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Breast cancer is the most commonly diagnosed cancer among women worldwide. Studies have reported minimal birth cohort effects on the incidence rates of breast cancer in Western countries but have reported notable birth cohort effects in some Asian countries. The risks of breast cancer may also vary within a country. In the present study, we abstracted female invasive breast cancer data from the Taiwan Cancer Registry for the period 1997–2016. We used the age–period–cohort model to compare birth cohort effects on breast cancer incidence rates between urban and rural regions in Taiwan. We identified a notable urban–rural disparity in birth cohort effects on breast cancer incidence rates in women in Taiwan. The incidence rates in the urban regions were higher than those in the rural regions across all cohorts. However, the incidence rates rose faster in the rural regions than in the urban regions across the cohorts. The risks of breast cancer observed for women born in 1992 were approximately 22 and 11 times than those observed for women born in 1917 in rural and urban regions, respectively. The observed gap in breast cancer incidence rates between the urban and rural regions gradually disappeared across the cohorts. Accordingly, we speculate that urbanization and westernization in Taiwan may be the drivers of female breast cancer incidence rates across birth cohorts.

Background Statistical data on the burden and relevant risk factors of lung cancer are valuable for policy‐making. This study aimed to compare the mortality of lung cancer attributable to smoking stratified by sex and age among adults in China and the United States (US). Methods We extracted age‐standardized mortality rates of lung cancer during 1990‐2017 using the comparative risk assessment framework of the 2017 Global Burden of Disease study. We performed an age‐period‐cohort analysis to estimate time trend of lung cancer mortality attributable to smoking. Results During 1990‐2017, the age‐standardized mortality rate of lung cancer was increasing in China but decreasing in the US for both sexes. The mortality attributable to smoking in China showed a generally increasing trend, while a continuous decrease was observed in the US. The age‐period‐cohort analysis showed a similar trend of age effect among adults between China and the US: the mortality substantially increased from the 30‐34 to 80‐84 age group and subsequently decreased in the 90‐94 age group. However, the period effect rapidly increased in Chinese adults during 1990‐2017, while it tended to be stable in the US although it was still slightly increasing in women. The cohort effect generally peaked in the earlier cohort born in 1902‐1906 in the two countries. Conclusions During 1990‐2017, the lung cancer mortality attributable to smoking and the period effect are generally increasing in Chinese adults; the mortality attributable to smoking is decreasing in the US adults, but the period effect tends to be stable. The rapid aging and prevalence of smoking may intensify the increasing mortality of lung cancer in China.

Background: The Eastern Mediterranean Region, including Iran, has the highest incidence and mortality of breast cancer (BC) in women. This study examined changes in BC mortality trends among Iranian women by age period cohort (APC) from 1990 to 2021. Methods: BC deaths and population by age (1990‒2021) were collected from the 2021 Global Burden of Disease (GBD) study and the average annual percentage change (AAPC) and relative risks (RRs) analyzed by joinpoint regression and the APC model. Results: From 1990 to 2021, the crude and adjusted BC mortality rates showed an increasing (AAPC=0.913%; 95% CI: 0.436%, 1.393%) and decreasing (AAPC=-0.384%, 95% CI: -0.759%, -0.008%) trend, respectively. The APC analysis exhibited an increasing trend in age effect except in the 60‒74 and 80‒84 age group. The period effect also presented an increasing trend from 1992 (RR=0.716; 95% CI: 0.697, 0.734) to 2021 (RR=1.410; 95% CI: 1.381, 1.441). Additionally, the cohort effect illustrated that the mortality rate decreased consistently from the earlier birth cohort to the later birth cohort (Coef=1.017 in<1901 cohort versus Coef=-0.928 in 2002-2006 cohort). Conclusion: Between 1990 and 2021, BC mortality in Iran showed an increase in crude rates but a decline in age-adjusted rates, with rising age and period effects and a decreasing cohort effect. These patterns may reflect improvements in early detection and treatment alongside demographic shifts, highlighting the need for continued monitoring to guide control strategies.

