County profile
Taylor County, Florida 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
18
−32 vs U.S. mean
Disease
72
+22 vs U.S. mean
Provider
8
−42 vs U.S. mean
SDOH
59
+9 vs U.S. mean
Specific health risk patterns
Taylor County, FL: 6 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 Taylor 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.
70,103,803 lbs of TRI-reported industrial releases (6,160,484 lbs of carcinogens).
Industrial emissions exposure × Surrounding population
Defend this finding — full lineage to source data5 sources cited
Industrial BurdenTaylor County: 93/100 (elevated above the 70th-percentile threshold)
Taylor County: 93/100 (elevated above the 70th-percentile threshold)
TRI facility density × PFAS contamination × pesticide use × total provider access deficit. Captures cumulative industrial environmental load on the surrounding population.
Methodology. Combines three distinct industrial exposure modes (point-source releases, drinking-water contamination, pesticide use) with a generalist provider-access leg since industrial pollution health effects span multiple specialties. 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)
Components (4)
Number of EPA Toxics Release Inventory (TRI) reporting facilities in the county.
EPA — Toxics Release Inventory (TRI) via Envirofacts
Vintage: TRI 2023 reporting year · Refresh: Annual · Lag: 18 months
How it's measured. Count of facilities reporting any TRI-listed chemical release in the most recent reporting year. TRI thresholds (10K-25K lb manufacturing; 500 lb persistent-bioaccumulative) mean smaller polluters are excluded from this count.
Caveat. TRI is industrial self-report. Underreporting is documented for some sectors and chemicals; the count is a floor, not a ceiling.
Coverage. All 3,222 US counties (zero-inflated; many rural counties = 0)
Composite 0–100 severity score for per- and polyfluoroalkyl substance (PFAS) contamination in the county's drinking water and environment.
EPA — UCMR5 (Unregulated Contaminant Monitoring Rule) + ECHO
Vintage: UCMR5 sampling 2023–2025 · Refresh: Quarterly · Lag: 3–6 months
How it's measured. Composite score combining detection frequency, peak concentration relative to EPA Health Advisory Levels, and number of PFAS species detected from UCMR5 public water system sampling and ECHO enforcement records.
Caveat. UCMR5 only samples public water systems serving 3,300+ people; private well users in small or rural communities are not represented.
Coverage. Counties with at least one UCMR5-eligible PWS
4.8K kg/year
Total estimated agricultural pesticide use in the county for the year, in kilograms (EPest_HIGH conservative estimate).
USGS — Pesticide National Synthesis Project (PNSP)
Vintage: PNSP 2019 (preliminary; 2018 unavailable; 2020+ unreleased) · Refresh: Annual when published · Lag: 2–3 years (and the program is on medium-low update reliability)
How it's measured. USGS PNSP estimates county-level pesticide application from USDA Census of Agriculture acreage by crop, multiplied by crop-specific application rates from proprietary surveys. EPest_HIGH is the regional-pool imputation that's conservative against undercounting.
Caveat. PNSP funding was nearly cut in 2023 and the program now publishes irregularly. 2018 has no data; 2020+ is unreleased as of methodology v1.8.0. Use with the data-quality note shown on the platform.
Coverage. 3,054 of 3,222 US counties
Total provider access deficit20%
Inverted national percentile rank of total healthcare specialists per 100K, with a 50/50 adjacency adjustment.
Methodology. Same shape as the specialty-specific deficits. Used by Industrial Burden where the relevant access dimension isn't a single specialty (industrial pollution health effects span pulmonary, cardiovascular, oncologic, and developmental medicine).
Components (2)
All active healthcare specialists 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 — all specialty taxonomy codes — geocoded to practice address, summed per county, divided by Census population estimate.
Caveat. NPPES is registration-time data, not practice attestation.
Coverage. All 3,222 US counties
All active healthcare specialists 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 — all specialty taxonomy codes — geocoded to practice address, summed per county, divided by Census population estimate.
