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Keywords:

  • pre-ART;
  • linkage to care;
  • sub-Saharan Africa;
  • mortality;
  • loss to follow-up
  • pré-ART;
  • liens avec les soins;
  • Afrique subsaharienne;
  • mortalité;
  • perte au suivi
  • pre-TAR;
  • nexo con atención médica;
  • África subsahariana;
  • mortalidad;
  • pérdida durante el seguimiento

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Objectives  To assess the proportion of patients lost to programme (died, lost to follow-up, transferred out) between HIV diagnosis and start of antiretroviral therapy (ART) in sub-Saharan Africa, and determine factors associated with loss to programme.

Methods  Systematic review and meta-analysis. We searched PubMed and EMBASE databases for studies in adults. Outcomes were the percentage of patients dying before starting ART, the percentage lost to follow-up, the percentage with a CD4 cell count, the distribution of first CD4 counts and the percentage of eligible patients starting ART. Data were combined using random-effects meta-analysis.

Results  Twenty-nine studies from sub-Saharan Africa including 148 912 patients were analysed. Six studies covered the whole period from HIV diagnosis to ART start. Meta-analysis of these studies showed that of the 100 patients with a positive HIV test, 72 (95% CI 60–84) had a CD4 cell count measured, 40 (95% CI 26–55) were eligible for ART and 25 (95% CI 13–37) started ART. There was substantial heterogeneity between studies (P < 0.0001). Median CD4 cell count at presentation ranged from 154 to 274 cells/μl. Patients eligible for ART were less likely to become lost to programme (25%vs. 54%, P < 0.0001), but eligible patients were more likely to die (11%vs. 5%, P < 0.0001) than ineligible patients. Loss to programme was higher in men, in patients with low CD4 cell counts and low socio-economic status and in recent time periods.

Conclusions  Monitoring and care in the pre-ART time period need improvement, with greater emphasis on patients not yet eligible for ART.

Objectifs:  Evaluer la proportion de patients perdus du programme (décédés, perdus de vue, transférés) entre le diagnostic du VIH et le début de la thérapie antirétrovirale (ART) en Afrique subsaharienne, et déterminer les facteurs associés aux perdus du programme.

Méthodes:  Revue systématique et méta-analyse. Nous avons cherché dans les bases de données PubMed et EMBASE pour les études chez des adultes. Les objectifs étaient le pourcentage de patients qui décèdent avant de commencer l’ART, le pourcentage de perdus au suivi, le pourcentage avec un taux de CD4 mesuré, la distribution des taux initiaux de CD4 et le pourcentage de patients admissibles à commencer l’ART. Les données ont été combinées en utilisant la méta-analyse des effets aléatoires.

Résultats:  29 études d’Afrique subsaharienne, portant sur 148.912 patients ont été analysées. 6 études couvraient toute la période allant du diagnostic du VIH au début de l’ART. La méta-analyse de ces études a montré que sur 100 patients ayant un test positif pour le VIH, 72 (IC95%: 60-84) avaient un taux de CD4 mesuré, 40 (IC95%: 26-55) étaient éligibles pour l’ART et 25 (IC95% : 13-37) ont commencé l’ART. Il y avait une hétérogénéité importante entre les études (P < 0.0001). Le taux médian de CD4 lors de la présentation variait de 154 cellules/μl à 274 cellules/μl. Les patients éligibles pour l’ART étaient moins susceptibles d’être perdus du programme (25% versus 54%, P < 0.0001), mais ils étaient plus susceptibles de décéder (11% versus 5%, P < 0.0001) que les patients non éligibles. Les perdus du programme étaient plus nombreux chez les hommes, chez les patients ayant un taux de CD4 bas et un statut socio-économique faible, et dans les périodes récentes.

Conclusions:  La surveillance et les soins dans la période pré-ART doivent être améliorés en mettant davantage l’accent sur les patients non encore éligibles pour l’ART.

