## Shop Talk

# Positives - True or False

### We're not supposed to be writing. We’re on January break. But a funny thing happened this morning. While resting on the couch and doom scrolling over omicron, wildfires, and Betty White, we noticed that the Jan. 2nd, headline (headline!) of the New York Times (NYT) is about evidence-based practice. As if that’s not amazing enough, the article is perfectly on topic. It deals with diagnostic tests. Here’s a screenshot of the top story that caught our eye and drew our fingers to the keyboard:

The infographic and accompanying text describe the **positive predictive value (PPV)** of a particular type of prenatal screening. While the text and figure provide different values (85% vs. up to 90%), the point is clear. A positive test result is rarely correct. Let’s use the *NYT* example to define and compute PPV and better appreciate why the article is concerned about these tests.

**What is PPV?**

PPV is the proportion of individuals with **a positive** test result who truly **HAVE** a condition of interest. A perfect value for PPV is 1 (or 100%, if we use percentage format). When a diagnostic test has perfect PPV, we know that every positive test is a true case of the disease or condition.

**How is PPV computed? **

PPV is computed from 2x2 tables like the one below. Pay attention to A, B, and A + B. We’ll deal with C and D another day.

A + B = the **total** number of people with a **positive** test result

A = the number of people with a **positive** result who also **HAVE **the condition,

B = the number of people with a **positive **result who do **NOT** have the condition

Before you look at the equation, see if you can figure it out yourself. We want a value that represents the **proportion** of individuals with **a positive** test who really **HAVE** the condition.

Here’s the equation:

PPV = A / (A + B)

Now, let’s insert the values from the NYT infographic. It tells us that if **100 **people get a **positive** test result, **15** will **HAVE** the condition and **85** will **NOT** have the condition.

The table looks like this:

A + B + C + D

A + B = 100 (**total** number of people with a **positive** test)

A = 15 (number of people with a **positive** test who **HAVE** the condition)

B = 85 (the number of people with a **positive** test who do **NOT** have the condition)

PPV = 15 / (15 + 85) = 0.15 (or 15%).

As the headline suggests, a positive test result is not very helpful. According to the values provided, only 15% of positive tests represent true cases. The other 85% are **false positives.** While subsequent (and more accurate) testing could eventually rule out concerning conditions and allay parents’ fears, tests with poor PPV risk unwarranted worry.

Thanks for reading. Unless the news compels us to write sooner, we’ll be back in February. If you see something in the headlines related to our mission, drop us a note in the contact section.

January Edition

Jan. 02, 2022

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