Surveillance and Epidemiological Investigation (22 questions)

Question 1

Quantitative

Answer 1

Generates numerical data, represents counts or values. Seeks to establish casual relationships between two or more variables- uses statistic methods to test strength and significance

Question 2

Cross-sectional study

Answer 2

Survey of INDIVIDUALS that assesses both exposure and disease at the same time.

examine outcome and risk factors in a population group at one point in time. Provide “snapshot” (Prevalence, correlation or survey studies)

Question 3

Case-control study

Answer 3

assemble group of individuals with disease as well as a comparative group without disease; then investigate proportions with the exposure of interest in each group.

examine population of individuals with and without an OUTCOME of interest, studied for exposure to one or more risk factors.
Quicker, less expensive and easier

Calculate odds ratio

Ex: Wanting to figure out source of outbreak

Question 4

Cohort study

Answer 4

assemble a group of individuals with an EXPOSURE and a comparative group without. Ensure all individuals are free of disease at start of the study, then follow this group over time investigating the proportion that subsequently develop disease in each group.

same of individuals with and without EXPOSURE to potential risk factor who are followed for incidence of outcome in each group
less pt selection and stronger evidence of casual association

Calculate Relative risk/risk ratio

Question 5

Qualitative

Answer 5

based on description and observation. for theory verification

Question 6

Rate

Answer 6

x/ y x k

x= numerator (# times event occurred)
y = denominator, population (ex. # pt at risk)
k = constant used to transform results of division into uniform quality

Question 7

Incidence rate

Answer 7

a measure of the frequency with which new cases or events occur among a population during a specified period.

NEW cases/(pop at risk x constant)

ex: new cases/ total patient days x constant

Question 8

Attack Rate

Answer 8

new cases/#exposed to risk factor

Type of incidence rate

(# new cases/pop at risk) x100

new cases/ # people exposed

Question 9

Prevalence Rate

Answer 9

of EXISTING cases/ pop at risk x 100

Question 10

Mortality rate

Answer 10

(# of deaths/pop at risk) x 10,000

crude= all causes of death
cause specific = rate from a certain disease

Question 11

SIR

Answer 11

observed/predicted

Question 12

Sensitivity

Answer 12

% of true positive
a/(a+c) x100

TP/ (TP+FN)

true positives/ # with outcome x 100%

If someone has the outcome, what is the likelihood the test will be positive?

Question 13

Specificity

Answer 13

true negatives/# individuals without outcome x 100%

% of true negatives
d/ (b+d) x 100

TN/ (TN+FP)

If someone does not have the outcome, what is the likelihood the test will be negative?

Question 14

Positive predictive value

Answer 14

true positives/ # individuals with positive result (TP+FP) x 100%

If the test result is positive, what is the likelihood that the person truly has the outcome?

Question 15

Negative predictive value

Answer 15

true negatives/ # with neg result x 100%

If the test result is negative, what is the likelihood that the person truly does not have the outcome?

Question 16

Validity

Answer 16

the degree to which a screening test or other data collection tool measures what it is intended to measure.

Question 17

P value

Answer 17

between 0 to 1:

helps determine significance of results
- small P value (≤ 0.05) strong evidence against the null, reject null hypothesis. statistically significant.
- Large p value (>0.05) weak evidence against null hypothesis, fail to reject null. Not enough evidence to suggest null is false.

Question 18

Power

Answer 18

probably that you will reject the null hypothesis when you should

The power of a test is its ability to detect a specified difference
(e.g., the probability of rejecting the null hypothesis when it is false). The
power of a hypothesis test is affected by three factors:
1. Sample size (n). In general, the greater the sample size, the greater
the power of the test.
2. Significance level (α). The higher the significance level, the higher
the power of the test.
3. The “true” value of the parameter being tested. The greater the
difference between the “true” value of a parameter and the value
specified in the null hypothesis, the greater the power of the test.
That is, the greater the effect size, the greater the power of the test.

