County profile
Prince of Wales-Hyder Census Area, Alaska Community Health Profile
Environmental risk, disease burden, provider access, and SDOH scores for community health needs assessment and service line planning. Fused from EPA, CDC, CMS, and Census data into a single free view.
Opportunity Score
Env
0
−50 vs U.S. mean
Disease
62
+12 vs U.S. mean
Provider
31
−19 vs U.S. mean
SDOH
66
+16 vs U.S. mean
Specific health risk patterns
Prince of Wales-Hyder Census Area, AK: 2 specific risk patterns triggered
Each pattern below combines a specific environmental exposure with a population that is more vulnerable to that exposure. When both are present at meaningful levels in Prince of Wales-Hyder Census Area, the pattern triggers. These are the most concrete data points for documenting a significant health need in a Community Health Needs Assessment and for planning where services or community investment would land hardest.
Internally, we call these “Compound Signals.” Each is a versioned, weighted composite scored against the national distribution. The full formula and citations live on the methodology page.
against an asthma + COPD prevalence of 11.9% + 9.4%.
Air pollution exposure × Respiratory-vulnerable population
Defend this finding — full lineage to source data5 sources cited
Respiratory BurdenPrince of Wales-Hyder Census Area: 82/100 (elevated above the 70th-percentile threshold)
Prince of Wales-Hyder Census Area: 82/100 (elevated above the 70th-percentile threshold)
PM2.5 exposure × respiratory disease prevalence × pulmonology access deficit. Surfaces counties where chronic air-quality exposure lands on a population with elevated asthma/COPD and inadequate specialty access.
Methodology. Each leg is converted to a national percentile rank before weighting. The composite is then itself rank-percentiled to produce the 0–100 published score. Methodology v1.8.0.
Threshold. Elevated when score ≥ 70th national percentile across all US counties evaluated for this signal
Peer set. All US counties evaluated for the signal (~3,222, less coverage gaps)
Evidence base
- · Pope CA et al. 'Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution.' JAMA 2002.
- · Schraufnagel DE et al. 'Air pollution and noncommunicable diseases.' Chest 2019 (American Thoracic Society + ERS joint review).
Components (3)
Yearly average fine particulate matter (PM2.5) concentration at ground level, in micrograms per cubic meter.
EPA — Air Quality System (AQS) + EJSCREEN modeled fallback
Vintage: AQS 2016–2025; EJSCREEN modeled 2024 · Refresh: AQS monthly; EJSCREEN quarterly · Lag: AQS: 6–18 months. EJSCREEN: 1 year.
How it's measured. EPA AQS reports monitor-network annual means where a county hosts a regulatory monitor. For counties without a monitor, the platform falls back to EPA EJSCREEN modeled PM2.5 (a downscaled NAAQS-grade product) so every county has a value.
Caveat. AQS undercounts wildfire-attributable PM2.5 by 10–30% in fire-affected counties; the platform reports wildfire smoke separately via Stanford Childs/Burke.
Coverage. All 3,222 US counties (mix of monitored + modeled)
Asthma + COPD prevalence blend30%
60/40 dominant/secondary percentile blend of asthma and COPD prevalence — the higher-percentile condition gets 60%, the lower gets 40%.
Methodology. The dominant/secondary blend ensures counties with both conditions elevated score higher than those with only one — a cardiometabolic-style cluster signal that a max() or simple average would miss. Introduced in methodology v1.1.0 to replace the original max() rule across all multi-condition disease components.
Components (2)
11.9%
Percent of adults age 18+ self-reporting current asthma diagnosis.
CDC — PLACES — Local Data for Better Health
Vintage: PLACES 2022–2023 (BRFSS source year ≈ 2 years prior) · Refresh: Monthly (PLACES release cadence) · Lag: 1–2 years
How it's measured. PLACES applies multilevel small-area estimation to BRFSS adult survey responses, producing county-level prevalence estimates with model-based uncertainty intervals. Self-reported, not provider-confirmed.
Coverage. All 3,222 US counties
9.4%
Percent of adults age 18+ self-reporting chronic obstructive pulmonary disease diagnosis.
