Abstract
Development of HAART in the mid-1990’s and its continued scale up has revolutionized the treatment of HIV-infected patients and led to remarkable reductions in HIV associated morbidity and mortality. However, recent studies have suggested a higher risk for early mortality in adults receiving ART in low-income countries as compared to those in high-income countries. There is dearth of data from developing countries where the burden of disease is high. The objective is to describe the burden and correlation between early vs. delayed mortality associated with HIV/AIDS in resource poor settings using data from Tanzania in East Africa. We performed a cross-sectional evaluation of routinely collected program data for 991 HIV-positive deceased adult patients who were placed on ART treatment, and died between January 1, 2007 and December 31, 2012. Data used were abstracted from records of patients who were treated at six health facilities in the Lake-zone Region of Tanzania in the timeframe. Bivariate and multivariate regression models were used to identify independent predictors of mortality and to calculate odds ratios. From the population, early deaths (within 3 months of ART initiation) occurred in 359 of the 991 cases, which represented 36.2%; while delayed deaths (after 3 months of ART initiation) occurred in 632 of 991 (63.8%). The average time to death for those who died within 3 months was 1 month compared to 22 months among those who died at > 3 months since initiation of ARV. In multivariate analysis, patients who were on WHO stage IV, had fever and cough symptoms at 6 months prior to death and patients with 0-1, 2-3, and 4-6 clinic visits had a higher risk of death in the first 3 months. Mortality among patients started on ART seems to be high. Where possible, healthcare providers should do more to vigorously monitor patients before starting them on ART for better outcomes. Additionally, public health efforts to encourage early testing and entry into treatment must be scaled up in resource poor countries to gain some lead-time and to keep the virus under control, sustain immune function, and delay the onset of opportunistic infections.
Keywords: Early, Mortality, Delayed, HAART, HIV
Introduction
Highly active antiretroviral therapy (HAART) has improved the survival of people living with the human immunodeficiency virus (PLHIV) (Cockerham et al., 2010). Although human immunodeficiency virus and AIDS/HIV related mortality in low-income settings remains high in the first months of therapy (Falster et al., 2009). Despite improvements in access to HAART, between 8%-26% of PLHIV on highly active anti-retroviral therapy (HAART) in sub-Saharan Africa die within a few months of the first year of antiretroviral treatment initiation largely due to the fact that some PLHIV only access HAART when they have advanced symptomatic disease (Lawn et al., 2008). There is an increased rate of mortality occurring within the first 12 months post-HAART initiation observed in low-to-middle income countries (LMIC) compared to high-income countries (Braitstein et al., 2006). One of the four targets for the World Health Organization’s Global Health Sector Strategy on HIV/AIDS 2011-2015 is to reduce HIV-related mortality by 25% from the 2009 baseline (WHO, 2011).
There are several factors illustrated to be associated with premature HIV/AIDS related mortality across different studies. In one observational clinical cohort study, 215 (5.1%) of the 4252 patients died during follow-up. AIDS related causes accounted for 41.4% of the mortality while CD4 cell count and HIV viral load were associated with AIDS mortality (Falster et al., 2009). According to Modrich et al. (2010), low baseline CD4 count, smoking, and older age were associated with HIV mortality (Cockerham et al., 2010). The major cause of early mortality was found to be tuberculosis (TB), which accounts for 21% of HIV related deaths (Lawn et al., 2008). Other studies have consistently shown TB to be a leading cause of mortality (Grant et al., 1998). In contrast, a study in Nepal found TB (30%) to be most common OI followed by candidiasis (14%) (Dhungel et al., 2008). The study showed a 32% mortality rate within 2 months of follow-up. A substantial proportion (about 70%) of these deaths occur very early (within the first 3 months) after starting ART (Zachariah et al., 2006). Routine program data indicates that patients starting HAART in resource-poor settings have increased mortality rates in the first months of therapy, compared with those in developed countries (Braitstein et al., 2006). Early HAART initiation could lead to improved outcomes (Kyeyune et al., 2010).
Despite increased access to HAART, people living with HIV continue to die; deaths that we believe could be averted. Appropriate diagnoses of opportunistic infections in people living with HIV in resource-limited settings are essential for the success of HIV care and treatment as well as the elimination of early mortality. There is dearth of data from developing countries where the burden of disease is high. The objective of this study is to describe the burden and correlate of early versus delayed mortality associated with HIV/AIDS in resource poor settings using data from Tanzania in East Africa.
