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2025 PHPM Royal College Study - based on AN Summary Notes > Epi & biostats - part 1 > Flashcards

Epi & biostats - part 1 Flashcards

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1
Q

what is confounding (2)

A

1- a variable other than the main one you’re studying is associated with both outcome and exposure
2- this distorts the true relationship between exposure and outcome

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2
Q

is a confounder on the causal pathway between exposure and outcome? (1)

A

1- NO - a confounder is NOT on the causal pathway

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3
Q

what is a simple example of an exposure-confounder-outcome triad? list the three (1, 2, 3) and give brief explanation of relationship (4)

A

1- exposure - coffee drinking
2- confounder - smoking
3- outcome - lung cancer
4- we’re trying to see whether there is a relationship between coffee drinking and lung cancer; at the outset there may seem to be, but if you stratify by smoking status, you’ll see that in smokers and in non-smokers, coffee drinking does not lead to lung cancer

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4
Q

how do you control for confounding through study design (3)

A

1- randomization
2- restriction
3- matching

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5
Q

what is randomization as a way of controlling for confounding (1)

A

1- it is a selection method that decides who is exposed and who is unexposed

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6
Q

what is restriction as a way of controlling for confounding (1)

A

1- method where you limit enrollment based on known confounders (e.g. do not include alcohol drinkers in study)

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7
Q

what is matching as a way of controlling for confounding (1)

A

1- match based on potential confounders (e.eg. match cohort based on age)

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8
Q

how do you control for confounding through data analysis (3)

A

1- stratification
2- standardization
3- multivariate analysis

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9
Q

what is stratification as a way of controlling for confounding (2)

A

1- stratification by particular variable (e.g. age, sex)
2- can tell whether or not the relationship between an exposure and outcome is due to a confounder

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10
Q

what is standardization as a way of controlling for confounding (2)

A

1- can standardize by age or sex, commonly
2- adjusting results to remove the effect of a characteristic responsible for differences in comparison

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11
Q

what is multivariate analysis as a way of controlling for confounding (1)

A

1- you can control for multiple confounders using regression models

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12
Q

what is effect modification (1)

A

1- the concept that the magnitude of effect between exposure and outcome is modified by a third variable

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13
Q

what is an example of effect modification (2)

A

1- smoking is an effect modifier for the effect of radon on lung cancer
2- smokers exposed to radon have a higher risk of lung cancer than non-smokers exposed to radon

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14
Q

what is a causal association (1)

A

1- it is a real association where change in exposure produces a change in outcome

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15
Q

what is a not-causal association (2)

A

1- a real association where change in exposure does not necessarily produce a change in outcome
2- an initial apparent association may be due to confounding factor

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16
Q

what is a spurious association (1)

A

1- a false association due to various causes of bias, or simply due to chance

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17
Q

what are koch’s postulates - background (AGIR)(2)

A

1- they are 4 criteria used to establish a causative relationship between a microbe and disease
2- the 4 criteria are AGIR = association, grown & isolated, inoculation, re-isolation

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18
Q

what is koch’s postulate ‘association’ (1)

A

1- association: microorganism must be found in abundance in those who have disease, but should not be found in healthy groups

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19
Q

what is koch’s postulate ‘grown and isolated’ (1)

A

1- isolated microorganism from a diseased host can be grown in pure culture

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20
Q

what is koch’s postulate ‘inoculation’ (1)

A

1- cultured microorganism can cause disease when introduced into a healthy host

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21
Q

what is koch’s postulate ‘re-isolated’ (1)

A

1- the microorganism of interest must be re-isolated from the inoculated, diseased experimental host

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22
Q

what are the 9 Bradford hill criteria of causality (C-TB-SPACES) (9)

A

1- consistency
2- temporality
3- biological gradient
4- specificity
5- plausibility
6- analogy
7- coherence
8- experiment
9- strength

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23
Q

which of the bradford hill criteria is the only necessary criteria for causality (1)

A

1- temporality

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24
Q

bradford hill causality criteria: what is strength (1)

