Modeling the Prescription Drug Epidemic with Mathematical Functions

Overview

The prescription drug epidemic is skyrocketing due to their addictive qualities. Using the Flesch Reading Ease Number and Flesch-Kincaid Grade Level Number of a specific prescription drug, one can predict the drug’s number of abuse cases for a specific year. This is because drug literature is complexly written and may lead people to misuse the drugs due to a lack of specific knowledge/terminology, thus misunderstandings of usage and/or side-effects on the user’s part. Logically, the fewer years of schooling an individual possesses, the less specific or general knowledge they have at their disposal. Therefore, younger individuals, due to their lack of schooling, would be more prone to drug misuse due to the lack of understanding surrounding drug literature involving literary concepts or vocabulary. The math-models generated would relate the complexity (scales) of the drug literature to the amount of abuse cases. The Flesch Reading Ease scale is used to measure the complexity/comprehensibility of the literature based upon sentence structure, defined by: 206.835-1.015(words/sentences)-84.6(syllables/words). The Flesch-Kincaid Grade Level scale is used to measure the amount of education required to understand the literature, based upon word complexity, defined by: 0.39(words/sentences)+11.8(syllables/words)-15.59. Six drugs were randomly picked from six prescription drug categories. Using the National Library of Medicine, Microsoft Word and regression software, the Flesch Reading Ease and Flesch-Kincaid Grade Level numbers were calculated to generate a model to predict the number of abuse cases for each drug. The equation: 23.9276*1.90166x (r2=0.95, p=0.000230168), predicted the number of abuse cases given by the Grade Level Number scale. The equation: 3896470-1084330*ln(x) (r2= 0 .90, p=0.003863714), predicted the number of abuse cases using the Reading Ease scale. These models, with high correlation coefficients (r2) and statistically significant p-values, suggest the complexity of prescription drug facts can predict the number of abuse cases. These models can be used to force drug manufacturers to reduce the scales’ numbers, thus complexity of the drug literature, perhaps lessening abuse cases, solving the prescription drug epidemic.

Competition history

• AJAS 2019 Category not listed

Related projects

Closest projects by meaning, across every fair and year in the corpus.

Browse more like this

• AJAS None projects
• Every AJAS 2019 project