Applied Epid

APPLIED EPIDEMIOLOGY Prepared by Antonio E. Chan, M.D.

Learning objectives Define epidemiology and outline its scope Differentiate epidemiology from clinical epidemiology Describe approaches to establishing “normality” Describe criteria and measures of disease occurrence commonly used in epidemiology Enumerate some routinely available data use in epidemiology

Learning objectives Understand diagnostic test in relation to disease Describe the main types of epidemiological studies Enumerate the advantages and disadvantages of observational studies compared with experimental studies Explain cause of disease Outline the steps necessary to establish the cause of disease

Learning objectives Appreciate the differing approaches used in epidemiology to compare the occurrence of disease Outline the role of epidemiology in describing the natural history of a disease and prognosis Understand the role of epidemiology in the prevention and control of disease through identification of the causes of disease Relate the different stages of the development of a disease to the phases of prevention

What is 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

AIMS OF EPIDEMIOLOGY To understand the course of the disease (natural history of the disease) To identify the causes or risk factors To provide effective measures of treatment and prevention

Uses of epidemiology Genetic factors Causation Environmental factors (including lifestyle) 2. Natural history 3. Description of health status of population Proportion with ill health, change over time, change with age, etc Good health Ill health Good health Subclinical changes Clinical disease Death Recovery Good health ILL health Time

Uses of epidemiology Evaluation of intervention Good health Ill health Treatment Medical care Health promotion Preventive measures Public health services

APPLIED EPIDEMIOLOGY Clinical epidemiology Communicable disease epidemiology Environmental and occupational epidemiology Molecular epidemiology

CLINICAL EPIDEMIOLOGY Definition is the application of epidemiological principles and methods to the practice of clinical medicine is the science of making predictions about individual patients by counting clinical events in similar patients, using scientific methods for studies of groups of patients to ensure that the predictions are accurate

CLINICAL EPIDEMIOLOGY Purpose: to develop and apply methods of clinical observations that will lead to valid conclusions by avoiding being misled by systematic error and chance to make good decisions in the care of patients

The Relationship Between EPIDEMIOLOGY + CLINICAL MEDICINE Populations Individuals Studies/Assessments Prevention Evaluation Planning Diagnosis Treatment Curing Caring

Clinical Question Issue Question Abnormality Is the patient sick or well ? Diagnosis How accurate are tests used to diagnose disease ? Frequency How often does a disease occur ? Risk What factors are associated with an increased risk of disease ? Prognosis What are the consequences of having a disease ? Treatment How does treatment change the course of disease ? Prevention Does an intervention on well people keep disease from arising ? Does early detection and treatment improve the course of disease ? Cause What conditions lead to disease ? What are the pathogenetic mechanisms of disease Cost How much will care for an illness cost ?

Sources of data useful for epidemiology studies Data on vital events – birth and death Morbidity or disease statistics Data on physiologic and or pathologic condition Statistics on health resources and services Statistics pertaining to the environment Demographic data Socio-cultural data

Measuring Health and Disease Clinical question: Is the patient sick or well? Health is defined as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” Epidemiologist’s definition of health states “ disease present” or “disease absent”

Measuring Health and Disease Clinical question: Is the patient sick or well? Diagnostic tests qualitative diagnostic test quantitative diagnostic test Normal (Gaussian) distribution method Percentile method Therapeutic method Predictive value method

Measuring Health and Disease Diagnostic criteria are usually based on symptoms, signs and test results 1. Hepatitis presence of antibodies in the blood 2. Asbestosis - symptoms and signs of specific changes in lung function, - radiographic demonstration of fibrosis of the lung tissue or pleural thickening and - history of exposure to asbestos fibers.

Major Manifestations Minor Manifestations Carditis Clinical: Polyarthritis fever Chorea athralgia (joint pains) Erythema marginatum previous rheumatic fever or Subcutaneous nodules rheumatic heart disease Laboratory: Acute phase reactants: Abnormal ESR, CRP, leukocytosis Prolonged P-R interval The Jones Criteria (revised) for Guidance in the Diagnosis of Acute Rheumatic Fever A high probability of rheumatic fever is indicated by the presence of two major or one major and two minor, manifestations, if supported by evidence of a preceding Group A streptococcal infection

MAJOR SIGNS MINOR SIGNS Weight loss > 10% Persistent cough > 1 month Fever > 1 month General pruritic dermatitis Chronic diarrhea > 1 month Recurrent herpes zoster General lymphadenopathy Chronic herpes simplex Oral candidiasis WHO CASE-DEFINITION FOR AIDS The presence of disseminated Kaposis sarcoma or cryptococcal meningitis or Two major signs in association with at least one minor sign

Measuring Health and Disease Diagnostic criteria must be clearly stated, easy to use and easy to measure in a standard manner under a wide variety of circumstances by different people Diagnostic criteria may change quite rapidly as knowledge or techniques improve. Definitions used in clinical practice are less rigidly specified and clinical judgment is more important in diagnosis

Measuring Health and Disease The development of criteria to establish the presence of disease requires definition of normality and abnormality Difficult to define what is normal No clear distinction between normal and abnormal

Approaches in establishing “normality” Clinical question: Is the patient sick or well ? Problem (misclassification) Clinical measurements nominal asymptomatic ordinal cut-off point interval or ratio Clinical measurements have skewed distributions Percentile method ( same prevalence rates)

Level at which treatment does more good than harm - Cost In specific age groups for men and women at which treatment makes economic as well as medical sense Criteria change from time to time

Approaches in establishing “normality” Clinical question: Is the patient sick or well ? Normal Abnormal common or usual being unusual well being sick not being treatable being treatable

Measures of disease frequency Clinical question: How often does a disease occur ? Prevalence of a disease is the number of cases in a defined population at a specified point in time Point prevalence Period prevalence Incidence is the number of new cases arising in a given period in a specified population

Measuring disease frequency Clinical question: How often does a disease occur ? The prevalence rate (P) for a disease is calculated as follows: Number of people with the disease or condition P = ----------------------------------------------------------------- (x factor) Number of people in the population at risk at the specified time

Measuring disease frequency Clinical question: How often does a disease occur ? Incidence rate (I) Number of people who get a disease in a specified period I = ---------------------------------------------------- X (factor) Sum of the length of time during which each person in the population is at risk

Measuring disease frequency Clinical question: How often does a disease occur ? Incidence rate The numerator is the number of new events that occur in a defined time period The denominator is the population at risk of experiencing the event during this period The most accurate way of calculating incidence rate is to calculate the person-time incidence rate ( Incidence density )

Measuring disease frequency Clinical question: How often does a disease occur ? Cumulative incidence rate or risk (CI) Number of people who get a disease during a specified period CI = ---------------------------------------------------- X (factor) Number of people free of the disease in the population at risk at the beginning of the period

Factors influencing observed prevalence rate Increased by: Decreased by: Longer duration of the disease Shorter duration of disease Prolongation of life of patient High case-fatality rate from disease without cure Increase in new case Decrease in new cases (increase in incidence) (decrease in incidence) In-migration of cases In-migration of healthy people Out-migration of healthy people Out-migration of cases In-migration of susceptible people Improved cure rate of cases Improved diagnostic facilities (better reporting)

Measuring disease frequency Clinical question: How often does a disease occur ? Prevalence studies do not usually provide strong evidence of causality It is helpful in assessing the need for health care and the planning of health services Prevalence rates are often used to measure the occurrence of conditions for which the onset of disease may be gradual

Measuring disease frequency Clinical question: How often does a disease occur ? Cumulative incidence rate Unlike incidence rate, it measures the denominator only at the beginning of a study This rate has a simplicity that makes it suitable for the communication of health information to decision makers Easy to interpret and provide a useful summary measure It is useful approximation of incidence rate when the rate is low or when the study period is short

274 CI = ------------ x 1000 = 2.3 per 1000 118,539 Example Relationship between cigarette smoking and incidence rate Stroke in a cohort of 118,539 women Never smoked 70 395,594 17.7 Ex-smoker 65 232,712 27.9 Smoker 139 280,141 49.6 Total 274 908,447 30.2 Person-years Stroke incidence rate Smoking No. of cases of observation (per 100,000 Category of stroke (over 8 years) person-years)

Measuring disease frequency Clinical question: How often does a disease occur ? Case-fatality rate a measure of the severity of a disease No. of deaths from a disease in a specified period Case fatality rate = ------------------------------------------ X 100 (CFR) No. of diagnosed cases of the disease in the same period

USE OF AVAILABLE INFORMATION (Mortality) Number of deaths in a specified period Crude mortality rate = --------------------------------------------------------- X F (CMR) Average total population during that period This mortality can be made specific as to age, sex or cause Not appropriate to use for comparison because death varies according age, sex, race, socio-economic class and other factors Comparison of mortality rates between groups of diverse age structure are usually based on age-standardized rates

Standardization of rates (Adjustment of rates) 1. Direct adjustment of rates This requires the selection of some population, called a standard population , to which the age-specific rates for each population can be applied. 2. Indirect adjustment of rates Standardization is based on age-specific rates rather than age composition The population whose rates form the basis for comparison is referred to as the “standard population” The larger of the two populations is usually chosen as standard because its rates tend to be more stable

Standardization of rates (Adjustment of rates) If developed and an undeveloped country are compared, the developed country would probably be taken as the standard A common way of carrying out indirect age-adjustment is to relate the total expected deaths thus obtained to observed deaths through a formula known as the Standardized Mortality Ratio (SMR) Total observed deaths in a population SMR = ------------------------------------------------------- Total expected deaths in that population

Standardization of rates (Adjustment of rates) Interpretation : If this mortality ratio is greater than 1, it means that more deaths are observed in the smaller or comparison population than would be expected on the basis of rates in the larger (standard) population If the ratio is less than 1, fewer deaths are observed than expected

Example: Direct method Comparison of death rates in two populations by age Annual Annual Age-specific Number Crude Age Population Death rate of Death rate (years) Number Proportion (per 1000) Deaths (per 1000) (1) (2) (3) (4) (5) (6) Population A < 15 1,500 0.30 2 3 15 – 44 2,000 0.40 6 12 ≥ 45 1,500 0.30 20 30 45 All ages 5,000 1.00 45 --------- = 9.0 5,000 Population B < 15 2,000 0.40 2 4 15 – 44 2,500 0.50 6 15 ≥ 45 500 0.10 20 10 29 All ages 5,000 1.00 29 -------- = 5.8 5,000

