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RESEARCH AND PRACTICE |
Denise Dion Hallfors and Bonita J. Iritani are with the Pacific Institute for Research and Evaluation, Chapel Hill, NC. William C. Miller is with the Departments of Epidemiology and Medicine, University of North Carolina, Chapel Hill. Daniel J. Bauer is with the Department of Psychology, University of North Carolina, Chapel Hill.
Correspondence: Requests for reprints should be sent to Denise Dion Hallfors, PhD, 1516 E Franklin St, Suite 200, Chapel Hill, NC 27514 (e-mail: hallfors{at}pire.org).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Objectives. We used nationally representative data to examine whether individuals’ sexual and drug behavior patterns account for racial disparities in sexually transmitted disease (STD) and HIV prevalence.
Methods. Data were derived from wave III of the National Longitudinal Study of Adolescent Health. Participants were aged 18 to 26 years old; analyses were limited to non-Hispanic Blacks and Whites. Theory and cluster analyses yielded 16 unique behavior patterns. Bivariate analyses compared STD and HIV prevalences for each behavior pattern, by race. Logistic regression analyses examined within-pattern race effects before and after control for covariates.
Results. Unadjusted odds of STD and HIV infection were significantly higher among Blacks than among Whites for 11 of the risk behavior patterns assessed. Across behavior patterns, covariates had little effect on reducing race odds ratios.
Conclusions. White young adults in the United States are at elevated STD and HIV risk when they engage in high-risk behaviors. Black young adults, however, are at high risk even when their behaviors are normative. Factors other than individual risk behaviors and covariates appear to account for racial disparities, indicating the need for population-level interventions.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Although there are probably many reasons for these racial disparities,5 a major question is whether individual behaviors account for most of the differences in infection rates. Research indicates that Blacks initiate sexual intercourse at younger ages than do Whites.6–8 Early age at first sexual intercourse is associated with both a history of STD infection8 and current STD infection, although the association diminishes with age among young adults.9 Black adolescents are also more likely than are White adolescents to report multiple sexual partners.6,10 Studies have shown no race-specific differences in condom use among young men10 and higher rates of use among Black adolescents than among White adolescents.6 When included as covariates, sexual risk behaviors have not fully accounted for race differences in STD prevalence in nationally representative surveys.8,11–13 Likewise, it has been shown that odds ratios for HIV infection among Black compared with White young men who have sex with men are not reduced in models adjusting for covariates such as number of partners and condom use.14
Drug use behaviors also have been implicated in STD and HIV infections as a result of individuals sharing needles and engaging in sexual behaviors with multiple or risky sexual partners to obtain drugs.15 Substance use proximal to a sexual encounter has been found as well to be associated with increased sexual risk taking.16 Sexual and substance use risk behaviors are known to co-occur,17,18 yet relatively little research has examined patterns of these risk behaviors among young people. The few studies that have been conducted have been helpful in understanding patterns,19–23 but only 1 study examined behavior patterns in relation to STDs.19 That study showed that Black girls were less likely than Black boys or White boys and girls to engage in high-risk behavior patterns but more likely to report a previously diagnosed STD.
Previous research involving the use of nationally representative data from the National Health and Nutrition Examination Survey revealed that disparities between Blacks and Whites in 3 viral blood-borne or sexually transmissible diseases (hepatitis B, hepatitis C, and herpes simplex type 2) were greater among individuals in low-risk groups than among those in high-risk groups, suggesting that public health attention should be directed toward individuals typically considered to be at low risk.24 This study, however, did not include data on other important STDs.
