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

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,...

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Autores principales: Ngo, Anh D., Paquet, Catherine, Howard, Natasha J., Coffee, Neil T., Taylor, Anne W., Adams, Robert J., Daniel, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3924477/
https://www.ncbi.nlm.nih.gov/pubmed/24406665
http://dx.doi.org/10.3390/ijerph110100830
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author Ngo, Anh D.
Paquet, Catherine
Howard, Natasha J.
Coffee, Neil T.
Taylor, Anne W.
Adams, Robert J.
Daniel, Mark
author_facet Ngo, Anh D.
Paquet, Catherine
Howard, Natasha J.
Coffee, Neil T.
Taylor, Anne W.
Adams, Robert J.
Daniel, Mark
author_sort Ngo, Anh D.
collection PubMed
description 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.
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spelling pubmed-39244772014-02-18 Area-Level Socioeconomic Characteristics, Prevalence and Trajectories of Cardiometabolic Risk Ngo, Anh D. Paquet, Catherine Howard, Natasha J. Coffee, Neil T. Taylor, Anne W. Adams, Robert J. Daniel, Mark Int J Environ Res Public Health Article 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. MDPI 2014-01-08 2014-01 /pmc/articles/PMC3924477/ /pubmed/24406665 http://dx.doi.org/10.3390/ijerph110100830 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0/ This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Ngo, Anh D.
Paquet, Catherine
Howard, Natasha J.
Coffee, Neil T.
Taylor, Anne W.
Adams, Robert J.
Daniel, Mark
Area-Level Socioeconomic Characteristics, Prevalence and Trajectories of Cardiometabolic Risk
title Area-Level Socioeconomic Characteristics, Prevalence and Trajectories of Cardiometabolic Risk
title_full Area-Level Socioeconomic Characteristics, Prevalence and Trajectories of Cardiometabolic Risk
title_fullStr Area-Level Socioeconomic Characteristics, Prevalence and Trajectories of Cardiometabolic Risk
title_full_unstemmed Area-Level Socioeconomic Characteristics, Prevalence and Trajectories of Cardiometabolic Risk
title_short Area-Level Socioeconomic Characteristics, Prevalence and Trajectories of Cardiometabolic Risk
title_sort area-level socioeconomic characteristics, prevalence and trajectories of cardiometabolic risk
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3924477/
https://www.ncbi.nlm.nih.gov/pubmed/24406665
http://dx.doi.org/10.3390/ijerph110100830
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