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Epidemiology Study Guide

by: Bahja Benford

Epidemiology Study Guide HCMG 3701

Bahja Benford
Clayton State
GPA 3.37

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About this Document

Intro to Epidemiology
Michael E. Dalmat
Study Guide
epi, Butler, Epidemiology, HCM
50 ?




Popular in Intro to Epidemiology

Popular in Nursing and Health Sciences

This 14 page Study Guide was uploaded by Bahja Benford on Sunday February 21, 2016. The Study Guide belongs to HCMG 3701 at Clayton State University taught by Michael E. Dalmat in Summer 2015. Since its upload, it has received 94 views. For similar materials see Intro to Epidemiology in Nursing and Health Sciences at Clayton State University.

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Date Created: 02/21/16
Study Guide: Notes and Test Prep! Analytical Epidemiology 1. Observational: cannot control exposure; cannot choose control vs. experimental groups. - Ecological (worldwide), case-control, and cohort. 2. Experimental design: can control exposure; can choose who gets intervention, placebo, etc. Random Control Trial: Gold standard Q&A: Q: Who controls exposure? A: If changed, experimental; if not, observational. Q: How many times are you going to view? A: It depends. Q: Which way does the exposure go? A: Positively or negatively. Q: How are you going to collect the data? A: What type? (Primary or secondary) Q: How often are you going to collect it? Q: Group or individual? (Group= unit) Q: How available are the study subjects? Exposure a.k.a: Risk Factor X (independent variable) RF: health behavior- direct, exercise - Environmental: coal plant, power plant - Genetics: Chronic/family history - Biological: Male or female - Can come from many areas - Exposures can be beneficial or can harm you. - Close response- radon exposure is linked to lung cancer (found in basements) OAS: Ecological, cohort, case-control 1. Ecological: groups of populations (county, city, district, etc.) Aggregate Data- other groups nearby. Ex: rates of cancer incidences, av. Sunlight exposure, Seasonal Defective Disorder (SAD)  depression Advantages Disadvantages 1. More information about 1. Ecologic fallacy (misconception) Health (context) 2. May not represent your area. What is the issue? 3. Not accurate due to secondary data 2. Used when indiv. Levels aren’t there. 3. Quick and easy (Cheap, dirty, and easy) 4. Secondary data 2. Case-Control Studies: outcome of interest- do they have it or don’t they? - Cases: HAVE - Controls: DO NOT HAVE DISEASE OR OUTCOMES ODDS: A/C, B/D, OR: AD/BC Same group, same pop. - More controls than cases - Very important about matching - Must be alike: age, race, gender, or other variables 1. 1 to 1: case control 2. Frequency: how many people are needed: 2:1, 3:1 - Aids in confounding (bias) - Establish some degree of risk (relative risk; RR= only found in COHORT STUDIES)  ODDS RATIO (OR): Incidence cannot be specifically (directly) calculated. - Frequency exposure/frequency outcome (case-control studies ONLY) - A/C: odds in favor of exposure among diseased group. - B/D: odds in favor of exposure among non-diseased group. - A*D & B*C- cross tabulation OR > 1.0 (+) between exposure and the disease. OR< 1.0 (-) protective factor OR= 1.0 no association at all! - NEVER HAVE A NEGATIVE VALUE IN ANY BOX ADVANTAGES DISADVANTAGES 1. Low prevalence 1. Measurements may be inaccur. (recall exposure) Outbreaks: food 2. Representative may be unknown. 2. Quick and easy 3. Provides indirect estimates of risk 3. Cheap 4. The real-time exposure vs. outcome may not always 4. Smaller # of subjects be discovered. 3. Cohort Studies: followed over a period of time; begin w/ exposed vs. non-exposed, to which none have the disease. - Can be measured 1. Prospective: forward; disease free! Health related issues over time. - Selection of cohort= is vital (can have bias) - Characteristics= defined - Baseline= before inclusion into study - Health exam= crucial 2. Retrospective: backwards! 1. Framingham Heart Study (1940) - Still looking- heart disease 2. Doll Hill Physician’s study (1951-2001) - Smoking/lung cancer (Britain) 3. Seven County Study - Diet, lifestyle and heart disease/stroke (1957-97) 4. Nurses Heart Study (1976) - HRT Primpo: Breast cancer 5. Woman’s Health Initiative (1990) - Estrogen and progesterone  Increase HD and BC Cohort Studies (cont.) - Historical data, determine baseline (past) - Follow-up between baseline and present  Distinguishing featuring: after all outcomes have occurred  Is used if you want to look @ a particular pop. or people w/a particular exposure(s)  Ie: workplaces (powerplants, factories, etc.) Examples: Ship yard naval workers during a time in past. Husbands who had a vasectomy and later developed prostate cancer. Past medical records b/twn health conditions and exposures. RR= Incidence exposed/non-exposed A/ A+B/C/C+D *Can be calculated directly Interpretation: (same as case control, OR) Reference group: denominator= unexposed group ADV. DISADV. - Direct observation of risk - Expensive/time consuming - Well-defined exposure factor - Complex to carry out - Can study uncommon pop. exp. – Subjects may be lost in follow up - Factor and outcome relationship - Misclassified exposure (wrong group) Is known. 1. Attributive risk: IR in exposed & IR in non-exposed AR= [A/A+B]- [C/C+D] * per population Experimental Studies -Intervention studies (intentional changes) 1. Randomized Control Trial: GOLD STANDARD.  