HIV remains a public health concern, with an estimated 2.6 million new infections occurring annually.1 Despite treatment advances, HIV presents significant health and social challenges.2 HIV is also costly, with medical care in the United States from the time of infection until the time of death estimated at $385,200.3 Global costs associated with antiretroviral therapy are estimated to be US $896 annually per person.4 In addition, new infections cost the US $29.7 billion annually in productivity losses.5 If incidence trends continue, the added annual cost of HIV care in the US will be between $128 and $237 billion dollars.6 To reduce the burden of HIV, prevention efforts need to be expanded.7 Identifying and evaluating successful behavioral interventions is critical to ensure that effective interventions are implemented with appropriate samples and in appropriate settings to achieve reductions in HIV transmission. To achieve this goal, meta-analytic techniques can help to quantitatively synthesize the behavioral intervention literature.
Meta-analyses of behavioral interventions have documented improvements in behavioral outcomes (eg, condom use) among a number of groups (eg, adolescents).8 Relatively fewer meta-analyses have examined biological outcomes, likely due to an insufficient number of studies measuring sexually transmitted infections (STIs) and/or HIV available to date. As a result, many of those meta-analyses have been underpowered.9–14 A recent meta-analysis found behavioral interventions improved both behavioral and biological outcomes among clients at STI clinics in the United States.15 Although there was sufficient power to test incident STIs (excluding HIV), this study was limited in that HIV incidence could be evaluated in only 5 of the 48 studies reviewed.15 Overall, it is unclear whether incident STIs, including HIV, are reduced after behavioral risk reduction interventions. Because condom use and incident STIs are independent outcomes,16 examining both behavioral and biological endpoints within a wider range of studies can increase our understanding of the efficacy of behavioral interventions.
The current study extends previous meta-analyses by focusing on the effects of behavioral interventions to improve both behavioral and biological outcomes. Specifically, meta-analytic techniques were used to evaluate the efficacy of behavioral interventions to reduce incident STIs, including HIV, and to promote condom use. We hypothesize that those participants receiving a behavioral intervention targeting HIV prevention would increase their condom use and would be less likely to acquire STIs, including HIV, relative to controls.
We also examine the extent to which the efficacy of behavioral interventions was a function of participant or intervention characteristics. Moderators included the following: (A) gender, age, race, and sexual orientation; (B) baseline STI or HIV; (C) intervention content (motivation and/or skills training, addressing sociocultural barriers, providing condoms, and tailored or targeted content); (D) matching facilitators to participants (on gender or race), and (E) intervention length. We hypothesize that interventions will be more efficacious when they (A) sampled greater proportions of those who are most affected by HIV (women, young adults, Blacks, Latinos, MSM, and heterosexuals);1,17 (B) sampled patients diagnosed with an STI or HIV at baseline, as infected participants may be more motivated to decrease their risk behavior;18 (C) targeted motivation and provided skills training, consistent with motivational and skill-based theories of HIV prevention;19,20 (D) tailored content to the individual, targeted content toward a specific group (eg, women), or matched facilitators to the gender or race of the participants, as tailoring and/or targeting the intervention content may enhance message relevancy;21 and (E) were longer, providing participants with additional opportunities to practice skills.
Search Strategy and Study Selection
Studies were located using 3 strategies. First, electronic databases (PubMed, PsycINFO, and other EBSCO-hosted databases) were searched using a Boolean search strategy for abbreviated (truncated words followed by an asterisk) and full keywords related to interventions that reported at least 1 laboratory-tested incident STI: [(HIV OR STD OR STI OR (human AND immun* AND virus) OR (acquir* AND immun* AND syndrome) AND (sexual* AND transmit* AND disease) OR (sexual* AND transmit* AND infect*) OR Chancroid OR (Chlamydia AND Infect*) OR Conjunctivitis OR (Lymphogranuloma and Venereum) OR Trachoma OR Gonorrhea OR (Granuloma AND Inguinale) OR Syphilis OR (Condylomata AND Acuminata) OR (Herpes AND Genitalis) OR (AIDS AND opportunistic AND infect*)] AND [(intervent* OR prevent*) AND (condom OR swab* OR sero*conver* OR (biological AND outcome)]. Second, we searched databases and document archives of HIV-related interventions held by the National Institute of Mental Health–funded Syntheses of HIV/AIDS Risk Reduction Project (a database of HIV-related interventions, 1981 to present) at the University of Connecticut and in the Prevention Research Synthesis Project database at the Centers for Disease Control and Prevention. Finally, reference sections of obtained articles were examined.22 Studies fulfilling the selection criteria and available by July 2010 were included.
The selection process for study inclusion in the meta-analysis appears in Figure 1. Studies were included if they examined an HIV risk-reduction strategy, used a randomized controlled trial or a quasi-experimental design, assessed condom use and laboratory-confirmed incident STIs, including HIV, and provided sufficient information to calculate effect sizes (ES). Studies were excluded if the intervention(s) did not focus on reducing the risk of HIV, had no comparison condition, or evaluated a structural-level (eg, mass media) intervention. Of the initially relevant reports, 1 had insufficient information for the calculation of ES; the study authors did not respond to our request and the report was excluded. When multiple papers evaluated intervention efficacy with the same sample, we calculated ES for all of the outcomes. Forty-two independent studies, including 67 separate interventions (k), met the selection criteria and were included in the meta-analysis.23–82
Coding and Reliability
Three trained independent raters coded study information, sample characteristics (eg, ethnicity, gender), design characteristics (eg, recruitment method, number of follow-ups), and content of control and intervention condition(s) (eg, number of sessions). Study quality was assessed using 16 items (eg, random assignment) from validated measures;83,84 scores ranged from 0 to 22. Interrater reliability for categorical variables was calculated as Cohen kappa (κ).85 Mean κ was 0.73 (median = 0.74), and the mean agreement was 83%, signifying high reliability. For continuous variables, we calculated an intraclass correlation; the mean effective reliability = 0.91, also high. Disagreements between coders were resolved through discussion.
