Overall, 82 (8%) patients died after transfer date (Table 2). McCord transferred a far higher proportion of patients than the other sites (21% vs. <6%) and had a higher proportion of deaths among TFOs (9% vs. 6%–8%). The majority of deaths after transfer took place during later ART (>3 months on ART), ranging from 77% (Khayelitsha) to 100% (Themba Lethu). Among transferred patients who died, the median time from ART to TFO was 3.9 months (IQR, 1.1–10.9 months) and was shorter in Khayelitsha and McCord hospital (<3 months) than in the other sites (7–8 months). The median baseline CD4+ cell count in patients who died was nearly half the median count in patients who survived (53 vs. 94 cells/μL, P = 0.005).
The proportion LTF varied by site from 8% in Khayelitsha to 19% in Themba Lethu (Table 2). Mortality among LTF patients was 37% (n = 972) (Table 2), ranging from 33% to 43% (Khayelitsha and McCord, respectively). Mortality was high during later ART (51%–71%). Among those who died during later ART, the majority of deaths occurred within 3 months of the LTF date, ranging from 58% (Themba Lethu) to 79% (Khayelitsha), P < 0.001. The median time from ART enrollment to LTF in patients who died was 2.3 months. The median baseline CD4+ cell count in LTF patients who died was about half that of patients who survived (59 vs. 104 cells/μL, P < 0.001).
Transfer was predicted by CD4+ cell count and site of ART initiation (see Table S3, Supplemental Digital Content,http://links.lww.com/QAI/A546). In multivariable analysis, patients with a CD4+ cell count ≥200 cells per microliter were less likely to be TFO than patients with a baseline count <25 cells per microliter [adjusted hazard ratio (aHR), 0.74; 95% confidence interval (CI): 0.60 to 0.91]. McCord was 5 times as likely as Khayelitsha to transfer patients (aHR, 5.24; 95% CI: 4.27 to 6.42) and Hlabisa and Themba Lethu less likely (respectively, aHR, 0.74; 95% CI: 0.60 to 0.91 and aHR, 0.70; 95% CI: 0.57 to 0.87) (see Table S3, Supplemental Digital Content,http://links.lww.com/QAI/A546). The effect of baseline CD4+ cell count on the likelihood of transfer was different in McCord from the other cohorts (P = 0.04). Patients with higher baseline CD4 counts were less likely to be transferred except at McCord where CD4 count was not associated with risk of transfer. TFOs increased cumulative program loss by 24 months from 22% to 28% compared with accounting only for mortality and LTF (Figure 2A).
Figure 2B shows mortality with LTF and TFO treated as time varying to remove potential survivor bias (presented separately by cohort in Figure S2, Supplemental Digital Content,http://links.lww.com/QAI/A546). Patients TFO had higher mortality than patients retained. At 24 months on ART, 21% of the TFO patients compared with 8% of those retained had died. Patients who were LTF had extremely high early mortality. Twenty four months after starting ART, cumulatively 81% of LTF patients had died.
In crude analysis, mortality was associated with male gender, age, baseline CD4+ cell count, cohort, and having been TFO or LTF (Table 3, model 1). Men were more likely than women to die (hazard ratio [HR], 1.65; 95% CI: 1.52 to 1.79). The risk of death increased with age, and adults aged older than 45 years compared with those 16–24 years had the highest mortality risk (HR, 1.58; 95% CI: 1.30 to 1.93). Mortality was inversely associated with baseline CD4+ cell count (HR, 0.32; 95% CI: 0.26 to 0.39; CD4+ cell count ≥200 vs. 0–24 cells/μL). Compared with Khayelitsha, patients in McCord and Hlabisa had higher crude mortality risk (HR = 2.26 and 1.48, respectively) and Themba Lethu patients had comparable risk (HR, 1.06; 95% CI: 0.94 to 1.22). In comparison with patients retained, the risk of death was higher for LTF (HR, 20.2; 95% CI: 18.5–22.05) and TFO patients (HR, 3.71; 95% CI: 2.96 to 4.65).
