County profile

Jefferson County, Oklahoma 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

80Highout of 100

Env

+16 vs U.S. mean

Disease

+31 vs U.S. mean

Provider

+31 vs U.S. mean

SDOH

+27 vs U.S. mean

FIPS: 40067Population: 5,347Risk overview: 3 of 4 major risks elevated

Specific health risk patterns

Jefferson County, OK: 7 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 Jefferson County, 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.

Respiratory Burden· 91Very Highmedium confidence

PM2.5 averages 8.4 µg/m³ against an asthma + COPD prevalence of 11.7% + 11.6%.

Air pollution exposure × Respiratory-vulnerable population

→ Signal Brief → Compare to peer counties → Tract drill-down

Defend this finding — full lineage to source data5 sources cited

Respiratory Burden

Jefferson County: 91/100 (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.

0.40 × percentile(pm25_annual_mean) + 0.30 × percentile(asthma_copd_blend) + 0.30 × percentile(pulmonology_access_deficit)

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)

PM2.5 annual mean concentration40%

8.4 µg/m³

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.

Source page →

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%.

0.6 × max(percentile(casthma), percentile(copd)) + 0.4 × min(percentile(casthma), percentile(copd))

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)

Current asthma prevalenceweighted leg

11.7%

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

Source page →

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

COPD prevalenceweighted leg

11.6%

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

Source page →

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.

100 − [0.5 × percentile(pulmonology_per_100k, this county) + 0.5 × percentile(pulmonology_per_100k, neighbor counties weighted by population)]

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)

Pulmonologists per 100,000 population50%

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

Source page →

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

Pulmonologists per 100,000 populationneighbor adjusted

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

Source page →

How it's measured. NPPES registry filtered to active pulmonology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.

Coverage. All 3,222 US counties

Heat Vulnerability· 86Very Highhigh confidence

102 days above 95°F against a heart-disease + diabetes prevalence of 10.1% + 16.3%.

Extreme heat exposure × Heat-vulnerable population

→ Signal Brief → Compare to peer counties → Tract drill-down

Defend this finding — full lineage to source data5 sources cited

Heat Vulnerability

Jefferson County: 86/100 (elevated above the 70th-percentile threshold)

Extreme heat exposure × cardiometabolic comorbidity × cardiology access deficit. Surfaces counties where a hot-day mortality event would land hardest.

0.40 × percentile(summer_max_temp) + 0.30 × percentile(chd_diabetes_blend) + 0.30 × percentile(cardiology_access_deficit)

Methodology. Heat-related cardiovascular mortality is the canonical climate-health linkage. The cardiometabolic blend identifies populations with the comorbidity profile that most amplifies heat-event mortality; the cardiology access leg captures whether the local system can absorb a heat-event surge.

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

· Bobb JF et al. 'Heat-related mortality and adaptation to heat in the United States.' Environmental Health Perspectives 2014.
· Khatana SAM et al. 'Association of extreme heat with all-cause mortality in the contiguous US.' JAMA Network Open 2022.

Components (3)

Average summer maximum temperature40%

90.7 °F

Mean of the daily maximum temperature across the meteorological summer (June–August).

NOAA — Applied Climate Information System (ACIS) — RCC-ACIS

Vintage: Multi-year mean (2018–2023 typical) · Refresh: Monthly · Lag: Current year

Source page →

How it's measured. NOAA ACIS aggregates GHCN-Daily station observations to county-level summer (JJA) daily-max means using inverse-distance weighting. Smooths year-to-year noise; captures the structural heat profile.

Coverage. All 3,222 US counties

Coronary heart disease + diabetes blend30%

60/40 dominant/secondary percentile blend of CHD and diabetes prevalence.

0.6 × max(percentile(chd), percentile(diabetes)) + 0.4 × min(percentile(chd), percentile(diabetes))

Methodology. Heat-vulnerability cardiometabolic cluster — counties with both conditions elevated face compounding heat-event mortality risk. Same dominant/secondary rule as the asthma+COPD blend.

Components (2)

Coronary heart disease prevalenceweighted leg

10.1%

Percent of adults age 18+ self-reporting coronary heart disease diagnosis.

CDC — PLACES — Local Data for Better Health

Vintage: PLACES 2022–2023 · Refresh: Monthly · Lag: 1–2 years

Source page →

How it's measured. PLACES small-area estimation from BRFSS self-report. Self-reported CHD undercounts asymptomatic disease.