Study Outcomes and Calculation of Effect Sizes
ES estimates for condom use and STIs, including HIV, were calculated. Trials varied in their measures of “condom use” (eg, count vs. percent of condom-protected sexual events); thus, outcomes included protected or unprotected vaginal, anal, or unspecified intercourse across an array of contexts. Because the majority of the studies reported continuous measures, ES were defined as the mean difference between the treatment and control groups divided by the pooled standard deviation (d).86 In the absence of means and standard deviations, other statistical information (eg, F values) was used.87,88 If a study reported dichotomous outcomes (eg, frequencies), we calculated an odds ratio and transformed it to d using the Cox transformation.89 If no statistical information was available and the study reported a nonsignificant or significant between-group difference, we estimated that ES to be zero or calculated an ES based on the minimum statistically significant P value (ie, P = 0.05), respectively.88 In calculating d, we (A) adjusted for baseline differences between condition(s) when preintervention measures were available;90 and (B) corrected for sample size bias.91 Positive ES indicated that treated participants increased condom use and reduced the incidence of STIs compared with controls.
Multiple ES were calculated from individual studies when they had more than 1 outcome, multiple intervention conditions, or when outcomes were separated by sample characteristics (eg, gender). When a study contained multiple measures of the same outcome, the ES were averaged. ES calculated for each intervention and by sample characteristics were analyzed as separate studies.88 From the 42 studies that met the selection criteria, 67 interventions were analyzed. Of these 67, all measured self-reported condom use, 57 reported incident STIs, 8 reported incident HIV, and 5 reported both incident STIs and HIV, separately. If the study reported more than 1 follow-up, the last follow-up was used.
Fixed-effect and random-effect analyses were conducted in Stata 1192 using published macros.88 The homogeneity statistic, Q, determined whether each set of weighted mean ESs shared a common ES; a significant Q indicates a lack of homogeneity and an inference of heterogeneity. To assess the extent to which studies' outcomes were consistent, the I2 index and its corresponding 95% confidence intervals were calculated;93,94I2 varies between 0 (homogeneous) and 100% (heterogeneous).95 If the confidence interval around I2 includes zero, the set of ES is considered homogeneous. Asymmetries in distributions of ES, which can suggest reporting bias, were examined with Begg technique,96 trim and fill,97 and Egger technique.98
To explain variability in ES, the relation between sample, methodological, or intervention characteristics and the magnitude of the effects were examined using a modified weighted least squares regression analyses with weights equivalent to the inverse of the variance for each ES.88,99 For the mixed-effect regression models, the inverse variance for each ES included both study-level sampling error and additional between-study population variance, following the restricted maximum likelihood solution; these models are more conservative than purely fixed-effects models.88 A priori determined moderators included sample characteristics (eg, proportion women, age group), intervention content (eg, motivation or behavioral skills), matching the facilitators with participants and intervention dose. Significant moderators were simultaneously entered into multiple regression models to evaluate whether they explained unique variance. Continuous variables (eg, proportion Black, proportion Hispanic/Latino) were mean centered to reduce multicolinearity. To retain all studies in multiple-moderator models, missing values of significant moderators were imputed from the mean of other studies that reported the information.
Study, Sample, and Intervention Details
Descriptive features of the studies are provided in Table S1 (see Supplemental Digital Content 1,http://links.lww.com/QAI/A223). Methodological quality of the studies ranged from 5 to 20 (median = 15). Publication year and methodological quality score were correlated (r = 0.52, P < 0.001); studies published ≥ 2003 received higher quality scores (median = 17) than earlier studies (median = 14).
Studies were conducted in North America (62%), Asia (17%), Africa (14%), Europe (5%), and South America (2%). Of the 40,665 participants sampled, 68% were women, 69% were Black, and their mean age was 26 years (SD = 7.62; range, 15%–44; 41% age 24 or younger). Several studies targeted women (50%) and Blacks and/or Hispanics (19%). Of the studies reporting alcohol (10 of 42) and/or drug use (14 of 42), 34% and 36% of participants reported current use of alcohol or illegal drugs, respectively. Twenty studies reported participants' HIV status; most participants (80%) were HIV negative and 3 studies sampled only those with HIV.23–25 Of the 33 studies reporting STI status at baseline, many participants (42%) had a biologically confirmed STI (excluding HIV); 6 studies restricted their samples only to those with a current STI.26,27,100–103
Participants were typically assessed at a single follow-up (M = 1.04; range = 1 to 2). The assessment typically occurred 13 weeks postintervention (M = 23.53; range = 0 – 208 weeks). When more than 1 follow-up was reported, we used the last assessment because biological outcomes tended to be reported at longer assessments. The last assessment typically occurred 52 weeks postintervention (M = 45.04; SD = 29.98). Most participants (79%) were offered incentives (eg, money) for participating in the research and/or completing the assessment(s).
