In the U.S. many employers pay a portion of health care costs for employees. As of 2019, the total annual medical costs for employees was just over 13 thousand U.S. dollars. That cost is expected to increase to 13.7 thousand U.S. dollars by 2020. There have been recent changes to employer-offered health care through the Affordable Care Act that requires employers with over 50 employees to offer affordable health care options to their employees.

U.S. health benefits at work

In the United States, both employers and employees may pay health care costs, depending on the work. In a recent survey U.S. residents were asked what benefits they expected from their employers, a vast majority of them said that they expect health care benefits. Despite the demand from employer-sponsored healthcare coverage, not all companies feel that they would be able to offer health coverage as an employment benefit. Another recent survey has illustrated that employer confidence in offering health insurance can change dramatically from year-to-year.

U.S. sick leave benefits

Another aspect of workplace health and wellness, is annual sick leave. In general, a majority of U.S. workers have access to a fixed number of paid sick days per year. However, a very small proportion of employees had access to paid sick leave as needed. As of 2017, around half of all employees utilized up to 5 days of sick leave per year. Despite that, there was still a large proportion, especially among those aged 18-30 years that went to work even though they were ill.

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

Health and Safety Code section 1288.7(a) requires California acute care hospitals to offer influenza vaccine free of charge to all healthcare providers (HCP) or sign a declination form if a HCP chooses not to be vaccinated. Hospitals must report HCP influenza vaccination data to the California Department of Public Health (CDPH), including the percentage of HCP vaccinated. CDPH is required to make this information public on an annual basis [Health and Safety Code section 1288.8 (b)].

California acute care hospitals are required to offer free influenza vaccine to HCP. Hospital HCP must receive an annual vaccine or sign a declination form. Hospitals collect vaccination data for all HCP physically working in the hospital for at least one day during influenza season, regardless of clinical responsibility or patient contact. Hospitals report HCP vaccination rates to the California Department of Public Health (CDPH) and CDPH publishes the hospital results annually. CDPH reports data separately for hospital employees, licensed independent practitioners such as physicians, other contract staff, and trainees and volunteers (Health and Safety Code section 1288.7-1288.8).

Detailed information about the variables included in each dataset are described in the accompanying data dictionaries for the year of interest.

For general information about NHSN, surveillance definitions, and reporting requirements for HCP influenza vaccination, please visit: https://www.cdc.gov/nhsn/hps/vaccination/index.html

To link the CDPH facility IDs with those from other Departments, including OSHPD, please reference the "Licensed Facility Cross-Walk" Open Data table at: https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk.

For information about healthcare personnel influenza vaccinations in California hospitals, please visit: https://www.cdph.ca.gov/Programs/CHCQ/HAI/Pages/HealthcarePersonnelInfluenzaVaccinationReportingInCA_Hospitals.aspx

Abstract (en): In January 2013, the Urban Institute launched the Health Reform Monitoring Survey (HRMS), a quarterly survey of the nonelderly population, to explore the value of cutting-edge, Internet-based survey methods to monitor the Affordable Care Act (ACA) before data from federal government surveys are available. Topics covered by the second round of the survey (second quarter 2013) include self-reported health status, type of and satisfaction with current health insurance coverage, access to and use of health care, health care affordability, whether the respondent considered purchasing or tried to purchase health insurance coverage directly from an insurance company, whether the respondent considered obtaining coverage through Medicaid or other government sponsored assistance plan based on income or disability, sources of information about health insurance, and the importance of various criteria in choosing a health insurance plan. Additional information collected by the survey includes age, education, race, Hispanic origin, gender, income, household size, housing type, marital status, employment status, number of employees at place of work, United States citizenship, smoking, internet access, home ownership, body mass index, sexual orientation, and whether the respondent reported an ambulatory care sensitive condition or a mental or behavioral condition. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: The HRMS response rate is roughly five percent each quarter. Datasets:DS0: Study-Level FilesDS1: Public-use DataDS2: Restricted-use Data Household population aged 18-64. Each quarterly HRMS sample is drawn from the KnowledgePanel, a probability-based, nationally representative Internet panel maintained by GfK Custom Research. Beginning with the second quarter of 2013, the HRMS includes oversamples of adults with family incomes at or below 138 percent of the federal poverty level and adults from selected state groups based on (1) the potential for gains in insurance coverage in the state under the ACA as estimated by the Urban Institute's microsimulation model and (2) states of specific interest to the HRMS funders. Additional funders have supported oversamples of adults from individual states or subgroups of interest (including children). However, ICPSR received data only for the adults in the general national sample and the income and state group oversamples. 2019-07-10 Variable Q7_F was removed from public dataset. An updated codebook excluding this variable was provided for public use. Current release will feature DS1 as public-use data only and DS2 as restricted-use data. Previous release included both public and restricted versions of DS1. Study title updated to include geographic information.2017-06-20 The principal investigators added a new weight variable to the data file and the technical documentation was updated accordingly.2015-03-23 The principal investigators deleted the multiple imputation variables _1_famsize, _2_famsize, _3_famsize, _4_famsize and _5_famsize. ICPSR revised the codebook accordingly and added to the collection a plain text version of the data with a Stata setup and record layout file. Funding institution(s): Ford Foundation. Urban Institute. Robert Wood Johnson Foundation (71390). web-based survey

