It has long been recognized that sexually transmitted infection (STI) risk in the United States is strongly patterned by race/ethnicity, with much highest rates among African Americans.1–5 Black men have higher-risk sexual behaviors than white men; however, even within strata of sexual and substance use behavior, there remain large differences in STI rates between race/ethnicities, particularly for black individuals.6
We expect STI risk to be associated with income because lower income is associated with less access to preventative information and health care as well as increased used of sex for economic purposes and as a psychosocial coping mechanism.7 Past studies of income and STI in the United States have found mixed results. Ecological studies have found a positive correlation between STI rates and area-level socioeconomic status.8–10 Two nationally representative studies have collected individual-level information on STI infection and income. Among adults in the National Health and Nutrition Examination Survey, chlamydia was associated with poverty.11 Among adolescents in the National Longitudinal Study of Adolescent Health (Add Health), higher household income did not predict bacterial STI risk, whereas by early adulthood, Add Health respondents’ childhood experiences of low income were only crudely associated with increased STI risk.12
Race/Ethnicity is an important predictor of income in US society; if race/ethnicity confounds the association between income and STIs, income may be a mediator or moderator of the race/ethnicity-STI relationship.13 The former would reflect a pathway from racial/ethnic identity through income that acts similarly for all racial/ethnic groups; the latter would reflect a pathway that acts differentially by race/ethnicity. To fully explore how income interacts with race/ethnicity with respect to STIs and, in particular, how income predicts STI risk within racial/ethnic groups, it is necessary to examine race/ethnicity and income jointly.
Previous analysis has highlighted the interplay of incarceration, social and sexual network segregation, and impoverished circumstances that places some racial/ethnic groups, for example, African Americans, at far higher risk for infection than the rest of the population.14–16 Analysis of within-race/ethnicity risk gradients has been less well explored. One study of gonorrhea risk in California by area-level poverty found that although race/ethnicity was the strongest predictor of risk, gradients existed within all racial/ethnic groups; the gradient was steepest for whites and shallowest for Hispanics.10 Another study of the relationship between education and STI risk in Add Health found steeper gradients among white women for self-reported diagnosis and steeper gradients among black women for laboratory-confirmed STI.17 Finally, a study of poverty and STI risk in Add Health found a nonsignificant trend toward a significant relationship among black adolescent men,18 but no clear gradient for whites or black women.
None of these existing studies provide national evidence regarding income gradients in STI diagnosis within racial/ethnic categories using individual-level data, with the exception of Newbern and colleagues,18 who focus on school-aged respondents. We extend their analysis to cover the period up to young adulthood, to determine how adolescent economic circumstances predict STI risk during individuals’ most high-risk years.
MATERIALS AND METHODS
This analysis used Waves I to III of the Add Health survey, which has followed a nationwide cohort since their adolescence in the mid-1990s19; understanding sexual behavior and health was one of its primary design interests. A sample of 80 US high schools (plus 52 of these schools’ largest feeder schools) were selected to represent US schools with respect to region of country, urbanicity, school size, school type, and ethnicity. Wave I (1994–1995) surveyed a sample of all enrolled students in grades 7 through 12 at home. Wave II (1996) resurveyed those who had been in grades 7 through 11 at Wave I. Wave III (2001–2002; ages, 18–26 years) sought to locate and interview all those surveyed at home in Wave I.
The base study population for this analysis comprised all respondents who were interviewed at Waves I and III, provided information on their age and sex, and were affiliated with 1 of the 132 core schools. We then excluded respondents whose parents did not provide information on family income or household size. Ethical approval for the Add Health study was obtained from the institutional review board at the University of North Carolina, Chapel Hill. This analysis was exempted by the Harvard School of Public Health institutional review board as a secondary analysis of existing data.
The primary outcome for this study was a binary measure reflecting whether a respondent had self-reported or laboratory-confirmed Chlamydia trachomatis, Neisseria gonorrhoeae, or Trichomonas vaginalis at either Wave II or III. At Wave III, respondents were asked to provide a urine sample testing; detailed descriptions of the testing methods and evidence of their sensitivity and specificity are available elsewhere.20 Also at Wave III, respondents were asked whether a health professional had, within the past 12 months, told them that they were infected with each of these STIs. At Wave II, respondents were asked whether they had been diagnosed since Wave I, and at Wave I, they were asked if they had ever been diagnosed.
