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.

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. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. 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. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

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.

As of January 11, 2023, the highest number of deaths due to the coronavirus in Sweden was among individuals aged 80 to 90 years old. In this age group there were 9,124 deaths as a result of the virus. The overall Swedish death toll was 22,645 as of January 11, 2023.

The first case of coronavirus (COVID-19) in Sweden was confirmed on February 4, 2020. The number of cases has since risen to over 2.68 million, as of January 2023. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed 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.

Read the associated blogpost for a detailed description of how this dataset was prepared; plus extra code for producing animated maps.

Context

The 2019 Novel Coronavirus (COVID-19) continues to spread in countries around the world. This dataset provides daily updated number of reported cases & deaths in Germany on the federal state (Bundesland) and county (Landkreis/Stadtkreis) level. In April 2021 I added a dataset on vaccination progress. In addition, I provide geospatial shape files and general state-level population demographics to aid the analysis.

Content

The dataset consists of thre main csv files: covid_de.csv, demgraphics_de.csv, and covid_de_vaccines.csv. The geospatial shapes are included in the de_state.* files. See the column descriptions below for more detailed information.

• covid_de.csv: COVID-19 cases and deaths which will be updated daily. The original data are being collected by Germany's Robert Koch Institute and can be download through the National Platform for Geographic Data (the latter site also hosts an interactive dashboard). I reshaped and translated the data (using R tidyverse tools) to make it better accessible. This blogpost explains how I prepared the data, and describes how to produces animated maps.

• demographics_de.csv: General Demographic Data about Germany on the federal state level. Those have been downloaded from Germany's Federal Office for Statistics (Statistisches Bundesamt) through their Open Data platform GENESIS. The data reflect the (most recent available) estimates on 2018-12-31. You can find the corresponding table here.

• covid_de_vaccines.csv: In April 2021 I added this file that contains the Covid-19 vaccination progress for Germany as a whole. It details daily doses, broken down cumulatively by manufacturer, as well as the cumulative number of people having received their first and full vaccination. The earliest data are from 2020-12-27.

• de_state.*: Geospatial shape files for Germany's 16 federal states. Downloaded via Germany's Federal Agency for Cartography and Geodesy . Specifically, the shape file was obtained from this link.

Column Description

COVID-19 dataset covid_de.csv:

• state: Name of the German federal state. Germany has 16 federal states. I removed converted special characters from the original data.

• county: The name of the German Landkreis (LK) or Stadtkreis (SK), which correspond roughly to US counties.

• age_group: The COVID-19 data is being reported for 6 age groups: 0-4, 5-14, 15-34, 35-59, 60-79, and above 80 years old. As a shortcut the last category I'm using "80-99", but there might well be persons above 99 years old in this dataset. This column has a few NA entries.

• gender: Reported as male (M) or female (F). This column has a few NA entries.

• date: The calendar date of when a case or death were reported. There might be delays that will be corrected by retroactively assigning cases to earlier dates.

• cases: COVID-19 cases that have been confirmed through laboratory work. This and the following 2 columns are counts per day, not cumulative counts.

• deaths: COVID-19 related deaths.

• recovered: Recovered cases.

Demographic dataset demographics_de.csv:

• state, gender, age_group: same as above. The demographic data is available in higher age resolution, but I have binned it here to match the corresponding age groups in the covid_de.csv file.

• population: Population counts for the respective categories. These numbers reflect the (most recent available) estimates on 2018-12-31.

Vaccination progress dataset covid_de_vaccines.csv:

• date: calendar date of vaccination

• doses, doses_first, doses_second: Daily count of administered doses: total, 1st shot, 2nd shot.

• pfizer_cumul, moderna_cumul, astrazeneca_cumul: Daily cumulative number of administered vaccinations by manufacturer.

• persons_first_cumul, persons_full_cumul: Daily cumulative number of people having received their 1st shot and full vaccination, respectively.

Acknowledgements

All the data have been extracted from open data sources which are being gratefully acknowledged:

• The [Robert ...

