Antiretroviral (ART) programmes rapidly scaled up in many countries in sub-Saharan Africa in recent years. To promote this process, WHO and the Joint United Nations Programme on HIV/AIDS developed a public health approach, based on clinical decision-making, standardized and simplified treatment protocols, regimens, decentralized and free services at the point of care and a coordinated response to the epidemic, including a single monitoring and evaluation system [1,2].
Monitoring of ART outcomes has been challenging . The lack of individual-level data is a major limitation of reports of many national ART programmes. Though large databases have been analysed [4–9], to our knowledge Thailand is the only country that has published nation-wide data at the level of the individual [10,11]. Rwanda collected data on a representative sample of patients on ART . With few exceptions , reports of adults and children are published separately, making a direct comparison of outcomes within a programme difficult.
By the end of 2009 Malawi achieved an ART coverage of 46%, based on the eligibility criteria of the 2010 WHO guidelines, using the public health approach . The quality of the national ART monitoring and evaluation system has been assessed [13–15] and an analysis of aggregated data including adults and children concluded that the rapid ART scale-up between 2004 and 2006 was successful . However, the use of quarterly and facility-level data limited this analysis to cumulative outcomes and survival probabilities at 6 and 12 months.
Our aim was to analyse ART outcomes across all age groups and to examine factors related to progression to death, loss to follow-up (LTFU) and programme retention, based on individual-level data from the national ART register.
The scale-up of antiretroviral therapy in Malawi
The national scale-up of ART was planned and implemented strategically . Clinics are staffed with at least one clinician (usually clinical officer grade), one nurse (who is certified to provide ART), a counsellor and a clerk. Clinics dispense ART free of charge and start 25 (low), 50 (medium) or 150 (high burden sites) new patients on treatment each month. Initially, only first-line drugs were supplied, consisting of fixed-dose combination (FDC) tablets of stavudine (d4T) 30 or 40 mg, lamivudine (3TC) 150 mg and nevirapine (NVP) 200 mg (Triomune 30 or 40). Alternative drugs, including efavirenz (EFV), were only available for NVP-associated toxicity, and these drugs as well as paediatric ART were limited to central hospitals. By December 2005, 98 sites had started patients on ART, including all Central and District Hospitals. The revised 2006 national ART guidelines recommended Triomune 30 for all patients, children could be started in all sites and the WHO paediatric 4 stage system was adopted .
Patients register at the ART clinics, are clinically staged and receive cotrimoxazole (CTX) prophylaxis except adults in WHO stage 1. Patients are deemed eligible for ART if they are in WHO clinical stages 3 or 4, or have absolute CD4 cell counts below 200 (until 2005) or 250/μl in adults or the age-dependent absolute or percentage CD4 threshold in children. They attend a standardized session to understand the implications of ART, start ART with a 2-week lead in phase with a lower dose of NVP unless on tuberculosis (TB) treatment, and receive monthly supplies of Triomune. If adherence is good, patients receive ART for 2 months.
Monitoring and evaluation system
The monitoring and evaluation system has been described elsewhere [13–15]. The system is based on tools recommended by WHO, including a master card for each ART patient and a patient register at each facility. At ART initiation, clinicians enter date, type of regimen and demographics on the patient's treatment card and this information is later transcribed into the ART register by a clerk. At each scheduled visit, the following primary outcomes are assessed and recorded on the treatment card by the clinician or nurse: alive and on ART, stopped all ART (for any reason, such as treatment fatigue or toxicity), LTFU (not been seen in the clinic more than 2 months after dispensed drugs have run out), transferred to another ART clinic, and death (based on a reliable report about the patient's death). Secondary outcomes included side effects, change of regimen and pill counts.
Outcomes are transcribed at least quarterly to the ART register and information is updated in the register when appropriate. For example, a patient who was lost to follow-up and came back to restart ART would be reclassified as alive and on ART, a patient found to receive ART elsewhere would be classified as transferred out, and a patient found to have died would be recorded as a death. A Ministry of Health supervision team, consisting of two trained supervisors, visits each site quarterly and checks all treatment cards and ART site registers for completeness and consistency . The supervision teams aggregate data in quarterly national reports. Since 2006, an Electronic Medical Records System using touchscreen computers has been introduced at sites exceeding 2000 patients . A recent audit of data quality found that monitoring and evaluation of the ART programme was well organized and produced good-quality data .