Materials and Methods
Study Design
This was a cross-sectional evaluation examining mortality among HIV/AIDS patients who received treatment between January 1st 2007- December 31st 2012 from select hospitals in rural Tanzania. The data was collected as part of a routine quality improvement exercise involving a review of medical records for 991 deceased patients who were on ART within the defined time period. Data on socio-demographic and clinical variables were collected and entered into a Microsoft excel spreadsheet.
Study Population
Currently, many African countries are receiving ART support through the President’s Emergency Plan for AIDS Relief in Africa (PEPFAR), the Global Fund, and other private donors. These funds have made ART available to thousands of people who are now receiving life-sustaining medications. The government of Tanzania implemented a regionalization approach leading stakeholders to direct attention to specific areas in an attempt to bring cohesive therapy and management to an entire geographical region across the entire country. This was directed first at reference-level hospitals followed by Regional and District Hospitals and later extending to health centers and dispensaries. In this paper, we specifically elaborate the characteristics of all mortality patients in six hospitals: 1 referral, 2 regional, 2 district hospitals, and 1 faith-based.
Analytical Methods and Statistical Analysis
Quality Improvement teams from the six hospitals were trained in data ethics and data abstraction procedures before they started the review of patients files. The data were cleaned and recoded by senior level program staff who also performed exploratory data analysis to identify any anomalies during the entry into the spreadsheet and potential outliers in the data.
Statistical analysis was carried out using STATA 12 (StataCorp LP, College Station, TX, USA for Windows). Descriptive statistics (mean, standard deviation) were used to examine the distribution of demographic characteristics of the study population, and other clinical characteristics such as patient symptoms, WHO staging and CD4 Cell count. At the bivariate, level we performed Chi-square (χ2) test of independence for categorical variables and Student T-test for continuous variables. Unadjusted odds ratios (OR), as well as 95% confidence intervals, were generated to identify factors directly associated with early mortality. Variables found to be statistically significant (p < 0.05) on unadjusted analysis were included in the adjusted logistic regression model.
Ethical Considerations
The current study is part of a routine quality improvement exercise for patient care. Permission for the use of the data was also obtained from the respective Hospital Management teams before embarking on this quality improvement exercise. This exercise was a highly participatory approach with the hospital administration and quality improvement teams taking a lead. There were no patient identifiers, data was kept confidential and remained anonymous. Results of this exercise were jointly summarized and communicated with the respective hospitals in the region.
Result
From the deceased population, mortality or death occurring within the first three months after ART Initiation (defined as Early Death) occurred in 359 (36.2%) cases while Delayed deaths (after 3 months of ART initiation) occurred in 632 (63.8%) of the samples. The average time to death for those who died within 3 months vs. less than 3 months was 35.9 days (SD:33.3-38) and 656 days (SD: 611.2-700.3), respectively. The mean age for the deceased patients was 39.1 years with a standard deviation (SD: 37.9 – 40.3) for early death and 41.2 years (SD of 40.3 – 42.1) for delayed deaths (Table 1).
Table 1.
Distribution of baseline socio demographic, clinical and laboratory characteristics of deceased ART patients in Tanzania.
