Epidemiology Screening Test Calculator

Calculator Input

Example Data Table

Formula Used

Sensitivity = TP / (TP + FN)

Specificity = TN / (TN + FP)

Positive Predictive Value = TP / (TP + FP)

Negative Predictive Value = TN / (TN + FN)

Accuracy = (TP + TN) / Total

Prevalence = (TP + FN) / Total

False Positive Rate = FP / (FP + TN)

False Negative Rate = FN / (FN + TP)

Positive Likelihood Ratio = Sensitivity / (1 - Specificity)

Negative Likelihood Ratio = (1 - Sensitivity) / Specificity

Diagnostic Odds Ratio = Positive Likelihood Ratio / Negative Likelihood Ratio

Youden Index = Sensitivity + Specificity - 1

Wilson score intervals are used for proportion confidence limits.

How to Use This Calculator

Enter the four values from a two by two table.

Use true positives for diseased people with positive results.

Use false positives for healthy people with positive results.

Use false negatives for diseased people with negative results.

Use true negatives for healthy people with negative results.

Select a confidence level and decimal precision.

Press Calculate to view results above the input form.

Use CSV or PDF buttons to save the current output.

Understanding Epidemiology Screening Test Results

Purpose of Screening Measures

Epidemiology screening tests help researchers compare a test result with true disease status. The basic table has four counts. These counts are true positives, false positives, false negatives, and true negatives. From them, the calculator estimates the main measures used in screening studies.

Sensitivity and Specificity

Sensitivity shows how well a test finds people with disease. A high value means fewer diseased cases are missed. Specificity shows how well a test identifies people without disease. A high value means fewer healthy people receive positive results. These two measures describe test performance against real disease status.

Predictive Values

Positive predictive value shows the chance of disease after a positive result. Negative predictive value shows the chance of no disease after a negative result. These values depend strongly on prevalence. The same test can have different predictive values in different populations. This is important when moving a tool from clinics to communities.

Likelihood Ratios

Likelihood ratios summarize how much a result changes disease suspicion. A larger positive likelihood ratio supports disease after a positive test. A smaller negative likelihood ratio supports ruling out disease after a negative test. These measures are useful because they connect screening results with clinical probability.

Accuracy and Prevalence

Accuracy is the share of all correct classifications. It can look high when disease is rare. That is why sensitivity, specificity, and predictive values should also be reviewed. Prevalence shows how common disease is inside the tested sample.

Confidence Intervals

Confidence intervals show uncertainty around proportion estimates. Small samples usually give wider intervals. Larger samples usually give narrower intervals. This calculator uses Wilson score intervals for sensitivity, specificity, predictive values, and accuracy. Reports should include both point estimates and intervals.

Interpreting the Output

A strong screening test should fit its purpose. Some programs prefer high sensitivity to reduce missed cases. Others need high specificity to reduce unnecessary follow-up. The best threshold depends on disease severity, cost, risk, and available care. Use these results as a study aid, not medical advice.

FAQs

What is a screening test calculation?

It converts a two by two disease table into performance measures. These include sensitivity, specificity, predictive values, likelihood ratios, accuracy, and prevalence.

What does sensitivity mean?

Sensitivity measures the proportion of diseased people correctly identified by the test. Higher sensitivity means fewer false negative results.

What does specificity mean?

Specificity measures the proportion of non-diseased people correctly identified as negative. Higher specificity means fewer false positive results.

Why are predictive values important?

Predictive values describe what a positive or negative result means for tested people. They change when disease prevalence changes.

What is a positive likelihood ratio?

It shows how much a positive test result increases disease likelihood. Higher values give stronger evidence for disease presence.

What is a negative likelihood ratio?

It shows how much a negative test result lowers disease likelihood. Smaller values give stronger evidence against disease presence.

Why does prevalence affect results?

Prevalence changes predictive values. Rare disease often lowers positive predictive value, even when sensitivity and specificity are good.

Can this calculator replace clinical judgment?

No. It supports statistical review of screening data. Clinical decisions need professional judgment, patient history, and validated medical guidance.