Share this article on:

Family Socioeconomic Status and Self-Reported Sexually Transmitted Diseases Among Black and White American Adolescents

Newbern, Elizabeth Claire PhD, MPH*; Miller, William C. MD, PhD, MPH*†; Schoenbach, Victor J. PhD*; Kaufman, Jay S. PhD*

doi: 10.1097/01.olq.0000137898.17919.35

Objective: The objective of this study was to assess associations between socioeconomic status (SES) and adolescent sexually transmitted diseases (STDs) stratified by race and gender.

Study: In cross-sectional analyses of the National Longitudinal Study of Adolescent Health Wave One (1995), unadjusted and adjusted associations between 4 family SES indicators and STD reports for black and white 7th through 12th graders were examined.

Results: Lower maternal education and nonprofessional maternal occupations were associated with higher STD reports in all groups except white females. Generally, STD reports were higher for adolescents not living in 2-parent homes, and lower income was only associated for black males.

Conclusion: Overall, SES is only a weak to moderate marker for adolescent STD risks. The relationship of SES and STDs varies by the SES measure used and differs across race-gender groups. Other individual factors such as risk behaviors or community factors such as income inequality could play a more critical role for adolescent STDs than family SES.

There are differences in the association between self-reported sexually transmitted diseases and 4 different measures of socioeconomic status among a national representative sample of black and white adolescents.

From the *Department of Epidemiology and the †Division of Infectious Diseases, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

The authors acknowledge David Savitz, Kathie Harris, Olga Sarmiento, Dionne Gesink Law, Rebecca James Baker, Lynne Sampson, Marlene Smurzynski, and Rachel Williams for their contributions to this manuscript.

Support was provided in part by the UNC STD Clinical Research Center (NIAID UO131496) (WCM, VJS) and the Clinical Associate Physician Program of the General Clinical Research Center (RR00046), Division of Research Resources, National Institutes of Health (WCM). The Add Health project is a program project designed by J. Richard Udry (PI) 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 Cancer 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 Behavior and Social Science Research, NIH; the Office of the Director, 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 of Minority Health, Centers for Disease Control and Prevention, DHHS; the Office of Minority Health, Office of Public Health and Science, DHHS; the Office of the Assistant Secretary for Planning and Evaluation, DHHS; and the National Science Foundation.

Correspondence: William C. Miller, MD, PhD, MPH, Department of Epidemiology, 2105F McGavran-Greenberg, Campus Box 7435, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7435. E-mail:

Received for publication December 4, 2003, and accepted March 31, 2004.

SEXUALLY TRANSMITTED DISEASES (STDs) are prevalent among adolescents between 15 and 20 years old in the United States.1,2 Racial and gender disparities in STDs still exist between groups of adolescents. In 2001, the reported number of chlamydial infections, gonorrhea, and primary and secondary syphilis for black females were 3.5 times that of black males, 2 times that of white females, and 16 times that of white males.3

Beyond identification of racial and gender disparities in adolescent STD outcomes,4,5 few studies have considered factors related to these differences. Socioeconomic status (SES) has been assessed in relation to racial disparities in self-reported STDs in the Youth Risk Behavior Surveillance System (YRBSS) and gonorrhea or chlamydial infections rates among 12 to 20 year olds in San Francisco.6,7 In the YRBSS, black-white differences in reported STDs actually increased after adjustment for parental education, family income, and regional location.7 In San Francisco, racial differences in gonorrhea rates decreased with declining neighborhood SES, but disparities in chlamydial infections were not consistently affected by neighborhood SES.6

Direct statistical comparisons of adjusted risks in racial/ethnic or gender groups have been criticized because the exchangeability conditions that give adjusted estimates their interpretability are hard to justify in substantive terms.8–10 In these comparisons, black is the exposed group and white is the unexposed group, although this counterfactual cannot be justified as a result of the practical impossibility of conditioning on a sufficient number or type of measured variables. Statistical comparisons of adjusted risks in racial/ethnic groups remain problematic even when considering only a single aspect in these comparisons. For example, when racial comparisons are adjusted for SES, residual associations between race and social position persist after adjustment because measured SES quantities are an incomplete representation of true social position and its many correlates.8,11,12 Stratification by race/ethnicity and gender has been suggested as one way to consider differences between groups while avoiding problems of direct statistical comparisons that would require unreasonable assumptions.13

In this study, we assessed associations between indicators of family SES and self-reported STD outcomes stratified by race and gender. SES was measured by 4 different indicators: maternal educational attainment, maternal occupation, household composition, and adjusted household income. We hypothesized that family SES markers would differ by race and gender and that income would be a relatively weak marker, particularly among blacks. These analyses were performed on black and white adolescents between the ages of 12 and 21 participating in the National Longitudinal Study of Adolescent Health (Add Health) Wave 1 (1995).

