Epidemiology Flashcards

(41 cards)

1
Q

Define epidemiology:

A

A public health discipline basic science which studies the DISTRIBUTION and DETERMINANTS of disease in POPULATIONS to control disease and illness and promote health

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2
Q

Define the core function of epidemiology: pubic health surveillance

A

Surveying populations to find patterns of disease occurrence

  • Data interpretation is a key skill, other key skills are designing & using data collection instruments, data management, and scientific writing and presentation
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3
Q

Define syndromes surveillance system:

A

Looks for predefined signs/symptoms of patients related to track able but rare diseases/conditions

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4
Q

Define epidemic:

A

Occurrence of disease clearly in excess of normal expectancy

Spans greater than a localized outbreak

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5
Q

Define Passive Surveillance System

A

Relies on healthcare system to follow regulations on required reportable diseases

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6
Q

Define active surveillance system:

A

Health officials go into communities to search for new disease cases

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7
Q

Define Outbreak:

A

An epidemic limited to a LOCALIZED increase in occurrence of disease (aka cluster)

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8
Q

Define Endemic:

A

The constant presence of a disease within a given area or population in excess of normal levels in other areas

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9
Q

Define Pandemic:

A

An epidemic spread world-wide (global health)

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10
Q

Define absolute difference:

A

A subtraction

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11
Q

Define relative difference:

A

A ratio

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12
Q

Define incidence:

A

New cases of disease

[aka Risk, aka Attack Rate, aka Cumulative Incidence]

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13
Q

Define prevalence

A

Existing cases of disease + new cases of disease

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14
Q

What causes prevalence to decrease?

A

If someone is cured or dies (removed from the pool)

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15
Q

What causes prevalence to go up?

A

When someone receives therapy to stay alive with the disease, such as insulin to treat diabetes

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16
Q

How do you calculate incidence?

A

(# of new cases of illness)/(# of people at risk of illness or in population)

[always subtract out (from the starting population, if known), those who’re not ‘at risk’ (already have the disease/outcome or are immune)]

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17
Q

What can incidence also be referred to as?

A

Risk, attack rate, or cumulative incidence

18
Q

What is the equation used to calculate prevalence?

A

of existing cases of disease/# of persons in population

19
Q

Define absolute difference:

A

A subtraction of 2 frequencies

Ex. 45 surgeries in males and 17 surgeries in females… 45-17 = 28, males had 28 more surgeries OR 17-45 = 28 (absolute value), females had 28 fewer surgeries

20
Q

Define Relative difference:

A

A division of frequencies or proportions

21
Q

Give an example of a relative difference frequency using 45 surgeries and 17 surgeries in females

A

45/17 = 2.6X the number of surgeries for males than females

OR

17/45 = 38%; Females had only 38% of the number of surgeries as males did

22
Q

Give an example of a relative difference proportion using 45 surgeries in males and 17 surgeries in females

A

45 males with surgeries/ total number of males = X% surgeries in males

OR

17 women with surgeries/total number of women = X% surgeries in women

23
Q

Define risk

A

Probability of outcome in an individual group

24
Q

Explain how to calculate risk using smokers and nonsmokers as your example:

A

(# smokers with disease)/(total # of smokers)

OR

(# of nonsmokers with disease)/(total # of nonsmokers)

25
Define absolute risk reduction (ARR):
The risk difference of the outcome attributable to exposure difference between groups
26
How do you calculate absolute risk reduction (ARR)?
Good outcome% - bad outcome% = X% The ARR tells you that for example the drug tested works X% better than the placebo
27
How is the relative risk reduction (RRR) calculated?
(AAR)/(R of the unexposed) Ex. AAR/outcome of placebo = X%, so the drug lowers your risk of disease by X%
28
Define the number needed to treat/number needed to harm
Number of patients needed to be treated to receive the stated benefit or harm: 1/ARR = # needed to treat Ex. Say ARR = 3.8% so, 1/.038 = 27 patients would need to be treated with the drug to reduce 1 disease/death case **Answer MUST be a whole number!!**
29
Define Risk Ratio (RR or relative risk):
Ratios of the risks from 2 different groups | Risk of outcome in exposed)/(Risk of outcome in the non-exposed
30
What do risk, odds, and hazard ratios all have in common?
- All compare 2 groups and describe the likelihood of an event/outcome in 1 group compared to another
31
What does it mean if a risk, odds, or hazard ratio is equal to 1?
The event/outcome is equally likely for both groups being compared
32
What does it mean if a risk, odds, or hazard ratio is >1?
The event/outcome is MORE likely to occur in the comparison group
33
How is a risk, odds, or hazard ratio interpreted if it is >/= 2?
Use the phrase "X" times greater for interpretation - The comparator group has X times greater odds of getting the disease
34
What are the 3 things to look for when interpreting ratios?
1) Group comparison orientation (drug vs. placebo or placebo vs. drug) 2) Direction of words (increased or decreased) 3) Magnitude (80% (1.8 times) or 20% (.8 times))
35
Define Odds
Frequency of an outcome occurring vs not occurring - The frequency of exposure vs frequency of not being exposed [in a 2x2 table, divide A/C or B/D]
36
What is the simplest way (shortcut) to calculate an odds ratio?
Cross multiply | A X D)/(B X C
37
Attributable risk is also known as ______
Absolute risk reduction (ARR) [defines the risk difference of the outcome attributable to exposure difference between groups]
38
Calculation of incidence rate
of new cases of illness/person-time of people at risk of illness [person-time: 100 person-years is 100 people followed for 1 year each; 10 people followed for 10 years each; 25 people followed for 4 years each, etc.]
39
How would you interpret a RR, OR, or HR of 1.8?
80% increased risk/odds/hazard
40
How would you interpret a RR of 0.25?
75% decreased risk/odds/hazard
41
T/F: When looking at the confidence interval for a ratio, if BOTH values are on the SAME side of 1.0 (equality) — it is ALWAYS statistically significant!
True