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Epidemiology > Exam 1 > Flashcards

Exam 1 Flashcards

1
Q

3 aspects of internal validity before statistical association:

A

-check for bias, counfounding/effect modification, statistical significance

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2
Q

Bias

A

systematic (non-random) error in study design or conduct leading to erroneous results (distorts relationship btw exposure & outcome)

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3
Q

after study end- fix bias?

A

Nothing you can do to fix it

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4
Q

prospective (prestudy)

A

adjustment minimize bias (assess to confirm internal validity and conclusions)

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5
Q

components of bias

A
  1. source 2. magnitude 3. direction
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6
Q

bias magnitude

A

how much of an impact bias has on changing odds ratio

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7
Q

bias direction

A

move Odds Ratio/Risk Ratio away or towards 1.0 (groups equal) enhance or minimizing

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8
Q

main categories of bias

A

how you measure (collect data) or Selection (ppl) -measurement- related biases -selection-related biases * both result in grps different–dont want– want grps to ne as equal as possible except the one thing you are studying biases make grps different & creates the error

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9
Q

measurement- related biases

A

way the researcher collects information, or measures/observes subjects which created a systemic difference between groups in quality/accuracy of info.

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10
Q

selection-related biases

A

way the researcher selects or acquires study subjects which creates a systemic difference in the composition between groups (not from same pop/group)

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11
Q

types of selection bias

A

health-worker bias self-selection/participant (res ponder) bias control selection bias

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12
Q

selection bias

A

-selecting study subjects that are not representative of your primary pop. of interest or generates differences in grps being compared -how you pick your people- bias– made groups different (inappropriate) -convenience study: mall on wed.

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13
Q

health-worker bias

A

environmental employer research -sick & dead not at job site

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14
Q

self-selection/participant (responded) bias

A

people who want to be in survey -volunteer different than nonresponse

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15
Q

control selection bias

A

only diff btw grps not clear based on disease definition -call 1st 50, not accounting for if they have a phone/can they answer

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16
Q

recall/reporting bias

A

subject-related variation -differential level of accuracy/detail in info btw grps - ex: recall after bad event, after disease; diabetics monitor eating more than regular ppl

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17
Q

hawthorne effect

A

overly encouraging, give more than needed, overly positive/helpful

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18
Q

types of measurement bias

A

subject related: recall bias, contamination bias, compliance bias, lost to follow up bias observer related: interviewer bias, diagnosis bias,

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19
Q

contamination bias

A

aspirin v placebo: dont want other drugs in same family (how investigators account for what else taking) -control grp accidentally receive tx of exposed to intervention being studied

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20
Q

compliance/adherence bias

A

comply, follow instructions- worry one grp different in complying -grps being interventionally studied have different compliances

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21
Q

lost to follow-up bias (attrition)

A

follow up diff in 1 grp or another; diff withdrawal or lost to follow-up rates or other differences -differential v. non-differential ex: surgery-good pain control drugs vs. placebo– hear from person in pain or drops out

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22
Q

interviewer (proficiency) bias

A
  • not trained in what to do -interviewer expects certain answers -conscious or unconscious –> mask inverstigator so they dont know who meds/who placebo -body language, inflection, expect diff. answers- skewed/false info
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23
Q

diagnosis/surveilance (expection) bias

A

diff evlauation, classification, diagnosis, observation preconceived expectations (hawthorne-like effects from researchers) expect drug to feel better

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24
Q

controlling for biases

A

*Select precise, accurate, & medically-appropriate measures of assessment and evaluation/observation -validated screening -specifics of data collection ex: polygraph test *blinging/masking *multiple sources data *randomly allocate observers for data collection *methods to minimize loss to follow up

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25
Q

misclassification bias

A

error in classifying either disease or exposure status or both -measurment (information/observation) bias put ppl in wrong group

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26
Q

misclassification bias types

A

non-differential & differential

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27
Q

non-differential misclassification bias

A

error in both grps equally misclass. of exposure/disease is unrelated to the other move ratio towards 1; attenuates effect of association

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28
Q

differential misclassification bias

A

error in one grp differently than other put ppl in wrong grps missclass. of exposure/disease is related to the other move ratio in either direction in relation 1.o (inflate or attenuate)

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29
Q

controlling for misclassification

A

want to minimize error and balance equation -similat tech. to limiting biases- its a measurement-related bias -technology on both grps

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30
Q

**differential misclassification

A

?

