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Longitudinal engagement trajectories and risk of death among new ART starters in Zambia: A group-based multi-trajectory analysis


Autoři: Aaloke Mody aff001;  Ingrid Eshun-Wilson aff001;  Kombatende Sikombe aff002;  Sheree R. Schwartz aff003;  Laura K. Beres aff003;  Sandra Simbeza aff002;  Njekwa Mukamba aff002;  Paul Somwe aff002;  Carolyn Bolton-Moore aff002;  Nancy Padian aff005;  Charles B. Holmes aff006;  Izukanji Sikazwe aff002;  Elvin H. Geng aff001
Působiště autorů: Division of HIV, ID and Global Medicine, University of California, San Francisco, Zuckerberg San Francisco General Hospital, San Francisco, California, United States of America aff001;  Centre for Infectious Diseases Research in Zambia, Lusaka, Zambia aff002;  Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America aff003;  Division of Infectious Diseases, University of Alabama at Birmingham, Alabama, United States of America aff004;  Division of Epidemiology, University of California, Berkeley, California, United States of America aff005;  Department of Medicine, Georgetown University, Washington, District of Columbia, United States of America aff006
Vyšlo v časopise: Longitudinal engagement trajectories and risk of death among new ART starters in Zambia: A group-based multi-trajectory analysis. PLoS Med 16(10): e32767. doi:10.1371/journal.pmed.1002959
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pmed.1002959

Souhrn

Background

Retention in HIV treatment must be improved to advance the HIV response, but research to characterize gaps in retention has focused on estimates from single time points and population-level averages. These approaches do not assess the engagement patterns of individual patients over time and fail to account for both their dynamic nature and the heterogeneity between patients. We apply group-based trajectory analysis—a special application of latent class analysis to longitudinal data—among new antiretroviral therapy (ART) starters in Zambia to identify groups defined by engagement patterns over time and to assess their association with mortality.

Methods and findings

We analyzed a cohort of HIV-infected adults who newly started ART between August 1, 2013, and February 1, 2015, across 64 clinics in Zambia. We performed group-based multi-trajectory analysis to identify subgroups with distinct trajectories in medication possession ratio (MPR, a validated adherence metric based on pharmacy refill data) over the past 3 months and loss to follow-up (LTFU, >90 days late for last visit) among patients with at least 180 days of observation time. We used multinomial logistic regression to identify baseline factors associated with belonging to particular trajectory groups. We obtained Kaplan–Meier estimates with bootstrapped confidence intervals of the cumulative incidence of mortality stratified by trajectory group and performed adjusted Poisson regression to estimate adjusted incidence rate ratios (aIRRs) for mortality by trajectory group. Inverse probability weights were applied to all analyses to account for updated outcomes ascertained from tracing a random subset of patients lost to follow-up as of July 31, 2015. Overall, 38,879 patients (63.3% female, median age 35 years [IQR 29–41], median enrollment CD4 count 280 cells/μl [IQR 146–431]) were included in our cohort. Analyses revealed 6 trajectory groups among the new ART starters: (1) 28.5% of patients demonstrated consistently high adherence and retention; (2) 22.2% showed early nonadherence but consistent retention; (3) 21.6% showed gradually decreasing adherence and retention; (4) 8.6% showed early LTFU with later reengagement; (5) 8.7% had early LTFU without reengagement; and (6) 10.4% had late LTFU without reengagement. Identified groups exhibited large differences in survival: after adjustment, the “early LTFU with reengagement” group (aIRR 3.4 [95% CI 1.2–9.7], p = 0.019), the “early LTFU” group (aIRR 6.4 [95% CI 2.5–16.3], p < 0.001), and the “late LTFU” group (aIRR 4.7 [95% CI 2.0–11.3], p = 0.001) had higher rates of mortality as compared to the group with consistently high adherence/retention. Limitations of this study include using data observed after baseline to identify trajectory groups and to classify patients into these groups, excluding patients who died or transferred within the first 180 days, and the uncertain generalizability of the data to current care standards.

