Evidence map›Paper›PMID 31005552›Full record

ArticleAnnals of epidemiology2019

Mind the gap: observation windows to define periods of event ascertainment as a quality control method for longitudinal electronic health record data.

Keri N Althoff, Cherise Wong, Brenna Hogan, Fidel Desir, Bin You, Elizabeth Humes, Jinbing Zhang, Yuezhou Jing, Sharada Modur, Jennifer S Lee and 11 more

Abstract read
In one paragraph

Article in Annals of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Clinical Predictors and Incidence of Kaposi Sarcoma Among Males With HIV in the Treat-All Era in the United States and Canada.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Five-Year Mortality for Adults Entering Human Immunodeficiency Virus Care Under Universal Early Treatment Compared With the General US Population.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022
    Article
  11. Association of the VACS Index With Hospitalization Among People With HIV in the NA-ACCORD.Journal of acquired immune deficiency syndromes (1999) · 2022
    Article
  12. CD4 Count at Entry into Care and at Antiretroviral Therapy Prescription among Adults with Human Immunodeficiency Virus in the United States, 2005-2018.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
    Article
  13. 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

21 authors.

Keri N AlthoffJohns Hopkins University, Baltimore, MD. Electronic address: kalthoff@jhu.edu.
Cherise WongJohns Hopkins University, Baltimore, MD.
Brenna HoganJohns Hopkins University, Baltimore, MD.
Fidel DesirJohns Hopkins University, Baltimore, MD.
Bin YouJohns Hopkins University, Baltimore, MD.
Elizabeth HumesJohns Hopkins University, Baltimore, MD.
Jinbing ZhangJohns Hopkins University, Baltimore, MD.
Yuezhou JingJohns Hopkins University, Baltimore, MD.
Sharada ModurJohns Hopkins University, Baltimore, MD.
Jennifer S LeeJohns Hopkins University, Baltimore, MD.
Aimee FreemanJohns Hopkins University, Baltimore, MD.
Mari KitahataUniversity of Washington, Seattle.
Stephen Van RompaeyUniversity of Washington, Seattle.
W Christopher MathewsUniversity of California San Diego, San Diego.
Michael A HorbergKaiser Permanente Mid-Atlantic Permanente Research Institute, Rockville, MD.
Michael J SilverbergKaiser Permanente Northern California, Oakland, CA.
Angel M MayorUniversidad Central del Caribe, Bayamon, Puerto Rico.
Kate SaltersBritish Columbia Centre for Excellence in HIV/AIDS, Vancouver, British Columbia, Canada.
Richard D MooreJohns Hopkins University, Baltimore, MD.
Stephen J GangeJohns Hopkins University, Baltimore, MD.
North American AIDS Cohort Collaboration on Research and Design