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31
Q

**non-differential misclassification

A

?

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32
Q

confounding variable

A

3rd variable that distorts an observed relationship btw exposure & outcome (disease) -associated with both but independent of both (not in causal pathway) (something else that gets involved with exposure & outcome; getting in the way)

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33
Q

confounding affect

A

can over or under estimate an association (RR/OR/HR) and change direction of effect

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34
Q

impact of confounders

A

intensity/magnitude/strength: association more or less extreme than true association direction: association that moves association in + or - direction

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35
Q

confounding example: coffee and low birth rate

A

smoking has relationship with both outcome and exposure= confounder

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36
Q

factor effect of confounder

A

report adjusted odds ratio

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37
Q

testing for confounding

A

step 1: crude outcome measure of association (OR/RR) btw exposure & outcome - unadjusted [linear 2x2 factoring] step 2: re-calculated outcome measure of association (OR/RR) btw exposure & outcome while statistically controlling the effects of the confounder [add 3rd variable for outcome of interest] step 3: compare the crude vs. adjusted measure of association btw exposure & outcome [compare the 2]

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38
Q

crude & adjusted estimate (RR/OR)

A

20% is confounding present

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39
Q

purpose of controlling for confounding

A

to get more accurate estimate of the true association btw exposure & outcome

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40
Q

causal pathway btw exposure & outcome

A

confounder NOT in pathway

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41
Q

ways to control confounding

A

can do before!! 1. study design stage 2. analysis of data stage

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42
Q

study design stage

A

randomization (blocked or stratified) restriction matching

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43
Q

analysis of data stage

A

stratification multivariate statistical analysis

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44
Q

randomization

A

hopefully allocates an equal number of subjects with the known & unknown confounders into each intervention grp (make grps equal) strengths: sufficient sample size, randomization makes grps equal weakness:sample size might not be large enough to control for known & unknown confounders, only intervention studies

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45
Q

restriction

A

limit who can enter study, decrease ability to take results & transfer to rest of community restricted to only subjects who do not fall within pre-specified categories of confounder strengths: straight forward, convenient, inexpensive, does not neg. impact internal validity weakness: sufficiently narrow restriction criteria reduces sample size, residual confounding effects, neg impact external validity

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46
Q

matching

A

more difficult, hands on researchers activity- difficult to find exactly like pt in other grp selected in matched pairs related to confounding variable to equally distribute confounder among each grp (let in but need one for other grp) strengths: intrusive weakness: difficult to match on everything needed to match, to find same person; overmatching

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47
Q

stratification

A

statistical analysis of data by evaluating association btw exposure and disease within various strata within confounding variables strengths: intrusive, straight- forward and enhances outstanding of data weakness: impractical

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48
Q

multivariate analysis

A

statistical analysis of the data by mathematically factoring out the effects of the confounding variables strengths: simultaneously control for mult. confounding variables; beta coefficients converted to OR’s weakness: not understand data

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49
Q

effect modification (interaction)

A

3rd variable modifies the magnitude of effect of association by varying it within different levels of 3rd variable (effect modifier) MUST report for each strata (informative)

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50
Q

effect modification (interaction) example: mortality odds of newborn’s born as singleton

A

birth weight (3rd variable) may impact odds of mortality- variable checked for confounding and effect modification–> Unadjusted mortality odds: 1.06 children born as single babies 6% more likely to die adjusted (for birth weight) mortality odds: 1.02 birth weight not confounder NEEDS TO CHANGE MORE THAN 20%

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51
Q

effect modification (interaction) example: mortality odds of newborn’s born as singleton– check strata

A

reduced risk—> increased risk as weight increased **effect modification is present b/c OR changes acc. diff. strata of effect-modifying variable

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52
Q

effect modifier

A

changes layers/strata ex: birth weight not confounding- didnt change outcome & exposure BUT layers of weight does change (at least 20%) btw highest and lowest OR

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53
Q

testing for effect modification

A

Step 1: crude outcome measure of association btw exposure & outcome (OR/RR) Step 2: crude outcome measure of association (OR/RR) btw exposure & outcome FOR EACH STRATA OF EFFECT-MODIFYING VARIABLE step 3: compare stratum-specific measure of association btw exposure & outcome (OR/RR)

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54
Q

point-estimate (RR/OR)

A

difference by 20% btw lowest & highest strata of effect-modifying variable IF effect present

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55
Q

detecting confounding & effect modification: how does the change in RR/OR change in the presence of confounding & effect modification?

