The deadliest animals in the world based on the number of human deaths per year is not a creature that humans usually find scary, such as a lion or snake. Mosquitos are by far the deadliest creature in the world when it comes to annual human deaths, causing around one million deaths per year, compared to 100,000 deaths from snakes and 250 from lions. Perhaps surpringly, dogs are the third deadliest animal to humans. Dogs are responsible for around 30,000 human deaths per year, with the vast majority of these deaths resulting from rabies that is transmitted from the dog.

Malaria

Mosquitos are the deadliest creature in the world because they transmit a number of deadly diseases, the worst of which is malaria. Malaria is a mosquito-borne disease caused by a parasite that results in fever, chills, headache, vomiting and, if left untreated, death. Malaria disproportionately affects poorer regions of the world such as Africa and South-East Asia. In 2020, there were around 627,000 deaths from malaria worldwide.

Mosquito-borne diseases in the U.S.

The most common mosquito-borne diseases reported in the United States include West Nile virus, malaria, and dengue viruses. Many of these cases, however, are from travelers who contracted the disease in another country - this is especially true for malaria, Zika, and dengue. In 2018, the states of California, New York, and Texas reported the highest number of mosquito-borne disease cases in the United States.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

In Italy, approximately ******* deaths were registered in 2022. According to the data, ischemic heart diseases were the most common cause of death in the country, with ****** cases registered, closely followed by cerebrovascular diseases. COVID-19 was the third illness causing the largest number of deaths in Italy. COVID-19 death comorbidities Most patients admitted to the hospital and later deceased with the coronavirus (COVID-19) infection showed one or more comorbidities. Hypertension was the most common pre-existing health condition, detected in **** percent of patients who died after contracting the virus. Type 2-diabetes, ischemic heart disease, and atrial fibrillation were also among the most common comorbidities in COVID-19 patients who lost their lives. Cancer deaths The number of people who died from a tumor in Italy decreased constantly between 2006 and 2021. Indeed, the rate of deaths due to cancer among Italians dropped from **** deaths per 10,000 inhabitants in 2006 to **** in 2021. The Italian region with the highest cancer mortality rate was Campania, followed by Sardinia, and Sicily.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON JULY 30

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia.

Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file.

Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death.

Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly.

The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.

Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).

Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year.

Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

On September 20, 2017, Hurricane Maria made landfall in Puerto Rico, leaving widespread destruction in its path. The official death count for Puerto Rico after Hurricane Maria was 64 excess deaths, but that controversial death toll has been debated by a number of academic and independent researcher journalists. With the loss of electrical power and telecommunication systems for much of the island, it was unclear how many deaths in Puerto Rico were an immediate result of Hurricane Maria's destruction as opposed to the access to care conditions that prolonged. Santos-Burgoa et al. applied a time-series analysis of the Puerto Rico Vital Statistics data to estimate the death count over time. To consider how many people died as opposed to emigrated away from Puerto Rico, two counterfactual assumptions were used, a Census-based scenario and a Displacement-based scenario for expected population change. Under the Census scenario and the Displacement scenario, the estimated death counts in Puerto Rico was approximately 1200 deaths and 3000 deaths, respectively, where the Displacement scenario was acclaimed as the preferred model.

Due to copy-right issues, the article and supplementary materials should be accessed at the source website. Please use the following reference citation and doi to redirect there: Santos-Burgoa C, Sandberg J, Suárez E, Goldman-Hawes A, Zeger S, Garcia-Meza A, Pérez CM, Estrada-Merly N, Colón-Ramos U, Nazario CM, Andrade E. Differential and persistent risk of excess mortality from Hurricane Maria in Puerto Rico: a time-series analysis. The Lancet Planetary Health. 2018 Nov 1;2(11):e478-88. http://dx.doi.org/10.1016/S2542-5196(18)30209-2

The number of deaths in Sweden in 2020 amounted to over 98,000. A high share of the deaths in 2020 were related to the coronavirus pandemic. However, in 2021, the number sank below 92,000, before increasing to over 94,000 in 2022 and 2023. The highest number of coronavirus deaths were among individuals age 70 and older. Sweden is the Nordic country that has reported the highest number of COVID-19-related deaths since the outbreak of the pandemic.

