In 2023, there were, on average, 2.32 hospital beds per 1,000 population in the United States. Hospital bed density varied widely between the states, with District of Columbia having 4.87 beds per thousand population, while there were just 1.57 hospital beds per thousand population available in Washington.

In 2023, community hospitals in the United States had an average of 2.3 beds per 1,000 population. The share of community hospital beds ranged from 1.6 to 4.9 beds per 1,000 persons across the country. The number of community hospital beds per 1,000 population in the United States decreased slightly from 2000 to 2023.

Hospital bed density varies significantly across countries, with South Korea and Japan leading the pack at over ** beds per 1,000 population in 2022. This stark contrast becomes apparent when compared to countries like the United States, which reported just **** beds per 1,000 people. These figures highlight the disparities in healthcare infrastructure and capacity among nations, potentially impacting their ability to respond to health crises and provide adequate care. Global trends in hospital bed density While some countries maintain high bed densities, others have experienced declines over time. Canada, for instance, saw its hospital bed rate decrease from **** per 1,000 inhabitants in 1980 to **** in 2022, mirroring trends seen in other developed nations. Similarly, Russia's hospital bed density fell from ** beds per 10,000 inhabitants in 2012 to ** beds per 10,000 in 2023. These reductions may reflect changes in healthcare delivery models and efficiency improvements. Regional variations and healthcare implications Despite having one of the highest bed densities globally, Japan has seen a slight decrease in recent years, from ***** beds per 100,000 inhabitants in 2014 to ******* in 2023. However, Japan still maintains a high capacity, which supports its notably long average hospital stay of **** days in 2022. In contrast, Brazil reported just under *** beds per 1,000 inhabitants in 2022, highlighting the significant disparities that exist between countries and regions in terms of healthcare infrastructure and potential impacts on patient care.

The average number of hospital beds available per 1,000 people in the United States was forecast to continuously decrease between 2024 and 2029 by in total *** beds (**** percent). After the eighth consecutive decreasing year, the number of available beds per 1,000 people is estimated to reach **** beds and therefore a new minimum in 2029. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the average number of hospital beds available per 1,000 people in countries like Canada and Mexico.

Forecast: Density of Physicians Employed in Hospitals in the US 2022 - 2026 Discover more data with ReportLinker!

Forecast: Density of Physicians Employed in Hospitals in the US 2024 - 2028 Discover more data with ReportLinker!

Context

Dataset aims to facilitate a state by state comparison of potential risk factors that may heighten Covid 19 transmission rates or deaths. It includes state by state estimates of: covid 19 positives/deaths, flu/pneumonia deaths, major city population densities, available hospital resources, high risk health condition prevalance, population over 60, means of work transportation rates, housing characteristics (ie number of large apartment complexes/seniors living alone), and industry information.

Content

The Data Includes:

1) Covid 19 Outcome Stats:

Covid_Death : Covid Deaths by State

Covid_Positive : Covid Positive Tests by State

2) US Major City Population Density by State: CBSA_Major_City_max_weighted_density

3) KFF Estimates of Total Hospital Beds by State:

Kaiser_Total_Hospital_Beds

4) 2018 Season Flu and Pneumonia Death Stats:

FLUVIEW_TOTAL_PNEUMONIA_DEATHS_Season_2018

FLUVIEW_TOTAL_INFLUENZA_DEATHS_Season_2018

5)US Total Rates of Flu Hospitalization by Underlying Condition:

Fluview_US_FLU_Hospitalization_Rate_....

6) State by State BRFSS Prevalance Rates of Conditions Associated with Higher Flu Hospitalization Rates

BRFSS_Diabetes_Prevalance BRFSS_Asthma_Prevalance BRFSS_COPD_Prevalance
BRFSS_Obesity BMI Prevalance BRFSS_Other_Cancer_Prevalance BRFSS_Kidney_Disease_Prevalance BRFSS_Obesity BMI Prevalance BRFSS_2017_High_Cholestoral_Prevalance BRFSS_2017_High_Blood_Pressure_Prevalance Census_Population_Over_60

7)State by state breakdown of Means of Work Transpotation:

COMMUTE_Census_Worker_Public_Transportation_Rate

8) State by state breakdown of Housing Characteristics

9) State by State breakdown of Industry Information

Acknowledgements

Links to data sources:

https://worldpopulationreview.com/states/

https://covidtracking.com/data/

https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/#stateleveldata

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

Census Tables: ACSST1Y2018.S1811 ACSST1Y2018.S0102 ACSST1Y2018.S2403 ACSST1Y2018.S2501 ACSST1Y2018.S2504

https://www.census.gov/library/visualizations/2012/dec/c2010sr-01-density.html

https://gis.cdc.gov/grasp/fluview/mortality.html

Inspiration

I hope to show the existence of correlations that warrant a deeper county by county analysis to identify areas of increased risk requiring increased resource allocation or increased attention to preventative measures.

