Skip to content

Advertisement

  • Research note
  • Open Access

Predictors associated with HIV/AIDS patients dropout from antiretroviral therapy at Mettu Karl Hospital, southwest Ethiopia

BMC Research Notes201912:232

https://doi.org/10.1186/s13104-019-4267-3

  • Received: 18 January 2019
  • Accepted: 12 April 2019
  • Published:

Abstract

Objective

The aim of this study was to determine the major risk factors of antiretroviral therapy dropout. The retrospective cohort research design was applied. 1512 HIV patients were included from Mettu Karl Hospital in Illubabor Zone, southwest part of Ethiopia from September 2005 to January 2018. Kaplan–Meier comparison and log-logistic regression accelerated failure time model were used.

Results

From the log-logistic regression result, the risk of dropout for patients with primary education status was 10.58% greater as compared to illiterate (p < 0.0110). The probability of dropout for patients with marital status separated was about 16.82% higher than those patients with marital status divorced (p < 0.0070). Being merchant, farmer and daily labour had a greater risk of dropout as compared to a housewife. Most of the HIV/AIDS patients on ART were dropout in a short period due to patients separated marital status, primary education, CD4, being merchants, farmer and daily labour. Investigation on the cause of antiretroviral therapy dropout from a number of AIDS clinics in the country is highly appreciated.

Keywords

  • AIDS
  • ART
  • Ethiopia

Introduction

HIV is the most responsible causes of mortality worldwide and the primary predictor of death in sub-Saharan Africa region. The prevalence of new infections in the area accounted for 66.6% of the world. Above 68% of adults and 90% of children infected with the disease were found in this area, and more than 76% of HIV/AIDS-related deaths were occurred in Africa [1]. In sub-Saharan Africa more than 2.2 million people were died per year due to HIV/AIDS and related causes [2, 3].

In Ethiopia, 780, 000 HIV/AIDS patients were on antiretroviral therapy [4] and around one million people are reportedly living with HIV. Of all people who have ever been reported as beginning antiretroviral treatment, 249,174 are adhering to their treatment regimen and there were 55,200 AIDS-related deaths in 2013 [5].

Antiretroviral therapy dropout is a serious challenge to the success of HIV/AIDS treatment. According to the world health organization report, from all patients enrolled in HIV, the percentage of success was only 23% [6]. Antiretroviral therapy dropout negatively affects the improvement of an immunological advantage of antiretroviral therapy and increases HIV/AIDS-related mortality [7]. Dropout of patients receiving antiretroviral therapy will be the reason for drug toxicity, treatment failure due to poor adherence, and drug resistance [810] this directly leads to death [1115]. 40% of all patients on antiretroviral therapy were dropout in sub-Saharan Africa [16, 17]. Of all dropout patients in the region of sub-Saharan Africa, 46% of them were died [16].

Antiretroviral therapy can reduce HIV replication and it develops the immune ability [18]. There are limited data accesses about the results of the ART in Ethiopia. In Oromia region, there were 194,370 HIV/AIDS patients and of the 115,334 were on antiretroviral therapy. Of them, only 59.3% of HIV/AIDS patients were on ART which was far from adequate [19]. Another investigation also explained that the rate of antiretroviral therapy failure in private health facilities in Ethiopia was 20.4% [20]. In Jimma, one out of five adults had to antiretroviral therapy dropout which is a disaster for once country which aims to minimize the effect of HIV/AIDS [21].

HIV/AIDS patients with poor antiretroviral therapy follow up outcome are at high risk of death by two times than patients with good follow up adherence [22]. Patients who have poor follow up status were at risk of death by four times than who have well-adhered patients in Addis Ababa [23]. The risk of death of poor adhered patients is five times greater than better-adhered patients [24]. The study in Ethiopia also showed that around 50% of the antiretroviral therapy dropout patients were dead [25]. HIV/AIDS Patients who dropout antiretroviral therapy will likely die in a short period of time [26]. Ethiopia is among one of the most HIV/AIDS prevalence countries globally. ART treatment has a great role to prolong the life of HIV patients but, there were a high percentage of dropouts from antiretroviral therapy which causes directly facilitate death [2729]. A study which was conducted in the Illubabor Zone recommended that investigation on antiretroviral therapy dropout in the area is timely [30]. Therefore, the aim of this study was to determine predictors of antiretroviral therapy dropout of HIV/AIDS patients at Mettu Karl Hospital in Illubabor, Ethiopia.