The most common causes of death

The most common cause of death in 2022 was diseases of the circulatory system (cardiovascular diseases). This cause was followed by cancerous tumors.

 Ischemic heart disease

Among the diseases in the circulatory system, the one that caused the most deaths was chronic ischemic heart disease. Chronic ischemic heart disease is when the blood flow to the heart is reduced because the arteries of the heart are blocked. In 2020, ischemic heart disease caused more than 50,000 deaths per 100,000 inhabitants.

In 2022, there were around **** deaths from suicide per 100,000 population among males in the U.S. aged ** years and *****. Males aged 75 years and older were more likely to die from suicide than any other age group for both males and females. The suicide death rate for males in general is constantly greater than that for females. Suicide method by gender Not only do suicide rates differ by gender, but the method of suicide varies as well. Suicide by firearm accounts for ** percent of suicides among males, but only ** percent of those among females. However, suicide by poisoning accounts for a much larger share of suicides among females than males. In 2019, there were a total of ****** firearm suicides and ***** poisoning suicides. Substance abuse, mental health, and suicide Those who suffer from substance abuse and certain mental health disorders are at a much greater risk of falling victim to suicide. It’s been found that around ** percent of those with drug or alcohol dependence or abuse had serious thoughts of suicide in the past year, compared to just ***** percent of those with no such substance dependence of abuse. Similarly, around *** percent of those with a major depressive episode in the past year had attempted suicide, while only *** percent of those without a major depressive episode had done so.

Deaths per week in Poland fluctuated strongly between 2019 and 2024. Nevertheless, the number of deaths in 2024 has generally fallen compared to previous years. To give an illustration: In the 52nd week of the calendar in 2024, 402 fewer people died compared to the same period in 2023. The most deaths in the observation period were recorded in the 45th calendar week of 2020, with 16,242. Diseases and their consequences The main reason for the sharp rise in deaths in 2020 was probably the outbreak of the Covid-19 pandemic. By March 2023, almost 3,000 per one million people in Poland had died as a result of the virus. Although this is comparatively low compared to other CEE countries, the peak phase strongly influences the weekly death figures. Bulgaria was hit particularly heavily among the CEE countries in terms of deaths. Another factor can be various disease patterns in Poland. In May 2022, although 32 percent of diagnosed diseases were Covid-19, 29 percent were hypertension. Death rate in comparison In absolute figures, however, 2021 is the year with the most deaths in the observation period, with 519,500 deaths. In comparison, Hungary only had 155,620 deaths in the same period. Despite this, the average age in Poland has been rising for decades and will continue to do so in the coming decades. It is, therefore, interesting to note that while the death rate fluctuates, life expectancy is rising continuously.

The graph displays the number of deaths per year in the United States from 1950 to 2025. The x-axis represents the years, abbreviated from '50 to '25, while the y-axis indicates the annual number of deaths. Over this 75-year period, the number of deaths ranges from a low of 1,479,684 in 1950 to a high of 3,492,879 in 2021. Notable figures include 2,430,923 deaths in 2001 and 3,090,000 projected deaths in 2024. The data exhibits a general upward trend in annual deaths over the decades, with significant increases in recent years. This information is presented in a line graph format, effectively highlighting the long-term trends and yearly variations in deaths across the United States.

Most estimates place the total number of deaths during the Second World War at around 70-85 million people. Approximately 17 million of these deaths (20-25 percent of the total) were due to crimes against humanity carried out by the Nazi regime in Europe. In comparison to the millions of deaths that took place through conflict, famine, or disease, these 17 million stand out due to the reasoning behind them, along with the systematic nature and scale in which they were carried out. Nazi ideology claimed that the Aryan race (a non-existent ethnic group referring to northern Europeans) was superior to all other ethnicities; this became the justification for German expansion and the extermination of others. During the war, millions of people deemed to be of lesser races were captured and used as slave laborers, with a large share dying of exhaustion, starvation, or individual execution. Murder campaigns were also used for systematic extermination; the most famous of these were the extermination camps, such as at Auschwitz, where roughly 80 percent of the 1.1 million victims were murdered in gas chambers upon arrival at the camp. German death squads in Eastern Europe carried out widespread mass shootings, and up to two million people were killed in this way. In Germany itself, many disabled, homosexual, and "undesirables" were also killed or euthanized as part of a wider eugenics program, which aimed to "purify" German society.

