Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. SOURCES CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

The deadliest energy source worldwide is coal. It is estimated that there are roughly 33 deaths from brown coal (also known as Lignite) and 25 deaths from coal per terawatt-hour (TWh) of electricity produced from these fossil fuels. While figures take into account accidents, the majority of deaths associated with coal come from air pollution.

Air pollution deaths from fossil fuels

Air pollution from coal-fired plants has been of growing concern as it has been linked to asthma, cancer, and heart disease. Burning coal can release toxic airborne pollutants such as mercury, sulfur dioxide, nitrogen oxides, and particulate matter. Eastern Asia accounts for roughly 31 percent of global deaths attributable to exposure to fine particulate matter (PM2.5) generated by fossil fuel combustion, which is perhaps unsurprising given the fact China and India are the two largest coal consumers in the world.

Safest energy source

Clean and renewable energy sources are unsurprisingly the least deadly energy sources, with 0.04 and 0.02 deaths associated with wind and solar per unit of electricity, respectively. Nuclear energy also has a low death rate, even after the inclusion of nuclear catastrophes like Chernobyl and Fukushima.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dict

Number and percentage of deaths, by place of death (in hospital or non-hospital), 1991 to most recent year.

According to Cognitive Market Research, the global Death Care market size is USD 122584.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 4.50% from 2024 to 2031.

North America holds the major market of more than 40% of the global revenue with a market size of USD 49033.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.7% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 36775.26 million.
Asia Pacific holds the market of around 23% of the global revenue with a market size of USD 28194.37 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.5% from 2024 to 2031.
Latin America holds the market of more than 5% of the global revenue with a market size of USD 6129.21 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.9% from 2024 to 2031.
Middle East and Africa holds the major market of around 2% of the global revenue with a market size of USD 2451.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.2% from 2024 to 2031.
Corporate holds the highest Death Care market revenue share in 2024.

Market Dynamics of Death Care Market

Key Drivers for Death Care Market

Rising Geriatric Population to Increase the Demand Globally

The growing number of senior persons worldwide is driving the geriatric population growth. Longer life expectancies increase mortality rates, which expands the need for death care services' customers. The need for all-inclusive end-of-life solutions, such as funeral planning, cremation services, and grief assistance, is highlighted by this shift in the population. The death care industry is seeing constant demand and innovation as societies struggle with the effects of an aging population. It is crucial for service providers to adjust to the changing needs of this particular demographic group in order to provide dignified and courteous end-of-life experiences.

Increasing urbanization to Propel Market Growth

The increasing trend of urbanization encourages a move toward smaller living areas, which reduces the number of house burials that are customary. In order to meet the demands of urban people, funeral houses, cemeteries, and cremation services are in high demand. The accessibility and availability of death care facilities become critical factors as cities grow and populations congregate. In metropolitan settings, funeral houses and crematoriums are essential for providing effective end-of-life care that are sensitive to cultural differences. This shift brought about by urbanization highlights the need for flexible death care methods so that people can pay their loved ones a proper tribute even in the face of physical limitations.

Restraint Factor for the Death Care Market

Lack of Awareness to Limit the Sales

There's a widespread ignorance in many poor countries about the range of death care services available. This shortfall limits the market's ability to grow in terms of new and creative solutions. People who lack information are frequently forced to use traditional methods, which hinders the adoption of more modern, effective, and culturally sensitive death care options. In order to close this informational gap and enable communities to make knowledgeable decisions about end-of-life preparations, education and outreach programs become essential. A more dynamic and responsive death care sector that caters to the changing needs and preferences of varied people worldwide can be fostered by stakeholders by raising knowledge and understanding of the wide range of services that are offered.

Impact of Covid-19 on the Death Care Market

The COVID-19 pandemic has had a major effect on the death care industry, leading to changes in consumer behavior and disruptions. Health and safety concerns have prompted modifications in funeral customs, including gathering bans and social distancing measures that modify customary funeral settings. Due to the perception that cremations pose a reduced risk of transmission than traditional burials, demand for these services has surged. Furthermore, the state of the economy has affected how people spend their money, with some choosing more straightforward and affordable final arrangements. Funeral homes and death care providers have had to quickly adjust to accommodate the changing demands of bereaved families while adhering to health s...

