In 2020, there were a total of 384,536 deaths in the United States caused by COVID-19. Males accounted for 208,718 COVID deaths that year. This statistic shows the total number of deaths due to COVID-19 in the United States in 2020, 2021, and 2022, by gender.

In 2020, the death rate for COVID-19 in the United States among males was 117 per 100,000 population. That year there was a total of 208,718 deaths from COVID-19 among males in the United States. This statistic shows the death rate for COVID-19 in the United States in 2020, 2021, and 2022, by gender.

Between July 2021 and June 2022, males in the United States reported higher death rates per million population than females for both COVID-19 and Long COVID. During this period, the death rate from COVID-19 for males was around 1,312 per million population, while roughly 7.3 men per million people died due to Long COVID. This statistic displays the death rates from COVID-19 and Long COVID per million population in the United States from July 2021 to June 2022, by gender.

It was estimated that around 6 percent of males and 4.8 percent of females 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 gender.

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

Context

The intention of this dataset was to encourage deeper exploration into the relationship between race/ethnicity, gender, poverty and severe health conditions and Covid 19 morbidity and mortality. Public health experts have long reported about the health disparities that exist for people who live in poverty and minorities populations. These reports also find that minorities who live in poverty are often doubly disadvantaged.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

Data is drawn from: 1. USA Facts/U.S CDC, 2. SAIPE/U.S Census, 3. Population Estimates/U.S Census, 4. Policy Map/NY Times/2017 SMART-BRFSS, U.S CDC Links to sources are in the file description below.

Special thanks to: 1. My instructors Andrew Worsely, Lydia Peabody, the team at General Assembly and my peers in GA Data Science June-August 2020. 2. Julian Hatwell

Inspiration

Questions to be answered? 1. What correlation exists between Covid 19 morbidity and mortality and poverty, race or gender, if any? 2. What can be observed about incidence of Covid 19 morbidity and mortality in U.S. counties where people living in poverty are the majority or counties where minority populations are the majority? 3. Capacity of U.S. county health systems and coverage of preventive health measures are not accounted for in this model, what features could be added to address these limitations? 4. In which countries outside the U.S. can this type of analysis be replicated? 5. How else can this dataset be improved?

In 2020, there were around 129 deaths per 100,000 men in large central metropolitan areas in the U.S. due to COVID-19, while there were only around 73 deaths per 100,000 women in the same urban area. This statistic illustrates the death rate for COVID-19 in the United States in 2020, by urbanicity and gender.

The dataset includes COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by gender.

Note: Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This table summarizes COVID-19 case and death data submitted to CDC as case reports for the line-level dataset. Case and death counts are stratified according to sex, age, and race and ethnicity at regional and national levels. Data for US territories are included in case and death counts, but not population counts. Weekly cumulative counts with five or fewer cases or deaths are not reported to protect confidentiality of patients. Records with unknown or missing sex, age, or race and ethnicity and of multiple, non-Hispanic race and ethnicity are included in case and death totals. COVID-19 case and death data are provisional and are subject to change. Visualization of COVID-19 case and death rate trends by demographic variables may be viewed on COVID Data Tracker (https://covid.cdc.gov/covid-data-tracker/#demographicsovertime).

In 2020, there were around 667 deaths per 100,000 men in the United States aged 65 to 74 years due to COVID-19, while among women the death rate was 433 per 100,000. This graph illustrates the death rates for COVID-19 among adults aged 65 and over in the United States in 2020, by age and gender.

In 2020, there were around 41.5 deaths per 100,000 men under 65 years in large central metropolitan areas in the U.S. due to COVID-19, while there were only around 20 deaths per 100,000 women under 65 years living in the same urban area. This statistic illustrates the death rate for COVID-19 among individuals under 65 years in the United States in 2020, by urbanicity and gender.

Context

Understanding gender is essential to understanding the risk factors of poor health, early death and health inequities. The COVID-19 outbreak is no different. At this point in the pandemic, we are unable to provide a clear answer to the question of the extent to which sex and gender are influencing the health outcomes of people diagnosed with COVID-19. However, experience and evidence thus far tell us that both sex and gender are important drivers of risk and response to infection and disease.

http://globalhealth5050.org/covid19 https://data.humdata.org/dataset/covid-19-sex-disaggregated-data-tracker

Content

In order to understand the role gender is playing in the COVID-19 outbreak, countries urgently need to begin both collecting and publicly reporting sex-disaggregated data. At a minimum, this should include the number of cases and deaths in men and women.

In collaboration with CNN, Global Health 50/50 began compiling publicly available sex-disaggregated data reported by national governments to date and is exploring how gender may be driving the higher proportion of reported deaths in men among confirmed cases so far.

Acknowledgements

http://globalhealth5050.org/covid19 https://data.humdata.org/dataset/covid-19-sex-disaggregated-data-tracker

Photo by Nick Fewings on Unsplash

Inspiration

Covid-19 Pandemic.

