Marginal structural models to assess delays in second-line HIV treatment initiation in South Africa
dc.contributor.author | Rohr, Julia K. | |
dc.contributor.author | Ive, Prudence | |
dc.contributor.author | Horsburgh, C. Robert | |
dc.contributor.author | Berhanu, Rebecca | |
dc.contributor.author | Shearer, Kate | |
dc.contributor.author | Maskew, Mhairi | |
dc.contributor.author | Long, Lawrence | |
dc.contributor.author | Sanne, Ian | |
dc.contributor.author | Bassett, Jean | |
dc.contributor.author | Ebrahim, Osman | |
dc.contributor.author | Fox, Matthew P. | |
dc.date.accessioned | 2016-09-21T06:03:53Z | |
dc.date.available | 2016-09-21T06:03:53Z | |
dc.date.issued | 2016-08-22 | |
dc.description | S1 Fig. Illustration of allocation of person time in marginal structural models. Hypothetical person time contributed to each of the 6 exposure groups in marginal structural models. | en_ZA |
dc.description | S1 Table. Alternative stratifications for adjusted marginal structural models for hazard ratios of death after first-line failure. | en_ZA |
dc.description | S2 Table. Adjusted marginal structural model hazard ratios for death after first-line failure, limiting to patients with 2 weeks to <8 months between failing viral loads on first-line (n = 4908). | en_ZA |
dc.description | S3 Table. Adjusted Cox proportional hazards ratios for alternative virologic outcomes on second-line ART, stratified by peak CD4 count prior to first-line failure. | en_ZA |
dc.description | S4 Table. Adjusted marginal structural models for hazard ratios of death after first-line failure (a) and adjusted Cox proportional hazards ratios for confirmed failure on second-line ART (b), with weighting by inverse probability of censoring after second-line switch to account for loss to follow-up. | en_ZA |
dc.description.abstract | BACKGROUND South African HIV treatment guidelines call for patients who fail first-line antiretroviral therapy (ART) to be switched to second-line ART, yet logistical issues, clinician decisions and patient preferences make delay in switching to second-line likely. We explore the impact of delaying second-line ART after first-line treatment failure on rates of death and virologic failure. METHODS We include patients with documented virologic failure on first-line ART from an observational cohort of 9 South African clinics. We explored predictors of delayed second-line switch and used marginal structural models to analyze rates of death following first-line failure by categorical time to switch to second-line. Cox proportional hazards models were used to examine virologic failure on second-line ART among patients who switched to second- line. RESULTS 5895 patients failed first-line ART, and 63% switched to second-line. Among patients who switched, median time to switch was 3.4 months (IQR: 1.1–8.7 months). Longer time to switch was associated with higher CD4 counts, lower viral loads and more missed visits prior to first-line failure. Worse outcomes were associated with delay in second-line switch among patients with a peak CD4 count on first-line treatment 100 cells/mm3. Among these patients, marginal structural models showed increased risk of death (adjusted HR for switch in 6–12 months vs. 0–1.5 months = 1.47 (95% CI: 0.94–2.29), and Cox models showed increased rates of second-line virologic failure despite the presence of survivor bias (adjusted HR for switch in 3–6 months vs. 0–1.5 months = 2.13 (95% CI: 1.01–4.47)). CONCLUSIONS Even small delays in switch to second-line ART were associated with increased death and second-line failure among patients with low CD4 counts on first-line. There is opportunity for healthcare providers to switch patients to second-line more quickly. | en_ZA |
dc.description.department | Medical Microbiology | en_ZA |
dc.description.librarian | am2016 | en_ZA |
dc.description.sponsorship | JKR, KS, MM, LL and MPF were funded for this work by United States Agency for International Development (USAID) through the following agreement: 674-A-12-00029. Additional support to KS was provided by the National Institutes of Health (NIH) (T32AI102623). | en_ZA |
dc.description.uri | http://www.plosone.org | en_ZA |
dc.identifier.citation | Rohr JK, Ive P, Horsburgh CR, Berhanu R, Shearer K, Maskew M, et al. (2016) Marginal Structural Models to Assess Delays in Second-Line HIV Treatment Initiation in South Africa. PLoS ONE 11(8): e0161469. DOI: 10.1371/journal.pone.0161469. | en_ZA |
dc.identifier.issn | 1932-6203 | |
dc.identifier.other | 10.1371/journal.pone.0161469 | |
dc.identifier.uri | http://hdl.handle.net/2263/56766 | |
dc.language.iso | en | en_ZA |
dc.publisher | Public Library of Science | en_ZA |
dc.rights | © 2016 Rohr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License. | en_ZA |
dc.subject | Patients | en_ZA |
dc.subject | CD4 Counts | en_ZA |
dc.subject | South African clinics | en_ZA |
dc.subject | Treatment | en_ZA |
dc.subject | Antiretroviral therapy (ART) | en_ZA |
dc.subject | Human immunodeficiency virus (HIV) | en_ZA |
dc.title | Marginal structural models to assess delays in second-line HIV treatment initiation in South Africa | en_ZA |
dc.type | Article | en_ZA |
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