CDC — PLACES — Local Data for Better Health
Vintage: PLACES 2022–2023 · Refresh: Monthly · Lag: 1–2 years
How it's measured. PLACES small-area estimation from BRFSS self-report. Underestimates true prevalence by an unknown factor since many cases go undiagnosed in low-access areas.
Coverage. All 3,222 US counties
Pulmonology access deficit30%
Inverted national percentile rank of pulmonologists per 100K, with a 50/50 in-county/neighbor-county adjacency adjustment.
Methodology. Inversion turns 'fewer providers' into a higher deficit score (so the signal weights point the same direction as exposure). The 50/50 adjacency adjustment uses Census Bureau county-adjacency files to reduce false positives where a county borders a major medical center: a small county next to Houston shouldn't read as 'no pulmonology' just because the practice happens to sit across the county line.
Components (2)
Active pulmonology specialists practicing in the county, normalized to population.
CMS — NPPES — National Plan and Provider Enumeration System
Vintage: Current month (NPPES is registration-time data) · Refresh: Monthly · Lag: Same month
How it's measured. NPPES registry filtered to active pulmonology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.
Caveat. NPPES is registration-time data, not practice attestation — providers may have moved or retired without updating their record. The 50/50 adjacency adjustment in the access deficit derivation reduces but does not eliminate this noise.
Coverage. All 3,222 US counties
Active pulmonology specialists practicing in the county, normalized to population.
CMS — NPPES — National Plan and Provider Enumeration System
Vintage: Current month (NPPES is registration-time data) · Refresh: Monthly · Lag: Same month
How it's measured. NPPES registry filtered to active pulmonology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.
Caveat. NPPES is registration-time data, not practice attestation — providers may have moved or retired without updating their record. The 50/50 adjacency adjustment in the access deficit derivation reduces but does not eliminate this noise.
Coverage. All 3,222 US counties
Wildfire smoke exposure × Respiratory-vulnerable population
Defend this finding — full lineage to source data8 sources cited
Smoke BurdenPrince of Wales-Hyder Census Area: 82/100 (elevated above the 70th-percentile threshold)
Prince of Wales-Hyder Census Area: 82/100 (elevated above the 70th-percentile threshold)
Wildfire smoke exposure (acute + chronic) × respiratory-vulnerable population × pulmonology access deficit. The merged successor (methodology v1.8.0) to the legacy Wildfire Smoke Vulnerability + Wildfire-Attributable Burden signals.
Methodology. Six-component blend captures both acute exposure (active fires, recent AQI peak) and chronic exposure (wildfire-attributable PM2.5 annual mean and smoke-day count from Stanford Childs/Burke). Merging the two legacy signals avoids the cannibalization problem where they shared 2 of 4 components and both fired in the same fire-exposed counties. Saved cohort URLs that reference the legacy signals soft-redirect to this merged signal.
Threshold. Elevated when score ≥ 70th national percentile across all US counties evaluated for this signal
Peer set. CONUS counties only — Alaska, Hawaii, Puerto Rico, Virgin Islands, and Guam fall back to acute legs only
Evidence base
- · Childs ML et al. 'Daily local-level estimates of ambient wildfire smoke PM2.5 for the contiguous US.' Environmental Science & Technology 2022.
- · Burke M et al. 'The contribution of wildfire to PM2.5 trends in the USA.' Nature 2023.
- · Ma Y et al. 'Mortality attributable to PM2.5 from wildland fires in California from 2008 to 2018.' PNAS 2024 (~11,415 attributable deaths/year nationally).
Components (6)
Count of active wildfire incidents reported within a 200-kilometer radius of the county centroid.
NIFC — WFIGS — Wildland Fire Interagency Geospatial Services
Vintage: Live (refreshed during fire season) · Refresh: Sub-daily during fire season
How it's measured. Geospatial proximity count of WFIGS incident points to the county geometric centroid. Captures the acute exposure window in the smoke burden composite.
Coverage. All 3,222 US counties
Highest daily Air Quality Index value recorded in the trailing 30-day window.
How it's measured. Maximum of the daily AQI values reported by AQS monitors in the county over the trailing 30-day window. A single bad day pushes this above 100; sustained smoke episodes push it above 150.
Coverage. Counties hosting an AQS monitor; ~2,400 of 3,222 US counties have at least one.