Main text

Study area

This study was conducted at Mettu Karl referral Hospital which is found in Ilubabor Zone, Oromia region, southwest part of Ethiopia. This is 600 km far from the capital city of Ethiopia. Mettu is known for its waterfalls such as Sor fall and surrounding evergreen forest.

Study design

The study was applied a retrospective cohort study design. All patients on antiretroviral therapy from September 2005 up to January 2018 were considered in the study. Secondary data from the Hospital registry was used to retrieve data all about HIV AIDS patients on antiretroviral therapy follow up. There were 3517 patients in a given time interval. Of which a total of 1512 patients were included in the study in a given time interval depending on exclusion criteria (see Additional file 1).

Variables

The dependent variable is survival time to dropout from the ART starting from September 2005 up to January 2018. The predictor variables were sex, occupation, WHO clinical stage, marital status, baseline regimen type, age, religion, educational level, CD4 level, religion, and body weight.

Exclusion criteria

Patients with; an incomplete variable of interest, transfer out and death outcomes were excluded from inferential analysis.

Survival data analysis

Factors associated with predictors of time to dropout from ART were analyzed using Kaplan–Meier comparison and log-logistic regression AFT model. Variables with p value < 0.05 was considered statistically significant.

Kaplan–Meier estimation

The Kaplan–Meier is a nonparametric method used to estimate the survival experience. The survival experience of two or more groups of between-subjects factor can be compared for equality. It is a nonparametric estimator of the survivor function S(t).
$$\hat{S}(t) = \prod\limits_{{t_{J} < t}} {\left(1 - \frac{{d_{j} }}{{n_{j} }}\right)}$$
where \(d_{j}\), is the number of individuals who experience the event at time \(t_{j}\), and, \(n_{j}\) is the number of individuals.

Log-logistic accelerated failure time model

The log-logistic distribution provides the most commonly used AFT model. The log-logistic regression can exhibit a non-monotonic hazard function which increases at early times and decreases at later times. It is similar in shape to the log-normal distribution but its cumulative distribution function has a simple closed form, which becomes important computationally when fitting data with censoring. The log-logistic survival and hazard function for a log-linear model with no covariates (logT = μ + δε) are;
$${\text{S}}\left( {\text{t}} \right) = \frac{1}{{1 + {\text{e}}^{\theta } {\text{t}}^{\gamma } }}$$
$${\text{H}}\left( {\text{t}} \right) = \frac{{{\text{e}}^{\theta } \gamma {\text{t}}^{\gamma - 1} }}{{1 + {\text{e}}^{{\theta {\text{t}}^{\gamma } }} }}$$
where θ =  \(\frac{ - \mu }{\sigma }\) and \(\gamma = \frac{1}{\sigma }\) are unknown parameters.

Results

There were 1512 patients in the cohort study out of which 243 (16.1%) were LTFU. From the total of HIV/AIDS patients, 933 (61.7%) of them were female and 579 (38.3%) were male. The majority of patients 817 (54%) of them were married. From all, 1109 (73.3%) of them were Christians others were Muslim. On the subject of education, 663 (43.8%) of them were primary education complete, 338 (22.4%) of them were secondary education complete, 267 (17.7%) of them were unable to read and write (illiterate), 244 (16.1%) were above secondary education level. Majority of patients 459 (30.4%) were merchants. Of all patients, 520 (34.4%) were started ART at WHO clinical stage three. On the regimen type, there were 120 (7.9%), 488 (32.3%), 493 (32.6%) and 411 (27.2%) patients who took AZT-3TC-EFV, D4t-3TC-NVP, D4t-3TC-EFV and AZT-3TC-NVP medication type respectively. The average age of patients was 33 years and the mean follow up time of patients were 6 years (Table 1).
Table 1

Descriptive analysis of variables

N = 1512

Number of events

(%)