Other studies involving nationally representative survey data have had to rely on self-reported STD information.11–13,19,25 A handful of important surveys have incorporated biological STD testing, but each has had notable limitations. The National Survey of Adolescent Males showed associations between chlamydial infection and unprotected sexual intercourse, but these associations were not examined by race and were limited to adolescent boys.10 The multisite Young Men’s Survey conducted HIV testing of participants but was limited to a venue-based sample of young men having sex with men.14
Wave III of the National Longitudinal Study of Adolescent Health (Add Health) provides nationally representative data on biological measures of STD and HIV infection as well as interview measures of risk behaviors collected from male and female respondents who were young adults in 2002.3,4,26 Using this representative sample of young adults in the United States, we examined whether race associations persist in STD and HIV infection after stratification according to substance use and sexual behavior patterns and control for other important covariates such as gender, socioeconomic status (SES), marital status, and age at first intercourse. Given that racial disparities in STDs and HIV are a critical and growing public health problem, we sought to use the best data set available to examine whether behaviors or other underlying differences might account for such unequal disease burdens.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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At wave III, Add Health researchers sought to interview all of the original participants who were living in the United States. The (weighted) response rate for the wave III probability sample was 75.6% (n = 14322); analyses of nonresponse bias showed that the wave III sample adequately represented the wave I sample.27 As a means of maintaining confidentiality, questionnaires were administered via laptop computers. Computer-assisted self-interviewing technology was used to gather data on sensitive topics such as sexual and substance use histories. After completion of the interview, consent for STD and HIV testing was obtained, and urine and oral mucosal transudate (OMT) specimens were collected. Participants were provided with information about HIV and other STDs, and a toll-free telephone number was provided for participants to call for their test results (i.e., for chlamydia, gonorrhea, and HIV results).
Measures
STD and HIV infection.
Urine specimens were tested for chlamydial, gonococcal, and trichomonal infections. Ligase chain reaction assays (Abbott Laboratories, Abbott Park, Ill) were used in identifying Chlamydia trachomatis and Neisseria gonorrhoeae in urine specimens. Trichomonas vaginalis was detected using a polymerase chain reaction enzyme-linked immunosorbent assay developed at the Microbiology Core Laboratory of the University of North Carolina Sexually Transmitted Diseases Cooperative Research Center.28–30 About 8% of the wave III study participants refused to provide a urine specimen for testing. An additional 6% of N gonorrhoeae results were excluded as a result of product recall by the test manufacturer. Other reasons test results were not available were inability of participants to provide a specimen at the time of the interview (2%) and shipping or laboratory problems (3%).
The OraSure HIV-1 oral specimen collection device (Epitope, Beaverton, Ore) was used in collecting OMT specimens for HIV testing. Presence of antibodies to HIV-1 was assessed with the Oral Fluid Vironostika HIV-1 Microelisa System (Organon Teknika Corp, Durham, NC). The OraSure HIV-1 Western Blot Kit (Organon Teknika Corp, Durham, NC) was used to confirm repeatedly reactive results. About 5% of the wave III participants chose not to provide an OMT sample for HIV testing. Other participants provided a sample that could not be tested owing to specimen collection, shipping, or other problems (about 3%), and still others provided a sample that resulted in an indeterminate test outcome (less than 1%). Detailed information about Add Health’s procedures for collecting, transporting, and testing urine and OMT samples is available elsewhere.3,4,28
A composite STD and HIV measure was created to identify participants who had tested positive for at least 1 of the 4 STD and HIV infections considered (coded 1) or had tested negative for all 4 infections (coded 0). Participants who had missing data for any of the 4 infections were coded as "missing" on the composite variable.
Race. Identification of race and Hispanic ethnicity was based on respondents’ self-reports. Participants were asked to indicate their race from among the options White, Black or African American, American Indian or Native American, and Asian or Pacific Islander. They were told that they could select more than 1 option. Respondents who self-identified more than 1 racial group were asked "Which 1 category best describes your racial background?" Our analyses included only participants who identified themselves as either non-Hispanic Black or non-Hispanic White.
