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epi > midterm 1 > Flashcards

midterm 1 Flashcards

1
Q

Definition of epidemiology

A

the study of the distribution and determinants of health related states and events in populations and the application of this study to control of health problems

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2
Q

Definition of epidemic

A
  • the occurance in a community or region of a group of illnesses of similar nature, clearly in excess of normal expectancy, and derived from a common/propagated source
  • rapid spread of a disease to a large number of people in a given population within a short period of time
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3
Q

endemic

A
  • the habitual presence of a disease within a given geographic area
  • regularly found among particular people or in a particular area
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4
Q

pandemic

A

a worldwide epidemic

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5
Q

Different types of prevention

A

primary, secondary, tertiary

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6
Q

Different types of prevention: PRIMARY

A

prevention of disease before it occurs

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7
Q

Different types of prevention: SECONDARY

A

early detection of existing disease to reduce severity and complications

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8
Q

Different types of prevention: TERTIARY

A

reducing the impact of disease (once it has already occurred)

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9
Q

purposes of epidemiology

A
  • identify causes and risk factors for disease
  • determine the extent of disease in the community
  • study natural history and prognosis of disease
  • evaluate preventive and therapeutic measures
  • provide foundation for public policy
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10
Q

Key measures in disease occurrence

A

counts, prevalence, incidence

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11
Q

Key measures in disease occurrence: COUNTS

A

the raw number of occurrences of an event

ex. the number of cases of COVID in a nursing home

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12
Q

Key measures in disease occurrence: PREVALENCE

A

the proportion of a population who have a specific [disease/characteristic] in a given time period or point in time

  • point prevalence = proportion
  • period prevalence = proportion
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13
Q

Key measures in disease occurrence: INCIDENCE

A

new disease [occurrence of new cases] that develop over time in an at-risk population

  • cumulative incidence = proportion
  • incidence rate = rate
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14
Q

3 Critical Factors in Measuring Frequency of Disease

A

Number of people affected by disease
Size of population that gave rise to cases
Length of follow up time for the population

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15
Q

numerator

A
  • the subset of the total population that we are interested in looking at; the number of individuals with a specific outcome (can be excluded or included from the denominator)
  • To assess the magnitude of disease occurrence, we need to know not only the number of cases…
  • the number of people who are affected by the disease
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16
Q

denominator

A
  • tells us the number of individuals in the underlying population (time can also be included in the denominator)
  • …but also the size of the population from which the cases emerged (denominator)
  • the size of the population from which the cases arise
  • the length of time that the population has been followed
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17
Q

x 10^n

A

allows us to convert disease measures into useful quantities such as percentages, or number of occurrences per unit of the total population (eg. 5 cases per 100,000 people)

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18
Q

Interrelationship between prevalence and incidence

A

-interrelationship: P ≅ I x D
high prevalence may reflect:
-high risk of disease (incidence)
-prolonged survival without cure (long duration)
low prevalence may reflect
-low risk (incidence)
rapid fatal disease
-progression
-rapid cure
-for conditions of short duration and high incidence, one may infer from this formula that, when the duration of a disease becomes short and the incidence is high, the prevalence becomes similar to incidence
-for diseases of short duration, cases recover rapidly or are fatal → eliminating the buildup of prevalent cases

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19
Q

Cumulative incidence

A
  • number of new events or cases of disease / the total number of individuals in the population at risk for a specific time interval.
  • cumulative incidence is a proportion that ranges from 0 to 1 (or 0% to 100%
  • represents the proportion of the at-risk population that developed the outcome
  • assumes that all subjects have the same follow up time or time at risk
  • is used when all individuals in the population are thought to be at risk of the health-related event being investigated, as in a prospective cohort study in which the population is fixed
  • cumulative incidence estimates the risk of a particular health related outcome in the cohort
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20
Q

incidence rate

A

-incidence rate = number of NEW events during a time period/ total person-time “at risk” x multiplier (eg. 10,0000)
incidence rate = a true rate based on person-time (contains time in the denominator)
-not a proportion; ranges from 0 to infinity
-incidence rate includes:
a numerator: the number/frequency of new cases
individuals who have a history of the disease are NOT included
a denominator: the population at risk
the denominator for incidence rates is the population at risk, which is defined as those members of a population who are at risk for contracting a specific disease or adverse health outcome
-can be interpreted as the time rate of change from “at risk” to diseased state
-used in follow-up studies, and accounts for variable lengths of follow up and timing of an event of interest
-incidence rates reflect the speed at which disease occurs in cases per person-time
-to determine an incidence rate, one must be able to specify the date of onset for the condition during the time period

