Nursing homes with residents positive for COVID-19 from 4/22/2020 to 6/19/2020. Starting in July 2020, this dataset will no longer be updated and will be replaced by the CMS COVID-19 Nursing Home Dataset, available at the following link: https://data.ct.gov/Health-and-Human-Services/CMS-COVID-19-Nursing-Home-Dataset/w8wc-65i5. Methods: 1) Laboratory-confirmed case counts are based upon data reported via the FLIS web portal. Nursing homes were asked to provide cumulative totals of residents with laboratory confirmed covid. This includes residents currently in-house, in the hospital, or who are deceased. Residents were excluded if they tested positive prior to initial admission to the nursing home. 2) The cumulative number of deaths among nursing home residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable). Limitations: 1) As of the week of 5/10/20, Point Prevalence Survey testing is being offered to all asymptomatic nursing home residents to inform infection prevention efforts. Point prevalence surveys will be conducted over a period of several weeks. Some nursing homes had adequate testing resources available to conduct surveys prior to this date. Differences in survey timing will impact the number of positive results that a nursing home reports. 2) Cumulative totals of residents testing positive are being collected rather than individual resident data. Thus we cannot verify the counts, de-duplicate, and/or verify whether there is a record of a positive lab test. This may result in either under- or over-counting. 3) The number of COVID-19 positive residents and the number of confirmed deaths among residents are tabulated from different data sources. Due to the timing of availability of test results for deceased residents, it is not appropriate to calculate the percent of cases who died due to COVID-19 at any particular facility based upon this data. 4) The count of deaths reported for 4/14 are not included in this dataset, as they were not broken out by laboratory-confirmed or probable. They can be viewed in the DPH Report here: https://portal.ct.gov/-/media/Coronavirus/CTDPHCOVID19summary4162020.pdf?la=en

As of September 27, 2020, there were around 125 COVID-19 deaths per 1,000 residents in nursing homes in Massachusetts. This statistic illustrates the rate of COVID-19 deaths in nursing homes in the United States as of September 27, 2020, by state.

Archived as of 3/16/22: Due to changing LTC reporting requirements, this dataset will no longer be updated after 3/16/2022. For data on Indiana's long term case facilities, please visit: https://data.cms.gov/covid-19/covid-19-nursing-home-data Number of verified COVID-19 related cases and deaths from Long-Term Care Facilities for residents and staff members. Historical case data are aggregated at the facility-level and are reported from 3/1/2020 and updated weekly. Facilities that are in non-compliance with historical case data reporting needs are denoted by "Facility has not submitted data" in the "Facility Submission Status" column. Facilities listed as non-compliant will be updated as necessary with any new submissions of their historical cases to the Indiana State Department of Health. Cases and deaths in this file include records reported by Long-Term Care Facilities and have been verified by ISDH through a positive COVID-19 diagnostic lab result. This data file was constructed to aggregate verified cases and deaths for LTC staff and residents at the facility level. Because residents and staff may be moved between facilities, calculating total verified counts from this data file is not advised. Users should refer to the ISDH LTC dashboard for total counts.

Abstract The article addresses the most basic nuances and key issues involved in the high mortality of doctors, nurses, technicians and nursing assistants, as a result of COVID-19 in Brazil. This is a study based on data from the Federal Councils of Medicine and Nursing (CFM and Cofen, respectively) and the study on the death inventory of the Oswaldo Cruz Foundation (Fiocruz), and aims to understand and analyze this reality in the light of the sociology of professions. The work makes a relevant and unprecedented contribution to the understanding of the past, present and future of working class segments that work at the bedside, on the front line, providing direct care to patients.

A List of UK Health Workers Who Have Died from COVID-19

Made machine-readable by hand from data from the UK newspaper "The Guardian", in this article: "Doctors, nurses, porters, volunteers: the UK health workers who have died from Covid-19" https://www.theguardian.com/world/2020/apr/16/doctors-nurses-porters-volunteers-the-uk-health-workers-who-have-died-from-covid-19

The Guardian is continuing to update the list day-by-day, as the COVID-19 pandemic continues. I do not plan to update this dataset, assuming, since the data collection biases are unknown, that nobody else will find it very interesting. I am not a copyright lawyer and do not know if this data is protected copyright, and if so, in which parts of the world.

Caveat: Creating this dataset from a newspaper article required a lot of hand work. I've done my best, but there may be mistakes.

