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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
Commentary

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.

Keywords

Public health systems research Public health practice Health planning Computer simulation Statistical models Theoretical models Population-based planning Target population Early medical intervention Chronic disease 

Résumé

La recherche interventionnelle en santé des populations (RISP), un domaine en expansion, explore les effets sur la santé des interventions en population menées à l’intérieur et à l’extérieur du secteur de la santé. La modélisation de simulation—le recours à des modèles mathématiques pour prédire les résultats de santé au sein de populations selon un jeu d’intrants spécifiés— est un outil sous-utilisé en RISP. Il peut être employé à plusieurs stades du processus de recherche : 1) la planification et la conception d’une étude de RISP; 2) sa mise en œuvre; et 3) l’application des constatations d’un milieu et d’une population à l’autre. En prenant l’exemple d’interventions de proximité sur l’environnement bâti qui visent à prévenir le diabète de type 2, nous démontrons que les modèles de simulation peuvent constituer une puissante technique pour étudier la prévention des maladies chroniques en RISP. Avec la disponibilité croissante de données sur les facteurs de risque et les issues des maladies chroniques, le recours à la modélisation de simulation en RISP pour la prévention des maladies chroniques devrait augmenter. Bien entendu, il faut s’assurer que les modèles sont dûment validés, et la prudence est de mise dans l’interprétation des extrants de ces modèles, étant donné l’incertitude inhérente des démarches de modélisation de simulation. Néanmoins, vu la complexité de la progression des maladies et les difficultés méthodologiques des études de RISP, les modèles de simulation peuvent être de précieux outils pour les chercheurs qui étudient les interventions en population susceptibles d’améliorer la santé et de réduire les inégalités de santé.

Mots-clés

Recherche sur les systèmes de santé publique Pratique en santé publique Planification en santé Simulation numérique Modèles statistiques Modèles théoriques Planification basée sur la population Population cible Intervention médicale précoce Maladie chronique 

Notes

Acknowledgements

We would like to acknowledge and thank Behnam Sharif, Dr. Sam Harper, and other members of the STAR team who provided valuable feedback on an earlier draft of the manuscript.

Funding

This commentary was unfunded. PT was the first author and receives support from the Department of Medicine at the University of Ottawa, Bruyère Research Institute, and the Ottawa Hospital Research Institute. Funders did not have any influence in any aspect of this manuscript.

Compliance with ethical standards

Conflict of interests

The authors declare that they have no conflict of interests.

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Copyright information

© The Canadian Public Health Association 2018

Authors and Affiliations

  • Peter Tanuseputro
    • 1
    • 2
    • 3
    • 4
  • Trevor Arnason
    • 5
  • Deirdre Hennessy
    • 2
  • Brendan Smith
    • 6
    • 7
  • Carol Bennett
    • 3
  • Jacek Kopec
    • 8
  • Andrew D. Pinto
    • 6
    • 9
    • 10
    • 11
  • Richard Perez
    • 3
  • Meltem Tuna
    • 3
  • Douglas Manuel
    • 1
    • 2
    • 3
    • 12
  1. 1.Bruyère Research InstituteOttawaCanada
  2. 2.Ottawa Hospital Research Institute, Ottawa Hospital - Civic CampusOttawaCanada
  3. 3.Institute for Clinical Evaluative SciencesOttawaCanada
  4. 4.Department of Medicine, Division of Palliative CareUniversity of OttawaOttawaCanada
  5. 5.Department of Community Health and EpidemiologyDalhousie UniversityHalifaxCanada
  6. 6.Dalla Lana School of Public HealthUniversity of TorontoTorontoCanada
  7. 7.Public Health OntarioTorontoCanada
  8. 8.School of Population and Public HealthUniversity of British ColumbiaRichmondCanada
  9. 9.Department of Family and Community MedicineSt. Michael’s HospitalTorontoCanada
  10. 10.Department of Family and Community Medicine, Faculty of MedicineUniversity of TorontoTorontoCanada
  11. 11.Centre for Urban Health Solutions, Li Ka Shing Knowledge InstituteSt. Michael’s HospitalTorontoCanada
  12. 12.Department of Family MedicineUniversity of OttawaOttawaCanada

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