A ranking of the 30 most common causes of death each year in Alberta, by ranking and total number of deaths. Vital Statistics cause of death data from 2023 onward is available on the Interactive Health Data Application under the Mortality category - Interactive Health Data Application - Mortality category

A ranking of the 30 most common causes of death each year in Alberta, by ranking and total number of deaths. Vital Statistics cause of death data from 2023 onward is available on the Interactive Health Data Application under the Mortality category - Interactive Health Data Application - Mortality category

IT: Completeness of Death Registration with Cause-of-Death Information data was reported at 100.000 % in 2010. This stayed constant from the previous number of 100.000 % for 2009. IT: Completeness of Death Registration with Cause-of-Death Information data is updated yearly, averaging 98.100 % from Dec 1992 (Median) to 2010, with 5 observations. The data reached an all-time high of 100.000 % in 2010 and a record low of 95.200 % in 1992. IT: Completeness of Death Registration with Cause-of-Death Information data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Population and Urbanization Statistics. Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.; ; World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/gho/data/node.main.1?lang=en).; Weighted average;

This layer represents the estimated percentage of post-neonatal (1 month old to 5 years old) deaths due to malaria out of the total number of post-neonatal deaths for each country in 2015. Those estimates are provided by the World Health Organization and by the Maternal and Child Epidemiology Estimation Group (MCEE-John Hopkins University). Data for neonatal and under five children are also available by clicking in a specific country.You can access the report here:http://apps.who.int/iris/bitstream/10665/43840/1/9789241596435_eng.pdfFor more information and to access the raw data, visit the WHO website: http://apps.who.int/gho/data/view.main.ghe1002015-CH8?lang=en

The quantity and condition of downed dead wood (DDW) is emerging as a major factor governing forest ecosystem processes such as carbon cycling, fire behavior, and tree regeneration. Despite this, systematic inventories of DDW are sparse if not absent across major forest biomes. The Forest Inventory and Analysis program of the United States (US) Forest Service has conducted an annual DDW inventory on all coterminous US forest land since 2002 (~1 plot per 38,850 ha), with a sample intensification occurring since 2012 (~1 plot per 19,425 ha). The data are organized according to DDW components and by sampling information which can all be linked to a multitude of auxiliary information in the national database. As the sampling of DDW is conducted using field efficient line-intersect approaches, several assumptions are adopted during population estimation that serve to identify critical knowledge gaps. The plot- and population-level DDW datasets and estimates provide the first insights into an understudied but critical ecosystem component of temperate forests of North America with global application. Resources in this dataset:Resource Title: Data files. File Name: Web Page, url: https://www.nature.com/articles/sdata2018303#Sec9 Data Citations: USDA Forest Inventory and Analysis DataMart https://apps.fs.usda.gov/fia/datamart/datamart.html (2018); Woodall, C. W. et al. Dryad Digital Repository https://doi.org/10.5061/dryad.9sv4765 (2018) (data links appear at the bottom of the References section)

