Area-Level Socioeconomic Characteristics, Prevalence and Trajectories of Cardiometabolic Risk

International Journal of Environmental Research and Public Health

Anh D. Ngo¹  Catherine Paquet²  Natasha J. Howard²  Neil T. Coffee²  Anne W. Taylor⁴  Robert J. Adams³ 

[1] Clinical and Population Perinatal Research, Kolling Institute of Medical Research, University of Sydney at Royal North Shore Hospital, St Leonards, New South Wales, NSW 2065, Australia;Social Epidemiology and Evaluation Research Group, School of Population Health and Sansom Institute for Health Research, University of South Australia, Adelaide, SA 5001, Australia; E-Mails: Catherine.;The Health Observatory, Discipline of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia; E-Mail:;Population Research and Outcome Studies, Discipline of Medicine, The University of Adelaide, Adelaide, SA 5005, Australia; E-Mail: Anne.

关键词: metabolic syndrome;    trajectories;    socioeconomic position;    income;    education;    cohort study;    residence characteristics;   

DOI  :  10.3390/ijerph110100830

来源: mdpi

【 摘 要 】

This study examines the relationships between area-level socioeconomic position (SEP) and the prevalence and trajectories of metabolic syndrome (MetS) and the count of its constituents (i.e., disturbed glucose and insulin metabolism, abdominal obesity, dyslipidemia, and hypertension). A cohort of 4,056 men and women aged 18+ living in Adelaide, Australia was established in 2000–2003. MetS was ascertained at baseline, four and eight years via clinical examinations. Baseline area-level median household income, percentage of residents with a high school education, and unemployment rate were derived from the 2001 population Census. Three-level random-intercepts logistic and Poisson regression models were performed to estimate the standardized odds ratio (SOR), prevalence risk ratio (SRR), ratio of SORs/SRRs, and (95% confidence interval (CI)). Interaction between area- and individual-level SEP variables was also tested. The odds of having MetS and the count of its constituents increased over time. This increase did not vary according to baseline area-level SEP (ratios of SORs/SRRs ≈ 1; p ≥ 0.42). However, at baseline, after adjustment for individual SEP and health behaviours, median household income (inversely) and unemployment rate (positively) were significantly associated with MetS prevalence (SOR (95%CI) = 0.76 (0.63–0.90), and 1.48 (1.26–1.74), respectively), and the count of its constituents (SRR (95%CI) = 0.96 (0.93–0.99), and 1.06 (1.04–1.09), respectively). The inverse association with area-level education was statistically significant only in participants with less than post high school education (SOR (95%CI) = 0.58 (0.45–0.73), and SRR (95%CI) = 0.91 (0.88–0.94)). Area-level SEP does not predict an elevated trajectory to developing MetS or an elevated count of its constituents. However, at baseline, area-level SEP was inversely associated with prevalence of MetS and the count of its constituents, with the association of area-level education being modified by individual-level education. Population-level interventions for communities defined by area-level socioeconomic disadvantage are needed to reduce cardiometabolic risks.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.