Age analysis assignment
Age analysis assignment
Age analysis assignment
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Conduct in-depth analysis of a pertinent topic related to aging. Define the problem, specific population affected, cultural implications, and financial/legal/ethical implications. What interventions can be used to improve the problem? What resources are available? What are the associated costs? Is this idea sustainable?
Submit your paper in a 6–7-page Microsoft Word document.
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On a separate references page, cite all sources using APA format.
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References must be within 3 years.
Description
Age Period Cohort Effect
Age period cohort (APC) analysis plays an important role in understanding time-varying elements in epidemiology. In particular, APC analysis discerns three types of time varying phenomena: Age effects, period effects and cohort effects. (1)
Age effects are variations linked to biological and social processes of aging specific to individuals.(2) They include physiologic changes and accumulation of social experiences linked to aging, but unrelated to the time period or birth cohort to which an individual belongs. In epidemiological studies age effects are usually denoted by varying rates of diseases across age groups.
Period effects result from external factors that equally affect all age groups at a particular calendar time. It could arise from a range of environmental, social and economic factors e.g. war, famine, economic crisis. Methodological changes in outcome definitions, classifications, or method of data collection could also lead to period effects in data. (3)
Cohort effects are variations resulting from the unique experience/exposure of a group of subjects (cohort) as they move across time. The most commonly defined group in epidemiology is the birth cohort based on year of birth and it is described as difference in the risk of a health outcome based on birth year. Thus a cohort effect occurs when distributions of disease arise from an exposure affect
age groups differently. In epidemiology, a cohort effect is conceptualized as an interaction or effect modification due to a period effect that is differentially experienced through age-specific exposure or susceptibility to that event or cause.(4)
In contrast to this conceptualization of cohort effect as an effect modification in epidemiology, sociological literature consider cohort effect as a structural factor representing the sum of all unique exposures experienced by the cohort from birth. In this case, age and period effect are conceived as confounders of cohort effect and APC analysis aims to disentangle the independent effect of age, period and cohort.(4) Most of the APC analysis strategies are based on the sociological model of cohort effect, conceptualize independent effect of age, period and cohort effect.
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Identification problem in APC: APC analysis aims at describing and estimating the independent effect of age, period and cohort on the health outcome under study. The different strategies used aims to partition variance into the unique components attributable to age, period, and cohort effects (4). However, there is a major impediment to independently estimating age, period, and cohort effects by modeling the data which is know as the “identification problem” in APC. This is due to the exact linear dependency among age, period, and cohort: Period – Age = Cohort; that is, given the calendar year and age, one can determine the cohort (birth year) (5). The presence of perfectly collinear predictors (age, period and cohort) in a regression model will produce a singular non-identifiable design matrix, from which it is statistically impossible to estimate unique estimates for the three effects. (5)
Conventional solutions to APC identification problem
Constrained Coefficients GLIM estimator (CGLIM)
A popular approach to resolving the identification problem was by using constraint based regression analysis (Constrained Coefficients GLIM estimator (CGLIM)). In this strategy additional constrains are placed on one of the categories of at least one predictor to simultaneously estimate the age period and cohort effect. Thus assuming some categories of age groups, cohorts or time periods have identical effects on the dependent variable it becomes possible to estimate independent effect of age period and cohort (6). However, the results from this analysis will depend on constrains chosen by the investigator based on external information. The validity of the constraints chosen will depend on the theoretical preconception about the categories of parameter that are identical, is often subjective and there is no empirical way to confirm the validity of the chosen constraints (4).
Proxy variables approach
Use one or more proxy variables as surrogates for the age, period, or cohort coefficients (7)
Nonlinear parametric (algebraic) transformation approach
Define a nonlinear parametric function of one of the age, period, or cohort variables so that its relationship to others is nonlinear.
Intrinsic estimator method
Is a new technique developed over the last 10 years and it is related to principal component analysis that addresses identification problem when explanatory variables are highly correlated. Though IE also imposes constraint on parameters similar to CGLM, the constraint are less subjective and doesn’t affect the estimation of regression parameters for age, period or cohort (4,5). Model validation studies have confirmed the robustness of the statistical properties of IE by comparing findings from an IE analysis of empirical data with results from an analysis of the same data by a different family of models that do not use the same identifying constraint (5).
Median Polish Analysis
The epidemiological definition of a cohort effect as an age by period interaction is basis for median polish analysis. It extracts the non-linearity in age and period effects and partition the non-linear variance into cohort effect and random error (4). In other words this approach evaluates the age and period interaction that is beyond what would be expected of their additive influences.
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