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.

This dataset includes the number of nursing home, or adult care facility-reported fatalities for residents with lab-confirmed COVID-19 disease that occurred at the facility, lab-confirmed COVID-19 disease that occurred outside of the facility, and COVID-19 presumed disease that occurred at the facility.

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.

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 late January, 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.

Estimating nursing home COVID-19 deaths by U.S. Health and Human Service (HHS) Regions, 25-May to 14-June 2020: Zero-inflated negative binomial models1.

Context

Upon reviewing the CMS website (https://data.cms.gov/covid-19/covid-19-nursing-home-data), it was apparent a number of nursing home providers were missing from a map plot that was intended to show COVID-19 statistics. I wanted to take a deeper look into the data and just play around with a few visualizations via the CMS provided data set, however I noticed the provided set did not contain any long/lat values for the nursing homes. It could also be seen that certain providers were not being mapped on the CMS website due to string being mixed with numbers in Provider IDs assigned to each provider. New Provider IDs were assigned in rank order, alphabetically and by State. Each nursing home, along with their address was pulled and used to obtain a set of coordinates for their facility and can be joined to the original dataset via Provider ID for use.

Content

Original dataset was sourced from the cms.gov website, with Geocodio being used to geocode the coordinates for the nursing homes. Per the CMS, "The data posted by CMS is what nursing homes submitted through the Centers of Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN) system. CMS and CDC perform quality assurance checks on the data and may suppress data that appear to be erroneous. The data is not altered from what nursing homes report to the NHSN system. Data regarding numbers of new cases, suspected cases, or deaths are aggregated.". Nursing homes reported weekly COVID statistics spanning 05/24/20 - 08/05/2021, ranging from case, death, vaccination, equipment, etc. for both residents and staff. A separate table containing address information and coordinates for each individual provider is available for joining, in order to map each facility for visualization.

Acknowledgements

Original Data Source: https://data.cms.gov/covid-19/covid-19-nursing-home-data

Geocode Source: https://www.geocod.io

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

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

The death rate in New York City for adults aged 75 years and older was around 4,135 per 100,000 people as of December 22, 2022. The risk of developing more severe illness from COVID-19 increases with age, and the virus also poses a particular threat to people with underlying health conditions.

What is the death toll in NYC? The first coronavirus-related death in New York City was recorded on March 11, 2020. Since then, the total number of confirmed deaths has reached 37,452 while there have been 2.6 million positive tests for the disease. The number of daily new deaths in New York City has fallen sharply since nearly 600 residents lost their lives on April 7, 2020. A significant number of fatalities across New York State have been linked to long-term care facilities that provide support to vulnerable elderly adults and individuals with physical disabilities.

The impact on the counties of New York State Nearly every county in the state of New York has recorded at least one death due to the coronavirus. Outside of New York City, the counties of Nassau, Suffolk, and Westchester have confirmed over 11,500 deaths between them. When analyzing the ratio of deaths to county population, Rockland had one of the highest COVID-19 death rates in New York State in 2021. The county, which has approximately 325,700 residents, had a death rate of around 29 per 10,000 people in April 2021.

Descriptive statistics on nursing home to CBSA COVID-19 mortality rate ratio, June 2020—January 2021.