Outcome

 Number of dropout

243

16.1

 Number of censored

1269

83.9

Sex

 Female

933

61.7

 Male

579

38.3

Marital status

 Divorced

188

12.4

 Married

817

54.0

 Separated

154

10.2

 Widow

176

11.6

 Never married

177

11.7

Educational level

 Illiterate

267

17.7

 Primary school

663

43.8

 Secondary school

338

22.4

 Above secondary

244

16.1

Religion

 Christian

1109

73.3

 Muslim

403

26.7

WHO clinical stage

 Stage I

475

31.4

 Stage II

352

23.3

 Stage III

520

34.4

 Stage IV

165

10.9

Original regimen

 D4t-3TC-NVP

488

32.3

 D4t-3TC-EFV

493

32.6

 AZT-3TC-NVP

411

27.2

 AZT-3TC-EFV

120

7.9

Occupation

 Housewife

344

22.8

 Daily labour

296

19.6

 Farmer

189

12.5

 Government worker

224

14.8

 Merchant

459

30.4

From the Chi square test result, dropout was significantly associated with WHO clinical stage (p value = 0.018) and marital status (p-value = 0.007) (see Additional file 2).

Kaplan–Meier survival estimates

The Kaplan–Meier graph showed that the survival ability of patients marital status married is less than patients with never married (see Additional file 3). From the Kaplan–Meier, log-rank test in Table 2 shows that the survival experience of patients related with occupation and original regimen type status had a significant difference on time to ART dropout at 5% of a significant level.
Table 2

Kaplan Meier long rank test result

Variables

Mean estimate

Median estimate

p

Estimate

95% CI

Estimate

95% CI

LCI

UCI

LCI

UCI

Sex

 Female

182.905

133.502

232.308

135.000

132.501

137.499

0.0889

 Male

131.761

119.924

143.597

131.000

124.685

137.315

Marital

 Divorced

116.161

110.373

121.948

126.000

114.974

137.026

0.0001

 Married

148.991

131.994

165.987

135.000

121.870

148.130

 Separated

140.209

134.111

146.308

149.000

128.970

169.030

 Widow

117.070

108.881

125.258

124.000

112.716

135.284

 Never married

171.348

104.636

238.061

130.000

121.385

138.615

Education

 Illiterate

214.268

165.411

263.126

133.000

124.420

141.580

0.1716

 Primary school

137.598

127.585

147.610

135.000

129.390

140.610

 Secondary school

151.528

135.514

167.542

132.000

128.993

135.007

 Above secondary

126.392

117.717

135.068

130.000

117.280

142.720

Religion

 Christian

160.112

124.299

195.926

132.000

127.944

136.056

0.0694

 Muslim

150.494

132.746

168.241

156.000

123.592

188.408

Occupation

 Housewife

233.275

168.920

297.630

149.000

133.260

164.740

0.0001

 Daily labour

137.090

117.988

156.192

130.000

120.881

139.119

 Farmer

122.867

116.163

129.571

138.000

110.609

165.391

 Government worker

117.358

109.166

125.550

118.000

113.796

122.204

 Merchant

125.597

119.114

132.080

129.000

122.795

135.205

WHO clinical stage

 Stage I

134.390

128.543

140.238

   

0.8367

 Stage II

130.374

123.499

137.250

138.000

115.751

160.249

 Stage III

165.794

125.912

205.675

134.000

131.714

136.286

 Stage IV

144.512

120.156

168.868

133.000

127.310

138.690

Regimen type

 D4t-3TC-NVP

209.679

156.318

263.041

134.000

129.245

138.755

0.0001

 D4t-3TC-EFV

117.646

111.596

123.696

127.000

118.607

135.393

 AZT-3TC-NVP

134.049

129.517

138.581

135.000

129.151

140.849

 AZT-3TC-EFV

125.931

117.382

134.481

123.000

113.594

132.406

Model selection

The study used the AIC criterion to compare different models. For each model, the value is computed as AIC = −2 log (likelihood) + 2(p + k). Based on the following statistics value of the AIC/BIC criteria parametric model with log-logistic was preferable for modelling since the smallest value is preferable (see Additional file 4).

