Banana AnalyticsBANANAANALYTICS

County profile

Martin County, Texas 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

46Moderateout of 100

Env

30

−20 vs U.S. mean

Disease

17

−33 vs U.S. mean

Provider

93

+43 vs U.S. mean

SDOH

54

+4 vs U.S. mean

FIPS: 48317Population: 5,216Risk overview: Near national averages

Specific health risk patterns

Martin County, TX: 3 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 Martin 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.

Heat-Dialysis Vulnerability· 96Very Highmedium confidence

11 dialysis-dependent Medicare beneficiaries (12.46 per 1k) and 108 days above 95°F.

Extreme heat exposure × Dialysis-dependent population

Defend this finding — full lineage to source data3 sources cited
Heat-Dialysis Vulnerability

Martin County: 96/100 (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%

93.5 °F

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

NOAAApplied 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

Dialysis-dependent Medicare beneficiaries per 1,00035%

12.5 per 1,000 Medicare benes

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

HHS / ASPRemPOWER 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.

CDCPLACES — 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· 96Very Highlow confidence

Pesticide intensity at 321.3 kg per sq mi, summer max temperatures averaging 93.5°F.

Pesticide + heat exposure × Farmworker population

Defend this finding — full lineage to source data3 sources cited
Field Burdenneeds review

Martin County: 96/100 (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

80th percentile

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

USGSPesticide 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

93.5 °F

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

NOAAApplied 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

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.

USDANASS — 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· 71Highmedium confidence

0.11% customer-hours of outage exposure against 72 DME-dependent Medicare beneficiaries (81.5 per 1k).

Power outage risk × Electricity-dependent medical population

Defend this finding — full lineage to source data6 sources cited
Outage Vulnerability

Martin County: 71/100 (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%

93.5 °F

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

NOAAApplied 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

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

37.4K customer-hours

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

DOE / ORNLEAGLE-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%

81.5 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 / ASPRemPOWER 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

5.5%

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

CDCPLACES — 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

6.0%

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

CDCPLACES — 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

44.2%

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

Census BureauAmerican 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)

2 signals near threshold: Industrial Burden (70) · Heat Vulnerability (63)

8 signals evaluated. See all signal methodologies →

Where Martin County stands

Health risks here sit near national averages

Martin County, Texas has elevated doctor and specialist shortages — primary care and specialty access rank worse than 93% of U.S. counties. Pollution exposure, chronic disease rates, and social and economic conditions all sit closer to the middle of the national distribution. The issue here is healthcare infrastructure — not enough providers for the population — rather than vulnerability piling up across multiple dimensions. Counties in this profile are candidates for provider-recruitment and capacity-building 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

Martin County compared to Texas 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.

Martin County four-pillar profile20406080100Disease BurdenEnv RiskSDOH StressProvider Gap

Provider Gap (93) is worse than at least 70% of U.S. counties, the largest contributor to community health risk here.

SDOH Stress (54) is moderately worse than the U.S. average of 50.

Environmental Risk (30) and Disease Burden (17) are at or better than the U.S. average.

  • Martin County
  • Texas state mean
  • U.S. mean (50)
  • Signal threshold (70)

Current Conditions

Today's air quality, fires, and weather alerts

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

Current Air Quality
24Good
PM2.5: 4.3 µg/m³ · 2026-05-28
Source: EPA AirNow
Nearest Active Wildfire
SEVEN CABINS
348 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 Martin County with state and national benchmarks. Full profile covers 40+ metrics on the platform.

IndicatorMartin CountyTX avgUS avg
EPA AQS / EJSCREEN
6.8
µg/m³
-10% vs TX
7.67.4
EPA AQS / EJSCREEN
58.3
ppb
-1.6% vs TX
59.257.1
Traffic Proximity
EJSCREEN
62,408
index
-63% vs TX
167,061291,320
NOAA ACIS
108
days/yr
+36% vs TX
7925
Superfund Proximity
EPA EJSCREEN
0.00
score
-100% vs TX
0.040.16
EPA EJSCREEN
18.89
score
+108% vs TX
9.093.39

Wildfire-Attributable Air Quality

Smoke PM2.5 the EPA doesn't count

Stanford peer-reviewed wildfire-attributable PM2.5 for Martin 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
0.57 µg/m³
6% of the 9 µg/m³ federal annual standard, on top of background air
Smoke days > 55 µg/m³
0
EPA “unhealthy for sensitive groups” threshold · Negligible
Smoke days > 100 µg/m³
0
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 Martin 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
0.11%
of all customer-hours in the year · Above routine
Peak customers out
1,483
in a single 15-minute interval · the year's worst quarter-hour
Intervals > 10,000 out
0
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 Martin County, 2010–2026. Cumulative + last 5 years of recorded weather events with deaths, injuries, and damages.