Nursing homes with residents positive for COVID-19 from 4/22/2020 to 6/19/2020.

Starting in July 2020, this dataset will no longer be updated and will be replaced by the CMS COVID-19 Nursing Home Dataset, available at the following link: https://data.ct.gov/Health-and-Human-Services/CMS-COVID-19-Nursing-Home-Dataset/w8wc-65i5.

Methods: 1) Laboratory-confirmed case counts are based upon data reported via the FLIS web portal. Nursing homes were asked to provide cumulative totals of residents with laboratory confirmed covid. This includes residents currently in-house, in the hospital, or who are deceased. Residents were excluded if they tested positive prior to initial admission to the nursing home. 2) The cumulative number of deaths among nursing home residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable).

Limitations: 1) As of the week of 5/10/20, Point Prevalence Survey testing is being offered to all asymptomatic nursing home residents to inform infection prevention efforts. Point prevalence surveys will be conducted over a period of several weeks. Some nursing homes had adequate testing resources available to conduct surveys prior to this date. Differences in survey timing will impact the number of positive results that a nursing home reports. 2) Cumulative totals of residents testing positive are being collected rather than individual resident data. Thus we cannot verify the counts, de-duplicate, and/or verify whether there is a record of a positive lab test. This may result in either under- or over-counting. 3) The number of COVID-19 positive residents and the number of confirmed deaths among residents are tabulated from different data sources. Due to the timing of availability of test results for deceased residents, it is not appropriate to calculate the percent of cases who died due to COVID-19 at any particular facility based upon this data. 4) The count of deaths reported for 4/14 are not included in this dataset, as they were not broken out by laboratory-confirmed or probable. They can be viewed in the DPH Report here: https://portal.ct.gov/-/media/Coronavirus/CTDPHCOVID19summary4162020.pdf?la=en

Summary of COVID-19 community outbreaks in Ottawa based on the most up to date information available in the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM).

Accuracy: Points of consideration for interpretation of the data:

• The data was extracted by Ottawa Public Health from the Ontario Ministry of Health Public Health Case and Contact Management Solution (CCM). The CCM is a dynamic disease reporting system that allows for ongoing updates to data previously entered. The data extracted from The CCM represent a snapshot at the time of extraction and may differ in previous or subsequent reports.

• Data are for confirmed outbreaks and the number Ottawa residents with laboratory confirmed COVID-19 associated to each outbreak is provided. Please note, individuals may be linked to multiple outbreaks.

• All the outbreaks reflect the outbreak definitions at the time they were declared open:

o Community: From April 1st 2020, 2 or more laboratory-confirmed COVID-19 cases with an epidemiological link in the setting within a 14-day period where at least 2 cases could have reasonably acquired their infection in the setting. Examples of epidemiological links in community settings include community organization (e.g. attended same social or volunteer club meeting), religious/spiritual organization (e.g. attended same service), residential (e.g. multi-unit dwelling - from different households in the same apartment building but rode the elevator together, used a common room at the same time), social event (e.g. attended same one-time party, wedding or funeral together), sports and recreation (e.g. attended same sports team practice or fitness class), or workplace (e.g. same work area, same shift).

• Public health is only required to formally declare outbreaks for workplace community settings but has chosen to declare outbreaks in other community settings when there is more risk to the public, there are challenges in contact tracing and/or capacity allows. Since October 2020, OPH has systematically reported outbreaks in other community settings. Please see the definitions for community outbreaks posted on the OPH COVID-19 Dashboard web page for more information.

Attributes: Data fields:

• Outbreak ID

• Setting - text

• Sub-category - text

• Start Date - outbreak start date

• End Date – outbreak end date

• Cases – total number of people with confirmed COVID-19 linked to the outbreak

• Deaths – total number of people with confirmed COVID-19 linked to the outbreak who died

Update Frequency: Daily

Contact: OPH Epidemiology Team

Annual data on death registrations by area of usual residence in the UK. Summary tables including age-standardised mortality rates.