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21
Q

incidence density

A

is the “average person-time incidence rate”
incidence density = number of new cases during the time period/ total person-time of observation
this variation in the incidence rate is calculated by using the person-time of observation as the denominator
when period of observation is measured in years → incidence density = number of new cases during the time period/ total person-years of observation

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22
Q

person-time

A

person time = the sum of the periods of time at risk for each of the subjects
total time each person in a population is “at risk”; but how is person-time accrued through follow up
most widely used measure is person-years → person time is used when the amounts of time of observation of each of the subjects in the study varies instead of remaining constant for each subject
differential follow up time and person-time at risk → factor follow-up time in the denominator to get a new quantity known as person years
how to calculate person-years
# of persons (population) x follow up time (in years) → incidence rate

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23
Q

crude death rate

A

cumulative incidence (every death is a new event)
proportion
occurrence = death
since everyone at risk, population = entire defined population
like CI, need to specify time period a
is the total number of deaths to residents in a specified geographic area (country, state, county, etc.) divided by the total population for the same geographic area (for a specified time period, usually a calendar year) and multiplied by 100,000.

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24
Q

case fatality rate

A

how many of infected persons die because of the disease
also is NOT a rate as it does not provide any information about the duration of the disease before death

number of persons with the disease who die during a specified period of time/ number of individuals with the disease

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25
Q

epidemiologic triad

A

host, agent, environment

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26
Q

agent

A

agent: characteristics specific to the pathogen causing virus or bacteria
sensitivity of specific bacteria that causes lyme disease
resistance to pesticides
genetic mutations
the infecting agent
think about what causes malaria

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27
Q

host

A

host: personal factors of the people impacted
socioeconomic, genetic characteristics
age, sex, race, genetic profile, previous diseases, immune status, religion, customs, occupation, marital status, family background
typically the person

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28
Q

environment

A

environment: crowding of houses, outside temperature, the food → make the transmission more common
temperature, humidity, altitude, crowding
changes we make in environment that affect the triad

29
Q

mode of disease transmission: Direct

A

direct contact through sexual, skin, blood, or body fluid contact as well as droplet inhalation
example: hepatitis C virus (HCV)

30
Q

mode of disease transmission: Indirect

A

indirect contact through a common vehicle (contaminated air, water, or good) or vector-borne (mosquito, tick, snail, etc)
common vehicle: (group of people who have eaten the food)
single exposure
food that was served at one event

31
Q

course of infection

A

exposure → infection → infectiousness → transmission → onset of disease → cure

32
Q

incubation period

A

incubation period (the time of exposure to the onset of disease)

33
Q

latency period

A 
latency period ( time from point of exposure to actual infectiousness)
infectiousness can be different from when you see symptoms
34
Q

carrier status

A

period where individuals are infectious but do not experience symptoms

35
Q

reproductive number

A

the basic reproductive number R0 is the average number of secondary cases a primary case generates
how likely this disease is to spread
infection will spread in the population if R0 > 1
infection will die out in the population if R0 < 1
infection will reach an endemic (be present in population but not increase or decrease) if R0 =1
R0 is expected number of secondary cases produced by a single infection in a completely susceptible population
a higher R0 will lead to a higher herd immunity threshold
more people will need to be immune to achieve herd immunity
V = 1 - 1/ R0
example with polio

36
Q

herd immunity

A

indirect protection from infectious disease that occurs when a large percentage of the population is immune (through vaccination)
once a certain threshold has been reached, herd immunity will result in the elimination of the disease from the population
conditions to achieve herd immunity
disease agent must be restricted to a single host species
transmission must be relatively direct
infections and vaccination must induce total immunity
different herd immunity thresholds that vary by disease
relationship to basic reproductive number
the average number of people who get exposed and therefore why you need a larger number immune for herd immunity to be operating

37
Q

Epidemic curves: POINT SOURCE

A

persons are exposed over a brief time to the same source, such as a single meal or an event. The number of cases rises rapidly to a peak and falls gradually. The majority of cases occur within one incubation period of the disease.
indicates indirect transmission
represents an incubation period (the period of exposure to the onset of symptoms/disease)

