EBP Flashcards
Prevention
Action that prevent disease occurrence / slow disease progression / minimise impact of disease
Examples of clinical preventive interventions:
- Immunisation
- Behavioural counselling e.g. smoking cessation
- Screening
- Chemoprevention e.g. statins for CVS disease prevention
Levels of prevention
Primary:
- aim to ↓ incidence
- targets ***risk factors / causes / disease in populations / individuals
- e.g. immunisation
Secondary:
- aim to ↓ prevalence
- targets individuals with ***early disease
- e.g. screening
Tertiary:
- aim to ↓ complications
- targets individuals with ***established disease
- e.g. stroke rehabilitation
Primordial:
- targets underlying ***social, economic, environmental determinants of health
- accomplished through **public policies and **intersectoral action
- e.g. control of air pollution
Quaternary:
- identifies individuals and groups at risk of ***over-diagnosis / over-treatment
- accomplished through actions that prevent iatrogenesis
- e.g. evidence-based medicine
Individual vs Population approach
Individual (high-risk) approach (only target individuals at high risk):
- Intervention appropriate to individual —> high motivation for physician and subject
- Screening has inherent diffulcties and costs —> only palliative, not radical
- Limited potential for populations —> large number of people at small risk may outweigh small number of people at high risk
Population approach (lower risk for whole population):
- Large potential for populations
—> aims to shift whole distribution of exposure in a favourable direction (i.e. lower prevalence of disease of whole population: shifting the normal distribution curve)
—> behaviourally appropriate; changes norms - Limited by ***prevention paradox
—> individuals perceive little benefit from intervention even though measure may benefit population
—> lower physician, subject motivation
Screening
Presumptive identification of an unrecognised disease/defect by application of tests/examinations/other procedures which can be applied rapidly
- Sort out apparently well (asymptomatic) people who probably have a disease from those who probably do not
- NOT intended to be diagnostic
Goals of screening
Early detection —> Early intervention —> Reduction in morbidity / mortality
- Reduction in morbidity / mortality from the newly identified disease among screened individuals due to ***early treatment
- Reduce disease burden for the population
- Provide cost-effective screening to target population
Wilson and Jungner screening criteria
Knowledge of diseases:
- Condition should be ***important health problem
- There should be a ***recognisable latent / early symptomatic stage
- ***Natural history of condition, including development from latent to declared disease, should be adequately understood
Knowledge of test:
- ***Suitable tests / examinations
- Test ***acceptable to the population
- Case-finding should be ***continuous (NOT “once and for all”)
Treatment of disease:
- ***Accepted treatment for patients with recognised disease
- ***Facilities for diagnosis and treatment should be available
- ***Agreed policy on whom to treat as patients
Cost considerations:
1. Cost of case-finding (including diagnosis and treatment of patients diagnosed) should be ***economically balanced in relation to possible expenditure on medical care as a whole
Assessing a screening test: Reliability and Validity
Reliability: degree to which results can be replicated
- depends on
—> Variability between people
—> Measurement error
Validity: degree to which results obtained by a test are true
- Sensitivity
- Specificity
***Sensitivity and Specificity
Used to decide whether a test should be used
Sensitivity (probability of true positive):
With disease + Positive test / Number of people with disease
Specificity (probability of true negative):
Without disease + Negative test / Number of people without disease
There will be trade-off between Sensitivity and Specificity
—> ↑ Sensitivity —> ↓ Specificity
Receiver operator characteristic (ROC) curve
- used to help decide best cut-off point
- Sensitivity plot against Specificity
- maximising **True positive (Sensitivity), minimise **False positive (1-Specificity)
- cut-off point usually at “shoulder” of ROC curve
- ***Area under ROC curve: used to compare overall accuracy of tests
***Predictive values
Predictive values:
- used to determine how likely a patient has / not has disease
- ***Post-test probability
PPV = True positive / All positive —> ***↑ with prevalence of disease (given constant SN, SP) (∵ ↑ true positive) NPV = True negative / All negative —> ***↓ with prevalence of disease (given constant SN, SP) (∵ ↓ true negative)
Sensitivity = True positive / All disease
—> ability to pick up all diseased people
—> high sensitivity
—> high NPV (very few false negative) (無就一定無, 有病就一定可以pick up到)
—> low type II error
Specificity = True negative / All healthy
—> ability to exclude normal people (無就一定無)
—> high specificity
—> high PPV (very few false positive) (有就一定有, 所有無病一定exclude)
—> low type I error
Use of sensitive test: correctly identify people WITH disease, rule out dangerous conditions (negative result tends to rule out diagnosis)
Use of specific test: correctly identify people WITHOUT disease, false positive results are highly undesirable (positive result tends to rule in diagnosis)
***Bayes’ theorem
Prevalence of disease:
- reflects Pre-test probability (probability of disease before diagnostic test result is known)
Predictive values:
- reflects Post-test probability (probability of disease after diagnostic test)
—> ***influenced by Prevalence (Pre-test probability)
SN, SP:
- ***Inherent characteristics of test
- ***Independent of Pre-test portability / prevalence
Implications of Predictive values dependent on Prevalence:
1. Better estimate prevalence by using best available information sources
—> high quality surveillance data
—> epidemiological studies with minimal bias
- Increase Pre-test probability may increase PPV
—> Apply diagnostic tests to more susceptible patients (use sparingly in primary care)
—> Interpret results of studies with care (tertiary care have higher disease prevalence, cannot be applied to primary / secondary settings)
Risks of screening
- Discomfort and inconvenience
- Psychosocial consequences
- Anxiety
- Family discord on further procedures - Adverse health outcomes
- False-positives: possibly invasive diagnostic procedures
- False-negatives: undetected disease
Screening for colorectal cancer
Asymptomatic Hong Kong residents 50-75
- Faecal occult blood test every 1 to 2 years OR - Flexible sigmoidoscopy every 5 years OR - Colonoscopy every 10 years
PICO question
Patient/Population in question
Intervention being considered
Comparison group
Outcome
***Biases associated with screening
- Lead-time bias
- Lead-time: Time between detection by screening and usual diagnosis
- Apparently improved survival time with no change in time from disease onset to death
- To avoid: Use ***mortality rather than survival rates - Length-time bias
- Slow-growing lesions are more likely to be detected by screening than fast-growing lesions
- Screening tends to pick up slower growing lesions which are associated with better prognoses
- To avoid: Compare outcomes in ***RCT - Compliance bias
- Individuals who are adherent to interventions usually have better prognosis than non-compliant individuals
- tend to be more health conscious and higher socioeconomic position
- To avoid: Compare outcomes in ***RCT
Why studies showed that screening cannot save lives?
- Studies may be underpowered to detect small overall mortality benefit
- Any reductions in mortality may be offset by deaths due to ***downstream effects of screening
- e.g. PSA screening yields numerous false-positive —> numerous prostate biopsies —> unnecessary harms and deaths
Misuse and Overuse of unnecessary tests/interventions:
Misuse: Interventions more harmful than beneficial
Overuse: Interventions that yield little useful information
Appraising a study
Relevance:
- Is population similar to the patient?
- Intervention feasible?
- Comparison group reasonable?
- Outcome patient-oriented?
Validity (3 factors affecting):
- Random error
- Systematic error / bias
- Selection bias
- Information bias - Confounding
Variations in responses:
- Participant variability
- Result of errors
Validity:
- Study sample
- Selection bias (representative of target population?)
—> inclusion / exclusion criteria reasonable?
—> where were patients recruited?
—> enrolled consecutively?
—> response rates reasonable? - Study design
- Systematic reviews > individual studies
- RCT for effectiveness of intervention
- Prospective cohort studies for prognosis / etiology
For assessing prognosis:
- is there an inception cohort? (group of patients assembled at onset of disease)
- at the same point / stage in their illness?
- Study conduct
- Selection bias (allocation to groups concealed? follow-up reasonably complete? drop-out rate differ between groups?)
—> loss to follow-up —> reduce statistical power
—> need >=70-80% patients follow up
- Information bias (observer bias, recall bias)
—> measurement of outcomes differ by exposure status?
- Confounding (adjust for important potential confounders) - Study results
- Statistically significant (confidence intervals, P values, adequate power: 0.8 (sample size) for null associations)
—> bigger effect size —> smaller sample size needed
- Clinically significant (effects in clinical practice, relative risk reduction, absolute risk reduction, NNT)
- How to apply results to patient
- Adjusted for confounders - Other factors that could affect interpretation of results
- STEPS (Safety, Tolerability, Effectiveness, Price, Simplicity)
- Conflicts of interest
Bias
- Information bias
—> Observer bias (by investigators)
—> Recall bias (by subjects, particular problem if public is aware of exposure-disease association)
- reduce bias:
—> Blind assessment of outcome status (investigator blinded)
—> Prospective assesssment of outcome (no need to recall)
—> Use objective measures - Selection bias:
- systematic difference between subjects and target population
- subjects not representative of target population —> affects EXTERNAL validity
- subjects groups are not comparable —> affects INTERNAL validity
- reduce bias:
—> random sample
—> maximise response rate
—> minimise attrition rate
—> select case and control from same underlying population
***Relative risk (RR)
Risk of benefit / harm of intervention compared to another
- depend only on relative difference and does not account for risk of no treatment (absolute risk at baseline)
EER (experimental event rate) = treated with effect / total treated
CER (control event rate) = control with effect / total control
Relative risk reduction (RRR) = (1-RR) x 100% OR ***CER-EER / CER
Absolute risk reduction (ARR) = Incidence in control group - Incidence in intervention group (i.e. CER-EER)
Number needed to treat (NNT) = 1 / ARR
***Number needed to treat (NNT)
NNT = 1 / ARR
- Number of patients need to be treated for one patient to receive benefit/prevent disease
- Ideal NNT = 1
- take into account that not everyone benefit from an intervention
- may be more useful than RR
—> combines relative benefit with background risk of patients
Prognosis
Likely course and outcome of disease / condition over time
Clinical course = prognosis of a condition that has received intervention
Natural history = prognosis of a condition that has NOT received intervention (determines when to intervene, disease staging etc.)