The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 5 procedures performed (Abdominal Aortic Aneurysm Repair, Unruptured/Open, Abdominal Aortic Aneurysm Repair, Unruptured/Endovascular, Carotid Endarterectomy, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2022 IMIs were generated using AHRQ Version 2023, while previous years' IMIs were generated with older versions of AHRQ software (2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

Abstract copyright UK Data Service and data collection copyright owner.

The National Survey of Bereaved People (VOICES - Views of Informal Carers - Evaluation of Services) is an annual survey designed to measure the quality of end of life care. The VOICES survey particularly focuses on the last three months of life. Results are used to inform policy decisions and enable evaluation of the quality of end of life care by age group, sex, in different settings (home, hospital, care homes and hospices) and by different causes of death. Quality of end of life care is also included as an indicator in the NHS Outcomes Framework and the VOICES survey is used to monitor progress against this.

The Department of Health (DH) first commissioned the survey in 2011 to follow up on a commitment made in the End of Life Care Strategy. Previously, very little systematic information was available about the quality of care delivered to people approaching the end of life, despite reports from the Healthcare Commission and the Neuberger review highlighting deficiencies in care. The commissioning responsibility for the survey moved from DH to NHS England following the restructuring of the Health and Care systems in England in April 2013.

Each year a sample of approximately 49,000 adults who died in England is selected from the deaths registration database held by the Office for National Statistics (ONS). To ensure the sample represents the deaths in England for the given period and covers the key domains of interest, the sample is stratified according to the cause of death, place of death and geography. For the 2011 and 2012 surveys, geography was based on Primary Care Trust (PCT) clusters. For the 2013 survey onwards, this is based on NHS Area Teams (NHS Area Team 2013 has also been applied to the earlier datasets).

The VOICES questionnaire is sent by post to the person who registered the death of the deceased; this is usually a relative or friend of the deceased. Questionnaires are sent out between 4 and 11 months after the patient has died. As is standard in most postal surveys, if no response is received, this first questionnaire is then followed up with two reminders. Once fieldwork, data capture, cleaning and processing are complete, findings are disseminated at both the national and sub-national level.

Further information about the survey and links to related publications may be found on the ONS National Bereavement Survey (VOICES) QMI webpage.

End User Licence and Secure Access versions available
The UK Data Service holds standard End User Licence (EUL) and Secure Access versions of the National Survey of Bereaved People data. EUL data are available to registered users but Secure Access data are only available to ONS Accredited Researchers (in addition, project approval and successful completion of a stringent training course are required before access can be granted). The Secure Access version contains finer detail variables (e.g. IMD deciles as opposed to quintiles in the EUL data, Strategic Clinical Network in addition to NHS Area Teams, and more detailed information on age, causes, dates and place of death). Users are strongly advised to check whether the EUL data are sufficient for their research needs before making an application for the Secure Access version.


Main Topics:
Date, cause and place of death; quality and standards of medical, nursing, social and pastoral care in the last three months of life; support for relatives/carers; demographics of deceased person and respondent.

The National Survey of Bereaved People (VOICES - Views of Informal Carers - Evaluation of Services) is an annual survey designed to measure the quality of end of life care. The VOICES survey particularly focuses on the last three months of life. Results are used to inform policy decisions and enable evaluation of the quality of end of life care by age group, sex, in different settings (home, hospital, care homes and hospices) and by different causes of death. Quality of end of life care is also included as an indicator in the NHS Outcomes Framework and the VOICES survey is used to monitor progress against this.

The Department of Health (DH) first commissioned the survey in 2011 to follow up on a commitment made in the End of Life Care Strategy. Previously, very little systematic information was available about the quality of care delivered to people approaching the end of life, despite reports from the Healthcare Commission and the Neuberger review highlighting deficiencies in care. The commissioning responsibility for the survey moved from DH to NHS England following the restructuring of the Health and Care systems in England in April 2013.