Back to Top | Article Outline

Materials and Methods

The design and methods for Add Health have been described previously.14 Briefly, Add Health is a longitudinal study of a multistaged sample of adolescents in grades 7 through 12. The Add Health sample with appropriate application of sampling weights is representative of in-school American adolescents. In addition to in-depth adolescent interviews during 1995, a parent or caregiver was interviewed when available.

The current study population includes non-Hispanic black and white adolescents who reported a history of sexual intercourse (N = 4962), comprising 847 black females, 939 black males, 1691 white females, and 1485 white males. The sample was limited to sexually experienced adolescents, because STD risks are minimal among those with no sexual history.15 Of the 14,003 non-Hispanic black and white Add Health Wave 1 participants, 1288 (59%) black females, 956 (47%) black males, 3395 (68%) white females, and 3402 (71%) white males were excluded as a result of lack of sexual experience.

The outcome, any self-reported STD history, was based on responses to questions about having ever been told by a clinician that they had gonorrhea, chlamydial infection, syphilis, trichomoniasis, genital warts, herpes simplex virus, or HIV/AIDS.

We assessed 4 socioeconomic indicators: maternal educational attainment, maternal occupation, family household composition, and 1994 adjusted household income. Self-reported maternal educational attainment was categorized into high school graduate or less, some post-high school education, and college graduate or further education. The categories of adolescent-reported maternal occupation were no occupation, service or manual labor, nonprofessional office, and professional. The adolescent report of maternal occupation was used because only 1 caregiver was interviewed, and the survey question related to the occupation of that individual rather than the occupation of both parents. Household composition, as reported by the adolescent, was grouped into 2-parent, single-mother, and other household structures, which included single-father homes, foster placements, and households headed by nonparent family members. Parental report of household income was divided by the federal poverty threshold income for the same household size to represent the relative amount of income beyond basic subsistence.16,17 Income was then categorized relative to the poverty threshold: high (over 300%), medium (151–300%), and low (less than 151%).

Analyses were performed separately by race and sex to allow for investigation of factors important for each of these groups. Prevalence odds ratios (ORs) were estimated using generalized estimating equations (GEE) that accounted for the complex survey design of Add Health (stratified, unequally weighted sample with multiple sampling levels) with the GENMOD procedure in SAS 8.2.18

For each race-gender group, simple models were developed separately for each of the 4 SES measures. The influence of several other variables on the SES-STD associations was assessed in multiple logistic regression using GEE, accounting for the complex survey design. Variables assessed as potential confounding or modifying factors included age, sexual behaviors (number of sexual partners, history of trading sex for money or drugs, and history of rape experience [female as survivor/male as perpetrator]), sexual risk perceptions (sexual orientation, condom use knowledge, and feelings regarding protection against STD and HIV risks), nonsexual risk behaviors (substance use, including cigarettes, alcohol, marijuana, and cocaine; history of being held back a grade in school; perceived health status; and lack of health insurance), and parental factors (parents belonging to the parent-teacher organization [PTO] and parent's satisfaction with the parent-child relationship). Directed acyclic graphs (DAGs) were used to identify intervening variables not appropriate for inclusion in the models.19,20

In models containing each SES measure and 1 additional factor, factors were tested for effect modification and were retained, as such, if stratum-specific ORs differed by 150% or more and the Breslow-Day test of homogeneity was significant (P ≤0.20); however, no factors met these criteria. Initial assessment of confounding was based on models with each SES factor and a single additional variable. Factors creating 10% or greater change in the SES-STD ORs were placed in full models. Backward elimination was used to eliminate variables from full models, dropping each adjustment factor 1 at a time. Factors that created a 10% or greater change the SES-STD associations were retained in final models.

To improve robustness, final models are based on analysis of imputed data from multiple Markov Chain Monte Carlo (MCMC) imputations performed in the SAS MI procedure.21 Because response to the household income measure was low (74%), sensitivity analysis on a range of possible values for missing incomes was performed. For the sensitivity analysis, 1 of the following was used to replace missing income values: 1) values imputed in SAS MI, 2) median tract income, or 3) placement of all missing values in the high, or 4) low income categories. Results from these replacements were compared with results for individuals with nonmissing income data (complete cases).

This study was approved by the University of North Carolina Medical Institutional Review Board.

