The Digital Economy Act 2017 register of information sharing agreements link contains a:

• list of information sharing agreements

• list of information sharing powers and objectives available under chapters 1, 2, 3 and 4 of Part 5 of the Digital Economy Act 2017

• list of specified persons (controllers and processors) for the information sharing agreements under the public service delivery, debt, fraud and civil registration provisions within the Digital Economy Act 2017, explaining which information sharing powers or objectives each specified person can disclose and receive information

Deaths registration data (all deaths in England and Wales) collected from The Registrar General for England and Wales. Record-level person data set, where a record represents one death registration.

The dataset is an index to digitized historical death certificates from 1862-1948 from all 5 NYC boroughs. Details about certificates in DORIS's collection and their digitization status can be found on our website (https://a860-historicalvitalrecords.nyc.gov/digital-vital-records).

The dataset is an index to digitized historical birth certificates from 1855-1909 from all 5 NYC boroughs. Details about certificates in DORIS's collection and their digitization status can be found on our website (https://a860-historicalvitalrecords.nyc.gov/digital-vital-records).

Cause-specific infant mortality fraction by WHO VA category and age group in Eastern Ethiopia, 2016–18.

Register of information sharing agreements that have been made under the public service delivery, debt, fraud and civil registration provisions within the Digital Economy Act 2017.

Abstract

Marriages data Statistics South Africa (Stats SA) publishes marriage data on citizens and permanent residents that are collected through the national civil registration systems. The data in this dataset is based only on registered marriages and divorces that are stipulated and governed by the country’s legal frameworks. The management of registered marriages is the responsibility of the Department of Home Affairs (DHA). Two main legislations cover the registration of civil marriages and customary marriages. Civil marriages are administered through the Marriage Act, 1961 (Act No. 25 of 1961) as amended, and its associated regulations. Customary marriages are governed by the Recognition of Customary Marriages Act, 1998 (Act No. 120 of 1998) that came into effect on 15 November 2000. An additional legislation is the registration of civil unions - relationships between same-sex couples that are legally recognized by a state authority. These unions are covered by the Civil Union Act, 2006 (Act No. 17 of 2006) that came into operation on 30 November 2006. After the solemnisation ceremony of a marriage or a civil union, the marriage officer submits the marriage /civil union register to the nearest office of the DHA, where the marriage / civil union details are recorded in the National Population Register (NPR). With respect to customary marriages, the two spouses and their witnesses present themselves at a DHA office in order to register a customary marriage. Hence the province of registration is not necessarily the province of the place of usual residence of the couple since the registration of the marriage can take place in any DHA office. Statistics South Africa obtains data on marriages and civil unions in digital format from DHA through the State Information Technology Agency (SITA) and the Marriages and Divorces 2010 dataset is compiled from this data.

Divorces data The dissolution of registered marriages and civil unions falls under the jurisdiction of the Department of Justice and Constitutional Development (DoJ&CD). This responsibility of the department is mandated through the Divorce Act, 1979 as amended, and its associated regulations (Act No.70 of 1979) and the Jurisdiction of Regional Courts Amendment Act, 2008 (Act No. 31 of 2008) as amended which came into effect on 9 August 2010.

The divorces data file only provides 2010 data on divorces from civil marriages. It is limited in its usability by this and by the fact that the data is on divorces that were granted in 2010 by the Department of Justice and Constitutional Development at 12 of the 62 divorce courts mandated to deal with divorce cases in South Africa. The lack of geographical data in the dataset also compromises its usability.

Geographic coverage

The Marriages and Divorces 2010 has national coverage.

