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Evaluative Sciences 1 > Unit 1 > Flashcards

Unit 1 Flashcards

1
Q

Epidemiology

A

The study of distribution of disease and determinants of disease frequency in populations

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2
Q

Goal of Epidemiology

A

Is to control health problems and improve health at the population level

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3
Q

Operationally

A

Counting the causes of morbidity and mortality

Determining variables associated with causes of morbidity and mortality

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4
Q

In operationally why identify factors that are causes?

A

They are potentially modifiable.

-Guiding and evaluation interventions to improve public health

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5
Q

Basic Assumptions of Epidemiology

A

1) Death disease and disability do not occur at random
2) There are causal factors that can be identified through the systematic investigations of human populations
3) Identifying these causal factors can lead to preventative intervention

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6
Q

Clinical and Research Concerns: Exposure

A

Good or bad: Chemical, biological, physical, psychological, educational etc.

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7
Q

Clinical and Research Concerns: Outcome

A

Good or bad: Disease, cure, improved attitude, longer life, better QOL

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8
Q

We generally know either the exposure or the outcome and_____________

A

Want to measure the other

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9
Q

Endemic

A

The usual occurrence of a disease in a given population

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10
Q

Epidemic

A

A meaningful increase in the occurrence of a disease in a given population

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11
Q

Pandemic

A

Spread of a disease across a large region or worldwide

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12
Q

Epidemiological Reasoning

A

1) Suscpicion of an E–> D relationship
2) Hypothesis formation
3) Test E–>D hypothesis
4 Rule out alternative explanations: Chance, bias, confounding

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13
Q

Epidemiology is fundamentally concerned with________

A

Populations

Measuring distributions of disease in populations and the factors associated with those distributions

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14
Q

Association

A

An identifiable relation between an exposure and a disease

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15
Q

Methodological question

A

How do we look for a cause

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16
Q

Ontological question

A

What is a cause

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17
Q

Ethical Question

A

How do we decide if there is enough evidence to act on a cause

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18
Q

What is a cause

A

A cause of a disease is an event condition or characteristic the preceded the disease and without chick the disease WOULD NOT have occurred at all or would not have occurred until some later time.

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19
Q

Criteria for assessing Causality

A

1) Strength of association
2) Dose-response relationship
3) Temporal Sequence
4) Biological credibility
5) Consistency of findings across studies

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20
Q

Strength of Association

A

Is there a strong E-D relationship

BUT doesn’t imply that a weak association cant be judged as cause and effect

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21
Q

Dose-Response Relationship

A

Does risk increase with increase exposure?

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22
Q

Temporal Sequence

A

E–> D

Does the exposure precede the disease

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23
Q

Biological Credibility

A

Is there a known biological basis for the relationship

BUT DEPENDS ON CURRENT STATE OF KNOWLEDGE

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24
Q

Consistency of Findings

A

Do multiple studies report similar findings

Studies can differ by:
Investigator
Methodology
Study population

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25
Q

Epidemiologic Approach

A 
Identify DISEASE
Identify EXPOSURE
Statistical associations between E/D
Hold constant factors that may be mixed up in this measure of association
Inference causal association
Recommend intervention
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26
Q

Risk Factor

A

A factor whic if present increases the probability of disease occurrence

  • The exposure must precede disease onset
  • Must be associated with an increase disease frequency
  • Absence of error and bias
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27
Q

Quantification in Epidemiology

A

Measuring diesel occurrence is fundamentel.
4 Types of scales

Nominal Scale
Ordinal Scale
Interval
Ratio

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28
Q

Nominal Scale

A

Uses names

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29
Q

Ordinal Scale

A

Follows an order based on severity

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30
Q

Interval Scale

A

Follows a mathematical order but has NO TRUE ZERO

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31
Q

Ratio Scale

A

Follows a mathematical order HAS a defined true zero

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32
Q

Quantification in Epidemiology

A

Depending on the time element we can also quantify cases as prevalent or incident by measuring:

  • Prevalence proportion
  • Incidence rate
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33
Q