COVID-19Plots and analysis relating to the coronavirus pandemic. Includes five sets of plots and associated R code to generate them.1) HeatmapsUpdated every few days - heatmaps of COVID-19 case and death trajectories for Local Authorities (or equivalent) in England, Wales, Scotland, Ireland and Germany.2) All cause mortalityUpdated on Tuesday (for England & Wales), Wednesday (for Scotland) and Friday (for Northern Ireland) - analysis and plots of weekly all-cause deaths in 2020 compared to previous years by country, age, sex and region. Also a set of international comparisons using data from mortality.org3) ExposuresNo longer updated - mapping of potential COVID-19 mortality exposure at local levels (LSOAs) in England based on the age-sex structure of the population and levels of poor health.There is also a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here: https://victimofmaths.shinyapps.io/covidmapper/, on GitHub https://github.com/VictimOfMaths/COVIDmapper and uploaded to this record4) Index of Multiple Deprivation No longer updated - preliminary analysis of the inequality impacts of COVID-19 based on Local Authority level cases and levels of deprivation. 5) Socioeconomic inequalities. No longer updated (unless ONS release more data) - Analysis of published ONS figures of COVID-19 and other cause mortality in 2020 compared to previous years by deprivation decile.Latest versions of plots and associated analysis can be found on Twitter: https://twitter.com/victimofmathsThis work is described in more detail on the UK Data Service Impact and Innovation Lab blog: http://lab.ukdataservice.ac.uk/2020/05/21/visualising-high-risk-areas-for-covid-19-mortality/Adapted from data from the Office for National Statistics licensed under the Open Government Licence v.1.0.http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Approximately 30% of deaths in Shanghai either occur at home or are not medically attended. The recorded cause of death (COD) in these cases may not be reliable. We applied the Smart Verbal Autopsy (VA) tool to assign the COD for a representative sample of home deaths certified by 16 community health centers (CHCs) from three districts in Shanghai, from December 2017 to June 2018. The results were compared with diagnoses from routine practice to ascertain the added value of using SmartVA. Overall, cause-specific mortality fraction (CSMF) accuracy improved from 0.93 (93%) to 0.96 after the application of SmartVA. A comparison with a “gold standard (GS)” diagnoses obtained from a parallel medical record review investigation found that 86.3% of the initial diagnoses made by the CHCs were assigned the correct COD, increasing to 90.5% after the application of SmartVA. We conclude that routine application of SmartVA is not indicated for general use in CHCs, although the tool did improve diagnostic accuracy for residual causes, such as other or ill-defined cancers and non-communicable diseases.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

This layer represents the estimated percentage of post-neonatal (1 month old to 5 years old) deaths due to diarrhoea out of the total number of post-neonatal deaths for each country in 2015. Those estimates are provided by the World Health Organization and by the Maternal and Child Epidemiology Estimation Group (MCEE-John Hopkins University). Data for neonatal and under five children are also available by clicking in a specific country.You can access the report here:http://apps.who.int/iris/bitstream/10665/43840/1/9789241596435_eng.pdfFor more information and to access the raw data, visit the WHO website: http://apps.who.int/gho/data/view.main.ghe1002015-CH3?lang=en

Here are a few use cases for this project:

1. Historical Research: The TombReader model can be used in the field of historical studies. With the ability to read text from resources like tombstones, it could provide a wealth of information about birth and death years (0-5), places of birth (pob), death (pod), etc., which could significantly aid researchers in building chronological data and genealogy trees.

2. Smart Tourism and Heritage Preservation: Tour operators or heritage conservators can use TombReader to quickly read and catalog information from historical cemeteries to create interactive digital experiences for tourists or for documentation purposes.

3. Forensic Investigations: Forensic teams can use this model to quickly gather information from tombstones when tracing family history or investigating unmarked graves, thus speeding up the investigative process.

4. Genealogy Research: Genealogists can use TombReader to efficiently trace family lineages. It can automate the laborious process of collecting birth, death, and location data.

5. Augmented Reality Apps: AR developers can use the TombReader model to create apps for history enthusiasts and tourists that would allow users to simply point their device at a gravestone, have the details recognized and translated into a readable format, and potentially search a database with those details.

In this paper, performance of hurdle models in rare events data is improved by modifying their binary component. Rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. This research aimed to develop and assess the performance of the Poisson and Negative Binomial (NB) Hurdle Rare Event Weighted Logistic Regression (REWLR) models, applied to simulated data with various degrees of zero inflation and to Nairobi county’s maternal mortality data. The study data on maternal mortality were pulled from JPHES. The data contain the number of maternal deaths, which is the outcome variable, and other obstetric and demographic factors recorded in MNCH facilities in Nairobi between October 2021 and January 2022. The models were also fit and evaluated based on simulated data with varying degrees of zero inflation. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best performing model for zero inflated count data due to rare events.