Columns: Name age institution city: I have filled this in myself; I am ignorant of UK geography and there may well be mistakes date_of_death possible_ppe_issue: mostly blank, but I have filled in "yes" where the article mentions a person who had doubts about the adequacy of PPE (personal protective equipment) MED_SPEC: I have attempted to fill in a medical specialty from the values used on the Eurostat web site for Physicians by Medical Specialty" and "Nursing and caring professionals" tables. The idea is to be able to calculate a fraction of affected individuals by specialty.

As of March 7, 2021, there had been a total number of 641,608 confirmed COVID-19 cases and 130,296 deaths among nursing home residents in the United States. The number of COVID-19 cases among nursing home staff in the United States reached 130,296 cases, as of March 7, 2021.

The purpose of this study is to describe, using bibliometric techniques and methods, the research published in Scopus-indexed nursing journals on various aspects of the death and dying process during the COVID-19 pandemic period.

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

As of June 2021, 524 healthcare workers in Poland have died from coronavirus infection. The highest number of COVID-19 infections has been reported among nurses, over 72.4 thousand.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

IntroductionItaly is one of the high-income countries hit hardest by Covid-19. During the first months of the pandemic, Italian healthcare workers were praised by media and the public for their efforts to face the emergency, although with limited knowledge and resources. However, healthcare workers soon had to face new challenges at a time when the national health system was working hard to recover. This study focuses on this difficult period to assess the impact of the COVID-19 pandemic on the mental health of Italian healthcare workers.Materials and MethodsHealthcare workers from all Italian regions [n = 5,502] completed an online questionnaire during the reopening phase after the first wave lockdown. We assessed a set of individual-level factors (e.g., stigma and violence against HCWs) and a set of workplace-level factors (e.g., trust in the workplace capacity to handle COVID-19) that were especially relevant in this context. The primary outcomes assessed were score ≥15 on the Patient Health Questionnaire-9 and score ≥4 on the General Health Questionnaire-12, indicators of clinically significant depressive symptoms and psychological distress, respectively. Logistic regression analyses were performed on depressive symptoms and psychological distress for each individual- and workplace-level factor adjusting for gender, age, and profession.ResultsClinically significant depressive symptoms were observed in 7.5% and psychological distress in 37.9% of HCWs. 30.5% of healthcare workers reported having felt stigmatized or discriminated, while 5.7% reported having experienced violence. Feeling stigmatized or discriminated and experiencing violence due to being a healthcare worker were strongly associated with clinically significant depressive symptoms [OR 2.98, 95%CI 2.36–3.77 and OR 4.72 95%CI 3.41–6.54] and psychological distress [OR 2.30, 95%CI 2.01–2.64 and OR 2.85 95%CI 2.16–3.75]. Numerous workplace-level factors, e.g., trust in the workplace capacity to handle COVID-19 [OR 2.43, 95%CI 1.92–3.07] and close contact with a co-worker who died of COVID-19 [OR 2.05, 95%CI 1.56–2.70] were also associated with clinically significant depressive symptoms. Similar results were found for psychological distress.ConclusionsOur study emphasizes the need to address discrimination and violence against healthcare professionals and improve healthcare work environments to strengthen the national health system's capacity to manage future emergencies.

ObjectivesTo assess excess mortality among older adults institutionalized in nursing homes within the successive waves of the COVID-19 pandemic in Catalonia (north-east Spain).DesignObservational, retrospective analysis of population-based central healthcare registries.Setting and participantsIndividuals aged >65 years admitted in any nursing home in Catalonia between January 1, 2015, and April 1, 2022.MethodsDeaths reported during the pre-pandemic period (2015–2019) were used to build a reference model for mortality trends (a Poisson model, due to the event counting nature of the variable “mortality”), adjusted by age, sex, and clinical complexity, defined according to the adjusted morbidity groups. Excess mortality was estimated by comparing the observed and model-based expected mortality during the pandemic period (2020–2022). Besides the crude excess mortality, we estimated the standardized mortality rate (SMR) as the ratio of weekly deaths’ number observed to the expected deaths’ number over the same period.ResultsThe analysis included 175,497 older adults institutionalized (mean 262 days, SD 132), yielding a total of 394,134 person-years: 288,948 person-years within the reference period (2015–2019) and 105,186 within the COVID-19 period (2020–2022). Excess number of deaths in this population was 5,403 in the first wave and 1,313, 111, −182, 498, and 329 in the successive waves. The first wave on March 2020 showed the highest SMR (2.50; 95% CI 2.45–2.56). The corresponding SMR for the 2nd to 6th waves were 1.31 (1.27–1.34), 1.03 (1.00–1.07), 0.93 (0.89–0.97), 1.13 (1.10–1.17), and 1.07 (1.04–1.09). The number of excess deaths following the first wave ranged from 1,313 (2nd wave) to −182 (4th wave). Excess mortality showed similar trends for men and women. Older adults and those with higher comorbidity burden account for higher number of deaths, albeit lower SMRs.ConclusionExcess mortality analysis suggest a higher death toll of the COVID-19 crisis in nursing homes than in other settings. Although crude mortality rates were far higher among older adults and those at higher health risk, younger individuals showed persistently higher SMR, indicating an important death toll of the COVID-19 in these groups of people.

