SCREENING

PRESENTER: DR. SHAILAJA DARAL

MODERATOR: DR. ANITA VERMAASSO. PROFESSORDEPT. OF COMMUNITY MEDICINEVMMC AND SAFDARJUNG HOSPITAL

DEFINITION OF SCREENING

The search for unrecognized disease or defect by means of rapidly applied tests, examinations or other procedures in apparently healthy individuals.*

The presumptive identification of unrecognized disease or defect by the application of tests, exams or other procedures which can be applied rapidly to sort out apparently well persons who probably have a disease from those who probably do not.*

Tests done in individuals with no symptom or sign of an illness are referred to as screening tests.*

(* K. Park Textbook of PSM 21st edition;* J M Last Dictionary of Epidemiology 4th edition, WHO Public Health Papers 1968;* J H Abramson and Z H Abramson Survey methods in Community Medicine 5th edition)

BASIS OF SCREENING

Iceberg phenomenon of disease

tip of the icebergCLINICAL DISEASE

submerged portionHIDDEN BURDEN OF DISEASE

BASIS OF SCREENING

LEAD TIME

BASIS OF SCREENING

Screening is a form of secondary prevention.

It detects disease in its early asymptomatic phase whereby early treatment can be given and disease can be cured or its progression can be delayed.

It has both diagnostic (?) and therapeutic components.

SCREENING TEST VERSUS DIAGNOSTIC TEST

Screening test1. Done on apparently

healthy individuals2. Applied to groups3. Results are arbitrary and

final4. Based on one criteria

and cut-off5. Less accurate6. Less expensive7. Not a basis for treatment8. Initiative comes from

investigator

Diagnostic test1. Done on sick or ill

individuals2. Applied on single patient3. Diagnosis is not final4. Based on evaluation of a no.

of signs/symptoms & lab findings

5. More accurate6. More expensive7. Used as a basis for

treatment8. Initiative comes from a

patient

COMMON SCREENING TESTS

• FASTING BLOOD GLUCOSE FOR DIABETES• BLOOD PRESSURE FOR HYPERTENSION• PSA TEST FOR PROSTATE CANCER• PAP SMEAR FOR CERVICAL CANCER• MAMMOGRAPHY FOR BREAST CANCER• FECAL OCCULT BLOOD FOR COLON CANCER

TYPES OF SCREENING

1. MASS SCREENINGApplication of screening test to large, unselected population. Everyone in the group is screened regardless of the probability of having the disease or condition.

Example: a) visual defects in school childrenb) mammography in women aged 40 years or lessc) newborn screening program in Japan

2. HIGH RISK / SELECTIVE / TARGETED SCREENINGThe screening of selected high-risk groups in the population.

Example: a) screening fetus for Down’s syndrome in a mother who already has a baby with Down’s syndromeb) screening for familial cancers, HTN and DMc) screening for CA Cervix in low SES womend) screening for HIV in risk groups

3. MULTIPURPOSE SCREENINGThe screening of a population by more than one test done simultaneously to detect more than one disease

Example: a) screening of pregnant women for VDRL, HIV, HBV by serological tests

4. MULTIPHASIC SCREENINGThe screening in which various diagnostic procedures are employed during the same screening program.

Example: a) DM – FBS, Glucose tolerance testb) Sickle cell anemia – CBC, Hb electrophoresis

5. OPPORTUNISTIC / CASE FINDING SCREENINGThere is no accurate or precise diagnostic test for the disease and where the frequency of its occurrence in the population is small. The main objective is to detect disease and bring patients to treatment.

Example: a) RHD in children

USES OF SCREENING

1. CASE DETECTION prescriptive screening, people are screened for their own benefit(cancer, diabetes, hypertension)

2. CONTROL OF DISEASE prospective screening, people are screened for the benefit of others(HIV, STI)

3. RESEARCH to know the natural history of a disease

4. EDUCATION public awareness

CRITERIA FOR CHOOSING A SCREENING TEST

1. DISEASEa) Significant burden of diseaseb) Detectable and long preclinical stage of diseasec) Adequately understood natural history of diseased) Appropriate test available for early detection of diseasee) Facilities for diagnosis of diseasef) Early detection of disease has outcome benefitg) Effective treatment available for diseaseh) Policy of screening program for disease

2. SCREENING TESTa) Inexpensiveb) Acceptablec) Validd) Reliablee) Yielding

GOLD STANDARD

• GOLD STANDARD: an external source of truth regarding the disease status of each individual in the population. Gold standard is a benchmark and its results are considered definitive.

• Infections – CULTURE

• Cancers – BIOPSY

• Drug testing – RANDOMIZED CONTROLLED TRIAL

• Cause of death – AUTOPSY

WHAT IS VALID AND RELIABLE?

VALIDITY IS THE ACCURACY OF A TEST.

