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Running head: SEXUAL RISK BEHAVIOR ADOLESCENTS 1
Sexual risk behavior: A multi-system model of risk and protective factors in South African
adolescents
by
Kaymarlin Govender
Richard G. Cowden
Kwaku Oppong Asante
Gavin George
Candice Reardon
Manuscript accepted for publication in Prevention Science (March 2019).
SEXUAL RISK BEHAVIOR ADOLESCENTS 2
Abstract
Adolescent sexual risk behavior has typically been studied within singular, isolated systems.
Using a multi-system approach, this study examined a combination of individual, proximal,
and distal factors in relation to sexual risk behavior among adolescents. A large cross-
sectional sample of 2561 adolescent (Mage = 14.92, SDage = 1.70) males (n = 1282) and
females in Grades 8 (n = 1225) and 10 completed a range of self-report measures.
Hierarchical ordinal logistic regression results supported a multi-system perspective of
adolescent sexual risk behavior. Although individual and peer levels were identified as the
primary contributors to the final model, a range of factors at varying levels of proximity to
the individual were associated with sexual risk behavior. Specifically, being male, black,
attaining increased age, greater alcohol use (individual level), parent risk behavior
(family/home level), and peer risk behavior, feeling more pressure from peers to have sex
(peer level), and lower social cohesion (community level) were associated with increased
sexual risk behavior. These findings suggest multiple individual, proximal, and distal factors
are salient to understanding sexual risk behavior among adolescents. Implications of the
findings for interventions targeting the prevention of adolescent sexual risk behavior are
discussed.
Keywords: adolescents; ecological; multi-system; protective factors; risk factors; sexual risk
behavior; youth
(193 words)
SEXUAL RISK BEHAVIOR ADOLESCENTS 3
Adolescence is a critical developmental period characterized by marked changes in a
person’s internal attributes (e.g., physiological, psychological) and external environment
(e.g., social, cultural). During this phase, individuals formulate sexual, gender, and self-
identities (Schlüter-Müller, Goth, Jung, & Schmeck, 2015). They also interrogate the
assumptions and people (i.e., parents) that governed their past thoughts and actions. As
adolescents develop their sense of autonomy (Labouvie-Vief, 2015), they often experience a
shift in the circle of individuals that influence them (i.e., away from parents towards peers)
and begin to participate in experimental behaviors and activities (Liao, Huang, Huh, Pentz, &
Chou, 2013; van de Bongardt, Reitz, Sandfort, & Deković, 2014). Often underprepared for
the typically abrupt transformations that occur, adolescents must apply their developing
decision-making abilities to potentially risky situations (Leijenhorst et al., 2010) in which
they may be conflicted by a combination of individual (e.g., sensation-seeking tendencies)
and socio-contextual (e.g., peers) influences (Casey, 2011). As reward-seeking processes tend
to develop earlier in adolescence than self-regulatory processes (Steinberg, 2008), decisions
can favor risk behaviors that may have detrimental health consequences (Reyna, Wilhelms,
McCormick, & Weldon, 2015).
Although a variety of health risk behaviors (e.g., alcohol use, physical inactivity)
emerge during adolescence (Houck et al., 2016; Sawyer et al., 2012), susceptibility to adverse
health consequences is pronounced for adolescents growing up in countries that carry the
global burden of infectious diseases (Patton et al., 2016). In South Africa, where HIV
prevalence (up to 7.1%) and incidence (up to 1.5%) rates among individuals between 15 and
24 years of age (Shisana et al., 2014; Zuma et al., 2016) are among the highest globally (Piot
et al., 2015), adolescents are especially vulnerable to experiencing undesirable health
consequences of high-risk sexual behavior (Govender, Naicker, Meyer-Weitz, Fanner,
Naidoo, & Penfold, 2013).
SEXUAL RISK BEHAVIOR ADOLESCENTS 4
In light of this apparent vulnerability, an abundance of research has focused on
identifying factors that affect the likelihood of South African adolescents’ engaging in
sexually risky behaviors, but most studies have isolated selected risk or protective factors
(e.g., Harrison et al., 2012; Kaufman et al., 2014). A recent review found that across Sub-
Saharan Africa, few studies have examined factors beyond the individual or family level, and
studies involving a combination of factors across a range of distal (e.g., community) and
proximal (e.g., individual) levels have been rare (Mmari & Sabherwal, 2013). With the
success of adolescent sexual risk intervention programs depending on the appropriate
targeting of specific, adaptable areas at multiple systemic levels (Patton et al., 2016),
comprehensive approaches to studying sexual risk are required in order for evidence-based,
context-specific interventions to be developed. In this study, a multi-system approach is used
to examine individual, proximal, and distal factors associated with sexual risk behavior in a
sample of South African school-going adolescents residing in KwaZulu-Natal, a high HIV
burdened region in South Africa.
