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Sexual Health Interventions: A Meta-Analysis a

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Jeffrey S. Becasen , Jessie Ford & Matthew Hogben a

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Centers for Disease Control and Prevention

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New York University Published online: 11 Sep 2014.

To cite this article: Jeffrey S. Becasen , Jessie Ford & Matthew Hogben (2014): Sexual Health Interventions: A Meta-Analysis, The Journal of Sex Research, DOI: 10.1080/00224499.2014.947399 To link to this article: http://dx.doi.org/10.1080/00224499.2014.947399

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JOURNAL OF SEX RESEARCH, 0(0), 1–11, 2014 Copyright # The Society for the Scientific Study of Sexuality ISSN: 0022-4499 print=1559-8519 online DOI: 10.1080/00224499.2014.947399

Sexual Health Interventions: A Meta-Analysis Jeffrey S. Becasen Centers for Disease Control and Prevention

Jessie Ford New York University

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Matthew Hogben Centers for Disease Control and Prevention In the second of two companion papers, we conducted a meta-analysis of sexual health interventions in three domains. The interventions chosen for the meta-analysis were a subset of studies presented in a narrative review (the first of the two companion papers); these in turn were selected on the basis of fit to principles derived from the World Health Organization (WHO) and other definitions of sexual health. Studies (n ¼ 20) were drawn from Medline and PsycINFO databases (English language, adult populations, 1996–2011) and fell into three domains: knowledge, attitudes, and sexual behaviors. We estimated intervention effects via Hedges’ g, using the random-effects approach. Initial estimates revealed a large effect for knowledge, g ¼ 1.32 (95% CI ¼ 0.51–2.14), and smaller effects for attitude change, g ¼ 0.17 (0.11–0.24) and behavior, g ¼ 0.21 (0.13–0.29). After removing outliers to produce more precise estimates, the final effect sizes for knowledge, attitudes, and sexual behavior were, respectively, 0.25 (0.03–0.48), 0.18 (0.12–0.24), and 0.18 (0.11–0.24). Interventions yielded positive effects across populations and in all the domains studied.

There is long-standing interest in the concept of sexual health, with efforts by the World Health Organization (WHO) to define the term globally dating back at least as far as the 1970s (WHO, 1975). This interest includes examination of the rationale for public health interest in sexual health and in defining a role for public health relevance in sexual health. That rationale is that, at least in the U.S., public health is concerned with many of the behaviors, services and outcomes that are pertinent to sexual health (Ivankovich, Fenton, & Douglas, 2013). In a companion paper to this effort, Hogben, Ford, Becasen, and Brown (2014) cited three more recent definitions (Centers for Disease Control and Prevention= Health Resources and Services Administration [CDC= HRSA] Advisory Committee, 2012; U.S. Public Health Service, 2001; WHO, 2006), which overlap substantially

The authors wish to acknowledge the assistance of Julia E. Hood for her insight and in the coding of articles in this review. A preliminary version of these results was presented at the 2013 meeting of the International Society for STD Research in Vienna, Austria. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Correspondence should be addressed to Jeffrey S. Becasen, Centers for Disease Control and Prevention, Mail Stop E-44, Atlanta, GA 30333. E-mail: [email protected]

in part because the efforts are largely cognizant of one another (see Figure 1). Hogben and colleagues (2014) derived two broad elements common to the three definitions of sexual health cited in the previous paragraph: (1) sexuality or relationships with a sexual or romantic component have intrinsic value as a part of health and (2) healthy sexual relationships require positive experiences for individuals and their partners. Based on a CDC (2010) consultation report, Hogben and colleagues (2014) also identified a conceptual framework of six domains in which sexual health should be reflected. The domains span cognitive variables, behaviors (individual and interpersonal), and outcomes, specifically: (1) knowledge, (2) attitudes, norms, intentions, and self-efficacy, (3) negotiation and communication, (4) health care use, (5) sexual behavior, (6) adverse health outcomes (e.g., sexually transmitted diseases [STDs], unintended pregnancy). In a narrative review of sexual health interventions, the authors coded intervention studies for sexual health content explicitly in the intervention and then reviewed the effects of these interventions in the six domains. They found that nearly all studies across a broad selection of adults produced positive effects, although many also found null effects (there were no harmful effects). Generally, the strongest and most consistent effects on behaviors and adverse

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BECASEN, FORD, AND HOGBEN

Figure 1.

