# Principles of Epidemiology Lecture 12 Dona Schneider, PhD, MPH, FACE

## Presentation on theme: "Principles of Epidemiology Lecture 12 Dona Schneider, PhD, MPH, FACE"— Presentation transcript:

Principles of Epidemiology Lecture 12 Dona Schneider, PhD, MPH, FACE
Screening Principles of Epidemiology Lecture 12 Dona Schneider, PhD, MPH, FACE

Principles Underlying Screening Programs
Validity – the ability to predict who has the disease and who does not Sensitivity – the ability of a test to correctly identify those who have the disease A test with high sensitivity will have few false negatives Specificity – the ability of a test to correctly identify those who do not have the disease A test that has high specificity will have few false positives

Principles Underlying Screening Programs (cont.)
An ideal screening test would be 100% sensitive and 100% specific – that is there would be no false positives and no false negatives In practice these are usually inversely related It is possible to vary the sensitivity and specificity by varying the level at which the test is considered positive

Calculating Measures of Validity
True Diagnosis Test Result Disease No Disease Total a b a+b Positive Negative Sensitivity = a/(a+c); the probability of having a positive test if you are positive Specificity = d/(b+d); the probability of having a negative test if you are negative Positive Predictive Value = a/(a+b); the probability of having the disease if you test positive Negative Predictive Value = d/(c+d); the probability of not having the disease if you test negative Prevalence = (a+c)/(a+b+c+d) Accuracy (efficiency of test) = (a+d)/(a+b+c+d) c d c+d Total a+c b+d a+b+c+d

Note the Following Screening Relationships
Specificity + false positive rate = 1 d/(b+d) + b/(b+d) = 1 If the specificity is increased, the false positive rate is decreased If the specificity is decreased, the false positive rate is increased Sensitivity + false negative rate = 1 a/(a+c) + c/(a+c) = 1 If the sensitivity is increased, the false negative rate is decreased If the sensitivity is decreased, the false negative rate is increased

Probability of Disease
Pre-test probability of disease = disease prevalence Post-test probability of disease = If normal, c/(c+d) If negative, a/(a+b)

Interrelationship Between Sensitivity and Specificity

Sensitivity and Specificity of a Blood Glucose Level
Sensitivity and Specificity of a Blood Glucose Level of 110 mg/100 ml for Presumptive Determination of Diabetes Status Blood Glucose Level (mg/100 ml) Diabetics (Percent) Nondiabetics (Percent) All those with level over 110 mg/100 ml are classified as diabetics 92.9 (true positives) 51.6 (false positives) All those with level under 110 mg/100 ml are classified as nondiabetics 7.1 (false negatives) 48.4 (true negatives) 100.0 100.0

Which is Preferred: High Sensitivity or High Specificity?
If you have a fatal disease with no treatment (such as for early cases of AIDS), optimize specificity If you are screening to prevent transmission of a preventable disease (such as screening for HIV in blood donors), optimize sensitivity

Remember…. Sensitivity and specificity are functions of the screening test If you use a given screening test on a low prevalence population, you will have a low positive predictive value and potentially many false positives

Translated into Real Life…..
Elisa is about 90% sensitive and 99% specific Population Prevalence of HIV PV+ PV- NJ (7 million) 1.5% 58% 99.8% Disease Yes Disease No Total Test + 94,500 68,950 163,450 Test - 10,500 6,826,050 6,836,550 Total 105,000 6,895,000 7 million Efficiency of test = (TP + TN)/Total tested = 98.9% But, 10,500 people who are HIV+ think they are disease free Another 68,950 are frightened into believing they have the disease and require more testing

If You Change To a High Risk Population, You Get Better Results….
PV+ PV- Prevalence of HIV IV Drug User 50% 98.9% 90.8% Total Disease Yes Disease No Test + 3,150 35 3,185 Test - 350 3,465 3,185 Total 3,500 3,500 7,000 Efficiency of test = (TP + TN)/Total tested = 94.5% Now 350 people who are HIV+ think they are disease free But only 35 are frightened into believing they have the disease and require more testing

Suppose You Have a Very High Prevalence?
HIV seropositivity is 90% among IV drug users in Newark PV+ = 99.9% PV- = 52% But, why bother to screen?

Example: Breast Cancer Screening
Mammogram Results Disease No Disease Total Positive 132 983 1,115 Sensitivity = 132/177 = 74.6% Specificity = 63,650/64,633 = 98.5% Positive Predictive Value = 132/1,115 = 11.8% Negative Predictive Value = 63,650/63,695 = 99.9% You can improve sensitivity and specificity by using more than one screening test, using multiphasic screening and targeting high risk populations If prevalence of a disease is low, even a highly valid test will yield a low predictive value Negative 45 63,650 63,695 Total 177 64,633 64,810

Example: Disease X (prevalence = 2%) True Diagnosis of Disease X
Test Results Disease No Disease Total Positive 18 67 49 Negative 2 933 931 The prevalence of Disease X is 2% (1,000 x 0.02 = 20) Sensitivity = 18/20 = 90% Specificity = 931/980 = 95% Positive Predictive Value = 18/67 = 27% Negative Predictive Value = 931/933 = 99.8% Total 1000 20 980

