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Refractive Errors, Myopia, and Presbyopia
Published in Ching-Yu Cheng, Tien Yin Wong, Ophthalmic Epidemiology, 2022
Ka Wai Kam, Chi Pui Pang, Jason C. S. Yam
In summary, myopia is more prevalent among both children and adults in East Asians, i.e., Japanese, Koreans, and Chinese, than in other populations, while South Asians like Indians and Malays are more myopic than Caucasians. Nevertheless, there are cautions in interpretations of reported data. First, there are age and cohort effects. Second, the prevalence of high myopia was higher in teenagers than adults mostly in cross-sectional studies conducted in different periods. High prevalence of myopia or high myopia in teenagers indicates that the myopia boom will arrive later in the working age. Third, many studies reported non-cycloplegic refraction, which may lead to over-estimations, as cycloplegic refraction should be the gold standard, especially among children. Fourth, both axial length and cornea curvature should be reported. The ratio of axial length to corneal radius of curvature (AL/CR), which is highly correlated to spherical equivalent but not affected by cycloplegia, is an important parameter, especially in studies without cycloplegic refraction.
Structural Equation Modeling with Longitudinal Data
Published in Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle, Structural Equation Modeling for Health and Medicine, 2021
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle
The analysis to study age-related development using multiple group multiple cohort LGM as discussed earlier can be viewed as a special case of age-period-cohort (APC) analysis. APC models are used to study the variability in trajectories of change over time and include age, period and cohort effects. Age effects have been described as phenomena associated with growing older; period effects as general influences that vary through time or epochs; and cohort effects as phenomena associated with individuals born around the same time [39].
Constraints to Progress in Leprosy Control
Published in Max J. Miller, E. J. Love, Parasitic Diseases: Treatment and Control, 2020
Declining trends have now been identified in several countries (Norway, Venezuela, the Philippines, China, and India). The decrease in incidence varies and seems to be associated with the level of endemicity. Specific incidence rates for age, sex, type, and the changes in their distribution over time are useful for predicting expected trends of the disease. Mean age of onset increases when incidence decreases. The trends observed in the distributions by type and sex over time need to be further evaluated. Further analysis is needed to assess cohort effects, as well as the specific epidemiological characteristics which could influence these indicators. For this purpose also, a standardized information system will be of great help.
Trends and Projections of Stomach Cancer Incidence in Hong Kong: A Population-Based Study
Published in Cancer Investigation, 2023
Liping Yang, Haifeng Sun, Yan Bai, Shengzhi Sun, Xiaoming Wu, Zhenhai Gan, Jianqiang Du, Jianfei Du
To perform APC analysis, we tabulated incidence and population data into 12 five-year age groups from 30–34 years to 85+ years, and 5 five-year calendar periods from 1994–1998 to 2014–2018. The output estimates of the APC analysis mainly include longitudinal age-specific rates, period and cohort rate ratios, and local drifts with net drift. The longitudinal age curve indicates the expected age-specific rate in a reference cohort adjusted for period effects. Period effects are changes that affect all age groups simultaneously and may be caused by changes in the social, cultural, or economic environment. Cohort effects are associated with differences between groups of individuals with the same birth year. We calculated the cohort rate ratio (RR) using the central birth cohort (1947–1951) as the reference, as well as the period RR using the central calendar period (2004–2006) as the reference. Net drift indicates the overall annual percentage change in the expected age-adjusted rate, and local drifts represent the estimated annual percentage change over time specific to age groups. We performed the analyzes using the APC Web Tool (Biostatistics Branch, National Cancer Institute, Bethesda, USA) (15). The Wald chi-square test was used to evaluate the significance of the APC model. All statistical tests were two-sided, and a p-value of less than 0.05 was considered statistically significant.
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/).
Sexual Variety in Norwegian Men and Women of Different Sexual Orientations and Ages
Published in The Journal of Sex Research, 2022
Bente Træen, Nantje Fischer, Ingela Lundin Kvalem
Another central finding is the generational shift in types of sexual acts tried or desired to try between participants <60 and ≥60 years old. Our findings indicate that most participants had these experiences with various sexual acts by the time they were between 30 and 59 years; thus, for these generations – born from 1960 onwards – sexual variety is a function of time and age. In contrast, participants born between 1931 and 1959 neither tried nor wanted to try many acts of sexual variety. This could indicate a cohort or generational effect rather than an age effect. Cohort effects exist as people from different generations construct specific social realities. It must be mentioned that the oldest participants in this study (≥70 years old) were teenagers prior to the so-called sexual revolution, whereas participants less than 70 years old were teenagers during or after this period. This is likely to have influenced their attitudes and behavior (Kontula & Haavio-Mannila, 1995; Træen & Stigum, 1998).