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Cohort Profile: The international epidemiological databases to evaluate AIDS (IeDEA) in sub-Saharan Africa

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.
International Journal of Epidemiology (Impact Factor: 9.18). 05/2011; 41(5):1256-1264. DOI: 10.1093/ije/dyr080
Source: PubMed
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Available from: Francois Dabis, Feb 08, 2016
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    • "We performed a cohort analysis among HIV-infected adults (≥18 years old) receiving care at one of three medical centers in Kenya and Uganda that participate in the East Africa IeDEA Consortium. Established in 2005 by the U.S. National Institutes of Health, the IeDEA Consortium has as its main procedural objective the harmonizing of data collected by geographically disparate, but representative, cohorts of persons infected with HIV or at risk for HIV141516. The scientific goal is to generate inferences about the natural or treated history of HIV, particularly regarding uncommon exposures or outcomes for which large samples are needed. "
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    ABSTRACT: In resource-limited areas, such as sub-Saharan Africa, problems in accurate cancer case ascertainment and enumeration of the at-risk population make it difficult to estimate cancer incidence. We took advantage of a large well-enumerated healthcare system to estimate the incidence of Kaposi sarcoma (KS), a cancer which has become prominent in the HIV era and whose incidence may be changing with the rollout of antiretroviral therapy (ART). To achieve this, we evaluated HIV-infected adults receiving care between 2007 and 2012 at any of three medical centers in Kenya and Uganda that participate in the East Africa International Epidemiologic Databases to Evaluate AIDS (IeDEA) Consortium. Through IeDEA, clinicians received training in KS recognition and biopsy equipment. We found that the overall prevalence of KS among 102,945 HIV-infected adults upon clinic enrollment was 1.4%; it declined over time at the largest site. Among 140,552 patients followed for 319,632 person-years, the age-standardized incidence rate was 334/100,000 person-years (95% CI: 314-354/100,000 person-years). Incidence decreased over time and was lower in women, persons on ART, and those with higher CD4 counts. The incidence rate among patients on ART with a CD4 count >350 cells/mm(3) was 32/100,000 person-years (95% CI: 14-70/100,000 person-years). Despite reductions over time coincident with the expansion of ART, KS incidence among HIV-infected adults in East Africa equals or exceeds the most common cancers in resource-replete settings. In resource-limited settings, strategic efforts to improve cancer diagnosis in combination with already well-enumerated at-risk denominators can make healthcare systems attractive platforms for estimating cancer incidence.
    Full-text · Article · Jan 2016 · Cancer Medicine
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    • "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 [76]. Within DI, there are potential data consistency and validity issues specifically related to the way patient data is reported and recorded over time. "
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    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. Discussion 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.
    Full-text · Article · Jul 2014 · PLoS ONE
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    • "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]. "
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    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. Methods 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. Results 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. Conclusions 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.
    Full-text · Article · Jun 2013 · BMC Health Services Research
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