Content Flashcards
descriptive
surveillance, ecologic, cross-sectional
no advance hypothesis
accept association may be causal or not
use pre-existing data
analytic
hypothesis testing
hypothesis poses casual link
collect new data or make use of existing data
observational studies - case-control, cohort
experimental studies - RCT, interventions
John Graunt
1662
demographer
counted number of people who have died by looking through by age and sex –> looked at patterns
analyzed weekly mortality reports
contributions: use of existing data, descriptive epi, describe mortality rates by person, place, time
James Lind
1747
surgeon
First clinical trial - sailors with scurvy in different treatment groups
contributions: develped method of experimental study design, tested hypothesis, group assignment, intervention
William Farr
Collected vital statistics and reported death by time, location, age, sex, occupation and cause
supported miasma
association between altitude and cholera mortality
contributions: collection of accurate health statistics data, interpret data to identify etiology of disease, developed method for standard mortality rate
Miasma theory
mid-1800s
cholera caused by polluted gases from decaying organic matter inhaled
prevented by better sanitation
John Snow
1854
Medical practitioner, anesthesiologist
Challenged miasma theory of cholera transmission
Grand experiment - water companies moved water source above tidal flow of thames river
Father of modern epi
formation of causal hypothesis based on prior knowledge and observation
design study to test causal hypothesis
collection of primary data
address problems of confounding, adherence, missing data
Austin Bradford Hill
1940
Randomized Control Trial of streptomycin for treatment of TB
patients with TB assigned to strep + bed rest vs bed rest alone
Contributions: randomization, comparison to standard of care, blinding, equipoise
Richard Doll
1950s
Prospective cohort study of heavy smokers –> lung cancer
case control study of people with and without long cancer and previous smoking history
contributions to epi: study design development, obtain outcome data from existing data, matching, causality - Hill’s criteria
Hill’s criteria for causality
temporal relationship, strength of association, dose-response relationship, biological plausibility, consideration of alternate explanations or confounding factors, replication of findings, cessation of exposure, consistency with other knowledge, specificity of association
Framingham Heart Study and Nurses’ Health Study
1958 and 1980
Framingham: establish risk factors for CVD
Nurses’: prospective cohort study for long-term effects of oral contraceptives
Contributions: landmark prospective cohort studies, detailed prospective data collection for prospective cohort study, analytic method
Epi objectives
determine extent of disease in pop
identify patterns and trends in disease occurrence
identify causes and risk factors for disease
evaluate effectiveness of preventive and therapeutic interventions and public health programs
applications of epi
study history of disease and describe health events
identify risk factors and causes of diseases
monitor and evaluation
identify control and preventive measures
odds
ratio of probability of occurrence of event to probability of nonoccurrence
part/non-part or p/(1-p)
p=probability
odds vs proportions
small proportions (part/whole) aka rare events = approximates odds large proportions = value does not approximate value for odds (non-rare events)
prevalence
of existing cases at point in time / # in total pop
factors that increase prevalence
increase incidence, longer duration, prolongation of life without cure, in-migration of cases, in-migration of susceptible people, out-migration of healthy people, better diagnosis of reporting
factors that decrease prevalence
decreased incidence, shorter duration, high case fatality, in-migration of health people, out-migration of cases, improved cure rates
incidence
of new cases of disease / # of population initially at risk
cumulative incidence
# of new cases over specified time / # at risk in population value increases with increased period of follow-up
attack rate
subjects who develop infectious outcome / # at risk during outbreak
case fatality rate
subjects who die / those who develop a disease
survival rate
1 - cumulative incidence for death # of participants who don't die during follow-up / number of subjects at risk for dying
concerns of cumulative incidence
assumes every is followed for same period of time
lost to follow up (censoring aka lack of knowledge for outcome of the individual)
competing risks (removes participant from being at risk from outcome of interest)