A
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56
Q

problem we want to eliminate (control/adjust for via several means) in the study

A

confounding

-crude vs. adjusted

is adjusted >20% difference than crude

control @ beg. & end

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57
Q

natural occurenece that we want to describe and study further

A

effect modification

not ignore, explain- informative

compating stratum-specific measures of association

is stratum-specific estimates >20% differnece from each other?

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58
Q

practice exercise

A

confounded?

interacted?

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59
Q

measures of association

A

exposure & disease

exposed v. non-exposed

diseased v. non-diseased

rows: exposure
column: disease

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60
Q

descriptive group comparisons common

A

absolute differences

relative differences

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61
Q

absoulte differences

A

SUBTRACTION

  • subtracting frequencies
  • males had 28 more surgeries
  • females had 28 fewer surgeries
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62
Q

relative differences

A

DIVISION

division (ratio) of frequencies

  • males had 2.6 times the number of surgeries compared to females (>160% increase)
  • females had just under 40% number of surgeries of males
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63
Q

relative differences

A

DIVISION

division (ratio) of proportions

*more common

divide proportions than interpret

  • males had more than 2.06 times the proportion of surgeries compared to females
  • females had just over 45% lower proportion of surgeries of males
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64
Q

absolute differences vs. relative differences

A

absolute differences will always be smaller then relative differences

EX: absoulte: A-B

20-10=10%

2-1=1%

.2-.1=.1%

relative: B/A

10/20= 50%

or 20/10=2 (100% higher)

1/2=50%

.1/.2=50%

*risk of disease of B is 1/2% A

or group B has 50% rate of disease compared to group A

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65
Q

pharma companies

A

relative differece because absoulute diff. is always smaller than relative diff.

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66
Q

risk ratio/ relative risk (RR)

A

used in studies where subjects are allocated based on exposure and outcome

ex: cohort studies

risk- incidence rate (IR), attack rate (part/whole)

ratio of 2 risks from different groups

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67
Q

Ramapril Example

Outcome?– bad (death/MI)

Ramapril: 14% had outcome

17% placebo had outcome

A

absoulte difference in event rates 3.8%

placebo 3.8% more likely

or

Ramapril had 3.8% absoulte lower event rate

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68
Q

% rates

A

the risks of event in each group

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69
Q

Risk and Risk ratio

A

risk is a proportion

probablility of outcome in exposed and in non-exposed

probability of outcome in exposed A/(A+B)

probablility of outcome in non-exposed C/(C+D)

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70
Q

Incidence risk (IR)

A

Risk is a proportion

risk of outcome in exposed: A/(A+B)

risk of outcome in nonexposed: C/(C+D)

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71
Q

Risk Ratio

A

ratio of risk from 2 different groups

risk in exposed/risk in nonexposed

2/2 = 1

17/17 = 1

always equalness, no differences

bigger/less = >1

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72
Q

(Ramapril EX) Risk ratio

A

MI ramipril: 14% MI placebo:17.8%

RR= 14/17.8=.77

RR (.77) < 1

ramapril has a decreased risk

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73
Q

numerator

A

always study group/ reason for study

interest/reference

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74
Q

Interpreting ratios- risk/odds/hazard

A

=1 no difference in risk/odds

>1 increased ratio (larger, bigger,above)

>2 “times control”

<1 decreased ratio (from one how much do you have to go down to get to odds ratio below one)

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75
Q

increased RR >1

A

use decimal value (converted to %) for interpretation

ratio over one-increased

If RR 1.53: then 53% increased risk in comparative grp

“the study grp of interest had a 53% increase. stop when get to 2 –> 2xs, 3xs…”

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76
Q

>2

A

OR=6.18

comparator group is 6.18 times greater odds

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77
Q

Decreased Ratio < 1

A

subtract decimal value from 1 (answer converted to %)

