Outcomes of patients’ loss to follow -up after antiretroviral therapy initiation in north- eastern South Africa Julie Ambia 1 , Chodziwadziwa Kabudula 2 , David Etoori 1 , F. Xavier Gomez-Olive 2 , Ryan Wagner 2 , Kathleen Kahn 2 , Georges Reniers 1 1 London School of Hygiene and Tropical Medicine 2 MRC/Wits Agincourt, Johannesburg, South Africa Introduction High rates of loss to follow-up (LTFU) have been reported in many HIV care and treatment programmes in sub-Saharan Africa. Perception of good health, use of alternative medicine, stigma, treatment fatigue, lack of knowledge, transport fare and competing demands for time are some of the factors that contribute to these high rates of disengagement [1]. However, some patients could have switched facilities, and still be adhering to ART [2, 3]. It is important to better understand the vital and treatment status of the LTFU patients both for improving the delivery of HIV services and for estimating the impact of ART on HIV associated mortality. For several years now, the SPECTRUM model has been used by UNAIDS for making most national and subnational estimates of HIV epidemic trends. ART coverage and the survival of patients on ART are key inputs on SPECTRUM that influence various components in the model, including mortality, fertility and onwards transmission. Its inputs need to be carefully calibrated with empirical evidence [4]. Data on the survival of people living with HIV (PLHIV) on ART often come from clinical cohorts, and it was quickly understood that high LTFU rates could not be ignored. Several strategies have been employed to obtain information on vital status of patients LTFU, including the review of obituaries in newspapers and record linkage with Civil Registration and Vital Statistics databases; both with the intent to identify patients who deceased after being LTFU [5, 6]. The IeDEA Network of East Africa has resorted to tracing a random sample of patients LTFU patients by phone or home visits, which has the added advantage that data can be used for estimating undocumented transfers to other facilities providing treatment [3, 7, 8]. In this study, we use data from Agincourt Health Demographic Surveillance System (HDSS) [9] that was individually linked with clinic visit records of chronic care patients at eight 1
Primary Health Care facilities. The vital status (also migration status) of any patient LTFU can thus be ascertained via the HDSS. Moreover, the link with the Agincourt HDSS will allow us to identify patients who are LTFU at one facility and are subsequently registered at another facility. Study setting and population The AHDSS study area covers 475 km 2 in Bushbuckridge, Mpumalanga province, north- eastern South Africa. As of 2014 the population was approximately 115,000 people living in 17,000 households spread in 31 villages [10]. Overall, 47.9% of the population are men and 52.1% are women [11]. Three fifths of the men aged between 30 and 54 years and 25% of the women aged between 25 and 49 years are regarded as labour migrant [12]. These labour migrants work in farms, mines and manufacturing industries outside the study site, returning home whenever possible. One third of the population are former Mozambican refugees who moved into the area during the civil war that began in 1977 and ended in 1992. Data sources Since 30 th April 2014, records of HIV patients from eight facilities (7 government facilities and 1 public/private partnership) have been linked with their Agincourt HDSS records. Currently, clinic visit information on test dates and results of HIV diagnosis, CD4 counts, and viral loads have been linked with individual Agincourt HDSS records. Operational definitions A patient was considered LTFU if they did not return for more than 90 days after their scheduled appointment. Time on ART was measured from the date ART was initiated to the earliest of the date of either LTFU, transferring out to another facility, death or end of analysis period (12 th August 2017). Time lost to follow-up was measured from the date of LTFU to the earliest date re-engagement, out-migration, death or end of analysis period (12 th August 2017). Duplicate records were checked in the data set to identify those who had transferred to another health facility (both self and documented transfers). Migration was considered if an individual was reported to have left the Agincourt HDSS study area. Re- engagement was defined as the resumption of clinic visits in the same clinic or another clinic. 2
