Abstract
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Assessing the Theory of Gender and Power: HIV Risk Among Heterosexual Minority Dyads
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Alia A. Al-Tayyib Abstract
This study drew on the Theory of Gender and Power (TGP) as a framework to assess power inequalities within heterosexual dyads and their effects on women. Structural equation modeling was used to better understand the relationship between structural and interpersonal power and HIV sexual risk within African American and Latina women’s heterosexual dyads. The main outcome variable was women’s sexual HIV risk in the dyad and was created using women’s reports of condomless sex with their main male partners and partners’ reports of their HIV risk behaviors. Theoretical associations developed a priori yielded a well-fitting model that explained almost a quarter of the variance in women’s sexual HIV risk in main partner dyads. Women’s and partner structural power were indirectly associated with women’s sexual HIV risk through substance use and interpersonal power. Interpersonal power was directly associated with risk. In addition, this study found that not identifying as heterosexual was directly and indirectly associated with women’s heterosexual sex risk. This study provides further support for the utility of the TGP and the relevance of gender-related power dynamics for HIV prevention among heterosexually-active women.
Introduction
Women with HIV are primarily infected through heterosexual sex [1], a behavior that usually occurs within the social context of a dyad. However, most HIV prevention interventions are based on individual-level theories and often fail to adequately incorporate the social context within which risk behavior takes place. Understanding how societal inequalities manifest as power imbalances within relationships is considered especially important in understanding sexual risk behaviors among women [2–6].
The Theory of Gender and Power (TGP) [7] is a social theory that has been used to characterize how the socially constructed concept of gender creates inequalities that can in turn place women at an increased risk of HIV infection [5, 6, 8]. TGP identifies three social structures that create inequalities: the sexual division of labor (e.g., poverty, low education), the sexual division of power (e.g., abuse, partner unwilling to engage in safer sex behaviors), and the structure of cathexis (e.g., social norms). These structures are described as operating at societal and institutional levels, which can create multi-level inequalities for women that impact individual health. Furthermore, TGP has been operationalized to identify the particular exposures and risk factors in each structure that increase women’s vulnerability and risk of HIV infection [6].
Although TGP is a social structural theory, the majority of empirical research regarding power using TGP has focused on furthering the understanding of interpersonal power dynamics regarding issues such as communication, decision making, and relationship equality, and how these imbalances influence condom use. However, theory suggests that larger macro-level issues may set the stage or precede the manifestation of interpersonal power inequities. Prior studies have also highlighted the multidimensionality of power and the differential influence of these dimensions on sexual risk behavior [9, 10]. A better understanding of how structural-level inequalities influence interpersonal dyad interactions and thus individual behavior is less explored.
The objectives of this study were to quantify key TGP constructs and use structural equation modeling to explore relationships and assess fit of a TGP-guided model of women’s sexual HIV risk using data from National HIV Behavioral Surveillance (NHBS). Based on TGP and available data, an a priori model was developed to predict women’s sexual HIV risk within the context of heterosexual relationships with primary partners (Fig. 1). The structure of cathexis was not included as appropriate measures were not available in NHBS. It was hypothesized that the TGP constructs of dyadic structural power and interpersonal power would be directly related to women’s sexual HIV risk, as would substance use. Women’s individual structural power (e.g., age, education level, family income, and housing status), sexual history, and relationship context were expected to predict the TGP constructs which would, in turn, mediate their relationship to sexual HIV risk.
A priori model
Methods
National HIV Behavioral Surveillance
We conducted a secondary analysis of data collected in 2006–2007 as part of the National HIV Behavioral Surveillance (NHBS) system’s pilot for behavioral surveillance among heterosexuals for increased risk of HIV [11]. The principal objective of the NHBS system is to monitor risk behaviors and access to prevention services among populations at highest risk for HIV infection in the United States. The 2006–2007 NHBS cycle included a one-time supplemental partner study that aimed to collect information from heterosexual minority women and their male sex partners to better understand male partner risk behaviors and the accuracy of the women’s perceptions of these risk behaviors. The NHBS Partner Study included information from both the male and female partners, which allowed for the creation of a dependent variable that included condom use as well as male partner risk behavior.
African American and Hispanic women, 18–50 years old, who reported having vaginal or anal sex with a man in the past 3 months were eligible to participate. Women were recruited at venues and by peers through respondent-driven sampling and, after completing the NHBS behavioral survey and HIV testing, were asked to recruit up to two male sex partners. A total of 11 participating NHBS study sites are included in this analysis of the Partner Study data. Due to small sample sizes, the analyses were limited to main partner dyads and dyads where both partners were HIV-negative. All NHBS data were anonymous and study procedures were approved by each study site’s local Institutional Review Boards.