RELIABILITY IS THE PRECISION OF A TEST.

ACCURACY: “how close is result of a test to its true value?”

PRECISION: “how close are the results of a test on repetition?”

GOLD STANDARD AND SCREENING TEST

VALIDITY

An expression of the degree to which a test measures what it purports to measure.

How to understand validity?

ESTIMATE: a measurement or statement about the value of a quantity under study.An epidemiological estimate is the end product of the study design, the study conduct and the data analysis.Error is a false or mistaken result obtained in a study or experiment.Systematic error is one sided variation of measurements from the true value. It has a recognizable source.A study that has little systematic error is described as VALID.

COMPONENTS OF VALIDITY

Ds present Ds absent

Test positive

Test negative

SENSITIVITY

The ability of a test to correctly identify those who have the disease. TPa/ (a + c) expressed as percentage.Problem of FP.

Ds present Ds absent

Test positive

Test negative

SPECIFICITY

The ability of a test to correctly identify those who do not have the disease. TNd/ (b + d) expressed as percentage.Problem of FN.

Ds present Ds absent

Test positive

Test negative

ACCURACY

ACCURACY = TP + TN / TP + FP + TN + FN

ACCURACY = (SENSITIVITY) X (PREVALENCE)

+(SPECIFICITY) X (1 - PREVALENCE)

PROBLEM OF FN AND FP

FN: false reassuranceignoring of disease signs and symptomspostponement of treatmentdetrimental to overall health

FP: further testingdiscomfort, inconvenience, anxietyburden on health facilitiesemotional traumadifficulty in “de-labeling”

CHANGING CUT-OFF POINTS:

SENSITIVTY AND SPECIFICITY OF IMPORTANT TESTS

1. Mammography: Sensitivity 75 – 95%Specificity 83 – 98%

2. PAP test: Sensitivity 29 – 56%Specificity 94 – 100%

3. PSA test (4 ng/ml): Sensitivity 20 – 32%Specificity 94 – 97%

4. FBS (5mmol/L): Sensitivity 85 – 89%Specificity 70 – 77%

5. RAPID ELISA: Sensitivity 99.5%Specificity 98%

USE OF MULTIPLE TESTS

•Sequential Testing (Two-Stage Screening)After the first (screening) test is conducted, those who tested positive are brought back for the second test to further reduce false positives.Consequently, the overall process will increase specificity but with reduced sensitivity.

LIKELIHOOD RATIO

The percentage of sick people with a given test result divided by the percentage of well individuals with the same result.

Positive LR: The relative probability of a positive test in a diseased individual in comparison to a disease-free individual.

TP / FP = SENSITIVITY / 1 – SPECIFICITY

Negative LR: The relative probability of a negative test in a diseased individual in comparison to a disease-free individual.

FN / TN = 1 – SENSITIVITY / SPECIFICITY

WHY DO WE NEED LIKELIHOOD RATIO?

• Many tests in clinical medicine have continuous results or multiple ordinal levels. Putting multiple categories into either positive or negative test causes loss of information. Likelihood ratios enable clinicians to interpret and use the full range of diagnostic test results.

• While predictive values relate test characteristics to populations, likelihood ratios can be applied to a specific patient.

• Likelihood ratios refine clinical judgment. Application of a likelihood ratio to a working diagnosis generally changes the diagnostic probability—sometimes radically.

EXAMPLE OF INTER-OBSERVER AND INTRA-OBSERVER VARIATION

• INTRA-OBSERVER VARIATION:two readings of blood pressure measurement

• INTER-OBSERVER VARIATION:chest x-ray films by different radiologists

KAPPA STATISTIC

Type of inter-observer agreement.Takes into account agreement between two observers purely by chance.It is the inter-observer agreement over and above agreement that is purely by chance.It is standardized to lie between -1 to 1.-1 = no agreement1 = perfect agreement0 = agreement purely due to chance

EXAMPLE OF KAPPA STATISTIC

Resident 2

Resident 1Are afternoon

lectures helpful?

yes no total

yes 15a

5b

20

no 10c

70d

80

total 25 75 100

PERCENTAGE AGREEMENT(15 + 70) / 100 = 0.85 or 85%

KAPPA statistic = PO - PE / 1 – PE

Where

PE = (a + b) / (a + b + c + d) X (a + c) / (a + b + c + d)

+(b + d) / (a + b + c + d) X (c + d) / (a + b + c + d)

So here, KAPPA = 0.57

YIELD

YIELD is the amount of unrecognized disease that is detected and brought to treatment as a result of screening.

YIELD = TP + FP / TP + FP + TN + FN

It depends on prevalence of the disease and sensitivity of the screening test.

Hence, yield of a screening test is high in high – risk screening.