| Characteristics | Early mortality (N= 359) |
Delayed mortality (N=632) |
P-value |
|---|---|---|---|
| Age (Mean, SD) | 39.1 (37.9 – 40.3) | 41.2 (40.3 – 42.1) | 0.003 |
| Gender (n=991) | |||
| Male | 190 (52.9%) | 290 (45.9%) | 0.033 |
| Female | 169 (47.1%) | 342 (54.1%) | |
| Time in days to death (Mean, SD) | 35.9 (33.3 – 38.5) | 656.1 (611.2 – 700.3) | <0.001 |
| Deaths per each site (n=991) | <0.001 | ||
| Referal Hospital | 98 (27.3%) | 179 (28.3%) | |
| Regional Hospital 1 | 64 (17.8%) | 130 (20.57%) | |
| District Hospital | 33 (9.2%) | 47 (7.4%) | |
| Regional Hospital 2 | 55 (15.3%) | 133 (21.0%) | |
| District Hospital 2 | 65 (18.1%) | 115 (18.2%) | |
| Faith Based District Hospital | 44 (12.3%) | 28 (4.4%) | |
| Baseline WHO staging (n=980) | 0.030 | ||
| Stage 1 | 6 (1.7%) | 41 (6.6%) | |
| Stage 2 | 42 (11.7%) | 142 (22.8%) | |
| Stage 3 | 148 (41.3%) | 295 (47.4%) | |
| Stage 4 | 162 (45.3%) | 144 (23.2%) | |
| Baseline CD4 (n=859) Mean (SD) | 112 (98.5 – 127.2) | 171 (158.5 – 184.4) | <0.001 |
| 0 – 200 /mm3 | 228 (80.0%) | 387 (67.4%) | |
| 201 – 350 /mm3 | 43 (15.1%) | 120 (20.9%) | |
| >350 /mm3 | 14 (4.9%) | 67 (11.7%) | |
| Baseline Weight (in Kgs) (n=921) | <0.001 | ||
| <45 | 123 (41.6%) | 173 (28.7%) | |
| 45-54 | 120 (37.6%) | 167 (27.7%) | |
| 55+ | 76 (23.8%) | 262 (43.5%) | |
| Last Weight Recorded (in Kgs) (n=966) | <0.001 | ||
| <45 | 118 (33.6) | 175 (28.4%) | |
| 45-54 | 141 (40.2%) | 182 (29.6)%) | |
| 55+ | 92 (26.2%) | 258 (41)%) | |
| No of Clinic visits Recorded(n=983) | <0.001 | ||
| 0-1 | 138 (38.6%) | 49 (7.8%) | |
| 2-3 | 193 (53.9%) | 143 (22.9%) | |
| 4-6 | 17 (4.7%) | 80 (12.8%) | |
| 7+ | 10 (2.8%) | 353 (56.5%) | |
| Symptoms 6 months prior to death | |||
| Fever and cough (n=977) | 197 (56.3%) | 288 (45.9%) | 0.002 |
| Shortness of breath (n=987) | 79 (22.2%) | 101 (16.0%) | 0.016 |
| OIs 6 months prior to death | |||
| Tuberculosis (n=989) | 111 (31.0%) | 140 (22.2%) | 0.002 |
| Cryptococcal Meningitis (n=988) | 32 (8.9%) | 31 (4.9%) | 0.013 |
| Pneumocystic Jiroveci Pneumonia (n=975) | 28 (8.0%) | 34 (5.5%) | 0.125 |
| CD4 level 6 months prior to death (n=196) | 0.446 | ||
| 0 – 200 /mm3 | 2 (25.0%) | 88 (46.8%) | |
| 201 – 350 /mm3 | 4 (50.0%) | 60 (31.9%) | |
| >350 /mm3 | 2 (25.0%) | 40 (21.3%) | |
| WHO staging at last visit (n=972) | 0.030 | ||
| Stage 1 | 3 (0.9%) | 9 (1.4%) | |
| Stage 2 | 16 (4.6%) | 60 (9.6%) | |
| Stage 3 | 144 (41.3%) | 252 (40.5%) | |
| Stage 4 | 186 (53.3%) | 302 (23.2%) | |
| Laboratory testing during illness | |||
| Biochemistry (n=991) | 158 (44.0%) | 325 (51.4%) | 0.077 |
| Haematology (n=991) | 210 (58.5%) | 394 (62.3%) | 0.490 |
Clinical characteristics
In regards to the Baseline WHO staging (n=980), 162 (45.3%) Early death cases and 144 (48.5%) late death cases were in stage IV. The mean baseline CD4 cell count (n=859) were 112 (SD: 98.5 – 127.2) and 171 (SD: 158.5 – 184.4) for early and delayed mortality cases. While 228 (80.0%) and 387 (67.4%) of the early and delayed cases had a baseline CD4 count of between 0 – 200 /mm3. The presence of fever and cough as symptoms at 6 months prior to death were 197 (56.3%) and 288 (45.9%) for early and delayed deceased cases (Table 2).
Table 2.