Analytic Approach
The analytical goal was to use structural equation modeling (SEM) to identify a parsimonious model using TGP latent variables and other variables to explain sexual HIV risk among a geographically diverse sample of high risk minority women. Measurement analyses (bivariate and exploratory and confirmatory factor analysis) were conducted first to determine how to best quantify the key TGP constructs identified in the a priori model [12, 13]. A detailed description of the results of these measurement analyses is beyond the scope of this paper; however, measurement and modeling modifications made from the a priori model to the final model are briefly described in the measurement section below. Structural modeling analyses were conducted using MPlus version 6.1 [14], and began by assessing the fit of the TGP variables and then adding in the remaining variables with paths specified in the a priori model. Model fit was assessed at each iteration, using the Chi square (X2) statistic, comparative fit index (CFI), and Root Mean Square Error of Approximation (RMSEA). For CFI, values exceeding 0.90 were considered indicative of adequate fit and values exceeding 0.95 as demonstrating good fit. For the RMSEA, fit was considered good if the values were ≤ 0.05. The R2 values were reviewed as well. Mplus output, including modification indices and residual matrices, were reviewed with each iteration and recommendations with high index values, if in line with theory, were assessed in subsequent analyses.
Measures
Table 1 provides detailed information on the TGP and substance use variables retained in the final model. Where there is deviation from the a priori model an explanation is provided below.
Table 1
Dependent and independent variables used in structural model
| Concept | Variables | Data source | |
|---|---|---|---|
| Women | Main male sex partner | ||
| Dependent | |||
 Women’s sexual HIV risk | Women’s HIV sex risk in the dyad (0–5) Condomless sex (0–2) * male risk behavior (1–5) where 0 represents no unprotected sex to 10 represents unprotected anal sex with a partner with all risk behaviors. Scores of 5 or higher were collapsed due to skewed distribution. | Condomless sex in the dyad (past 3 months): 0 = no unprotected sex (7%); 1 = condomless vaginal sex (73%); 2 = condomless anal sex (could also have had unprotected vaginal sex; 20%) | Sum of 5 binary items: Condomless sex with a man Condomless sex with one other woman Condomless sex with multiple other women STD diagnosis (past 12 months) |
| Independent | |||
| Structural power | Women’s structural power (0–7); Male partners structural power (0–7) | Sum of 7 binary items: Not currently homeless Not in poverty Employed part- or full-time Completed high school/GED or more education Currently insured Visited a health care provider (past 12 months) Received HIV prevention services (past 12 months) | Sum of 7 binary items: Not currently homeless Not in poverty Employed part- or full-time Completed high school/GED or more education Currently insured Visited a health care provider (past 12 months) Received HIV prevention services (past 12 months) |
| Interpersonal power | Risk communication (latent) | 7 items measuring partner risk behaviors discussed with partner (past 3 months): # of current sex partners # of past sex partners HIV status History of sex with men History of drug use History of STDs Using condoms | |
| Disrespect in the dyad (latent) | 2 items: Ever physically and/or sexually abused by main partner Partner had concurrent partners during the relationship | ||
| Comfort asking partner to use condoms (manifest variable) | 1 item: Comfort asking partner to use condoms | ||
| Substance use (latent) | 5 items regarding substance use (past 12 months): Binge drinking (4 + alcoholic drinks in one sitting on weekly or more frequent basis) Injected drugs Used marijuana Used crack cocaine Used other drugs | ||
Dependent Variable:Women’s Sexual HIV Risk in the Dyad
This variable includes self-reported data from both the female participants and their main male sex partners and ranges from 0 to 5 with a higher score indicating more women’s sexual HIV risk in dyads.
Division of Labor: Structural Power Indices
The a priori model included two variables related to division of labor: women’s structural power at an individual level (women’s age, education level, household income, and homelessness) and within the dyad (the status of female participants relative to their main male sex partners based on age, homelessness, education, employment, income, insurance coverage, and health care utilization). Bivariate analyses indicated that women’s dyadic power relative to their main partners was not associated with interpersonal power or women’s sexual HIV risk; however, separate structural power variables created to describe the women and their male partners were. Therefore, the final model used two summary index variables, one to describe women’s structural power and one to describe that of women’s main male partners; each score ranges from 0 to 7 with a higher score indicating more structural power. Women’s age at the time of the interview was not included in the structural power index as it was not significantly related to the dependent variable. Age was retained separately as a continuous exogenous manifest variable.