Income was based on parental reports at Wave I of 1994 total pretax household income (in $1000 increments, top coded at $999,000; no income data were collected at Wave II). Household incomes were equivalized by dividing them by the square root of the number of individuals in the household—an approach adopted by the “Luxembourg Income Study,” which accounts for economies of scale arising from some household consumption being nonrivalrous in consumption; that is, use by one member does not diminish the amount available for others.21 Incomes were categorized into quintiles, using the highest quintile as the reference category, to allow for the detection of nonlinearities in STI diagnosis gradients. We classified race/ethnicity into 4 categories based on respondents’ self-report of Hispanic ethnicity and their primary racial identification: white non-Hispanic, black non-Hispanic, any Hispanic, and other non-Hispanic (hereafter “white,” “black,” “Hispanic,” and “other”).
Additional covariates considered as potential confounders of the relationships between race/ethnicity, income, and STIs included respondents’ age (in years) and sex at Wave I and school urbanicity (urban, suburban, or rural), regional location (West, Midwest, South, Northeast), and type (public or private).
We calculated cumulative risk proportions for each combination of race/ethnicity and income quintile, and their adjusted Wilson score 95% approximate binomial confidence intervals (CIs). All statistical tests were 2 sided at α = 0.05, and regression analyses were conducted in SAS version 9.3 (SAS Institute, Cary, NC). Multivariable analysis was conducted using logistic regression models, using survey procedures that allow for clustering at the school level and sampling weights that adjust for nonresponse and the unequal probability of selection. We initially established the relationship between racial/ethnic category and STI risk, adjusting for age, sex and school-level covariates. We then added income quintile as a covariate to assess the degree to which income mediated the race/ethnicity-STI relationship, and finally, we included interactions of race/ethnicity and income quintile to assess effect modification. We also ran models stratified by sex and considering each STI separately.
We conducted 3 sensitivity analyses: first, given a low likelihood of reverse causation, we included as cases those individuals reporting an STI diagnosis at any age before Wave I; second, we restricted our sample to respondents who were interviewed at all 3 waves; and third, because the impact of family income might be expected to exert its greatest effect while students were in school, we restricted our sample to those interviewed at Waves I and II and used self-reported STI diagnosis at either wave as our outcome.
A total of 10,791 respondents were interviewed at both Waves I and III, were affiliated with a core schools, provided information on age and sex, had parents who reported household size and income, and either answered questions relating to STI history at Waves II and III or provided a valid urine sample for STI testing at Wave III. Age or sex information was missing for 13 Wave III respondents, a further 82 were not from core schools, 3594 more had no household income information, and 423 others lacked sampling weights.
Respondents were almost all aged between 13 and 18 at baseline with an even sex split (Table 1). The sample was more black and Hispanic than the general US population and the schools that they attended, reflecting Add Health’s intentional oversampling of minorities. Median equivalized per-capita income was US$22,660 (95% CI, US$20,972–US$24,348). Respondents falling in the poorer quintiles of the sample were more likely to be black or Hispanic and less likely to be white. They were also more likely to come from the South and to attend urban or rural, as opposed to suburban, schools.
Across Waves II and III, prevalence of either a recent diagnosis of, or positive test for, at least 1 STI was 9.2%. The most common diagnosis was of chlamydia (6.7%), followed by trichomoniasis (2.6%) and then gonorrhea (1.5%). Diagnosis risk was highest for blacks (26.1%), followed by Hispanics (10.6%), others (9.3%), and, finally, whites (5.4%). The risk of diagnosis fell as income increased, from 14.7% in the poorest quintile to 5.2% in the richest quintile. This gradient was observed for all 4 racial/ethnic groups, although the patterns were not strictly monotonic in every instance (Fig. 1, values in Supplementary Table 1, http://links.lww.com/OLQ/A67). Bivariate regression analysis confirmed that whites were at significantly lower risk for STI diagnosis than all other groups and that all income quintiles were at significantly higher risk compared with the richest quintile (Supplementary Table 2, http://links.lww.com/OLQ/A67).
