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.

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

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.

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

RRR = relative risk ratios, conf. interval = 95% conf. interval, p-values from likelihood ratio tests.model also included control variablHes nationality and age as cubic spline, results not shown.* = average number of nursing home beds per 100 habitants above 65 years (per 106 regions).Data source: Swiss Federal Statistical Office, MedStat/SOMED/SNC.Results of the multinomial logistic regression analysis for four different places of death (reference value: death at home), Switzerland, 2007 & 2008, individuals born before 1942.

This dataset is derived from a secondary analysis of a retrospective cohort study conducted in 2024. Participants included those who were aged 65 or older, had stayed in a special nursing home for more than six months before death, were able to take oral intake, and died in the nursing home. Exclusion criteria included deaths outside the nursing home and inability to take oral intake during the study period.The nursing homes were managed by three organizations across different regions in Japan. Data were collected from electronic care files maintained by the nursing homes. The food intake data, visually assessed by care providers, are recorded on a scale of 1 to 10 for each meal component. The average weekly food intake for the 24 weeks leading up to each resident’s death was calculated.

The average number of years that a group of individuals could expect to live at a given age if they are at risk of dying observed at each age during the reference year(s). The calculation is done over several years in order to have a more stable estimate.

Note: The entity's life expectancy may be influenced by the presence or absence of a nursing home in the entity's territory. Although the calculation includes all the deaths observed over the selected period, the impact of some deaths on life expectancy remains greater in a sparsely populated entity. The classification of entities according to their life expectancy should therefore be interpreted with caution.

According to Cognitive Market Research, the global Retirement Home Services market is growing at a compound annual growth rate (CAGR) of 3.90% from 2023 to 2030. Rising Global Life Expectancy Is Driving The Growth of the Market

People are living longer lives than they were a few decades ago. This is due to low rates of cardiovascular and infectious disease mortality. The majority of deaths in the world were caused by three primary health conditions: ischemic heart disease, chronic obstructive pulmonary disease (COPD), and stroke.

Since the 1990s, the average number of fatalities has grown. The number of people dying from illnesses such as heart disease has increased as the world population has grown.

The decrease in age-specific mortality rates for various illnesses is evidence of the healthcare industry's success.Life expectancy increases as a result of breakthroughs in public healthcare facilities and significant developments in the healthcare business, as well as higher living standards, increased nutrition, better education, and lifestyle changes. An individual's global average age is mostly determined by living conditions and place of residence. These factors will boost market growth during the forecast period.

Technological Developments Will Boost Market Expansion

During the forecast period, technological advancements in long-term healthcare are anticipated to propel market expansion. This is brought on by the increase in Internet usage, which has sparked the development of online marketplaces, mobile apps, and mHealth. There is a rising need for support services including smartphone apps, trackers, wearables, communication tools, and smart alarms. These tools allow nurses and caregivers to monitor, document, and observe patients as well as connect with medical specialists.The use of computer and mobile phone-based patient data management among these technologies is spreading throughout long-term care.

Apps that create electronic health records (EHRs) and mobile health records (MHRs) are now available, making it simpler for consumers and healthcare professionals to access and exchange health information.

(Source:health-e.in/blog/phr-apps-india/)

The main technological advancements are mHealth and mobile-based healthcare applications that produce electronic health records (EHRs) and mobile health records (MHRs). When there are medical emergencies, other technologies, like alarm integration methods, are employed to notify service providers and caregivers. As they lessen the dependency on carers, smart houses are becoming more popular in industrialized nations. Thus, the market's expansion over the course of the forecast period will be fueled by the rising acceptance of such cutting-edge technical solutions.

The Aspects of the Retirement Home Services Market are Limitingits Growth

Negative Reputation Of Retirement Homes Is A Significant Barrier To Market Growth

Though living in the comfort of one's own home is always preferable, living in an old age home has its advantages. However, just a few old age facilities provide the bare minimum of quality for a comfortable stay. The cost of services supplied by old age homes is heavily influenced by the quality of those services. Many individuals enroll in retirement homes that lack basic infrastructure and services because they cannot afford the hefty service fees. Residents at nursing facilities are rarely given privacy. The environment in certain nursing facilities frequently results in despair, boredom, neglect, and, in some cases, abuse.

Impact of COVID-19 on The Retirement Home Services Market

Due to the risk of getting the virus in communal living arrangements, the pandemic has reduced demand for retirement homes. However, the epidemic has increased demand for retirement homes that provide specialized nursing care services. Retirement homes that provide specialized services for nursing care are growing more popular as individuals seek a safe and comfortable place to live. Introduction of Retirement Home Services

A retirement home is a multi-residence living complex designed for the elderly, sometimes known as an old people's home or old age home. Everyone or a couple resides in a room or suite of rooms that is akin to an apartment. There are more facilities in the building. This will include places for gathering, eating, playing, and receiving some kind of healt...

