Matthew Lamb mrl2013@columbia ICAP-M&E

1. Barriers to Retention and Factors Associated with LTF in HIV Programs The literature and ICAP Matthew Lamb mrl2013@columbia.eduICAP-M&E

2. Barriers to retention

4. Questions asked by Geng et al. • What happened to patients who were LTF? • vital status • current care and ART status • 2. What reasons do patients LTF give for no longer attending clinic?

5. Study design and sampling frame Cumulative LTF Incidence: 12 mo: 16% 24 mo: 30% 36 mo: 39% 3,628 ART patients 23% (829) LTF 77% (2,799) remained in care Automatically generated from electronic medical records when patient has not been seen for 6 months Outreach Worker: Visits location of patient, asks around ~ 1 afternoon/patient 15% (128) tracked 25% (32) died 13% (17) not found 62% (79) alive 61% (48) patient interviewed 39% (31) informant interviewed Questionnaire: reasons for LTF; current care and ART status

6. Reasons for LTF among 48 patients directly interviewed

7. Patient characteristics associated with Death among those LTF 111 tracked and vital status ascertained 79 alive 32 died (25%) * death rate highest 1-3 mo > last clinic visit Predictors of Survival in LTF Patients

8. Study design and sampling frame 3,628 ART patients 23% (829) LTF 77% (2,799) remained in care 15% (128) tracked 25% (32) died 13% (17) not found 62% (79) alive 61% (48) patient interviewed 39% (31) informant interviewed 83% (40) in care elsewhere in last 3 months 71% (34) taking ART in the last month *self report

9. Extrapolating to all LTF patients Patient attends clinic Unknown (LTF) Recorded death Recorded transfer Recorded survival and retention ~ 50% ~ 25% ~ 25% Unrecorded withdrawal Self-reported transfer Unrecorded death

10. Conclusions and points for future discussion • Structural barriers to retention dominate the given reasons in this study • Are there program characteristics that address enablers to retention? • Among those LTF later ascertained to be dead, highest death rate shortly after last clinic visit • Clinical/demographic factors associated with death among LTF patients suggests areas of potential intervention • How can this inform clinic monitoring of patients at high risk of death? • LTF is a mix of undocumented deaths (bad!), unknown (bad!) and transfers (problematic!)

11. Program characteristics associated with non-retention, LTF, and death at ICAP sites Preliminary work Matthew Lamb

12. Aims • Are program-level characteristics (e.g., adherence support, outreach) associated with retention, LTF, or death at ICAP-supported sites? • Are the observed associations similar when using aggregate (URS) and patient-level data?

13. Program characteristics • Measured from PFaCTS • Only gets at program availability, not quality or coverage • Reliability study ongoing, results soon! • Current ICAP ‘retention’ programs focus primarily on psychosocial interventions to improve adherence to ART in addition to retention

14. Data sources Program characteristics: PFaCTS PLD: 84 sites, 5 countries, 80,000 patients URS: 242 sites, 5 countries, 156,000 patients URS: 349 sites, 10 countries, 233,000 patients

15. Study Design • Aggregate estimates of LTF, Death, and Non-retention (LTF + Death) rates obtained from Track 1.0 indicators reported to URS • Cumulative number on ART – cumulative number LTF or dead • Excluding known transfers • Patient-level estimates based on person-years since ART initiation until (a) documented death or (b) 6 months with no visit • Excluding known transfers • Information combined with PFaCTS to assess association between characteristics targeting adherence and retention and the two measures of LTF rates

16. Program characteristics associated with LTF: aggregate data LTF Rate Ratio (95% CI) Food support Frequent counseling Peer educators Educational materials Outreach Support groups Reminder tools >1 directed counseling N = 384 sites with PFaCTS and URS care and treatment data through July, 2009 (10 countries) N = 242 sites with PFaCTS in countries providing electronic PLD, to ICAP-NY (5 countries) N = 84 sites with PFaCTS, electronic PLD, and URS care and treatment data through July, 2009 (5 countries) Through June 2009. Adjusting for urban/rural, facility type, year facility began providing ART care, cumulative number of patients seen in care

17. Preliminary results: focusing on two programmatic services (active patient outreach and food support): 84 sites with patient-level data Aggregate analysis 1st bar = crude, 2nd bar = adjusted Patient-level analysis 1st bar = crude, 2nd bar = adjusted for site-level factors 3rd bar = adjusted for site- and patient-level factors

18. LTF since ART initiation, by urban/rural: • 100 ICAP sites with patient-level data

19. LTF since ART initiation, by facility type: • 100 ICAP sites with patient-level data

20. LTF since ART initiation, by year of ART initiation: • 100 ICAP sites with patient-level data

21. ICAP analysis: Strengths and limitations • Strengths • Limitations • Routinely-collected data • Aggregate analyses can use all ICAP care and treatment sites • Patient-level analyses show that results from aggregate are largely reliable • Routinely-collected data • PFaCTS doesn’t get at program quality or coverage • Potential misclassification in PFaCTS harder to detect true associations

22. Conclusions • Routinely-collected data provide evidence that program services may influence patient retention • Structural barriers may be important (Geng), and one intervention aimed at these barriers (food support) is associated with reduced LTF • Use of routinely collected data for program evaluation can provide insights for further research

23. Acknowledgements • ICAP country programs • ICAP M&E Advisors • Ministries of Health, provincial and district-level programs • Non-governmental organizations and partners • PEPFAR • Doris Duke Charitable Foundation ORACTA program • ICAP M&E NY team • Molly McNairy • Denis Nash