From the log-logistic regression model; when a CD4 level added by one unit, the risk of dropout increased by 0.05% (AHR = 1.0005). Likewise, a unit change of weight could accelerate time to dropout by 0.31% (AHR = 1.0031). The risk of dropout of patients with married marital status was 9.8% greater as compared with divorced. Patients ART dropout with separated marital status were at risk as compared to married by 16.82%. The probability of ART dropout with primary education level was 10.58% greater than the illiterate patients. The risks of dropout of patients with daily labour were 87.44% greater than that of housewife. Similarly, the risks to dropout of being farmer were 82.73% as compared to housewife. Being dropout from ART with government worker was increased by 73.72% as compared to a housewife (p < 0.001). Being a merchant also had a negative impact on dropout as compared to housewife. Patients who took D4t-3TC-EFV medication type had a greater risk of dropout as compared to patients who took D4t-3TC-NVP by 84.23% (Table 3).
Table 3

Log-logistic AFT model result

Model

AHR

p

95% confidence interval

Age

1.0034

0.0630

0.9998

1.0070

Marital status

 Divorced (ref)

  Married

1.0980

0.0390

1.0049

1.1999

  Separated

1.1682

0.0070

1.0444

1.3067

  Widow

0.9323

0.2000

0.8376

1.0377

  Never married

1.0987

0.1030

0.9812

1.2302

Education

 Illiterate (ref)

  Primary school

1.1058

0.0110

1.0236

1.1945

  Secondary school

1.0526

0.2680

0.9612

1.1527

  Above secondary

1.0724

0.2670

0.9480

1.2131

Occupation

 Housewife (ref)

  Daily labour

0.8744

0.0150

0.7848

0.9743

  Farmer

0.8273

0.0010

0.7413

0.9233

  Government worker

0.7372

0.0001

0.6709

0.8100

  Merchant

0.8293

0.0001

0.7656

0.8984

  CD4

1.0005

0.0001

1.0003

1.0007

  Weight

1.0031

0.0890

0.9995

1.0066

Original regimen

 D4t-3TC-NVP (ref)

  D4t-3TC-EFV

0.8423

0.0001

0.7811

0.9083

  AZT-3TC-NVP

1.0467

0.1990

0.9762

1.1222

  AZT-3TC-EFV

0.9707

0.5720

0.8757

1.0760

AHR, adjusted hazard ratio; p, p value; Ref, reference category

Discussion

In this survival retrospective cohort study, there were 243 dropouts from 1512 patients, yielding antiretroviral therapy dropout prevalence were 17/100 patients. In Gambia, only 17.2% dropout was observed [31]. Another study in Nigeria stated that there were 74.9% had been ART dropout which is greater than this investigation [32]. A study which found in sub-Saharan Africa stated that this percentage will vary from 5.7 to 28.9% [33]. A study which was conducted in the region also stated that the percentage of patients dropout was estimated to be up to 31% [34]. The average age of all patients was 33 which is the most productive age group, another study also in Zambia same echo shows that the median age were 34 [35]. Other studies across the country also statement between 31 and 33 [27, 36, 37], which is almost consistent with this study. Even though many manuscript papers stated that age was as a significant factor for antiretroviral therapy dropout, this study explained that age was not a significant impact on antiretroviral therapy dropout. This is inconsistent with findings from other studies [38]. Unlike other studies, weight and WHO clinical stage were not a responsible cause of antiretroviral therapy dropout [3944]. Patients with higher CD4 level have a greater risk of dropout [AHR = 1.0005 (1.0003–1.0007)], which is directly related with the study in the UK [45] and Hospital of Bergamo cohorts [46], where dropout was related with a higher CD4 count level. Another study in French found that patients with higher CD4 count have increased the risk of antiretroviral therapy dropout [35, 47]. This study stated that sex was not a responsible factor for loss from treatment, but another study in Ethiopia stated that being male was one of the predictors for antiretroviral therapy dropout [48]. Likewise, no association was found between sex and loss from treatment [4951], but not other studies [5254]. The difference may arise because of sample size, study design and follow up time difference. Some previous studies suggest that marital status can predict dropout among ART initiators [5557]. In this data, the patient’s initially receiving D4t-3TC-EFV regimens had decreased risk of dropout as compared with patients who took D4t-3TC-NVP medication type. But the regimen type AZT was not a significant predictor as compared to D4T based which is consistent with another study [57]. This study will serve as resource material for researchers, managers, policymakers. Additionally, the study will be used as a baseline for further researchers.

Conclusion

In conclusion, HIV/AIDS patients on antiretroviral therapy were dropout in a short period due to patients marital status married and separated, primary education level, high level of CD4 count, being merchants, farmer and daily labour. Investigation on the cause of antiretroviral therapy dropout from a number of HIV/AIDS clinics in the country is highly appreciated.

