1. A Twin Response to Twin Epidemics: Integrated Syphilis/HIV Testing at STI Clinics in Guangdong Province China Li-Gang Yang, MD, MS, Joseph D. Tucker, MD, MA Guangdong Provincial STD Center UNC Chapel Hill School of Medicine Harvard Medical School

2. GuangdongProvince

3. Syphilis and sexually transmitted HIV in Guangdong Province 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Guangdong Province reported cases 20032004200520062007 Year Sexually transmitted HIV cases Primary syphilis cases Reported primary syphilis cases (purple) and heterosexually transmitted HIV cases (blue) in Guangdong Province. L1 = primary syphilis cases. Source: Guangdong STD Control Center and MMWR 2009; 58:396-400.

4. 0 10 20 30 40 50 60 198519901995200020052010 Total syphilis incidence (per 100,000 total population) Congenital syphilis incidence (per 100,000 live births) 35.3 13.3 Reported overall syphilis and congenital syphilis incidence per 100,000 population and per 100,000 live births respectively. Data are from the National Center for STD Control and Prevention in Nanjing, China.

5. 0 10 20 30 40 50 60 198519901995200020052010 Total syphilis incidence (per 100,000 total population) Congenital syphilis Incidence (per 100,000 live births) 35.3 13.3 Reported overall syphilis and congenital syphilis incidence per 100,000 population and per 100,000 live births respectively. Data are from the National Center for STD Control and Prevention in Nanjing, China. Primary & Secondary Syphilis Comparison: United States (2005) – 2.9/100,000 PRC (2005) – 5.6/100,000

6. Social Ecological Framework*: Determining HIV test uptake** * Choice of framework based on data from STI physicians (Tucker et al, BMC Public Health, 2010), policy experts (Tucker et al, Bulletin of WHO, 2010), and high risk groups (Wang et al, AIDS & Behavior, 2008). ** Defined as receipt of HIV test results by an STI patient at an STI clinic. Marital status Dyadic Interpersonal Physician-Level Age, sex, medical training Alone or accompanied HIV stigma, syphilis stigma HIV training, syphilis training Individual-Level Age, sex, income Prior testing Clinic-Level Availability of free rapid HIV tests Guidelines, clinic norms, and doctor incentives

7. Clustered observations among patients seeing the same physician Generalized Estimated Equations (GEE) -Other methods that treat clustering as a nuisance -More difficult to alter variance structures or characterize level-1 and level-2 interactions Multi-level modeling (MLM) -Substantive and technical advantages when dealing with clustered data. -Logit MLM can be used to describe binomial outcome (HIV test uptake as dichotomous outcome): logit (π i ) = log [ π i / (1- π i )] = β 0j + β 1 x ij β 0j = β 0 + u 0j

8. Plum Blossom Project Free rapid syphilis & free rapid HIV testing available at each STI clinic. 3 cities, 6 clinics - 62 physicians recruited 2061 STI participants. STI physicians filled out a survey with three domains – sociodemographics & training background, responses to clinical vignettes, and HIV/syphilis stigma. STI patients filled out a survey with seven domains. Physicians filled out a 10 item refuser form for those who did not want to be tested for HIV. Participation was voluntary and no incentives were provided to patients or physicians for participating. Rapid syphilis testing does not require trained lab personnel.

9. Syphilis & HIV Comparison of syphilis/HIV testing among those who filled out the complete Plum Blossom Survey (n = 1792). *Spearman correlation coefficient comparing responses to respective syphilis and HIV items. VariableSyphilisHIVCorrelation Coefficient* p-value Patient not tested in the past 1280 (71.4%)1316 (73.4%)0.719<0.001 Doctor asked patient about this disease 1758 (98.1%)1749 (97.6%)0.637<0.001 Patient willing to be tested1705 (95.1%)1697 (94.7%)0.939<0.001 Patient accepted testing1702 (95.0%)1693 (94.5%)0.815<0.001 Test performed1699 (94.8%)1691 (94.4%)0.957<0.001 Patient returned for test results 1681 (93.8%)1673 (93.4%)0.887<0.001 Rapid test positive139 (8.3%)3 (0.2%)0.0390.109

10. Patient reasons for HIV test refusal (n=1299) Reason for HIV test refusal (%) Reason for syphilis test refusal (%) I don’t have risk factors652 (50.2)794 (61.9) I don’t have time315 (24.3)272 (21.2) Follow-up plan or next steps unclear 203 (15.6)149 (11.6) I don’t have the money122 (9.4%)112 (8.7) Fear loss of face, discrimination100 (7.7)71 (5.5) Fear loss of confidentiality23 (1.8%)NA Doubt the test results25 (1.9)10 (0.8)

