) and cannabis abuse (6.7%) were comparable to what has been found in the population. Rates for alcohol abuse, based on recent general population studies, range from 3.5% [10] to 5– 6% [11]. For cannabis abuse, prevalence rates range from 5% to 7% [11]. There are no data available to determine population prevalence rates among adolescents based on the DSM-IV criteria [12]. A prevalence rate of 1% was found for major depressive disorder (single episode). In a representative sample of high school students, prevalence rates of 2.9% and 3.1% [13] were reported for major depressive disorder. The fact that the prevalence rates in this study generally were comparable to or lower than prevalence rates found in other epidemiological studies can in part be explained by the fact that information was not available for each sub-component. It is important for researchers developing large-scale surveys, such as Add Health, to take into consideration all the criteria that are used to assess problem behaviors in a classification system such as the DSM-IV. This enables researchers to develop problem behavior constructs with meaningful thresholds of severe problem behavior. Alternatively, the somewhat low prevalence rates might also be owing to the Add Health design. The Add Health study is a schoolbased design and therefore adolescents who were not currently in school were not included in the study. This is an important limitation of the Add Health study. We do not know whether the prevalence rates would have been different if adolescents
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who are not in school had been included in the Add Health study. The lower prevalence rate for major depressive disorder (single episode) can also be explained by the fact that the Add Health design did not provide information on some of criteria for major depressive disorder (single episode). Specifically, the in-home interview did not provide information on the following DSM-IV criteria for major depressive disorder (single episode): insomnia or hypersomnia nearly every day, psychomotor agitation or retardation nearly every day, and diminished ability to think or concentrate, or indecisiveness, nearly every day.
Comorbidity Rates The comorbidity rates found in this study were also comparable to what has been found in the general population. The comorbidity between conduct disorder/oppositional defiant disorder and depression ranging from 2.9% to 8.2% in general population studies using DSM-IV criteria [14], is consistent with the 5.3% found in this study for the comorbidity between conduct disorder and major depressive disorder (single episode). As described in the results section we found the following comorbidity rates for other constructs: conduct disorder and alcohol abuse was 11.7%, conduct disorder and drug abuse was 15.5%, cannabis abuse and alcohol abuse was 19.4%. Rohde et al. found point prevalence rates for cannabis abuse and problem drinking (meeting at least one criterion of DSM-IV substance abuse/dependence but no diagnosis) at 15.5% and for cannabis abuse and alcohol abuse/dependence 57.4%. Note that the alcohol abuse/dependence group in the study by Rohde et al. consisted mostly of adolescents who had received an alcohol dependence diagnosis. Comorbidity between substance abuse and major depressive disorder (single episode) was about 3.4% (cannabis abuse) and 3.6% (alcohol abuse) in this same study [15].
Limitations Using a categorical classification system (such as the DSM-IV) in research has limitations. Because the proxy variables are not diagnoses, researchers must be careful when they interpret their data. Furthermore, use of the proxies implies acceptance of the DSM-IV system and the implicit theory behind it.
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Conclusion Although a direct diagnosis might be greatly preferred by clinicians, our approach offers researchers a way to identify adolescents who are likely to be at risk. Most survey data sets like Add Health do not include complete measures of maladaptation and therefore researchers have to develop their own scales. Even if these survey data sets would include complete scales, researchers still have to make decisions about where to draw the line between adaptive and maladaptive functioning. These decisions are generally not guided by prior theory but based on a percentage cut-off point such as the top 10% or 15%. The approach illustrated in this paper identifies adolescents at risk based on the DSM-IV. Therefore, the decision where to draw the line between maladaptive and adaptive functioning is not arbitrary but based on an existing classification system and can be applied to other data sets that measure maladaptive functioning. We hope that this work will stimulate researchers to think about how survey data can be most useful in studies of mental health-related issues using existing data. A benchmarking approach such as we have presented might be useful with other types of data as well. Time efficiency, cost effectiveness, availability, and generalizability are among the advantages we see in working with survey data. We encourage researchers to consider maximizing use of these valuable resources by building further crosswalks between survey data and other theoretically or conceptually important constructs, and also by studying the relationship between these constructs and the developmental context in which the adolescent is situated. We gratefully acknowledge support provided by grant HD 36479 from the National Institute of Child Health and Human Development, to Brent C. Miller, P.I. We especially thank DenYelle Baete, Blaine Kirkpatrick, Matthew May, Reed Reichwald, and Sarah Scott who made key contributions to this paper. This research is based on data from the Add Health project, a program designed by J. Richard Udry (P.I.) and Peter Bearman, and funded by grant P01-HD31921 from the National Institute of Child Health and Human Development to the Carolina Population Center, University of North Carolina at Chapel Hill, with cooperative funding participation by the National Center Institute; the National Institute of Alcohol Abuse and Alcoholism; the National Institute on Deafness and Other Communication Disorders; the National Institute on Drug Abuse; the National Institute of General Medical Sciences; the National Institute of Mental Health; the National Institute of Nursing Research; the Office of AIDS research, NIH; the Office of Research on Women’s Health, NIH; the Office of Population Affairs, DHHS; the National Center for Health Statistics, Centers for Disease Control and Prevention, DHHS, the Office
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of the Assistant Secretary for Planning and Evaluation, DHHS; and the National Science Foundation. Persons interested in obtaining data files from the National Longitudinal Study of Adolescent Health should contact Add Health, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 27516-3997 (E-mail [email protected]).
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