Age-period-cohort analysis for trends in body mass index in Ireland

Hdl Handle:
http://hdl.handle.net/10147/303556
Title:
Age-period-cohort analysis for trends in body mass index in Ireland
Authors:
Jiang, Tao; Gilthorpe, Mark S; Shiely, Frances; Harrington, Janas M; Perry, Ivan J; Kelleher, Cecily C; Tu, Yu-Kang
Citation:
BMC Public Health. 2013 Sep 25;13(1):889
Journal:
BMC Public Health
Issue Date:
25-Sep-2013
URI:
http://dx.doi.org/10.1186/1471-2458-13-889; http://hdl.handle.net/10147/303556
Abstract:
Abstract Background Obesity is a growing problem worldwide and can often result in a variety of negative health outcomes. In this study we aim to apply partial least squares (PLS) methodology to estimate the separate effects of age, period and cohort on the trends in obesity as measured by body mass index (BMI). Methods Using PLS we will obtain gender specific linear effects of age, period and cohort on obesity. We also explore and model nonlinear relationships of BMI with age, period and cohort. We analysed the results from 7,796 men and 10,220 women collected through the SLAN (Surveys of Lifestyle, attitudes and Nutrition) in Ireland in the years 1998, 2002 and 2007. Results PLS analysis revealed a positive period effect over the years. Additionally, men born later tended to have lower BMI (−0.026 kg·m-2 yr-1, 95% CI: -0.030 to −0.024) and older men had in general higher BMI (0.029 kg·m-2 yr-1, 95% CI: 0.026 to 0.033). Similarly for women, those born later had lower BMI (−0.025 kg·m-2 yr-1, 95% CI: -0.029 to −0.022) and older women in general had higher BMI (0.029 kg·m-2 yr-1, 95% CI: 0.025 to 0.033). Nonlinear analyses revealed that BMI has a substantial curvilinear relationship with age, though less so with birth cohort. Conclusion We notice a generally positive age and period effect but a slightly negative cohort effect. Knowing this, we have a better understanding of the different risk groups which allows for effective public intervention measures to be designed and targeted for these specific population subgroups.
Item Type:
Article
Language:
en
Keywords:
BODY MASS INDEX; OBESITY

Full metadata record

DC FieldValue Language
dc.contributor.authorJiang, Taoen_GB
dc.contributor.authorGilthorpe, Mark Sen_GB
dc.contributor.authorShiely, Francesen_GB
dc.contributor.authorHarrington, Janas Men_GB
dc.contributor.authorPerry, Ivan Jen_GB
dc.contributor.authorKelleher, Cecily Cen_GB
dc.contributor.authorTu, Yu-Kangen_GB
dc.date.accessioned2013-10-17T16:56:59Z-
dc.date.available2013-10-17T16:56:59Z-
dc.date.issued2013-09-25-
dc.identifier.citationBMC Public Health. 2013 Sep 25;13(1):889en_GB
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2458-13-889-
dc.identifier.urihttp://hdl.handle.net/10147/303556-
dc.description.abstractAbstract Background Obesity is a growing problem worldwide and can often result in a variety of negative health outcomes. In this study we aim to apply partial least squares (PLS) methodology to estimate the separate effects of age, period and cohort on the trends in obesity as measured by body mass index (BMI). Methods Using PLS we will obtain gender specific linear effects of age, period and cohort on obesity. We also explore and model nonlinear relationships of BMI with age, period and cohort. We analysed the results from 7,796 men and 10,220 women collected through the SLAN (Surveys of Lifestyle, attitudes and Nutrition) in Ireland in the years 1998, 2002 and 2007. Results PLS analysis revealed a positive period effect over the years. Additionally, men born later tended to have lower BMI (−0.026 kg·m-2 yr-1, 95% CI: -0.030 to −0.024) and older men had in general higher BMI (0.029 kg·m-2 yr-1, 95% CI: 0.026 to 0.033). Similarly for women, those born later had lower BMI (−0.025 kg·m-2 yr-1, 95% CI: -0.029 to −0.022) and older women in general had higher BMI (0.029 kg·m-2 yr-1, 95% CI: 0.025 to 0.033). Nonlinear analyses revealed that BMI has a substantial curvilinear relationship with age, though less so with birth cohort. Conclusion We notice a generally positive age and period effect but a slightly negative cohort effect. Knowing this, we have a better understanding of the different risk groups which allows for effective public intervention measures to be designed and targeted for these specific population subgroups.-
dc.language.isoenen
dc.subjectBODY MASS INDEXen_GB
dc.subjectOBESITYen_GB
dc.titleAge-period-cohort analysis for trends in body mass index in Irelanden_GB
dc.typeArticleen
dc.identifier.journalBMC Public Healthen_GB
dc.language.rfc3066en-
dc.rights.holderTao Jiang et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2013-10-17T15:11:03Z-
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