Variety of data files supporting CDC vaccination dashboards, downloaded 2.4.25. Includes weekly vaccination data for children, adults;COVID vaccination coverage overall and for pregnant women, nursing home residents, adults;Laboratory-confirmed RSV, COVID-19, and flu hospitalizations (source: RESPNet);Deaths from COVID-19, influenza, and RSV overall, by state, by race and ethnicity;ED visits with COVID-19, influenza, RSV, by demographics;NIS-ACM data on COVID-19 for adults (source:RespVaxView);Cumulative COVID-19 vaccination by age, jurisdictionCDC wastewater surviellance data tablesFluView Phase 2 Data***For CDC Covid-19 Nursing Home Data:Microdata: YesLevel of Analysis: Nursing HomesVariables Present: YesFile Layout: .csvCodebook: Yes Methods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC NHSN Report State HCP Influenza Vaccination:Microdata: NoLevel of Analysis: StateVariables Present: YesFile Layout: N/ACodebook: NoMethods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Adult Covid NIS-ACM RespVax Data: Microdata: YesLevel of Analysis: Local - county, cityVariables Present: YesFile Layout: .csvCodebook: YesMethods: YesWeights (with appropriate documentation): YesPublications: NoAggregate Data: No***For NSSP Emergency Department Visits - COVID-19, Flu, etc. Microdata: YesLevel of Analysis: AilmentsVariables Present: YesFile Layout: .csvCodebook: NoMethods: Yes (https://docs.google.com/spreadsheets/d/19Po9Ir57Q-81Q5DfE1yKnW9NDLHXqPXc2307QY1hq24/edit?gid=1803019...) Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***For CDC Percentage of Emergency Department Visits with Diagnosed COVID-19 in US:Microdata: YesLevel of Analysis: Demographic GroupsVariables Present: YesFile Layout: .csvCodebook: NoMethods: Yes (https://archive.cdc.gov/www_cdc_gov/ncird/surveillance/respiratory-illnesses/index.html)Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***CDC Provisional COVID-19, Flu, and Pneumonia Death Counts:Microdata: YesLevel of Analysis: State, Demographic GroupsVariables Present: YesFile Layout: .csvCodebook: Yes (https://www.cdc.gov/nchs/nvss/vsrr/covid19/index.htm)Methods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Rates of Laboratory Confirmed RSV, Covid Hospitalizations:Microdata: YesLevel of Analysis: Weekly Rates by StateVariables Present: YesFile Layout: .csvCodebook: YesMethods: YesWeights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Vaccination Rates Among Adults 18 Years and Older :Microdata: YesLevel of Analysis: Yearly State Rate by Demographic Variables Present: YesFile Layout: .csvCodebook: YesMethods: Yes https://www.cdc.gov/adultvaxview/publications-resources/vaccination-coverage-adults-2021.html Weights (with appropriate documentation): NoPublications: NoAggregate Data: No***For CDC Vaccination Rates Among Pregnant Women:Microdata: YesLevel of Analysis: Percent Vaccinated Per Year by Demographic Type and Vaccination StatusVariables Present: YesFile Layout: .csvCodebook: YesMethods: Yes https://www.cdc.gov/fluvaxview/coverage-by-season/pregnant-april-2024.htmlWeights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***For CDC Weekly Cum. COVID-19 Vaccination Coverage by Season, Race and Ethnicity, Medicare FFS aged 65+:Microdata: YesLevel of Analysis: Demographic Groups Variables Present: YesFile Layout: .csvCodebook: Yes https://data.cdc.gov/Vaccinations/Weekly-Cumulative-COVID-19-Vaccination-Coverage-an/ksfb-ug5d/about...Methods: Yes (above link)Weights (with appropriate documentation): NoPublications: NoAggregate Data: Yes***CDC Weekly Cum. Est No COVID-19 Vax Admin in Pharmacy...:Microdata: YesLevel of Analysis: National (delineated by Age Group)Variables Present: Yes - separate document https://data.cdc.gov/Vaccinations/Weekly-Cumulative-Estimated-Number-of-COVID-19-Vac/ewpg-rz7g/about...File Layout: .csvCodebook: Yes (see above link)Methods: Yes (see above link)Weights (with appropriate documentation): NoPublications: NoAggregate Data: No***CDC Weekly Cum. Doses (in millions) of Influenza Vaccinations...:Microdata: YesLevel of Analysis: National Variables Present: Yes Fi

In the United States between February 12 and March 16, 2020, the percentage of COVID-19 patients hospitalized with the disease increased with age. Findings estimated that up to 70 percent of adults aged 85 years and older were hospitalized.