HR= 0.73

27% lower probablility of hazard outcome

amount you went down to get to it, CANT JUST GRAB THE DECIMAL

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78
Q

forest plots

A

plot with indicator line at 1.o

talk about where OR # lands

higher or less than 1

vertical line at 1.0 represents equalness/sameness

ppl who take statins are about 40% lower hazard risk for dying of MI

higher end of 1.o

magnitude btw 1-3

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79
Q

when interpreting ratio’s (RR, OR, HR) looking for…

A
  1. direction of words
  2. magnintude
  3. group compairison

*target for wrong answers ex: grps backwards

(increased or decreased)

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80
Q

Absolute Risk Reduction (ARR)

attributable risk

A

Absoulte Risk difference (ARD)

SUBTRACTION

simple absoulte difference (subtraction) in risks

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81
Q

AR

A

risk difference in outcome among exposed that can be attributed to actual exposure

ex: ramipril 14% placebo 17.8%

ARR= 3.8%

subtract them - claim difference soly to use of ramipril

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82
Q

attribute

A

risk difference

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83
Q

*** example exam page

A
84
Q

relative risk reduction (RRR)

A

relative risk difference (RRD)

DIVISION

(ARR)/ R exposed

ex: ramipril 3.8%/17.8%=21.3% relative risk reduction in Ramipril (exposed) grp relative to placebo (unexposed)

85
Q

Number needed to treat (NNT)

A

how many ppl needed to tx to get benefit

(vital to understand & explain to pts)

1/ absoulte risk reduction [subtract risk then 1/ #]

(1/ARR) ex: 1/0.038= 26.3=27 pts

interpretation: # of pts needed to be tx to experience the studied event outcome (benefitial or harmful)

86
Q

Ramipril number needed to treat

A

if ramipril harmful, AR would be negative and NNT would translate “for every 27 pts 1 would be harmed/bad event”

how many to avoid one outcome of death, stroke, MI

1/ARR

87
Q

Number needed to treat

ex:opiod

A

NNT easier for ppl to comprehend

for every 4 pts, i given opioid to 1 pt is going to get nausea (seems common)

on average 1 in 4 pts will have nausea

constipation: 1 in 5 got constipatin (common)

88
Q

NNH

A

same as NNT

negative event

Number needed to harm

ex: 1 out of 822 had MI

would have to tx 822 pts with this drug before 1 had MI

89
Q

*** Practice

A

Rexp=

Rnexp=

ARR=

RR=

RRR=

NNT=

90
Q

**Pracice with Bivalirudin vs Heparin

Bivalirudin = 190 N=2289

Heparin= 199 N=2281

A

Rexp=diseased/total= 190/2289=8.3%

Rnexp=nondiseased/total= 199/2281=8.7%

ARR=subtract= 8.7-8.3=.4%

RR=division=8.3/8.7=.954=95.4% <1 so lower risk

RRR= 1-.954=0.046=4.6% risk reduction relative to unexpected risk

NNT= 1/ARR= 1/.004= 250

tx 250 ppl with Bivalirudin in order to prevent 1 person from death or MI

91
Q

Risk ratio

A

part/whole

92
Q

odds ratio

A

occurance/nonoccurance

93
Q

odds & odds ratio (OR)

A

odds is a ratio

used in studies where subjects are allocated based on disease presence (y/n) and evaluated for exposure

odds of exposure vs. odds of not exposed (cases)

A/C

odds of exposure vs. odds not exposed (controls)

B/D

94
Q

odds of exposure in cases

odds of exposure in controls

A

A/C

B/D

95
Q

Odds ratio (OR)

A

division of odds

(A/C)/ (B/D)

96
Q

**Practice

A

odds of exposure in cases= A/C=457/362=1.26

odds of exposure in controls=26/85=0.305

odds ratio (OR)=1.26/0.305=4.13

97
Q

practice HPV

A

Not pt over whole; odds of occuring/odds of not oc

odds of exposure in cases= 37/63=0.587

<1

odds of exposure in controls=11/189=0.058

< likely

odds ratio (OR)= 0.587/0.058= 10.1

10xs more likely to be involved; those with ph cancer are more likely to have had HPV

98
Q

interpreting OR

A

looking at all diabetics in the US

were US diabetics more likely to be on a statin or not & characteristcs btw statins and nonstain users

*one grp designated as reference group (odds ratio compared to 1)

compared to female US diabetics, males 38% more likely to be tx with statin to lower cholesterol