Total events (20102026)
84
18 in the last 5 years
Deaths · injuries
0· 0
cumulative across all event types
Property + crop damage
$172K
cumulative reported damages
Events by type
Thunderstorm69
Flood12
Tornado2

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 Martin County, normalized to land area and ranked nationally. Animal Units (AU) follow the EPA federal definition under 40 CFR §122.23.

CAFO density rank
15thpercentile · Low
National rank of animal units per square mile.
Animal units per sq mi
5.5
Federal CAFO thresholds: 300 AU = “Medium”, 1,000 AU = “Large.” Total AU: 4,998 across 915 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 Martin 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)
80thpercentile · Elevated
National rank of kilograms applied per square mile.
Total mass applied
294.0K kg
321.3 kg/sq mi across 33 distinct compounds.
Top compounds by mass
  1. 1.GLYPHOSATE191.5K kg
  2. 2.DICAMBA39.4K kg
  3. 3.2,4-D22.3K kg
  4. 4.ACETOCHLOR13.2K kg
  5. 5.DIURON7.0K 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 Martin County. Full profile covers 15+ health outcomes plus mortality on the platform.

Martin County chronic disease prevalence vs. CDC PLACES national benchmarksFrequent mental distress (14+ days)14.517.8Depression21.122.2Cancer (any, excl. skin)7.16.1Diabetes11.412.1COPD6.66.0Coronary heart disease6.05.5Current asthma (adults)9.89.6Stroke3.23.0510152025Prevalence (%)
Martin County adult disease prevalence vs. CDC PLACES national benchmarks, ranked by absolute divergence. Green connectors mark conditions where Martin County is below the benchmark; terracotta where above.National benchmarkMartin County
ConditionMartin CountyTX avgUS avg
Current Asthma
% of adults with current asthma
9.6%
-2.3% vs TX
9.8%10.6%
COPD
% of adults with diagnosed COPD
6.0%
-22% vs TX
7.7%8.6%
Diabetes
% of adults with diagnosed diabetes
12.1%
-17% vs TX
14.6%13.7%
Coronary Heart Disease
% of adults with CHD
5.5%
-26% vs TX
7.4%7.9%
Depression
% of adults ever diagnosed with depression
22.2%
+0.4% vs TX
22.1%23.1%
Frequent Mental Distress
% of adults with 14+ poor mental health days/month
17.8%
+2.3% vs TX
17.4%17.2%

Vulnerable Medicare Population

Who needs the grid to stay alive

Medicare beneficiaries in Martin 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.

PopulationCountPer 1,000 Medicare
Total Medicare beneficiaries
Denominator
883
Electricity-dependent (any DME)
Ventilators, oxygen concentrators, IV pumps, motorized wheelchairs
72
81.5
+44% vs TX
Dialysis-dependent
ESRD beneficiaries needing in-center or home dialysis
≤10
12.46
+158% vs TX
Oxygen-dependent
Home oxygen concentrators (outage-vulnerable)
35
39.6
+86% vs TX

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 Martin 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.

SpecialtyMartin CountyUS avg
Primary Care
Family medicine, internal medicine, general practice, pediatrics.
95.6
per 100k
-27% vs US
130.4
Cardiology
Cardiovascular disease, electrophysiology, interventional cardiology.
19.1
per 100k
+58% vs US
12.1
Psychiatry
Mental health prescribers; complements behavioral health access.
19.1
per 100k
+2.4% vs US
18.7

Source: CMS National Plan and Provider Enumeration System (NPPES). Counts reflect providers with a primary practice address in Martin 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 Martin 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 Martin 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 Martin 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 Martin County ranks for respiratory, oncology, cardiovascular, renal, endocrine, and behavioral health service line opportunity.

Multi-County Comparison

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

See pricing →

Nearby Counties

Counties bordering Martin 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