Graph and download economic data for All Employees, Health Care (CES6562000101) from Jan 1990 to Sep 2025 about health, establishment survey, education, services, employment, and USA.

This is an overview document covering the Healthcare Workforce as at March 2019 and refers to numbers of staff in three areas: i) Those directly employed by the NHS in Hospital and Community Health Services (HCHS) ii) GPs and their staff iii) A proportion of the staff working in Independent Healthcare Providers (see key facts below). More information on all of these areas are available within the accompanying documents and also via the 'Related Links' section below. This includes a link to the Independent Healthcare Provider Workforce report. We are now reviewing the content, format and purpose of this publication and welcome your feedback. In particular we are considering whether it would be useful to provide something more like a data hub to help guide users around the workforce publications including interactive visualisations rather than continuing with the current publication. Please email us with your comments and suggestions, clearly stating Healthcare Workforce Statistics as the subject heading, via enquiries@nhsdigital.nhs.uk

In 2023, there were around ** personal care and home health aides per 1,000 adults aged 65 years or older in the United States. Meanwhile, the rate of home health care workers per elderly population varied widely by state, with New York having the highest rate and Florida the lowest.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

In 2024, there were approximately 1.4 million persons employed in home health care services in the United States. This number has fluctuated since reaching a peak of 1.5 million in 2016. This statistic shows the number of persons employed in U.S. home health care services from 2000 to 2024.

Demographic trends play a major role in shaping the healthcare landscape, as economic factors and an aging population contribute to fast-rising healthcare spending. While consumers are spending more on healthcare services in the US, healthcare providers are confronting complex challenges related to labor, competition and tech advances. COVID-19 exposed healthcare and social assistance providers to unprecedented financial and operating pressures, with the lasting impacts still shaping every corner of the sector in 2025. Providers continue to grapple with workforce shortages intensified by the pandemic, resulting in ongoing staffing and recruitment challenges that pressure wage growth and new strategies to recruit and retain. At the same time, consolidation activity is reshaping the landscape, with more patients than ever receiving care from massive, integrated health systems rather than independent ones. Meanwhile, social assistance providers are finding it difficult to meet rising demand for services like food banks and emergency shelters. Despite this challenging operating environment, revenue has been expanding at a CAGR of 4.0% to an estimated $4.3 trillion over the past five years, with revenue rising an expected 2.3% in 2025. Healthcare and social assistance providers are struggling to address staffing challenges. The pandemic exacerbated existing staffing shortages, as the physical and mental toll of the pandemic pushed some to leave the sector entirely. Persistent labor shortages jeopardize healthcare and social assistance providers' ability to address demand, creating widespread staff burnout, high turnover rates and wage inflation. While the health sector labor market began stabilizing in 2024, alleviating wage pressures, an undersized workforce still leaves hundreds of thousands of jobs open. Staff shortages have been a driver of AI adoption in the health sector, as organizations adopt tech solutions to maintain care quality and efficiency with fewer personnel. Automating time- and cost-intensive administrative task helps organizations cope with labor shortages, but also enhances operating efficiency and patient outcomes amid workforce gaps. Demographic trends will remain the driving force behind rising healthcare spending moving forward. However, increasing demand and elevated costs will pressure healthcare and social assistance providers to shift how they operate. For example, investments in digital tools, including AI, and telehealth will accelerate because of their ability to lower costs, increase capacity and improve patient outcomes. As this occurs, cybersecurity will become a core priority, as health systems must mitigate the impact of increasingly disruptive and sophisticated cyberattacks. The sector will also face significant challenges from Medicaid cuts resulting from the OBBBA, as estimates suggest that nearly 17.0 million people will lose health coverage between 2026 and 2034. This substantial loss of coverage is likely to strain providers, particularly those serving large Medicaid and uninsured populations, creating new financial pressures. These dynamics will reinforce and accelerate the ongoing consolidation activity, as providers increasingly seek mergers or acquisitions to access resources, achieve operating efficiencies and ensure stability. In all, sector revenue will grow at a CAGR 3.4% to reach an estimated $5.0 trillion over the next five years.

Graph and download economic data for Employed full time: Wage and salary workers: Healthcare practitioner and technical occupations: 16 years and over: Men (LEU0254593800A) from 2000 to 2024 about healthcare, occupation, full-time, health, males, salaries, workers, 16 years +, wages, employment, and USA.

