This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

The HCUP Summary Trend Tables include monthly information on hospital utilization derived from the HCUP State Inpatient Databases (SID) and HCUP State Emergency Department Databases (SEDD). Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD. The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics: Overview of monthly trends in inpatient and emergency department utilization All inpatient encounter types Inpatient stays by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Inpatient encounter type -Normal newborns -Deliveries -Non-elective inpatient stays, admitted through the ED -Non-elective inpatient stays, not admitted through the ED -Elective inpatient stays Inpatient service line -Maternal and neonatal conditions -Mental health and substance use disorders -Injuries -Surgeries -Other medical conditions Emergency department treat-and-release visits Emergency department treat-and-release visits by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Description of the data source, methodology, and clinical criteria

A 2024 survey found that over half of U.S. individuals indicated the cost of accessing treatment was the biggest problem facing the national healthcare system. This is much higher than the global average of 32 percent and is in line with the high cost of health care in the U.S. compared to other high-income countries. Bureaucracy along with a lack of staff were also considered to be pressing issues. This statistic reveals the share of individuals who said select problems were the biggest facing the health care system in the United States in 2024.

Dataset Overview

This dataset presents a focused snapshot of Primary Health Care (PHC) Expenditure per Capita across 114 countries. The data spans from 2016 to 2022, though not all years are represented for each country. It reflects the financial commitment of nations to primary health care, providing a basis for comparative analysis of health spending priorities and trends over time.

Data Science Applications

Despite its modest size, this dataset is ripe for exploratory data analysis, trend analysis, and cross-country comparisons. It can be used to model health expenditure growth, forecast future spending, and identify outliers. Data scientists can also merge it with other datasets to study correlations between PHC expenditure and health outcomes or economic indicators.

Column Descriptors

• Countries: The nation to which the data pertains.
• Indicators: Specifies the type of data, here it's PHC Expenditure per Capita.
• 2016 - 2022: Yearly expenditure data in US dollars. Note that not all countries have data for each year.

Ethically Collected Data

The data was sourced from the WHO's publicly available Global Health Expenditure Database, ensuring ethical collection and sharing practices. It adheres to international standards for health data transparency and accessibility.

Acknowledgements

I extend my gratitude to the United Nations and its specialized agencies for compiling and maintaining the health expenditure data and to Dall E3 for enhancing my dataset presentation with relevant imagery.

This data set contains various information about individuals' sleep habits and physical activities. The data provides important indicators of individuals' overall health and quality of life. Below is detailed information about the columns in the data set and their contents:

User ID: An individual's unique identification number.

Age: The age of the individual.

Gender: The sex of the individual ('f' female, 'm' male)

Sleep Quality: The quality of an individual's sleep (a scale of 1-10, with 10 indicating the highest quality)

Bedtime: The individual's bedtime (in 24-hour format)

Wake-up Time: The individual's wake-up time (in 24-hour format)

Daily Steps: Number of steps per day

Calories Burned: The amount of calories burned per day

Physical Activity Level: The individual's physical activity level (low, medium, high)

Dietary Habits: Dietary habits of the individual (healthy, medium, unhealthy)

Sleep Disorders: Whether the individual has sleep disorders (yes, no)

Medication Usage: Whether the individual uses medication for sleep disorders (yes, no)

Explanation: These data are imaginary data. It was created entirely for the purpose of improving users, it has nothing to do with reality.

Healthcare fraud is considered a challenge for many societies. Health care funding that could be spent on medicine, care for the elderly, or emergency room visits is instead lost to fraudulent activities by materialistic practitioners or patients. With rising healthcare costs, healthcare fraud is a major contributor to these increasing healthcare costs.

Try out various unsupervised techniques to find the anomalies in the data.