Computation of Expected Number of Deaths by Direct Method Example 1 : Identical Age-specific Rates Population A Population B Age-specific Age-specific Age Standard Population Death Rate Expected Death Rate Expected (years) (A and B Combined) per 1000 Deaths per 1000 Deaths (1) (2) (3)=(2)x(1) (4) (5)=(4)x(1) < 15 3,500 2 7 2 7 15 – 44 4,500 6 27 6 27 ≥ 45 2,000 20 4 0 20 40 All ages 10,000 74 74 Conclusion : There is truly no difference between A and B in risk of death

Computation of Expected Number of Deaths by Direct Method Example 2 : Different Age-specific Rates Population A Population B Age-specific Age-specific Age Standard Population Death Rate Expected Death Rate Expected (years) (A and B Combined) per 1000 Deaths per 1000 Deaths < 15 3,500 2 7 2 7 15 – 44 4,500 6 27 10 45 ≥ 45 2,000 20 40 20 40 All ages 10,000 74 92 74 92 ---------- = 7.4 ---------- = 9.2 10,000 10,000 Conclusion : There is difference between A and B in risk of death

Example of Indirect Method Deaths by Age and Photofluorogram Reading (Whites) for Three-and-a-Half Year Observation Period, Muscogee County, Georgia, 1946 Negative for Cardiovascular Disease Suspect for Cardiovascular Age-specific Disease Age in 1946 Number of death rates Number of (years) Population Deaths per 100 Population Deaths 15 – 34 13,681 35 0.25 23 1 35 – 54 8,838 102 1.15 24 5 55 and over 2,253 149 6.61 65 14 ---------- ------- ------- ----- All ages 24,772 286 112 20 Crude death rate per 100 1.15 17.9

Percentage Distribution by Age of Negatives and Suspects, Muscogee County, Georgia 15 – 34 13,681 55.2 23 20.5 35 – 54 8,838 35.7 24 21.4 55 and over 2,253 9.1 65 58.0 All ages 24,772 100.0 112 99.9 Negative for Suspect for Cardiovascular Disease Cardiovascular Disease Age Percentage Percentage (years) Number of Population Number of Population

Calculation of Standardized Mortality Ratio for Suspects Compared with Negatives, Muscogee County, Georgia (1) (2) (3) = (1) x (2) (4) 15 – 34 23 0.25 .1 1 35 – 54 24 1.15 .3 5 55 and over 65 6.61 4.3 14 All ages 4.7 20 Death Rates per 100 Expected Deaths Observed for Persons Negative among “Suspects” Deaths Age Number of for Cardiovascular According to Rates among (years) “Suspects” Disease for Negatives “Suspects” Observed deaths 20 SMR = -------------------------- = --------- = 4.25 Expected deaths 4.7

No. of deaths in a year of children less than 1 year of age Infant mortality rate = ------------------------------------------------------ X F No. of live births in the same year A measure of overall health status for a given population It is based on the assumption that it is particularly sensitive to socio-economic changes and to health care intervention Other measures of mortality in early childhood are : 1. Fetal death rate 2. Stillbirth or late fetal death rate 3. Perinatal mortality rate 4. Neonatal mortality rate 5. Postneonatal mortality rate Mortality

Child mortality rate is based on deaths of children aged 1 – 4 years and is important because accidental injuries, malnutrition and infectious diseases are common in this age group Maternal pregnancy-related deaths in a year Maternal mortality rate = ------------------------------------- Total births in the same year Life expectancy is the average number of years an individual of a given age is expected to live if current mortality rates continue Mortality

Life Expectancy (years) at selected ages for four countries Age Mauritius Bulgaria USA Japan Birth 65.0 68.3 71.6 75.8 45 years 25.3 27.3 30.4 32.9 65 years 11.7 12.6 15.0 16.2

DIAGNOSIS Clinical question: How accurate are tests used to diagnose disease ? Diagnostic test – the objective is to diagnose any treatable disease present Characteristics of a diagnostic test Reliable – gives the same measurement when repeated more than once Valid - measures what it intends to measure Accurate – correctly determines those with disease and those without Easy to use – can be performed by other people without difficulty Not expensive – affordable Safe and acceptable

Gold standard – a sounder indication of truth or a standard of accuracy - a new diagnostic test is compared - elusive (not available) - expensive and risky – biopsy, surgical exploration, autopsy - sometimes simple – throat swab culture DIAGNOSIS Clinical question: How accurate are tests used to diagnose disease ?

Cut-off points 80 90 100 110 120 130 140 150 160 170 Normal Group Abnormal Group B l o o d L e v e l ( mg / 100 ml )

DIAGNOSIS Clinical question: How accurate are tests used to diagnose disease ? a + c b + d a + b + c + d a + b c + d DISEASE Present Absent TEST Positive a b Negative c d

Validity of a diagnostic test a = no. of true positives, b = no. of false positives c = no. of false negatives, d = no. of true negatives Sensitivity = probability of a positive test in people with the disease = a/(a + c) Specificity = probability of a negative test in people without the disease Positive predictive value = probability of the person having the disease when the test is positive = a /(a + b) Negative predictive value = probability of the person not having the disease when the test is negative = d / (c + d)

62 87 37 112 149 Group A  -Hemolytic Streptococcus on Throat Culture Present Absent Clinical Diagnosis of Strep Pharyngitis Yes 27 35 No 10 77

DISEASE Clinical question: How accurate are tests to diagnose disease ? Use of multiple diagnostic tests use of imperfect diagnostic tests, with less than 100% sensitivity and specificity, a single test frequently results in a probability of disease that is neither very high or very low.

DISEASE Clinical question: How accurate are tests to diagnose disease ? Parallel tests (all at once) - used when rapid assessment is necessary as in hospitalized or emergency patients, or for ambulatory patients who cannot return easily for evaluation because they have come from a long distance Parallel tests generally increase the sensitivity and, therefore, the negative predictive value for a given disease prevalence above those of each individual test. On the otherhand, specificity and positive predictive value are lowered Parallel testing is useful when the clinician is faced with the need for a very sensitive test but has available only two or more relatively insensitive ones.

DISEASE Clinical question: How accurate are tests to diagnose disease ? Serial testing (consecutively, based on previous test result) - used when rapid assessment is not required - used when some of the tests are expensive or risky - maximizes specificity and positive predictive value but lowers sensitivity and the negative predictive value. - the process is more efficient if the test with the highest specificity is used first.

Effect of Sequence is Serial Testing: A Then B versus B Then A Prevalence of Disease Number of patients tested 1000 Number of patients with disease 200 (20% prevalence) Sensitivity and Specificity of the Tests Test Sensitivity Specificity A 80 90 B 90 80 Sequence of Testing Begin with Test A Begin with Test B Disease Disease + - + - A + 160 80 240 B + 180 160 340 - 40 720 760 - 20 640 660 200 800 1000 200 800 1000 240 Patients Retested with B 340 Patients Retested with A Disease Disease + - + - B + 144 16 160 A + 144 16 160 - 16 64 80 - 46 144 180 160 80 240 180 160 340

DISEASE Clinical question: How accurate are tests used to diagnose disease ? Statements about validity test Sensitivity and specificity are inversely related. A sensitive test can pick up most cases of the disease but it will erroneously label as positive many persons who do not have the disease. A highly specific test will correctly label as negative those who do not have the disease but it will miss many cases.

Trade-Off between Sensitivity and Specificity when Diagnosing Diabetes Blood Sugar Level 2 hr after Eating Sensitivity Specificity (mg/100 mL) (%) (%) 70 98.6 8.8 80 97.1 25.5 90 94.3 47.6 100 88.6 69.8 110 85.7 84.1 120 71.4 92.5 130 64.3 96.9 140 57.1 99.4 150 50.0 99.6 160 47.1 99.8 170 42.9 100.0 180 38.6 100.0 190 34.3 100.0 200 27.1 100.0

DISEASE Clinical question: How accurate are tests to diagnose disease ? A very sensitive test gives a low positive predictive value since it produces many false positive. Conversely, a very specific test gives a high positive predictive value. Sensitivity and specificity are unaffected by the prevalence of the disease or condition. Since sensitivity depends only on those with the disease or condition and specificity only on those without the disease or condition. The positive predictive value of a test increases with the prevalence of the disease.

Positive Test 0 20 40 60 80 100 100 80 60 40 20 0 Prevalence of Disease (Percentage) Predictive value (Percentage) Negative Test

DISEASE Clinical question: How accurate are tests to diagnose disease ? Uses of sensitive tests A sensitive test should be chosen when there is an important penalty for missing a disease (dangerous but treatable condition) A sensitive test is most helpful to the clinician when the test result is negative (to rule out disease) Uses of specific tests Highly specific tests are needed when false-positive results can harm the patient physically, emotionally, or financially. A specific test is most helpful when the test result is positive (to confirm or “rule in” the disease)

LIKELIHOOD RATIOS Alternative way of describing the performance of a diagnostic test Summarize the same kind of information as sensitivity and specificity Used to calculate the probability of disease after a positive or negative test (positive or negative predictive value) Advantage – can be used at multiple level of test results.

LIKELIHOOD RATIOS Use of likelihood ratios depends on odds Probability Used to express sensitivity, specificity and predictive value Is the proportion of people in whom a particular characteristic, such as a positive test, is present

LIKELIHOOD RATIOS Odds Is the ratio of two probabilities (the probability of an event to that of 1 – probability of event Odds and probability contain the same information, but they express it differently

LIKELIHOOD RATIOS The two can be interconverted using simple formulas: Probability of event Odds = ------------------------------- 1 – Probability of event Odds Probability = ------------------------- 1 + Odds

LIKELIHOOD RATIOS Express how many times more (or less) likely a test is to be found in diseased, compared with non-diseased, people. If a test yields dichotomous results (both positive and negative) Two types of likelihood ratios described its ability to discriminate between diseased and non-diseased people

LIKELIHOOD RATIOS Test’s positive likelihood ratio (LR+) the ratio of the proportion of diseased people with a positive test result (sensitivity) to the proportion of non-diseased with a positive test result (1 – specificity) Test’s negative likelihood ratio (LR-) the proportion of diseased people with a negative test result (1 – sensitivity) divided by the proportion of non-diseased people with a negative test result (specificity)

LIKELIHOOD RATIOS Example: Diagnostic Characteristics of a D-dimer Assay in Diagnosing Deep Venous Thrombosis (DVT) Test Disease DVT according to Gold Standard (Compression ultrasonography and /or 3 month follow up)` D-dimer Assay for Diagnosis of DVT Present Absent Total Positive 34 168 202 Negative 1 282 283 Total 35 450 485

LIKELIHOOD RATIOS Sensitivity 34 / 35 LR + = ----------------- = --------------- = 2.6 1 – Specificity 168 / 450 1 – Sensitivity 1 / 35 LR - = ------------------- = ---------------- = .045 ~ .05 Specificity 282 / 450

INTERPRETATION OF LIKELIHOOD RATIOS Likelihood Ratio is the probability of a particular test result for a person with the disease of interest divided by the probability of that test result for a person without the disease of interest An LR+ of one indicates a test with no value in sorting out persons with and without the disease of interest, since the probability of a positive test result is equally likely for affected and unaffected persons.

