In the United States, one in four patients were enrolled a total of 5 days or less in hospice before they passed away*. Yet one in ten received care for more than 275 days across their lifetime. Hospice care involves caring for those who are terminally ill. Such care usually does not include treatment but focuses instead on making the end of life as comfortable as possible. Hospice teams can include nurses, home health aides, social workers and physicians. Hospice providers Hospice care can be provided at the patient’s home or in a facility, such as a nursing home, assisted living, hospital or hospice care center. In 2022, there were around 5,899 Medicare certified hospices in the United States. The large majority of theses are freestanding independent hospices, while a much smaller portion are part of a hospital system or part of a home health agency. Hospice patients In 2022, there were around 1.72 million hospice patients in the U.S. Female Medicare beneficiaries were more likely than male to use hospice services. Expectedly, older adults (over 84 years) were more likely to be a hospice patient than younger peers. The most common diagnoses were neurological and cancer

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.

Health Statistics at a Glance tables contain information on socio-economic risk factors or determinants of health, health status, new information on health outcomes and expanded information on utilization of the health care system. The aim of Health Statistics at a Glance tables is to present a core data set using the most recent information available. The indicator tables show extended time series for Canada, provinces and territorial levels of geography. Depending on the indicator, cross-classifications are by age and sex, and, in some cases by education. Due to the large amount of sample survey data used to construct the indicators, many tables cannot be produced for sub-provincial areas. Health Statistics at a Glance is an integrated information product. Its content reflects the growing demand for analysis of many current health issues supplemented by the underlying data. Within this CD-ROM there are three major components: the Statistical Report on the Health of Canadians, 17 Health Reports articles cited in the Statistical Report, and all of the components of Health Indicators, including Causes of Death. Users access the data as tabulations that they can display in various formats according to their own needs. The Health Statistics at a Glance CD-ROM contains the entire database of over 100 indicators and the software to access the information on a personal computer. The database can be accessed on the mainframe computer by using Statistics Canada's CANSIM cross-classified database.

IntroductionIn case of COVID-19 related scarcity of critical care resources, an early French triage algorithm categorized critically ill patients by probability of survival based on medical history and severity, with four priority levels for initiation or continuation of critical care: P1 –high priority, P2 –intermediate priority, P3 –not needed, P4 –not appropriate. This retrospective multi-center study aimed to assess its classification performance and its ability to help saving lives under capacity saturation.MethodsICU patients admitted for severe COVID-19 without triage in spring 2020 were retrospectively included from three hospitals. Demographic data, medical history and severity items were collected. Priority levels were retrospectively allocated at ICU admission and on ICU day 7–10. Mortality rate, cumulative incidence of death and of alive ICU discharge, length of ICU stay and of mechanical ventilation were compared between priority levels. Calculated mortality and survival were compared between full simulated triage and no triage.Results225 patients were included, aged 63.1±11.9 years. Median SAPS2 was 40 (IQR 29–49). At the end of follow-up, 61 (27%) had died, 26 were still in ICU, and 138 had been discharged. Following retrospective initial priority allocation, mortality rate was 53% among P4 patients (95CI 34–72%) versus 23% among all P1 to P3 patients (95CI 17–30%, chi-squared p = 5.2e-4). The cumulative incidence of death consistently increased in the order P3, P1, P2 and P4 both at admission (Gray’s test p = 3.1e-5) and at reassessment (p = 8e-5), and conversely for that of alive ICU discharge. Reassessment strengthened consistency. Simulation under saturation showed that this two-step triage protocol could have saved 28 to 40 more lives than no triage.ConclusionAlthough it cannot eliminate potentially avoidable deaths, this triage protocol proved able to adequately prioritize critical care for patients with highest probability of survival, hence to save more lives if applied.

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.

