Evidence map›Paper›PMID 27236380›Full record

ReviewPreventing chronic disease2016

Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions.

Yan Li, Mark A Lawley, David S Siscovick, Donglan Zhang, José A Pagán

Abstract readReview
In one paragraph

Review in Preventing chronic disease, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

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

  1. Pooled it
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  12. Applications of agent-based modeling in trauma research.Psychological trauma : theory, research, practice and policy · 2023
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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

5 authors.

Yan LiResearch Scientist, Center for Health Innovation, The New York Academy of Medicine, 1216 Fifth Avenue, New York, NY 10029. Email: yli@nyam.org.
Mark A LawleyCenter for Remote Health Technologies and Systems and Department of Industrial and Systems Engineering, Texas A&M University, College Station, Texas.
David S SiscovickThe New York Academy of Medicine, New York, New York.
Donglan ZhangDepartment of Health Policy and Management, University of California, Los Angeles, California.
José A PagánCenter for Health Innovation, The New York Academy of Medicine, New York, New York, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.

Indexed as

Models, TheoreticalSystems AnalysisChronic DiseaseComorbidityHumansResearchUnited States

Identifiers

PMID27236380
PMCPMC4885681

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.