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

a+b+c+db+da+cTotal

c+ddcNegative

a+bbaPositive

TotalNo DiseaseDiseaseTest Result

True Diagnosis

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

100.0100.0

48.4

(true negatives)

7.1

(false negatives)

All those with level under 110 mg/100 ml are

classified as nondiabetics

51.6

(false positives)

92.9

(true positives)

All those with level over 110 mg/100 ml are

classified as diabetics

Nondiabetics

(Percent)

Diabetics

(Percent)

Blood Glucose Level

(mg/100 ml)

Sensitivity and Specificity of a Blood Glucose Level of 110 mg/100 ml for Presumptive Determination of Diabetes Status

Adjusting Sensitivity and Specificity by Adjusting Cut Points

Which is Preferred: High Sensitivity orHigh 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…..

Another 68,950 are frightened into believing they have the disease and require more testing

But, 10,500 people who are HIV+ think they are disease free

Efficiency of test = (TP + TN)/Total tested = 98.9%

7 million6,895,000105,000Total

6,836,5506,826,05010,500Test -163,45068,95094,500Test +

TotalDisease NoDisease Yes

99.8%58%1.5%NJ (7 million)

PV-PV+Prevalence of HIVPopulation

Elisa is about 90% sensitive and 99% specific

If You Change To a High Risk Population, You Get Better Results….

But only 35 are frightened into believing they have the disease and require more testing

Now 350 people who are HIV+ think they are disease free

Efficiency of test = (TP + TN)/Total tested = 94.5%

7,0003,5003,500Total

3,1853,465350Test -

3,185353,150Test +

TotalDisease NoDisease Yes

90.8%98.9%50%IV Drug UserPV-PV+Prevalence of HIVPopulation

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

64,81064,633177Total

63,69563,65045Negative

1,115983132Positive

TotalNo DiseaseDiseaseMammogram

Results

Breast Cancer

Example: Disease X (prevalence = 2%)

100098020Total

9339312Negative

674918Positive

TotalNo DiseaseDiseaseTest Results

True Diagnosis of Disease X

Example: Disease X (prevalence = 1%)

100098010Total

941.5940.51Negative

58.549.59Positive

TotalNo DiseaseDiseaseTest Results

True Diagnosis of Disease X

To increase positive predictive value increase prevalence by screening high risk populations

Importance of Prevalence in Screening

100,00099.99010Total

99,98599,9850Negative

15510Positive

TotalNo DiseaseDiseaseTest Results

True Diagnosis of HIVFemale Donors

Assume we have a test for AIDS which has a sensitivity of 100% and a specificity of 99.995%. 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:

100,00050,00050,000Total

49,99749,9970Negative

50,003350,000Positive

TotalNo DiseaseDiseaseMale Homosexuals

True Diagnosis of HIV

PV+ = 0.66667

PV+ = 0.99994

Relationship of Specificity to Predictive Value

Prev = 20%, Sens = 50%, Spec = 90%,

PV = 100/180 = 56%

1,000800200

820100- 720Test

18080100+

-+

Disease

400

Prev = 20%, Sens = 50%, Spec = 50%,

PV = 100/500 = 20%

1,000800200

500100-

Test

500400100+

-+Disease

- 40020

Prev = 20%, Sens = 90%, Spec = 50%,

PV = 180/520 = 31%

1,000800200

420

Test

580400

180+

-+

Disease

500250

Prev = 50%, Sens = 50%, Spec = 50%,

PV = 250/500 = 50%

1,000500500

250-

Test500250250+

-+

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.)

1) Set up a 2x2 table

1000900100Total

True NegativeFalse Negative Negative

False PositiveTrue PositivePositive

TotalNo DiseaseDiseaseTest Results

True Diagnosis of Disease X

Suppose You Are Faced With the Following Brain Teaser (cont.)

1000900100Total

8158105Negative

1859095Positive

TotalNo DiseaseDiseaseTest Results

True Diagnosis of Disease X

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

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

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.)

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