Reporting question
Population health · Public product
Premature mortality / YPLL75
I developed the analysis and public report to help a health system understand premature mortality and inform discussions about population-health priorities.
- Public health analysis
- Data validation
- TypeScript
- Next.js
- My role
- Project lead
- Audience
- Health-system leadership & public users
- Data
- CDC WONDER mortality data
- Delivery
- Public interactive web report
Practical value
Turns mortality extracts into an interactive report with linked views, source documentation, and explicit interpretation limits.
Working product
Explore the public product.
Purpose & ownership
The reporting need.
A death count alone does not show how early deaths occur. Years of Potential Life Lost before age 75 adds that perspective to conversations about causes, communities, and intervention priorities.
The report lets readers inspect mortality patterns by cause, year, and geography, review definitions, and consult source material. It continues to evolve as leadership provides feedback. Jacob Blevins is a co-contributor.
Technical approach
What I built.
- 01
Prepare comparable extracts
Align cause, age, year, and geography across mortality extracts. Keep the analysis frame explicit when measures use different populations or age ranges.
- 02
Validate the source bridge
Check overlapping years between source series before combining them. Retain suppression rules and avoid treating suppressed observations as known zeros.
- 03
Explain the measure
Present premature mortality alongside its definition, time period, and source. Distinguish absolute burden from population-based comparisons.
- 04
Build a public product
Develop the report in Next.js and TypeScript, with linked views, source downloads, and methodological notes readers can inspect.
Quality & interpretation
How I check it.
- Reconcile totals across causes, years, and geographic groupings.
- Check overlap between source series.
- Retain source suppression and analysis-frame notes.
- Verify charts against the underlying prepared data.