Objetivos:  Evaluar la proporción de pacientes perdidos del programa (muertos, perdidos durante el seguimiento, transferidos fuera del área) entre el diagnóstico del VIH y el comienzo de la terapia antirretroviral (TAR) en África subsahariana, y determinar los factores asociados con la pérdida del programa.

Métodos:  Revisión sistemática y meta-análisis. Hemos buscado en las bases de datos de PubMed y EMBASE, estudios en adultos. Los resultados eran el porcentaje de pacientes que morían antes de comenzar TAR, el porcentaje perdido durante el seguimiento, el porcentaje con un conteo de células CD4, la distribución de los primeros conteos de CD4 y el porcentaje de pacientes que, cumpliendo criterios, iniciaban el TAR. Los datos se combinaron utilizando un meta-análisis de efectos aleatorios.

Resultados:  Se analizaron 29 estudios en África subsahariana, incluyendo 148 912 pacientes. 6 de los estudios cubrían el periodo completo, desde el diagnóstico del VIH al inicio del TAR. Un meta-análisis de estos estudios mostró que de 100 pacientes con una prueba positiva para VIH, 72 (95% IC 60–84) tenían un conteo de células CD4 medidas, 40 (95% IC 26–55) cumplían criterios para iniciar TAR y 25 (95% IC 13–37) comenzaron TAR. Había una heterogeneidad sustancial entre estudios (P < 0.0001). La mediana de conteo de células CD4 en el momento de presentarse para el estudio estaba entre 154 células/μl a 274 células/μl. Los pacientes con criterios para iniciar TAR tenían menos posibilidad de perderse durante el programa (25% versus 54%, < 0.0001) pero también tenían un mayor probabilidad de morir (11% versus 5%, P < 0.0001) que los pacientes que no cumplían criterios. La pérdida del programa era mayor entre hombres, en pacientes con un bajo conteo de células CD4 y un bajo estatus socio-económico, y en periodos de tiempo recientes.

Conclusiones:  La monitorización y los cuidados durante el tiempo pre-TAR necesita mejoras, con un mayor énfasis en pacientes que aún no cumplen criterios para iniciar TAR.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Attrition of HIV infected patients in care before starting antiretroviral therapy (ART) is high in low-income settings (Amuron et al. 2009; Bassett et al. 2009). However, much research has focused on clinical outcomes of ART, while few studies have analysed loss to programme (i.e. because of mortality, loss to follow-up or transfer out to another site) between HIV testing and ART initiation. Clinical documentation is often poor or non-existent during this time period. Healthcare systems are overloaded, testing might take place at a different site than the provision of ART and patients ineligible for ART are less sick and do not require monitoring of ART-related side effects. A recent systematic review showed substantial variation in loss to programme across sites, but did not distinguish between loss to follow-up, mortality and transfer out (Rosen & Fox 2011). Only two studies covered the whole time period from diagnosis of HIV infection to ART initiation (Kranzer et al. 2010; Tayler-Smith et al. 2010), and predictors of loss to programme were not explored.

Despite the fact that ART coverage in sub-Saharan Africa has increased substantially in recent years and had reached 6.6 million people by the end of 2010, approximately 9 million people remain untreated (World Health Organisation 2011). The majority of the patients who start ART do so too late with low CD4 cell counts and opportunistic infections (Fairall et al. 2008; Keiser et al. 2008; Kigozi et al. 2009). As a result, mortality in the first few months after therapy start is also high (Braitstein et al. 2006; May et al. 2010).

If the reasons for patient attrition between diagnosis and beginning of ART were known, programmes could plan more efficiently and better allocate their resources. ART coverage would be increased, and early mortality on ART would be reduced. Our goal was to ascertain why patients are lost to programme before they begin ART. We therefore performed a systematic review to determine the magnitude and predictors of mortality, loss to follow-up and transfer out between HIV diagnosis and start of ART in sub-Saharan Africa.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Data sources

We searched PubMed and EMBASE databases on August 9 2011, limiting the search to studies in humans, studies from sub-Saharan Africa and to English-language publications. We also limited the search to studies that were published after 2001, because ART scale up in resource-limited settings began in 2002 (Gilks et al. 2006; Keiser et al. 2008). We used both free text words and medical subject headings (MeSH) and a combination of the following words and their variations: antiretroviral agents, therapeutic use, pre-treatment, pre-ART, prior to treatment, eligibility, loss to care and loss to follow-up. We examined the references of all included studies. Further details of the search strategy are given in the Appendix S1.