Limitations

There were a lot of patients with incomplete records which were excluded from this investigation; this may affect the conclusion of the study.

Abbreviations

WHO: 

World Health Organization

HIV: 

human immunodeficiency virus

AIDS: 

acquired immune deficiency syndrome

AFT: 

accelerated failure time

ART: 

antiretroviral therapy

Declarations

Authors’ contributions

This research paper entire activity was done by MT. The author read and approved the final manuscript.

Acknowledgements

The author wishes to thank Mettu Karl Hospital workers specifically Mr. Tadele Mitiku, for his willingness and help during the entire data collection process.

Competing interests

The author declares no competing interests.

Availability of data and materials

If needed the raw data in excel format for this article is available.

Consent for publication

Not applicable.

Ethics approval and consent to participate

This study used secondary data from medical case records and patients were not contacted. The data from the case records were handled with strong responsibility and confidentiality. The study was started after ethical clearance was obtained from Mettu University research committee and permission was taken from Mettu Karl Hospital medical director to collect data from records.

Funding

There was no fund.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Statistics, Injibara University, Injibara, Amhara, Ethiopia

References

  1. Joint United Nations Programme on HIV and AIDS and World Health Organization. AIDS Epidemic Update. Geneva: Joint United Nations Programme on HIV and AIDS; 2007. http://data.unaids.org/pub/EPISlides/2007/2007_epiupdate_en.pdf.
  2. United Nations General Assembly. Political Declaration on HIV/AIDS: intensifying our efforts to eliminate HIV/AIDS. New York, United Nations, 2011. http://www.un.org/Docs/journal/asp/ws.asp?m=A/65/L.77.
  3. Global plan towards the elimination of new HIV infections among children by 2015 and keeping their mother alive—2011–2015. Geneva: UNAIDS; 2011. http://www.unaids.org/en/media/unaids/contentassets/documents/unaidspublication/2011/20110609_JC2137_Global-Plan-Elimination-HIV-Children_en.pdf.
  4. World health organization, HIV/AIDS, Ethiopia. http://apps.who.int/gho/data/node.main.626.
  5. Federal democratic republic of Ethiopia, Country progress report on the HIV response, 2014.Google Scholar
  6. World Health Organization. Retention in HIV Programmes Defining the Challenges and Identifying Solutions. 2011. http://apps.who.int/iris/bitstream/10665/44878/1/9789241503686_eng.pdf.
  7. Hogg RS, Heath K, Bangsberg D, Yip B, Press N, O’shaughnessy MV, Montaner JS. Intermittent use of triple-combination therapy is predictive of mortality at baseline and after 1 year of follow-up. AIDS. 2002;16(7):1051–8.View ArticlePubMedGoogle Scholar
  8. Kaplan JE, Hanson D, Dworkin MS, Frederick T, Bertolli J, Lindegren ML, Holmberg S, Jones JL. Epidemiology of human immunodeficiency virus-associated opportunistic infections in the United States in the era of highly active antiretroviral therapy. Clin Infect Dis. 2000;30(1):S5–14.View ArticlePubMedGoogle Scholar
  9. Low-Beer S, Yip B, O’shaughnessy MV, Hogg RS, Montaner JS. Adherence to triple therapy and viral load response. J Acquir Immune Defic Syndr. 2000;23(4):360–1.View ArticlePubMedGoogle Scholar
  10. Taiwo B. Understanding transmitted HIV resistance through the experience in the USA. Int J Infect Dis. 2009;13(5):552–9.View ArticlePubMedGoogle Scholar
  11. Dalal RP, MacPhail C, Mqhayi M, Wing J, Feldman C, Chersich MF, Venter WD. Characteristics and outcomes of adult patients lost to follow-up at an antiretroviral treatment clinic in Johannesburg, South Africa. J Acquir Immune Defic Syndr. 2008;47(1):101–7.PubMedGoogle Scholar