Analysis unit

The units of anaylsis for the Marriages and Divorces 2010 are individuals.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Description: The data set consists of 15 transcripts of interviews with traditional birth attendants. All nine provinces were included in the study sample, however, no data were collected for TBAs in Eastern Cape and KwaZulu Natal, urban or rural, Limpopo, urban, North West rural, or Western Cape urban, as TBAs’ practice is not encouraged in many communities and finding willing participants was thus a challenge. Abstract: This project was conceived to investigate the functionality of the South African civil registration and vital statistics system, and to identify those shortcomings that undermine its effectiveness and accuracy in the surveillance of birth and death statistics relative to expectant mother, infants and children. Through a Gap analysis the challenges, bottlenecks and short circuits (the 'gaps') within the surveillance system that serve to compromise the efficiency and effectiveness of the system were identified. The project's aim was to strengthen the existing surveillance strategies for monitoring maternal and child morbidity and mortality in South Africa. The objectives of the project were the following: assess the current surveillance system and strategies for monitoring maternal and child morbidity and mortality in order to identify gaps and challenges within the system; and to describe the availability and performance of maternal, obstetric and infant health care services. The gap analysis included gathering information related to the functioning, challenges and efficiency of the surveillance system at all levels including communities and assessment of the availability and performance of the Civil Registration and Vital Statistics system (CRVS) and maternal and child health (MCH) services. All nine provinces of South Africa were included in the gap analysis. A total of eighteen (18) districts, one urban and one rural per province participated in the gap analysis. The target population included Department of Home Affairs and Department of Health officials working in different components of the CRVS system and MCH respectively at community, facility, district, provincial and national level. It also included all community leaders (these included traditional leaders, community leaders and farm owner/representative), Traditional Birth Attendants (TBAs), community health workers and pregnant women. Digital audio recording Face-to-face interview Focus group Self-completion TBAs (women assisting/who have assisted other women during childbirth) who work or live in the participating communities and were present at the time of the study and have agreed to participate and signed the consent form.

Data collection tools and sample size.

A set of genealogical data consisting of Civil Registry marriages. Many of these records are provided with a link to the digital image of the original deed as well as a direct link to the relevant deed on the website of the City Archives of Breda

The dataset is an index to digitized historical marriage licenses from 1908-1949 from all 5 NYC boroughs. Details about certificates in DORIS's collection and their digitization status can be found on our website (https://a860-historicalvitalrecords.nyc.gov/digital-vital-records).

Description: The data set consists of 39 transcripts of interviews with professional health workers. Gauteng province was excluded from the study as approval from Department of Health could not be obtained. Abstract: This project was conceived to investigate the functionality of the South African civil registration and vital statistics system, and to identify those shortcomings that undermine its effectiveness and accuracy in the surveillance of birth and death statistics relative to expectant mother, infants and children. Through a Gap analysis the challenges, bottlenecks and short circuits (the 'gaps') within the surveillance system that serve to compromise the efficiency and effectiveness of the system were identified. The project's aim was to strengthen the existing surveillance strategies for monitoring maternal and child morbidity and mortality in South Africa. The objectives of the project were the following: assess the current surveillance system and strategies for monitoring maternal and child morbidity and mortality in order to identify gaps and challenges within the system; and to describe the availability and performance of maternal, obstetric and infant health care services. The gap analysis included gathering information related to the functioning, challenges and efficiency of the surveillance system at all levels including communities and assessment of the availability and performance of the Civil Registration and Vital Statistics system (CRVS) and maternal and child health (MCH) services. All nine provinces of South Africa were included in the gap analysis. A total of eighteen (18) districts, one urban and one rural per province participated in the gap analysis. The target population included Department of Home Affairs and Department of Health officials working in different components of the CRVS system and MCH respectively at community, facility, district, provincial and national level. It also included all community leaders (these included traditional leaders, community leaders and farm owner/representative), Traditional Birth Attendants (TBAs), community health workers and pregnant women. For the health facility assessment, the target population included selected public health facilities across the nine provinces and health professionals in charge of MCH and notification of diseases and stillbirths. Data collection included both qualitative and quantitative approaches. Digital audio recording Face-to-face interview Focus group Self-completion Professional health care workers responsible for MHC, who support birth and death registration and work or live in the participating communities and were present at the time of the study and have agreed to participate and signed the consent form. Sampling Different sampling methods were used to select districts and participants for the different components of the project. The sampling methods used are discussed below. Sampling of the districts A total of 18 districts (9 rural and 9 urban) were selected. From each province one urban and one rural district were selected to be included in the surveillance. Convenient sampling approach was used to ensure that the selected urban district is the district that hosts the provincial offices and rural district to be the one furthest from the provincial capital. Within each district the following were included in the sample: villages, Department of Home Affairs (DHA) offices (district and local office), urban and informal areas. In total, 18 rural communities (9 rural villages and 9 farms); 18 rural district (local) DHA offices; 18 urban settlements (9 formal & 9 informal); 18 urban district (local) DHA offices were selected. These selected areas formed part of the gap analysis conducted at community level. Convenient sampling approach was used to ensure that the selected DHA offices within the sampled districts were servicing the selected village, farm, informal settlement and formal settlement in that particular district. The sampled villages formed part of the catchment area of the selected health facilities. That is, the health facilities of focus were treated together with their catchment areas as communities. Detailed sampling approaches used to select participants are discussed below.

