AIDS:
13 November 2010 - Volume 24 - Issue 17 - p 2717–2725
doi: 10.1097/QAD.0b013e32833fb71f
Epidemiology and Social
Outcomes in patients waiting for antiretroviral treatment in the Free State Province, South Africa: prospective linkage study
Ingle, Suzanne Ma; May, Margareta; Uebel, Kerryb; Timmerman, Venessab; Kotze, Eduanc; Bachmann, Maxd; Sterne, Jonathan ACa; Egger, Matthiase; Fairall, Larab,f; for IeDEA-Southern Africa
Author Information
aDepartment of Social Medicine, University of Bristol, UK
bKnowledge Translation Unit, University of Cape Town Lung Institute, University of Cape Town, South Africa
cFree State Department of Health, Bloemfontein, South Africa
dSchool of Medicine, Health Policy and Practice, University of East Anglia, Norwich, UK
eInstitute of Social & Preventive Medicine (ISPM), University of Bern, Switzerland
fDepartment of Medicine, University of Cape Town, South Africa.
Received 6 May, 2010
Revised 26 July, 2010
Accepted 17 August, 2010
Correspondence to Suzanne M. Ingle, Department of Social Medicine, 39 Whatley Road, Bristol BS8 2PS, UK. Tel: +44 117 928 7279; fax: +44 117 928 7325; e-mail: s.ingle@bristol.ac.uk
Abstract
Objective: In South Africa, many HIV-infected patients experience delays in accessing antiretroviral therapy (ART). We examined pretreatment mortality and access to treatment in patients waiting for ART.
Design: Cohort of HIV-infected patients assessed for ART eligibility at 36 facilities participating in the Comprehensive HIV and AIDS Management (CHAM) program in the Free State Province.
Methods: Proportion of patients initiating ART, pre-ART mortality and risk factors associated with these outcomes were estimated using competing risks survival analysis.
Results: Forty-four thousand, eight hundred and forty-four patients enrolled in CHAM between May 2004 and December 2007, of whom 22 083 (49.2%) were eligible for ART; pre-ART mortality was 53.2 per 100 person-years [95% confidence interval (CI) 51.8–54.7]. Median CD4 cell count at eligibility increased from 87 cells/μl in 2004 to 101 cells/μl in 2007. Two years after eligibility an estimated 67.7% (67.1–68.4%) of patients had started ART, and 26.2% (25.6–26.9%) died before starting ART. Among patients with CD4 cell counts below 25 cells/μl at eligibility, 48% died before ART and 51% initiated ART. Men were less likely to start treatment and more likely to die than women. Patients in rural clinics or clinics with low staffing levels had lower rates of starting treatment and higher mortality compared with patients in urban/peri-urban clinics, or better staffed clinics.
Conclusions: Mortality is high in eligible patients waiting for ART in the Free State Province. The most immunocompromised patients had the lowest probability of starting ART and the highest risk of pre-ART death. Prioritization of these patients should reduce waiting times and pre-ART mortality.
Introduction
HIV-infected patients in South Africa may experience delays in accessing antiretroviral therapy (ART). Until recently, South African Department of Health guidelines recommended ART when the CD4 cell count drops below 200 cells/μl or if WHO stage IV illness develops. Despite this low threshold, many eligible patients do not receive treatment. A recent study reported that in 2008, only 40.2% of eligible patients in South Africa were receiving ART [1].
Pretreatment mortality rates in HIV-infected individuals are high [2,3]. Such in-program mortality has been termed ‘unseen’, because many studies and evaluations report only on outcomes in treated patients[4]: few programs in resource-limited settings have good data prior to treatment. A study from the Free State Province in South Africa suggested that 87% of all deaths were in patients who had not started ART [2]. Starting eligible patients on treatment as quickly as possible is therefore a priority [3], but resource limitations mean that patients often have to wait for treatment [5,6].
