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A New Approach to Age-Period-Cohort Analysis Using Partial Least Squares Regression: The Trend in Blood Pressure in the Glasgow Alumni Cohort

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  • Yu-Kang Tu
  • George Davey Smith
  • Mark S Gilthorpe

Abstract

Due to a problem of identification, how to estimate the distinct effects of age, time period and cohort has been a controversial issue in the analysis of trends in health outcomes in epidemiology. In this study, we propose a novel approach, partial least squares (PLS) analysis, to separate the effects of age, period, and cohort. Our example for illustration is taken from the Glasgow Alumni cohort. A total of 15,322 students (11,755 men and 3,567 women) received medical screening at the Glasgow University between 1948 and 1968. The aim is to investigate the secular trends in blood pressure over 1925 and 1950 while taking into account the year of examination and age at examination. We excluded students born before 1925 or aged over 25 years at examination and those with missing values in confounders from the analyses, resulting in 12,546 and 12,516 students for analysis of systolic and diastolic blood pressure, respectively. PLS analysis shows that both systolic and diastolic blood pressure increased with students' age, and students born later had on average lower blood pressure (SBP: −0.17 mmHg/per year [95% confidence intervals: −0.19 to −0.15] for men and −0.25 [−0.28 to −0.22] for women; DBP: −0.14 [−0.15 to −0.13] for men; −0.09 [−0.11 to −0.07] for women). PLS also shows a decreasing trend in blood pressure over the examination period. As identification is not a problem for PLS, it provides a flexible modelling strategy for age-period-cohort analysis. More emphasis is then required to clarify the substantive and conceptual issues surrounding the definitions and interpretations of age, period and cohort effects.

Suggested Citation

  • Yu-Kang Tu & George Davey Smith & Mark S Gilthorpe, 2011. "A New Approach to Age-Period-Cohort Analysis Using Partial Least Squares Regression: The Trend in Blood Pressure in the Glasgow Alumni Cohort," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-9, April.
    • Handle: RePEc:plo:pone00:0019401
    DOI: 10.1371/journal.pone.0019401
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    Cited by:

    1. Shih-Yung Su & Wen-Chung Lee, 2019. "Age-period-cohort analysis with a constant-relative-variation constraint for an apportionment of period and cohort slopes," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-21, December.
    2. Andrew Bell & Kelvyn Jones, 2015. "Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 255-266, January.
    3. Page, Andrew & Milner, Allison & Morrell, Stephen & Taylor, Richard, 2013. "The role of under-employment and unemployment in recent birth cohort effects in Australian suicide," Social Science & Medicine, Elsevier, vol. 93(C), pages 155-162.
    4. Myck, Michał & Oczkowska, Monika, 2022. "Healthier over time? Period effects in health among older Europeans in a step-wise approach to identification," Social Science & Medicine, Elsevier, vol. 297(C).
    5. Andrew Bell & Kelvyn Jones, 2015. "Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 255-266, January.
    6. Paulo Cavalcante Apratto Junior & Mônica Bastos De Lima Barros & Regina Paiva Daumas & Mônica Kramer De Noronha Andrade & Denise Leite Maia Monteiro & Beatriz Rodrigues Lopes Vincent & Valéria Teresa , 2014. "Trends in AIDS Incidence in Individuals Aged 50 Years or Older in the City of Rio de Janeiro, Brazil, 1982–2011: An Age-Period-Cohort Analysis," IJERPH, MDPI, vol. 11(8), pages 1-14, July.
    7. Bischofberger, Stephan M. & Hiabu, Munir & Mammen, Enno & Nielsen, Jens Perch, 2019. "A comparison of in-sample forecasting methods," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 133-154.
    8. Andrew Bell & Kelvyn Jones, 2014. "Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(11), pages 333-360.

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