Method
Participants
The sample (N = 2561) characteristics are presented in Table 1. Participants (Mage =
14.92, SDage = 1.70) were drawn from 12 secondary schools located in the Bergville
Education (N = 7) and Central Durban Education (N = 5) circuits within the province of
KwaZulu-Natal, South Africa. For state funding allocations, the schools are categorized into
poverty quintiles
1
, with the Bergville circuit comprising rural, lower income, Black families
(poverty quintiles 1 to 3). The Central Durban circuit has greater racial diversity and families
of higher socioeconomic status (poverty quintiles 4 and 5). The majority of the participants
were male (50.08%), in Grade 10 (52.17%), and Black (85.55%) students.
1
All South African public schools are classified into one of five quintiles based on
socioeconomic indicators of the communities that surround each school (Murray, 2016).
SEXUAL RISK BEHAVIOR ADOLESCENTS 5
Materials
Participants completed the following measures. Response options for all single-item
measures, along with the number of items, scoring ranges, and internal consistency estimates
for all scale measurements, are reported in Table 1.
Sexual risk behavior. Given the complexities of measuring sexual risk behavior
(Wilkinson et al., 2017), we used several items commonly applied in the measurement of
sexual risk behavior (for reviews, see Ssewanyana, Mwangala, van Baar, Newton, &
Abubakar, 2018; Toska et al., 2017). Participants who first indicated that they had
experienced sexual intercourse were presented with six additional items assessing a variety of
sexual risk behaviors, including age of sexual debut, condom use at last sex, number of
sexual partners, any partner > 5 years older, prior pregnancy, and transactional sex.
Responses to each item were dichotomized into categories representing low and high levels
of sexual risk behavior (first category for each item represents sexual practices characterized
by low risk): sexual debut (≥ 15 years, < 15 years), condom use at last sex (Yes, No),
multiple sexual partners (No, Yes), any partner > 5 years older (No, Yes), prior pregnancy
(No, Yes), and transactional sex (No, Yes). Our approach to grading participants’ sexual risk
behavior is detailed in the Figure 1.
Substance use. Substance use (not including alcohol use) was measured using an
aggregated score of three items that inquired about the frequency with which participants had
used tobacco, marijuana, and other illegal drugs (e.g., heroin, cocaine, ecstasy) in the last 30
days, respectively. Responses were provided using a three-point rating scale (1 = None; 3 =
More than two times).
Alcohol use. We administered five items that were combined for a total index of
alcohol use. Items measured the frequency of general alcohol use, typical quantity of
alcoholic beverages when consuming alcohol, frequency of binge drinking occurrences,
involvement in alcohol-related incidents, and whether others have expressed concern about
SEXUAL RISK BEHAVIOR ADOLESCENTS 6
participants’ drinking patterns. Sample items include “How often do you have a drink
containing alcohol?” (1 = Never; 5 = Four or more times a week) and “How many drinks
containing alcohol do you have on a typical day when drinking?” (1 = I don’t drink alcohol; 6
= Ten or more drinks).
Leisure opportunities. A nine-item scale was constructed to measure adolescents’
perceptions about leisure time, the leisure time activities they partake in, and the activities
available in the community for them to engage in during their free time. All items were rated
using a five-point response scale (1 = Strongly disagree; 5 = Strongly agree) and aggregated
for a total score. Sample items include “I know of places in the community where there are
lots of things to do” and “For me, free time just drags on and on” (reverse scored).
Parental vital status and HIV status of household residents. Participants completed
two single items to determine if their biological parents (none, one, or both) were alive and
whether a household resident is living with HIV.
Parent risk behaviors. Four items were administered to assess whether participants’
parents/caregivers had smoked cigarettes, been visibly intoxicated in front of them, used
illegal substances, and been in trouble with the police in the last six months. These items
were rated using a two-point scale (1 = No; 2 = Yes) and combined for a total measure of
parent risk behavior.
Parental support. Three items were developed to measure supportive parent/caregiver
behaviors. Participants were prompted to respond to each item (e.g., “I talk to my
parents/caregiver about my homework assignments and projects”) by selecting one of two
dichotomous options (1 = No; 2 = Yes). The items were aggregated for an index of family
support.