The coronavirus disease (COVID-19) pandemic has had unprecedented consequences for Canada's aging population with the majority of COVID-19 deaths (approximately 80% during 2020) occurring among adults aged 65 years and older. Both advanced age and underlying chronic diseases and conditions contribute to these severe outcomes. Excess mortality refers to additional mortality above the expected level (based on mortality in the same period in the preceding year or averaged over several preceding years in the same population). This measure allows for the measurement of death directly and indirectly related to COVID-19 and provides a summary measure of its whole system impact in addition to its impact on mortality.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

As of February 17, 2022, there had been approximately 139.5 thousand deaths due to COVID-19 recorded in England. When broken down by age, almost 37 percent of these deaths occurred in the age group 80 to 89 years, while a further fifth of deaths were recorded among over 90 year olds. For further information about the COVID-19 pandemic, please visit our dedicated Facts and Figures page.

Pre-existing conditions of people who died due to COVID-19, broken down by country, broad age group, and place of death occurrence, usual residents of England and Wales.

Context

COVID-19 Cases and Deaths by Race/Ethnicity

Content

COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The age-adjusted rates are directly standardized using the 2018 ASRH Connecticut population estimate denominators (available here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-State--County-Population-with-Demographics).

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age-adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

This dataset will be updated on a daily basis. Data are subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differs from the timestamp in DPH's daily PDF reports.

Acknowledgements

Thanks to catalog.data.gov.

In March 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) to be a pandemic, stating that those with underlying health conditions are most susceptible, including motor neuron disease (MND). To examine the effect the COVID-19 pandemic had on deaths from MND in the United States. Death certificate data for all MND deaths aged 20 years and older were analyzed from 2017 to 2019 (pre-COVID), then expanded to include 2020 and 2021 (COVID) deaths to evaluate if COVID-19 impacted MND deaths. The average number of MND deaths documented during the COVID-19 years was 8009, up from 7485 MND deaths pre-COVID. The age-adjusted mortality rate among the non-Hispanic population increased during COVID to 2.78 per 100,000 persons (95% CI = 2.73–2.82) from 1.81 (95% CI = 1.78–1.84). The Hispanic population also saw an increase in mortality rate during COVID (1.61, 95% CI = 1.51–1.71) compared with pre-COVID (1.10, 95% CI = 1.03–1.17). Decedent’s home as a place of death also saw a mortality rate increase during COVID (1.51, 95% CI = 1.48–1.54) compared with pre-COVID (1.30, 95% CI = 1.27–1.32). For the Hispanic population, the rate peaked at 80–84 years pre-COVID, but for the COVID years, the rate peaked earlier, at 75–79 years. The total number of MND deaths was greater during COVID than in the preceding years. The analysis suggests there might have been a consequence of circumstances surrounding the global pandemic and the associated restrictions.

Background and aimPatients with interstitial lung diseases, including asbestosis, showed high susceptibility to the SARS-CoV-2 virus and a high risk of severe COVID-19 symptoms. Italy, highly impacted by asbestos-related diseases, in 2020 was among the European countries with the highest number of COVID-19 cases. The mortality related to malignant mesotheliomas and asbestosis in 2020 and its relationship with COVID-19 in Italy are investigated.MethodsAll death certificates involving malignant mesotheliomas or asbestosis in 2010–2020 and those involving COVID-19 in 2020 were retrieved from the National Registry of Causes of Death. Annual mortality rates and rate ratios (RRs) of 2020 and 2010–2014 compared to 2015–2019 were calculated. The association between malignant pleural mesothelioma (MPM) and asbestosis with COVID-19 in deceased adults ≥80 years old was evaluated through a logistic regression analysis (odds ratios: ORs), using MPM and asbestosis deaths COVID-19-free as the reference group. The hospitalization for asbestosis in 2010–2020, based on National Hospital Discharge Database, was analyzed.ResultsIn 2020, 746,343 people died; out of them, 1,348 involved MPM and 286 involved asbestosis. Compared to the period 2015–2019, the mortality involving the two diseases decreased in age groups below 80 years; meanwhile, an increasing trend was observed in subjects aged 80 years and older, with a relative mortality risks of 1.10 for MPM and 1.17 for asbestosis. In subjects aged ≥80 years, deaths with COVID-19 were less likely to have MPM in both genders (men: OR = 0.22; women: OR = 0.44), while no departure was observed for asbestosis. A decrease in hospitalization in 2020 with respect to those in 2010–2019 in all age groups, both considering asbestosis as the primary or secondary diagnosis, was observed.ConclusionsThe increasing mortality involving asbestosis and, even if of slight entity, MPM, observed in people aged over 80 years during the 1st year of the COVID-19 pandemic, aligned in part with the previous temporal trend, could be due to several factors. Although no positive association with COVID-19 mortality was observed, the decrease in hospitalizations for asbestosis among individuals aged over 80 years, coupled with the increase in deaths, highlights the importance of enhancing home-based assistance during the pandemic periods for vulnerable patients with asbestos-related conditions.