A

1- larger the effect size, the more likely the association is causal

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25
Q

bradford hill causality criteria: what is specificity (1)

A

1- a single risk factor consistently relates to a single effect

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26
Q

bradford hill causality criteria: what is plausibility (1)

A

1- the effect must have biologic plausibility

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27
Q

bradford hill causality criteria: what is analogy (1)

A

1- when one causal agent is known, a second, similar agent may cause same or similar disease

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28
Q

bradford hill causality criteria: what is consistency (1)

A

1- the reproducibility of association in various populations and situations

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29
Q

bradford hill causality criteria: what is coherence (1)

A

1- any new data should not be in opposition to the current evidence

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30
Q

bradford hill causality criteria: what is experiment (1)

A

1- association can be demonstrated in experimental evidence

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31
Q

bradford hill causality criteria: what is temporality (1)

A

1- exposure must always precede outcome

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32
Q

bradford hill causality criteria: what is biological gradient (1)

A

1- a causal relationship is more likely if a dose-response gradient is demonstrated

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33
Q

what is a proportion (1)

A

1- the fraction or percentage one quantity makes up of another quantity

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34
Q

is the numerator included in the denominator in a proportion? (1)

A

1- yes - numerator is part of denominator in a proportion

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35
Q

what is an example of a proportion (1)

A

1- number of canadians with cancer divided by the total population

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36
Q

what is a ratio (1)

A

1- the comparison of one quantity to another

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37
Q

is the numerator included in the denominator in a ratio? (1)

A

1- no - numerator is not included in the denominator of a ratio

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38
Q

what is an example of a ratio (1)

A

1- male to female ratio in a class

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39
Q

what is a rate (1)

A

1- Measure of the frequency with which an event occurs in a defined population in a defined time

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40
Q

what is included in the denominator of ‘rate’ which is not part of the denominator for ‘ratio’ or ‘proportion’ (1)

A

1- time is in the denominator for rate, and not for ratio or proportion

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41
Q

what is an example of a rate (1)

A

1- number of deaths per hundred thousand Canadians in one year

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42
Q

what is crude rate (1)

A

1- overall rate for a defined population without adjustment for confounders

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43
Q

what is adjusted rate (1)

A

1- summary rate that has been statistically modified to remove the effect of one or more confounding factors

44
Q

what is incidence rate (1)

A

1- Number of new cases of a disease that occur during a specified period of time in a population at
risk of developing the disease

45
Q

how can incidence rate be measured (3)

A

1- frequency count
2- proportion of population at risk
3- rate per unit of time

46
Q

what is prevalance (1)

A

1- Proportion of people with a condition divided by total number of persons at risk of the condition in
the population at that time

47
Q

what are factors that influence prevalance (6)

A

1- incidence
2- duration that prevalence is being measured over
3- increased case fatality
4- differential migration of people with or without disease
5- changes in diagnosis/definition of disease
6- changes in reporting or case finding

48
Q

what is attack rate (1)

A

1- number of people who become ill over the number of people exposed, or proportion of exposed persons who become infected

49
Q

over what time period is an attack rate generally calculated (1)

A

1- The time period may not be indicated, but would typically refer to the period of the outbreak

50
Q

what is the calculation for attack rate (1)

A

1- Calculation: AR = number of new cases / number of persons at risk

51
Q

for a measure of association (RR, OR, etc.), what does the strength of association indicate? (3)

A

1-
< 1.0 = Exposure associated with decreased risk of outcome
2-
1.0 = No association between exposure and outcome
3-
> 1.0 = Exposure associated with increased risk of outcome

52
Q

what is absolute risk (AR) (1)

A

1- Incidence of a disease in a population

53
Q

how would you calculate absolute risk of disease in the exposed as per 2x2 table(1)

A

1-
AR in exposed (Ie) = a/a+b

54
Q

how would you calculate absolute risk of disease in the unexposed as per 2x2 table (1)

A

1-
AR in unexposed (Iu) = c/c+d

55
Q

what is relative risk/RR (or risk ratio) (1)

