Evidence map›Paper›PMID 32181122›Full record

ArticlePreventive medicine reports2020

Evaluation of family health history collection methods impact on data and risk assessment outcomes.

R Ryanne Wu, Rehena Sultana, Yasmin Bylstra, Saumya Jamuar, Sonia Davila, Weng Khong Lim, Geoffrey S Ginsburg, Lori A Orlando, Khung Keong Yeo, Stuart A Cook and 1 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Preventive medicine reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03114553 (Integration of Family Health History Data Into a Singaporean Healthy Population Cohort Database to Understand the Associations Between Family Health History and Genomics), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
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.

NCT03114553 completednot on this map

Integration of Family Health History Data Into a Singaporean Healthy Population Cohort Database to Understand the Associations Between Family Health History and Genomics

TypeobservationalSponsorDuke UniversityRan2017 to 2024Enrolled1,000ConditionsAllArmsMeTree
3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. 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 at 4 institutions in 2 countries.

R Ryanne WuCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, USA.
Rehena SultanaCentre for Quantitative Medicine, Duke-National University of Singapore Medical School, Singapore.
Yasmin BylstraSingHealth Duke-NUS Institute of Precision Medicine, Duke-National University of Singapore Medical School, Singapore.
Saumya JamuarSingHealth Duke-NUS Institute of Precision Medicine, Duke-National University of Singapore Medical School, Singapore.
Sonia DavilaSingHealth Duke-NUS Institute of Precision Medicine, Duke-National University of Singapore Medical School, Singapore.
Weng Khong LimSingHealth Duke-NUS Institute of Precision Medicine, Duke-National University of Singapore Medical School, Singapore.
Geoffrey S GinsburgCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, USA.
Lori A OrlandoCenter for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, USA.
Khung Keong YeoProgramme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore.
Stuart A CookSingHealth Duke-NUS Institute of Precision Medicine, Duke-National University of Singapore Medical School, Singapore.
Patrick TanSingHealth Duke-NUS Institute of Precision Medicine, Duke-National University of Singapore Medical School, Singapore.
National University of Singapore · SGDuke University · USAgency for Science, Technology and Research · SGDuke-NUS Medical School · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Information technology applications for patient-collection of family health history (FHH) increase identification of elevated-risk individuals compared to usual care. It is unknown if the method of collection impacts data collected or if simply going directly to the patient is what makes the difference. The objective of this study was to examine differences in data detail and risk identification rates between FHH collection directly from individuals using paper-based forms and an interactive web-based platform. This is a non-randomized epidemiologic study in Singaporean population from 2016 to 2018. Intervention was paper-based versus web-based interactive platform for FHH collection. Participant demographics, FHH detail, and risk assessment results were analyzed. 882 participants enrolled in the study, 481 in the paper-based group and 401 in the web-based group with mean (SD) age of 45.4 (12.98) years and 47.5% male. Web-based FHH collection participants had an increased number of conditions per relative (p-value <0.001), greater frequency of reporting age of onset (p-value <0.001), and greater odds of receiving ≥1 risk recommendation both overall (OR: 3.99 (2.41, 6.59)) and within subcategories of genetic counselling for hereditary cancer syndromes (p-value = 0.041) and screening and prevention for breast (p-value = 0.002) and colon cancer (p-value = 0.005). This has significant implications for clinical care and research efforts where FHH is being assessed. Using interactive information technology platforms to collect FHH can improve the completeness of the data collected and result in increased rates of risk identification. Methods of data collection to maximize benefit should be taken into account in future studies and clinical care.

Indexed as

Data accuracyFamily health historyHealth risk assessmentPreventive health services

Identifiers

PMID32181122
PMCPMC7066218
OpenAlexW3012436808

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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.