Study selection

We included all studies that reported on numbers of participants followed between HIV diagnosis and start of ART, including studies that did not cover the entire period. We excluded studies on children and on the prevention of mother-to-child transmission (PMTCT). We also excluded qualitative studies and reports from national programmes. Studies that reported on specific topics that were not the primary area of interest (i.e. modelling studies without primary data, studies on drug resistance, adherence or drug interactions, cancer-specific studies or studies on pre-ART HIV transmission) were also excluded. We used no other selection criteria. Two reviewers (CM, OK) independently assessed the eligibility of articles and abstracts. Discrepancies were resolved by consensus.

Data extraction

To minimise transcription errors, we used a double-entry system to enter data from each publication into a standardised extraction sheet. The following data were extracted: eligibility criteria of participants, the characteristics of the programme (setting, location, country), characteristics of participants (age, gender and CD4 cell count at different time points), eligibility criteria for ART start, methods for tracing patients lost to follow-up and the number of patients alive and lost to programme (i.e. lost to follow-up, transferred out and dead) at different time points. We selected four time points: (i) HIV testing, (ii) CD4 testing with eligibility assessment for ART, (iii) becoming eligible for ART, and (iv) start of ART. These defined three time periods: stage 1 (HIV testing to CD4 testing), stage 2 (CD4 testing to ART eligibility) and stage 3 (ART eligibility to ART start). We extracted loss to programme, mortality, loss to follow-up and transfer out at each stage before the initiation of ART. In addition, we extracted the length of time between these time points and mortality rates in the different stages. Further we also extracted predictors for loss to programme, mortality and loss to follow-up between HIV and CD4 testing and between meeting eligibility criteria for ART and ART start. We recorded whether there was a significant (P < 0.05) positive or negative association or whether there was no significant association. Discrepancies were resolved by consensus. Data were entered into an EpiData database (version 3.1.).

Statistical analysis

We calculated the percentage of people who reached each of the four time points and combined data from relevant studies using random-effects meta-analysis on the logit scale. Combined estimates were transformed back to percentages. Because results were heterogeneous, we both calculated approximate prediction intervals (PrI) based on the whole random-effects distribution and traditional 95% confidence intervals (CI) around the mean of the distribution. PrI predict the likely mean percentages in new studies and are the most sensible way to summarise the results of heterogeneous studies (Higgins et al. 2003). We examined sources of heterogeneity using meta-regression for treatment-eligible patients starting ART (stage 3; the only stage for which enough estimates were available). We considered the following study characteristics: country (South Africa vs. others); degree of urbanisation (rural, urban, semi-urban); mode of entry into programme (voluntary counselling and testing, provider-initiated counselling and testing, entry through tuberculosis (TB) or sexually transmitted disease clinic); programme costs (free vs. fee for service); ART eligibility criteria (CD4 cell count ≤ 200 vs. > 200 cells/μl); age (median age at baseline); gender (percentage female at baseline); and study period (1 January 2001–31 December 2003, 1 January 2004–31 December 2006, ≥1 January 2007). Data were analysed using STATA version 11.2 (StataCorp, TX, USA).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Study characteristics

We identified 81 potentially eligible full-text articles of 2122 identified studies, based on titles and abstracts. After screening the full-text articles, we included 29 studies of 148 912 treatment naïve patients. Twenty-five studies were included in the meta-analysis. Four studies were excluded because they reported on the same study population and time period as another article that was published later (Lawn et al. 2005; Kaplan et al. 2008; Larson et al. 2010a; Losina et al. 2010). The selection of the studies is shown in detail in a flowchart in the Appendix (Figure S1).

