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Epidemiology > Exam > Flashcards

Exam Flashcards

1
Q

Epidemiology definition

A

The study of diseases
-science of epidemics
-science of illness
-science of distribution of disease

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2
Q

Historical figures in epidemiology

A

John Grunt- bills of mortality
James Lind- scurvy
Pierre Charles-Alexandre Louis- inflammation of organs
John Snow- link between cholera and water supply
Doll and Hill- tobacco

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3
Q

Descriptive statistics

A

Focused on population rather than individuals
-quantitatively describes or summarizes features from a collection of information

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4
Q

Descriptive study

A

Describes characteristics of a population or phenomenon being studied. It does not answer questions about how/when/why the characteristics occurred.
 Simple description of health status of a community
 No link between cause and effect
 First step in examining patterns of disease

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5
Q

Health definition

A

a state of complete physical, mental and social well-being and not merely the absence of disease and infirmity

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6
Q

Disease definition

A

a pathological process causing illness

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7
Q

Illness definition

A

feeling or experience of unhealth which is entirely personal

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8
Q

Prevalence

A

Frequency of existing cases, the number of people in a population who currently have a particular outcome
=number of people with the disease at a specified time / number of people in the population who could get the disease (at risk) at the time

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9
Q

Point prevalence

A

cases existing at a certain point in time (generally a day)

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10
Q

Period prevalence

A

cases existing over a specified period of time (week, month, year)

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11
Q

Incidence

A

Frequency of new cases over a period of time (rate)
=number of new events in a specified period / number of persons exposed to risk during this period

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12
Q

Risk/cumulative incidence

A

probability that an individual will develop an outcome over a specified period of time
=number of people who get a disease during a specified period / number of people free of the disease in the population at risk at the beginning of the period

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13
Q

Crude rate

A

rates that apply to the entire population (rate of spread)

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14
Q

Specific rate

A

rates that apply to those within a population with certain characteristics (rate of spread)

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15
Q

Case fatality (%)

A

=number of deaths from diagnosed cases in a given period / number of diagnosed cases of the disease in the same period *100

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16
Q

Impairment

A

any loss or abnormality of psychological, physiological or anatomical structure or function

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17
Q

Disability

A

any restriction or lack (resulting from an impairment) of ability to
perform an activity in the manner or within the range considered
normal for a human being

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18
Q

Handicap

A

a disadvantage for a given individual, resulting from an impairment or a disability, that limits or prevents the fulfilment of a role that is normal (depending on age, sex, and social and cultural factors) for that individual.

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19
Q

Years of life lost due to death (YLL)

A

Takes into account the age at which deaths occur by giving greater weight to deaths at younger age and lower weight to deaths at older age

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20
Q

Years of life lost due to disability (YLD)

A

Takes into account the number of healthy years lost due to living with a disability or with the symptoms of disease

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21
Q

Disability adjusted life year (DALY)

A

A year of healthy life lost, either through premature death or equivalently through living with disability due to illness or injury.
=YLL + YLD = DALY

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22
Q

Quality adjusted life years (QALY)

A

Measures the quality and quantity of life lived and is based on the
number of years of life that is added by an intervention/treatment

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23
Q

Validity

A

If the study is repeated in another setting with same population = same results
 Systematic error

an expression of the degree to which a measurement actually measures what it claims to measure
– Conformity
– Correctness
– Accuracy

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24
Q

Reliability

A

If the study is repeated under same conditions with same population = same results
 Random error

The degree of stability exhibited when a
measurement is repeated under identical conditions
– Consistency
– Repeatability
– Precision
– Reducibility

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25
Q

Probability sampling

A

Random and not based on choice of researchers
- Simple random
– Stratified
– Systematic
– Cluster

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26
Q

Non-probability sampling

A

Researchers pick
– Convenience
– Snowball
– Purposive

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27
Q

Volunteer bias

A

People volunteer to participate in the study
– One sample of the target population is more likely to be included/excluded than others
– Self-selection or study inclusion/exclusion
 Participants in screening programs tend to be healthier than those who don’t volunteer or comply
 Impact on disease specific and overall mortality