The larger the value of the LR+, the stronger the association between having a positive test result and having the disease of interest The larger the size of the LR+ the better the diagnostic value of the test. Although somewhat arbitrary, an LR+ value of 10 or greater is often perceived as in indication of a test of high diagnostic value INTERPRETATION OF LIKELIHOOD RATIOS

INTERPRETATION OF LIKELIHOOD RATIOS An LR- with a value of one indicates a test with no value in sorting out persons with and without the disease of interest as the probability of a negative test result is equally likely among persons affected and unaffected. The smaller the value of the LR-, the stronger the association between having a negative test result and not having the disease of interest.

INTERPRETATION OF LIKELIHOOD RATIOS The smaller the size of the LR-, the better the diagnostic value of the test. On somewhat arbitrary grounds, an LR- value of 0.1 or less is often perceived as an indication of a test with high diagnostic value.

TECHNIQUES FOR USING LIKELIHOOD RATIOS Mathematical approach Using a likelihood ratio nomogram Simple “Rule of Thumb” for determining effect of likelihood ratios on disease probability

Mathematical Approach Convert Pretest Probability (Prevalence) to Pretest odds Pretest odds = Prevalence / (1 – Prevalence) Multiply Pretest odds by Likelihood ratio to obtain Posttest odds Pretest odds X Likelihood ratio = Posttest odds Convert Posttest odds to Posttest probability (predictive value) Posttest probability = Posttest odds / (1 + Posttest odds)

USING A LIKELIHOOD RATIO NOMOGRAM Place a straight edge at the correct prevalence and likelihood ratio values and read off the posttest probability where the straight edge crosses the line

SIMPLE “RULE OF THUMB” Approximate Change in Likelihood ratio Disease Probability (%) 10 +45 9 +40 8 7 6 +35 5 +30 4 +25 3 +20 2 +15 1 No Change 0.5 - 15 0.4 - 20 0.3 - 25 0.2 - 30 0.1 - 45

SIMPLE “RULE OF THUMB” Mnemonic Likelihood ratio of 2, 5, 10 increases the probability of disease approximately 15%, 30% and 45% respectively, and the inverse of these likelihood ratios of 0.5, 0.2, and 0.1 decrease the probability of disease similarly 15%, 30%, and 45%

LIKELIHOOD RATIOS Likelihood ratios must be used with odds, not probability The main advantage of likelihood ratios is that they make it possible to go beyond the simple and clumsy classification of a test result as either abnormal or normal, as is usually done when describing the accuracy of a diagnostic test only I terms of sensitivity and specificity at a single cutoff point.

LIKELIHOOD RATIOS Disease is more likely in the presence of an extremely abnormal test result than it is for a marginal one With likelihood ratios, it is possible to summarize information contained in a test result at different levels In computing likelihood ratios across range of test results, a limitation of sensitivity and specificity is overcome.

LIKELIHOOD RATIOS Can accommodate the common and reasonable, clinical practice of putting more weight on extremely high (or low) test results than on borderline ones when estimating the probability (or odds) that a particular disease is present.

Distribution of Values for Serum Thyroxine in Hypothyroid and Normal Patients, With Calculation of Likelihood Ratios Patients with Test Result Total Serum Thyroxine Hypothyroid Normal Likelihood Ratio (Ug/dL) number (percent) number (percent) <1.1 2(7.4) 1.1 – 2.0 3(11.1) Ruled in 2.1 – 3.0 1(3.7) 3.1 – 4.0 8(29.6) 4.1 – 5.0 4(14.8) 1(1.1) 13.8 5.1 – 6.0 4(14.8) 6(6.5) 2.3 6.1 – 7.0 3(11.1) 11(11.8) .9 7.1 – 8.0 2(7.4) 19(20.4) .4 8.1 – 9.0 17(18.3) 9.1 – 10 20(21.5) 10.1 – 11 11(11.8) Ruled out 11.1 – 12 4(4.3) > 12 4(4.3) Total 27(100) 93(100)

DISEASE Clinical question: How accurate are tests to diagnose disease ? Problems: Lack of information on negative tests Lack of information on test results in the nondiseased Lack of objective standards for disease Consequences of imperfect standards If a new test is compared with an old (but inaccurate) standard test, the new test may seem worse even when it is actually better

DISEASE Clinical question: How accurate are tests to diagnose disease? Reliability and validity Measurement error Instrument The means of making the measurement Observer The person making the measurement Biologic variation Within individuals Changes in people with time and situation Among individuals Biologic differences from person to person

DISEASE Clinical question: How accurate are tests to diagnose disease?

EARLY DIAGNOSIS Strategies Screening test (uni- or multi-phasic) Periodic health examination Case finding Objectives Early detection of asymptomatic disease Identification of predictors or risk factors of disease

EARLY DIAGNOSIS NATURAL HISTORY OF DISEASE (FOUR STAGES) Biologic onset initial interaction between man, causal factors, and the rest of the environment cannot detect the presence of disease Early diagnosis possible mechanisms of disease produce structural or functional changes individual remains free of any symptoms

EARLY DIAGNOSIS NATURAL HISTORY OF DISEASE (FOUR STAGES) Usual clinical diagnosis disease progresses to the point where symptoms appear and affected individual becomes ill Outcome recovery, permanent disability or death

EARLY DIAGNOSIS NATURAL HISTORY OF DISEASE (FOUR STAGES) T I M E EARLY USUAL BIOLOGIC DIAGNOSIS CLINICAL ONSET POSSIBLE DIAGNOSIS OUTCOME Recovery Disability Death D X

EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE 1 2 3 CP CP CP EARLY USUAL BIOLOGIC DIAGNOSIS CLINICAL ONSET POSSIBLE DIAGNOSIS OUTCOME Recovery Disability Death D X

EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE Position 1 The screening test and case finding would be too late to be of help in early detection of disease Position 2 The test will have a promise of improving the outcomes of those who have the target disorder Position 3 Early detection of the disease is a waste of time

EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE How do we tell a disease has a critical point at position 2 and its detection is worth our critical effort?

EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE Data modified from S. Shapiro. Evidence of screening for breast cancer from a randomized trial (Suppl.) 39:2772, 1977 Breast cancers diagnosed early in the Health Insurance Plan Study Age at diagnosis Percentage with positive axillary nodes 40-49 50-59 60+ Total Mode of early diagnosis Only by mammography 6 (19%) 27 (42%) 11 (31%) 44 (33%) 16% Only by clinical exam 19 (62%) 26 (40%) 14 (38%) 59 (45%) 19% Detected by both modes 6 (19%) 12 (18%) 11 (31%) 29 (22%) 41% 31 (100%) 65 (100%) 36 (100%) 132 (100%)

EARLY DIAGNOSIS CRITICAL POINTS IN THE NATURAL HISTORY OF DISEASE Some results of the H.I.P. randomized trial of early diagnosis in breast cancer Data modified from S. Shapiro. Evidence of screening for breast cancer from a randomized trial, Cancer(Suppl.) 39:2772, 1977 Deaths per 10,000 women per year From breast cancer From all other causes From cardiovascular disease 40-49 50-59 60-69 Control women 2.4 5.0 5.0 54 25 Experimental women 2.5 2.3 3.4 54 24

HOW TO DECIDE WHEN TO SEEK AN EARLY DIAGNOSIS Does early diagnosis really lead to improved clinical outcomes ( in terms of survival, function, and quality of life)? Can you manage the additional clinical time required to confirm the diagnosis and provide long-term care for those screen positive? Will the patients in whom an early diagnosis is achieved comply with your subsequent recommendations and treatment regimen

HOW TO DECIDE WHEN TO SEEK AN EARLY DIAGNOSIS Has the effectiveness of individual components of a periodic health examination or multiphasic screening program been demonstrated prior to their combination? Does the burden of disability from the target disease warrant action? Are the cost, accuracy, and acceptability of the screening test adequate for your purpose?

Does early diagnosis really lead to improved clinical outcomes (in terms of survival, function, and quality of life)? Claims for therapeutic benefit must withstand close scrutiny and experimental evidence from randomized trials is a prerequisite. Long-term beneficial effects of therapy outweigh the long-term detrimental effects of the treatment regimen and labeling of patients as diseased.

Can you manage the additional clinical time required to confirm the diagnosis and provide long-term care for those screen positive? Increased demands on your time start with early diagnosis and you need to be sure that you have enough of it. Large numbers of labeled but untreated hypertensive attest to the size of this problem

Will the patients in whom an early diagnosis is achieved comply with your subsequent recommendations and treatment regimens If patients will not take their medicine, all the screening and diagnosis made are nullified. Labeled patient

Have the effectiveness of individual components of a periodic health examination or multiphasic screening program been demonstrated prior to their combination? The appropriateness of a mix of tests must consider whether differences in the distributions of two diseases render the combination of their respective screening tests nonsensical. It was this consideration that led the Canadian Task Force on the Periodic Health Examination to propose quite different “health protection packages” for patients of different age, sex, and social status.

