Hospital counts differ across counties because facilities locate where patients, funding, and referral networks concentrate, not in proportion to residents. The 24/7 Wall St. analysis of CMS data found about one hospital for every 63,500 people nationwide. Its least-served counties had more than 160,000 people per hospital.
Why hospitals per capita varies so much by county
Three structural forces drive the gap. Each one affects where facilities are built and how many stay open, so the same county can look well served or under-served depending on which force dominates.
Population density and facility location
A hospital needs enough patient volume to cover its fixed costs. In population density terms, a sparsely populated area may support only one small facility across a wide geography, so the total count is low even though each hospital serves few residents and a large area. In dense areas, many facilities can serve the same population, yet the county ratio still depends on how many hospitals fall inside its boundaries.
Economic base and tax revenue
Local hospitals rely on payer mix, local tax revenue, and the economic strength of the community they serve. Counties with a larger economic base may be able to sustain more facilities, while weaker revenue may limit how many remain open. The HRSA Area Health Resources Files include an Economics area alongside Health Facilities and Population Characteristics, so these links can be tested at county level rather than assumed.
Regional referral patterns
Patients cross county lines for specialty care, trauma services, and procedures a local hospital does not offer. A county with few hospitals can still deliver care to residents through neighboring facilities, and a county with many hospitals can draw patients from elsewhere. Raw per-capita counts miss these referral patterns, so a ratio should be read alongside the regional network around it.
Where can you find county-level hospital data?
Three federal sources cover the two inputs of a county hospital ratio: facility counts and population. The HRSA Area Health Resources Files (AHRF) is the broadest of them. It covers counties, states and the nation, and holds more than 6,000 variables grouped into eight areas: Health Care Professions, Health Facilities, Population Characteristics, Economics, Health Professions Training, Hospital Utilization, Hospital Expenditures and Environment. HRSA states that the file can be used to build a county hospitals or beds per capita ratio, so it supplies facility and population variables in one place.
The CMS Medicare facility registry is the source for facility counts by county. It lists facilities registered with Medicare, including hospitals and ambulatory surgery centers, so counts must be filtered by facility type before they are used as a hospital measure. A 24/7 Tempo analysis based on CMS data counted 5,339 Medicare-registered facilities this way.
The Census Bureau American Community Survey (ACS) 5-year estimates provide the population denominators. The 24/7 analysis used 2018 ACS 5-year population figures.
| Source | Publisher | What it covers | Role in a county ratio |
|---|---|---|---|
| Area Health Resources Files (AHRF) | HRSA, U.S. Department of Health and Human Services | Counties, states and nation; more than 6,000 variables; annual refresh cycle | Facility, population and hospital utilization variables in one file |
| Medicare facility registry | CMS | Medicare-registered hospitals and ambulatory surgery centers | Facility counts by county |
| ACS 5-year estimates | U.S. Census Bureau | County population figures (2018 vintage in the 24/7 analysis) | Population denominator |
The sources do not share a reference year by default, so check the vintage of each before dividing one figure by another. A count from one year divided by a population from another year produces a ratio that does not describe either year.
How do you calculate hospitals per 100,000 residents?
The formula is hospitals per 100,000 residents = hospitals ÷ population × 100,000. Divide the number of hospitals in an area by its resident population, then multiply by 100,000. The result shows how many hospitals serve every 100,000 people, which allows areas of different sizes to be compared on the same scale. For example, Queens, NY has 7 hospitals for 2,278,906 people, so 7 ÷ 2,278,906 × 100,000 ≈ 0.31 hospitals per 100,000 residents.
To convert a ratio expressed as people per hospital, reverse the operation: 100,000 ÷ people per hospital. A national ratio of 1 hospital per 63,500 people gives 100,000 ÷ 63,500 ≈ 1.57 hospitals per 100,000 residents. The same conversion applied to the counties in the ranking gives the values below.
| Area | People per hospital (as reported) | Hospitals per 100,000 residents (calculated) |
|---|---|---|
| Nationwide | 63,500 | 1.57 |
| Hampshire, MA | 161,159 | 0.62 |
| Chittenden, VT | 162,052 | 0.62 |
| Portage, OH | 162,644 | 0.61 |
| Queens, NY | 325,558 (2,278,906 ÷ 7) | 0.31 |
Choosing the year-matched denominator is the step that most affects accuracy. The numerator (the hospital count) and the denominator (the population estimate) should refer to the same reference period. The 24/7 analysis divided CMS facility counts by 2018 ACS 5-year population figures, so its facility and population years may not match. Treat its ratios as approximate.
Work through each county in four steps:
- Count the hospitals in the county from a facility list for the chosen year.
- Take the county population for that same year.
- Divide the hospital count by the population.
- Multiply by 100,000 and round to two decimals.
What does the national average look like?
The nationwide benchmark in the 24/7 Wall St. ranking is one hospital for about every 63,500 people. Any county can be measured against that figure. A county with more residents per hospital than 63,500 has fewer hospitals relative to its population than the national norm.
The counties with the fewest hospitals per resident in the ranking sit well above that benchmark:
| County | People per hospital | Multiple of national average |
|---|---|---|
| Nationwide average | about 63,500 | 1.0 |
| Hampshire, MA | 161,159 | about 2.5 |
| Chittenden, VT | 162,052 | about 2.6 |
| Portage, OH | 162,644 | about 2.6 |
The multiples are simple divisions of each county figure by 63,500, rounded to one decimal place.