Deaths or serious injuries among emergency medical technicians (EMTs) and other ambulance occupants occur at a high rate during transport. According to a study by the National Institute for Occupational Safety and Health (NIOSH), EMTs and paramedics have higher fatality rates when compared to all workers, with forty-five percent of EMT deaths resulting from highway incidents, primarily due to vehicle collisions.1 Data from the National Highway and Traffic Safety Administration showed that among the persons killed in crashes involving an ambulance between 1992 and 2011, twenty one percent were EMTs and patients, while four percent were ambulance drivers.2 To reduce injury potential to the EMTs and other ambulance occupants, NIOSH, the Department of Homeland Security, the U.S. General Services Administration, and the National Institute of Standards and Technology, along with private industry partners, have committed to improving the workspace design of ambulance patient compartments for safe and effective perfo

In 2023, there were over 7.4 million people employed in hospitals across the United States. This is the highest number in the recorded time period and hospital employment numbers have returned to and surpassed pre-pandemic levels.

The number of hospitals in the United States was forecast to continuously decrease between 2024 and 2029 by in total 13 hospitals (-0.23 percent). According to this forecast, in 2029, the number of hospitals will have decreased for the twelfth consecutive year to 5,548 hospitals. Depicted is the number of hospitals in the country or region at hand. As the OECD states, the rules according to which an institution can be registered as a hospital vary across countries.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 150 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 number of hospitals in countries like Canada and Mexico.

Data showing the vaccination rates of healthcare workers in Oregon Hospitals for the 2016 - 2017 flu season

Abstract

Nigeria has one of the largest stocks of human resources for health (HRH) in Africa. However, great disparities in health status and access to health care exist among the six geo-political zones, and between rural and urban areas. This assessment measures the size, skills mix, distribution, and growth rate of HRH in the public health sector in Nigeria. The assessment also quantifies the increase in HRH requirements in the public health sector necessary for reaching key PEPFAR targets and the health Millennium Development Goals. The findings are based on a survey conducted in April-May 2006 in 290 public health facilities representing all levels of care (primary, secondary, and tertiary). The study data enabled us to estimate the total number of doctors, nurses, midwives, lab and pharmacy staff, and community health workers currently employed in the public sector. The distribution of health workers by level of care, and HRH availability in rural and urban areas was also quantified.Staff attrition rates, measuring the number of those leaving the public sector as percent of total staff, were determined among all staff categories. The annual growth in HRH in the public sector from new graduates was also measured.

Geographic coverage

National

Analysis unit

Public Health Facilities

Universe

The survey focused on public health facilities representing all levels of care (primary, secondary, and tertiary).

Kind of data

Sample survey data [ssd]

Sampling procedure

Two-Stage Stratified Random Sample A survey was conducted in 290 public health facilities representing all levels of care (primary, secondary, and tertiary). The facilities were selected using two-stage stratified sampling. First, two states were selected from each of the six geo-political zones in Nigeria, with probability of selection of each state proportional to its population size. In addition, the Federal Capital Territory of Abuja (FCT) was added to the two states selected in the North Central zone. The selected states in each zone cover between 32 and 50 percent of the zone's population and in total, the 13 states included in the sample account for 40 percent of Nigeria's population. In the second stage of sampling, a sample of facilities at each level of care was chosen in each selected state. All Federal Medical Centers and teaching hospitals in the sampled states were selected with certainty. All other facilities were selected using systematic random sampling. A higher proportion of hospitals, compared to smaller facilities, were included in the sample in order to increase the number of facilities that have most of the data being collected. Primary care facilities include health centers, health clinics, maternities, and dispensaries. There was non-response from two facilities selected with certainty.