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28
Q

Healthy volunteer effect

A

Example of self-selection whereby
outcome, over time, directly affects the
exposure
More healthy people may be found in
potentially hazardous environments

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29
Q

Selection bias

A

Systematic difference between those in the study /
intervention / exposure and those not
Overcome by;
– Randomisation
– Transparent selection process

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30
Q

Allocation bias

A

Process of allocating participants to groups is compromised
Overcome by;
– Randomisation
– Concealment of the
randomisation process

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31
Q

Performance bias

A

Once allocated, occurs when any
differences in outcomes may be
attributed to the ‘intervention’ or
exposure
– Guards against ‘placebo’ effect
– Provides ‘natural’ prognosis
Overcome by;
– Blinding

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32
Q

Hawthorne effect

A

Participants perform/behave differently due to being involved in the study (they know they are being observed)

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33
Q

Placebo effect

A

Responses (positive/negative) to the perceived intervention

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34
Q

Attrition bias

A

Attrition rate, or drop-outs, within a study
Important to identify reasons for withdrawals due to;
– Missing data
– Adverse events
– Motivation
– Other…
Overcome by;
– Intention-to-treat analysis

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35
Q

CONSORT statement

A

CONSORT encompasses various initiatives developed by the CONSORT Group to alleviate the problems arising from inadequate reporting of randomized controlled trials
(complete and transparent reporting)

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36
Q

Detection bias

A

Also known as ascertainment bias
-An investigator may distort or
misclassify the outcome measured
if participant group is known
Overcome by;
– Blinding

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37
Q

Measuring / information bias

A

Errors in measuring outcomes that lead to misclassification
 Non-differential vs differential misclassification

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38
Q

Non-differential misclassification

A

When measurement error and misclassification occurs equally
in all groups being compared
 Due to;
– Random error
– Instrument bias
 Results in dilution of ‘true’ effect

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39
Q

Differential misclassification

A

Measurement error and misclassification occurs to a greater
extent in one group over others
 Due to systematic error
 Examples include;
– Recall bias
– Response bias
– Interviewer / observer bias

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40
Q

Recall bias

A

Differences in accuracy or recollections of events/exposures from participants
 Bias is unintentional and
often based on expectation
– MMR vaccination and autism

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41
Q

Response bias

A

Often occurs in patient self-reported data
 Bias is intentional
– Portraying oneself in good light
– Lack of understanding

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42
Q

Interviewer / observer bias

A

Recording of information in different ways between interviewers / observers
 Corrected by;
– Standardised questions
– Inter-observer / inter-rater
reliability

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43
Q

Will Rogers phenomenon

A

‘When the Okies left Oklahoma and moved to California, they raised the average intelligence level in both states.’
1. Individuals who are misclassified (Okies) who are
moving, are below average for the current
context (Oklahoma)
2. Individuals who are misclassified (Okies) are
above the average for the context that they enter
(California)

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44
Q

Confounding bias

A

Occurs when the effect of the study factor on the outcome is mixed in the data with the effect on another (third variable, or
confounder)
Overcome by stratification

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45
Q

Publication bias

A

Only studies which show a certain result are published

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46
Q

Case report / series

A

Detailed report by one or more health professionals on the profile of a single patient
Case series is a report on a series of patients with an outcome
of interest
-Strengths: Hypothesis generating, quick, cheap
-Limitations: Generalisability, bias

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47
Q

Ecological study

A

 Also known as correlational studies
 Units of analysis are groups, rather than individuals
 Compares disease frequencies between;
– Different populations during the same period of time, or
– Same population at different time periods
Strengths: Fast, easy, cheap
Limitations: Bias, association only, possible misclassification

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48
Q

Cross-sectional study

A

Exposure and outcome determined simultaneously
 Cross-sectional studies measure;
– Prevalence of disease
– Presence/absence of exposure
 Disease and exposure can be assessed at the same point in
time in a cross-sectional study
Strengths: Estimate prevalence of outcome, identifies association
Limitations: Can’t generate cause and effect, only offers a snap-shot