Coverage. All 3,222 US counties

Diabetes prevalenceweighted leg

16.3%

Percent of adults age 18+ self-reporting diabetes diagnosis (excludes gestational).

CDC — PLACES — Local Data for Better Health

Vintage: PLACES 2022–2023 · Refresh: Monthly · Lag: 1–2 years

Source page →

How it's measured. PLACES small-area estimation from BRFSS self-report. Excludes gestational diabetes per the BRFSS question framing.

Coverage. All 3,222 US counties

Cardiology access deficit30%

Inverted national percentile rank of cardiologists per 100K, with a 50/50 in-county/neighbor-county adjacency adjustment.

100 − [0.5 × percentile(cardiology_per_100k, this county) + 0.5 × percentile(cardiology_per_100k, neighbor counties weighted by population)]

Methodology. Same adjacency-adjusted inversion as pulmonology deficit. Reduces false positives near major cardiac centers.

Components (2)

Cardiologists per 100,000 population50%

Active cardiology specialists practicing in the county, normalized to population.

CMS — NPPES — National Plan and Provider Enumeration System

Vintage: Current month · Refresh: Monthly · Lag: Same month

Source page →

How it's measured. NPPES registry filtered to active cardiology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.

Caveat. NPPES is registration-time data, not practice attestation. The 50/50 adjacency adjustment helps but does not eliminate location noise.

Coverage. All 3,222 US counties

Cardiologists per 100,000 populationneighbor adjusted

Active cardiology specialists practicing in the county, normalized to population.

CMS — NPPES — National Plan and Provider Enumeration System

Vintage: Current month · Refresh: Monthly · Lag: Same month

Source page →

How it's measured. NPPES registry filtered to active cardiology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.

Caveat. NPPES is registration-time data, not practice attestation. The 50/50 adjacency adjustment helps but does not eliminate location noise.

Coverage. All 3,222 US counties

Heat-Dialysis Vulnerability· 84Highmedium confidence

11 dialysis-dependent Medicare beneficiaries (6.67 per 1k) and 102 days above 95°F.

Extreme heat exposure × Dialysis-dependent population

→ Signal Brief → Compare to peer counties → Tract drill-down

Defend this finding — full lineage to source data3 sources cited

Heat-Dialysis Vulnerability

Jefferson County: 84/100 (elevated above the 70th-percentile threshold)

Extreme heat × dialysis-dependent Medicare beneficiaries × chronic kidney disease prevalence. Anchored on Taiwan NHIRD findings of 5.3× CKD heat-hospitalization rate, 9× ESRD heat-stroke mortality.

0.40 × percentile(summer_max_temp) + 0.35 × percentile(dialysis_per_1k_medicare) + 0.25 × percentile(kidney_prevalence)

Methodology. Dialysis patients are uniquely heat-vulnerable: missed dialysis sessions during heat-related power loss or transport disruption cause electrolyte cascades within hours. The Taiwan NHIRD analysis (NHIRD = National Health Insurance Research Database) is the strongest population-level evidence we have for the magnitude of the effect.

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

· Lin Y-K et al. 'Extreme heat and ESRD heat-stroke mortality.' Taiwan NHIRD analysis.
· Remigio RV et al. 'Association of extreme heat events with hospital admission or mortality among patients with end-stage renal disease.' JAMA Network Open 2019.

Components (3)

Average summer maximum temperature40%

90.7 °F

Mean of the daily maximum temperature across the meteorological summer (June–August).

NOAA — Applied Climate Information System (ACIS) — RCC-ACIS

Vintage: Multi-year mean (2018–2023 typical) · Refresh: Monthly · Lag: Current year

Source page →

Coverage. All 3,222 US counties

Dialysis-dependent Medicare beneficiaries per 1,00035%

6.7 per 1,000 Medicare benes

Rate of Medicare beneficiaries on at-home or in-center dialysis per 1,000 county Medicare beneficiaries.

HHS / ASPR — emPOWER Map — Medicare beneficiary DME data

Vintage: Current month · Refresh: Monthly · Lag: Same month

Source page →

How it's measured. HHS ASPR derives dialysis-dependent counts from Medicare claims for ESRD-related at-home or in-center service codes, aggregated to county. Reported per 1,000 county Medicare beneficiaries to normalize for size.

