Evidence map›Paper›PMID 19527598›Full record

ReviewPreventing chronic disease2009

Complex systems modeling for obesity research.

Ross A Hammond

Abstract readReview
In one paragraph

Review in Preventing chronic disease, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
107citing papers in PubMed, 5 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

107 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  19. A Neuroeconomics Approach to Obesity.Biological psychiatry · 2022
    Review
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47 more citing papers are in PubMed but not listed here.

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

1 author.

Ross A HammondCenter on Social and Economic Dynamics, Economic Studies Program, The Brookings Institution, 1775 Massachusetts Ave, NW, Washington, DC 20036, USA. rhammond@brookings.edu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The obesity epidemic has grown rapidly into a major public health challenge, in the United States and worldwide. The scope and scale of the obesity epidemic motivate an urgent need for well-crafted policy interventions to prevent further spread and (potentially) to reverse the epidemic. Yet several attributes of the epidemic make it an especially challenging problem both to study and to combat. This article shows that these attributes--the great breadth in levels of scale involved, the substantial diversity of relevant actors, and the multiplicity of mechanisms implicated--are characteristic of a complex adaptive system. It argues that the obesity epidemic is driven by such a system and that lessons and techniques from the field of complexity science can help inform both scientific study of obesity and effective policies to combat obesity. The article gives an overview of modeling techniques especially well suited to study the rich and complex dynamics of obesity and to inform policy design.

Indexed as

Models, BiologicalSystems BiologyComputer SimulationHealth PolicyHealth PromotionHumansObesityUnited States

Identifiers

PMID19527598
PMCPMC2722404

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.