Risk behavior patterns. A key goal of the investigation was to determine whether racial disparities in STD and HIV prevalence persist after stratification of the sample on the basis of sexual and substance use risk behaviors. We used a stratification strategy for both methodological and theoretical reasons. Methodologically, treating each risk behavior as a covariate is not optimal given the potentially nonlinear effects of sexual and substance use behaviors on STD and HIV risk. Theoretically, subgroups of individuals may engage in qualitatively distinct patterns of substance use and sexual risk behaviors. Aggregating over these subgroups could produce misleading results (i.e., "Simpson’s paradox"31).
Given their known relation to STD and HIV risk, 4 risk strata were predefined as follows: abstaining, injection drug use (since June 1995), male–male sexual activity (since June 1995), and engaging in sex for money (since the previous interview in 1996). All individuals reporting injection drug use were assigned first, followed by those reporting male–male sexual relations and those engaging in sex for money.
Individuals not meeting criteria for these initial strata were then cluster analyzed (using the K-means method, which minimizes the sum of within-cluster variances and thus, maximizes differences between clusters, with wave III sampling weights) to identify other distinctive risk patterns. Variables entered into the cluster analysis included number of cigarettes smoked in the past month, number of days on which alcohol was consumed in the past 12 months (7 categories), number of days on which 5 or more drinks were consumed consecutively in the past 12 months (7 categories), number of times marijuana was used in the past month, average number of days cocaine or other illegal drugs were used in the past month, condom use at most recent vaginal intercourse (4 categories: never had sexual intercourse, no sexual intercourse in past year, used condom at most recent sexual intercourse, did not use condom at most recent sexual intercourse), sexual situation in past 12 months that respondent later regretted because she or he had been drinking or using drugs (2 categories: yes or no), and number of sexual partners since June 1995.
This analysis resulted in the identification of 12 additional risk patterns. In total, the 16 risk patterns assessed explained 73% of individual differences in sexual and substance use risk behaviors, indicating a high level of within-pattern homogeneity. Thus, within each risk pattern, Black and White adolescents were highly similar (i.e., matched) in their sexual and substance use risk behavior levels. Blacks and Whites were initially cluster analyzed separately; then, because identical patterns emerged for the 2 groups, they were analyzed in combination.19 We used SAS version 8.2 (SAS Institute Inc, Cary, NC) to conduct the cluster analysis.
Control variables. Covariates included gender, marital status (married or not married), high-school dropout (yes or no), functional poverty (whether or not respondents or their households did not pay the full amount of either the rent or mortgage or utility [gas, electricity, or oil] bill because they did not have enough money), and early initiation of sexual intercourse (first sexual intercourse at age 14 years or younger vs first sexual intercourse at an older age). Given the ages of participants, school dropout and functional poverty were selected as the optimal available indicators of SES.
Statistical Analyses
First, we analyzed prevalences by race (Black or White) for each of the 4 infections individually. We conducted bivariate analyses to examine STD and HIV infection by each of the control variables, using an F test that accounted for survey design effects to assess significance. Next, we examined individual race-specific infection prevalences stratified according to risk behavior pattern. Because race-specific patterns were similar for HIV and all of the STDs, we collapsed them into a single composite variable to avoid any chance of discerning an individual respondent’s identity and responses through the known characteristics of that person. We used separate logistic regression models to determine prevalence odds ratios and 95% confidence intervals (CIs) for each of 15 risk behavior patterns (abstaining was not included) and assess the effects of race both before and after control for covariates.
We used poststratification sampling weights developed by the Add Health research team to calculate estimates representative of the national population. Procedures for survey data in Stata SE 8.0 (Stata Corp, College Station, Tex) were used to account for the complex sampling design of the Add Health study. SAS was used for analyses (e.g., frequency analyses) not requiring adjustment of standard errors. All percentages reported here are weighted; all sample sizes reported are un-weighted.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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STD and HIV Prevalence
The prevalence of combined STD and HIV infection within the analysis sample was 6%. Most of those with an infection had a chlamydial infection (65%). Approximately 10% of the participants tested positive for 2 or more infections. Chlamydia and gonorrhea were the most common co-occurring positive infections (0.35% prevalence), followed by chlamydia and trichomoniasis (0.30%). Among those with a chlamydial infection, 9% also had gonorrhea, and 8% had trichomoniasis. Among all participants with gonorrhea, 72% also had a chlamydial infection.