Risk factors associated with early mortality among HAART patients in Tanzania.
| Characteristics | Unadjusted OR | Adjusted OR | ||
|---|---|---|---|---|
| OR (95%CI) | P value | OR (95%CI) | P value | |
| Gender | ||||
| Female | 1 | 1 | ||
| Male | 1.3 (1.02-1.71) | 0.033 | 1.3 (0.95-1.77) | 0.08 |
| CD4 level 6 months prior to death (n=196) | ||||
| 0 – 200 /mm3 | 2.8 (1.54,5.13) | <0.001 | 2.6 (0.96-1.80) | 0.084 |
| 201 – 350 /mm3 | 1.7 (0.87,3.36) | 0.116 | 60 (0.84-3.58) | 0.136 |
| >350 /mm3 | 1 | 1 | ||
| WHO staging at last visit (n=972) | ||||
| Stage 1 | 1 | 1 | ||
| Stage 2 | 2 (0.80-5.0) | 0.135 | 1.2 (0.44-3.17) | 0.733 |
| Stage 3 | 3.4 (1.4-8.25) | 0.006 | 2.11 (0.84-5.33) | 0.113 |
| Stage 4 | 7 (3.17-18.64) | <0.001 | 5.5 (2.13-14.06) | <0.001 |
| Symptoms 6 months prior to death | ||||
| Fever and cough (n=977) | ||||
| Yes | 1.5 (1.16-1.97) | 0.02 | 1.8 (1.23-2.57) | 0.002 |
| No | 1 | 1 | ||
| Shortness of breath (n=987) | ||||
| Yes | 1.5 (1.07-2.07) | 0.016 | 1.2 (0.79-1.91) | 0.355 |
| No | 1 | 1 | ||
| OIs 6 months prior to death | ||||
| Tuberculosis (n=989) | ||||
| Yes | 1.6 (1.17-2.11) | 0.002 | 1.4 (0.97-2.09) | 0.069 |
| No | 1 | 1 | ||
| Cryptococcal Meningitis (n=988) | ||||
| Yes | 1.9 (1.13-3.16) | 0.014 | 1.4(0.74-2.70) | 0.297 |
| No | 1 | 1 | ||
| Pneumocystic Jiroveci Pneumonia (n=975) | ||||
| Yes | 1.55 (0.93-2.59) | 0.092 | 1.1 (0.59-2.22) | 0.689 |
| No | 1 | 1 | ||
| Baseline Weight (in Kgs) (n=921) | ||||
| <45 | 1.8 (1.35-2.63) | <0.001 | 1.3 (0.50-3.10) | 0.62 |
| 45-54 | 2.2 (1.57-3.00) | <0.001 | 1.5 (0.74-2.97) | 0.25 |
| 55+ | 1 | 1 | ||
| Last Weight Recorded (in Kgs) (n=966) | ||||
| <45 | 2.5 (1.73,3.46) | <0.001 | 1.8 (0.90-3.49) | 0.09 |
| 45-54 | 2.4 (1.75,3.50) | <0.001 | 1.9 (0.81-4.82 | 0.13 |
| 55+ | 1 | 1 | ||
| No of Clinic visits Recorded(n=983) | ||||
| 0-1 | 99 (48.97-201.81) | <0.001 | 112 (48.16-62.33) | <0.001 |
| 2-3 | 47 (24.51-92.61) | <0.001 | 52 (24.50-112.66) | <0.001 |
| 4-6 | 7 (3.31-16.99) | <0.001 | 6 (2.32-15.71) | <0.001 |
| 7+ | 1 | 1 | ||
Factors associated with early mortality
At the bivariate analysis, gender; CD4 cell count level at 6 months prior to death; WHO staging at last visit; the hematological test; the presence of fever and cough and shortness of breath symptoms at 6 months prior to death; the presence of Tuberculosis; Cryptococcal Meningitis; Pneumocystic Jirovecii Pneumonia (PJP) at 6 months prior to death; baseline weight (in kg); last visit weight (in kg); and number of clinic visits attended were statistically and significantly associated with early mortality (p<0.05).
At the multivariate analysis level (Table 2), patients who were on WHO stage IV were 5 times likely to have an early death as compared to those on WHO stage I [OR = 5.5, 95% CI: 2.13-14.06). Patients who had fever and cough symptoms at 6 months prior to death were more likely to have an early death as compared to those who didn’t experience the symptoms (OR = 1.8; 95% CI: 1.23-2.57). We observed a gradient relationship in which patients with less clinic visits were more likely to die in three month (p <0.001).