Note: Data elements were retired from HERDS on 10/6/23 and this dataset was archived.

This dataset includes the cumulative number and percent of healthcare facility-reported fatalities for patients with lab-confirmed COVID-19 disease by reporting date and age group. This dataset does not include fatalities related to COVID-19 disease that did not occur at a hospital, nursing home, or adult care facility. The primary goal of publishing this dataset is to provide users with information about healthcare facility fatalities among patients with lab-confirmed COVID-19 disease.

The information in this dataset is also updated daily on the NYS COVID-19 Tracker at https://www.ny.gov/covid-19tracker.

The data source for this dataset is the daily COVID-19 survey through the New York State Department of Health (NYSDOH) Health Electronic Response Data System (HERDS). Hospitals, nursing homes, and adult care facilities are required to complete this survey daily. The information from the survey is used for statewide surveillance, planning, resource allocation, and emergency response activities. Hospitals began reporting for the HERDS COVID-19 survey in March 2020, while Nursing Homes and Adult Care Facilities began reporting in April 2020. It is important to note that fatalities related to COVID-19 disease that occurred prior to the first publication dates are also included.

The fatality numbers in this dataset are calculated by assigning age groups to each patient based on the patient age, then summing the patient fatalities within each age group, as of each reporting date. The statewide total fatality numbers are calculated by summing the number of fatalities across all age groups, by reporting date. The fatality percentages are calculated by dividing the number of fatalities in each age group by the statewide total number of fatalities, by reporting date. The fatality numbers represent the cumulative number of fatalities that have been reported as of each reporting date.

Estimating nursing home COVID-19 deaths by U.S. Health and Human Service (HHS) Regions, 6-July to 26-July 2020: Zero-inflated negative binomial models1.

