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78 Cards in this Set
- Front
- Back
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Primary prevention
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Preventing initialization (immunization)
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Secondary Prevention
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Early detection (cancer screening)
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Tertiary Prevention
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Reducing impact - rehab
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Ignaz Semmelweis
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Childbred fever/washing hands
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Edward Jenner
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Smallpox/vaccination
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John Snow
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Cholera/water pump
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Wade Hampton Frost
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Epi of infectious diseases/life tables
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Clinical vs. non-clinical disease
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Clinical shows symptoms
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4 non-clinical disease types
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Preclinical - before symptoms, going to have symptoms
Subclinical - Not apparent, won't become apparent Persistent - Can't shake off Latent - No active multiplication |
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Epidemiological Triad
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Host
Agent Environment (Vector) |
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Endemic
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Routine appearance of disease
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Epidemic
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Occurrence of disease in a certain region, in excess of normal, derived from a common source
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Pandemic
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World-wide epidemic
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Herd immunity
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Group resistance due to a large portion being immune
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Incubation period
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Interval from infection to the time of onset of clinical illness
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3 variables of epidemiological investigation
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- Time of exposure
- Time of disease onset - Incubation period |
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Steps of epi investigation
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1. Define
2. Examine 3. Look for relevant variables 4. Develop hypothesis 5. Test hypothesis 6. Recommend control measures |
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Attack rate
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# of cases among people at risk / Total number at risk
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Prevalence equation
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Prevalence = Incidence * Duration
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Incidence
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Number of new cases in a defined population and time / people at risk
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Incidence/Prevalence/Attack rate - Rate or proportion?
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Incidence - rate
prevalence - proportion attack rate - proportion |
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Prevalence
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Number of cases present at time / number of person at that time
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Crude mortality rate
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Total deaths from all causes / Population at midyear
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Case Fatality Rate
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People who died from a specific disease in a time period / people with that specific disease
(proportion) |
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Proportionate mortality
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Number of deaths from a specific disease / Total number of deaths
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Direct Age Adjustment
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find death rate for each age group, apply to standard population
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Indirect Age adjustment
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Standardized Mortality Ratio = Observed # of deaths per year / expected # of deaths per year
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Cohort Effect
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Plot prevalence by birth group, not age group
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YLL
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years of life lost due to death
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YLD
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years of healthy life lost due to disability
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DALY
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YLL + YLD = years of healthy life lost due to disability/death
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Two assumptions in life tables
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- No changes in survivorship
- Those lost to follow have the same experience |
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Observed survival rate
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Use life tables, Kaplan-Meier, or person-years
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Relative survival rate
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Observed survival rate / Expected survival rate
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Validity measures
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Sensitivity and specificity
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Prevalence vs. Predictive Value
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Higher prevalence = higher positive predictive value
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Biggest factor of PPV
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Specificity
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Simultaneous vs. Sequential: Spec/Sens
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Simult: Sens increases, Spec decreases
Seq: Sens decreases, spec increases |
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Reliability
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How well an observer classifies the same individual under different circumstances
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Validity
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How well a given test reflects another test of greater accuracy
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Kappa
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(% agree - % chance) / ( 1 - % chance)
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Kappa grades
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.4 - .75 moderate
.7+ good |
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Advantages of Case study vs. Cohort study
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Case is less expensive
Case is better for infrequent diseases Case is quicker |
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Nested case-control studies
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Controls are a pool of people at risk when the disease develops. When an individual gets sick, he goes into the case cohort, and someone simultaneously goes into the control group.
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Case-cohort studies
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Defined cohort at beginning, # of cases that are developed over time are matched to a control group all at the end.
Can use multiple diseases |
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Advantages of nested case-control and case-cohort studies
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All samples are taken before, so no selection bias and no "chicken or egg" problem
Only need to test those infected and matching controls, not all samples More comparability |
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Case-crossover study
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Studies etiology of acute outcomes.
Environmental exposure at time of case is compared to a certain time before case. Adv - Cheap, same person is control Dis - Recall bias, disregards other factors |
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Cross-sectional study
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Study both exposure and outcome simultaneously
Compare prevalence of those exposed to those not exposed Adv - Cheap, easy Dis - Chicken or egg AKA prevalence study |
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Confounding by indication
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The bias to systematically pick people for treatment who are most ill
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Stratification
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Allocating randomization by certain characteristics
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Masking/blinding
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Patient doesn't know what they're on
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Double blinding
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Patient and investigator both don't know what treatment the patient is on
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Levels of data analysis
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Primary - Intention to treat
Secondary - observing those already treated Subgroup - planned in advance |
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ITT
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intention to treat - analyze purely on randomization, ignore non-adherence, switched, ineligibility, etc.
Most conservative approach |
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How to deal with non-compliance
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- pilot studies
- active monitoring |
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Cross-over design
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Each patient does both treatments
Adv - Serves as own control Dis - generalizability |
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Factorial design
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Testing two treatments at once
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Group allocation design
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Randomization is group of individuals, i.e. school, neighborhood
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Relative Risk (efficacy)
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Rates of bad events: (rate in treated) / (rate in untreated)
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Relative risk reduction
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Bad events: (rate in untreated - rate in treated) / (rate in untreated)
Also: 1-relative risk = 1-(treated/untreated) |
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Absolute risk reduction
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(bad events): Rate in untreated - rate in treated
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Number needed to treat
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bad events: 1 / (rate in untreated - rate in treated)
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Generalizability
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Validity of test to reflect population
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Clinical phases of new drugs
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Phase 1: Determine level of toxicity
Phase 2: Clinical investigation on a small amount of people Phase 3: Clinical trials on large population after safety is shown Phase 4: Post-marketing |
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Types of error
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Type 1: Treatments are same, conclude they are different (alpha)
Type 2: Treatments are different, conclude they are the same (beta) |
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Power of study
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Ability to correctly identify that treatments are different (1 - beta)
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Cohort study
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Start with exposed/not exposed to environmental factor (smoking), divide into disease or no disease
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Relative risk in cohort study
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(incidence among exposed) / (incidence among non-exposed)
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Attributable risk in cohort study
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(incidence rate for exposed) - (incidence rate for non-exposed)
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Bias in assessment of outcome
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Unmasked investigator might skew results towards hypothesis
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Information bias
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Those treated might have more in-depth information on treatment/disease, especially in retrospective study
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Non-response bias
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Healthy are less apt to respond
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Analytic bias
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Investigator bias while analyzing data, may use prior biases to help hypothesis
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Selection bias
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Who gets into the program - healthy people volunteer more
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When to use cohort study
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- Good evidence of association of disease/exposure
- Incidence of disease among exposure is high - Time between exposure and disease is short |
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Adv/Dis of cohort study
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Adv: Can assess several outcome at once
Adv: Control of time and outcome measurements Adv: Less potential for bias Dis: Large samples Dis: long follow up Dis: not efficient for rare outcomes |
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Case control study
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Start with disease/no disease, see who was exposed
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When to do case control
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- Rare disease
- Need quick answers about exposure - Limited funding - Not able to follow exposed/non-exposed easily |