Between April and June 2008, two photography clerks visited all public and private ART sites and photographed ART registers to create a set of digital images using two digital cameras, four 2GB SD memory cards, two sets of re-chargeable batteries, chargers and two wooden camera frames (jigs) for fixed-distance image collection. The jig allowed a constant focal distance to ensure that the camera captured the entire page with maximum clarity. The left and right pages were labelled with blue tags with page numbers to ensure proper pairing of pages. Patients’ and guardians’ names and addresses were covered and were invisible on the pictures to protect confidentiality. Images were enhanced and aligned using Google's Picasa software for optimal data transcription; images were backed up on a 150 GB external hard drive. Thereafter, six data clerks transcribed the data into an Excel database by double entry using the enhanced digital images. The project manager supervised data entry and conducted data validation, using the Excel Synkronizer plug-in. The database contained the following variables: ART registration number and date of registration, age, sex, transfer in, date of starting ART, reason for ART, and primary outcomes with dates. The data from nine sites with electronic records were merged with the database.
The analysis includes patients who started treatment between 1 January 2004 and 31 December 2007 in the public sector through their first 12 months on ART and excludes patients with unknown age, sex, start date and date and type of outcome (Fig. 1). In addition, patients of private sector sites had to be excluded since data quality was poor. Patients who started ART elsewhere and were transferred in had to be excluded from the analysis to avoid double counting: the programme does not use a unique national identification number. Patients transferred out were censored at their last follow-up visit. We considered three endpoints: mortality, LTFU and retention on ART, defined as the reciprocal of death, LTFU and treatment stop combined.
We used competing risk models to analyse the time to LTFU and the time to death, as measured from the start of ART (i.e. baseline) . Competing risk analysis assumes that each patient is exposed to two or more risks, which may not be independent. One of these outcomes is the event of interest, whereas the others are competing events. The analyses were censored 1 year after starting ART. The multivariable models included patient-level (age group, sex, WHO stage and year of ART start) and clinic-level (degree of urbanization, region and health care facility level) variables. Age groups included infants 0–1 years of age, children 2–5 and 6–14 years, and adults 15–24, 25–34, 35–44 and at least 45 years. We investigated possible interactions between age group and all other variables. Retention on ART was analysed by fitting a Cox proportional hazards regression model for outcomes death, LTFU and stopping treatment combined, adjusted for the same variables as in the competing risk analyses. Results are shown as subhazard ratios (sHRs) from the competing risk analysis and hazard ratios from the Cox regression, with 95% confidence intervals (95% CIs).
In a separate analysis we used a web calculator developed by the International epidemiological Databases to Evaluate AIDS (IeDEA, available at http://www.iedea-sa.org) to obtain estimates of mortality that are corrected for LTFU. The calculations are based on the fact that overall mortality is the average of mortality in patients retained in care and mortality in patients lost to follow-up, weighted by the proportion of patients lost to follow-up . Mortality in patients lost to follow-up was estimated based on a meta-analysis of studies that assessed the vital status of patients by tracing them .
Confidentiality and ethical approval
Measures are in place in all ART facilities to ensure patient confidentiality, consent for HIV testing, and counselling and support for those who receive a positive HIV test result. Studies using data collected routinely within the context of monitoring and evaluation, such as ART registers, do not require formal approval by the Malawi National Health Science Research Committee.
The dataset included 189 931 patients from 211 clinics; 117 945 patients from 148 clinics met the eligibility criteria and were analysed (Fig. 1). The median age of patients was 34 years and 1.0% were infants below 2 years, 7.4% children 2–14 years, 7.5% were young people 15–24 years and 84.2% were 25 years and older (Table 1). Sixty percent of all patients were female and the sex ratio showed a characteristic pattern across age groups: women outnumbered men from age 14 to 40 years, with a peak female-to-male ratio of 6.3 at the age of 20 (web appendix Figure S1, http://links.lww.com/QAD/A195). The proportion of patients with WHO clinical stage 3 or 4 was similar for adults (85.8%), children 2–14 years (85.2%), and infants (84.4%). Between 2004 and 2007 the proportion of annual ART initiations that were in infants and children increased from 6.0 to 9.6%.