For English, see below As of 1 January 2023, RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home . File description: - This file contains the following numbers: (number of newly reported) positively tested individuals aged 70 and older living at home*, by safety region, per date of the positive test result. - (number of newly reported) deceased individuals aged 70 and older living at home who tested positive*, by safety region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. Reports from 01-07-2020 are regarded as individuals aged 70 and older living at home if, according to the information known to the GGD, they: • Do not live in an institution AND • Are aged 70 or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as individuals aged 70 and older living at home if they: • Can be linked to a known location of a disability care institution or nursing home on the basis of their 6-digit zip code OR • Have 'Disabled care institution' or 'Nursing home' as the location of the contamination mentioned. OR • Based on the content of free text fields, can be linked to a disability care institution or nursing home. The file is structured as follows: A set of records per date of with for each date: • A record for each security region (including 'Unknown') in the Netherlands, even if there are no reports for the relevant security region. The numbers are then 0 (zero). • Security region is unknown when a record cannot be assigned to one unique security region. A date 01-01-1900 is also included in this file for statistics whose associated date is unknown. The following describes how the variables are defined. Description of the variables: Version: Version number of the dataset. This version number is adjusted (+1) when the content of the dataset is structurally changed (so not the daily update or a correction at record level. The corresponding metadata in RIVMdata (https://data.rivm.nl) is also changed. Version 2 update (January 25, 2022): • An updated list of known nursing or care home locations and private residential care centers was received from the umbrella organization Patient Federation of the Netherlands on 03-12-2021. taken to determine whether individuals live in an institution Version 3 update (February 8, 2022) • From February 8, 2022, positive SARS-CoV-2 test results will be reported directly from CoronIT to RIVM. such as Testing for Access) and healthcare institutions (such as hospitals, nursing homes and general practitioners) that enter their positive SARS-CoV-2 test results via the Reporting Portal of GGD GHOR directly to RIVM. Reports that are part of the source and contact investigation sample and positive SARS-CoV-2 test results from healthcare institutions that are reported to the GGD via healthcare email are reported to RIVM via HPZone. From 8 February, the date of the positive test result is used and no longer the date of notification to the GGD. Version 4 update (March 24, 2022): • In version 4 of this dataset, records have been compiled according to the municipality reclassification of March 24, 2022. See description of the variable security_region_code for more information. Version 5 update (August 2, 2022): • The classification of persons aged 70 years and parents living independently has not been applied to reports that have only been received by RIVM since February 8, 2022 via an alternative reporting route. From 8 February to 1 August 2022, the number of reports from independently living persons aged 70 and parents was therefore underestimated by approximately 14%. As of August 2, 2022, this format will be retroactively updated. Version 6 update (September 1, 2022): - From September 1, 2022, the data will no longer be updated every working day, but on Tuesdays and Fridays. The data is retroactively updated on these days for the other days. - As of September 1, 2022, this dataset is split into two parts. The first part contains the dates from the start of the pandemic to October 3, 2021 (week 39) and contains "tm" in the file name. This data will no longer be updated. The second part contains the data from October 4, 2021 (week 40) and is updated every Tuesday and Friday. Date_of_report: Date and time on which the data file was created by RIVM. Date_of_statistic_reported: The date used for reporting the 70plus statistic living at home. This can be different for each reported statistic, namely: • For [Total_cases_reported] this is the date of the positive test result. • For [Total_deceased_reported] this is the date on which the patients died. Security_region_code: Security region code. The code of the security region based on the patient's place of residence. If the place of residence is not known, the safety region