![]() The risk of dying increases exponentially with age, in humans as well as in many other species. This increase is often attributed to the “accumulation of damage” known to occur in many biological structures and systems. The aim of this paper is to describe a generic model of damage accumulation and death in which mortality increases exponentially with age. The damage-accumulation process is modeled by a stochastic process know as a queue, and risk of dying is a function of the accumulated damage, i.e., length of the queue. The model has four parameters and the main characteristics of the model are: (i) damage occurs at random times with a constant high rate (ii) the damage is repaired at a limited rate, and consequently damage can accumulate (iii) the efficiency of the repair mechanism decays linearly with age (iv) the risk of dying is a function of the accumulated damage. the model now presented the probability of a tumor as a function of dose. Using standard results from the mathematical theory of queues it is shown that there is an exponential dependence between risk of dying and age in these models, and that this dependency holds irrespective of how the damage-accumulation process is modeled. Then for an incremental risk of 1 in 1,000,000, the dose would be the solution. These generic features suggest that the model could be useful when interpreting changes in the relation between age and mortality in real data.įurthermore, the ways in which this exponential dependence is shaped by the model parameters are also independent of the details of the damage accumulation process. Potential human carcinogenic risks associated with chemical exposure are expressed in terms of an increased probability of developing cancer during a person's lifetime. In this section we discuss correlation analysis which is a technique used to quantify the associations between two continuous variables.To exemplify, historical mortality data from Sweden are interpreted in the light of the model. 1.10-6 lifetime cancer risk means that there is one additional case of cancer during a lifetime in a population of a million persons. For example, we might want to quantify the association between body mass index and systolic blood pressure, or between hours of exercise per week and percent body fat. Regression analysis is a related technique to assess the relationship between an outcome variable and one or more risk factors or confounding variables (confounding is discussed later). The outcome variable is also called the response or dependent variable, and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted "Y" and the independent variables are denoted by "X". [ NOTE: The term "predictor" can be misleading if it is interpreted as the ability to predict even beyond the limits of the data. Also, the term "explanatory variable" might give an impression of a causal effect in a situation in which inferences should be limited to identifying associations. Compute and interpret coefficients in a linear regression analysis.Compute and interpret a correlation coefficient.Define and provide examples of dependent and independent variables in a study of a public health problem.Learning ObjectivesĪfter completing this module, the student will be able to: The terms "independent" and "dependent" variable are less subject to these interpretations as they do not strongly imply cause and effect.
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