Most studies (83%) randomly assigned individuals or groups to conditions. The control condition was most often HIV education (48%), but 38% used an active comparison condition (eg, brief form of intervention), and 14% used an assessment-only control. Interventions involved face-to-face delivery to a group (45%), an individual (45%), or a combination (10%). Group interventions met for a median of 4 sessions of 120 minutes each with a median of 2 facilitators and 10 participants per session; individual interventions met for a median of 1 session of 39 minutes each with 1 facilitator. Most interventions provided education (90%). Most interventions (84%) addressed proximal (eg, risk awareness, decisional balance exercises, and attitudes toward risk reduction) motivation, whereas 46% addressed distal (eg, life goals, personal values) motivational components.19 Many provided skills training (ie, with rehearsal and feedback) relating to intrapersonal (eg, self-management; 58%), interpersonal (eg, partner negotiation; 55%), and/or condom-specific (eg, placing condoms on model; 43%) aspects of risk reduction. Condoms were provided in 39% of the interventions. Most interventions (94%) included counseling and testing. Boosters were provided in 46% of the interventions. In 66% of the interventions, formative research was used to target intervention content to the sample. Facilitators were often paraprofessionals (85%); some interventions reported matching the facilitators to the ethnicity (28%) or gender (45%) of the sample.
Overall Efficacy of the Interventions
Table 1 provides the weighted mean ES for the 42 studies (k = 67) at the final postintervention assessment. Overall, analyses indicate that behavioral interventions improved condom use and reduced incident STIs and HIV compared with controls. By the final assessment, intervention participants significantly increased their condom use (d+ = 0.17) and reduced their incidence of STIs (d+ = 0.16), including HIV (d+ = 0.46), compared with controls. The pattern of results was consistent using fixed-effect or random-effect assumptions. There were no asymmetries that might be interpreted as publication bias: Trim and fill results for each outcome suggested that no study was missing; Begg test96 results were nonsignificant (zcondom use = 1.15, P = 0.25; zSTI = 0.53, P = 0.60; zHIV = 0.37, P = 0.71) as were Egger test98 results (biascondom use = 0.31, t = 0.65, P = 0.52; biasSTI = 0.94, t = 1.14, P = 0.26; biasHIV = 0.091, t = 0.030, P = 0.98).
The hypothesis of homogeneity was rejected for each outcome; examination of I2 confirmed high levels of heterogeneity. Moderator tests examined whether a priori determined sample or intervention characteristics related to the variability in ES (reported below; Table 2 for specific moderators). Analyses with and without moderator imputation revealed the same patterns; therefore, only the analyses with imputation appear.
Moderators of Intervention Impact on Condom Use
Bivariate regression analyses under mixed-effect assumptions examined potential moderators of condom use. As shown in Table 2, interventions increased participant's condom use when the content of the intervention excluded self-management skills training (β = −0.28, P = 0.04), addressed sociocultural barriers (β = 0.31, P = 0.03), and the intervention targeted Blacks and/or Hispanics (β = 0.32, P = 0.02). When significant bivariate moderators were simultaneously entered into a regression model, addressing sociocultural barriers was the only variable that remained a significant moderator of condom use (β = 0.31, P =0.03).
Moderators of Intervention Impact on Sexually Transmitted Infections
As Table 2 shows, intervention participants succeeded in reducing incident STIs when sampling participants diagnosed with an STI, including HIV, at baseline (β = 0.32, P = .04), the intervention excluded self-management skills training (β = −0.35, P = 0.03), and when condoms were not provided (β = −0.34, P = 0.03). When entered simultaneously, only STI/HIV diagnosed at baseline (β = 0.31, P = 0.04) and self-management skills training (β = −0.32, P = .04) remained significant moderators of incident STIs and accounted for 32% of the variance.
Moderators of the Intervention Impact on Incident HIV
Moderator tests for the intervention impact on incident HIV appear in Table 2. HIV incidence was reduced when studies sampled fewer Blacks or more Hispanic/Latinos, intervention content included a distal motivational component, condom skills training, active interpersonal skills training, did not provide condoms, and was targeted to women. When entered simultaneously in a regression model, only proportion of Hispanic/Latino participants remained significant; thus, interventions were successful in reducing the incidence of HIV when sampling more Hispanic/Latino participants (β = 0.88, P < 0.01).
Results from the current meta-analysis of 42 studies evaluating 67 interventions and measuring both behavioral and biological outcomes demonstrated that behavioral interventions increase condom use and lower the incidence of STIs, including HIV, for durations up to 4 years. Although research indicates that between-group ES are generally smaller, especially when comparing an intervention to an active control,104 weighted mean ES in the current meta-analysis were of small to medium magnitude (0.16 to 0.46). To our knowledge, this meta-analytic review is the first to examine the incidence of HIV in a wide range of populations at-risk for HIV. Contrary to recent reports,105 these findings show that behavioral interventions reduce behavioral risk and incident infections in a wide range of samples.
Several sample and intervention characteristics moderated intervention efficacy. First, interventions were more successful at improving condom use when sociocultural barriers of safer sex were addressed. Because a number of social, cultural, and economic factors influence condom use,106 and because poverty, gender inequality, and stigma influence individual's risk for HIV, this result is not surprising.107 Other life challenges often overshadow HIV as a concern, with survival needs forcing people into riskier practices and relationships. Results from this meta-analysis suggest that addressing sociocultural barriers within interventions seems to make them more efficacious at reducing sexual risk.
Second, contrary to expectations, providing participants with active self-management training was less successful in improving condom use and incident STIs at the final assessment. In our prior meta-analytic review,108 interventions were successful at improving condom use at “short-term” (less than 3 months postintervention), but not at “long-term” (>52 weeks), when self-management skills training was provided. In the current meta-analysis, the last assessment typically occurred at 52 weeks postintervention. One possible explanation for our findings is that self-management skills learned during the intervention were not sustained 1 year later. Active self-management training may dissipate over time; to improve sustainability, participants may benefit from boosters targeting self-management skills.
