Evidence map›Paper›PMID 30039263›Full record

ArticleCanadian journal of public health = Revue canadienne de sante publique2019

Simulation modeling to enhance population health intervention research for chronic disease prevention.

Peter Tanuseputro, Trevor Arnason, Deirdre Hennessy, Brendan Smith, Carol Bennett, Jacek Kopec, Andrew D Pinto, Richard Perez, Meltem Tuna, Douglas Manuel

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Canadian journal of public health = Revue canadienne de sante publique, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  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

10 authors.

Peter TanuseputroBruyère Research Institute, 43 Bruyère Street, Ottawa, ON, K1N 5C8, Canada. ptanuseputro@ohri.ca.ORCID 0000-0002-4409-0795
Trevor ArnasonDepartment of Community Health and Epidemiology, Dalhousie University, Halifax, NS, B3H 1V7, Canada.
Deirdre HennessyOttawa Hospital Research Institute, Ottawa Hospital - Civic Campus, 1053 Carling Ave Box 693, 2-005 Admin Services Building, Ottawa, ON, K1Y 4E9, Canada.
Brendan SmithDalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada.
Carol BennettInstitute for Clinical Evaluative Sciences, Civic Campus, Administrative Services Building, 1st Floor, 1053 Carling Avenue, Box 684, Ottawa, ON, K1Y 4E9, Canada.
Jacek KopecSchool of Population and Public Health, University of British Columbia, Milan Ilich Arthritis Research Centre, 5591 No. 3 Road, Richmond, BC, V6X 2C7, Canada.
Andrew D PintoDalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, ON, M5T 3M7, Canada.
Richard PerezInstitute for Clinical Evaluative Sciences, Civic Campus, Administrative Services Building, 1st Floor, 1053 Carling Avenue, Box 684, Ottawa, ON, K1Y 4E9, Canada.
Meltem TunaInstitute for Clinical Evaluative Sciences, Civic Campus, Administrative Services Building, 1st Floor, 1053 Carling Avenue, Box 684, Ottawa, ON, K1Y 4E9, Canada.
Douglas ManuelBruyère Research Institute, 43 Bruyère Street, Ottawa, ON, K1N 5C8, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Population Health Intervention Research (PHIR) is an expanding field that explores the health effects of population-level interventions conducted within and outside of the health sector. Simulation modeling-the use of mathematical models to predict health outcomes in populations given a set of specified inputs-is a useful, yet underutilized tool for PHIR. It can be employed at several phases of the research process: (1) planning and designing PHIR studies; (2) implementation; and (3) knowledge translation of findings across settings and populations. Using the example of community-wide, built environment interventions for the prevention of type 2 diabetes, we demonstrate how simulation models can be a powerful technique for chronic disease prevention research within PHIR. With increasingly available data on chronic disease risk factors and outcomes, the use of simulation modeling in PHIR for chronic disease prevention is anticipated to grow. There is a continued need to ensure models are appropriately validated and researchers should be cautious in their interpretation of model outputs given the uncertainties that are inherent with simulation modeling approaches. However, given the complexity of disease pathways and methodological challenges of PHIR studies, simulation models can be a valuable tool for researchers studying population interventions that hold the potential to improve health and reduce health inequities.

Indexed as

Models, TheoreticalPopulation HealthCanadaChronic DiseaseHealth Services ResearchHumansResearch DesignChronic diseaseComputer simulationEarly medical interventionHealth planningPopulation-based planningPublic health practicePublic health systems researchStatistical modelsTarget populationTheoretical models

Identifiers

PMID30039263
PMCPMC6964592

What OpenQuestion holds

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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.