Division of Power: Women’s Interpersonal Power
Several variables were initially identified to describe interpersonal power within women’s main partnerships (women’s report of intimate partner violence, communication about sexual history, comfort requesting their main partners use condoms, use of substances before/during sex, sex with other partners). Based on exploratory factor analysis (EFA) and during the modeling process, two latent variables were retained to describe women’s interpersonal power within dyads: risk communication and disrespect in the dyad. The variable regarding comfort asking their main partners to use condoms was retained as a manifest variable.
Women’s Substance Use
Substance use is considered to be a risk factor related to Division of Power [6]. The final model included 5 variables related to women’s level of substance use in the last year.
Other Variables in the Final Model
Of the remaining exogenous variables identified in the a priori model, age of first sex, women’s number of sex partners in the past 12 months, and currently living with the main partner were retained. In addition, although all Partner Study participants had recent opposite-sex partners, only 86% reported a heterosexual orientation. Therefore, a dichotomous variable of heterosexual orientation yes/no was included (collapsing homosexual/gay/lesbian, bisexual, and other into not heterosexual). The final model controlled for geographic region of the NHBS project site, marital status (never married; separated/widowed/divorced; or married/living as married), and race/ethnicity (Hispanic/Latina; Black/African American; or multiracial).
Results
The final dataset had a total of 533 main partner dyads. Overall demographics and demographics by geographic region are shown in Table 2. There were significant geographic differences related to homelessness (past 12 months X2 = 24.64, p < 0.001 and current X2 = 33.42, p < 0.001), race/ethnicity distribution (X2 = 42.99, p < 0.001), employment status (X2 = 63.27, p = 0.005) and poverty (X2 = 14.10, p = 0.003).
Table 2
Women’s demographic characteristics by geographic region
| Total | Easta | Southb | Midwestc | Westd | Test statistic | p value | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No. | % | No. | % | No. | % | No. | % | No. | % | |||
| Age (mean, SD) | 32.9 | (9.6) | 34.3 | (9.9) | 31.7 | (9.5) | 32.8 | (9.6) | 33.4 | (9.4) | F = 1.47 | 0.222 |
| Race/Ethnicity | ||||||||||||
| Black | 448 | (84) | 69 | (78) | 127 | (96) | 160 | (82) | 92 | (79) | X2 = 42.99 | <0.001 |
| Hispanic or Latinoe | 72 | (14) | 20 | (22) | 5 | (4) | 32 | (17) | 15 | (13) | ||
| Multiracial | 13 | (2) | 0 | (0) | 0 | (0) | 3 | (2) | 10 | (9) | ||
| Homelessness | ||||||||||||
| Past 12 months | 137 | (26) | 30 | (34) | 16 | (12) | 47 | (24) | 44 | (38) | X2 = 24.68 | <0.001 |
| Currently homeless | 77 | (15) | 22 | (25) | 6 | (5) | 19 | (10) | 30 | (26) | X2 = 33.42 | <0.001 |
| Education | ||||||||||||
| < High school | 189 | (36) | 37 | (42) | 46 | (35) | 70 | (36) | 36 | (31) | X2 = 17.60 | 0.505 |
| High school/GED | 199 | (37) | 27 | (14) | 54 | (41) | 68 | (35) | 50 | (43) | ||
| > High school | 145 | (27) | 25 | (28) | 32 | (24) | 57 | (29) | 31 | (27) | ||
| Employment status | ||||||||||||
| Unemployed | 342 | (64) | 52 | (58) | 87 | (66) | 114 | (59) | 89 | (76) | X2 = 63.27 | 0.005 |
| Employed part-time | 81 | (15) | 11 | (12) | 25 | (19) | 31 | (16) | 14 | (12) | ||
| Employed full-time | 110 | (21) | 26 | (29) | 20 | (15) | 50 | (26) | 14 | (12) | ||
| Income | ||||||||||||
| Living in poverty | 385 | (73) | 52 | (60) | 108 | (82) | 138 | (71) | 87 | (74) | X2 = 14.10 | 0.003 |
| Marital status | ||||||||||||
| Married/living as married | 137 | (26) | 24 | (27) | 38 | (29) | 49 | (25) | 26 | (22) | X2 = 2.56 | 0.862 |
| Never married | 299 | (56) | 49 | (55) | 68 | (55) | 114 | (59) | 68 | (58) | ||
| Separated/divorced/widowed | 97 | (18) | 16 | (17) | 26 | (20) | 32 | (16) | 23 | (20) | ||
| Sexual orientation | ||||||||||||
| Heterosexual | 457 | (86) | 77 | (87) | 121 | (92) | 167 | (86) | 92 | (79) | X2 = 15.87 | 0.07 |
| Homosexual | 1 | (0) | 1 | (1) | 0 | (0) | 0 | (0) | 0 | (0) | ||
| Bisexual | 73 | (14) | 11 | (12) | 11 | (8) | 26 | (13) | 25 | (21) | ||
| Other | 1 | (0) | 0 | (0) | 0 | (0) | 1 | (1) | 0 | (0) | ||
| All | 533 | (100) | 89 | (100) | 132 | (100) | 194 | (100) | 117 | (100) | ||
Table 3 presents the distribution of the indicator variables retained in the final structural model by geographic location. There were significant geographic differences in women’s structural power (F = 5.55, p = 0.001), sexual orientation (X2 = 8.76, p = 0.033), all 5 substance use variables (marijuana X2 = 21.55, p < 0.001; crack X2 = 8.21, p = 0.042; other illicit drugs X2 = 12.05, p = 0.007; binge drinking X2 = 10.03, p = 0.019; and injection drug use X2 = 17.93, p = <0.001) and two of the risk communication variables (ever had sex with a man X2 = 8.58, p = 0.035 and HIV status X2 = 14.47, p = 0.002).