Statistical methods With LTFU defined as more than 90 days late for a scheduled appointment, we used competing risks survival analysis to estimate the cumulative incidence of (i) LTFU, (ii) transferring out to another facility and (iii) death after initiating ART. The complement of these three probabilities represents the proportion of patients that are still living in the Agincourt HDSS and taking ART. We used competing risks survival analysis to also estimate the cumulative incidence of (i) re-engagement, (ii) death and (iii) migration out of Agincourt HDSS after LTFU. The complement of these three probabilities represents the proportion of patients that are LTFU but still alive, living in the study site, and not on treatment elsewhere. We used clinical data from the eight facilities to calculate naïve estimates of mortality among PLHIV who start ART, and to adjust the mortality estimates after incorporating LTFU outcomes from linked datasets. We used Cox regression to estimate cause-specific hazard ratios for LTFU. To model the hazard of LTFU after starting ART, patients were right censored at the time of death or transferring out. Covariates with evidence of association were included in the adjusted model. Schoenfeld residuals were used to evaluate proportional hazards assumption. 3
Results Table 1. Characteristics of patients initiating ART in the Agincourt sub-district in Mpumalanga Province, South Africa Patient characteristic Total n=1,138 % Patient category Men 242 21.2 Women (non-pregnant) 704 61.9 Women (pregnant or breastfeeding) 192 16.9 Nationality South African 752 66.0 Other nationalities 386 34.0 Age at ART/ care initiation (in years) <1-19 61 5.4 20-29 319 28.0 30-39 356 31.3 40-49 214 18.8 ≥50 187 16.5 Missing 1 Year of ART/care initiation 2014 132 11.6 2015 415 36.5 2016 548 48.1 2017 43 3.8 Baseline CD4 count <350 cells/µL 722 65.1 351-500 cells/µL 244 22.0 >501 cells/µL 143 12.9 Missing 29 WHO stage at ART initiation WHO I/II 1,008 91.1 WHO III/IV 99 8.9 Missing 31 Tuberculosis at ART initiation Yes 29 2.6 No 1,092 97.4 Missing 17 Baseline characteristics There were more women (78.8%) than men (21.2%) who initiated ART. In total, 66% were South Africans and 78.1% were between 20 and 49 years old. Median age was 39.4 years [IQR: 31.4-47.2 years] among men, 36.1 years [IQR: 28.5-47.1 years] among non-pregnant women and 27.4 years [IQR: 23.3-32.2 years] among pregnant women. The highest number of patients initiated ART in 2016 (48.1%) compared to 2015 (36.5%) and 2014 (11.6%). Overall, 91.1% of the patients were in WHO stage I/II and 2.6% were co-infected with TB at ART initiation. The median follow-up time for these patients on treatment was 13.9 months 4
[IQR: 0.2-44.5 months]. The 1,138 patients have contributed a total of 1,369 person-years on ART. Outcomes of patients initiating ART Cumulative incidence of LTFU (n=456) was 25.8% (23.1%-28.5%) at one year and 37.0% (33.6%-40.4%) at two years following ART initiation (see Figure 1). The cumulative incidence of transferring out (n=12) was 0.9% (0.4%-2.5%) at one year after initiation of ART. The cumulative incidence of death (n=27) was 1.57% (0.93%-2.48%) at one year after initiation of ART and 3.2% (2.04%-4.64%) at two years. Overall, 25 of 27 (93%) patients died within three months of their last clinic visit, median number of days was 37 [IQR: 20-58 days]. Outcomes of patients initiating ART .6 .5 .4 .3 .2 .1 0 0 1 2 3 4 Years since initiating ART Lost to follow-up Dead TransferOut Figure 1. Cumulative incidence curves of outcomes of patients after ART initiation Outcomes of patients LTFU Among those LTFU, cumulative incidence of re-engaging (n=103) which was 24.4% (19.6%- 29.6%) at one year after being LTFU and 42.4% (33.9%-50.6%) at two years (see Figure 2). Cumulative incidence of out-migration (n=77) was 13.4% (9.6%-17.9%) at one year after being LTFU and 32.0% (24.0%-40.4%) at two years. Only one patient died and two patients never returned after initiating ART. 5
Outcomes of patients LTFU .6 .5 .4 .3 .2 .1 0 0 1 2 3 4 Years lost to follow-up Reengaged Dead Migrated Figure 2. Cumulative incidence curves of outcomes of patients LTFU Risk factors associated with loss to follow-up among ART patients Factors associated with LTFU among ART patients are presented in Table 2. In crude and adjusted analysis, being pregnant was associated with a higher risk of LTFU (HR: 2.30; 95% CI 1.80-2.93, AHR: 1.78; 95% CI 1.38-2.30). Being a man was also associated with increased hazard of LTFU in both crude and adjusted analyses (HR: 1.48; 95% CI 1.15-1.90, AHR: 1.55; 95% CI 1.20-2.00). Younger age was associated with higher incidence of LTFU, and those above age 50 had lower LTFU. Other individual attributes such as nationality or the WHO staging were not associated with LTFU. Evaluation of Schoenfeld residuals suggested no violation of the proportional hazards assumption for any of the variables included in the analyses. 6
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