Each year a sample of approximately 49,000 adults who died in England is selected from the deaths registration database held by the Office for National Statistics (ONS). To ensure the sample represents the deaths in England for the given period and covers the key domains of interest, the sample is stratified according to the cause of death, place of death and geography. For the 2011 and 2012 surveys, geography was based on Primary Care Trust (PCT) clusters. For the 2013 survey onwards, this is based on NHS Area Teams (NHS Area Team 2013 has also been applied to the earlier datasets).

The VOICES questionnaire is sent by post to the person who registered the death of the deceased; this is usually a relative or friend of the deceased. Questionnaires are sent out between 4 and 11 months after the patient has died. As is standard in most postal surveys, if no response is received, this first questionnaire is then followed up with two reminders. Once fieldwork, data capture, cleaning and processing are complete, findings are disseminated at both the national and sub-national level.

Further information about the survey and links to related publications may be found on the ONS National Bereavement Survey (VOICES) QMI webpage.

End User Licence and Secure Access versions available
The UK Data Service holds standard End User Licence (EUL) and Secure Access versions of the National Survey of Bereaved People data. EUL data are available to registered users but Secure Access data are only available to ONS Accredited Researchers (in addition, project approval and successful completion of a stringent training course are required before access can be granted). The Secure Access version contains finer detail variables (e.g. IMD deciles as opposed to quintiles in the EUL data, Strategic Clinical Network in addition to NHS Area Teams, and more detailed information on age, causes, dates and place of death). Users are strongly advised to check whether the EUL data are sufficient for their research needs before making an application for the Secure Access version.

Background: In 2019, 80% of the 7.4 million global child deaths occurred in low- and middle-income countries (LMICs). Global and regional estimates of cause of hospital death and admission in LMIC children are needed to guide global and local priority setting and resource allocation but are currently lacking. The study objective was to estimate global and regional prevalence for common causes of pediatric hospital mortality and admission in LMICs. Methods: We performed a systematic review and meta-analysis to identify LMIC observational studies published January 1, 2005-February 26, 2021. Eligible studies included: a general pediatric admission population, a cause of admission or death, and total admissions. We excluded studies with data before 2000 or without a full text. Two authors independently screened and extracted data. We performed methodological assessment using domains adapted from the Quality in Prognosis Studies tool. Data were pooled using random-effects models where possible. We reported prevalence as a proportion of cause of death or admission per 1000 admissions with 95% confidence intervals (95%CI). Findings: ur search identified 29,637 texts. After duplicate removal and screening, we analyzed 253 studies representing 21.8 million pediatric hospitalizations in 59 LMICs. All-cause pediatric hospital mortality was 4.1% [95%CI 3.4-4.7%]. The most common causes of mortality (deaths/1000 admissions) were infectious (12 [95%CI 9-14]); respiratory (9 [95%CI 5-13]); and gastrointestinal (9 [95%CI 6-11]). Common causes of admission (cases/1000 admissions) were respiratory (255 [95%CI 231-280]); infectious (214 [95%CI193-234]); and gastrointestinal (166 [95%CI 143-190]). We observed regional variation in estimates. Pediatric hospital mortality remains high in LMICs. Implications: Global child health efforts must include measures to reduce hospital mortality including basic emergency and critical care services tailored to the local disease burden. Resources are urgently needed to promote equity in child health research, support researchers, and collect high-quality data in LMICs to further guide priority setting and resource allocation. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

The main causes of death in Nigeria in 2021 were neonatal disorders and malaria. More specifically, nearly 14 percent and 13 percent of all deaths in the country were caused by neonatal disorders and malaria, respectively. Other common causes included lower respiratory infects and COVID-19.

As part of the Pacific Dataviz Challenge 2024,the New Caledonian government's Department of Health and Social Affairs (DASS) is making available the list of medical causes of death in New Caledonia between 2011 and 2023.

This dataset includes medical causes of death in New Caledonia from 2011 to 2023.

For reasons of confidentiality linked to medical secrecy, people's ages have been classified by broad category.

Medical causes are coded in ICD-10 (International Classification of Diseases established by the World Health Organization).

The study of medical causes of death in New Caledonia, whether or not the person is domiciled in the territory, is essential for public health. It enables us to identify the most pressing health problems, as well as vulnerable populations, in order to guide public policy and set up appropriate health programs.