Parental monitoring. Six items were selected from the Parental Monitoring Scale
(Small & Kerns, 1993) to measure the extent to which participants were monitored by their
SEXUAL RISK BEHAVIOR ADOLESCENTS 7
parents/caregivers. A five-point response format (1 = Never; 5 = Very often) was used to rate
the items, which were summed for a total parental monitoring score.
Parent-child communication about sex. Participants responded to sixteen items
measuring quality of communication about sexual topics with their parents/primary
caregivers (Jaccard, Dittus, & Gordon, 2000). Items were rated on a five-point scale (1 =
Strongly disagree; 5 = Strongly agree) and combined for a total score.
Sibling risk behaviors. Five items were developed to assess whether participants’
siblings had used tobacco, been intoxicated, used illegal substances, engaged in sexual
activity, and been in trouble with the police during the last six months. Participants rated each
item using a four-point response scale (1 = I don’t have siblings; 4 = Yes), which were
combined for an index of sibling risk behavior.
Peer risk behavior. Four items assessed peer involvement in problem behaviors and
sexual activity (Brook, Morojele, Zhang, & Brook, 2006). Items were rated on a three-point
response scale (1 = None; 3 = Most) and combined for a total score.
Prosocial peers. Prosocial peer behavior was assessed using two items (e.g., “My
friends try to do what is right”). Both items were rated on four-point scale (1 = Not at all
true; 4 = Very much true) and summed for a total measure of peer prosociality.
Peer support. Three items inquired about the extent to which adolescents received
support from peers. Participants used a four-point response format (1 = Not at all true; 4 =
Very much true) to respond to each of the items, which were combined for an index of
perceived peer support. Sample items include “I have a friend my own age who really cares
about me” and “I have a friend my own age who helps me when I’m having a difficult time.”
School connectedness. The Psychological Sense of School Membership scale
(Goodenow, 1993) was used to measure school connectedness. The 18 items assess
participants’ sense of inclusion, acceptance, respect, encouragement, and belonging at school.
SEXUAL RISK BEHAVIOR ADOLESCENTS 8
Items were rated on a five-point response scale (1 = Strongly disagree; 5 = Strongly agree)
and aggregated for a total school connectedness score.
Ease of learner engagement in risk behaviors at school. Four items were constructed
to determine the ease with which learners are able to engage in problem behaviors and sexual
activity on school premises (e.g., “How easy is it for learners to drink alcohol on the school
premises and not get caught?”). Items were rated using a four-point response scale (1 = You
would get caught for sure; 4 = Very easy, no educator would notice) and aggregated for a
composite score.
Information about HIV, sex, and availability of support. We developed five single-
item measures to assess frequency of HIV and sex education at school (along with the
perceived helpfulness of such information) and whether schools had provided participants
with information about organizations in the community (e.g., health clinics) where they could
receive support.
Violence scale. Adolescents completed 12 items from the Screen for Adolescent
Violence Exposure scale (Hastings & Kelly, 1997) to assess experiences of violence in the
home, school, and community. Using a five-point response format (1 = Never; 5 = Always),
participants rated the 12 items with reference to each of the three aforementioned contexts
(36 responses in total). Respective responses were combined for subscale totals referring to
each context.
Social cohesion. Four items from the Collective Efficacy Scale (Sampson,
Raudenbush, & Earls, 1997) were used to measure perceived social cohesion in the
community. Items were completed using a five-point response scale (1 = Strongly disagree; 5
= Strongly agree) and aggregated for an index of social cohesion.
Community support. A single item was used to assess perceptions of community
support.
Procedure
SEXUAL RISK BEHAVIOR ADOLESCENTS 9
Permission to conduct the study was granted by the University of KwaZulu-Natal
Human and Social Science Research Ethics Committee and the Provincial Department of
Basic Education, KwaZulu-Natal. Subsequently, the permission of the 12 schools in the
Bergville and Central Durban Education circuits was acquired in order to obtain access to the
students. This process also involved informing adolescents’ parents about the study and the
nature of their child’s prospective participation in it. For those students that volunteered to
participate in the study, written consent (from parents or legal guardians) and assent (from
adolescents) was obtained prior to their participation. The questionnaire was translated from
English into isiZulu, which was checked for accuracy using backtranslation. Participants
from schools in the Bergville Education circuit completed the questionnaire in IsiZulu,
whereas those within the Central Durban Education circuit requested to complete the English
version. A multilingual team of fieldworkers was available to provide guidance and support
to participants. Questionnaires were completed in school classrooms during predetermined
timeframes allocated by each school for data collection.
