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NS 3600 > Content > Flashcards

Content Flashcards

1
Q

descriptive

A

surveillance, ecologic, cross-sectional
no advance hypothesis
accept association may be causal or not
use pre-existing data

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2
Q

analytic

A

hypothesis testing
hypothesis poses casual link
collect new data or make use of existing data
observational studies - case-control, cohort
experimental studies - RCT, interventions

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3
Q

John Graunt

A

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

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4
Q

James Lind

A

1747
surgeon
First clinical trial - sailors with scurvy in different treatment groups
contributions: develped method of experimental study design, tested hypothesis, group assignment, intervention

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5
Q

William Farr

A

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

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6
Q

Miasma theory

A

mid-1800s
cholera caused by polluted gases from decaying organic matter inhaled
prevented by better sanitation

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7
Q

John Snow

A

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

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8
Q

Austin Bradford Hill

A

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

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9
Q

Richard Doll

A

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

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10
Q

Hill’s criteria for causality

A

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

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11
Q

Framingham Heart Study and Nurses’ Health Study

A

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

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12
Q

Epi objectives

A

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

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13
Q

applications of epi

A

study history of disease and describe health events
identify risk factors and causes of diseases
monitor and evaluation
identify control and preventive measures

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14
Q

odds

A

ratio of probability of occurrence of event to probability of nonoccurrence
part/non-part or p/(1-p)
p=probability

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15
Q

odds vs proportions

A 
small proportions (part/whole) aka rare events = approximates odds
large proportions = value does not approximate value for odds (non-rare events)
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16
Q

prevalence

A

of existing cases at point in time / # in total pop

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17
Q

factors that increase prevalence

A

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

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18
Q

factors that decrease prevalence

A

decreased incidence, shorter duration, high case fatality, in-migration of health people, out-migration of cases, improved cure rates

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19
Q

incidence

A

of new cases of disease / # of population initially at risk

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20
Q

cumulative incidence

A 
# of new cases over specified time / # at risk in population
value increases with increased period of follow-up
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21
Q

attack rate

A

subjects who develop infectious outcome / # at risk during outbreak

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22
Q

case fatality rate

A

subjects who die / those who develop a disease

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23
Q

survival rate

A 
1 - cumulative incidence for death 
# of participants who don't die during follow-up / number of subjects at risk for dying
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24
Q

concerns of cumulative incidence

A

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)

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25
Q

incidence rate

A 
solution to difficulty with cumulative incidence
# of new cases of disease / total person-time of observation
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26
Q

odds ratio

A 
odds of developing the disease among exposed compared to odds of developing the disease among unexposed
case-control
small risks: OR = RR
larger risks (common): OR =/= RR
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27
Q

risk ratio

A

relative effect associated with exposure
null value = 1
>1: risk or odds of disease is greater in exposed

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28
Q

risk difference

A

absolute effect associated with exposure
null value = 0
>0: risk of disease greater in exposed

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29
Q

Cochrane

A

gold standard for causal inference
trasnparent method and guidelines from WHO
standardized approaches
look at all the evidence to date and make decisions according to this

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30
Q

ecological study

A

look at associations and interpret data
strengths - hypothesis generating, less time and resource intensive
limitations - country-level data, ecological fallacy, confounding

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31
Q

cross-sectional study

A

snapshot of population
strengths - one stop shopping, less expensive, can detect effect of exposure that do not vary over time
limitations - temporal relationship not clear, prevalence in function of incidence and duration, prevalent cases may be over-represented by cases with long disease duration

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32
Q

case-control study

A

investigator enrolls cases and controls and records exposure status
strengths - efficient design for rare outcomes, quick to complete, inexpensive, easy to study multiple exposures, disease has long induction or latent periods, exposures and risk factors are not known well, exposure is difficult to obtain
limitations - no estimate of incidence is possible, control choice may be different, recall bias is possible, can’t infer something about overall population
ODDS RATIO

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33
Q

cohort study

A

observational; enroll participants free of disease, but at risk; follow up over time
strengths - exposure measured before outcome, estimate incidence in exposure group, easy to study multiple outcomes, allow study of rare exposures (risk ratio, hazard ratio)
limitations - inefficient for rare outcomes, costly and time consuming, confounding, size of exposure and unexposed may not reflect source population
Calculate - incidence rate, risk ratio

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34
Q

nested case control study

A

use data from cohort, but info you are looking at hasn’t been studied
Faster and easier, because the data is already there

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35
Q

randomized clinical trial

A

gold standard for causality, random allocation, equipoise, blinding
strengths - gold standard for causality, avoid bias by confounding, clear that exposure precedes outcome, estimate incidence, can study several outcomes
limitations - not always possible or ethical (equipoise), inefficient for rare or delayed outcomes

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36
Q

case cohort study

A

controls selected from cohort at beginning of time period

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37
Q

density case-control study

A

controls sampled each time case occurs (risk set sampling)