Prevalence Proportion

A

=#Cases/#person in population ) at a specific time

Specific time can be a point or period of time

Ex. 45 D1 students /105 have at least one active carie

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34
Q

Incidence Rate

A

=# of new cases of disease/population at risk) over a time period

Ex.
7 new cases of periodontal disease per 105 D1 in 2010

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35
Q

_____Is not a rate but ______ is and is not meaningful without a time unit

A

Prevalence

Incidence

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36
Q

Issues with Incidence and prevalence

A

How do we know someone is a case
How do we count population at risk
What specific time period

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37
Q

Evidence Based Dentistry

A

EBD is an approach to oral heath care that requires INTEGRATION clinically relevant SCIENTIFIC EVIDENCE, relating to the patients oral and medical condition and history with the DENTISTS CLINICAL EXPERTISE and the PATIENTS TREATMENT NEEDS AND PREFERENCES

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38
Q

EBD is based on 3 components

A

1) Best available scientific evidence
2) A dentists clincal skill and judgement
3) Patients needs and preferences

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39
Q

What approach does EBD take

A

Patient centered!

It is an approach to practice and to making clinical decision. Just one component used to arrive at the best treatment. Provides personalized dental care based on the most current scientific knowledge.

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40
Q

EBD IS:

A

Systematic approach to practicing good dentistry:

Provide right care
To the right patients
At the right time

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41
Q

EBD is NOT

A
  • Cook Book Dentistry
  • A standard of care
  • Only about randomized trials (EBD process is not a rigid methodolical evaltion of scientific evidence)
  • Impossible to practice
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42
Q

EBD: Outcome

A

Better quality of patient care and outcomes
-Evaluation of science underlying clinical care
-Quality of decision making
-Reduciton in variations in clinical practice
Improvements in research and dissemination of results

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43
Q

EBD can facilitate_______

A

BUT does not guarantee, making better declines in the provision of dental care

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44
Q

EBD Steps *5

A

1 Formulate a relevant question
2 Find the best available evidence
3 Review the evidence for its validity and applicability
4 Integrate the best research evidence with your clinical expertise and patients needs/desires
5 Evaluate your efforts

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45
Q

PICO

A

Anatomy of well formulated question:

P-population
I-intervention
C-comparison
O-Outcome

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46
Q

Quality of information (from best to worst 6)

A 
1 RCT
2 We'll controlled cohort studies, case control and nonrandomize clinical trials
3 Cross sectional studies
4 Descriptive surveys
5 Case Reports
6 Personal opinion
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47
Q

Primary Source

A

Must be the first disclosure containing sufficient information to enable peers to assess obseravations repeat experiments and to evaluate intellectual processes

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48
Q

Secondary Source

A

Includes most books, review, articles, and indexes to the literature and usually summarize reviews or organize information

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49
Q

Conducting a Search

A

1 Defining the question
2 choosing a resource
3 Keyword vs classification systems
4 Search techniques

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50
Q

Defining the question

A

The most difficult step

PICO
Patient, intervention, comparison, outcomes

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51
Q

Boolean Operators

A

AND, OR, NOT

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52
Q

Truncation

A

Allow you to place a symbol at the end of a word which tells the engine to retrieve the word stem with different endings (usually an *)

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53
Q

Phrases

A

Use quotation marks this limits the results to the exact phrase

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54
Q

Parentheses

A

Use to appropriately group the terms and operators to control the order of the search

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55
Q

Explode

A

Instructs the search engine to retrieve the information with a broad subject heading that are broken down into narrower subject headings

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56
Q

Internal Validity

A

The degree to which the results of a study are likely to approximate the truth

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57
Q

External Validity

A

The extent to which the effects observed are applicable to a broader population

Inference can only correctly be made to the population from which the sample was drawn

We often leap to inference far beyond the targeted population

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58
Q

Observational Studeis

A

They observe the outcomes without intervening to affect them

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59
Q

Experimental Studies

A

The researcher manipulates the exposure(usually a drug or treatment) to compare it to the standard of care

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60
Q

Observational Studies (ex)

A

Cohort studies
Case Control Studies
Cross Sectional Studies

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61
Q

Cohort Studies

A

Subjects are selected based on their exposure status

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62
Q

2 Types of cohort studies

A

Prospective Cohort-compares disease prevalence in the exposed and unexposed

Retrospective cohort- They begin with the exposure of interest and probe back for exposure information

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63
Q

Advantages of Cohort Studies

A

Maintains temporal sequence (assesses exposure before outcome)
Good for assessing rare EXPOSURES and rapidly fatal disease