Caveat. NPPES is registration-time data, not practice attestation.
Coverage. All 3,222 US counties
Pesticide intensity at 4.6 kg per sq mi, summer max temperatures averaging 92.1°F.
Pesticide + heat exposure × Farmworker population
Defend this finding — full lineage to source data3 sources cited
Field Burdenneeds reviewTaylor County: 90/100 (elevated above the 70th-percentile threshold)
Taylor County: 90/100 (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.
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)
14th 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
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
92.1 °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
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
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
5 days above 95°F against a heart-disease + diabetes prevalence of 9.7% + 15.9%.
Extreme heat exposure × Heat-vulnerable population
Defend this finding — full lineage to source data5 sources cited
Heat VulnerabilityTaylor County: 89/100 (elevated above the 70th-percentile threshold)
Taylor County: 89/100 (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)
92.1 °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)
9.7%
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
15.9%
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)
18.7 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
18.7 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
1.97% customer-hours of outage exposure against 263 DME-dependent Medicare beneficiaries (50.2 per 1k).
Power outage risk × Electricity-dependent medical population
Defend this finding — full lineage to source data6 sources cited
Outage VulnerabilityTaylor County: 81/100 (elevated above the 70th-percentile threshold)
Taylor County: 81/100 (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.
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)
92.1 °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
2.1M 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)
50.2 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
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.
Methodology. Outage-vulnerability cluster — the conditions most directly harmed when a sustained outage halts both CPAP/BiPAP and rescue-medication workflows.
Components (2)
9.7%
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
11.5%
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
25.7%
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
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)
22 dialysis-dependent Medicare beneficiaries (4.20 per 1k) and 5 days above 95°F.
Extreme heat exposure × Dialysis-dependent population
Defend this finding — full lineage to source data3 sources cited
Heat-Dialysis VulnerabilityTaylor County: 80/100 (elevated above the 70th-percentile threshold)
Taylor County: 80/100 (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.
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)
92.1 °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
4.2 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
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)
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
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
PM2.5 averages 7.4 µg/m³ against an asthma + COPD prevalence of 10.5% + 11.5%.
Air pollution exposure × Respiratory-vulnerable population
Defend this finding — full lineage to source data5 sources cited
Respiratory BurdenTaylor County: 73/100 (elevated above the 70th-percentile threshold)
Taylor County: 73/100 (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)
7.4 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.
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)
10.5%
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
11.5%
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)
9.3 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
9.3 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: Smoke Burden (56) · Runoff Burden (56)
8 signals evaluated. See all signal methodologies →
Where Taylor County stands
Health risks here sit near national averages
Taylor County, Florida has elevated chronic disease rates — respiratory disease, cardiovascular disease, cancer, and behavioral health conditions rank worse than 72% of U.S. counties. Pollution exposure, doctor access, and social and economic conditions all sit closer to the middle of the national distribution. The pattern here is concentrated disease burden rather than multiple risks piling up — typically this points to legacy disease patterns or an older population rather than emerging environmental or access drivers.
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
Taylor County compared to Florida 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 (72) is worse than at least 70% of U.S. counties, the largest contributor to community health risk here.
SDOH Stress (59) is moderately worse than the U.S. average of 50.
Environmental Risk (18) and Provider Gap (8) are at or better than the U.S. average.