Back to Top | Article Outline


The mean age for each race-gender group of study participants was 17 years old (standard error [SE] = 0.1). Over 50% of black males reported engaging in sexual intercourse with more than 1 partner (Table 1), whereas less than 50% of the other race-gender groups reported multiple partners. Reports of all types of drug use, including cigarette smoking, alcohol consumption, and marijuana or cocaine use, were more common among white adolescents than black adolescents with white males having the most prevalent drug use.



Back to Top | Article Outline

Indicators of Socioeconomic Status

Nationally representative estimates of family SES were generally lower for black adolescents (Table 2). Eleven percent to 15% of black adolescents had college-graduate mothers, whereas close to 20% of white adolescents had college-graduate mothers. Close to 70% of each race-gender group had mothers who worked in service fields, manual labor, or nonprofessional office positions. Single-mother homes were the most common living arrangements for black adolescents, whereas over two thirds of white adolescents lived with 2 parents. Over 50% of black adolescents were in the lowest-income group, whereas fewer than 25% of white adolescents had this same income level.



Back to Top | Article Outline

Sexually Transmitted Disease Prevalence

The prevalence of STD reports was lowest among white males (2.0%; 95% confidence interval [CI], 0.9–3.1), higher among white females (5.8%; 95% CI, 4.5–7.0) and black males (6.9%; 95% CI, 4.2–9.6), and markedly higher among black females (19.9%; 95% CI, 15.6–24.1). The most prevalent STD for each group was chlamydial infection.

Back to Top | Article Outline

Maternal Education

Lower maternal education was weakly, but not significantly, associated with higher STD reports across all groups, except white females. The odds of STDs for black females with mothers in the high school or less group were 1.8 (95% CI, 0.6–5.6) times higher than for black females with college-graduate mothers (Table 3). The odds of STDs were 2.8 (95% CI, 0.6–12.7) times higher for black males with mothers in the lowest educational group than for those with college-graduate mothers. STD levels were lower or similar for white females in the lower maternal education groups compared with those with college-educated mothers. For white males, a strong but imprecise association existed between lower maternal education and STDs. After adjusting for additional factors, maternal education-STD associations changed very little (Table 4).





Back to Top | Article Outline

Maternal Occupation

In bivariate analyses, nonprofessional maternal occupations appeared to be associated with higher STD reports for all groups except white females (Table 3). Black females with mothers employed in office work (OR, 2.9; 95% CI, 1.1–7.6) and service or manual labor (OR, 2.8; 95% CI, 1.2–5.8) were more likely to report STDs than black females with mothers in professional occupations. Black males experienced similar or greater magnitudes of effect as black females, but these associations were less precise and not statistically significant. For white females, maternal occupation was not associated with STD reports in contrast to the other race-gender groups. STD reports appeared to be higher for white males with mothers in nonprofessional occupations with strong associations for service and office-position categories, but these associations were imprecise.

After adjustment, associations between nonprofessional maternal occupations and reported STDs increased slightly for black females (Table 4). For the other race-gender groups, these associations changed little, typically toward the null.

Back to Top | Article Outline

Household Composition

In bivariate models, self-reported STDs were consistently higher for those living in single-father, nonparental, or foster homes (Table 3). For all groups, the odds of STDs were approximately 2 times higher for adolescents in these households compared with those in 2-parent homes. Single-mother households were not strongly associated with STDs for black females, black males, and white females. With only 1 STD report among white males in single-mother homes, a negative association with STDs was found. After adjustment, most household composition-STD associations remained unchanged or decreased slightly (Table 4). Household composition associations increased for black males to 2.9 times (95% CI, 1.1–7.5) higher odds of STDs for other household types and 2.0 times (95% CI, 0.7–5.8) higher odds of STDs for single-mother homes when compared with 2-parent homes.

Back to Top | Article Outline

Adjusted Household Income

A possible association of lower household income and STD reports was observed only among black males (Table 3). In the lowest-income group, STD odds for black males were 2.7 (0.5–15.4) times higher than those with the highest income, but this estimate is imprecise. The associations between lower household income and reported STDs changed little in multivariate analysis (Table 4).

Back to Top | Article Outline

Sensitivity Analysis of Missing Household Income Data

As a result of the high level of missing household income data (26%), which was unlikely to be missing at random,12 we performed a sensitivity analysis of unadjusted income-STD associations with missing incomes replaced with different values. Associations for black females, white females, and white males varied little across the different replacement methods (Table 5), suggesting that income was missing approximately at random in these groups. For black males, however, when missing values were placed into the lowest-income group, the results were similar to complete case associations. Given that STDs were more prevalent among black males with missing income (9.2%; 95% CI, 3.9–14.4) than those with income data (6.0%; 95% CI, 3.4–8.5), these differences suggest that reported crude and adjusted lower income-STD associations for black males could be underestimates of the true association.