is based on the GGD that submitted the report, except for the Central and West Brabant and Brabant-Noord safety regions, since the GGD and safety region are not comparable for these regions. See also: https://www.cbs.nl/nl-nl/figures/detail/84721ENG?q=Veiliteiten From March 24, 2022, this file has been compiled according to the municipality classification of March 24, 2022. The municipality of Weesp has been merged into the municipality of Amsterdam . With this division, the Gooi- en Vechtstreek safety region has become smaller and the Amsterdam-Amstelland safety region larger; GGD Amsterdam has become larger and GGD Gooi- en Vechtstreek has become smaller (Municipal division on 1 January 2022 (cbs.nl). Security_region_name: Security region name. Security region name is based on the Security Region Code. See also: https://www.rijksoverheid.nl /topics/safety-regions-and-crisis-management/safety-regions Total_cases_reported: The number of new COVID-19 infected over-70s living at home reported to the GGD on [Date_of_statistic_reported].The actual number of COVID-19 infected over-70s living at home is higher than the number of reports in surveillance, because not everyone with a possible infection is tested. In addition, it is not known for every report whether this concerns a person over 70 living at home. Date_of_statistic_reported] The actual number of deceased people over 70 living at home who died of COVID-19 is higher than the number of reports in the surveillance, because not all deceased patients are tested and deaths are not legally reportable. Moreover, it is not known for every report whether this concerns a person over 70 living at home. Corrections made to reports in the OSIRIS source system can also lead to corrections in this database. In that case, numbers published by RIVM in the past may deviate from the numbers in this database. This file therefore always contains the numbers based on the most up-to-date data in the OSIRIS source system. The CSV file uses a semicolon as a separator. There are no empty lines in the file. Below are the column names and the types of values ​​in the CSV file: • Version: Consisting of a single whole number (integer). Is always filled for each row. Example: 2. • Date_of_report: Written in format YYYY-MM-DD HH:MM. Is always filled for each row. Example: 2020-10-16 10:00 AM. • Date_of_statistic_reported: Written in format YYYY-MM-DD. Is always filled for each row. Example: 2020-10-09. • Security_region_code: Consisting of 'VR' followed by two digits. Can also be empty if the region is unknown. Example: VR01. • Security_region_name: Consisting of a character string. Is always filled for each row. Example: Central and West Brabant. • Total_cases_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 12. • Total_deceased_reported: Consisting of only whole numbers (integer). Is always filled for each row. Example: 8. ---------------------------------------------- ---------------------------------- Covid-19 statistics for persons aged 70 and older living outside an institution, by security region and date As of 1 January 2023, the RIVM will no longer collect additional information. As a result, from January 1, 2023, we will no longer report data on infections among people over 70 living at home. File description: This file contains the following numbers: - Number of newly reported persons aged 70 and older living at home who tested positive*, by security region, by date of the positive test result. - Number of newly reported deceased persons aged 70 and older living at home who tested positive*, by security region, by date on which the patient died. The numbers concern COVID-19 reports since the registration of the (residential) institution in OSIRIS with effect from questionnaire 5 (01-07-2020). * For reports from 01-07-2020, it is recorded whether the patient lives in an institution. For reports from 01-07-2020 persons aged 70 and older are considered to be living at home if, according to the information known to the PHS, they: • were not living in an institution AND • Are aged 70 years or older AND • The person is not employed and is not a healthcare worker Persons whose residential facility/institution is not listed can still be excluded as being an persons aged 70 and older living at home if they: • Based on their 6-digit zip code, can be linked to a known location of a care institution for the disabled or a nursing home OR • Have 'Disability care institution' or 'Nursing home' as the stated location of transmission. OR • Based on the content of free text fields, links can be made to a care institution for the disabled or a nursing home. The file is structured as follows: A set of records by date, with for