Seasonal influenza epidemics have a substantial public health and economic burden in the United States (US). On average, over 200,000 people are hospitalized and an estimated 23,000 people die from respiratory and circulatory complications associated with seasonal influenza virus infections each year. Annual direct medical costs and indirect productivity costs across the US have been found to average respectively at $10.4 billion and $16.3 billion. The objective of this study was to estimate the economic impact of severe influenza-induced illness on the US Veterans Affairs population. The five-year study period included 2010 through 2014. Influenza-attributed outcomes were estimated with a statistical regression model using observed emergency department (ED) visits, hospitalizations, and deaths from the Veterans Health Administration of the Department of Veterans Affairs (VA) electronic medical records and respiratory viral surveillance data from the Centers for Disease Control and Prevention (CDC). Data from VA’s Managerial Cost Accounting system were used to estimate the costs of the emergency department and hospital visits. Data from the Bureau of Labor Statistics were used to estimate the costs of lost productivity; data on age at death, life expectancy and economic valuations for a statistical life year were used to estimate the costs of a premature death. An estimated 10,674 (95% CI 8,661–12,687) VA ED visits, 2,538 (95% CI 2,112–2,964) VA hospitalizations, 5,522 (95% CI 4,834–6,210) all-cause deaths, and 3,793 (95% CI 3,375–4,211) underlying respiratory or circulatory deaths (inside and outside VA) among adult Veterans were attributable to influenza each year from 2010 through 2014. The annual value of lost productivity amounted to $27 (95% CI $24–31) million and the annual costs for ED visits were $6.2 (95% CI $5.1–7.4) million. Ninety-six percent of VA hospitalizations resulted in either death or a discharge to home, with annual costs totaling $36 (95% CI $30–43) million. The remaining 4% of hospitalizations were followed by extended care at rehabilitation and skilled nursing facilities with annual costs totaling $5.5 (95% CI $4.4–6.8) million. The annual monetary value of quality-adjusted life years (QALYs) lost amounted to $1.1 (95% CI $1.0–1.2) billion. In total, the estimated annual economic burden was $1.2 (95% CI $1.0–1.3) billion, indicating the substantial burden of seasonal influenza epidemics on the US Veterans Affairs population. Premature death was found to be the largest driver of these costs, followed by hospitalization.

Effect of staff tract measures and own tract measures on facility deaths per bed.

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.

There were 667,479 deaths in the United Kingdom in 2021, compared with 689,629 in 2020. Between 2003 and 2011, the annual number of deaths in the UK fell from 612,085 to just over 552,232. Since 2011 however, the annual number of annual deaths in the United Kingdom has steadily grown, with the number recorded in 2020, the highest since 1918 when there were 715,246 deaths. Both of these spikes in the number of deaths can be attributed to infectious disease pandemics. The great influenza pandemic of 1918, which was at its height towards the end of World War One, and the COVID-19 pandemic, which caused a large number of deaths in 2020.  Impact of the COVID-19 pandemic The weekly death figures for England and Wales highlight the tragic toll of the COVID-19 pandemic. In two weeks in April of 2020, there were 22,351 and 21,997 deaths respectively, almost 12,000 excess deaths in each of those weeks. Although hospitals were the most common location of these deaths, a significant number of these deaths also took place in care homes, with 7,911 deaths taking place in care homes for the week ending April 24, 2020, far higher than usual. By the summer of 2020, the number of deaths in England and Wales reached more usual levels, before a second wave of excess deaths hit the country in early 2021. Although subsequent waves of COVID-19 cases resulted in far fewer deaths, the number of excess deaths remained elevated throughout 2022. Long-term life expectancy trends As of 2022 the life expectancy for men in the United Kingdom was 78.57, and almost 82.57 for women, compared with life expectancies of 75 for men and 80 for women in 2002. In historical terms, this is a major improvement in relation to the mid 18th century, when the overall life expectancy was just under 39 years. Between 2011 and 2017, improvements in life expectancy in the UK did start to decline, and have gone into reverse since 2018/20. Between 2020 and 2022 for example, life expectancy for men in the UK has fallen by over 37 weeks, and by almost 23 weeks for women, when compared with the previous year.

Legacy unique identifier: P00778

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 average number of years that a group of individuals could expect to live at a given age if they are at risk of dying observed at each age during the reference year(s). The calculation is done over several years in order to have a more stable estimate. Note: The entity's life expectancy may be influenced by the presence or absence of a nursing home in the entity's territory. Although the calculation includes all the deaths observed over the selected period, the impact of some deaths on life expectancy remains greater in a sparsely populated entity. The classification of entities according to their life expectancy should therefore be interpreted with caution.

The average number of years that a group of individuals could expect to live at a given age if they are at risk of dying observed at each age during the reference year(s). The calculation is done over several years in order to have a more stable estimate. Note: The entity's life expectancy may be influenced by the presence or absence of a nursing home in the entity's territory. Although the calculation includes all the deaths observed over the selected period, the impact of some deaths on life expectancy remains greater in a sparsely populated entity. The classification of entities according to their life expectancy should therefore be interpreted with caution.

Legacy unique identifier: P00770

Nombre moyen d'années qu'un groupe d'individus pourrait s'attendre à vivre à un âge donné s'il encourt les risques de mourir observés à chaque âge au cours de l'année (ou des années) de référence. Le calcul se fait sur plusieurs années afin d'avoir une estimation plus stable. Note: Les espérances de vie de l'entité peuvent être influencées par la présence ou l'absence de maison de repos sur le territoire de l'entité. Bien que le calcul intègre l'ensemble des décès observés sur la période retenue, l'impact de quelques décès sur l'espérance de vie reste plus important dans une entité peu peuplée. Il convient donc d'interpréter avec prudence le classement des entités selon leurs espérances de vie.