Life-course and cohort trajectories of mental health in the UK, 1991–2008 – A multilevel age–period–cohort analysis

Bell, Andrew

doi:10.1016/j.socscimed.2014.09.008

Cited by 103 publications

(139 citation statements)

References 56 publications

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“…We have argued before that there are some situations where the HAPC model could be used (e.g. when periods and cohorts have no continuous trends) and it can easily be adapted to incorporate theory where appropriate (Bell, 2014;Bell & Jones, 2014b, 2015a). However the model does not work as a general purpose APC model; no model does.…”

Section: Discussionmentioning

confidence: 99%

Should age-period-cohort analysts accept innovation without scrutiny? A response to Reither, Masters, Yang, Powers, Zheng and Land

Bell

Jones

2015

Social Science & Medicine

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Section: Discussionmentioning

confidence: 99%

Should age-period-cohort analysts accept innovation without scrutiny? A response to Reither, Masters, Yang, Powers, Zheng and Land

Bell

Jones

2015

Social Science & Medicine

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“…Because age, period and cohort are linearly related, the linear effects of the three factors cannot be modeled simultaneous without imposing restrictions on at least one of the parameters. For this study, we conceptualized the models within a multilevel framework [42, 43]. We estimated age and cohort as fixed effects with period as a random effect.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2017

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BackgroundThe use of complementary and alternative medicine (CAM) is growing. However the factors contributing to changes over time and to birth cohort differences in CAM use are not well understood.SettingWe used data from 10186 participants, who were aged 20–69 years at the first cycle of data collection in the longitudinal component of the Canadian National Population Health Survey (1994/95-2010/11). We examined chiropractic and other practitioner-based CAM use with a focus on five birth cohorts: pre-World War II (born 1925–1934); World War II (born 1935–1944); older baby boomers (born 1945–1954); younger baby boomers (born 1955–1964); and Gen Xers (born 1965–1974). The survey collected data every two years on predisposing (e.g., sex, education), enabling (e.g., income), behavior-related factors (e.g., obesity), need (e.g., chronic conditions), and use of conventional care (primary care and specialists).ResultsThe findings suggest that, at corresponding ages, more recent cohorts reported greater CAM (OR = 25.9, 95% CI: 20.0; 33.6 for Gen Xers vs. pre-World War) and chiropractic use than their predecessors (OR = 2.2, 95% CI: 1.7; 2.8 for Gen Xers vs. pre-World War). There was also a secular trend of increasing CAM use, but not chiropractic use, over time (period effect) across all ages. Factors associated with cohort differences were different for CAM and chiropractic use. Cohort differences in CAM use were partially related to a period effect of increasing CAM use over time across all ages while cohort differences in chiropractic use were related to the higher prevalence of chronic conditions among recent cohorts. The use of conventional care was positively related to greater CAM use (OR = 1.8, 95% CI: 1.6; 2.0) and chiropractic use (OR = 1.2, 95% CI: 1.1; 1.4) but did not contribute to changes over time or to cohort differences in CAM and chiropractic use.ConclusionThe higher CAM use over time and in recent cohorts could reflect how recent generations are approaching their healthcare needs by expanding conventional care to include CAM therapies and practice for treatment and health promotion. The findings also underscore the importance of doctors discussing CAM use with their patients.

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“…The inclusion of period into the models complicates matters, because adding a fixed effect for period, typically a linear fixed effect, may produce unstable estimates due to the high collinearity of the age, period, and cohort effects. To overcome this problem we used hierarchical age–period–cohort (HAPC) models adapted for longitudinal panel data to examine the 3 effects simultaneously . The HAPC model included a third level (period): individuals were nested within time periods.…”

Section: Methodsmentioning

confidence: 99%

Population‐Based Study of Changes in Arthritis Prevalence and Arthritis Risk Factors Over Time: Generational Differences and the Role of Obesity

Badley

Cañizares

Perruccio

2017

Arthritis Care & Research

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Objective. To investigate cohort effects in arthritis prevalence across 4 birth cohorts: World War II (born 1935-1944), older and younger baby boomers (born 1945(born -1954(born and 1955(born -1964(born , respectively), and Generation X (born 1965(born -1974, and to determine whether birth cohort effects in arthritis prevalence were associated with differences in risk factors over time or period effects. Methods. Analysis of biannually collected data from the longitudinal Canadian National Population Health Survey, 1994-2011 (n = 8,817 at baseline). Data included self-reported arthritis diagnosed by a health professional, risk factors (years of education, household income, smoking, physical activity, sedentary behavior, body mass index [BMI]), and survey year as an indicator of period. We used hierarchical age-period-cohort analyses to compare the age trajectory of arthritis by birth cohort and to examine the contribution of changes in risk factors and period to cohort differences. Results. More recent cohorts had successively a greater prevalence of arthritis. Risk factors were significantly associated with arthritis prevalence independently of cohort differences. The effects of increasing education and income over time on potentially reducing the arthritis prevalence were almost counter-balanced by effects of increasing BMI. Significant cohort-BMI and age-BMI interactions indicated an earlier age of arthritis onset for obese individuals than those of normal weight. Conclusion. Projections that only take into account the changing age structure of the population may underestimate future trends. Our understanding of the impact of BMI on arthritis is likely an underestimate. Cohort differences focus attention on the need to target arthritis management education to young and middle-aged adults.

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Life-course and cohort trajectories of mental health in the UK, 1991–2008 – A multilevel age–period–cohort analysis

Cited by 103 publications

References 56 publications

Should age-period-cohort analysts accept innovation without scrutiny? A response to Reither, Masters, Yang, Powers, Zheng and Land

Should age-period-cohort analysts accept innovation without scrutiny? A response to Reither, Masters, Yang, Powers, Zheng and Land

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

Population‐Based Study of Changes in Arthritis Prevalence and Arthritis Risk Factors Over Time: Generational Differences and the Role of Obesity

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