The study reports on outcomes in HIV-infected patients from the time they were enrolled in the Comprehensive HIV and AIDS Management (CHAM) program in the Free State Province, South Africa. The Free State has the third highest HIV prevalence of all nine South African provinces, and a population-based survey in 2008 reported a prevalence of 12.6% [7]. The CHAM program began enrolments in May 2004, initially in three assessment sites and one treatment site, expanding to a total of 20 sites by the end of 2004 [8]. By July 2010 the Free State was providing ART at 47 nurse-led assessment and combined sites and 11 doctor-led treatment sites and planned to provide ART in all 220 nurse run primary care facilities over the next 3 years.
We investigated times to treatment and pre-ART death, and analyzed CD4 count changes in patients who were not yet eligible for treatment. We ascertained deaths by linking the clinical database with the national death register, and used a competing risks framework to jointly model access to treatment and pretreatment mortality, and their determinants.
Methods
Setting and patients
All patients aged at least 15 years enrolled in 36 facilities in the Free State public-sector treatment program from May 2004 through December 2007 were followed until December 2008. Patients were followed from their first point of contact with the program. All care, including ART and co-trimoxazole prophylaxis, was provided free of charge. Approximately 50% of documented eligible patients received co-trimoxazole but almost none received isoniazid to prevent tuberculosis. In 2005, just over 50% of patients presenting to the program had a prior positive HIV test and around 45% had voluntary counseling and testing (VCT) for the first time [9]. Of those undergoing VCT at ART facilities, 95% were HIV-positive. After testing HIV-positive in any Free State clinic, patients were referred to nurse-run assessment sites for assessment of treatment eligibility, including CD4 cell count measurements. According to protocol [10] patients with CD4 cell counts between 200 and 500 cells/μl were asked to return for another assessment after 6 months and patients with CD4 cell counts above 500 cells/μl were asked to return after 12 months. Patients with CD4 cell counts 200 cells/μl or less, or with WHO disease stage IV (AIDS) were enrolled in a 3-week Drug Readiness Training (DRT) program and referred for initiation of ART. Patients starting ART obtain monthly supplies of medication from their assessment site and return to treatment sites every 6 months, for repeat prescriptions.
By December 2007, 28 clinics and EIGHT hospital sites were participating in the program. Seventeen clinics were assessment-only and 11 combined assessment and treatment. Seven hospital sites provided treatment for patients referred from assessment clinics: the eighth functions as a specialist referral facility for the province.
Data sources, outcomes and definitions
Clinical data from standardized forms were entered into the province's Electronic Medical Records System by trained data capturers [11]. Data were downloaded weekly to the Free State Department of Health (FSDOH) Data Warehouse. Unique patient identifiers allowed tracking of patients across facilities. Program staff provided information on type of facility (assessment-only, treatment-only or combined assessment and treatment clinic), location of facility (rural/peri-urban/urban) and, if applicable, distance between assessment and ART initiation sites (kilometers). Patient load, numbers of staff and staff vacancies were obtained from the Human Resources Data Mart, part of the Data Warehouse [12]. The number of patients enrolled per year was the total number enrolled throughout follow-up divided by the total time that the clinic had been part of the CHAM program. The mean number of staff in each clinic from April 2005 to September 2008 was divided by the number of patients enrolled in that clinic per year in the program to obtain the number of staff per 1000 patients per year. Patients became eligible for ART as soon as a CD4 cell count of 200 cells/μl or less was measured. WHO disease staging was not used to assess eligibility, as it was not routinely recorded. Deaths were ascertained by linking the database with the National Death Register (estimated to capture >90% of adult deaths in South Africa [13]) each month, using the National Identity Number. Patients without an identity number may belong to more marginalized populations, although we cannot infer whether they have higher mortality rates. The Data Warehouse was also linked with the National Health Laboratory Services (NHLS) Database, to obtain all measured CD4 cell counts (not all CD4 cell counts were recorded in medical records due to constraints on data entry). CD4 results were reported by the laboratory and added to medical records after the clinic visit when blood was drawn. The visit schedule was used to define whether patients were considered to be in care at the end of December 2008. Patients not seen by 6 months after an expected visit were defined as not in care (the 6-month threshold allowed for patients who arrived late and also for visits to be recorded in the database). Untreated patients were defined as not in care according to CD4 cell count 200 cells/μl or less and not seen in the last 6 months; 201–500 cells/μl and not seen in the last year; or more than 500 cells/μl and not seen in the last 18 months. Untreated patients with no recorded CD4 cell count were defined as not in care if not seen in the last 6 months.