Caveat. emPOWER masks counts of 1–10 to the literal value 11 for beneficiary privacy. Per-1k rates derived from masked counts respect the same floor — a small county showing exactly 11 beneficiaries may have anywhere from 1 to 11 actual.

Coverage. All 3,222 US counties (subject to the 1–10 mask)

Chronic kidney disease prevalence25%

Percent of adults age 18+ self-reporting chronic kidney disease diagnosis.

CDC — PLACES — Local Data for Better Health

Vintage: PLACES 2022–2023 · Refresh: Monthly · Lag: 1–2 years

Source page →

How it's measured. PLACES small-area estimation from BRFSS self-report. CKD self-report substantially undercounts true prevalence (most CKD is asymptomatic until late stages).

Coverage. All 3,222 US counties

Field Burden· 84Highlow confidence

Pesticide intensity at 31.1 kg per sq mi, summer max temperatures averaging 90.7°F.

Pesticide + heat exposure × Farmworker population

→ Signal Brief → Compare to peer counties → Tract drill-down

Defend this finding — full lineage to source data3 sources cited

Field Burdenneeds review

Jefferson County: 84/100 (elevated above the 70th-percentile threshold)

Pesticide intensity × summer heat × farmworker population proxy. Surfaces counties where outdoor agricultural workers face simultaneous heat-illness and pesticide-exposure risk.

Weighted composite of pesticide_density_pct_high + summer_max_temp + farmworker_proxy (component weights documented in the gold pipeline manifest, ticket #90)

Methodology. Demographic identifies the population HRSA 330(g) migrant/seasonal worker centers were created to serve. v1 weights are pending finalization — see ticket #90 — and the score is published with medium confidence pending the curation pass.

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)

Components (3)

Pesticide density rank (kg per square mile, EPest_HIGH)weighted leg

39th percentile

National percentile rank of pesticide application intensity per square mile, conservative-against-undercounting (EPest_HIGH) basis.

USGS — Pesticide National Synthesis Project (PNSP)

Vintage: PNSP 2019 (preliminary) · Refresh: Annual when published · Lag: 2–3 years

Source page →

How it's measured. Total kg / county land area in sq mi, then rank-percentile against all PNSP-covered US counties. EPest_HIGH is the regional-pool imputation that errs against undercounting.

Caveat. PNSP is on medium-low update reliability — see pesticide_total_kg caveat.

Coverage. 3,054 of 3,222 US counties

Average summer maximum temperatureweighted leg

90.7 °F

Mean of the daily maximum temperature across the meteorological summer (June–August).

NOAA — Applied Climate Information System (ACIS) — RCC-ACIS

Vintage: Multi-year mean (2018–2023 typical) · Refresh: Monthly · Lag: Current year

Source page →

Coverage. All 3,222 US counties

Farmworker exposure proxy (USDA NASS livestock + crop area)weighted legneeds review

Composite proxy for outdoor agricultural worker exposure, derived from USDA NASS livestock counts and crop acreage indicators.

USDA — NASS — National Agricultural Statistics Service

Vintage: NASS Quick Stats current vintage · Refresh: Annual · Lag: 1–2 years

Source page →

How it's measured. Weighted blend of farmworker-intensive crop acreage and livestock operations, used as a proxy for the population that HRSA 330(g) migrant/seasonal worker centers were created to serve. Direct farmworker counts are unreliable below state level; this proxy is the structural-pattern stand-in.

Coverage. Counties with non-zero ag activity

Outage Vulnerability· 82Highmedium confidence

0.19% customer-hours of outage exposure against 117 DME-dependent Medicare beneficiaries (71.0 per 1k).

Power outage risk × Electricity-dependent medical population

→ Signal Brief → Compare to peer counties → Tract drill-down

Defend this finding — full lineage to source data6 sources cited

Outage Vulnerability

Jefferson County: 82/100 (elevated above the 70th-percentile threshold)

Summer heat × DOE/ORNL outage burden × emPOWER electricity-dependent Medicare × CHD/COPD prevalence × pre-1980 housing AC proxy. The compound that no aggregator surfaces.

