1 / 59

Networks, Neighborhoods, and HIV prevention

Networks, Neighborhoods, and HIV prevention. Carl A. Latkin Department of Health, Behavior & Society clatkin@jhsph.edu. Grants: DA010446, DA09951, DA13142 Lighthouse team Study participants. Colleagues & Co-Investigators Amy Knowlton Karin Tobin Melissa Davey- Rothwell

axl
Download Presentation

Networks, Neighborhoods, and HIV prevention

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Networks, Neighborhoods, and HIV prevention Carl A. Latkin Department of Health, Behavior & Society clatkin@jhsph.edu

  2. Grants: DA010446, DA09951, DA13142 Lighthouse team Study participants Colleagues & Co-Investigators Amy Knowlton Karin Tobin Melissa Davey- Rothwell Wei Hua Acknowledgements

  3. Goals • Understand how social network processes influence individuals’ health behaviors • Examine how physical context may impede or abet behavior change • Develop, sustainable, cost-effective preventive interventions by capitalizing on naturally occurring social processes

  4. A Social Ecological Model of Health Behavior • Microsocial environment • Network structure • Network function • Physical environment • Behavioral settings • Blocks • Neighborhoods • Communities

  5. An individual (X) and her social ties (0) as defined by interactions of interest, e.g., drug sharing, sex. Personal Network o o o o X o o o 0 o

  6. Networks Defined: Interactions of Interest • Support networks • Emotional • Financial or material • Informational (health advice) • Instrumental (household, caregiving tasks) • Risk networks • Sex partners • Drinking/drug partners • Overlapping support & risk ties

  7. Network Elicitation: 2 Steps • Identify names of important ties based on interactions of interest • Sample name generating questions: • Emotional support • Who can you talk to about something personal or private? • Financial support • Who would give or loan you $25 or something of value? • Drug sharing • Who do you do drugs with? • Elicit attributes of network members • Demographics: age, gender, education • Risk behaviors, Formal and informal economic resources

  8. Risk Network Attributes • Drug use (function) • Types: heroin, cocaine, speedball • Modes of administration: inject, snort, smoke • Role relation • Main partner, kin, non-kin, professionals • Females, males • Qualities of ties • Closeness • Conflict • Frequency of contact

  9. The SHIELD Study, Baltimore (1997-2004) • Social network-oriented experimental HIV prevention intervention • Recruitment strategies • geocoding, focused ethnography, targeted street outreach, word-of-mouth • N=1,637 at baseline • Semi-annual assessments • 5 waves of data

  10. SHIELD Study Eligibility Criteria 18 years or older ≥ weekly contact with active drug users Willingness to conduct HIV outreach education Willingness to bring in network members

  11. Demographics of SHIELD Index Participants (n=1,051) Variables % Female 36 Education: <12th grade 45 Unemployed 78 Homeless 29 Injected drugs 47 HIV seropositive 17

  12. Correlates of Selling Sex among Men & Women AOR (95% CI) Female gender 6.51 (3.49, 2.12) Crack smoking <Everyday 2.09 (1.08, 4.05) ≥Everyday 2.09 (0.78, 5.55) Crack smokers in network, no. 1.27 (1.08, 1.49) Kin in network, no. 0.83 (0.73, 0.94) Latkin, Hua, Forman., 2003; n=674

  13. Correlates of Buying Sex among Men AOR (95% CI) Age: > 40 years old1.62 (0.96, 2.74) Crack smoking < Everyday 3.43(1.89, 6.23) ≥ Everyday 1.56 (0.59, 4.14) Crack smokers in network, no.1.52(1.28, 1.81) Injectors in network, no. 1.25(1.05, 1.48) Kin in network, no. 0.85(0.75, 0.96) n=435

  14. Drug overdose correlates among recent heroin users(SHIELD; logistic regression; n=361)

  15. Change in social network characteristics (T1–T4) of 659 SHIELD participants

  16. Network Dynamic Measures • Network Turnover-in • # of new members entering the network between baseline & follow-up • Network Turnover-out • # of members disappearing from the network between baseline & follow-up

  17. Network Stability and Turnover

  18. Predicted probabilities of classification inHIV risk behavior change group

  19. Predictors of Entry into Drug Treatment

  20. Suboptimal ER use among HIV+ injectors(SAIL study; n=295) ** p<0.01, * p<0.05 Knowlton, Hua, Latkin, 2005

  21. All Connections: largest network component among SHIELD participants (N=5,615)

  22. Network Conclusions • Network factors are strongly associated with health behaviors • Influence behavior • Regulate daily activities • Promote and maintain social norms • High specificity of network member function on health outcomes

  23. Neighborhoods & Psychological Distress • People living in impoverished neighborhoods have high levels of psychological distress and HIV • Is their psychological distress associated with their HIV risk? • How might neighborhood factors influence distress and HIV?

