Prevention, Screening & Health Maintenance
Basics & Essentials XHypothesis Driven History Taking.htm
Ideal Screening Test: Real Clinical Testing:
Always correct Know operating characteristics of test
Repeatable Standardization, controls
Safe, painless, quick, inexpensive Quantify risks, cost effectiveness
Results make clinical difference Actual or significant benefit vs. bias
Screening Decisions: [scroll down]
What diseases warrant screening?
Who should be screened?
How should I screen? What test?
When should screening begin? For how long?
Consider - Natural History of Disease
Morbidity & Mortality
Treatment Effective if Given Early
Concepts in Screening:
Sensitivity and Specificity = functions of the operating characteristics of test
Predictive Values are related to disease prevalence or pre-test probability
Sensitivity: True positives by screening test / All with disease by gold standard
Specificity: True negatives by screening test / All without disease by gold standard
Positive Predictive Value: True Positives / All who test positive (true and false positives)
Negative Predictive Value: True Negatives / All who test negative (true and false negatives)
Rule: As Prevalence Increases-
As Prevalence Decreases-
Number Needed to Treat: (NNT): the number of patients who must be exposed to an intervention before the clinical outcome
of interest occurred; for example, the number of patients needed to treat to prevent one adverse outcome. ( Therapy) To Calculation
Determinants of Evidence
Presence/Insufficiency of evidence What evidence is there?
Quality of Evidence: Good, Fair, Poor or What is the quality of the evidence?
Service Effective vs Ineffective Does evidence show effectiveness?
Benefit outweigh risk Do benefits outweigh risks?
Levels of Evidence [U.S. Preventive Services Task Force Formula]
Hierarchy of Experimental Study Design
RCT: Randomized, controlled trial.
(Double-blinded, placebo controlled)
Study design where treatments, interventions, or enrollment into different study groups are assigned by random allocation rather
than by conscious decisions of clinicians or patients. If the sample size is large enough, this study design avoids problems of bias
and confounding variables by assuring that both known and unknown determinants of outcome are evenly distributed between
treatment and control groups.
Case Control Study:
Retrospective comparison of exposures of persons with disease (cases) with those of persons without the disease (controls).
Follow-up of exposed and non-exposed defined groups, with a comparison of disease rates during the time covered.
Report of a number of cases of disease.
EBM: The User's Guide to the Medical Literature
Are the results valid?
1. Was the assignment of patients to treatment randomized?
2. Were all patients who entered the trial accounted for and attributed at its conclusion?
3. Was follow-up complete?
4. Were patients analyzed in the groups to which they were randomized (intention to treat
5. Were patients, their clinicians and study personnel kept `blind' to treatment received?
6. Were the groups similar at the start of the trial?
7. Aside from the experimental intervention, were the groups treated equally?
What are the results?
1. How large is the treatment effect?
2. How precise is the estimate of the treatment effect? What is the confidence interval
around the effect?
Will the results help me in my patient care?
1. Can the results be applied to my patients?
2. Were all clinically relevant outcomes considered?
3. Are the benefits worth the harms and costs?
REMEMBER: Evidence makes an important
contribution to good clinical decision making but evidence,
though necessary, is not always sufficient. A clinician must also deal with a patient's values and preferences.
In addition to clinical expertise, a good
clinician must demonstrate compassion, sensitive listening skills,
and broad perspectives from the humanities and social sciences. These attributes allow for an understanding
of patients' illnesses in the context of their experience, personalities, and cultures.
Clinical Epidemiology Glossary
Absolute risk: The observed or calculated probability of an event in the population under study.
Absolute risk difference: the difference in the risk for disease or death between an exposed population and an unexposed population.
Absolute risk reduction (ARR): the difference in the absolute risk (rates of adverse events) between study and control populations.
Adjustment: A summarizing procedure for a
statistical measure in which the effects of differences in
composition of the populations being
compared have been minimized by statistical methods.
Association: Statistical dependence between two or
more events, characteristics, or other variables. An
association may be fortuitous or may
be produced by various other circumstances; the presence of an association does not necessarily imply a causal relationship.
Bias (Syn: systematic error): Deviation of results or inferences from the truth, or processes leading to such deviation. See also Referral Bias,Selection Bias.