Does the burden of disability from the target disease warrant action? The disease you are searching for should be either so common or so awful as to warrant all the work and expense of detecting it in its presymptomatic state

Types of epidemiological study Type of study Alternative name Unit of study Observational studies Descriptive studies Analytical studies Ecological Correlational Population Cross-sectional Prevalence Individuals Case-control Case-reference Individuals Cohort Follow-up Individuals Experimental studies Interventional studies Randomized controlled trials Clinical trials Patients Field trials Healthy people Community trials Community intervention Communities studies

Types of epidemiological study (Descriptive studies) Case reports - detailed presentations of a single case or a handful of cases - means of describing rare clinical events - describe unusual manifestations of disease - elucidate the mechanisms of disease and treatment - place issues before medical community and often trigger more decisive studies - susceptible to bias

Types of epidemiological study (Descriptive studies) Case-series - a simple descriptive account of interesting characteristics observed in a group of patients - study larger group of patients (e.g. 10 or more) with particular disease - describe the clinical manifestations of disease and treatments in a group of patients assembled at one point in time - absence of a comparison group, not conclusive - hypothesis-generating - selection bias

Types of epidemiological study (Observational studies) Ecological studies - aggregate risk studies - units of analysis are populations or groups of people rather than individuals - rely on data collected for other purposes; data on different exposures and on socioeconomic factors may not be available - ecological fallacy (bias) - useful in raising hypothesis

Types of epidemiological study (Observational studies) Study subjects With outcome Without outcome Population at risk Defined population Onset of study TIME No direction of inquiry Cross-sectional study (Prevalence study)

Types of epidemiological study (Observational Studies) Cross-sectional studies (Prevalence studies) - measure the prevalence of disease - measurements of exposure and effect are made at the same time - useful for investigating exposures that are fixed characteristics of individuals, such as ethnicity, socio-economic status and blood group, or chronic diseases or stable conditions

Types of epidemiological study (Observational studies) Cross-sectional studies (Prevalence studies) - In sudden outbreaks of disease it is the most convenient first step in an investigation into the cause - Rare disease, conditions of short duration or diseases with high case fatality are often not detected

Types of epidemiological study (Observational studies) Cross-sectional studies (Prevalence studies) - short-term and therefore less costly - provide no direct estimate of risk - prone to bias from selective survival - estimates of prevalence may be biased by the exclusion of cases in which death or recovery are rapid

Types of epidemiological study (Observational studies) CASES (people with disease) CONTROLS (people without disease) Exposed Not exposed Exposed Not exposed Population direction of inquiry T I M E Design of a case-control study

Types of epidemiological study (Observational studies) Case-control studies - longitudinal studies (looking backward from the disease to a possible cause) - use new (incident) cases - used to investigate cause (etiology) of disease, esp. rare diseases - used odds ratio

Types of epidemiological study (Observational studies) Case-control studies - relatively efficient, requiring smaller sample than cohort study - completed faster and more economical - earliest practical observational strategy for determining an association - antecedent-consequence uncertainty

Table arrangement and formula for Odds ratio (OR) Disease No disease Total Risk factor present A B A+B Risk factor absent C D C+D Total A+C B+D [A / (A+C)] / [C / (A+C)] A/C AD OR = ------------------------------- = ------- = ------- [B / (B+D)] / [D / (B+D)] B/D BC

Types of epidemiological study (Observational study) Interpretation of Odds ratio Value of OR less than 1 indicates a negative association (i.e., protective effect) between the risk factor and the disease For rare disease (e.g., most chronic diseases with disease prevalence of less than 10%), OR approximates RR

Example of case-control study Association between recent meat consumption and enteritis necroticans in Papua New Guinea Exposure (recent meat ingestion) Yes No Total Disease Yes 50 11 61 (enteritis necroticans) No 16 41 57 Total 66 52 118

Example of case-control study [A / (A+C)] / [C / (A+C)] A / C AD OR = -------------------------------- = -------- = ----- [B / (B+D)] / [D / (B+D)] B / D BD 50 X 41 OR = ------------- = 11.6 11 X 16 The cases were 11.6 times more likely than the controls to have recently ingested meat

Types of epidemiological study (Observational studies) Population People without the disease Exposed Not exposed disease no disease disease no disease direction of inquiry T I M E Design of a cohort study

Past Present Future Cohort Follow-up assembled Historical cohort Cohort Follow-up assembled Concurrent cohort

Types of epidemiological study (Observational studies) Cohort studies - longitudinal studies (forward) - provide the best information about the causation of disease - most direct measurement of the risk of developing disease - provide the possibility of estimating the attributable risks - use relative risk

Types of epidemiological study (Observational studies) Cohort studies - most closely resemble experimental studies - Long-term, not always feasible - Sample size required for the study extremely large - Attrition is most serious problem

Table arrangement and formula for relative risk (RR) Disease No Disease Total Risk factor present A B A + B Risk factor absent C D C + D Total A + C B + D A / (A + B) RR = ----------------- C / (C + D)

Types of epidemiological study (Observational studies) Interpretation of relative risk (RR) The disease (or other health related outcome) is RR times more likely to occur among those exposed than among those with no exposure The larger the value of RR , the stronger the association between the disease in question and exposure to the risk factor

Types of epidemiological study (Observational studies) Interpretation of relative risk (RR) Value of RR close to 1 indicates that the disease and exposure to the risk factor are unrelated Value of RR less than 1 indicates a negative association between the risk factor and the disease (i.e., protective rather than detrimental)

Example of cohort study Problem: A county school system provides lunch to 10,000 school children. During the first week of school, 2,500 of these children ate chicken salad later shown to be contaminated with salmonella. The entire population of 10,000 students was subsequently followed for one month to determine whether exposure to salmonella increased the risk of diarrhea.

Example of cohort study Diarrhea No Diarrhea Exposure (D+) (D-) Totals E+ 30 2,470 2,500 E- 60 7,440 7,500 Totals 90 9,910 10,000 A / (A+B) 30 / 2,500 RR = --------------- = ----------------- = 1.5 C / (C+D) 60 / 7,500 1.5 times greater than in children with no such exposure

Advantages and disadvantages of different observational study designs Probability of: selection bias NA medium high low recall bias NA high high low loss to follow-up NA NA low high confounding high medium medium low Time required low medium medium high Cost low medium medium high Ecological Cross- Case- Cohort sectional control

Applications of different observational study designs Investigation of rare disease ++++ - +++++ - Investigation of rare cause ++ - - +++++ Testing multiple effect of + ++ - +++++ cause Study of multiple exposure ++ ++ ++++ +++ and determinants Measurements of time ++ - + +++++ relationship Direct measurement of - - + +++++ incidence Investigation of long - - +++ - latent periods Ecological Cross- Case- Cohort sectional control

Types of epidemiological study (Experimental studies) Non-participants Do not meet Selection criteria Potential participants Participants Non-participants Control Treatment Study population Randomization Invitation to participate Selection by defined criteria Design of a randomized clinical trial

Types of epidemiological study (Experimental studies) Randomized controlled trials (RCTs) Gold standard or reference in medicine Provide the greatest justification for concluding causality Subject to the least number of problems or biases Best study design to establish the efficacy of a treatment or a procedure

Types of epidemiological study (Experimental studies) Randomized controlled trials (RCTs) - Expensive and time-consuming - Difficult to obtain approval to perform properly designed clinical trials

Relative ability of different types of study to “prove” causation Type of study Ability to “prove” causation Randomized controlled trials Strong Cohort studies Moderate Case-control studies Moderate Cross-sectional studies Weak Ecological studies Weak

Bias in Clinical Observation Selection bias occurs when comparisons are made between groups of patients that differ in determinants of outcome other than the one under study Measurement bias occurs when the methods of measurement are dissimilar among groups of patients Confounding bias occurs when two factors are associated (“travel together”) and the effect of one is confused with or distorted by the effect of the other

Methods of Controlling Selection Bias Phase of Study Method Description Design Analysis Randomization Assign patients to groups in a way that + gives each patient equal chance of falling into one or the other group Restriction Limit the range of characteristics of + of patients in the study Matching For each patient in one group select one + or more patients with the same characteristics (except for the one under study) for a comparison group Stratification Compare rates within subgroups (strata) + with otherwise similar probability of the outcome

Methods for Controlling Selection Bias Phase of Study Method Description Design Analysis Adjustment Simple Mathematically adjust crude rates for one + or few characteristics so that equal weight is given to strata of similar risk Multiple Adjust for difference in large number of factors + related to outcome, using mathematical modelling techniques Best case/ Describe how different the results could be + Worse case under the most extreme or simply very unlikely) conditions of selection bias

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Webster’s definition: “something that brings about an effect or a result” “ A factor is a cause of an event if its operation increases the frequency of an event” In medicine : “etiology” “pathogenesis” “mechanisms” or “risk factors” Importance: prevention, diagnosis and treatment of disease

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Concepts of Cause Single causation (Koch’s postulates) a particular disease has one cause and a particular cause results in one disease The organism must be present in every case of the disease The organism must be isolated and grown in pure culture The organism must cause a specific disease when inoculated into an animals and The organism must then be recovered from the animal and identified

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Multiple causation (Web of causation) Effects never depend on single isolated causes, but rather develop as the result of chains of causation in which each link itself is the result of “a complex genealogy of antecedents.” Many factors act together to cause disease

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Concept of Cause A cause must precede a disease A cause is termed sufficient when it inevitably produces or initiates a disease A cause is termed necessary if a disease cannot develop in its absence

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? INCREASED SUSCEPTIBILITY INGESTION OF CHOLERA VIBRIO CHOLERA Causes of cholera Exposure to contaminated water Effect of cholera toxins on bowel wall cells Genetic factors Malnutrition Crowded housing Poverty Risk factors for cholera Mechanisms for cholera

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? A sufficient cause is not usually a single factor, but often comprises several components It is not necessary to identify all the components of a sufficient cause before effective prevention can take place Each sufficient cause has a necessary cause as a component A causal factor on its own is often neither necessary nor sufficient

SUFFICIENT CAUSES U A B U A E U B E I II III

Causation Causal relationship in the physical sciences are often simple, as in Boyle’s law relating pressure and volume of a gas, or the effect of heat on a metal bar. The causal agent is sufficient, the time relationship is short, and replication is easy. In the Boyle’s law situation, a change in pressure was both necessary and sufficient for a change in volume, given that the other circumstances were fixed.

Causation In the metal bar example, heat was sufficient but not a necessary cause; there are other ways of lengthening a metal bar Causal relationship in human health and disease are rarely simple

Causation In human health and disease not all causal agents are sufficient. In the disease tuberculosis, infection by the tubercle bacillus does not invariably lead to clinical tuberculosis. Only a small proportion of those who are infected by the bacillus develop clinical tuberculosis

Causation Most situations in health and disease do not fulfill the criteria either necessary or for sufficient causation. An healthy man is admitted to hospital with multiple fractures, having been hit by a bus just outside the hospital

Causation We can conclude that there was a causal relationship between being hit by the bus and having multiple fractures But the relationship implies neither that the cause is sufficient nor that it is necessary. Not all people hit by buses have multiple fractures. Not all patients with multiple fractures have been hit by buses.