0.30 × percentile(summer_max_temp) + 0.25 × percentile(eaglei_outage_burden) + 0.20 × percentile(power_dependent_per_1k_medicare) + 0.15 × percentile(chd_copd_blend) + 0.10 × percentile(pre1980_housing_pct)

Methodology. Combines structural heat exposure with an outage-burden track record AND a population uniquely harmed by outages (electricity-dependent DME) AND comorbidities that amplify outage harm (CHD/COPD) AND a housing-stock proxy for AC penetration. The pre-1980 housing leg is currently null pending Census ACS B25034 derivation; the score still computes from 4 of 5 components.

Threshold. Elevated when score ≥ 70th national percentile across all US counties evaluated for this signal

Peer set. 3,050 of 3,222 US counties (DOE EAGLE-I coverage); Alaska + sparsely-served rural may have no signal

Evidence base

· McBrien H, Casey JA. 'Power outages and respiratory hospitalization risk in the US.' PLOS Medicine 2026 (8+ hour county outages → respiratory hospitalization RR 1.05).
· Stone B et al. 'Compound climate and infrastructure events.' Environmental Science & Technology 2023 (heat × blackout doubles all-cause mortality in modeling; Phoenix worst-case ~13,000 deaths).
· Brelsford C et al. 'A dataset of recorded electricity outages by US county 2014–2022.' Scientific Data 2024 (validates DOE/ORNL EAGLE-I).

Components (5)

Average summer maximum temperature30%

90.7 °F

Mean of the daily maximum temperature across the meteorological summer (June–August).

NOAA — Applied Climate Information System (ACIS) — RCC-ACIS

Vintage: Multi-year mean (2018–2023 typical) · Refresh: Monthly · Lag: Current year

Source page →

Coverage. All 3,222 US counties

Annual customer-hours-out (EAGLE-I)25%

40.1K customer-hours

Total customer-hours of electrical outage in the county for the year, summed from 15-minute interval data.

DOE / ORNL — EAGLE-I — Environment for Analysis of Geo-Located Energy Information

Vintage: Annual rollup of 15-minute interval data · Refresh: Annual · Lag: 1 year

Source page →DOI: 10.6084/m9.figshare.24237376

How it's measured. DOE/ORNL aggregates utility-reported 15-minute outage counts across ~93% of US electricity customers, stitched into a continuous county-level outage timeline. Validated against utility filings in Brelsford et al, Scientific Data 2024.

Caveat. Excludes some smaller cooperatives; the 7% missing customers are concentrated in rural Alaska and parts of the rural West.

Coverage. 3,050 of 3,222 US counties (AK + sparsely-served rural may have no block)

Electricity-dependent DME Medicare beneficiaries per 1,00020%

71.0 per 1,000 Medicare benes

Rate of Medicare beneficiaries using power-dependent durable medical equipment (oxygen concentrators, ventilators, IV pumps, hospital beds) per 1,000 county beneficiaries.

HHS / ASPR — emPOWER Map — Medicare DME claims

Vintage: Current month · Refresh: Monthly · Lag: Same month

Source page →

How it's measured. HHS ASPR derives power-dependent DME counts from Medicare claims for HCPCS codes covering ventilators, suction pumps, oxygen concentrators, NPWT pumps, and infusion pumps. The population most directly harmed by sustained outages.

Caveat. Same 1–10 masking as the dialysis rate. Underestimates total power-dependent population by Medicaid + commercial-insured + uninsured exclusion.

Coverage. All 3,222 US counties

Coronary heart disease + COPD blend15%

60/40 dominant/secondary percentile blend of CHD and COPD prevalence.

0.6 × max(percentile(chd), percentile(copd)) + 0.4 × min(percentile(chd), percentile(copd))

Methodology. Outage-vulnerability cluster — the conditions most directly harmed when a sustained outage halts both CPAP/BiPAP and rescue-medication workflows.

Components (2)

Coronary heart disease prevalenceweighted leg

10.1%

Percent of adults age 18+ self-reporting coronary heart disease diagnosis.

CDC — PLACES — Local Data for Better Health

Vintage: PLACES 2022–2023 · Refresh: Monthly · Lag: 1–2 years

Source page →

How it's measured. PLACES small-area estimation from BRFSS self-report. Self-reported CHD undercounts asymptomatic disease.