Third, consistent with expectations, interventions were more successful at lowering incident STIs with patients diagnosed with an STI or HIV at study entry. Prior research examining the effects of STI diagnosis “alone” has found little change in sexual risk behavior compared with individuals not diagnosed with an STI.109,110 Consistent with this research, a diagnosis of an STI or HIV was not a significant moderator of condom use in our meta-analysis. In contrast, an STI or HIV diagnosis at baseline was the sole moderator of incident STIs in our multiple-moderator analyses. Being diagnosed and receiving treatment for an STI before a behavioral HIV intervention may increase receptivity to the intervention thus behavioral changes (eg, partner reduction, monogamy) and reducing STI incidence. Moreover, research indicates that the association between STIs and condom use varies by type of infection, and increasing condom use may not avert all STIs equally.16 Future research might examine the contexts in which STI diagnosis moderates both behavioral and biological outcomes.
Finally, interventions were more successful at reducing the incidence of HIV when sampling more Latinos. Globally, Latinos are disproportionately affected by HIV.7 Interventions targeting Latinos are urgently needed to avert HIV infections among this group. Our meta-analysis shows that interventions reduced incidence of HIV in samples with greater proportions of Latino participants. In an earlier review, Noguchi et al. found that sampling greater proportions of Latinos was related to acceptance and retention of HIV prevention programs.111 Acceptance and retention of an intervention has the potential to increase participants' exposure to targeted content. Thus, one potential explanation for these findings is that exposure to targeted intervention content increases the relevancy of the message, thereby facilitating behavioral change and ultimately reducing the incidence of HIV.21 Consistent with prior meta-analytic reviews, the current findings suggest that interventionists developing behavioral interventions to reduce HIV should conduct formative research to identify the specific needs of Latinos.9,112
Because the ultimate goal of most behavioral interventions is to reduce the transmission of HIV, measuring HIV incidence is a desirable but rarely feasible outcome.113 Given the low incidence of HIV in many populations, testing for HIV is often impractical as large sample sizes would be required to detect a statistically significant effect of the behavioral intervention.16 Therefore, most behavioral intervention trials use self-reported behaviors and STIs as proxy measures of the impact of an intervention on the incidence of HIV within a given subpopulation. Although prior research indicates that improvements in self-reported behaviors and incident STIs after exposure to a behavioral intervention, these findings should not imply that the same intervention was also successful in reducing HIV incidence.16 In the current meta-analysis, we show that behavioral interventions are successful both at improving condom use and at reducing incident STIs and HIV. Although it may seem that changes in behavioral outcomes resulted in changes in biological outcomes, exploratory analyses do not support that assertion. In fact, there was no association between condom use, incident STIs, and HIV (P ≥ 0.10). Weighted regression analyses predicting incident HIV from condom use and incident STIs indicated that behavioral (β = −0.11, P = .56, k = 13) and biological (β = −0.03, P = .89, k = 8) outcomes are not associated with changes in HIV incidence. Moreover, simply providing condoms to individuals was insufficient in reducing the incidence of STIs, as shown in our moderator analyses. These findings warrant further investigation as the current meta-analysis was limited in the number of studies available to test the behavioral–biological association. The association between behavioral and biological outcomes is complex. Transmission of STIs depends upon a number of factors including partner type, characteristics, and perceptions of partner safety.113 Thus, examining both behavioral and biological outcomes, and factors associated with sexual risk behaviors, should be important in determining the efficacy of behavioral interventions.
Limitations of the underlying literature should be considered when interpreting these findings. First, self-reports are vulnerable to cognitive and social biases.114,115 Nonetheless, most researchers employed methods designed to optimize data quality.16 Second, follow-ups typically rely on a single assessment, which may not be representative of long-term ongoing risk. Still, the data do provide support for long-term efficacy of behavioral interventions given that the studies' last assessment occurred ≥1 year postintervention. Third, variations in how condom use was measured (eg, frequency, proportions) and STIs (eg, use of composite measures) required that we employ heterogeneous markers of risk reduction that are rarely partner specific. Fourth, the diversity of samples may have obscured nuanced patterns in the study outcomes. Fifth, most studies were conducted in the U.S.; it was not feasible to examine how well findings generalize across other important geographical regions of the world. Finally, only a few studies (6 studies, see Table S1, Supplemental Digital Content 1, http://links.lww.com/QAI/A223) used a wait-list/no treatment/assessment-only control group, which precluded an evaluation of the impact of behavioral interventions relative to type of control group (eg, no treatment vs. active comparisons).
Behavioral interventions to reduce HIV among studies measuring both behavioral and biological outcomes are successful at improving condom use and reducing incident STIs, including HIV, with effects that persist for durations as long as 4 years. Because the association between behavioral and biological outcomes is complex,113 measuring both behavioral and biological outcomes can help to determine the efficacy of behavioral interventions. Implementation of efficacious behavioral interventions within a wide range of population groups should be a high priority.
We thank the study authors who provided additional intervention details or data for this investigation and other SHARP (Synthesis of HIV/AIDS Research Project) team members who contributed to the development of this article. We thank Nicole Crepaz for access to the Prevention Research Synthesis Project Database available from the Centers for Disease Control and Prevention.