Funding

Statistical and Data Management Center (SDMC), AIDS Clinical Trials Group (ACTG)UM1AI068634 · NIAID · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI Marlene Ann Cooper, Michael David Hughes · 2011 to 2026
$246.6M
WOMEN'S HEALTH AGENDAU01AI038858 · NIAID · SOCIAL AND SCIENTIFIC SYSTEMS, INC. · PI BENSON, CONSTANCE ANN · 1996 to 2008
$157.5M
AIDS Clinical Trials Group NetworkU01AI068636 · NIAID · SOCIAL AND SCIENTIFIC SYSTEMS, INC. · PI KURITZKES, DANIEL R. · 2006 to 2010
$147.1M
UCSF CLINICAL AND TRANSLATIONAL SCIENCE INSTITUTEUL1RR024131 · NCRR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI JOHNSTON, S. CLAIBORNE · 2006 to 2011
$111.2M
University of Washington/Fred Hutch Center for AIDS ResearchP30AI027757 · NIAID · UNIVERSITY OF WASHINGTON · PI Judith Tsui · 1988 to 2026
$104.9M
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
Virology CoreP30AI027763 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI STEVEN Grant DEEKS · 1988 to 2026
$93.6M
Virology CoreP30AI027767 · NIAID · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI OLAF KUTSCH · 1988 to 2026
$84.1M
STATISTICAL AND DATA MANAGEMENT CENTERU01AI038855 · NIAID · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI HUGHES, MICHAEL DAVID · 1996 to 2006
$78.1M
Engaging University of California Stakeholders for Biorespository ResearchUL1TR000004 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GRANDIS, JENNIFER RUBIN · 2012 to 2015
$78.0M
Studies of Ocular Complications of AIDS - Coordinating CenterU10EY008057 · NEI · JOHNS HOPKINS UNIVERSITY · PI VAN NATTA, MARK L · 1988 to 2012
$78.0M
Virology, Immunology, and Microbiology CoreP30AI050410 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVID M. MARGOLIS · 2001 to 2026
$76.9M
Intramural NIH HHS Z01 CP010176NCATS NIH HHS KL2 TR000421NCATS NIH HHS KL2 TR002317NCATS NIH HHS UL1 TR000004NCATS NIH HHS UL1 TR000083NCATS NIH HHS UL1 TR000454NCI NIH HHS N01 CP001004NCI NIH HHS N02 CP055504NCI NIH HHS R01 CA165937NCRR NIH HHS M01 RR000052NCRR NIH HHS UL1 RR024131NEI NIH HHS K23 EY013707NEI NIH HHS U10 EY008052NEI NIH HHS U10 EY008057NEI NIH HHS U10 EY008067NHLBI NIH HHS U01 HL146194NIAAA NIH HHS R01 AA016893NIAAA NIH HHS U01 AA013566NIAAA NIH HHS U01 AA020790NIAAA NIH HHS U24 AA020794NIAID NIH HHS F31 AI124794NIAID NIH HHS K01 AI093197NIAID NIH HHS K01 AI131895NIAID NIH HHS K24 AI065298NIAID NIH HHS K24 AI118591NIAID NIH HHS P30 AI027757NIAID NIH HHS P30 AI027763NIAID NIH HHS P30 AI027767NIAID NIH HHS P30 AI036219NIAID NIH HHS P30 AI050410NIAID NIH HHS P30 AI094189NIAID NIH HHS P30 AI110527NIAID NIH HHS R24 AI067039NIAID NIH HHS U01 AI031834NIAID NIH HHS U01 AI034989NIAID NIH HHS U01 AI034993NIAID NIH HHS U01 AI034994NIAID NIH HHS U01 AI035004NIAID NIH HHS U01 AI035039NIAID NIH HHS U01 AI035040NIAID NIH HHS U01 AI035041NIAID NIH HHS U01 AI035042NIAID NIH HHS U01 AI037613NIAID NIH HHS U01 AI037984NIAID NIH HHS U01 AI038855NIAID NIH HHS U01 AI038858NIAID NIH HHS U01 AI042590NIAID NIH HHS U01 AI068634NIAID NIH HHS U01 AI068636NIAID NIH HHS U01 AI069432NIAID NIH HHS U01 AI069434NIAID NIH HHS U01 AI069918NIAID NIH HHS U01 AI103390NIAID NIH HHS U01 AI103397NIAID NIH HHS U01 AI103401NIAID NIH HHS U01 AI103408NIAID NIH HHS UM1 AI035043NIAID NIH HHS UM1 AI068634NIA NIH HHS R01 AG053100NICHD NIH HHS U01 HD032632NIDA NIH HHS F31 DA037788NIDA NIH HHS K24 DA000432NIDA NIH HHS R01 DA011602NIDA NIH HHS R01 DA012568NIDA NIH HHS U01 DA036935NIMHD NIH HHS G12 MD007583NIMHD NIH HHS U54 MD007587NIMH NIH HHS P30 MH062246
6 · The paper itself

Abstract

purposeUse of electronic health records (EHRs) in health research may lead to the false assumption of complete event ascertainment. We estimated "observation windows" (OWs), defined as periods within which the assumption of complete ascertainment of events is more likely to hold, as a quality control approach to reducing the likelihood of this false assumption. We demonstrated the impact of OWs on estimating the rates of type II diabetes mellitus (diabetes) from HIV clinical cohorts.

methodsData contributed by 16 HIV clinical cohorts to the NA-ACCORD were used to identify and evaluate OWs for an operationalized definition of diabetes occurrence as a case study. Procedures included (1) gathering cohort-level data; (2) visualizing and summarizing gaps in observations; (3) systematically establishing start and stop dates during which the assumption of complete ascertainment of diabetes events was reasonable; and (4) visualizing the diabetes OWs relative to the cohort open and close dates to identify immortal person-time. We estimated diabetes occurrence event rates and 95% confidence intervals in the most recent decade that data were available (January 1, 2007, to December 31, 2016).

resultsThe number of diabetes events decreased by 17% with the use of the diabetes OWs; immortal person-time was removed decreasing total person-years by 23%. Consequently, the diabetes rate increased from 1.23 (95% confidence interval [1.20, 1.25]) per 100 person-years to 1.32 [1.29, 1.35] per 100 person-years with the use of diabetes OWs.

conclusionsAs the use of EHR-curated data for event-driven health research continues to expand, OWs have utility as a quality control approach to complete event ascertainment, helping to improve accuracy of estimates by removing immortal person-time when ascertainment is incomplete.

Indexed as

Quality ControlDiabetes Mellitus, Type 2Electronic Health RecordsHIV InfectionsHumansIncidenceDiabetesElectronic medical recordsHealth researchHIVImmortal person-timeQuality control

Identifiers

PMID31005552
PMCPMC6502687

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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.