1. The Health Communication Partnership October 2, 2007 End-of-Project Meeting AIDS Communication Programs and HIV Prevention in South Africa

2. Four basic questions Does HIV prevention behavior actually reduce HIV prevalence? Do AIDS communication programs increase HIV prevention behavior? Can the impact of individual programs be separated from others? Are these interventions cost-effective? Advancing Health Communication, Saving Lives

3. That require appropriate theories and advanced methods to answer Multivariate Causal Attribution Analysis (MCA) 1. An intervention: a full-scale, population-level program with pre/post or follow-up sample survey, 2.An appropriate theory of communication and behavior change 3.A valid counter-factual condition to estimate the net effects of the intervention “What would have happened without it.” Advancing Health Communication, Saving Lives

4. 1.Representative sample survey 2.Structural equation modeling (SEM) to estimate causal relationships 3.Propensity score matching (PSM) Approximates the conditions of a randomized experimental design by constructing a matched control group that is statistically equivalent to the treatment group MCA Analysis with Survey Data Advancing Health Communication, Saving Lives external validity causal inference internal validity counter-factual condition

5. y 1 Communication y 3 HIV Status y 2 Behavior W Z X Exogenous Socio- Demographic Controls Endogenous Variables 1 1 33 22 Direct or indirect effect ? direct Causal Pathways: Communication Exposure to HIV Status Advancing Health Communication, Saving Lives

6. South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey, 2005 Commissioned by the Nelson Mandela Foundation Secondary statistical analysis by: D. Lawrence Kincaid (JHU) & Warren Parker (CADRE) Advancing Health Communication, Saving Lives Conducted by: Human Science Research Council (HSRC) Centre for AIDS Development Research & Evaluation (CADRE) Medical Research Council (MRC)

7. Percent How would you rate yourself in terms of risk of becoming HIV positive? N = 9,764 (weighted); Have had sex and HIV test 63.6 % do not expect to get infected Advancing Health Communication, Saving Lives

8. N = 9,764 (weighted); Have had sex and HIV test Reasons for believing one is not at risk of HIV infection 64.2 % reported one or more reasons Percent Advancing Health Communication, Saving Lives Prevention Behavior

9. Statistically Significant Predictors of HIV Negative Status South Africa, 2005 Advancing Health Communication, Saving Lives Positively Related Negatively Related to HIV Negative Status to HIV Negative Status Prevention behavior Tertiary education A student On a pension Not Black White Coloured Indian Single Female Ages 15-24 years Ages 25-43 years Poverty Frequency of 4-5 alcoholic drinks Ever used injectable drugs HIV prevalence in one’s cluster Negative but not stat. significant: No. of regular sex partners (12 mo.) No. of non-regular partners (1 mo.) N = 9,764; subsample that has had sex and HIV test; logistic regression analysis

10. Percent Percent who are HIV positive by HIV prevention behavior N = 9,764; Have had sex and HIV test Advancing Health Communication, Saving Lives Using Prevention Behavior No Prevention Behavior Difference Would have been HIV positive without prevention behavior Treatment GroupMatched Control Group

11. Percent who are HIV negative by HIV prevention behavior 90.5 86.1 4.4 0 20 40 60 80 100 Percent Treatment GroupMatched Control GroupDifference Using Prevention Behavior No Prevention Behavior Difference p<0.001 Advancing Health Communication, Saving Lives Would have been HIV positive without prevention behavior

12. Percent Reduction in HIV Prevalence Due to Prevention Behavior Advancing Health Communication, Saving Lives 16,702,263 Using prevention behavior x 4.4 % Difference in HIV negative status 734,890 Would have been HIV positive in 2005 without prevention behavior 3,990,876 HIV positive in 2005 + 734,890 Would have been HIV positive 4,725,776 Expected to be HIV positive in 2005 15.6 % Reduction in HIV infections due to HIV prevention behavior (734,890 / 4,725,776)

13. Expected Life-Time Savings in ARV Treatment of HIV Positive Cases Advancing Health Communication, Saving Lives 15.6 % Reduction in HIV infections due to HIV prevention behavior 734,890 Additional HIV/AIDS cases x US $ 8,000 Lifetime costs for treatment (20 years)* $ 5.9 Billion Estimated savings over 20 years due to HIV prevention as of 2005 * Source: Kahn, Marseille, & Auvert (2006)