1. Joint United Nations Programme on HIV/AIDS (UNAIDS). . Global report: UNAIDS report on the global AIDS epidemic 2010 Available at: http://www.unaids.org/globalreport/Global_report.htm
. Accessed July 15, 2011
2. Antoni MHFolkman S. Stress, coping, and health in HIV/AIDS The Oxford Handbook of Stress, Health, and Coping. 2011 New York, NY Oxford University Press:428–451
3. Schackman BR, Gebo KA, Walensky RP, et al. The lifetime cost of current human immunodeficiency virus care in the United States. Med Care. 2006;44:990–997
4. Menzies NA, Berruti AA, Berzon R, et al. The cost of providing comprehensive HIV treatment in PEPFAR-supported programs. AIDS. 2011;25:1753–1760
5. Hutchinson AB, Farnham PG, Dean HD, et al. The economic burden of HIV in the United States in the era of highly active antiretroviral therapy: evidence of continuing racial and ethnic differences. J Acquir Immune Defic Syndr. 2006;43:451–457
6. Hall HI, Green TA, Wolitski RJ, et al. Estimated future HIV prevalence, incidence, and potential infections averted in the United States: a multiple scenario analysis. J Acquir Immune Defic Syndr. 2010;55:271–276
7. WHO, UNAIDS, UNICEF. Towards Universal Access: Scaling Up Priority HIV/AIDS Interventions in the Health Sector. Progress Report. 2010 Geneva, Switzerland World Health Organization
8. Noar SM. Behavioral interventions to reduce HIV-related sexual risk behavior: review and synthesis of meta-analytic evidence. AIDS Behav. 2008;12:335–353
9. Herbst JH, Kay LS, Passin WF, et al. A systematic review and meta-analysis of behavioral interventions to reduce HIV risk behaviors of Hispanics in the United States and Puerto Rico. AIDS Behav. 2007;11:25–47
10. Crepaz N, Marshall KJ, Aupont LW, et al. The efficacy of HIV/STI behavioral interventions for African American females in the United States: a meta-analysis. Am J Public Health. 2009;99:2069–2078
11. Neumann MS, Johnson WD, Semaan S, et al. Review and meta-analysis of HIV prevention intervention research for heterosexual adult populations in the United States. J Acquir Immune Defic Syndr. 2002;30(suppl 1):S106–S117
12. Ward DJ, Rowe B, Pattison H, et al. Reducing the risk of sexually transmitted infections in genitourinary medicine clinic patients: a systematic review and meta-analysis of behavioural interventions. Sex
Transm Infect. 2005;81:386–393
13. Crepaz N, Horn AK, Rama SM, et al. The efficacy of behavioral interventions in reducing HIV risk sex
behaviors and incident sexually transmitted disease in black and Hispanic sexually transmitted disease clinic patients in the United States: a meta-analytic review. Sex
Transm Dis. 2007;34:319–332
14. Crepaz N, Lyles CM, Wolitski RJ, et al. Do prevention interventions reduce HIV risk behaviours among people living with HIV? A meta-analytic review of controlled trials. AIDS. 2006;20:143–157
15. Scott-Sheldon LA, Fielder RL, Carey MP. Sexual risk reduction interventions for patients attending sexually transmitted disease clinics in the United States: a meta-analytic review, 1986 to early 2009. Ann Behav Med. 2010;40:191–204
16. Fishbein M, Pequegnat W. Evaluating AIDS prevention interventions using behavioral and biological outcome measures. Sex
Transm Dis. 2000;27:101–110
17. Centers for Disease Control and Prevention. . HIV Surveillance Report, 2009 2011 Available at: http://www.cdc.gov/hiv/surveillance/resources/reports/2009report/index.htm
. Accessed Feburary 2011
18. Weinhardt LS, Carey MP, Johnson BT, et al. Effects of HIV counseling and testing on sexual risk behavior: a meta-analytic review of published research, 1985-1997. Am J Public Health. 1999;89:1397–1405
19. Carey MP, Lewis BP. Motivational strategies can augment HIV-risk reduction programs. AIDS Behav. 1999;3:269–276
20. Fisher JD, Fisher WA, Shuper PADiClemente RJ, Crosby RA, Kegler MC. The information-motivation-behavioral skills model of HIV prevention behavior Emerging Theories in Health Promotion Practice and Research. 2009 San Francisco, CA Jossey-Bass:21–63
21. Kreuter MW, Wray RJ. Tailored and targeted health communication: strategies for enhancing information relevance. Am J Health Behav. 2003;27(suppl 3):S227–S232
22. Rosenthal R. The “file-drawer” problem and tolerance for null results. Psyc Bull. 1979;86:638–641
23. Saleh-Onoya D, Reddy PS, Ruiter RAC, et al. Condom use promotion among isiXhosa speaking women living with HIV in the Western Cape Province, South Africa: a pilot study. AIDS Care. 2009;21:817–825
24. Wingood GM, DiClemente RJ, Mikhail I, et al. A randomized controlled trial to reduce HIV transmission risk behaviors and sexually transmitted diseases among women living with HIV: The WiLLOW Program. J Acquir Immune Defic Syndr. 2004;37(suppl 2):S58–S67
25. Wolitski RJ, Parsons JT, Gomez CA, et al. Prevention with gay and bisexual men living with HIV: rationale and methods of the Seropositive Urban Men's Intervention Trial (SUMIT). AIDS. 2005;19(suppl 1):S1–S11
26. Crosby R, DiClemente RJ, Charnigo R, et al. A brief, clinic-based, safer sex
intervention for heterosexual African American men newly diagnosed with an STD: a randomized controlled trial. Am J Public Health. 2009;99(suppl 1):S96–S103
27. Shain RN, Piper JM, Newton ER, et al. A randomized, controlled trial of a behavioral intervention to prevent sexually transmitted disease among minority women. N Engl J Med. 1999;340:93–100