Single Blind: subjects don’t know  Double Blind: researchers know  Triple Blind: no one knows, including data analysis - Pill, procedure, surgical technique or intervention 2. Quasi-experimental design: (Not randomly assigned, maps!) - Community trial: 3 Program eval. - QE Study 1. Prophylactic trial: treatment effectiveness or substance used for disease prevention (New Vaccine) 2. Therapeutic trail: effectiveness of treatment that brings about improved patient’s health 3. Clinical trial: placebo vs. actual meds. 4. Crossover design: flip groups Challenges (Validity) External Validity: does it work outside of the scope? (Generalization) Sampling error Internal Validity: Outcome vs exposure (hypothesis right or wrong) Bias: systematic error - Hawthorne: people changed behavior purposely. - Recall Bias: when they got disease (case-control) - Selection Bias: person put in group purposely - Healthy worker effect: workers who are healthier are favored - Confounding: can affect outcome and controls  State the uses for descriptive epidemiologic studies 1. Permits evaluation of trends in health and disease (monitoring known diseases, identifying emerging problems) 2. Provide a basis for planning, provision, and evaluation of health services. 3. Identify problems to be studied by analytic methods and suggest area that may be fruitful for investigation  Name the characteristics of person, place and time variables that are used in descriptive epidemiologic studies and give an example of each. -Person Variables- Age, sex, race, socioeconomic status, marital status, nativity, migration, and religion. -Place Variables- geographic regions that are being compared (international, national, regional, urban- rural, local) occurrences of disease. -Time Variables- secular trends, cyclic fluctuation (seasonality), point epidemics, and clustering.  Descriptive studies: be able to define the terms case reports, case series and cross-sectional studies. How are case reports and cases series similar? How are these different? Case Reports- are accounts of a single occurrence of a noteworthy health-related incident or small collection of such events. Case Series- in comparison with case report, a case series is a larger collection of cases of disease, often grouped consecutively and listing common features such as the characteristics of affected patients. Cross-Sectional Studies- examines the relationship between diseases and other variables of interest as they exist in a defined population at one particular time. More complex than case report & case series.  What is the difference between continuous variables and a dichotomous variables? Provide examples of each. Continuous Variable- type of variable that can have an infinite number of values within a specified range. Ex: height, weight Dichotomous Variables- are nominal variables which have only two categories or levels. For example, if we were looking at gender, we would most probably categorize somebody as either "male" or "female".  Define the following: Hypothesis, null hypothesis, method of difference, method of concomitant variation, and operationalization Hypothesis- Supposition tested by collecting facts that lead to its acceptance or rejection. Null-Hypothesis- a hypothesis of no difference in a population parameter among the groups being compared. Method of Difference-refers to a situation in which all of the factors in two or more domains are the same except for a single factor. Method of Concomitant Variation- refers to a type of association in which the frequency on an outcome increases with the frequency of exposure to a factor. EX: dose response relationship between number of cigarettes smoked and mortality levels from lung cancer. Operationalization- refers to the process of defining measurement procedures for the variables used in a study. EX: studying the association between tobacco use and lung disease.  Describe the differences between mean, median, and mode. How do you find each of these measurements? Mean (or average) - can be used with both discrete and continuous data, although its use is most often with continuous data. The mean is equal to the sum of all the values in the data set divided by the number of values in the data set. So, if we have n values in a data set and they have values x1, x2, ..., xn, the sample mean, usually denoted by (pronounced x bar), is: Median- is the middle score for a set of data that has been arranged in order of magnitude. The median is less affected by outliers and skewed data. In order to calculate the median, suppose we have the data below: 65 55 89 56 35 14 56 55 87 45 92 We first need to rearrange that data into order of magnitude (smallest first): 14 35 45 55 55 56 56 65 87 89 92 Mode- is the most frequent score in our data set. On a histogram it represents the highest bar in a bar chart or histogram. You can, therefore, sometimes consider the mode as being the most popular option.  Define and understand the different types of associations found among variables: Positive associations, negative associations, no associations. Positive association- as the value of one variable increases so does the value of the other variables. Negative association- when the value of one variable increases, the value of the other variable decreases. No association- x is unrelated to y  Define Pearson’s Correlation coefficient (r). What does -1, 0, +1 mean in terms of r? Coefficient (r) used with continuous variables Correlation coefficients range from -1 to 0 to +1. When the value of r is negative the relationship between two variables is inverse; a positive r denotes a positive association. The closer r is to either +1 or -1, the stronger the association between two variables. As r approaches 0, the association becomes weaker, the value 0 means that there is no association.  Be able to describe and sketch a contingency table in demonstrating associations between an exposure and an outcome  Distinguish between a confidence interval estimate and a point estimate Confidence interval estimate-a range of values that with a certain degree of probability contain the population parameter. Pointe estimate-which is a single value chosen to represent the population parameter.  