Coverage. All 3,222 US counties

COPD prevalenceweighted leg

11.6%

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

Source page →

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

Pre-1980 housing stock (%)10%needs review

47.7%

Percent of housing units built before 1980 — a proxy for central air conditioning penetration (post-1980 codes typically include central AC).

Census Bureau — American Community Survey — Table B25034 (Year Structure Built)

Vintage: ACS 5-Year 2019–2023 · Refresh: Quarterly · Lag: 1 year

Source page →

How it's measured. Sum of B25034 categories for housing units built 1979 and earlier, divided by total occupied units. Used as a proxy for the share of housing without central AC; the post-1978 lead-paint ban and the post-1980 construction-code uplift roughly coincide.

Caveat. Currently null in the production JSON pending pipeline derivation (methodology v1.8.0). Outage Vulnerability score still computes from 4 of 5 components.

Coverage. All 3,222 US counties (when populated)

Smoke Burden· 75Highmedium confidence

Wildfire-attributable PM2.5 averaged 0.8 µg/m³ with 0 days above 55 µg/m³ in a county where 11.7% of adults have asthma and 11.6% have COPD.

Wildfire smoke exposure × Respiratory-vulnerable population

→ Signal Brief → Compare to peer counties → Tract drill-down

Defend this finding — full lineage to source data8 sources cited

Smoke Burden

Jefferson County: 75/100 (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.

0.125 × percentile(active_fires_within_200km) + 0.125 × percentile(aqi_max_30day) + 0.15 × percentile(wildfire_pm25_annual_mean) + 0.15 × percentile(smoke_days_above_55) + 0.25 × percentile(asthma_copd_blend) + 0.20 × percentile(pulmonology_access_deficit)

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)

Active fire incidents within 200 km13%needs review

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

Source page →

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

30-day maximum AQI13%needs review

Highest daily Air Quality Index value recorded in the trailing 30-day window.

EPA — Air Quality System (AQS) — daily AQI

Vintage: Trailing 30 days · Refresh: Daily

Source page →

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.

Wildfire-attributable PM2.5 annual mean15%

0.8 µg/m³

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)

Smoke days above 55 µg/m³15%

0 days/year

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%.

0.6 × max(percentile(casthma), percentile(copd)) + 0.4 × min(percentile(casthma), percentile(copd))

Components (2)

Current asthma prevalenceweighted leg

11.7%

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

Source page →

Coverage. All 3,222 US counties

COPD prevalenceweighted leg

11.6%

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

Source page →

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.

100 − [0.5 × percentile(pulmonology_per_100k, this county) + 0.5 × percentile(pulmonology_per_100k, neighbor counties weighted by population)]

Components (2)

Pulmonologists per 100,000 population50%

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

Source page →

How it's measured. NPPES registry filtered to active pulmonology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.

Coverage. All 3,222 US counties

Pulmonologists per 100,000 populationneighbor adjusted

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

Source page →

How it's measured. NPPES registry filtered to active pulmonology taxonomy codes, geocoded to practice address, summed per county, divided by Census population estimate.

Coverage. All 3,222 US counties

Runoff Burden· 74Highhigh confidence

207.4 CAFO animal-units per sq mi (84th national percentile) led by cattle.

Agricultural runoff + flood exposure × Uninsured rural population

→ Signal Brief → Compare to peer counties → Tract drill-down

Defend this finding — full lineage to source data3 sources cited

Runoff Burdenneeds review

Jefferson County: 74/100 (elevated above the 70th-percentile threshold)

CAFO density × flood exposure × rural Medicaid coverage gap. Pattern after Hurricane Florence inundated 91 NC swine + 36 poultry CAFOs in 2018.

Weighted composite of cafo_density_pct + flood_exposure + rural_medicaid_gap (component weights documented in the gold pipeline manifest, ticket #90; flood_exposure pending Lynch/Parks 2025 ingestion per ticket #76)

Methodology. Surfaces counties where concentrated animal feeding operations sit in flood-exposed terrain and the local health system is least equipped to absorb the public-health spillover. v1 uses placeholder-friendly formulation until Lynch/Parks 2025 lands.

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)

Components (3)

CAFO density rank (national percentile)weighted leg

84th percentile

National percentile rank of animal-unit density per square mile, derived from USDA livestock head counts and EPA Animal Unit conversion factors.

