EPID 301 Midterm

study of distribution and determinants of health related states, AND application to control health problems

diagnosed with disease

dying from disease

Descriptive

Analytic

describes health status of a population (descriptive epidemiology)

offers way to figure out causations of disease

evaluation of treatments

identify natural history and prognosis

when is it?

who is it?

where did it happen?

what is it?

How did it happen?

why did it happen?

NEW cases in a period of time

measure of disease burden

# new cases in 1 year/avg number of people in the population in the same year

# new cases in a specified period/total person-time when people were at risk of getting the disease

TOTAL cases AT A POINT in time

measure of disease risk

one point in time

number of people with the disease at one time/total number of people in the population at that time

incidence rate x average duration of disease

allows for comparison between groups

standardized between age, gender

census

vital statistics

disease registries

health records

surveys

randomized controlled trial

case control

cohort

cross sectional

Identify case, match with controls

Disadvantage: recall bias, difficult to select controls, reverse causality

Advantage: good for rare/uncommon disease, quick, economical, multiple exposures

gives ODDS RATIO

volunteers

low response rate

ascertainment

interviewer

recall

interviewer

immortal time bias

misclassofication of variable

loss of follow up

ascertainment

healthy worker effect

Follow group of people over time

disadvantage: time consuming, expensive, loss of follow up

advantage: no recall bias, no reverse causality, explore exposures which change over time, direct measure of incidence, test multiple effects

gives RELATIVE RISK

Looks at a situation in one point in time

advantage: one point in time, less time consuming, little ethical problems, study multiple exposures

disadvantage: reverse causality (did x cause y or did y cause x), recall bias

gives ODDS RATIO

best way to ensure groups are exchangeable

strengths: best to avoid confounding, no reverse causality, direct measure of incidence

weaknesses: long time, high cost, ethics

Do no harm

do good

autonomy (informed consent)

justice/equity/fairness/impartiality

<0.5 = less outcome when exposure increased

1 more outcome when exposure increased

statistical measure of risk

NOT causation

random sample error

measurement bias

selection bias

having enough people to find a true association

however, having a huge sample every time is not feasable

complete sample size calculation to find sample size large enough to find true association

Biases arising from method of measurement

prevent using standardized instruments (i.e., standardized questionnaires)

chance

bias

confounding

alternative explanations for study findings, must be addressed before accepting findings

mixing or muddling effects when the relationship is confused by the addition of another factor

illusory/biased association between exposure and outcome, which may be due to a 3rd factor

good matching

restriction (do not include confounders)

randomization

done during analysis

stratification

multivariable modelling

NOT on causal pathway

risk factor for disease

associated with the exposure of interest

information from every person in a population

birth records

death records

marriages

i.e., cancer registry

data from practitioners and pathology labs

most diseases are poorly reported, only cover minority of conditions

electronic medical record

admissions data

ER visits

surgery data

ONLY WHEN ADMITTED

collects data from a sample of a larger population

Canadian community health survey

age

sex

education

location

family structure

ratio of disease to non disease

can get from case control and cross sectional study

(exposed with disease x unexposed without disease)/(exposed without disease x unexposed with disease)

Ratio of disease incidence in exposed vs unexposed

(exposed with disease/(all exposed))/(unexposed with disease/(all unexposed)

proportion of a defined population that develops the outcome of interest in a specific time period

number of people who develop the disease in a period/number of people at risk at the start of the period

compares levels of exposure/disease across populations, not individuals

participants serve as own controls

removes variability that cannot be eliminated by randomization

single patient trial

one individual receives both experimental and control treatments in a random order

people randomized together

used when intervention cannot occur at individual level

cluster trial, intervention is implemented at community level

range placed around a point estimate, in which the true result likely lies

95% CI will likely contain the true value 95% of the time

narrower CI = less sampling error

selection of the study sample differs from the population in a non-random directional way, and relates to the exposure

clearly define study population

study sample represents population of interest

High response rate of cases and controls

systematic or non-random error in the collection of data

ROAR: recall bias, observer bias, attention bias, response bias

blinding RCTs

standardized instruments

quality control

unlikely to arise by chance

useful practically

in large study: small differences that are not clinically useful come up as statistically significant

in small study: a clinical use may arise, but is not statistically significant, so hard to know if result was by chance

SMR

• matching

• restriction

ASM

• stratification

• multivariable modelling