Who is at higher risk from COVID-19? The same study also found that coronavirus patients aged 85 and older were at the highest risk of death. There are other risk factors besides age that can lead to serious illness. People with pre-existing medical conditions, such as diabetes, heart disease, and lung disease, can develop more severe symptoms. In the U.S. between January and May 2020, case fatality rates among confirmed COVID-19 patients were higher for those with underlying health conditions.

How long should you self-isolate? As of August 24, 2020, more than 16 million people worldwide had recovered from COVID-19 disease, which includes patients in health care settings and those isolating at home. The criteria for discharging patients from isolation varies by country, but asymptomatic carriers of the virus can generally be released ten days after their positive case was confirmed. For patients showing signs of the illness, they must isolate for at least ten days after symptom onset and also remain in isolation for a short period after the symptoms have disappeared.

Status of COVID-19 cases in Ontario

This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Effective April 13, 2023, this dataset will be discontinued. The public can continue to access the data within this dataset in the following locations updated weekly on the Ontario Data Catalogue:

• Ontario COVID-19 testing percent positive by age group
• Confirmed positive cases of COVID-19 in Ontario
• Ontario COVID-19 testing metrics by Public Health Unit (PHU)
• Ontario COVID-19 testing percent positive by age group
• COVID-19 cases in hospital and ICU, by Ontario Health (OH) region
• Cumulative deaths (new methodology)
• Deaths Involving COVID-19 by Fatality Type

For information on Long-Term Care Home COVID-19 Data, please visit: Long-Term Care Home COVID-19 Data.

Data includes:

• reporting date
• daily tests completed
• total tests completed
• test outcomes
• total case outcomes (resolutions and deaths)
• current tests under investigation
• current hospitalizations
  • current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness
  • current patients in Intensive Care Units (ICUs) testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) no longer testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness
  • current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID-19
  • current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID-19
• Long-Term Care (LTC) resident and worker COVID-19 case and death totals
• Variants of Concern case totals
• number of new deaths reported (occurred in the last month)
• number of historical deaths reported (occurred more than one month ago)
• change in number of cases from previous day by Public Health Unit (PHU).

This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

Cumulative Deaths

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

The methodology used to count COVID-19 deaths has changed to exclude deaths not caused by COVID. This impacts data captured in the columns “Deaths”, “Deaths_Data_Cleaning” and “newly_reported_deaths” starting with data for March 11, 2022. A new column has been added to the file “Deaths_New_Methodology” which represents the methodological change.

The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1, 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred.

On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. A small number of COVID deaths (less than 20) do not have recorded death date and will be excluded from this file.

CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags.

Related dataset(s)