Discussion and Conclusion
In our findings, it is demonstrated that 359 (36.2%) of the cases have early mortality with the time duration of less than three months after the start of treatment. Several variables were significantly associated with early mortality including WHO staging at last visit, patients who had fever and cough symptoms at 6 months prior to death, and number of clinic visits/consultation.
Several studies in low- and middle-income countries have reported that patients who undergo treatment have early mortality after initiating HAART. Empirical evidence in Malawi showed that 61%) (n=116) of the death occurred within the first 3 months and 150 (79%) within the first 6 months (Zachariah et al., 2006). Along with this, in the same country, 206 (74%) people died within 3 months of initiating HAART at a primary health center (Zachariah et al., 2009). In a Tanzania study, from 95 patients who died, 59 of the patients died within 3 months of starting HAART (Johannessen et al., 2008). These studies from Malawi, Tanzania, and Ethiopia (Tadesse & Hiruy, 2014) demonstrated that majority of the patients were in WHO stage 3 and 4 and had a CD4 count of less than 200 mm3 at the baseline; and concurred that most of our study participants who presented to the health facilities for HAART had advanced diseases. It may be necessary to improve HIV testing strategies so that those found HIV-positive may be initiated on HAART early before the immune system is severely weakened and susceptible to opportunistic disease. Similarly, it might also be an indication that there is need for improved monitoring and increased access to HAART for patients awaiting HIV treatment to obtain better outcomes. More qualitative research needs to be done to better understand the reasons for delayed access to HAART among patients initiated late to improve both treatment strategies and disease management. Early testing should be encouraged at the facility and community level.
In our findings, advanced disease progression with WHO stage 4 were independent predictors of early mortality. Similar findings have been reported elsewhere. In a Nepal study, HIV-infected patients with WHO clinical stage III had a 3-fold increased risk of death compared to patients with stage I or II and the risk of death among WHO clinical stage 4 patients was even higher- compared to stage I or II patients (Bhatta et al., 2013). Another study found that patients in stage 3 and 4 were two to four times more likely to die than patients in stage 1 and 2 (Sieleunou et al., 2009). In the same way, mortality has also been found to be strongly associated with WHO stage 3 & stage 4 disease (Stringer et al., 2006). Even in a sub-Saharan Africa study, excess mortality was lower in women as compared to men, and higher in patients starting ART with advanced stage of disease (Brinkhof et al., 2009). High mortality during the first months of treatment was strongly associated with the advanced clinical stage in Ethiopia (Jerene et al., 2006). Among ART patients, WHO stages were strong predictors of mortality in ART patients. There was an eight fold higher hazard of death for WHO stage 4 vs. stage 1 (Mugisha et al., 2014) and in a Tanzanian study, WHO stage 4 at the start of ART was associated with four times higher death rate compared to WHO stage 1–3 combined (Mageda et al., 2012). In a Burkina Faso study, clinical staging (WHO stages) were significantly associated with death (Kouanda et al., 2012).
Another finding was that missing a clinic visit was associated with mortality. Mortality rates were more than 2 times higher for patients with missed visits, compared to mortality rates of patients who attended all scheduled appointments during the first year of care (2.3 vs. 1.0 deaths per 100 patient-years of follow-up (HR =2.90; p = 0.02) (Mugavero et al., 2009) and with persons with visits in all 4 quarters during the first year. The adjusted hazard ratio of death was 1.42, 1.67, and 1.95 for persons with visits in 3 quarters, 2 quarters, and 1 quarter, respectively (Giordano et al., 2007) and had a higher risk of hospital admissions (OR: 2.4) and mortality (OR: 6.7) (Colubi et al., 2012). Similarly, advanced clinical stage and longer total elapsed time without clinical visits for 1 year after HAART were all significant risk factors for the occurrence of new AIDS-defining illnesses or death (Park et al., 2007) and adherence to visits (Gupta et al., 2011).
There was missing data (undocumented) and this limited the richness of the findings; this is a concern in terms of patient management without adequate documented data. The cause of death was also not ascertained by verbal biopsy or other modality. As a subsequent to our quality improvement exercise, it was important to train and advocate for improvements in medical records documentation for improved care by health workers. However, the retrieval and abstraction of a large number of patient’s medical records across different types of facilities could still represent a real reflection of a program setting data; therefore, this enables the enforcement of policy-making and can be taken as a major strength of this quality improvement exercise.