Conclusions

Among new ART starters in Zambia, we observed 6 patient subgroups that demonstrated distinctive engagement trajectories over time and that were associated with marked differences in the subsequent risk of mortality. Further efforts to develop tailored intervention strategies for different types of engagement behaviors, monitor early engagement to identify higher-risk patients, and better understand the determinants of these heterogeneous behaviors can help improve care delivery and survival in this population.

Klíčová slova:

Death rates – Patients – Probability distribution – Public and occupational health – Tuberculosis – Zambia


Zdroje

1. Chammartin F, Zurcher K, Keiser O, Weigel R, Chu K, Kiragga AN, et al. Outcomes of patients lost to follow-up in African antiretroviral therapy programs: individual patient data meta-analysis. Clin Infect Dis. 2018;67(11):1643–52. doi: 10.1093/cid/ciy347 29889240

2. Holmes CB, Bengtson A, Sikazwe I, Bolton-Moore C, Mulenga LB, Musonda P, et al. Using the side door: non-linear patterns within the HIV treatment cascade in Zambia. Conference on Retroviruses and Opportunistic Infections 2014; 2014 Mar 3–6; Boston, MA, US.

3. Hallett TB, Eaton JW. A side door into care cascade for HIV-infected patients? J Acquir Immune Defic Syndr. 2013;63(Suppl 2):S228–32. doi: 10.1097/QAI.0b013e318298721b 23764640

4. Sikombe K, Kadota JL, Simbeza S, Eshun-Wilson I, Pry J, Beres LK, et al. Understanding patient mobility in HIV-positive adults across multiple clinic in Zambia. Conference on Retroviruses and Opportunistic Infections 2018; 2018 Mar 4–7; Boston, MA, US.

5. Mody A, Sikazwe I, Savory T, Mwanza Mw, Sikombe K, Eshun-Wilson I, et al. Substantial mortality and loss prior to treatment in ART-eligible patients in Zambia. Conference on Retroviruses and Opportunistic Infections 2018; 2018 Mar 4–7; Boston, MA, US.

6. Haber N, Tanser F, Bor J, Naidu K, Mutevedzi T, Herbst K, et al. From HIV infection to therapeutic response: a population-based longitudinal HIV cascade-of-care study in KwaZulu-Natal, South Africa. Lancet HIV. 2017;4(5):e223–30. doi: 10.1016/S2352-3018(16)30224-7 28153470

7. Colasanti J, Kelly J, Pennisi E, Hu YJ, Root C, Hughes D, et al. Continuous retention and viral suppression provide further insights into the HIV care continuum compared to the cross-sectional HIV care cascade. Clin Infect Dis. 2016;62(5):648–54. doi: 10.1093/cid/civ941 26567263

8. Geng EH, Odeny TA, Lyamuya R, Nakiwogga-Muwanga A, Diero L, Bwana M, et al. Retention in care and patient-reported reasons for undocumented transfer or stopping care among HIV-infected patients on antiretroviral therapy in eastern Africa: application of a sampling-based approach. Clin Infect Dis. 2016;62(7):935–44. doi: 10.1093/cid/civ1004 26679625

9. Camlin CS, Neilands TB, Odeny TA, Lyamuya R, Nakiwogga-Muwanga A, Diero L, et al. Patient-reported factors associated with reengagement among HIV-infected patients disengaged from care in East Africa. AIDS. 2016;30(3):495–502. doi: 10.1097/QAD.0000000000000931 26765940

10. Haber N, Pillay D, Porter K, Barnighausen T. Constructing the cascade of HIV care: methods for measurement. Curr Opin HIV AIDS. 2016;11(1):102–8. doi: 10.1097/COH.0000000000000212 26545266

11. Powers KA, Miller WC. Critical review: building on the HIV cascade: a complementary “HIV states and transitions” framework for describing HIV diagnosis, care, and treatment at the population level. J Acquir Immune Defic Syndr. 2015;69(3):341–7. doi: 10.1097/QAI.0000000000000611 25835604

12. Sikazwe I, Holmes CB, Sikombe K, Czaicki N, Padian N, Wilson I, et al. A sampling-based approach to evaluate retention and its barriers across clinics in Zambia. 9th IAS Conference on HIV Science; 2017 Jul 23–26; Paris, France.