ABSTRACT Objective: To evaluate the impact of the COVID-19 pandemic on hospital admission and mortality indicators in older adults with fractures of the proximal femur. Methods: Observational and retrospective study that took place from June 2016 to 2020. Patients of both genders who underwent surgical treatment for fractures of the proximal end of the femur, aged over 60 years, were included. Results: The population consisted of 379 patients, treated before (group 1; N = 278; 73.35%) and during the pandemic (group 2; N = 101; 26.65%). Higher mortality was observed in group 2 (N = 24; 23.8%) versus group 1 (N = 10; 3.6%), p < 0.001. The highest proportion of deaths in group 2 was maintained in patients aged 70-79 years (p = 0.011), 80-89 years (p ≤ 0.001) and > 90 years (p ≤ 0.001). In addition, the preoperative time and hospital stay were longer in group 2 compared to group 1 (p ≤ 0.001). Conclusion: The present study demonstrated that the pandemic period increased the mortality rate and the preoperative and hospitalization time in older patients with femur fractures. Thus, the pandemic has affected the care of fractures of the proximal femur in older adults, which reinforces the need to adopt measures to reduce complications and mortality. Level of Evidence II, Retrospective Study.

Objective: To study the differences in clinical characteristics, risk factors, and complications across age-groups among the inpatients with the coronavirus disease 2019 (COVID-19).Methods: In this population-based retrospective study, we included all the positive hospitalized patients with COVID-19 at Wuhan City from December 29, 2019 to April 15, 2020, during the first pandemic wave. Multivariate logistic regression analyses were used to explore the risk factors for death from COVID-19. Canonical correlation analysis (CCA) was performed to study the associations between comorbidities and complications.Results: There are 36,358 patients in the final cohort, of whom 2,492 (6.85%) died. Greater age (odds ration [OR] = 1.061 [95% CI 1.057–1.065], p < 0.001), male gender (OR = 1.726 [95% CI 1.582–1.885], p < 0.001), alcohol consumption (OR = 1.558 [95% CI 1.355–1.786], p < 0.001), smoking (OR = 1.326 [95% CI 1.055–1.652], p = 0.014), hypertension (OR = 1.175 [95% CI 1.067–1.293], p = 0.001), diabetes (OR = 1.258 [95% CI 1.118–1.413], p < 0.001), cancer (OR = 1.86 [95% CI 1.507–2.279], p < 0.001), chronic kidney disease (CKD) (OR = 1.745 [95% CI 1.427–2.12], p < 0.001), and intracerebral hemorrhage (ICH) (OR = 1.96 [95% CI 1.323–2.846], p = 0.001) were independent risk factors for death from COVID-19. Patients aged 40–80 years make up the majority of the whole patients, and them had similar risk factors with the whole patients. For patients aged

This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

This table displays the number of COVID-19 deaths among Cambridge residents by race and ethnicity. The count reflects total deaths among Cambridge COVID-19 cases.

The rate column shows the rate of COVID-19 deaths among Cambridge residents by race and ethnicity. The rates in this chart were calculated by dividing the total number of deaths among Cambridge COVID-19 cases for each racial or ethnic category by the total number of Cambridge residents in that racial or ethnic category, and multiplying by 10,000. The rates are considered “crude rates” because they are not age-adjusted. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts.

