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Medical skills For Scientist > Epidemiology > Flashcards

Epidemiology Flashcards

(57 cards)

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1
Q

Epidemic

A

Notable excess of disease over a period of time in a given region
Associated with an acute outbreak e.g. Cholera, Malaria, Measles

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2
Q

Point Source epidemic

A

All members of the population at risk are exposed to the causal agent over a short period of time.
Incubation period may vary amongst individuals which is reflected in the intensity of exposure/immune response of people exposed.
Predictable distribution.
#cases increase rapidly, peak, gradually taper off.
Right skewed curve
One Source of infection

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3
Q

Propagative Epidemic

A

Multiple peaks. Wave after wave of infection spreads through the population.
Index case-likely source for sources in secondary and tertiary peak.
Contagious
E.g. measles

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4
Q

Endemic

A

Constant presence of disease agent within given geographical area or population
E.g. Malaria, Ross river virus

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5
Q

Cluster

A

Aggregation of relativity uncommon diseases in space and/or time in amounts that are perceived to be greater than chance.
Usually environmental cause, rare, non-infectious.

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6
Q

Steps to investigating outbreaks

A
  • confirm problem
  • extent of outbreak
  • population at risk
  • hypotheses
  • analyse data
  • modify hypothesis
  • control measures
  • evaluate effectiveness
  • prevention
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7
Q

Attack rates

A

Percentage of: (those who became ill )/ (those who were exposed ) x 100

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8
Q

Relative risk

A

Attack rate(%) if exposed / Attack rate (%) not exposes

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9
Q

Observational Stuides

A

Descriptive and analytical
Case series, cross sectional, cohort, case control
Do not intervene
Investigator measures and describes the occurrence/pattern of disease

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10
Q

Descriptive studies

A

1st step in epidemiological investigation
Limited to describing occurrence
Case report-1 person
Case series-more than 1 person
E.g. Thalidomide, SARS, HIV/AIDS
Detailed report of single patient or group of patients

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11
Q

Analytical Studies

A

Cross sectional and cohort studies

Assment of patterns/relationships between health states and variables

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12
Q

Cross-sectional studies

A

Survey
Examine frequency of health problems and how common in a specified population
Not easy to assess reasons for associations
E.g. deliberate self-harm in adolescence

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13
Q

Cohort studies

A

Begin with people free of disease of interest
Classify people into subgroups depending on exposure to potential cause of disease
Variable of interest is specified and measured over time
No intervention

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14
Q

Cohort study benefits

A

Observe only-no intervention or experiment
Provide best info about causation or risk
Can examine multiple outcomes of single exposure e.g. effect of high protein diet on cholesterol and weight loss
Reduce bias in assessing exposure (prospective)
Directly measures incidence of disease in exposed/unexposed populations

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15
Q

Cohort study drawbacks

A

Need large number of participants inefficient for rare diseases expensive and time-consuming validity can be affected by losses due to follow-up (long study) and death

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16
Q

Case-control studies

A

Begin with group of people with disease (cases) AND group of people without disease (controls) look at possible cause of disease in both groups

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17
Q

Case-control studies benefits

A

Good for rare diseases or diseases with long latency example cancer
quick
inexpensive
small number of participants
can study more than one potential disease

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18
Q

Case-control study’s drawbacks

A

Rely on records/recall of past exposure (recall bias)
selection of appropriate control group is difficult
cannot establish sequence of events (cause and effect)
can show Association But can’t show causation unable to determine prevalence of disease in population

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19
Q

Experimental studies

A

Includes randomised control trials (RCT) or intervention studies involves attempt by researcher to change a disease determinate/progress of disease in one or more groups of people through treatment or exposure

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20
Q

Randomise control trials

A

Use patient as subjects investigate affect of a treatment or intervention on specific disease

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21
Q

Field trials

A

Participants are healthy people purpose is to prevent low frequency disease for example vaccine trials

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22
Q

Community trials

A

Participants are communities

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23
Q

benefits of experimental studies

Study These Flashcards
A
  • Considered goal standard high level of evidence national health and medical research council
  • reduced risk of bias due to randomisation
  • blinding of participants and assessors to group allocation double blinding -no bias
  • can compare between treatments which drug better at achieving outcomes
24
Q

