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NR 503 Epidemiology Final Exam Study Guide ( version-2) / NR 503 Epidemiology Final Exam Study guide(Latest 2020): Chamberlain College of Nursing (Already graded A)

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NR 503 Epidemiology Final Exam Study Guide ( version-2) / NR 503 Epidemiology Final Exam Study guide(Latest 2020): Chamberlain College of Nursing (Already graded A)

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NR 503
Study Guide for Final Exam
May, 2018


1. Objectives of epidemiology
a. Understand the aetiology or cause of a disease (risk factors)
b. Find out the extent that a disease or health problem affects a community or
population
c. Determine the natural history or prognosis
d. Evaluate existing and newly developed preventative therapeutic measures and
modes of healthcare delivery
2. Define, compare, calculate, and interpret Measures of Morbidity
a. Incidence rate: measurement of the number of new individuals who contract a
disease during a particular period of time
i. Calculation: Number of new cases of disease or injury during specified
period DIVIDED BY Size of population at start of period
b. Attack rate: Same as risk, proportion of individuals in a population (initially free
of disease) who develop the disease within a specified time interval.
i. Calculation: Total number of new cases DIVIDED BY The total population
c. Prevalence: measurement of all individuals affected by the disease at a
particular time
i. Calculation: Number of new cases of disease or injury during specified
period DIVIDED BY Time each person was observed, totaled for all
persons
d. These rates are used to measure disease occurrence and make comparisons
between population groups. They are commonly used measures that help our
understanding of the distribution of disease in a given population.
3. Understand why incidence data are important for measuring risk.
a. Incidence is important for measuring risk because it tells you the rate at which
new people are contracting the disease
4. Define, compare, calculate, and interpret Measures of Mortality
a. Mortality: a measure of the frequency of occurrence of death in a defined
population during a specified interval. Morbidity and mortality measures are
often the same mathematically; it's just a matter of what you choose to
measure, illness or death.
i. Calculation: Deaths occurring during a given time period DIVIDED BY Size
of the population among which the deaths occurred TIMES 10n
b. Cause-specific mortality rate: The mortality rate from a specified cause for a
population.
i. Calculation: The number of deaths attributed to a specific cause DIVIDED
BY The size of the population at the midpoint of the time period
c. Annual mortality rate: The rate of death in a one-year period.
i. Calculation: Deaths occurring within the one-year period DIVIDED BY Size
of population in which the deaths occured

, d. Case-fatality: the proportion of deaths within a designated population of "cases"
(people with a medical condition) over the course of the disease
i. Calculation: he number of deaths from a specified disease over a defined
period of time DIVIDED BY The number of individuals diagnosed with the
disease during that time TIMES 100 (final answer should be percentage)
e. Proportionate mortality: the proportion of deaths in a particular population over
a specified period of time, attributable to different causes
i. Calculation: Number of deaths within a population due to a specific
disease or cause DIVIDED BY the total number of deaths in the population
during a time period such as a year.
5. Assess the Validity and Reliability of Diagnostic and Screening Tests
a. Define, compare and calculate measures of validity, including sensitivity and
specificity.
i. Sensitivity identifies the proportion of individuals who truly DO have the
disease AND are given a positive test result. I find it helpful to remember:
sensiTivity = sensitive to the Truth (i.e. do have disease + do have positive
result)
1. We want to know what proportion of individuals who have the
disease (a+c) were given a positive test result (a), therefore…
a. Sensitivity = a/(a+c)
ii. Specificity identifies the proportion of individuals who truly DO NOT have
the disease AND were given the correct negative test result. I find it
helpful to remember: specificity = speciFies the False (i.e. do not have
disease and do not have positive test result
1. This time we want to know what proportion of people who do not
have the disease (b+d) were given the correct negative test result
(d), therefore…
a. Specificity = d/(b+d).
b. Define and calculate positive predictive value.
i. Positive Predictive Value: The probability that subjects with a positive
screening test truly have the disease.
1. Calculation: a/(a+c)
c. Understand positive predictive value (PPV) and relationship to disease
prevalence and specificity of a screening test.
i. Higher prevalence/specificity rates=higher ppv rates
6. Epidemiologic transition
a. Phase of development witnessed by a sudden and stark increase in population
growth rates brought by improved food security and innovations in public health
and medicine, followed by a re-leveling of population growth due to subsequent
declines in fertility rates.
7. Study designs (case-control, cross sectional, prospective and retrospective cohort, clinical
trial)
a. Case-control study: An observational study that compares patients who have a
disease or outcome of interest (cases) with patients who do not have the disease
or outcome (controls) and looks back retrospectively to compare how frequently

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