Background: The prevalence of diabetes mellitus is rapidly increasing in China, but the secular trends in incidence and mortality remain unknown. This study aims to examine time trends from 1990 to 2017 and the net age, period, and cohort effects on diabetes incidence and mortality. Methods: Incidence and mortality rates of diabetes (1990–2017) were collected for each 5-year age group (from 5–9 to 80–84 age group) stratified by gender from the Global Burden of Disease 2017 Study. The average annual percentage changes in incidence and mortality were analyzed by joinpoint regression analysis; the net age, period, and cohort effects on the incidence and mortality were estimated by age-period-cohort analysis. Results: The joinpoint regression analysis showed that age-standardized incidence significantly rose by 0.92% (95% CI: 0.6%, 1.3%) in men and 0.69% in women (95% CI: 0.3%, 1.0%) from 1990 to 2017; age-standardized mortality rates rose by 0.78% (95% CI: 0.6%, 1.0%) in men and decreased by 0.12% (95% CI: −0.4%, 0.1%) in women. For age-specific rates, incidence increased in most age groups, with exception of 30–34, 60–64, 65–69 and 70–74 age groups in men and 25–29, 30–34, 35–39 and 70–74 age groups in women; mortality in men decreased in the younger age groups (from 20–24 to 45–49 age group) while increased in the older age groups (from 50–54 to 80–84 age group), and mortality in women decreased for all age groups with exception of the age group 75–79 and 80–84. The age effect on incidence showed no obvious changes with advancing age while mortality significantly increased with advancing age; period effect showed that both incidence and mortality increased with advancing time period while the period trend on incidence began to decrease since 2007; cohort effect on incidence and mortality decreased from earlier birth cohorts to more recent birth cohorts while incidence showed no material changes from 1982–1986 to 2012–2016 birth cohort. Conclusions: Mortality decreased in younger age groups but increased in older age groups. Incidence increased in most age groups. The net age or period effect showed an unfavorable trend while the net cohort effect presented a favorable trend. Aging likely drives a continued increase in the mortality of diabetes. Timely population-level interventions aiming for obesity prevention, healthy diet and regular physical activity should be conducted, especially for men and earlier birth cohorts at high risk of diabetes.

•Generalized additive mixed models (GAMMs) fit lifespan brain trajectories more accurately than traditional methods.•Optimal formulation of GAMMs for longitudinal data analysis is discussed, and compared in realistic simulation experiments and two application examples.•We discuss and contrast questions which can be answered with a single measurement per participant, and which questions require repeated measurements.•R code shows how GAMMs can be used in practice, with packages “gamm4” and “mgcv”. We address the problem of estimating how different parts of the brain develop and change throughout the lifespan, and how these trajectories are affected by genetic and environmental factors. Estimation of these lifespan trajectories is statistically challenging, since their shapes are typically highly nonlinear, and although true change can only be quantified by longitudinal examinations, as follow-up intervals in neuroimaging studies typically cover less than 10% of the lifespan, use of cross-sectional information is necessary. Linear mixed models (LMMs) and structural equation models (SEMs) commonly used in longitudinal analysis rely on assumptions which are typically not met with lifespan data, in particular when the data consist of observations combined from multiple studies. While LMMs require a priori specification of a polynomial functional form, SEMs do not easily handle data with unstructured time intervals between measurements. Generalized additive mixed models (GAMMs) offer an attractive alternative, and in this paper we propose various ways of formulating GAMMs for estimation of lifespan trajectories of 12 brain regions, using a large longitudinal dataset and realistic simulation experiments. We show that GAMMs are able to more accurately fit lifespan trajectories, distinguish longitudinal and cross-sectional effects, and estimate effects of genetic and environmental exposures. Finally, we discuss and contrast questions related to lifespan research which strictly require repeated measures data and questions which can be answered with a single measurement per participant, and in the latter case, which simplifying assumptions that need to be made. The examples are accompanied with R code, providing a tutorial for researchers interested in using GAMMs.

Social commentators have argued that changes over the last decades have coalesced to create a relatively unique generation of young people. However, using large samples of U.S. high-school seniors from 1976 to 2006 (Total N = 477,380), we found little evidence of meaningful change in egotism, self-enhancement, individualism, self-esteem, locus of control, hopelessness, happiness, life satisfaction, loneliness, antisocial behavior, time spent working or watching television, political activity, the importance of religion, and the importance of social status over the last 30 years. Today's youth are less fearful of social problems than previous generations and they are also more cynical and less trusting. In addition, today's youth have higher educational expectations than previous generations. However, an inspection of effect sizes provided little evidence for strong or widespread cohort-linked changes.

Although the effects of age, period, and cohort (APC) on suicide are important, previous work in this area may have been invalid because of an identification problem. We analyzed these effects under three different scenarios to identify vulnerable groups and thus overcame the identification problem. We extracted the annual numbers of suicides from the National Death Register of Korea (1992–2015) and estimated the APC effects. The annual average suicide rates in 1992–2015 were 31.5 and 14.7 per 100,000 males and females, respectively. The APC effects on suicide were similar in both sexes. The age effect was clearly higher in older subjects, in contrast to the minimal changes apparent during earlier adulthood. The birth cohort effect showed an inverted U shape; a higher cohort effect was evident in females born in the early 1980s when period drift was larger than 3.7%/year. Period effect increased sharply during the early 1990s and 2000s. We found that elderly and young females may be at a particularly high risk of suicide in Korea.