Example: Disease X (prevalence = 1%) True Diagnosis of Disease X
Test Results Disease No Disease Total Positive 9 49.5 58.5 Negative 940.5 941.5 1 The prevalence of Disease X is 1% (1,000 x 0.01 = 10) Sensitivity = 9/10 = 90% Specificity = 940.5/990 = 95% Positive Predictive Value = 9/58.5 = 14.5% Negative Predictive Value = 940.5/941.5 = 99.9% Total 10 980 1000 To increase positive predictive value increase prevalence by screening high risk populations

Importance of Prevalence in Screening
Assume we have a test for AIDS which has a sensitivity of 100% and a specificity of %. We wish to apply it to female blood donors who have an HIV prevalence of 0.01% and we wish to apply it to male homosexuals in San Francisco, in whom the prevalence is 50%. For every 100,000 screened we find: Female Donors True Diagnosis of HIV Test Results Disease No Disease Total 10 5 15 Positive 99,985 Negative 99,985 Total 10 99.990 100,000 PV+ = True Diagnosis of HIV Male Homosexuals Disease No Disease Total 50,003 Positive 50,000 3 49,997 Negative 49,997 Total 50,000 50,000 100,000 PV+ =

Relationship of Specificity to Predictive Value
500 250 Prev = 50%, Sens = 50%, Spec = 50%, PV = 250/500 = 50% 1,000 - Test + Disease 400 Prev = 20%, Sens = 50%, Spec = 50%, PV = 100/500 = 20% 1,000 800 200 500 100 - Test + Disease - 400 20 Prev = 20%, Sens = 90%, Spec = 50%, PV = 180/520 = 31% 1,000 800 200 420 Test 580 180 + Disease Prev = 20%, Sens = 50%, Spec = 90%, PV = 100/180 = 56% 1,000 800 200 820 100 - 720 Test 180 80 + Disease

Suppose You Are Faced With the Following Brain Teaser
In a given population of 1,000 persons, the prevalence of Disease X is 10%. You have a screening test that is 95% sensitive and 90% specific. What is the positive predictive value? What is the efficiency of the test?

Suppose You Are Faced With the Following Brain Teaser (cont.)
Set up a 2x2 table True Diagnosis of Disease X Test Results Disease No Disease Total Positive True Positive False Positive Given that the prevalence is 10% and the population is 1,000, then 100 people are sick This means 900 are not Since the sensitivity is 95%, 95% of the 100 truly sick will test as true positives (n=95) Since the specificity is 90%, 90% of the 900 truly well will test as true negatives (n=180) Fill in the blanks Negative False Negative True Negative Total 100 900 1000

Suppose You Are Faced With the Following Brain Teaser (cont.)
True Diagnosis of Disease X Test Results Disease No Disease Total Positive 95 90 185 Negative PV + = True positives / All positives a / (a+b) = 95 / 185 = 51.4% PV - = True negatives / All negatives d / (c+d) = 810 / 815 = 99.4% Efficiency of the Test = Total correct / Total predictions (a+d) / (a+b+c+d) = ( ) / 1000 = 90.5% 5 810 815 Total 100 900 1000

Principles Underlying Screening Programs
Reliability – the ability of a test to give consistent results when performed more than once on the same individual under the same conditions Variation in the method due to variability of test chemicals or fluctuation in the item measured (e.g., diurnal variation in body temperature or in relation to meals) Standardize fluctuating variables Use standards in laboratory tests, run multiple samples whenever possible Observer variation Train observers Use more than one observer and have them check each other

Principles Underlying Screening Programs
Yield – the amount of previously unrecognized disease that is diagnosed and brought to treatment as a result of the screening program Sensitivity You must detect a sufficient population of disease to be useful Prevalence of unrecognized disease Screen high risk populations Frequency of screening Screening on a one time basis does not allow for the natural history of the disease, differences in individual risk, or differences in onset Diseases have lead time Participation and follow-up Tests unacceptable to those targeted for screening will not be utilized

Conditions for Establishing Screening Programs
The condition should be an important health problem There should be an accepted treatment for patients with recognized disease If there is no treatment, it is premature to institute screening Facilities for diagnosis and treatment should be available It is unethical to screen without providing possibilities for follow-up There should be a recognizable latent or early symptomatic stage If early detection does not improve survival, there is no benefit from screening

Conditions for Establishing Screening Programs (cont.)
There should be a suitable test for examination, with sufficient sensitivity and specificity to be of use in identifying new cases The test should be acceptable to the population The natural history of the condition, including development from latent to declared disease, should be adequately understood There should be an agreed-upon policy concerning whom to treat as patients

Conditions for Establishing Screening Programs (cont.)
The cost of case-finding should be economically balanced in relation to possible expenditure on medical care as a whole Case-finding should be in a continuing process and not a one-time project

Biases in Screening Referral Bias (volunteer bias) Length Bias
Screening selectively identifies those with a long preclinical and clinical phase (i.e., those who would have a better prognosis regardless of the screening program)

Biases in Screening (cont.)
Lead Time Bias The apparently better survival that is observed for those screened is not because these patients are actually living longer, but instead because diagnosis is being made at an earlier point in the natural history of the disease

Biases in Screening (cont.)
Overdiagnosis Bias (a misclassification bias) Enthusiasm for a new screening program may result in a higher rate of false positives and give false impression of increased rates of diagnosis and detection Also, false positives would result in unrealistically favorable outcomes in persons thought to have the disease

Download ppt "Principles of Epidemiology Lecture 12 Dona Schneider, PhD, MPH, FACE"

Similar presentations