Detailed Data File:

The following variables are included in the detailed Physician and Other Supplier data file (see Appendix A for a condensed version of variables included)).

npi – National Provider Identifier (NPI) for the performing provider on the claim. The provider NPI is the numeric identifier registered in NPPES.

nppes_provider_last_org_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s last name. When the provider is registered as an organization (entity type code = ‘O’), this is the organization's name.

nppes_provider_first_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s first name. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_mi – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s middle initial. When the provider is registered as an organization (entity type code= ‘O’), this will be blank.

nppes_credentials – When the provider is registered in NPPES as an individual (entity type code=’I’), these are the provider’s credentials. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_gender – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s gender. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_entity_code – Type of entity reported in NPPES. An entity code of ‘I’ identifies providers registered as individuals and an entity type code of ‘O’ identifies providers registered as organizations.

nppes_provider_street1 – The first line of the provider’s street address, as reported in NPPES.

nppes_provider_street – The second line of the provider’s street address, as reported in NPPES.

nppes_provider_city – The city where the provider is located, as reported in NPPES.

nppes_provider_zip – The provider’s zip code, as reported in NPPES.

nppes_provider_state – The state where the provider is located, as reported in NPPES. The fifty U.S. states and the District of Columbia are reported by the state postal abbreviation. The following values are used for all other areas:

'XX' = 'Unknown' 'AA' = 'Armed Forces Central/South America' 'AE' = 'Armed Forces Europe' 'AP' = 'Armed Forces Pacific' 'AS' = 'American Samoa' 'GU' = 'Guam' 'MP' = 'North Mariana Islands' 'PR' = 'Puerto Rico' 'VI' = 'Virgin Islands' 'ZZ' = 'Foreign Country'

nppes_provider_country – The country where the provider is located, as reported in NPPES. The country code will be ‘US’ for any state or U.S. possession. For foreign countries (i.e., state values of ‘ZZ’), the provider country values include the following: AE=United Arab Emirates IT=Italy AG=Antigua JO= Jordan AR=Argentina JP=Japan AU=Australia KR=Korea BO=Bolivia KW=Kuwait BR=Brazil KY=Cayman Islands CA=Canada LB=Lebanon CH=Switzerland MX=Mexico CN=China NL=Netherlands CO=Colombia NO=Norway DE= Germany NZ=New Zealand ES= Spain PA=Panama FR=France PK=Pakistan GB=Great Britain RW=Rwanda GR=Greece SA=Saudi Arabia HU= Hungary SY=Syria IL= Israel TH=Thailand IN=India TR=Turkey IS= Iceland VE=Venezuela

provider_type – Derived from the provider specialty code reported on the claim.

medicare_participation_indicator – Identifies whether the provider participates in Medicare and/or accepts the assigned assignment of Medicare allowed amounts.

place_of_service – Identifies whether the place of service submitted on the claims is a facility (value of ‘F’) or non-facility (value of ‘O’). Non-facility is generally an office setting; however other entities are included in non-facility.

hcpcs_code – HCPCS code used to identify the specific medical service furnished by the provider.

hcpcs_description – Description of the HCPCS code for the specific medical service furnished by the provider.

hcpcs_drug_indicator –Identifies whether the HCPCS code for the specific service furnished by the provider is an HCPCS listed on the Medicare Part B Drug Average Sales Price (ASP) File.

line_srvc_cnt – Number of services provided; note that the metrics used to count the number provided can vary from service to service.

bene_unique_cnt – Number of distinct Medicare beneficiaries rec...

Limit state-purchased health care cost growth to 2% less than the projected national health expenditures average every year through 2019.

Contains data on Community Services Statistics for June 2023 and a provisional data file for July 2023 (note this is intended as an early view until providers submit a refresh of their data).

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

Key health nutrition & population statistics gathered from the World Bank, gathered from various international sources.

Data includes:

• Background
  • Economy
  • Education
  • Labor force
  • Poverty
• Cause of deaths
• Health financing
• HIV/AIDS
• Immunization
• Infectious diseases
• Medical resources and usage
• Non-communicable diseases
• Nutrition
• Population dynamics
  • Other demographic Indicators
  • Population
  • Population by age group
• Public Sector
  • Policy & institutions
• Reproductive health
• Water and sanitation

The national averages for critical quality measures of Home Health Agencies.

This dataset describes the number and density of health care services in each census tract in the United States. The data includes counts, per capita densities, and area densities per tract for many types of businesses in the health care sector, including doctors, dentists, mental health providers, nursing homes, and pharmacies.

