ABSTRACT
Background: The misunderstanding of numbers and probabilities is a problem that runs across practically the entire population. This problem concerns both physicians, nurses, and patients, affecting medical decisions and informed consent. Notably, this problem hurts the proper interpretation of a positive mammogram, which covers the computation of the probability of a woman having breast cancer under a positive test, called the positive predictive value.
Method: In this study, one ascertains the literacy of health professionals and the general population concerning the knowledge and computation of the positive predictive value under a positive mammogram. This knowledge is required to enable women with the necessary information to make an informed decision before and after screening. The consequences of a lack of this knowledge run from psychological distress to more harmful situations such as overdiagnosis and overtreatment. The information was collected through a questionnaire available online from 02/01/2019 to 12/04/2019.
Results: Most physicians overestimated the actual probability of a woman having breast cancer, with the majority equating this value with the sensitivity of the test. They also had the lowest rate of correct answers. A little less than one-third of nurses matched the positive predictive value with the prevalence, and, unexpectedly, the general population had the highest percentage of correct answers.
Conclusions: These results point to the need for the reinforcement of statistical education among all participants involved, especially health professionals.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Carina Filipa Costa Ferreira
Carina Ferreira. BSc degree in Gerontological Social Education; Master in Palliative Care.
Teresa Paula Amaral Abreu
Teresa Abreu. BSc degree in Mathematics; PhD in Advanced Mathematics.
Mário João Freitas de Sousa Basto
Mário Basto. BSc degree in Medicine; BSc degree in Applied Mathematics; Master in Applied Mathematics; PhD in Engineering Sciences.