Definitions of sexual health used to develop intervention selection criteria.

outcomes were found for vulnerable populations (e.g., physically abused, human immunodeficiency virus [HIV] or STD infected) and men who have sex with men (MSM; persons thus defined formed nearly all sexual health interventions in a category originally intended for lesbian, gay, bisexual, transgender, queer, and questioning [LGBTQ] populations). Interventions with populations comprising heterosexual adults brought about knowledge increases and more positive attitudes toward sexual health. The narrative review is broad in scope, but the precision is limited to direction and relatively rough estimates of effect sizes. In this companion article, we drew data from Hogben and colleagues (2014) to conduct meta-analyses of research in three sexual health domains with sufficient studies: knowledge, attitudes, and behaviors.

Method This study employed the quantitative analysis of relevant studies, a subset of studies from the larger companion article (Hogben, Ford, Becason, & Brown, 2014). As stated, the narrative review is broader in scope and contains the overarching themes important to the synthesis of past research. The additional requirements for the quantitative analysis resulted in a more narrow 2

scope and fewer studies than the companion article; however, because the magnitude and direction of each relevant statistical relationship was accounted for, the effect sizes calculated for each of the studies can be compared, making these effect sizes sensitive to variabilities in the strength of the relationships. One clear benefit of this quantitative method was that studies could be combined by domains and summary effect measures could be calculated and presented. Search Methods, Study Identification, and Selection Studies included in the meta-analysis were derived from a larger review of sexual health interventions (Hogben et al., 2014), as noted. Searches were conducted in the OVID Medline and PsycINFO electronic databases. The search terms comprised language from the sexual health definitions, and three adverse health outcomes: HIV=AIDS, sexually transmitted disease, and unintended pregnancy. Eligible studies were written in English and published between 1996 and 2011. Only intervention studies that reported outcomes aligned with one of the six domains and included adults from the United States or a country with comparable public health infrastructure (e.g., Canada, Australia, Western European countries) were retained for further review. The studies also were also required to include components of the sexual health definition to be integral

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SEXUAL HEALTH INTERVENTIONS: A META-ANALYSIS

to the intervention. The final sample of studies also needed to meet a minimum quality threshold determined using the Critical Appraisal Skills Programs (CASP; Fowkes & Fulton, 1991). A more detailed description of study selection and identification of studies for the larger review can be found in Hogben and colleagues (2014). Generally, eligible studies comprised adults (18 years old and older) regardless of race=ethnicity, sexual orientation, or identity (gay, bisexual, heterosexual, transgender). Two studies included participants younger than 18 years old, but the majority of each sample was adults. For the meta-analysis, we applied additional exclusion criteria. First, we excluded studies that focused on vulnerable populations, because the heterogeneity of qualifying conditions for outcomes (e.g., STD infection versus drug use versus miscarriage) among the populations in these studies was not amenable to meta-analysis. We acknowledge that MSM may be considered an at-risk population; however, that alone did not warrant exclusion from this study. Second, we chose studies based on a narrower research design (randomized control trials and quasiexperimental designs) than in the narrative review. Interventions fitting these design parameters included studies where alternative health education treatments were given to the comparison group, as well as studies with no or minimal treatment control groups. Delivery methods of intervention could be communities, small group, individual, or self-administered. Nonrandomized studies were eligible only if there was an independent comparison group. These were behavioral or social interventions designed to directly affect health outcomes that have intrapersonal and interpersonal components which are congruent with the six outcome domains of sexual health. Studies in the knowledge domain measured treatment effects on knowledge about sexual health—in other words, STDs and HIV=AIDS; cause, transmission, and prevention of disease; or pregnancy prevention. The attitude domain included studies measuring treatment effects on evaluations of various sexual health– related behaviors or other concepts such as ‘‘attitude objects’’ (Breckler & Wiggins, 1989): that is, sexuality, condoms and contraceptives, sexual behaviors, or sex education. Sexual behavior studies measured treatment effects on both risky and protective sexual behaviors, including condom use, unprotected=protected sex, and number of sexual partners. Data Extraction and Analysis All statistical analyses, calculations of effect sizes, summary estimates, and funnel plots were conducted using Comprehensive Meta-Analysis software (Borenstein, Hedges, Higgins, & Rothstein, 2005). Data concerning specific study design characteristics,