OR=1.38: more bc >1 38 into percentage

99
Q

compare 2 groups and describe likelihood of outcome in 1 group compared to another

A

all “ratios”

RR,OR,HR

100
Q

(all ratios) if ratio is 1.0

A

outcome is equally likely for both groups

101
Q

(all ratios) if ratio is > 1.0

A

outcome in more likely to occur in main study (comparison) group

102
Q

(all ratios) if ratio is < 1.0

A

outcome is less likely to occur in the main study (comparison) group

103
Q

association

A

connection, linkage, NOT cause

(relationships OR/RR/HR) btw exposure & disease

104
Q

cause

A

precursor event/condition/characteristic required for the occurance of the disease

105
Q

main types of associations

A
  1. artifactual (false)
  2. non-causal

but has a role (smoking & lung cancer)

  1. casual

(one of (not only) causal process)

106
Q

artifactual associations

A

can arise from significant bias and or extensive confounding

(false associations)

107
Q

non-causal associations

A
  1. disease may cause the exposure
  2. disease & exposure both associated with a third factor (confounding)
108
Q

non-causal associations-disease may cause the exposure

A

chicken before egg or egg before chicken

RA leading to physical inactivity

(Disease [RA] may cause exposure [inactivity])

109
Q

non-causal association-disease & exposure both associated with a third factor (confounding)

A

third factor-

positive association shown btw coff & CHD or Downs & birth order

110
Q

koch’s postulate’s for infectious disease

A

if implying causative must have 4 things *limitations

  1. Must be present in every case of disease
  2. Must not be found in cases of other diseases or healthy individuals
  3. Must becapable of isolation, culture and reproducing disease in experimental animals
  4. Must be recovered from experimentally-induced diseased animals
111
Q

koch’s postulate’s limitstions

A

-Disease production may require presence of “co‐factors” that postulates don’t address
-Viruses can’t be cultured similar to bacteria
-Not all viruses/bacteria induce clinical disease
•Carrier & Sub‐clinical disease

112
Q

Mill’s Canons

A

cause of any effect must be consist of a constellation of concepts that act in concert

(ex: heart disease- overweight,cholesterol,hypertension)

113
Q

types of causal relationships

A

sufficient cause

necessary cause

component cause (risk factor)

114
Q

Suffcient Cause

A

set of minimal conditions/events that inveitably produce disease

  • if present the disease will always occur
  • rare/genetic abnormalities
  • can have multiple risk factors/component causes that induce disease
115
Q

necessary cause

A

cause precedes a disease

cause must be present for disease to occur, yet the cause may also be present without the disease occuring

EX: Tb

  • carriers, have in lungs but not sick
  • sick- it has to be in lungs
116
Q

component cause (risk factor)

A

characteristic that if present and active increases the probability of a particular disease

(increase likelihood of outcome)

ex: High HDL

some pts primed/susceptible to disease before component causes induce disease (multi-factoral)

117
Q

multi-factoral

A

some pts primed/susceptible to disease before component causes induce disease

118
Q

multiple causation

A

to examine the influece of a single factor, necessary to adjust/control for the effects of other factors

119
Q

3 ways to control/adjust variables

A
  1. restriction
  2. matching
  3. stratification
120
Q

keep other factors out from study

only <65

no smokers

A

resriction

121
Q

similar characteristics in each grp

age, gender, disease, smoking status

A

matching

122
Q

categorize patients on exposure levels or disease severity or other important pt characteristics

A

stratification

123
Q

Hill’s Guidelines

A

how close to causation is an association

  1. strength
  2. consistency
  3. temporality
  4. biologic gradient
  5. plausibility

the higher the # of criteria met, when evaluating an association, the more likely it may be causal

124
Q

Strength

A

size of association (RR,OR,HR)

bigger the association, bigger the relationship

ex: smokers 20xs greater risk of lung cancer

*stong association is neither necessary or sufficient for causality & weakness of association is neither necessary nor sufficient for absence of causality

125
Q

consistency

A

reproducability

repeated observation of association in different populations under different circumstances in different studies

ex: smoking & CHD

*may still obscure truth-Menopausal Hormone Therapy (consistency can still be wrong)

126
Q

temporality

A

necessity that the cause precede the effect/outcome in time

time/order - proximate cause (short term interval)