Table 3
Significant path variables a in the model by geographic region
| Total | Eastb | Southc | Midwestd | Weste | Test statistic | p-value | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No. | % | No. | % | No. | % | No. | % | No. | % | |||
| All | 533 | 89 | 132 | 195 | 117 | |||||||
| Male partner’s structural power (0–7)f | 3.8 | (± 1.4) | 3.8 | (± 1.4) | 3.5 | (± 1.3) | 3.8 | (± 1.5) | 4.0 | (± 1.5) | F = 1.92 | 0.126 |
| Woman’s structural power (0–7)f | 3.9 | (± 1.4) | 4.3 | (± 1.4) | 3.5 | (± 1.4) | 4.0 | (± 1.4) | 3.9 | (± 1.4) | F = 5.55 | 0.001 |
| Heterosexual | 457 | (86) | 77 | (87) | 121 | (92) | 167 | (86) | 92 | (79) | X2 = 8.76 | 0.033 |
| Age at first sex | ||||||||||||
| 13 or younger | 116 | (22) | 25 | (28) | 23 | (17) | 45 | (23) | 23 | (20) | X2 = 17.97 | 0.265 |
| 14 | 86 | (16) | 14 | (16) | 18 | (14) | 28 | (14) | 26 | (22) | ||
| 15 | 102 | (19) | 13 | (15) | 27 | (21) | 36 | (19) | 26 | (22) | ||
| 16 | 98 | (18) | 17 | (19) | 32 | (24) | 33 | (17) | 16 | (14) | ||
| 17 | 58 | (11) | 7 | (8) | 18 | (14) | 25 | (13) | 8 | (7) | ||
| 18 or older | 73 | (14) | 13 | (15) | 14 | (11) | 28 | (14) | 18 | (15) | ||
| Currently living with partner | 282 | (54) | 45 | (51) | 72 | (55) | 108 | (57) | 57 | (50) | X2 = 1.87 | 0.599 |
| Agef | 32.9 | (± 9.6) | 34.3 | (± 9.9) | 31.7 | (± 9.5) | 32.8 | (± 9.6) | 33.4 | (± 9.4) | F = 1.47 | 0.222 |
| Substance use, past 12 months | ||||||||||||
| Marijuana use | 260 | (49) | 36 | (41) | 46 | (35) | 109 | (56) | 69 | (59) | X2 = 21.55 | <0.001 |
| Crack use | 114 | (21) | 21 | (24) | 18 | (14) | 42 | (22) | 33 | (28) | X2 = 8.21 | 0.042 |
| Other illicit drug use | 158 | (30) | 19 | (21) | 36 | (27) | 54 | (28) | 49 | (42) | X2 = 12.05 | 0.007 |
| Binge drinking | 134 | (25) | 17 | (19) | 23 | (17) | 59 | (30) | 35 | (30) | X2 = 10.03 | 0.019 |
| Injection drug use | 42 | (8) | 8 | (9) | 3 | (2) | 12 | (6) | 19 | (16) | X2 = 17.93 | <0.001 |
| Disrespect in the dyad | ||||||||||||
| Any physical or sexual abuse | 186 | (35) | 36 | (41) | 47 | (36) | 62 | (32) | 41 | (35) | X2 = 2.06 | 0.559 |
| Partner has other partners (0–3) | ||||||||||||
| Definitely did not | 138 | (27) | 21 | (24) | 31 | (24) | 53 | (29) | 33 | (29) | X2 = 3.11 | 0.960 |
| Probably did not | 130 | (25) | 24 | (28) | 31 | (24) | 46 | (25) | 29 | (26) | ||