Groundwater withdrawal estimates for 1913-2016 for the Death Valley regional groundwater flow system (DVRFS) are compiled in this Microsoft® Access database to support a regional, three-dimensional, transient groundwater flow model (Belcher and others, 2017; Halford and Jackson, 2020). This database (version 2) updates previously published databases that compiled estimates of groundwater withdrawals for 1913-1998 (Moreo and others, 2003), 1913-2003 (Moreo and Justet, 2008), and 1913-2010 (Elliott and Moreo, 2018; version 1 of this data release). Version 2 of this data release is the most current version of the database and supersedes all previous versions. A total of about 41,000 acre-ft of groundwater were withdrawn from DVRFS in 2016 of which 51 percent was used for irrigation, 20 percent for domestic, and 27 percent for public supply, commercial, and mining activities. The total groundwater withdrawals for Pahrump Valley (hydrographic area 162) increased from 17,000 acre-ft in 2010 to 19,900 acre-ft in 2016. During the same period irrigation withdrawals increased from 3,700 to 5,900 acre-ft and other water uses, primarily public supply and domestic, increased from 13,300 to 14,000 acre-ft. The increase in total water use is attributed primarily to an increase in irrigated acreage. Total irrigated acreage increased from 720 acres in 2010 to 1,100 acres in 2016. The total groundwater withdrawals for Amargosa Desert (hydrographic area 230) in 2010 (18,100 acre-ft) were similar to 2016 (18,400 acre-ft). Irrigation withdrawals for 2016 totaled 15,100 acre-ft. In 2010, 800 of 2,460 irrigated acres were metered (33 percent). By 2016, 1,370 of 2,700 irrigated acres were metered (51 percent). Due to this increase in the number of metered irrigated fields, metered irrigation withdrawals continue to be compiled as reported instead of compiling acreage and applying estimated application rates as was done for previous database versions (Moreo and others, 2003; Moreo and Justet, 2008). The mean application rate for metered fields (5.4 ft/yr) was lower than the mean application rate estimated for fields without meters (6.5 ft/yr; Moreo and Justet, 2008). It is unclear whether the mean application rate for metered fields is representative of application rates in fields that are not metered. An accuracy of ± 5 percent is assumed for all metered withdrawals. Groundwater withdrawals for all water-use categories other than irrigation in Amargosa Desert were 3,300 acre-ft in 2016 (18 percent of total withdrawals). Commercial water use decreased from 2,200 acre-ft in 2010 to 1,800 acre-ft in 2016. Groundwater use for mining increased from 310 acre-ft in 2010 (less than 2 percent) to 690 acre-ft in 2016 (4 percent). This increase in mining use can be attributed to an increase in water usage by Industrial Mineral Ventures Nevada. Domestic and public supply water use in 2016 was 830 acre-ft, about 4 percent of the total water use. Other significant areas of groundwater withdrawals are Penoyer Valley (hydrographic area 170) and the Nevada National Security Site (NNSS). Penoyer Valley was excluded from this update because it lies outside of the version 3 model boundary; however, it should be noted that withdrawal estimates through 2003 and withdrawal points for Penoyer Valley and other areas outside of the DVRFS version 3 preliminary boundary but within the DVRFS version 2 boundary have not been removed from the database. Groundwater withdrawals supporting NNSS activities were updated for the period of record based on updated estimates published by Elliott and Moreo (2011) and U.S. Geological Survey (2017).

