Module 8 Flashcards

(49 cards)

1
Q

Descriptive epidemiology

A

Describes characteristics of a population

  • Health needs
  • Health events
  • Health outcomes
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2
Q

Inferential epidemiology

A

Compares two or more populations for differences or similarities

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3
Q

Sampling

A

Done when data can’t be collected from entire population

  • Subset of population provides estimate
  • Sample is drawn from a sampling frame or list of those available to be sampled (ex: phone book)
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4
Q

Sampling methods

A
  1. Convenience sample
  2. Probability sample
  3. Systematic sampling
  4. Stratified random sampling
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5
Q

Convenience sample

A

Nonrandom selection, e.g., first 50 to enter clinic

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6
Q

Probability sample

A
  • Uses some random mechanism to draw sample from sampling frame
  • Each member in sample equally as likely to be chosen
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7
Q

Systematic sampling

A

e.g., Randomly choose one of first 20 patient charts, then every 20th chart thereafter to get a 5 percent sample

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8
Q

Stratified random sampling

A

Stratify sample into categories (e.g., age within gender) and then randomly sample from within each category

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9
Q

Statistical measures of effect

A
  1. Significance tests
  2. The p value
  3. Confidence Interval
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10
Q

Levels of measurement: 2 classes of data

A
  1. Continuous

2. Categorical

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11
Q

Continuous variable

A
  • Distance between points meaningful
  • Variable can take any value between points
  • Age, height, weight, blood pressure
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12
Q

Categorical variable

A

Take values in fixed number of categories

  • Ordinal—categories can be ordered in some way (ex: patient satisfaction —from not satisfied to very satisfied)
  • Nominal—categories are “qualitative” (race, gender)
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13
Q

Descriptive statistics for continuous variables

A
  1. Measures of central tendency

2. Measures of dispersion/variation

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14
Q

Measures of central tendency

A
  1. Mean: average value

2. Median: half observation below, half above

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15
Q

Measures of dispersion/variation

A
  1. Standard deviation: average distance that variables fall
    from the mean
  2. Variance: square of standard deviation
  3. Range: distance from lowest to highest value
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16
Q

Descriptive statistics for categorical variables

A
  1. Frequency distribution presented graphically
  2. Proportion: number with attribute/total #
  3. Rate: a proportion multiplied by some number
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17
Q

Inferential statistics

A

Compare two or more samples for some characteristic
Tests specific hypotheses regarding populations
-Two-sided hypothesis: makes no assumptions regarding which population has the higher (or lower) value
-One-sided hypothesis: assumes one population has a higher or lower value

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18
Q

p Value

A

Probability of observed differences being due to random chance
- Statistically significant: p

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19
Q

Null hypothesis

A

States that there is no difference among the groups being compared
*Underlying all statistical tests is a null hypothesis

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20
Q

Significance tests

A

Used to decide whether to reject or fail to reject a null hypothesis

21
Q

Significance level

A

Chance of rejecting the bull hypothesis when it is actually true

22
Q

Confidence intervals

A
The test statistic +/- some quantity
An alternative to the hypothesis test
Provides a range in which the true value will probably lie
Depend on: 
-Variability: (higher > larger CI)
-Sample size: smaller > larger CI)
23
Q

Statistical power

A

Ability of a study to demonstrate an association if one exists; probability that we will correctly determine that the null is false and reject it
Determined by:
-Frequency of the condition under study – Magnitude of the effect
-Study design
-Sample size

24
Q

Two-sample T test

A
Compare mean values of a continuous value from 2 groups
Need to know:
-Mean of each group
-Size of each group
-Variance for each group
25
Z test for difference in proportion
Compare proportion with attribute from two populations Need to know: -Proportion with attribute in each population -Size of each population
26
Study designs (overview)
1. Experimental studies 2. Observational studies – Descriptive studies: cross-sectional surveys – Analytic studies: many ecologic studies, case-control studies, cohort studies
27
Descriptive studies
– Used to identify a health problem that may exist – Characterize the amount and distribution of disease
28
Analytic studies
– Follow descriptive studies | – Used to identify the cause of the health problem
29
Validity for etiologic inference
``` #1 Experimental study #2 Prospective cohort study #3 Retrospective cohort study #4 Nested case-control study ```
30
Ecologic studies
Correlations are obtained between exposure rates and disease rates among different groups or populations *Unit of analysis is the group, not individual
31
Ecologic fallacy
Observations made at the group level may not represent the exposure-disease relationship at the individual level *Occurs when incorrect inferences about the individual are made from group level data
32
Advantages of ecologic studies
– Quick, simple, inexpensive | – Good approach for generating hypotheses when a disease is of unknown etiology
33
Disadvantages of ecologic studies
– Ecological fallacy | – Imprecise measurement of exposure and disease
34
Cross-sectional study (PREVALENCE STUDY)
o Survey done at particular point in time o Exp and dis measures obtained at individual level o Exp and dis outcome determined simultaneously o Cases of disease are prevalent (not incident) o Single period of observation o Both probability and non-probability sampling used
35
Uses of cross-sectional studies
– Hypothesis generation – Intervention planning – Estimation of the magnitude and distribution of a health problem
36
Limitations of cross-sectional studies
– Do not provide incidence data – Cannot study low prevalence diseases – Cannot determine temporality of exposure & disease
37
Case-control studies
- Compare persons with disease (cases) with those without disease (controls) - Explore whether differences between cases and controls result from exposures to risk factors. * Useful when population is not well-defined
38
Classification of case-control studies
- When disease is identified—PRESENT - When exposure/treatment is recognized—PAST - When analysis is conducted—PRESENT
39
When to use case-control studies
- Exposure data are difficult/expensive to obtain o Disease is rare - Disease has long induction/latent period - Little is known about disease - Underlying population is dynamic
40
Advantages of case-control studies
– Tend to use smaller sample sizes than surveys or prospective studies. – Quick and easy to complete. – Cost effective. – Useful for studies of rare diseases
41
Limitations of case-control studies
– Provide indirect estimate of risk – Timing of exposure-disease relationship difficult to determine – Representativeness of cases/controls often unknown
42
Selecting cases for CC Studies
``` o Cases usually have the disease o Define cases specifically o Signs/symptoms o Clinical exams o Diagnostic tests ```
43
Sources of cases for CC studies
o Clinic patient rosters o Death certificates o Surveys o Cancer/birth defect registries
44
Sources of controls for CC studies
o Population controls o Hospital/clinic controls o Illnesses must be unrelated to exposure o Illnesses must have same referral pattern to HCF o Dead controls—some cases deceased o Friend/spouse/relative controls
45
Sources of exposure info for CC studies
o In-person/telephone interview o Questionnaires o Existing datasets: medical records, pharmacy databases, registry, employment/insurance/birth/death records o Biological specimens—biomarkers
46
Case control assumptions
o Frequency disease population is small o Cases/controls are representative o Can’t calculate relative risk (RR) directly
47
Relative risk in case control
RR = # times more likely cases are to get disease than controls given exposure
48
Odds ratio
``` An estimate of relative risk o When cases/controls are representative o If disease prevalence is small OR = 1 implies no association o If > 1.0, then increased risk o If ```
49
OR provides good approx of risk when:
– Controls are representative of a target population – Cases are representative of all cases – The frequency of disease in the population is small