The prevalence of each type of infection was higher among Blacks than among Whites (Table 1
). The prevalence of an STD including HIV was highest among Black women (22.8%; 95% CI = 19.3%, 26.8%), followed by Black men (14.6%; 95% CI = 11.6, 18.2), White women (3.7%; 95% CI = 2.9, 4.6), and White men (2.6%; 95% CI = 2.0, 3.4). Unmarried participants (F = 10.5, P< .01), high school dropouts (F = 24.2, P< .001), participants meeting the criteria for functional poverty (F = 11.5, P< .001), and those who had first engaged in sexual intercourse at age 14 years or younger (F = 19.4, P< .001) were more likely to have an STD. Condom use at most recent sexual intercourse was not associated with current infection (F = 3.2, P= .08).
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, risk behavior patterns are ordered from most to least normative. Characteristics of the members within each group are presented. In the sample overall, the most normative behavior pattern was the "few partners, low alcohol, tobacco, and other drug use" pattern, and more than a third of Blacks (37.6%) were in this group. By contrast, Whites were more broadly distributed among the behavior groups, with "light alcohol consumption and sexual activity" being the modal pattern.
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, with prevalence of infection presented according to race and behavior pattern. Among White young adults, the STD and HIV infection prevalence was lower than the overall prevalence of 6% for all of the risk behavior patterns with the exception of the 4 least normative and most risky patterns (engaging in sex for money, injection drug use, male–male sexual activity, and marijuana or other drug use). By contrast, prevalences for Black young adults were above 6% for all 15 risk behavior patterns.
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). For example, in the few partners and low alcohol, tobacco, and other drug use group, odds of any STD or HIV infection were 7.8 (95% CI = 4.5, 13.4) times higher among Black than among White young adults. Odds ratios for race were not statistically significant for the multiple partners, engaging in sex for money, injection drug use, and marijuana or other drug use risk behavior patterns, which were all among the least normative patterns. In each case, and especially for those in the injection drug use and marijuana or other drug use groups, odds ratios were large. However, because of the small sample sizes, estimates were imprecise, as evidenced by the wide confidence intervals. As can be seen in Table 4, adjustment for gender, marital status, school dropout status, functional poverty, and age at first sexual intercourse had little effect on the relation between race and infection status.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Racial disparities probably reflect environmental, institutional, and contextual differences between Blacks and Whites. For example, sexual mating patterns are largely segregated according to race,34,35 and Blacks are more likely than Whites to cross high-and low-risk behavior groupings when choosing sexual partners.35 This combination of assortative (like with like) mixing by race and disassortative mixing by risk group may create a "perfect storm" effect for Blacks that results in very high STD and HIV rates. Considering that the most important determinant of STD and HIV acquisition is the infection status of one’s partner, Blacks in both high- and low-risk groups are more likely to encounter an infected partner given this combination of assortative and disassortative mixing.
Addressing racial disparities in STD and HIV prevalence is extremely sensitive, and policy makers must proceed carefully, with dialogue and consensus among all community groups. Nevertheless, it is critical that the problem be addressed with due speed and guided by scientific findings. Given the documented higher prevalence of STDs among Black than White young adults, a more aggressive strategy to reach all Black young adults is needed, such as offering free universal screening to this population.
We recommend a media campaign informing Blacks of the high prevalence of HIV and other STDs across behavior groups, reducing the stigma associated with testing and an STD diagnosis, and encouraging young adults to be tested on an annual basis. We also recommend that information, testing, and treatment services be offered through nontraditional venues such as churches and beauty salons, as well as through colleges, prison and jail facilities, and health care venues. In addition, we recommend that surveillance be conducted to monitor whether prevalence is reduced over time and whether subgroups (such as men) are routinely being tested.