Define what is meant by a causal association. Be able to define Hill’s criteria of causality (what are these? See table 5-5) Casual association- Strength- strong associations give support to causal relationship between factor and disease. Consistency- constant association is one that has been observed repeatedly. Specificity- specific association that is constrained to a particular disease exposure relationship Temporality-criterion that specifies that we must observe the case before the effect Biological Gradient- known as dose-response curve, shows a linear trend in the association between exposure and disease. Plausibility- criterion that requires an association must be biologically plausible from the standpoint of contemporary biological knowledge. Coherence- criterion suggest that the cause and effect interpretation of our data should not seriously conflict with generally known facts of natural history and biology of the disease. Experiment Analogy-final criterion relates to the correspondence between known associations and one that is being evaluated for causality.  Describe an be able to sketch a scatter plot, epidemic curve, multi-modal curve, dose-response curve  Define or describe the dose-response threshold and latency Dose-response threshold- refers to the lowest dose at which a particular response occurs. Latency- refers to the time period between initial exposure and a measurable response.  What is risk, exposure or outcome variable?  What is the difference between an observational study design and an experimental study design? Observational study design- includes cross sectional studies, cohort studies, and case control studies (the researcher studies but does not alter, what occurs) Experimental study design- includes Randomized control trials and quasi experimental designs (the researcher intervenes to change reality, then observes what happens)  Analytic Study designs: Ecologic, case-control, cohort, experimental design, what are the advantages and disadvantages of each type of study?  Prospective & retrospective cohort studies; what are the differences? Be able to give an example of each. Retrospective Cohort Study: Person already has the disease. Using historical data determines exposure level at some baseline in the past. Ex: study of mortality among an occupational cohort of shipyard workers employed by a specific naval yard during a defined time interval in the past. Prospective Cohort Study: must establish baseline for each member before inclusion in the study, follows people over time to document the occurrence of new cases. EX:  What are the different types of experimental study designs? The different types of RCT’s? Types of experimental designs 1. Randomized control trials 2. Quasi- experimental study 3. Program evaluation Types of RCT’s 1. Prophylactic trial- designed to evaluate the treatment of a substance to prevent disease. EX: efficacy of a new vaccine 2. Therapeutic trial- evaluate the effectiveness of the treatment that is used to bring about improvement in a patient health. Ex: new surgical procedure- vs- old surgical procedure 3. Clinical trial- placebo VS new medicine or procedure 4. Crossover trial- participants st  Odds Ratio (how to calculate, when it is used [what type of study], interpretation of results) Odds Ratio used in Case control studies, AD/BC  Risk Ratio (how to calculate, when it is used [what type of study], interpretation of results) A/ (A+B) / C/(C+D) used in cohort studies, interpret the exposure to the non-exposure group of the disease  Be able to interpret values for an OR and RR (< 1.0, > 1.0 or = 1.0) If RR>1 positive association between disease and risk factor If RR= 1 no association If RR < 1 negative association between disease and risk factor  Types of bias in studies (i.e. Hawthorne effect, healthy worker effect, confounding) 1. Hawthorne effect- change in behavior because they are in a study 2. Recall bias-doesn’t remember when they got the disease Selection bias- purposely putting someone into a study 3. Healthy worker effect- workers are found to be healthier than ones that don’t work 4. Confounding- these are variables that can affect the controls and may lead to biased or misguided associations between disease and cause (exposure)  Attributable risk (AR) [how to calculate this, when is it use {what type of study} and interpretation of results] AR= [A/A+B x per population] – [C/C+D x per population], Cohort study, interpretation EX: 15 of the cases of CHD among the people who smoke can be attributed to their smoking. Difference between the incidence rate of a disease in an exposed group and the incidence rate in the non-exposed group  Population Risk Differences (PRD) Know how to calculate this and why is it used. Provides an indication of the benefit to the population derived by modifying a risk factor. This measure is the difference between the rates of disease in the non-exposed [A/A+Bx1000] – [C/C+Dx1000] used in cohort studies  What are the challenges to the validity of study designs? External validity-refers to one’s ability to generalize from the results of the study to an external population. Sampling error- a type of error that arises when values (statistics) obtained for a sample differ from the values (parameters) of the parent population. Internal validity- refers to the degree to which the study has used methodologically sound procedures  What is confounding? Confounding-Another example of a type of study bias. It denoted distortion of a measure of the effect of an exposure on an outcome due to the association of the exposure with other factors that influence the occurrence of the outcome.  Herd immunity- resistance of an entire community to an infectious disease due to the immunity of a large proportion of individuals in that community to the disease.


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