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Geographic Clustering of Cardiometabolic Risk Factors in Metropolitan Centres in France and Australia

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  • Catherine Paquet

    (Centre for Population Health Research, School of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide SA 5001, Australia
    Research Center of the Douglas Mental Health University Institute, Montréal, QC H4H 1R3, Canada)

  • Basile Chaix

    (Inserm, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis Team, Paris 75012, France
    Sorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis Team, Paris 75012, France)

  • Natasha J. Howard

    (Centre for Population Health Research, School of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide SA 5001, Australia)

  • Neil T. Coffee

    (Centre for Population Health Research, School of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide SA 5001, Australia)

  • Robert J. Adams

    (Discipline of Medicine, The University of Adelaide, Adelaide SA 5001, Australia)

  • Anne W. Taylor

    (Discipline of Medicine, The University of Adelaide, Adelaide SA 5001, Australia)

  • Frédérique Thomas

    (Centre d’Investigations Préventives et Cliniques, Paris 75116, France)

  • Mark Daniel

    (Centre for Population Health Research, School of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide SA 5001, Australia
    Department of Medicine, The University of Melbourne, St Vincent’s Hospital, Melbourne, Fitzroy VIC 3065, Australia)

Abstract

Understanding how health outcomes are spatially distributed represents a first step in investigating the scale and nature of environmental influences on health and has important implications for statistical power and analytic efficiency. Using Australian and French cohort data, this study aimed to describe and compare the extent of geographic variation, and the implications for analytic efficiency, across geographic units, countries and a range of cardiometabolic parameters (Body Mass Index (BMI) waist circumference, blood pressure, resting heart rate, triglycerides, cholesterol, glucose, HbA 1c ). Geographic clustering was assessed using Intra-Class Correlation (ICC) coefficients in biomedical cohorts from Adelaide (Australia, n = 3893) and Paris (France, n = 6430) for eight geographic administrative units. The median ICC was 0.01 suggesting 1% of risk factor variance attributable to variation between geographic units. Clustering differed by cardiometabolic parameters, administrative units and countries and was greatest for BMI and resting heart rate in the French sample, HbA 1c in the Australian sample, and for smaller geographic units. Analytic inefficiency due to clustering was greatest for geographic units in which participants were nested in fewer, larger geographic units. Differences observed in geographic clustering across risk factors have implications for choice of geographic unit in sampling and analysis, and highlight potential cross-country differences in the distribution, or role, of environmental features related to cardiometabolic health.

Suggested Citation

  • Catherine Paquet & Basile Chaix & Natasha J. Howard & Neil T. Coffee & Robert J. Adams & Anne W. Taylor & Frédérique Thomas & Mark Daniel, 2016. "Geographic Clustering of Cardiometabolic Risk Factors in Metropolitan Centres in France and Australia," IJERPH, MDPI, vol. 13(5), pages 1-17, May.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:5:p:519-:d:70593
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    References listed on IDEAS

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    1. Chaix, Basile & Jouven, Xavier & Thomas, Frédérique & Leal, Cinira & Billaudeau, Nathalie & Bean, Kathy & Kestens, Yan & Jëgo, Bertrand & Pannier, Bruno & Danchin, Nicolas, 2011. "Why socially deprived populations have a faster resting heart rate: Impact of behaviour, life course anthropometry, and biology – the RECORD Cohort Study," Social Science & Medicine, Elsevier, vol. 73(10), pages 1543-1550.
    2. Mitchell, Richard & Dujardin, Claire & Popham, Frank & Farfan Portet, Maria-Isabel & Thomas, Isabelle & Lorant, Vincent, 2011. "Using matched areas to explore international differences in population health," Social Science & Medicine, Elsevier, vol. 73(8), pages 1113-1122.
    3. Lisa A. Matricciani & Catherine Paquet & Natasha J. Howard & Robert Adams & Neil T. Coffee & Anne W. Taylor & Mark Daniel, 2013. "Investigating Individual- and Area-Level Socioeconomic Gradients of Pulse Pressure among Normotensive and Hypertensive Participants," IJERPH, MDPI, vol. 10(2), pages 1-19, February.
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    Cited by:

    1. Darren J. Mayne & Geoffrey G. Morgan & Bin B. Jalaludin & Adrian E. Bauman, 2019. "Area-Level Walkability and the Geographic Distribution of High Body Mass in Sydney, Australia: A Spatial Analysis Using the 45 and Up Study," IJERPH, MDPI, vol. 16(4), pages 1-29, February.
    2. Timotheus B. Darikwa & Samuel O. Manda, 2020. "Spatial Co-Clustering of Cardiovascular Diseases and Select Risk Factors among Adults in South Africa," IJERPH, MDPI, vol. 17(10), pages 1-16, May.
    3. Renin Toms & Xiaoqi Feng & Darren J Mayne & Andrew Bonney, 2020. "Role of Area-Level Access to Primary Care on the Geographic Variation of Cardiometabolic Risk Factor Distribution: A Multilevel Analysis of the Adult Residents in the Illawarra—Shoalhaven Region of NS," IJERPH, MDPI, vol. 17(12), pages 1-21, June.

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