RECEIVER OPERATING CHARACTERISTICS(ROC) ANALYSIS

For a given test, the true positive rate (TPR) or SENSITIVITY against false positive rate (FPR) or 1 – SPECIFICITY can be measured. All possible combinations of TPR and FPR compose a ROC space.

One TPR and one FPR together determine a single point in the ROC space, and the position of a point in the ROC space shows the trade – off between sensitivity and specificity, i.e. the increase in sensitivity is accompanied by a decrease in specificity.

Thus the location of the point in the ROC space depicts whether the screening test classification is good or not.

Plotting SENSITIVITY and 1 – SPECIFICITY at different values of a screening test gives the ROC curve.

The area under the curve (AUC) gives a way to measure the accuracy of the screening test.

In an ideal situation, a point determined by both TPR and FPF yields coordinates (0, 1). This ideal point indicates that the screening test has a sensitivity of 100% and specificity of 100%.

Screening test with 50% sensitivity and 50% specificity lies on the diagonal determined by coordinate (0, 0) and coordinates (1, 0). Theoretically, random error would give a point along this diagonal.

A point predicted by a screening test that falls into the area above the diagonal represents a good screening classification.

METHOD TO FIND THE OPTIMUM CUT – OFF POINT OF A SCREENING TEST

YOUDEN INDEX AND COST CONSIDERATIONS

EVALUATION OF SCREENING TEST

1. METHODS

a) Experimental: conduct an RCT of the screening test to compare the disease specific cumulative

mortality rate between the intervention and control group.

this also eliminates confounding.

allows study of distribution of lead time, effects of early treatment and

identification of prognostic factors.

b) Non – experimental:cohort study (comparison of advanced disease or death rates in those who choose to screen and those who do not)

case - control study (comparison of screening history in those who have advanced disease and those who are healthy)

ecological study (correlation of screening pattern and disease experience of several populations)

2. MEASURES OF EFFECT

a) Comparison of survival experience

b) Comparison of disease specific mortality rate

ISSUES WITH SCREENING

1. Lead time bias - the systematic error of apparent increased survival from detecting disease in an early stage.

2. LENGTH TIME BIASDiseases with a long pre-clinical phase are more likely to be detected during screening. Moreover, pre-clinical phase for the same disease may be variable in different individuals.

3. SELECTION BIASNot everyone will take part in a screening program. There are factors that differ between those willing to get tested and those who are not.Willingness depends on perceived risk of disease and intelligence of patient.Example: a) breast cancer screening – more positive outcome if only intelligent people participate. More negative outcomes if only high risk patients participate.

4. OVERDIAGNOSISScreening may identify abnormalities that would never cause a problem in a person's lifetime. Causes overestimate of disease as well as survival.

Example: a) PAP testing and Cervical CISb) PSA testing and low grade prostate cancerc) mammography and DCIS

BAYES THEOREM

A prior (pre-test) probability is an initial probability value originally obtained before any additional information is obtained.A posterior (post-test) probability is a probability value that has been revised by using additional information that is later obtained.

Example: Road traffic accident patients in a hospital are listed and one of the patients is randomly selected.a) what is the probability that he will have cerebral hemorrhage?b) during the later analysis of this patient it was found that he has CT scan positive for cerebral hemorrhage. Now, what is the probability that he actually has cerebral hemorrhage?

For the first question, we know thatProbability of cerebral hemorrhage in a RTA patient is 28%.

For the second question, we know thatCT scan has a sensitivity of 90% and specificity of 95% for detecting cerebral hemorrhage.

Bayes theorem gives us a way of calculating probability of an event in the light of presence of a second event, which itself occurs in the presence of the first one.

Probability that our patient has cerebral hemorrhage, in view of an already positive CT scan for cerebral hemorrhage, is…

Pre-test probability of event (P1) X sensitivity of test (Sn)--------------------------------------------------------------------------- X 100

(P1 X Sn) + (1 – P1) X (1 – Sp)Here,

P1 = 0.28Sn = 0.90Sp = 0.95

So,

Post-test probability (P2) = 0.875 or 87.5%

REFERENCES

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305 – 358.• Abramson JH, Abramson ZH. Survey methods in community medicine;5:171 –

204.• Last JM. Dictionary of epidemiology;4:165-166.• Rothman KJ, Greenland S, Lash TL. Modern epidemiology;3:128 – 182.• Park K. Textbook of preventive and social medicine;21:124 – 131.• Suryakantha AH. Community medicine;2:288 – 291.• Oxford textbook of public health;4:621 – 637.• Matthews DE, Farewell VT. Using and understanding medical statistics;4:282 –

309.• Lectures on epidemiology by John Hopkins Bloomberg School of Public Health.• Reeves M. Fundamentals of medical epidemiology and biostatistics.• WHO Public health papers, 1968.

THANK YOU…