| Probably did | 143 | (28) | 23 | (27) | 38 | (29) | 50 | (27) | 32 | (28) | ||
| Definitely did | 105 | (20) | 18 | (21) | 31 | (24) | 37 | (20) | 19 | (17) | ||
| Risk communication, past 3 months | ||||||||||||
| # Current sex partners | 283 | (53) | 40 | (45) | 75 | (57) | 109 | (56) | 59 | (50) | X2 = 4.06 | 0.255 |
| # Past sex partners | 286 | (54) | 45 | (51) | 76 | (58) | 106 | (54) | 59 | (50) | X2 = 1.69 | 0.640 |
| Ever had sex with a man | 224 | (42) | 25 | (28) | 58 | (44) | 88 | (45) | 53 | (45) | X2 = 8.58 | 0.035 |
| HIV status | 282 | (53) | 59 | (66) | 79 | (60) | 91 | (47) | 53 | (45) | X2 = 14.47 | 0.002 |
| Drug use history | 321 | (60) | 55 | (62) | 82 | (62) | 113 | (58) | 71 | (61) | X2 = 0.72 | 0.868 |
| STD history | 251 | (47) | 38 | (43) | 70 | (53) | 96 | (49) | 47 | (40) | X2 = 5.49 | 0.139 |
| Using condoms | 296 | (56) | 49 | (55) | 77 | (58) | 107 | (55) | 63 | (54) | X2 = 0.60 | 0.897 |
| Comfort asking partner to use condoms (0–3) | ||||||||||||
| Never asked | 217 | (41) | 37 | (42) | 54 | (41) | 75 | (39) | 51 | (44) | X2 = 8.89 | 0.448 |
| Not comfortable | 30 | (6) | 6 | (7) | 8 | (6) | 8 | (4) | 8 | (7) | ||
| Somewhat comfortable | 45 | (9) | 3 | (3) | 14 | (11) | 15 | (8) | 13 | (11) | ||
| Very comfortable | 241 | (45) | 43 | (48) | 56 | (42) | 97 | (50) | 45 | (39) | ||
| Number of male sex partners, past 12 months | ||||||||||||
| 1 | 205 | (39) | 40 | (20) | 50 | (38) | 72 | (38) | 43 | (37) | X2 = 3.65 | 0.723 |
| 2–3 | 159 | (30) | 24 | (27) | 45 | (34) | 57 | (36) | 33 | (28) | ||
| 4 or more | 166 | (31) | 25 | (28) | 37 | (22) | 63 | (38) | 41 | (35) | ||
| Woman’s sexual HIV risk in the dyad (0–5)f | 2.3 | (± 1.5) | 2.5 | (± 1.5) | 1.9 | (± 1.3) | 2.4 | (± 1.5) | 2.4 | (± 1.5) | F = 3.83 | 0.010 |
SEM Structural Model Building
The final model included 522 dyads (11 were dropped due to missing data). Figure 2 shows the significant direct paths in the model, the standardized path coefficients for each path, and the factor loadings for all indicator variables in each latent factor. The model fit was adequate (N = 522, X2 = 472.782, DF = 250, p < 0.001, RMSEA = 0.041, CFI = 0.916) and the amount of variance explained by the model was 0.236 (24%). Table 4 presents all specified paths in the final model, the unstandardized coefficients, standard error and Z scores. Indirect and total effects were reviewed but are not presented.