13. Pence BW, Bengtson AM, Boswell S, Christopoulos KA, Crane HM, Geng E, et al. Who will show? Predicting missed visits among patients in routine HIV primary care in the United States. AIDS Behav. 2019;23(2):418–26. doi: 10.1007/s10461-018-2215-1 30006790

14. Reveles KR, Juday TR, Labreche MJ, Mortensen EM, Koeller JM, Seekins D, et al. Comparative value of four measures of retention in expert care in predicting clinical outcomes and health care utilization in HIV patients. PLoS ONE. 2015;10(3):e0120953. doi: 10.1371/journal.pone.0120953 25794182

15. Fox MP, Rosen S. Retention of adult patients on antiretroviral therapy in low- and middle-income countries: systematic review and meta-analysis 2008–2013. J Acquir Immune Defic Syndr. 2015;69(1):98–108. doi: 10.1097/QAI.0000000000000553 25942461

16. Grimsrud A, Bygrave H, Doherty M, Ehrenkranz P, Ellman T, Ferris R, et al. Reimagining HIV service delivery: the role of differentiated care from prevention to suppression. J Int AIDS Soc. 2016;19(1):21484. doi: 10.7448/IAS.19.1.21484 27914186

17. Grimsrud A, Barnabas RV, Ehrenkranz P, Ford N. Evidence for scale up: the differentiated care research agenda. J Int AIDS Soc. 2017;20(Suppl 4):22024. doi: 10.7448/IAS.20.5.22024 28770588

18. Hagey JM, Li X, Barr-Walker J, Penner J, Kadima J, Oyaro P, et al. Differentiated HIV care in sub-Saharan Africa: a scoping review to inform antiretroviral therapy provision for stable HIV-infected individuals in Kenya. AIDS Care. 2018;30(12):1477–87. doi: 10.1080/09540121.2018.1500995 30037312

19. Powers KA, Samoff E, Weaver MA, Sampson LA, Miller WC, Leone PA, et al. Longitudinal HIV care trajectories in North Carolina. J Acquir Immune Defic Syndr. 2017;74(Suppl 2):S88–95. doi: 10.1097/QAI.0000000000001234 28079718

20. Nagin D. Group-based modeling of development. Cambridge: Harvard University Press; 2005.

21. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38. doi: 10.1146/annurev.clinpsy.121208.131413 20192788

22. Nagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Stat Methods Med Res. 2018;27(7):2015–23. doi: 10.1177/0962280216673085 29846144

23. Holmes CB, Sikazwe I, Sikombe K, Eshun-Wilson I, Czaicki N, Beres LK, et al. Estimated mortality on HIV treatment among active patients and patients lost to follow-up in 4 provinces of Zambia: findings from a multistage sampling-based survey. PLoS Med. 2018;15(1):e1002489. doi: 10.1371/journal.pmed.1002489 29329301

24. Ministry of Health. Adult and adolescent antiretroviral therapy protocols 2010. Lusaka: Zambian Ministry of Health; 2010 [cited 2019 Oct 9]. Available from: http://www.who.int/hiv/pub/guidelines/zambia_art.pdf.

25. Ministry of Health, Ministry of Community Development, Mother and Child Health. Zambia consolidated guidelines for treatment and prevention of HIV infection. Lusaka: Zambian Ministry of Health; 2014 [cited 2019 Oct 9]. Available from: http://www.moh.gov.zm/docs/reports/Consolidated%20Guidelines%20Final%20Feb%202014.pdf.

