Intro to Epidemiology Flashcards
Define epidemiology.
The study of the distribution and determinants of health-related states or events in specified populations and the application of this study to control of health problems
Define Mortality Rate.
A measure of the frequency of occurrence of death in a defined population during a specified interval (denominator population and specific time frame necessary).
Provide examples of possible denominators in epidemiology.
- Health board
- City
- Hospital
- Disease register
- Recruited to a study
Identify main types of timeframes in epidemiology ? Provide an example for each.
Person-time
(10 deaths per 10,000 person years which could be 10,000 people for 1 year, 5,000 people for 2 years, 2,000 people for 5 years)
n-year follow-up
(5-year mortality of 10 per 10,000 people)
Define incidence. How do we calculate this ?
Number of new cases
Incidence rate = (Number of new people with outcome over a time period x 100,000) / Total number of people in the group at risk
Define prevalence. How do we calculate this?
Proportion of population that has disease
Point prevalence rate (at a specified time, e.g. in 2010) = (Number of people with outcome at a point in time x 100) / Total number of people in the group
Period prevalence rate (over a specified period, e.g. lifetime) = (Number of people with outcome during a time period x 100) / Average number of people in the group
Distinguish prevalence and incidence.
INCIDENCE
• A rate or a proportion
• Useful for identifying causes of diseases
• Occurs, by definition, only in people without the disease
PREVALENCE
• A proportion
• Identifies disease burden
• Useful for planning services
• Depends partly on incidence
Describe how the following will affect prevalence/incidence ?
- Improved medication, procedures, rehabilitation
- Improved long-term management
- Increased diagnosis
Improved medication, procedures, rehabilitation, etc. –> Decreased prevalence
Improved long-term management –> Increased prevalence
Increased diagnosis –> Increased prevalence
What are the different patterns of outcome occurrence ?
- Sporadic: Occasional cases occurring irregularly.
- Endemic: Persistent background level of occurrence (low to moderate levels)
- Epidemic: Occurrence in excess of the expected level for a given time period
- Pandemic: Epidemic occurring in or spreading over more than one continent
Define outcome in the context of epidemiology, giving examples.
Any defined disease, state of health, health-related event or death
– Death – Hospitalisation – First diagnosis with a disease – Recurrence (e.g. cancer) – Quality of life – Surrogates (e.g. blood pressure, lung function, etc.)
Define exposures in the context of epidemiology.
Any factor that may be associated with an outcome of interest.
Identify the main categories of exposures, providing examples for each.
• Non-modifiable
– age, sex, genotype
• Modifiable
– smoking, weight, diet, alcohol consumption
• Interventions
– drug therapy
– surgery
– lifestyle advice
How do we calculate risk in epidemiology ?
(Number of outcomes in a group x 100) / Number of people in the group
How do we calculate Relative Risk ?
Relative risk (RR; Risk ratio) = Risk in exposed / Risk in unexposed
How do calculate Relative Risk Reduction (RRR) ?
(1 – Relative risk) x 100
How do we calculate Absolute Risk Reduction (ARR; risk difference) ?
Risk in unexposed - Risk in exposed
How do we calculate Number needed to treat (NNT) ?
1 / Absolute risk reduction
Describe the main features of confidence intervals.
– Represents range of plausible values
– Values near the limits less plausible than those in the middle
– The wider the interval the greater the uncertainty
– The higher the line, the more plausible the value.
Describe the hierarchy of study designs/evidence.
From top down:
- Systematic reviews and meta-analyses
- EXPERIMENTAL DESIGNS: Randomised controlled Trials and Pseudo RCTs
- QUASI-EXPERIMENTAL DESIGNS: quasi-experimental prospectively controlled studies, pre-test or post-test or historic/retrospective control group study
- OBSERVATIONAL-ANALYTIC DESIGNS: Cohort study, Case-controlled study
- OBSERVATIONAL-DESCRIPTIVE DESIGNS: Cross-sectional studies, case series, case study
- Background information/expert opinion
Describe the main features of cross-sectional study.
• Sample a population
• Estimate the proportion:
– Different exposures
– Different signs/symptoms
– Different outcomes
• Use data
– to describe prevalence/burden
– to explore associations
Describe the main features of case-control study.
- Select cases with an outcome
- Select controls without the outcome
- Explore EXPOSURES in cases and controls
- Compare exposures in cases and controls
- Identify association
Describe the main features of cohort study.
- Select people without an outcome
- Classify according to an exposure
• Follow-up
– Prospective (The investigators design the questions and data collection procedures carefully in order to obtain accurate information about exposures before disease develops in any of the subjects. After baseline information is collected, subjects in a prospective cohort study are then followed “longitudinally,” i.e. over a period of time)
– Retrospective (conceived after some people have already developed the outcomes of interest. The investigators jump back in time to identify a cohort of individuals at a point in time before they have developed the outcomes of interest, and they try to establish their exposure status at that point in time. They then determine whether the subject subsequently developed the outcome of interest.)
• Compare RISK of disease in exposed and unexposed
Describe the main features of a Randomised Controlled Trial.
• Random allocation
– Intervention group
– Control/comparator group
• Compare RISK of outcome in intervention and control groups
Identify the objective, and timeframe (future/past) of the following study designs:
- RCT
- Cohort Study
- Case-Control Study
- Cross-sectional Study
RCT: Treatment effect (Future)
Cohort: Cause, Prognosis, Incidence (Prospective= Future, Retrospective=Past)
Case-control: Cause (Past)
Cross-sectional: Prevalence (Past)
Define confounding variable. Give an example.
“Variable that influences both the dependent variable and independent variable causing a spurious association.
Spurious correlation between ice cream and murder”(confounding variable is season/weather).
Define bias.
Bias is systematic error. It might be due to errors in: – what data are collected – how data are collected – how data are analysed – how data are interpreted – how data are reported
Bias leads to wrong conclusions concerning:
- Effectiveness
- Causation
Are study designs at the top of the evidence pyramid more, or less prone to confounding and bias ?
Less prone to confounding and bias
What are the criteria to infer causality ?
- STRENGTH
A causal link is more likely with strong
associations (RR or OR) but, a small association does not mean that there is not a causal effect - CONSISTENCY (REPRODUCIBILITY)
A causal link is more likely if the association is observed in different studies and different sub-groups - SPECIFICTY
A causal link is more likely when a disease is associated with one specific factor. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship - TEMPORALITY
A causal link is more likely if exposure to the putative cause has been shown to precede the outcome (i.e. RCT, prospective cohort) - BIOLOGICAL GRADIENT
A causal link is more likely if different levels of exposure to the putative factor lead to different risk of acquiring the
outcome
- Greater exposure should generally lead to greater
incidence of the effect.
- In some cases, the mere presence of the factor can
trigger the effect.
- In other cases, an inverse proportion is observed:
greater exposure leads to lower incidence - PLAUSIBILITY
A causal link is more likely if a biologically plausible mechanism is likely or demonstrated
But, knowledge of the mechanism is limited by current knowledge - COHERENCE
A causal link is more likely if the observed association conforms with current knowledge (epidemiological and laboratory findings, but lack of laboratory evidence cannot
invalidate an epidemiological association) - EXPERIMENT
A causal link is very likely if removal or prevention of the putative factor leads to a reduced or non-existent risk of acquiring the outcome (experimental evidence) - ANALOGY
A causal link is more likely if an analogy exists with other diseases, species or settings