Statistical analysis
Patients were included in survival models from the time they were eligible for treatment (first CD4 cell count ≤200 cells/μl, baseline) to the earlier of ART initiation or death. Patients not in care were censored at the date they were last seen in the program. We estimated the cumulative incidence of starting ART (overall and stratified by CD4) while accounting for censoring due to the competing risk of death, and the cumulative incidence of pre-ART death accounting for the competing risk of starting ART. We also estimated the cumulative incidence of starting ART and pre-ART death from the time of enrolment, among patients with no recorded pretreatment CD4 cell count.
Competing risks proportional hazards regression models [14] were used to estimate adjusted associations of patient and facility characteristics with rates of starting ART and pre-ART death. We controlled for sex, age, weight, CD4 cell count, year of enrolment, staffing levels per 1000 patients per year, facility location and distance to site for ART initiation. We estimated subdistribution hazard ratios (SHRs) for these competing events: SHRs can be interpreted similarly to hazard ratios estimated in standard Cox models, but they account for the hazard of the competing event (see Web Appendix 1,http://links.lww.com/QAD/A90 for further details). Data on weight were missing for 6012 of 22 083 (27.2%) eligible patients and were assumed missing at random (MAR) [15]. We used multiple imputation using chained Eqs. [16] to derive 25 imputed datasets. Results from models fitted on each dataset were combined using Rubin's rules [15]. Analyses restricted to individuals with complete data were also conducted [17], and in sensitivity analyses we first excluded patients without a valid identity and second assumed that patients without an identity and lost to follow-up were dead. Complete case analyses were also done within CD4 strata. All analyses used Stata version 11 statistical software [18].
The FSDOH gave permission for the data to be analyzed for this study and the Human Research Ethics Committee of the University of Cape Town approved the protocol. No individual patient consent was deemed necessary as data were collected routinely for the CHAM program, and patient identifiers were removed from data extracts used for analyses.
Results
Table 1 shows characteristics of the 44 844 treatment-naïve patients enrolled in CHAM between May 2004 and December 2007. Those without an identity number were younger, had a higher median pretreatment CD4 cell count and were more likely to be enrolled in a site which could provide ART initiation. New enrolments to the program substantially increased with each calendar year.
Figure 1 shows the flow of patients through the program. A total of 33 182 patients (74%) had at least one pretreatment CD4 cell count. For the remaining 26% of patients, CD4 either could not be linked from the NHLS, or pretreatment CD4 was never measured. Of those patients with a pretreatment CD4 cell count, 22 083 (67%) were eligible for ART before December 2008. The percentage of patients eligible at their first CD4 cell count was 57% in 2004; decreasing to 54% in 2005 and 2006 and increasing to 65% in 2007. There was a corresponding large increase in the number of facilities in 2007, resulting in more people presenting for HIV care with advanced disease. Of those eligible, 19 089 (86%) were eligible at their first CD4 measurement. Of the 11 662 patients with no recorded pretreatment CD4 cell count, 3528 (30%) subsequently started ART. Patients in this group may have had a CD4 200 or less which was not recorded, or have qualified for treatment on the basis of a stage IV illness. Patients with no recorded pretreatment CD4 were more likely to be male and to have enrolled in 2007 and less likely to have a National Identity Number.