  • distant cause (long term interval)

induction period & latent period

Ex: (med) side effects & rns / food poisoning

testicular cancer

longer the time period harder to connect

127
Q

biological gradient

A

observation of gradient of risk (dose-response) associated with the degree of exposure

prove gradient- down causal pathway

minimum cumulative exposure before disease pop up

threshold effect

128
Q

plausibility

A

biological feasibility the association can be understood and explained

make any sense? is event biologically plausible if really true?

plausibility decision on criterion-based from prior beliefs, which may be flawed or incomplete

ex: stomach ulcers

129
Q

pitfalls in causal research

A

Bias

confounding

effect modification

synergism

130
Q

synergism

A

interacting of 2 or more presumably-causal variables so that the combined effect is clearly greatrer than the sum of the individual effects

EX: health compromising behaviors on preterm births

multiple risk factors, when present put together, worse in risk than alone

131
Q

induction period

A

component cause to disease onset

132
Q

latent period

A

disease onset to diagnosis/clinical presentation

(somethings take time to develop)

133
Q

example of temporality

A

studies show higher lung cancer rates among former smokers during 1st year cessation than current smoking

infer: cont. smoking must decrease risk of cancer
actuality: those who stop b/c early symptoms of illness

(already diseased or already sick and cont. to smoke)

134
Q

distribution of diease

A

frequencies & patterns of disease occurance

135
Q

frequencies of disease occurrences

A

counts in relation to size of population

136
Q

patterns of disease occurances

A

person, place, time

3 Ws: who, where, when

Descriptive Epidemiology

137
Q

descriptive epidemiology

A

who, where, when

used to know if location is experiencing disease occurance more frequently than usual

138
Q

surveillance systems

A

passive

active

syndromic

139
Q

passive surveillance system

A

relies on healthcare system for required reportable diseases

140
Q

active surveillance systems

A

public health officials into community to search for new cases

141
Q

syndromic surveillance system

A

pre-defined symptoms reported or evaluated

bio survelliance

certain symptoms connected to bad diseases

142
Q

case definition

A

most critical element that must be defined

(diagnostic criteria)

*set of unifrom criteria used to define a diseas for public health survellance

change over time as learn more about disease

must accurately define & execute how

confirmed v. probable

143
Q

CSTE

NNDSS

A

councile of state and territorial epidemiologists

CDC’s national notifiable diseases surveillance system

144
Q

classic epi curve

A

visual depiction- represent data (derived from line table)

who when where

1.pattern of spread (shape)

  • common or point source (continuous & intermittenet)
  • propagated source
  1. magnitude of impact
  • sentinel case/ peak/ outliers
  • time trends
  • start/stop/duration

helps to form hypothesis

145
Q

occurrence of disease clearly in excess of normal expectancy

increased # of disease above what is customary

A

epidemic

146
Q

epidemic limited to a localized increase in occurrence of disease

-more concentrated

A

outbreak

(cluster)

147
Q

constant presence of a disease within a given area/pop. in excess of normal in other areas

always high for area

ex: HIV in africa

A

endemic

148
Q

epidemic occuring over a very wide area involving a large number of people

epidemic across globe

(1918 flu; swine flue h1n1)

A

pandemic

149
Q

epi curve pattern of spread: common or point source (continous & intermittent)

A

not person-to-person

from a common, single point source for the outbreak

150
Q

epi curve pattern of spread: propagated

A

person-to-person spread

151
Q

epi curve magnitude of impact

A

sentinal case/ peak/outliers

time trends (rate of occurance)

start/ stop/ duration

152
Q

epi curve helps to form thypotheses on:

A

routes of transmission

probable exposure periods

incubation period (help id/eliminate causes)

153
Q

one blob of increasing and decreasing disease

A

Common or point source outbreak

continous

NOT repeated or propagated (or spread)

NO sentinel/ index case (ppl together and exposed to something common) (no blob early as index/start case)

154
Q

common location, see single pop up

A

common or point source

outbreak continuous

NOT repeated or propagated

Semtinel/index case

155
Q
A

common or point source

intermittent outbreak

IS repeated

propagated (persontoperson or source of illness still there)

ex: local lake, getting same exposure over and over

156
Q
A

propagated transmission

infected subjects infect others who spread infection

Index case

(see incubation period)

157
Q

incubation period

A

(dont know when building retrospectivley, can change time)

build from initial exposure

NO index case bc initial exposure not a person

ex: state fair - what did you eat? rides? ??