Levels of evidence national health and medical research council

Study These Flashcards
A

Level 4 Descriptive studies cross-sectional studies case report and Case series
level 3 observational studies cohort and case control
level 2 experimental studies randomise control trials (RCT)
level 1 systemic review of relevant randomise control control trials meta-analysis

25
Describe an occurrence(s)
Case report or case series
26
Investigate prevalence
Cross-sectional studies
27
Investigate natural history and cause affect relationships
Cohort studies
28
Identify association
Case-control study’s
29
Literature review
Defined by research question. An account of what has been published on a topic by accredited scholars and researchers. Conveys knowledge established on a topic and strengths and weaknesses. Explain how studies are similar in how they vary. Identify new ways to interpret in shed light on gaps in previous research.
30
Efficacy of treatments or interventions
Experimental studies randomise control trials
31
systematic error
Also called bias. All measures are out. Not corrected by taking more measures.
32
Random error
Two types: unexplained error limits of measuring tool variation due to extraneous factors error in both directions some measures higher some measures lower
33
Null hypothesis
Statistical assumption that no difference exists between the two groups usually opposite of what researcher believes to be true studies should be designed to disprove the null hypothesis
34
Alternative hypothesis
Groups are different researcher believes true
35
Controls
Eliminate confounding variable is positive control expect positive response negative control expect negative response
36
P of less than 0.05
There is less than one in 20 chance 5% that the difference is due to noise
37
Disease
Incorrectly functioning part of the body abnormal body structure various causes internal and ex-turn all characteristic signs and symptoms
38
Sign
Objective evidence of disease that can be observed or measured such as blood and faeces or a skin rash
39
Symptom
Generally subjective and can only be detected by patient such as a stomach ache
40
Categorical data
Involve categories label nominal example male female chemical element yes no
41
Continuous data
Numerical involve a scale of meaningful numbers example age test score height income time until chemical reaction
42
Type of data for frequency counts in percentages or proportions
Nominal or categorical data summaries
43
Graft as pie chart or bar chart
Nominal categorical data summaries
44
Type of data for mean and standard deviation symmetrical or median and interquartile range IQR non-symmetrical data
Continuous data summary
45
Graphed as box plot histogram scatterplot
Continuous data summaries
46
When is median (middle value) used?
Nine symmetrical data skewed report with interquartile range
47
Interquartile range
Interquartile range IQR equals 75th percentile to the 25th percentile Q3 through Q1 The box in the box plot middle value is the median value line through the middle of the box
48
Note that blank is affected by the extreme value the most so blank is always leaning toward the tail compare to the other two measures
Note that mean is affected by the extreme value the most so mean is always leaning towards the tail compare to the other two measures
49
Null hypothesis
Typically no change or no difference
50
Alternate hypothesis
There is a change or a difference
51
P value
Helps you determine the significance of your results a small period value typically less than or equal to 0.05 indicates strong evidence against the null hypothesis so you reject the null hypothesis if the P value is low the Null has to go a large P value greater than 0.05 indicates weak evidence against the null hypothesis so you failed to reject the null hypothesis
52
Intention to treat analysis ITT
All subjects are analysed according to the groups to which they are allocated if someone dies halfway through the study missing values are imported guess they’re likely result and included in the analysis
53
Why are randomise control trials superior to observational studies
Randomise control trials +intention to treat = group equal groups at the start change between groups equals treatment/drug effectiveness observational studies = unequal groups at start
54
Inferential statistics
Makes inferences about populations using data drawn from samples allows you to make predictions inferences from that data instead of using the entire population to gather the data the statistician will collect a Sample or samples for the millions of residents and make inferences about the entire population using the Sample
55
Confounding
A con founder is another variable or variable is related to outcome confounding is the effect of another variable covariate or factor that cannot be separated out this can cause in balances between groups at the start
56
Type one error
False positive reject the null hypothesis is when the groups are really equal conclude different when there is no significant difference
57
Type two error
False negative fail to reject the null when the groups are in fact different conclude not different when there is significant difference

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