NR503 Epidemiology Final Exam Study Guide (Latest-2022/2023, Version-1)/ NR 503 Epidemiolo, Study Guides, Projects, Research of Nursing

Download NR503 Epidemiology Final Exam Study Guide (Latest-2022/2023, Version-1)/ NR 503 Epidemiolo and more Study Guides, Projects, Research Nursing in PDF only on Docsity! NR 503 - Study Guide for Final Exam 1. Objectives of epidemiology.2-7 Step 1. Understanding the etiology or cause of a disease (risk factors) Step 2. Finding out the extent that a disease or health problem affects a community or population. Step 3. Determine the natural history or prognosis Step 4. Evaluate existing and newly developed preventative therapeutic measures and modes of healthcare delivery Step 5. Provide the foundation for developing public policy relating to environmental problems, genetic issues, and other considerations regarding disease prevention and health promotion. 2. Define, compare, calculate, and interpret Measures of Morbidity .41.58 a. Incidence rate.41.51 (# of NEW cases of a disease occurring in the population during a specified period of time / # of persons who are at risk of developing the disease during that period of time) x 1000 = Incidence rate per 1,000 : the NEW cases of a disease over a period of time. b. Attack rate.46 - special form of cumulative incidence - Used for diseases of short observation time period - Not a true rate because the time dimension is often uncertain (food borne outbreaks) = (# of new cases among population during period / population at risk at beginning of period) x 100 (expressed as %) c. Prevalence.46 : the EXISTING cases of a disease over a period of time. (# of cases of a disease present in the population at a specified time / # of persons in the population at that specified time) x 1,000 = Prevalence per 1,000 3. Understand why incidence data are important for measuring risk.  Measures risk (probability of developing disease)  Useful for investigating determinants of disease (not survival)  To investigate causes of disease  Need to know population ‘at risk’ or ‘person-time’ at risk 4. Define, compare, calculate, and interpret Measures of Mortality a. Cause-specific mortality rate.64.65 =(Total number of deaths due to x cause / total population at mid year) x 1,000 =(No.of deaths from leukemia in one year in children younger than 10 years of age / No.of children in the population younger than 10 years of age at midyear) x 1,000 b. Annual mortality rate .64 -annual death rate, or mortality rate from all causes. (total # of deaths from all causes in 1 year / total population at mid year ) x 1,000 c. Case-fatality.65 --what percentage of people who have a certain disease die within a certain time after their disease was diagnosed? =(# of deaths due to a certain disease/total # suffering from that disease) x 100 % -Measure of the severity of the disease -As therapy improves, case-fatality would be expected to decline. d. Proportionate mortality. This is Not a rate. 66 -the proportionate mortality from cardiovascular disease in the U.S. in 2010 (percent, %) -of all deaths in the US, what proportion was caused by cardiovascular disease? =( # of deaths from cardiovascular diseases in US in 2010 / total deaths in the US in 2010) x 100 5. Assess the Validity.89 and Reliability105.110 of Diagnostic and Screening Tests a. Define, compare and calculate measures of validity, including sensitivity and specificity. -Validity of test: its ability to distinguish between who has a disease and who does not. -Sensitivity of test is defined as the ability of the test to identify correctly those who have the disease. -Specificity of test is defined as the ability of the test to identify correctly those who do not have the disease. Have a Disease Do not have Disease Positive True Positive (TP) Have the disease And test positive a False Positive (FP) Do not have the disease But test positive b Positive predictive value = (TP/TP+FP)x10 0 =(a/a+b)x100 Negative False Negative (FN) Have the disease But test negative c True Negative (TN) Do not have the disease And test negative d Negative Predictive value =TN/TN+FN Sensitivity = TP /TP+FN Specificity =TN /TN+FP b. Define and calculate positive predictive value.100. -what is the probability that the patient has the disease? -PPV is proportion of people who screened positive and actually have the disease (True positive/All positives)x100 c. Relationship between Positive predictive value (PPV) and disease prevalence.101 : the higher the prevalence, the higher the predictive value. Therefore, a screening program is most productive and efficient if it is directed to a high- risk target population. Sensitivity = 99%, Specificity = 95% from disease) in the two group. Randomize d Controlled Trials Prospectiv e Concurrent Retrospecti ve Nonconcurre nt Historical cohort Case- control Retrospectiv e - Cases then exposed and not exposed. Controls (people w/o the disease) then exposed and not exposed. -You can infer an association b/w exposure and disease. But not causality b/c this is a case control study. -begin with knowledge of disease Cross sectional Prevalence studies b/c describe the prevalence of the exposure and disease in a particular population. -Design: define a population then look at who is exposed and who is not exposed. Then for exposed see who developed the disease and who did not develop the disease. Do the same for not exposed. -take place at 1 point in time; compare to cohort (done over time), --therefore can’t give you info about causality; time course of variables is unknown Clinical trial Case report Case series -Describes instance of disease -Gold standard for study design -b/c it can potentially establish a causal relationship. -but dose not mean that every randomized study will Investigator identifies the original population at the beginning of the study. Use historical data from the past so that we can telescope the frame of calendar time for the study and obtain our results sooner. To exam the possible relation of an exposure to a certain disease. We identify a group of individuals with that disease (called cases) and, -is at a single point in time, looking at a particular population, and noting how many people have the exposure and how many people have the disease. Case report to generation of hypothesis to hypothesis being proven (general flow of study design) establish a causal relationship for purposes of comparison, a group of people without that disease (called controls). -think snapshot of a defined population at a single point in time. -Survey a population; for each participant, we determine the serum cholesterol level and perform an ECG for evidence of CHD. Both exposure and disease outcome are determined simultaneousl y for each