38
Q

Epidemic curves: EXTENDED SOURCE

A

In a continuous common source (extended) outbreak, persons are exposed to the same source but exposure is prolonged over a period of days, weeks, or longer. The epi curve rises gradually and might plateau.
indicates indirect transmission

39
Q

Epidemic curves: PROPAGTED SOURCE

A

In a propagated outbreak, there is no common source because the outbreak spreads from person-to-person. The graph will assume the classic epi curve shape of progressively taller peaks, each being one incubation period apart.
indicates direct transmission
COSI → calculated from the index case to the secondary case → successive generations → you can estimate the COSI between the two

40
Q

attack rate

A

among a group of exposed susceptible hosts, the proportion who develop a certain illness [is an alternative form of the incidence rate that is used when the nature of the disease/condition is such that a population is observed for a short period of time, often as a result of specific exposure)
proportion
measured from beginning to end of outbreak
attack rate calculated by the following formula
# of persons with clinical symptoms/ # of people exposed and at risk of developing disease
AR = ill/(ill + well) x 100 (during a time period)

41
Q

outbreak

A

an outbreak is a type of epidemic but it typically refers to a specific geographic location, for a shorter period of time and in some cases (not always) thought only to apply to infectious agents and diseases
obesity epidemic or opioid epidemic (not infectious disease related), in contrast outbreaks are often localized & refer to infectious disease

42
Q

case definition

A

Select a clear case definition
includes the accepted, usual presentation of the disease with or without standard laboratory confirmation
categorize into “confirmed”, “probable”, and “possible”
self report symptoms but not seen by clinician/ don’t have lab testing = probable
look back at ED patient records and ask about past patient records symptoms in line with what’s expected = possible case

43
Q

Index case

A

the first case in a defined outbreak that is recognized by public health authorities

44
Q

primary case

A

person who first brings a disease into a group of people (often only determined in retrospect)
**occasionally the primary and index case = same

45
Q

secondary case

A

cases who were infected by the index case

46
Q

epidemic curve use

A

we use an epidemic curve to measure and track an outbreak

histogram of the number of cases against the time of onset of disease

47
Q

COSI - Clinical onset serial interval

A

defined as the average time between symptom onset in an index case and a secondary case(s)
**not the same as the incubation period because it is a function of the time to transmission from one generation to the next + latency period + time until symptoms
time between index case and secondary case = important # because reflects the cleanest data possible
gives an understanding for things like a propagated outbreak
Derived from analysis of symptom onset times between index and secondary case
sometimes estimated by calculating time between successive generations (of a propagated outbreak)

48
Q

What is a cause

A

a cause of a disease occurrence is an event, condition, or characteristic that preceded the disease onset and that, had the event, condition, or characteristic been different in a specified way, the disease would either not have occurred at all or would not have occurred until some time later
to measure a causal effect, we have to contrast the experience of exposed people with what would have happened in the absence of exposure

49
Q

Determinants of health-related states

A

determinants = factors that bring about a change in health status, either positive or negative
→ determinant implies causation
one of the primary goals of epidemiology is to identify precisely and without bias the causes of human disease

50
Q

causality as inference

A

Causality is an inference, not an observation of fact
ultimately requires judgement [continuum of certainty]
epidemiology does not provide definitive approaches to make causal inferences, but rather useful conceptual frameworks and methodical strategies (including study design)
frameworks for how to conceptualize causes
designs and considerations hta support or challenge causal inference

51
Q

Counterfactual Ideal

A

Counterfactual outcomes never observed

  1. the ideal comparison would be the result of a thought experiment: the comparison of people with themselves followed through time simultaneously in both an exposed and unexposed state → such comparison envisions the impossible because it requires the person to exist in two incarnations (one exposed and one unexposed) but if such an impossible goal were achievable it would allow us to know the effect of exposure because only the difference between the two settings would be the exposure → because this situation is impossible = counterfactual
  2. because we can never achieve the counterfactual ideal we strive to come as close as possible to it in the design of epidemiologic studies → instead of comparing the experience of an exposed group with its counterfactual ideal we must compare their experience with that of a real unexposed population [give a result that is close to that from a counterfactual comparison] → why we resort to design methods that promote comparability
52
Q

Hypothetical condition “what if…”