Causation Where the time relation is not clear, and the concepts of necessary and sufficient cause do not hold, we need a quantitative assessment of the relationship, based on observations not on one individual but on a number of individuals. Hence, the definition of causation is quantitative

Causation A direct test of the quantitative definition of causation is by randomized trial approach

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Concept of cause Proximity of cause to effect Disease is also determined by less specific, more remote causes or risk factors, such as people’s behavior or characteristics of their environment. These factors may be even more important causes of disease than are pathogenetic mechanisms If the pathogenetic mechanism is not clear, knowledge of risk factors may still lead to very effective treatments and prevention

SUSCEPTIBLE HOST INFECTION TUBERCULOSIS Exposure to Mycobacterium Tissue Invasion and Reaction Crowding Malnutrition Vaccination Genetic Risk Factors for Mechanisms of Tuberculosis Pathogenesis Tuberculosis Distant from Outcome Proximal to Outcome Causes of tuberculosis

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Concept of cause Interplay of multiple causes Synergism – the joint effect is greater than the sum of the effects of the individual causes Antagonism – the joint effect is lesser Effect Modification – a special type of interaction A substantial impact on a patient’s health by changing only one or a small number of the causes

Cause as a risk factor Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Risk refers to the probability of some untoward event Risk indicates the likelihood that people who are exposed to certain factors (risk factors) will subsequently develop a particular disease Risk factor refers to condition, physical characteristic, or behavior that increases the probability (i.e., risk) that a currently healthy individual will develop a particular disease.

Cause as a risk factor Clinical question: What conditions lead to disease ? What are the pathogenetic mechanism of disease ? Exposure to risk factor can occur at a single point in time or over a period of time ever exposed current dose largest dose taken total cumulative dose years of exposure years since first contact

Cause as a risk factor Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Recognizing risk Large risks associated with effects that occur rapidly after exposure are easy for anyone to recognize Most morbidity and mortality are caused by chronic diseases. The relationship between exposure and disease are far less obvious – latency period

Comparing disease occurrence among exposed and unexposed Absolute comparison Risk difference, also called attributable risk (exposed), excess risk or absolute risk Attibutable fraction (exposed) or etiological fraction (exposed) Population attributable risk or attributable fraction (population) Relative comparison Risk ratio Standardized mortality ratio

Relationship between cigarette smoking and incidence rate of stroke in a cohort of 118,539 women Never smoked 70 395,594 17.7 Ex-smoker 65 232,712 27.9 Smoker 139 280,141 49.6 Total 274 908,447 30.2 Smoking Person-y ears Stroke incidence rate category No. of cases of observation (per 100,000 of stroke (over 8 years) person-years)

Comparing disease occurrence among exposed and unexposed Risk difference is the difference in rates of occurrence between exposed and unexposed groups useful measure of the extent of the public health problem caused by the exposure Example: 49.6 – 17.7 = 31.9 per 100,000 person-years

Comparing disease occurrence among exposed and unexposed Attributable fraction (exposed) is the proportion of the disease in the specific population that would be eliminated in the absence of exposure determined by dividing the risk difference by the rate of occurrence among the exposed population Example: [(49.6 – 17.7) / 49.6] x 100 = 64% Interpretation: One would expect to achieve a 64% reduction in the risk of stroke among the women smokers if smoking were stopped, on the assumption that smoking is both causal and preventable

Comparing disease occurrence among exposed and unexposed Population attributable risk [attributable fraction (population)] is a measure of the excess rate of disease in a total study population which is attributable to an exposure useful for determining the relative importance of exposures for the entire population and is the proportion by which the incidence rate of the outcome in the entire population would be reduced if exposure were eliminated. 30.2 – 17.7 = ------------------ = 0.414 o r 41.4% 30.2

Comparing disease occurrence among exposed and unexposed Risk ratio or relative risk the ratio of the risk of occurrence of a disease among exposed people to that among the unexposed better indicator of the strength of an association than the risk difference used in assessing the likelihood that an association represents a causal relationship Example: RR = 49.6 / 17.7 = 2.8

Cause as a risk factor Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Uses of risk factor predict the occurrence of disease marker of disease outcome improve the positive predictive value of a diagnostic test prevent disease

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Establishing cause In clinical medicine, it is not possible to prove causal relationship beyond any doubt. It is only possible to increase one’s conviction of a cause and effect relationship, by means of empiric evidence, cause is established.

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Establishing cause Factors that are considered causes at one time are sometimes found to be indirectly related to disease later, when more evidences are available

Cause Clinical question: What conditions lead to disease ? What are the pathogenetic mechanisms of disease ? Establishing cause Two factors – the suspected cause and the effect – obviously must appear to be associated if they are to be considered as cause and effect However, not all associations are causal Two factors may be associated but not causal due to the presence of selection and measurement biases, chance and confounder

Could it be due to selection or measurement bias Could it be due to confounding? Could it be a result of chance? Could it be causal? Apply guidelines and make judgment ASSESSING THE RELATIONSHIP BETWEEN A POSSIBLE CAUSE AND OUTCOME No No Probably not

GUIDELINES FOR CAUSATION Temporal Does the cause precede the effect ? relationship (essential) Plausibility Is the association consistent with other knowledge ? (mechanism of action; evidence from experimental animals) Consistency Have similar results been shown in other studies ? Strength What is the strength of the association between the cause and the effect ? (relative risk)

GUIDELINES FOR CAUSATION Dose-response Is increased exposure to the relationship possible cause associated with increased effect ? Reversibility Does the removal of a possible cause lead to reduction of disease risk ? Study design Is the evidence based on a strong study design ? Judging the How many lines of evidence lead evidence to conclusions?

Treatment Clinical question: How does treatment change the course of disease? DECIDING ON THE BEST THERAPY

IS THE ULTIMATE OBJECTIVE TO ACHIEVE CURE, PALLIATION, SYMPTOMATIC RELIEF, OR WHAT? DOES THE PATIENT REQUIRE ANY TREATMENT AT ALL? WHAT SORTS OF EVIDENCE, FROM WHAT SOURCES, SHOULD DETERMINE THE CHOICE OF THE SPECIFIC TREATMENT TO BE USED TO REACH THIS GOAL HOW WILL YOU KNOW WHEN TO STOP TREATMENT, CHANGE ITS INTENSITY, OR SWITCH TO SOME OTHER TREATMENT? THREE PRINCIPAL DECISIONS THAT DETERMINE THE RATIONAL TREATMENT OF ANY PATIENT

Example A PATIENT WITH SYMPTOMLESS BUT MODERATELY SEVERE ESSENTIAL HYPERTENSION (FIFTH-PHASE DIASTOLIC BLOOD PRESSURE 110 mm Hg).

Example ULTIMATE OBJECTIVE OF TREATMENT To prevent (further) target organ damage to the brain, eye, heart, kidney, and large vessels that would cause disability or untimely death. CHOICE OF SPECIFIC TREATMENT On the basis of randomized clinical trials of active agents versus placebo, antihypertensive drugs TREATMENT TARGET A fifth-phase diastolic blood pressure of less than 90 mm Hg, or as close to that as tolerable in the face of drug side effects.

SIX OBJECTIVES OF TREATMENT Cure (e.g. kill the microbe, cut out the tumor, desensitize the phobic patient) Prevent a recurrence (e.g. give prophylactic antibiotics following recovery from acute rheumatic fever, or major tranquilizers following discharge for schizophrenia ) Limit structural or functional deterioration (e.g. reconstruct, rehabilitate) Prevent the later complication (e.g. give diuretics to symptomless hypertensives and aspirin to threatened strokes).

SIX OBJECTIVES OF TREATMENT Relieve the current distress (e.g. replace the hormone, provide emotional support or counseling, give painkillers, anti-depressants and anti-inflammatory drugs) Deliver reassurance (e.g. “un-label” the misdiagnosed, transmit the truly favorable prognosis) Allow to die with comfort and dignity (e.g. cancel further diagnostic testing and focus on the relief of current symptoms and the preservation of self-esteem).

THREE ELEMENTS OF A SICKNESS THE DISEASE OR TARGET DISORDER THE ANATOMIC, BIOCHEMICAL, PHYSIOLOGIC, OR PSYCHOLOGIC DERANGEMENT THE ILLNESS THE SIGNS, SYMPTOMS, AND BEHAVIORS EXHIBITED BY THE PATIENT AS A RESULT OF, AND RESPONDING TO, THE TARGET DISORDER THE PREDICAMENT THE SOCIAL, PSYCHOLOGICAL, AND ECONOMIC FASHION IN WHICH THE PATIENT IS SITUATED IN THE ENVIRONMENT

Need to know exactly what is being treated Its prognosis when treated and untreated Its risk of relapse and recurrence Its permanent disabilities Its ultimate outcomes Need for the correct and accurate assessment of illness as this is the key to setting treatment objectives (symptomatic relief) Need to assess the patient’s predicament in order to identify the limits of one’s treatment options

SELECTING THE SPECIFIC TREATMENT The first element of selecting the specific treatment is to decide first whether any treatment is required.

SELECTING SPECIFIC TREATMENT Modern manufacturers have introduced exotic machines that can select, punch, drill, bend, fit, and weld raw materials into finished goods all by themselves, they sharpen their own tools when they become dull, replace bits of themselves when they wear out, and even sense and correct their own mistakes.

SELECTING SPECIFIC TREATMENT One problem they have not been able to overcome, however, is the almost irresistable temptation they present to their human attendants to adjust, reset, and otherwise tinker with them, even when they are functioning fine. The results are often disastrous. In desperation, some plant managers have installed prominent notices along their automated assembly lines: IF IT AIN’T BROKE, DON’T FIX IT!