Back to Top | Article Outline


The relationships among SES, race, gender, and negative health outcomes have been repeatedly identified as important public health issues.12,22–24 However, these associations are difficult to assess because of complexities in the relationships among race, gender, and SES, as well as the difficulty in identifying appropriate SES measures.8,9,11 In this examination of family SES and adolescent self-reports of STDs, we performed analyses stratified by race and gender, because direct comparisons have limited validity owing to the inability to adjust for the myriad of factors responsible for observed differences. We examined 4 SES measures to assess differences in their associations with adolescent STD reports for these race-gender groups.

We observed substantial differences in the family SES and STD relationships by race, gender, and SES measure. Generally, measures indicating lower SES were more consistently related to STD among blacks. Family structure, maternal occupation, and, to a lesser extent, maternal education were associated with STDs among black females and males. For black males, the association of STDs with maternal education and occupation were not statistically significant; however, the magnitude of the point estimates were comparable to those of black females, suggesting important, but imprecise, effects. In contrast, only nontraditional family structure was associated with STD among white females. Income was generally a poor indicator of STD risk in all groups, as expected.

The observed differences in the association of SES measures and STDs by race and gender could represent racial differences in contextual and individual risks. The single absolute, and difficult-to-measure, risk factor for STD acquisition is sexual contact with an infected partner. One step removed from this risk factor is participation in a sexual network with infected persons. In the United States, sexual networks are highly segregated by race.25,26 Furthermore, the geographic distribution of blacks and whites by SES is not equivalent. Lower SES blacks tend to be concentrated in urban areas and the rural southeast,27 both areas with a high prevalence of STDs. In contrast, lower SES whites are spread throughout rural areas in the remainder of the United States, which tend to have low STD prevalence. Our results suggest that for blacks, economic advantage, measured by maternal education or maternal occupation, could be associated with removal from a high STD-prevalence sexual network to a lower-risk network, perhaps through relocation to a more affluent area. In contrast, white adolescents with lower SES could be living in areas with historically low STD prevalence. It would be of interest to assess the contextual settings that these adolescents reside within to determine whether the geographic context of their communities are clearly associated with the individual SES measures and STD prevalence.

We expected family SES to have a stronger impact on adolescent STDs because family SES should affect the adolescent's proximity to and interactions with high-risk sexual networks. We anticipated that family SES would influence residential location,26,28,29 access to health care, particularly STD screening,30–32 and parental capacity to discuss sexual risks.29,33–43 Instead, the relatively weak associations found suggest that family SES might be less important than other factors such as individual risk behaviors and community-level influences, including community SES. Individual factors such as number of sexual partners and trading sex for drugs or money have been associated with STD reports in racial-gender subgroups.44 Additionally, when adjusted for individual risk behaviors, black-white disparities in adolescent STDs were reduced, whereas they increased when adjusted for SES.7 Although neighborhood poverty levels were not found to consistently reduce racial disparities in adolescent STDs,40 other community-level influences such as income inequality, segregation, and social capital investment have yet to be investigated and could prove to be critical for disparities in adolescent STDs as they have for other types of health disparities.6,12,45–47

We did not anticipate an absence of association between maternal education or occupation and STDs for white females. In addition to the potential contextual explanations, differences in individual risk behavior could contribute to the observed differences in SES measures. Among female adolescents, lower parental education has been associated with early sexual activity but not with number of sexual partners,48 suggesting less variation in sexual risks across SES levels for white females compared with other groups. Although we adjusted for the number of sexual partners, we could not assess other partnership characteristics such as concurrency49,50 and partnership age or racial disparity,26,49,51–54 which could affect white female SES-STD associations.

Unfortunately, in the United States, race remains a marker of STD prevalence. The substantial disparity in STDs by race is unlikely to be the result of biologic mechanisms, but rather, results from a complex interaction of individual and contextual factors, including SES. The complexity of these numerous relationships contributes to the fact that observed racial disparities cannot be adequately assessed in single models directly comparing racial groups. In epidemiologic terms, direct race comparisons are difficult to justify because the implicit counterfactual comparison is not justifiable.8–10 By examining the relationship of SES measures and STD after stratification by race and gender, we clearly demonstrated that the association of these measures with STDs differs by race, which supports our underlying rationale for using this stratified approach.

SES is a multifaceted construct that cannot be described adequately with a single measure. We observed, as have others investigating similar outcomes including adult STDs55 and adolescent initiation of sexual intercourse,48 that household income had weaker associations than other SES measures. Although measurement error and missing data are plausible explanations for this weak association, income could simply not reflect the complexity of SES. Other measures of family SES such as wealth,12,56 parental (not only maternal) measures, or composites of multiple SES measures56 could have provided different insights than the 4 SES measures used.