Table 1 summarises the characteristics of the 29 studies included in our review. The majority (n = 16) were single-site studies of public sector ART programmes; 14 were performed in South Africa. Twenty-two studies included patients from the general population, and 11 were performed in urban sites only. Ten studies reported on ART eligible participants, 15 on eligible and not yet eligible participants and two only on patients not yet eligible for ART. Eligibility criteria for ART differed between studies. A CD4 threshold of 200 cells/μl was most commonly used (n = 18). Other CD4 thresholds were 250 (n = 4) and 300 cells/μl (n = 1). Two studies did not report on eligibility. The definition of loss to follow-up (LTFU) also varied: ‘missing appointments for more than 1 month’ or ‘no visit at the clinic for 6 or 12 months’ was used in several studies (Table 1). Phone calls, home visits or linkage to the death registry was used to ascertain deaths among patients lost to follow-up.

Table 1. Characteristics of studies included in review, first CD4 cell count measurement after HIV diagnosis and mortality rates prior to initiation of antiretroviral therapy (ART)
StudyLocationSettingStudy PeriodPopulationNo patients ART naiveEligibility criteria: CD4 cell count/WHO stageEligibility status of subjectsDefinition of loss to follow-upTracing of patients lost to follow-up/ascertainment of vital statusFirst CD4 cell count; median (IQR)Pre-ART mortality rate (per 100person-years)
  1. *Most recent year (2006).

  2. †For eligible patients.

  3. ‡Overall pre-ART mortality rate.

  4. §Excluded from meta-analysis.

  5. ¶Only 930 were traced.

  6. TB, tuberculosis; WHO, World Health Organization; n.r., not reported; IQR, interquartile range; CDC, Centers for Disease Control and Prevention.