USDA + EPA — USDA Census of Agriculture + EPA 40 CFR §122.23 AU formula

Vintage: USDA Census of Ag 2022 (most recent quinquennial) · Refresh: Every 5 years · Lag: 1–2 years

Source page →

How it's measured. Per-county livestock head counts (hogs, cattle, dairy, broilers, layers, turkeys, sheep) from USDA Census, multiplied by EPA 40 CFR §122.23 Animal Unit conversion factors, divided by county land area in square miles, then rank-percentile against all US counties.

Caveat. USDA Census suppresses cells where disclosure would identify individual farms, biasing the AU total downward in concentrated-producer counties. Census is quinquennial — between releases the value goes stale.

Coverage. Counties with non-zero animal-unit totals

Flood exposure (Lynch/Parks 2025)weighted legneeds review

County-level flood exposure index from Lynch & Parks 2025 — combines historical flood footprints, FEMA SFHA coverage, and 100-year floodplain population overlap.

Lynch / Parks — Lynch & Parks 2025 — county flood exposure index (pending ingestion #76)

Vintage: Pending pipeline ingestion (ticket #76) · Refresh: TBD

How it's measured. Peer-reviewed flood-exposure composite combining historical inundation footprints with current FEMA Special Flood Hazard Area coverage. Pending ingestion as of methodology v1.8.0; the v1 Runoff Burden signal uses placeholder-friendly formulation until this lands.

Caveat. Not yet in production JSON. Runoff Burden score is computable from CAFO density + uninsured rural alone in the interim; this leg is reserved for the post-#76 score refresh.

Coverage. All 3,222 US counties when ingested

Rural Medicaid coverage gapweighted legneeds review

Composite of rural-classified census tract share and Medicaid coverage shortfall — proxy for the rural population least insured against environmental health spillover.

Census Bureau — ACS 5-Year + Census urban-rural classification

Vintage: ACS 5-Year 2019–2023; rural classification 2020 decennial · Refresh: Quarterly (ACS); decennial (rural class) · Lag: 1 year (ACS)

Source page →

How it's measured. Weighted blend of rural-classified tract share (Census urban-rural classification) and Medicaid + uninsured rate (ACS). Captures the population that bears the brunt of agricultural-runoff health events without coverage to absorb the medical cost.

Coverage. All 3,222 US counties

8 signals evaluated. See all signal methodologies →

Where Jefferson County stands

3 of 4 major health-risk areas are worse than national averages

Jefferson County, Oklahoma faces stronger-than-average challenges across three of four major health-risk areas: chronic disease rates (worse than 81% of U.S. counties), doctor and specialist access (worse than 81% of U.S. counties), and social and economic challenges (worse than 77% of U.S. counties) are all worse than most U.S. counties. Pollution and environmental hazards are not the primary driver here, which means the main issues are chronic disease prevalence, shortages of doctors and specialists, and economic and social conditions like poverty, housing instability, and limited insurance coverage. This pattern is more common in non-industrial communities facing deep, long-standing access and economic challenges.

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

Jefferson County compared to Oklahoma 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 (81), Provider Gap (81), and SDOH Stress (77) are worse than at least 70% of U.S. counties, the largest contributors to community health risk here.

Environmental Risk (66) is moderately worse than the U.S. average of 50.

Jefferson County
Oklahoma state mean
U.S. mean (50)
Signal threshold (70)

Current Conditions

Today's air quality, fires, and weather alerts

Live operational data for Jefferson County: real-time AQI from EPA AirNow, active fires from NIFC, and any National Weather Service advisories. Updated daily.

Current Air Quality

25Good

PM2.5: 4.4 µg/m³ · 2026-05-28

Source: EPA AirNow

Nearest Active Wildfire

RX SIMMONS SUA

294 km away · 0 acres

0 fires within 100 km · 0 within 200 km

Source: NIFC active fire perimeters

Environmental Factors

Air, water, and exposure indicators

Top environmental indicators for Jefferson County with state and national benchmarks. Full profile covers 40+ metrics on the platform.

