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The Use of a Chronic Disease and Risk Factor Surveillance System to Determine the Age, Period and Cohort Effects on the Prevalence of Obesity and Diabetes in South Australian Adults - 2003–2013

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  • Anne W Taylor
  • Zumin Shi
  • Alicia Montgomerie
  • Eleonora Dal Grande
  • Stefano Campostrini

Abstract

Background: Age, period and cohort (APC) analyses, using representative, population-based descriptive data, provide additional understanding behind increased prevalence rates. Methods: Data on obesity and diabetes from the South Australian (SA) monthly chronic disease and risk factor surveillance system from July 2002 to December 2013 (n = 59,025) were used. Age was the self-reported age of the respondent at the time of the interview. Period was the year of the interview and cohort was age subtracted from the survey year. Cohort years were 1905 to 1995. All variables were treated as continuous. The age-sex standardised prevalence for obesity and diabetes was calculated using the Australia 2011 census. The APC models were constructed with ‘‘apcfit’’ in Stata. Results: The age-sex standardised prevalence of obesity and diabetes increased in 2002-2013 from 18.6% to 24.1% and from 6.2% to 7.9%. The peak age for obesity was approximately 70 years with a steady increasing rate from 20 to 70 years of age. The peak age for diabetes was approximately 80 years. There were strong cohort effects and no period effects for both obesity and diabetes. The magnitude of the cohort effect is much more pronounced for obesity than for diabetes. Conclusion: The APC analyses showed a higher than expected peak age for both obesity and diabetes, strong cohort effects with an acceleration of risk after 1960s for obesity and after 1940s for diabetes, and no period effects. By simultaneously considering the effects of age, period and cohort we have provided additional evidence for effective public health interventions.

Suggested Citation

  • Anne W Taylor & Zumin Shi & Alicia Montgomerie & Eleonora Dal Grande & Stefano Campostrini, 2015. "The Use of a Chronic Disease and Risk Factor Surveillance System to Determine the Age, Period and Cohort Effects on the Prevalence of Obesity and Diabetes in South Australian Adults - 2003–2013," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-11, April.
    • Handle: RePEc:plo:pone00:0125233
    DOI: 10.1371/journal.pone.0125233
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    References listed on IDEAS

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    3. Keyes, Katherine M. & Utz, Rebecca L. & Robinson, Whitney & Li, Guohua, 2010. "What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971-2006," Social Science & Medicine, Elsevier, vol. 70(7), pages 1100-1108, April.
    4. 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.
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