Blind(ed) study (Syn: masked
study): A study in which observer(s) and/or subjects are
kept ignorant of the group to which the subjects are
in an experimental study, or of the population from which the subjects come, as in a nonexperimental or observational study. Where both observer and
subjects are kept ignorant, the study is termed a double-blind study. If the statistical analysis is also done in ignorance of the group to which subjects
belong, the study is sometimes described as triple blind. The purpose of "blinding" is to eliminate sources of bias.
Case-series: Report of a number of cases of disease.
Retrospective comparison of exposures of persons with
disease (cases) with those of persons without the disease
(see Retrospective study).
Causality: The relating of causes to the effects
they produce. Most of epidemiology concerns causality and
several types of causes can be distinguished.
It must be emphasized, however, that epidemiological evidence by itself is insufficient to establish causality, although it can provide powerful circumstantial evidence.
other than the treatment under study that are applied
differently to the treatment and control groups.
Cointervention is a
serious problem when double blinding is absent or when the use of very effective non-study treatments is permitted.
Cohort study: Follow-up of exposed and non-exposed defined groups, with a comparison of disease rates during the time covered.
Comparison group: Any group to which the index group is compared. Usually synonymous with control group.
Co-morbidity: Coexistence of a disease or diseases in a study participant in addition to the index condition that is the subject of study.
Confidence interval (CI): The
range of numerical values in which we can be confident (to a
computed probability, such as 90 or 95%) that the population
value being estimated will be found. Confidence intervals indicate the strength of evidence; where confidence intervals are wide, they indicate less precise
estimates of effect. The larger the trial's sample size, the larger the number of outcome events and the greater becomes the confidence that the true relative
risk reduction is close to the value stated. Thus the confidence intervals narrow and "precision" is increased. In a "positive finding" study the lower boundary
of the confidence interval, or lower confidence limit, should still remain important or clinically significant if the results are to be accepted. In a "negative finding"
study, the upper boundary of the confidence interval should not be clinically significant if you are to confidently accept this result.
Confounding variable, Confounder:
A variable that can cause or prevent the outcome of
interest, is not an intermediate variable, and is associated
factor under investigation. A confounding variable may be due chance or bias. Unless it is possible to adjust for confounding variables, their effects cannot be
distinguished from those of factor(s) being studied.
Control event rate(CER): The percentage of the control/nonexposed group who experienced outcome in question.
Dose-response relationship: A
relationship in which change in amount, intensity, or
duration of exposure is associated with a change-either an
decrease-in risk of a specified outcome.
Determinant: Any definable factor that effects a
change in a health condition or other characteristic.
Effectiveness: a measure
of the benefit resulting from an intervention for a given
health problem under usual conditions of clinical care for a
this form of evaluation considers both the efficacy of an intervention and its acceptance by those to whom it is offered, answering the question, "Does the
practice do more good than harm to people to whom it is offered?" See Intention to treat.
Efficacy: a measure of the
benefit resulting from an intervention for a given health
problem under the ideal conditions of an investigation; it
answers the question,
"Does the practice do more good than harm to people who fully comply with the recommendations?"
Exclusion Criteria: Conditions which preclude entrance of candidates into an investigation even if they meet the inclusion criteria.
Experimental event rate(EER): The percentage of intervention/exposed group who experienced outcome in question.
Follow-up: Observation over a
period of time of an individual, group, or initially defined
population whose relevant characteristics have been assessed
to observe changes in health status or health-related variables.
Gold standard: A method, procedure, or measurement that is widely accepted as being the best available.
Incidence: The number of new cases of illness commencing, or of persons falling ill, during a specified time period in a given population. See also Prevalence.
Intention to treat analysis: A
method for data analysis in a randomized clinical trial in
which individual outcomes are analyzed according to the
group to which
they have been randomized, even if they never received the treatment they were assigned. By simulating practical experience it provides a better measure of
effectiveness (versus efficacy).
Interviewer bias: Systematic error due to interviewer's subconscious or conscious gathering of selective data.
Lead-time bias: If prognosis study patients are
not all enrolled at similar, well-defined points in the
course of their disease, differences in outcome over time
merely reflect differences in duration of illness.
Likelihood ratio: Ratio of the probability that a given diagnostic test result will be expected for a patient with the target disorder rather than for a patient without the disorder.
