epi

Data types

Categorical: male vs. female, ethnicity, SES, age group, educational level Continuous: range of values (height, weight, physical activity level)

Descriptive variables are: a. Collected when epidemiologists ask questions of their study subjects. b. Most often biased and have to be taken as such. c. The outcome of interest in the context of model building. d. Measures of Relative Risk (RR). e. p-values.

Collected when epidemiologists ask questions of their study subjects

Reversibility

Does removal of cause lead to reduction in risk? -yes=strong causal relationship -no= question relationship OR seek alternate pathways/explanations ex. smokers who give up smoking have a lower risk of lung canc than those who continue to smoke

Temporality

Does the cause (exposure) precede the effect (outcome)? -Timing, cause must precede effect -post hoc fallacy: simply because one thing happens after another, the first even caused the second, accidents DO in fact exist ex. lower rate of injuries in drivers after seat belts became law

Epidemiologists typically use descriptive studies to: a. Reject null hypotheses having to do with exposure-outcome associations. b. Generate hypotheses. c. Examine potential biases having to do with data entry into computer programs. d. Refute causal models. e. Teach public health practitioners how to perform analytical studies

Generate hypotheses

The main purpose of this diagram is to: a. Help visualize the possible relationship between women who may have taken cocaine prenatally and the ability of their offspring to perform on a neuropsychological exam during the early school years. b. Demonstrate, based on analytical designs, that there is indeed a strong association between children who perform poorly on a standardized neuropsychological exam and their gestational age. c. Assess the linkage between birth weight and birth length. d. Test covariates that are not included in the model. e. Assess if the proposed model is backwards; that is, if reverse causality is really at work and the outcome of interest should really be the exposure.

Help visualize the possible relationship between women who may have taken cocaineprenatally and the ability of their offspring to perform on a neuropsychological exam duringthe early school years.

Judging the evidence

How many lines of evidence lead to conclusion? -ex. diabetes is caused by many lines, like obesity, BMI, genetics, age, ethnicity etc.

Inductive logic was discussed in the context of: a. Hypothesis generation b. Hypothesis testing c. Negative feedback d. The self-reinforcing nature of feedback perceived by the general public. e. Hypothesis refutation

Hypothesis generation

Study Design

Is the evidence based on strong study design? -sample size? enough power? sample composition? reference group/controls? anonymity?

Plausibility

Is this association consistent with existing biological/medical knowledge? -consistent with other knowledge increases plausibility ex. relationship between obesity and skeletal health? we know it is hard on joints so can "assume" it is plausible

Consistency

Persistent association between exposure and outcome from multiple studies of adequate power in different persons, place, circumstances, times. -similar results in other studies -greater emphasis should be given to best-designed studies -ex. tobacco smoking and lung cancer, many different studies on this topic backing it up

Odds Ratio Calculation

Table: a. b. a+b c. d. c+d a+c. b+d. a+b+c+d OR= (a/c)/(b/d) =axd/bxc Upper left to lower right multiply Upper right to lower left multiply divide first over second

Population and Samples

Target population: world, country, culture Study population: students, residents of place, people of occupation Study sample: active participants in the study

Strength

What is the strength of the relationship between the potential cause and effect? -strong association is more likely to be causal than a weak association -strong=statistically significant (p-values) ex. risk of lung cancer is 20x higher in smokers than non-smokers -RR: probability of lung canc for smokers/prob of lung canc for non smokers

Incidence

"Risk" # of new health-related events in a defined population within a specific period of time -measure via frequency count, rate, cumulative ex. percentage of HS freshman boys who develop acne over the course of their HS years

Inductive Logic/Reasoning (descriptive)

"instinct" -used to generate hypothesis & discover relationships a conclusion is drawn from particular cases or specific instances, based on facts or observations -premise may be true with a false conclusion

What are the 8 causal criteria

1. Temporality/Temporal relationship 2. Plausibility 3. Consistency 4. Strength of Association 5. Dose-Response relationship 6. Reversibility 7. Study Design 8. Judging the evidence

What are the steps of descriptive and analytical epi?

1. use descriptive analysis to generate a hypothesis/model 2. use analytics to find the strength of association along main or secondary pathway via hypotheses testing (parameter estimates, Odds Ratio, Relative Risks, Hazard Ratios) 3.Interpret results

Dose-Response Relationship

Are varying amounts of exposure to the cause associated with varying magnitude of the effect? -increase dosage=increase effect? ex. greater nose level and longer exposure=higher prevalence of hearing loss -risk vs. benefit relative to possible side effects

Main Causal Pathway

arrow from exposure to outcome -main exposure is not necessarily directly causal to the outcome, but the possibility can be determined through causal criteria

Contingency Table

cross-classification, subcategories of one characteristics are horizontal and other are vertical

Incidence & Prevalence comparison

do you currently have otitis media? point prevalence have you had OM during the last 3 years? period prevalence have you ever had OM cumulative incidence

Covariates placed along one of the secondary pathways included :a. IQ and memory learning b. language, fetal growth and neurological function c. birth length and neurological function d. gestational age, IQ and neurological function e. fetal growth and IQ

e. fetal growth and IQ

Gating

example: those who respond "no" are not asked further questions

Indirect/Secondary Causal Pathways

indirect associations of the exposure relative to the outcome

Stratification

layering/grouping variables by other covariates ex. grouping those exposed to cocaine by marital status and age group

Corollaries

logical extensions from the assumption that an idea is considered to be true without controversy (axiom) Corollary 1: nonrandom clusters of disease can be observed, understood, measured based on individual and community characteristics -"golden triangle": agent, host, environment Corollary 2: variates in disease frequency occur d/t variations in exposure to etiologic agents or remote causes, or variation in susceptibility

Prevalence

measure of disease occurrence -# of cases of disease present in population at a specified time/# of persons in population at a specified time -does not take into account when disease developed/duration -therefore do not have a measure of disease risk, just prevalence -point prevalence: at specific point in time -period prevalence: cases of disease at any point during a certain time period -proportion not a rate "Burden of disease" measurement

The model, as proposed, is a _____ model. a. univariate b. reversed c. temporally incorrect d. multivariate e. mis-specified

multivariate

Univariate Model

no secondary pathways, only one exposure of interest and its relation to the outcome

Axiom

something that cannot be proven to be true, it is assumed true without controversy ex. disease is not randomly distributed in human population groups -epidemiology studies difference in distribution -disease pattern can be identified, measured, and this is the most effect way to modify and prevent disease

Epidemiology

study of distribution of disease (states of health) and determinants of disease (deviations from health) in human populations Goals: -identify etiology of disease -Provide knowledge to prevent.control disease via interventions -Provide info to maximize timing/effectiveness of interventions

Hazard Ratio

theoretical measure of probability of occurrence of an event per unit time at risk "instantaneous incidence rate" ex.death at time "t"

Epi Model

tool to help epidemiologists study the potential association between an exposure and an outcome of interest

Deductive Logic/Reasoning (analytical)

used to test, refute, falsify a hypothesis -conclusions drawn from general principles or premises -conclusions have no uncertainty; they are certain provided the premises are true

Model efficiency

using fewest number of variables needed to explain an association "most bang for your buck"

Covariates

variables in indirect pathways, may impact the outcome & help better predict the outcome of interest

Multivariate Model

when there are multiple variables of interest that may help explain the relationship of interest