There are two circumstances in which patients “ain’t broke” and ought not attempt to “fix” them False-positive diagnostic errors that label patients as diseased. When either the treatment is worse than the disease or when their illness is trivial, self-limited, or well within the recuperative and reparative powers of the patient’s body and mind

DR. CLIFTON MEADOR NICELY SUMMARIZED THESE “NON-DISEASES” MIMICKING SYNDROMES (round-faced fat women with hairy upper lips but normal steroids have non-Cushing’s disease) UPPER-LOWER LIMIT SYNDROMES (borderline laboratory values) NORMAL VARIATION SYNDROMES (Short children of short parents have non-dwarfism) LABORATORY ERROR SYNDROMES

DR. CLIFTON MEADOR NICELY SUMMARIZED THESE “NON-DISEASES” ROENTGENOLOGIC-OVERINTERPRETATION SYNDROMES CONGENITALLY ABSENT-ORGAN SYNDROMES (“Non-functioning” kidneys and gall bladders that are not there) OVERINTERPRETATION-OF-PHYSICAL FINDINGS SYNDROMES

Conditions among patients who “ain’t broke, so don’t fix them” Adie’s pupil Café au lait spots Campbell de Morgan spots Non-dwarfism Pregnancy Pityriasis rosea Silent gallstones Ptosis of the kidney (in normotensive) “ Letter-reversal” in a 4-year old Umbilical hernia in infancy 11. Symptomless hypotension 12. Symptomless hiatus hernia 13. Symptomless hyperuricemia 14. Symptomless colonic diverticulae 15. Small degrees of stable scoliosis 16. Non-Cushing’s disease 17. Symptomless hypokalemia in thiazide- treated hypertensives who are not taking digitalis

THREE WAYS OF PICKING UP THERAPY YOUR OWN UNCONTROLLED CLINICAL EXPERIENCE (INDUCTION METHOD) FORMAL RANDOMIZED CLINICAL TRIALS (DEDUCTION METHOD) RECOMMENDATIONS OF OTHERS (ABDICATION OR SEDUCTION METHOD)

SELECTING SPECIFIC TREATMENT THE HYPOTHETICO-DEDUCTIVE METHOD IS PREFERRED FOR SELECTING SPECIFIC TREATMENTS THE BEST INFORMATION ON WHETHER A GIVEN TREATMENT DOES MORE GOOD THAN HARM TO PATIENTS WITH A GIVEN DISORDER IS THE RESULTS OF A RANDOMIZED CLINICAL TRIAL

SIX GUIDES TO DISTINGUISH USEFUL FROM USELESS OR EVEN HARMFUL THERAPY Was the assignment of patients to treatments really randomized ? Were all clinically relevant outcomes reported ? Were the study patients recognizably similar to your own ? Were both clinical and statistical significance considered ? Is the therapeutic maneuver feasible in your practice? Were all the patients who entered the study accounted for at its conclusion

SIX GUIDES TO DISTINGUISH USEFUL FROM USELESS OR EVEN HARMFUL THERAPY Guides 1 & 6 deal mostly with validity (Are the article’s conclusions true?) Guides 2, 3, & 5 deal mostly with applicability (Are the article’s conclusions relevant to your own patients?) Guide 4 deals with both validity (statistical significance) and applicability (clinical significance)

Clinically relevant outcomes in a randomized trial of clofibrate in the prevention of coronary heart disease PLACEBO CLOFIBRATE Average change in serum cholesterol (%) +1 - 9 Non-fatal myocardial infarctions per 1000 subjects 7.2 5.8 Fatal and nonfatal myocardial infarctions per 1000 subjects 8.9 7.4 Total deaths per 1000 subjects 5.2 6.2

WERE THE STUDY PATIENTS RECOGNIZABLY SIMILAR TO YOUR OWN ? The clinical and socio-demographic status of study patients must be described in sufficient detail The study patients should be at least roughly similar to patients in your practice.

WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? CLINICAL SIGNIFICANCE refers to the importance of a difference in clinical outcomes between treated and control patients. usually described in terms of the magnitude of a result. STATISTICAL SIGNIFICANCE tells us whether the conclusions the authors have drawn are likely to be true (regardless of whether or not they are clinically important).

WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? If the difference is statistically significant, is it clinically significant as well ? If the difference is not statistically significant, was the trial big enough to show a clinically important difference if it had occurred ? “ CLINICAL SIGNIFICANCE ” GOES BEYOND ARITHMETIC AND IS DETERMINED BY CLINICAL JUDGMENT .

WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? An article that reports on a randomized double-blind clinical trial comparing a new drug ( Drug A ) with an identical appearing placebo ( Drug B ) for the control of an important clinical disorder. Based on the results, the authors of the article will have drawn one of two conclusions: either Drug A is better than Drug B or Drug A is no better than Drug B.

Comparing the conclusions drawn from a clinical trial with the true state of affairs w x y z TP=true positive; FP= false positive; FN= false negative; TN= true negative THE CLINICAL TRIAL IS THE DIAGNOSTIC TEST The true state of affairs Drug A is better than drug B Drug A is no better than drug B Conclusion drawn from a clinical trial Drug A is better than drug B TP Correct FP Error Drug A is no better than drug B Error FN Correct TN

Naming the erroneous conclusions from a clinical trial w x y z The true state of affairs Drug A is better than drug B Drug A is no better than drug B Conclusion drawn from a clinical trial Drug A is better than drug B TP Correct (1-  = power) FP Type I error (risk of making this error=  =P value) Drug A is no better than drug B Type II error (risk of making this error=  ) FN Correct TN

WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? The relationships between Type I and Type II errors are used in both planning and interpreting randomized trials. In planning such a trial, investigators can decide beforehand just how great a risk they are willing to run of drawing erroneous conclusions of both sorts Most authors decide to set the false-positive (  ) risk at .05 and the false-negative (  ) risk at .20 – conventional levels of statistical significance.

WERE BOTH CLINICAL AND STATISTICAL SIGNIFICANCE CONSIDERED ? In other clinical situations, esp. in the growing number of cases in which clinicians want to find out whether a new treatment is not better than, but as good as; a standard treatment of higher toxicity or cost, the false-negative risk may be set lower.

IF THE DIFFERENCE IS STATISTICALLY SIGNIFICANT, IS IT CLINICALLY SIGNIFICANT AS WELL ? One of the landmark U.S. Veterans Administration trials of whether treating hypertension would prevent fatal and nonfatal target organ damage. In this trial, patients with and without prior target organ damage (to the heart, brain, eye, kidney, or major vessels) at entry were randomized to receive either active anti-hypertensive drugs or identical appearing placebos, and the clinical course were observed over the next 3 years for the subset of men who entered before the age of 50 with diastolic blood pressures between 90 and 114

Occurrence of death, stroke, or other major complications How might these benefits be expressed in terms of clinical significance ? Patient status at entry Adverse event rates Placebo P Active RX A Prior target organ damage .22 .08 No prior organ damage .10 .04

Occurrence of death, stroke, or other major complications These relative risk reductions mean that the risk of death, stroke, or other complications of hypertension was reduced by almost two-third through active treatment Patient status at entry Adverse event rates Relative Risk Reduction RRR Placebo P Active A (P – A) -----------= RRR P Prior target organ damage .22 .08 .22 - .08 ----------- = 64% .22 No prior organ damage .10 .04 .10 - .04 ----------- = 60% .01

IF THE DIFFERENCE IS STATISTICALLY SIGNIFICANT, IS IT CLINICALLY SIGNIFICANT AS WELL ? YARDSTICK FOR Relative Risk Reduction (RRR) Relative risk reductions of  50% almost always, and of  25% often, are considered to be clinically significant. A quick and useful measure of clinical significance.

Occurrence of death, stroke, or other major complications The decimal form of absolute risk reduction is foreign to most clinicians Patient status at entry Adverse events Absolute risk reduction ARR Placebo P Active A RRR P – A = ARR Prior target organ damage .22 .08 64% .22-.08=.14 No prior organ damage .10 .04 60% .10-.04=.06

IF THE DIFFERENCE IS STATISTICALLY SIGNIFICANT, IS IT CLINICALLY SIGNIFICANT AS WELL ? For easy interpretation of absolute risk reduction, we take the reciprocal of it. The reciprocal of the absolute risk reduction is the number of patients we need to treat in order to prevent one complication of their disease This measure of clinical significance is called the number needed to treat (NNT)

Occurrence of death, stroke, or other major complications Patient status at entry Adverse events Number Needed to Treat (NNT) Placebo P Active A RRR ARR 1 ----- = NNT ARR Prior target organ damage .22 .08 64% .14 1 ---- = 7 .14 No prior organ damage .10 .04 60% .06 1 ---- = 17 .06

The effect of different baseline risks and relative risk reductions on the number needed to treat Baseline risk (with no treatment) Relative risk reduction on treatment 50% 40% 30% 25% 20% 15% 10% .9 .6 .3 2 3 7 3 4 8 4 6 11 4 7 13 6 8 17 7 11 22 11 17 33 .2 .1 .05 10 20 40 13 25 50 17 33 67 20 40 80 25 50 100 33 67 133 50 100 200 .01 .005 .001 200 400 2000 250 500 2500 333 667 3333 400 800 4000 500 1000 5000 667 1333 6667 1000 2000 10000

The effect of different baseline risks and relative risk reductions on the number needed to treat Conclusions: When the absolute baseline risk of the bad clinical outcome is high, even modest relative risk reductions generate gratifyingly small NNT. Small changes in the absolute baseline risk of a rare clinical event lead to big changes in the numbers of patients we need to treat in order to prevent one.

IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED? Sample case When Hill and his colleagues performed their randomized trial of home-versus-hospital care for patients with suspected myocardial infarction (in the days before thrombolytic therapy), they observed a 6-week case- fatality rate of 20% among the 132 patients who were randomized to be treated at home. This rate was not statistically significantly different from the 6-week case- fatality rate of 18% they documented among the other 132 patients who were randomized to treatment in hospital

IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED? Can we conclude that it was safe in those days to treat such coronary patients at home ? Was this trial big enough to show a clinically significant difference (say a 25% or 50% better among hospitalized coronaries) if it did occur ?

Was the trial big enough to show a relative risk reduction of  25% if it had occurred ? Observe rate of events in the experimental group .95 .90 .85 .80 .75 .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .05 Observed rate of events in the control group .95 .90 .85 .80 .75 .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .05 14 27 68 391 11 18 38 110 1057 14 25 54 185 4889 11 18 33 78 326 13 22 44 112 635 11 16 28 57 165 1524 13 20 35 75 250 6349 10 15 24 43 99 402 12 17 28 53 132 722 13 20 33 65 180 1607 10 15 22 38 79 254 11 16 25 44 98 381 12 18 28 50 121 634 13 19 30 57 1296 10 13 20 33 64 196 4537 10 14 20 34 71 261 10 14 20 35 78 371 10 13 20 34 80 589 12 17 30 74 1245

Was the trial big enough to show a relative reduction of  50% if it had occurred ? Observed rate of events in the experimental group .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .08 .06 .04 .02 Observed rate of events in the control group .98 .95 .90 .85 .80 .75 .70 .65 .60 .55 .50 .45 .40 .35 .30 .25 .20 .15 .10 .08 .06 .04 .02 14 24 50 165 5803 12 19 37 102 921 14 26 58 236 12 19 38 108 995 10 15 27 63 256 12 21 41 116 1059 16 29 66 268 13 22 43 120 1082 11 17 30 68 270 13 22 42 119 1059 11 17 30 66 260 13 22 42 113 987 11 16 28 62 239 13 20 38 102 867 10 15 26 55 205 12 18 33 86 699 13 22 45 160 10 15 26 64 482 11 17 32 102 254 2017 14 25 66 131 453 12 20 44 76 179 1313 10 16 31 47 87 274 12 22 30 47 97 561

IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED? The trial needed 261 patients per group to be confident that it had not missed a risk reduction of 25% in the 6-week case-fatality rate of coronary patients treated in hospital The trial needed 45 patients per group (50%) The trial was too small to reject a 25% improvement, but large enough to reject a 50% improvement in the 6-week case-fatality rates of coronary patients treated in hospital

IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED? 95% CONFIDENCE INTERVAL OR CONFIDENCE LIMIT ON RISK REDUCTION, NNT OR OTHER MEASURE OF EFFICACY

IF THE DIFFERENCE IS NOT STATISTICALLY SIGNIFICANT, WAS THE TRIAL BIG ENOUGH TO SHOW A CLINICALLY IMPORTANT DIFFERENCE IF IT HAD OCCURRED ? This is a Swedish Co-operative Stroke Study carried out to determine whether patients with cerebral infarcts might have fewer subsequent strokes if they took aspirin. Placebos were given to 252 controls patients (n C ), and 18 of these (p C = 18 / 252 = .07) had a subsequent nonfatal stroke. Aspirin was given to 253 experimental patients (n E ), of whom 23 (p E = 23 / 253 = .09) had a recurrent nonfatal stroke. The results certainly did not favor aspirin. There was an absolute increase of .02 between the two groups, generating a relative risk increase (rather than reduction) of 29%.

CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL Control (Placebo) Experimental (aspirin) Patients with recurrent strokes n c = 252 p c = .07 n E = 253 p E = .09 Absolute risk reduction = p c – p E = .07 - .09 = -.02 Relative risk reduction = (p c – p E ) / p c = .02 / .07 = -29%

CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL = = From to

CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL The result appears quite definitive in terms of excluding any possible benefit from aspirin Based on confidence interval analysis (- .02 - .05 =) - .07, generating a relative risk increase of recurrent stroke from aspirin of (- .07 / .07 =) – 100%, support the prior suspicion that aspirin cannot be beneficial in this situation.

CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL (-.02 + .05 =) + .03, generating a relative risk reduction of recurrent stroke from aspirin of (.03 / .07 =) + 43% If we believe that a risk reduction of 30% or more would be clinically significant, we cannot regard the Swedish study as definitively excluding a benefit of aspirin.

CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL In summary, when an article draws a negative conclusion about a treatment (because P  .05), you can focus on the upper end of the confidence interval for the relative risk reduction, for this place the treatment in the most favorable light. If this upper boundary lies below what you’d consider to be the smallest clinically significant risk reduction, you are reading about a definitively negative trial

CONFIDENCE INTERVAL IN A “NEGATIVE” RANDOMIZED TRIAL If, on the otherhand, this upper end of the confidence interval includes clinically important relative risk reductions, the trial hasn’t ruled them out and cannot be regarded as definitively negative.

IS THE THERAPEUTIC MANEUVER FEASIBLE IN YOUR PRACTICE The therapeutic maneuver has to be described in sufficient detail for readers to replicate it with precision. Must be clinically sensible Must be available Must note whether the authors avoid two specific biases in its application

IS THE THERAPEUTIC MANEUVER FEASIBLE IN YOUR PRACTICE Contamination (in which control patients accidentally receive the experimental treatment Cointervention ( the performance of additional diagnostic or therapeutic acts on experimental but not the control patients)

WERE ALL PATIENTS WHO ENTERED THE STUDY ACCOUNTED FOR AT ITS CONCLUSION What can a reader do when outcomes are not reported for missing subjects ? Best case/worse case approach Arbitrarily assign a bad outcome to all missing members of the group which fared better, and good outcome to all missing members of the group that fared worse.

WERE ALL PATIENTS WHO ENTERED THE STUDY ACCOUNTED FOR AT ITS CONCLUSION ? What can a reader do when outcomes are not reported for missing subjects ? Best case/worse case approach If this maneuver fails to cancel the statistical or clinical significance of the results, the reader can accept the study’s conclusions

WERE ALL PATIENTS WHO ENTERED THE STUDY ACCOUNTED FOR AT ITS CONCLUSION ? Example A cohort of 123 morbidly obese patients was studied 19 – 47 months after surgery. Success was defined as having lost more than 30% of excess weight. Only 103 patients (84%) could be located. In these, the success rate of surgery was 60/103 (58%)

WERE ALL PATIENTS WHO ENTERED THE STUDY ACCOUNTED FOR AT ITS CONCLUSION ? Solution: Best case success rate = (60 + 20) / 123 = 65% Worse case success rate = 60 / 123 = 49% Thus the true rate must have been 49 and 65%

Treatment Clinical question: How does treatment change the course of disease? Usually the effects of treatment are much less obvious and most interventions require research to establish their value Specific interventions must do more good than harm among patients who use them (efficacious and effective) The most desirable method for measuring efficacy and effectiveness is that of the randomized controlled trial

Treatment Clinical question: How does treatment change the course of disease? Intervention studies Clinical trials Controlled trials Uncontrolled trials Concurrent control

Treatment Clinical question: How does treatment change the course of disease? Types of clinical trial (according to purpose) Prophylactic trials, e.g. immunization, contraception Therapeutic trials (drug treatment, surgical procedures Safety trials (side-effects of drug) Effectiveness trials (theoretical, use, and extended use effectiveness of contraceptive methods) Risk factor trials (proving etiology of disease) Efficiency trials

Treatment Clinical question: How does treatment change the course of disease? Phases of Clinical Trials Phase I Clinical Trials experimental animals used to establish that the new agent is effective and suitable for human use 1 st phase in humans – pharmacologic and toxicologic studies Phase 2 Clinical Trials assess the effectiveness of the drug or device determine the appropriate dose investigate its safety

Treatment Clinical question: How does treatment change the course of disease? Phase 3 Clinical Trials (Classical phase) performed on patients with consent carried out mostly on hospital in-patients assess the effectiveness, safety and continued use of the drug/device Phase 4 Clinical Trials a trial in normal field or program setting reassess effectiveness, safety, acceptability and continued use of the drugs

Natural history of a disease and prognosis Clinical question: What are the consequences of having a disease ? Prognosis is a prediction of the future course of disease following its onset Natural history of disease refers to the stages of a disease D x time P R E- S Y M P T O M A T I C CLINICAL DISEASE EARLY USUAL BIOLOGIC DIAGNOSIS CLINICAL ONSET POSSIBLE DIAGNOSIS OUTCOME RECOVERY DISABILITY DEATH

Natural history of disease and prognosis Clinical question: What are the consequences of having a disease ? Prognostic factors are conditions that are associated with a given outcome of the disease Risk factors Prognostic factors events being counted is a variety of consequences the onset of disease of disease are counted predict low probability describe relatively events frequent events

Outcomes of Disease (the Five Ds) Death A bad outcome if untimely Disease A set of symptoms, physical signs, and laboratory abnormalities Discomfort Symptoms such as pain, nausea, dyspnea, itching, and tinnitis Disability Impaired ability to go about usual activities at hoe, work, or recreation Dissatisfaction Emotional reaction to disease and its care, such as sadness or anger

Natural history of disease and prognosis Clinical question: What are the consequence of having a disease ? Multiple prognostic factors and prediction rules A combination of factors may give a more precise prognosis than each of the same factors taken one at a time Clinical prediction rules estimate the probability of outcomes according to a set of patient characteristics

TUBERCULOUS MENINGITIS (STAGING) STAGE I Characterized by non-specific symptoms such as fever, headache, irritability, drowsiness and body malaise. Focal neurologic signs are absent STAGE II Characterized by lethargy nuchal rigidity, seizures, positive Kernig or Brudzinski signs, hypertonia, vomiting, cranial nerve palsies and other focal neurologic signs

TUBERCULOUS MENINGITIS (STAGING) STAGE III Characterized by coma, hemiplegia or paraplegia, hypertension, decerebrate posturing, deterioration of vital signs and eventually death

Natural history of disease and prognosis Clinical question: What are the consequence of having a disease ? Rates Commonly Used to Describe Prognosis Rate Definition 5-year survival Percent of patients surviving 5 years from some point in the course of their disease Case fatality Percent of patients with a disease who die of it Disease-specific mortality Number of people per 10,000 population dying of a specific disease Response Percent of patients showing some evidence of improvement following an intervention Remission Percent of patients entering a phase in which disease is no longer detectable Recurrence Percent of patients who have return of disease after a disease-free interval

Natural history of disease and prognosis Clinical question: What are the consequences of having a disease ? Survival analysis (Kaplan-Meir analysis) a way of estimating the survival of a cohort over time Life table analysis

MAKING A PROGNOSIS What do we tell the patient? Should we keep mum, reassure him that his illness is trivial, or advise him to make out his will? What do we do for the patient? Should we reassure him and leave him alone, simply watch and wait, or treat him as soon as possible?

MAKING A PROGNOSIS The answers to these questions depend on our understanding of the natural history of the disease time course of the interactions between the patient, the causal factors for his disease, and the rest of his environment beginning with the biologic onset of disease and ending with his recovery, death, or arrival at some other physical, social, and emotional state

MAKING A PROGNOSIS In deciding what to tell and what to do for the patient, we will be extrapolating from what we know about the likely clinical course of the patient’s disease in order to make judgments about the patient’s prognosis In some situations, making a prognosis is clear cut and easy but in some it is difficult

MAKING A PROGNOSIS (Sample cases) Suppose that you discover a symptom-less subcutaneous lipoma on the back of an anxious steelworker who has come to you for insomnia and dyspepsia, which began after being laid off by the mill. Suppose the biopsy of a mass discovered on rectal examination of an otherwise robust 62-year-old waitress with recent rectal bleeding reveals a well-differentiated carcinoma.

MAKING A PROGNOSIS (Sample cases) Suppose you detect 10-15 degrees of scoliosis in an otherwise healthy 12-year-old student who has come for her preschool examination. Do you tell her and her parents, refer her to an “orthopod” or what? Suppose you have finally controlled a 37-year-old accountant’s left-sided ulcerative colitis that had troubled him since he was 32. Should you now recommend a prophylactic colectomy to obviate the risk of subsequent cancer?

MAKING A PROGNOSIS What to do for difficult situations Seek an expert opinion Read up on clinical literature about clinical course and prognosis Prognosticate based on your own clinical experience

GUIDES FOR READING ARTICLES TO LEARN THE CLINICAL COURSE AND PROGNOSIS OF DISEASE Was an “inception cohort” assembled? Was the referral pattern described? Was complete follow-up achieved? Were objective outcome criteria developed and used? Was the outcome assessment “blind”? Was adjustment for extraneous prognostic factors carried out?

WAS AN “INCEPTION COHORT” ASSEMBLED? Patients should have been identified at an early and uniform point (inception) in the course of their disease (e.g. onset of symptoms, time of diagnosis or beginning of treatment), so that those who succumbed or completely recovered are included with those whose disease persisted. The starting point is called zero time Descriptions of prognosis should include the full range of manifestations that would be considered important to patients

WAS AN “INCEPTION COHORT” ASSEMBLED? Failure to start a study of clinical course and prognosis with an inception cohort has an unpredictable effect on its results Failure to assemble a proper inception cohort of patients constitutes a fatal flaw in studies of prognosis.