BackgroundDespite recent achievements to reduce child mortality, neonatal deaths continue to remain high, accounting for 41% of all deaths in children under five years of age worldwide, of which over 90% occur in low- and middle-income countries (LMICs). Infections are a leading cause of death and limitations in care seeking for ill neonates contribute to high mortality rates. As estimates for care-seeking behaviors in LMICs have not been studied, this review describes care seeking for neonatal illnesses in LMICs, with particular attention to type of care sought. Methods and FindingsWe conducted a systematic literature review of studies that reported the proportion of caregivers that sought care for ill or suspected ill neonates in LMICs. The initial search yielded 784 studies, of which 22 studies described relevant data from community household surveys, facility-based surveys, and intervention trials. The majority of studies were from South Asia (n = 17/22), set in rural areas (n = 17/22), and published within the last 4 years (n = 18/22). Of the 9,098 neonates who were ill or suspected to be ill, 4,320 caregivers sought some type of care, including care from a health facility (n = 370) or provider (n = 1,813). Care seeking ranged between 10% and 100% among caregivers with a median of 59%. Care seeking from a health care provider yielded a similar range and median, while care seeking at a health care facility ranged between 1% and 100%, with a median of 20%. Care-seeking estimates were limited by the few studies conducted in urban settings and regions other than South Asia. There was a lack of consistency regarding illness, care-seeking, and care provider definitions. ConclusionsThere is a paucity of data regarding newborn care-seeking behaviors; in South Asia, care seeking is low for newborn illness, especially in terms of care sought from health care facilities and medically trained providers. There is a need for representative data to describe care-seeking patterns in different geographic regions and better understand mechanisms to enhance care seeking during this vulnerable time period. Please see later in the article for the Editors' Summary

Data are mean ± SD, median [25–75 interquartile], or number (percentage). LOS: length of stay; ICU: intensive care unit;a : excluding death during acute care;b : institution was considered as “home” in patients previously living in an institution;c : excluding patients previously living in an institution;d : excluding patients who died in acute care and/or rehabilitation.

Effective June 7th, 2024, this dataset will no longer be updated.This file contains data on:

1. Cumulative count of Ottawa residents with laboratory-confirmed COVID-19 by episode date (i.e. the earliest of symptom onset, testing or reported date), including active cases and resolved cases.

2. Cumulative count of Ottawa residents with laboratory-confirmed COVID-19 who died by date of death.

3. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by reported date and episode date.

4. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by outbreak association and episode date.

5. Daily count of Ottawa residents with laboratory-confirmed COVID-19 newly admitted to the hospital, currently in hospital, and currently in the intensive care unit (ICU).

6. Cumulative rate of confirmed COVID-19 for Ottawa residents by age group and episode date.

7. Cumulative rate of confirmed COVID-19 for Ottawa residents by gender and episode date.

8. Daily count of Ottawa residents with laboratory-confirmed COVID-19 by source of infection and episode date.

Data are from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM).

Accuracy: Points of consideration for interpretation of the data:

The percent of cases with no known epidemiological (epi) link, during the current day and previous 13 days, is calculated as the number of cases with no known epi link among all cases. The percent of cases with no known epi link is unstable during time periods with few cases.

Source of infection is based on a case's epidemiologic linkage. If no epidemiologic linkage is identified, source of infection is allocated using a hierarchy of risk factors: related to travel prior to April 1, 2020 > part of an outbreak > close or household contact of a known case > related to travel since April 1, 2020 > unspecified epidemiological link > no known source of infection > no information available.

Data are entered into and extracted by Ottawa Public Health from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM). The CCM is a dynamic disease reporting system that allows for ongoing updates; data represent a snapshot at the time of extraction and may differ from previous or subsequent reports.

As the cases are investigated and more information is available, the dates are updated.

A person’s exposure may have occurred up to 14 days prior to onset of symptoms. Symptomatic cases occurring in approximately the last 14 days are likely under-reported due to the time for individuals to seek medical assessment, availability of testing, and receipt of test results.

Confirmed cases are those with a confirmed COVID-19 laboratory result as per the Ministry of Health Public health management of cases and contacts of COVID-19 in Ontario. March 25, 2020 version 6.0.

Counts will be subject to varying degrees of underreporting due to a variety of factors, such as disease awareness and medical care seeking behaviours, which may depend on severity of illness, clinical practice, changes in laboratory testing, and reporting behaviours.

Data on hospital admissions, ICU admissions and deaths are likely under-reported as these events may occur after the completion of public health follow up of cases. Cases that were admitted to hospital or died after follow-up was completed may not be captured in iPHIS or local health unit reporting tools.

Cases are associated with a specific, isolated community outbreak; an institutional outbreak (e.g. healthcare, childcare, education); or no known outbreak (i.e., sporadic).

The distribution of the source of infection among confirmed cases is impacted by the provincial guidance on testing.

Surveillance testing for COVID-19 began in long term care facilities on April 25, 2020.

Source of infection is allocated using a hierarchy: Related to travel prior to April 1, 2020 > Close contact of a known case or part of a community outbreak or source of infection is an institutional outbreak > Related to travel since April 1, 2020 > No known source of infection > Missing.

