Overview & Definitions Flashcards
The basis of Epidemiology
Populations
John Snow
Broad Street Pump
Cholera
Father of Epi
Epidemiology
Public health discipline that studies the distribution and determinants of disease in populations
Distribution
Spread
Who, when, where or person, place, time
Descriptive Epi
Determinants
Cause
Why, How
Analytic Epi
6 Core Functions of Epidemiology
- Public Health Surveillance
- Field Investigation
- Analytic Studies
- Evaluation
- Linkages
- Policy Development
Passive Surveillance
Healthcare System
I.e. Reporting diseases
Active Surveillance
Going into communities and searching for disease cases.
E.g. John Snow
Syndromic Surveillance
Predefined signs/systems of patient related to trackable diseases.
When multiple of a community are being treated for the same symptoms
Incubation (Induction)
Time between exposure and onset of disease
Latency Period
Time between onset of disease and disease detection (symptoms or diagnosis)
Case Definition
Set criteria used to define a disease for public health surveillance
Epidemic
Disease in excess of normal expectancy
Outbreak
An epidemic limited to a certain or localized increase of disease.
Also called cluster.
E.g. Disease occurring at a school
Endemic
An area where the occurrence of a disease is higher than normal for other places, but normal for that particular place
Emergency of International Concern
Endemic that alerts the world.. pre-pandemic
Pandemic
World-wide endemic
Epidemic Curve
Shows all 3 elements of descriptive epi.
I.e. Who, when and where
Incidence
New cases of disease
Aka: risk, attack rate
New cases/ people at risk
PROPORTION
Prevalence
Existing cases + New cases
PROPORTION
Cumulative Incidence
Incidence sum over multiple periods of time
Incidence Rate
New cases/ person-time at risk
Incidence Density
Sum of Incidence rates over multiple periods of time
Prevalence
Existing cases/population
Point Prevalence
Prevalence at given period of time
Period Prevalence
Prevalence over a given period of time
Morbidity
Sickness or disease
Crude
Out of everybody
OR
Unadjusted
Mortality
Death
Crude Morbidity Rate
of persons with disease/# in population
Crude Mortality Rate
of deaths (all causes)/population
Cause-Specific Morbidity Rate
with cause specific disease/population
Cause Specific Mortality Rate
of cause specific deaths/population
Case-Fatality Rate
of cause specific deaths/# of disease cases
Case Specific Survival Rate
of cause-specific cases alive/# of cases of disease
Proportional Mortality Rate (PMR)
of cause specific deaths/total deaths in population
Live Birth-Rate
of live births/ 1000 population
Fertility Rate
of live births/ 1000 women of childbearing age
Neonatal Mortality Rate
of death in <28 days of ages/ 1000 live births
Postnatal Mortality Rate
of deaths > 28 days but <1 year/ 1000 live births
Infant Mortality Rate
of deaths in those <1 year / 1000 live births
Maternal Mortality Ratio
of female deaths related to pregnancy / live births divided by 100,000
OR
# of female deaths related to preg/#of live births = ... ... Times 100,000
Infectivity
The ability to invade a host
infected/ # at risk
Pathogenicity
The ability too cause clinical disease
with clinical disease/# infected
Virulence (Case-Fatality Rate)
The ability to cause death
of deaths/# with infectious disease
Absolute Risk Reduction (ARR)
Attributable Risk
Risk difference of outcome attributable to exposure difference between groups..
Relative Risk Reduction (RRR)
ARR/ R(unexposed)
Number Needed to Treat or Harm (NNT or NNH)
1/ARR in decimal form
ALWAYS round up.
Strive for NNT=1, NNH = infinity
Risk Ratio or Relative Risk (RR)
Ratio of risk from 2 different groups
Risk of outcome in exposed/risk of outcome in non-exposed
Odds
Ratio
Frequency of outcome occurring / not occurring
Did/didn’t, yes/no
Odds Ratio
ratio of odds from 2 different groups
Odds of exposure in diseased/odds of exposure in non-diseased
Confounding
Lack of exchangeability
The confounder has to be associated with both exposure and outcome.
Has to be an independent relationship. Cannot be directly linked.
Crude Association / Unadjusted
Ignores all other characteristics and explanatory factors. Only looks at exposure and outcome.
Adjusted Ratio
Ration calculated with both the exposure and outcome compared to the 3rd variable.
Impact of Confounding
- Magnitude: an association more or less extreme that true association
- Direction: can produce an association in the opposite direction
Purpose of controlling for confounders
To get a more accurate estimate of the measure of association between exposure and outcome
Ways to Control Confounding
- Study Design Stage
Randomization
Restriction
Matching - Analysis of Data Stage
Stratification (w/ weighting)
Multivariate statistical analysis (regression analysis)
Randomization
Hopefully gives equal numbers of subjects with known confounders to each group.
Strength: With sufficient sample size, will likely be successful.
Stratified version more precisely assures equal-ness
Weakness: Sample size may not be large enough
No guarantee for all known and unknown confounders
Only practical for interventional studies
Restriction
Participants are restricted to only subjects that do not have pre-specified confounder
Strength:
Straight forward, convenient, inexpensive
Weakness:
May affect ability to enroll subjects
If not narrow enough, can allow other confounding effects
Eliminates ability to evaluate varying levels of excluded factor
Negatively affects EXTERNAL VALIDITY (Generalizability)
Matching
Subjects selected in pairs and equally distributed
Strength:
Intuitive, gives more analytic efficiency
Weakness:
Difficult to accomplish, time consuming, may be expensive
Doesn’t control for ant confounders other than those matched.
Stratification
Descriptive & Statistical analysis of data. Within various levels within the confounding variable
Strength:
Intuitive to some, straight forward and enhances understanding of data
Weakness:
Impractical for simultaneous control of multiple confounders
Multivariate Analysis
Statistical analysis of data by factoring out effects of confounding variables - mathematically
Strength:
Can simultaneously control for multiple confounding variables
OR’s can be obtained and interpreted in statistical regressions
Weakness:
Requires individuals to clearly understand data
Can be time consuming for researcher
Effect Modification (Interaction)
A 3rd variable that modifies the magnitude of effect of a true association by varying it within different levels.
Modifies the effect across the strata. (Levels/layers)
If present, researcher must report the measures of association of each strata individually.
