Beyond the HIV Care Cascade: A Rapid Review of Patient Trajectories, Care Transitions and Multistate Modeling Approaches
DOI:
https://doi.org/10.58812/wsshs.v4i10.3166Keywords:
HIV Care Cascade, Multistate Model, Care Transitions, Retention in Care, Re-Engagement, Loss to Follow-up, Competing RisksAbstract
The conventional HIV care cascade reports cross-sectional proportions and conceals repeated movement between care states. This rapid review examined how studies published from 2021 to 2026 document patient trajectories, care transitions, and multistate modeling in HIV care. Searches were run on 12 October 2026 with a general web search tool because bibliographic database counts could not be obtained; one reviewer screened 47 records, and most full texts were inaccessible, so extraction is largely abstract-level. Thirteen studies were included: six multistate analyses, one descriptive cyclical-cascade study, three group-based trajectory analyses, and three competing-risk analyses. Disengagement and re-engagement recurred across studies from Africa and North America, and loss to follow-up often conflated transfer, death, and disengagement. Younger age, depression, alcohol use, travel distance, and health-system factors were associated with transitions in individual studies. Definitions of disengagement ranged from 28 days to 18 months, which prevented pooling, and no study modeled diagnosis-to-linkage transitions. The evidence is heterogeneous and associational. Standardized state definitions, explicit handling of transfers and mortality, and complete database searching are needed.
References
[1] E. M. Gardner, M. P. McLees, J. F. Steiner, C. del Rio, and W. J. Burman, “The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection,” Clin. Infect. Dis., vol. 52, no. 6, pp. 793–800, Mar. 2011, doi: 10.1093/cid/ciq243.
[2] UNAIDS, Global AIDS Strategy 2021–2026: End Inequalities. End AIDS. Geneva, Switzerland: Joint United Nations Programme on HIV/AIDS, 2021.
[3] H. Masanja et al., “Statistical methods applied for the assessment of the HIV cascade and continuum of care: a systematic scoping review,” BMJ Open, vol. 13, no. 11, Art. no. e071392, Nov. 2023. [Online]. Available: https://bmjopen.bmj.com/content/13/11/e071392
[4] M. J. Mugavero et al., “Measuring retention in HIV care: The elusive gold standard,” J. Acquir. Immune Defic. Syndr., vol. 61, no. 5, pp. 574–580, Dec. 2012, doi: 10.1097/QAI.0b013e318273762f.
[5] S. Rosen, M. P. Fox, and C. J. Gill, “Patient retention in antiretroviral therapy programs in sub-Saharan Africa: A systematic review,” PLoS Med., vol. 4, no. 10, Art. no. e298, Oct. 2007, doi: 10.1371/journal.pmed.0040298.
[6] M. P. Fox and S. Rosen, “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., vol. 69, no. 1, pp. 98–108, May 2015, doi: 10.1097/QAI.0000000000000553.
[7] K. Kranzer, D. Govindasamy, N. Ford, V. Johnston, and S. D. Lawn, “Quantifying and addressing losses along the continuum of care for people living with HIV infection in sub-Saharan Africa: A systematic review,” J. Int. AIDS Soc., vol. 15, no. 2, Art. no. 17383, 2012, doi: 10.7448/IAS.15.2.17383.
[8] K. Zürcher et al., “Outcomes of HIV-positive patients lost to follow-up in African treatment programmes,” Trop. Med. Int. Health, vol. 22, no. 4, pp. 375–387, Apr. 2017, doi: 10.1111/tmi.12843.
[9] M. J. Mugavero, K. R. Amico, T. Horn, and M. A. Thompson, “The state of engagement in HIV care in the United States: From cascade to continuum to control,” Clin. Infect. Dis., vol. 57, no. 8, pp. 1164–1171, Oct. 2013, doi: 10.1093/cid/cit420.
[10] P. Ehrenkranz et al., “The revolving door of HIV care: Revising the service delivery cascade to achieve the UNAIDS 95-95-95 goals,” PLoS Med., vol. 18, no. 5, Art. no. e1003651, May 2021, doi: 10.1371/journal.pmed.1003651.
[11] A. Mody et al., “Longitudinal care cascade outcomes among people eligible for antiretroviral therapy who are newly linking to care in Zambia: A multistate analysis,” Clin. Infect. Dis., vol. 71, no. 10, pp. e561–e570, Nov. 2020, doi: 10.1093/cid/ciaa268.
[12] S. Saeed et al., “Frequent disengagement and subsequent mortality among people with HIV and hepatitis C in Canada: A prospective cohort study,” Open Forum Infect. Dis., vol. 11, no. 5, Art. no. ofae239, Apr. 2024, doi: 10.1093/ofid/ofae239.
[13] J. Euvrard et al., “The cyclical cascade of HIV care: Temporal care engagement trends within a population-wide cohort,” PLoS Med., vol. 21, no. 5, Art. no. e1004407, May 2024, doi: 10.1371/journal.pmed.1004407.