In multivariable analysis of the total time period, adjustment for baseline characteristics, site of ART initiation, and TFO/LTF status attenuated the association between mortality and male gender (aHR, 1.19; 95% CI: 1.09 to 1.31) and strengthened the association with age (Table 3, model 1). The impact of ART initiation site on mortality varied: the effect at Hlabisa and McCord was attenuated after controlling for the effect of LTF on mortality (see model 2, Table S3, Supplemental Digital Content,http://links.lww.com/QAI/A546), with little additional impact of controlling for the effect of TFO on mortality (see model 3, Table S3, Supplemental Digital Content,http://links.lww.com/QAI/A546). In Themba Lethu, adjustment for baseline characteristics reduced mortality estimates relative to other cohorts (aHR, 0.59; 95% CI: 0.51 to 0.69), with or without the inclusion of deaths among patients TFO or LTF. In sensitivity analyses, mortality was extremely high in the 3-month period directly after LTF/TFO: aHR, 32.21 (95% CI: 29.18 to 35.56) and aHR, 3.54 (95% CI: 2.52 to 4.98) for LTF and TFO, respectively, compared with retained (Table 3, model 2). Excluding deaths in the 3 months after TFO and LTF substantially changed mortality estimates for the effect of ART initiation site and TFO/LTF status (Table 3, model 3). Compared with Khayelitsha, the effect of Hlabisa and McCord was strengthened and the effect of Themba Lethu was attenuated. Compared with patients retained, the risk among LTF was reduced from aHR of 22.03 to aHR of 2.85 (95% CI: 2.43 to 3.33), and patients who were TFO had comparable mortality risk (aHR, 0.75; 95% CI: 0.54 to 1.03). In testing for interaction, Khayelitsha had higher mortality after TFO than the other cohorts, consistent with Figure S1 (see Supplemental Digital Content,http://links.lww.com/QAI/A546). However, the impact of these additional deaths on mortality in the cohort was negligible (see Figure S2, Supplemental Digital Content,http://links.lww.com/QAI/A546).
Figure 2C shows the impact of correcting mortality estimates through NPR linkage. In all cohorts, crude (site-reported) mortality substantially underestimated mortality. Correction for deaths among patient LTF substantially increased mortality estimates, whereas additionally accounting for unascertained deaths in the smaller proportion of patients TFO had a limited effect on overall cumulative mortality estimates.
In this analysis of 19,481 ART-naive adults with civil identification numbers starting ART between 2004 and 2009, patients who were transferred had higher mortality than patients retained at the ART initiation site. Mortality among TFOs was low, with one-third occurring in the 3-month period directly after TFO. Mortality after LTF was far higher, particularly during early ART and in the period directly after LTF. Excluding the mortality directly after TFO/LTF, the mortality risk among patients TFO and retained was similar, but patients LTF had 3 times the mortality risk of those retained. After linkage to the NPR, correction for deaths among patients TFO had limited impact on mortality, but correction for LTF substantially increased mortality estimates. The inclusion of deaths after LTF, but not those after TFO, changed the association between treatment cohort and mortality.
The South African ART program has undergone rapid expansion since its inception in 2004. In 2009–2010, a total of 550 accredited facilities were established to offer ART.19 By 2012, 3686 facilities (80% of the total) offered ART.20 This expansion in facilities offering ART has provided more opportunity for TFOs within the health system. Indeed, the probability of TFO at 1 year increased from 1.4% in patients enrolled 2002–2004 to 8.9% in patients enrolled in 2009.12 Lowering the threshold of ART eligibility is likely to increase patient numbers further11 and may mean that more mobile individuals are enrolled on ART. In addition, evidence suggests that even in low-income countries, patients actively seek better quality health care despite higher costs if they believe that this may improve their outcomes.21 There is thus a need for a robust system to ensure that patients who are TFO successfully reengage in care in another facility without increased risk of mortality. Our study highlighted a number of issues related to patient transfers that have programmatic implications.
First, over one-third of deaths in those TFO during later ART occurred in the 3-month period directly after TFO. It is plausible that patients may have been requesting transfer at a time of severe illness to be cared for at home or in the expectation of death22 or may have been actively transferred to better equipped services due to illness. South Africa has a long history of circular labor migration. Individuals leave home to find work in urban areas and return home to receive care and to die in rural areas where their families remain.23 Our findings suggest the need for close monitoring after TFO to ensure that TFO patients have successfully linked to care and for the rapid recall of lost patients.
