Evidence map›Paper›PMID 32483067›Full record

ArticleEpidemiology (Cambridge, Mass.)2020

Gone But Not Lost: Implications for Estimating HIV Care Outcomes When Loss to Clinic Is Not Loss to Care.

Jessie K Edwards, Catherine R Lesko, Michael E Herce, Gad Murenzi, Christella Twizere, Patricia Lelo, Kathryn Anastos, Olga Tymejczyk, Marcel Yotebieng, Denis Nash and 2 more

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. A Framework for Descriptive Epidemiology.American journal of epidemiology · 2022
    Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Jessie K EdwardsFrom the Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Catherine R LeskoDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Michael E HerceInstitute for Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Gad MurenziRwanda Military Hospital, Kigali, Rwanda.
Christella TwizereCentre Hospitalo, Universitaire de Kamenge, Bujumbura, Burundi.
Patricia LeloKalembelembe Pediatric Hospital, Kinshasa, Democratic Republic of the Congo.
Kathryn AnastosDepartments of Medicine and Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY.
Olga TymejczykInstitute for Implementation Science in Population Health, City University of New York, New York, NY.
Marcel YotebiengDepartment of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY.
Denis NashInstitute for Implementation Science in Population Health, City University of New York, New York, NY.
Adebola AdedimejiDepartment of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY.
Andrew EdmondsFrom the Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC.

Funding

"Enhancing Cancer Research in Central Africa IeDEA"U01AI096299 · NIAID · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI Denis Nash, Marcel Yotebieng · 2011 to 2026
$35.8M
Comparative effectiveness of tailored HIV treatment plans and mortalityK01AI125087 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI EDWARDS, JESSIE · 2017 to 2021
$586k
Understanding Longitudinal Clinical Outcomes and Post-release Retention in Care among HIV-infected Prisoners in Lusaka, ZambiaK01TW010272 · FIC · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HERCE, MICHAEL EMMANUEL · 2015 to 2018
$538k
FIC NIH HHS K01 TW010272NIAID NIH HHS K01 AI125087NIAID NIH HHS U01 AI096299
6 · The paper itself

Abstract

backgroundIn some time-to-event analyses, it is unclear whether loss to follow up should be treated as a censoring event or competing event. Such ambiguity is particularly common in HIV research that uses routinely collected clinical data to report the timing of key milestones along the HIV care continuum. In this setting, loss to follow up may be viewed as a censoring event, under the assumption that patients who are "lost" from a study clinic immediately enroll in care elsewhere, or a competing event, under the assumption that people "lost" are out of care all together.

methodsWe illustrate an approach to address this ambiguity when estimating the 2-year risk of antiretroviral treatment initiation among 19,506 people living with HIV who enrolled in the IeDEA Central Africa cohort between 2006 and 2017, along with published estimates from tracing studies in Africa. We also assessed the finite sample properties of the proposed approach using simulation experiments.

resultsThe estimated 2-year risk of treatment initiation was 69% if patients were censored at loss to follow up or 59% if losses to follow up were treated as competing events. Using the proposed approach, we estimated that the 2-year risk of antiretroviral therapy initiation was 62% (95% confidence interval: 61, 62). The proposed approach had little bias and appropriate confidence interval coverage under scenarios examined in the simulation experiments.

conclusionsThe proposed approach relaxes the assumptions inherent in treating loss to follow up as a censoring or competing event in clinical HIV cohort studies.

Indexed as

Continuity of Patient CareHIV InfectionsAfricaAnti-Retroviral AgentsCohort StudiesHumansLost to Follow-UpTreatment OutcomeAnti-Retroviral Agents

Identifiers

PMID32483067
PMCPMC8344105

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.