Our selected socioeconomic indicators have potential limitations, including appropriateness, number of missing observations, differential reporting, and measurement error. We held the SES variable categories constant across race-gender groups to allow for comparison between the groups, but disproportionate representation in the categories could influence the observed results. With fewer blacks in the highest SES groups and fewer whites in the lower groups, we lose the ability to create categories representing the nonoverlapping tails of these distributions, which could be critical in health disparities.12,22,57 Also, the same absolute levels of SES do not imply the same level of benefits for each group with blacks and females benefiting less than whites and males at the same level.11,22

The cross-sectional study design limits assessment of the causal relationship between SES and STD reports. However, directions of potential causal relationships are not in doubt because the family SES indicators (maternal education, maternal occupation, household composition, and annual household income) are unlikely to result from an adolescent's STD status. Temporality could be a problem, particularly in relation to other variables included in this study. The outcome includes infections occurring throughout the sexual histories of these adolescents, so we cannot be certain that risk behaviors and attitudes preceded STD diagnosis.58

Although the STD prevalences in this study are similar to those from other adolescent STD research,7 our prevalence underestimates the true prevalence of infections because it is based on self-reports. Potential causes for underestimation include underreporting, asymptomatic infections, and lack of screening in high-risk populations. Underreporting of STD diagnoses can result from embarrassment or misunderstanding of a diagnosis.59,60 In Add Health, audio computer-assisted interviews (A-CASI) were conducted to limit bias and nonresponse for sensitive items such as STD histories.61–64 Asymptomatic or mildly symptomatic infections are common among females and would be missed unless screening occurred.65,66 Female patients commonly undergo STD screening, but regular contact with the medical system is less frequent for adolescent males. However, because sexually transmitted infections are more commonly symptomatic in males, treatment is typically sought.30,67

We combined multiple STDs in a single measure to maximize the STD prevalence. Different STDs could have distinct patterns of occurrence, which might be related to SES, possibly affecting observed results. Chlamydial infection was the most predominant STD in this sample; however, the use of a composite STD outcome measure limits the ability to interpret the results for any single STD.

Add Health used a school-based sample and did not include adolescents who dropped out of school before the survey. Consequently, youths presumably at high risk for STDs and relatively lower SES could have been excluded disproportionately.68 However, a recent evaluation of the adequacy of the sampling of Add Health suggests that bias arising from school dropouts could be minimal.69

STDs are among the most common health problems faced by American adolescents. Race,4,6,7,35,51,70–78 gender,4,51,74,79,80 and socioeconomic status6,7,81 have repeatedly been demonstrated to be associated with STDs among adolescents. However, more in-depth considerations of adolescent STD disparities have been limited. Our results demonstrate important differences in family SES and increased adolescent STD reports when stratified by race and gender. Future investigation of adolescent STD disparities, particularly with biologic measures of prevalent infections, should stratify by race and gender when possible. In addition, studies considering the role of SES should include multiple indicators. For most adolescents, family SES has minimal effects on STD risks, but other factors could still influence these outcomes, including adolescent-specific factors and community-specific factors.