A

1- Ratio of the absolute risk of disease among the exposed group to the absolute risk of the disease
among the non-exposed group, i.e. RR = Ie / Iu

56
Q

what does relative risk indicate (1)

A

1- Quantifies how much more (hazard) or less (protective) exposed persons develop an outcome

57
Q

how do you calculate relative risk, as per 2x2 table (1)

A

1- Calculation =
1 - RR = Ie / Iu = (a/a+b) / (c/c+d)

58
Q

how would you interpret, for example, a relative risk of 2.5 for smoking (exposure) and cancer (outcome) (1)

A

1-
if RR = 2.5 for smoking and cancer, smokers are 2.5 times more likely to develop cancer than non-smokers

59
Q

what is relative risk reduction (RRR) (1)

A

1- Relative decrease in risk of an event in the exposed group compared to the unexposed group

60
Q

how do you calculate relative risk reduction (1)

A

1- Calculation = (Ie - Iu) / Iu

61
Q

what is odds ratio (2)

A

1- Probability of an event occurring relative to it not occurring
2- When a health outcome is rare, OR approximates RR

62
Q

how do you calculate odds ratio (1)

A

1-
ad/bc

63
Q

what is Attributable Risk (AR) (i.e. Risk Difference (RD), or Absolute Risk Reduction (ARR)) (1)

A

1- Number or incidence of cases of disease among exposed individuals that can be attributed to that exposure

64
Q

does attributable risk consider exposed, unexposed or total population (1)

A

1- attributable risk considers exposed population only

65
Q

what is the purpose of attributable risk (1)

A

1- it is a measure of the potential for prevention of disease if the exposure could be eliminated

66
Q

how do you calculate attributable risk (1)

A

1-
Ie - Iu

67
Q

how would you interpret an attributable risk (1)

A

1- if AR is X, smokers had X number of additional cases per 100 participants compared to non-
smokers

68
Q

what is number needed to treat (NNT) (1)

A

1- Number of patients who need to be treated to prevent one additional bad outcome

69
Q

how do you calculate number needed to treat (1)

A

1- Calculation =
1 / ARR (or attributable risk)

70
Q

what is Attributable Risk Percent (AR%) (i.e. Attributable Fraction (AF), or Attributable Risk in Exposed) (1)

A

1- Percentage or proportion of cases of a disease among exposed individuals that can be attributed to
that exposure as a percentage/fraction

71
Q

does attributable risk percent consider exposed, unexposed or total population (1)

A

1- 1- attributable risk percent considers exposed population only

72
Q

how do you calculate attributable risk percent (1)

A

1-
Calculation =
(Ie - Iu) / Ie

73
Q

how would you interpret attributable risk percent (1)

A

1- if AR% is X, among smokers, X percent of lung cancers were attributable to smoking

74
Q

what is Population Attributable Risk (PAR) (1)

A

1- Number or incidence of cases of disease in the population that can be attributed to an exposure

75
Q

does population attributable risk consider exposed, unexposed or total population (1)

A

1- Considers both those exposed and unexposed in
the entire population

76
Q

what is the purpose of population attributable risk (1)

A

1- to determine the number of cases of
disease that would not occur in a population if the
factor were eliminated or no one was exposed

77
Q

how do you calculate population attributable risk (1)

A

1-
Calculation: PAR = Itotal – Iunexp

It = # disease total / population, i.e. (a+c/n)

Iu = # disease in unexposed / # unexposed i.e. (c/c+d)

78
Q

how do you interpret population attributable risk (1)

A

1- if PAR = X, there were X number of additional
cases of lung cancer per 100 study participants
attributable to smoking

79
Q

what is population attributable risk % (or population attributable fraction, PAF) (1)

A

1- Percentage or proportion of cases of disease in
the population that can be attributed to an exposure

80
Q

does population attributable fraction consider exposed, unexposed or total population (1)

A

1- Considers both those exposed and unexposed in
the entire population

81
Q

what is the purpose of population attributable fraction (1)

