Evidence map›Paper›PMID 25867779›Full record

ArticleJournal of acquired immune deficiency syndromes (1999)2015

Examining clinic-based and public health approaches to ascertainment of HIV care status.

Katerina A Christopoulos, Susan Scheer, Wayne T Steward, Revery Barnes, Wendy Hartogensis, Edwin D Charlebois, Stephen F Morin, Hong-Ha M Truong, Elvin H Geng

Abstract read
In one paragraph

Article in Journal of acquired immune deficiency syndromes (1999), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

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

  1. Guideline
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  11. HIV Data to Care-Using Public Health Data to Improve HIV Care and Prevention.Journal of acquired immune deficiency syndromes (1999) · 2019
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  12. Observational
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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

9 authors.

Katerina A Christopoulos*HIV/AIDS Division, San Francisco General Hospital, University of California, San Francisco, San Francisco, CA; †HIV Epidemiology Section, San Francisco Department of Public Health, San Francisco, CA; ‡Center for AIDS Prevention Studies, University of California, San Francisco, San Francisco, CA; and §Department of Family Medicine, University of California, Los Angeles, Los Angeles, CA.
Susan Scheer
Wayne T Steward
Revery Barnes
Wendy Hartogensis
Edwin D Charlebois
Stephen F Morin
Hong-Ha M Truong
Elvin H Geng

Funding

Unsolicited R24 for the CFAR-Network of Integrated Clinical Sciences, CNICSR24AI067039 · NIAID · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Michael S. Saag · 2006 to 2026
$99.4M
Ujima Mentoring ProgramP30MH062246 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Sheri Ann Lippman · 2001 to 2026
$57.4M
The District of Columbia Developmental Center for AIDS Research (DC D-CFAR)P30AI087714 · NIAID · GEORGE WASHINGTON UNIVERSITY · PI BUKRINSKY, MICHAEL ILYA · 2010 to 2014
$8.4M
Optimizing Rapid and Full Engagement in Care for Persons with Newly Diagnosed HIVK23MH092220 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHRISTOPOULOS, KATERINA A · 2010 to 2013
$635k
NIAID NIH HHS P30 AI087714NIAID NIH HHS R24 AI067039NIMH NIH HHS 5P30 MH062246NIMH NIH HHS K23 MH092220NIMH NIH HHS K23MH092220NIMH NIH HHS P30 MH062246
6 · The paper itself

Abstract

introductionClinic-based tracing efforts and public health surveillance data can provide different information about HIV care status for the same patients. The relative yield and how best to use these sources to identify and reengage out-of-care patients is unknown.

methodsAt a large public HIV clinic in San Francisco, we selected a 10% random sample of active patients who were at least 210 days "late" for an HIV primary care visit as of April 1, 2013, for clinic-based outreach. Patients were considered out of care if they did not have an HIV primary care visit in the 210 days before April 1, 2013. We then matched the sample with the San Francisco Department of Public Health HIV surveillance registry. Patients with a CD4 or viral load result in the 210-day period were classified as in care. We compared results from both sources and estimated the cumulative incidence of disengagement from care for the full cohort of clinic patients.

resultsOf 940 patients lost to follow-up, 95 were sampled. Clinic tracing found 60 (63%) in care, 23 (24%) not located, 9 (10%) out of care, 2 (2%) incarcerated, and 1 (1%) had died. Of 42 individuals surveillance classified as out of care, tracing found 22 (52%) were in care. Of 52 patients found to be in care by surveillance, 12 (23%) were out of care by clinic tracing or unable to be located. The naive estimate of the cumulative incidence of disengagement from care at 3 years for the active clinic cohort was 41.1% [95% confidence interval (CI): 37.6 to 44.5]. The use of surveillance data reduced this estimate to 12.7% (95% CI: 18.2 to 25.4), and when further corrected using tracing outcomes, the estimate dropped to only 6.4% (95% CI: 3.4 to 9.4).

conclusionsClinic-based tracing and surveillance data together provide a better understanding of care status than either method alone. Using surveillance data to inform clinic-based outreach efforts may be an effective strategy, although tracing efforts are most likely to be successful if conducted in real time.

Indexed as

Continuity of Patient CarePatient Acceptance of Health CarePublic Health SurveillanceAdultAmbulatory Care FacilitiesCD4 Lymphocyte CountCohort StudiesElectronic Health RecordsFemaleHIV InfectionsHumansIncidenceLost to Follow-UpMaleMiddle AgedPatient Identification Systems

Identifiers

PMID25867779
PMCPMC4495955

What OpenQuestion holds

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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.