Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

In 2020, there were around 872 deaths per 100,000 population among those aged 65 years and older in New Jersey due to COVID-19. The COVID-19 death rate in Hawaii was significantly lower at around 90 deaths per 100,000 population. This statistic illustrates the death rates due to COVID-19 among adults aged 65 and over in the United States in 2020, by state.

According to a medical analysis of 44,672 confirmed COVID-19 cases in China, the overall fatality rate of the novel coronavirus was 2.3 percent. As of February 11, 2020, the fatality rate of patients aged 80 years and older was 14.8 percent.

As of June 2022, death rates from COVID-19 and Long COVID per million people in the United States were both higher among individuals aged 85 and older. Within the analyzed period, approximately 117 people per million in this age group died due to Long COVID, and around 14,122 individuals per million died from COVID-19. This statistic shows the death rates from COVID-19 and Long COVID per million population in the United States from July 2021 to June 2022, by age group.

Background: The older population is often disproportionately and adversely affected during humanitarian emergencies, as has also been seen during the COVID-19 pandemic. Data regarding COVID-19 in older adults is usually over-generalised and does not delve into details of the clinical characteristics in them. This study was conducted to analyse clinical and laboratory characteristics, risk factors, and complications of COVID-19 between older adults who survived and those who did not. Methods: We conducted a case-control study among older adults(age > 60 years) admitted to the Intensive Care Unit(ICU) during the COVID-19 pandemic. The non-survivors (cases) were matched with age and sex-matched survivors (control) in a ratio of 1: 3. The data regarding socio-demographics, clinical characteristics, complications, treatment, laboratory data, and outcomes were analysed. Results: The most common signs and symptoms observed were fever (cases vs controls) (68.92 vs. 68.8%), followed by shortn..., A hospital-based case-control study was undertaken. Data was collected from the Intensive Care Unit(ICU) from December 2020 to September 2022. The sample size was calculated with a two-sided confidence level(1-α) of 95, 80% power, and with a ratio of controls to cases at 3:1. A sample size of 260 was calculated consisting of 195 controls and 65 cases. A Case was defined as a COVID-19-positive individual older than 60 years who, after being admitted or transferred to the ICU, did not survive, i.e., non-survivor. A Control was defined as a COVID-19-positive individual with age greater than 60 years who was admitted or transferred to the ICU, following which the patient recovered(survived) and was discharged alive from the hospital, i.e., survivor. Those patients who were admitted for post-COVID-19 complications or for COVID-19 unrelated medical conditions following discharge after initial treatment for COVID-19 pneumonia were excluded. The cases (non-survivors) were recruited according to..., Microsoft Excel, Word

Deaths were determined to be COVID-associated if they met the Department of Public Health's surveillance definition at the time of death.The cumulative COVID-19 mortality rate can be used to measure the most severe impacts of COVID-19 in a community. There have been documented inequities in COVID-19 mortality rates by demographic and geographic factors. Black and Brown residents, seniors, and those living in areas with higher rates of poverty have all been disproportionally impacted.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Results data for the thesis on estimating the age-, sex-, cause-specific excess mortality during the COVID-19 pandemic in Hong Kong and South Korea.Thesis abstractBackgroundFew studies used a consistent methodology and adjusted for the risk of influenza-like illness (ILI) in historical mortality trends when estimating and comparing the cause-specific excess mortality (EM) during the COVID-19 pandemic. Previous studies demonstrated that excess mortality was widely reported from CVD and among the elderly. This study aims to estimate and compare the overall, age-, sex-, and cause-specific excess mortality during the COVID-19 pandemic in Hong Kong (HK) and South Korea (SK) with consideration of the impact of ILI.MethodsIn this population-based study, we first fitted a generalized additive model to the monthly mortality data from Jan 2010 to Dec 2019 in HK and SK before the COVID-19 pandemic. Then we applied the fitted model to estimate the EM from Jan 2020 to Dec 2022. The month index was modelled with a natural cubic spline. Akaike information criterion (AIC) was used to select the number of knots for the spline and inclusion of covariates such as monthly mean temperature, absolute humidity, ILI consultation rate, and the proxy for flu activity.FindingsFrom 2020 to 2022, the EM in HK was 239.8 (95% CrI: 184.6 to 293.9) per 100,000 population. Excess mortality from respiratory diseases (RD) (ICD-10 code: J00-J99), including COVID-19 deaths coded as J98.8, was 181.3 (95% CrI: 149.9 to 210.4) per 100,000. Except for RD, the majority of the EM in HK was estimated from cardiovascular diseases (CVD) (22.4% of the overall EM), influenza and pneumonia (16.2%), ischemic heart disease (8.9%), ill-defined causes (8.6%) and senility (6.7%). No statistically significant reduced deaths were estimated among other studied causes.From 2020 to 2022, the EM in SK was 204.7 (95% CrI: 161.6 to 247.2) per 100,000 population. Of note, COVID-19 deaths in SK were not included in deaths from RD but were recorded with the codes for emergency use as U07.1 or U07.2. The majority of the EM was estimated from ill-defined causes (32.0% of the overall EM), senility (16.6%), cerebrovascular disease (6.8%) and cardiovascular diseases (6.1%). Statistically significant reduction in mortality with 95 CrI lower than zero was estimated from vascular, other and unspecified dementia (-26.9% of expected deaths), influenza and pneumonia (-20.7%), mental and behavioural disorders (-18.8%) and respiratory diseases (-7.7%).InterpretationExcluding RD in HK which includes COVID-19 deaths, the majority of the EM in HK and SK was from CVD and senility. Mortality from influenza and pneumonia was estimated to have a statistically significant increase in HK but a decrease in SK probability due to different coding practices. HK had a heavier burden of excess mortality in the elderly age group 70-79 years and 80 years or above, while SK had a heavier burden in the age group of 60-69 years. Both HK and SK have a heavier burden of excess mortality from males than females. Better triage systems for identifying high-risk people of the direct or indirect impact of the epidemic are needed to minimize preventable mortality.

Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions—a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita—influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular “playbook”—a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country’s past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity.

Provisional deaths registration data for single year of age and average age of death (median and mean) of persons whose death involved coronavirus (COVID-19), England and Wales. Includes deaths due to COVID-19 and breakdowns by sex.

In 2020, the U.S. had the highest COVID-19 pandemic-related excess mortality rate among non-elderly people compared to other peer countries. “Excess deaths” represent the number of deaths beyond what is expected in a typical year. This measure illustrates the mortality directly or indirectly associated with the COVID-19 pandemic. This statistic presents the COVID-19 pandemic-related excess mortality rate in the U.S. and select countries in 2020, by age group (per 100,000 people in age group).

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group.

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

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

Data includes:

• Date on which the death occurred
• Age group
• 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated
• 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster

Additional notes

As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm.

As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category.

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.

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.

The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON.

“Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results.

Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts.

Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different.

Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported.

Rates for the most recent days are subject to reporting lags

All data reflects totals from 8 p.m. the previous day.

This dataset is subject to change.

Why was there considerable variation in initial COVID-19 mortality impact across countries? Through a configurational lens, this paper examines which configurations of five conditions—a delayed public-health response, past epidemic experience, proportion of elderly in population, population density, and national income per capita—influence early COVID-19 mortality impact measured by years of life lost (YLL). A fuzzy-set qualitative comparative analysis (fsQCA) of 80 countries identifies four distinctive pathways associated with high YLL rate and four other different pathways leading to low YLL rate. Results suggest that there is no singular “playbook”—a set of policies that countries can follow. Some countries failed differently, whereas others succeeded differently. Countries should take into account their situational contexts to adopt a holistic response strategy to combat any future public-health crisis. Regardless of the country’s past epidemic experience and national income levels, a speedy public-health response always works well. For high-income countries with high population density or past epidemic experience, they need to take extra care to protect elderly populations who may otherwise overstretch healthcare capacity.

COVID-19 causes significant mortality in elderly and vulnerable people and spreads easily in care homes where one in seven individuals aged > 85 years live. However, there is no surveillance for infection in care homes, nor are there systems (or research studies) monitoring the impact of the pandemic on individuals or systems. Usual practices are disrupted during the pandemic, and care home staff are taking on new and unfamiliar roles, such as advanced care planning. Understanding the nature of these changes is critical to mitigate the impact of COVID-19 on residents, relatives and staff. 20 care homes staff members were interviewed using semi-structured interviews.

The COVID-19 pandemic poses a substantial risk to elderly and vulnerable care home residents and COVID-19 can spread rapidly in care homes. We have national, daily data on people with COVID-19 and deaths, but there is no similar data for care homes. This makes it difficult to know the scale of the problem, and plan how to keep care home residents safe. We also want to understand the impact of COVID-19 on care home staff and residents. Researchers from University College London (UCL) will measure the number of cases of COVID-19 in care homes, using data from Four Seasons Healthcare, a large care home chain. FSHC remove residents' names and addresses before sending the dataset to UCL, protecting resident's confidentiality. Since we cannot visit care homes during the pandemic, we will hold virtual (online) discussion meetings with care home stakeholders (staff, residents, relatives, General Practice teams) every 6-8 weeks, to learn rapid lessons about managing COVID-19 in care homes and identify pragmatic solutions. Our findings will be shared with FHSC, GPs and Public Health England, patients and the public, and support the national response to COVID-19. Patients and the public will be involved in all stages of the research.

The spread of coronavirus (COVID-19) in Italy has hit every age group uniformly and claimed over 190 thousand lives since it entered the country. As the chart shows, however, mortality rate appeared to be much higher for the elderly patient. In fact, for people between 80 and 89 years of age, the fatality rate was 6.1 percent. For patients older than 90 years, this figure increased to 12.1 percent. On the other hand, the death rate for individuals under 60 years of age was well below 0.5 percent. Overall, the mortality rate of coronavirus in Italy was 0.7 percent.