outcomes, intervention details, and participant demographics were extracted. To examine main effects, only those reporting results for group comparison were included in the analysis. We used results that incorporated appropriate statistical controls (e.g., baseline measurements, age) when available. For studies that reported multiple follow-up outcome measures, the most commonly reported times after end of intervention were three and six months; we selected outcome data measured closest to six months after intervention to measure more sustained effects. Studies that reported outcome measures for different duration of treatment were combined in the analysis to represent one data point. For studies that compared multiple groups, only the fullest or most complete treatment and truest control group comparison data were extracted. For example, Bigman, Cappella, and Hornik (2010) employed a 1
5 factorial design comparing various combinations of treatment with a control group. Only the pure, full treatment and control comparison data were included in the analysis. Comparison groups that underwent no or minimal treatment and groups that underwent alternative interventions were included. For each domain, we calculated a standardized mean difference (Hedges’ g) to assess main intervention effects in each study and an overall summary Hedges’ g to describe the combined intervention effects for all the studies in the domain. The standardized mean difference statistic is applicable to this synthesis of intervention studies where the primary assessment is the comparison of means of outcome measure between treatment and comparison groups. For this study, Hedges’ g was chosen as our standardized mean difference statistic because it incorporates an adjustment that corrects for any biases introduced when studies have small sample sizes (Lipsey & Wilson, 2001). Because effect sizes for interventions that compared treatment groups to comparison groups with minimal or no treatment conditions tend to be higher than if contrasted against alternative treatments, subgroup analysis was conducted based on comparison condition (n of studies permitting). We calculated pooled Hedges’ g estimates using a randomeffects model to allow that the true effect of treatments could vary from study to study (Borenstein et al., 2005). Heterogeneity between studies was evaluated using Q-test statistics and I2. The amount of heterogeneity was indicated by the I2 value, with the following scale: 0 < Low < 20%; 20% < Medium > 80%; high > 80% (Lipsey & Wilson, 2001). To evaluate heterogeneity between studies, we reran the analyses, removing one study at a time, observing how much each study accounted for heterogeneity. Sensitivity analysis evaluated subgroups of studies based on methodological methods that were likely to produce valid estimates of effect sizes. Using a combination of these processes we identified potential outliers. 3

BECASEN, FORD, AND HOGBEN

We examined the possibility of publication bias using funnel plots (Light, Singer, & Willett, 1994).

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Results Table 1 summarizes the studies included in the analysis. Project FIO (Future Is Ours), a small group, cognitive-behavioral HIV=AIDS risk-reduction intervention, was represented in multiple papers, from which we selected Ehrhardt and colleagues (2002) as the study with the appropriate variables in the domains we were analyzing. We identified 20 studies across three of the six sexual health domains for meta-analysis. Seven studies measured treatment effects for sexual health knowledge, 13 studies examined treatment effects for sexual health attitudes, and 14 studies examined treatment effects for sexual behavior. Because analyses were conducted separately by domain, studies could appear in more than one domain. Too few studies reported on measured treatment effects on communication or healthseeking behavior to be included. Interventions that intended to affect sexual health knowledge used mostly alternative or standard care as comparison conditions to the treatment groups (four of seven studies). Seven of the interventions for attitudes compared treatment groups to minimal or no treatment comparison groups, while the remaining six used alternative or standard care comparison groups. The majority of sexual behavior interventions in our study used minimal or no treatment comparison groups (10 of 14 studies). Assignment to treatment conditions was randomized for most interventions. In studies where full randomization was not possible, members of the treatment group served as their own controls, with control condition measurements taken prior to intervention. Across all the studies, more than half had only female participants (11 of 20), and most interventions were delivered to individuals or in small groups. Knowledge Across the seven interventions that measured sexual health knowledge (Figure 2), the treatment groups showed higher mean scores compared to the comparison groups (Hedges’ g ¼ 1.32, 95% CI ¼ 0.51–2.14). Total sample size in these seven interventions was 1,515, with five out of the seven studies containing only female participants. There was considerable heterogeneity across the seven studies (I2 ¼ 97.7%). After the removal of three outliers (Card et al., 2011; Lee & Yen, 2007; Rogers, McRee, & Arntz, 2009) that spuriously affected the overall summary effect size, the treatment effect for sexual health knowledge interventions decreased but remained statistically significant (Hedges’ g ¼ 0.25, 95% CI ¼ 0.03–0.48), and heterogeneity improved (p ¼ 0.05, I2 ¼ 61.9%). 4