Emergency medical services (EMS) workforce demographics in the United States do not reflect the diversity of the population served. Despite some efforts by professional organizations to create a more representative workforce, little has changed in the last decade. This scoping review aims to summarize existing literature on the demographic composition, recruitment, retention, and workplace experience of underrepresented groups within EMS. Peer-reviewed studies were obtained from a search of PubMed, CINAHL, Web of Science, ProQuest Thesis and Dissertations, and non-peer-reviewed (“gray”) literature from 1960 to present. Abstracts and included full-text articles were screened by two independent reviewers trained on inclusion/exclusion criteria. Studies were included if they pertained to the demographics, training, hiring, retention, promotion, compensation, or workplace experience of underrepresented groups in United States EMS by race, ethnicity, sexual orientation, or gender. Studies of non-EMS fire department activities were excluded. Disputes were resolved by two authors. A single reviewer screened the gray literature. Data extraction was performed using a standardized electronic form. Results were summarized qualitatively. We identified 87 relevant full-text articles from the peer-reviewed literature and 250 items of gray literature. Primary themes emerging from peer-reviewed literature included workplace experience (n = 48), demographics (n = 12), workforce entry and exit (n = 8), education and testing (n = 7), compensation and benefits (n = 5), and leadership, mentorship, and promotion (n = 4). Most articles focused on sex/gender comparisons (65/87, 75%), followed by race/ethnicity comparisons (42/87, 48%). Few articles examined sexual orientation (3/87, 3%). One study focused on telecommunicators and three included EMS physicians. Most studies (n = 60, 69%) were published in the last decade. In the gray literature, media articles (216/250, 86%) demonstrated significant industry discourse surrounding these primary themes. Existing EMS workforce research demonstrates continued underrepresentation of women and nonwhite personnel. Additionally, these studies raise concerns for pervasive negative workplace experiences including sexual harassment and factors that negatively affect recruitment and retention, including bias in candidate testing, a gender pay gap, and unequal promotion opportunities. Additional research is needed to elucidate recruitment and retention program efficacy, the demographic composition of EMS leadership, and the prevalence of racial harassment and discrimination in this workforce.

WHO Health Indicators for Algeria

Healthcare Delivery, Socioeconomic and Demographic Statistics

By Humanitarian Data Exchange [source]

About this dataset

This dataset from the World Health Organization (WHO) contains comprehensive data on various health indicators for Algeria. It covers various topics such as mortality, sustainable development, global health estimates, health systems, malaria and tuberculosis, child and infection diseases, public health and environment, substance use and mental health tobacco injuries and violence HIV/AIDS nutrition urban Health noncommunicable diseases financial protection medical equipment demographic socioeconomic statistics essential health technologies medical equipment insecticide resistance oral Health universal healthcare global observatory for eHealth human resources information systems youth AMR glass noncommunicable diseases mental healthcare workforce neglected tropical diseases AMR GASP ICD sexual reproductive care and many more.

It provides resource descriptions that allow users to access individual indicator metadata as well as detailed coverage on different countries in the world. The dataset also includes methods related to registry interlinked with last updated information from WHO’s data portal and license terms provided under a variety of other sources.

The analysis of this dataset allows us to know more about the state of public healthcare in Algeria which can eventually lead us not only to an improved understanding but also better initiatives that are designed to benefit the wellbeing of citizens across this nation

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How to use the dataset

This dataset contains a wide variety of health-related indicators for Algeria from the World Health Organization’s (WHO) global health portal. This data can be used to gain insights into various socio-economic conditions, health systems, and public policy strategies in Algeria.

Getting Started

Firstly, you should download the dataset from Kaggle and unzip it into a folder of your choice. Then open the .csv file with your favorite spreadsheet or text editor application. After you have opened the dataset file, you will be able to see all of the available categories and indicator variables included in this dataset.