Question 19

Confidence interval

Answer 19

estimated range of values likely to include an unknown pop parameter.
width gives an idea of how uncertain. Wide interval = more data needed
usually collected at 95%

Question 20

standard deviation

Answer 20

variability in values around the mean. Distance between data point and mean.
- positive deviation= greater than mean
- negative deviation= less than mean
- no deviation= equals mean
small= tight grouped, precise
large= spread out

emperical rule:
68% = 1 standard deviation from mean
95%= 2 standard deviation from mean
99.7= 3 standard deviation from mean

Question 21

Data normally distributed

Answer 21

Mean, Median and Mode are equal, bell shape curve

Question 22

? Deep SSI, 90 days

Answer 22

Craniotomy, coronary artery bypass graft

Question 23

Process measure

Answer 23

focuses on process that lead to specific outcome. Process measures are commonly used to evaluate
compliance with desired care or support practices or to monitor variation in
these practices (Ex: app abx usage, med errors, flu vaccination rates, sterilization quality assurance testing, compliance with iso protocols, TB skin testing, HH, environmental cleaning, communicable disease reporting, antimicrobial prescribing and administrations, maintaining barriers during constuction)

Question 24

Outcome measure

Answer 24

indicates the result of the performance (or non-performance) of a function or process (ex: HAI’s, infection or colonization of specific org, flu or TB skin test conversions, sharps injuries, decubitus ulcers, pt falls, CLABSI)

Question 25

Attributable risk percent

Answer 25

gives the proportion of cases attributable (and avoidable) to this exposure in relation to all cases. calculate in order to figure out diseases that could be prevented by eliminating exposure in the entire study population

(relative risk - 1) / relative risk

Question 26

precision of relative risk is related to

Answer 26

the power of the study. Power is affected by size of the effect and the size of study/sample size

Question 27

most common reservoir for highly pathogenic avian flu H5N1 virus

Answer 27

birds, esp domestic poultry (chicken, turkeys, geese, ducks)

Question 28

Casual Web

Answer 28

causation is essential concept in epidemiology. The web of causation refers to the interrelationship of multiple factors that contribute to the occurrence of disease

indirect and direct causes of disease may form a complex network of events that determines the level of disease in a community

Question 29

Higher prevalence of disease

Answer 29

will increase the PPV (positive predictive value) and decrease the NPV (neg predictive value)

Question 30

Most common cause of epidemic gastroenteritis worldwide and leading cause of foodborne outbreaks in US

Answer 30

Norovirus

Question 31

Attributable risk

Answer 31

Difference in rate of a condition between an exposed pop and unexposed
AR= IE - IU

IE (Incidence of exposed) = # exposed who get disease / total # exposed

IU (unexposed) = # unexposed with get disease / total # unexposed

Question 32

Odds ratio

Answer 32

probability of having a particular risk factor if a condition/disease is present divided by the probability of having the condition/disease is not present

ad / bc

2x2 table:
A| B
—–
C| D

Question 33

(p = 0.001) p value indicates
more evidence in support of which

Answer 33

The alternative hypothesis (reject null and accept alternative hypothesis)

Rationale: The p value is the probability of obtaining the observed sample
results (or a more extreme result) when the null hypothesis is actually true.
If the p value is small (≤ the significance level), it suggests that the observed
data is inconsistent with the assumption that the null hypothesis is true, and
thus that hypothesis must be rejected and the alternative hypothesis accepted
as true.

Question 34

Risk ratio or relative risk

Answer 34

Used in cohort studies

% Risk of outcome in group WITH exposure/ risk of outcome of group WITHOUT exposure

Incidence proportion= cases/ exposed or unexposed. Use this to calculate % of With and without exposure

Question 35

Type I error

Answer 35

rejecting the null hypothesis when it is true

p value = probability of type 1 error

A Type I error occurs when one rejects the null hypothesis (H0)
when it is true. This is also called a false-positive result (as we incorrectly
conclude that the research hypothesis is true when in fact it is not). The p
value or calculated probability is the estimated probability of rejecting the
null hypothesis of a study question when that hypothesis is true.