Among all 44 844 patients enrolled in CHAM, 9232 died before starting ART [pretreatment mortality rate 32.4 per 100 person-years; 95% confidence interval (CI) 31.7–33.0]. Among the 22 083 patients eligible for treatment, 5125 (23%) died before starting ART (pretreatment mortality rate 53.2 per 100 person-years; 95% CI 51.8–54.7). In eligible patients the median time to death was 92 (IQR 33–216) days. Among 12 963 patients who were eligible and started ART the median wait (time from eligibility to initiation) was 95 (53–170) days. Waiting times decreased from median 122 (67–200) in 2004 to 78 (45–128) days in 2007. A total of 2148 eligible patients died on ART (mortality rate 10.1 per 100 person-years; 95% CI 9.7–10.6).
The median CD4 cell count at eligibility moderately increased from 87 (37–142) in 2004 to 101 (48–154) cells/μl in 2007. However, in the 20 sites that opened at the start of the program in 2004, median CD4 decreased from 102 to 97 cells/μl over the same period. This is surprising as we expected initial CD4s to increase as the sites became more established in the local area, and this suggests the backlog of eligible patients remains for a number of years after a site has opened. This may be consistent with other evidence of barriers to care previously noted [1]. Among 2990 eligible patients with a previous CD4 cell count more than 200 cells/μl, the median of this count was 260 (227–318) cells/μl, and was measured a median 183 (105–309) days previously. Their median CD4 cell count at eligibility was 160 (124–183) cells/μl.
Figure 2a shows cumulative percentages of patients starting ART, and pre-ART mortality, up to 2 years after eligibility. At 2 years, 68% (67–68%) of eligible patients had started ART, 26% (26–27%) had died before starting ART: thus 6% were alive and untreated. Figure 2b shows cumulative percentages for starting ART and pre-ART mortality by CD4 cell count at eligibility. Among patients with CD4 cell count 25 cells/μl or less at eligibility, 48% died before starting ART and 51% initiated ART. Corresponding percentages in patients with 101–200 cells/μl were 20 and 78%. In patients without CD4 cell counts, 42% died by 2 years and 53% started ART. In a sensitivity analysis, follow-up time was extended to the database close date for those patients defined as not in care as the percentage alive and untreated at 2 years may be underestimated. This showed that by 2 years, 60% of patients were estimated to have started ART, 23% died pre-ART and 17% remained alive and untreated. Risk factor associations with both outcomes were similar to those in the main analysis.
Table 2 shows adjusted SHRs for the association of facility and patient level characteristics with time to starting ART and time to pre-ART death, accounting for the other (competing) event. Compared with women, men were less likely to start treatment (SHR 0.81, 95% CI 0.78–0.84) and more likely to die (SHR 1.36, 95% CI 1.28–1.44). Higher weight and CD4 cell counts increased the probability of starting treatment. The risk of death before starting ART decreased with higher weight and CD4 cell count. Mortality declined and access to treatment increased over calendar time. After accounting for the competing risk of starting treatment, the cumulative incidence function estimates that 13% of patients without an identity died before starting ART, compared to 31% of patients with an identity. However, sensitivity analyses restricted to patients who had a valid identity and 1 year of follow-up showed the same pattern of declining mortality over calendar time, although estimates were attenuated. Enrolment in a clinic with low staffing levels was associated with a decreased probability of starting treatment, compared with better staffed clinics. Enrolment in a rural facility was associated with lower rates of starting treatment and higher mortality rates compared with those enrolled in urban or peri-urban facilities. Increasing distance from the initiation site was associated with a decreased probability of treatment and increased probability of death.
Complete case analyses (Web Appendix 2,http://links.lww.com/QAD/A90) showed a stronger effect of year of enrolment, staffing levels and location on the probability of starting ART, and a stronger effect of weight, year of enrolment and location on the probability of pre-ART death. The effect of distance to the initiation site on the probability of starting treatment and pre-ART death were somewhat attenuated. Sensitivity analyses that assumed patients without a valid identity and were lost to follow-up were dead suggested that higher staffing levels were associated with lower mortality, but other results were similar to the main analysis. Associations estimated within CD4 strata were consistent with those shown in Table 2 (available from the authors on request).