Data Analyses
Statistical computations were performed in R (R Core Team, 2018). Analyses were
performed using a pairwise deletion approach. For all measures containing at least two items,
internal consistency was estimated using omega. We used ordinal (proportional odds) logistic
regression to examine bivariate relationships between the study variables and sexual risk
behavior. Variables that revealed significant (p < .05) bivariate associations with sexual risk
were retained for use in the primary analysis.
Systemic level predictors of sexual risk behavior were estimated using a hierarchical
ordinal (proportional odds) logistic regression model. Variables were entered into the model
in a series of sequential blocks (five in total), beginning with those at the individual level.
The remaining blocks of variables were entered from most proximal (i.e., family) to most
SEXUAL RISK BEHAVIOR ADOLESCENTS 10
distal (i.e., community) with regard to the individual
2
. For the full model, collinearity
diagnostics (all VIF values ≤ 3.28) did not reveal any multicollinearity issues (Tabachnick &
Fidell, 2013). Analysis of parallel lines (all p-values ≥ .065) indicated the proportional odds
assumption was appropriately met (Williams, 2016).
Results
Bivariate Analyses
Results of the bivariate relations between sexual risk and each of the study variables
are displayed in Table 1. Within the individual level, sexual risk behavior was higher among
adolescents who were older, male, black, in Grade 10, and those who reported higher alcohol
and substance use. For the family/home level, participants who had someone diagnosed with
HIV/AIDS living in their home, were paternal or double orphans, reported less parental
monitoring and communication about sex with their parents, experienced violence at home,
and endorsed greater parent risk behavior tended to engage in higher sexual risk behavior.
Associations with the peer-level variables indicated sexual risk behavior was higher among
adolescents who experienced more pressure to have sex and reported greater peer risk
behavior, whereas sexual risk behavior was lower when peers were more prosocial. Within
the school level, sexual risk behavior was higher when it was easier for learners to engage in
risk behaviors at school and there was greater violence at school. Lower sexual risk behavior
was found among two schools in quintile 5. For the community level, greater violence in the
community and lower social cohesion were associated with higher sexual risk behavior.
Primary Analysis
Results of the hierarchical ordinal logistic regression are presented in Table 2. The
overall model containing five blocks was statistically significant (Nagelkerke R2 = .39, p <
.001), with systemic-level effects found for the individual (p < .001) and peer (p < .001)
2
Block 1 (Individual), Block 2 (Individual + Family/home), Block 3 (Individual +
Family/home + Peer), Block 4 (Individual + Family/home + Peer + School), Block 5
(Individual + Family/home + Peer + School + Community).
SEXUAL RISK BEHAVIOR ADOLESCENTS 11
levels. Although the family/home (p = .051), school (p = .878), and community (p = .110)
level blocks did not contribute significantly to model fit, the results indicated a combination
of individual, proximal, and distal variables were associated with sexual risk behavior.
Specifically, being older, male, black, higher alcohol use (individual level), higher
parent/caregiver engagement in risk behavior (family/home level), feeling more pressure
from peers to have sexual intercourse, higher peer risk behavior (peer level), and lower social
cohesion (community level) were associated with increased sexual risk behavior. All other
determinants were unrelated to sexual risk behavior (p > .05)
Discussion
Adolescents growing up in countries where the global burden of sexually transmitted
infections (including HIV) is highest are especially vulnerable to the health-related
consequences of sexual risk behavior. Developing effective prevention programing requires a
comprehensive understanding of contextually-relevant factors associated with sexual risk
behavior among key populations living in such countries. In this study, a multi-system
approach was used to examine relations between a combination of individual, proximal, and
distal factors and sexual risk behavior in a sample of South African adolescents living in a
high HIV burdened region of the country. The findings supported a multi-system perspective
of sexual risk behavior among adolescents, with a range of factors associated with sexual risk
behavior at varying levels of proximity to the individual. This highlights the importance of
considering a multi-level combination of psychosocial factors to understanding sexual risk
behavior in adolescence (Salazar et al., 2010; Tenkorang & Maticka-Tyndale, 2014). The
findings also suggest that there may be distinctions in the importance of systemic levels
depending on where factors fit in within the proximal-distal spectrum, as predominant effects
were found for the individual and peer systemic levels. This notion is consistent with prior
research that has found proximal factors tend to be stronger predictors of adolescent sexual
SEXUAL RISK BEHAVIOR ADOLESCENTS 12
risk behavior (James, Montgomery, Leslie, & Zhang, 2013; Lansford, Dodge, Fontaine,
Bates, & Pettit, 2014).
