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.

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.

A longitudinal study which follows the cohort of current residents and discharged residents sampled from the 1985 National Nursing Home Survey (NNHS), thus permitting study of nursing home and hospital utilization over time. The study was conducted in three waves. To supplement the current and discharged resident components, the 1985 NNHS included a new component - the Next-of-Kin (NOK). The NOK, using a Computer Assisted Telephone Interviewing (CATI) system, was designed to collect information about current and former nursing home residents that is not generally available from patient records or other sources in the nursing home. The NNHSF obtains additional information on a portion of the residents for whom a Current Resident Questionnaire (CRQ) or a Discharged Resident Questionnaire (DRQ) was completed. In September 1994, the NNHSF Mortality Public Use Data Tape was released, covering the years 1984-1990. It contains the multiple cause-of-death information for 6,507 subjects from the NNHSF found to be deceased after linking and matching of files with the National Death Index. Information on the mortality tape includes the date of death, region of occurrence and residence, etc. All NNHSF tapes include a patient identification number common across files to allow linkage among them. Data Availability: Public Use data tapes for each wave and the mortality tape are available through the National Technical Information Office (NTIS), NACDA and the ICPSCR at the University of Michigan. The 1985 survey tape includes eight files: the facility questionnaire, nursing staff questionnaire, current resident questionnaire, discharged resident questionnaire, expense questionnaire, nursing staff sampling list, current resident sampling list, discharged resident sampling list. The next-of-kin questionnaire is available on a separate tape. * Dates of Study: 1987-1990 * Study Features: Longitudinal * Sample Size: ** 1987: 6,001 (Wave I) ** 1988: 3,868 (Wave II) ** 1990: 3,041 (Wave III) Links: * Wave I (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09813 * Wave II (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09838 * Wave III (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06142

Provisional counts of deaths in care homes caused by coronavirus (COVID-19) by local authority. Published by the Office for National Statistics and Care Quality Commission.

Number and percentage of deaths, by place of death (in hospital or non-hospital), 1991 to most recent year.

Provisional counts of the number of care home resident deaths registered in England and Wales, by region, including deaths involving coronavirus (COVID-19), in the latest weeks for which data are available.

Multivariable regression results–role of nursing home quality, full models.

A data set of a multicohort study of persons 70 years of age and over designed primarily to measure changes in the health, functional status, living arrangements, and health services utilization of two cohorts of Americans as they move into and through the oldest ages. The project is comprised of four surveys: * The 1984 Supplement on Aging (SOA) * The 1984-1990 Longitudinal Study of Aging (LSOA) * The 1994 Second Supplement on Aging (SOA II) * The 1994-2000 Second Longitudinal Study of Aging (LSOA II) The surveys, administered by the U.S. Census Bureau, provide a mechanism for monitoring the impact of proposed changes in Medicare and Medicaid and the accelerating shift toward managed care on the health status of the elderly and their patterns of health care utilization. SOA and SOA II were conducted as part of the in-person National Health Interview Survey (NHIS) of noninstitutionalized elderly people aged 55 years and over living in the United States in 1984, and at least 70 years of age in 1994, respectively. The 1984 SOA served as the baseline for the LSOA, which followed all persons who were 70 years of age and over in 1984 through three follow-up waves, conducted by telephone in 1986, 1988, and 1990. The SOA covered housing characteristics, family structure and living arrangements, relationships and social contracts, use of community services, occupation and retirement (income sources), health conditions and impairments, functional status, assistance with basic activities, utilization of health services, nursing home stays, and health opinions. Most of the questions from the SOA were repeated in the SOA II. Topics new to the SOA II included use of assistive devices and medical implants; health conditions and impairments; health behaviors; transportation; functional status, assistance with basic activities, unmet needs; utilization of health services; and nursing home stays. The major focus of the LSOA follow-up interviews was on functional status and changes that had occurred between interviews. Information was also collected on housing and living arrangements, contact with children, utilization of health services and nursing home stays, health insurance coverage, and income. LSOA II also included items on cognitive functioning, income and assets, family and childhood health, and more extensive health insurance information. The interview data are augmented by linkage to Medicare enrollment and utilization records, the National Death Index, and multiple cause-of-death records. Data Availability: Copies of the LSOA CD-ROMs are available through the NCHS or through ICPSR as Study number 8719. * Dates of Study: 1984-2000 * Study Features: Longitudinal * Sample Size: ** 1984: 16,148 (55+, SOA) ** 1984: 7,541(70+, LSOA) ** 1986: 5,151 (LSOA followup 1) ** 1988: 6,921 (LSOA followup 2) ** 1990: 5,978 (LSOA followup 3) ** 1994-6: 9,447 (LSOA II baseline) ** 1997-8: 7,998 (LSOA II wave 2) ** 1999-0: 6,465 (LSOA II wave 3) Link: * LSOA 1984-1990 ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08719

Note: As of 4/16/25, this dataset is no longer being updated.