Amuron et al. (2009) Jinja, UgandaRural, semi-rural2004–2007General4321<200/μl III and IVEligible and not yet eligibleEligible participants not starting ART until 12/2007Visits222 (111–338)*n.r.
Bassett et al. (2010) 2 facilities in Durban, South AfricaSemi-rural, urban2006–2009General1477<200/μlEligible and not yet eligibleNot reachable 6–12 months after enrolmentPhone callsn.r.n.r.
Bassett et al. (2009) Durban, South AfricaUrban2006–2006General501<200/μl III and IVEligibleNot starting ART ≤3 months of scheduled ART training appointmentPhone calls159 (65–299)n.r.
Geng et al. (2010) Mbarara, UgandaRural2009–2010General1309<250/μlEligiblen.r.n.r.n.r.n.r.
Ingle et al. (2010) 28 facilities in Free State Province, South AfricaRural, semi-rural, urban2004–2008General44 844<200/μl IVEligible and not yet eligibleNo visit, length depending on CD4 cell countLinkage to death registry170 (76–318)53.2†, 32.4‡
Kaplan et al. (2008)§Cape Town, South AfricaUrban2002–2007Women only2131n.r.Eligiblen.r.n.r.n.r.n.r.
Kohler et al. (2011) Nairobi, KenyaUrban2005–2008General5854<250/μl III an IVEligible and not yet eligibleNo return to clinic >30 days after scheduled appointmentPhone callsn.r.n.r.
Kranzer et al. (2010) 2 facilities in Cape Town, South AfricaSemi-rural2004–2009Random sample988<200/μlEligible and not yet eligibleNo CD4 cell count within 6 months of HIV test, no ART initiation within 6 months of CD4 cell countn.r.n.r.n.r.
Larson et al. (2010a)§Johannesburg, South AfricaUrban2007–2009General356<200/μlNot yet eligibleNo return ≥52 weeksn.r.n.r.n.r.
Larson et al. (2010b) Johannesburg, South AfricaUrban2008–2009General389<200/μlEligible and not yet eligibleNo completed CD4 test ≤6 weeks, 6–12 weeks, ≤12 weeksn.r.n.r.n.r.
Lawn et al. (2006) Cape Town, South AfricaUrban2002–2005General1235<200/μl IVEligibleOnly for subjects on ARTn.r.n.r.33.6
Lawn et al. (2005)§Cape Town, South AfricaSemi-rural2002–2005General712<200/μl IVEligiblen.r.n.r.n.r.n.r.
Lessells et al. (2011) 16 facilities in Kwa-Zulu-Natal, South AfricaRural2007–2009General, with CD4 cell count >200/μl4223¶<200/μl IVNot yet eligibleNo CD4 cell count ≤13 months of HIV testLinkage to African Demographic Info Systemn.r.n.r.
Losina et al. (2010)§2 facilities in Durban, South AfricaRural, semi-rural, urban2006–2007General454<200/μln.r.No CD4 cell count ≤8 weeks of HIV testn.r.n.r.n.r.
Loubiere et al. (2009) 27 facilities in Cameroonn.r.2006–2007Random sample180<200/μl IVEligible and not yet eligiblen.r.n.r.n.r.n.r.
McGrath et al. (2010) Karonga, Malawin.r.2005–2006General730<250/μl III and IVEligible and not yet eligibleEligible participants not starting ARTVisitsn.r.n.r.
McGuire et al. (2010) 11 facilities in Chiradzulu, MalawiRural2004–2007General1561n.r.Eligible and not yet eligibleMissed appointment >1 monthVisitsn.r.n.r.
Micek et al. (2009) 14 facilities in Beira and Chimoio, MozambiqueUrban2004–2006General3950<200/μl IV III and CD4 < 350/μlEligible and not yet eligiblen.r.n.r.n.r.n.r.
Mulissa et al. (2010) Arba Minch, EthiopiaRural, urban2003–2009General2191n.r.n.r.n.r.n.r.n.r.13.1‡
Murphy et al. (2010) Durban, South AfricaUrban2006–2007Patients presenting with opportunistic infection (OI)49<200/μl IVEligiblen.r.n.r.n.r.n.r.
Ochieng-Ooko et al. (2010) 23 facilities in Kenyan.r.2001–2007General50 275n.r.Eligible and not yet eligibleNo return >6 months if not on ARTn.r.Women: 225 (98–408) Men: 154 (57–302)n.r.
Pepper et al. (2011) Cape Town, South AfricaUrbann.r.TB-infected HIV+176<200 μl IV without extrapulmonary TBEligible and not yet eligibleNot traceable after 6 months of starting TB treatmentn.r.n.r.n.r.
Scott et al. (2011) 133 facilities in Cape Town, South AfricaUrban2010Generaln.r.n.r.Eligible and not yet eligiblen.r.n.r.n.r.n.r.
Tayler-Smith et al. (2011) 3 facilities in Kibera, KenyaUrban2005–2008General2471<200/μl IV and III III with CD4 < 350/μlEligibleMissed appointment >1 monthn.r.n.r.n.r.
Tayler-Smith et al. (2010) Thyolo District, MalawiRural2008–2009General1633<250/μl III and IVEligible and not yet eligiblen.r.n.r.246 (133–414)n.r.
Togun et al. (2011) Banjul, The GambiaRural, urban2004–2010General790<200 μlEligibleMissed appointment >90 daysVisits, Interviewsn.r.21.9
Van der Borght et al. (2009) 16 facilities in Nigeria, Burundi, Rwanda, Democratic Republic of Congo, Congon.r.2001–2007Heineken employees and families428<300/μl IV CDC stage CEligible and not yet eligiblen.r.n.r.274 (139–435)1.6
Zachariah et al. (2011) Nairobi, Kenya and Thyolo MalawiRural, urban2004–2008General14 942<200 μl for Malawi also: III or IV and CD4 < 250/μlEligibleEligible patients missed appointment ≥1 month (appointments >2 months apart)n.r.n.r.n.r.
Zachariah et al. (2006) Thyolo District, MalawiRural2003–2004Registered for TB treatment742III and IVEligiblen.r.n.r.n.r.n.r.