In multivariable models (Table 2, complete results in Supplementary Table 3, http://links.lww.com/OLQ/A67), Hispanics and others had approximately double the odds of STI diagnosis compared with whites, and blacks had more than 6 times the odds. The addition of income reduced the race/ethnicity differentials marginally. However, income had an independent association with STIs, with the poorest quintile having 83% increased odds and the middle 3 quintiles having roughly 50% increased odds, compared with the richest quintile. When we interacted race/ethnicity and income, the racial/ethnic differences in the highest-income quintile changed little for blacks and Hispanics. Income gradients were steepest among others, followed by Hispanics and blacks, and flattest for whites. As a result, disparities between whites and others were most pronounced among the poorest (Supplementary Table 4, http://links.lww.com/OLQ/A67).
When we stratified the analysis by sex, there were no clear gradients in income for white men or women (Table 3; Supplementary Tables 4, http://links.lww.com/OLQ/A67 and 5, http://links.lww.com/OLQ/A67 present the same results with direct comparisons within income groups and within racial/ethnic groups, respectively). Among all other groups, income gradients were steeper for women than for men. The strongest gradient existed for black women, among whom the 2 poorest quintiles had more than 2.5 times the odds of STI diagnosis compared with the richest quintile (odds ratios [ORs], 2.68 [95% CI, 1.48–4.85] and 2.70 [95% CI, 1.37–5.30]). The average diagnosis ratio between blacks and all others was wider for women than for men; this reflected white-black disparities, which were larger among poor women than men, but became roughly equal by the highest-income quintile.
Analyzing each STI outcome separately, the greatest racial/ethnic disparities existed for gonorrhea, reflecting a particularly large gap between blacks and all other groups. Income gradients were visible for chlamydia and gonorrhea; the gradient for trichomoniasis was shallow. Within race/ethnicities, significant differences existed between richest and poorest quintiles for all groups for chlamydia diagnosis; for other infections, small numbers of diagnoses led to unstable estimates and gradients, although a gradient was notable by its absence for trichomoniasis among whites. The 3 sensitivity analyses had a limited impact on the key findings (Supplementary Table 6, http://links.lww.com/OLQ/A67).
This study provides the first analysis of income gradients in STIs within race/ethnicity groups using a national US sample of individuals including young adults. In line with existing studies, including a previous report using this data set,3 we find large differentials in STI risk across racial/ethnic groups—more than a 6-fold increase in the odds of either physician or laboratory report for blacks compared with whites. This study moves beyond prior analyses in finding that this racial/ethnic disparity continues and perhaps strengthens into young adulthood. We find that these racial/ethnic disparities are only weakly related to income; adding income to a model containing measures of race/ethnicity reduced the point estimates on the latter by between 5% and 15%. Income is nonetheless an independent predictor of STI risk. This is consistent with existing race/ethnicity-adjusted ecological analyses linking area-level poverty and gonorrhea rates in California.10 In contrast, previous studies of STI diagnosis and parental income in Add Health have suggested little relationship using single-wave outcomes,12,18 although parental and own education was predictive.17,22 Pooling diagnoses across waves may have increased our power to detect an association.
When we allow for income and race/ethnicity to interact in our models, we find some evidence for effect modification, reflected in the better fit of the interaction model as measured by the Akaike information criterion (Table 2). Our analyses show that income affected STI diagnosis probability less for whites than for other groups and that racial/ethnic disparities were least pronounced among the rich. Moreover, stratification by sex led to income gradients among whites disappearing entirely, suggesting that income is only related to STI diagnosis among whites insofar as it reflects sex differences in income. Our finding of steeper risk gradients among blacks is congruent with existing studies of socioeconomic status, race/ethnicity, and STIs in Add Health: past research has found maternal education and occupation at Wave I and own education at Wave III to be associated with STIs among blacks but not whites.17,18
When we further stratify by sex, we find a stronger income gradient for women than for men among blacks and Hispanics. Combined with the finding of greater disparities by income within nonwhite groups, this result highlights that sex, race/ethnicity, and income interact to place poor black women at increased risk for STIs.