The number of clinics grew steadily from 17 in January 2004 to 148 in December 2007 and more patients started treatment each year: from 6809 in 2004 to 51 810 in 2007, reaching the cumulative number of 117 945 by December 2007 (Fig. 2). Most clinics (n = 66, 45%) were in southern Malawi, where the majority of patients were started on ART (n = 62 544, 53%). Though the majority of clinics were health centres (n = 53, 36%), most patients started in district hospitals (56% of adults and 48% of children 2–14 years) and clinics located in urban areas (52% of adults and 48% of children 2–14 years). Thirty-three clinics had only adults and in two clinics the majority of patients were children and infants. Most ‘other’ facilities were run by Malawian nongovernmental organizations.
Mortality, loss to follow-up and retention on antiretroviral therapy
Patients were followed for a total of 85 246 person-years. At 12 months outcomes were as follows: 81 980 (69.5%) patients were alive and on ART in the same clinic, 14 425 (12.2%) had died, 11 827 (10.0%) were lost to follow-up, 9432 (8.0%) had transferred to another clinic and 281 (0.2%) had stopped ART.
Mortality rates in children and infants decreased from 24.3 per 100 person-years during the period of the first 3 months on ART to 5.9 per 100 person-years during months 4–12; the corresponding numbers for adults were 37.0 per 100 person-years and 8.0 per 100 person-years. Figure 3a shows the mortality for the two periods by age at start of ART: infants below 2 years (47.0 per 100 person-years) and young people aged 15–24 years (40.8 per 100 person-years) had the highest early mortality. Infants and young people had also the highest rates for LTFU over 12 months (24.7 and 19.3 per 100 person-years, respectively). As a result, retention in these age groups was lowest, whereas children 6–14 years were most likely to remain in care, followed by children aged 2–5 years (Fig. 3b).
The higher mortality and LTFU rates in infants and young people were confirmed in the multivariable analysis with adjusted sHRs for mortality of 1.37 (95% CI 1.17–1.60) in infants and 1.17 (95% CI 1.10–1.25) in young people compared to adults 25–34 years. For LTFU the corresponding sHRs were 1.37 (95% CI 1.18–1.59) and 1.27 (95% CI 1.19–1.35; Table 2). Male sex was associated with a faster progression to death and a higher risk of LTFU (Fig. 3c and d), and this was particularly pronounced in adults 25–34 years of age (the sHR of mortality 1.50, 95% CI 1.42–1.59, P value for interaction <0.001; sHR for LTFU 1.29, 95% CI 1.22–1.38, P value for interaction <0.001; data not shown). WHO stages 3 and 4 were associated with higher mortality; this was more pronounced in adults of all ages compared to children (P value for interaction <0.001). Over calendar years, the risk of death decreased, whereas the risk of LTFU increased: the sHR comparing 2004 with 2007 was 1.65 (95% CI 1.54–1.76) for mortality and 0.56 (95% CI 0.51–0.62) for LTFU, resulting in a similar probability of retention each year (Table 2; web appendix Figure S2, http://links.lww.com/QAD/A195). Finally, there were differences between healthcare facilities. For example, mortality in central hospitals was lower and LTFU higher compared to district hospitals.
Mortality corrected for loss to follow-up
Observed mortality rates for each clinic together with estimated rates of mortality that take deaths among patients lost to follow-up into account are presented in the web appendix (Figure S3, http://links.lww.com/QAD/A195). Across all clinics, the median [interquartile range (IQR)] observed mortality rate was 14.6/100 person-years (9.2–21.6). After correcting mortality for LTFU, the median (IQR) mortality rate increased to 18.5/100 person-years (12.9–26.1).
Our analysis of patient-level data of the national ART programme in Malawi between 2004 and 2007 showed that infants less than 2 years and young people 15–24 years had the highest mortality and LTFU rates in the first year of ART, in particular during the first 3 months, when mortality rates were up to five times higher than in the subsequent 9 months. Multivariable regression confirmed that patients starting in these age groups had the highest risk of death or LTFU. From the age of 20 years, men were more likely to die than women, with a 50% higher risk in those aged 25–34 years. Overall, there was a trend of decreasing mortality from 2005 onwards, but LTFU increased across all age groups, and consequently overall programme retention remained stable.