Based on the overall model, biological factors (i.e., age, race, and sex) were among
the strongest predictors of sexual risk behavior at the individual level. These findings are
largely consistent with a number of reviews reporting distinctions in sexual risk behavior
propensities based on biographical attributes (e.g., Toska et al., 2017). Further, the results
coincide with a wealth of research highlighting the salience of alcohol use as a behavioral
antecedent of sexual risk behavior (Patrick, Maggs, & Lefkowitz, 2015). A noteworthy
finding was the relative importance of alcohol use, as compared to substance use, in
predicting sexual risk, advocating the importance of separating the two behavioral choices
when examining sexual risk behavior. Although alcohol and substance use are often highly
correlated among adolescents (Capaldi, 2014; Kelly et al., 2015), the neurocognitive effects
of alcohol use (i.e., reduction in behavioral inhibition and impulse control) on behavior
(Winward, Hanson, Tapert, & Brown, 2014) may have a greater influence on sexually risky
behavioral choices. Considering the prominence of alcohol use with regard to sexual risk
behavior found in this study, the finding that leisure opportunities was unrelated to sexual
risk behavior is of particular interest. With adolescents less likely to partake in alcohol use
outside of leisure time periods (Weybright, Caldwell, Ram, Smith, & Wegner, 2016), the
types of activities adolescents engage in during their free time may be of greater importance
than their perceptions of and attitudes towards leisure opportunities.
Comparable to prior research that has found links between risky parental behavior
and health-risk behaviors among adolescents (e.g., Donaldson, Handren, & Crano, 2016),
parent risk behavior was the single predictor of sexual risk behavior at the family/home level.
Adolescents’ sexual risk behavior was linked to their engagement in activities that resembled
those included in the measure of parent risk behavior (e.g., alcohol use), suggesting that
exposure to parents’ maladaptive alcohol use may have a profound effect on adolescents’
SEXUAL RISK BEHAVIOR ADOLESCENTS 13
decisions to participate in alcohol consumption (van der Zwaluw et al., 2008). That is,
parental behaviors may indirectly endorse the kinds of activities that heighten adolescents’
proclivity to engage in such behaviors themselves. Further, parent risk behavior (e.g., alcohol
intoxication) impairs parents’ functioning and the ability to effectively fulfill parental
responsibilities (e.g., monitoring), which may lead to parent-child conflict that drives
adolescents towards other extrafamilial influences and affords adolescents greater freedom to
partake in health-risk behaviors (Latendresse et al., 2008).
Within the peer level, peer risk behavior and feeling greater pressure to have sex
were associated with heightened sexual risk behavior. Peer influences affect adolescents’
sexual risk behavior directly through their need to belong and feel accepted (Selikow,
Ahmed, Flisher, Mathews, & Mukoma, 2009), as well as indirectly through the types of
sexual risk-related activities (e.g., alcohol use) that are promoted by peers who engage in
delinquent behavior (Tomé, Matos, Simões, Diniz, & Camacho, 2012). The present findings
underscore the increased role of peer influences during adolescence (Liao et al., 2013), with
negative peer influences exerting a stronger effect on sexual risk behavior than positive peer
influences (e.g., peer support).
Similar to previous research that has found facets of social capital tend to promote
safer sexual practices (Crosby, Holtgrave, DiClemente, Wingood, & Gayle, 2003), social
cohesion emerged as a community-level factor associated with lower sexual risk behavior.
This finding iterates the relevance of distal levels of influence on risky sexual behavior
(Salazar et al., 2010), likely due to the impact distal systems (e.g., community) have on more
proximal (e.g., individual) systems (Hutchison & Wood, 2007). For example, in communities
characterized by higher levels of social cohesion, adolescents have more opportunities to
develop bonds with community members who can supervise, monitor, and positively shape
their values (Sampson, Morenoff, & Gannon-Rowley, 2002). Moreover, interconnected
communities might have a greater influence over the norms and behavioral choices (e.g.,
SEXUAL RISK BEHAVIOR ADOLESCENTS 14
parental monitoring) of families living within such communities (Valdimarsdóttir &
Bernburg, 2015). Collective social capital may also produce health benefits by diffusing
knowledge about health-related issues in communities and invoking informal social control
over health-related behaviors (Boyce, Davies, Gallupe, & Shelley, 2008).