28. Archibald CP, Chan RK, Wong ML, et al. Evaluation of a safe-sex
intervention programme among sex
workers in Singapore. Int J STD AIDS. 1994;5:268–272
29. Chan RK, Goh A, Goh CL, et al. “Project Protect”–an STD/AIDS prevention intervention programme for sex
workers and establishments in Singapore Paper presented at: International Conference AIDS; June 6-11, 1993; Berlin, Germany
30. Bhave G, Lindan C, Hudes E, et al. Impact of an intervention on HIV, sexually transmitted diseases, and condom use among sex
workers in Bombay, India. AIDS. 1995;9:S21–S30
31. Boyer C, Barrett D, Peterman T, et al. Sexually transmitted disease (STD) and HIV risk in heterosexual adults attending a public STD clinic: evaluation of a randomized controlled behavioral risk-reducation intervention trial. AIDS. 1997;11:359–367
32. Boyer CB, Shafer MA, Shaffer RA, et al. Evaluation of a cognitive-behavioral, group, randomized controlled intervention trial to prevent sexually transmitted infections and unintended pregnancies in young women. Prev Med. 2005;40:420–431
33. Benner TACard JJ, Benner TA. FOCUS: preventing sexually transmitted infections and unwanted pregnancies among young women. Model Programs for Adolescent Sexual Health: Evidence-Based HIV, STI, and Pregnancy Prevention Interventions. 2008 New York, NY Springer:217–226
34. Boyer CB, Shafer MA, Pollack LM, et al. Sociodemographic markers and behavioral correlates of sexually transmitted infections in a nonclinical sample of adolescent and young adult women. J Infect Dis. 2006;194:307–315
35. Hwang LY, Shafer MA, Pollack LM, et al. Sexual behaviors after universal screening of sexually transmitted infections in healthy young women. Obstet Gynecol. 2007;109:105–113
36. Brems C, Dewane SL, Johnson ME, et al. Brief motivational interventions for HIV/STI risk reduction among individuals receiving alcohol detoxification. AIDS Educ Prev. 2009;21:397–414
37. Carey MP, Senn TE, Vanable PA, et al. Brief and intensive behavioral interventions to promote sexual risk reduction among STD clinic patients: results from a randomized controlled trial. AIDS Behav. 2010;14:504–517
38. Chacko MR, Wiemann CM, Kozinetz CA, et al. Efficacy of a motivational behavioral intervention to promote chlamydia and gonorrhea screening in young women: a randomized controlled trial. J Adolesc Health. 2010;46:152–161
39. DiClemente RJ, Wingood GM, Harrington KF, et al. Efficacy of an HIV prevention intervention for African American adolescent girls: a randomized controlled trial. JAMA. 2004;292:171–179
40. Wingood GM, DiClemente RJ, Harrington KF, et al. Efficacy of an HIV prevention program among female adolescents experiencing gender-based violence. Am J Public Health. 2006;96:1085–1090
41. Wingood G, Sales J, Braxton N, et al.Lecroy C, Mann J The handbook of prevention and intervention programs for adolescent girls Behavioural Case Formulation and Intervention: A Functional Analytical Approach. 2008 Hoboken, NJ Wiley:163–186
42. DiClemente RJ, Wingood GM, Rose ES, et al. Efficacy of sexually transmitted disease/human immunodeficiency virus sexual risk-reduction intervention for african american adolescent females seeking sexual health services: a randomized controlled trial. Arch Pediatr Adolesc Med. 2009;163:1112–1121
43. Downs JS, Murray PJ, Bruine de Bruin W, et al. Interactive video behavioral intervention to reduce adolescent females' STD risk: a randomized controlled trial. Soc Sci Med. 2004;59:1561–1572
44. Ford K, Wirawan DN, Reed BD, et al. The Bali STD/AIDS Study: evaluation of an intervention for sex
Transm Dis. 2002;29:50–58
45. Ford K, Reed BD, Wirawan DN, et al. The Bali STD/AIDS study: human papillomavirus infection among female sex
workers. Int J STD AIDS. 2003;14:681–687
46. Grimley DM, Hook EW III. A 15-minute interactive, computerized condom use intervention with biological endpoints. Sex
Transm Dis. 2009;36:73–78
47. Hadden BR An HIV/AIDS prevention intervention with female and male STD patients in a peri-urban settlement in KwaZulu Natal, South Africa. 1997 KwaZulu Natal, South Africa University of Natal:4
48. Hobfoll SE, Jackson AP, Lavin J, et al. Effects and generalizability of communally oriented HIV-AIDS prevention versus general health promotion groups for single, inner-city women in urban clinics. J Consult Clin Psychol. 2002;70:950–960
49. Imrie J, Stephenson JM, Cowan FM, et al. A cognitive behavioural intervention to reduce sexually transmitted infections among gay men: randomised trial. BMJ. 2001;322:1451–1456
50. James NJ, Gillies PA, Bignell CJ. Evaluation of a randomized controlled trial of HIV and sexually transmitted disease prevention in a genitourinary medicine clinic setting. AIDS. 1998;12:1235–1242
51. Jemmott LS, Jemmott JB III, O'Leary A. Effects on sexual risk behavior and STD rate of brief HIV/STD prevention interventions for African American women in primary care settings. Am J Public Health. 2007;97:1034–1040
52. O'Leary A, Jemmott LS, Jemmott JB. Mediation analysis of an effective sexual risk-reduction intervention for women: the importance of self-efficacy. Health Psychol. 2008;27(2 suppl):S180–S184
53. Jemmott JB III, Jemmott LS, Braverman PK, et al. HIV/STD risk reduction interventions for African American and Latino adolescent girls at an adolescent medicine clinic: a randomized controlled trial. Arch Pediatr Adolesc Med. 2005;159:440–449