Italy's death toll was one of the most tragic in the world. In the last months, however, the country started to see the end of this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

Since the first case was detected at the end of January in Italy, coronavirus has been spreading fast. As of May, 2023, the authorities reported over 25.8 million cases in the country. The area mostly hit by the virus is the North, in particular the region of Lombardy.

For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Introduction: Older adults are more susceptible to severe COVID-19, with increased all-cause mortality. This has been attributed to their multimorbidity and disability. However, it remains to be established which clinical features of older adults are associated with severe COVID-19 and mortality. This information would aid in an accurate prognosis and appropriate care planning. Here, we aimed to identify the chronic clinical conditions and the comorbidity clusters associated with in-hospital mortality in a cohort of older COVID-19 patients who were admitted to the IRCCS Policlinico San Martino Hospital, Genoa, Italy, between January and April 2020.Methods: This was a retrospective cohort study including 219 consecutive patients aged 70 years or older and is part of the GECOVID-19 study group. During the study period, upon hospital admission, demographic information (age, sex) and underlying chronic medical conditions (multimorbidity) were recorded from the medical records at the time of COVID-19 diagnosis before any antiviral or antibiotic treatment was administered. The primary outcome measure was in-hospital mortality.Results: The vast majority of the patients (90%) were >80 years; the mean patient age was 83 ± 6.2 years, and 57.5% were men. Hypertension and cardiovascular disease, along with dementia, cerebrovascular diseases, and vascular diseases were the most prevalent clinical conditions. Multimorbidity was assessed with the Cumulative Illness Rating Scale. The risk of in-hospital mortality due to COVID-19 was higher for males, for older patients, and for patients with dementia or cerebral-vascular disease. We clustered patients into three groups based on their comorbidity pattern: the Metabolic-renal-cancer cluster, the Neurocognitive cluster and the Unspecified cluster. The Neurocognitive and Metabolic-renal-cancer clusters had a higher mortality compared with the Unspecified cluster, independent of age and sex.Conclusion: We defined patterns of comorbidity that accurately identified older adults who are at higher risk of death from COVID-19. These associations were independent of chronological age, and we suggest that the identification of comorbidity clusters that have a common pathophysiology may aid in the early assessment of COVID-19 patients with frailty to promote timely interventions that, in turn, may result in a significantly improved prognosis.

Note: Starting April 27, 2023 updates change from daily to weekly.

Summary The cumulative number of confirmed COVID-19 deaths among Maryland residents by age: 0-9; 10-19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80+; Unknown.

Description The MD COVID-19 - Confirmed Deaths by Age Distribution data layer is a collection of the statewide confirmed COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by designated age ranges. A death is classified as confirmed if the person had a laboratory-confirmed positive COVID-19 test result. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Probable deaths are available from the MD COVID-19 - Probable Deaths by Age Distribution data layer.

Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

PIONEER: Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 4.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases& more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS)& death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records& clinic letters. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

As of April 26, 2023, around 27 percent of total COVID-19 deaths in the United States have been among adults 85 years and older, despite this age group only accounting for two percent of the U.S. population. This statistic depicts the distribution of total COVID-19 deaths in the United States as of April 26, 2023, by age group.

Introduction: The COVID-19 pandemic has posed major challenges to all aspects of healthcare. Malta's population density, large proportion of elderly and high prevalence of diabetes and obesity put the country at risk of uncontrolled viral transmission and high mortality. Despite this, Malta achieved low mortality rates compared to figures overseas. The aim of this paper is to identify key factors that contributed to these favorable outcomes.

Methods: This is a retrospective, observational, nationwide study which evaluates outcomes of patients during the first wave of the pandemic in Malta, from the 7th of March to the 24th of April 2020. Data was collected on demographics and mode of transmission. Hospitalization rates to Malta's main general hospital, Mater Dei Hospital, length of in-hospital stay, intensive care unit admissions and 30-day mortality were also analyzed.

Results: There were 447 confirmed cases in total; 19.5% imported, 74.2% related to community transmission and 6.3% nosocomially transmitted. Ninety-three patients (20.8%) were hospitalized, of which 4 were children. Patients with moderate-severe disease received hydroxychloroquine and azithromycin, in line with evidence available at the time. A total of 4 deaths were recorded, resulting in an all-cause mortality of 0.89%. Importantly, all admitted patients with moderate-severe disease survived to 30-day follow up.

Conclusion: Effective public health interventions, widespread testing, remote surveillance of patients in the community and a low threshold for admission are likely to have contributed to these favorable outcomes. Hospital infection control measures were key in preventing significant nosocomial spread. These concepts can potentially be applied to stem future outbreaks of viral diseases. Patients with moderate-severe disease had excellent outcomes with no deaths reported at 30-day follow up.