This dataset contains data for the Healthcare Payments Data (HPD): Medical Out-of-Pocket Costs and Chronic Conditions report. The data covers three measurement categories: annual member count, annual median out-of-pocket count, annual median claim count. The annual member count quantify the number of unique individuals who received at least one medical service in the reporting year. Annual median out-of-pocket measurements quantifies the sum of copay, coinsurance, and deductible incurred by members. Annual median claim count measurements quantifies the number of distinct claims or encounters associated with members. Both 25th and 75th percentiles for out-of-pocket cost and claim count are also included. Measures are grouped by payer types, chronic conditions flag, chronic condition types, and chronic condition numbers.

The big data in healthcare market size is estimated to grow from USD 78 billion in 2024 to USD 540 billion by 2035, representing a CAGR of 19.20% till 2035

Graph and download economic data for All Employees, Home Health Care Services (CEU6562160001) from Jan 1985 to Sep 2025 about health, establishment survey, education, services, employment, and USA.

We developed an Australianised version of Synthea. Synthea is a synthetic data generation software that uses publicly available population aggregate statistics such as demographics, disease prevalence and incidence rates, and health reports. Synthea generates data based on manually curated models of clinical workflows and disease progression that cover a patient’s entire life and does not use real patient data; guaranteeing a completely synthetic dataset. We generated 117,258 synthetic patients from Queensland.

Description: Welcome to the Diabetes Prediction Dataset, a valuable resource for researchers, data scientists, and medical professionals interested in the field of diabetes risk assessment and prediction. This dataset contains a diverse range of health-related attributes, meticulously collected to aid in the development of predictive models for identifying individuals at risk of diabetes. By sharing this dataset, we aim to foster collaboration and innovation within the data science community, leading to improved early diagnosis and personalized treatment strategies for diabetes.

Columns: 1. Id: Unique identifier for each data entry. 2. Pregnancies: Number of times pregnant. 3. Glucose: Plasma glucose concentration over 2 hours in an oral glucose tolerance test. 4. BloodPressure: Diastolic blood pressure (mm Hg). 5. SkinThickness: Triceps skinfold thickness (mm). 6. Insulin: 2-Hour serum insulin (mu U/ml). 7. BMI: Body mass index (weight in kg / height in m^2). 8. DiabetesPedigreeFunction: Diabetes pedigree function, a genetic score of diabetes. 9. Age: Age in years. 10. Outcome: Binary classification indicating the presence (1) or absence (0) of diabetes.

Utilize this dataset to explore the relationships between various health indicators and the likelihood of diabetes. You can apply machine learning techniques to develop predictive models, feature selection strategies, and data visualization to uncover insights that may contribute to more accurate risk assessments. As you embark on your journey with this dataset, remember that your discoveries could have a profound impact on diabetes prevention and management.

Please ensure that you adhere to ethical guidelines and respect the privacy of individuals represented in this dataset. Proper citation and recognition of this dataset's source are appreciated to promote collaboration and knowledge sharing.

Start your exploration of the Diabetes Prediction Dataset today and contribute to the ongoing efforts to combat diabetes through data-driven insights and innovations.

The US Healthcare Visits Statistics dataset includes data about the frequency of healthcare visits to doctor offices, emergency departments, and home visits within the past 12 months in the United States by age, race, Hispanic origin, poverty level, health insurance status, geographic region and other characteristics between 1997 and 2016.

A 10,000-patient database that contains in total 10,000 virtual patients, 36,143 admissions, and 10,726,505 lab observations.

The Medical Expenditure Panel Survey (MEPS) Household Component (HC) collects data from a sample of families and individuals in selected communities across the United States, drawn from a nationally representative subsample of households that participated in the prior year's National Health Interview Survey (conducted by the National Center for Health Statistics). During the household interviews, MEPS collects detailed information for each person in the household on the following: demographic characteristics, health conditions, health status, use of medical services, charges and source of payments, access to care, satisfaction with care, health insurance coverage, income, and employment. The panel design of the survey, which features several rounds of interviewing, makes it possible to determine how changes in respondents' health status, income, employment, eligibility for public and private insurance coverage, use of services, and payment for care are related. Public Use Files for Household data are available on the MEPS website.