The ranking was produced by a Tempo analysis published on 2020-12-02.
The 2020 snapshot reflects conditions at the time of the analysis and does not show current hospital counts. Hospital openings, closures, and population shifts since then would change these ratios.
Why hospital counts can mislead
A raw count of hospitals says little about how many residents can reach one. National averages hide wide differences between counties. Queens, NY, has 7 hospitals serving 2,278,906 residents. By this measure Queens (325,558 people per hospital) has roughly twice as many residents per hospital as Hampshire, MA (161,159), Chittenden, VT (162,052) or Portage, OH (162,644), the counties the ranking flags for very few hospitals. Queens would therefore look worse on this measure than the flagged counties, so the “fewest hospitals” label does not follow from people per hospital alone. A count alone cannot show whether Queens residents are served by hospitals in other counties, and the gap is a reason to check how facilities were counted and located.
Three distortions matter when reading these figures:
- Facility type. The Tempo analysis counted both hospitals and ambulatory surgery centers per county. An ambulatory surgery center is not a hospital, so a combined total overstates hospital capacity. Check whether a source separates the two before comparing counties.
- Location, not residency. Facilities are counted where they are located. A county can host hospitals used mainly by people who live next door, while a county with few hospitals may send its residents elsewhere. Raw counts do not capture these cross-county flows.
- Population denominator. A ratio depends on the population figure it divides by.
Beds, ICU capacity and drive time as better measures
A facility count treats every hospital as equal, whatever its size, services or location. Three indicators separate access from presence: inpatient capacity, critical-care capacity and travel time. The HRSA Area Health Resources Files (AHRF) cover county-level data in eight areas, including Health Facilities and Hospital Utilization, so they supply inputs for the capacity measures.
Staffed beds per 100,000 residents
Divide the staffed beds in each county by its population and express the result per 100,000 residents. Use the bed count and population from the same AHRF release so both numerator and denominator refer to the same year. Mixing years distorts the ratio, especially in counties with large facility openings or closures.
ICU bed availability and specialist services
A high general bed ratio says little about whether a county can treat critical cases. Record ICU beds separately, then check which specialist services the county’s hospitals offer, such as trauma care or neonatal intensive care. A county can score well on general beds and still have no ICU, so these two indicators must be read together.
Drive time to the nearest emergency department
Drive time measures what residents actually face. Calculate minutes by road from a population-weighted point in each county to the nearest emergency department, then report the median or population-weighted average. A single county centroid misrepresents counties with several population centers. Large, sparsely populated counties often show longer times than dense ones with the same facility count, which is the access gap a count hides.
| Measure | What it captures | Main limitation |
|---|---|---|
| Facility count | Number of hospitals present | Ignores size, services and distance |
| Staffed beds per 100,000 residents | Inpatient capacity relative to population | Does not measure emergency-only access |
| ICU bed availability | Capacity for critical care | Says nothing about travel time |
| Drive time to emergency department (minutes) | Travel burden to emergency care | Depends on routing assumptions and point placement |
How to compare counties when choosing where to live (using the three ranked counties as an example)
Shortlisting counties works best as a side-by-side table with one row per county and one column per metric, using the same source and year for every row. The health access metric used here is hospitals per capita, expressed as people per hospital. A 2020 analysis by 24/7 Wall St. ranked counties on this measure.
The following counties appear in that ranking as having very few hospitals relative to population:
| County | People per hospital | Rank among these three (1 = most hospitals per person) |
|---|---|---|
| Hampshire, MA | 161,159 | 1 |
| Chittenden, VT | 162,052 | 2 |
| Portage, OH | 162,644 | 3 |
The ranking is only one input. To build a weighted shortlist, assign each metric a weight that reflects your priorities, score each county from 0 to 10 on each metric, then calculate the total. The formula is: total score = sum of (weight × metric score). For example, a buyer who gives housing 40%, health access 30%, and climate 30% would multiply each county’s housing score by 0.4, its health score by 0.3, and its climate score by 0.3, then add the results.
Matching hospital access to age and health needs requires a different check. A low people-per-hospital figure describes the county as a whole, not the area near a given address. Households with older members or ongoing care needs should also check drive times to the nearest facility, which this ranking does not measure.
Combining housing prices and health access in one table means adding a housing column next to the health column for each county, using prices from the same time period. Pull those prices from US public property-sale records, such as county recorder or assessor data, for each county, then apply the same weighted formula to both columns. Keep the table to the counties that pass your minimum requirements, so the comparison stays focused on a short list of three to five options.
Frequently asked questions
What does “people per hospital” actually measure?
It divides a county’s population by the number of hospitals located inside its boundaries, so a higher figure means each facility serves more residents. The national reference from the 24/7 Wall St. analysis works out to about 63,500 people per hospital, which gives you a yardstick for judging any county’s people per hospital ratio.
Why does Queens look worse than the counties the ranking flags?
Queens shows about 325,558 people per hospital, roughly double the figures for Hampshire, MA, Chittenden, VT and Portage, OH, yet the ranking does not flag it as under-served. Check the 7-hospital count against another source before drawing conclusions, because a count inside county lines does not show whether Queens residents use hospitals in neighboring counties.
Where can I get official county-level hospital data?
The HRSA Area Health Resources Files cover counties, states and the nation, with more than 6,000 variables, including health facility and population data. The files are updated annually, so you can build a county hospitals-per-capita ratio from data that you match to the same year.