Discussion
We found that the most immunocompromised patients had the lowest probability of starting treatment of all patients eligible for ART after accounting for the competing risk of death. This was clearly demonstrated by the graphs of the cumulative incidence of starting treatment and of pretreatment mortality stratified by CD4 cell count, and was confirmed by the findings of the multivariable adjusted competing risks regression. Time to ART and death were both strongly associated with clinic-level factors, including location, distance to ART initiation site and staffing of health facilities [19]. The distance between assessment and initiation site was a proxy measure of the distance from place of residence to initiation site, and therefore can represent the potential inconvenience to the patient of attending for ART initiation. Patients also had variable distances from home to assessment clinics, but these were not recorded.
The program shows improvements in increasing access to treatment and decreasing mortality over the period 2004–2007, this reflects increasing numbers of facilities participating in the ART scale-up and increasing experience with the program. However, these data do not include patients who enrolled during the Free State drugs moratorium, when new initiations onto ART were suspended for three months over the New Year period of 2008/2009 [20]. Not surprisingly, the patterns of associations of covariates with mortality were the inverse of those with the outcome starting treatment.
A number of patients were followed up in the clinic before they became eligible. These patients waited a median of approximately 6 months for their next CD4 cell count to be taken, in line with national guidelines. However, CD4 cell counts had often dropped significantly below the threshold of 200 cells/μl before patients were seen again. Patients who are initially ineligible for treatment should therefore be monitored more frequently to ensure timely initiation, and should be encouraged to return promptly for their repeat CD4 cell count, as has also been reported in a recent study on pre-ART care from Johannesburg [21].
Our study has several strengths. We were able to follow patients from their first point of contact with the program, and patient records were linked to the National Death Register and the NHLS laboratory database. This ensured that information on deaths and CD4 cell counts in our data are accurate and relatively complete. Although CD4 measurements were routinely taken on almost all patients, only half of the patients had these recorded in the clinical database. Completeness of CD4 recording was improved to 74% by linkage with the NHLS using deterministic matching of records. Another strength is the use of a competing risk framework which appropriately adjusts probabilities of starting ART for the probability of death pre-ART, andvice versa. As shown in the Web appendix, standard methods such as Kaplan–Meier survival estimates may give a misleading impression of pretreatment mortality in the presence of competing risks.
The amount of missing data is nevertheless a weakness of our study. Weight data were missing on a large proportion of patients and these had to be imputed based on measured patient characteristics. Results from complete case analyses and analyses based on imputed data were relatively consistent. Over a quarter of enrolled patients did not have a pretreatment CD4 and so were not included in our analysis. These patients were more likely to be men and it may be that they did not return for follow-up. It is well documented that HIV-infected men have different health-seeking behaviors to women [22–24]. These patients also had increased rates of pre-ART death, implying that many of these patients were ill and did need treatment urgently. CD4 cell counts were not measured in these patients, the counts could not be identified in the NHLS database due to variation in names, or they were considered eligible for treatment based on unrecorded criteria. A large number of patients were not in care at the database close date. These patients may have transferred out of the province to receive their care in another facility, or they may re-enter the CHAM at a later date. Improved surveillance of patients has been achieved in CHAM by monthly linkage to the death register and the NHLS database, as well as concerted efforts to collect identity numbers. Reasons for missing identity numbers included patients not knowing them or, if not legal residents, not having them. Few other provincial programs have electronic surveillance data comparable to this program. Improved monitoring of pre-ART patients with CD4 cell counts close to the treatment initiation threshold might reduce pre-ART deaths. Data are not collected on WHO disease stage at enrolment. Inclusion of these data would have helped identify patients eligible to start treatment.
Previous studies have shown that patients in a wide variety of resource-limited settings including sub-Saharan Africa, Asia and Latin America, are starting ART well below the recommended treatment thresholds [25,26]. However, to our knowledge, only three other studies have assessed waiting times for ART. A study based on the first 5 years of ART scale-up in Cambodia reported median waiting times for ART of 11.3 months in 2003, which were drastically reduced to 1.3 months in 2007 [27]. A study from Cape Town found that patients waited a median of 34 days from enrolment to treatment initiation [28]. This is considerably quicker than we have seen in the Free State program. However, the Cape Town program only included patients who were known to be eligible at enrolment and so waiting time did not include the time spent assessing patients for eligibility. A study at a fee-paying clinic in Durban reported a mean waiting time of 3.6 months from first CD4 cell count to first ART training session [29], similar to that in the publicly funded Free State program. However, the Durban study found that waiting time to first ART training session decreased with decreasing CD4 cell count, indicating that sicker patients were fast-tracked, which was not the case in the Free State program.