Updated weekly on Thursdays Older adults and people with disabilities who live in long term care facilities are at high risk for COVID-19 illness and death. The data below describes the impacts of COVID-19 on the residents and staff of Long Term Care Facilities licensed by the State Department of Social and Health Services (DSHS), including Skilled Nursing Facilities (nursing homes); Adult Family Homes and Assisted Living Facilities.

Cases and deaths are also occurring in other forms of senior housing not licensed by DSHS, including subsidized housing for people age 50+, Permanent Supportive Housing, and naturally occurring retirement communities (NORCs) and among people with disabilities living in Supportive Living Facilities (also licensed by DSHS).

Morbidity and mortality attributable to COVID-19 is devastating global health systems and economies. Bacillus Calmette Guérin (BCG) vaccination has been in use for many decades to prevent severe forms of tuberculosis in children. Studies have also shown a combination of improved long-term innate or trained immunity (through epigenetic reprogramming of myeloid cells) and adaptive responses after BCG vaccination, which leads to non-specific protective effects in adults. Observational studies have shown that countries with routine BCG vaccination programs have significantly less reported cases and deaths of COVID-19, but such studies are prone to significant bias and need confirmation. To date, in the absence of direct evidence, WHO does not recommend BCG for the prevention of COVID-19. This project aims to investigate in a timely manner whether and why BCG-revaccination can reduce infection rate and/or disease severity in health care workers during the SARS-CoV-2 outbreak in South Africa. These objectives will be achieved with a blinded, randomised controlled trial of BCG revaccination versus placebo in exposed front-line staff in hospitals in Cape Town. Observations will include the rate of infection with COVID-19 as well as the occurrence of mild, moderate or severe ambulatory respiratory tract infections, hospitalisation, need for oxygen, mechanical ventilation or death. HIV-positive individuals will be excluded. Safety of the vaccines will be monitored. A secondary endpoint is the occurrence of latent or active tuberculosis. Initial sample size and follow-up duration is at least 500 workers and 52 weeks. Statistical analysis will be model-based and ongoing in real time with frequent interim analyses and optional increases of both sample size or observation time, based on the unforeseeable trajectory of the South African COVID-19 epidemic, available funds and recommendations of an independent data and safety monitoring board. The study will be supported by a novel 3D lung organoid model of SARS-CoV-2 infection system that can mimic the cascade of immunological events after SARS-CoV-2 infection to determine and analyse the contribution of cellular components to the impact of BCG revaccination in this study. Given the immediate threat of the SARS-CoV-2 epidemic the trial has been designed as a pragmatic study with highly feasible endpoints that can be continuously measured. This allows for the most rapid identification of a beneficial outcome that would lead to immediate dissemination of the results, vaccination of the control group and outreach to the health authorities to consider BCG vaccination for all qualifying health care workers. Methods This dataset was collected in a clinical randomised control trial under the TASK008-BCG CORONA protocol. The trial was conducted in South Africa. This trial was registered with ClinicalTrials.gov, NCT04379336.

The dataset consists of quantitative data derived mainly from international datasets (ILO, WHO), supplemented by data from national datasets and modelled data to complete missing values. It shows the statistical data we collated and used to calculate estimates of Covid-19 deaths among migrant health care workers and includes details on how missing information was imputed. It includes spreadsheet estimates for India, Nigeria, Mexico, and the UK for excess and reported Covid-19 deaths amongst foreign-born workers and for all workers in the human health and social work sector and in three specific health occupations: doctors, nurses, and midwives. For each group the spreadsheets provide a basic estimate and an age-sex standardised estimate.

This project aims to give proper attention to the place of migrant workers in health care systems during the Covid-19 pandemic. Migrant workers are of substantial and growing significance in many countries' health and care systems and are key to realising the global goal of universal health care, so it is vital that we understand much better how Covid-19 is impacting on them.