Figure 1 shows the number of studies that reported on different time periods between HIV diagnosis and ART start. Six studies, conducted in four different countries covered the whole time period (Micek et al. 2009; Bassett et al. 2010; Ingle et al. 2010; Kranzer et al. 2010; Tayler-Smith et al. 2010; Kohler et al. 2011) from HIV testing to start of ART. Most studies provided information for the time between ART eligibility and ART start (21 studies, 18 included in meta-analysis). Only a few studies reported on outcomes of patients without CD4 cell counts and on outcomes of patients not yet eligible for ART.

image

Figure 1.  Routes from HIV testing to start of antiretroviral therapy (ART). Shaded boxes show the different pre-ART care points, and shaded circles show the number of studies included in the systematic review (number of studies included in the meta-analysis in parentheses) at each of these stages. The three areas (number 1–3) represent the different stages in the cascade, which are described in more detail in the text (i.e. stage 1: from HIV diagnosis to CD4 testing; stage 2: eligibility assessment; stage 3: from eligibility to start of ART). #Completed: the patient was informed about the CD4 test result, or the CD4 test was carried out within a certain time period after the HIV test; *LTP, loss to programme

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Meta-analyses

Six studies covering period from HIV diagnosis to ART start (stages 1–3).

Figure 2 summarises the meta-analyses of the six studies that provided information on the period spanning HIV infection to start of ART. Of 100 patients who tested positive for HIV, 72 (95% CI 60–84) patients had a CD4 cell count measured, and 40 (95% CI 26–55) were eligible for ART and 25 (95% CI 13–37) started ART.

image

Figure 2.  Percentage of HIV positive patients completing different stages between testing positive for HIV infection and start of antiretroviral therapy (ART). Results from meta-analysis of six studies covering the period from HIV testing to start of ART. The six studies include 58 746 patients (Micek et al. 2009; Bassett et al. 2010; Ingle et al. 2010; Kranzer et al. 2010; Tayler-Smith et al. 2010; Kohler et al. 2011).

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Stage 1: From HIV diagnosis to CD4 testing.

CD4 cell count was measured in 77.6% (95% CI 71.0–84.2) of patients, with substantial between-study heterogeneity (I= 99.9%, P-value <0.0001). The median time from HIV testing to the first CD4 cell count was 56 days in Losina et al. 2010 and 60 days in Micek et al. 2009.Loubiere et al. 2009 reported that 56.8% of patients had their CD4 cell count measured within 30 days of the HIV test, and Larson et al. (2010a) reported that CD4 cell counts were measured on the same day that the HIV test was administered. Kranzer et al. 2010 was the only study assessing the different modes of entry into care during this stage: the median time was between 2 and 3 days, regardless of whether the patients entered via voluntary counselling and testing, via antenatal care or from a TB or sexually transmitted infections (STI) clinic.

Stage 2: Assessment of eligibility for ART.

The median CD4 cell count at presentation to the clinic ranged from 154 (IQR 57–302) to 274 (IQR 139–435) cells/μl in the six studies where this information was provided (Table 1). The percentage of patients with a CD4 cell count who were eligible for ART was 56.5% (95% CI 49.3–63.6), again with substantial between-study heterogeneity (I= 99.5%, P-value <0.0001).

Stage 3: From eligibility to start of ART.

Eighteen studies reported the percentage of patients who started ART after becoming eligible: the overall estimate was 62.9% (95% CI 55.2–70.7); I= 99.7%, heterogeneity P-value <0.0001. In meta-regression, the estimated percentage of patients starting ART increased from 44% to 81% as the percentage of women increased from 50% to 75%, and no other factors were associated with the percentage of patients starting ART. Of note, two programmes in which very few eligible patients started ART (Zachariah et al. 2006; Murphy et al. 2010) included only patients who had entered the HIV programme from a TB clinic. Five of the 25 studies reported on the median duration from becoming eligible to starting ART. The median duration was 22 days (McGrath et al. 2010), 34 days (Lawn et al. 2006), 83 days (Murphy et al. 2010), 95 days (Ingle et al. 2010) and 108 days (Bassett et al. 2009). Figure S2 shows the forest plots from the meta-analyses of all three stages.

Pre-ART loss to programme, loss to follow-up, mortality and transfer out.