54. Jewkes R, Nduna M, Levin J, et al. Impact of stepping stones on incidence of HIV and HSV-2 and sexual behaviour in rural South Africa: cluster randomised controlled trial. BMJ. 2008;337:A506
55. Kalichman SC, Cain D, Weinhardt L, et al. Experimental components analysis of brief theory-based HIV/AIDS risk-reduction counseling for sexually transmitted infection patients. Health Psychol. 2005;24:198–208
56. Kamb ML, Fishbein M, Douglas JM, et al. Efficacy of risk-reduction counseling to prevent human immunodeficency virus and sexually tranmsited diseases. J Am Med Assoc. 1998;280:1161–1167
57. Gottlieb SL, Douglas JM Jr., Foster M, et al. Incidence of herpes simplex virus type 2 infection in 5 sexually transmitted disease (STD) clinics and the effect of HIV/STD risk-reduction counseling. J Infect Dis. 2004;190:1059–1067
58. Metcalf CA, Malotte CK, Douglas JM Jr, et al. Efficacy of a booster counseling session 6 months after HIV testing and counseling: a randomized, controlled trial (RESPECT-2). Sex
Transm Dis. 2005;32:123–129
59. Warner L, Newman DR, Kamb ML, et al. Problems with condom use among patients attending sexually transmitted disease clinics: prevalence, predictors, and relation to incident gonorrhea and chlamydia. Am J Epidemiol. 2008;167:341–349
60. Kershaw TS, Magriples U, Westdahl C, et al. Pregnancy as a window of opportunity for HIV prevention: effects of an HIV intervention delivered within prenatal care. Am J Public Health. 2009;99:2079–2086
61. Koblin B, Chesney M, Coates T. Effects of a behavioural intervention to reduce acquisition of HIV infection among men who have sex
with men: the EXPLORE randomised controlled study. Lancet. 2004;364:41–50
62. Li X, Wang B, Fang X, et al. Short-term effect of a cultural adaptation of voluntary counseling and testing among female sex
workers in China: a quasi-experimental trial. AIDS Educ Prev. 2006;18:406–419
63. Mhalu F, Hirji K, Ijumba P, et al. A cross-sectional study of a program for HIV infection control among public house workers. J Acquir Immune Defic Syndr. 1991;4:290–296
64. NIMH. . The NIMH multisite HIV prevention trial: reducing HIV sexual risk behavior. Science. 1998;280:1889–1894
65. Orr DP, Langefeld CD, Katz BP, et al. Behavioral intervention to increase condom use among high-risk female adolescents. J Pediatr. 1996;128:288–295
66. Patterson TL, Mausbach B, Lozada R, et al. Efficacy of a brief behavioral intervention to promote condom use among female sex
workers in Tijuana and Ciudad Juarez, Mexico. Am J Public Health. 2008;98:2051–2057
67. Patterson TL, Orozovich P, Semple SJ, et al. A sexual risk reduction intervention for female sex
workers in Mexico: design and baseline characteristics. J HIV/AIDS Soc Serv. 2006;5:115–137
68. Patterson TL, Semple SJ, Fraga M, et al. An HIV-prevention intervention for sex
workers in Tijuana, Mexico: A pilot study. Hispanic J Behav Sci. 2005;27:82–100
69. Peipert JF, Redding CA, Blume JD, et al. Tailored intervention to increase dual-contraceptive method use: a randomized trial to reduce unintended pregnancies and sexually transmitted infections. Am J Obstet Gynecol. 2008;198:630 E631–E638
70. Rekart ML, Wong T, Wong E, et al. The impact of syphilis mass treatment one year later: self-reported behaviour change among participants. Int J STD AIDS. 2005;16:571–578
71. Roye C, Silverman PP, Krauss B. A brief, low-cost, theory-based intervention to promote dual method use by black and Latina female adolescents: A randomized clinical trial. Health Educ Behav. 2007;34:608–621
72. Champion JD, Shain RN, Korte JE, et al. Behavioral interventions and abuse: secondary analysis of reinfection in minority women. Int J STD AIDS. 2007;18:748–753
73. Sherman SG, Sutcliffe C, Srirojn B, et al. Evaluation of a peer network intervention trial among young methamphetamine users in Chiang Mai, Thailand. Soc Sci Med. 2009;68:69–79
74. The Voluntary HIV-1 Counseling and Testing Efficacy Study Group. . Efficacy of voluntary HIV-1 counselling and testing in individuals and couples in Kenya, Tanzania, and Trinidad: a randomised trial. Lancet. 2000;356:103–112
75. Wilson TE, Hogben M, Malka ES, et al. A randomized controlled trial for reducing risks for sexually transmitted infections through enhanced patient-based partner notification. Am J Public Health. 2009;99(suppl 1):S104–S110
76. Wolitski RJ, Gomez CA, Parsons JT. Effects of a peer-led behavioral intervention to reduce HIV transmission and promote serostatus disclosure among HIV-seropositive gay and bisexual men. AIDS. 2005;19(suppl 1):S99–S109
77. Wong ML, Chan R, Lee J, et al. Controlled evaluation of a behavioral intervention programme on condom use and gonorrhoea incidence among sex
workers in Singapore. Health Educ Res. 1996;11:423–432
78. Bishop GD, Wong MLMacLachlan M. Designing sustainable health promotion: STD and HIV prevention in Singapore Cultivating Health: Cultural Perspectives on Promoting Health. 2001 New York, NY Wiley
79. Wong ML, Chan KW, Koh D. A sustainable behavioral intervention to increase condom use and reduce gonorrhea among sex
workers in Singapore: 2-year follow-up. Prev Med. 1998;27:891–900
80. Wong ML, Chan R, Koh D. Long-term effects of condom promotion programmes for vaginal and oral sex
