Epi Midterm Review Flashcards
Objectives of Epi
Distribution -frequencies -patterns Determinants -risk factors
John Snow
Father of Epi
3 Assumptions made by Epi
- Disease not random
- Investigation can lead to association, causes and prevention
- Making comparisons is cornerstone
6 Core Functions
Public Health surveillance Field investigation Analytic studies Evaluation Linkages Policy development
Population vs. Sample
Population is all ind making up common group from which a sample can be obtained
Generalizability
Inferential statistics transposed from sample to full population
Inferential Stats
Inferences made about random data relative to a sample
Epidemic
Occurrence of disease clearly in excess of normal expectance
Outbreak
AKA upsurge/cluster
An epidemic linked to a localized increase in occurrence of disease
Pandemic
Epidemic occurring over very wide area, large number of people
Endemic
Constant presence of a disease within given area or population in excess of normal levels in other areas
Cluster
AKA outbreak
2 Main stages of disease prevention
Primary - preventing disease
Secondary - Interrupts disease process
MeSH
Medical Subject Headings
Boolean terms
AND, OR, NOT
CONSORT
Clinical trial checklist
STROBE
Observational trial checklist
cohort, case-control, cross-sectional
STARD
Diagnostic study checklist
PRISMA
Systematic reviews/Meta-analyses checklist
3 Characteristics of data
- Magnitude
- Interval
- Rational Zero
Magnitude
Bigger is more, lower is less
Interval
Equal, even spacing
Ex. time
Nominal Data
No magnitude, No interval
Non-ranked categories
Ex. Sex, race, handed-ness
Ordinal Data
Magnitude, No interval
Categories, ranking something
Ex. Pain scale
Interval Data
Magnitude and Interval
Ex. height, weight, age, income, days until surgery
Mean
Average
Median
Value that divides group values into half
Mode
Most frequently occurring value
Variation
Difference between max and min
Spread or dispersion of data
Variance Standard Deviation (square root of variance)
Graph Types
Pyramid Stacked Pie Line Graph - trend Scatter plot Box plot
Null Hypothesis
H0
Study hypothesis that states no true difference between groups being compared
Alternative Hypothesis
H1
Hypothesis states there IS a true difference
P Score
Want less than 0.05 (5%) so it is a statistically significant result. NOT by chance
P < .05
Statistically significant, result is not by chance
Reject null hypothesis
Type 1 Error
(alpha)
Rejecting null hypothesis when it is actually true
False positive
Type 2 Error
(beta)
NOT rejecting null hypothesis when it is actually false. There is a real diff but you don’t reject H0
Power
Ability of a study to find difference in study if it was there
Sample size
Larger increases power. More likely to find a difference if it is there
Sensitivity
Box A
Proportion of time that a test is positive in pt that does have disease
Specificity
Box D
How well test can detect absence of disease when in fact disease is absent
Diagnostic Accuracy
Proportion of time a pt is correctly identified as either having a disease or not having disease
Positive Likelihood Ratio
LR+
Probability of positive test in presence of disease
Sensitivity/1-Specificity
PPV
How accurately a positive test predicts presence of disease
Validity
Ability to accurately discern between those that have disease and those that don’t
Internal validity
Extent to which results accurately reflect the true situation of study population
External validity
Extent results are applicable to other, non studied populations
Reliability
=Reproducibility
3 factors necessary to compare dz frequencies in diff popultions
number of people affected by disease
Size of the source population
Length of time population followed
Ratios
Division of 2 unrelated numbers
Risk
AKA : Attack rate, incidence, cumulative incidence
Common measure of disease frequency
Mortality rate Death rate Survival rate Live birth rate Fertility rate
Absolute Difference
Subtracting differences
ARR
How much is difference in risk attributed to your exposure
Observational
designs considered natural
Observe outcomes
Useful for unethical study designs
Experimental
Intervention
Can demonstrate causation
Investigator selects interventions
Advantages of Clinical
Cause precedes effect - shows causation
Only design used by FDA for new drug/device
Disadvantages of Clinical
Cost
Time
Ethical
Generalizability
Simple study design
Subjects randomized once
Usually to test one hypothesis
Factorial study design
Randomized at least twice
Multiple hypotheses
Disadv: risk of drop out, restricts generalizability
Parallel Study design
groups concurrently and parallel
NOT cross-over
Cross-over study design
Groups serve as own control
1 person multiple data pts
Wash out period to reset to normal
Direct Endpoints
Most clinically relevant, publishable
Ex death, stroke, hospitalization
Surrogate Markers
Number that is associated with bad outcome VS direct endpts
Ex. cholesterol, BP
Blocked randomization
Make sure group is equal
1:1, 2:1, etc
ensures balance within each group
Stratified randomization
Ensures balance by known confounding variable
ex. age, gender, disease severity
Placebo/Dummy
Inert treatments
30-50%
Hawthorne Effect
Desire of study subject to please investigator
Double Dummy
more than one placebo
Run in/Lead in phase
ALL study subjects given one or more placebos for initial therapy to determine baseline of disease
4 Principles of Bioethics
- Autonomy
- Beneficience
- Justice
- Nonmaleficence
Autonomy
self rule/determination
ex. not in place is mental capacity, age
Beneficiance
Benefit/do good for patient
Justice
Equal fair treatment regardless of pt characteristics
Nonmaleficence
Do no harm
- withhold info
- lie
Consent
Agree to participate
Legal age
Assent
Not able to consent, legal guardian must consent
Belmont Report
1978
- Respect for persons
- Beneficiene
- Justice
IRB
Protect human subjects before study
OHRD is law above IRB, could mean jail
DSMB
Data safety and Monitoring Board
Assess for undue risk or benefit AFTER study starts
Can stop study early
Equipoise
Genuine confidence that an intervention may be worthwhile in order to use it in humans
-principal people who argue against placebo use
Full Board IRB
ALL drug trials
Interventional trials with more than minimal/no risk to pts
Expedited IRB
Minimal risk and no pt identifiers
Exempt IRB
No pt identifiers, low/no risk, enviro, use of existing data/specimens
Kappa Statistic
Agreement between evaluators
+1 perfectly agree
-1 exactly opposite
Kappa Interpretation
.8-1 excellent
.6-.8 good
.4-.6 fair
0-.4 poor
Positive Post Hoc
If sub groups are pre-defined and NOT fishing
3 Factors for Sample Size
- Anticipated difference between groups
- Background rate of outcome
- Alpha and Beta (power)
Ways to Handle Drop outs
- Include them anyway
- Ignore them
- Treat them as “as treated” - move subject
Improving Adherence
Frequent follow ups
Treatment reminders
Med blister packs, dosage containers