Motivation

Why did I create this dataset? This is my first time creating a notebook in Kaggle and I am interested in learning more about COVID-19 and how different countries are affected by it and why. It might be useful to compare different metrics between different countries. And I also wanted to participate in a challenge, and I've decided to join the COVID-19 datasets challenge. While looking through the projects, I noticed https://www.kaggle.com/koryto/countryinfo and it inspired me to start this project.

Method

My approach is to scour the Internet and Kaggle looking for country data that can potentially have an impact on how the COVID-19 pandemic spreads. In the end, I ended up with the following for each country:

• Monthly temperature and precipitation from Worldbank
• Latitude and longitude
• Population, density, gender and age
• Airport traffic from Worldbank
• COVID-19 date of first case and number of cases and deaths as of March 26, 2020
• 2009 H1N1 flu pandemic cases and deaths obtained from Wikipedia
• Property affordability index and Health care index from Numbeo
• Number of hospital beds and ICU beds from Wikipedia
• Flu and pneumonia death rate from Worldlifeexpectancy.com (Age Adjusted Death Rate Estimates: 2017)
• School closures due to COVID-19
• Number of COVID-19 tests done
• Number of COVID-19 genetic strains
• US Social Distancing Policies from COVID19StatePolicy’s SocialDistancing repository on GitHub
• DHL Global Connectedness Index 2018 (People Breadth scores)
• Datasets have been merged by country name whenever possible. I needed to rename some countries by hand, e.g. US to United Sates, etc. but it's possible that I might have missed some. See the output file covid19_merged.csv for the merged result.

See covid19_data - data_sources.csv for data source details.

Notebook: https://www.kaggle.com/bitsnpieces/covid19-data

Caveats

Since I did not personally collect each datapoint, and because each datasource is different with different objectives, collected at different times, measured in different ways, any inferences from this dataset will need further investigation.

Other interesting sources of information

• IMF Policy Tracker
• nCov strain analysis across the globe!
• Google Mobility Report
• Oxford government response tracker
• Impact on aviation
• Impact on restaurants
• Harvard
• Tableau datahub
• Unemployment outlook from ILO

Acknowledgements

I want to acknowledge the authors of the datasets that made their data publicly available which has made this project possible. Banner image is by Brian.

I hope that the community finds this dataset useful. Feel free to recommend other datasets that you think will be useful / relevant! Thanks for looking.

The features in the order shown under “Feature name” are: GDP, inter-state distance based on lat-long coordinates, gender, ethnicity, quality of health care facility, number of homeless people, total infected and death, population density, airport passenger traffic, age group, days for infection and death to peak, number of people tested for COVID-19, days elapsed between first reported infection and the imposition of lockdown measures at a given state.

Worries about the impact of COVID-19 on pregnant mothers and their offspring are widespread. Data from New York City in 2020 and Philadelphia in 1918 were used to compare to COVID-19 mortality rates versus Spanish Flu mortality rates by age and gender. The data show that COVID-19 mortality rates have been much higher for individuals over 60 compared to the Spanish Flu, which had much higher mortality rates for people between the ages of 20-40. Data on COVID-19 death counts for New York City from Centers for Disease Control and Prevention (2020) combined with population estimates for 2017 from New York State Department of Health (2017). Data on Spanish Influenza from Rogers (1920). Data is accessible to people who have an OPEN ICPSR account.

Overview and Inspiration

I wanted to make some geospatial visualizations to convey the current severity of COVID19 in different parts of the U.S..

I liked the NYTimes COVID dataset, but it was lacking information on county boundary shape data, population per county, new cases / deaths per day, and per capita calculations, and county demographics.

After a lot of work tracking down the different data sources I wanted and doing all of the data wrangling and joins in python, I wanted to open-source the final enriched data set in order to give others a head start in their COVID-19 related analytic, modeling, and visualization efforts.

This dataset is enriched with county shapes, county center point coordinates, 2019 census population estimates, county population densities, cases and deaths per capita, and calculated per day cases / deaths metrics. It contains daily data per county back to January, allowing for analyizng changes over time.

UPDATE: I have also included demographic information per county, including ages, races, and gender breakdown. This could help determine which counties are most susceptible to an outbreak.