Can study multiple diseases/outcomes from a given exposure

Can calculate INCIDENCE among exposed and unexposed

Minimizes error in ascertainment of exposure(least prospective)

Provides complete description of experience after exposure including rate of progression and natural history of disease

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64
Q

Disadvantages of Cohort Studies

A

1) Expensive
2) Inefficient for rare diseases
3) Long follow up
4) Secular trends may influence behavior and study characteristics over time
5) Diagnostic trends

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65
Q

Cohort: Selection of “exposed” and “unexposed” depends on:

A

Research question
Ability to collect exposure and disease information
How common/rare the exposure

Unexposed
Similar to exposed other than exposure factor
Appropriate selection and investigation methods to avoid bias and confounding

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66
Q

Case-Control Studies

A

Subjects are selected based on their disease status

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67
Q

Case studies should theoretically mimic ________ studies

A

Cohort studies

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68
Q

In case control studies__________ are compared to________

A 
Diseased people (cases)
Non-diseased people (controls)
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69
Q

Cases and controls should be:

A

Different only on their past exposure

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70
Q

Case control Studies

A

Can demonstrate risk INDICATORS and not risk FACTORS due to the retrospective nature of the study design (temporality cannot be assessed)

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71
Q

Case Control Studies: Exposure

A

Cases and controls must have had an EQUAL CHANCE OF BEING EXPOSED

The exposure has to be assessed retrospectively and proportions of cases and controls who are exposed are unknown at the beginning of the study

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72
Q

Case Control Advantages

A

1) Efficient for rare diseases**
2) Relatively efficient in terms of time and money
3) Can study diseases with long latency period
4) Allow for the evaluation of multiple exposures that may increase risk for a specific disease

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73
Q

Case Control Studies: Disadvantages

A

1) cannot DIRECTLY compute incidence of diseases in exposed and non-exposed persons
2) Temporal relationship cannot be established wit certainty
3) Prone to errors in selection of cases/controls and in errors pertaining o the collection of information
4) Not optimal for rare exposures***

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74
Q

Case Control: Selection of Cases and Controls

?

A 
Cases:
Cases (disease) definition
Diagnostic criteria
Hospital based or population based
Incident or prevalence

Controls:
Would be cases if had the disease
Potential for bias and confounding

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75
Q

Cross Sectional Studies

A

Selection of subjects based on neither exposure or disease status

  • Most basic study design
  • Point in time or snapshot information
  • Subject selected without regards to exposure or disease status
  • Does not need explained etiologic objectives
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76
Q

Cross Sectional Studies: Advantages

A

Sampling and analytic methods provide for statistically valid inference to populations

Exposure and disease are assessed at the individual level

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77
Q

Cross-Sectional Studies: Disadvantages

A

Temporality cannot be assessed

78
Q

Experimental Studies (2 types)

A

Randomized Clinical trials

Community Intervention Trials

79
Q

Randomized Clinical Trial

A

Principles of all experimental studies follow those from clinical trials

Have a long history in clinical medicine

Are sub-types of Cohort studies in which exposure is randomly assigned by the investigator

80
Q

Randomization

A

The process by which each participants treatment is determined by some random mechanism

The PRIMARY PURPOSE of randomization is the minimizing of confounding

81
Q

Why Randomize?

A
  • To create groups (experimental and control) that are not determined by any other factor other than chance
  • Minimize confounding (known and unknown)
82
Q

RCT: Blinding

A

The investigator and or participant do not know what arm the participant is in

83
Q

Single Blinded

A

The participant does not know but the investigators does know treatment assignment

Ex.
Toothpaste trial where participant does not know if she is using the experimental or the control paste

84
Q

Double blinded

A

Where neither the participant nor investigator know treatment assignment

EX. Drug trials, where participants simply take a blue or red pill and no one knows which pill is which; data are coded and code only broken outside by monitors or at the end of the study

85
Q

Purpose of Blinding

A

Removes bias or systematic error

86
Q

Information Bias

A

Drawing different conclusions depending on their knowledge of which study arm particular participant is in

87
Q

Selection Bias

A

Study recruiters can be eager to recruit “sick persons” into experimental arm

88
Q

Key Elements of RCT

A
  • Selection of study population
  • Allocation of treatment/intervention
  • Study conduct and compliance
  • Follw up and establishing outcomes
89
Q

