Analysis of the level of polypharmacy in patients from an isolated rural area: effect of age, sex, and chronic diseases.

Introduction: The increase in life expectancy and the greater number of chronic diseases have led to a greater use of medications. This polypharmacy can cause a greater number of drug-related problems and negative results on the patient's health associated with medication, which is why most health services are focused on solving these problems. Machine learning uses different techniques to generate knowledge in health, one of them is regression, whose model establishes that a prognosis is created from a dependent variable and a series of independent variables. Materials and methods: Data collection was conducted during 2021-2022 in an isolated rural pharmacy. The screening of participants susceptible to being part of the study began at the time of dispensing, verifying that they were part of the personalized dosing system (PDS) service. Results: The study population consisted of 78 participants, predominantly female. The sociodemographic profile was characterized by being female, between 66 and 80 years of age. The number of chronic diseases per participant was 4.25 ± 1.49. During the study phase, a total of 450 drug-related problems (DRPs) were detected, with an average of 5.64 ± 2.69 DRPs per participant. Discussion: Age and the assigned polypharmacy level are the factors that most influence the final polypharmacy level. However, it is necessary to include the variable "chronic diseases" since in some situations it seems to be significant. Conclusion: The factors that most influence the polypharmacy index are patient age and initial polypharmacy level and, to a lesser extent, but no less important, the number of chronic diseases.