This dataset includes the cumulative number of healthcare facility-reported fatalities for patients with lab-confirmed COVID-19 disease by reporting date, patient county of residence, and patient fatalities that occurred based on the facility county. 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 county fatality numbers in this dataset are calculated by summing the number of fatalities by patient county of residence and reporting date, and patient fatalities that occurred based on the facility county, respectively. The statewide fatality numbers are calculated by summing the number of fatalities across all patient counties of residence, and across all facilities by county, by reporting date, respectively. The fatality numbers represent the cumulative number of fatalities that have been reported as of each reporting date.

Number of participants and number of deceased persons during the study period.

Adjusted rate ratiosa for all-cause hospitalization, hospitalization with delayed discharge, and hospitalization with death, comparing COVID-19 pandemic vs historical (2018/19) monthly periods, among Alberta Assisted Living (AL) and Nursing Home (NH) residents.

Abstract

This dataset contains statistics on deaths in South Africa in 2012. The registration of deaths in South Africa is regulated by the Births and Deaths Registration Act, 51 of 1992. The South African Department of Home Affairs (DHA) is responsible for the registration of deaths in South Africa. The data is collected with two instruments: The death register and the medical certificate in respect of death. The staff of the DHA Registrar of Deaths section fills in the former while the medical practitioner attending to the death completes the latter. Causes of death are coded by the Department of Home Affairs according to the tenth revision of the International Classification of Diseases (ICD-10) ICD-10, as required by the World Health Organization for their member countries. The data is used by the Department of Home Affairs to update the Population Register. The forms are sent to Statistics South Africa (Stats SA) for their use for statistical purposes. From the two forms sent to Stats SA, the following data items of the deceased are extracted: place of residence, place of death, date of death, month and year of registration, sex, marital status, occupation, underlying cause of death, whether or not the death was certified by a medical practitioner, and whether or not the deceased died in a health institution or nursing home. From 1991 death notifications do not require data on population group, and therefore this dataset includes death data for all population groups. This dataset excludes 2012 deaths that were not registered, and late registrations which would not have been available to Stats SA in time for the production of the dataset.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

The data covers all deaths that occurred in 2012 and registered at the Department of Home Affairs in South Africa.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The data is collected with two instruments: the death register and the medical certificate in respect of death.

This collection contains time series data, spanning 35 years, on general social indicators. The file includes data on vital statistics, population, the labor force, health, education, and leisure activities. Variables on vital statistics cover birth and death rates and life expectancy. Variables on population cover marriage, divorce, and age by sex. Labor force data contain information on employment and unemployment, percentages of the population employed in certain occupations, labor turnover, and family income. Health-related data include hospital and doctor visits, prescription costs, and nursing home expenses. Education data cover degrees conferred, educational attainment rates, and number of children enrolled in kindergarten and primary and secondary schools. Data on leisure activities cover sporting event attendance, automobile ownership, and utility consumption. Most of the data are reported on an annual basis extending from 1946 through 1980. Some data, such as the education data, are based on a less frequent observation cycle, and other data, such as health data, do not extend back to 1946. The study consists of a documentation file and a data file. The documentation contains a list of the variables, an alphabetized listing of the variables, and SPSS descriptives. The data file contains both the SPSS control cards and the data.

This dataset comprises the third follow-up of the baseline Hispanic EPESE, HISPANIC ESTABLISHED POPULATIONS FOR THE EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1993-1994: ARIZONA, CALIFORNIA, COLORADO, NEW MEXICO, AND TEXAS, and provides information on 1,682 of the original respondents. The Hispanic EPESE collected data on a representative sample of community-dwelling Mexican-American elderly, aged 65 years and older, residing in the five southwestern states of Arizona, California, Colorado, New Mexico, and Texas. The primary purpose of the series was to provide estimates of the prevalence of key physical health conditions, mental health conditions, and functional impairments in older Mexican Americans and to compare these estimates with those for other populations. The Hispanic EPESE attempted to determine whether certain risk factors for mortality and morbidity operate differently in Mexican Americans than in non-Hispanic White Americans, African Americans, and other major ethnic groups. The public-use data cover background characteristics (age, sex, type of Hispanic race, income, education, marital status, number of children, employment, and religion), height, weight, social and physical functioning, chronic conditions, related health problems, health habits, self-reported use of dental, hospital, and nursing home services, and depression. The follow-ups provide a cross-sectional examination of the predictors of mortality, changes in health outcomes, and institutionalization and other changes in living arrangements, as well as changes in life situations and quality of life issues. The vital status of respondents from baseline to this round of the survey may be determined using the Vital Status file (Part 2). This file contains interview dates from the baseline as well as vital status at Wave IV (respondent survived, date of death if deceased, proxy-assisted, proxy-reported cause of death, proxy-true). The first follow-up of the baseline data (Hispanic EPESE Wave II, 1995-1996 [ICPSR 3385]) followed 2,438 of the original 3,050 respondents, and the second follow-up (Hispanic EPESE Wave III, 1998-1999 [ICPSR 4102]) followed 1,980 of these respondents. Hispanic EPESE, 1993-1994 (ICPSR 2851), was modeled after the design of ESTABLISHED POPULATIONS FOR EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1981-1993: EAST BOSTON, MASSACHUSETTS, IOWA AND WASHINGTON COUNTIES, IOWA, NEW HAVEN, CONNECTICUT, AND NORTH CENTRAL NORTH CAROLINA and ESTABLISHED POPULATIONS FOR EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1996-1997: PIEDMONT HEALTH SURVEY OF THE ELDERLY, FOURTH IN-PERSON SURVEY DURHAM, WARREN, VANCE, GRANVILLE, AND FRANKLIN COUNTIES, NORTH CAROLINA.