Final dyad model (N = 522): significant standardized direct path coefficients and factor loadings
*p≤0.05, **p≤0.01, ***p≤0.001
Table 4
Final model paths and factor loadings: unstandardized coefficients, standard errors and Z scores
| Paths | Unstandardized coefficient | SE | Z score | p-value | |
|---|---|---|---|---|---|
| From | To | ||||
| Women’s structural power | Substance involvement | − 0.215 | 0.06 | − 3.595 | < 0.001 |
| Heterosexual Orientation | − 1.385 | 0.281 | − 4.932 | < 0.001 | |
| Male structural power | Disrespect | − 0.092 | 0.040 | − 2.286 | 0.022 |
| Age at first sex | − 0.116 | 0.036 | − 3.183 | 0.001 | |
| Living with sex partner | − 0.306 | 0.128 | − 2.383 | 0.017 | |
| Substance involvement | 0.269 | 0.076 | 3.546 | < 0.001 | |
| Women’s structural power | Risk communication | 0.091 | 0.039 | 2.372 | 0.018 |
| Age | − 0.024 | 0.006 | − 3.906 | < 0.001 | |
| Disrespect (with) | Risk communication | 0.175 | 0.067 | 2.609 | 0.009 |
| Substance involvement | # Of sex partners | 0.268 | 0.051 | 5.255 | < 0.001 |
| Risk communication | Comfort requesting condoms | 0.315 | 0.097 | 3.234 | 0.001 |
| Heterosexual Orientation | Women’s sexual risk | − 0.414 | 0.186 | − 2.232 | 0.026 |
| Comfort requesting condoms | − 0.191 | 0.045 | − 4.289 | < 0.001 | |
| # Of sex partners | 0.415 | 0.089 | 4.650 | < 0.001 | |
| Disrespect | 0.401 | 0.203 | 1.980 | 0.048 | |
| Geographic region | − 0.023 | 0.006 | − 3.515 | < 0.001 | |
| Substance involvement | − 0.093 | 0.095 | − 0.977 | 0.329 | |
| Risk communication | 0.06 | 0.079 | 0.763 | 0.446 | |
| Women’s structural power | − 0.067 | 0.045 | − 1.493 | 0.135 | |
| Male structural power | − 0.064 | 0.049 | − 1.309 | 0.191 | |
| Age at first sex | − 0.055 | 0.044 | − 1.258 | 0.208 | |
| Living with sex partner | 0.052 | 0.14 | 0.374 | 0.708 | |
| Marital status | − 0.136 | 0.087 | − 1.573 | 0.116 | |
| Age | 0.012 | 0.008 | 1.598 | 0.110 | |
| Race/Ethnicity | 0.004 | 0.047 | 0.090 | 0.929 | |
| Factor loadings | |||||
| Substance use | Crack usea | 1 | 0 | 999 | 999 |
| Marijuana use | 0.763 | 0.145 | 5.246 | < 0.001 | |
| Other drug use | 1.432 | 0.334 | 4.288 | < 0.001 | |
| Injection drug use | 0.668 | 0.168 | 3.962 | < 0.001 | |
| Problem drinking | 0.474 | 0.097 | 4.882 | < 0.001 | |
| Risk communication | # Current sex partnersa | 1 | 0 | 999 | 999 |
| # Past sex partners | 1.310 | 0.200 | 6.603 | < 0.001 | |
| Ever MSM | 1.440 | 0.157 | 6.647 | < 0.001 | |
| HIV status | 1.278 | 0.194 | 6.599 | < 0.001 | |
| Drug use history | 1.427 | 0.222 | 6.441 | < 0.001 | |
| STD history | 1.826 | 0.299 | 6.097 | <.001 | |
| using condoms | 0.775 | 0.121 | 6.402 | < 0.001 | |
| Disrespect in Dyad | Any physical or sexual abusea | 1 | 0 | 999 | 999 |
| Partner has other partners | 0.755 | 0.188 | 4.01 | < 0.001 |
Neither women’s (b = − 0.067, p = 0.135) nor male partners’ (b = − 0.064, p = 0.191) structural power had significant direct paths to women’s sexual HIV risk. Both were initially entered into the modeling with a direct path specified to risk. These paths were significant but when paths were added to interpersonal power and substance use, as specified a priori and as recommended by modification indices, the direct paths to risk were no longer significant.
Higher male partner structural power had a significant negative path to disrespect in the dyad (i.e., greater male partner economic resources was associated with less disrespect in dyads; b = − 0.092, p = 0.022). The indirect path from male structural power through disrespect to risk was not significant but male structural power did have a small but significant total effect on risk (b = − 0.099, p = 0.026) suggesting that male structural power has an effect on women’s sexual HIV risk in the dyad but this model didn’t identify a significant pathway.
Higher women’s structural power had a significant direct path to decreased substance use (b = − 0.215, p < 0.001) and increased risk communication (b = 0.091, p = 0.018), indicating that women with greater economic resources have less substance use and more communication with their main partners about partner risk behavior. The indirect path from women’s structural power to substance use to number of sex partners to risk was significant (b = − 0.024, p = 0.003) and women’s structural power had a small but significant total effect on risk (b = − 0.094, p = 0.029).
Two of the interpersonal power constructs had significant direct paths to risk: disrespect in the dyad (b = 0.401, p = 0.048) and comfort asking their partners to use condoms (b = − 0.191, p < 0.001). The latent risk communication construct did not have a significant direct path to risk and did not have a significant indirect path or total effect on risk. In addition, disrespect and risk communication were modeled as being correlated with each other and this correlation was significant (b = 0.175, p = 0.009). Therefore, the model takes this correlation into account and is able to create a more precise estimate of the effects of these variables (partial effects of each controlling for the other). In addition, the variables included in the model explained a substantial proportion of the variance in the disrespect variables (40% of abuse and 34% in perception of other sex partners).
Substance use did not have a significant direct path to risk but did have a significant indirect path to risk through number of sex partners (b = 0.111, p = 0.001) and a significant total effect on risk (b = 0.127, p = 0.043).