  12. Brennan AT, Maskew M, Sanne I, Fox MP. The importance of clinic attendance in the first six months on antiretroviral treatment: a retrospective analysis at a large public sector HIV clinic in South Africa. J Int AIDS Soc. 2010;13(1):49.View ArticlePubMedPubMed CentralGoogle Scholar
  13. Bygrave H, Kranzer K, Hilderbrand K, Whittall J, Jouquet G, Goemaere E, Vlahakis N, Triviño L, Makakole L, Ford N. Trends in loss to follow-up among migrant workers on antiretroviral therapy in a community cohort in Lesotho. PLoS ONE. 2010;5(10):e13198.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Malcolm S, Ng J, Rosen R, Stone V. An examination of HIV/AIDS patients who have excellent adherence to HAART. AIDS Care. 2003;15(2):251–61.View ArticlePubMedGoogle Scholar
  15. Murphy DA, Sarr M, Durako SJ, Moscicki A-B, Wilson CM, Muenz LR. Barriers to HAART adherence among human immunodeficiency virus-infected adolescents. Arch Pediatr Adolesc Med. 2003;157(3):249–55.View ArticlePubMedGoogle Scholar
  16. Brinkhof MW, Pujades-Rodriguez M, Egger M. Mortality of patients lost to follow-up in antiretroviral treatment programmes in resource-limited settings: systematic review and meta-analysis. PLoS ONE. 2009;4(6):e5790.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Fatti G, Meintjes G, Shea J, Eley B, Grimwood A. Improved survival and antiretroviral treatment outcomes in adults receiving community-based adherence support: 5-year results from a multicentre cohort study in South Africa. J Acquir Immune Defic Syndr. 2012;61(4):e50–8.View ArticlePubMedGoogle Scholar
  18. Federal HIV/AIDS Prevention and Control Office, Federal Ministry of Health. Guidelines for management of opportunistic infections and antiretroviral treatment in adolescences and adults in Ethiopia. http://www.who.int/hiv/pub/guidelines/ethiopia_art.pdf.
  19. Yassin S, Gebretekle GB. Magnitude and predictors of antiretroviral treatment failure among HIV-infected children in Fiche and Kuyu hospitals, Oromia region, Ethiopia: a retrospective cohort study. Pharmacol Res Persp. 2017;5(1):e00296.View ArticleGoogle Scholar
  20. Yimer YT, Yalew AW. Magnitude and predictors of anti-retroviral treatment (ART) failure in private health facilities in Addis Ababa, Ethiopia. PLoS ONE. 2015;10(5):e0126026.View ArticleGoogle Scholar
  21. Gesesew HA, Ward P, Woldemichael K, Mwanri L. Prevalence, trend and risk factors for antiretroviral therapy discontinuation among HIV-infected adults in Ethiopia in 2003–2015. PLoS ONE. 2017;12(6):e0179533.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Abebe N, Alemu K, Asfaw T, Abajobir AA. Survival status of hiv positive adults on antiretroviral treatment in Debre Markos Referral Hospital, Northwest Ethiopia: retrospective cohort study. Pan Afr Med J. 2014;17:88.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Bedru A, Worku A. Assessment of predictors of survival in patients living with HIV/AIDS after the advent of highly active antiretroviral therapy in Addis Ababa Ethiopia [MPH thesis]. Addis Ababa University. 2009.Google Scholar
  24. Abose G, Enkusilassie F. Survival status among patient living with HIV/AIDS who are on art treatment in Durame and Hossana hospitals: a retrospective longitudinal study. Thesis; 2012.Google Scholar
  25. Wubshet M, Berhane Y, Worku A, Kebede Y. Death and seeking alternative therapy largely accounted for lost to follow-up of patients on ART in northwest Ethiopia: a community tracking survey. PLoS ONE. 2013;8(3):e59197.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Keiser O, Tweya H, Braitstein P, Dabis F, MacPhail P, Boulle A, Nash D, Wood R, Lüthi R, Brinkhof MW. Mortality after failure of antiretroviral therapy in sub-Saharan Africa. Trop Med Int Health. 2010;15(2):251–8.View ArticlePubMedGoogle Scholar
  27. Alemu AW, Sebastián MS. Determinants of survival in adult HIV patients on antiretroviral therapy in Oromiyaa, Ethiopia. Glob Health Action. 2010;3(1):5398.View ArticleGoogle Scholar
