CHDS faculty Nicolas Menzies and colleagues analyzed the identification of Tuberculosis (TB) cases in 111 low and middle-income countries using a mathematical model of TB diagnosis, and estimated the number of individuals correctly and incorrectly diagnosed. They found significant numbers of both false-negative and false-positive diagnoses: for 2023 they estimated that there were as many as one million false-negative and two million false-positive diagnoses.
Worldwide, TB is the greatest cause of infectious disease deaths. The most frequently used test to diagnose TB has limited sensitivity, and many diagnoses are based on clinical evaluation, which results in additional TB diagnoses. However, this can result in some individuals being incorrectly diagnosed. A false-negative diagnosis delays treatment for individuals with TB, allowing for lung damage to progress and TB to be transmitted to other individuals. A false-positive diagnosis results in an individual without TB being initiated on a TB treatment regimen, which not only exposes the patient to the health risks and financial costs associated with TB treatment, but also delays treatment of the patient’s true health condition, some of which can be severe.
Recently, a number of PCR-based tests have been developed with higher sensitivity for TB, yet these tests are not yet universally available. Menzies and colleagues found that increased access to these new tests would substantially reduce false-negative diagnosis but only have a small effect on false-positive diagnoses. Major improvements will need higher-sensitivity tests and consequently reduced reliance on clinical diagnosis.
Learn more: Read the full article, Estimating the Number of Incorrect Tuberculosis Diagnoses in Low- and Middle-Income Countries
Learn more: Read the Harvard Chan School press release, Tuberculosis Misdiagnoses Are Common, and Can Be Deadly
Learn more: Read the NPR feature, New Study Estimates 3 Million Cases of This Killer Disease Are Misdiagnosed Each Year
Learn more: Read about the CHDS approach to Models and Tools
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