WAS THE REFERRAL PATTERN DESCRIBED? The pathways by which patients entered the study sample should be described. Did they come from a primary care center or were they assembled in a tertiary care center? It is in the assembly of patients that studies of the course and prognosis of disease often flounder

WAS THE REFERRAL PATTERN DESCRIBED? (Different forms of bias) A major clinical center’s reputation results in part from its particular expertise in a specialized area of clinical medicine, it will be referred problem cases likely to benefit from this expertise ( Centripetal bias ) And its experts may preferentially admit and keep track of these cases over other, less challenging or less interesting ones ( popularity bias )

WAS THE REFERRAL PATTERN DESCRIBED? (Different forms of bias) The selection that occurs at each stage of the referral process can generate patient samples at tertiary care centers that are much different from those found in the general population ( referral filter bias ) Patients differ in their financial and geographic access to the clinical technology that identifies them as eligible for studies of the course and prognosis of disease ( diagnostic access bias )

WAS COMPLETE FOLLOW-UP ACHIEVED? All members of the inception cohort should be accounted for at the end of the follow-up period, and their clinical status should be known. This is because patients do not disappear from a study for trivial reasons (refuse therapy or recover or die or retire or simply grow tired of being followed.) Difficult for the authors to achieve perfection, they are bound to lose a few members of their inception cohort

WERE OBJECTIVE OUTCOME CRITERIA DEVELOPED AND USED? The prognostic outcomes should be stated in explicit, objective terms so that you, as the reader of the subsequent report, will be able to relate them to your own practice These criteria are applied in a consistent manner.

WAS THE OUTCOME ASSESSMENT “BLIND”? The examination for important prognostic outcomes should have been carried out by clinicians who were “blind” to the other features of these patients.

WAS THE OUTCOME ASSESSMENT “BLIND”? The clinician who knows that a patient possesses a prognostic factor of presumed importance may carry out more frequent or more detailed searches for the relevant prognostic outcome ( diagnostic-suspicion bias ) Pathologists and others who interpret diagnostic specimens can have their judgments dramatically influenced by prior knowledge of the clinical features of the case ( expectation bias )

WAS ADJUSTMENT FOR EXTRANEOUS PROGNOSTIC FACTORS CARRIED OUT? Is there mathematical adjustment for extraneous prognostic factors mentioned in the article? Clinicians may not be familiar “ Rules of thumb” to apply to the “predictive model”.

FIRST RULE OF THUMB If the article concludes that some constellation of symptoms, signs, and laboratory results accurately predicts a certain prognosis, demand evidence that the authors have confirmed the constellation’s predictive power in a second independent sample of patients (the test sample)

SECOND RULE OF THUMB It has to do with the numbers of patients that should have been included in the training and test samples. There should at least be 10 patients for every prognostic factor the authors studied.

Prevention Clinical question: Does an intervention on well people keep disease from arising? Does early detection and treatment improve the course of disease ? Prevention (Webster’s definition) –” the act of keeping from happening” In clinical medicine, the definition is restricted; depending on when in the course of disease interventions are made

Prevention Clinical question: Does an intervention on well people keep disease from arising? Does early detection and treatment improve the course of disease? ASYMPTOMATIC NO DISEASE DISEASE CLINICAL COURSE Onset Clinical Diagnosis Primary Secondary Tertiary Remove risk Early detection Reduce factors and treatment complications Levels of prevention

Prevention Clinical question: Does an intervention on well people keep the disease from arising? Does early detection and treatment improve the course of disease? Level of prevention Phase of disease Target Primary Specific causal factor Total population, selected groups and healthy individuals Secondary Early stage of disease Patients Tertiary Late stage of disease Patients (treatment, rehabilitation)

Prevention Clinical question: Does an intervention on well people keep disease from arising? Does early detection and treatment improve the course of disease? Primary prevention Immunization (communicable diseases) Folic acid administration to prevent neural tube defects Counseling patients to adopt healthy lifestyles Chlorination and fluoridation of the water supply Laws mandating seatbelt use in automobile and helmets for motorcycle use Use of earplugs or dust masks in certain occupational setting

Prevention Clinical question: Does an intervention on well people keep disease from arising? Does early detection and treatment improve the course of disease? Secondary prevention Pap smear Screening test – identification of an unrecognized disease or risk factor by history taking, physical examination, laboratory test or other procedure that can be applied rapidly

Criteria for instituting a screening program Disease Serious High prevalence of preclinical stage Natural history understood Long period between first signs and overt disease Diagnostic test Sensitive and specific Simple and cheap Safe and acceptable Reliable Diagnosis and Facilities are adequate Treatment Effective, acceptable, and safe treatment available

Prevention Clinical question: Does an intervention on well people keep disease from arising? Does early detection and treatment improve the course of disease? Tertiary prevention Limitation of disability Rehabilitation The goal here is not to prevent death but to maximize the amount of high-quality time a patient has left.

Prevention Clinical question: Does an intervention on well people keep disease from arising? Does early detection and treatment improve the course of disease? Health maintenance or Periodic health examination Procedures are performed on patients without specific complaints, to identify and modify risk factors to avoid the onset or to find disease early in its course so that by intervening patients remain well

Criteria for Deciding Whether a Medical Condition Should Be Included in Periodic Health Examination How great is the burden of suffering caused by the condition in terms of: Death Discomfort Disease Dissatisfaction Disability Destitution How good is the screening test, if one is to be performed, in terms of: Sensitivity Cost Specificity Safety Simplicity Acceptability 3. a. For primary prevention, how effective is the intervention? or b. For secondary prevention, if the condition is found, how effective is the ensuing treatment in terms of: Efficacy Patient compliance Early treatment being more effective than later treatment

Prevention Clinical question: Does an intervention on well people keep the disease from arising? Does early detection and treatment improve the course of disease? Health maintenance or Periodic health examination How much harm for how much good? Before undertaking a health promotion procedure on a patient, especially if the procedure is controversial among expert groups, the clinician should discuss both the pros (probability of and hoped for health benefits) and cons (probability of unintended effects) of the procedure with the patient.

The spectrum of illness from communicable disease INAPPARENT MILD SEVERE DEATH INFECTION DISEASE DISEASE No signs or Clinical illness with signs and symptoms symptoms

Origin Over 2,000 years ago, Hippocrates “environmental factors can influence the occurrence of disease” In the early 19 th century, the distribution of disease in specific human population groups was measured

John Snow’s epidemiological studies on the risk factor of Cholera in London Deaths from Cholera in districts of London Supplied by two water companies, 8 July to 26 August 1854 Water Supply Population No. of deaths Cholera death Company 1851 from cholera rate per 1000 population ________________________________________________________ Southwark 167,654 844 5.0 Lambeth 19,133 18 0.9

ACHIEVEMENTS IN EPIDEMIOLOGY Eradication of Smallpox Identification of methylmercury – “Minamata Disease” Identification of factors causing Rheumatic fever and Rheumatic heart disease Iodine deficiency disease AIDS, SARS

PROGNOSIS Survival curve Actuarial or life table analysis (Cutler-Ederer method) Kaplan-Meier curve

Patient Date of Transplant Date lost to Follow-up Date of Kidney Failure Months in Study 1 1 – 11 - 1979 4 – 8 - 1978 2 2 1 – 18 - 1978 23 3 1 – 29 – 1978 23 4 4 – 4 – 1978 4 – 24 – 1978 < 1 5 4 – 19 – 1978 20 6 5 – 10 – 1978 19 7 5 – 14 – 1978 8 – 28 – 1978 3 8 5 – 21 – 1978 11 – 2 – 1978 5 9 6 – 6 – 1978 11 – 15 – 1978 17 6 – 17 – 1978 18 6 – 21 – 1978 18 7 – 22 – 1978 11 – 7 – 1978 3 9 – 27 – 1978 15 10 – 5 – 1978 1 – 20 – 1979 3 10 – 22 – 1978 14 11 – 15 – 1978 13 12 – 6 – 1978 12 12 – 12 – 1978 12 2 – 1 – 1979 10 2 – 16 – 1979 10 4 – 8 – 1979 8 4 – 11 – 1979 8 4 – 18 – 1979 8 6 – 26 – 1979 8 – 4 – 1979 1 7 – 3 – 1979 5 7 – 12 – 1979 5 7 – 18 – 1979 8 – 1 – 1979 4 8 – 23 – 1979 4 10 – 16 – 1979 2 12 – 12 – 1979 < 1 12 – 24 – 1979 < 1

Data for Actuarial (Life Table) Analysis of Rejection (Deaths) of Kidneys Arrangement of survival data Months since Alive at beginning Rejection during Withdrawn alive or Entry into study of interval interval lost to follow-up n i d i W i 0 up to 2 31 3 2 2 up to 4 26 3 2 4 up to 6 21 1 3 6 up to 9 17 0 3 9 up to 12 14 0 2 12 up to 15 12 0 4 15 up to 18 8 1 1 18 up to 21 6 0 4 21 up to 24 2 0 2

B. Actuarial Calculation Months since Probability of Probability of Cumulative Probability entry into study rejection or death kidney retention of kidney retention q i = d i / [n i – (w/2)] p i = 1 – q i s i = p i p i-1 p i-2 ….p 1 0 up to 2 3/[31 – (2/2)] =.10 .90 .90 2 up to 4 3/[26 – (2/2)] =.12 .88 .79 4 up to 6 1/[21 – (3/2)] =.05 .95 .75 6 up to 9 0/[17 – (3/2)] = 0 1.00 .75 9 up to 12 0/[14 – (2/2)] = 0 1.00 .75 12 up to 15 0/[12 – (4/2)] = 0 1.00 .75 15 up to 18 1/[8 – (1/2)] = .13 .87 .65 18 up to 21 0/[6 – (4/2)] = 0 1.00 .65 21 up to 24 0/[2 – (2/2)] = 0 1.00 .65

Calculation for confidence band for actuarial curve Interval q i n i d i w i s i 0 – 2 0.10 31 3 2 0.0037 2 – 4 0.12 26 3 2 0.0055 4 – 6 0.05 21 1 3 0.0027 6 – 9 0 17 0 3 0 9 – 12 0 14 0 2 0 12 – 15 0 12 0 4 0 15 – 18 0.13 8 1 1 0.0200 18 – 21 0 6 0 4 0 21 – 24 0 2 0 2 0

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