Profiles of HIV Care Disruptions Among Adult Patients Lost to Follow-up in Zambia: A Latent Class Analysis.
Journal
Journal of acquired immune deficiency syndromes (1999)
ISSN: 1944-7884
Titre abrégé: J Acquir Immune Defic Syndr
Pays: United States
ID NLM: 100892005
Informations de publication
Date de publication:
01 01 2021
01 01 2021
Historique:
pubmed:
27
10
2020
medline:
16
7
2021
entrez:
26
10
2020
Statut:
ppublish
Résumé
Patients report varied barriers to HIV care across multiple domains, but specific barrier patterns may be driven by underlying, but unobserved, behavioral profiles. We traced a probability sample of patients lost to follow-up (>90 days late) as of July 31, 2015 from 64 clinics in Zambia. Among those found alive, we ascertained patient-reported reasons for care disruptions. We performed latent class analysis to identify patient subgroups with similar patterns of reasons reported and assessed the association between class membership and care status (ie, disengaged versus silently transferred to a new site). Among 547 patients, we identified 5 profiles of care disruptions: (1) "Livelihood and Mobility" (30.6% of the population) reported work/school obligations and mobility/travel as reasons for care disruptions; (2) "Clinic Accessibility" (28.9%) reported challenges with attending clinic; (3) "Mobility and Family" (21.9%) reported family obligations, mobility/travel, and transport-related reasons; (4) "Doubting Need for HIV care" (10.2%) reported uncertainty around HIV status or need for clinical care, and (5) "Multidimensional Barriers to Care" (8.3%) reported numerous (mean 5.6) reasons across multiple domains. Patient profiles were significantly associated with care status. The "Doubting Need for HIV Care" class were mostly disengaged (97.9%), followed by the "Multidimensional Barriers to Care" (62.8%), "Clinic Accessibility" (62.4%), "Livelihood and Mobility" (43.6%), and "Mobility and Family" (23.5%) classes. There are distinct HIV care disruption profiles that are strongly associated with patients' current engagement status. Interventions targeting these unique profiles may enable more effective and tailored strategies for improving HIV treatment outcomes.
Sections du résumé
BACKGROUND
Patients report varied barriers to HIV care across multiple domains, but specific barrier patterns may be driven by underlying, but unobserved, behavioral profiles.
METHODS
We traced a probability sample of patients lost to follow-up (>90 days late) as of July 31, 2015 from 64 clinics in Zambia. Among those found alive, we ascertained patient-reported reasons for care disruptions. We performed latent class analysis to identify patient subgroups with similar patterns of reasons reported and assessed the association between class membership and care status (ie, disengaged versus silently transferred to a new site).
RESULTS
Among 547 patients, we identified 5 profiles of care disruptions: (1) "Livelihood and Mobility" (30.6% of the population) reported work/school obligations and mobility/travel as reasons for care disruptions; (2) "Clinic Accessibility" (28.9%) reported challenges with attending clinic; (3) "Mobility and Family" (21.9%) reported family obligations, mobility/travel, and transport-related reasons; (4) "Doubting Need for HIV care" (10.2%) reported uncertainty around HIV status or need for clinical care, and (5) "Multidimensional Barriers to Care" (8.3%) reported numerous (mean 5.6) reasons across multiple domains. Patient profiles were significantly associated with care status. The "Doubting Need for HIV Care" class were mostly disengaged (97.9%), followed by the "Multidimensional Barriers to Care" (62.8%), "Clinic Accessibility" (62.4%), "Livelihood and Mobility" (43.6%), and "Mobility and Family" (23.5%) classes.
CONCLUSION
There are distinct HIV care disruption profiles that are strongly associated with patients' current engagement status. Interventions targeting these unique profiles may enable more effective and tailored strategies for improving HIV treatment outcomes.
Identifiants
pubmed: 33105396
doi: 10.1097/QAI.0000000000002530
pmc: PMC7722465
pii: 00126334-202101010-00010
doi:
Substances chimiques
Anti-HIV Agents
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
62-72Subventions
Organisme : NCATS NIH HHS
ID : KL2 TR002346
Pays : United States
Organisme : NIAID NIH HHS
ID : T32 AI060530
Pays : United States
Organisme : NIAID NIH HHS
ID : K24 AI134413
Pays : United States
Références
Chammartin F, Zurcher K, Keiser O, et al. Outcomes of patients lost to follow-up in African antiretroviral therapy programs: individual patient data meta-analysis. Clin Infect Dis. 2018;67:1643–1652.
Holmes CB, Bengtson A, Sikazwe I, et al. Using the Side Door: Non-linear Patterns within the HIV Treatment Cascade in Zambia. Conference on Retroviruses and Opportunistic Infections, Boston, MA, March 3-6, 2014.
Hallett TB, Eaton JW. A side door into care cascade for HIV-infected patients? J Acquir Immune Defic Syndr. 2013;63(suppl 2):S228–S232.
Haberer JE, Sabin L, Amico KR, et al. Improving antiretroviral therapy adherence in resource-limited settings at scale: a discussion of interventions and recommendations. J Int AIDS Soc. 2017;20:21371.
Geng EH, Holmes CB, Moshabela M, et al. Personalized public health: an implementation research agenda for the HIV response and beyond. PLoS Med. 2019;16:e1003020.
Pascoe SJS, Scott NA, Fong RM, et al. “Patients are not the same, so we cannot treat them the same”—a qualitative content analysis of provider, patient and implementer perspectives on differentiated service delivery models for HIV treatment in South Africa. J Int AIDS Soc. 2020;23:e25544.
Collins LM, Lanza ST. Latent Class and Latent Transition Analysis : With Applications in the Social Behavioral, and Health Sciences . Hoboken, NJ: Wiley; 2010.
