HIV Care Cascade among Prisoners of the Mandalay Central Prison in Myanmar: 2011-2018.
Nang A Mwe NomKhine Wut Yee KyawAjay M V KumarSan HoneThida ThidaThet Wai NwePyae SoanThurain HtunHtun Nyunt OoPublished in: Tropical medicine and infectious disease (2020)
Prisoners have a higher HIV prevalence and higher rates of attrition from care as compared with the general population. There is no published evidence on this issue from Myanmar. We assessed (1) HIV test uptake, HIV positivity, and enrollment in care among newly admitted prisoners between 2017 and 18 (2) Treatment outcomes among HIV-positive prisoners enrolled in care between 2011 and 18. This was a cohort study involving secondary analysis of program data. Among 26,767 prisoners admitted to the Mandalay Central Prison between 2017 and 2018, 10,421 (39%) were HIV-tested, 547 (5%) were HIV-positive, and 376 (69%) were enrolled in care. Among the 1288 HIV-positive prisoners enrolled in care between 2011 and 2018, 1178 (92%) were started on antiretroviral therapy. A total of 883 (69%) were transferred out (post-release) to other health facilities, and among these, only 369 (42%) reached their destination health facilities. The final outcomes (censored on 30 June 2019) included the following: (i) Alive and in care 495 (38%), (ii) death 138 (11%), (iii) loss to follow-up 596 (46%), and (iv) transferred out after reaching the health facilities 59 (5%). We found major gaps at every step of the HIV care cascade among prisoners, both inside and outside the prison. Future research should focus on understanding the reasons for these gaps and designing appropriate interventions to fill these gaps.
Keyphrases
- hiv positive
- antiretroviral therapy
- men who have sex with men
- healthcare
- hiv infected
- south africa
- human immunodeficiency virus
- palliative care
- hiv testing
- quality improvement
- hiv infected patients
- hiv aids
- public health
- affordable care act
- pain management
- metabolic syndrome
- machine learning
- risk assessment
- type diabetes
- skeletal muscle
- artificial intelligence
- combination therapy
- deep learning
- weight loss
- insulin resistance
- big data
- meta analyses
- climate change
- data analysis
- physical activity