on sexually transmitted infections among sex
workers in Singapore. AIDS. 2004;18:1195–1199
81. Wong ML, Chan R, Koh D, et al. Theory and action for effective condom promotion: illustrations from a behavior intervention project for sex
workers in Singapore. Int Q Community Health Educ. 1994;15:405–421
82. Wynendaele B, Bobma W, Manga W, et al. Impact of counselling on safer sex
and STD occurence among STD patients in Malawi. Int J STD AIDS. 1995;6:105–109
83. Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. 1996;17:1–12
84. Miller WR, Brown JM, Simpson TL, et al.Hester RK, Miller WR What works? A methodological analysis of the alcohol treatment outcome literature Handbook of Alcoholism Treatment Approaches: Effective Alternatives. 19952nd ed Needham Heights, MA Allyn & Bacon:12–44
85. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46
86. Cohen J Statistical Power Analysis for the Behavioral Sciences. 19982nd ed New York, NY Erlbaum
87. Johnson BT, Eagly AHReis HT, Judd CM. Quantitative synthesis of social psychological research Handbook of Research Methods in Social and Personality Psychology. 2000 New York, NY Cambridge University Press:496–528
88. Lipsey MW, Wilson DB Practical Meta-Analysis. 2001 Thousand Oaks, CA Sage
89. Sanchez-Meca J, Marin-Martinez F, Chacon-Moscoso S. Effect-size indices for dichotomized outcomes in meta-analysis. Psychol Methods. 2003;8:448–467
90. Becker BJ. Synthesizing standardized mean-change measures. Br J Math Stat Psychol. 1988;41:257–278
91. Hedges LV. Distribution theory for Glass's estimator of effect size and related estimators. J Educ Stat. 1981;6:107–128
92. Stata Statistical Software: Release 11. 2009 College Station, TX StataCorp LP [computer program]
93. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Statistics Med. 2002;21:1539–1558
94. Huedo-Medina TB, Sanchez-Meca J, Marin-Martinez F, et al. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychological Methods. 2006;11:193–206
95. Higgins JP, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–560
96. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50:1088–1101
97. Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56:455–463
98. Egger M, Davey Smith G, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–634
99. Hedges LVCooper H, Hedges LV. Fixed effects models. The Handbook of Research Synthesis. 1994 New York, NY Russell Sage Foundation:285–299
100. Hadden BR An HIV/AIDS prevention intervention with female and male STD patients in a peri-urban settlement in KwaZulu Natal, South Africa. 1997 Washington, DC International Center for Research on Women
101. Orr DP, Langefeld CD, Katz BP, et al. Behavioral intervention to increase condom use among high-risk female adolescents. J Pediatr. 1996;128:288–295
102. Wilton L, Herbst JH, Coury-Doniger P, et al. Efficacy of an HIV/STI prevention intervention for black men who have sex
with men: findings from the Many Men, Many Voices (3MV) project. AIDS Behav. 2009;13:532–544
103. Wynendaele B, Bomba W, M'Manga W, et al. Impact of counselling on safer sex
and STD occurrence among STD patients in Malawi. Int J STD AIDS. 1995;6:105–109
104. Grissom RJ. The magical number .7 +/− .2: meta-meta-analysis of the probability of superior outcome in comparisons involving therapy, placebo, and control. J Consult Clin Psychol. 1996;64:973–982
105. Padian NS, McCoy SI, Balkus JE, et al. Weighing the gold in the gold standard: challenges in HIV prevention research. AIDS. 2010;24:621–635
106. Sarkar NN. Barriers to condom use. Eur J Contracept Reprod Health Care. 2008;13:114–122
107. Johnson BT, Redding CA, DiClemente RJ, et al. A network-individual-resource model for HIV prevention. AIDS Behav. 2010;14(suppl 2):204–221
108. Johnson BT, Scott-Sheldon LA, Smoak ND, et al. Behavioral interventions for African Americans to reduce sexual risk of HIV: a meta-analysis of randomized controlled trials. J Acquir Immune Defic Syndr. 2009;51:492–501
109. Kershaw TS, Ickovics JR, Lewis JB, et al. Sexual risk following a sexually transmitted disease diagnosis: the more things change the more they stay the same. J Behav Med. 2004;27:445–461
110. Wilson TE, Jaccard J, Levinson RA, et al. Testing for HIV and other sexually transmitted diseases: implications for risk behavior in women. Health Psychol. 1996;15:252–260
111. Noguchi K, Albarracin D, Durantini MR, et al. Who participates in which health promotion programs? A meta-analysis of motivations underlying enrollment and retention in HIV-prevention interventions. Psychol Bull. 2007;133:955–975
112. Huedo-Medina TB, Boynton MH, Warren MR, et al. Efficacy of HIV prevention interventions in Latin American and Caribbean nations, 1995–2008: a meta-analysis. AIDS Behav. 2010;14:1237–1251
113. Pequegnat W, Fishbein M, Celentano D, et al. NIMH/APPC workgroup on behavioral and biological outcomes in HIV/STD prevention studies: a position statement. Sex
Transm Dis. 2000;27:127–132
114. Schroder KE, Carey MP, Vanable PA. Methodological challenges in research on sexual risk behavior: II. Accuracy of self-reports. Ann Behav Med. 2003;26:104–123
115. Weinhardt LS, Forsyth AD, Carey MP, et al. Reliability and validity of self-report measures of HIV-related sexual behavior: progress since 1990 and recommendations for research and practice. Arch Sex