26. Geng EH, Emenyonu N, Bwana MB, Glidden DV, Martin JN. Sampling-based approach to determining outcomes of patients lost to follow-up in antiretroviral therapy scale-up programs in Africa. JAMA. 2008;300(5):506–7. doi: 10.1001/jama.300.5.506 18677022

27. Geng EH, Odeny TA, Lyamuya RE, Nakiwogga-Muwanga A, Diero L, Bwana M, et al. Estimation of mortality among HIV-infected people on antiretroviral treatment in East Africa: a sampling based approach in an observational, multisite, cohort study. Lancet HIV. 2015;2(3):e107–16. doi: 10.1016/S2352-3018(15)00002-8 26424542

28. Kabore L, Muntner P, Chamot E, Zinski A, Burkholder G, Mugavero MJ. Self-report measures in the assessment of antiretroviral medication adherence: comparison with medication possession ratio and HIV viral load. J Int Assoc Provid AIDS Care. 2015;14(2):156–62. doi: 10.1177/2325957414557263 25421930

29. Wu P, Johnson BA, Nachega JB, Wu B, Ordonez CE, Hare AQ, et al. The combination of pill count and self-reported adherence is a strong predictor of first-line ART failure for adults in South Africa. Curr HIV Res. 2014;12(5):366–75. 25426940

30. Hong SY, Jerger L, Jonas A, Badi A, Cohen S, Nachega JB, et al. Medication possession ratio associated with short-term virologic response in individuals initiating antiretroviral therapy in Namibia. PLoS ONE. 2013;8(2):e56307. doi: 10.1371/journal.pone.0056307 23509605

31. Jones BL, Nagin DS. A note on a Stata plugin for estimating group-based trajectory models. Sociol Methods Res. 2013;42:608–13.

32. Bray BC, Lanza ST, Tan X. Eliminating bias in classify-analyze approaches for latent class analysis. Struct Equ Modeling. 2015;22(1):1–11. doi: 10.1080/10705511.2014.935265 25614730

33. Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression methods in biostatistics: linear, logistic, survival, and repeated measures models. New York: Springer-Verlag; 2012.

34. Bakk Z, Tekle FB, Vermunt JK. Estimating the association between latent class membership and external variables using bias-adjsuted three-step approaches. Sociol Methodol. 2013;43(1):272–311.

35. Rao A, McCoy S. Fostering behavior change for better health. Stanford Social Innovation Review. 2015 Aug 17 [cited 2019 Oct 9]. Available from: https://ssir.org/articles/entry/fostering_behavior_change_for_better_health.

36. Hermans LE, Moorhouse M, Carmona S, Grobbee DE, Hofstra LM, Richman DD, et al. Effect of HIV-1 low-level viraemia during antiretroviral therapy on treatment outcomes in WHO-guided South African treatment programmes: a multicentre cohort study. Lancet Infect Dis. 2018;18(2):188–97. doi: 10.1016/S1473-3099(17)30681-3 29158101

37. Wang R, Haberlen SA, Palella FJ Jr, Mugavero MJ, Margolick JB, Macatangay BJ, et al. Viremia copy-years and mortality among cART-initiating HIV-positive individuals: how much viral load history is enough? AIDS. 2018;32(17):2547–56. doi: 10.1097/QAD.0000000000001986 30379686

38. Antiretroviral Therapy Cohort Collaboration (ART-CC), Vandenhende MA, Ingle S, May M, Chene G, Zangerle R, et al. Impact of low-level viremia on clinical and virological outcomes in treated HIV-1-infected patients. AIDS. 2015;29(3):373–83. doi: 10.1097/QAD.0000000000000544 25686685

39. Bernal E, Gomez JM, Jarrin I, Cano A, Munoz A, Alcaraz A, et al. Low-level viremia is associated with clinical progression in HIV-infected patients receiving antiretroviral treatment. J Acquir Immune Defic Syndr. 2018;78(3):329–37. doi: 10.1097/QAI.0000000000001678 29543636

40. Bulsara SM, Wainberg ML, Newton-John TRO. Predictors of adult retention in HIV care: a systematic review. AIDS Behav. 2018;22(3):752–64. doi: 10.1007/s10461-016-1644-y 27990582

41. Bassett IV, Coleman SM, Giddy J, Bogart LM, Chaisson CE, Ross D, et al. Barriers to care and 1-year mortality among newly diagnosed HIV-infected people in Durban, South Africa. J Acquir Immune Defic Syndr. 2017;74(4):432–8. doi: 10.1097/QAI.0000000000001277 28060226

42. Heltberg R, Oviedo AM, Talukdar F. What are the sources of risk and how do people cope? Insights from household surveys in 16 countries. Washington (DC): World Bank; 2013.