Mortality among ART patients is a cause for concern, especially since a significant proportion of patients die without a confirmed diagnosis. Healthcare providers need to do more vigorous monitoring of patients before starting them on ARTs for better outcomes. Simple tools should be developed for assessing opportunistic infections among high-risk patients. There is a need to do more systematic studies in order to explore early mortality in detail.
Acknowledgments
Our appreciation goes to Dr. Redfield for his great insight and stewardship in the program. Special thanks to the entire facility quality improvement teams for assisting in data collection. Thanks to LEAD program and to the Presidential Emergency Plan for AIDS Relief fund, the patients, and the Tanzanian government for their continued support.
Footnotes
Conflict of Interests
All authors declare that they have no conflict of interests associated with the publication of this paper.
References
- Cockerham LSR, Zolopa A, Rimland D, Lewis CE, Bacchetti P, Grunfeld C, Shlipak M, Tien PC, 2010. Association of HIV infection, demographic and cardiovascular risk factors with all-cause mortality in the recent HAART era. J Acquir Immune Defic Syndr, 53(1): 102–106. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Falster KCJ, Donovan B, Duncombe C, Mulhall B, Sowden D, Zhou J, Law MG, 2009. Australian HIV Observational Database; TREAT Asia HIV Observational Database: AIDS-related and non-AIDS-related mortality in the Asia-Pacific region in the era of combination antiretroviral treatment. AIDS, 23(17): 2323–2336 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lawn SDHA, Anglaret X, Myer L, Wood R, 2008. Early mortality among adults accessing antiretroviral treatment programmes in sub-Saharan Africa. AIDS 2008, 22(15):1897–1908. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Braitstein PBM, Dabis F, Schechter M, Boulle A, Miotti P, Wood R, Laurent C, Sprinz E, Seyler C, Bangsberg DR, Balestre E, Sterne JA, May M, Egger M, 2006. Antiretroviral Therapy in Lower Income Countries (ART-LINC) Collaboration; ART Cohort Collaboration (ART-CC) groups: Mortality of HIV-1-infected patients in the first year of antiretroviral therapy: comparison between low-income and high-income countries. Lancet, 367(9513):817–824. [DOI] [PubMed] [Google Scholar]
- WHO, 2011. Global health sector strategy on HIV/AIDS 2011-2015 World Health Organization, ISBN: 978 92 4 150165 1. [Google Scholar]
- Grant AD1 DCK, 1998. The growing challenge of HIV/AIDS in developing countries. Br Med Bull, 54(2):369–381. [DOI] [PubMed] [Google Scholar]
- Dhungel BADK, Easow JM, Singh YI, 2008. Opportunistic infection among HIV seropositive cases in Manipal Teaching Hospital, Pokhara, Nepal. Kathmandu Univ Med J (KUMJ), 6(23):335–339. [DOI] [PubMed] [Google Scholar]
- Kyeyune R dBS, Cattamanchi A, Davis JL, Worodria W, Yoo SD, Huang L, 2010. Causes of early mortality in HIV-infected TB suspects in an East African referral hospital J Acquir Immune Defic Syndr, 55(4):446–450 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Zachariah R1FM, Massaquoi M, Pasulani O, Arnould L, Makombe S, Harries AD, 2006. Risk factors for high early mortality in patients on antiretroviral treatment in a rural district of Malawi. AIDS, 20(18):2355–2360. [DOI] [PubMed] [Google Scholar]
- Zachariah R1HK, Moses M, Manzi M, Line A, Mwagomba B, Harries AD, 2009. Very early mortality in patients starting antiretroviral treatment at primary health centres in rural Malawi. Trop Med Int Health, 4(7):713–721. [DOI] [PubMed] [Google Scholar]