There has been significant research describing how sexual networks and hence sexual risks are heavily structured by race/ethnicity and how this leads to racial/ethnic disparities in STI rates.15,16 Although being poor, female, and African American are all independent risk factors for STIs,23 our finding of a stronger income gradient for black women is somewhat surprising. This is because African Americans have relatively low risk homophily—that is, women who are otherwise low risk tend to have higher-risk partners because of a range of factors (including racial/ethnic homophily, imbalanced sex ratios [due to higher male mortality rates] and extremely high incarceration rates) limiting their choice of sexual partners.14 Such disassortative mixing should theoretically lead to a less variation in STI risk across the income gradient. A possible explanation is that being “poor” in this study does not have the same meaning for all racial/ethnic groups. Median net worth within the bottom income quintile in the 2000 census was $24,000 for whites but less than $100 for blacks.13 Income quintiles may therefore not reflect the same socioeconomic circumstances for each race/ethnicity, and thus, steeper gradients for blacks may reflect the greater depth of their poverty. (We note that this does not explain why black women have a steeper income-risk gradient than black men.)
We find variation in income gradients by race/ethnicity. One potential explanation of this finding relates to spatial concentration. Sexually transmitted infections with a low population prevalence (for example, syphilis, gonorrhea) tend to be most concentrated by geography,24 by race/ethnicity,5 and by income.3 Within a single STI, concentration of rates also seems to be highest among African Americans10 and to be highest in areas where blacks experience certain dimensions of geographic and economic segregation.25 These neighborhoods exhibit high levels both of prevalent STIs and of social risk factors (such as drug use rates and high-risk sexual norms), which are likely to increase risky sexual behavior.26,27 Our observed steeper income gradients among racial/ethnic minorities might then reflect the higher likelihood of poor minority individuals living in these areas of concentrated high STI risk—compared with poor whites. Such an argument is congruent with STI risk being concentrated among poor minority individuals. Given existing evidence that sexual risk behaviors do not explain racial/ethnic disparities in STI rates,6 it might also be of interest to explore whether they explain the income gradients seen in this study.
Our use of the Add Health data set provides some notable strengths. The prospective, longitudinal nature of the data set should limit concerns regarding the temporal direction of any associations, especially because attrition is relatively low and does not seem to greatly affect prevalence estimates.28 Using multiple waves of outcome data additionally raises our power to detect effects. Furthermore, the study’s national coverage allows us to draw nationwide conclusions. In addition, our use of both laboratory testing (avoiding bias arising from variation in health care access) and audio computer–assisted interview self-report data (ensuring that prior but treated cases are captured) strengthens our approach and has the added benefit that social desirability bias in self-reports should be limited by respondents’ knowledge that they are also being laboratory tested.
Nevertheless, there are also a number of potential limitations to our analysis. First, we rely on school (and residential) location at a single time point, which may have resulted in misclassification of context across waves. Second, although our STI outcomes combine self-reported clinical diagnosis and laboratory testing, there is a 5-year gap in respondents’ self-report (from Wave II up to 1 year before Wave III), which is troubling if diagnostic patterns (by race/ethnicity or income) differ systematically across respondents’ lifetimes. This concern is somewhat allayed by the fact that Add Health effectively comprises 6 cohorts (because enrolment covered 6 grades), and thus, all ages between 14 and 26 years are covered by both self-report and laboratory testing. Consequently, these gradients would need to vary systematically both by age of respondent and by birth cohort to generate bias. We have no hypothesis as to why this form of systematic variation might exist; however, were it the case, then our results would not be generalizable to other birth cohorts.
Third, as is common in survey-based analyses, many individuals do not have income data (24.3%), which may have led to selection bias. Those with missing income data were significantly more likely to be nonwhite; however, they were not more likely to have a positive test result across the whole sample or within racial/ethnic groups (Supplementary Table 7, http://links.lww.com/OLQ/A67). Finally, it is important to note the context of this study—US youth in the 1990s and early 2000s—when chlamydia rates were rising and gonorrhea (and perhaps trichomoniasis) rates were falling,29 when extrapolating results elsewhere.
Our study provides evidence that although racial/ethnic differentials are significantly larger than income differentials in STI rates nationwide, both factors are independent predictors of increased risk. The Centers for Disease Control and Prevention are committed to integrating consideration of social determinants of health into STI prevention program design.30 Prevention efforts for STIs in the United States often focus on African American populations,31,32 reflecting their very high infection rates at all income levels. Our analysis highlights that there may be added benefit in targeting interventions to assist the poorest within other racial/ethnic groups, particularly other minorities, given their independently higher risk of STIs. This does not imply singling such individuals out for targeted prevention messages, but rather the importance of providing interventions relevant to such individuals, including consideration of structural interventions that lower such individuals’ vulnerability to high-risk behaviors, partners, and settings.33
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