Limitations and Future Research Directions
While our application of a multi-system approach offers promising insight into the
proximal and distal factors associated with sexual risk behavior among adolescents living in
an HIV endemic region of South Africa, selected methodological limitations ought to be
considered. First, the findings of this study are based on cross-sectional data, thereby
preventing determinations of causality. Second, participants were conveniently sampled from
two school districts within a single province. Given South Africa’s geographically varied
demography and socioeconomic climates, indiscriminate application of the conclusions
drawn in this study may neglect to appreciate contextually-specific distinctions in
adolescents’ sexual risk behavior. Third, gradation of sexual risk behavior was based on a
combination of items that captured a relatively heterogenous range of risky sexual behaviors.
While this approach has the advantage of capturing a comprehensive range of behaviors,
caution should be applied in generalizing the findings to any singular sexual risk behavior.
Fourth, all variables were derived from self-report measures, the accuracy of which may have
been affected by task-related demands and the social context in which participation occurred
(Schroder, Carey, & Vanable, 2003). Although self-report ratings are commonly applied to
the study of sexual risk behavior (DiClemente, Swartzendruber, & Brown, 2013), a broader
range of factors (particularly at more distal levels) could be captured in future research by
gathering data from other informants. Research is also needed to identify the relevant
processes, contexts, and interplay between the determinants hitherto identified as affecting
risk sexual behavior, a precursor to HIV infection. Additionally, sophisticated research
designs are necessary to investigate the longitudinal influences of various individual,
SEXUAL RISK BEHAVIOR ADOLESCENTS 15
proximal, and distal factors on adolescents’ health risk behaviors, particularly among at-risk
and marginalized populations. Policies and programs that attend to the economic and social
needs of families and communities, as well as those that seek to build individual
competencies, will be critical for adolescents to safely navigate their development,
particularly within AIDS-affected communities (Govender, Masebo, Nyamaruze, Cowden,
Schunter, & Bains, 2018).
Conclusion
This study represents one of the few studies that has adopted a multi-system
approach to examining South African adolescents’ sexual risk behavior, a country that
continues to have some of the highest global high HIV incidence and prevalence rates
(Shisana et al., 2014; Zuma et al., 2016). The findings of this study support the understanding
that narrowly focusing on individual risk and protective factors, while ignoring risk and
protective factors across multiple levels, will likely undermine efforts targeting maladaptive
health-risk behaviors, including the effectiveness of HIV prevention programing.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
Ethical Approval
All procedures involving participants in this study were performed in accordance with the
ethical standards of the institutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical standards. Study
approval was granted by the University of KwaZulu-Natal Human and Social Science
Research Ethics Committee and the Provincial Department of Basic Education, KwaZulu-
Natal.
SEXUAL RISK BEHAVIOR ADOLESCENTS 16
Informed Consent
Informed consent was obtained from all individual participants included in the study.
Running head: SEXUAL RISK BEHAVIOR ADOLESCENTS 17
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Table 1
Summary statistics and bivariate associations (ordinal logistic regression) with sexual risk behavior
Systemic Level
Variable
n (%)
Items
Item/Scale
Range
M (SD)
ω
DV = Sexual risk behavior