A

counterfactual ideal: literally, counter to the fact → counterfactual outcomes are hypothetical
ie. if I hadn’t lived in a neighborhood with high pollution levels, would I have developed heart disease?
if I hadn’t turned my head so quickly, would I have fallen?
the central problem of epidemiology: only one outcome is ever observed;
bad part → we never truly know the counterfactual outcome for an individual (individual causal effect)
good part → we can estimate the average counterfactual outcomes for a population (sometimes)

53
Q

risk rations (relative risk) and rate ratios

A

risk in exposed/ risk in non exposed
also referred to as relative risks
range from 0 to positive infinity
no dimension (units)
assuming that the event we are studying is a “bad” outcome (ie death)
= 1 (no association)
> 1 (harmful)
risk in exposed persons is greater than the risk in unexposed → evidence of a positive association which may be causal
< 1 (protective)
the risk in exposed is less than risk in unexposed → negative association which may be indicative of protective effect
same applies to odds ratios

54
Q

risk difference

A 
risk in exposed - risk in non exposed
ranges from -1 to +1 
no dimension (units) 
assuming event is a “bad” outcome (ie death) 
= 0 no association
> 0 harmful
< 0 protective
we would rarely calculate an “odds difference”
55
Q

Incidence rate ratio

A

RIR = (IRE+/IRE-)

56
Q

measures of association

A

odds (Number of cases / Number of Non Cases) and probability (Number of cases/Entire population)

57
Q

confounder

A

-Non-causal relationships due to spurious associations between exposure and outcome
-“a distortion in the magnitude of the true effect of a study exposure on a study outcome due to a mixing of effects between the exposure and some extraneous factor(s)”
-a distortion in the estimated association between a study exposure and a study outcome due to the mixing effects between the exposure and some extraneous factor(s)
-unlike bias, not the “fault” or systematic error of investigator
-function of conducting research in humans
unevenly distributed characteristics in real life
present in nearly every observational study
-mixing of effects owing to the presence of third extraneous factor/set of factors
-results in distortion of true exposure-disease association

58
Q

properties of a confounding variable

A
  • must be associated with exposure
  • must be associated with the outcome
  • must not be intermediate variable in the causal pathway
59
Q

how do we control for a confounding variable?

A

stratified analysis

60
Q

stratification

A

purpose of stratification is to identify potential confounding to get unbiased estimates of the true relationship between exposure and disease

61
Q

bias

A

Any systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of disease.”

62
Q

difference btwn confounding and bias

A

The major difference between bias and confounding is bias is a result of the design, conduct or analysis of a study that results in a mistaken estimate of an exposure’s effect on the risk of disease. Contrary, confounding is not due to the actions of the investigator/researcher and it just happens.

63
Q

Selection bias

A

If the way in which study participants were selected results in a mistaken estimate of an exposure’s effect on the risk of disease
refers to distortions that result from procedures used to select subjects and form factors that influence participation in the study
arises when the relation between exposure and disease is different for those who participate and those who theoretically would be eligible for study but do not participate
results in a mistaken estimate of an exposure’s effect on the risk of disease

64
Q

information bias

A

bias due to the procedures used to collect or analyze information that results in misclassification of exposure and/or outcome status (measurement error)
results in a mistaken estimate of an exposures effect on the risk of disease
recall bias is a form of information bias

65
Q

odds ratio

A 
odds in exposed/ odds in non exposed
shortcut = ad/bc
Odds Ratios
range from 0 to positive infinity
no dimension “units”
assuming that the event we are studying is a “bad outcome” like death
= 1 (no association)
> 1 (harmful) 
< 1 (protective)
Odds (disease) = number of cases/ number of “non-cases”
AD/BC
66
Q

how to interpret odds ratio

A

If the exposure is not related to the disease = odds will equal 1
if the exposure is positively related to the disease = odds will be greater than 1
if the exposure is negatively related to the disease/protective = odds will be less than 1

67
Q

When is it appropriate to use crude odds ratio versus stratified odds ratio

A

general analytic approach to controlling confounding:
estimate the crude or overall association [estimated effect in total sample]
stratify by levels of the potential confounder
estimate stratum-specific measures of effect (conditional association)
compare the crude value to the stratum-specific values
if the same = no confounding
if different = confounded → report the stratum-specific estimates

68
Q

dichotomous/binary variable

A

two level categorical variable

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