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38
Q

cumulative case-control study

A

controls sampled from disease-free for entire period (chosen at end of study)

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39
Q

prospective cohort study

A

follow-up period occurs chronologically after start of study period

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40
Q

retrospective cohort study

A

follow-up period occurred chronologically before start of study period

41
Q

ambidirectional cohort study

A

follow-up period began chronologically before the start of the study period, but continues into the future

42
Q

open cohort

A

dynamic - (membership defined by changeable characteristic (location, relationship status)
exposure status can change over time
follow up - new participants added or eliminated during follow up
incidence rate

43
Q

fixed cohort

A

defined by irrevocable element (exposure to man-made or natural disaster, inhabitants of a location at a specific point in time)
exposure - doesn’t change
may have loss to follow up, but no enrollees
incidence rate

44
Q

closed cohort

A

irrevocable event
no losses to follow up
cumulative incidence or incidence rate

45
Q

induction period

A

time between exposure and development of disease

46
Q

latent period

A

time period between disease development and detection

47
Q

empirical latent period

A

induction period + latent period

48
Q

equipoise

A

uncertainty about risks and benefits of treatment

49
Q

PICO

A

population, intervention, comparison, outcome

50
Q

community RCT

A

randomize groups or clusters to groups
less statistically significant
people within groups are more similar to each other than people in other groups

51
Q

RCT Phases

A

Phase 1: Formulation - safety and toxicity, dosage, side effects
Phase 2: Activity - Evidence for potential treatment, effect of intervention
Phase 3: efficacy - RCT, effects of intervention on outcome
Phase 4: effectiveness - investigate wide-scale use and effectiveness

52
Q

effectiveness

A

strategy practical to use in real world
realistic
broad sample from potential target population

53
Q

efficacy

A

theory predicts it will work
testable in ideal and controlled conditions
greatest effect of intervention; high adherence
sensitive to predicted effects; may be surrogate

54
Q

parallel design of RCT

A

two or more arms of treatment

different participants in each

55
Q

crossover design of RCT

A

two or more arms
participants receive all treatments
outcome must be acute and reversible
key assumption: participants at beginning second phase returned to initial status (washout period)

56
Q

simple

A

single new treatment vs comparison treatment

treatment vs standard of care

57
Q

factorial

A

multiple treatments
2x2 factorial design (each participant undergoes separate randomization assignment for 2 treatments)
potential to examine effect of multiple treatments within signle study design

58
Q

simple randomization

A

no guarantee of equal size groups
coin flip, random number generator
still possible for imbalance, if small trial

59
Q

blocked randomization

A

within block, 1/2 get intervention, 1/2 get placebo

block sizes vary under randomization process

60
Q

stratified randomization

A

ensure balance by key characteristics in all groups
assign patients to strata, according to baseline cahracteristics
within strata, assign patients using blocked randomiation

61
Q

Random error

A

error caused by some factor that changes from one measurement to another
decreases with larger study size

62
Q

selection bias

A

error due to systematic differences between the characteristics of the people selected for a study and those who are not (follow up and selection)
source:design, sampling, Berkson’s bias, self-selection, healthy worker effect, non-response
Problems: selecting inappropriate control group, exposure influences detection of cases, outcome influences choices of exposed and non-exposed participants, loss to follow up related to exposure and outcome

63
Q

information bias

A

Error due to differences in manner in which data for exposure or outcome are obtained from various groups, which may lead to misclassification of study participants.
sources: recall, prevarication, reporting, loss to follow up, missing data, digit preference, boserver, instrumental, detection, lead time, length

64
Q

random/non-differential information bias

A

variability in data that is not readily explained
measurement of disease is not different for exposed and unexposed; diseased and non-diseased
validity and reliability
tends to result in measure of association that is biased towards null value

65
Q

systematic/differential information bias

A

effect estimate may be incorrect
case-control study - cases recall or report exposure differently from controls
avoid this via blinding, change control group, etc.
measures of association can be biased in any direction

66
Q

confounding

A

associated with exposure, associated with disease, not part of causal pathway of exposure
calculate adjusted measure of association to only reflect effect of exposure (without confounder)
(RRcrude - RRadjusted) / RRadjusted > 10%

67
Q

3C’s of confounding

A

comparability - distribution of Z differes between exposed and unexposed
collapsibility - 2x2xk table can be collapsed into 2x2 table without changing effect estimate (stratification)
counterfactual - factual and counterfactual outcomes of exposed participants are not exchangeable

68
Q

positive confounder

A

variable that is positively related to disease and positively related to exposure or both inversely related
RRadjusted < RRcrude

69
Q

negative confounder

A

variable that is either positively related to disease and inversely related to exposure or vice versa
RRadjusted > RRcrude

70
Q

How to reduce for confounding

A

study design - randomization, restriction (limit study to one category of potential confounder), matching
analysis: stratification, change in effect estimate (adjust after stratification), multivariate analysis