  28. Teklu AM, Nega A, Mamuye AT, Sitotaw Y, Kassa D, Mesfin G, Belayihun B, Medhin G, Yirdaw K. Factors associated with mortality of TB/HIV co-infected patients in Ethiopia. Ethiop J Health Sci. 2017;27(1):29–38.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Bitew S, Mekonen A, Assegid M. Predictors on mortality of human immunodeficiency virus infected children after initiation of antiretroviral treatment in Wolaita zone health facilities, Ethiopia: retrospective cohort study. J AIDS HIV Res. 2017;9(4):89–97.Google Scholar
  30. Tadege M. Time to death predictors of HIV/AIDS infected patients on antiretroviral therapy in Ethiopia. BMC Res Notes. 2018;11(1):761.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Togun T, Peterson I, Jaffar S, Oko F, Okomo U, Peterson K, Jaye A. Pre-treatment mortality and loss-to-follow-up in HIV-1, HIV-2 and HIV-1/HIV-2 dually infected patients eligible for antiretroviral therapy in The Gambia, West Africa. AIDS Res Ther. 2011;8(1):24.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Agolory SG, Auld AF, Odafe S, Shiraishi RW, Dokubo EK, Swaminathan M, Dalhatu I, Onotu D, Abiri O, Debem H. High rates of loss to follow-up during the first year of pre-antiretroviral therapy for HIV patients at sites providing pre-ART care in Nigeria, 2004–2012. PLoS ONE. 2017;12(9):e0183823.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Brinkhof MW, Spycher BD, Yiannoutsos C, Weigel R, Wood R, Messou E, Boulle A, Egger M, Sterne JA. AIDS IeDtE: adjusting mortality for loss to follow-up: analysis of five ART programmes in sub-Saharan Africa. PLoS ONE. 2010;5(11):e14149.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Rosen S, Fox MP, Gill CJ. Patient retention in antiretroviral therapy programs in sub-Saharan Africa: a systematic review. PLoS Med. 2007;4(10):e298.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Li MS, Musonda P, Gartland M, Mulenga PL, Mwango A, Stringer JS, Chi BH. Predictors of patient attrition according to different definitions for loss to follow-up: a comparative analysis from Lusaka, Zambia. J Acquir Immune Defic Syndr. 2013;63(3):e116.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Amanzi P, Michelo C, Simoonga C, Dambe R, Chongwe G. Survival of people on antiretroviral treatment in Zambia: a retrospective cohort analysis of HIV clients on ART. Pan Afr Med J. 2016;24:144.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Sieleunou I, Souleymanou M, Schönenberger AM, Menten J, Boelaert M. Determinants of survival in AIDS patients on antiretroviral therapy in a rural centre in the Far-North Province, Cameroon. Trop Med Int Health. 2009;14(1):36–43.View ArticlePubMedGoogle Scholar
  38. Fox MP, Shearer K, Maskew M, Meyer-Rath G, Clouse K, Sanne I. Attrition through multiple stages of pre-treatment and ART HIV care in South Africa. PLoS ONE. 2014;9(10):e110252.View ArticlePubMedPubMed CentralGoogle Scholar
  39. Lawn SD, Harries AD, Anglaret X, Myer L, Wood R. Early mortality among adults accessing antiretroviral treatment programmes in sub-Saharan Africa. AIDS (London, England). 2008;22(15):1897–908.View ArticleGoogle Scholar
  40. Feldacker C, Johnson D, Hosseinipour M, Phiri S, Tweya H. Who starts? Factors associated with starting antiretroviral therapy among eligible patients in two, public HIV clinics in Lilongwe, Malawi. PLoS ONE. 2012;7(11):e50871.View ArticlePubMedPubMed CentralGoogle Scholar
  41. IeDEA T. Immunodeficiency at the start of combination antiretroviral therapy in low-, middle-and high-income countries. J Acquir Immune Defic Syndr. 2014;65(1):8.Google Scholar
  42. Hassan AS, Fielding KL, Thuo NM, Nabwera HM, Sanders EJ, Berkley JA. Early loss to follow-up of recently diagnosed HIV-infected adults from routine pre-ART care in a rural district hospital in Kenya: a cohort study. Trop Med Int Health. 2012;17(1):82–93.View ArticlePubMedGoogle Scholar