Back to Top | Article Outline


1. Healthy People 2000 Final Review. US Department of Health and Human Services, Atlanta: Centers for Disease Control and Prevention (CDC) [pdf]. Available at: Accessed January 3, 2002.
2. Eng T, Butler WE. The Hidden Epidemic: Confronting Sexually Transmitted Diseases, vol 1. Washington, DC: National Academy Press, 1997.
3. Sexually Transmitted Disease Surveillance, 2000. US Department of Health and Human Services, Atlanta: Centers for Disease Control and Prevention (CDC) Division of STD Prevention [pdf]. Available at:, 2002.
4. Valleroy LA, MacKellar DA, Karon JM, Janssen RS, Hayman CR. HIV infection in disadvantaged out-of-school youth: Prevalence for US Job Corps entrants, 1990 through 1996. J Acquir Immune Defic Syndr 1998; 19:67–73.
5. Buzi RS, Weinman ML, Smith PB. Ethnic differences in STD rates among female adolescents. Adolescence 1998; 33:313–318.
6. Ellen J, Kohn R, Bolan G, Shiboski S, Krieger N. Socioeconomic differences in sexually transmitted disease rates among black and white adolescents, San Francisco, 1990 to 1992. Am J Public Health 1995; 85:1546–1548.
7. Ellen J, Aral S, Madger L. Do differences in sexual behaviors account for the racial/ethnic differences in adolescents' self-reported history of a sexually transmitted disease? Sex Transm Dis 1998; 25:125–129.
8. Kaufman JS, Cooper RS. Commentary: Considerations for use of racial/ethnic classification in etiologic research. Am J Epidemiol 2001; 154:291–298.
9. Kaufman JS, Cooper RS. Seeking causal explanations in social epidemiology. Am J Epidemiol 1999; 150:113–120.
10. Kaufman JS, Cooper RS. In search of the hypothesis. Public Health Rep 1995; 110:662–666.
11. Kaufman JS, Cooper RS, McGee DL. Socioeconomic status and health in blacks and whites: The problem of residual confounding and the resiliency of race. Epidemiology 1997; 8:621–628.
12. Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: Concepts, methodologies, and guidelines. Annu Rev Public Health 1997; 18:341–378.
13. Jones CP. Invited commentary: 'Race,' racism, and the practice of epidemiology. Am J Epidemiol 2001; 154:299–304; discussion 296–305.
14. Resnick M, Bearman P, Blum R, et al. Protecting adolescents from harm: Findings from the National Longitudinal Study on Adolescent Health. JAMA 1997; 278:823–832.
15. Aral S, Cates W Jr. The multiple dimensions of sexual behavior as risk factor for sexually transmitted disease the sexually experienced are not necessarily sexually active. Sex Transm Dis 1989; 16:173–177.
16. Poverty Thresholds in 1994, by Size of Family and Number of Related Children Under 18 Years. US Bureau of the Census [web page]. 9/25/2001. Available at: Accessed November 1, 2001.
17. Kawachi I, Kennedy BP. The relationship of income inequality to mortality: Does the choice of indicator matter? Soc Sci Med 1997; 45:1121–1127.
18. Version 8.2 [computer program]. Cary, NC: SAS, 2001.
19. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999; 10:37–48.
20. Hernan M, Henandez-Diaz S, Werler M, Mitchell A. Causal knowledge as a prerequisite for confounding evaluation: An application to birth defects epidemiology. Am J Epidemiol 2002; 155:176–184.
21. The MI Procedure (SAS Manual Chapter 11). SAS [pdf]. Available at: Accessed on July 13, 2004.
22. Williams D, Collins C. US socioeconomic and racial differences in health: Patterns and explanations. Annu Rev Sociol 1995; 21:349–386.
23. Lin SS, Kelsey JL. Use of race and ethnicity in epidemiologic research: Concepts, methodological issues, and suggestions for research. Epidemiol Rev 2000; 22:187–202.
24. Healthy People 2010. US Department of Health and Human Services [pdf]. Available at: Accessed January 5, 2002.
25. Laumann E, Gagnon JH, Michael RT, Michaels S. The Social Organization of Sexuality: Sexual Practices in the United States. Chicago, IL: University of Chicago Press; 1994.
26. Laumann EO, Youm Y. Racial/ethnic group differences in the prevalence of sexually transmitted diseases in the United States: a network explanation. Sex Transm Dis 1999; 26:250–261.
27. Harris RP, Zimmerman JN. Children and poverty in the rural south. SRDC Policy Series; The Southern Rural Development Center. Available at: Accessed March 20, 2004.
28. Aral SO, Hughes JP, Stoner B, et al. Sexual mixing patterns in the spread of gonococcal and chlamydial infections. Am J Public Health 1999; 89:825–833.
29. Ramirez-Valles J, Zimmerman M, Newcomb M. Sexual risk behavior among youth: Modeling the influence of prosocial activities and socioeconomic factors. J Health Soc Behav 1998; 39:237–253.
30. Fortenberry J. Health care seeking behaviors related to sexually transmitted diseases among adolescents. Am J Public Health 1997; 87:417–420.
