P1-S6.07 Ecological analysis of the factors influencing changes in HIV prevalence over time among FSW following a targeted intervention. (10th July 2011)

• Record Type: Journal Article
• Authors: wen, C; Boily, M C; Pickles, M R E H; Verma, S; Ramesh, B M; Isac, S; Adhinkari, R; Mainkar, M K; Alary, M; Vickerman, P
• Is Part Of: Sexually transmitted infections. Volume 87(2011)Supplement 1
• Page Start: A198
• Page End: A199
• Publication Date: 2011-07-10
• Subjects: Sexually transmitted diseases -- Periodicals; HIV infections -- Periodicals; 616.951005
• DOI: 10.1136/sextrans-2011-050108.231
• Languages: English
• ISSNs: 1368-4973

Abstract

Abstract : Background: Avahan is a large scale intervention that targets high-risk groups, including female sex workers (FSW), in many epidemiologically heterogeneous districts in southern India. Changes in HIV prevalence post intervention may depend on setting and intervention characteristics. We conducted an ecological analysis to identify which factors were associated with greater changes in FSW HIV prevalence after Avahan start in 2004. Methods: All variables were derived from two serial rounds (R1, R2) of cross-sectional FSW surveys, conducted ∼3–4 years apart, from 24 districts of 4 Southern Indian states. We examined the association between the difference in FSW HIV prevalence between rounds (R2−R1)(D.FSW HIV) and different classes of factors (Abstract P1-S6.07 table 1 ). Intervention factors included differences between rounds in consistent condom use (CCU) with occasional clients, difference in STI prevalence, or fraction of FSW in contact with the intervention at R1, and others (see Abstract P1-S6.07 table 1 ). Baseline contextual factors included FSW HIV or STI prevalence, fraction of FSW ever asked for anal intercourse (AI), weekly client number per FSW etc at R1, estimates of CCU in 1998, and increase in CCU before R1. Design factors (date of R1, time between R2−R1, differences in response rate between R2 and R1), and differences in contextual factors between rounds as listed Abstract P1-S6.07 table 1 were also explored. Pearson correlations, univariate and multiple linear regression analysis were performed. Results: In univariate analyses, D.FSW HIV prevalence was negatively associated with R1 FSW HIV prevalence (r=−0.53), R1 HSV-2 and Tp prevalence, difference in response rate, % asked for AI at R1 (Abstract P1-S6.07 table 1 ). D.FSW HIV prevalence was positively associated with differences in syphilis (r=0.36) or in HSV-2 prevalence or in % asked for AI. In multivariate analysis, R1.FSW HIV prevalence (slope=−0.57) and estimated CCU in 1998 (slope=0.29)(R=0.73), or R1.FSW HIV (slope=0.19) and D. FSW HSV-2 (slope=−0.83) prevalence (R=0.66) were significantly associated with D.FSW HIV prevalence (p<0.01). Conclusion: Contemporary time trends in HIV prevalence are influenced by epidemic stages and historical condom use for many years. HIV prevalence is more (less) likely to decline after effective interventions introduced in mature (early) epidemics. R2 was conducted too early after R1 to expect large decline in HIV. Without control group, mathematical modelling is required to simulate counterfactuals and estimate intervention impact.