Enhancing Public Health Data Understanding by Leveraging PCA for Aggregated Measures

Music City Center 

Principal Component Analysis (PCA) is a robust technique for data reduction and clustering analysis, yet its interpretative power in public health data remains underexploited. This study elucidates effective practices for applying PCA in clinical data science, using public health datasets to assist policymakers in interpreting national trends. We analyze data from the CDC Covid Tracker (ED visits and deaths due to Covid-19), the CDC Drug Overdose Surveillance and Epidemiology (DOSE) System (suspected drug overdoses), and the AMA End the Epidemic initiative (buprenorphine, naloxone, opioid prescriptions). These datasets include aggregated measures such as weekly ED visits and monthly percentage changes across all states over at least five years. Through PCA, we reveal latent relationships within the data. Preliminary results indicate that the first principal component may represent a national trend, while the second captures regional variations. By translating mathematical constructs into practical interpretations, we enhance the accessibility of these analyses and support public health policy aimed at reducing geographic health disparities.

principal component analysis

PCA

Data reduction

Public health

Covid-19

Clustering analysis 

Section on Teaching of Statistics in the Health Sciences