Second, patients who were transferred had comparable or lower mortality than those retained beyond the 3 months after transfer (aHR, 0.75; 95% CI: 0.54 to 1.03; Table 3, model 2). This is important new information on a group of patients whose outcomes have been largely unknown. Our finding differs from a Malawi study that reported improved survival among TFOs compared with patients retained for more than 24 months (5% vs. 12%).24 Our results suggest that once patients have stabilized on treatment, TFO may not impact on mortality or that beyond the early months on ART stable patients are more likely than others to be transferred. Indeed, in Malawi, patients who were transferred had less-advanced clinical stage of disease and better survival than those retained.
Third, some transfers may have been due to resource constraints among patients battling to access health care. For example, McCord Hospital transferred a far larger proportion of patients and experienced far higher mortality after TFO. In this cohort, patients were required to make a small co-payment toward their treatment. The co-payment covered all HIV-related outpatient care for the month, with no additional costs for any investigation or treatment, but did not cover inpatient admission care. Previous research has found free provision of ART associated with lower mortality in low-income countries.25 It is plausible that patients requesting transfer in this cohort were unable to afford even the small co-payment, which increased from R120 per consultation in 2005 to R140 in 2008.
Fourth, our study confirms the major threat that LTF poses to program effectiveness.4,10,26–29 Almost half of the LTF patients had died, mostly within 3 months of being LTF. The timing of deaths was similar to deaths after TFO, but mortality was far higher. Even excluding the 3-month period directly after LTF, LTF patients still had nearly 3 times the risk of death compared with retained patients (aHR, 2.85; 95% CI: 2.43 to 3.33). Retention in chronic HIV care has long been recognized as a major challenge,10,13,28,30,31 with LTF increasing by calendar year as ART programs scale-up enrollment.10,20,31 Numerous strategies that have been proposed to retain patients in care include decentralizing ART provision and taskshifting,32 reducing clinic caseloads,33 and managing ART at community-34 and home-level.35 Urgent attention is needed to prevent LTF particularly in the first few months on ART.
Finally, although in all cohorts the median CD4+ cell count at ART initiation was lower among TFOs and LTF than those retained, there was substantial heterogeneity across sites including in the proportion TFO/LTF, the incidence of mortality, the median time from ART initiation to TFO/LTF in patients who died, and the median baseline CD4+ cell count in patients who died compared with those who survived. Such variability suggests that although TFO is reported as a single outcome, it may have different meanings in different sites, which may impact on mortality. Sites need to understand what TFO means in their own context.
In addition to the implications for patient care, our analysis has implications in terms of program evaluation. Accurate ascertainment of mortality poses major challenges, particularly in large programs in developing countries with limited capacity to actively follow patients. In the absence of additional outcome ascertainment, many studies censor the follow-up time of patients who are TFO, assuming that mortality is the same as among patients retained. Using linkage, our study provides evidence that in a context of low TFO rates and mortality rates after TFO, censoring follow-up time at TFO date did not lead to a substantial underestimation of mortality. In contrast, including deaths among LTF compared with censoring at the time of LTF impacted on mortality estimates and the effect of baseline characteristics as well as cohort on mortality. Thus, if the proportion transferred and the event rates were higher, it might be necessary to analytically treat TFO in a similar way to LTF. In situations where additional outcome information is available from, for example, tracing studies or linkage to a population register, statistical methods such as inverse probability weighting and/or multiple imputation can be used.36 In the absence of such information, options include the use of a nomogram to correct mortality estimates7 and use of selection and pattern-mixture models.37
So what are the implications of our findings, particularly in the context of policy initiatives to test and treat all people with HIV? The study highlights the need for improved follow-up especially in the months after TFO and LTF. South Africa has a number of factors that should support good patient follow-up. ART is widely available; by 2013, approximately 80% of all primary health care facilities were offering ART services and this number seems to increase.38 In addition, there are standardized ART guidelines that are widely disseminated, ensuring a fairly unified approach to treatment across facilities and providers. These guidelines could be substantially strengthened by a standardized approach to transferring and following patients, with a particular focus on the timing of patients transferred. We recommend (1) extreme caution in transferring patients until they are clinically stable on ART; (2) prompt and comprehensive reassessment at the receiving facility to ensure continuity of care, not only in terms of ART but also for comorbid conditions, particularly in the 3-month period directly after TFO/LTF; (3) prompt follow-up of patients who are LTF; and (4) a single patient identifier for all health facilities and much improved national health information systems to support monitoring and evaluation.