Types:
- Average / Overall prognosis: “most likely course of a condition”
- Prognostic factors: factors associated with outcome
- Risk prediction models: risk groups who are likely to have worse outcomes
Finding out:
1. Clinical experience (prone to bias)
(2. Systematic review of cohort studies)
- Cohort studies (exposure睇outcome)
- **better evidence on prognosis
- recruit based on **exposure characteristic:
—> prognostic factor: exposed (e.g. smoking) vs not exposed (e.g. no smoking)
- prospective —> look for ***outcome (morbidity / mortality) - (Control arm of) RCT
- treatment arm —> clinical course
- control arm —> natural history
- problem: participants may not be representative of population of interest
- may give information on prognosis but ***participants may not be representative of population of interest - Case-control studies (outcome睇exposure)
- recruit based on **outcome:
—> Cases
—> Control
- retrospective —> look for prognostic factors / **exposure
- problem: does not provide information on outcome rates (∵ you select cases) - Cross-sectional studies
- cannot know temporal sequence
Causation
Necessary cause: outcome would not occur without exposure
Sufficient cause: exposure guarantees cause will occur (very few in medicine)
E.g.
- HIV is a necessary cause of AIDS (must have HIV to have AIDS)
- but not a sufficient cause for AIDS (not everyone has HIV develop AIDS)
Finding cause:
- Experimental studies e.g. RCT
- Observational studies only tells ***associations
Bradford Hill’s Criteria
- Consistency (on replication)
- Strength (of association)
- Specificity
- Dose response relationship
- Temporality
- Biological plausibility
- Coherence
OA risk factors:
Non-modifiable:
- age
- sex
- genetics
- bone deformities
Modifiable:
- obesity
- joint injuries
- occupational exposure
Dealing with confounding
- Multivariable analysis
- adjust / control for confounders
- Regression analysis
Regression analysis:
- mathematical model to describe relationship between Dependent variable and ***>=1 Independent variables
- allow estimation of a range of exposures, each one adjusted for potential confounding effect of others
- describe relationship between exposure and outcome adjusted for effects of potential confounders
—> “all else being equal, what is effect of X on Y?”
Types of regression analysis:
- Logistic regression —> Binary outcome
- Linear regression —> Quantitive outcome
- Cox regression —> Time to occurrence of an event (e.g. survival)
- ***effects of several variables on time to occurrence of an event
- Hazard: instantaneous rate of the event at time t
- Cox proportional hazards model assume that Hazard Ratios comparing different exposure groups remain constant over time
Hazard ratio
Ratio of hazards between 2 groups
- interpreted in the same ways as risk ratio
HR>1: high hazard than comparison group
HR=1: no difference in hazard from comparison group
HR<1: lower hazard than comparison group
Survival curve vs Survival rate
Survival curve:
- Probability of survival starts at 100%, decreases over time
- Shows proportion of study population that is still alive at successive time points
- Patients are censored when lost from study
Survival rate:
- alternative to survival curve
- survival rates at different time points (1 year, 5 years), median survival rate, disease-free survival rate etc.
Mortality
All-cause mortality
- includes all causes of mortality, even those seemingly unrelated to study exposure
- ***less prone to misclassification
Disease-specific mortality - heavily relies on accuracy and completeness of death certification —> immediate cause —> intervening cause —> underlying cause - ***more prone to misclassification
—> intervention may just reduce one of the above (All-cause / Disease-specific mortality)
Doctors when issuing death certificates, causes of death must be consistent with those in ICD-10 (classification system by WHO)
Standardised mortality ratio (SMR):
- Mortality of study group to Mortality of standard population
- SMR =1: same mortality rate as standard population
- SMR >1: higher mortality rate in study group
- SMR <1: lower mortality rate in study group
(Sometimes 100 is used rather than 1)
Diagnosis
Diagnostic tests:
- Laboratory tests
- Imaging
- Endoscopy
- History + Physical examination
- combination of above
Diagnosis is a ***Probability based on:
- Frequency of disease:
- Prevalence (more commonly available)
- Incidence - Index of suspicion:
- Risk factors / risk predictors: age, smoking status etc.
—> Effect measures: Odds ratios, Risk ratios
- Careful history and clinical judgement - Accuracy and validity of diagnostic tests
Screening vs Diagnosis
Screening:
- for Asymptomatic patients with early disease
—> secondary prevention
—> lower prevalence of disease
- Aims to identify people with high risk of disease where interventions may reduce morbidity / mortality
- NOT intended to diagnose
Diagnosis: - for Symptomatic patients —> higher prevalence of disease - Aim: make a definitive diagnosis / narrow down DDx - NOT intended to prevent disease
Issues with determining validity
- An appropriate “gold standard” (reference standard)
- best available test to determine presence / absence of disease
—> new test should not be compared to imperfect test - sometimes too invasive / costly
—> simpler tests should only be used as proxies when risk of misclassification is low - disease may lack objective standards for diagnosis e.g. mental disorders
- Lack of information on negative tests
- clinicians reluctant to further test when initial test is negative