Considerations in Experimental studies (3)

A

1) stopping rules
2) Sample Size
3) Analysis and Interpretation

90
Q

Systematic Review

A

Systematic complete summary of the literature

91
Q

Meta-Analysis

A

Combined analysis of data from different studies following strict guidelines

92
Q

Scales of Measurement Data Types

A 
Nominal
Ordinal 
Continuous
-Interval
-Ration
93
Q

Nominal

A

A measurement scale based on the classification of an observation according to the group to which it belongs

Ex. Gender, political party, marital status

94
Q

Ordinal

A

A measurement scale based on the classification of an observation according to its relationship to other observations

Ex. Poor-fair-good rating scale

95
Q

“Uneven Intervals”

A

Just because numbers are used doesn’t mean that they represent “true numbers’ that can be added and subtracted.

Numbers can represents nominal or ordinal scale values

96
Q

Interval

A

A measurement scale characterized by equal units of measurement

  • The distance between any 2 numbers is known size
  • Zero point is arbitrary

Ex. Fahrenheit/Centigrade temperature scales

97
Q

Ratio

A

A measurement scale characterize by equal units of measurement and a true zero point at its origin

Ex Mass, time

98
Q

Populations Vs. Samples

A

Populations (Greek symbols)
Constants, not variables

Samples
Roman characters X, S (or SD)
Variables

99
Q

Measures of Location or Central Tendency

A

Mode
Median
Mean

100
Q

Mode

A

The value of the most frequent measurement
Useful with the nominal scale but may be used with any
There can be more than one mode

101
Q

Median

A

The value of measurement that falls in the middle when the measurement are arranged in order of magnitude.
-Point at which 50% of the measurement fall

-Not useful with ordinal scale, but may be used with higher order scales

102
Q

Mean

A

The arithmetical average: The sum of the measurements divided by the total number of measurements

Most useful with interval or ratio measurement

103
Q

Measures of Variability or Dispersion

A 
Ranges
Interquartile Range
Variance
Standard Deviation
Coefficient of Variation
Standard error of the meant
104
Q

Range

A

Range=Xmax-Xmin

105
Q

Interquartile Range

A

IR=X25th -X75th

106
Q

Variance

A

The average of the square of the deviations of the measurements about their mean

Considered in terms of distance of each measurement from the mean
“Degrees of freedom”

S^2

107
Q

How to Calculate Variance

A

1) Find the mean
2) Subtract this sample mean from each of the measurements and squaring the results to eliminate negative numbers.
- The sum of these=sum of squares (ss)
3) Dividing ss by the number in the sample minus one gives the sample variance

Substracting one from the sample total=UNBIASED estimate of POPULATION variance

108
Q

Standard Deviation

A

“Positive square root of the variance”
-The standard deviation is calculated by taking the square root of variance

Subtracting one from N gives us an unbiased estimator or the population standard deviation

109
Q

Coefficient of Variation (CV)

A

Measures the percentage of spread
Unitless
Allows for comparisons

CV=100*SD/X

110
Q

Central Limit Theorem

A

The sampling distribution of the mean of any independent random variable will be normal if the sample size is large enough

The More closely the sampling distribution needs to resemble a normal distribution the more sample points required

The more closely the original populations resembles a normal distribution the fewer sample points will be required

111
Q

Standard Error of the Mean (SE)

A

Took several samples from a population, calculated the mean of each sample, then calculated SD=SE

SE may be calculated from a single sample by divine the SD by the square root of N

112
Q

_______Is an estimate of the variability of individual measurements within a sample

A

SD

SD is used to measure variability of individual/subjects around a sample mean

113
Q

_______ is an estimate of the variability of sample means about the population mean

A

SE

SE is used to assess how accurately a sample mean reflects a population mean

114
Q

Interpretation of Mean (anesthesia study)

A

Best scientific guess of how long it takes to achieve anesthesia

115
Q

Interpretation: SD

A

How much variantion there is in onset time

116
Q

Interpretation: SE

A

If experiment repeated the true population mean would be within 1 SE

117
Q

3 Ds of Epidemiology

A

Distribution
Determinants
Descriptive

118
Q

Determinants are influenced by

A 
Heredity
Biology 
Physical environment
Social environment
Lifestyle
119
Q

Descriptive (epidemiology)