To our knowledge, this is the first study to report mortality among patients TFO, using data from linkage to the NPR. Most analyses from large ART programs underestimate mortality because of high LTF and poor vital registration. This study was strengthened by our ability to explore the vital status of patients after leaving a program, which would generally only be possible by undertaking expensive tracing studies with limited success. The analysis only included patients with ID numbers, ensuring good mortality ascertainment. A limitation is that patients with ID numbers may have been different from those without IDs, which may have led to some underestimation of the true mortality after TFO. However, in sensitivity analysis, there was no evidence of any substantial differences between those with and those without IDs (see Table S5, Supplemental Digital Content,http://links.lww.com/QAI/A546). A further limitation is the possible misclassification of outcomes. Patients classified as LTF may be silent TFOs, whereas some patients LTF may be incorrectly classified as TFO. In addition, different reasons for patient transfer may independently impact on mortality risk but cohorts did not capture patients' reasons for TFO. Finally, because of the observational nature of the study, we were unable to determine whether the increased risk of mortality in those transferred was causally related to the transfer itself or was related to unmeasured characteristics of the individuals transferred, or information bias due to misclassification.
In summary, improved administrative and clinical procedures to ensure continuity and quality of care for patients TFO and LTF are needed. As the proportion of patients TFO grows, it will become increasingly important in cohort analyses to consider the potential for differential outcomes in TFO patients.
1. Cornell M, et al.. Monitoring the South African national antiretroviral treatment programme, 2003-2007: the IeDEA southern africa collaboration. S Afr Med J. 2009;99:653–660.
2. Cornell M, et al.. Gender differences in survival among adult patients starting antiretroviral therapy
in South Africa: a multicentre cohort study. Plos Med. 2012;9:e1001304.
3. Brinkhof MWG, et al.. Mortality
of HIV-infected patients starting antiretroviral therapy
in sub-Saharan Africa: comparison with HIV-unrelated mortality
. Plos Med. 2009;6:e1000066.
4. van Cutsem G, et al.. Correcting for mortality
among patients lost to follow-up
on antiretroviral therapy
in South Africa: a cohort analysis. PLoS One. 2011;6:e14684.
5. Yu J, et al.. True outcomes for patients on antiretroviral therapy
who are “lost to follow-up
” in Malawi. Bull World Health Organ. 2007;85:550–554.
6. Chi BH, et al.. Universal definition of loss to follow-up in HIV treatment programs: a statistical analysis of 111 facilities in Africa, Asia, and Latin America. Plos Med. 2011;8:e1001111.
7. Egger M, et al.. Correcting mortality
for loss to follow-up: a nomogram applied to antiretroviral treatment programs in sub-Saharan Africa. Plos Med. 2011;8:e1000390.
8. Fenner L, et al.. Early mortality
and loss to follow-up in HIV-infected children starting antiretroviral therapy
in Southern Africa. J Acquir Immune Defic Syndr. 2010;54:524–532.
9. Geng EH, et al.. Sampling-based approach to determining outcomes of patients lost to follow-up
in antiretroviral therapy
scale-up programs in Africa. JAMA. 2008;300:506–507.
10. Cornell M, et al.. Temporal changes in programme outcomes among adult patients initiating antiretroviral therapy
across South Africa, 2002-2007. AIDS. 2010;24:2263–2270.
11. Mutevedzi PC, Lessells RJ, Newell M-L, Disengagement from care in a decentralised primary health care antiretroviral treatment programme: cohort study in rural South Africa. Trop Med Int Health. 2013;n/a-n/a.
12. Nglazi MD, et al.. Increasing transfers
-out from an antiretroviral treatment service in South Africa: patient characteristics and rates of virological non-suppression. PLoS One. 2013;8:e57907.
13. Auld AF, et al.. Four-year treatment outcomes of adult patients enrolled in Mozambique's rapidly expanding antiretroviral therapy
program. PLoS One. 2011;6:e18453.
14. Banda AC, et al.. Antiretroviral therapy
in the Malawi Defence Force: access, treatment outcomes and impact on mortality
. PLoS One. 2008;3:e1445.