  43. Ingle S, Margaret M, Uebel K, Timmerman V, Kotze E, Bachmann M, Sterne JA, Egger M, Fairall L. Outcomes in patients waiting for antiretroviral treatment in the Free State Province, South Africa: prospective linkage study. AIDS (London, England). 2010;24(17):2717.View ArticleGoogle Scholar
  44. Mulissa Z, Jerene D, Lindtjørn B. Patients present earlier and survival has improved, but pre-ART attrition is high in a six-year HIV cohort data from Ethiopia. PLoS ONE. 2010;5(10):e13268.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Hill T, Bansi L, Sabin C, Phillips A, Dunn D, Anderson J, Easterbrook P, Fisher M, Gazzard B, Gilson R. Data linkage reduces loss to follow-up in an observational HIV cohort study. J Clin Epidemiol. 2010;63(10):1101–9.View ArticlePubMedGoogle Scholar
  46. Arici C, Ripamonti D, Maggiolo F, Rizzi M, Finazzi M, Pezzotti P, Suter F. Factors associated with the failure of HIV-positive persons to return for scheduled medical visits. HIV Clin Trials. 2002;3(1):52–7.View ArticlePubMedGoogle Scholar
  47. Ndiaye B, Ould-Kaci K, Salleron J, Bataille P, Bonnevie F, Choisy P, Cochonat K, Fontier C, Guerroumi H, Ajana F. Incidence rate and risk factors for loss to follow-up in HIV-infected patients from five French clinical centres in Northern France-January 1997 to December 2006. Antivir Ther. 2009;14(4):567–75.PubMedGoogle Scholar
  48. Mekuria LA, Prins JM, Yalew AW, Sprangers MA, Nieuwkerk PT. Retention in HIV care and predictors of attrition from care among HIV-infected adults receiving combination anti-retroviral therapy in Addis Ababa. PLoS ONE. 2015;10(6):e0130649.View ArticlePubMedPubMed CentralGoogle Scholar
  49. Li L, Lee SJ, Wen Y, Lin C, Wan D, Jiraphongsa C. Antiretroviral therapy adherence among patients living with HIV/AIDS in Thailand. Nurs Health Sci. 2010;12(2):212–20.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Pinheiro C, Carvalho-Leite J, Drachler M, Silveira V. Factors associated with adherence to antiretroviral therapy in HIV/AIDS patients: a cross-sectional study in Southern Brazil. Braz J Med Biol Res. 2002;35(10):1173–81.View ArticlePubMedGoogle Scholar
  51. Collazos J, Asensi V, Cartón JA. Adherencia GEpeEMdl: Sex differences in the clinical, immunological and virological parameters of HIV-infected patients treated with HAART. Aids. 2007;21(7):835–43.View ArticlePubMedGoogle Scholar
  52. Mehta S, Moore RD, Graham NM. Potential factors affecting adherence with HIV therapy. Aids. 1997;11(14):1665–70.View ArticlePubMedGoogle Scholar
  53. Cahn P, Vibhagool A, Schechter M, Soto-Ramirez L, Carosi G, Smaill F, Jordan JC, Pharo CE, Thomas NE, Steel HM. Predictors of adherence and virologic outcome in HIV-infected patients treated with abacavir-or indinavir-based triple combination HAART also containing lamivudine/zidovudine. Curr Med Res Opin. 2004;20(7):1115–23.View ArticlePubMedGoogle Scholar
  54. Bonolo P, César CC, Acúrcio FA, Graças BC, Pádua CA, Álvares J, Campos LN, Carmo RA, Guimarães MD. Non-adherence among patients initiating antiretroviral therapy: a challenge for health professionals in Brazil. Aids. 2005;19:S5–13.View ArticleGoogle Scholar
  55. Meloni ST, Chang C, Chaplin B, Rawizza H, Jolayemi O, Banigbe B, Okonkwo P, Kanki P. Time-dependent predictors of loss to follow-up in a large HIV treatment cohort in Nigeria. In: Open forum infectious diseases. Oxford University Press; 2014.Google Scholar
  56. Berheto TM, Haile DB, Mohammed S. Predictors of loss to follow-up in patients living with HIV/AIDS after initiation of antiretroviral therapy. N Am J Med Sci. 2014;6(9):453.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Asiimwe SB, Kanyesigye M, Bwana B, Okello S, Muyindike W. Predictors of dropout from care among HIV-infected patients initiating antiretroviral therapy at a public sector HIV treatment clinic in sub-Saharan Africa. BMC Infect Dis. 2015;16(1):43.View ArticleGoogle Scholar

Copyright

© The Author(s) 2019

Advertisement