The US infusion pump sector grows at 6.38% CAGR, driven by rising hospital bed density and demand for advanced infusion technologies.

Forecast: Density of Associate Nurses Employed in Hospitals in the US 2023 - 2027 Discover more data with ReportLinker!

Public reporting of measures of hospital performance is an important component of quality improvement efforts in many countries. However, it can be challenging to provide an overall characterization of hospital performance because there are many measures of quality. In the United States, the Centers for Medicare and Medicaid Services reports over 100 measures that describe various domains of hospital quality, such as outcomes, the patient experience and whether established processes of care are followed. Although individual quality measures provide important insight, it is challenging to understand hospital performance as characterized by multiple quality measures. Accordingly, we developed a novel approach for characterizing hospital performance that highlights the similarities and differences between hospitals and identifies common patterns of hospital performance. Specifically, we built a semi-supervised machine learning algorithm and applied it to the publicly-available quality measures for 1,614 U.S. hospitals to graphically and quantitatively characterize hospital performance. In the resulting visualization, the varying density of hospitals demonstrates that there are key clusters of hospitals that share specific performance profiles, while there are other performance profiles that are rare. Several popular hospital rating systems aggregate some of the quality measures included in our study to produce a composite score; however, hospitals that were top-ranked by such systems were scattered across our visualization, indicating that these top-ranked hospitals actually excel in many different ways. Our application of a novel graph analytics method to data describing U.S. hospitals revealed nuanced differences in performance that are obscured in existing hospital rating systems.

This dataset contains measures of the number and density of hospitals per United States Census Tract or ZIP Code Tabulation Area (ZCTA) in 2023. The dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau). The four geographies include: Census Tract 2010 Census Tract 2020 ZIP Code Tabulation Area (ZCTA) 2010 ZIP Code Tabulation Area (ZCTA) 2020

Forecast: In Hospitals Magnetic Resonance Imaging Units Density in the US 2023 - 2027 Discover more data with ReportLinker!

Data set from the article Van Bulck L, Goossens E, Luyckx K, Apers S, Oechslin E, Thomet C, Budts W, Enomoto J, Sluman MA, Lu CW, Jackson JL, Khairy P, Cook SC, Chidambarathanu S, Alday L, Eriksen K, Dellborg M, Berghammer M, Johansson B, Mackie AS, Menahem S, Caruana M, Veldtman G, Soufi A, Fernandes SM, White K, Callus E, Kutty S, Moons P; APPROACH-IS consortium and the International Society for Adult Congenital Heart Disease (ISACHD). Healthcare system inputs and patient-reported outcomes: a study in adults with congenital heart defect from 15 countries. BMC Health Serv Res. 2020 Jun 3;20(1):496. doi: 10.1186/s12913-020-05361-9. PMID: 32493367; PMCID: PMC7268498.

This is the abstract:

Background: The relationship between healthcare system inputs (e.g., human resources and infrastructure) and mortality has been extensively studied. However, the association between healthcare system inputs and patient-reported outcomes remains unclear. Hence, we explored the predictive value of human resources and infrastructures of the countries' healthcare system on patient-reported outcomes in adults with congenital heart disease.

Methods: This cross-sectional study included 3588 patients with congenital heart disease (median age = 31y; IQR = 16.0; 52% women; 26% simple, 49% moderate, and 25% complex defects) from 15 countries. The following patient-reported outcomes were measured: perceived physical and mental health, psychological distress, health behaviors, and quality of life. The assessed inputs of the healthcare system were: (i) human resources (i.e., density of physicians and nurses, both per 1000 people) and (ii) infrastructure (i.e., density of hospital beds per 10,000 people). Univariable, multivariable, and sensitivity analyses using general linear mixed models were conducted, adjusting for patient-specific variables and unmeasured country differences.

Results: Sensitivity analyses showed that higher density of physicians was significantly associated with better self-reported physical and mental health, less psychological distress, and better quality of life. A greater number of nurses was significantly associated with better self-reported physical health, less psychological distress, and less risky health behavior. No associations between a higher density of hospital beds and patient-reported outcomes were observed.

Conclusions: This explorative study suggests that density of human resources for health, measured on country level, are associated with patient-reported outcomes in adults with congenital heart disease. More research needs to be conducted before firm conclusions about the relationships observed can be drawn.

Top Covid19 Countries and Health Demographic Trend

Context

This is a time-series trend data collection with a series of json files primarily focused on countries most impacted by Covid-19. The tree formatted time series data should be able to enable various different kinds of analysis to answer questions about what may make a country's health system vulnerable to Covid-19 and what health demographics may help reducing the impact.

