This dataset presents the footprint of the rate of potentially avoidable deaths per 100,000 people, age-standardised, by sex. Potentially avoidable deaths are deaths below the age of 75 from conditions that are potentially preventable through individualised care and/or treatable through existing primary or hospital care. The data spans the years of 2009-2016 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is based on analysis of the Australian Institute of Health and Welfare (AIHW) National Mortality Database (NMD). The database includes cause of death information which is sourced from the Registrars of Births, Deaths and Marriages in each state and territory, the National Coronial Information System, and compiled and coded by the Australian Bureau of Statistics (ABS). For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Life Expectancy and Potentially Avoidable Deaths 2014-2016 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. Rates have been age-standardised to facilitate comparisons between populations with different age structures.

This dataset, released December 2016, contains statistics relating to avoidable mortalities during the year 2010-2014 from the following causes: cancer, diabetes, circulatory systems diseases, respiratory system diseases and external causes. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS).

Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure.

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

Source: Data compiled by PHIDU from deaths data based on the 2010 to 2014 Cause of Death Unit Record Filessupplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries ofBirths, Deaths and Marriages and the National Coronial Information System.

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available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells.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 Local Government Area (LGA) 2016 geographic boundaries. 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.

This dataset presents the footprint of the average number of years a person is expected to live at birth by sex, assuming that the current age-specific death rates are experienced throughout their life. The data spans the years of 2011-2016 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

The data is based on the Australian Institute of Health and Welfare (AIHW) analysis of life expectancy estimates as provided by the Australian Bureau of Statistics (ABS). Life expectancies at birth were calculated with reference to state/territory and Australian life tables (where appropriate) for a three year period. The disaggregation used for reporting life expectancy at birth is PHN area. These values are provided by the ABS.

For further information about this dataset, visit the data source: Australian Institute of Health and Welfare - Life Expectancy and Potentially Avoidable Deaths 2014-2016 Data Tables.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Life expectancy for 2014-2016 are based on the average number of deaths over three years, 2014-2016, and the estimated resident population (ERP) as at 30 Jun 2015.

This dataset, released December 2016, contains statistics relating to avoidable mortalities during the year 2010-2014 from the following causes: cancer, diabetes, circulatory systems diseases, respiratory system diseases and external causes. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2010 to 2014 Cause of Death Unit Record Filessupplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries ofBirths, Deaths and Marriages and the National Coronial Information System. 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 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.

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.

The leading cause of death in low-income countries worldwide in 2021 was lower respiratory infections, followed by stroke and ischemic heart disease. The death rate from lower respiratory infections that year was 59.4 deaths per 100,000 people. While the death rate from stroke was around 51.6 per 100,000 people. Many low-income countries suffer from health issues not seen in high-income countries, including infectious diseases, malnutrition and neonatal deaths, to name a few. Low-income countries worldwide Low-income countries are defined as those with per gross national incomes (GNI) per capita of 1,045 U.S. dollars or less. A majority of the world’s low-income countries are located in sub-Saharan Africa and South East Asia. Some of the lowest-income countries as of 2023 include Burundi, Sierra Leone, and South Sudan. Low-income countries have different health problems that lead to worse health outcomes. For example, Chad, Lesotho, and Nigeria have some of the lowest life expectancies on the planet. Health issues in low-income countries Low-income countries also tend to have higher rates of HIV/AIDS and other infectious diseases as a consequence of poor health infrastructure and a lack of qualified health workers. Eswatini, Lesotho, and South Africa have some of the highest rates of new HIV infections worldwide. Likewise, tuberculosis, a treatable condition that affects the respiratory system, has high incident rates in lower income countries. Other health issues can be affected by the income of a country as well, including maternal and infant mortality. In 2023, Afghanistan had one of the highest rates of infant mortality rates in the world.