Attitudes For the 13 studies that evaluated interventions targeting participants’ attitudes or self-efficacy about sexual health (Figure 3), treatment groups showed significantly higher mean scores compared to the control groups (Hedges’ g ¼ 0.17, 95% CI ¼ 0.11–0.24). Total sample size for these 13 studies was 5,618, with nine of the 13 studies having only female participants. In subgroup analysis, interventions that used standard or alternative treatments for comparison groups showed significant effects (Hedges’ g ¼ 0.16, 95% CI ¼ 0.10–0.23). Studies that compared treatment groups to minimal or no treatment comparison groups were significant and were also significantly different (more effective) than studies that used standard or alternative care comparison groups (p ¼ 0.01). Interventions that used minimal or no treatment for comparison groups showed large effects (Hedges g ¼ 1.01, 95% CI ¼ 0.36– 1.65). The studies using standard or alternative treatments as comparison groups were homogenous (I2 ¼ 0%, p ¼ .72). However, there were high levels of heterogeneity among all the studies (I2 ¼ 94%) and among interventions using minimal or no treatment for the comparison groups (I2 ¼ 96%). After removing two outliers (Card et al., 2011; Rew, Fouladi, Land, & Wong, 2007) based on weight and effect size, homogeneity improved and the summary effect of all interventions and the interventions that used minimal or no treatment comparison groups remained significant (Hedges’ g ¼ 0.18, 95% CI ¼ 0.12–0.24; Hedges’ g ¼ 0.71, 95% CI ¼ 0.38–1.05, respectively). Behavior The 14 interventions that measured treatment effects on sexual behavior (Figure 4) showed significantly higher effect sizes for the treatment groups when compared to controls (Hedges’ g ¼ 0.21, 95% CI ¼ 0.13–0.29). There was a total sample size of 12,077, with six studies including only females, three including only males, and five including both sexes. The four interventions that measured against standard or alternative treatments showed small but significant treatment effects (Hedges’ g ¼ 0.20, 95% CI ¼ 0.11–0.29), and similarly the 10 interventions that measured against minimal or no treatment comparison groups showed only slightly higher intervention effects (Hedges’ g ¼ 0.25, 95% CI ¼ 0.08–0.42). Heterogeneity was substantial for all the studies and interventions that compared against minimal or no treatment (I2 ¼ 66% and 74%, respectively). Removal of one outlier (Card et al., 2011) improved homogeneity (I2 ¼ 10% and 15%, respectively) and the overall effect decreased slightly but remained consistent with prior results (Hedges’ g ¼ 0.18, 95% CI ¼ 0.11–0.24). Publication Bias We used funnel plots of the standard error against the effect size (across studies) to assess for publication bias.