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48
Q

Case-control study

A

 Compares the occurrence of possible cause in ‘cases’ and
‘controls’
 Data is collected at one point in time
 Exposures are collected at a previous point in time
 Case-control studies are retrospective as the investigator is
looking backward from disease to possible cause
Strengths: Good for rare outcomes & long diseases, quick, cheap
Limitations: Bias

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49
Q

Cohort study

A

 Involves follow-up of people with a common characteristic
 The incidence of an outcome is compared between those
exposed and those not exposed to a risk factor during the
study time
Strengths: Identifies natural history, temporal sequence
Limitations: Loss to follow up, expensive, time consuming

Retrospective cohort: Participants identified on the basis of previously recorded exposure

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50
Q

Randomised controlled trial

A

 An RCT is an experimental comparative study in which
participants are allocated to treatment/intervention or
control/placebo groups using a random mechanism
 Participants have an equal chance of being allocated to an
intervention or control group
Strengths: Reduced risk of bias, cause and effect
Limitations: Expensive, follow up duration, ethics

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51
Q

Cross-over RCT

A

 Participants receive a series of treatments
 Participants are then ‘crossed-over’ to receive the alternate
treatment
Strengths: Patients serve as own control, sample size
Limitations: Feasibility, ethics, order of events?

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52
Q

Cluster RCT

A

Clusters rather than individuals are randomized
– Geography
– Communities
– Social
– Educational
– Occupational

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53
Q

Component cause

A

– A variety of separate requirements contributing to the cause
 Obesity, insulin resistance, hypertension, low LDL cholesterol, high triglycerides

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54
Q

Sufficient cause

A

– When all components are part of the one sufficient cause that will lead to the effect
 Metabolic syndrome

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55
Q

Necessary cause

A

– When an outcome can’t develop in its absence
 Environmental, biological, social determinants of health
– e.g. breast cancer and BRCA gene

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56
Q

Bradford Hill criteria for determining a causal relationship

A

 Temporal relation (this is necessary)
 Plausibility
 Consistency
 Strength
 Does-response relationship
 Reversibility
 Study design
 Judging the evidence

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57
Q

Temporal relationship

A

 The cause must precede the
effect
 Sometimes difficult to
demonstrate with casecontrol and cross-sectional
studies
– Patients with stomach cancer
have low levels of Vitamin C…

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58
Q

Plausibility

A

 Is the association consistent with other knowledge?
 Issues to consider include;
– Mechanism of action
– Evidence from animal, biological studies

59
Q

Consistency

A

 Have similar results been shown in other studies?

60
Q

Strength

A

 Strength of a relationship as measured by the relative risk (RR)
 Rule of thumb: RR ≥ 2 considered to demonstrate a strong
relationship

61
Q

Dose–response relationship

A

 A dose–response relationship occurs when changes in the level
of a possible cause are associated with changes in the
prevalence or incidence of the effect

62
Q

Reversibility

A

 When the removal of a possible cause results in a reduced
disease risk, there is a greater likelihood that the association is
causal
– Use of NSAIDs and gastrointestinal bleeding / ulcers
– Challenge / re-challenge strategy

63
Q

Study design

A

How strong is the study design

64
Q

Judging the evidence

A

 Temporal relationship is essential, then… judge the evidence

65
Q

Surrogate endpoint

A

 Surrogate endpoint, or marker
– Measure of an effect of a treatment / exposure that may correlate with
a real clinical endpoint
– But the relationship / correlation is not certain
 Why use surrogate endpoints?
– Regulatory approval (phase III trials)
– Reduction in duration and cost of studies

66
Q

Primary outcomes

A

The main thing you are investigating

67
Q

Secondary outcomes

A

Side effects

68
Q

Ad-hoc outcomes

A

Confounding factors

69
Q

Data types

A

Numerical
 Continuous (blood pressure)
 Discrete (number of children)
Categorical
 Nominal (blood type)
 Ordinal (measure of pain)

70
Q

Interviews

A
  1. Scheduled standardized interview
    – Structure is uniform
  2. Non-scheduled standardized interview
    – Phrasing may be altered
  3. Non-standardized interview
    – Conversation
71
Q

Questionnaires

A

 The design process;
1. Introductory statement explaining its purpose
2. Demographic questions
3. Simple factual questions
4. Complex questions asking for opinions or attitudes
5. Closing question / statement

72
Q

Double barreled question

A

– How useful did you find meeting with the practice nurse before
attending your appointment and did the online system speed up the process?