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection instrument In each of the selected facilities, a questionnaire was administered to eligible facility managers and health staff. These were staff in charge of the services included in the survey – for example, information regarding immunizations in a hospital was obtained from the nurse in charge at the hospital’s child health clinic. The questionnaire collected information on: 1. Number of staff employed in 2004, 2005, and at the time of survey (April 2006); 2. Number of incoming and outgoing staff in 2005 by reason for leaving or starting work at the facility; 3. Types of services provided at the facility for HIV/AIDS, TB, malaria, maternal and child health, and family planning; 4. Number of patients seen at the facility in the three months preceding the survey for each of these services; 5. Which types of health staff provide each service; 6. Average time spent per patient-visit for each of the services related to the five focus areas.

Cleaning operations

Data from the survey questionnaires was entered electronically using an EpiInfo database, and all data analysis was performed using Stata v.8 software.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

Data showing the vaccination rates of Healthcare Workers in Oregon Dialysis facilities for the 2016 - 2017 flu season

Abstract (en): This study comprises the second round of the physician survey component of the Community Tracking Study (CTS) sponsored by the Robert Wood Johnson Foundation. The CTS is a national study designed to track changes in the American health care system and the effects of the changes on care delivery and on individuals. Central to the design of the CTS is its community focus. Sixty sites (51 metropolitan areas and 9 nonmetropolitan areas) were randomly selected to form the core of the CTS and to be representative of the nation as a whole. As in the first round of the physician survey (COMMUNITY TRACKING STUDY PHYSICIAN SURVEY, 1996-1997: UNITED STATES), the second round was administered to physicians in the 60 CTS sites and to a supplemental national sample of physicians. The survey instrument collected information on physician supply and specialty distribution, practice arrangements and physician ownership of practices, physician time allocation, sources of practice revenue, level and determinants of physician compensation, provision of charity care, career satisfaction, physicians' perceptions of their ability to deliver care, views on care management strategies, and various other aspects of physicians' practice of medicine. In addition, primary care physicians (PCPs) were asked to recommend courses of action in response to some vignettes of clinical presentations for which there was no prescribed method of treatment. Dataset 3, the Site and County Crosswalk Data File, identifies the counties that constitute each CTS site. Dataset 4, the Physician Survey Summary File, contains site-level estimates and standard errors of the estimates for selected physician characteristics, e.g., the percentage of physicians who were foreign medical school graduates, the mean age of physicians, and the mean percentage of patient care practice revenue from Medicaid. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Physicians practicing in the 48 states of the contiguous United States who provided direct patient care for at least 20 hours per week and were not federal employees, specialists in fields in which the primary focus was not direct patient care, or graduates of foreign medical schools who were only temporarily licensed to practice in the United States. Residents, interns, and fellows were excluded. The CTS sites were selected using stratified sampling with probability proportional to population size. The supplemental sample, which was selected using stratified random sampling, was included in the survey in order to increase the precision of national estimates. The sample frame was developed by combining lists of physicians from the American Medical Association and the American Osteopathic Association. For both the site and supplemental samples, the sampling design involved randomly selecting physicians who were part of the Round 1 survey and physicians who were not covered by Round 1. Thus, about 58 percent of the Round 2 respondents also participated in Round 1. PCPs were oversampled in the site sample. 2009-02-02 Stata setups produced by ICPSR were added to the collection.2004-02-24 The user guide for the restricted-use version of the main data file has been revised. As noted on the "What's New" page in the guide, there are minor changes to the text related to the recommended SUDAAN parameters.2002-03-01 The user guides for the public- and restricted-use versions of the main data file have been revised. A discussion was added about how to pool data from Round 1 and Round 2 in order to increase sample size. In addition, the data definition statements have been enhanced. Funding insitution(s): Robert Wood Johnson Foundation (29275). computer-assisted telephone interview (CATI) For additional information about this study see the Web site of the Center for Studying Health System Change.

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 0.1 beds (-3.7 percent). After the eighth consecutive decreasing year, the number of available beds per 1,000 people is estimated to reach 2.63 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 150 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.