Women’s sexual orientation was the only exogenous variable with a direct significant path to sexual HIV risk (b = − 0.414, p = 0.026). Identifying as heterosexual was significantly directly related to decreased sexual HIV risk as well as decreased substance use (b = − 1.385, p < 0.001). Additionally, the indirect path of heterosexual orientation to substance use to number of sex partners to risk was significant (b = − 0.154, p = 0.001) as was the total effect (b = − 0.590, p < 0.001)).
Women’s age at first sex (b = − 0.116, p = 0.001) and currently living with the main partner (b = − 0.306, p = 0.017) were negatively associated with disrespect in the dyad. Women’s age (b = − 0.024, p < 0.001) at the time of the interview was significantly related to less risk communication. This direction was not expected but may indicate that older women feel more secure in their relationships and/or have been in the relationship longer and thus had not communicated with their partner about risk in the recent past. The final model controlled for race/ethnicity (b = 0.004, p = 0.929), geographic site (b = − 0.023, < 0.001) and marital status (b = − 0.136, p = 0.116). Of these three control variables, geographic site was significantly related to the women’s sexual HIV risk.
Discussion
This study identified a structural model based on TGP theory that had good model fit indices and explained almost a quarter (24%) of the variance in women’s sexual HIV risk in the dyad. The model also provided important information on the associations of the TGP constructs with risk.
The development and use of a dyadic measure of structural power, mostly defined by economic exposures, was not supported in this study. Thus, two summary variables were created to represent women’s structural power and male partners’ structural power. In the final model, neither of these constructs were directly associated with women’s sexual HIV risk; however, both were directly associated with interpersonal power constructs and had small overall effects on sexual risk. This suggests that interventions focused on bolstering economic opportunities and stability for both women and men may improve women’s interpersonal power and reduce substance use which in turn may reduce sexual risk. The only significant path to risk among the structural variables was women’s structural power through substance use and number of sex partners; thus, interventions focused on improving women’s economic opportunities may be particularly relevant for reducing women’s substance use and in turn, sexual HIV risk in their sexual relationships with men.
Like other studies [15], this study found that interpersonal power had a more proximal and direct relationship to sexual risk. Among the three constructs used to measure interpersonal power, disrespect in the dyad had the strongest association with risk followed by comfort in asking their male partner to use condoms. These findings suggest that women who are in relationships with less respect are also at increased sexual risk within their dyad, thus, interventions focused on abuse, healthy relationships, and respect are important. Additionally, the relatively high proportion of the variance in the disrespect variables explained by the preceding variables in the model suggests that substance use, less male structural power, and young age of sexual debut might be used as proxy measures to identify dyads at risk of interpersonal power imbalances.
Although risk communication was not directly associated with women’s sexual HIV risk, women’s comfort requesting condom use was. This suggests that interventions that affect women’s comfort asking their partner to use condoms are important. Given that women’s structural power was associated with communication, economic interventions could be important in influencing women’s ability to discuss risk behaviors with their partner.
This study did not find substance use to be directly related to the composite measure of women’s sexual HIV risk; however, it was strongly associated with women’s number of sex partners and with having more disrespect in the dyad. In addition, two other constructs (women’s structural power and heterosexual orientation) had indirect significant paths to risk through substance use. Screening for substance use problems and providing brief interventions and referral to treatment, especially among low income and/or non-heterosexual populations, may be helpful in identifying and supporting women at risk. In addition, substance abuse treatment providers should continue to screen and provide support to women around domestic abuse and in fostering healthy partnerships.
An unexpected finding was the significant direct and indirect relationship of not having a heterosexual orientation with women’s sexual HIV risk. Although not hypothesized in the a priori model, this study found that not identifying as heterosexual was significantly associated with increased sexual HIV risk among women both directly and indirectly through substance use and number of sex partners. Additional bivariate analyses found that compared to heterosexual women, bisexual women in this study engaged in more unprotected anal sex, had higher risk male study partners, had higher rates of substance use and problematic drinking, had more forced sex with their male study partner, had higher reported STIs in the last year, and had a higher number of other sex partners in the last year. This is consistent with previous studies reporting lower knowledge and perceived risk [16, 17], higher rates of substance use [18, 19], and greater numbers of male sex partners [20, 21] among women who do not identify as heterosexual, compared to those who do. Research targeting women has not typically included sexual orientation as a variable to explain heterosexual sex risk and it is unclear how sexual orientation would fit into a model regarding gender and power. Bisexually identified women may have fewer social resources and be more disenfranchised than women who identify as being in a particular group (e.g., heterosexual or lesbian). Clearly more research is needed to better understand the relevance of bisexual identity for prevention of HIV among heterosexually-active women.