A number of studies have reported on pretreatment mortality in HIV-infected patients. A study in Uganda reported 35 deaths per 100 person-years in the first month of screening patients for ART eligibility[30]. Similar, in Cape Town, a study found pretreatment mortality rates of 33.3 deaths per 100 person-years [28]. A study of the DART trial comparison cohort in Entebbe, Uganda, reported a mortality rate of 57.7 per 100 patient-years for those with CD4 below 200 [31], which is comparable with the rate of 53.2 per 100 patient-years reported for eligible patients in the Free State cohort. Thai et al. [27] reported on pre-ART mortality using standard survival analysis and conducting sensitivity analyses making different assumptions about outcomes in patients lost to follow-up. In their worst and best-case scenarios (assuming, respectively, that patients lost to follow-up had all died or were all alive), 1-year mortality among patients eligible for treatment was estimated at 49 and 20%, respectively. However, these authors did not specifically allow the competing risk of starting ART.
Many of the most immunocompromised patients will not survive drug readiness training, which is required before initiating ART. Although such training may improve patient adherence and reduce the emergence of drug resistance, it also increases waiting time for treatment. Our study shows high early mortality which suggests that severely immunosuppressed patients cannot afford the luxury of extended pre-ART education. Improved ways of delivering DRT are required, but there have been no formal evaluations of alternative modes of delivery in low-income settings. Assessment of patient readiness for treatment through DRT should not be used to ration treatment [32]. New South African treatment guidelines for 2010 state that those who urgently need ART, that is CD4 below 100 or are pregnant or stage IV or co-infected with MDR or XDR TB, should be initiated within 2 weeks [33]. DRT would then have to be shortened or occur at the same time as the patient is starting ART. In general, delayed initiation was due to inadequate resources, and the shortage of doctors to assess patients and to initiate ART also contributes to the long waiting times. Prioritization of ART to those with the lowest CD4 cell counts, the ability to initiate ART in as many facilities as possible, earlier diagnoses and closer monitoring of ineligible patients may all contribute to reduced waiting times and pretreatment mortality. Increased provision of co-trimoxazole could also improve outcomes for patients not yet on ART, as it has been seen to have an independent effect on mortality [2]. All these strategies require more human resources, which are severely lacking in South Africa. Task-shifting may prove to be the way forward and is a recent addition to the South African HIV/AIDS National Strategic Plan. Trials of this are currently underway and there have been promising results so far [34–37].
As most patients start treatment well below the current initiation threshold of 200 cells/μl, increasing this may help to increase the likelihood of patients initiating at around 200 cells/μl. On 1 December 2009, the South African government announced that the treatment threshold would be raised to 350 cells/μl for pregnant women and those co-infected with TB. Although this is a step in the right direction, it must be noted that raising the treatment threshold will add to the already overwhelming burden of maintaining patients on ART, as discussed in a recent commentary in The Lancet [6], and could lead to more delays for many other patients. To combat this, the South African government announced that ART would be available in all healthcare sites, not only specialist HIV clinics, a decision with implications for training and staffing levels in all facilities.
