1 research Flashcards
routine data
Routine data describes non-targeted information that is obtained in a standardised and consistent manner.
strengths and weakness of routine data
epidemiological trends
Time: secular, periodic, epidemic
person
place
DALY Vs QALY
DALY: the LOSS of the equivalent of one year of full health.
sum of the years of life lost to due to premature mortality (YLLs) and the years lived with a disability (YLDs)
QALY: one year of life GAIN in perfect health
Random error
Chance differences in the true and recorded values may result in an apparent association between an exposure and an outcome, and such variations may arise fro:
- unbiased measurement errors
- biological variation within an individual
Bias
any systematic error in an epidemiological study that results in an incorrect estimate of the true effect of an exposure on the outcome of interest
Either: Selection and information bias
Misclassification (Information bias)
classification of an individual, a value or an attribute into a category other than that to which it should be assigned.
differential or non-differential.
non diff fine as tends to null.
Types of information bias (differential)
- observer bias
- interview bias
- recall bias
- social desirability bias
- performance bias
- detection bias
selection bias
- loss to follow up
- sampling bias
- allocation bias
- attrition bias
- healthy worker effect (occupational cohort studies)
control bias (case-controls)
confounding control
- design:
- randomisation, matching, restriction - analysis
- stratification (mantel Haenszal)
- multivariate analysis
- standardisation
reduce bias
- randomisation
- blinding
- training
- protocol
- ease of follow up
- high risk cohort
- duplication
confounder
Independently associated with exposure and outcome but not on causal pathway
residual confounding
residual confounding: unknown confounding left after taking into account known
should be equal if RANDOMISATION done
Descriptive studies
example
pros/ cons
ecological fallacy
case studies and ecological studies
ecological studies: population level
- aggregate, environmental, global
- geographical and time series
Good:
rapid, low-cost, if only aggregate data available, exposures in different areas, spatial framework
bad:
control: selection
no causation, publication bias, confounding control, recording difference
ECOLOGICAL FALLACY
effect on population level not seen at individual level
- no possible to link exposure to individual
- data collected for another reason
- average exposure not linear
- confounding control
example: new migrants states with high literacy: individual migrants lower literacy
study designs
Observational: cross section, case-control, cohort
interventional: RCT, non randomised trial
cross sectional
collect exposure and disease outcomes at the same time
- descriptive, analytical (or ecological level)
prevalence and odds
pros:
Can study multiple exposures and outcomes. Rapid and cheap.
Useful for rare diseases.
Useful for detecting disease burden.
cons:
Because cross-sectional studies measure prevalence, not incidence, the findings cannot differentiate between the determinants of aetiology and survival.
difficult to determine if an outcome or an exposure came first, because both were assessed simultaneously (i.e. risk of reverse causation).
May be subject to recall bias
cross-sectional ecological study: the study by Drain et al. [4] that compared HIV prevalence and rates of male circumcision in 118 developing countries and found that HIV prevalence was lower in countries where male circumcision
Cohort
looks at association between exposure and outcome by following a group of exposed individuals over a period of time (often years) to see whether they develop the disease or outcome of interest.
prospective: follow until get outcome or until time limit reached
retrospective: exposure and outcome have already occurred at the start of the study. Pre-existing data, such as medical notes, can be used to assess any causal links, so lengthy follow-up is not required.
pros:
- prospective (temporal)
- multiple effects
- rare exposure
- long latency
cons;
- time consuming
- expensive
- rare disease
- recall bias
nested case-control: can be within a cohort
examples:
- farringham cohort study:
variations of RCT
Parallel (traditional)
crossover (person their own control, will need to ensure Rx washout)
factorial design
wo or more interventions are compared singly and in combination against a
comparison group (i.e. there may be four groups: intervention A, intervention B,
interventions A and B, and control).
This design allows the investigator to study
eg PEACE study 2x2 facotrial for prostate cancer (4 groups)
cluster:
eg ochomo et al: spatial malaria nets in Kenya
types of randomisation
systematic allocation
simple randomisation (random number generated)
block randomisation (AABB)
stratified (separate age, sex then randomise)
stepped wedge (pop divided then gradually introduce in random)
what type of spread
This is POINT SOURCE
In a point source outbreak, persons are exposed over a brief time to the same source, such as a single meal or an event. The number of cases rises rapidly to a peak and falls gradually. The majority of cases occur within one incubation period of the disease.
eg. food poisoning
what type of spread
Mode of Spread: Continuous Common Source
In a continuous common source outbreak, persons are exposed to the same source but exposure is prolonged over a period of days, weeks, or longer.
The epi curve rises gradually and might plateau.
eg salmonella from salami
what type of spread
Mode of Spread: Propagated Outbreak
there is no common source because the outbreak spreads from person-to-person.
The graph will assume the classic epi curve shape of progressively taller peaks, each being one incubation period apart.
eg measles, flu