A

Prevalence
Severity
Age adjusted distribution in population

120
Q

Analytic

A

Try to answer a specific question

121
Q

Primary Data

A

Mail survey
Epidemiological screening
Telephone interview

122
Q

Secondary Data

A

Medicaid
Vital statistics
Cancer Registry

123
Q

Chemical Acid Theory

A

17th and 18th Centuries. Decay arises from acids formed in the oral cavity. Assumed acids were inorganic

124
Q

Parasitic (septic) Theory

A

Microorganism inifiltrae the enamel, leading to decomposition. Recognition that enamel is organic

125
Q

Non-Specific Plaque Hypothesis

A

Total plaque microflora

126
Q

Specific plaque hypothesis

A

Only a few species involved

127
Q

Ecological Plaque Hypothesis

A

Shift in hemeostatic balance

128
Q

Extended ecological Plaque hypothesis

A

Non-pathogenic bacteria can adapt to produce acid

129
Q

Dental Caries

A

An infection communicable disease resulting in destruction of tooth structure by acid forming bacteria found in dental plaque, an intra oral biofilm in the presence of sugar.

130
Q

Epidemiological triangle

A

Host
Agent
Environment

131
Q

Agents of Caries

A

Streptococcus mutants (most cariogenic for enamel)
streptococcus sobnrinus
Vile lonely

132
Q

Mutans Streptococci (ms)

A 
Greater ms counts greater caries prevalence
Caries conducive because
-Ability to adhere to tooth
-Produce copious amounts of acid
-Survive at low pH
133
Q

Hope would House Study

A

Sucrose restricted diet a outing 81 children age 4-9
At the start 78% cavity free
53% continued caries free at age 13
This was significantly higher than caries free 13 yers old within the general residential population-only .4%

134
Q

Dental Caires Risk Factors

A 
Age
Gender
Race and Ethnicity
Socioeconomic status
Geography
135
Q

Deterrence

A 
Saliva
Plaque removal -OH
Dietary Habits
Fluoride Therapy
Sealants
Caries Vaccine
Antibiotics
Other
136
Q

How To measure Dental caries

A

Counts
Proportions
Rates
Indices

137
Q

Prevalence

A

The number of people in a population who have a given disease at ag vein point in time. Prevalence measures the frequency of all current cases of disease (old and new)

138
Q

Incidence

A

A measure of the number of lesions/period of time

Limited in that they only measure the numbers of new initial lesions per unit of time

139
Q

DMFT (DMFS)

A

Decayed
Missing
Filled
Teeth/Surface

Describe the amount of prevalence of dental caries in an individual

140
Q

Maximum value for DMF(S)

A

128

141
Q

DMFS value of ______ or higher can be considered to indicate sever disease in children up to age 17

A

7

142
Q

Limitations of prevalence

A

Provides pas history only
Does not provide rate of lesion development
Does not indicated if caries is active or inactive
Does not provide the frequency of occurrence of new lesions

143
Q

D+M+F

A

Prevalence

144
Q

D/DMF

A

Untreated caries

145
Q

F/DMF

A

Treated caries

146
Q

M/DMF

A

Tooth fatality

147
Q

DEFT or DFT

A

Decayed
Extraction
Filled

Index is useful up to the age of 6

148
Q

Significant Caries Index

A

Bring attention to the individual with the highest caries values in each population under investigation

149
Q

SiC index

A

Individuals are sorted according to their DMFT values

One third of the population with the highest caries scores is selected

The mean DMFT for this subgroup is calculated. This value is the SiC index

150
Q

Overarching Goals of Healthy People 2010

A

Increase quality and years of healthy life
Eliminates health disparities

28 Focus areas
467 specific objectives

151
Q

Practical Significance of the epidemiology of Dental Caries

A

Planning, funding, and delivery of services
-Example water fluoridation, clinics, Medicaid

Training: numbers and types of professionals

152
Q

Why are Statistics important

A

It allows us to understand information and make clinical decisions based on DATA from:

Scientific journals
Clinical study reports
Product manufacturers
Presentations at dental conferences