This paper presents a method that uses observed data from an age-period table to set bounds on the age, period, and cohort effects in an age-period-cohort multiple classification (APCMC) model. The rationale is that with enough periods over a long time span the age distributions within periods on the dependent variable will be affected by different sets of cohorts for each of the periods. This is likely to result in different trends in these separate period age distributions such that the trends in the age distributions will encompass the trend in the age effects that generated the dependent variable values. This approach can help to identify bounds that likely encompass the age, period, cohort parameters that generated the data. The data used in this papers are estimated homicide arrests by single years for those aged 15–64 for the periods 1964 to 2019 in the United States. I utilize the observed trends in the age-distributions for each of the 56 periods as different constraints on the trends for the age effects in the APCMC fixed effects model. These estimates are used to form bounds on the age effects, period effects, and cohort effects.

Background Cognitive abilities serves as a critical indicator of healthy aging. As China progresses into a stage of advanced population aging, there has been a significant increase in the number of elderly individuals experiencing age-related cognitive decline. Despite this demographic shift, there is a paucity of longitudinal research examining cognitive abilities among older Chinese adults over extended time periods. This study aims to investigate changes in cognitive abilities and explore group differences among older Chinese adults aged 65 to 110 years, employing a multidimensional temporal approach that encompasses age, period, and birth cohort effects. Methods This study utilizes data from eight waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS), spanning from 1998 to 2018. The dataset comprises 94,116 observations from 36,157 unique participants. Cognitive abilities are assessed using Mini-Mental State Examination (MMSE) scores as a proxy measure. To address the issue of perfect collinearity in the temporal dimension, the study employs the Hierarchical Age-Period-Cohort Cross-Classified Random Effects Model (HAPC-CCREM). This model allows for the examination of age effects, period effects, and cohort effects on cognitive abilities among older Chinese adults. In the model specification, age is treated as a fixed effect, while period and birth cohort are incorporated as random effects. Drawing upon the cognitive reserve hypothesis, the study investigates significant factors influencing cognitive abilities in this population. Results At the fixed-effect level, demographic factors, health behaviors, self-rated health, subjective well-being, and childhood adversity significantly influence cognitive abilities among older Chinese adults. The age effects are significant, with cognitive abilities exhibiting an inverted U-shaped curve across the lifespan. At the random-effect level, period effects are significant, revealing a gradual annual increase in overall cognitive levels among older Chinese adults since 2008. Cohort effects are also significant, demonstrating an increasing trend in overall cognitive levels for the earlier-born cohorts in the first six groups. Conversely, later-born cohorts in the latter five groups show a declining trend in overall cognitive levels. Notably, period effects significantly enhance cohort effects. Conclusions The cognitive reserve hypothesis supports the significance of the majority of identified influencing factors. Cognitive abilities demonstrate an accelerating decline with increasing age, following an evolutionary trajectory consistent with physiological principles among older Chinese adults. Since 2008, cognitive abilities have shown a monotonic increasing trend annually, further validating the Flynn effect in this population. The cognitive abilities of the six earlier-born cohorts exhibit an increasing trend, supporting the compression of morbidity hypothesis. Conversely, the cognitive abilities of the five later-born cohorts show a declining trend, supporting the expansion of morbidity hypothesis. These findings collectively contribute to our understanding of cognitive aging patterns and their underlying mechanisms among older Chinese adults.

Objectives To determine the contributions of period effects (controlling for ages and cohorts) and cohort effects (controlling for ages and periods) to the changes in the homicide arrest rates in the United States over the time span from 1965 to 2015. Methods This determination faces the age–period–cohort identification problem: the linear trends of these three factors are linearly dependent and there are an infinite number of solutions that fit the data equally well. I address this problem in multiple ways: by setting reasonable bounds around the age-distribution of homicide arrests using constrained estimates, presenting estimable functions that are identified, and by using an age–period–cohort–characteristic model. Results The cohort effects on homicide arrest rates have on average trended upward since the cohort born in 1945–1949; and the period coefficients have on average trended downward since 1965, but trended upward during the mid-1960s to the early-1970s. These trends over time are somewhat different depending on the bounds under consideration. Conclusions Theories that posit period effects as responsible for at least part of the drop in homicide rates from 1990 to 2015 and the increase in homicide rates from the mid-1960s to the early-1970 are consistent with this analysis. Cohort effects worked in the opposite direction of the period effects for the years from 1990 to 2015. The existence of both sets of effects has implications for criminological theories and for policy prescriptions.