How this data can be used

Geospatial analysis and visualization - Which counties are currently getting hit the hardest (per capita and totals)? - What patterns are there in the spread of the virus across counties? (network based spread simulations using county center lat / lons) -county population densities play a role in how quickly the virus spreads? -how does a specific county/state cases and deaths compare to other counties/states? Join with other county level datasets easily (with fips code column)

Content Details

See the column descriptions for more details on the dataset

Visualizations and Analysis Examples

COVID-19 U.S. Time-lapse: Confirmed Cases per County (per capita)

https://github.com/ringhilterra/enriched-covid19-data/blob/master/example_viz/covid-cases-final-04-06.gif?raw=true" alt="">-

Other Data Notes

• Please review nytimes README for detailed notes on Covid-19 data - https://github.com/nytimes/covid-19-data/
• The only update I made in regards to 'Geographic Exceptions', is that I took 'New York City' county provided in the Covid-19 data, which has all cases for 'for the five boroughs of New York City (New York, Kings, Queens, Bronx and Richmond counties) and replaced the missing FIPS for those rows with the 'New York County' fips code 36061. That way I could join to a geometry, and then I used the sum of those five boroughs population estimates for the 'New York City' estimate, which allowed me calculate 'per capita' metrics for 'New York City' entries in the Covid-19 dataset

Acknowledgements

• Special thanks to NYTimes for all of their hard work gathering and consolidating all of the U.S. COVID19 related data on daily basis. Their git repo https://github.com/nytimes/covid-19-data/
• Also, thanks to ykzeng for the county population density estimates: https://github.com/ykzeng/covid-19/tree/master/data-

PurposeCardiovascular disease (CVD) is the leading cause of death in the United States, and sepsis significantly contributes to hospitalization and mortality. This study aims to assess the trends of sepsis-associated CVD mortality rates and variations in mortality based on demographics and regions in the US.MethodsThe Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database was used to identify CVD and sepsis-related deaths from 1999 to 2022. Data on gender, race and ethnicity, age groups, region, and state classification were statistically analyzed to obtain crude and age-adjusted mortality rates (AAMR). The Joinpoint Regression Program was used to determine trends in mortality within the study period.ResultsDuring the study period, there were a total of 1,842,641 deaths with both CVD and sepsis listed as a cause of death. Sepsis-associated CVD mortality decreased between 1999 and 2013, from AAMR of 65.7 in 1999 to 58.8 in 2013 (APC −1.06*%, 95% CI: −2.12% to −0.26%), then rose to 74.3 in 2022 (APC 3.23*%, 95% CI: 2.18%–5.40%). Throughout the study period, mortality rates were highest in men, NH Black adults, and elderly adults (65+ years old). The Northeast region, which had the highest mortality rate in the initial part of the study period, was the only region to see a decline in mortality, while the Northwest, Midwest, and Southern regions experienced significant increases in mortality rates.ConclusionSepsis-associated CVD mortality has increased in the US over the past decade, and both this general trend and the demographic disparities have worsened since the onset of the COVID-19 pandemic.

This dataset compiles publicly available sex-disaggregated data reported by national governments to date and is exploring how gender may be driving the higher proportion of reported deaths in men among confirmed cases so far.

It is estimated that in 2021 the COVID-19 pandemic caused around 21,776 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 2021, by age and gender.

Background The United States has experienced high surge in COVID-19 cases since the dawn of 2020. Identifying the types of diagnoses that pose a risk in leading COVID-19 death casualties will enable our community to obtain a better perspective in identifying the most vulnerable populations and enable these populations to implement better precautionary measures. Objective To identify demographic factors and health diagnosis codes that pose a high or a low risk to COVID-19 death from individual health record data sourced from the United States. Methods We used logistic regression models to analyze the top 500 health diagnosis codes and demographics that have been identified as being associated with COVID-19 death. Results Among 223,286 patients tested positive at least once, 218,831 (98%) patients were alive and 4,455 (2%) patients died during the duration of the study period. Through our logistic regression analysis, four demographic characteristics of patients; age, gender, race and region, were deemed to be associated with COVID-19 mortality. Patients from the West region of the United States: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming had the highest odds ratio of COVID-19 mortality across the United States. In terms of diagnoses, Complications mainly related to pregnancy (Adjusted Odds Ratio, OR:2.95; 95% Confidence Interval, CI:1.4 - 6.23) hold the highest odds ratio in influencing COVID-19 death followed by Other diseases of the respiratory system (OR:2.0; CI:1.84 – 2.18), Renal failure (OR:1.76; CI:1.61 – 1.93), Influenza and pneumonia (OR:1.53; CI:1.41 – 1.67), Other bacterial diseases (OR:1.45; CI:1.31 – 1.61), Coagulation defects, purpura and other hemorrhagic conditions(OR:1.37; CI:1.22 – 1.54), Injuries to the head (OR:1.27; CI:1.1 - 1.46), Mood [affective] disorders (OR:1.24; CI:1.12 – 1.36), Aplastic and other anemias (OR:1.22; CI:1.12 – 1.34), Chronic obstructive pulmonary disease and allied conditions (OR:1.18; CI:1.06 – 1.32), Other forms of heart disease (OR:1.18; CI:1.09 – 1.28), Infections of the skin and subcutaneous tissue (OR: 1.15; CI:1.04 – 1.27), Diabetes mellitus (OR:1.14; CI:1.03 – 1.26), and Other diseases of the urinary system (OR:1.12; CI:1.03 – 1.21). Conclusion We found demographic factors and medical conditions, including some novel ones which are associated with COVID-19 death. These findings can be used for clinical and public awareness and for future research purposes.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.