A public health case study focused on identifying patterns between prescription activity and mortality risk indicators.
The value of this project is not only the visual output, but the way it translates difficult health data into interpretable signals for decision-makers.
Focus: morbidity trend interpretation
Stack: R, statistical analysis, visual communication
Value: regional risk insight from federal-style data
This project investigates how prescription-related patterns may align with broader morbidity and mortality signals. The emphasis is on interpreting the data responsibly and presenting risk clusters clearly.
It demonstrates the ability to work with complex healthcare-adjacent data and shape the output into something usable for analysts, administrators, and public-health stakeholders.
Test whether prescription behavior aligns with worsening outcome patterns across locations and subgroups.
Use visual and statistical comparisons to isolate meaningful correlations without overstating causality.
Present high-risk relationships in a way that remains readable for non-technical healthcare stakeholders.
Surfaces patterns that point to concentrated public-health risk rather than isolated noise.
Shows careful distinction between pattern detection and unsupported causal claims.
Turns difficult statistical evidence into a usable summary for decision-makers.
Demonstrates healthcare-relevant analytical storytelling under sensitive subject matter.