5

180 gay and Latino bisexual men 135 African American women

329 Asian and Pacific Islander men who have sex with men 198 women enlisted in the U.S Navy

Carballo-Dieguez et al., 2005 Card et al., 2011

Choi et al., 1996

Chung-Park, 2008

229 low-income, high-risk women

Sexual behavior domain Baker et al., 2003

Rogers et al., 2009 Swartz et al., 2011

21

29

33 24

30

20 41

NR

19

89 homeless youth recruited at an outreach center 152 primarily low-income, urban, African American women 128 undergraduate students 164 women

Robinson et al., 2002

29 NR NR

166 women who recently gave birth 268 Black and Latino girls and 258 parents 3,706 low-income, at-risk men and women

Lee & Yen, 2007 O’Donnell et al., 2010 National Institute of Mental Health Multisite HIV Prevention Trial Group, 2001 Rew et al., 2007

24 21 21

30 50 19

135 African American women 198 women enlisted in the U.S Navy 76 African American and Latin urban women

domain 229 low-income, high-risk women 334 198 unmarried undergraduates

20 41

NR

24 21 29 19

Age in Years

Card et al., 2011 Chung-Park, 2008 Jones, 2008

Attitudes, norms, and intentions Baker et al., 2003 Bigman et al., 2010 Bryan, Aiken, & West, 1996

Rogers et al., 2009 Swartz et al., 2011

Robinson et al., 2002

135 African American women 198 women enlisted in the U.S Navy 166 women who recently gave birth 89 homeless youth recruited at an outreach center 152 primarily low-income, urban, African American women 128 undergraduate students 164

Sample Size

Study Characteristic by Sexual Health Domain

Knowledge domain Card et al., 2011 Chung-Park, 2008 Lee & Yen, 2007 Rew et al., 2007

Study

Table 1.

100

0

0 100

100

59 100

100

37

100 100 58

100 100 100

100 52 100

59 100

100

100 100 100 37

% Female

RCT

RCT

RCT RCT

RCT

Quasi RCT

RCT

Quasi

RCT RCT RCT

RCT RCT RCT

RCT RCT RCT

Quasi RCT

RCT

RCT RCT RCT Quasi

Design

No treatment

Wait-listed

Wait-listed Standard health education

Health education

HIV pamphlets and a certificate for a local beauty school Assessment only Website with reproductive health content

Assessment only

Standard health education No treatment Video on careers in health care and computer technology Routine postpartum teaching No treatment Standard treatment

Health education No treatment Stress management session

HIV pamphlets and a certificate for a local beauty school Assessment only Website with reproductive health content

Standard health education No treatment Routine postpartum teaching Assessment only

Control Condition

Outcome Domains and Primary Measure

(Continued )

Vaginal Episode Equivalent Index (including unprotected sex acts) Unprotected anal intercourse Consistent condom use, percentage of protected sex acts Number of sexual partners, unprotected anal intercourse Sexual activity, contraceptive use

Homophobia Attitudes, self-efficacy, behavioral intentions

Sexual attitudes

Condom self-efficacy and intentions

Risk-reduction skills Attitudes toward the HPV vaccine Attitudes toward condoms, self-efficacy, intentions to use condoms Condom self-efficacy Mediators (attitude=self-efficacy) Reduce stereotypical gender expectations to engage in unprotected sex Attitudes, sexual and contraceptive self-efficacy Parents’ self-efficacy to communicate Self-efficacy

Knowledge and understanding of sexuality Knowledge

Sexual anatomy knowledge

STI knowledge Knowledge Knowledge Knowledge

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6

176 college students

Ferrer, Fisher, Buck, & Amico, 2011 Lauby, Smith, Stark, Person, & Adams, 2000 O’Donnell et al., 2010

19

89 homeless youth recruited at an outreach center 218 primarily low-income, urban, African American women 388 Black men 30

NR

NR

0

100

37

58

100

NR

3,706 low-income, at-risk men and women

100

52

50

100

% Female

25

NR

NR

22

Age in Years

RCT

RCT

Quasi

RCT

RCT

Quasi

RCT

RCT

RCT

Design

HIV pamphlets and a certificate for a local beauty school Wait-listed

Assessment only

Standard treatment

No treatment

No treatment

Standard care

Information only

No treatment

Control Condition

Sexual risk and protective behaviors

Sexual risk behaviors

Risk behaviors

Daughters delay in sexual intercourse, alcohol use 100% condom use or abstinence, proportion of intercourse acts during which a condom was used, unprotected intercourse acts