- Johannessen A1NE, Ngowi BJ, Sandvik L, Matee MI, Aglen HE, Gundersen SG, Bruun JN, 2008. Predictors of mortality in HIV-infected patients starting antiretroviral therapy in a rural hospital in Tanzania. BMC Infect Dis, 8(52). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jerene D EA, Hailu Y, Lindtjørn B, 2006. Predictors of early death in a cohort of Ethiopian patients treated with HAART. BMC Infect Dis, 6(136). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tadesse K HF, Hiruy N, 2014. Predictors of Mortality among Patients Enrolled on Antiretroviral Therapy in Aksum Hospital, Northern Ethiopia: A Retrospective Cohort Study. PLoS One, 9(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bhatta L KE, Deuba K, Shrestha R, Karki DK, Ekstrom AM, Ahmed LA, 2013. Survival on antiretroviral treatment among adult HIV-infected patients in Nepal: a retrospective cohort study inFar-western region, 2006-2011. BMC Infect Dis, 13(604). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sieleunou I SM, Schönenberger AM, Menten J, Boelaert M, 2009. Determinants of survival in AIDS patients on antiretroviral therapy in a rural centre in the Far-North Province,Cameroon. Trop Med Int Health, 14(1):36–43 [DOI] [PubMed] [Google Scholar]
- Stringer JS ZI, Levy J, Stringer EM, Mwango A, Chi BH, Mtonga V, Reid S, Cantrell RA, Bulterys M, Saag MS, Marlink RG, Mwinga A, Ellerbrock TV, Sinkala M, 2006. Rapid scale-up of antiretroviral therapy at primary care sites in Zambia: feasibility and early outcomes. JAMA, 296(7):782–793. [DOI] [PubMed] [Google Scholar]
- Brinkhof MW BA, Weigel R, Messou E, Mathers C, Orrell C, Dabis F, Pascoe M, Egger M, 2009. International Epidemiological Databases to Evaluate AIDS (IeDEA): Mortality of HIV-infected patients starting antiretroviral therapy in sub-Saharan Africa: comparison with HIV-unrelated mortality. PLoS Med 6(4). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mugisha V TC, Wang C, Lahuerta M, Nuwagaba-Biribonwoha H, Tayebwa E, Ingabire E, Ingabire P, Sahabo R, Twyman P, 2014. Abrams EJ;Identifying Optimal Models for HIV Care in Rwanda Collaboration: Determinants of Mortality and Loss to Follow-Up among Adults Enrolled in HIV Care Services in Rwanda. PLoS One, 9(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mageda K LG, Mmbaga EJ, 2012. High Initial HIV/AIDS-Related Mortality and -Its Predictors among Patients on Antiretroviral Therapy in the KageraRegion of Tanzania: A Five-Year Retrospective Cohort Study. AIDS Res Treat. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kouanda S MI, Nikiema L, Tiendrebeogo S, Doulougou B, Kaboré I, Sanou MJ, Greenwell F, Soudré R, Sondo B, 2012. Determinants and causes of mortality in HIV-infected patients receiving antiretroviral therapy in Burkina Faso: a five-year retrospective cohort study. AIDS Care, 24(4):478–490. [DOI] [PubMed] [Google Scholar]
- Mugavero MJ1LH, Willig JH, Westfall AO, Ulett KB, Routman JS, Abroms S, Raper JL, Saag MS, Allison JJ, 2009. Missed visits and mortality among patients establishing initial outpatient HIV treatment Clin Infect Dis, 48(2):248–256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Giordano TP1GA, White AC Jr, Suarez-Almazor ME, Rabeneck L, Hartman C, Backus LI, Mole LA, Morgan RO, 2007. Retention in care: a challenge to survival with HIV infection. Clin Infect Dis, 44(11):1493–1499 [DOI] [PubMed] [Google Scholar]
- Colubi MMP-EM, Eliás L, Pumares M, Muriel A, Zamora AM, Casado JL, Dronda F, López D, Moreno S, 2012. SEAD Study Group Missing scheduled visits in the outpatient clinic as a marker of short-term admissions and death HIV Clin Trials 2012, 13(5):289–295 [DOI] [PubMed] [Google Scholar]
- Park WB CP, Kim SH, Jo JH, Bang JH, Kim HB, Kim NJ, Oh M, Choe KW, 2007. One-year adherence to clinic visits after highly active antiretroviral therapy: a predictor of clinical progress in HIV patients J Intern Med, 261(3):268–275 [DOI] [PubMed] [Google Scholar]
- Gupta A NG, Yang WT, Chandrasekhar A, Gupte N, Bisson GP, Hosseinipour M, Gummadi N, 2011. Early mortality in adults initiating antiretroviral therapy (ART) in low- and middle-income countries (LMIC): a systematic review and meta-analysis. PLoS One, 6(12). [DOI] [PMC free article] [PubMed] [Google Scholar]