Annual mean concentration of PM2.5 specifically attributable to wildfire smoke, isolated from background and other anthropogenic sources.
Stanford / Harvard — Childs/Burke wildfire smoke PM2.5 — daily local-level estimates
Vintage: Annual rollup (most recent: see WildfireAttributable.year) · Refresh: Annual · Lag: 3–4 years
Source page →DOI: 10.7910/DVN/DJVMTV
How it's measured. Daily 10-km grid estimates of wildfire-attributable PM2.5 derived in Childs et al (Environmental Science & Technology 2022) using satellite smoke plumes, monitor data, and atmospheric chemistry models. Aggregated to county-year annual means by population-weighted overlay. Validated in Burke et al (Nature 2023).
Caveat. CONUS only — Alaska, Hawaii, Puerto Rico, Virgin Islands, and Guam have no Childs/Burke coverage and fall back to the acute legs of the smoke burden signal.
Coverage. CONUS counties only (~3,108 of 3,222)
Count of days in the year where wildfire-attributable PM2.5 exceeded 55 micrograms per cubic meter (the EPA AQI 'unhealthy for sensitive groups' threshold).
Stanford / Harvard — Childs/Burke wildfire smoke PM2.5 — daily local-level
Vintage: Annual rollup (most recent: see WildfireAttributable.year) · Refresh: Annual · Lag: 3–4 years
Source page →DOI: 10.7910/DVN/DJVMTV
How it's measured. Day count of the same Childs/Burke daily 10-km grid where the population-weighted county-day exceeds 55 µg/m³. Captures sustained smoke exposure that the annual mean smooths over.
Coverage. CONUS counties only (~3,108 of 3,222)
Asthma + COPD prevalence blend25%
60/40 dominant/secondary percentile blend of asthma and COPD prevalence — the higher-percentile condition gets 60%, the lower gets 40%.
Methodology. The dominant/secondary blend ensures counties with both conditions elevated score higher than those with only one — a cardiometabolic-style cluster signal that a max() or simple average would miss. Introduced in methodology v1.1.0 to replace the original max() rule across all multi-condition disease components.
Components (2)
11.9%
Percent of adults age 18+ self-reporting current asthma diagnosis.
CDC — PLACES — Local Data for Better Health
Vintage: PLACES 2022–2023 (BRFSS source year ≈ 2 years prior) · Refresh: Monthly (PLACES release cadence) · Lag: 1–2 years
How it's measured. PLACES applies multilevel small-area estimation to BRFSS adult survey responses, producing county-level prevalence estimates with model-based uncertainty intervals. Self-reported, not provider-confirmed.
Coverage. All 3,222 US counties
9.4%
Percent of adults age 18+ self-reporting chronic obstructive pulmonary disease diagnosis.
CDC — PLACES — Local Data for Better Health
Vintage: PLACES 2022–2023 · Refresh: Monthly · Lag: 1–2 years
How it's measured. PLACES small-area estimation from BRFSS self-report. Underestimates true prevalence by an unknown factor since many cases go undiagnosed in low-access areas.
Coverage. All 3,222 US counties
Pulmonology access deficit20%
Inverted national percentile rank of pulmonologists per 100K, with a 50/50 in-county/neighbor-county adjacency adjustment.
Methodology. Inversion turns 'fewer providers' into a higher deficit score (so the signal weights point the same direction as exposure). The 50/50 adjacency adjustment uses Census Bureau county-adjacency files to reduce false positives where a county borders a major medical center: a small county next to Houston shouldn't read as 'no pulmonology' just because the practice happens to sit across the county line.
Components (2)
Active pulmonology specialists practicing in the county, normalized to population.
CMS — NPPES — National Plan and Provider Enumeration System
Vintage: Current month (NPPES is registration-time data) · Refresh: Monthly · Lag: Same month
How it's measured. NPPES registry filtered to active pulmonology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.
Caveat. NPPES is registration-time data, not practice attestation — providers may have moved or retired without updating their record. The 50/50 adjacency adjustment in the access deficit derivation reduces but does not eliminate this noise.
Coverage. All 3,222 US counties
Active pulmonology specialists practicing in the county, normalized to population.