- Taylor County
- Florida state mean
- U.S. mean (50)
- Signal threshold (70)
Current Conditions
Today's air quality, fires, and weather alerts
Live operational data for Taylor 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 Taylor County with state and national benchmarks. Full profile covers 40+ metrics on the platform.
| Indicator | Taylor County | FL avg | US avg |
|---|---|---|---|
PM2.5 (annual mean) EPA AQS / EJSCREEN | 7.4 µg/m³ ▼ -6.1% vs FL | 7.8 | 7.4 |
Ozone EPA AQS / EJSCREEN | 53.2 ppb ▼ -5.1% vs FL | 56.1 | 57.1 |
Traffic Proximity EJSCREEN | 66,184 index ▼ -81% vs FL | 351,155 | 291,320 |
Days Above 95°F NOAA ACIS | 5 days/yr ▼ -72% vs FL | 18 | 25 |
Superfund Proximity EPA EJSCREEN | 0.00 score ▼ -100% vs FL | 0.09 | 0.16 |
Drinking Water Violations EPA EJSCREEN | 25.33 score ▲ +923% vs FL | 2.48 | 3.39 |
Wildfire-Attributable Air Quality
Smoke PM2.5 the EPA doesn't count
Stanford peer-reviewed wildfire-attributable PM2.5 for Taylor 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 Taylor 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 Taylor 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 Taylor 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 Taylor 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.
- 1.2,4-D3.4K kg
- 2.TRICLOPYR432 kg
- 3.ATRAZINE279 kg
- 4.METOLACHLOR & METOLACHLOR-S142 kg
- 5.METOLACHLOR-S142 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 Taylor County. Full profile covers 15+ health outcomes plus mortality on the platform.
| Condition | Taylor County | FL avg | US avg |
|---|---|---|---|
Current Asthma % of adults with current asthma | 10.5% ▲ +6.9% vs FL | 9.8% | 10.6% |
COPD % of adults with diagnosed COPD | 11.5% ▲ +29% vs FL | 8.9% | 8.6% |
Diabetes % of adults with diagnosed diabetes | 15.9% ▲ +12% vs FL | 14.2% | 13.7% |
Coronary Heart Disease % of adults with CHD | 9.7% ▲ +15% vs FL | 8.4% | 7.9% |
Depression % of adults ever diagnosed with depression | 19.3% ▲ +5.5% vs FL | 18.3% | 23.1% |
Frequent Mental Distress % of adults with 14+ poor mental health days/month | 19.0% ▲ +11% vs FL | 17.1% | 17.2% |
Vulnerable Medicare Population
Who needs the grid to stay alive
Medicare beneficiaries in Taylor 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 | 5,236 | — |
Electricity-dependent (any DME) Ventilators, oxygen concentrators, IV pumps, motorized wheelchairs | 263 | 50.2 ▲ +15% vs FL |
Dialysis-dependent ESRD beneficiaries needing in-center or home dialysis | 22 | 4.20 ▲ +49% vs FL |
Oxygen-dependent Home oxygen concentrators (outage-vulnerable) | 68 | 13.0 +0.8% vs FL |
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 Taylor 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 | Taylor County | US avg |
|---|---|---|
Primary Care Family medicine, internal medicine, general practice, pediatrics. | 158.7 per 100k ▲ +22% vs US | 130.4 |
Cardiology Cardiovascular disease, electrophysiology, interventional cardiology. | 18.7 per 100k ▲ +55% vs US | 12.1 |
Pulmonology Respiratory disease specialists — relevant to PM2.5 and wildfire smoke exposure. | 9.3 per 100k ▲ +55% vs US | 6.0 |
Psychiatry Mental health prescribers; complements behavioral health access. | 18.7 per 100k -0.0% vs US | 18.7 |
Oncology / Hematology Cancer specialists. | 9.3 per 100k ▲ +46% vs US | 6.4 |
Neurology Neurological disease specialists. | 9.3 per 100k ▲ +18% vs US | 7.9 |
Source: CMS National Plan and Provider Enumeration System (NPPES). Counts reflect providers with a primary practice address in Taylor 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 Taylor 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 Taylor 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 Taylor 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 Taylor County ranks for respiratory, oncology, cardiovascular, renal, endocrine, and behavioral health service line opportunity.
Multi-County Comparison
Compare Taylor 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 Taylor County with methodology citations. Drops straight into a CHNA or grant application.
Nearby Counties
Counties bordering Taylor County
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