Condom consistency and use at last sex

First time and repeated female condom use, total number of sex acts by activity (anal, oral, vaginal), condom use, unprotected vaginal and anal sex Unprotected sex acts, 100% condom use, proportion of protected sex acts Condom use

Outcome Domains and Primary Measure

Note. HIV ¼ human immunodeficiency virus; HPV ¼ human papillomavirus; NR ¼ not reported; RCT ¼ randomized control trial; STI ¼ sexually transmitted infection.

Wilton et al., 2009

Robinson et al., 2002

National Institute of Mental Health Multisite HIV Prevention Trial Group, 2001 Rew et al., 2007

217 heterosexual couples

El-Bassel et al., 2005

1,695 primarily African American and low-income women 268 Black and Latino girls and 258 parents

360 women recruited from a Planned Parenthood clinic

Sample Size

Ehrhardt et al., 2002

Study

Table 1. Continued

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SEXUAL HEALTH INTERVENTIONS: A META-ANALYSIS

Effect sizes should be more dispersed as one moves down the plot, due to increasing standard errors in smaller studies (the typical shape is that of a funnel). With few studies in the knowledge domain interventions, there was evidence of some asymmetry. The plot indicated we could be missing larger studies with strong effects and smaller studies with weaker effects. The attitude and sexual behavior domains exhibited little asymmetry, except for some smaller studies with weak effects that may not have been retrieved.

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Discussion Figure 2. Interventions on sexual health knowledge. Heterogeneity of totals: Q ¼ 262.4, p < 0.05, I2 ¼ 97.7%; heterogeneity of totals with outliers removed: Q ¼ 7.87, p ¼ 0.05, I2 ¼ 61.9%. (Note. Outliers that were removed: Card et al., 2011; Lee & Yen, 2007; Rogers et al., 2009.)

If no publication bias is present the studies should be symmetrically plotted around the mean effect size. Larger, more highly powered studies with relatively small standard errors at one end of the plot would show the studies symmetrically clustered closely around the mean.

The principal goal of this meta-analysis was to provide refined estimates of intervention effect sizes compared to those reported in a companion narrative review (Hogben et al., 2014). We were able to calculate sufficient numbers of effect sizes for meta-analyses in three of five eligible domains. (We excluded adverse events because these covered substantially different outcomes that often also covaried with population; there were enough effects per se.) Across the three domains— knowledge, attitudes, and sexual behaviors—we found

Figure 3. Interventions on sexual health attitudes, self-efficacy and norms grouped by comparison conditions. Heterogeneity in alternative=usual groups: Q ¼ 2.86, p ¼ 0.72, I2 ¼ 0%; heterogeneity in minimal=none groups: Q ¼ 150.9, p < 0.05, I2 ¼ 96%; heterogeneity of minimal=none groups with outliers removed: Q ¼ 15.67, p < 0.05, I2 ¼ 74.5%; heterogeneity of totals: Q ¼ 185.9, p < 0.05, I2 ¼ 93.5%; heterogeneity of totals with outliers removed: Q ¼ 55.72, p < 0.05, I2 ¼ 82.0%; between-group differences: p < 0.05. (Note. Outliers that were removed: Card et al., 2011; Rew et al., 2007.)

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BECASEN, FORD, AND HOGBEN

Figure 4. Interventions on sexual health behaviors grouped by comparison conditions. Heterogeneity in alternative=usual groups: Q ¼ 3.24, p ¼ 0.36, I2 ¼ 7.4%; heterogeneity in minimal=none groups: Q ¼ 35.2, p < 0.05, I2 ¼ 74.4%; heterogeneity in minimal=none groups with outlier removed: Q ¼ 9.46, p ¼ 0.31, I2 ¼ 15.4%; heterogeneity of totals: Q ¼ 38.6, p < 0.05, I2 ¼ 66.3%; heterogeneity of totals with outlier removed: Q ¼ 13.34, p ¼ 0.34, I2 ¼ 10.1%. Between-group differences: p ¼ 0.60; between-group differences with outlier removed: p ¼ 0.48. (Note. Outlier that was removed: Card et al., 2011).