A

1- determine proportional reduction in population disease that would occur if an exposure to a risk factor were reduced to an alternative ideal
exposure scenario (e.g., no tobacco use)

82
Q

how do you calculate population attributable fraction (2)

A

1-
Calculation =
(IT – IU) / IT

2-
Alternative =
[Pexp(RRexp -1)] / [(Pexp(RRexp -1) + 1]
where RR = relative risk
Pexp = probabilty of exposure (a+b)/n

83
Q

how do you interpret population attributable fraction (1)

A

1- in the study population, X percent of lung cancer is attributable to smoking

84
Q

what is sensitivity (true positive rate, SnOUT) (1)

A

1- Ability of a test to identify correctly those who have the disease when disease is present

85
Q

how do you calculate sensitivity (1)

A

1- Calculation =
TP / (TP + FN)

86
Q

how do you interpret sensitivity - when sensitivity is high (1)

A

1- If sensitivity is high there are a high number of True Negatives and the test can rule out disease - you rarely miss people with the disease

87
Q

how do you interpret sensitivity - when sensitivity is low (1)

A

1- If the sensitivity is low the test may mistakenly classify people who do actually have the condition as not having it (False Negatives)

88
Q

how do you calculate the false negativity rate (1)

A

1-
False negativity rate = 1 - sensitivity

89
Q

for what purpose is a highly sensitive test good for (1)

A

1- highly sensitive tests are good as screening tests

e.g., COVID-19 testing would benefit from high sensitivity so all potential cases can be isolated quickly, even if that means briefly isolating those who do not have the disease until follow-up test results return

90
Q

what is specificity (rue negative rate, SpIN) (1)

A

1- Ability of a test to correctly identify those without a
disease when disease is absent

91
Q

how do you calculate specificity (1)

A

1- Calculation =
TN / (FP + TN)

92
Q

how do you interpret specificity - when specificity is high (1)

A

1- If specificity is high there are high number of True
Positives and the test can rule in a disease
- Rarely misclassify people as having the disease

93
Q

how do you interpret specificity - when specificity is low (1)

A

1- If the specificity is low the test will mistakenly
classify people who do not actually have the condition as having it (False Positive)

94
Q

how do you calculate the false positivity rate (1)

A

1-
False positive rate = 1 – specificity

95
Q

for what purpose is a highly specific test good for (1)

A

1- highly specific tests are good confirmatory tests

e.g., after a patient screens positive for HIV on a rapid test, the confirmatory test should be highly specific to ensure that the person is not given a false positive diagnosis of a serious illness

96
Q

what is positive predictive value (PPV) (1)

A

1- Probability that an individual with a positive test
actually has the disease

97
Q

how do you calculate positive predictive value (1)

A

1-
Calculation =
TP / (TP + FP)

98
Q

what happens to positive predictive value as the prevalence of a disease increases (1)

A

1- PPV increase as prevalence increases

99
Q

what is Negative predictive value (NPV) (1)

A

1- Probability that an individual with a negative test
does not have a disease

100
Q

how do you calculate negative predictive value (1)

A

1-
Calculation =
TN / (TN + FN)

101
Q

what happens to negative predictive value as the prevalence of a disease increases (1)

A

1- NPV increases as prevalence decreases

102
Q

what is a likelihood ratio (1)

A

1- Combines sensitivity and specificity to indicate “by how much having a test result will reduce the uncertainty of making a given diagnosis”

103
Q

what is Positive likelihood ratio (LR+) (1)

A

1- expresses how
much a positive test result increases the odds that a patient has the disease

104
Q

how do you calculate positive likelihood ratio (1)

A

1-
Calculation = sensitivity / (1 – specificity)

105
Q

what is Negative likelihood ratio (LR-) (1)

A

1- expresses how
much a negative test result decreases the odds that a patient has the disease

106
Q

how do you calculate negative likelihood ratio (1)

A

1- Calculation =
(1 – sensitivity) / specificity

2025 PHPM Royal College Study - based on AN Summary Notes (10 decks)

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