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Disease Screening Interpretation

Screening testing in health care: Getting it right.
The Journal of the American Dental Association, Volume 153, Issue 4, April 2022, Pages 365-370.
Glick M, Carrasco-Labra, A.

Oral health care professionals often wish to screen individuals for contagious diseases and other medical conditions to identify systemic conditions that may impact their oral care. Whether screening for COVID-19, HPV, high blood pressure or a host of other medical conditions, it is important to not only choose an appropriate test, but also be able to effectively interpret results.

To assist oral health care professionals in interpreting test results, Penn Dental Medicine has created this electronic web-based tool – the Disease Screening Interpretation Calculator (DSIC) – that provides information to assist a clinician in making appropriate clinical decisions that are based on the outcome of a screening test.

A screening test for a medical condition can usually not provide a 100% certainty of a patient’s medical status – there will always be a certain percentage of false negative (FN) and false positive (FP) results. However, the DSIC will provide the probability of a positive test result being a true positive (TP). This probability is called the positive predictive value (PPV). The DSIC will also provide the probability of a negative test result being a true negative (TN). This probability is called the negative predictive value (NPV).

There are three different variables that will determine a PPV and NPV – the prevalence of the condition (i.e. for COVID-19, the transmission level in the patient’s community or for other medical conditions, the prevalence in a patient population), the sensitivity, and the specificity of the test being used. A test’s sensitivity is the test’s ability to detect TP cases among all the cases with the condition, while the specificity is a test’s ability to detect TN cases among all the cases without the condition. A test’s sensitivity and specificity values are acquired from the test/device manufacturer.

Once care providers know the PPV and the NPV of a test result and how to communicate positive and negative test results with patients, oral health care professionals will be able to better decide on how to treat a patient with a certain test result, generate appropriate medical referrals, determine the need for further testing, as well as provide a public service.

Follow the instructions below to use the calculator.

Population Prevalence
(Pre-test probability)
Sensitivity Specificity
Insert Value
Test results Diseased Healthy Output Accuracy
True positive False positive Positive predictive value
False negative True negative Negative predictive value

Instructions:

1. Insert a population value if you have a population figure and it is relevant. NOTE: The population value will not change the positive predictive value or the negative predictive value.

2. Select the prevalence. The value can be changed by dragging the slide bar or by clicking on the slide bar and then change the input value with the arrow keys on the computer keyboard. Prevalence is the amount of disease at one particular point in time; the proportion of people who have the disease.

3. Select the sensitivity. The value can be changed by dragging the slide bar or by clicking on the slide bar and then change the input value with the arrow keys on the computer keyboard. A test’s sensitivity, available from the manufacturer, provides an estimate of a test’s ability to detect a disease in an individual with the disease. For example, suppose the sensitivity of a test is 95%, 95 out of 100 individuals with the disease of interest will have a positive test result – a true positive result, and 5 individuals with the disease will have a negative test result – a false negative result.

4. Select the specificity. The value can be changed by dragging the slide bar or by clicking on the slide bar and then change the input value with the arrow keys on the computer keyboard. A test’s specificity, available from the manufacturer, provides an estimate of a test’s ability to detect absence of a disease in an individual without the disease. For example, if the specificity of a test is 90%, 90 individuals without the disease of interest will have a negative test result – a true negative result, and 10 individuals without the disease of interest will have a positive test result – a false negative result.

Interpretation Values

Positive predictive value (PPV) is the probability that a person with a positive screening test has the disease, i.e., the probability that the obtained result is a true positive result.

Negative predictive value (NPV) is the probability that a person with a negative screening test doesn’t have the disease, i.e., the probability that the obtained result is a true negative result.

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