  24. Measure of Neighborhood Disorder Assessment of: • Vandalism • Teens on streets • Vacant housing • Litter or trash on streets • Selling drugs • People getting robbed • Burglary Perkins and Taylor (1992)

  25. Baseline neighborhood disorder predicting depression (CES-D) at 9 months follow-up;

  26. Modeling of Neighborhood Factors • Psychological Distress: CES-D served as indicators for psychological distress. • Risk Behaviors: • Injection risk behaviors: sharing needles and cookers • Sexual risk behaviors: exchange money or drugs for sex, sex with injectors, sex with crack user • Analytic Procedure: Structural equation modeling (SEM) techniques were conducted using Mplus

  27. Modeling of Neighborhood Effects on Injection Risk Behaviors Beat Up Sell Drugs Robbery Neighborhood Disorder Loitering Litter Vacant Vandalism Latkin, Williams et al., 2004

  28. Neighborhood Factors and HIV Risk Behaviors Among Males Buying Sex(structural equation modeling) 0.19 Buying Sex with Money or Drugs Crack Use Past 6 Months 0.21 Psychological Distress 0.20 0.13 Bolded coefficients significant at p<.05 Coefficients are standardized Coefficient in ( ) represents the direct effect of Neighborhood Disorder 0.33 0.09 (0.19) Neighborhood Disorder

  29. Neighborhood Factors & HIV Risk Behaviors among Females Selling Sex 0.08 Crack Use Past 6 Months Psychological Distress 0.17 0.41 Selling Sex for Money or Drugs -0.07 Bolded coefficients significant at p<.05 Coefficients are standardized Coefficient in ( ) represents the direct effect of Neighborhood Disorder 0.16 (0.17) 0.24 Neighborhood Disorder

  30. Geocoded SHIELD Study Participants’ Addresses

  31. Path Model of Indirect Effects of Block Group Level Violence on Psychological Distress Among Drug Users in Baltimore, Maryland

  32. HPTN 037 A phase III randomized study to evaluate the efficacy of a network-oriented peer intervention for HIV prevention among injection drug users and their network members • A Study of the HIV Prevention Trials Network • Sponsored by: NIAID, NICHD, NIDA, NIMH, NIH

  33. . Key study personnel Apinun Aramrattna, MD, PhD Namtip Srirak, RN, PhD Tasanai Vongchak, RN, MPH Susan Sherman, MPH, PhD Vu Minh Quan, MD Annet Davis-Vogel, RN, MSW Valerie Simpson Monica Ruiz, PhD Kevin Ryan, PhD Tom Perdue, MPH Sharavi Gandham, M.S. Deborah Hilgenberg, MPH Kanokporn Wiboonnatakul David Celentano, Sc.D David Metzger, Ph.D. Deborah Donnell, Ph.D HPTN 037

  34. Enrollment criteria • Behavioral inclusion criteria for index participants: 1. HIV ELISA negative within 60 days prior to randomization, 2. Injected drugs at least 12 times in the last three months, 3. Been out of treatment for at least three months, and 4. Willing to identify at least two study eligible risk network members, and able to recruit at least one eligible member. • Behavioral inclusion criteria for network members: 1. Recruited for the study by an index participant, 2. Injected drugs and/or had sex with the index within 3 months

  35. Study design

  36. Results • The 414 networks, with 1123 participants, were randomized to treatment and experimental conditions. • Philadelphia enrolled 696 participants in 232 networks • Thailand enrolled 427 participants in 182 networks. • 951 persons who injected drugs in the month prior to enrollment, • The overall retention rate at 6 months, 83% at 12 months, 85% at 18 months, 82% at 24 months. • In Philadelphia 5-7% of eligible participants were incarcerated during follow-up. • Few networks have follow-up on none of the network members, • 44 deaths • HIV rate 0.69 per 100 PY ( 95% CI 0.33, 1.27) • Stopped trial due to low HIV seroconversion rate

  37. Baseline: Network member relationship to index participant - by site Chiang Mai Philadelphia % % . Number Enrolled 35 65 Type of Relationship with Index Sex and Drugs 4 17 Sex Only 22 10 Drugs Only 74 73 ..

  38. Baseline behaviors Chiang Mai Philadelphia % % . In jail/prison# 5 17 Lived on street # 11 25 Smoked crack* 0 55 Smoked amphetamines* 41 1 Drug treatment # 1 27 Drink alcohol # 86 59 Number of days injected* 1 to 10 days 72 11 10+ to 20 days 12 17 20+ to every day 16 71 . * -last month, #- last 6 months

  39. Results: Participants who injected drugs in the prior month(Philadelphia=560, Chiang Mai=356) . Chiang Mai Philadelphia % % Baseline 83.5 97.8 6 Months 25.3 60.8 12 Months 23.5 51.7 18 Months 9.6 44.3 24 Months 9.2 39.3 .

  40. Statistical analysis methods of intervention effects • Behavioral Data • Each person reports behavior every 6 months • Each network measures multiple people • Treatment randomized by network within each site • Repeated measures analysis, nested within networks • Analysis assesses the difference between arms • Intent-to-treat-analysis • Each site: possibly different treatment effects; different proportions in the control arm • SUDAAN statistical software to fit models

  41. Cumulative number of intervention sessions attended Chiang Mai Philadelphia % % . At least 1 visit 98 85 At least 2 visits 97 81 At least 3 visits 96 80 At least 4 visits 96 72 At least 5 visits 89 66 All 6 visits 74 44_____ .

  42. Results: Ten or more HIV risk reduction conversations

  43. Results: Philadelphia Effect of treatment vs. control on HIV risk behavior

  44. Results: Treatment Effects forChiang Mai

  45. 037 Conclusions • Both sites: Dramatic HIV risk reduction in both conditions • Philadelphia: Intervention effect with patterns of meaningful reductions in injection risk behaviors; dose-response pattern of risk reduction, with greater change in indexes as compared to network members • Thailand: No intervention effect • Possible factors: Few risk behaviors to detect change, sampling bias, regression toward the mean, contamination, powerful control condition, differential attrition, historic factors • What are the mechanism of behavior change?

More Related