Literature DB >> 25617033

Should age-period-cohort studies return to the methodologies of the 1970s?

Eric N Reither1, Ryan K Masters2, Yang Claire Yang3, Daniel A Powers4, Hui Zheng5, Kenneth C Land6.   

Abstract

Social scientists have recognized the importance of age-period-cohort (APC) models for half a century, but have spent much of this time mired in debates about the feasibility of APC methods. Recently, a new class of APC methods based on modern statistical knowledge has emerged, offering potential solutions. In 2009, Reither, Hauser and Yang used one of these new methods - hierarchical APC (HAPC) modeling - to study how birth cohorts may have contributed to the U.S. obesity epidemic. They found that recent birth cohorts experience higher odds of obesity than their predecessors, but that ubiquitous period-based changes are primarily responsible for the rising prevalence of obesity. Although these findings have been replicated elsewhere, recent commentaries by Bell and Jones call them into question - along with the new class of APC methods. Specifically, Bell and Jones claim that new APC methods do not adequately address model identification and suggest that "solid theory" is often sufficient to remove one of the three temporal dimensions from empirical consideration. They also present a series of simulation models that purportedly show how the HAPC models estimated by Reither et al. (2009) could have produced misleading results. However, these simulation models rest on assumptions that there were no period effects, and associations between period and cohort variables and the outcome were perfectly linear. Those are conditions under which APC models should never be used. Under more tenable assumptions, our own simulations show that HAPC methods perform well, both in recovering the main findings presented by Reither et al. (2009) and the results reported by Bell and Jones. We also respond to critiques about model identification and theoretically-imposed constraints, finding little pragmatic support for such arguments. We conclude by encouraging social scientists to move beyond the debates of the 1970s and toward a deeper appreciation for modern APC methodologies.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Age-period-cohort models; Body mass index; Cohort effects; Hierarchical modeling; Obesity epidemic; Random effects; Research methods; Social change

Mesh:

Year:  2015        PMID: 25617033      PMCID: PMC4357521          DOI: 10.1016/j.socscimed.2015.01.011

Source DB:  PubMed          Journal:  Soc Sci Med        ISSN: 0277-9536            Impact factor:   4.634


  18 in total

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2.  Time changes, so do people.

Authors:  Etsuji Suzuki
Journal:  Soc Sci Med       Date:  2012-04-22       Impact factor: 4.634

3.  The impossibility of separating age, period and cohort effects.

Authors:  Andrew Bell; Kelvyn Jones
Journal:  Soc Sci Med       Date:  2013-05-04       Impact factor: 4.634

4.  Recent advances in age-period-cohort analysis. A commentary on Dregan and Armstrong, and on Reither, Hauser and Yang.

Authors:  David J Harding
Journal:  Soc Sci Med       Date:  2009-09-18       Impact factor: 4.634

5.  Educational Differences in U.S. Adult Mortality: A Cohort Perspective.

Authors:  Ryan K Masters; Robert A Hummer; Daniel A Powers
Journal:  Am Sociol Rev       Date:  2012-08-01

6.  The cohort as a concept in the study of social change.

Authors:  N B Ryder
Journal:  Am Sociol Rev       Date:  1965-12

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Authors:  Liying Luo
Journal:  Demography       Date:  2013-12

8.  A changing pattern of childhood BMI growth during the 20th century: 70 y of data from the Fels Longitudinal Study.

Authors:  William Johnson; Laura E Soloway; Darin Erickson; Audrey C Choh; Miryoung Lee; William C Chumlea; Roger M Siervogel; Stefan A Czerwinski; Bradford Towne; Ellen W Demerath
Journal:  Am J Clin Nutr       Date:  2012-03-14       Impact factor: 7.045

9.  Trends in body mass index in adolescence and young adulthood in the United States: 1959-2002.

Authors:  Hedwig Lee; Dohoon Lee; Guang Guo; Kathleen Mullan Harris
Journal:  J Adolesc Health       Date:  2011-07-12       Impact factor: 5.012

10.  Do birth cohorts matter? Age-period-cohort analyses of the obesity epidemic in the United States.

Authors:  Eric N Reither; Robert M Hauser; Yang Yang
Journal:  Soc Sci Med       Date:  2009-09-19       Impact factor: 4.634

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  14 in total

1.  Clarifying hierarchical age-period-cohort models: A rejoinder to Bell and Jones.

Authors:  Eric N Reither; Kenneth C Land; Sun Y Jeon; Daniel A Powers; Ryan K Masters; Hui Zheng; Melissa A Hardy; Katherine M Keyes; Qiang Fu; Heidi A Hanson; Ken R Smith; Rebecca L Utz; Y Claire Yang
Journal:  Soc Sci Med       Date:  2015-07-31       Impact factor: 4.634

2.  COHORT CHANGES IN THE SOCIAL DISTRIBUTION OF TOLERANT SEXUAL ATTITUDES.

Authors:  Fred C Pampel
Journal:  Soc Forces       Date:  2016-11-07

3.  Fitting Age-Period-Cohort Models Using the Intrinsic Estimator: Assumptions and Misapplications.

Authors:  Ryan K Masters; Daniel A Powers; Robert A Hummer; Audrey Beck; Shih-Fan Lin; Brian Karl Finch
Journal:  Demography       Date:  2016-08

4.  Recent cohort effects in suicide in Scotland: a legacy of the 1980s?

Authors:  Jane Parkinson; Jon Minton; James Lewsey; Janet Bouttell; Gerry McCartney
Journal:  J Epidemiol Community Health       Date:  2016-07-18       Impact factor: 3.710

5.  An analysis of suicide trends in Scotland 1950-2014: comparison with England & Wales.

Authors:  Nadine Dougall; Cameron Stark; Tim Agnew; Rob Henderson; Margaret Maxwell; Paul Lambert
Journal:  BMC Public Health       Date:  2017-12-20       Impact factor: 3.295

6.  The unrealized potential: cohort effects and age-period-cohort analysis.

Authors:  Jongho Heo; Sun-Young Jeon; Chang-Mo Oh; Jongnam Hwang; Juhwan Oh; Youngtae Cho
Journal:  Epidemiol Health       Date:  2017-12-05

7.  Changes in the use practitioner-based complementary and alternative medicine over time in Canada: Cohort and period effects.

Authors:  Mayilee Canizares; Sheilah Hogg-Johnson; Monique A M Gignac; Richard H Glazier; Elizabeth M Badley
Journal:  PLoS One       Date:  2017-05-11       Impact factor: 3.240

8.  The hierarchical age-period-cohort model: Why does it find the results that it finds?

Authors:  Andrew Bell; Kelvyn Jones
Journal:  Qual Quant       Date:  2017-02-24

Review 9.  Cancer Risk Studies and Priority Areas for Cancer Risk Appraisal in Uganda.

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10.  Drug-related deaths in Scotland 1979-2013: evidence of a vulnerable cohort of young men living in deprived areas.

Authors:  Jane Parkinson; Jon Minton; James Lewsey; Janet Bouttell; Gerry McCartney
Journal:  BMC Public Health       Date:  2018-03-27       Impact factor: 3.295

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