It is estimated that from 2020 to 2021, the mean rate of excess deaths associated with the COVID-19 pandemic from all-causes was highest in Peru. In 2020-2021, there were around 437 excess deaths due to the COVID-19 pandemic per 100,000 population in Peru. This statistic shows the mean number of excess deaths associated with the COVID-19 pandemic from all-causes in 2020-2021 in select countries worldwide, per 100,000 population.

Excess Death excl COVID: Predicted: Single Excess Est: Florida data was reported at 0.000 Number in 16 Sep 2023. This stayed constant from the previous number of 0.000 Number for 09 Sep 2023. Excess Death excl COVID: Predicted: Single Excess Est: Florida data is updated weekly, averaging 0.000 Number from Jan 2017 (Median) to 16 Sep 2023, with 350 observations. The data reached an all-time high of 526.000 Number in 21 Aug 2021 and a record low of 0.000 Number in 16 Sep 2023. Excess Death excl COVID: Predicted: Single Excess Est: Florida data remains active status in CEIC and is reported by Centers for Disease Control and Prevention. The data is categorized under Global Database’s United States – Table US.G012: Number of Excess Deaths: by States: All Causes excluding COVID-19: Predicted (Discontinued).

It is estimated that by the end of 2021 the COVID-19 pandemic had caused around 14.9 million excess deaths worldwide. South-East Asia accounted for the highest number of these deaths with about 5.99 million excess deaths due to the pandemic. This statistic shows the cumulative mean number of excess deaths associated with the COVID-19 pandemic worldwide as of the end of 2021, by region.

It is estimated that in 2020 the COVID-19 pandemic caused around 762,927 excess deaths among females worldwide aged 80 years and older. This statistic shows the mean number of excess deaths associated with the COVID-19 pandemic from all-causes worldwide in 2020, by age and gender.

It is estimated that by the end of 2021 the COVID-19 pandemic had caused around 14.9 million excess deaths worldwide. This statistic shows the cumulative mean number of excess deaths associated with the COVID-19 pandemic worldwide in 2020-2021, by month.

Provisional data on excess mortality (excluding COVID-19) during heat-periods in the 65 years and over age group estimates in England, including the estimated number of deaths where the death occurred within 28 days of a positive COVID-19 result and the mean central England temperature.

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.

Objective: To determine the effects of current age and disease duration on excess mortality in multiple sclerosis, we described the dynamics of excess deaths rates over these two time scales and studied the impact of age at multiple sclerosis clinical onset on these dynamics, separately in each initial phenotype.

Methods: We used data from 18 French multiple sclerosis expert centers participating in the Observatoire Français de la Sclérose en Plaques. Patients with multiple sclerosis living in metropolitan France and having a clinical onset between 1960 and 2014 were included. Vital status was updated on January 1st, 2016. For each multiple sclerosis phenotype separately (relapsing onset (R-MS) or primary progressive (PPMS)), we used an innovative statistical method to model the logarithm of excess death rates by a multidimensional penalized spline of age and disease duration.

Results: Among 37524 patients (71% women, mean age at multiple sclerosis onset ± standard deviation 33.0 ± 10.6 years), 2883 (7.7%) deaths were observed and 7.8% of patients were lost-to-follow-up. For R-MS patients, there was no excess mortality during the first 10 years after disease onset; afterwards, whatever age at onset, excess death rates increased with current age. From current age 70, the excess death rates values converged and became identical whatever the age at disease onset, which means that disease duration had no more impact. Excess death rates were higher in men with an excess hazard ratio of 1.46 (95% confidence interval 1.25-1.70). In contrast, in PPMS patients, excess death rates rapidly increased from disease onset, and were associated with age at onset, but not with sex.

Conclusions: In R-MS, current age has a stronger impact on multiple sclerosis mortality than disease duration while their respective effects are not so clear in PPMS.

It is estimated that in December 2021, the United States had around 112,441 excess deaths associated with the COVID-19 pandemic from all causes. Cumulatively, it was estimated that the U.S. had a mean of around 932,458 excess deaths due to the pandemic as of the end of 2021. This statistic shows the mean number of excess deaths associated with the COVID-19 pandemic from all-causes in the United States in 2020-2021, by month.