Of note:

This chart reflects the time period of March 25 (first known Cambridge death) through present.

It is important to note that race and ethnicity data are collected and reported by multiple entities and may or may not reflect self-reporting by the individual case. The Cambridge Public Health Department (CPHD) is actively reaching out to cases to collect this information. Due to these efforts, race and ethnicity information have been confirmed for over 80% of Cambridge cases, as of June 2020.

Race/Ethnicity Category Definitions: “White” indicates “White, not of Hispanic origin.” “Black” indicates “Black, not of Hispanic origin.” “Hispanic” refers to a person having Hispanic origin. A person having Hispanic origin may be of any race. “Asian” indicates “Asian, not of Hispanic origin.” To protect individual privacy, a category is suppressed when it has one to four people. Categories with zero cases are reported as zero. "Other" indicates multiple races, another race that is not listed above, and cases who have reported nationality in lieu of a race category recognized by the US Census. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts. "Other" also includes a small number of people who identify as Native American or Native Hawaiian/Pacific islander. Because the count for Native Americans or Native Hawaiian/Pacific Islanders is currently < 5 people, these categories have been combined with “Other” to protect individual privacy.

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.

It was estimated that around 30 percent of those aged 80 years and older who had COVID-19 in the United States from January 22 to May 30, 2020 died from the disease. Deaths due to COVID-19 are much higher among those with underlying health conditions such as cardiovascular disease, chronic lung disease, or diabetes. This statistic shows the percentage of people in the U.S. who had COVID-19 from January 22 to May 30, 2020 who died, by age.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Context

The 2019–20 coronavirus pandemic is an ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Source: https://en.wikipedia.org/wiki/2019%E2%80%9320_coronavirus_pandemic.

Content

Coronavirus COVID-19 confirmed cases, deaths, case mortality ratios, country, latitude, and longitude.

Disclaimer: Data will be more accurate as more data comes in. Deaths/Infections will be a better measure of mortality rate after a pandemic is over, when the estimates of the number of infections start to get closer to the true number of infected individuals. Note discussion of case mortality ratio (numbers as they are reported) vs infection mortality ratio (estimates of the actual numbers). This dataset discusses case mortality ratios.

Acknowledgements

Banner photo by Adhy Savala on Unsplash.

Data generated from the notebook https://www.kaggle.com/paultimothymooney/does-latitude-impact-the-spread-of-covid-19 using data from https://www.kaggle.com/paultimothymooney/latitude-and-longitude-for-every-country-and-state and https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset, all of which were released under open data licenses.

Background: Mainland China experienced a major surge in SARS-CoV-2 infections in December 2022-January 2023, but its impact on mortality was unclear given the underreporting of coronavirus disease 2019 deaths. Methods: Using obituary data from the Chinese Academy of Engineering (CAE), we estimated the excess death rate among senior CAE members by taking the difference between the observed rate of all-cause death in December 2022-January 2023 and the expected rate for the same months in 2017-2022, by age groups. We used this to extrapolate an estimate of the number of excess deaths in December 2022-January 2023 among urban dwellers in Mainland China. Results: In December 2022-January 2023, we estimated excess death rates of 0.94 per 100 persons (95% confidence interval [CI] = -0.54, 3.16) in CAE members aged 80-84 years, 3.95 (95% CI = 0.50, 7.84) in 85-89 years, 10.35 (95% CI = 3.59, 17.71) in 90-94 years, and 16.88 (95% CI = 0.00, 34.62) in 95 years and older. Using our baseline assumptions, this extrapolated to 917,000 (95% CI = 425,000, 1.45 million) excess deaths among urban dwellers in Mainland China, much higher than the 81,000 in-hospital deaths officially reported from 9 December 2022 to 30 January 2023. Conclusions: As in many jurisdictions, we estimate that the coronavirus disease 2019 pandemic had a much wider impact on mortality than what was officially documented in Mainland China.