• Confirmed positive cases of COVID-19 in Ontario

Estimates of excess deaths can provide information about the burden of mortality potentially related to the COVID-19 pandemic, including deaths that are directly or indirectly attributed to COVID-19. Excess deaths are typically defined as the difference between the observed numbers of deaths in specific time periods and expected numbers of deaths in the same time periods. This visualization provides weekly estimates of excess deaths by the jurisdiction in which the death occurred. Weekly counts of deaths are compared with historical trends to determine whether the number of deaths is significantly higher than expected.Counts of deaths from all causes of death, including COVID-19, are presented. As some deaths due to COVID-19 may be assigned to other causes of deaths (for example, if COVID-19 was not diagnosed or not mentioned on the death certificate), tracking all-cause mortality can provide information about whether an excess number of deaths is observed, even when COVID-19 mortality may be undercounted. Additionally, deaths from all causes excluding COVID-19 were also estimated. Comparing these two sets of estimates — excess deaths with and without COVID-19 — can provide insight about how many excess deaths are identified as due to COVID-19, and how many excess deaths are reported as due to other causes of death. These deaths could represent misclassified COVID-19 deaths, or potentially could be indirectly related to the COVID-19 pandemic (e.g., deaths from other causes occurring in the context of health care shortages or overburdened health care systems).Estimates of excess deaths can be calculated in a variety of ways, and will vary depending on the methodology and assumptions about how many deaths are expected to occur. Estimates of excess deaths presented in this webpage were calculated using Farrington surveillance algorithms (1). A range of values for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound of the 95% prediction interval), by week and jurisdiction.Provisional death counts are weighted to account for incomplete data. However, data for the most recent week(s) are still likely to be incomplete. Weights are based on completeness of provisional data in prior years, but the timeliness of data may have changed in 2020 relative to prior years, so the resulting weighted estimates may be too high in some jurisdictions and too low in others. As more information about the accuracy of the weighted estimates is obtained, further refinements to the weights may be made, which will impact the estimates. Any changes to the methods or weighting algorithm will be noted in the Technical Notes when they occur. More detail about the methods, weighting, data, and limitations can be found in the Technical Notes.This visualization includes several different estimates:Number of excess deaths: A range of estimates for the number of excess deaths was calculated as the difference between the observed count and one of two thresholds (either the average expected count or the upper bound threshold), by week and jurisdiction. Negative values, where the observed count fell below the threshold, were set to zero.Percent excess: The percent excess was defined as the number of excess deaths divided by the threshold.Total number of excess deaths: The total number of excess deaths in each jurisdiction was calculated by summing the excess deaths in each week, from February 1, 2020 to present. Similarly, the total number of excess deaths for the US overall was computed as a sum of jurisdiction-specific numbers of excess deaths (with negative values set to zero), and not directly estimated using the Farrington surveillance algorithms.Select a dashboard from the menu, then click on “Update Dashboard” to navigate through the different graphics.The first dashboard shows the weekly predicted counts of deaths from all causes, and the threshold for the expected number of deaths. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.The second dashboard shows the weekly predicted counts of deaths from all causes and the weekly count of deaths from all causes excluding COVID-19. Select a jurisdiction from the drop-down menu to show data for that jurisdiction.The th

Results of multivariate analysis with linear model, adjusting for case rate.

Descriptions and sources for variables included in the study.

Abstract The COVID-19 pandemic poses difficulties for long-term care institutions for the elderly, with increased mortality rates for the residents. This study aims to estimate the impact of COVID-19 on mortality of institutionalized elderly in Brazil. Estimates of the percentage of elderly deaths occurring in care homes were calculated for Brazil, States and Regions using estimates for the total number of deaths. The estimation was based upon information available for other countries. The weighted percentage was 44.7% and 107,538 COVID-19 deaths were estimated for the elderly in these institutions in Brazil in 2020. Higher numbers of deaths were expected in the Southeast Region (48,779 deaths), followed by the Northeast Region (28,451 deaths); São Paulo was the most affected State (24,500 deaths). The strong impact of COVID-19 on the elderly population living in long-term care facilities is clear. Estimates for the country exceeded 100,000 elderly people, potentially the most fragile and vulnerable, and are based upon a conservative number of total deaths, in view of other estimates and the alarming situation of death growth in Brazil from COVID-19.

Key: rsus: relative susceptibility to infection; psym: probability of developing symptoms; psev: probability of developing severe symptoms (i.e., sufficient to justify hospitalization); pcri: probability of developing into a critical case (i.e., sufficient to require ICU); pdea: probability of death (i.e., infection fatality ratio). Relative susceptibility values are derived from odds ratios presented in Zhang et al. [47]. Mortality rates are based on O’Driscoll et al. [48] for ages 90. All other values are derived from Verity et al. [45] and Ferguson et al. [50], which did not differentiate 80–89 and 90+. Values were validated from model fits to data on numbers of cases, numbers of people hospitalized and in intensive care, and numbers of deaths from Washington and Oregon states. Note that "overall" values depend on the age structure of the population being modeled. For a population like the USA or UK, the symptomatic proportion is roughly 70%, while for populations skewed towards younger ages, this proportion is lower. Similarly, overall mortality rates are estimated to vary from 0.2% in Kenya to 0.9% in the USA and 1.4% in Italy.

Percentage of COVID-19 cases/deaths and population for African Americans and Hispanic/Latinos by DMV state.