Question 36

Type II error

Answer 36

failing to reject a false null hypothesis

Question 37

What information will be needed to calculate a
CLABSI rate for the ICU?

Answer 37

BSI and # device days

Need device days, NOT # central line

The number of patients who had bloodstream infections
identified and the number of device days for the time period

X/Y x K
#BSI/device days x K

Question 38

superficial SSI

Answer 38

occur within 30 days-only skin or subcutaneous tissue.

In addition, one of the following must be met:
*Purulent drainage,
*Organisms isolated from an aseptically obtained culture of fluid or tissue

And patient has at least one of the following:
°° Purulent drainage from the superficial incision
°° Organisms isolated from an aseptically obtained culture of fluid or
tissue from the superficial incision
°° Superficial incision that is deliberately opened by a surgeon,
attending physician, or other designee*

And patient one signs or symptoms: pain
or tenderness, localized swelling, redness, or heat. A culture negative
finding does not meet this criterion.
°° Diagnosis of superficial incisional SSI by the surgeon or attending
physician or other designee

Question 39

Chi-square tests (χ2)

Answer 39

-To evaluate the effect of a variable on outcomes
-To calculate an odds ratio or relative risk
- If each cell of the table is greater than 5

Chi-square tests (χ2) can be used to test the association between
two classifications of a set of counts or frequencies (discrete data). This data
are commonly displayed as a contingency table or 2 x 2 table where rows
represent one variable and columns represent the other. The null hypothesis
is that there is no association between the two variables. Row and column
totals (marginal totals) are used to predict what count would be expected for
each cell if the null hypothesis were true. A test statistic is calculated from the
observed and expected frequencies. The larger the test statistic (for given
degrees of freedom) the more likely there is to be a statistically significant
association between the two variables. Chi-square tests are used for medium
to large samples (see Figure 4-1). The Fisher’s exact test is used in place of the
χ2 when the sample size number is less than 20 or any one cell in the table is
less than 5.
Figure 4-1. Formula for chi-square
χ2 =
(0-E)2 /
E
Where:
O = observed frequency
E = expected frequency

Question 40

The measure of central tendency most affected by outliers is

Answer 40

Mean

Question 41

Which statistical test is used when the data are small in numbers?

Answer 41

Fisher’s exact

Rationale: Fisher’s exact test is a statistical significance test used in the
analysis of contingency tables. Although in practice it is employed when
sample sizes are small, it is valid for all sample sizes.

Question 42

A measure of dispersion that reflects the variability in values around the mean is called the:

Answer 42

Standard deviation

Question 43

Steps to hypothesis testing include

Answer 43

• State the research question
• Specify the null and alternative hypotheses
• Calculate test statistic
• Compute probability of test statistic or rejection region
• State conclusions

Analysis and dealing with outliers is an important component of statistical analysis. Sometimes careful analysis of outliers, their removal, or weighting down
can change the conclusions considerably. Outliers should be investigated to
determine the optimal method of analysis.

Question 44

If the index of kurtosis is –1.99, then the curve is

Answer 44

Relatively flat

Rationale: Two terms are used to describe the shape of a frequency
distribution: “skewness” and “kurtosis.”
Kurtosis refers to how flat or peaked a curve is (see Figure 4-3):
* Leptokurtic is the more peaked curve. (+) positive numbers
* Mesokurtic is a typical bell-shaped curve or normal distribution. A value of 0
* Platykurtic is the flatter curve (-) negative numbers.

Question 45

The term for an extraneous variable that systematically varies
with the independent variable and influences the dependent
variable is a

Answer 45

Confounding variable
Rationale: A confounding variable is a variable that has an important
confounding effect on the result but is not among the variables being
studied. It can suggest a false relationship between variables, or it can
hide a relationship that exists.