The mortality in our cohort was higher compared to other paediatric [5,26] and adult  studies. A comparable early mortality rate in infants was reported from Côte d’Ivoire  and Malawi . In 2004–2005, at primary care facilities in Lusaka in neighbouring Zambia, the overall mortality in adults was less than in our national cohort with 16.1/100 person-years, but younger people 15–29 years also had the highest mortality rate among adults (17.2/100 person-years). The early mortality in this age group was 25.6/100 person-years, considerably lower than in our study . This might be caused by differences in baseline characteristics, quality of care or deaths not accounted for among patients lost to follow-up. Recent data from Tanzania's national ART programme report poor retention of young people on ART who had advanced disease at ART initiation .
We found decreasing mortality and increasing LTFU over the 4 years of this study, resulting in an overall stable retention of patients on ART over time. This pattern was also observed in four provinces in South Africa , where errors in the documentation of patients transferred out and insufficient capacity to manage the many patients contributed to the higher rates of LTFU. Our dataset included no information on individual visits: only the most recent outcome was recorded for each patient. Patients who became lost to follow-up recently therefore had less time to return to the clinic than patients lost in earlier years. This could partly explain the increase in the rate of LTFU in more recent years, but the bias is likely to be small: a study from Zambia found that the 60-day threshold for the number of days patients could be late for an appointment before being classified as lost to follow-up performed well, with a sensitivity of 84% and misclassification of only about 5% of patients . The change of policy in the treatment of HIV/TB co-infected patients may have contributed to the decline in mortality. Before 2006, HIV/TB patients had to wait with ART until completion of TB treatment or had to stop and restart ART, when TB was diagnosed while on ART. In 2006 co-treatment of HIV and TB was introduced .
Our study relied on routine operational data collected during normal service provision. With high workloads, errors are unavoidable, but routine supervision should have minimized their frequency. Of note, the dataset was limited to the variables required for national monitoring. It is well known that CD4 cell count and viral load as well as socioeconomic and anthropometric parameters are associated with patients’ outcomes, but we could not examine these variables as they were not a part of our core dataset. We also did not collect information on cotrimoxazole prophylaxis, AIDS-related diseases such as Kaposi's sarcoma and tuberculosis, or prior exposure to antiretroviral prophylaxis as part of prevention of mother-to-child transmission (MTCT). By excluding patients who transferred in at ART sites we may have introduced some bias, since patients who move between clinics may have different outcomes compared to patients who stay in care at the same site . Our method of taking photographs of ART registers using a simple digital camera supported by a jig, to standardize distance and focus is a novel and untested approach. However, the method was piloted, and all photographs were reviewed twice and data entry errors corrected.
In conclusion, routine data from Malawi's ART monitoring and evaluation system not only provides useful aggregated data to guide policy , but also patient-level data that specifically point to the needs of infants, young people and men, extending previous findings [34–36]. Malawi national guidelines have now adopted the new WHO recommendations to start adults and children earlier on ART , and go beyond WHO recommendations in the prevention of MTCT . Late presentation and start of ART may be one reason for poor outcomes both in young people and men [38,39]. Unfortunately, outcomes of men on ART have received little attention, despite increasing evidence that in sub-Saharan Africa their prognosis is poorer than in women . Gender norms may prevent men to seek healthcare early or make them feel uncomfortable admitting ill health, resulting in lower and later uptake of ART [35,41]. Male champions, popular men advocating for a change in behaviour, may have an impact . Several factors facilitating access to health services have been identified in young people : services should be free, respect young people's need for privacy and involve them in decision-making. In Zambian children on ART, factors associated with poor adherence included changing residence, school attendance, lack of HIV disclosure to the child, and increasing household income . ART programmes should collaborate more closely with schools, and with youth-friendly services in the community [44,45], for example teen clubs , and involve these services in efforts to improve retention on ART.
Lydia Lo contributed to the design, data collection and data management of the study. Chris Buck commented and helped editing the paper.
R.W., A.H., S.M., H.T. and A.J. coordinated data collection, H.T. and A.J. performed the data management. J.E., O.K., M.E. and A.J. designed the statistical analyses which were performed by J.E. and O.K. R.W. and J.E. wrote the first draft of the manuscript which was revised by O.K., A.H. and M.E. All authors approved the final manuscript.
Conflicts of interest
The study was supported by the Clinton Health Access Initiative, the National Institute of Allergy and Infectious Diseases (NIAID), Grant 5U01-AI069924-05, a PROSPER fellowship to O.K. supported by the Swiss National Science Foundation (Grant 32333B_131629) and a PhD student fellowship to J.E. from the Swiss School of Public Health.
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