15. Bussmann H, et al.. Five year outcomes of initial patients treated in Botswana's national antiretroviral treatment program. AIDS. 2008;22:2303–2311.
16. Wandeler G, et al.. Outcomes of antiretroviral treatment programs in rural Southern Africa. J Acquir Immune Defic Syndr. 2012;59:e9–e16.
17. Rosen S, Fox MP, Gill CJ, Patient retention in antiretroviral therapy
programs in sub-Saharan Africa: a systematic review. Plos Med. 2007;4:e298.
18. Ekouevi DK, et al.. Low retention of HIV-infected patients on antiretroviral therapy
in 11 clinical centres in West Africa. Trop Med Int Health. 2010;15:34–42.
19. Department of Health, Department of Health Annual report 2009/10, 2010.
20. WHO, UNICEF, and UNAIDS, Global Update on HIV Treatment 2013: Results, Impact and Opportunities. Geneva, Switzerland: WHO, UNICEF, and UNAIDS; 2013.
21. Leonard KL. Active patients in rural African health care: implications for research and policy. Health Policy Plan. 2013;1–11.
22. Welaga P, et al.. Coming home to die? The association between migration and mortality
in rural South Africa. BMC Public Health. 2009;9:193.
23. Clark SJ, et al.. Returning home to die: circular labour migration and mortality
in South Africa. Scand J Public Health Suppl. 2007;69:35–44.
24. Yu JK-L, et al.. What happens to patients on antiretroviral therapy
who transfer out to another facility? PLoS One. 2008;3:e2065.
25. Braitstein P, et al.. Mortality
of HIV-1-infected patients in the first year of antiretroviral therapy
: comparison between low-income and high-income countries. Lancet. 2006;367:817–824.
26. Brinkhof M, et al.. Early loss of HIV-infected patients on potent antiretroviral therapy
programs in lower-income countries. Bull World Health Organ. 2008;86:559–567.
27. Fox MP, Rosen S. Patient retention in antiretroviral therapy
programs up to three years on treatment in sub-Saharan Africa, 2007–2009: systematic review. Trop Med Int Health. 2010;15:1–15.
28. Harries AD, et al.. Strategies to improve patient retention on antiretroviral therapy
in sub-Saharan Africa. Trop Med Int Health. 2010;15:70–75.
29. Tweya H, et al.. Early active follow-up of patients on antiretroviral therapy
(ART) who are lost to follow-up
: the “Back-to-Care” project in Lilongwe, Malawi. Trop Med Int Health. 2010;15:82–89.
30. Boyles TH, et al.. Factors influencing retention in care after starting antiretroviral therapy
in a rural South African programme. PLoS One. 2011;6:e19201.
31. Brinkhof MWG, Pujades-Rodriguez M, Egger M, Mortality
of patients lost to follow-up
in antiretroviral treatment programs in resource-limited settings: systematic review and meta-analysis. PLoS One. 2009;4:e5790.
32. Long L, et al.. Treatment outcomes and cost-effectiveness of shifting management of stable ART patients to nurses in South Africa: an observational cohort. Plos Med. 2011;8:e1001055.
33. Fatti G, et al.. The effect of patient load on antiretroviral treatment programmatic outcomes at primary health care facilities in South Africa: a multicohort study. J Acquir Immune Defic Syndr. 2011;58:E17–E19.
34. Decroo TMD, et al.. Distribution of antiretroviral treatment through self-forming groups of patients in Tete Province, Mozambique. J Acquir Immune Defic Syndr. 2011;56:e39–e44.
35. Jaffar S, et al.. Rates of virological failure in patients treated in a home-based versus a facility-based HIV-care model in Jinja, southeast Uganda: a cluster-randomised equivalence trial. Lancet. 2009;374:2080–2089.
36. Schomaker M, et al.. Non-ignorable loss to follow-up: correcting mortality
estimates based on additional outcome ascertainment. Stat Med. 2013.
37. Little R. In: Fitzmaurice G, et al., eds. Selection and Pattern-mixture Models, in Longitudinal Data Analysis. CRC Press; 2009:409–432.
38. Department of Health, The National Health Care Facilities Baseline Audit: National Summary Report 2012. Durban, South Africa: 2013.
antiretroviral therapy; mortality; transfers; lost to follow-up
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