The project's overarching research questions are, in the relation to Covid-19, what risks do migrant health care workers experience, what are the pressures on resilient and sustainable health care workforces, and how are stakeholders responding to these risks and pressures?

We develop a research method to estimate Covid-19 migrant health care worker mortality rates and trial the method, undertaking statistical analysis and modelling using quantitative data drawn from WHO and OECD data and other demographic and bio-statistical data as available.

In addition to strengthening the methodological techniques and empirical evidence base on the risks of Covid-19 infection and death among migrant health care workers our project also tracks, through documentary analysis, collective responses to such risks and challenges to sustainable health workforces for universal health coverage.

This project is attuned to the urgent need for high quality data and for 'real world' solutions-focused Covid-19 research forged from collaboration. We are focused on the immediate application of proof-of concept findings to a rapidly-evolving global health crisis.

Effective April 1, 2022, the Cook County Medical Examiner’s Office no longer takes jurisdiction over hospital, nursing home or hospice COVID-19 deaths unless there is another factor that falls within the Office’s jurisdiction. Data continues to be collected for COVID-19 deaths in Cook County on the Illinois Dept. of Public Health COVID-19 dashboard (https://dph.illinois.gov/covid19/data.html).

This filtered view contains information about COVID-19 related deaths that occurred in Cook County that were under the Medical Examiner’s jurisdiction.This view was created by looking for "covid" in any of these fields: Primary Cause, Primary Cause Line A, Primary Cause Line B, Primary Cause Line C, or Secondary Cause.

For more information see: https://datacatalog.cookcountyil.gov/stories/s/ttk4-trbu

Not all deaths that occur in Cook County are reported to the Medical Examiner or fall under the jurisdiction of the Medical Examiner. The Medical Examiner’s Office determines cause and manner of death for those cases that fall under its jurisdiction. Cause of death describes the reason the person died. This dataset includes information from deaths starting in August 2014 to the present, with information updated daily.

Changes: December 16, 2022: The Cook County Commissioner District field now reflects the boundaries that went into effect December 5, 2022.

For English, see below

Per 1 juli 2023 is COVID-19 geen meldingsplichtige ziekte meer. Daarom wordt de data vanaf 11 juli 2023 niet meer bijgewerkt.

Beschrijving bestand: - Dit bestand bevat de volgende aantallen: (aantal nieuw gemelde) positief geteste bewoners van verpleeghuizen*, naar veiligheidsregio, per datum van de positieve testuitslag. - (aantal nieuw gemelde) positief geteste overleden bewoners van verpleeghuizen*, naar veiligheidsregio, per datum waarop patiënt overleden is. - (aantal) (op)nieuw besmette verpleeghuislocaties**, naar veiligheidsregio, per datum waarop de gegevens zijn gepubliceerd door het RIVM. - (totaal) besmette verpleeghuislocaties**, naar veiligheidsregio, per datum waarop de gegevens zijn gepubliceerd door het RIVM. De aantallen betreffen Covid-19 meldingen sinds de eerste melding in Nederland (27-02-2020). * Voor meldingen voor 01-09-2020 werd een patiënt aangemerkt als bewoner van een verpleeghuis indien deze volgens de gegevens van OSIRIS: • Op basis van zijn 6 cijferige postcode gekoppeld konn worden aan een bekende locatie van een verpleeg- of verzorgingshuis of particulier woonzorgcentrum óf • ‘Verpleeghuis’ als locatie van de besmetting had óf • Op basis van de inhoud van vrije tekstvelden gelinkt konden worden aan een verpleeghuis Bovendien moest gelden dat deze patiënt: • Op het moment van melding 70 jaar of ouder was én • Geen gezondheidsmedewerker was én • Geen beroep had. Bij meldingen vanaf 01-09-2020 tot en met 31 december 2022 werd ook dppr de GGD'en geregistreerd of de patiënt woonachtig was in een verpleeghuisinstelling of woonzorggroep voor ouderen. Meldingen vanaf 01-09-2020 tot en met 31 december 2022 werden aangemerkt als bewoner van een verpleeghuis indien deze volgens de gegevens bekend bij de GGD: • Woonachtig was in een verpleeg- of verzorgingshuis of woonzorgcentrum voor ouderen • Indien de woonsituatie ontbrak werd de definitie van meldingen voor 01-09-2020 gehanteerd Voor meldingen vanaf 1 januari 2023 wordt een patiënt aangemerkt als bewoner van een verpleeghuis indien deze volgens de gegevens van OSIRIS: • Op basis van zijn 6 cijferige postcode gekoppeld kan worden aan een bekende locatie van een verpleeg- of verzorgingshuis of particulier woonzorgcentrum óf • Op het moment van melding 70 jaar of ouder is De bijgewerkte lijst van bekende verpleeg- of verzorgingshuislocaties en particuliere woonzorgcentra is verkregen van de koepelorganisatie Patiëntenfederatie Nederland op 03-12-2021 en bevat 2546 unieke 6 cijferige postcodes van verpleeg- of verzorgingshuislocaties en particuliere woonzorgcentra in Nederland. ** Voor datums voor 01-09-2020 werd een verpleeghuislocatie op een bepaalde datum als besmet aangemerkt, wanneer: 1. Het een bekende locatie van verpleeg- of verzorgingshuizen of particuliere woonzorgcentra betrof 2. Er tenminste 1 positief geteste persoon was die op basis van zijn 6 cijferige postcode aan deze locatie gekoppeld kon worden en waarvan de eerste ziektedag minder dan 28 dagen geleden was Bij datums vanaf 01-09-2020 tot en met 31 december 2022 werd een verpleeghuislocatie op een bepaalde datum als besmet aangemerkt, wanneer: 1. Het een bekende locatie van verpleeg- of verzorgingshuizen of particuliere woonzorgcentra betrof 2. Er tenminste 1 positief geteste verpleeghuisbewoner* was die op basis van zijn 6 cijferige postcode aan deze locatie gekoppeld kon worden en waarvan de eerste ziektedag minder dan 28 dagen geleden was. Bij datums vanaf 1 januari 2023 wordt een verpleeghuislocatie op een bepaalde datum als besmet aangemerkt, wanneer: 1. Het een bekende locatie van verpleeg- of verzorgingshuizen of particuliere woonzorgcentra betreft 2. Er tenminste 1 positief geteste persoon is die op basis van zijn 6 cijferige postcode aan deze locatie gekoppeld kan worden waarvan de eerste ziektedag minder dan 28 dagen geleden was

