title: Geographic Clustering of Cardiometabolic Risk Factors in Metropolitan Centres in France and 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, HbA1c). 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, HbA1c 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.
authors.affiliation: Centre for Population Health Research, School of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide SA 5001, Australia. catherine.paquet@unisa.edu.au.|Research Center of the Douglas Mental Health University Institute, Montréal, QC H4H 1R3, Canada. catherine.paquet@unisa.edu.au.
authors.forename: Catherine
authors.initials: C
authors.lastname: Paquet
authors.affiliation: Inserm, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis Team, Paris 75012, France. basile.chaix@iplesp.upmc.fr.|Sorbonne Universités, UPMC Univ Paris 06, UMR-S 1136, Pierre Louis Institute of Epidemiology and Public Health, Nemesis Team, Paris 75012, France. basile.chaix@iplesp.upmc.fr.
authors.forename: Basile
authors.initials: B
authors.lastname: Chaix
authors.affiliation: Centre for Population Health Research, School of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide SA 5001, Australia. natasha.howard@unisa.edu.au.
authors.forename: Natasha J
authors.initials: NJ
authors.lastname: Howard
authors.affiliation: Centre for Population Health Research, School of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide SA 5001, Australia. neil.coffee@unisa.edu.au.
authors.forename: Neil T
authors.initials: NT
authors.lastname: Coffee
authors.affiliation: Discipline of Medicine, The University of Adelaide, Adelaide SA 5001, Australia. robert.adams@adelaide.edu.au.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Adams
authors.affiliation: Discipline of Medicine, The University of Adelaide, Adelaide SA 5001, Australia. anne.taylor@adelaide.edu.au.
authors.forename: Anne W
authors.initials: AW
authors.lastname: Taylor
authors.affiliation: Centre d'Investigations Préventives et Cliniques, Paris 75116, France. Thomas@ipc.asso.fr.
authors.forename: Frédérique
authors.initials: F
authors.lastname: Thomas
authors.affiliation: Centre for Population Health Research, School of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide SA 5001, Australia. mark.daniel@unisa.edu.au.|Department of Medicine, The University of Melbourne, St Vincent's Hospital, Melbourne, Fitzroy VIC 3065, Australia. mark.daniel@unisa.edu.au.
authors.forename: Mark
authors.initials: M
authors.lastname: Daniel
mesh_terms: D015415:Biomarkers / Q000378:metabolism; D001786:Blood Glucose; D001794:Blood Pressure / Q000502:physiology; D015992:Body Mass Index; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000209:etiology* / Q000517:prevention & control; D008076:Cholesterol, HDL; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D005260:Female; D062305:Geographic Mapping; D007722:Health Knowledge, Attitudes, Practice; D006306:Health Surveys; D006801:Humans; D007333:Insulin Resistance; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control; D008875:Middle Aged; D009765:Obesity / Q000453:epidemiology* / Q000517:prevention & control; D010297:Paris / Q000453:epidemiology; D012307:Risk Factors; D013021:South Australia / Q000453:epidemiology; D014280:Triglycerides / Q000097:blood; D055105:Waist Circumference / Q000502:physiology
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D008076:Cholesterol, HDL; D014280:Triglycerides
keywords: Intra-Class Correlation; cardiometabolic risk factors; cross-country comparison; geographic clustering; geographic variation