73
Q

Leading questions

A

– We have recently introduced practice nurses into our clinics to ensure that patients who are discharged from the emergency room have continually care from the ED to home. What are your thoughts on this innovation?

74
Q

Coding questions

A

 Have you ever had a chest x-ray?
– Yes / No
 What was the natural colour of your hair when you were 20 years old?
– Red / Blond / Brown / Black
 How many packs of cigarettes would you smoke in a week?
– One / two / three / four / five +
 Which of the following symptoms are you currently experiencing?
– Headache / Dizziness / Cough / Temperature

75
Q

Visual analogue scales (VAS) and numerical rating scales (NRS)

A

refer to OneNote

76
Q

Adjectival scale

A

refer to OneNote

77
Q

Likert scale

A

refer to OneNote

78
Q

Semantic differential scale

A

refer to OneNote

79
Q

Inter-rater reliability

A

In statistics, inter-rater reliability is the degree of agreement among independent observers who rate, code, or assess the same phenomenon
– Reliability across multi investigators

80
Q

Intra-rater reliability

A

– Reliability of a single investigator

81
Q

Kappa statistic

A

A statistic which can inform researchers about the inter-rater agreement

82
Q

Measures of treatment effect

A

For binary (dichotomous) outcomes
 Absolute risk reduction/increase (ARR/I)
 Relative Risk (RR)
 Relative risk reduction/increase (RRR/I)
 Number needed to treat/harm (NNT/H)

83
Q

Absolute risk reduction (ARR)

A

Is simply the difference in the proportion of subjects with the
outcome of interest in each group.
ARR = X (control) – Y (intervention)
remember to put it in %

84
Q

Relative risk (RR)

A

Is defined as the probability of an event in the active treatment
group divided by the probability of an event in the control group.
A relative risk of 1 is the null value or no difference
RR = Y ÷ X or (intervention ÷ control)

85
Q

Relative risk reduction (RRR)

A

Can be thought of as a standardised measure of the absolute risk
reduction
It can be expressed as the absolute risk reduction divided by the
probability of an event in the control group
RRR = 1 – RR or
x-y / x * 100

86
Q

Number needed to treat (NNT)

A

Is the number of patients who would have to receive the treatment for 1 of them to benefit
The number needed to treat is the reciprocal of the absolute risk reduction
NNT = 1 ÷ ARR

87
Q

Odds ratio (OR)

A

An odds ratio is a statistic that quantifies the strength of the association between two events
AD / BC (refer to OneNote)

88
Q

Confidence intervals (CIs)

A

 Most research uses analysis from a sample to estimate the
value in the true population
 A CI is the range of values within which we are confident that
the true population value lies
 Bounded by the lower confidence limit (LCL) and upper
confidence limit (UCL)
- If they cross 1 then there is no difference

 We can also use CIs to compare two sample means and
determine whether the difference between groups is
statistically significant
 Critical value here is ZERO
 If the CI for the difference in means includes zero, the
difference is not statistically significant
 If the CI for the difference in means does not include
zero, the difference is statistically significant

89
Q

Mean difference (MD)

A

 Mean difference (MD) is the absolute difference between the means of
two continuous variables
 A mean difference of 0 means no effect

90
Q

P-Values

A

 P = Probability
 A p-value indicates the probability that an observed test
result is due to chance (i.e. not a true result)
 Standard accepted cut-off point is p<0.05
– This means that there is a 5/100 (5%) chance that our
result is due to chance
– We are willing to accept that we will be wrong 5% of
the time

