Epidemiology test 1 Flashcards
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Definition of epidemiology
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the study of how health related states are distributed in populations and the factors that influence or determine this distribution in order to reduce health problems
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Goals of epidemiology Look in the book
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1)to identify the etiology or cause of a disease and relevant risk factors that increase a person's risk for disease 2)determine the extent of disease found in the community 3)study the natural history and prognosis of disease 4)evaluate both existing and newly developed preventative and therapeutic measures and modes of health care delivery 5)provide foundation for developing public policy relating to environmental problems, genetic issues, and other considerations regarding disease prevention and health promotion
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Iceberg concept
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we only know what is on the surface; example HIV - 20% of HIV in US are unaware that they are infected -unaware HIV transmit approximately half of the new HIV infections -important to not only count the clinical cases because more people could have it that don't know
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Endemic/Epidemic/Pandemic
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endemic: habitual presence of a disease in a region epidemic: excess of normal expectancy pandemic: world-wide epidemic
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Exposure types (common-vehicle single/multiple periodic/continuous)
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common vehicle: all cases develop among people exposed to something single exposure: spoiled egg salad at a picnic vs. multiple: contaminated air supply in a building periodic vs continuous
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Herd immunity
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-relies on having enough immune hosts to interrupt the chain of transmission -a communicable disease is reliant on the chance that a person who is infected will transmit the infection to another susceptible host -but not all hosts need to be immune to stop the spread -depends on how effectively the disease is transmitted -exposures are not necessarily random!
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Incubation period
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how long it takes for infection to lead to clinical illness can vary based on: disease, rought of exposure/invasion, dose of exposure, -many diseases spread during the incubation period
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Attack rate*
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equation: AR = (# of at-risk who get sick) / (Total # at risk) Similar to incidence, but used more frequently for infectious disease Food-specific AR Time is implicit (diseases with known incubation period)
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Cross-tabulation
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determining which factor caused illness A table or matrix that shows the relationship between two or more variables
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Person-time (e.g. person-years)*
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different people may have been at risk for different lengths of time aka incidence density -accounts for people who were only following through part of the study -1 person observed for one full year= 1 person-year - use number of people time number of years each is at risk/observed (but each year is assumed to be the same)
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Morbidity measures*:
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incidence (person-time), cumulative incidence, point prevalence, period prevalence)
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incidence
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# new cases in population during a specified time period)/total person-time (sum of time periods of observation of each person who has been observed for all or part of the time period)
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cumulative incidence
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or incidence proportion is a measure of frequency, as in epidemiology, where it is a measure of disease frequency during a period of time.
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point prevalence
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is the number of persons with disease in a time interval (eg, one year) divided by number of persons in the population; that is, prevalence at the beginning of an interval plus any incident cases.
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period prevalence
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is the proportion of a population that has the condition at some time during a given period (e.g., 12 month prevalence), and includes people who already have the condition at the start of the study period as well as those who acquire it during that period.
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Know the relationship (conceptual and mathematical) between incidence and prevalence
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In "incidence", there is a notion of dynamic that is absent in "prevalence". Incidence refers to the number of new cases occurring during a given period of time (ex: in my village there were 12 new cases of flu during the winter), while prevalence corresponds to the proportion of cases at a specific point in time (on 3rd Feb, there was only one person who had flu in my village).