The Holocaust Of all races, the Nazi's viewed Jews as being the most inferior. Conspiracy theories involving Jews go back for centuries in Europe, and they have been repeatedly marginalized throughout history. German fascists used the Jews as scapegoats for the economic struggles during the interwar period. Following Hitler's ascendency to the Chancellorship in 1933, the German authorities began constructing concentration camps for political opponents and so-called undesirables, but the share of Jews being transported to these camps gradually increased in the following years, particularly after Kristallnacht (the Night of Broken Glass) in 1938. In 1939, Germany then invaded Poland, home to Europe's largest Jewish population. German authorities segregated the Jewish population into ghettos, and constructed thousands more concentration and detention camps across Eastern Europe, to which millions of Jews were transported from other territories. By the end of the war, over two thirds of Europe's Jewish population had been killed, and this share is higher still when one excludes the neutral or non-annexed territories.

Lebensraum Another key aspect of Nazi ideology was that of the Lebensraum (living space). Both the populations of the Soviet Union and United States were heavily concentrated in one side of the country, with vast territories extending to the east and west, respectively. Germany was much smaller and more densely populated, therefore Hitler aspired to extend Germany's territory to the east and create new "living space" for Germany's population and industry to grow. While Hitler may have envied the U.S. in this regard, the USSR was seen as undeserving; Slavs were the largest major group in the east and the Nazis viewed them as inferior, which was again used to justify the annexation of their land and subjugation of their people. As the Germans took Slavic lands in Poland, the USSR, and Yugoslavia, ethnic cleansings (often with the help of local conspirators) became commonplace in the annexed territories. It is also believed that the majority of Soviet prisoners of war (PoWs) died through starvation and disease, and they were not given the same treatment as PoWs on the western front. The Soviet Union lost as many as 27 million people during the war, and 10 million of these were due to Nazi genocide. It is estimated that Poland lost up to six million people, and almost all of these were through genocide.

In the 4th quarter of 2022, the category including heart diseases was the one recording most deaths among men and women in Colombia, with over ****** people dying due to such illnesses. The category of cerebrovascular diseases followed, with ***** and ***** cases for women and men, respectively. Other main causes of death in Colombia during that period include chronic respiratory diseases, hypertensive disorders, and some digestive diseases.

Every year the CDC releases the country’s most detailed report on death in the United States under the National Vital Statistics Systems. This mortality dataset is a record of every death in the country for 2005 through 2015, including detailed information about causes of death and the demographic background of the deceased.

It's been said that "statistics are human beings with the tears wiped off." This is especially true with this dataset. Each death record represents somebody's loved one, often connected with a lifetime of memories and sometimes tragically too short.

Putting the sensitive nature of the topic aside, analyzing mortality data is essential to understanding the complex circumstances of death across the country. The US Government uses this data to determine life expectancy and understand how death in the U.S. differs from the rest of the world. Whether you’re looking for macro trends or analyzing unique circumstances, we challenge you to use this dataset to find your own answers to one of life’s great mysteries.

Overview

This dataset is a collection of CSV files each containing one year's worth of data and paired JSON files containing the code mappings, plus an ICD 10 code set. The CSVs were reformatted from their original fixed-width file formats using information extracted from the CDC's PDF manuals using this script. Please note that this process may have introduced errors as the text extracted from the pdf is not a perfect match. If you have any questions or find errors in the preparation process, please leave a note in the forums. We hope to publish additional years of data using this method soon.