Figure 3 shows forest plots of overall loss to programme, loss to follow-up and mortality by ART eligibility. Among eligible patients, 24.6% (95% CI 18.8–30.3) were lost to programme (Figure 3A), whereas among ineligible patients 54.2% (95% CI 42.8–72.0) were lost to programme (Figure 3B). Mortality before treatment initiation in eligible patients was 10.8% (95% CI 4.6–17.0; Figure 3C), with rates ranging from 1.6 to 53.2 per 100 person-years (Table 1). In three studies reporting on patients not yet eligible for ART, 4.8% (95% CI 0–13.0) died (Figure 3D). For loss to follow-up, the corresponding numbers were 13.2% (95% CI 9.3–17.1, Figure 3E) in eligible patients and 57.3% (95% CI 34.3–80.2, Figure 3F) in ineligible patients. Data on transfers out were reported in only few studies: overall, 5.5% (95% CI 0–13.3%) of patients were transferred before ART initiation (two studies).

image

Figure 3.  Meta-analysis of loss to programme, mortality and loss to follow-up during the pre-antiretroviral therapy (ART) phase, according to whether patients were or were not eligible for ART. Panel a/b: percentage of ART eligible/ineligible patients becoming loss to programme (LTP) before ART start. LTP includes mortality, loss to follow-up, transfer out and alive but not in programme. Panel c/d: percentage of ART eligible/ineligible patients dying before ART start. Panel e/f: percentage of ART eligible/ineligible patients becoming lost to follow-up before ART start

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Predictors of mortality, loss to programme, CD4 cell count determination and ART initiation

These results are summarised in Tables S1 and S2. Briefly, men were more likely to be lost to programme and less likely to start ART than women. The same association was described for patients with a lower socio-economic status and lower CD4 count, and for later time periods. Conversely, older age and less advanced clinical stage were associated with start of ART. Only a few studies analysed predictors of mortality and of having a CD4 cell count determined.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

This systematic review and meta-analysis, based on more than 140 000 patients from 12 countries in sub-Saharan Africa, shows that pre-ART attrition is high: only about 25 of 100 newly diagnosed patients with HIV started ART even though in most clinics the median CD4 cell count at presentation was below 200 cells/μl. Loss to programme was twice as high in patients not yet eligible for ART as in patients eligible for ART, whereas mortality was more than twice as high in eligible patients compared with ineligible patients. The few studies that reported on predictors of loss to programme showed higher rates of loss in men, in patients with low CD4 cell counts, in patients of low socio-economic status and in recent time periods.

Many patients with a positive HIV test were lost before eligibility for ART was determined. This is illustrated by Micek et al. (2009), most of whose patients had to be referred to the ART clinic for CD4 measurements: immune status was assessed in fewer than half of the patients. Two recently published studies suggested that point-of-care CD4 tests could improve the linkage to care. Faal et al. found that providing the CD4 results at the time of HIV testing increased ART initiation rates (Faal et al. 2011), and a recent study from Mozambique showed that after the introduction of a point-of-care CD4 test, the proportion of patients lost to follow-up dropped from 57% to 21%(Jani et al. 2011). In some studies, CD4 counts were measured more than 2 months after the HIV test, and in only one site, CD4 testing was carried out on the same day as the HIV test (Larson et al. 2010a). Unfortunately, the latter study did not evaluate pre-ART loss to programme, and it is unclear whether this approach led to a higher proportion of patients starting ART.

The design of some studies may explain why the proportion of patients with a CD4 cell count differed (Larson et al. 2010b; Kohler et al. 2011; Pepper et al. 2011). Larson et al. distinguished between measured and completed CD4 cell count testing: 84.6% of the HIV positive patients had a CD4 cell count measured but only 53.1% of the eligible, and 45.7% of the not yet eligible patients picked up the results. Pepper et al. included only HIV positive patients on TB treatment; in this integrated TB/HIV clinic, the majority of patients had a pre-ART CD4 test carried out as part of the routine follow-up to determine eligibility for ART. In Kohler et al.’s study in (2011), the introduction of free cotrimoxazole (CTX) prophylaxis improved pre-ART retention, and CTX provision may therefore be associated with a larger proportion of patients with a CD4 cell count. Different study locations, patient management strategies and local guidelines are other possible sources of heterogeneity. Predictors of receiving a CD4 cell count were also investigated in a recent study by a mobile HIV testing unit in South Africa. Low CD4 cell count, female sex and the availability of a phone correlated with reception of test results. Patients with the lowest CD4 cell counts were most likely to be linked to facility-based HIV care (Govindasamy et al. 2011).