As data were available from both partners in the dyad, we were able to create a more robust measure of the women’s HIV risk in the dyad. Most HIV prevention research to date has used condom use to assess level of HIV risk. However, in certain relationships, especially main partnerships, condom use is less frequent [22] and may be a poor measure of actual risk. There was little consistent condom use among this sample of main partner dyads even though 73% of the women in this study believed there was some probability that their main male partner had other sex partners and half the male partners reported having other concurrent sex partners. A quarter of the women reported that they were married or living as married which could indicate a lack of commitment or stability among these main partnerships. However, given the low rates of condom use and high rates of multiple partners found in this study, the concept and expectation around monogamy in main partnerships needs further exploration.
There are several limitations to this study. First, this study used secondary data from a study not designed to examine TGP. Therefore, the questions asked of participants and contained in the data set were not the exact questions that would be asked if the goal of the study were to measure and analyze power. Second, all measures used in this study were from self-reported data and there can be a tendency for participants to underreport high risk behaviors such as sex and drug using behaviors [23]. However, as with all NHBS protocols, the protocol for the Partner Study emphasized privacy and required anonymity for all procedures and both privacy and anonymity may enhance reporting of sensitive behaviors [24, 25]. Third, data are cross-sectional in that the interview was conducted at one point in time. The nature of cross sectional data does not lend itself to the causal sequencing that may be implied by SEM. Last, as the number of dyads with non-main or HIV-discordant partners was too low to permit stratification, analyses were limited to main partner dyads in which both partners were HIV negative. Results might not be generalizable to non-main or HIV-discordant partnerships.
Conclusions
Overall, this study provides important information on power and sexual risk among women of color living in low-income areas. Like other studies, this study found that interpersonal power is an important construct in understanding sexual HIV risk. Interpersonal power, as measured by disrespect, had the strongest direct effect on women’s sexual HIV risk in their heterosexual dyad and was strongly associated with women’s substance use. In addition, this study found that women’s and male partners’ structural power were associated with women’s substance use, as well as women’s interpersonal power and indirectly with sexual HIV risk in the dyad. Finally, as male partners’ structural power was related to less disrespect in the dyad, it may be important to understand how financial constraints for men lead to power imbalances in their relationships with women. Impacting women’s sexual risk within the social context of a heterosexual dyad is complicated and there are opportunities to leverage both structural and interpersonal aspects to reduce women’s sexual risk.
Acknowledgements
The authors would like to acknowledge the support of the Denver, CO NHBS Partner Study site Principal Investigator Mark Thrun and data analyst Theresa Mickiewicz as well as the following NHBS Partner Study sites and researchers: Dallas, TX: Shane Sheu, Sharon Melville, Richard Yeager, Jim Dyer, Nandita Chaudhuri, Alicia Novoa; Detroit, MI: Renee McCoy, Vivian Griffin, Eve Mokotoff; Houston, TX: Marcia Wolverton, Jan Risser, Hafeez Rehman; Los Angeles, CA: Trista Bingham, Ekow Sey; Miami & Ft. Lauderdale, FL: Marlene LaLota, Lisa Metsch, David Forrest., Dano Beck, Stefanie White; New York City, NY: Alan Neaigus, Chris Murrill, Samuel Jenness, Holly Hagan, and Travis Wendel; San Francisco CA: H Fisher Raymond, Willi McFarland, Hong-Ha Truong; Seattle, WA: Maria Courogen, Hanne Thiede, Nadine Snyder, Richard Burt; St Louis, MO: Michael Herbert, Yelena Friedberg, Dean Klinkenberg, LaBraunna Friend. The authors would also like to acknowledge the support of the Center for Disease Control and Prevention’s Behavioral and Clinical Surveillance Branch, Division of HIV/AIDS Prevention, NHBS team: Teresa Finlayson, Nevin Krishna and Binh Le.
Funding
This study was funded in part of the Centers for Disease Control and Prevention (Grant #U62-PS000954-02). Data collection was based on CDC study protocols as part of the National HIV Behavioral Surveillance System.
Footnotes
Publisher's Disclaimer: Disclaimer The findings and conclusions of this paper are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.
Compliance with Ethical standards
Conflicts of interest The authors declare that they have no conflict of interest.
Ethical Approval All procedures performed in the study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. The current data analysis was approved under the Colorado Multiple Institutional Review Board (Protocol # 06-0517).
Informed Consent Informed consent was obtained from all individual participants included in the study.
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Funding
Funders who supported this work.
NCHHSTP CDC HHS (1)
Grant ID: U62 PS000954
National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention (1)
Grant ID: U62-PS000954-02
