EPI 390 Lecture 6Outline of Last Lecture I. Review of defining EpidemiologyII. Roles of the EpidemiologistIII. Continuation of Descriptive vs. Analytic EpidemiologyIV. Lab Science vs. Field ScienceV. Vectors for DiseaseVI. Quantifying DiseaseOutline of Current Lecture I. Populations in Epidemiologya. “Caseness”II. Review of Prevalence vs. IncidenceIII. Interpreting Odds Ratio/Risk Ratio and Relative RiskIV. Case Study: Chili PeppersV. Case Study: Colon cancer and meat consumptionCurrent LectureVI. Populations in Epidemiologya. Types of Populationi. Target population – a broad category of subjects, i.e. the world, a country,or a culture.ii. Study population – a more defined population; a feasible subset of the target population, i.e. students, people in Michigan, people of a certain occupation.iii. Study sample – the active participants in the study.1. The larger the study sample, the better. This is because a larger sample increases heterogeneity to include many variables and increases statistical significance of results.2. Epidemiology thrives on heterogeneity – clues to the etiology of disease stems from our differences in levels of exposure.a. Heterogeneity comes from presenting data in categorical and continuous sets.These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.b. Cut-offs for categories are hard to determine, though, since most disease is on a continuum rather than a definitive “yes or no” basisb. “Caseness” – this concept is based on determining how cases of disease are categorized, in the continuum of datai. It is practical to split the continuum into ‘case’ vs. ‘non-case’, defining the caseness on a statistical, clinical, or prognostic basis.ii. Caseness causes a loss of flexibility by imposing epidemiological case definitions, which are more rigid than clinically based definitions.1. This is considered the price for the standardization necessary to evaluate etiology.VII. Review of Prevalence vs. Incidencea. Each are measures of diseaseb. Prevalence – measures the proportion of individuals in a given population who have the disease at that particular moment in time. (# of cases of disease/total population)i. Point Prevalence – disease prevalence at one instant in time.ii. Period Prevalence – prevalence of disease over a specified period of time.c. Incidence – measures the number of new cases that occur in a population over a period of time; aka the rate of disease. i. Cumulative incidence – The number of new cases that occur during a period of time, divided by the number of case-free individuals at the beginning of the observation period.ii. Incidence Density – The number of new cases divide by the total time contributed by each individual in the population.1. This is a more precise measure, because once a person drops out of the study or becomes a case they no longer contribute to the denominator of cumulative incidence.VIII. Interpreting Odds Ratio/Risk Ratio and Relative Riska. Overview:i. Typically a population is stratified as to an exposure and a disease. The occurrence of cases in the exposed group compared to the unexposed group is measured.1. If an exposure causes a disease it should be highly associated with disease.ii. Relative Risk (RR) = (Incidence of cases in the exposed population) ÷ (Incidence of cases in the unexposed population)iii. Odds Ratio (OR) = (Odds of being a case in the exposed population) ÷ (Odds of being a case in the unexposed population)1. e.g. A risk ratio or odds ratio of 3.0 for lung cancer means that three times as many people showed up in that category with lung cancer as in the control groupb. Interpretation:i. When RR or OR > 1 – The exposure is more associated with the “cases” vs.“non-cases”, so the exposure may be a causeii. When RR or OR = 1 – The exposure occurs equally in “cases” and “non-cases”, so the exposure probably isn’t a cause.iii. When RR or OR < 1 – The disease/case occurs more frequently in the unexposed population. When this occurs, it indicates the exposure could be protective, or some other variable in the unexposed population may be the cause of the cases.IX. Case Study: Chili Peppersa. Purpose of the study – to measure the association between chili pepper consumption and gastric cancer.b. Study Design - Compare the proportion of those with gastric cancer who consumed chili peppers versus the proportion of non-cases who also consumed chili peppers.i. A strong positive relationship was found between chili pepper consumption and gastric cancer cases (The odds of being a chili pepper consumer w/ gastric cancer is 6 times higher than the non-cases)X. Case Study: Colon cancer and meat consumptiona. The risk – meat consumption is correlated to colon cancer.b. The observationsi. In a country with high meat consumption (exposure) is high, incidence of colon cancer (disease) are also high.ii. In a country where meat consumption is low, incidence of colon cancer is also lowc. The Hypothesis – there is a suggested relationship/correlation between meat consumption and colon cancer.d. Controls – i. Reasonable covariates1. Other parts of the diet that could be carcinogenic or contribute to colon cancer2. Patterns in meat consumption of the individual (quantity, frequency, meat quality, etc.)3. Non-dietary components contributing to colon cancer (environmental and behavioural components)4. Pre-disposition (family history) to colon cancerii. Other possible causes, according to the CDC1. Pre-disposition: The individual has Inflammatory Bowel Disease, a personal/family history, and/or has a genetic predisposition (i.e. familial polyposis or hereditary non-polyposis colorectal cancer/Lynch disease)2. Behavioural choices: limited physical activity, low levels of fruits, vegetables, or fiber and high levels of fats in the diet, obesity, alcohol and/or tobacco