31. Porter LE, Ku L. Use of reproductive health services among young men, 1995. J Adolesc Health 2000; 27:186–194.
32. Ellen JM, Lane MA, McCright J. Are adolescents being screened for sexually transmitted diseases? A study of low-income African American adolescents in San Francisco, California. West J Med 2000; 173:109–113.
33. Rosenthal SL, Von Ranson KM, Cotton S, Biro FM, Mills L, Succop PA. Sexual initiation: Predictors and developmental trends. Sex Transm Dis 2001; 28:527–532.
34. Manlove J, Terry E, Gitelson L, Papillo AR, Russell S. Explaining demographic trends in teenage fertility, 1980–1995. Fam Plann Perspect 2000; 32:166–175.
35. Crosby R, Leichliter JS, Brackbill R. Longitudinal prediction of sexually transmitted diseases among adolescents: Results from a national survey. Am J Prev Med 2000; 18:312–317.
36. Biglan A, Metzler CW, Wirt R, et al. Social and behavioral factors associated with high-risk sexual behavior among adolescents. J Behav Med 1990; 13:245–261.
37. Stiffman A, Dore P, Cunningham R, Earls R. Person and environment in HIV risk behavior change between adolescence and young adulthood. Health Education Quarterly 1995; 22:211–226.
38. Romer D, Black M, Ricardo I, et al. Social influences on the sexual behavior of youth at risk for HIV exposure. Am J Public Health 1994; 84:977–985.
39. Holtzman D, Rubinson R. Parent and peer communication effects on AIDS-related behavior among US high school students. Fam Plann Perspect 1995; 27:235–240, 268.
40. Crosby R, DiClemente R, Wingood G, Rose E, Lang D. Correlates of continued risky sex among pregnant African American teens: Implications for STD prevention. Sex Transm Dis 2002; 30:57–63.
41. Crosby R, Wingood G, DiClemente R, Rose E. Family-related correlates of sexually transmitted disease and barriers to care: A pilot study of pregnant African American adolescents. Family & Community Health 2003; 25:16–27.
42. Pinderhughes E, Dodge K, Bates J, Pettit G, Zelli A. Discipline responses: Influences of parents' socioeconomic status, ethnicity, beliefs about parenting, stress, and cognitive–emotional processes. J Fam Psychol 2000; 14:3800–3400.
43. Lammers C, Ireland M, Resnick M, Blum R. Influences on adolescents' decision to postpone onset of sexual intercourse: A survival analysis of virginity among youths aged 13 to 18 years. J Adolesc Health 2000; 26:42–48.
44. Newbern E. Individual Behaviors, Beliefs, and Sociodemographic Factors Related to Sexually Transmitted Disease Reports Among Black and White American Adolescents. Chapel Hill: Department of Epidemiology, University of North Carolina, 2002.
45. Kaplan GA, Pamuk ER, Lynch JW, Cohen RD, Balfour JL. Inequality in income and mortality in the United States: Analysis of mortality and potential pathways. BMJ 1996; 312:999–1003.
46. Lynch JW, Kaplan GA, Pamuk ER, et al. Income inequality and mortality in metropolitan areas of the United States. Am J Public Health 1998; 88:1074–1080.
47. Kawachi I, Kennedy BP, Lochner K, Prothrow-Stith D. Social capital, income inequality, and mortality. Am J Public Health 1997; 87:1491–1498.
48. Santelli JS, Lowry R, Brener ND, Robin L. The association of sexual behaviors with socioeconomic status, family structure, and race/ethnicity among US adolescents. Am J Public Health 2000; 90:1582–1588.
49. Ford K, Sohn W, Lepkowski J. American adolescents: Sexual mixing patterns, bridge partners, and concurrency. Sex Transm Dis 2002; 29:13–19.
50. Kelley S, Borawski E, Flocke S, Keen K. The role of sequential and concurrent sexual relationships in the risk of sexually transmitted diseases among adolescents. J Adolesc Health 2003; 32:296–305.
51. Boyer CB, Shafer MA, Wibbelsman CJ, Seeberg D, Teitle E, Lovell N. Associations of sociodemographic, psychosocial, and behavioral factors with sexual risk and sexually transmitted diseases in teen clinic patients. J Adolesc Health 2000; 27:102–111.
52. DiClemente R, Wingood G, Crosby R, et al. Sexual risk behaviors associated with having older sex partners: A study of black adolescent females. Sex Transm Dis 2002; 29:20–24.
53. Miller KS, Clark LF, Moore JS. Sexual initiation with older male partners and subsequent HIV risk behavior among female adolescents. Fam Plann Perspect 1997; 29:212–214.
54. Ford K, Norris A. Sexual networks of African-American and Hispanic youth. Sex Transm Dis 1997; 24:327–333.
55. Tanfer K, Cubbins L, Billy J. Gender, race, class and self-reported sexually transmitted disease incidence. Fam Plann Perspect 1995; 27:196–202.
56. Liberatos P, Link B, Kelsey J. The measurement of social class in epidemiology. Epidemiol Rev 1988; 10:87–121.