The number of potentially avoidable deaths from all causes by males, females and total people aged 0 to 74 years and their corresponding mortality rates/ratios with respective confidence intervals, …Show full descriptionThe number of potentially avoidable deaths from all causes by males, females and total people aged 0 to 74 years and their corresponding mortality rates/ratios with respective confidence intervals, 2010-14 (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/. For information on the avoidable mortality concept, please refer to the Australian and New Zealand Atlas of Avoidable Mortality, available from: http://phidu.torrens.edu.au/. Source: Data compiled by PHIDU from deaths data based on the 2010 to 2014 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, and ABS Estimated Resident Population (ERP), 30 June 2010 to 30 June 2014. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

Maternal mortality ratio shown by province from 2001 to 2018*Number of maternal deaths per 10,000 live births.The maternal mortality rate for every one hundred thousand live births is 38 according to 2016 data. Its fluctuations during the last decades demonstrate the need to focus on access and care by qualified professionals to reduce the causes of preventable deaths.Source: https://www.ossyr.org.ar/indicadores.php#Raz%C3%B3n-de-mortalidad-materna

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 Local Government Area (LGA) 2016 geographic boundaries.

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.

r/> 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.

Most common additional conditions (antecedent and immediate CoD) in the causal chain for the 5 leading underlying causes of child deaths in CHAMPS.b'*'

BackgroundSepsis is one of the leading causes of preventable deaths in hospitals. This study presents the evaluation of a quality collaborative, which aimed to decrease sepsis-related hospital mortality.MethodsThe German Quality Network Sepsis (GQNS) offers quality reporting based on claims data, peer reviews, and support for establishing continuous quality management and staff education. This study evaluates the effects of participating in the GQNS during the intervention period (April 2016–June 2018) in comparison to a retrospective baseline (January 2014–March 2016). The primary outcome was all-cause risk-adjusted hospital mortality among cases with sepsis. Sepsis was identified by International Classification of Diseases (ICD) codes in claims data. A controlled time series analysis was conducted to analyze changes from the baseline to the intervention period comparing GQNS hospitals with the population of all German hospitals assessed via the national diagnosis-related groups (DRGs)-statistics. Tests were conducted using piecewise hierarchical models. Implementation processes and barriers were assessed by surveys of local leaders of quality improvement teams.ResultsSeventy-four hospitals participated, of which 17 were university hospitals and 18 were tertiary care facilities. Observed mortality was 43.5% during baseline period and 42.7% during intervention period. Interrupted time-series analyses did not show effects on course or level of risk-adjusted mortality of cases with sepsis compared to the national DRG-statistics after the beginning of the intervention period (p = 0.632 and p = 0.512, respectively). There was no significant mortality decrease in the subgroups of patients with septic shock or ventilation >24 h or predefined subgroups of hospitals. A standardized survey among 49 local quality improvement leaders in autumn of 2018 revealed that most hospitals did not succeed in implementing a continuous quality management program or relevant measures to improve early recognition and treatment of sepsis. Barriers perceived most commonly were lack of time (77.6%), staff shortage (59.2%), and lack of participation of relevant departments (38.8%).ConclusionAs long as hospital-wide sepsis quality improvement efforts will not become a high priority for the hospital leadership by assuring adequate resources and involvement of all pertinent stakeholders, voluntary initiatives to improve the quality of sepsis care will remain prone to failure.

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.

In 2021, COVID-19 caused about 133 deaths per 100,000 population in high-income countries. This statistic displays the leading causes of death in high-income countries in 2021 by deaths per 100,000 population. Mortality from chronic diseases such as cancer and heart diseases are increasing around the world. Chronic deaths are especially prominent in Western countries, but have also recently began to increase in the developing world. Non-communicable disease burden This increase in chronic and degenerative non-communicable diseases globally stems from aging populations, modernization, and rapid urbanization. Though these are all signs of socioeconomic progress, the resulting shift in disease carries a heavy burden for societies. Health expenditure makes up around 10 percent or more of the GDP in most high-income countries, and the global spending on medicines is expected to more than double from 2010 to 2027. Non-communicable disease risk factors and prevention In most OECD countries, over 30 percent of adults are overweight. Lack of exercise, poor nutrition, and generally unhealthy lifestyles can often lead to a cluster of symptoms including abnormal blood levels, high blood pressure, and excess body fat, which in turn pose an increased risk of heart disease, stroke, and diabetes. However, most non-communicable diseases are preventable, and their modifiable risk factors can be lowered through lifestyle and behavioral changes.