91
Q

Hazard ratios

A

 The effects of possible prognostic factors, which are relative to
one another, can be interpreted using a Hazard Ratio (HR)
 Hazard ratios are similar to a Relative Risk, with the difference
being that the Hazard Ratio is derived from the time-to-event
analysis
 A Hazard Ratio of 1 (HR=1.0) suggests no benefit

92
Q

Infectious disease (ID)

A

Case is a source of infection for others
* Failure to detect early and treat is detrimental
Immunity
* Prior exposure may confer immunity.
* Vaccination is important measure
* Heard immunity
Urgency in response
* Prompt response is important. Surveillance and
preparedness is key
Multiple prevention measures is critical
* Prevent exposure and transmission
* Treatment is a key prevention
* Increase resilience of population

93
Q

Mode of transmission

A

Direct transmission
* Direct physical contact

Indirect transmission
* Vehicle-borne
-Contaminated inanimate materials or objects (fomites).
* Vector-borne *
-Mechanical: No reproduction of agent (i.e. fly) in vector
-Biological: Reproduction (i.e. Mosquitoes) in vector

Airborne
* Droplet
* Microbial aerosols usually the respiratory tract.
* Dust
* The small particles from soil clothes, bedding or
contaminated floors by wind or mechanical agitation.

94
Q

Endemic

A

refers to the constant presence of a disease or infectious agent in a population within a geographic area .

95
Q

Sporadic

A

refers to a disease that occurs infrequently and irregularly.

96
Q

Cluster

A

refers to an aggregation of cases grouped in place and time that are suspected to be greater than the number expected

97
Q

Outbreak

A

is a noticeable, often small but sudden, increase over the expected number of epidemiologically linked cases

98
Q

Epidemic

A

refers to an increase, often sudden, in the number of cases above what is normally expected in that population in that area.

99
Q

Pandemic

A

is an epidemic with a P ( p for passport) - A new pathogen that spreads from person to person across the globe”.

100
Q

Reproduction number (R)

A

-Reproduction number (R) is the
average number of new infections
caused by 1 infected individual
-Basic reproduction number (R0) is the
reproduction number (R) when the
entire population is susceptible.

101
Q

Epidemic curves

A
  • An “epidemic curve” shows the frequency of new cases
    over time based on the date of onset of disease.
  • The shape of the curve in relation to the incubation
    period for a particular disease can give clues about the
    source.
    -refer to the OneNote for different curves
102
Q

Source outbreak

A

Point source (common source)
* Single source of contamination (food borne outbreaks)
* Many people get sick concurrently (1 incubation period)
* Steep upslope and More gradual downslope
* Exposure period can be predicted from curve

103
Q

Propagated outbreaks

A
  • Can affect many people, over a long period
    of time E.g. person to person spread
  • Prolonged exposure
  • Can quantify transmissibility of propagated
    outbreaks using R
104
Q

Steps in an outbreak investigation

A
  1. Confirm existence of an outbreak
  2. Verify the diagnosis
  3. Construct a working case definition
  4. Find cases systematically, line list
  5. Perform descriptive epidemiology
  6. Develop hypotheses
  7. Evaluate & refine hypotheses, perform additional studies as necessary
  8. Implement control and prevention measures
  9. Communicate findings
  10. Follow up and maintain surveillance
    -details in OneNote
105
Q

Systematic review

A

A systematic review is a summary of the medical literature that uses explicit and reproducible methods to systematically search, critically appraise, and synthesize on a specific issue. It synthesizes the results of multiple primary studies related to each other by using strategies that reduce biases and random errors.

Key factors to consider to determine how good it is
– Inclusion criteria
– Inadequate literature search
– Publication bias
– Inadequate assessment of study quality

106
Q

Meta-analysis

A

Meta-analysis is a research process used to systematically synthesise or merge the findings of single, independent studies, using statistical methods to calculate an overall or ‘absolute’ effect.2 Meta-analysis does not simply pool data from smaller studies to achieve a larger sample size.