158
Q

probable exposure period

A

if know minimum & maximum incubation time- can build probable exposure period

ex: 6 days +/-3

rubella from disney

further back in time, harder for ppl to recall

159
Q

(relative) measures of disease frequency

A

Ratios

proportions

rates *time period important, need all same to compare

160
Q

divison of 2 unrelated numbers

(num not part of denom)

A

ratios

161
Q

division of 2 related numbers

num is part of denom

A

proportions

162
Q

division of 2 #s with time in denom

A

rates

163
Q

gender surgical ratio-general

A

ratio of female COB to male COB who have undergone surgical procedure

female/# male

164
Q

scientific academic acumen

A

proportion of students with undergrad science gpa 4.o

students with 4.0/all students

165
Q

biopsy ratio

A

female biopsy/# female students

166
Q

natural history of disease timeline

A

stage of susceptibility

stage of subclinical disease

stage of clinical disease

stage of recovery, disability or death

167
Q

factors in comparing measures of disease frequency between groups

A
  1. # people affected (frequecy)
  2. size of source pop. or at risk
  3. length of time

** need to standardized, similar denominator

168
Q

Comparing populations examples

breast cancer in 2 counties

100 cases in A

75 cases in B

population A 50,000

population B 5,000

A

which country appears to have most cancer? A

which county appears to have a higher rate? (rate= time in demon)

which appears to have a higher rate? (missing time)

100/50000=.2% vs. 75/5000=1.5% B

Cases in A 1 yr Cases in B 3 years which higher rate of cancer? MAKE SAME DENOM.

B

169
Q

disease rate

A

of events/ equal # person years

standard baseline comparison= standardized rate

170
Q

rates of disease MUST

A

population size & time period

must be equal to adeuately & appropriately compare frequencies btw grps

171
Q

incidence

A

INclude

inclusion of new cases

calc. those at risk

172
Q

Prevalence

A

existing cases of disease + new cases of disease

PREVious

take everyone

173
Q

indicence & prevalence are both

A

proportions & factors in the at risk or base population (denom)

174
Q

measures of disease frequency: incidence

A

risk, attack rate, cimulative incidence

new cases of disease/ 3 persons at risk for the disease (or in pop)

  • num and denom same time frame
  • NOT dynamic populations

-subtract immunized grp from total then take out those who already got disease
***denom only those at risk

ex: attack rate of nausea & vomitting= everyone N&V/ate pizza

175
Q

dynamic populations (fluctuations) denom (risk grp) used as:

A

pop at start of year

average pop over the year

pop at mid year

**** subtract out already diseased or immune

176
Q

new cases of disease/person-time at risk for disease

A

incidence rate

-useful when everyone followed the same amount of time

177
Q

person-time

A

100 person years: contributed 100 person years to population

100ppl for 1yr, 10ppl for 10yrs; 1 person for 100yrs; 25ppl for 4yrs each

4o pack years: packs/day X #yrs

178
Q

incidence density

A

new cases/ TOTAL PERSON-TIME of pop at risk

fluid time

contribute to denom

179
Q

* ? repeated disease

A
180
Q

measures of disease frequency: prevalence

A

existing cases of disease/# persons in pop

num & denom time frames same

denom: with disease + at risk

point-prevalence (pt in time- Dec 31st) & period-prevalence (period of time- 1 yr)

181
Q

practice- gonorrhea

A

incidence rate/100,000: 297/100,000

733151 new/246552000 pop X 100,000= 297

incidence rate/1,000: 2.97/1000

733151 new/246552000 pop X 1000= 2.97

182
Q

*practice-gastroenteritis

Of 46 that got sick, 18 ate tuna salad, 5 ate chocolate. attack rate of chocolate?

A

attack rate: 46/75= 61%

chocolate attack rate: 5/75???