14. D. Lawrence Kincaid, Patrick Coleman, Van Pham JHBSPH & JHHESA Warren Parker, Benjamin Makhubele, Helen Hajiyiannis, Pumla Ntlabati, Cathy Connolly, CADRE National HIV/AIDS Communication Survey 2006 A nationally representative sample of A national sample of 6,998 (ages 15-65 yrs) representative of a population 29,315,622 South Africans, using the same sampling frame as the HSRC 2005 survey.

15. 1. Age 2. Sex 3. Single vs. ever married 4. Level of education 5. No children for whom you’re parent or guardian 6. Level of Living Standard (Household Items) 7.Poverty: Lack of fuel, clean water, medicine, food 8.Knows someone who has died of AIDS 9.Owns one or more television sets 10.Frequency of watching television 11.Frequency of listening to radio 12.Listens to local community radio 13.Frequency of reading newspapers 14.Frequency of reading magazines 15.Frequency of internet use 16.Currently employed or student 17.Type of urban and rural residence 18.White, Indian, Colored versus Black 19.Province Socio- economic control variables used to estimate adjusted impact of communi- cation programs Advancing Health Communication, Saving Lives

16. Type of Communication Program Five Independent Television Programs 1. Tsha Tsha TV Drama 2. Soul City TV Drama 3, Khomanani Choice TV 4. Beat it Siyanqoba TV Program 5. loveLive TV Spots One Monthly Newspaper Supplement 6. S’camto Print (Sunday Times & loveLife) Percent Exposure 48 65 18 27 67 12 19 Communication Programs in South Africa Advancing Health Communication, Saving Lives N= 6,998 (15-65 yrs.) Population (weighted) = 29,315,622

17. Type of AIDS Communication Program Khomanani Communication Campaign 7. TV Program or Spot 8. Radio Program or Spot 9. Leaflet 10. Newspaper Advertisement Khomanani Community Intervention 11. Participated organized event 12. Spoke to community action ambassador Percent Exposure 53 47 36 32 4 5 Communication Programs in South Africa N= 6,998 (15-65 yrs.) Population (weighted) = 29,315,622 Advancing Health Communication, Saving Lives

18. Type of Communication Program Two National Radio Programs 13. Soul City Radio 14. loveLive radio Advert Two Community Radio Programs by ABC Ulwazi 15. The Journey Community Radio Drama 16. Body, Mind and Soul Community Radio Drama Three Programs for Children 17. Soul Buddyz TV Drama 18. Soul Buddyz Radio Program 19. Takalani Sesame TV Program Percent Exposure 36 57 4 6 54 25 56 Communication Programs in South Africa N= 6,998 (15-65 yrs.) Population (weighted) = 29,315,622 Advancing Health Communication, Saving Lives

19. Level of Exposure to 19 AIDS Communication Programs by Gender * Percent N= 6,998 (15-65 yrs.) Population (weighted) = 29,315,622 (not stat. sig.) * About 2,197,454 out of 29,315,622 are not exposed to any programs Number of Communication Programs

20. Measuring HIV Prevention Behavior Have you ever had sex before? Have you had sex in the past 12 mo.? With the person you most recently had sex with, did you do anything to prevent HIV infection? What did you do? [ DO NOT PROMPT. MULTIPLE RESPONSES. ] Nothing, used condoms, faithful to partner…etc. N = 4,844 of 7,006 Advancing Health Communication, Saving Lives

21. Percent condom use by the cumulative exposure to 19 AIDS communication programs ADJUSTED for 19 socio-economic control variables N= 4,844 (15-65 yrs.); if had sex in the last 12 months; p<0.001; logistic regression analysis Number of AIDS Communication Programs Seen or Heard Percent -19 18-point spread