This indicator provides information about medically underserved areas and/or populations (MUA/Ps), as determined by the federal Health Resources and Services Administration (HRSA). Each designated area includes multiple census tracts.State Primary Care Offices submit applications to HRSA to designate specific areas within counties as MUA/Ps. The MUA/P designation is made using the Index of Medical Underservice (IMU) score, which includes four components: provider per 1,000 population, percent of population under poverty, percent of population ages 65 years and older, and infant mortality rate. The IMU scores ranges from 0-100. Lower scores indicate higher needs. An IMU score of 62 or below qualifies for designation as an MUA/P. Note: if an area is not designated as an MUA/P, it does not mean it is not underserved, only that an application has not been filed for the area and that official designation has not been given.The MUAs within Los Angeles County consist of groups of urban census tracts (namely service areas). MUPs have a shortage of primary care health services for a specific population within a geographic area. These populations may face economic, cultural, or language barriers to health care, such as: people experiencing homelessness, people who are low-income, people who are eligible for Medicaid, Native Americans, or migrant farm workers. All the MUPs that have been designated within Los Angeles County are among low-income populations of selected census tract groups. Due to the nature of the designation process, a census tract may be designated as both an MUA and an MUP and as multiple MUAs. MUA/P designations help establish health maintenance organizations or community health centers in high-need areas.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Data showing the vaccination rates of healthcare workers in Oregon Ambulatory Surgery Centers for the 2016 - 2017 flu season

All data is provisional and subject to change The Centers for Medicare & Medicaid Services (CMS) Count Positive Rate is updated weekly - includes : Week ending, Tests in prior 14 days, 14 day test rate per 100K, Percent Positivity in prior 14 days, Percent Positive in prior 7 days, and Notes Source: Testing data: HHS Unified Testing Dataset; Population data: 2019 Census Rates of county positivity COVID-19 Viral (RT-PCR) Laboratory 14-Day Test Positivity Rates, by US County Documentation: The data presented represent viral COVID-19 laboratory diagnostic and screening test (reverse transcription polymerase chain reaction, RT-PCR) results and exclude antibody and antigen tests. COVID-19 Electronic Lab Reporting (CELR) state health department-reported data are used to describe county-level viral COVID-19 laboratory test (RT-PCR) result totals when information is available on patients’ county of residence or healthcare providers’ practice location. HHS Protect laboratory data (provided directly to Federal Government from public health labs, hospital labs, and commercial labs) are used otherwise. Some states did not report on certain days, which may affect the total number of tests resulted and positivity rate values. Total viral (RT-PCR) laboratory tests are the number of tests performed, not the number of individuals tested. Viral (RT-PCR) laboratory test positivity rate is the number of positive tests divided by the total number of tests performed and resulted. Resulted tests are assigned to a timeframe based on this hierarchy of test-related dates: 1. test date; 2. result date; 3. specimen received date; 4. specimen collection date. Resulted tests are assigned to a county based on a hierarchy of test-related locations: 1. patient residency; 2. provider facility location; 3. ordering facility location; 4. performing organization location. Special Note: During the week of Sept 14, 2020, the following updates were made to the county percent test positivity characterization methodology. In order to use a greater amount of data to calculate percent test positivity and improve the stability of values, the indicator was expanded to include 14 days of data instead of 7 days. Further, because there are instances where counties with high test positivity rates may reflect low testing levels rather than high levels of viral transmission, additional criteria were added to re-assign counties with low testing volume to lower nursing home staff testing tiers (i.e., communities with low levels of testing and high test positivity (>10%) are reassigned to either yellow or green testing tiers). Nursing homes may set their testing frequency based on the color-coded reassigned positivity classification. Counties that are classified as gray have not submitted testing data for this time period. Please refer to the state COVID-19 website for data on gray counties.: During the week of Sept 14, 2020, the following updates were made to the county percent test positivity characterization methodology. In order to use a greater amount of data to calculate percent test positivity and improve the stability of values, the indicator was expanded to include 14 days of data instead of 7 days. Further, because there are instances where counties with high test positivity rates may reflect low testing levels rather than high levels of viral transmission, additional criteria were added to re-assign counties with low testing volume to lower nursing home staff testing tiers (i.e., communities with low levels of testing and high test positivity (>10%) are reassigned to either yellow or green testing tiers). Nursing homes may set their testing frequency based on the color-coded reassigned positivity classification. Counties that are classified as gray have not submitted testing data for this time period. Please refer to the state COVID-19 website for data on gray counties. Background on Virus: About the Disease: Coronavirus disease 2019 (COVID-19) is a respiratory illness that is spreading from person to person in parts of the United States. The risk of infection with COVID-19 is higher for people who are close contacts of someone known to have COVID-19, for example healthcare workers, or household members. Other people at higher risk for infection are those who live in or have recently been in an area with an ongoing spread of COVID-19. The virus spreads mainly between people who are in close contact with one another (within about 6 feet) through respiratory droplets produced when an infected person coughs or sneezes. It also may be possible that a person can get COVID-19 by touching a surface or object that has the virus on it and then touching their own mouth, nose, or possibly their eyes, but this is not thought to be the main way the virus spreads.