The percent of cases with unknown source, during the current day and previous 13 days, is calculated as the number of cases with no known source among cases who source of infection is not an institutional outbreak. Calculated over a 14 day period (i.e. the day of interest and the preceding 13 days). The percent of cases with no known source is unstable during time periods with few cases.

Update Frequency: Wednesdays

Attributes: Data fields:

Data fields:

Date – Date in format YYYY-MM-DD H:MM. The date type varies based on the column of interest and could be:

 - Episode date – Earliest of

symptom onset, test or reported date for cases;

 - Date of death – The date

the person was reported to have died

 - Reported date – Date the

confirmed laboratory results were reported to Ottawa Public Health

 - Hospitalization date

Cumulative Cases by Episode Date – cumulative number of Ottawa residents with laboratory-confirmed COVID-19 by episode date. Cumulative Resolved Cases by Episode Date – cumulative number of Ottawa residents with laboratory-confirmed COVID-19 that have not died and are either (1) assessed as ‘recovered’ in The CCM or (2) 14 days past their episode date and not currently hospitalized. Cumulative Active Cases by Episode Date– cumulative number of Ottawa residents with an active COVID-19 infection. Calculated as the total number of Ottawa residents with COVID-19 excluding resolved and deceased cases. Cumulative Deaths by Date of Death - cumulative number of Ottawa residents with laboratory-confirmed COVID-19 who died by date of death. Deaths are included whether or not COVID-19 was determined to be a contributing or underlying cause of death. Daily Cases by Reported Date – number of Ottawa residents with laboratory-confirmed COVID-19 by reported date 7-Day Average of Newly Reported Cases by Reported Date – number of Ottawa residents with laboratory-confirmed COVID-19 by reported date. Calculated over a 7 day period (i.e. the day of interest and the preceding 6 days). Daily Cases by Episode Date - number of Ottawa residents with laboratory-confirmed COVID-19 by episode date. Daily Cases Linked to a Community Outbreak by Episode Date – number of Ottawa residents with laboratory-confirmed COVID-19 associated with a specific isolated community outbreak by episode date. Daily Cases Linked to an Institutional Outbreak – number of Ottawa residents with laboratory-confirmed COVID-19 associated with a COVID-19 outbreak in a healthcare, childcare or educational establishment by case episode date. Healthcare institutions include places such as long-term care homes, retirement homes, hospitals, other healthcare institutions (e.g. group homes, shelters). Daily Cases Not Linked to an Institutional Outbreak (i.e. Sporadic Cases) – number of Ottawa residents with laboratory-confirmed COVID-19 not associated to an outbreak of COVID-19. Cases Newly Admitted to Hospital – Daily number of Ottawa residents with confirmed COVID-19 admitted to hospital. Emergency room visits are not included in the number of hospital admissions. Cases Currently in Hospital – Number of Ottawa residents with confirmed COVID-19 currently in hospital, includes patients in intensive care. Emergency room visits are not included in the number of hospitalizations. Cases Currently in ICU - Number of Ottawa residents with confirmed COVID-19 currently being treated in the intensive care unit (ICU). It is a subset of the count of hospitalized cases. Cumulative Rate of COVID-19 by 10-year Age Groupings (per 100,000 pop) and Episode Date – The number of Ottawa residents with confirmed COVID-19 within an age group (e.g. 0-9 years) divided by the total Ottawa population for that age group. This fraction is then multiplied by 100,000 to get a rate of COVID-19 per 100,000 population for that age group. Cumulative Rate of COVID-19 by Gender (per 100,000 pop) and Episode Date – The number of Ottawa residents with confirmed COVID-19 of a given gender (e.g. female) divided by the total Ottawa population for that gender. This fraction is then multiplied by 100,000 to get a rate of COVID-19 per 100,000 population for that gender. Source of infection is travel by episode date: individuals who are most likely to have acquired their infection during out-of-province travel. Number of cases with missing information on source of infection by episode date: assessment for source of infection was not completed. Number of cases with no known epidemiological link by episode date: individuals who did not travel outside Ontario, are not part of an outbreak, and are not able to identify someone with COVID-19 from whom they might have acquired infection. The assessment for source of infection was completed, but no sources were identified. Source of infection is a close contact by episode date: individuals presumed to have acquired their infection following close contact (e.g. household member, friend, relative) with an individual with confirmed COVID-19. Source of infection is an outbreak by episode date: individuals who are most likely to have acquired their infection as part of a confirmed COVID-19 outbreak. Source of Infection is Unknown by Episode Date: Ottawa residents with confirmed COVID-19 who did not travel outside

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.