22. Impact on Other HIV/AIDS Outcomes Advancing Health Communication, Saving Lives Percentage HIV/AIDS Outcomes Point Spread Range Used condom to prevent HIV 18 (41-59) Talked to partner about HIV test24 (49-73) Talked to a friend about HIV test26(23-79) Ever had an HIV test21(36-57) HIV test within the last 12 months19(15-34) High reversed AIDS stigma10(37-47) Helped someone sick with AIDS16(6-22) Know ARV as treatment for AIDS34(60-26) Faithful to one’s partner 2(1 - 3) Abstain from sexn. s.(4 - 4) One vs. multiple partnersn. s. (87-87) One vs. multiple partners (last month)n. s. (97-97) Condom with non-regular partnern. s. (71-70)

23. Scatter plot of the correlation among 19 AIDS communication programs (Factor analysis of the tetrachoric correlation among 19 programs) Soul City TV Soul Buddyz TV Tsha Tsha TV Takalani Sesame TV loveLife TV Beat It Siyanqoba TV Khomanani Choice TV Khomanani newspaper loveLife radio Khomanani TV advert Khomanani radio advert Khomanani leaflet Soul City radio Soul Buddyz radio Journey radio drama Body Mind Soul radio drama Khomanani community ambassador Khomanani organised event loveLive S’Camtoprint TELEVISION PROGRAMS RADIO PROGRAMS KHOMANANI COMMUNITY PROGRAMS

24. Percent who watched the TV drama Tsha Tsha during the last 12 months by age and sex * N= 6998; not stat. significant by sex Percent Age Group Advancing Health Communication, Saving Lives

25. Impact of watching Tsha Tsha on Using Condoms to Prevent HIV/AIDS N = 4,844 who have had sex in last 12 months; p<0.001 Advancing Health Communication, Saving Lives

26. Average Treatment Effects on Those Who Watched Tsha Tsha on Television Advancing Health Communication, Saving Lives HIV/AIDS Outcomes Average Treatment Effects ( Percentage Point Difference) Used condom to prevent HIV7.0 Used condom with nonregular partner 5.7 Discussed HIV test with partner4.0 Ever tested for HIV4.3 Aware of ARV as AIDS treatment6.5 Attitude towards living with HIV/AIDS3.0 Helped cared for person sick with AIDS 5.2 Abstinence from sexn.s. Faithful to one partnern.s. One versus multiple sex partnersn.s.

27. Cost-Effectiveness analysis of the Tsha Tsha Television Serial Drama on Using Condoms to Prevent HIV

28. Estimation of the Cost-Effectiveness of Tsha Tsha on Using Condoms to Prevent HIV Additional condom users attributed to Tsha Tsha (7 % points): 724,971 Estimated production costs for 52 episodes broadcast before survey: 1 US $ 2,272,000 Cost per additional condom user: $ 3.13 ($ 2,272,000 / 724,971) Cost per person reached: $ 0.16 ($ 2,272,000 / 14,132,107) 1 Joint funding by SABC-Education and USAID; broadcast costs are not included but are presumed to be offset by commercial advertising (R 14,768,000 / 6.5 = US $ 2,272,000).

29. Conclusions HIV prevention behavior helps to reduce HIV prevalence. AIDS communication programs increase HIV prevention behavior. The impact of individual programs can be separated from others. HIV prevention communication programs and HIV prevention behavior are highly cost-effective.

30. Advancing Health Communication, Saving Lives Communicating Health … Saving Lives

31. Estimation of the Cost-Effectiveness of Tsha Tsha Total population ages 15-65 years = 29,366,512 70% of 29,366,512 had sex in last year = 20,565,661 50% watched Tsha Tsha = 10,356,727 Used Condoms to Prevent HIV 58.2% who watched Tsha Tsha = 6,027,615 51.2% matched control group = − 5,302,644 Condom users attributed to Tsha Tsha 724,971 * Also: net difference of 0.07 x 10,356,727 viewers = 724,971 condom adopters Advancing Health Communication, Saving Lives

32. The HIV Epidemic in South Africa The HIV epidemic is severe, affecting all age groups Large prevalence variations exist between sexes, age groups, race groups and communities Most severely affected are females <20; females aged 20 - 40 and males aged 25 - 45 Children and older age groups significantly affected HIV prevalence by age and gender, South Africa, 2005 (NM/HSRC)