Holmes CB, Sikazwe I, Sikombe K, 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:e1002489.
Geng EH, Emenyonu N, Bwana MB, et al. Sampling-based approach to determining outcomes of patients lost to follow-up in antiretroviral therapy scale-up programs in Africa. JAMA. 2008;300:506–507.
Geng EH, Odeny TA, Lyamuya RE, et al. Estimation of Mortality among HIV-infected people on antiretroviral therapy treatment in east Africa: a sampling based approach in an observational, multisite, cohort study. Lancet HIV. 2015;2:e107–e116.
Geng EH, Odeny TA, Lyamuya R, 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;62:935–944.
Bray BC, Lanza ST, Tan X. Eliminating bias in classify-analyze approaches for latent class Analysis. Struct Equ Model. 2015;22:1–11.
Vittinghoff E, Glidden DV, Shiboski SC, et al. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models . New York, NY: Springer-Verlag; 2012.
Bakk Z, Tekle FB, Vermunt JK. Estimating the association between latent class membership and external variables using bias-adjsuted three-step approaches. Sociological Methodol. 2013;43:272–311.
El-Sadr WM, Harripersaud K, Rabkin M. Reaching global HIV/AIDS goals: what got us here, won't get us there. PLoS Med. 2017;14:e1002421.
Rao A, McCoy S. Fostering behavior change for better health. Stanford Sci Innovation Review. 2015;17. Available at: https://ssir.org/articles/entry/fostering_behavior_change_for_better_health .
Sikombe K, Kadota JL, Simbeza S, et al. Understanding patient mobility in HIV-positive adults across multiple clinic in Zambia. Conference on Retroviruses and Opportunistic Infections, Boston, MA, March 4-7, 2018.
Mwamba C, Sharma A, Mukamba N, et al. “They care rudely!”: resourcing and relational health system factors that influence retention in care for people living with HIV in Zambia. BMJ Glob Health. 2018;3:e001007.
Eshun-Wilson I, Mukumbwa-Mwenechanya M, Kim HY, et al. Differentiated care preferences of stable patients on antiretroviral therapy in Zambia: a discrete choice experiment. J Acquir Immune Defic Syndr. 2019;81:540–546.
Zanolini A, Sikombe K, Sikazwe I, et al. Understanding preferences for HIV care and treatment in Zambia: evidence from a discrete choice experiment among patients who have been lost to follow-up. PLoS Med. 2018;15:e1002636.
Ware NC, Wyatt MA, Geng EH, et al. Toward an understanding of disengagement from HIV treatment and care in sub-Saharan Africa: a qualitative study. PLoS Med. 2013;10:e1001369; discussion e1001369.
Topp SM, Mwamba C, Sharma A, et al. Rethinking retention: mapping interactions between multiple factors that influence long-term engagement in HIV care. PLoS One. 2018;13:e0193641.
Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14:157–168.
Bassett IV, Coleman SM, Giddy J, 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;74:432–438.
Camlin CS, Neilands TB, Odeny TA, et al. Patient-reported factors associated with reengagement among HIV-infected patients disengaged from care in East Africa. AIDS. 2016;30:495–502.
Lanza ST, Rhoades BL, Nix RL, et al. Conduct Problems Prevention Research G. Modeling the interplay of multilevel risk factors for future academic and behavior problems: a person-centered approach. Dev Psychopathol. 2010;22:313–335.
Wilkinson AL, El-Hayek C, Fairley CK, et al. Measuring transitions in sexual risk among men who have sex with men: the novel use of latent class and latent transition analysis in HIV sentinel surveillance. Am J Epidemiol. 2017;185:627–635.
Gottert A, Pulerwitz J, Heck CJ, et al. Creating HIV risk profiles for men in South Africa: a latent class approach using cross-sectional survey data. J Int AIDS Soc. 2020;23(suppl 2):e25518.
Heidari O, Dangerfield DT, Hickson DA. Sexual risk and sexual healthcare utilization profiles among Black sexual minority men in the U.S. Deep South. AIDS Care. 2020;32:1602–1609.
Comins CA, Rucinski KB, Baral S, et al. Vulnerability profiles and prevalence of HIV and other sexually transmitted infections among adolescent girls and young women in Ethiopia: a latent class analysis. PLoS One. 2020;15:e0232598.
Nguyen N, Powers KA, Miller WC, et al. Sexual partner types and incident HIV infection among rural South African adolescent girls and young women enrolled in HPTN 068: a latent class Analysis. J Acquir Immune Defic Syndr. 2019;82:24–33.
Turpin RE, Slopen N, Chen S, et al. Latent class analysis of a syndemic of risk factors on HIV testing among black men. AIDS Care. 2019;31:216–223.
Okeke NL, Clement ME, McKellar MS, et al. Health care utilization behaviors predict disengagement from HIV care: a latent class Analysis. Open Forum Infect Dis. 2018;5:ofy088.
Mody A, Eshun-Wilson I, Sikombe K, et al. Longitudinal engagement trajectories and risk of death among new ART starters in Zambia: a group-based multi-trajectory analysis. PLoS Med. 2019;16:e1002959.
Powers KA, Samoff E, Weaver MA, et al. Longitudinal HIV care trajectories in North Carolina. J Acquir Immune Defic Syndr. 2017;74(suppl 2):S88–S95.
Knight L, Roberts BJ, Aber JL, et al. 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 Develop. 2015;27:213–233.
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;25:1581–1585.
Leserman J, Ironson G, O'Cleirigh C, et al. Stressful life events and adherence in HIV. AIDS Patient Care STDS. 2008;22:403–411.
Mugavero MJ, Raper JL, Reif S, 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;71:920–926.