Notation, definition, and formula of each column of an abridged life table to calculate life expectancy.

ObjectivesPropose a methodology to identify COVID-19 associated deaths using healthcare billing records and evaluate its effectiveness by comparing the results with excess mortality data from 2020 to 2022 and confirmed COVID-19 deaths.MethodsA retrospective quantitative analysis was conducted by merging healthcare billing records with cause of death data. The term “COVID-19 associated death” was defined as any death occurring within a defined timeframe following a confirmed contact with COVID-19. This category includes individuals who died directly due to COVID-19, with COVID-19 as a contributing factor, or as an aftermath of a COVID-19 infection, as well as those who died from other causes but had previously contracted COVID-19. This broader definition provides a more comprehensive measure of excess mortality compared to the officially confirmed COVID-19 deaths attributed to the virus.ResultsWe identified 35,399 COVID-19 associated deaths during the 3-year pandemic in Slovakia compared to 21,395 confirmed COVID-19 deaths.ConclusionThe identification of COVID-19 associated deaths with our methodology offers a more accurate explanation for the notably high excess mortality observed in Slovakia (31,789 deaths) during the pandemic, relative to the EU27. Given the high level of excess mortality, the officially confirmed deaths are likely underestimated, and the presented methodology provides a more precise measure of mortality. Additionally, healthcare billing records prove valuable in identifying these deaths at the individual patient level using claims data of health insurance companies, which is crucial for implementing targeted preventive measures and improving preparedness for future pandemics.

Expected, observed and excess deaths (expressed in absolute and percentage terms), highest and lowest five regions of the Russian Federation with greater than 3,000 predicted deaths per year, 2020, urban and rural areas.

It is estimated that in 2020 the COVID-19 pandemic caused around 101,900 excess deaths among females aged 80 years and older in the United States. This statistic shows the mean number of excess deaths associated with the COVID-19 pandemic from all-causes in the United States in 2020, by age and gender.

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.

This publication of the SHMI relates to discharges in the reporting period March 2023 - February 2024. The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. The SHMI covers patients admitted to hospitals in England who died either while in hospital or within 30 days of being discharged. To help users of the data understand the SHMI, trusts have been categorised into bandings indicating whether a trust's SHMI is 'higher than expected', 'as expected' or 'lower than expected'. For any given number of expected deaths, a range of observed deaths is considered to be 'as expected'. If the observed number of deaths falls outside of this range, the trust in question is considered to have a higher or lower SHMI than expected. The expected number of deaths is a statistical construct and is not a count of patients. The difference between the number of observed deaths and the number of expected deaths cannot be interpreted as the number of avoidable deaths or excess deaths for the trust. The SHMI is not a measure of quality of care. A higher than expected number of deaths should not immediately be interpreted as indicating poor performance and instead should be viewed as a 'smoke alarm' which requires further investigation. Similarly, an 'as expected' or 'lower than expected' SHMI should not immediately be interpreted as indicating satisfactory or good performance. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided, as well as a breakdown of the data by diagnosis group. Further background information and supporting documents, including information on how to interpret the SHMI, are available on the SHMI homepage (see Related Links).

It is estimated that in December 2020, there were around 995,085 excess deaths associated with the COVID-19 pandemic from all causes. Cumulatively, it was estimated that there was a mean of around 14.9 million excess deaths due to the pandemic as of the end of 2021. This statistic shows the mean number of excess deaths associated with the COVID-19 pandemic from all-causes worldwide in 2020-2021, by month.

There reference tables presents provisional estimated figures of excess winter mortality (EWM) for the winter period 2013/14, and final figures for the winter period 1950 to 2011 in England and Wales. These results are broken down by region, sex, age group and local authority.

Datasets include: 5 year moving average, mean number of daily deaths each month, mean monthly temperature each month, weekly deaths per 100,000 caused by influenza-like illnesses, mortality by sex, age, region, usual country of residence and local autority and the underlying case of death from 1991 - 2014.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

Notes:

Mean excess number of deaths.