Indicator	Jefferson County	OK avg	US avg
PM2.5 (annual mean) EPA AQS / EJSCREEN	8.4 µg/m³ +0.3% vs OK	8.3	7.4
Ozone EPA AQS / EJSCREEN	60.5 ppb ▲ +5.0% vs OK	57.6	57.1
Traffic Proximity EJSCREEN	4,644 index ▼ -96% vs OK	108,375	291,320
Days Above 95°F NOAA ACIS	102 days/yr ▲ +82% vs OK	56	25
Superfund Proximity EPA EJSCREEN	0.00 score ▼ -100% vs OK	0.16	0.16
Drinking Water Violations EPA EJSCREEN	145.32 score ▲ +678% vs OK	18.68	3.39

Wildfire-Attributable Air Quality

Smoke PM2.5 the EPA doesn't count

Stanford peer-reviewed wildfire-attributable PM2.5 for Jefferson County. The EPA classifies wildfire smoke as "exceptional events" and excludes it from official AQS monitoring; Childs/Burke fills that gap with daily county-level data.

Annual mean wildfire PM2.5

0.81 µg/m³

9% of the 9 µg/m³ federal annual standard, on top of background air

Smoke days > 55 µg/m³

EPA “unhealthy for sensitive groups” threshold · Negligible

Smoke days > 100 µg/m³

EPA “unhealthy” threshold · acute exposure days

Source: Childs et al, Environmental Science & Technology 2022 (Harvard Dataverse 10.7910/DVN/DJVMTV). Latest year shipped: 2020. Burke et al, Nature 2023 estimate that the EPA AQS network undercounts wildfire-attributable PM2.5 by 10–30% in fire-affected counties. Coverage is CONUS only. Full methodology →

Outage Burden

When the grid goes dark

DOE/ORNL EAGLE-I customer-hours-out for Jefferson County in 2024. The fraction is population-normalized via the Maximum Customer Count denominator (Brelsford et al, Sci Data 2024) so it's directly comparable across counties of any size.

Customer-hours-out, 2024

0.19%

of all customer-hours in the year · Above routine

Peak customers out

2,260

in a single 15-minute interval · the year's worst quarter-hour

Intervals > 10,000 out

count of 15-minute slots with 10k+ customers out · surge events

Source: DOE/ORNL EAGLE-I (figshare 10.6084/m9.figshare.24237376). Latest year shipped: 2024. Coverage: 3,050 of 3,222 US counties; AK and some sparsely-served rural counties may have no data. Full methodology →

Severe Weather History

Recorded storm events and damages

NOAA NCEI Storm Events Database for Jefferson County, 2010–2026. Cumulative + last 5 years of recorded weather events with deaths, injuries, and damages.

Total events (2010–2026)

122

24 in the last 5 years

Deaths · injuries

0· 0

cumulative across all event types

Property + crop damage

$244.5K

cumulative reported damages

Events by type

Thunderstorm98

Tornado13

Flood11

Source: NOAA NCEI Storm Events Database (full history rollup). NOAA buckets ~50 raw event_type strings into 8 health-relevant categories. Coverage: 3,107 of 3,222 US counties; the absent are typically Alaska boroughs and territories where NOAA codes events as forecast zones rather than counties. Full methodology →

Concentrated Animal Feeding Operations

Livestock density and federal-permit confidence

USDA Census of Agriculture (vintage 2022) animal-unit totals for Jefferson County, normalized to land area and ranked nationally. Animal Units (AU) follow the EPA federal definition under 40 CFR §122.23.

CAFO density rank

84thpercentile · Elevated

National rank of animal units per square mile.

Animal units per sq mi

207.4

Federal CAFO thresholds: 300 AU = “Medium”, 1,000 AU = “Large.” Total AU: 157,398 across 759 sq mi.

Dominant species

Cattle (beef)

Top contributor to the AU total. Other species may also be present.

Low federal coverage. Likely <20% of large CAFOs federally NPDES-permitted in this state (EPA-IG ~32% national average is heavily skewed toward delegated states).

Source: USDA Census of Agriculture 2022 (head counts) + EPA 40 CFR §122.23 (animal-unit conversion). The CAFO composite deliberately omits NPDES facility counts because federal coverage averages ~32% nationally per EPA-IG and is heavily state-skewed — adding it as a numerator would systematically bias the index toward delegated states. Full methodology →

Pesticide Use

USGS Pesticide National Synthesis

Annual pesticide application rollup for Jefferson County from the USGS Pesticide National Synthesis Project. Most recent year on file: 2019. Mass figures use the EPest_HIGH estimate (the conservative-against-undercounting framing); EPest_LOW is also retained on the underlying data.