Question 46

frequency polygon

Answer 46

useful for showing 2 sets of data on a graph, uses connecting lines and data points

Question 47

virus is the causative agent in
Kaposi’s sarcoma?

Answer 47

Human herpesvirus 8

Question 48

when the prevalence
of a disease is very low

Answer 48

The positive predictive value of a diagnostic test is lowered

Sensitivity and specificity are independent of prevalence of disease. PPV and
NPV are disease prevalence dependent. Generally a higher prevalence will
increase the PPV and decrease the NPV.

Question 49

Norovirus

Answer 49

Noroviruses (NoVs) are the most common cause of epidemic gastroenteritis worldwide and the leading cause of foodborne outbreaks in
the United States. Severe disease associated with NoV occurs most frequently among older adults, young children, and immunocompromised patients. NoV outbreaks occur year round, but activity increases in the United States duringthe winter months; 80 percent of reported outbreaks occur during Novem ber–April. Most NoV outbreaks are attributed to genotype GII.4, which evolves
rapidly over time.

Question 50

point source epidemics

Answer 50

exposed to same source over brief period of time (ex: meal or event)- transmission occurs through a vehicle (same person) rather than person-to- person

Question 51

criteria for causality

Answer 51

*The incidence of disease is higher in those who are exposed to the factor
*Evidence that the independent and dependent variables are related
*The association has been observed in numerous studies

Rationale: The criteria for causality are known as Hill’s criteria and use
epidemiological methods to determine whether a factor is causal for a given
disease. Hill’s criteria for causation are as follows:
1. Strength of association: The incidence of disease should be higher in
those who are exposed to the factor under consideration than in those
who are not exposed; that is, the stronger the association between an
exposure and a disease, the more likely the exposure is to be causal. For
example, lung cancer is common in those who smoke.
2. Consistency: This means that the association should be observed in
numerous studies, preferably by different researchers using different
research methodologies.
3. Specificity: Refers to an association between one factor and one
disease, and this association is more likely to be causal. This criterion
also refers to the extent to which the occurrence of one factor can be
used to predict the occurrence of another (disease). In reality, such a
one-to-one relationship is rare due to the multifactorial causes of most
diseases and because, sometimes, the same factor(s) can cause more
than one disease.
4. Temporality: This must also be addressed when determining cause of
disease. Essentially, exposure to the hypothesized causal factor must
precede the onset of disease.
5. Biological gradient: The biological gradient is a dose-response
relationship between increased exposure to a factor and increased
likelihood of disease. For example, the longer one smokes, the more
likely one is to develop lung cancer. If the association demonstrates a
biological gradient between the factor (exposure) and effect (disease),
the relationship is more likely to be causal.
6. Plausibility: The association in question should also be biologically
plausible in light of current knowledge. This criterion may be the most
elusive and variable of the nine. Because biological knowledge is ever
expanding, lack of biological plausibility does not necessarily disprove
a theoretical association.
7. Coherence: There should be coherence between known information
about the biological spectrum of the disease and the associated factor,
that is, the association should be in accordance with other facts known
about the natural history of the disease.
8. Analogy: Associations derived from experiments add considerable
weight to evidence supporting causal associations. These experiments
can be animal model studies or clinical trials; however, although animal
models may be helpful, many diseases do not manifest the same way in
animals and humans.
9. Finally, if similar associations have been shown to be causal, by analogy
the association is more likely to be causal. Determining causality may
also help to determine at which points the natural history of a disease
may be interrupted, so that prevention and control efforts are effective.
It can also add information on the natural history of a disease.

Question 52

scientific criteria for disease causation

Answer 52

Koch’s postulates consist of four points:

1. The organism must always be found with the disease, in accordance with the clinical stage observed.
2. The organism must then be grown in pure culture from a diseased host.
3. The same disease must be reproduced when a pure culture of the organism is inoculated into a healthy susceptible host.
4. The organism must then be recovered from the experimentally infected host.

A causal association should not be confused with causality, which requires a number of conditions to be met, one of which is the presence of causal associations