183
Q

Seven cases of Hep A among 70 kids attending a child care center.Each infected child from different family. The total number of persons residing in the homes of the 7 affected families was 32. One incubation period later, 5 family members of the 7 infected children also developed hepatitis A

A

secondary attack rate:

total–> 32

  • 7 <– already have

= 25 (denom) at risk

new cases–> 5 (nem)

5/25=20%

184
Q

practice-death

A

incidence rate of death:

2 deaths/19 person years = 0.11 deaths/ (1) person years

11 deaths/100 person years

185
Q

practice- incidence of disease

A

incidence of disease from 1990-1996:

NO TIME IN INCIDENCE JUST RATE

so cases/population= 2/6= .33=33%

186
Q

incidence rate of disease

A

cases/ person time= 2/ (2+2+3+7+2+6)= 22 –> 1/11 =.09/person years

or 9/100 person years

187
Q

*** ? practice- incident: just count; incidence: new cases/total pop; incidence rate: time (person years)

calc incidence of disease after Oct 1?

of 21 ppl, 4 already had at start, 7/21 vaccincated?

how many incident cases of death occurance?

cures?

what is denom in calc prevelance of disease on April 1st?

period prevalence of disease the whole year?

point prevalence Oct 1?

April 1?

A

incidence of disease: case/total pop= 6/? 21-4= 17ppl

21-4-7= 10ppl (denom)

death occurance: 5 deaths in 1 year

cures: 2cures in 1 yr
prevelance: 21 (everyone in study)
period: 21 (ever been pt of group)

point whole yr: (cases at pt) new + existing/ all cases= 10/21=.48

pt oct: 2/21

pt april: 6/20

188
Q

rates

A

person time

189
Q

practice- STD

A

period prevelence: 2 months

180cases/300 total= .60=60%

190
Q

father of epidemiology

A

John Snow

191
Q

public health-discipline basic science studies

distribution

determinants

of disease in populations to control disease and illness and promote health

A

epidemiology

subspecialties- disease, exposure, population, combined

192
Q

differences in disciplines

A

basic science: cells tissues biochem proccesses understanding disease mech/ pharam, microbio

clinical science: sick pts/ improving diag. and tx, internal med

public health: populations/communities/ preventing disease and promo health/ epidemiology policy and mgmt

193
Q

epidemiolgist

A

surveillance and descriptive epi

inference

analytical epi

community intervention

194
Q

objectives in Epi

A

id patterns in disease occurence

det. extent of disease

id causes of or risk factors

natural course of disease

evaluate effectiveness of measures

public health policy

POPULATIONS

195
Q

Epidemiological assumptions

A

disease occurence is NOT RANDOM

SYSTEMATIC INVESTIGATIONS of different populations (method)

MAKING COMPARISONS (data)

196
Q

distribution of disease

A

frequencies of disease occurences

  • counts in relation to size

patterns of disease occurences

  • person place time

DESCRIPTIVE EPI
who where when

197
Q

determinants of disease

A
  • factors of susceptibility/ exposure/ risk
  • etiology/ cause of disease
  • mode of transmission (source)
  • social/environmental/biologic elements that determine occurrence/presence of disease

ANALYTICAL EPI

Why, How

198
Q

core functions of epi

A

public health surveillance

field investigation analytical studies

evaluation

linkages

policy development

199
Q

Public health surveillance

A

to portray the ongoing patterns of disease occurrence so that investigation, control, and prevention measures can be developed and applied

  • registries
    skills: data collection, data mgmt, DATA INTERPRETATION, presentation
200
Q

field investigation

A

to determine the sources of vehicles of disease or to learn to simply learn more about the natural history, clinical spectrum, descriptive epi, & risk factors of disease

ex: ebola, SARS

201
Q

analytical studies

A

to advance the info (hypotheses) generated by descriptive epi techniques

comparison group

skills: design, conduct, analysis, cmun findings

202
Q

evaluation

A

determine, systematically & objectively, the relevance, effectiveness, efficiency and impact of activities with respect to established goals

203
Q

linkage

A

to collaborate (link) with other professionals

204
Q

policy development

A

provide input, testimony, & rec. regarding disease control and prevention strategies, reportable disease regulations and health care policy

205
Q

the epi approach

A

counting (frequencies): cases/events and describing person, place & time
*case/study definitions!

dividing (percentages): 3 cases/ denom to calc rates, ratios, & proportions *Standardized and unstandardized pop. sized

comparing: change in disesase over time, absolute & relative differences, statistical differences

Epidemiology (1 decks)

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