1. Is retention on ART underestimated due to patient transfers
Is retention on ART underestimated due to patient transfers? Estimating system-wide retention using a national labs database in South Africa
Matthew Fox1,2, Jacob Bor1,2, Bill MacLeod1,2, Mhairi Maskew2, Alana Brennan1,2, Wendy Stevens3, Sergio Carmona3
1 Department of Global Health, Boston University School of Public Health, USA, 2 Health Economics and Epidemiology Research Office, University of Witwatersrand, South Africa, 3 National Health Laboratory Service, South Africa and Department of Molecular Medicine and Haematology, University of the Witwatersrand
Funded by NIAID R01 AI115979

2. Background
Systematic reviews have shown high rates of attrition in patients receiving antiretroviral therapy
36 months retention averages 65%–70% (Fox JAIDS 2015)
Attrition includes both death and loss to follow up
Clinic perspective is limited:
Some patients who request transfer don’t appear at a new clinic
Some lost patients return but are counted as new patients (cycling)
Some patients lost re-enter care at another clinic (silent transfer)
Migration, transfers, silent transfers and cycling may lead to under-estimation of retention in care

3. Objective
To use a new national HIV patient cohort in South Africa created from South Africa’s national laboratory database (National NHLS HIV Cohort), that can identify movement between clinics to assess system-wide retention in care within the public sector
We compared system-wide retention to retention at the initiating clinic to explore the impact of transfer to new sites
We assessed demographic predictors of system-wide retention in care

4. Methods
The National Health Laboratory Services (NHLS) National HIV cohort
NHLS is the main provider of laboratory services for the public-sector program in South Africa
Cohort created using all routine CD4/Viral Loads done since 2004
A validated unique patient identifier
Exact match on first, last name, DOB, sex, facility
Identify candidate matches for probabilistic record linkage
Score candidate matches based on similarity (Fellegi-Sunter, 1969)
Use graph-based approaches to guide decisions about whether a pair are a match
90% Sensitivity, 90% Positive Predictive Value compared to manually-matched gold standard
We have limited information to match on and we have too many matches to score them all
Jaro Winkler is a string based comparison for first and last name – those with a weighted average of the two below 0.9 are screened out
We also use this again in the Fellegi sunter to weight the scores
Fellegi Sunter is a score that can be pos or neg and gives liklelihoods based on rareness of observations
Can’t compare all possible links so we limit with the candidate matches
Score all cases with the first/last inversion etc
Score all in the nickname file – created by looking for statistically rare last names and DOB within a clinic which could only be twins, then had RAs code if they were correct
Jaro Winkler done for all case with DOB within 10 years
Fellegi-Sunter takes into account how common a variables is
sim_scoreij = (first_sim, last_sim, DOB_sim, sex_sim, prov_sim, fac_sim)
Clusters are defined setting threshold = t_low
Clusters with diameter <=2 placed in final dataset
For clusters with diameter >2, threshold is raised, eliminating some potential matches, and network is constructed again.
Repeated until all clusters either have diameter <=2 or have all links with tot_sim scores > t_high.

5. Methods
Included all patients starting ART in 2004/2005 with any follow up
By guidelines, first viral load was collected at ART initiation
Assessed retention as time to a patient's most recent lab result (CD4/VL)
Followed patients through March 2015
“Still in care” if their last lab occurred April March 2015
Assessed two retention concepts:
(a) system-wide retention including all labs regardless of facility
(b) retention at initiating clinic, ignoring labs at other facilities
Both definitions reflect attrition from death and loss to follow up

6. Results NHLS National HIV Cohort
9.2 million people have ever sought care for HIV
About 40% are single CD4 counts
Many who test positive never return to care
Likely under-matching
4.2 million ever initiated ART (and VL monitored)
3.1 million currently on ART and VL-monitored

7. Population Characteristics at first CD4 count
Results
Population Characteristics at first CD4 count
Sex
Female
64%      
Age
<30
35%      
39%    
≥40
27%    
CD4
<100    
36%    
30%      
17%      
≥350
Patients initiating ART in 2004 and 2005
N = 66,865
9-year retention
…at initiating clinic:
16.7% (95% CI: )
…system-wide:
54.5% (95%CI: 53.5 – 55.4)

8. Retention: system-wide vs. clinic perspective
System-wide retention
% observed at a later date
Clinic retention
Retained w/in national program
Retained at initiating clinic
Years since ART initiation
1 year year year
System-wide  ( )  ( )  ( )
Clinic retention  ( ) ( ) ( )

9. System-wide retention, by first CD4 count
>=500
<50
% observed at a later date
<50
50-99
>=500
Years since ART initiation

10. System-wide retention, by age at first CD4
<25 years
% observed at a later date
>50 years
<25
25-30
30-40
40-50
>50
Years since ART initiation

11. System-wide retention by sex
Female
Male
% observed at a later date
Female
Male
Years since ART initiation

12. Adjusted predictors of attrition*
Factor
Clinic attrition
HR (95% CI)
System-wide attrition
CD4
<50
1.14 ( )
1.25 ( )
50-99
1.08 ( )
1.14 ( )
1.05 ( )
1.06 ( )
1.11 ( )
0.98 ( )
1.02 ( )
>=500
Reference
Age
<25
0.86 ( )
0.78 ( )
1.03 ( )
1.03 ( )
1.01 ( )
1.07 ( )
>=50
1.05 ( )
1.27 ( )
Sex
Female
0.95 ( )
0.84 ( )
Male
*Also adjusted for province

13. Conclusions Strengths: Limitations:
Size, national scope, ability to see movement between clinics
Limitations:
Limited data on predictors, over/under matching, no mortality data, doesn’t include patients who never return
Patient migration and transfer are common throughout South Africa
NHLS National Patient Cohort allows passive tracking of patients regardless of where they seek care
Overall retention in care is underestimated using only the clinic wide perspective

14. Thank you and Acknowledgments
Patients, care providers, and NHLS staff
Sue Candy (NHLS CDW)
Research colleagues at BUSPH, NHLS and HE2RO
National Department of Health
Funders
NIH (NIAID)
PEPFAR
USAID