Finally, we recommend further research into structural factors that may account for some of the racial STD and HIV disparities in evidence. For example, we believe that an important area for future research is the possible link between the disproportionate incarceration of Blacks (relative to Whites) and STD and HIV disparities. The incarceration disparity is enormous: the US Bureau of Justice Statistics estimates that, if current trends continue, 1 in 3 Black males will spend time in prison during their lives, compared with 1 in 17 White males.36 Several studies have noted a relation between STD and HIV infections and incarceration,37–40 but a better understanding of causal mechanisms and opportunities for prevention is needed.
This study involved a number of limitations. Sexual and substance use risk behaviors were measured via self-reports and could have been underestimated. However, computer-assisted self-interviewing technology was used in Add Health; this methodology has been found to increase reporting of sensitive behaviors, and there are indications that it is effective across racial/ethnic groups.41 The measure of condom use that was used to assign individuals to behavior pattern groups was limited to most recent intercourse, which may not have been representative of participants’ overall condom use behavior. Valid measurement of condom use has posed challenges to researchers generally, given that study participants may not recall condom use behavior accurately or may be influenced by social desirability issues.42
Moreover, SES is a complex construct, and its measurement is further complicated when, as a result of their age group, participants’ incomes may be equally low whether they are enrolled in college or have dropped out of school. Our 2 indicators were dichotomous and could have obscured effects of SES at more detailed levels. Nevertheless, the variables we used did capture a pair of important and different dimensions of SES: education (as measured using school dropout status) and a summary of the effects of low income (as measured using our functional poverty variable).
Our findings suggest that STD and HIV prevention efforts should be focused at the individual behavior level for Whites but at the population level for Blacks, because infection prevalence among Blacks is elevated regardless of behavior patterns. Given the consistent evidence that sexual mating patterns are segregated according to race, we suggest policy strategies that will reduce infection in the burdened population. Most STDs can be cured, and the health and life span of individuals infected with HIV can be greatly increased by current therapies. In addition, the spread of HIV may be reduced through prompt diagnosis and treatment, given that antiretroviral treatment reduces viral load and thus, risk of transmission.43 Many STDs increase the likelihood of both infectivity of and susceptibility to HIV infection, so reducing prevalence will also help to prevent the spread of HIV.44 The burden of HIV and STD morbidity and mortality is high. We have the tools to greatly lower disease rates, and appropriate efforts should be directed toward reaching this important public health goal.
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Acknowledgments |
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We thank Abenah Vanderpuije for her helpful review of and comments on the article. We used data from the National Longitudinal Study of Adolescent Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris and funded by a grant from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. We thank Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design of the Add Health study.
Human Participant Protection
This study was approved by the institutional review board of the Pacific Institute for Research and Evaluation. Participants in the National Longitudinal Study of Adolescent Health provided written informed consent.
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Footnotes |
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Contributors
D. D. Hallfors originated the ideas for this article, supervised all aspects of its development and execution, and wrote the discussion section. B. J. Iritani wrote the initial draft of the other sections and conducted all data analyses. W. C. Miller consulted on the framework of the article and critically reviewed and revised early drafts. D. J. Bauer provided leadership on the statistical methods for the article.
Accepted for publication February 15, 2006.
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References |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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 S. O Aral, J. Lipshutz, and J. Blanchard Drivers of STD/HIV epidemiology and the timing and targets of STD/HIV prevention Sex Transm Inf, August 1, 2007; 83(suppl_1): i1 - i4. [Full Text] [PDF]
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S. O Aral, K. A Fenton, and K. K Holmes Sexually transmitted diseases in the USA: temporal trends Sex Transm Inf, July 1, 2007; 83(4): 257 - 266. [Abstract] [Full Text] [PDF]
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