43. Knight L, Roberts BJ, Aber JL, Richter L, The Size Research Group. Household shocks and coping strategies in rural and peri-urban South Africa: baseline data from the SIZE study in Kwazulu-Natal, South Africa. J Int Dev. 2015;27:213–33.

44. Brinkley-Rubinstein L, Chadwick C, Graci M. The connection between serious life events, anti-retroviral adherence, and mental health among HIV-positive individuals in the Western Cape, South Africa. AIDS Care. 2013;25(12):1581–5. doi: 10.1080/09540121.2013.793270 23656514

45. Leserman J, Ironson G, O’Cleirigh C, Fordiani JM, Balbin E. Stressful life events and adherence in HIV. AIDS Patient Care STDS. 2008;22(5):403–11. doi: 10.1089/apc.2007.0175 18373416

46. Mugavero MJ, Raper JL, Reif S, Whetten K, Leserman J, Thielman NM, et al. Overload: impact of incident stressful events on antiretroviral medication adherence and virologic failure in a longitudinal, multisite human immunodeficiency virus cohort study. Psychosom Med. 2009;71(9):920–6. doi: 10.1097/PSY.0b013e3181bfe8d2 19875634

47. Pence BW, Raper JL, Reif S, Thielman NM, Leserman J, Mugavero MJ. Incident stressful and traumatic life events and human immunodeficiency virus sexual transmission risk behaviors in a longitudinal, multisite cohort study. Psychosom Med. 2010;72(7):720–6. doi: 10.1097/PSY.0b013e3181e9eef3 20595416

48. O’Donnell JK, Gaynes BN, Cole SR, Edmonds A, Thielman NM, Quinlivan EB, et al. Stressful and traumatic life events as disruptors to antiretroviral therapy adherence. AIDS Care. 2017;29(11):1378–85. doi: 10.1080/09540121.2017.1307919 28351158

49. Corless IB, Guarino AJ, Nicholas PK, Tyer-Viola L, Kirksey K, Brion J, et al. Mediators of antiretroviral adherence: a multisite international study. AIDS Care. 2013;25(3):364–77. doi: 10.1080/09540121.2012.701723 22774796

50. Topp SM, Mwamba C, Sharma A, Mukamba N, Beres LK, Geng E, et al. Rethinking retention: mapping interactions between multiple factors that influence long-term engagement in HIV care. PLoS ONE. 2018;13(3):e0193641. doi: 10.1371/journal.pone.0193641 29538443

51. Ware NC, Wyatt MA, Geng EH, Kaaya SF, Agbaji OO, Muyindike WR, et al. Toward an understanding of disengagement from HIV treatment and care in sub-Saharan Africa: a qualitative study. PLoS Med. 2013;10(1):e1001369. doi: 10.1371/journal.pmed.1001369 23341753

52. Ridgway J, Ramachandran A, Koenig H, Kumar A, Walsh J, Sung C, et al. Predictive analytics for retention in HIV care. 13th International Conference on HIV Treatment and Prevention Adherence; 2018 Jun 8–10; Miami, FL, US.

53. Haberer JE, Sabin L, Amico KR, Orrell C, Galarraga O, Tsai AC, et al. Improving antiretroviral therapy adherence in resource-limited settings at scale: a discussion of interventions and recommendations. J Int AIDS Soc. 2017;20(1):21371. doi: 10.7448/IAS.20.1.21371 28630651

54. Keene C, Cassidy T, Makeleni-Leteze T, Dutyulwa T, Dumile N, Flowers T, et al. Medecins Sans Frontieres’ Welcome Service: a collaborative reorganisation of HIV services to address disengagement from care in Khayelitsha, South Africa. 9th Annual SA AIDS Conference; 2019 Jun 11–14; Durban, South Africa.

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Interní lékařství

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