Clinical Epidemiology

Question 1

Normal as common

Answer 1

Classifies values that occur frequently as normal, and those that occur infrequently as abnormal.

Question 2

Operational definition of abnormality

Answer 2

Assume that an arbitrary cut-off point on the frequency distribution is the limit of normality and consider all values beyond this point as abnormal.
Usually two standard deviations above or below the mean

Question 3

Gaussian distribution

Answer 3

Normal in statistical sense

Cut-off would identify 2.5% of the population as abnormal using this cut off

Question 4

Percentile approach

Answer 4

Does not assume a statistically normal distribution, no biological basis

We can consider that the 95th percentile point is the dividing line between normal and abnormally high values, thus classifying 5% of the population as abnormal

Question 5

Abnormality associated with disease

Answer 5

Distinction between normal and abnormal can be based on the distribution of the measurements for both healthy and diseased people, we attempt to define the cut-off point that separates the two groups. Classification error can arise, comparison of two frequency distributions often shows considerable overlap.

Question 6

Abnormal as treatable

Answer 6

These difficulties in distinguishing accurately between normal and abnormal have led to the use of criteria determined by evidence from randomized controlled trials, which can be designed to detect the point at which treatment does more good than harm. Such trials are not always designed to account for other risk factors or the cost of treatment.

Question 7

Diagnostic tests

Answer 7

Diagnose treatable disease and to help confirm possible diagnoses suggested by the patient’s signs and symptoms. Usually involve laboratory investigations (genetic, microbiological, biochemical or physiological), the principles that help determine the value of these tests.

Question 8

Value of a test

Answer 8

Disease present or absent and a test result either positive or negative.

Question 9

Four combinations of disease status and test result

Answer 9

True positive, true negative, false negative, and false positive.

Only use these categories when there is an absolutely accurate method of determining the accuracy of other tests.

Question 10

Practical utility of a given test

Answer 10

A test’s positive and negative predictive value.

Depends on the sensitivity and specificity of the test, and the prevalence of the disease in the population being tested.

Depend critically on the prevalence of the abnormality in the patients being tested

Question 11

Positive predictive value

Answer 11

The probability of disease in a patient with an abnormal test result

Question 12

Negative predictive value

Answer 12

probability of a patient not having a disease when the test result is negative

Question 13

Natural history refers to

Answer 13

• Pathological onset
• The pre-symptomatic stage, from onset of pathological changes in the first appearance of symptoms or signs
• The stage when the disease is clinically obvious and may be subject to remissions and relapses, regress spontaneously or progress to death

Question 14

Prognosis

Answer 14

Prediction of the course of a disease and is expressed as the probability that a particular event will occur in the future.

Epidemiological information from many patients is necessary to provide sound predictions on prognosis and outcome. Clinical experience alone is inadequate for this purpose, since it is often based on a limited set of patients and inadequate follow-up.

Question 15

Quality of life

Answer 15

assessment of prognosis should include measurement of all clinically relevant outcomes and not just death, since patients are usually interested in the quality of life as they are in its duration. Randomly selected groups.

Question 16

Quantity of life

Answer 16

Prognosis in terms of mortality is measured as case-fatality rate or probability of survival. Both the date of onset and the duration of follow-up must be clearly specified. Survival analysis is a simple method of measuring prognosis.

Question 17

Clearly advantageous treatments

Answer 17

antibiotics for pneumonia and surgery for trauma. Rarely is there such clarity in clinical medicine. Usually effects are less obvious, and most interventions require research to establish their value. Specific treatments need to be shown to do more good than harm among patients who actually use them: efficacy.

Question 18

Compliance

Answer 18

The extent to which patients follow medical advice.

Question 19

Practical effectiveness

Answer 19

Determined by studying outcomes in a group of people offered treatment, only some of whom will be compliant.

Question 20

Best method of measuring efficacy and effectiveness of treatments

Answer 20

By randomized controlled trial, however, some times trials like this cannot be done, and only a small proportion of current medical interventions have been assessed with such trials.

Question 21

Use of evidence based guidelines

Answer 21

Assist practitioners and patients in making decisions about appropriate health care for specific clinical circumstances. Putting evidence into practice requires evidence-based guidelines.

Question 22

Prevention in clinical practice

Answer 22

Clinical practice prevention is often secondary or tertiary. But primary prevention can be implemented on a routine basis.

Question 23

Reducing risks

Answer 23

opportunities for health professionals to offer practical advice and support to patients with the hope of preventing new diseases or exacerbation of existing illnesses. Clinical epidemiologists are often involved in figuring out how effective these interventions are.

Question 24

Reducing risks in patients with established diseases

Answer 24

evidence based approaches to reducing the risk of adverse outcomes in those with the disease are very similar to the approaches used to reduce disease onset. Behavioural interventions, pharmacological interventions, other interventions.