Density rank (2019)

39thpercentile · Low

National rank of kilograms applied per square mile.

Total mass applied

23.6K kg

31.1 kg/sq mi across 22 distinct compounds.

Top compounds by mass

1.2,4-D18.4K kg
2.GLYPHOSATE2.2K kg
3.CHLORPYRIFOS716 kg
4.ATRAZINE637 kg
5.TRICLOPYR584 kg

Source: USGS Pesticide National Synthesis Project (2019). USGS PNSP nationally; year 2019 is preliminary; 2018 unavailable; 2020+ not released. Update reliability medium-low. Full methodology →

Health Outcomes

Chronic disease prevalence

CDC PLACES model-based prevalence estimates for adults in Jefferson County. Full profile covers 15+ health outcomes plus mortality on the platform.

Jefferson County adult disease prevalence vs. CDC PLACES national benchmarks, ranked by absolute divergence. Green connectors mark conditions where Jefferson County is below the benchmark; terracotta where above.National benchmarkJefferson County

Condition	Jefferson County	OK avg	US avg
Current Asthma % of adults with current asthma	11.7% +0.4% vs OK	11.7%	10.6%
COPD % of adults with diagnosed COPD	11.6% ▲ +23% vs OK	9.4%	8.6%
Diabetes % of adults with diagnosed diabetes	16.3% ▲ +15% vs OK	14.2%	13.7%
Coronary Heart Disease % of adults with CHD	10.1% ▲ +20% vs OK	8.4%	7.9%
Depression % of adults ever diagnosed with depression	25.4% +1.1% vs OK	25.1%	23.1%
Frequent Mental Distress % of adults with 14+ poor mental health days/month	18.5% +1.8% vs OK	18.2%	17.2%

Vulnerable Medicare Population

Who needs the grid to stay alive

Medicare beneficiaries in Jefferson County 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	1,649	—
Electricity-dependent (any DME) Ventilators, oxygen concentrators, IV pumps, motorized wheelchairs	117	71.0 ▲ +11% vs OK
Dialysis-dependent ESRD beneficiaries needing in-center or home dialysis	≤10	6.67 ▲ +21% vs OK
Oxygen-dependent Home oxygen concentrators (outage-vulnerable)	55	33.4 ▲ +22% vs OK

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 Jefferson County 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	Jefferson County	US avg
Primary Care Family medicine, internal medicine, general practice, pediatrics.	130.2 per 100k -0.2% vs US	130.4
Psychiatry Mental health prescribers; complements behavioral health access.	18.6 per 100k -0.4% vs US	18.7

Source: CMS National Plan and Provider Enumeration System (NPPES). Counts reflect providers with a primary practice address in Jefferson County; 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 Jefferson County, 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.

Respiratory Burden

PM2.5 · Asthma + COPD · Provider deficit

—

Wildfire Smoke Vulnerability

Fire activity · AQI · Asthma + COPD

—

Heat Health Risk

Temperature · Chronic disease · Provider deficit

—

Industrial Pollution Burden

TRI · PFAS · Pesticide · Provider deficit

—

Heat × Dialysis Vulnerability

Temperature · Dialysis-dependent · Kidney disease

—

Wildfire Attributable Burden

Smoke PM2.5 · Smoke days · Asthma + COPD

—

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 Jefferson County

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 Jefferson County

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 Jefferson County ranks for respiratory, oncology, cardiovascular, renal, endocrine, and behavioral health service line opportunity.

Multi-County Comparison

Compare Jefferson County 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 Jefferson County with methodology citations. Drops straight into a CHNA or grant application.

See pricing →

Nearby Counties

Counties bordering Jefferson County

Adjacent county profiles with their own scores and environmental health data. Source: Census Bureau County Adjacency File.

Cotton County

Oklahoma

High

Env 29Disease 76Provider 87SDOH 75

Montague County

Texas

Elevated

Env 29Disease 55Provider 86SDOH 69

Clay County

Texas

Elevated

Env 29Disease 52Provider 85SDOH 64

Carter County

Oklahoma

Elevated

Env 70Disease 55Provider 47SDOH 52

Love County

Oklahoma

Moderate

Env 28Disease 63Provider 56SDOH 60

Stephens County

Oklahoma

Moderate

Env 28Disease 69Provider 50SDOH 53

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