(None = 0, Low = 1, High = 2)
Individual
Age
2559
14.92 (1.70)
OR = 1.56***, 95% CI [1.47, 1.67]
Gender
2560
Female (ref)
1278 (49.92)
Male
1282 (50.08)
OR = 3.30***, 95% CI [2.65, 4.11]
Race
2546
Other (ref)
368 (14.45)
Black
2178 (85.55)
OR = 2.65***, 95% CI [1.83, 3.82]
School grade
2561
Grade 8 (ref)
1225 (47.83)
Grade 10
1336 (52.17)
OR = 3.26***, 95% CI [2.60, 4.08]
Leisure opportunities
2261
9
9 to 45
21.79 (5.54)
.73
OR = 1.00, 95% CI [.98, 1.02]
Substance use
2513
3
3 to 9
3.31 (.89)
.71
OR = 1.96***, 95% CI [1.75, 2.19]
Alcohol use
2212
5
5 to 22
6.18 (2.45)
.82
OR = 1.34***, 95% CI [1.29, 1.40]
Family/home
Household resident with HIV
2489
No (ref)
1532 (61.55)
I don’t know
856 (34.39)
OR = 1.07, 95% CI [.86, 1.33]
Yes
101 (4.06)
OR = 2.00**, 95% CI [1.28, 3.12]
Parental status
2448
Both parents living (ref)
1623 (66.30)
Father deceased
492 (20.10)
OR = 1.54***, 95% CI [1.20, 1.98]
Mother deceased
141 (5.76)
OR = 1.47, 95% CI [.96, 2.23]
Both parents deceased
192 (7.84)
OR = 1.99***, 95% CI [1.41, 2.82]
Parental monitoring
2396
6
6 to 30
22.45 (5.84)
.85
OR = .94***, 95% CI [.92, .96]
Communication about sex
2026
16
16 to 80
43.27 (12.42)
.89
OR = 1.02**, 95% CI [1.01, 1.02]
Sibling risk behavior
2410
5
5 to 20
7.78 (4.05)
.92
OR = 1.01, 95% CI [.99, 1.04]
Parental/caregiver support
2522
3
3 to 6
5.45 (.80)
.51
OR = .71***, 95% CI [.64, .80]
Violence at home
1997
12
12 to 60
14.75 (3.55)
.74
OR = 1.05**, 95% CI [1.01, 1.08]
Parent risk behavior
2496
4
4 to 8
4.53 (.86)
.62
OR = 1.29***, 95% CI [1.16, 1.44]
Peer
Pressure to have sex
2531
No (ref)
1687 (66.65)
I’m not sure
293 (11.58)
OR = 2.67***, 95% CI [1.96, 3.63]
A little bit
222 (8.77)
OR = 4.80***, 95% CI [3.50, 6.57]
Yes
329 (13.00)
OR = 7.39***, 95% CI [5.60, 9.75]
Prosocial peers
2496
2
2 to 8
6.21 (1.50)
.60
OR = .83***, 95% CI [.78, .89]
Peer support
2465
3
3 to 12
9.01 (2.70)
.77
OR = .98, 95% CI [.94, 1.01]
Peer risk behavior
2507
4
4 to 12
5.38 (1.84)
.80
OR = 1.56***, 95% CI [1.48, 1.65]
School
Frequency of lessons about HIV/AIDS
2540
Not often (ref)
854 (33.62)
Often
1109 (43.66)
OR = .84, 95% CI [.67, 1.06]
Very often
577 (22.72)
OR = .87, 95% CI [.67, 1.15]
Frequency of lessons about sex
2526
Not often (ref)
824 (32.62)
Often
1136 (44.97)
OR = 1.18, 95% CI [.93, 1.49]
Very often
566 (22.41)
OR = 1.32, 95% CI [1.00, 1.74]
Information about HIV/AIDS helpful
2537
No (ref)
323 (12.73)
Yes
2214 (87.27)
OR = .89, 95% CI [.66, 1.20]
Information about sex helpful
2528
No (ref)
409 (16.18)
Yes
2119 (83.82)
OR = .99, 95% CI [.75, 1.31]
School provides information about organizations to get help
2515
No (ref)
970 (38.57)
Yes
1545 (61.43)
OR = .96, 95% CI [.78, 1.18]
School
2561
A (quintile 5) (ref)
419 (16.36)
B (quintile 4)
237 (9.25)
OR = 1.15, 95% CI [.77, 1.71]
C (quintile 5)
242 (9.45)
OR = .39***, 95% CI [.23, .64]
D (quintile 3)
151 (5.90)
OR = 1.15, 95% CI [.73, 1.82]
E (quintile 2)
113 (4.41)
OR = 1.26, 95% CI [.78, 2.04]
F and G# (quintile 1)
187 (7.30)
OR = .85, 95% CI [.54, 1.33]
H (quintile 3)
113 (4.41)
OR = .93, 95% CI [.53, 1.62]
I (quintile 5)
301 (11.75)
OR = .51**, 95% CI [.33, .77]
J (quintile 3)
260 (10.15)
OR = 1.12, 95% CI [.76, 1.65]
K (quintile 5)
287 (11.21)
OR = .78, 95% CI [.53, 1.16]
L (quintile 2)
251 (9.80)
OR = 1.31, 95% CI [.90, 1.91]
Violence at school
1986
12
12 to 60
15.54 (3.68)
.74
OR = 1.06***, 95% CI [1.03, 1.09]
Ease of learner engagement in risk behaviors at school
2437
4
4 to 16
7.88 (3.17)
.81
OR = 1.07***, 95% CI [1.04, 1.11]
School connectedness
1997
18
18 to 90
55.60 (9.65)
.83
OR = .99, 95% CI [.98, 1.00]
Community
Community support
2534
No (ref)
1052 (41.52)
Yes
1482 (58.48)
OR = .94, 95% CI [.77, 1.15]
Violence in community
2141
12
12 to 60
19.49 (6.00)
.82
OR = 1.07***, 95% CI [1.05, 1.09]
Social cohesion
2297
4
4 to 20
13.10 (2.79)
.55
OR = .91***, 95% CI [.88, .95]
Note. OR = odds ratio, 95% CI = 95% confidence intervals for odds ratio.