that interventions produced (a) overall positive effects and (b) some heterogeneity in effect sizes. Given the lack of antagonistic findings in the studies, reducing heterogeneity effectively reduced overall effect size estimates, but in all cases the overall results remained positive and significantly different from 0. In the remainder of this discussion, we interpret findings by domain, noting where results extend the findings of the narrative review. We then discuss limitations and conclude with suggestions for future work. Domains Knowledge. The initial estimates for knowledge were the most heterogeneous of all the effects, indicating wide variance in how effective interventions were in increasing participant knowledge. This was so even though the content was similar across interventions (e.g., modes of transmission). There were no clear patterns of knowledge acquisition by population or setting: The larger effect sizes were derived from samples including college students, African American adult women, and women in the U.S. Navy (Robinson et al., 8

2002; Rogers et al., 2009; Chung-Park, 2008). One of the lowest effect size estimates was found with a population of 40- to 55-year-old women (Swartz et al., 2011). Women in this age range are often construed as at little risk of adverse outcomes relative to other female age groupings, with the possible exception of sexual violence. However, given some evidence that women in this age range are more likely to acquire new sexual partners now than in previous years—for example, as a result of divorce (Liddon, Leichliter, Habel, & Aral, 2010)—gaps in knowledge may need to be addressed. The participants in Swartz and colleagues (2011) reported mean knowledge scores of 50% at posttest (both intervention and control), so the lack of effect size was not due to high baseline knowledge. Mitigating such concerns is the fact that no intervention in this review relied only upon knowledge; indeed, the participants in Swartz and colleagues (2011) formed both more positive attitudes toward sexual health and safer behavioral intentions. Attitudes. Estimates for attitudes were generally positive, as reflected in the overall results, with and without outliers. For attitudinal variables we had enough

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SEXUAL HEALTH INTERVENTIONS: A META-ANALYSIS

effects to split the comparisons according to whether the control groups received any intervention, and we saw the effect size against no intervention was much larger than that against an alternative (not sexual health) intervention. Given alternative intervention comparison groups were often didactic or informational, generating more positive attitudes may require mostly knowledge (often paired with attitude change efforts) and familiarity with the intervention content. Nevertheless, sexual health–framed interventions were significantly more efficacious in producing positive attitude change compared to alternative interventions (g ¼ 0.16; see Figure 3), so we see our first evidence that a sexual health–framed intervention may improve positive attitudes over and above other intervention approaches, extending the conclusions from the narrative review. The source of overall heterogeneity appeared to reside in studies comparing the intervention to no or minimal intervention. As with knowledge, there were no clear patterns of effect size by population or setting—the two largest effects after removal of outliers were found with college students and Black or Latino parents (O’Donnell, Myint-U, Duran, & Stueve, 2010; Rogers et al., 2009). One study even drew on an Internet-based panel formed from a nationally representative sample (Bigman et al., 2010). There are not yet enough studies in the review to declare that sexual health–framed interventions have positive effects that are robust across all U.S. subpopulations, but the presence of positive effects across the populations in the 13 studies in this meta-analysis is promising. Sexual behaviors. We found positive overall effects for behavior change with low heterogeneity (after outliers were removed). As with attitudes, we found such effects whether sexual health intervention conditions were compared to alternative interventions or to minimal intervention. Unlike with attitudes, the effect sizes were almost the same, regardless of the control group composition. Hogben and colleagues (2014) emphasized in the narrative companion to this paper that LGBT-based samples (actually almost exclusively MSM) had more consistently positive sexual behavior outcomes than other adult samples. The studies in the meta-analysis were mostly (11 of 14) women or couples, so we see that interventions incorporating sexual health principles had positive effects for these groups as well—although the effect was small. Power for individual studies appeared to be an issue, as most effect sizes were small by conventional standards (g < .20). This situation illustrates the value of meta-analysis in that a series of small, statistically nonsignificant effects would otherwise mask a genuine difference. Limitations The primary limitations are the same as for the narrative review. As a retrospective exercise, we fit existing