153
Q

Mean

A

Average of the date; sensitive to extreme values

154
Q

Median

A

Middle point of the data; less sensitive to extreme values

155
Q

Mode

A

Most frequently occurring value in the data

156
Q

SD

A

Measure how much the individual data points vary around the mean

157
Q

Frequency

A

Count of a given outcome or in each category

158
Q

Percentage

A

Count of a given outcome per hundred showing proportion of each category out fo the total

159
Q

Correlation

A

Is there a linear relationship between an independent variable X and a dependent variable Y

160
Q

Value of correlation coefficient can lie between

A

-1 and +1

161
Q

+ Value

A

As x increases, Y increases

162
Q
  • Value
A

As x increases y decreases

163
Q

The closer r is to +1 or -1__________ the relationship

A

Stronger

164
Q

Square of correlation

A

Is the fraction of variation in Y and explained by X

The higher the r^2 the better the fit of the regression line

165
Q

Hypothesis Testing

A

A hypothesis is an explanation for certain observations. We use hypothesis testing to tell if what we observe in the population is consistent with the hypothesis

166
Q

Null Hypothesis H0

A

Usually staters that there is no difference between 2 groups being compared or no effect of a product or intervention

167
Q

Alternative Hypothesis Ha

A

States that there is a difference between 2 groups being compared or an effect of a product or intervention

168
Q

Type 1 Error

A

Rejecting the null hypothesis that is actually true in the population

169
Q

Alpha statistical significance

A

Type 1 error

Alpha is commonly set to .05 or 5% of incorrectly rejecting the null hypothesis when it is actually true

170
Q

Type 2 Error

A

Failing to reject (accept) the null of hypothesis that is actually false in the population.

Probability of a type 2 error is beta

171
Q

P-Value

A

The probability assuming that the null hypothesis is true of seeing an effect as extreme or more extreme than that in the study by chance

172
Q

Reject null hypothesis is P-value _____ Alpha

A

Less than

173
Q

Fail to reject (accept ) null hypothesis if P value is _____ alpha

A

Greater

174
Q

Confidence Intervals

A

Is a range of values about a sample statistic that we are confident that the true population parameter lies

175
Q

T-Test

A

Statistical test that can be used to determine whether the mean value of a continuous outcome variable differs significantly between 2 independent groups

176
Q

Alternative test may be

A

Directional or non directional

177
Q

One Sample T Test

A

Can be used when the outcome variable of interest is only being examined in one group

178
Q

Matched pair T test

A

Can be used when subjects are matched in paired and their outcomes are compared within each matched pair ( including where observations are teen on the same subjects before and after a given intervention(

179
Q

Chi-Squared Test

A

When examining categorical data, the chi square test can be sued to compare the proportion of subjects in each of two groups who have a dichotomous outcome

180
Q

ANOVA

A

Analysis of Variance is a sticaltcal method that allows for comparison of several population means

181
Q

F statistic

A

Used by ANVOA, rejects null hypothesis that the population means of all groups are equal if Pvalue of F statisistic is greater than alpha (.05)

182
Q

The Fundamental Issue

A

Quantifying our confidence on how well the findings reflect the truth. Two approaches

  • Hyptohesis and pavlues
  • Confidence Interval estimation
183
Q

Limitations of statistical inference

A

Only tells about the role of chance or random error in making inference from your study population to the source population.

  • Does not tell about the role bias or confounding
  • Statisitics do not tell you about causality
184
Q

Bias

A

Systematic error in the design, conduct or analysis of a study that results in a mistaken estimate of an exposures effect on disease

185
Q

Types of Bias

A

Selection Bias

Information Bias

186
Q

Selection Bias

A

Systematic error in selecting subjects into one or more of the study groups such as cases and controls or exposed and unexposed

187
Q

Information Bias

A

Errors in procedures for gathering relevant information

Ex. Bias in recall in collecting data interview

188
Q

Confounding

A

Sitituation in which a non-causal association between a given exposure and an outcome is observed as a result of the influence of a third variable usually designated a confounding variable or confounded

189
Q

A variable is a confounder if:

A

1) It is a known risk factor of the outcome

2) It is associated with the exposure but is not the result of the exposure

190
Q

Is a covariate a cofounder

A

1) Is it associated with exposure
2) Is it causally associated with outcome

If YES then:
Calculate crude association
Calculate stat rum specific association

191
Q

Confounding is not an _______ phenomenon

A

All or none

Evaluative Sciences 1 (1 decks)

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