PM10 (particulate matter less than 10 micrometres in diameter) comprises solid and liquid particles in the air. PM10 can be inhaled and the largest particles in this size fraction are deposited in the upper airways, while the smaller ones can deposit deep in the lungs. Children, the elderly, and people with existing heart or lung problems have a higher risk of health effects from PM10 exposure. Health effects include decreased lung function or heart attack, and mortality. We report on the modelled number of premature deaths for adults (30+ years), hospitalisations, and restricted activity days for people of all ages for years 2006 and 2016 only. The model only includes impacts that result from exposure to PM10 that comes from human activities. We focus on PM10 from human activities because these sources can be managed, unlike PM from natural sources such as sea salt. • Premature deaths are those, often preventable, occurring before a person reaches the age they could be expected to live to. • Hospitalisations relate to those for respiratory and cardiac illnesses (not including cases leading to premature death). • Restricted activity days occur when symptoms are sufficient to limit usual activities such as work or study. These days aren’t shared evenly across the population – people with asthma or other respiratory conditions would likely have more restricted activity days. More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf.

This dataset presents the footprint of statistics related to deaths due to all causes (combined) by sex. The reported statistics include year of death, total deaths, crude rates, age-standardised rates, rate ratio, median age at death, premature deaths, potential years of life lost and potentially avoidable deaths. The data spans the years of 2012-2016 and is aggregated to Local Government Area (LGA) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS). Mortality Over Regions and Time (MORT) books are workbooks that contain recent deaths data for specific geographical areas, sourced from the Australian Institute of Health and Welfare (AIHW) National Mortality Database. They present various statistics related to deaths by all causes and leading causes of death by sex for each geographical area. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - MORT Books. Please note: AURIN has spatially enabled the original data.

This dataset, released February 2021, contains statistics relating to avoidable mortalities during the year 2014-2018 from the following causes: cancer, diabetes, circulatory systems diseases, respiratory system diseases and external causes. The data is by Local Government Area (LGA) 2016 geographic boundaries. 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.

State-wise comparative analysis of neonatal mortality rate, India, 2005–2006 and 2015–2016 with reference to SDG3 target on preventable deaths among new borns.

The number of potentially avoidable deaths and their cause at age 0 to 74 years with corresponding mortality rates/ratios with respective confidence intervals, 2010 - 2014. The specified causes of …Show full descriptionThe number of potentially avoidable deaths and their cause at age 0 to 74 years with corresponding mortality rates/ratios with respective confidence intervals, 2010 - 2014. The specified causes of death are: cancers, colorectal cancer, breast cancer, circulatory system diseases, ischaemic heart disease, cerebrovascular disease, respiratory system diseases, chronic obstructive pulmonary disease, deaths from select external causes of mortality, suicide and self-inflicted injuries, other external causes of mortality, transport accidents. (all entries that were classified as not shown, not published or not applicable were assigned a null value; no data was provided for Maralinga Tjarutja LGA, in South Australia). The data is by LGA 2015 profile (based on the LGA 2011 geographic boundaries). For more information on statistics used please refer to the PHIDU website, available from: http://phidu.torrens.edu.au/. For information on the avoidable mortality concept please refer to the Australian and New Zealand Atlas of Avoidable Mortality, available from: http://phidu.torrens.edu.au/. Source: Data compiled by PHIDU from deaths data based on the 2010 to 2014 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, and ABS Estimated Resident Population (ERP), 30 June 2010 to 30 June 2014. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)