Acknowledgements
The study was funded by the National Institute of Allergy and Infectious Diseases (NIAID grant 1 U01 AI069924–01 to IeDEA Southern Africa). S.M.I. was funded by a UK Medical Research Council PhD Studentship. M.M. and J.A.C.S. were supported by UK Medical Research Council grant G0700820. The Free State ART data warehouse was developed with the help of a research grant from the International Development Research Centre, Canada (IDRC-102411). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We also gratefully acknowledge the data capturers, nurses, doctors and managers of the Free State program, in particular Ronald Chapman, Cloete van Vuuren and Dewald Steyn, MediTech for assisting with data downloads to the warehouse, Krista Dong for critical input into designing the structured clinical records, Terry Marshall and Sue Candy for assisting with the preparation of NHLS data, and Adrian Spoerri who helped with data linkage. S.M.I. and L.F. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. We acknowledge all those who died without access to ART.
E.K., V.T. and L.F. established the cohort database. K.U., V.T., and E.K. were involved in data acquisition. M.E., L.F., M.M. and J.S. were responsible for study design. S.I., M.M., and J.S. were responsible for statistical analysis and had full access to the data. S.I., M.M., J.S. and M.E. wrote the first draft of the manuscript. All authors helped in interpreting data, contributed to writing and approved the final version.
There are no conflicts of interest.
References
1. Adam MA, Johnson LF. Estimation of adult antiretroviral treatment coverage in South Africa. S Afr Med J 2009; 99:661–667.
2. Fairall LR, Bachmann MO, Louwagie GM, van Vuuren C, Chikobvu P, Steyn D, et al. Effectiveness of antiretroviral treatment in a South African program: a cohort study. Arch Intern Med 2008; 168:86–93.
3. Lawn SD, Myer L, Orrell C, Bekker LG, Wood R. Early mortality among adults accessing a community-based antiretroviral service in South Africa: implications for programme design. AIDS 2005; 19:2141–2148.
4. Lawn SD, Myer L, Wood R. Efficacy of antiretroviral therapy in resource-poor settings: are outcomes comparable to those in the developed world? Clin Infect Dis 2005; 41:1683–1684.
5. Hirschhorn LR, Skolnik R. Making universal access a reality: what more do we need to know? J Infect Dis 2008; 197:1223–1225.
6. Koole O, Colebunders R. ART in low-resource settings: how to do more with less. Lancet 2010;376:396–398.
7. Shisana O, Rehle T, Simbayi LC, Zuma K, Jooste S, Pillay-van-Wyk V, et al, and SABSSM III Implementation Team. South African national HIV prevalence, incidence, behaviour and communication survey 2008: a turning tide among teenagers? Cape Town: HSRC Press; 2009.
8. Free State Department of Health. Implementation of the Comprehensive Care, Management and Treatment of HIV and AIDS patients: outcomes of the first year, 2004. 2005.
9. Free State Department of Health. Implementation of the Comprehensive Care, Management and Treatment of HIV and AIDS Programme 2005 Third Quarter Report. 2006 Feb.
10. South African National Department of Health. National Antiretroviral Treatment Guidelines; 2004.
11. Meditech Information Technology I. Meditech. www meditech co za 2009. Available from: URL:http://www.meditech.co.za/01000000.html
12. Kotze JE, McDonald T. Challenges in developing a data warehouse to manage the rollout of antiretroviral therapy in a developing country. 18th international conference of the Information Resources Management Association (IRMA), Vancouver, Canada, 19–23 May 2007.
13. Statistics South Africa. Mortality and causes of death in South Africa, 2005: findings from death notification. Statistical Release P0309 3 2007. Available from: URL:http://www.statssa.gov.za/publications/P03093/P030932005.pdf
14. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94:496–509.
15. Rubin D. Multiple imputation for nonresponse in surveys. New York: Wiley; 1987.
16. Royston P. Multiple imputation of missing values: update of ice. Stata J 2005; 5:527–536.
17. Sterne JA, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. Br Med J 2009; 338:b2393.
18. StataCorp LP. STATA Statistical Software: Release 11. College Station, TX: Stata Corp LP; 2009;
19. Ingle SM, May M, Uebel K, Timmerman V, Kotze JE, Bachmann MO, et al. Differences in access and patient outcomes across antiretroviral treatment clinics in the Free State province: prospective cohort study. S Afr Med J 2010 (in press).
