# Slides — Youth Mental Health Access Gap V1 · Trellison Institute Briefing Deck
**Audience**: policy briefing · academic seminar · journalism brief
**Length**: 10 slides + title + closer = 12 total
**Speaker notes**: ~30 seconds per slide; deck designed for 8-minute briefing or 20-minute deep-read
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## SLIDE 0 — Title
**The Youth Mental Health Access Gap Is Structurally More Severe Than the Adult Gap and Wider Across States**
A working paper from Trellison Institute · May 2026 (v1.0 draft)
*Companion to: Mental Health Access Gap V1 (adults, May 2026)*
*Methodology rating: pending · Evidence-chain certificate: pending*
Speaker note: This is the second published application of the Need-vs-Access Framework v1. State geography for under-18 — the first state-level study.
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## SLIDE 1 — The 39.4% headline
> **39.4%** of high-school students reported 2+ weeks of persistent sadness or hopelessness in the past 12 months.
>
> **Approximately 16.5 million young people** across 35 participating states.
>
> **2.4×** the adult companion paper's 16.8% pop-weighted prevalence of frequent mental distress.
Source: CDC YRBSS 2023 (Mental Health Indicators) · State-level pop-weighted aggregation.
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## SLIDE 2 — The 85× supply range
[Visual: bar chart of youth-serving provider density per 100K under-18, sorted ascending]
| State | Providers / 100K under-18 |
|---|---:|
| Puerto Rico | 5.8 |
| North Carolina | 9.1 |
| New Jersey | 17.9 |
| Texas | 16.9 |
| ... | ... |
| Connecticut | 500 |
| Maine | 884.9 |
| Hawaii | 983.0 |
| Delaware | 1,049.4 |
| Vermont | 1,059.0 |
| Alaska | 1,085.1 |
The state-to-state range is **~85×**. Approximately **3× wider** than the comparable adult range.
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## SLIDE 3 — The state policy story
The youth gap is dominated by *state-level supply distribution*, not local geography. At state granularity:
- **States that have built youth-serving workforce capacity saturate the gap**: VT, AK, ME, DE, HI.
- **States that have not have structural shortfalls**: PR (extreme), NC, NJ, TX.
The two-problem split (geographic vs capacity) of the adult paper does not apply at state-level — but a parallel framing does: **state policy choice is the binding constraint**.
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## SLIDE 4 — Positive outliers
[Visual: residual scatter or labeled chloropleth]
States where the gap is *worse* than the SES proxy predicts (z > +1.5σ):
| State | z | Need % | Uninsured % | Density / 100K |
|---|---:|---:|---:|---:|
| Puerto Rico | +2.39 | 39.2 | 2.5 | 5.8 |
| North Carolina | +1.81 | 39.1 | 4.2 | 9.1 |
| New Jersey | +1.53 | 36.3 | 2.6 | 17.9 |
Single shared structural pattern: **youth-serving workforce under-investment not explained by insurance coverage**.
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## SLIDE 5 — Negative outliers — what's working
[Visual: contrast — VT and AK highlighted on a chloropleth]
States where the gap is *better* than the SES proxy predicts (z < -1.5σ):
| State | z | Need % | Density / 100K | Policy stack |
|---|---:|---:|---:|---|
| Vermont | -1.70 | 29.3 | 1,059 | University medical center + Medicaid expansion + Designated Agencies |
| Alaska | -1.69 | 43.2 | 1,085 | IHS + tribal health organizations + Title V MCH grants |
Two distinct successful patterns. Both **policy-driven, not market-driven**.
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## SLIDE 6 — The methodology
The Need-vs-Access Framework v1.1.0 — `atlas.need_vs_access_framework_v1` (DaedArch DB-native tool registry):
1. State-level pop-weighted national need
2. State-level supply density per 100K under-18
3. Gap ratio = (need × 1000) / state-supply
4. **National OLS** of log(gap) ~ uninsured rate
5. Z-score residual classification at ±1.5σ
6. Pop-weighted dartboard sample (4 per class)
**Adaptation from adult tract-level**: single parameter change (`regression_grouping="national"`). No other code changes.
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## SLIDE 7 — The dartboard (proof)
9 states. Pre-specified. Population-weighted. Stratified.
[Visual: chloropleth with 9 dart markers]
PR · NC · NJ (positive) · VT · AK (negative) · TX · IL · UT · NV (expected)
Per-state narratives in `mh_gap_youth_v1_dartboard_narratives.md` — each story checks out against the public record (Maria-era workforce migration in PR; UVM + Medicaid expansion in VT; IHS structural supply in AK).
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## SLIDE 8 — Why the adult-vs-youth comparison matters
| Metric | Adult (tract) | Youth (state) |
|---|---:|---:|
| Pop-weighted prevalence | 16.8% | 39.4% |
| Implied population in distress | 41.1M | 16.5M |
| Provider density range | ~10× | ~85× |
| Geographic gap (sub-state) | 6.3M / 2.6% | n/a — no sub-state data |
| Capacity gap (sub-state) | 238M / 97.4% | implicit at state level |
The youth case is *structurally different* from the adult case. Same framework, different policy implications.
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## SLIDE 9 — Policy implications
For **states like PR, NC, NJ** (workforce under-supplied):
- BHWET workforce expansion with youth-specific tracks
- CCBHC payment model with explicit youth-serving certification
- Title V MCH block grant youth mental-health line
- For PR specifically: federal Medicaid parity + IHS-style federal direct-employment
For **all states**:
- MHPAEA parity-law enforcement on commercial youth benefits
- 988 + 911 youth-specific crisis-response co-located funding
- State network-adequacy enforcement specific to under-18 panels
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## SLIDE 10 — The arsenal
Published content arsenal at **https://trellison.com/research/youth-mental-health-supply-demand-gap**:
- Working paper (~3,800 words)
- Methodology supplement
- Executive brief (2 pages)
- Data dictionary / codebook (12 fields)
- 9 dartboard state narratives
- Press release + Q&A
- Replication package
- Animated visualization (state chloropleth)
- This deck
Same arsenal spec as the adult companion paper. Same framework. Different bindings. Compound credibility per study.
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## SLIDE 11 — Q&A / Discussion
Anticipated questions cataloged in `mh_gap_youth_v1_press_qa.md` (12 entries).
Contact:
- [email protected]
- [email protected]
- [email protected]
*Thank you.*