Evidence map›Paper›PMID 32897240›Full record

ArticleJMIR medical informatics2020

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities.

Marissa Tan, Elham Hatef, Delaram Taghipour, Kinjel Vyas, Hadi Kharrazi, Laura Gottlieb, Jonathan Weiner

Abstract read
In one paragraph

Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 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, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  11. Public perspectives on the use of different data types for prediction in healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
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  17. The role of health-related quality of life in improving cancer outcomes.Journal of clinical and translational research · 2023
    Article
  18. Article
  19. Assessment of structured data elements for social risk factors.The American journal of managed care · 2022
    Article
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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

7 authors.

Marissa TanGeneral Preventive Medicine Residency Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0001-7001-8319
Elham HatefGeneral Preventive Medicine Residency Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0003-2535-8191
Delaram TaghipourGeneral Preventive Medicine Residency Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-1714-6175
Kinjel VyasDivision of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-4019-0166
Hadi KharraziDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Center for Population Health Information Technology, Baltimore, MD, United States.ORCID https://orcid.org/0000-0003-1481-4323
Laura GottliebSocial Interventions Research and Evaluation Network, Center for Health & Community, University of California, San Francisco, CA, United States.ORCID https://orcid.org/0000-0003-2669-4066
Jonathan WeinerDepartment of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Center for Population Health Information Technology, Baltimore, MD, United States.ORCID https://orcid.org/0000-0002-8299-3995

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

Indexed as

health care disparitiesinformation technologypopulation healthsocial determinants of health

Identifiers

PMID32897240
PMCPMC7509627

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.