It is worrisome that few patients started ART despite low CD4 cell counts at presentation. Although ART eligibility criteria varied between studies, the majority used a CD4 threshold of 200 cells/μl. With the new threshold of 350 cells/μl or a universal ‘test and treat’ approach, the number of ART eligible patients will increase substantially. It is unclear what the significance of our results for a ‘test and treat’ strategy is. Although all HIV patients would qualify for ART, the high loss to follow-up rate among ineligible patients may also demonstrate how difficult it is to retain asymptomatic patients in care. Only few studies evaluated the factors associated with loss to follow-up and, interestingly, these factors were previously described as predictors of LTFU in patients on ART (Makombe et al. 2007; Maskew et al. 2007; Ekouevi et al. 2010; Boyles et al. 2011). The high LTFU rate in younger men may have psychosocial and structural origins (Cornell et al. 2009), while the increase over time may be due to an overburdened health system as the number of patients continues to increase.

This systematic review has several limitations. First, in all meta-analyses, between-study heterogeneity was substantial. Second, differing definitions of loss to follow-up and ART eligibility criteria made the comparison of the studies difficult. Patients lost to follow-up may re-enter the health system later, when they are much sicker. Also, many studies did not describe the clinical and immunological criteria for ART eligibility in detail. Third, the number of included studies and countries was limited, and only few studies covered the whole pre-ART period from HIV diagnosis to ART initiation; generalisability may therefore also be limited. Nor could we determine the point at which LTFU and mortality rates were highest. Fourth, only a few studies reported on the proportion of patients transferred out. In other studies, these patients may have been assumed lost to follow-up, thus underestimating linkage to care. Fifth and finally, the reasons why patients are lost to care are poorly reported, and tracing of these patients is rarely described. Mortality is generally expected to be underestimated because patients lost to follow-up are at higher risk of death (Brinkhof et al. 2009).

Our findings illustrate the urgent need to find ways to improve linkage between HIV testing and care in low-income countries. Possible interventions to maximise retention in care include (i) making point-of-care CD4 cell counts available at HIV testing facilities to minimise losses between HIV diagnosis, CD4 count and pick-up of the result, (ii) reduce the number of required visits before ART initiation and thus the financial burden on patients, (iii) keep the time period between ART eligibility and initiation as short as possible without undermining ART counselling, (iv) motivate ineligible patients to return in regular intervals, and (v) introduce health information systems to better monitor the pre-ART period and patient movement and trace patients not returning to reassess treatment eligibility.

In conclusion, this systematic review shows that linkage from HIV diagnosis to HIV care is poor in resource-limited settings. To achieve satisfying ART coverage, monitoring of pre-ART patients needs to be improved, and strategies to increase retention in care need to be implemented.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We thank Kali Tal for commenting and editing the manuscript and Julia Bohlius for helpful discussions. The study was supported by the National Institute of Allergy and Infectious Diseases, a PROSPER fellowship to OK funded by the Swiss National Science Foundation and a PhD student fellowship to JE from the Swiss School of Public Health.

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  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Figure S1. Identification and selection of studies.

Figure S2. Meta-analysis from HIV diagnosis to start of antiretroviral therapy (ART).

Table S1. Predictors of loss to programme and mortality between determination of ART eligibility and start of ART.

Table S2. Predictors of having CD4 cell count determined and starting ART.

Appendix S1. Search terms of electronic databases.

FilenameFormatSizeDescription
tmi3089_sm_TableS1-S2FigS1-S2.docx183KSupporting info item

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