[14] P. K. Andersen and N. Keiding, “Multi-state models for event history analysis,” Stat. Methods Med. Res., vol. 11, no. 2, pp. 91–115, Apr. 2002, doi: 10.1191/0962280202SM276ra.
[15] H. Putter, M. Fiocco, and R. B. Geskus, “Tutorial in biostatistics: Competing risks and multi-state models,” Stat. Med., vol. 26, no. 11, pp. 2389–2430, May 2007, doi: 10.1002/sim.2712.
[16] L. Meira-Machado, J. de Uña-Álvarez, C. Cadarso-Suárez, and P. K. Andersen, “Multi-state models for the analysis of time-to-event data,” Stat. Methods Med. Res., vol. 18, no. 2, pp. 195–222, Apr. 2009, doi: 10.1177/0962280208092301.
[17] J. P. Fine and R. J. Gray, “A proportional hazards model for the subdistribution of a competing risk,” J. Am. Stat. Assoc., vol. 94, no. 446, pp. 496–509, Jun. 1999, doi: 10.1080/01621459.1999.10474144.
[18] P. C. Austin, D. S. Lee, and J. P. Fine, “Introduction to the analysis of survival data in the presence of competing risks,” Circulation, vol. 133, no. 6, pp. 601–609, Feb. 2016, doi: 10.1161/CIRCULATIONAHA.115.017719.
[19] D. S. Nagin and C. L. Odgers, “Group-based trajectory modeling in clinical research,” Annu. Rev. Clin. Psychol., vol. 6, pp. 109–138, 2010, doi: 10.1146/annurev.clinpsy.121208.131413.
[20] P.-P. Hung and H.-H. Wei, “From pediatric to adult HIV care: What works to keep adolescents engaged—A systematic review of transition strategies,” Trop. Med. Infect. Dis., vol. 10, no. 10, Art. no. 295, 2025, doi: 10.3390/tropicalmed10100295.
[21] I. Y. Mauleti, K. A. Wibisana, et al., “Factors associated with long-term retention in antiretroviral therapy among people living with HIV: Evidence from a tertiary hospital in Jakarta, Indonesia,” J. Prev. Med. Public Health, May 2024 [volume and pages to be verified].
[22] World Health Organization, Guideline on When to Start Antiretroviral Therapy and on Pre-Exposure Prophylaxis for HIV. Geneva, Switzerland: WHO, 2015.
[23] World Health Organization, Consolidated Guidelines on HIV Prevention, Testing, Treatment, Service Delivery and Monitoring: Recommendations for a Public Health Approach. Geneva, Switzerland: WHO, 2021.
[24] [Authors to be verified], “Time-homogeneous Markov modeling of HIV progression in patients receiving antiretroviral therapy treatment in the Ashanti Region, Ghana,” Can. J. Infect. Dis. Med. Microbiol., 2025, doi: 10.1155/cjid/5549653.
[25] P. Hougaard, “Multi-state models: A review,” Lifetime Data Anal., vol. 5, no. 3, pp. 239–264, Sep. 1999, doi: 10.1023/A:1009672031531.
[26] O. O. Aalen and S. Johansen, “An empirical transition matrix for non-homogeneous Markov chains based on censored observations,” Scand. J. Stat., vol. 5, no. 3, pp. 141–150, 1978.
[27] C. H. Jackson, “Multi-state models for panel data: The msm package for R,” J. Stat. Softw., vol. 38, no. 8, pp. 1–28, 2011, doi: 10.18637/jss.v038.i08.
[28] L. C. de Wreede, M. Fiocco, and H. Putter, “mstate: An R package for the analysis of competing risks and multi-state models,” J. Stat. Softw., vol. 38, no. 7, pp. 1–30, 2011, doi: 10.18637/jss.v038.i07.
[29] M. J. Crowther and P. C. Lambert, “Parametric multistate survival models: Flexible modelling allowing transition-specific distributions with application to estimating clinically useful measures of effect differences,” Stat. Med., vol. 36, no. 29, pp. 4719–4742, Dec. 2017, doi: 10.1002/sim.7448.
[30] J. D. Kalbfleisch and R. L. Prentice, The Statistical Analysis of Failure Time Data, 2nd ed. Hoboken, NJ, USA: Wiley, 2002.
[31] R. J. Cook and J. F. Lawless, Multistate Models for the Analysis of Life History Data. Boca Raton, FL, USA: CRC Press, 2018.
[32] J. Beyersmann, A. Allignol, and M. Schumacher, Competing Risks and Multistate Models with R. New York, NY, USA: Springer, 2012.
[33] B. Lau, S. R. Cole, and S. J. Gange, “Competing risk regression models for epidemiologic data,” Am. J. Epidemiol., vol. 170, no. 2, pp. 244–256, Jul. 2009, doi: 10.1093/aje/kwp107.