studies to definitions that were developed more recently than some of the studies in the review. The choice of search terms may have influenced the number of studies found with sexual violence outcomes. The meta-analysis is also limited in that we were only able to evaluate three of the six domains of interest. In particular, vulnerable populations were excluded from this review, although we note that many of the other samples included individuals with vulnerabilities, even though the researchers did not select participants on the basis of, for example, use of illicit drugs. The literature would be advanced if sexual health intervention effects on health care use outcomes and adverse events were evaluated in future reviews. Analyses for publication bias suggest the findings are largely robust to that potential limitation. In knowledge, the area in which a funnel plot indicated there was most likely to be bias (Light et al., 1994), the nature of the asymmetry suggested that the effect for knowledge might actually be underestimated (the putative ‘‘missing’’ studies included large studies with large effects). The two main literature searches produced many of the same studies in each year (higher than 80% overlap) but not identical lists (limiting to the same years and even with the same search terms). This unanticipated methodological event is not a limitation, but it is worth noting that single searches are perhaps more a large (possibly) random sample than a comprehensive one. We used random-effects approaches in the analyses, and search results reinforce the value of doing so. Limitations and qualifications noted, the interventions in this review fit the criteria for sexual health and produced positive effects under experimental conditions. The almost exclusively randomized trial settings add credence to the conclusions that effects were due to the interventions but typically raise questions of generalizability under less controlled settings. Positive findings, however, were drawn from samples that varied by race and ethnicity, socioeconomic status, family or relationship status, and even occupation or employment. Findings did not obviously vary according to those conditions, except as noted in the discussion. Therefore, we found reasonably precise effect sizes that were robust to a variety of conditions. Conclusions We conclude with three points. First, in this article and in the companion article, we have discussed findings largely in terms of populations (Hogben et al., 2014) and domains (this article). The larger point is that intervention content cuts across domains and often across populations. Thus, increases in knowledge complement more positive attitudes (e.g., reduced stigmatization or increased favorability toward getting sexual health care), and knowledge and attitudes predict behaviors. Occasionally, we found behavioral change without 9

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attitude change; this was often when baseline attitudes were favorable, and the participants learned some skill or had some channel factor facilitating behavior (Lewin, Adams, & Zener, 1935) opened via intervention, meaning that the commitment to participate in the intervention study may have facilitated the subsequent behavioral changes. This leads to our second point: Although this was not a review of theory, the interventions were typically theoretically based. That they were efficacious, typically along lines predicted from broad social cognitive models (e.g., Fishbein et al., 2001), speaks to the value of theory in behavioral intervention. Finally, we emphasize, as we did in Hogben and colleagues (2014), that we construe sexual health framing is an enrichment of the field of work devoted to reducing adverse outcomes associated with sex, not as a competitor. We have noted that sexual health intervention arms outperformed alternative interventions in attitudinal and behavioral outcomes (i.e., Hedges’ g > 0 against alternative interventions in Figures 3 and 4), but this does not necessarily represent the triumph of explicit sexual health framing over other approaches. Many risk-reduction approaches have elements compatible with this framework, and incorporation of the principles of sexual health could contribute to combinations of interventions or multilevel efforts that include approaches not evaluated in this review. For example, a risk-reduction intervention aimed at increasing condom use (purely because such use reduces the risk of STD transmission) in the context of a campaign focused on normalizing discussion of sexuality and sexual behavior among youth combines a communication campaign expressing elements of the definition with a risk-reduction approach that does not explicitly do so. One such example is the ‘‘GYT: Get Yourself Tested’’ campaign, with the twin goals of ‘‘normalizing the conversation’’ and increasing the proportion of sexually active youth who receive STD testing (Friedman et al., 2014). We suggest these combinations of approaches place sexual health interventions appropriately in the overall context of prevention, health promotion, and individual well-being.

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