Number Needed to Treat (NNT):
the number of patients who must be exposed to an
intervention before the clinical outcome of interest
occurred; for example, the
number of patients needed to treat to prevent one adverse outcome.
Odds: a proportion in which the numerator contains the number of times an event occurs and the denominator includes the number of times the event does not occur.
Odds Ratio (Syn: cross-product
ratio, relative odds): a measure of the degree of
association; for example, the odds of exposure among the
cases compared with the
odds of exposure among the controls.
Precision: The range in which the best estimates of a true value approximate the true value. See Confidence interval.
Predictive value: In
screening and diagnostic tests, the probability that a
person with a positive test is a true positive (i.e., does
have the disease), or that a person
with a negative test truly does not have the disease. The predictive value of a screening test is determined by the sensitivity and specificity of the test, and by the
prevalence of the condition for which the test is used.
Prevalence: the proportion of persons with a particular disease within a given population at a given time.
Prognosis: the possible
outcomes of a disease or condition and the likelihood that
each one will occur.
Prognostic factor: Demographic, disease-specific, or co-morbid characteristics associated strongly enough with a condition's outcomes to predict accurately the
eventual development of those outcomes. Compare with risk factors. Neither prognostic or risk factors necessarily imply a cause and effect relationship.
Prospective study: Study design where one or more
groups (cohorts) of individuals who have not yet had
the outcome event in question are monitored for the
number of such events which occur over time.
Randomized controlled trial:
Study design where treatments, interventions, or enrollment
into different study groups are assigned by random
than by conscious decisions of clinicians or patients. If the sample size is large enough, this study design avoids problems of bias and confounding variables by
assuring that both known and unknown determinants of outcome are evenly distributed between treatment and control groups.
Recall bias: Systematic error due to the differences in accuracy or completeness of recall to memory of past events or experiences.
Referral filter bias: The
sequence of referrals that may lead patients from primary to
tertiary centres raises the proportion of more severe or
thus increasing the likelihood of adverse or unfavorable outcomes.
Relative risk (RR): the ratio of
the probability of developing, in a specified period of
time, an outcome among those receiving the treatment of
exposed to a risk factor, compared with the probability of developing the outcome if the risk factor or intervention is not present.
Relative risk reduction (RRR): the extent to which a treatment reduces a risk, in comparison with patients not receiving the treatment of interest.
Reliability (Repeatability, Reproducibility): the results of a test or measure are identical or closely similar each time it is conducted.
Retrospective study: study
design in which cases where individuals who had an outcome
event in question are collected and analyzed after the
have occurred (see also Case-control study).
Risk factor: patient
characteristics or factors associated with an increased
probability of developing a condition or disease in the
first place. Compare with
prognostic factors. Neither risk or prognostic factors necessarily imply a cause and effect relationship.
Selection Bias: a bias in
assignment or a confounding variable
that arises from study design rather than by chance. These
can occur when the study
and control groups are chosen so that they differ from each other by one or more factors that may affect the outcome of the study.
Sensitivity (of a diagnostic test): the proportion of truly diseased persons, as measured by the gold standard, who are identified as diseased by the test under study.
Specificity (of a diagnostic test): the proportion of truly nondiseased persons, as measured by the gold standard, who are so identified by the diagnostic test under study.
Stratification: division into groups.
Stratification may also refer to a process to control for
differences in confounding variables, by making separate
groups of individuals who have the same values for the confounding variable.
Strength of Inference: the likelihood that an
observed difference between groups within a study represents
a real difference rather than mere chance or
the influence of confounding factors, based on both p values and confidence intervals. Strength of inference is weakened by various forms of bias and by small sample sizes.
Survival curve: A graph of
the number of events occurring over time or the chance of
being free of these events over time. The events must be
discrete and the
time at which they occur must be precisely known. In most clinical situations, the chance of an outcome changes with time. In most survival curves the earlier
follow-up periods usually include results from more patients than the later periods and are therefore more precise.
Validity: the extent to which a variable or intervention measures what it is supposed to measure or accomplishes what it is supposed to accomplish.
The internal validity of a study refers to the integrity of the experimental design.
The external validity of a study refers to the appropriateness by which its results can be applied to non-study patients or populations.