County profile
Hickory County, Missouri 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
26
−24 vs U.S. mean
Disease
92
+42 vs U.S. mean
Provider
22
−28 vs U.S. mean
SDOH
75
+25 vs U.S. mean
Specific health risk patterns
Hickory County, MO: 4 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 Hickory 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.
PM2.5 averages 6.9 µg/m³ against an asthma + COPD prevalence of 11.1% + 16.0%.
Air pollution exposure × Respiratory-vulnerable population
Defend this finding — full lineage to source data5 sources cited
Respiratory BurdenHickory County: 89/100 (elevated above the 70th-percentile threshold)
Hickory County: 89/100 (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)
6.9 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.
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)
11.1%
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
16.0%
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)
11.8 providers / 100K
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
11.8 providers / 100K
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
212.7 CAFO animal-units per sq mi (85th national percentile) led by cattle.
Agricultural runoff + flood exposure × Uninsured rural population
Defend this finding — full lineage to source data3 sources cited
Runoff Burdenneeds reviewHickory County: 86/100 (elevated above the 70th-percentile threshold)
Hickory County: 86/100 (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.
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)
85th 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
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
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
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)
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
10 days above 95°F against a heart-disease + diabetes prevalence of 13.1% + 19.7%.
Extreme heat exposure × Heat-vulnerable population
Defend this finding — full lineage to source data5 sources cited
Heat VulnerabilityHickory County: 75/100 (elevated above the 70th-percentile threshold)
Hickory County: 75/100 (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.
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)
86.9 °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
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.
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)
13.1%
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
How it's measured. PLACES small-area estimation from BRFSS self-report. Self-reported CHD undercounts asymptomatic disease.
Coverage. All 3,222 US counties
19.7%
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
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.
Methodology. Same adjacency-adjusted inversion as pulmonology deficit. Reduces false positives near major cardiac centers.
Components (2)
11.8 providers / 100K
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
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
11.8 providers / 100K
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
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
Wildfire-attributable PM2.5 averaged 0.6 µg/m³ with 0 days above 55 µg/m³ in a county where 11.1% of adults have asthma and 16.0% have COPD.
Wildfire smoke exposure × Respiratory-vulnerable population
Defend this finding — full lineage to source data8 sources cited
Smoke BurdenHickory County: 70/100 (elevated above the 70th-percentile threshold)
Hickory County: 70/100 (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.
0.6 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)
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%.
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)
11.1%
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
16.0%
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)
11.8 providers / 100K
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
11.8 providers / 100K
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
2 signals near threshold: Outage Vulnerability (64) · Field Burden (57)
7 signals evaluated. See all signal methodologies →
Where Hickory County stands
2 of 4 major health-risk areas are worse than national averages
In Hickory County, Missouri, two major health-risk areas stand out as worse than the national average: chronic disease rates (worse than 92% of U.S. counties) and social and economic challenges (worse than 75% of U.S. counties). Chronic disease rates and the economic conditions that drive them — poverty, food insecurity, housing instability — tend to reinforce each other. Addressing one without the other is hard. Effective response usually combines clinical care with upstream community investment.
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
Hickory County compared to Missouri 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 (92) and SDOH Stress (75) are worse than at least 70% of U.S. counties, the largest contributors to community health risk here.
Environmental Risk (26) and Provider Gap (22) are at or better than the U.S. average.
- Hickory County
- Missouri state mean
- U.S. mean (50)
- Signal threshold (70)
Current Conditions
Today's air quality, fires, and weather alerts
Live operational data for Hickory County: 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 Hickory County with state and national benchmarks. Full profile covers 40+ metrics on the platform.
| Indicator | Hickory County | MO avg | US avg |
|---|---|---|---|
PM2.5 (annual mean) EPA AQS / EJSCREEN | 6.9 µg/m³ ▼ -7.4% vs MO | 7.5 | 7.4 |
Ozone EPA AQS / EJSCREEN | 54.7 ppb ▼ -3.0% vs MO | 56.4 | 57.1 |
Traffic Proximity EJSCREEN | 10,022 index ▼ -94% vs MO | 159,297 | 291,320 |
Days Above 95°F NOAA ACIS | 10 days/yr ▼ -6.4% vs MO | 11 | 25 |
Superfund Proximity EPA EJSCREEN | 0.00 score ▼ -100% vs MO | 0.66 | 0.16 |
Drinking Water Violations EPA EJSCREEN | 2.41 score ▲ +427% vs MO | 0.46 | 3.39 |
Wildfire-Attributable Air Quality
Smoke PM2.5 the EPA doesn't count
Stanford peer-reviewed wildfire-attributable PM2.5 for Hickory 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.