CMS — NPPES — National Plan and Provider Enumeration System
Vintage: Current month (NPPES is registration-time data) · Refresh: Monthly · Lag: Same month
How it's measured. NPPES registry filtered to active pulmonology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.
Caveat. NPPES is registration-time data, not practice attestation — providers may have moved or retired without updating their record. The 50/50 adjacency adjustment in the access deficit derivation reduces but does not eliminate this noise.
Coverage. All 3,222 US counties
7 signals evaluated. See all signal methodologies →
Where Prince of Wales-Hyder Census Area stands
Health risks here sit near national averages
Prince of Wales-Hyder Census Area, Alaska has elevated social and economic challenges — poverty, housing instability, transportation barriers, and uninsured rates rank worse than 66% of U.S. counties. Pollution exposure, chronic disease rates, and doctor access all sit closer to the middle of the national distribution. The pattern here is social and economic conditions driving downstream health risk rather than a full pile-up across all four areas. Effective response usually requires upstream community investment alongside any clinical strategy.
Methodology: when three or more of the four major health-risk areas (pollution, chronic disease, doctor access, social and economic conditions) score above the 70th national percentile, we call the pattern “multi-pillar convergence.” The scoring approach and citations live on the methodology page.
Risk profile
Prince of Wales-Hyder Census Area compared to Alaska and the U.S. average
Four health-risk scores on a 0-100 scale, where 50 is the U.S. average. A higher score means that area is a stronger contributor to community health risk.
Disease Burden (62) and SDOH Stress (66) are moderately worse than the U.S. average of 50.
Environmental Risk (0) and Provider Gap (31) are at or better than the U.S. average.
- Prince of Wales-Hyder Census Area
- Alaska state mean
- U.S. mean (50)
- Signal threshold (70)
Current Conditions
Today's air quality, fires, and weather alerts
Live operational data for Prince of Wales-Hyder Census Area: real-time AQI from EPA AirNow, active fires from NIFC, and any National Weather Service advisories. Updated daily.
Environmental Factors
Air, water, and exposure indicators
Top environmental indicators for Prince of Wales-Hyder Census Area with state and national benchmarks. Full profile covers 40+ metrics on the platform.
| Indicator | Prince of Wales-Hyder Census Area | AK avg | US avg |
|---|---|---|---|
Traffic Proximity EJSCREEN | 162 index ▼ -100% vs AK | 70,291 | 291,320 |
Superfund Proximity EPA EJSCREEN | 0.02 score ▼ -85% vs AK | 0.13 | 0.16 |
Drinking Water Violations EPA EJSCREEN | 27.05 score ▲ +146% vs AK | 11.01 | 3.39 |
Health Outcomes
Chronic disease prevalence
CDC PLACES model-based prevalence estimates for adults in Prince of Wales-Hyder Census Area. Full profile covers 15+ health outcomes plus mortality on the platform.
| Condition | Prince of Wales-Hyder Census Area | AK avg | US avg |
|---|---|---|---|
Current Asthma % of adults with current asthma | 11.9% ▲ +9.5% vs AK | 10.9% | 10.6% |
COPD % of adults with diagnosed COPD | 9.4% ▲ +27% vs AK | 7.4% | 8.6% |
Diabetes % of adults with diagnosed diabetes | 14.0% ▲ +21% vs AK | 11.6% | 13.7% |
Coronary Heart Disease % of adults with CHD | 8.9% ▲ +27% vs AK | 7.0% | 7.9% |
Depression % of adults ever diagnosed with depression | 20.3% ▲ +5.7% vs AK | 19.2% | 23.1% |
Frequent Mental Distress % of adults with 14+ poor mental health days/month | 16.8% ▲ +3.1% vs AK | 16.3% | 17.2% |
Vulnerable Medicare Population
Who needs the grid to stay alive
Medicare beneficiaries in Prince of Wales-Hyder Census Area who depend on electricity for dialysis, oxygen, or other powered medical equipment. From the HHS emPOWER program, which CMS publishes monthly so emergency managers know who to find first when the power goes out.
