Institute of Social and Preventive Medicine (ISPM), University of Bern, Switzerland, Infectious Diseases Epidemiology Unit, School of Public Health and Family Medicine, University of Cape Town, South Africa, Programme PAC-CI, Abidjan, Côte d'Ivoire, Epidemiology Branch, Division of AIDS, National Institute of Allergy and Infectious Diseases, Bethesda, MD, USA, Morogoro Regional Hospital, Morogoro, Tanzania, AMOCONGO ARV Ambulatory Treatment Center, Kinshasa, Democratic Republic of the Congo, Indiana University School of Medicine, IN, USA, Moi University School of Medicine, Eldoret, Kenya, University of Toronto, Dalla Lana School of Public Health, Toronto, Canada, Department of Statistics and Epidemiology, RTI International, Durham, NC, USA, Centre for Infectious Disease Research in Zambia, Lusaka, Zambia and INSERM U897, Institut de Santé Publique, Epidémiologie et Développement (ISPED), Université Bordeaux Segalen, Bordeaux, France.
"Missing data is an important challenge with operational datasets derived from large clinical populations, particularly those in resource-poor settings. This can be attributable in large part to the lack of sufficient interest or resources needed to accurately collect, manage, and clean datasets . Within DI, there are potential data consistency and validity issues specifically related to the way patient data is reported and recorded over time. "
[Show abstract][Hide abstract] ABSTRACT: Background
Identifying follow-up (FU) visit patterns, and exploring which factors influence them are likely to be useful in determining which patients on antiretroviral therapy (ART) may become Lost to Follow-Up (LTFU). Using an operation and implementation research approach, we sought 1) to describe the timing of FU visits amongst patients who have been on ART for shorter and longer periods of time; and 2) to determine the median time to late visits, and 3) to identify specific factors that may be associated with these patterns in Zomba, Malawi.
Methods and Findings
Using routinely collected programme monitoring data from Zomba District, we performed descriptive analyses on all ART visits among patients who initiated ART between Jan. 1, 2007–June 30, 2010. Based on an expected FU date, each FU visit was classified as early (≥4 day before an expected FU date), on time (3 days before an expected FU date/up to 6 days after an expected FU date), or late (≥7 days after an expected FU date). In total, 7,815 patients with 76417 FU visits were included. Ninety-two percent of patients had ≥2 FU visits. At the majority of visits, patients were either on time or late. The median time to a first late visit among those with 2 or more visits was 216 days (IQR: 128–359). Various patient- and visit-level factors differed significantly across Early, On Time, and Late visit groups including ART adherence and frequency of, and type of side effects.
The majority of patients do not demonstrate consistent FU visit patterns. Individuals were generally on ART for at least 6 months before experiencing their first late visit. Our findings have implications for the development of effective interventions that meet patient needs when they present early and can reduce patient losses to follow-up when they are late. In particular, time-varying visit characteristics need further research.
PLoS ONE 07/2014; 9(7):e101875. DOI:10.1371/journal.pone.0101875 · 3.23 Impact Factor
"This has been attributed to poor infrastructure, a lack of trained personnel, and clinic characteristics including patient volume [35,38]. Often front line health workers involved in data collection lack a clear understanding as to how the data they collect will be used and how particularly, it is relevant for their day-to-day activities [34,40]. Improving quality data collection, however, may require incentives and ongoing training and supervision for personnel [35,38,40], specifically because data collection can be burdensome [35,37]. "
[Show abstract][Hide abstract] ABSTRACT: Background
Retention in antiretroviral therapy (ART) programmes remains a challenge in many settings including Malawi, in part due to high numbers of losses to follow-up. Concept Mapping (CM), a mix-method participatory approach, was used to explore why patients on ART are lost to follow-up (LTFU) by identifying: 1) factors that influence patient losses to follow-up and 2) barriers to effective and efficient tracing in Zomba, Malawi.
CM sessions (brainstorming, sorting and rating, interpretation) were conducted in urban and rural settings in Zomba, Malawi. Participants included ART patients, ART providers, Health Surveillance Assistants, and health managers from the Zomba District Health Office. In brainstorming, participants generated statements in response to “A specific reason why an individual on ART becomes lost to follow-up is…” Participants then sorted and rated the consolidated list of brainstormed items. Analysis included inductive qualitative methods for grouping of data and quantitative cluster identification to produce visual maps which were then interpreted by participants.
In total, 90 individuals brainstormed 371 statements, 64 consolidated statements were sorted (participant n = 46), and rated on importance and feasibility (participant n = 69). A nine-cluster concept map was generated and included both patient- and healthcare-related clusters such as: Stigma and Fears, Beliefs, Acceptance and Knowledge of ART, Access to ART, Poor Documentation, Social and Financial Support Issues, Health Worker Attitudes, Resources Needed for Effective Tracing, and Health Worker Issues Related to Tracing. Strategies to respond to the clusters were generated in Interpretation.
Multiple patient- and healthcare focused factors influence why patients become LTFU. Findings have implications particularly for programs with limited resources struggling with the retention of ART patients.
BMC Health Services Research 06/2013; 13(1):210. DOI:10.1186/1472-6963-13-210 · 1.71 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To compare outcomes of antiretroviral therapy (ART) in South Africa, where viral load monitoring is routine, with those in Malawi and Zambia, where monitoring is based on CD4 cell counts.
We included 18,706 adult patients starting ART in South Africa and 80,937 patients in Zambia or Malawi. We examined CD4 responses in models for repeated measures and the probability of switching to second-line regimens, mortality and loss to follow-up in multistate models, measuring time from 6 months.
In South Africa, 9.8% [95% confidence interval (CI) 9.1-10.5] had switched at 3 years, 1.3% (95% CI 0.9-1.6) remained on failing first-line regimens, 9.2% (95% CI 8.5-9.8) were lost to follow-up and 4.3% (95% CI 3.9-4.8) had died. In Malawi and Zambia, more patients were on a failing first-line regimen [3.7% (95% CI 3.6-3.9], fewer patients had switched [2.1% (95% CI 2.0-2.3)] and more patients were lost to follow-up [15.3% (95% CI 15.0-15.6)] or had died [6.3% (95% CI 6.0-6.5)]. Median CD4 cell counts were lower in South Africa at the start of ART (93 vs. 132 cells/μl; P < 0.001) but higher after 3 years (425 vs. 383 cells/μl; P < 0.001). The hazard ratio comparing South Africa with Malawi and Zambia after adjusting for age, sex, first-line regimen and CD4 cell count was 0.58 (0.50-0.66) for death and 0.53 (0.48-0.58) for loss to follow-up.
Over 3 years of ART mortality was lower in South Africa than in Malawi or Zambia. The more favourable outcome in South Africa might be explained by viral load monitoring leading to earlier detection of treatment failure, adherence counselling and timelier switching to second-line ART.
AIDS (London, England) 06/2011; 25(14):1761-9. DOI:10.1097/QAD.0b013e328349822f · 5.55 Impact Factor
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