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 Hickory 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.
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 Hickory County, 2010–2026. Cumulative + last 5 years of recorded weather events with deaths, injuries, and damages.
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 Hickory County, normalized to land area and ranked nationally. Animal Units (AU) follow the EPA federal definition under 40 CFR §122.23.
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 Hickory 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.
- 1.GLYPHOSATE3.2K kg
- 2.2,4-D2.1K kg
- 3.ATRAZINE902 kg
- 4.CHLORPYRIFOS858 kg
- 5.METOLACHLOR & METOLACHLOR-S855 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 Hickory County. Full profile covers 15+ health outcomes plus mortality on the platform.
| Condition | Hickory County | MO avg | US avg |
|---|---|---|---|
Current Asthma % of adults with current asthma | 11.1% ▲ +3.2% vs MO | 10.8% | 10.6% |
COPD % of adults with diagnosed COPD | 16.0% ▲ +57% vs MO | 10.2% | 8.6% |
Diabetes % of adults with diagnosed diabetes | 19.7% ▲ +44% vs MO | 13.6% | 13.7% |
Coronary Heart Disease % of adults with CHD | 13.1% ▲ +57% vs MO | 8.3% | 7.9% |
Depression % of adults ever diagnosed with depression | 24.9% -0.7% vs MO | 25.1% | 23.1% |
Frequent Mental Distress % of adults with 14+ poor mental health days/month | 17.9% -1.3% vs MO | 18.1% | 17.2% |
Vulnerable Medicare Population
Who needs the grid to stay alive
Medicare beneficiaries in Hickory 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 | 2,928 | — |
Electricity-dependent (any DME) Ventilators, oxygen concentrators, IV pumps, motorized wheelchairs | 183 | 62.5 ▼ -8.3% vs MO |
Dialysis-dependent ESRD beneficiaries needing in-center or home dialysis | 0 | 0.00 ▼ -100% vs MO |
Oxygen-dependent Home oxygen concentrators (outage-vulnerable) | 66 | 22.5 ▼ -14% vs MO |
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 Hickory 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 | Hickory County | US avg |
|---|---|---|
Primary Care Family medicine, internal medicine, general practice, pediatrics. | 129.4 per 100k -0.8% vs US | 130.4 |
Cardiology Cardiovascular disease, electrophysiology, interventional cardiology. | 11.8 per 100k ▼ -2.6% vs US | 12.1 |
Pulmonology Respiratory disease specialists — relevant to PM2.5 and wildfire smoke exposure. | 11.8 per 100k ▲ +95% vs US | 6.0 |
Psychiatry Mental health prescribers; complements behavioral health access. | 23.5 per 100k ▲ +26% vs US | 18.7 |
Oncology / Hematology Cancer specialists. | 11.8 per 100k ▲ +83% vs US | 6.4 |
Neurology Neurological disease specialists. | 11.8 per 100k ▲ +49% vs US | 7.9 |
Source: CMS National Plan and Provider Enumeration System (NPPES). Counts reflect providers with a primary practice address in Hickory 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 Hickory 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.
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 Hickory 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 Hickory 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 Hickory County ranks for respiratory, oncology, cardiovascular, renal, endocrine, and behavioral health service line opportunity.
Multi-County Comparison
Compare Hickory 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 Hickory County with methodology citations. Drops straight into a CHNA or grant application.
Nearby Counties
Counties bordering Hickory County
Adjacent county profiles with their own scores and environmental health data. Source: Census Bureau County Adjacency File.
Benton County
Missouri
56
Elevated
St. Clair County
Missouri
54
Moderate
Dallas County
Missouri
53
Moderate
Camden County
Missouri
47
Moderate
Polk County
Missouri
43
Moderate
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