Understanding The Dataset

The columns in this dataset are divided into two categories; GHO (Global Health Observatory) metadata fields and variable fields describing each indicator value. The GHO metadata fields provide contextual information on where each individual healthcare indicator was sourced from including its reference year(s), geographic region/country, data source code/url and publication state code/url among others. These types of fields can be helpful when interpreting more specific results related to an entire given region or country for example. The second category includes variable fields that contain individual healthcare indicators such as mortality rates or access to clean water for example related to a specific region or population group within Algeria as well as their corresponding statistical values such as low & high values collected over a period of time etc.. Additionally it is important to note that columns with ** after them indicate labels which are relevant only if applicable e..g Low**

Best Practices For Analysing Data

When analysing this type of data consider which comparison type(s) would work best given your end goal: absolute comparison between 2+ geographies over same timeframe? Two periods compared comparatively within same geography? Or different measurements all using same base geography (ie one country)? Once you decide what type of analysis makes sense then use applicable filters/areas such as regions , provinces etc & start slicing up datasets according to whatever measure works best until desired outcomes are found e..g filter out by age groups / sex / marital status / ethnicity etc rather than downloading entire table with all stats together thereby simplifying efforts & narrowing down scope greatly improving accuracy along way whilst identifying potential must know trends quickly through visualisations generated Charts / tables when combined often highlight underlying relationships quickly which is key before analysing further Deep diving combined datasets by cross referencing various indices allowing viewers gain even better insights specially when combined structured narrative explanations composed backed up by facts

Research Ideas

• Creating visualizations that show changes in health indicators over tim...

The global healthcare workforce management system market size is projected to increase from USD 2.38 billion in 2025 to USD 8.22 billion by 2035, with CAGR growth above 13.2%. Top companies operating in the industry include Kronos, ADP, Infor, Workday, Cerner, shaping competitive strategies across the sector.

In 2018, around 9.6 million health care workers in the United States were native-born females, while there were around 2 million foreign-born female health care workers. This statistic illustrates the number of health care workers in the United States in 2018, by gender and nativity.

Graph and download economic data for Employed full time: Wage and salary workers: Other healthcare practitioners and technical occupations: 16 years and over: Men (LEU0254596700A) from 2000 to 2019 about healthcare, occupation, full-time, health, males, salaries, workers, 16 years +, wages, employment, and USA.

BackgroundGrowing physician maldistribution and population demographic shifts have contributed to large geographic variation in healthcare access and the emergence of advanced practice providers as contributors to the healthcare workforce. Current estimates of geographic accessibility of physicians and advanced practice providers rely on outdated “provider per capita” estimates that have shortcomings.PurposeTo apply state of the art methods to estimate spatial accessibility of physician and non-physician clinician groups and to examine factors associated with higher accessibility.MethodsWe used a combination of provider location, medical claims, and U.S. Census data to perform a national study of health provider accessibility. The National Plan and Provider Enumeration System was used along with Medicare claims to identify providers actively caring for patients in 2014 including: primary care physicians (i.e., internal medicine and family medicine), specialists, nurse practitioners, and chiropractors. For each U.S. ZIP code tabulation area, we estimated provider accessibility using the Variable-distance Enhanced 2 step Floating Catchment Area method and performed a Getis-Ord Gi* analysis for each provider group. Generalized linear models were used to examine associations between population characteristics and provider accessibility.ResultsNational spatial patterns of the provider groups differed considerably. Accessibility of internal medicine most resembled specialists with high accessibility in urban locales, whereas relative higher accessibility of family medicine physicians was concentrated in the upper Midwest. In our adjusted analyses independent factors associated with higher accessibility were very similar between internal medicine physicians and specialists–presence of a medical school in the county was associated with approximately 70% higher accessibility and higher accessibility was associated with urban locales. Nurse practitioners were similar to family medicine physicians with both having higher accessibility in rural locales.ConclusionsThe Variable-distance Enhanced 2 step Floating Catchment Area method is a viable approach to measure spatial accessibility at the national scale.

In 2024, 73 percent of employees working for private industries in the U.S. had access to health care benefits through employer-sponsored benefit plans, while 95 percent of state government workers had access to healthcare benefits. This statistic illustrates the share of employees who had access to employer sponsored healthcare benefit plans in the U.S. in 2024, by type of worker.

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.

Financial overview and grant giving statistics of Home Healthcare Workers Of America

Additional file 1: Supplemental Appendix 1. Top PA Performers Ranked by Number of Graduates from 2014-2018.

Financial overview and grant giving statistics of Local 2020 Home Healthcare Workers Of America

Additional file 1: Table S1. Summary Statistics (2001–2017). Percentage of full-time, full-year workers, aged 18–75, by year, regression variable and gender.