| Population | Count | Per 1,000 Medicare |
|---|---|---|
Total Medicare beneficiaries Denominator | 1,191 | — |
Electricity-dependent (any DME) Ventilators, oxygen concentrators, IV pumps, motorized wheelchairs | 55 | 46.2 ▼ -23% vs AK |
Dialysis-dependent ESRD beneficiaries needing in-center or home dialysis | 0 | 0.00 ▼ -100% vs AK |
Oxygen-dependent Home oxygen concentrators (outage-vulnerable) | ≤10 | 9.2 ▲ +103% vs AK |
Source: HHS emPOWER Map (ArcGIS county layer), May 2026. Counts of 1–10 are masked as “≤10” per HHS privacy rules; per-1,000 rates are derived and still respect the privacy floor. Full methodology →
Provider Supply
Specialty physician density per 100,000 residents
Active providers in Prince of Wales-Hyder Census Area from the CMS National Plan and Provider Enumeration System (NPPES). Compared to the U.S. average for each specialty. Adjacency adjustment is applied separately in the Provider Gap pillar score.
| Specialty | Prince of Wales-Hyder Census Area | US avg |
|---|---|---|
Primary Care Family medicine, internal medicine, general practice, pediatrics. | 157.1 per 100k ▲ +20% vs US | 130.4 |
Psychiatry Mental health prescribers; complements behavioral health access. | 17.4 per 100k ▼ -6.6% vs US | 18.7 |
Source: CMS National Plan and Provider Enumeration System (NPPES). Counts reflect providers with a primary practice address in Prince of Wales-Hyder Census Area; specialty is taken from the provider's primary NUCC taxonomy code.
Pro analytical view
What drives this county's scores
The flagged signals and service-line opportunities for Prince of Wales-Hyder Census Area, plus the methodology decomposition behind each score. Visible to Pro, Consultant Studio, and Enterprise tiers.
Where to focus
Pro feature
Top flagged signals + service lines are a Pro feature
See how each signal's components blend into its final score, and which signals + service lines this county should prioritize. Available on Professional, Consultant Studio, and Enterprise.
Score decomposition
Each named signal's component breakdown with weights. The bar length is the component's percentile rank; the parenthetical is its weight in the final blend.
Pro feature
Score decomposition is a Pro feature
See how each signal's components blend into its final score, and which signals + service lines this county should prioritize. Available on Professional, Consultant Studio, and Enterprise.
Tract drill-down
Census tracts inside Prince of Wales-Hyder Census Area
Pro feature
Tract-level drill-down is a Pro feature
See how each signal's components blend into its final score, and which signals + service lines this county should prioritize. Available on Professional, Consultant Studio, and Enterprise.
On the full platform
What else is available for Prince of Wales-Hyder Census Area
The page above is a subset. The free Community account unlocks the full single-county profile: every indicator, every data source, demographics, historical trends, and mortality data. Professional unlocks multi-county comparison, compound signal analysis, service line rankings, and consultant-ready PDF reports.
Full Environmental Profile
All 40+ environmental metrics including toxic releases, hazardous site proximity, PFAS detection, pesticide exposure, and climate stress indicators.
Service Line Opportunities
See how Prince of Wales-Hyder Census Area ranks for respiratory, oncology, cardiovascular, renal, endocrine, and behavioral health service line opportunity.
Multi-County Comparison
Compare Prince of Wales-Hyder Census Area side-by-side with neighboring counties across every dimension.
Trend Analysis
5-year sparklines for health outcomes, SDOH measures, and mortality rates so you can see where the county is heading, not just where it is today.
PDF Report Export
Generate a consultant-ready environmental health briefing for Prince of Wales-Hyder Census Area with methodology citations. Drops straight into a CHNA or grant application.
Nearby Counties
Counties bordering Prince of Wales-Hyder Census Area
Adjacent county profiles with their own scores and environmental health data. Source: Census Bureau County Adjacency File.
Data sources: EPA AQS, EPA EJSCREEN, EPA TRI, CDC PLACES, CDC WONDER, CMS NPPES, Census ACS, County Health Rankings, NOAA ACIS, NCI State Cancer Profiles. Every score on this page is derived from publicly available federal data, fused by the Banana Analytics pipeline.
Methodology: See the full scoring methodology (v1.2.0) for weights, sensitivity analysis, and validation against county-level mortality data.
Last refreshed: May 28, 2026