epidemiology exam 3 final Flashcards
distinction between statistical and causal inference
statistical inference -Draws a conclusion about a population based on information from a sample
Probability is used to indicate the level of reliability in the conclusion.
The possibility that chance, bias, or confounding explain a statistical association should always be considered.
causal inference -
A conclusion about the presence of a health-related state or event and reasons for its existence
Causal inferences provide a scientific basis for medical and public health action.
Made with methods comprising lists of criteria or conditions applied to the results of scientific studies
difference between a null hypothesis and alternative hypothesis
null hypothesis -(Ho) contradicts what the researcher believes will be the findings.
alternative hypothesis -(Ha) is what the researcher believes will be the findings
understand significance testing
p value =the probability that the findings observed could have occurred by chance alone
The P value compared to set alpha
p = probability
Set alpha is what the researcher sets as a cut off value for rejection or failure to reject the null
what are the choices in null hypothesis given significance test results
Used to decide whether to reject or fail to reject a null hypothesis.
Only choices:
Reject Ho
Fail to reject Ho
know the p value, how to interpret the p value and possible reasons for non significant differences
p value =the probability that the findings observed could have occurred by chance alone
Often a p-value less than 0.05 reject Ho the results are statistically significant
P value < 0.05 = Reject the null, there is strong evidence against the null hypothesis
P value > 0.05 = Fail to reject the null, there is weak evidence against the null hypothesis
know the difference between clinical and significant significance and how are each determined
the clinical significance observes dissimilarity between the two groups or the two treatment modalities,
while statistical significance implies whether there is any mathematical significance to the carried analysis of the results or not.
to determine clinical significance - of a treatment makes a positive and noticeable improvement to  a patient,
to determine statical significance -if the p-value falls below the significance level, then the result is statistically significant.
know what determines statistical power
Frequency of the condition under study
Magnitude of the effect
Study design
Sample size
know what is statistical power
The ability of a study to demonstrate an association/correlation/causation if one exists.
what is confident interval and its interpretation
confidence interval - More meaningful than a P-value or other point estimates (e.g., RR, OR)
Contains true value of population parameter
Expressed as an interval
how is it interpretated -“we are 95% confident that the population parameter is between X and X.”
Name the 3 basic statistical tests that can be applied to examine associations, correlations, or differences. Know when each should be applied.
chi square -assesses associations
Two or more categorical variables
correlations -assess relationships
Most common are continuous variables
independent t tests - assess difference between groups
Categorical group variable and one continuous variable
know the five key questions when evaluating epidemiologic association such as chance , bias and confounding
- Could the association have been observed by chance? -Determined through the use of statistical tests (e.g., p-value)
- Could the association be due to bias? - Bias refers to systematic errors
how samples were selected
or how data was analyzed - Could other confounding variables have accounted for the observed relationship?
- To whom does this association apply?-Representativeness of sample
Participation rates (are they appropriate? Dropouts?) - Does the Association represent a Cause-and-Effect Relationship?-Examine list of criteria
Examine models
understand the criteria for causality
A.B. Hill’s criteria of causality
An expanded list of causal criteria
Strength
Consistency
Specificity
Temporality
Biological gradient
Plausibility
Coherence
Experiment
Analogy
John Stuart Mill’s 3 methods of hypothesis formulation in disease etiology, 1856
Smoking and Health, 1964 Surgeon General’s report
Presented several criteria for evaluation of a causal association
know the statistical associations causal ,direct and multiple
causal direct of indirect - C arrow A
direct -Compromised nurse caring for patient with Ebola touches body fluids without protective equipment will lead to Ebola transmission
indirect - C arrow B arrow A -Low education (C) leads to obesity (A)
B = lack of leisure time (intervening variable)
multiple causality- requirement that more than one factor be present for disease to develop…”
know all the models of multiple causality
causal pie model -
example lung cancer
An individual factor that contributes to cause disease is shown as a piece of a pie. After all the pieces of a pie fall into place, the pie is complete — and disease occurs.
web of causation -
heart disease
A web of causation is just that a web, and it should look like an entangled spider web as all causative factors are interconnected and there rarely is one causative factor to any disease or illness.
wheel model -
childhood lead poisoning
eliminates the agent as a sole cause of disease, but emphasizes the complex interaction of physical, biological, and social environments.
epidemiological triangle - TB
The triad consists of an external agent, a susceptible host, and an environment that brings the host and agent together.
explain the sufficient and necessary
sufficient cause -A set of minimal conditions and events that inevitable produce the disease
necessary cause -Required conditions and events to produce the disease
Example: Not everyone exposed to the flu will get the flu (immunity, health status). But, everyone that has the flu were exposed to the flu virus.
what is cause
is a specific event, condition, or characteristic that precedes the health outcome and is necessary for its occurrence.
Risk factor
At-risk behavior
Predisposing factors
sampling error
refers to the variations from the true population parameter which can result from random sampling.
environmental epidemiology
the study of diseases and conditions (occurring in the population) that are linked to environmental factors”
Exposure factors outside of the individual control fall under environmental epidemiology
social epidemiology
Concerned with the influence of a person’s position in the social structure upon the development of disease (Syme, 1974).
“ . . . the branch of epidemiology that studies the social distribution and social determinants of states of health.” (Berkman, Kawachi, 2000)
behavioral epidemiology
Studies the role of behavioral factors in health
psychosocial epidemiology
Broadly conceptualized term that includes psychological, behavioral, and social factors
Relevant to mental health states, e.g., grief and depression
Relevant to physical health states, e.g., chronic diseases
know types of exposures that are studied in each epidemiological study
exposures to environmental epidemiology- Chemical agents
Electromagnetic radiation
Ionizing radiation
Heavy metals
Air pollution
Biological Agents including Allergens and Molds
Dusts
Physical and mechanical energy
social epidemiology - social determinants of health and social distribution
social epidemiology - studying the social context of health
behavioral epidemiology - Health Behaviors. Alcohol. Drug use. Sexual activity.
understand some of the historical events that lead to a focus on environmental effects on health
rachel carson -drew attention to environmental chemicals like DDT and TRIS
considered to have an inspired the modern environmental movement
know the potential environmental hazards that humans are exposed to
Chemical agents
Electromagnetic radiation
Ionizing radiation
Heavy metals
Air pollution
Biological Agents including Allergens and Molds
Dusts
Physical and mechanical energy
know the health effects of environmental exposures on populations and workers
Various lung diseases
Dermatologic problems
Bladder cancer among dye workers (from agents such as azo and benzidiene)
Leukemia among workers exposed to benzene
Psychological conditions
know the difference between ionizing and nonionizing radiation examples
ionizing radiation -Consists of either particle energy (e.g., highly energetic protons, neutrons, and α and β particles) or electromagnetic energy (e.g., γ-rays and X-rays)
nonionizing chemicals -visible, infrared, and ultraviolet light; microwaves; radio waves; and radiofrequency energy from cell phones.
