Evidence map›Paper›PMID 36857086›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2023

Using electronic health record data to link families: an illustrative example using intergenerational patterns of obesity.

Amy E Krefman, Farhad Ghamsari, Daniel R Turner, Alice Lu, Martin Borsje, Colby Witherup Wood, Lucia C Petito, Fernanda C G Polubriaginof, Daniel Schneider, Faraz Ahmad and 1 more

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Adherence to Pediatric Screening.Journal of the American Heart Association · 2024
    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

11 authors.

Amy E KrefmanDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.ORCID 0000-0002-6692-0104
Farhad GhamsariDepartment of Internal Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.
Daniel R TurnerIT Research Computing Services, Northwestern University, Evanston, Illinois, USA.
Alice LuNorthwestern Medicine Enterprise Data Warehouse, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Martin BorsjeNorthwestern Medicine Enterprise Data Warehouse, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Colby Witherup WoodIT Research Computing Services, Northwestern University, Evanston, Illinois, USA.
Lucia C PetitoDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Fernanda C G PolubriaginofMemorial Sloan Kettering Cancer Center, New York, New York, USA.
Daniel SchneiderNorthwestern Medicine Enterprise Data Warehouse, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
Faraz AhmadDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.ORCID 0000-0002-2613-2541
Norrina B AllenDepartment of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
NCI NIH HHS P30 CA008748
6 · The paper itself

Abstract

objectiveElectronic health record (EHR) data are a valuable resource for population health research but lack critical information such as relationships between individuals. Emergency contacts in EHRs can be used to link family members, creating a population that is more representative of a community than traditional family cohorts. MATERIALS AND

methodsWe revised a published algorithm: relationship inference from the electronic health record (RIFTEHR). Our version, Pythonic RIFTEHR (P-RIFTEHR), identifies a patient's emergency contacts, matches them to existing patients (when available) using network graphs, checks for conflicts, and infers new relationships. P-RIFTEHR was run on December 15, 2021 in the Northwestern Medicine Electronic Data Warehouse (NMEDW) on approximately 2.95 million individuals and was validated using the existing link between children born at NM hospitals and their mothers. As proof-of-concept, we modeled the association between parent and child obesity using logistic regression.

resultsThe P-RIFTEHR algorithm matched 1 157 454 individuals in 448 278 families. The median family size was 2, the largest was 32 persons, and 247 families spanned 4 generations or more. Validation of the mother-child pairs resulted in 95.1% sensitivity. Children were 2 times more likely to be obese if a parent is obese (OR: 2.30; 95% CI, 2.23-2.37).

conclusionP-RIFTEHR can identify familiar relationships in a large, diverse population in an integrated health system. Estimates of parent-child inheritability of obesity using family structures identified by the algorithm were consistent with previously published estimates from traditional cohort studies.

Indexed as

Electronic Health RecordsObesityCohort StudiesFamilyHumansParentsPediatric Obesitycohort studieselectronic health recordfamily characteristicsobesitypopulation health

Identifiers

PMID36857086
PMCPMC10114127

What OpenQuestion holds

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Registered trials

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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.