107
Q

meta analysis vs systematic review

A

A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies.2 Apr 2018

108
Q

Literature review

A

A literature review explores and evaluates the literature on a specific topic or question. It synthesises the contributions of the different authors, often to identify areas that need further exploration.
Comparison to systematic review in OneNote

109
Q

Funnel plots

A

Good for detecting publication bias

110
Q

PRISMA flow chart

A

refer to OneNote for picture

111
Q

Data synthesis

A

 A meta-analysis does not just simply add up numbers across
studies!
 It identifies results from individual studies and calculates a
weighted average
 Simply adding studies would break;
– Power of randomisation
– Reduce variance
– Overestimate significance

112
Q

Forest plots

A

used in systematic reviews to determine the cumulative findings in a multitude of studies, examples in OneNote
dichotomous (1)
continuous (0)

113
Q

Heterogeneity

A

 Heterogeneity refers to the diversity that exists between
studies in a review
– clinical
– methodological
– statistical
 Identifying heterogeneity
– visual inspection of the forest plots
– chi-squared (chi2) test (Q test)
– I2 statistic to quantify heterogeneity

114
Q

Sensitivity analyses

A

Sensitivity analyses examine how the results vary under
different assumptions
– e.g. re-analysing data using ‘low’, ‘medium’ and ‘high’ quality studies
good quality trials vs poor quality trials

115
Q

Subgroup analyses

A

Subgroup analyses are meta-analysis on subgroups of the
studies
– e.g. sex, age, drug doses

116
Q

Diagnostic tests

A

– Usually performed when a patient who has a clinical problem & is more likely to have the disease

117
Q

Screening

A

– Performed on healthy people, before they have symptoms

118
Q

Pre-test probability

A

Pre-test probability + test info = Post-test probability
 Pre-test probability
– Clinical assessment
– Personal experience
– Published data (prevalence of a disease)

119
Q

Sensitivity

A

-Sensitivity and specificity always and only relate/refer to the test, they don’t relate to the patient
-How accurate is the test that we’re using
-Sensitivity measures true positive (if somebody has the disease how likely are they to test positive)

120
Q

Specificity

A

-Sensitivity and specificity always and only relate/refer to the test, they don’t relate to the patient
-How accurate is the test that we’re using
-Specificity measures true negative (if somebody doesn’t have the disease how likely are they to test negative)

121
Q

Positive predictive value

A

These are kinda like sensitivity and specificity but they relate to the participant/patient
-Positive predictive value (the chance that if somebody tests positive that they’ll actually have the disease)

Sensitivity and specificity relate to the test and the test won’t change. The problem with PPV and NPV is that demographics and disease prevalence does change based on setting which impacts the PPV and NPV

122
Q

Negative predictive value

A

These are kinda like sensitivity and specificity but they relate to the participant/patient
-Negative predictive value (the chance that if somebody tests negative that they’ll actually not have the disease)

Sensitivity and specificity relate to the test and the test won’t change. The problem with PPV and NPV is that demographics and disease prevalence does change based on setting which impacts the PPV and NPV

123
Q

Positive likelihood ratio

A

 Positive Likelihood ratio (LR+)
probability of positive test result in a patient with the disease / probability of positive test result in a patient without the disease
or
sensitivity / (1 – specificity)

  • When the positive likelihood ratio is close to 10 it means that when somebody tests positive we can be pretty certain they have the disease
124
Q

Negative likelihood ratio

A

 Negative Likelihood ratio (LR-)
probability of negative test result in a patient with the disease / probability of negative test result in a patient without the disease
or
(1 – sensitivity) / specificity

  • When the negative likelihood ratio is close to 0 it means that when somebody tests negative we can be pretty certain they don’t have the disease
125
Q

Nomogram

A

examples in OneNote

Using multiple tests (when they test positive for them all they build of likelihood of having the disease)

126
Q

Primary prevention

A

Primary prevention aims to prevent disease / injury before it occurs
– Vaccination against
infectious disease
– Education and awareness
of healthy and safe habits
– Legislation

127
Q

Secondary prevention

A

Secondary prevention aims to reduce the impact of disease / injury that has occurred
– Screening to detect disease at
an early stage
– Interventions to prevent further
disease / injury