Accuracy:Data are from the "Quarterly PHU opioid-related deaths report" published quarterly by the Office of the Chief Coroner for Ontario (OCCO).Opioid-related deaths are defined as an acute intoxication/toxicity death resulting from the direct effects of the administration of exogenous substance(s) where one or more of the substances is an opioid, regardless of how the opioid was obtained. This excludes deaths due to chronic substance use, medical assistance in dying, trauma where an intoxicant contributed to the circumstances of the injury and deaths classified as homicide.Reports only include confirmed opioid-related deaths for which death investigation results have indicated an opioid directly contributed to the cause of death; these reports are created when approximately 80% of the deaths in the most recent quarter for the province have been confirmed. A breakdown of confirmed vs probable cases can be found in the Interactive Opioid Tool on the PHO website (https://www.publichealthontario.ca/en/dataandanalytics/pages/opioid.aspx)Deaths have been assigned to public health unit based on six-digit postal code of the residence of the decedent. If residence postal code was unavailable, the postal code of the incident location was used. If postal code of the incident location was unavailable, the postal code of the death location was used. This methodology aligns with how deaths are assigned in PHO's Interactive Opioid Tool. Update Frequency: Quarterly

Attributes:Quarter - Year and quarter of death.Number of confirmed opioid-related deaths of Ottawa residents – number of confirmed opioid-related deaths of Ottawa residents. Contact: Ottawa Public Health Epidemiology Team

Weights and transformed weights by error type and SDI level.

The predicted number of cumulative death produced by the model over time for three different quarantine scenarios and three time periods together with the corresponding 90% prediction intervals.

Abstract

JHU Coronavirus COVID-19 Global Cases, by country

Documentation

PHS is updating the Coronavirus Global Cases dataset weekly, Monday, Wednesday and Friday from Cloud Marketplace.

This data comes from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This database was created in response to the Coronavirus public health emergency to track reported cases in real-time. The data include the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries, aggregated at the appropriate province or state. It was developed to enable researchers, public health authorities and the general public to track the outbreak as it unfolds. Additional information is available in the blog post.

Visual Dashboard (desktop): https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

Section 2

Included Data Sources are:

• World Health Organization (WHO): https://www.who.int/
• DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
• BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
• National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
• China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
• Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
• Macau Government: https://www.ssm.gov.mo/portal/
• Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
• US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
• Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
• Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
• European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
• Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
• Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
• 1Point3Arces: https://coronavirus.1point3acres.com/en
• WorldoMeters: https://www.worldometers.info/coronavirus/

%3C!-- --%3E

Section 3

**Terms of Use: **

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Section 4

**U.S. county-level characteristics relevant to COVID-19 **

Chin, Kahn, Krieger, Buckee, Balsari and Kiang (forthcoming) show that counties differ significantly in biological, demographic and socioeconomic factors that are associated with COVID-19 vulnerability. A range of publicly available county-specific data identifying these key factors, guided by international experiences and consideration of epidemiological parameters of importance, have been combined by the authors and are available for use:

https://github.com/mkiang/county_preparedness/

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

Cyprus CY: Completeness of Death Registration with Cause-of-Death Information data was reported at 68.000 % in 2016. This records a decrease from the previous number of 86.000 % for 2011. Cyprus CY: Completeness of Death Registration with Cause-of-Death Information data is updated yearly, averaging 82.800 % from Dec 1997 (Median) to 2016, with 5 observations. The data reached an all-time high of 86.000 % in 2011 and a record low of 68.000 % in 2016. Cyprus CY: Completeness of Death Registration with Cause-of-Death Information data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cyprus – Table CY.World Bank.WDI: Population and Urbanization Statistics. Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.;World Health Organization, Global Health Observatory Data Repository/World Health Statistics (http://apps.who.int/gho/data/node.main.1?lang=en).;Weighted average;

Japan JP: Suicide Mortality Rate: Female data was reported at 11.400 NA in 2016. This records a decrease from the previous number of 11.800 NA for 2015. Japan JP: Suicide Mortality Rate: Female data is updated yearly, averaging 13.600 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 14.100 NA in 2010 and a record low of 11.400 NA in 2016. Japan JP: Suicide Mortality Rate: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

India IN: Suicide Mortality Rate: Male data was reported at 17.800 NA in 2016. This records a decrease from the previous number of 18.000 NA for 2015. India IN: Suicide Mortality Rate: Male data is updated yearly, averaging 18.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.600 NA in 2000 and a record low of 17.700 NA in 2010. India IN: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;