For more information about Coronavirus (COVID-19) in the City of Austin visit our main page at https://austintexas.gov/covid19. This data will be updated daily. To view the case count for the State of Texas, visit Texas DSHS https://www.dshs.state.tx.us/coronavirus/. Please call 3-1-1 (512-974-2000) with questionsLTCF Dashboard (Desktop Version) | LTCF Dashboard (Mobile Version) Download Public Data from the COVID19 Hub: https://atc-covid19data-austin.hub.arcgis.com/

The number of hospital beds in the United States was forecast to continuously increase between 2024 and 2029 by in total 16.6 thousand beds (+1.75 percent). After the fifteenth consecutive increasing year, the number of hospital beds is estimated to reach 967.9 thousand beds and therefore a new peak in 2029. Notably, the number of hospital beds of was continuously increasing over the past years.Depicted is the estimated total number of hospital beds in the country or region at hand.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 150 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 number of hospital beds in countries like Mexico and Canada.

Department of State Hospitals (DSH)-wide Violence Data Annual Rates of Assault from 2010-2020 for the following groups: Patient Assault (A2), Staff Assault (A4).

A2 - Patient physical assaults are committed by another patient. Formally defined as “Aggressive Act to Another Patient - Physical: Hitting, pushing, kicking or similar acts directed against another individual to cause potential or actual injury.” This does not include verbal assault, which is coded as “A1.”

A4 – Staff physical assaults are committed by a patient. Formally defined as “Aggressive Act to Staff - Physical: Hitting, pushing, kicking, or similar acts directed against a staff person that could cause potential or actual injury.” This does not include verbal assault, which is coded as “A3.”

Please Note:

1.Please note that it is an update to the previously published dataset with additional datasets.

2.Violence Rates value (in previous publication) can be calculated as a number per 1000 Patient Days. This number is easily interpreted and enables more accurate comparisons across time.

3.Prior to January 1, 2016 DSH-Atascadero coded an assault as Patient on Staff (A4) only when physical contact was made between patient and staff. All other Department of State Hospitals (DSH)- facilities code an assault as Patient on Staff (A4) either when physical contact was made or when physical contact was attempted. On January 1, 2016 Department of State Hospitals (DSH)--Atascadero began coding assaults in the same manner as all other Department of State Hospitals (DSH)- facilities.

4.Prior to January 1, 2016 Violence incidents were not captured specifically as Physical Contact made or Physical Contact Attempted.

There are a range of problems associated with job on call centres. A range of problems associated with the job have become apparent, with time pressure, performance monitoring via computer, monitoring of phone calls, ergonomic deficiencies and musculoskeletal problems amongst the problems reported. An earlier study of a call centre in Sweden found inadequate working conditions and signs of ill health amongst a high percentage of the population in their 20s who had only been working for 2-3 years. The situation was worse there than amongst older employees in other industries with computer-intensive jobs. Inadequate working conditions and the high incidence of medical complaints amongst young employees may mean that call centres are failing to provide the sustainable work opportunities that many are counting on, e.g. in rural areas. Scientific studies of call centres are few and the state of knowledge of working conditions and health there is deficient. A cross-sectional study into working and health conditions at call centres in Sweden was conducted with the aim of contributing to a sustainable development of call centre work. The project was conducted in partnership with the Ergonomics Programme at the National Institute for Working Life, Occupational Medicine North, Sundsvall Hospital, and the Institute for Psychosocial Factors and Health at the Karolinska Institute. Data were collated at social, corporate and individual level from 15-20 larger call centres with different operating spheres, ownership structures and geographical location. Data were collected on work organisation, content and times, physical and psychosocial working conditions, and health and well-being with the aid of questionnaires, observations, measurements, medical examinations and company registers (including the computerised monitoring system). A total of approximately 1,500 people were included in the study. Models were tried out in order to evaluate the effects of ill health and inadequacies in working conditions at the company's expense.

In 2021, Medicare payments contributed to 18.9 percent of all hospital net revenue, while private/self/other payments accounted for almost 69 percent of hospital revenue. Medicaid charges made up the rest - 13.5 percent.