A more detailed overview of the data can be found here. You'll find that the fields are consistent within this time window, but some of data codes change every few years. For example, the 113_cause_recode entry 069 only covers ICD codes (I10,I12) in 2005, but by 2015 it covers (I10,I12,I15). When I post data from years prior to 2005, expect some of the fields themselves to change as well.

All data comes from the CDC’s National Vital Statistics Systems, with the exception of the Icd10Code, which are sourced from the World Health Organization.

Project ideas

• The CDC's mortality data was the basis of a widely publicized paper, by Anne Case and Nobel prize winner Angus Deaton, arguing that middle-aged whites are dying at elevated rates. One of the criticisms against the paper is that it failed to properly account for the exact ages within the broad bins available through the CDC's WONDER tool. What do these results look like with exact/not-binned age data?
• Similarly, how sensitive are the mortality trends being discussed in the news to the choice of bin-widths?
• As noted above, the data preparation process could have introduced errors. Can you find any discrepancies compared to the aggregate metrics on WONDER? If so, please let me know in the forums!
• WONDER is cited in numerous economics, sociology, and public health research papers. Can you find any papers whose conclusions would be altered if they used the exact data available here rather than binned data from Wonder?

Differences from the first version of the dataset

• This version of the dataset was prepared in a completely different many. This has allowed us to provide a much larger volume of data and ensure that codes are available for every field.
• We've replaced the batch of sql files with a single JSON per year. Kaggle's platform currently offer's better support for JSON files, and this keeps the number of files manageable.
• A tutorial kernel providing a quick introduction to the new format is available here.
• Lastly, I apologize if the transition has interrupted anyone's work! If need be, you can still download v1.

Long COVID-19 in Jordan

https://doi.org/10.5061/dryad.4b8gthtk6

The data represent responses to a self-reporting questionnaire that was designed to address long COVID-19 status and factors that may associate with it among Jordanians. It included questions regarding COVID-19 symptoms, pre-existing medical history, treatment and supplements, COVID-19 vaccination history, and symptoms recorded after vaccination. We adopted the definition of long COVID-19 that refers to individuals experiencing at least one symptom longer than four weeks.

Description of the data and file structure

The data were entered into SPSS data file and organized as follows: Demographic data (columns B-H) are sex (Male:0, Female: 1), age (18-34:2, 35-44:3, 45-54:4, >55: 5), marital status (single:1, married:2, other:3), smoking (No:0, Yes:1), employment status (not:0, goverment:1, private:2), and obesity (non- obese:0, obese:1), hospitalization required (column I, No:0, Yes:1), number of times of infected w...

Mortality from lung cancer, directly age-standardised rate, persons, under 75 years, 2004-08 (pooled) per 100,000 European Standard population by Local Authority by local deprivation quintile. Local deprivation quintiles are calculated by ranking small areas (Lower Super Output Areas (LSOAs)) within each Local Authority based on their Index of Multiple Deprivation 2007 (IMD 2007) deprivation score, and then grouping the LSOAs in each Local Authority into five groups (quintiles) with approximately equal numbers of LSOAs in each. The upper local deprivation quintile (Quintile 1) corresponds with the 20% most deprived small areas within that Local Authority. The mortality rates have been directly age-standardised using the European Standard Population in order to make allowances for differences in the age structure of populations. There are inequalities in health. For example, people living in more deprived areas tend to have shorter life expectancy, and higher prevalence and mortality rates of most cancers. Lung cancer accounts for 7% of all deaths among men and in England every year and 4% of deaths among women every year. This amounts to 24% of all cancer deaths among men in England and 18% of all cancer deaths among women in England1. Reducing inequalities in premature mortality from all cancers is a national priority, as set out in the Department of Health’s Vital Signs Operating Framework 2008/09-2010/111. This indicator has been produced in order to quantify inequalities in lung cancer mortality by deprivation. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01406

Number of deaths caused by diseases of the respiratory system, by age group and sex, 2000 to most recent year.