128
Q

Tertiary prevention

A

Tertiary prevention aims to alleviate the impact of ongoing illness / injury
– Rehabilitation
 Mental
 Physical
 Social

129
Q

‘high-risk’ prevention strategy

A

 Target a select group – usually vulnerable
 Intravenous drug users
– Needle-exchange program
– Vaccination (against hepatitis B)
 Screening pregnant women aged over 40 years
– MSAFP
– Ultrasound
– Amniocentesis
 Limitation: common disease / widespread cause

130
Q

‘mass’ prevention strategy

A

 Common disease / widespread cause
 Mass / population strategy
 Legislated use of seatbelts
– Targeting only ‘high’ risk e.g. males 18-25 years would
not work

131
Q

Opportunistic screening

A

going in for 1 test but decide to do 4 because we can
– ‘Case finding’
– e.g. PSA test at ‘general health check-up’

132
Q

Selective screening

A

– Screening those in a specific criteria
– e.g. mammography in women 50-69

133
Q

Mass screening

A

– Screening across an ‘entire population’
– e.g. Neonatal screening (i.e Guthrie test)

134
Q

Benefits of screening

A

 Potential benefits
– Early detection of the disease
– Early treatment of the disease (↑
treatment options)
– Psychological well being

135
Q

Limitations of screening

A

 Potential limitations
– Over-diagnosis (over-treatment)
– Interval disease (cancers – too late???)
– False negative/positive (implications)
– Side effects (screening and treatment)

136
Q

Bias in screening

A

 Lead time
 Length time
 Volunteer
 Over-diagnosis

137
Q

lead-time bias

A

 It does not take into account the natural history of the disease
 The period between screens when disease is detected by
screening and when it would have become symptomatic and
been diagnosed in the usual way

138
Q

length time bias

A

 Occurs due to heterogeneity in the disease (i.e. fast and slow
growing tumours)
 Screening tests more likely to find slow growing disease,
hence better apparent prognosis
 Over-representation of slowly progressing disease among
cases detected by screening

139
Q

Over-diagnosis

A

Identification of slow growing cancers that may never have
become apparent (‘false positive’)

140
Q

Interval disease

A

Can also be thought of as ‘false negatives’
 Interval disease occur between screening as they are found in
the time interval between screens
 How do you overcome interval disease???
– Increase screening frequency
– But, do you increase chances of over-diagnosis?

141
Q

Measures of screening effectiveness

A

A number of measures can be used to quantify how ‘effective’
a screening program is;
– RR, RRR
– Gain in life expectancy
– Cost per case detected
– Cost per life saved
– Gain in quality adjusted life years (QALYs)
– Number needed to screen (NNS)

a good screening test is…
-Safe
-Simple
-Reliable
-Accurate/valid

142
Q

Principles for the introduction of population
screening

A
  1. The condition should be an important health problem
  2. There should be a recognisable latent or early symptomatic stage
  3. The natural history of the condition, including development from latent to declared disease, should be adequately understood
  4. There should be an accepted treatment for patients with recognised disease
  5. There should be a suitable test or examination that has a high level of accuracy
  6. The test should be acceptable to the population
  7. There should be an agreed policy on whom to treat as patients
  8. Facilities for diagnosis and treatment should be available
  9. The cost of screening (including diagnosis and treatment of patients diagnosed) should be economically balanced in relation to possible expenditure on medical care as a whole, and
  10. Screening should be a continuing process and not a ‘once and for all’ project.
143
Q

Efficacy

A

Does the intervention ‘work’ under ideal, ‘laboratory’ conditions?

144
Q

Effectiveness

A

It’s public health impact
 If we administer the intervention in ‘real life’ situations, Is it effective?
Barriers include…
 Taste
 Mode of delivery
 Cost
 Accessibility
 Perception

145
Q

Efficiency

A

If the intervention is effective, what is the cost to benefit ratio?

Costs include: money, discomfort, pain, disability, quality of life and social implications

146
Q

Implementation science

A

Peer-reviewed journal
Implementation Science is an open access peer-reviewed academic journal in healthcare that was established in 2006

Epidemiology (2 decks)

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