#The initial analysis for school could not be estimated, likely due to the comparably smaller sample size in school F (n = 37, 1.44%) relative to the other schools. We
combined participants in school F with those in school G (n = 150, 5.86%), as both schools are located in the Bergville Education district and are classified as Quintile
1 schools. Neither school was associated with a higher odds of sexual risk behavior (p = .793).
*p <.05, **p < .01, ***p < .001.
Table 2
Summary statistics of the hierarchical ordinal regression model (sequential block entry) predicting sexual risk behavior
Systemic Level
Determinant
DV = Sexual risk behavior (None = 0, Low = 1, High = 2)
Estimate (SE)
AOR [95% CI]
Individual (Block 1)
Age
.29*** (.08)
1.33 [1.13, 1.57]
Gender
Female (ref)
Male
.98*** (.26)
2.68 [1.61, 4.46]
Race
Other (ref)
Black
1.46** (.46)
4.32 [1.76, 10.60]
School grade
Grade 8 (ref)
Grade 10
.49 (.29)
1.62 [.92, 2.86]
Substance use
.01 (.14)
1.01 [.77, 1.31]
Alcohol use
.18*** (.04)
1.19 [1.10, 1.29]
Model χ2 (df)
250.86*** (6)
Nagelkerke R2
.315
Family/home (Block 2)
Household resident with HIV
No (ref)
I don’t know
-.14 (.22)
.87 [.56, 1.33]
Yes
.34 (.53)
1.40 [.50, 3.97]
Parental status
Both parents living (ref)
Father deceased
.29 (.27)
1.33 [.79, 2.26]
Mother deceased
.45 (.37)
1.57 [.76, 3.22]
Both parents deceased
.42 (.35)
1.52 [.76, 3.03]
Parental monitoring
-.01 (.02)
.99 [.96, 1.03]
Communication about sex
.00 (.01)
1.00 [.98, 1.02]
Parental/caregiver support
-.12 (.12)
.89 [.70, 1.12]
Violence at home
.03 (.04)
1.03 [.96, 1.11]
Parent risk behavior
.25* (.12)
1.28 [1.01, 1.63]
Model χ2 (df)
269.14*** (16)
Block χ2 (df)
18.28 (10)
Nagelkerke R2 (R2 change)
.335 (.020)
Peer (Block 3)
Pressure to have sex
No (ref)
I’m not sure
.57* (.29)
1.77 [1.01, 3.12]
A little bit
1.07*** (.31)
2.90 [1.60, 5.28]
Yes
136*** (.31)
3.89 [2.10, 7.20]
Prosocial peers
.11 (.07)
1.12 [.97, 1.28]
Peer risk behavior
.29*** (.07)
1.33 [1.17, 1.52]
Model χ2 (df)
313.56*** (21)
Block χ2 (df)
44.43*** (5)
Nagelkerke R2 (R2 change)
.383 (.048)
School (Block 4)
School
A (quintile 5) (ref)
B (quintile 4)
.29 (.44)
1.33 [.57, 3.13]
C (quintile 5)
-.12 (.47)
.89 [.36, 2.22]
D (quintile 3)
-.37 (.49)
.69 [.26, 1.81]
E (quintile 2)
-.15 (.51)
.86 [.32, 2.33]
F and G# (quintile 1)
.11 (.43)
1.11 [.47, 2.60]
H (quintile 3)
-.62 (.73)
.54 [.13, 2.24]
I (quintile 5)
-.01 (.49)
.99 [.38, 2.61]
J (quintile 3)
.40 (.40)
1.49 [.68, 3.29]
K (quintile 5)
-.27 (.44)
.77 [.33, 1.80]
L (quintile 2)
-.15 (.42)
.86 [.37, 1.96]
Ease of learner engagement in risk behaviors at school
.01 (.03)
1.01 [.95, 1.08]
Violence at school
.01 (.04)
1.01 [.93, 1.09]
Model χ2 (df)
320.25*** (33)
Block χ2 (df)
6.69 (12)
Nagelkerke R2 (R2 change)
.390 (.007)
Community (Block 5)
Violence in community
-.01 (.02)
.99 [.94, 1.03]
Social cohesion
-.08* (.04)
.93 [.86, .99]
Model χ2 (df)
324.66*** (35)
Block χ2 (df)
4.41 (2)
Nagelkerke R2 (R2 change)
.394 (.004)
Note. *p < .05, **p < .01, ***p < .001.
