Analysis Over Alternative Metrics and Multiple Dimensions of Time
Lesa Hoffman in Longitudinal Analysis, 2015
These level-2 predictors will be referred to as birth cohort and death cohort, and for convenience each will be centered at the same constant as its level-1 counterpart (e.g., 84 years since birth or −7 years to death). In the models that follow, the cohort predictors will provide contextual effects that distinguish between-person from within-person effects of time. That is, a cohort effect means that it matters when you were at a given point in time, not just what point in time it was. For instance, would a person who was 84 years of age in 1950 have the same expected outcomes as a person who was 84 in the year 2000? If not, many possible factors could be responsible for these cohort effects that remain after controlling for age, such as generational differences in education, health care, working life, socialization, and so on. Our job here is not to determine the source of these cohort effects; instead, we aim to accurately describe the effects of cross-sectional differences and longitudinal changes within each accelerated metric of time.
Online relationship formation
Ciarán Mc Mahon in Psychological Insights for Understanding COVID-19 and Media and Technology, 2020
Adolescents’ online activity and subculture are unique. This distinctiveness stems from both an age effect and a generational effect. The age effect refers to the influence of life-stage characteristics on adolescents’ social conduct and network structure. The generational (or cohort) effect refers to the cultural change adolescents are exposed to in their formative years. Nowadays computer and Internet literacy, online communication, smartphone and hyper-textual literacy are skillfully mastered. Social networks sites are unique artefacts that allow early adolescents to share and discuss ideas and feelings, ask and answer each other’s questions, or showcase projects, all of which promote a pro-social attitude. If in the past the formation of the youth culture was limited to neighborhood hangouts, now spaces and channels of interaction are expanded, allowing individuals to find peers to share interests, hobbies, and feelings conditional upon having access and skills.
Cancer of the Breast
Jennifer L. Kelsey, Nancy G. Hildreth in Breast and Gynecologic Cancer Epidemiology, 2019
One possible explanation for the general increase in incidence rates is that more extensive screening has led to detection and removal of lesions which are really not cancer or has brought about a temporary increase in rates because of early detection of tumors that otherwise would not have become manifest until a later time. Evidence exists, in fact, that more “minimal” breast cancer is being included in incidence rates as a result of earlier diagnosis.19 However, it is unlikely that the increase over the past 30 years is entirely attributable to early case-finding specifically from screening programs since: Analyses by Holford20 indicate that the increase in incidence is more likely to be the result of a cohort effect (higher rates in successive cohorts of women) than a period effect (exposure to a risk factor beginning at or limited to a specific time period).The rate of increase appears to be greater among blacks than whites, even though whites have higher participation rates in screening programs than blacks.
Projection of global burden and risk factors for aortic aneurysm – timely warning for greater emphasis on managing blood pressure
Published in Annals of Medicine, 2022
Xuewei Huang, Zhouxiang Wang, Zhengjun Shen, Fang Lei, Ye-Mao Liu, Ze Chen, Juan-Juan Qin, Hui Liu, Yan-Xiao Ji, Peng Zhang, Xiao-Jing Zhang, Juan Yang, Jingjing Cai, Zhi-Gang She, Hongliang Li
Using the age-period-cohort model, the trends of AA-associated disease burden can be depicted and predicted, considering the impacts from age, periods, and cohorts. In detail, the age effect is the impact of age on disease occurrence. Differences in the risk of disease occurrence among subjects of the same age but at different periods can be considered the effect of period effects, such as advances in disease screening and treatment. The cohort effect is the effect of long-term exposure to risk factors or lifestyle habits on the risk of disease in subjects of the same birth cohort. Mortality due to AA is closely related to age. The increasing age of the population over the past 30 years has been accompanied by significant changes in AA-related risk factors and treatment. These changes may have an impact on the disease burden of AA. The age-period-cohort model allows for the analysis of changes in disease trends while controlling for age, period, and cohort effects. However, covariance among the three effects leads to the problem of unidentifiability in the classical age-period-cohort model. The Bayesian age-period-cohort model (BAPC) avoids this problem by including random effects, we completed the predictions using the BAPC package in R. The details have been explained elsewhere [14]. For prediction analysis at the national and regional levels, we used population data provided by the United Nations Economic and Social Council, which were available for a total of 187 countries and regions (https://population.un.org/wpp/Download/Standard/Population/).
Long-term trends in the incidence of endometriosis in China from 1990 to 2019: a joinpoint and age–period–cohort analysis
Published in Gynecological Endocrinology, 2021
Jinhui Feng, Shitong Zhang, Jiadong Chen, Jie Yang, Jue Zhu
The cohort effect revealed variable risks in different birth cohorts, potentially due to differential exposure to risk factors in early life. In this sense, individuals from the same birth cohort had the same opportunity to be exposed to specific risk factors. In the present study, the cohort effect exhibited a downward trend overall, accompanied by a slight fluctuation in risk for individual birth cohorts. Compared to the earliest birth cohort (1938–1942), the risk of endometriosis incidence in the 1998–2002 cohort declined by almost 50%. This indicates a decreased risk of endometriosis in younger generations. The earlier cohorts, specifically the 1938–1942 and 1943–1947 cohorts, in China experienced a higher incidence risk, which is likely related to social unrest, poor medical conditions, and a sluggish economy. Subsequently, with the gradual stabilization of society and improvements in living conditions, the risk of endometriosis has declined progressively. From 1950 to 1980, China underwent its first healthcare revolution in 30 years, with great improvements seen in health care environments [23]. In recent cohorts, due to improvements in public health policy, treatment of medical conditions, and implementation of endometriosis screening, women are experiencing significantly reduced exposure to endometriosis risk factors compared to earlier birth cohorts. In 2016, Healthy China 2030 was proposed, which will further promote health care in China and potentially reduce the risk of endometriosis [24].
Association of refraction and ocular biometry in highly myopic eyes
Published in Clinical and Experimental Optometry, 2021
Yanxian Chen, Decai Wang, Linxing Chen, William Yan, Mingguang He
The principal strengths of our study include a standardised protocol and the comprehensive assessment of ocular biometric parameters. The wide range of age in our study population allowed an overview observation in ocular biometry in high myopes. However, it should be pointed out that our data is cross-sectional, and therefore causality cannot be inferred from the recorded associations between refraction and ocular biometry. Cohort effect is another issue in cross-sectional study that may produce bias to the analysis. Given the rising prevalence of myopia during the last few decades, unlike the older high myopes, some of younger patients are more likely to be affected by environmental factors instead of genetic factors, and these two types of high myopia may have different pathologic outcomes.34 Though our results were consistent with the longitudinal data in highly myopic eyes with long-term follow-ups,11 further study is required to identify the cohort effect and investigate the difference between environment-driven and gene-driven high myopia