subject - documents unusual medical occurrence Case series - collections of individual case reports Limitation: Selection bias- What makes someone comparable to people in the cases, this is a broad area and can cause problems for case control studies. Strengths: good for study of rare conditions b/c you don’t have to wait for people to develop the disease, you start with people that have the disease already. -can study multiple exposures Limitation: Prevalence incidence bias is related to the fact that we have no information about the time course of variables in a crossectional study Strengths -cheap and level of exposure -Better than case report on: allow for control for other variable (control group), they can help reduce confounding variables Limitation: no control group Limitation -no compariso n group Strength -fast, cheap 8. Causal Inferences.262 a. Identify- the thought process, methods, and evidence used to support or refute a relationship as one of cause and effect Define- What is a conclusion about the presence of a health-related state or event and reason for its existence and provide examples for common biases in different epidemiological studies, including selection bias and information bias (e.g., recall, misclassification, differential classification, etc.). selection bias -Error due to systematic differences in exposures between those who are selected and those who are not Information bias -Systematic errors due to incorrect categorization -Measurement error in assessment of either exposure or outcome Nondifferential misclassification, (information bias) - If errors are about the same in both groups, it tends to minimize any true difference between the groups (bias toward the null). differential misclassification (information bias) -When there are more frequent errors in exposure or outcome classification in one of the groups Recall bias -Phenomenon where cases may recall past -exposures differently than controls recall past exposures b. Factors that could increase or decrease bias. c. Define confounding and be able to identify confounding factors in a study and approaches to handling confounding.268 -in a study of whether factor A is a cause of disease B, we say that a third factor, factor X, is a confounder 9. Identify causal association verses confounding in case control and cohort study designs.248 Necessary AND sufficient cause: a factor is both necessary and sufficient for producing the disease ex: lead exposure = lead poisoning Necessary but not sufficient cause: each factor is necessary but not, in itself, sufficient to cause the disease; multiple factors are needed ex: alcoholism sufficient but not necessary causes: factor is not required to produce a specified outcome BUT when present, is able to cause the outcome by itself. Means that there are other causes of outcome ex: exposure X = outcome Y exposure Z ALSO = outcome Y neither sufficient nor necessary causes: a cause that isn't required to produce the outcome and when present is not able to cause the outcome by itself; hence there b. Generalizability (external validity) -the ability to generalize results of the study to other situations. -Findings of the study are generalizable from the study population to the defined population, and presumably, to the total population. -refers to the extent to which study results can be applied to other individual or settings. -it is the ability of the results to be generalized to the ‘real world’ population (from clinical trials to practice) -concerns the generalizability of the trial results to persons(target population) other than the original study population 14. Evaluating Screening Programs a. Sources of bias that must be taken into account in assessing study findings, including referral bias, length-biased sampling, lead-time bias, five-year survival, and over-diagnosis bias. Pg326 referral bias do those who were voluntarily screened have the same characteristics as those who chose not to be screened? -volunteers healthier and more compliant, better prognosis length-biased sampling not from the type of person that comes for screening but comes from the type of disease that is selected for screening - does screening selectively identify cases of the disease which have a better prognosis? do cases that are found have a better prognosis regardless of how early therapy is initiated? - more likely to identify those with a long preclinical phase which might seem associated with living longer or better outcomes 1. compare with RCT 2. examine survival for both screened and unscreened - in the screened group, survival should be calculated for those in whom the disease is detected between screening examinations lead-time bias survival seems better because the interval from diagnosis to death is longer, but the patient is not any better off because death has not been delayed five-year survival 1. must take into account for an estimated lead time in an attempt to id any prolongation of survival above that resulting from the artifact of lead time (see below) 2. compare mortality from the disease in the entire screened group with that in the unscreened group, rather than just the case fatality rate in those in whom disease was detected by screening over-diagnosis bias people who initiate screening programs overdiagose because they are just so excited (more false-positives) overestimates association b. Characteristics and patterns of disease progression and effective screening program. 15. Considerations when evaluating individual data a) Are the characteristics of the two groups comparable—demographically, medically, and in terms of factors relating to prognosis? pg315 b) Are the measurement methods comparable (e.g., diagnostic methods and the way disease is classified) in both groups? pg315 16. Calculations and interpretations a. Risk ratio b. Relative risk, 161.217: The ratio of the risk of disease in exposed individuals to the risk of disease in non-exposed individuals. c. Odds ratio, 222: The ratio of the odds of development of disease in non- exposed person. d. Attributable risk,230: how much of the risk (incidence) of the disease we hope to prevent if able to eliminate exposure to the agent in question. e. Incidence rate, 41-46:The number of new cases of a disease that occurs during specified period of time in a population at risk for developing the disease. f. Disease incidence g. Annual mortality rate.64 h. Positive predictive value.100 i. Sensitivity and specificity.89 j. Prevalence rate.46:The number of affected persons present in the population at a specific time divided by the number of persons in the population at that same time. k. Concordance rate.290 RCT Cohort Case-control Caseseries,repor t observational observational