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Mortality measures*:
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all-cause, group specific, cause-specific, case fatality rate, proportionate mortality)
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all-cause
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measures rate of deaths. (#deaths from all causes in 1 year)/ (#persons in the population at mid-year)
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group specific
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(#deaths from all causes in 1 year in defined group)/ #persons in the population in defined group at mid-year)
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cause-specific
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(#deaths from specified cause in 1 year) / (#persons in the population at mid-year)
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case fatality rate
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(#ind. dying during specified period of time after disease onset or diagnosis) / (#ind with the specified disease)
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proportionate mortality
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(#deaths from particular disease in a year) / (total # deaths in a year)
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Also know the concept & purpose of age adjustment (not the calculation)
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no calculations
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Cohort effect Concept
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people may seem older over the study
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Screening tests*:
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validity, reliability, sensitivity, specificity, positive predictive value, negative predictive value
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validity
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ability to distinguish b/w who has a disease and who does not
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reliability
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repeatability
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sensitivity
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ability to correctly identify those with the disease
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positive predictive value
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proportion who test positive actually have the disease
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negative predictive value
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proportion who test negative who do not have the disease
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Survival*:
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5-year survival, median survival, relative survival
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5year survival
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percentage of indivs alive 5 years after diagnosis proportion/%alive after 5 years limitation: lead time, no info <5 years, trajectory may be different
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median survival
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the length of time that half of the study population survives -time at which 1/2 have died limitation: there is a high level summary measure that does not capture differences over time
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relative survival
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(observed survival in people with the disease) / (expected survival if disease were absent) -: Observed Survival / Expected Survival -Useful when comparing groups where survival is not close to 100%
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Survival: Life tables, Kaplan-Meier Be able to distinguish between these
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life tables: using fixed time intervals (ie year 1, year 2, year 3 etc) - more complex appraoch but can use more data - uses year by year probability of survival (given that you survived the preceding year) - allows us to view the overall probability of survival by year of follow-up -accommodates losses to follow-up by estimating the number at risk assumptions: 1)No change in survivorship over study 2)Withdrawals survivorship is the same as those retained 3)People lost to follow-up are lost uniformly over the interval 4)The event occurs uniformly over the interval -rates calculated at pre-specified intervals (e.g., years) -Limitations: changes in detection/Tx, LTFU (must make assumption that survivorship is the same)
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Kaplan-Meier
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using all event times ; calculate survival every time someone dies -: Calculate survival at each time of death Limitations: Similar to life tables, but LTFU is handled at calculation times
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Generalizability (external validity)
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is the study representative of the population in a way that is meaningful ; "common" experiences with diease (eg signs, symptoms, prognossi) vary according to population and clinic based samples
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Exchangeability
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everything should be the same except the independent variables (exposures of interest) should be different so you can compare the dependent variable (outcome)
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Randomization
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participants randomly assigned to one group or another which will remove the potential that researcher or self-selection will result in differences b/w groups beyond exposure
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stratified randomization
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a two-stage procedure in which patients who enter a clinical trial are first grouped into strata according to clinical features that may influence outcome risk. Within each stratum, patients are then assigned to a treatment according to separate randomization schedules [1].
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crossover (planned vs unplanned)
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Time when subjects may switch between study groups: Planned crossover, and Unplanned crossover
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planned crossover
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Planned crossover Randomly assign subjects to study groups Observe the subjects over time Switch the subjects to the other study group Observe them over time Compare the changes between the subjects with one treatment versus the other treatment
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unplanned crossover
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Unplanned crossover May occur if someone in one treatment group needs to be medically managed like the second treatment group instead because of health concerns You may have randomized someone to one treatment group but they decide they don't want to be in that treatment group
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factorial design
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Use one study population to study two drugs Drugs must be different and mode of action of each drug (how it works in the body) must be different Allows you to use a study population most economically
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type 1 error
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type 1: alpha treatment differs when they really don't
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type 2 error
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type 2: beta treatments don't differ when they really do
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power
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For example, 80% power in a clinical trial means that the study has a 80% chance of ending up with a p value of less than 5% in a statistical test (i.e. a statistically significant treatment effect) if there really was an important difference (e.g. 10% versus 5% mortality) between treatments
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p-value
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A p value of 0.5 suggests that there is a 50-50 chance that the findings of the study are significant.
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alpha
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With respect to estimation problems , alpha refers to the likelihood that the true population parameter lies outside the confidence interval . Alpha is usually expressed as a proportion. Thus, if the confidence level is 95%, then alpha would equal 1 - 0.95 or 0.05.
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beta
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Beta is the probability of Type II error in any hypothesis test-incorrectly concluding no statistical significance. (1 - Beta is power).
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Efficacy*
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Efficacy can be defined as the performance of an intervention under ideal and controlled circumstances, whereas effectiveness refers to its performance under 'real-world' conditions. Efficacy = ((rate in those who received old therapy) - (rate in those who received new therapy)/ rate in those ho received old therapy)
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Number needed to treat*
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The NNT is the average number of patients who need to be treated to prevent one additional bad outcome (e.g. the number of patients that need to be treated for one to benefit compared with a control in a clinical trial). It is defined as the inverse of the absolute risk reduction.
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Phases of a drug trial (US/FDA)
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Phase I - small studies that look at potential toxic effects and pharmacologic effects of new drugs Phase II - A larger study to determine how effective the drug is and how safe it is Phase III - Large-scale trial of the drug for effectiveness and safety (limited time) Phase IV - Long-term surveillance of safety of a drug
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Ethics-Core concepts including beneficence, respect for human dignity, and justice; informed consent; vulnerable groups
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beneficence: Doctors and researchers should act in the best interest of their patients respect for human dignity: people's ability to make their own decisions justice: People should equally benefit from research informed consent: vulnerable groups: Additional protections for vulnerable populations including pregnant women, children, and prisoners.