Medical causes of death are recorded on medical certificates. Their analysis focuses on the initial cause mentioned by the physician as being at the origin of the health event leading to death.

It should be noted that this dataset is subject to certain biases, as medical certificates are completed on the basis of the information available at the time of death, which means that the doctor does not systematically have all the information needed to identify the initial cause of death.

Data is from the California Department of Public Health (CDPH) Respiratory Virus Weekly Report.

The report is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week.

Laboratory surveillance for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (parainfluenza types 1-4, human metapneumovirus, non-SARS-CoV-2 coronaviruses, adenovirus, enterovirus/rhinovirus) involves the use of data from clinical sentinel laboratories (hospital, academic or private) located throughout California. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for influenza, respiratory syncytial virus, and other respiratory viruses in California. These laboratories report the number of laboratory-confirmed influenza, respiratory syncytial virus, and other respiratory virus detections and isolations, and the total number of specimens tested by virus type on a weekly basis.

Test positivity for a given week is calculated by dividing the number of positive COVID-19, influenza, RSV, or other respiratory virus results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19 and influenza-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset provided by the State of California Department of Finance (https://dof.ca.gov/forecasting/demographics/projections/). Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html).

CDPH collaborates with Northern California Kaiser Permanente (NCKP) to monitor trends in RSV admissions. The percentage of RSV admissions is calculated by dividing the number of RSV-related admissions by the total number of admissions during the same period. Admissions for pregnancy, labor and delivery, birth, and outpatient procedures are not included in total number of admissions. These admissions serve as a proxy for RSV activity and do not necessarily represent laboratory confirmed hospitalizations for RSV infections; NCKP members are not representative of all Californians.

Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify influenza, respiratory syncytial virus, and COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all influenza, respiratory syncytial virus, and COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

Wastewater data: This dataset represents statewide weekly SARS-CoV-2 wastewater summary values. SARS-CoV-2 wastewater concentrations from all sites in California are combined into a single, statewide, unit-less summary value for each week, using a method for data transformation and aggregation developed by the CDC National Wastewater Surveillance System (NWSS). Please see the CDC NWSS data methods page for a description of how these summary values are calculated. Weekly wastewater data are defined as Sunday through Saturday.

Preliminary figures for 2023 indicated that ischaemic heart disease was the leading cause of death in the Philippines. The number of people who died from this illness was estimated at 124,437. Following this, cancer resulted in the deaths of about 71,000 people. Eating habits Heart diseases have been linked to high meat consumption, among others. In the Philippines, pork has been the most consumed meat type, followed closely by chicken. While pork meat is typically produced domestically, the country also imports pork to supplement its supply. However, plant-based food has started gaining popularity among Filipinos. In fact, a 2024 survey revealed that 69 percent of surveyed Filipinos consumed plant-based products, including meat alternatives. Common diseases in the Philippines Aside from heart and cerebrovascular diseases, the Filipino population is also exposed to infections, diabetes, skin diseases, and illnesses resulting from high meat consumption. In 2020, over 700,000 Filipinos contracted acute respiratory tract infections, followed by over 400,000 diagnosed with hypertension. In areas with high exposure to rain, dengue infections and leptospirosis have also become prevalent.

This dataset, released February 2021, contains statistics relating to Deaths from all avoidable causes for males/females/persons aged 0 to 74 years, 2014-2018. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Data compiled by PHIDU from deaths data based on the 2014 to 2018 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. The population is the ABS Estimated Resident Population (ERP), 30 June 2014 to 30 June 2018.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

This dataset, released July 2018, contains statistics relating to Deaths from all avoidable causes for males/females/persons aged 0 to 74 years, 2011-2015. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible.

For more information please see the data source notes on the data.

Source: Data compiled by PHIDU from deaths data based on the 2011 to 2015 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. The population at the small area level is the ABS Estimated Resident Population (ERP), 30 June 2011 to 30 June 2015, Statistical Areas Level 2; the population standard is the ABS ERP for Australia, 30 June 2011 to 30 June 2015.

AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.