Sample of Studies and Selection Criteria
We searched for studies through the simultaneous use of several strategies. First, we searched electronic reference databases (MEDLINE, PsycINFO, AIDSearch, CINAHL, Dissertation Abstracts Online, ERIC) using search terms related to HIV and other sexually transmitted diseases (i.e., human or acquired and immu* and syndrome or virus, or AIDS, or HIV; sexually and transmitted and disease* or infection, or STD, or STI), intervention (intervene* or prevent*) and sexuality (sex*, condom*, or intercourse). Second, we checked HIV-related listservs, the NIH database of grant awardees, and conference proceedings. We also requested papers from individual researchers conducting HIV interventions and searched the reference lists of relevant, obtained papers. Finally, we manually searched journals likely to publish intervention results (e.g., American Journal of Public Health, Health Psychology, JAMA). These supplemental strategies ensured the comprehensiveness of the reference database searches. Studies that fulfilled the search criteria and that were available as of May 2003 were included.
Studies were included if they (1) examined a deliberate HIV-risk-reduction strategy in a non-perinatal context; (2) used a randomized controlled trial or a quasi-experimental design with rigorous controls (e.g., participants did not self-select into conditions); (3) measured a frequency-related marker (i.e., presence of sexual activity, frequency of intercourse, and number of partners) following the intervention; and (4) provided sufficient information to calculate effect size (ES) estimates. For the purposes of this review, a deliberate HIV-risk-reduction strategy was defined as at least 15 minutes of HIV-relevant instruction. Of the initially relevant articles, 22 had insufficient information for the calculation of effect sizes, and these study authors were contacted. Twelve authors (55%) sent requested information, and these studies were included.
The use of these criteria yielded 174 studies, which investigated 206 separate interventions [see references 23
for included manuscripts, some of which included more than one study]. Of the 174 studies, 32 were not published at the time the search was closed (May, 2003). In total, these interventions began with a total of 149,660 participants; the average retention rate was 78%, leaving 116,735 participants for analysis. Consistent with meta-analytic convention [125
], each intervention was treated as an individual study during analysis.
Two raters independently coded each study for descriptive purposes and to determine whether variation in ESs can be attributed to features of the studies. The following dimensions were coded: (1) sample demographics (e.g., ethnicity, gender), (2) risk characteristics (e.g., sex trade, drug or alcohol use, men who have sex with men (MSM)), (3) HIV serostatus, (4) design and measurement specifics (e.g., number of follow-ups), and (5) content of control and intervention condition(s) (e.g., total amount of time spent across all sessions). Finally, we also coded studies according to whether they provide information, motivational, and/or behavioral skills components, consistent with the IMB model [18
Across the study- and intervention-level categorical dimensions, coders agreed on the majority of judgments (M agreement = 83%). Disagreements were resolved through discussion. Effective reliability for the continuous variables was calculated by the Spearman-Brown result, which takes into account the mean interjudge correlation as well as the number of judges. For these variables, the effective reliability ranged from .60 to 1.00 (M = .86).
When studies did not include sufficient data regarding the moderators of interest, values were imputed. When only an age range or category was given (and not the mean age of the sample), we used the midpoint of the age category as the mean age of the sample. In addition, if authors did not report that their sample included MSM, IDU, sex traders or commercial sex workers, and/or excessive alcohol users, we imputed a zero value for these characteristics, assuming no prevalence.
Frequency-Related Study Outcomes and Effect Size Derivation
We calculated individual ESs for all frequency-related measures reported in each of the 206 separate interventions examined across the 174 included studies. Specifically, we analyzed self-reported sexual frequency measures including (1) number of sexual occasions, defined as the number of episodes of penetrative intercourse (vaginal or anal); (2) number of sexual partners, defined as the number of unique partners an individual had in a given amount of time; and (3) sexual activity status, defined as having or not having some type of intercourse.
The number of sexual occasions and partners were continuous “counts” whereas sexual activity status was dichotomous. Because two of the three outcomes were continuous rather than dichotomous, the ES calculated was the standardized mean difference (d
); odds ratios are meant for cases in which both the independent and the dependent variable are categorical. We represented the third dimension in d
for ease of comparison between the categories. The pooled standard deviation (SD) served as the denominator in the ES calculation, when it was available, or, in a minority of cases, another form of SD (e.g., the SD of the paired comparisons) was used because the pooled SD was not available and could not be calculated from the report. Other available statistical information (e.g., F
values) was used instead or as a supplement to means and SDs [130
]. In calculating the effect sizes, we used statistics that controlled for baseline differences. If a study reported a significant difference on an outcome between the studied groups at baseline and did not control for this difference in analyses of the post-intervention outcomes, we omitted the outcome in question. When studies reported odds ratios, we transformed them to d
using the Cox transformation [131
]. Within a follow-up measurement assessment, when the questions implied different intervals (e.g., previous week vs. previous month), we used the data from the interval that best matched the period since the end of the intervention.
The sign of each effect size was set so that it was positive when the outcome favored risk reduction and effect sizes were corrected for sample size bias [132
]. Forty-eight reports provided statistics separately by different groupings (e.g., by gender or race). When this occurred, effect sizes from each grouping were included in the analysis. As such, some studies contributed more than one effect size for the same outcome. Effect sizes were calculated on the measures provided at the first available follow-up after the intervention. When available, we also calculated d
for condom use, following the same procedures as above. Analyses followed fixed- and random-effects assumptions [133
] to evaluate the mean tendencies for each outcome variable, and fixed-effects assumptions to test whether features of the studies could explain variability in the magnitude of effect sizes [134
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