© 2010 Jones and Bartlett Publishers, LLC INTRODUCTION TO EPIDEMIOLOGY FIFTH EDITION © 2010 Jones and Bartlett Publishers, LLC

Statistical and Causal Associations Chapter 9 © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Objectives Understand the distinction between causal inference and statistical inference. Be familiar with selected criteria for establishing causal associations. Understand the roles of chance, bias, and confounding in statistical associations. Know the steps for hypothesis testing and be able to apply hypothesis testing in the search for causal associations. Understand how webs of causation can be used as tools in epidemiology. © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Aim in epidemiology At the heart of epidemiology is the notion of causality: The idea is that when a causal association is established, a protection and control attitude can occur rather than a mere reaction to the public health crises © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Related terms Risk factor At-risk behavior Predisposing factor © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Causal inference A conclusion about the presence of a health-related state or event and reasons for its existence Causal inferences provide a scientific basis for medical and public health action. Made with methods comprising lists of criteria or conditions applied to the results of scientific studies. © 2010 Jones and Bartlett Publishers, LLC

Statistical Inference Draws a conclusion about a population based on information from sampled data Probability is used to indicate the level of reliability in the conclusion The possibility that chance, bias, or confounding explain a statistical association should always be considered © 2010 Jones and Bartlett Publishers, LLC

Types of Causal Associations Direct Causal Association No intermediate factor and is more obvious Eliminating the exposure will eliminate the adverse health outcome Indirect causal association Involves one or more intervening factors Often much more complicated © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Factors of causation Predisposing factors Enabling factors Precipitating factors Reinforcing factors © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Predisposing factors Factors or conditions already present that produce a susceptibility or disposition in a host to a disease or condition without actually causing it © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Reinforcing factors Can be negative or positive Positive reinforcing factors Support, enhance, and improve the control and prevention of the causation of disease Negative reinforcing factors The factors that help aggravate and perpetuate disease, conditions, disability, or death © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Enabling factors Factors that can affect health through an environmental factor Services, living conditions, programs, societal supports, skills, and resources that facilitate a health outcome’s occurrence Can also be a result of a lack of services or medical programs © 2010 Jones and Bartlett Publishers, LLC

Precipitating factors Factors essential to the development of diseases, conditions, injuries, disabilities, and death An infectious agent Lack of seat belt use in cars Drinking and driving Lack of helmet use by motorcycle © 2010 Jones and Bartlett Publishers, LLC

Three methods of hypothesis formulation in disease etiology Method of difference Method of agreement Method of concomitant variation © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Method of Difference The frequency of disease occurrence is extremely different under different situations or conditions. If a risk factor can be identified in one condition and not in a second, it may be that factor, or the absence of it, that causes the disease. © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Method of Agreement If risk factors are common to a variety of different circumstances and the risk factors have been positively associated with a disease, then the probability of that factor being the cause is extremely high. © 2010 Jones and Bartlett Publishers, LLC

Method of Concomitant Variation The frequency or strength of a risk factor varies with the frequency of the disease or condition. Example. Increased numbers of children not immunized against measles causes the incidence rate for measles to go up. © 2010 Jones and Bartlett Publishers, LLC

Statistical association does not mean causal association For example, ice cream consumption and murder are strongly correlated. Does eating ice cream make people want to kill or does killing result in a desire for ice cream? The explanation may be that hot temperatures are related to both ice cream consumption and murder and that it is the heat, not the ice cream causally associated with murder © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Causal criteria Strength of association Consistency of association Specificity Temporality Biologic gradient Biological plausibility Coherence Analogy Experimental evidence © 2010 Jones and Bartlett Publishers, LLC

Direct and indirect causal associations Direct causal association has no intermediate factor and is more obvious Exposure to staphylococcal pathogens results in illness Indirect causal association involves one or more intervening factors and is often much more complicated A high fat diet is associated with polyps and polyps are associated with colon cancer © 2010 Jones and Bartlett Publishers, LLC

The role of chance The “luck of the draw” Most epidemiologic studies rely on sampled data Characteristics of subjects in a sample may vary from sample to sample. As a result, an association between an exposure and outcome, or lack thereof, may be the result of chance Sample size is directly related to chance To minimize chance, increase the sample size © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Confidence intervals A range of reasonable values in which a population parameter lies, based on a random sample from the population As sample size goes up, the role of chance goes down, as reflected by the confidence interval. Can also be used to evaluate statistical significance © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC The role of bias The deviation of the results from the truth and can explain an observed association between exposure and outcome variables Minimized by properly designing and conducting the research study © 2010 Jones and Bartlett Publishers, LLC

The role of confounding Occurs when the relationship between an exposure and a disease outcome is influenced by a third factor, which is related to the exposure and, independent of this relationship, is also related to the health outcome. Only the randomized experimental study allows us to balance out confounding among groups. © 2010 Jones and Bartlett Publishers, LLC

Confounding (continued) Should always be considered as a possible explanation for an observed association, particularly descriptive epidemiologic studies and non-randomized analytic epidemiologic studies May over or underestimate a true association Possible to control for at the design and analysis levels of a study © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Web of causation Webs are graphic, pictorial, or paradigm representations of complex sets of events or conditions caused by an array of activities connected to a common core or common experience or event © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Web of causation Webs have many arms, branches, sources, inputs, causes, etc., that are somehow interconnected or interrelated to the core Webs can also have a chain-of-events where some events must occur before others can happen © 2010 Jones and Bartlett Publishers, LLC

© 2010 Jones and Bartlett Publishers, LLC Decision Trees The yes–no response of decision trees leads the epidemiologist closer to discovering the cause than a web of causation alone. Helps eliminate possibilities of causation while leading the investigator down the correct path toward discovery, assuming the questions are answered correctly. © 2010 Jones and Bartlett Publishers, LLC