71
Q

effect measure modification

A

effect of exposure on outcome.disease is modified depending on value of third variable called “effect modifier”.
want to better understand and measure – effect is different for different people.
exposure having a different effect on outcome in different groups of patients.
detected by stratum-specific estimates of measure of effect

72
Q

generation time

A

time interval between one person getting infected and another person getting infected from the firt

73
Q

reproduction number

A

average number of infected persons resulting from contact with single infected person occur from contact with primary case

74
Q

secondary attack rate

A

risk of infection among susceptible individuals exposed to an infected source

75
Q

transmission probability

A

probability of transmission from infected person to susceptible person during contact

76
Q

virulence

A

degree to which a pathogen can cause disease and death

77
Q

direct transmission

A

airborne, direct contact, fecal-oral, STD

78
Q

indirect transmission

A

zoonoses, vector-borne diseases, environmental pathogens, intermediate host (tapeworm)

79
Q

serial interval

A

time between successive cases of disease

80
Q

basic reproductive number

A

average number of infections that would be caused by one infected person when everyone else is susceptible

81
Q

effective reproductive number

A

average number of infections resulting from one infected person given that not everyone is susceptible

82
Q

Reproductive rate

A

beta x k x D
beta = likelihood of transmission per individual contact
k = number of contacts a person has
D = duration of infectivity
– decreased by immunization rate (leads to herd immunity)

83
Q

What’s different about ID epi?

A

A person with infection is both the outcome and source of transmission.
A person with infection may resolve an infection and become immune
A person with an infection may have no symptoms yet still infect others without being recognized as a source
Since ID can be an epidemic, rugency may exist and preventive action must be taken.
There is strong biological basis for investigation and action (Koch’s and Hill’s)
People can become immune from having the disease or through immunizations

84
Q

Why is access to HIV diagnosis and treatment important?

A

life extension
reduce infection
HIV has short latency period and long incubation period

85
Q

Multicenter AIDS Cohort Study

A

longest US-based study of HIV-infected individuals
over 1000 publications
discoveries: how to best diagnose HIV infection, direct association between viral load and HIV disease progression, connection between low CD4 T-cell counts and progression to clinical AIDS, central role of immune activation in HIV disease, how to best treat and care for HIV

86
Q

trends in HIV infection

A

antiretroviral therapy retention decreases as months go on
new infections common in women in sub-saharan Africa
infections slightly more in men (overall)
prevalence is higher among females
East and Southern Africa - more common among young women (prime time for pregnancy and repro)

87
Q

Risk of MTCT of HIV

A

5-10% in pregnancy/in utero (medication)
10-20% labor and delivery / intrapartum (C-section)
10-20% breastfeeding / postpartum (supportive care during breast feeding)
overall: 30-45%

88
Q

Risk of MTCT of HIV

A

5-10% in pregnancy/in utero (medication)
10-20% labor and delivery / intrapartum (C-section)
10-20% breastfeeding / postpartum (supportive care during breast feeding)
overall: 30-45%

89
Q

how to design a successful screening program

A
  1. suitable disease (high prevalence, high burden, detectabl, treatment is available, early treatment is beneficial)
  2. Suitable test (simple, rapid, inexpensive, safe, acceptable)
  3. Suitable screening program (reliable, valid)
90
Q

sensitivity

A

% of individuals with the disease correctly classified by screening test as having disease
denominator would be dependent on another (diagnostic)test

91
Q

specificity

A

% of individuals without the disease correctly classified by screening test as not having the disease

92
Q

how can sensitivity and specificity of a test be improved?

A

retrain screeners, recalibrate screening instruments, use a different test, use more than one test, develop better screening tests

93
Q

positive predictive value

A

% of individuals classified by screening test as having the disease who actually have the disease

94
Q

negative predictive value

A

% of individuals classified by screening test as disease-free who do not have the disease

95
Q

lead time bias

A

amonunt of lead time gained by screening and degree that these lead times improve effectiveness of treatment
time between disease detection with screening and its usual clinical presentation and diagnosis
does the length of time before getting clinical symptoms matter?

96
Q

length time bias

A

evaluation of screening must also take into account length-biased sampling
screen-detected cases may not be representative of all cases (may have longer preclinical phases, biologically slower progression and somewhat better prognosis)
screening will preferentially identify those slowly developing disease

97
Q

volunteer bias

A

people who choose to participate in screening program may be healthier than those who do not participate
evaluations must be based on measures that are not affected by early diagnosis except to the extent that early treatment is beneficial

98
Q

healthy worker effect

A

special case of selection bias in which mortality related to occupational exposures is underestimated

99
Q

Berkson’s bias

A

special case of selection bias involving hospital cases and controls, where the higher exposure rate among those individuals admitted to a hospital leads to a distorted odds ratio effective size

NS 3600 (2 decks)

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