[34] Ganta et al. [initials to be verified], “Predictors of loss to follow up among adults on antiretroviral therapy before and after the start of treat-all strategy in public health facilities of Hawassa city, Ethiopia: A competing risk regression,” PLoS ONE, vol. 19, no. 3, Art. no. e0299505, Mar. 2024, doi: 10.1371/journal.pone.0299505.
[35] B. L. Jones, D. S. Nagin, and K. Roeder, “A SAS procedure based on mixture models for estimating developmental trajectories,” Sociol. Methods Res., vol. 29, no. 3, pp. 374–393, Feb. 2001, doi: 10.1177/0049124101029003005.
[36] A. C. Tricco et al., “A scoping review of rapid review methods,” BMC Med., vol. 13, Art. no. 224, Sep. 2015, doi: 10.1186/s12916-015-0465-6.
[37] C. Garritty et al., “Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews,” J. Clin. Epidemiol., vol. 130, pp. 13–22, Feb. 2021, doi: 10.1016/j.jclinepi.2020.10.007.
[38] M. J. Page et al., “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, Art. no. n71, Mar. 2021, doi: 10.1136/bmj.n71.
[39] M. L. Rethlefsen et al., “PRISMA-S: An extension to the PRISMA statement for reporting literature searches in systematic reviews,” Syst. Rev., vol. 10, Art. no. 39, Jan. 2021, doi: 10.1186/s13643-020-01542-z.
[40] S. Waffenschmidt, M. Knelangen, W. Sieben, S. Bühn, and D. Pieper, “Single screening versus conventional double screening for study selection in systematic reviews: A methodological systematic review,” BMC Med. Res. Methodol., vol. 19, Art. no. 132, Jun. 2019, doi: 10.1186/s12874-019-0782-0.
[41] M. Gusenbauer and N. R. Haddaway, “Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources,” Res. Synth. Methods, vol. 11, no. 2, pp. 181–217, Mar. 2020, doi: 10.1002/jrsm.1378.
[42] J. P. T. Higgins et al., Eds., Cochrane Handbook for Systematic Reviews of Interventions, version 6.4. Chichester, U.K.: Cochrane, 2023.
[43] K. Sikombe et al., “Identifying care gaps along the HIV treatment failure cascade: A multistate analysis of viral load monitoring, re-suppression, and regimen switches in Zambia,” PLoS Med., vol. 22, no. 9, Art. no. e1004720, Sep. 2025, doi: 10.1371/journal.pmed.1004720.
[44] A. Ramakrishnan et al., “A multistate analysis of longitudinal care outcomes among people newly linked to human immunodeficiency virus care in Missouri,” Open Forum Infect. Dis., vol. 13, no. 8, Art. no. ofag414, Aug. 2026, doi: 10.1093/ofid/ofag414.
[45] [Authors to be verified], “Alcohol use and the longitudinal HIV care continuum for people with HIV who enrolled in care between 2011–2019,” Ann. Epidemiol., vol. 85, pp. 6–12, 2023, doi: 10.1016/j.annepidem.2023.07.002.
[46] [Authors to be verified], “Depression and the revolving door of HIV care: using multistate approaches to understand complex longitudinal exposure/outcome relationships among a cohort of people entering HIV care in Cameroon from 2019 to 2020,” BMJ Public Health, Dec. 2025, PMID: 41394023.
[47] [Authors to be verified], “Multi-state Markov model for time to treatment changes for HIV/AIDS patients: a retrospective cohort national datasets, Ethiopia,” BMC Infect. Dis., 2024, doi: 10.1186/s12879-024-09469-9.
[48] A. Julien et al., “Trajectories and predictors of HIV care retention among individuals receiving ART in rural South Africa: a group-based trajectory modelling analysis,” J. Glob. Health, vol. 15, Art. no. 04187, Jul. 2025, doi: 10.7189/jogh.15.04187.
[49] [Authors to be verified], “Longitudinal trajectories of HIV care engagement since diagnosis among persons with HIV in the Florida Ryan White program,” AIDS Behav., 2022, doi: 10.1007/s10461-022-03659-9.
[50] [Authors to be verified], “Trajectories of HIV visit engagement during the perinatal period among women in Lilongwe, Malawi,” 2025, PMID: 39821057.
[51] [Authors to be verified], “Time-dependent predictors of loss to follow-up in HIV care in low-resource settings: A competing risks approach,” 2025. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC12289015
[52] [Authors to be verified], “Fine and Gray competing risk regression model of predictors of loss to follow up among female sex workers receiving antiretroviral therapy in Nigeria: 2016–2022,” 2026, PMID: 41928197.
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Copyright (c) 2026 Muhammad Ridwan Monisa, Jane Paula Jovita Hehanussa, Wa Ode Anabilla, Hana Gesti Pratiwi, Hana Chantika Waran, Damara Herlis Mailera, Santika Sari Rahayu Mahu

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