20. El-Khatib Z, Richter M. (ARV-) Free State? The moratorium's threat to patients' adherence and the development of drug-resistant HIV. S Afr Med J 2009; 99:412, 414.
21. Larson BA, Brennan A, McNamara L, Long L, Rosen S, Sanne I, et al. Early loss to follow up after enrolment in pre-ART care at a large public clinic in Johannesburg, South Africa. Trop Med Int Health 2010; 15(Suppl 1):43–47.
22. Box TL, Olsen M, Oddone EZ, Keitz SA. Healthcare access and utilization by patients infected with human immunodeficiency virus: does gender matter? J Womens Health (Larchmt) 2003; 12:391–397.
23. Mane P, Aggleton P. Gender and HIV/AIDS: what do men have to do with it? Curr Sociol 2001; 49:23–37.
24. Braitstein P, Boulle A, Nash D, Brinkhof MW, Dabis F, Laurent C, et al. Gender and the use of antiretroviral treatment in resource-constrained settings: findings from a multicenter collaboration. J Womens Health (Larchmt) 2008; 17:47–55.
25. Keiser O, Anastos K, Schechter M, Balestre E, Myer L, Boulle A, et al. Antiretroviral therapy in resource-limited settings 1996 to 2006: patient characteristics, treatment regimens and monitoring in sub-Saharan Africa, Asia and Latin America. Trop Med Int Health 2008; 13:870–879.
26. Cornell M, Technau K, Fairall LR, Wood R, Moultrie H, van Cutsem G, 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.
27. Thai S, Koole O, Un P, Ros S, De MP, Van DW, et al. Five-year experience with scaling-up access to antiretroviral treatment in an HIV care programme in Cambodia. Trop Med Int Health 2009; 14:1048–1058.
28. Lawn SD, Myer L, Harling G, Orrell C, Bekker LG, Wood R. Determinants of mortality and nondeath losses from an antiretroviral treatment service in South Africa: implications for program evaluation. Clin Infect Dis 2006; 43:770–776.
29. Bassett IV, Wang B, Chetty S, Mazibuko M, Bearnot B, Giddy J, et al. Loss to care and death before antiretroviral therapy in Durban, South Africa. J Acquir Immune Defic Syndr 2009; 51:135–139.
30. Amuron B, Namara G, Birungi J, Nabiryo C, Levin J, Grosskurth H, et al. Mortality and loss-to-follow-up during the pretreatment period in an antiretroviral therapy programme under normal health service conditions in Uganda. BMC Public Health 2009; 9:290.
31. Munderi P, Watera C, Nakiyingi J, Kasirye A, Walker S, French N, et al. Survival and causes of death, 2 years after introduction of antiretroviral therapy in Africa: a historical cohort comparison in Entebbe, Uganda; 2006.
32. Gebrekristos HT, Mlisana KP, Karim QA. Patients' readiness to start highly active antiretroviral treatment for HIV. Br Med J 2005; 331:772–775.
33. South African National Department of Health. The South African National Antiretroviral Treatment Guidelines 2010; 2010.
34. Vasan A, Kenya-Mugisha N, Seung KJ, Achieng M, Banura P, Lule F, et al. Agreement between physicians and nonphysician clinicians in starting antiretroviral therapy in rural Uganda. Hum Resour Health 2009; 7:75.
35. Sanne I, Orrell C, Fox MP, Conradie F, Ive P, Zeinekcer J, et al. Nurse versus doctor management of HIV-infected patients receiving antiretroviral therapy (CIPRA-SA): a randomised noninferiority trial. Lancet 2009; 376:33–40.
36. Jaffar S, Amuron B, Foster S, Birungi J, Levin J, Namara G, 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.
37. Fairall LR, Bachmann MO, Zwarenstein MF, Lombard CJ, Uebel K, van Vuuren C, et al. Streamlining tasks and roles to expand treatment and care for HIV: randomised controlled trial protocol. Trials 2008; 9:21.
Keywords:
ART; HIV; mortality; South Africa; treatment access; waiting times
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© 2010 Lippincott Williams & Wilkins, Inc.
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