The Excess Winter Mortality Index (EWD Index) shows excess winter deaths as a Percentage Ratio of the number of deaths expected in the (eight) warmer months either side of Winter (01 December to 31 March). So the data’s yearly time period is from 01 August to 31 July the following year. In other words, EWD is the ratio of extra deaths from all causes during the winter months compared to average non-winter deaths. The EWD Index is partly dependent on the proportion of Older People in the population, as most excess winter deaths affect Older People. This indicator covers all ages, but there is no standardisation in its calculation by age or any other factor. So figures for an area can be influenced for example by the proportion of Older People. This dataset is updated annually. Source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF), indicator 90360 / E14. Age breakouts, confidence intervals and metadata are shown on the PHE (PHOF) site.

SummaryThe number of cases interviewed who had a completed answer to the question asking if they attended any gatherings of more than 10 people in the 14 days before they became ill (or had a positive test) during their covidLINK interviews.DescriptionMD COVID-19 - Contact Tracing Cases Social Gatherings of More than 10 People layer reflects the number of cases interviewed who had a completed answer to the question asking if they attended any gatherings of more than 10 people in the 14 days before they became ill (or had a positive test) during their covidLINK interviews. Respondents may indicate that they attended more than one category of social gathering. For a variety of reasons, some individuals choose not to answer particular questions during the course of their interview.Events and locations where there is prolonged exposure to other people — including weddings, parties, stores, restaurants, etc. — are considered “high risk” for COVID-19 transmission. The more interaction at a gathering or location, the more likely a person may be to transmit or become infected with the virus. More information about considerations for events and gatherings — including how to assess risk levels and promote healthy behaviors that reduce spread — is available from the Centers for Disease Control and Prevention.Answers to interview questions do not provide evidence of cause and effect. Due to the nature of COVID-19 and the wide range of scenarios in which a person can become infected, most of the time it will not be possible to pinpoint exactly where and when a case became infected. Though a person may report attendance at a particular location, that does not mean that transmission happened at that location.The covidLINK interview questionnaire is updated as necessary to capture relevant information related to case exposure and potential onward transmission. These revisions should be taken into consideration when evaluating trends in case responses over time.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

BackgroundUnderstanding the effects of demographic drivers on lung cancer mortality trends is critical for lung cancer control. We have examined the drivers of lung cancer mortality at the global, regional, and national levels.MethodsData on lung cancer death and mortality were extracted from the Global Burden of Disease (GBD) 2019. Estimated annual percentage change (EAPC) in the age-standardized mortality rate (ASMR) for lung cancer and all-cause mortality were calculated to measure temporal trends in lung cancer from 1990 to 2019. Decomposition analysis was used to analyze the contributions of epidemiological and demographic drivers to lung cancer mortality.ResultsDespite a non-significant decrease in ASMR [EAPC = −0.31, 95% confidence interval (CI): −1.1 to 0.49], the number of deaths from lung cancer increased by 91.8% [95% uncertainty interval (UI): 74.5–109.0%] between 1990 and 2019. This increase was due to the changes in the number of deaths attributable to population aging (59.6%), population growth (56.7%), and non-GBD risks (3.49%) compared with 1990 data. Conversely, the number of lung cancer deaths due to GBD risks decreased by 19.8%, mainly due to tobacco (−12.66%), occupational risks (−3.52%), and air pollution (−3.47%). More lung cancer deaths (1.83%) were observed in most regions, which were due to high fasting plasma glucose levels. The temporal trend of lung cancer ASMR and the patterns of demographic drivers varied by region and gender. Significant associations were observed between the contributions of population growth, GBD risks and non-GBD risks (negative), population aging (positive), and ASMR in 1990, the sociodemographic index (SDI), and the human development index (HDI) in 2019.ConclusionPopulation aging and population growth increased global lung cancer deaths from 1990 to 2019, despite a decrease in age-specific lung cancer death rates due to GBD risks in most regions. A tailored strategy is needed to reduce the increasing burden of lung cancer due to outpacing demographic drivers of epidemiological change globally and in most regions, taking into account region- or gender-specific risk patterns.