57. Brewster K. Race differences in sexual activity among adolescent women: the role of neighborhood characteristics. Am Sociol Rev 1994; 59:408–424.
58. Roye CF. Condom use by Hispanic and African-American adolescent girls who use hormonal contraception. J Adolesc Health 1998; 23:205–211.
59. Harrington KF, DiClemente RJ, Wingood GM, et al. Validity of self-reported sexually transmitted diseases among African American female adolescents participating in an HIV/STD prevention intervention trial. Sex Transm Dis 2001; 28:468–471.
60. Clark LR, Brasseux C, Richmond D, Getson P, D'Angelo LJ. Are adolescents accurate in self-report of frequencies of sexually transmitted diseases and pregnancies? J Adolesc Health 1997; 21:91–96.
61. Riley ED, Chaisson RE, Robnett TJ, Vertefeuille J, Strathdee SA, Vlahov D. Use of audio computer-assisted self-interviews to assess tuberculosis-related risk behaviors. Am J Respir Crit Care Med 2001; 164:82–85.
62. Murphy DA, Durako S, Muenz LR, Wilson CM. Marijuana use among HIV-positive and high-risk adolescents: A comparison of self-report through audio computer-assisted self-administered interviewing and urinalysis. Am J Epidemiol 2000; 152:805–813.
63. Wight RG, Rotheram-Borus MJ, Klosinski L, Ramos B, Calabro M, Smith R. Screening for transmission behaviors among HIV-infected adults. AIDS Educ Prev 2000; 12:431–441.
64. Newman J, Des Jarlais D, Turner C, Gribble J, Cooley P, Paone D. The differential effects of face-to-face and computer interview modes. Am J Public Health 2002; 92:294–297.
65. Cohen DA, Nsuami M, Martin DH, Farley TA. Repeated school-based screening for sexually transmitted diseases: a feasible strategy for reaching adolescents. Pediatrics 1999; 104:1281–1285.
66. Todd CS, Haase C, Stoner BP. Emergency department screening for asymptomatic sexually transmitted infections. Am J Public Health 2001; 91:461–464.
67. Bolan G, Ehrhardt A, Wasserheit J. Gender perspectives and STDs. In: Holmes K, Mardh P, Sparling P, et al., eds. Sexually Transmitted Diseases, 3rd ed. New York: McGraw-Hill Health Professions Division, 1999:117–127.
68. Health risk behaviors among adolescents who do and do not attend school—United States, 1992. MMWR Morb Mortal Wkly Rep 1994; 43:129–132.
69. Udry J, Chantala K. Missing school dropouts in surveys does not bias risk estimates. Soc Sci Res 2003; 32:294–311.
70. Sexually Transmitted Disease Surveillance, 1999. US Department of Health and Human Services, Atlanta: Centers for Disease Control and Prevention (CDC) Division of STD Prevention [pdf]. September 2000. Available at: Accessed 2001.
71. Chokephaibulkit K, Patamasucon P, List M, Moore B, Rodriguez H. Genital Chlamydia trachomatis infection in pregnant adolescents in east Tennessee: A 7-year case-control study. J Pediatr Adolesc Gynecol 1997; 10:95–100.
72. Boyer CB, Shafer MA, Teitle E, Wibbelsman CJ, Seeberg D, Schachter J. Sexually transmitted diseases in a health maintenance organization teen clinic: Associations of race, partner's age, and marijuana use [erratum appears in Arch Pediatr Adolesc Med 2000; 154:433]. Arch Pediatr Adolesc Med 1999; 153:838–844.
73. Ho GY, Bierman R, Beardsley L, Chang CJ, Burk RD. Natural history of cervicovaginal papillomavirus infection in young women. N Engl J Med 1998; 338:423–428.
74. Rosenthal SL, Stanberry LR, Biro FM, et al. Seroprevalence of herpes simplex virus types 1 and 2 and cytomegalovirus in adolescents. Clin Infect Dis 1997; 24:135–139.
75. Shaw E, Roberts D, Connor PD. Prevalence of and risk factors for chlamydia in a rural pregnant population. J Fam Pract 1995; 41:257–260.
76. Ley C, Bauer HM, Reingold A, et al. Determinants of genital human papillomavirus infection in young women. J Natl Cancer Inst 1991; 83:997–1003.
77. Gibson JJ, Hornung CA, Alexander GR, Lee FK, Potts WA, Nahmias AJ. A cross-sectional study of herpes simplex virus types 1 and 2 in college students: Occurrence and determinants of infection. J Infect Dis 1990; 162:306–312.
78. McCormack WM, Rosner B, McComb DE, Evrard JR, Zinner SH. Infection with Chlamydia trachomatis in female college students. Am J Epidemiol 1985; 121:107–115.
79. Ford K, Norris AE. Sexually transmitted diseases: experience and risk factors among urban, low income, African American and Hispanic youth. Ethn Health 1996; 1:175–184.
80. Nsuami M, Cohen DA. Participation in a school-based sexually transmitted disease screening program. Sex Transm Dis 2000; 27:473–479.
81. Goodman E. The role of socioeconomic status gradients in explaining differences in US adolescents' health. Am J Public Health 1999; 89:1522–1528.
© Copyright 2004 American Sexually Transmitted Diseases Association