Matthew Lamb ICAP-M&E Barriers to Retention and Factors Associated with LTF in HIV Programs The literature and ICAP

Barriers to retention Structural Financial Transportation Competing priorities Biomedical Illness Health Clinical issues Psychosocial Forgetfulness Drug abuse Stigma/disclosure Available support systems Knowledge/beliefs

Questions asked by Geng et al. 1.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?

Study design and sampling frame 3,628 ART patients 77% (2,799) remained in care 23% (829) LTF 15% (128) tracked 13% (17) not found 25% (32) died 62% (79) alive 61% (48) patient interviewed 39% (31) informant interviewed Questionnaire: reasons for LTF; current care and ART status 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 Cumulative LTF Incidence: 12 mo: 16% 24 mo: 30% 36 mo: 39%

Reasons for LTF among 48 patients directly interviewed

Patient characteristics associated with Death among those LTF 32 died (25%) 79 alive 111 tracked and vital status ascertained Clinical measure at last visit Hazard Ratio95% CI Increasing AgePer 10 yr increase2.0( ) Low blood pressure 75 mm HG 3.0( ) CNS syndromeYes vs. no2.9( ) Pre-ART CD4 countPer 50 cells/mm 3 increase 0.6( ) * death rate highest 1-3 mo > last clinic visit Predictors of Survival in LTF Patients

Study design and sampling frame 3,628 ART patients 77% (2,799) remained in care 23% (829) LTF 15% (128) tracked 13% (17) not found 25% (32) died 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

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

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!)

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

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?

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

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

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

Program characteristics associated with LTF: aggregate data 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) Educational materials >1 directed counseling Frequent counseling Support groups Peer educators Reminder tools Food support Outreach Through June Adjusting for urban/rural, facility type, year facility began providing ART care, cumulative number of patients seen in care LTF Rate Ratio (95% CI)

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

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

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

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

ICAP analysis: Strengths and 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 Strengths Limitations

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

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