Background: It is not known if widespread vaccination can prevent the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in subpopulations at high risk, like older adults in nursing homes (NH). Objective: The objective of the study was to know if coronavirus disease 2019 (COVID-19) outbreaks can occur in NH with high vaccination coverage among its residents. Methods: We identified, using national professional networks, NH that suffered COVID-19 outbreaks despite having completed a vaccination campaign, and asked them to send data, using predefined collecting forms, on the number of residents exposed, their vaccination status and the number, characteristics, and evolution of patients infected. The main outcome was to identify outbreaks occurring in NH with high vaccine coverage. Secondary outcomes were residents’ risk of being infected, developing severe disease, or dying from COVID-19 during the outbreak. SARS-CoV-2 infection was defined by a positive reverse transcriptase-polymerase chain reaction. All residents were serially tested whenever cases appeared in a facility. Unadjusted secondary attack rates, relative risks, and vaccine effectiveness during the outbreak were estimated. Results: We identified 31 NH suffering an outbreak during March–April 2021, of which 27 sent data, cumulating 1,768 residents (mean age 88.4, 73.4% women, 78.2% fully vaccinated). BNT162b2 was the vaccine employed in all NH. There were 365 cases of SARS-CoV-2 infection. Median secondary attack rates were 20.0% (IQR 4.4%–50.0%) among unvaccinated residents and 16.7% (IQR 9.5%–29.2%) among fully vaccinated ones. Severe cases developed in 42 of 80 (52.5%) unvaccinated patients, compared with 56 of 248 (22.6%) fully vaccinated ones (relative risks [RR] 4.17, 95% CI: 2.43–7.17). Twenty of the unvaccinated patients (25.0%) and 16 of fully vaccinated ones (6.5%) died from COVID-19 (RR 5.11, 95% CI: 2.49–10.5). Estimated vaccine effectiveness during the outbreak was 34.5% (95% CI: 18.5–47.3) for preventing SARS-CoV-2 infection, 71.8% (58.8–80.7) for preventing severe disease, and 83.1% (67.8–91.1) for preventing death. Conclusions: Outbreaks of COVID-19, including severe cases and deaths, can still occur in NH despite full vaccination of a majority of residents. Vaccine remains highly effective, however, for preventing severe disease and death. Prevention and control measures for SARS-CoV-2 should be maintained in NH at periods of high incidence in the community.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

Results of the Cox model.

The distribution of coronavirus disease (COVID-19) cases in Japan as of March 16, 2022, showed that the highest number of patients were aged 20 to 29 years old, with a total of over one million cases. The highest number of deaths could be seen among the patients aged 80 years and older at about 15.5 thousand cases.

 Shortage of intensive care beds 

With over 1,200 hospital beds per 100,000 inhabitants available in the country, Japan is one of the best-equipped OECD nations regarding the medical sector. However, after the COVID-19 outbreak, country has faced a shortage of hospital beds, especially those required for intensive care. ICU beds only constitute a small share of the overall number of hospital beds in the country compared to European countries like Switzerland and Germany. To combat this problem, the Japanese government implemented financial incentives for hospitals upon acquisition of new intensive care beds. Another factor playing a significant part in the shortage of hospital beds is the comparably high average length of hospital stays, since some bedridden seniors are in long-term care in hospitals, as opposed to being cared for in nursing homes or at home.

Challenges for private hospitals Japan’s over eight thousand hospitals were opened by doctors, leading to the majority of the institutions being privately owned. As many of them are specialized and dependent on outpatient surgeries, COVID-19 patients pose new difficulties, as treating them in a converted ward would hinder day-to-day operations. Acquisition of intensive care beds involves financial and logistical challenges, which smaller private institutions have difficulty meeting, as they are not funded by tax revenues.

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