Demographic metrics

Healthcare GDP Expenditure 
Healthcare Employment
Hospital Bed Capacity
Air Pollution and Death Rate
Chronic illnesses and DALYs(Disability-Adjusted Life Years)
Body Weight 
Elderly(Aged 65+) Population
CT Scanner Density
Tobacco Consumption(Smoker population %)

More metrics can be added upon request.

Data Normalization

The raw CSV includes many different types of measurements such as number, percentage and per 1 million population. This data normalizes the time_series data by selecting data that is more about density, and number per capita data rather than absolute numbers. This could help doing comparison among nations since they may vary significantly on population.

Content

Most of the JSON files contain time_series data. For people who want to use the data as country metadata, the most-recent data attribute is collected in top_countries_latest_fact_summary.json

The JSON data focuses on the above mentioned demographic areas in a simple tree schema { Country_name: { metric_name:[ List of {year, value, unit} ] } }

Data source & License

The data is sourced from OECD(https://stats.oecd.org/) and GDHX(http://ghdx.healthdata.org/). The json files with prefix "gbd_" are from GDHX

Following citation is needed for using GDHX data:

GBD Results tool: Use the following to cite data included in this download: Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018. Available from http://ghdx.healthdata.org/gbd-results-tool.

Inspiration

• Where does US rank in term of Healthcare/Preventive spending in GDP, hospital bed/ICU bed/physician density and long-term illness? In which areas can US do more to prevent future Cov-19 crisis?

• Is there correlation in a nation's medical preparedness and the rate of growth in confirmation, death rate and recovery rate? From GBD data graphs, it seems that Dalys(DALYs (Disability-Adjusted Life Years), rate per 100k) can divided nations into different camps.

• How does death rate from Cov-19 correlate with Death rate related to Cardiovascular diseases and Chronic respiratory diseases?

• What trends can we discover in various nation's health demographics over time? Are some areas getting better while others getting worse?

• With time span from 2010 to 2018, this dataset can also correlate with data related to recent outbreaks such as seasonal flus, Avian influenza, etc.

Example Notebook

With some quick analysis, it shows that the US actually ranks higher than China for DALYs(Disability-adjusted life years) caused by Chronic Respiratory conditions, which could be due to seasonal allergies. It seems counter-intuitive that this may suggest that countries with cleaner air may have higher burden of people with Chronic Respiratory conditions that may have made them more vulnerable in the Covid-19 crisis.

Example Kernel: https://www.kaggle.com/timxia/bar-chart-comparison-of-countries https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2F2fce05195108856422b437316f34e837%2FTobacco.png?generation=1585936274243838&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fe8db14764a47a8bce48fa79bdfdfb0f1%2FChronicDisease.png?generation=1585936274372639&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4802460%2Fc534d40af042b9a503325f41c49b83cb%2FAirPollution.png?generation=1585936274337626&alt=media" alt="">

Forecast: Density of Professional Nurses and Midwives Employed in Hospitals in the US 2022 - 2026 Discover more data with ReportLinker!

Forecast: Density of Associate Nurses Employed in Hospitals in the US 2024 - 2028 Discover more data with ReportLinker!

Forecast: Density of Professional Nurses and Midwives Employed in Hospitals in the US 2024 - 2028 Discover more data with ReportLinker!

Forecast: In Hospitals Magnetic Resonance Imaging Exams Density in the US 2022 - 2026 Discover more data with ReportLinker!

Medical Service Study Areas - Census Detail, 2010California Health & Human Services Agency Open Data Portal DescriptionMedical Service Study Areas (MSSAs) are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSAs were developed in 1976 by the California Healthcare Workforce Policy Commission (formerly California Health Manpower Policy Commission) to respond to legislative mandates requiring it to determine "areas of unmet priority need for primary care family physicians" (Song-Brown Act of 1973) and "geographical rural areas where unmet priority need for medical services exist" (Garamendi Rural Health Services Act of 1976).MSSAs are recognized by the U.S. Health Resources and Services Administration, Bureau of Health Professions' Office of Shortage Designation as rational service areas for purposes of designating Health Professional Shortage Areas (HPSAs), and Medically Underserved Areas and Medically Underserved Populations (MUAs/MUPs).The MSSAs incorporate the U.S. Census total population, socioeconomic and demographic data and are updated with each decadal census. Office of Statewide Health Planning and Development provides updated data for each County's MSSAs to the County and Communities, and will schedule meetings for areas of significant population change. Community meetings will be scheduled throughout the State as needed.Adopted by the California Healthcare Workforce Policy Commission on May 15, 2002.Each MSSA is composed of one or more complete census tracts. MSSAs will not cross county lines. All population centers within the MSSA are within 30 minutes travel time to the largest population center.Urban MSSA - Population range 75,000 to 125,000. Reflect recognized community and neighborhood boundaries. Similar demographic and socio-economic characteristics.Rural MSSA - Population density of less than 250 persons per square mile. No population center exceeds 50,000.Frontier MSSA - Population density of less than 11 persons per square mile.