Unmet Mental Health Needs
A healthcare analytics case study examining whether failing to treat mental health needs is associated with measurable workforce detachment.
This project shows the full arc hiring managers care about: question framing, data preparation, testing approach, validation logic, and clearly communicated findings.
Focus: unmet need and employment status
Stack: PostgreSQL, SQL, statistical testing
Value: turns public-health data into workforce-relevant evidence
- Strong example of question-to-conclusion analytical discipline.
- Directly relevant to healthcare quality, utilization, and reporting work.
- Shows ability to translate complex methods into stakeholder-readable output.
Conception To Conclusion
Does failing to treat mental health needs cause measurable workforce detachment?
2019 NSDUH / SAMHSA dataset with adult cohort and standardized variables.
PostgreSQL ETL pipeline plus Chi-Square independence testing.
Statistically significant employment drop when unmet need is present.
Main Evidence
The primary chart makes the relationship legible quickly: unmet mental health need is associated with lower employment rates, with the difference remaining meaningful enough to support the broader conclusion.
What The Data Shows
The null hypothesis is rejected under a materially significant result.
Employment drops meaningfully when unmet mental health need is present.
Demonstrates ETL, schema normalization, testing, and communication in one workflow.
Connects directly to questions healthcare analytics teams solve around access, quality, and outcomes.