11. Physician non-offer of HIV testing Physicians reasons for not offering HIV testing to all patients (n=62) Low prevalence of disease 18 (29.0%) Not currently recommended by guidelines 12 (19.4%) Not enough time in my clinic 5 (8.1%) I am worried about HIV stigma affecting my patients 4 (6.5%) I cannot provide adequate HIV follow-up services 3 (4.8%) I feel uncomfortable or inadequately trained to deliver a new HIV diagnosis 2 (3.2%) I feel uncomfortable or inadequately trained to discuss HIV risk behaviors 1 (1.6%) Not my responsibility 1 (1.6%)

12. Physician non-offer of HIV testing Physicians reasons for not offering HIV testing to all patients (n=62) Low prevalence of disease 18 (29.0%) Not currently recommended by guidelines 12 (19.4%) Not enough time in my clinic 5 (8.1%) I am worried about HIV stigma affecting my patients 4 (6.5%) I cannot provide adequate HIV follow-up services 3 (4.8%) I feel uncomfortable or inadequately trained to deliver a new HIV diagnosis 2 (3.2%) I feel uncomfortable or inadequately trained to discuss HIV risk behaviors 1 (1.6%) Not my responsibility 1 (1.6%)

13. Multi-level model of HIV testing refusal The following factors were significantly associated with HIV test refusal in a two level binomial logit model: 1)Married (compared to all unmarried and not currently married) 2)Previous HIV testing (compared to those who have never been tested for HIV before) 3)Alone (compared to those who attended the STI clinic accompanied) 4)Participated in last two months (compared to first three months)

14. Physician-level predictors Physician-level variance accounted for 28.4% of all variance assuming level-1 variation is that of standard logistic distribution (3.29). None of the physician-level factors significantly improved the best MLM model: age, sex, HIV training, medical training, HIV stigma (using a validated instrument), number of patients evaluated, city.

15. HIV test refusal Factors associated with HIV test refusal (p<0.05): 1)Married (OR 13.9) 2)Prior HIV testing (OR 9.6) 1)Alone (OR = 1.9) 2)High income (OR = 1.8)

16. HIV test refusal Factors associated with HIV test refusal (p<0.05): 1)Married (OR 13.9) 2)Prior HIV testing (OR 9.6) 1)Alone (OR = 1.9) 2)High income (OR = 1.8) Factors associated with syphilis infection (p<0.05): 1)Married (OR 3.0) 2)Prior HIV testing (OR 3.1) 1)Women (OR 2.4) 2)Aged 40-60 (OR 1.7)

17. “SEEK” - Expanding syphilis/HIV testing in China The effects of a massive one-time compulsory HIV testing effort have lapsed (Wu et al., Science, 2006). HIV VCT centers have been plagued by poor test uptake (Ma et al., AIDS, 2000). Antenatal testing promising (Zhou et al., FRLBE105) although this represents a population with less sexual risk. TB institute testing with high HIV test uptake (Wang et al., Int. J. Tuberc. Lung Dis, 2010) but probably less associated with early HIV infections. STI-clinic based testing reaches a higher risk group in the setting of a sexual health infrastructure with the capacity to respond (Tucker et al., BMC Health Services, 2010).

18. Conclusions -Routine syphilis testing at STI clinics in China represents an opportunity to expand routine HIV testing. -Interpersonal factors (accompanied, marital status) influence individual HIV test uptake and may be leveraged to expand testing. -Multi-level modeling can be useful for analyzing clustered observations and accounting for hierarchical data structures.

19. Thank You Huizhou STI Clinic Xiao-Xiong Huang Hua Peng Sen-Miao Zhang Fang-Mei Chen UNC School of Medicine* Myron S. Cohen** Gail Henderson US NIH Fogarty Center* Sten Vermund Harvard University Arthur Kleinman SV Subramanian Rochelle Walensky Martin K. Whyte London Advisors Rosanna Peeling (LSHTM) Sarah Hawkes (UCL) *Main funders **Main mentor China National STD Control Center (Nanjing) Xiang-Sheng Chen Yue-Pin Yin Jin Bu Guangdong Provincial STD Control Center Bin Yang Ligang Yang Song-Ying Shen Cheng Wang Xuqi Ren Tinglu Ye Helena Chang Jiangmen Skin Hospital Zheng-Jun Zhu He-Kun Lu Bao-Yuan Zhang Shu-Jie Huang Xue-Ling Tan Wei-Jun Deng Xinhui District STI Clinic Jian-Xin Yu Yun Feng Jing-Feng Huang