Het bestand is als volgt opgebouwd: Een set records per datum van met voor elke datum: • Een record voor elke veiligheidsregio (inclusief “Onbekend”) van Nederland, ook als voor de betreffende veiligheidsregio geen meldingen zijn. De aantallen zijn dan 0 (nul). • Veiligheidsregio is onbekend wanneer een record niet toe te wijzen is aan één unieke veiligheidsregio Er is in dit bestand ook een datum 01-01-1900 opgenomen voor statistieken waarvan de bijbehorende datum onbekend is.

Hieronder wordt beschreven hoe de variabelen zijn gedefinieerd. Beschrijving van de variabelen: Version: Versienummer van de dataset. Dit versienummer wordt aangepast (+1) wanneer de inhoud van de dataset structureel wordt gewijzigd (dus niet de dagelijkse update of een correctie op record niveau. Ook de corresponderende metadata in RIVMdata (https://data.rivm.nl) wordt dan gewijzigd. Versie 2 update (25 januari 2022): • Een bijgewerkte lijst met bekende verpleeg- of verzorgingshuislocaties en particuliere woonzorgcentra van de koepelorganisatie Patiëntenfederatie Nederland is ontvangen op 03-12-2021. Op 25-01-2022 is deze bijgewerkte lijst in gebruik genomen. Versie 3 update (8 februari 2022) • Vanaf 8 februari 2022 worden de positieve SARS-CoV-2 testuitslagen rechtstreeks vanuit CoronIT aan het RIVM gemeld. Ook worden de testuitslagen van andere testaanbieders (zoals Testen voor Toegang) en zorginstellingen (zoals ziekenhuizen, verpleeghuizen en huisartsen) die hun positieve SARS-CoV-2 testuitslagen via het Meldportaal van GGD GHOR invoeren rechtstreeks aan het RIVM gemeld. Meldingen die onderdeel zijn van de bron- en contactonderzoek steekproef en positieve SARS-CoV-2 testuitslagen van zorginstellingen die via zorgmail aan de GGD worden gemeld worden wel via HPZone aan het RIVM gemeld. Vanaf 8 februari wordt de datum van de positieve testuitslag gebruikt en niet meer de datum van melding aan de GGD. Versie 4 update (24 maart 2022): • In versie 4 van deze dataset zijn records samengesteld volgens de gemeente herindeling van 24 maart 2022. Zie beschrijving van de variabele Security_region_code voor meer informatie. Versie 5 update (2 augustus 2022): • De indeling van personen als bewoner van een verpleeghuis of woonzorgcentrum op basis van postcode (bijv. 1234AB) en een leeftijd van boven de 70 jaar is niet toe gepast op meldingen die sinds 8 februari 2022 alleen via een alternatieve meldroute bij het RIVM binnenkwamen. Van 8 februari t/m 1 augustus 2022 is hierdoor het aantal meldingen van bewoners van verpleeghuizen/woonzorgcentra met ongeveer 9% onderschat. Vanaf 2 augustus 2022 wordt deze indeling met terugwerkende kracht bijgewerkt. Versie 6 update (1 september 2022): • Vanaf 1 september 2022 wordt de data niet meer iedere werkdag geüpdatet, maar op dinsdagen en vrijdagen. De data wordt op deze dagen met terugwerkende kracht bijgewerkt voor de andere dagen. • Vanaf 1 september 2022 is deze dataset opgesplitst in twee delen. Het eerste deel bevat de data vanaf het begin van de pandemie tot en met 3 oktober 2021 (week 39) en bevat ‘tm’ in de bestandsnaam. Deze data wordt niet meer geüpdatet. Het tweede deel bevat de data vanaf 4 oktober 2021 (week 40) en wordt iedere dinsdag en vrijdag geüpdatet. Versie 7 update (3 januari 2023): • Per 1 januari 2023 verzamelt het RIVM geen aanvullende informatie meer. Dit heeft als gevolg dat we vanaf 1 januari 2023 geen overlijdens meer rapporteren. Om deze reden wordt de kolom [Total_deceased_reported] niet meer aangevuld. • Doordat er niet meer wordt nagevraagd of iemand een verpleeghuis bewoner is, worden personen alleen gerapporteerd als bewoner van een verpleeghuis wanneer de postcode van deze persoon overeenkomt met de vestiging van een verpleeghuis en de persoon 70 jaar of ouder is. Hierdoor zal het aantal personen dat wordt gekenmerkt als verpleeghuis bewoner lager uit gaan vallen. Dit heeft invloed op de variabelen [Total_cases_reported], [Total_new_Infected_locations_reported] en [Total_Infected_locations_reported]. Versie 8 update (4 april 2023): • Vanaf 4 april 2023 zal dit bestand wekelijks op dinsdag worden geüpdatet. De data wordt met terugwerkende kracht bijgewerkt voor de andere dagen. Versie 9 update (23 mei 2023): • Een bijgewerkte lijst met bekende verpleeg- of verzorgingshuislocaties en particuliere woonzorgcentra van de koepelorganisatie Patiëntenfederatie Nederland is ontvangen op 09-03-2023. Op 17-05-2023 is deze bijgewerkte lijst in gebruik genomen.

Date_of_report: Datum en tijd waarop het databestand is aangemaakt door het RIVM.

Date_of_statistic_reported: De datum die gebruikt wordt voor het rapporteren van de verpleeghuisstatistiek. Deze kan voor iedere gerapporteerde statistiek anders zijn, namelijk: • Voor [Total_cases_reported] is dat de datum van de positieve testuitslag. • Voor [Total_deceased_reported] is dat de datum waarop de patiënten zijn overleden. • Voor [Total_new_infected_locations_reported] en [Total_infected_locations_reported] is dat de datum waarop de aantallen zijn gepubliceerd door het RIVM.

Security_region_code: Veiligheidsregiocode. Veiligheidsregio gebaseerd op de woonplaats van de patiënt. Indien de woonplaats niet bekend is, wordt Veiligheidsregio gebaseerd op de GGD die de melding heeft gedaan, behalve voor Veiligheidsregio Midden- en West-Brabant en Brabant-Noord aangezien voor deze regio’s GGD en Veiligheidsregio niet vergelijkbaar zijn. Zie ook: https://www.cbs.nl/nl-nl/cijfers/detail/84721NED?q=Veiligheid Vanaf 24 maart 2022 is dit bestand samengesteld volgens de gemeente indeling van 24 maart 2022. Gemeente Weesp is opgegaan in gemeente Amsterdam. Met deze indeling is de veiligheidsregio Gooi- en Vechtstreek kleiner geworden en de veiligheidsregio Amsterdam-Amstelland groter; GGD Amsterdam is groter geworden en GGD Gooi- en Vechtstreek is kleiner geworden ( https://www.cbs.nl/nl-nl/onze-diensten/methoden/classificaties/overig/gemeentelijke-indelingen-per-jaar/indeling-per-jaar/gemeentelijke-indeling-op-1-januari-2022).

Healthcare workers’ infection and death by region and facility type.