Abstract copyright UK Data Service and data collection copyright owner. The aims of the project were to examine and analyse demographic processes of fertility, nuptiality, marital fertility, mortality and migration during periods encompassing the demographic transition in England and Wales. In particular, the goal was to reveal underlying relationships between demographic processes in the context of changing socio-economic conditions. With this goal in mind, population, occupational, and education data were compilated, and demographic and statistical models were employed to estimate key measures and indicators of demographic change. The large majority of the data and estimates were compiled and made at the registration district level for the period 1851-1911. In addition decennial inter-county migration flows were estimated for the period 1851-1911. Main Topics: This digital resource is based on two sources of official data that were published regularly for the registration districts and counties of England and Wales between 1851 and 1911. The published volumes of seven censuses provided most of the information on age, sex and marital status distributions. Census data also provided information on occupational distributions. Vital registration provided the intercensal numbers of births, deaths and marriages. Some supplementary data were also used such as the series of English life tables. From these data demographic and statistical models were employed to estimate key measures and indicators of demographic change at the registration district level, such as population growth rates, population density, birth and death rates, net migration rates, Coale's indices of nuptiality, marital fertility and overall fertility, estimates of the timing of the onset of fertility transition, estimates of life expectancy and more. Most of the district level variables are available for the period 1841-1911, although the data is less complete and less reliable for the period 1841-1850. In addition, decennial inter-county migration flows were estimated from lifetime migration information for the period 1851-1911. Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. Convenience sample Compilation or synthesis of existing material

Sociodemographic characteristics of healthcare providers.

Description of the Data Set

This dataset is taken from the Arkiv Digital AD AB image and index database. When a child was born he or she was registered in a church record book called Birth and Christening records by the priest. They registered the name of the child, when the child was born and baptized, where the child was living and information about the father and mother of the child. The index is based on manual annotation of images from several books between the year 1800 to 1840.

The dataset consists of 191,301 index rows and 15,000 images and has been divided into train: 133,941 index rows and 10,500 images eval: 28,303 index rows and 2,250 images test: 29,057 index rows and 2,250 images

Swedish county (län)

Gävleborgs län - 23 982 index rows Gotlands län - 9 925 index rows Norrbottens län - 12 198 index rows Västerbottens län - 16 118 index rows Västernorrlands län - 21 014 index rows Västmanlands län - 21 141 index rows Älvsborgs län - 52 988 index rows Örebro län - 33 935 index tows

Description of the index columns

• id: Arkiv Digital AD AB ID in database
• index_aid: Index AID (Arkiv Digital AD AB external ID)
• county: County where the child was born or registered (usually not in the image)
• parish: Parish where the child was born or registered (can be written at the top of the page or entirely missing from the image)
• child_first_name: Given name of the child
• birth_date: Date of birth, format YYYYMMDD (on the image it is usually written DD/MM with the year on top of page)
• baptism_date: Date of baptism, format YYYYMMDD (on the image it usually written DD/MM with the year on top of page)
• birth_place: Place of birth
• father_title: Title or occupation of the father
• father_first_name: Given name of the father
• father_last_name: Surname of the father
• father_age: Age of the father when the child was born <== (available only in the master dataset SHIBRm)
• mother_title: Title or occupation of the mother
• mother_first_name: Given name of the mother
• mother_last_name: Surname of the mother
• mother_age: Age of the mother when the child was born
• image_aid: Image AID (Arkiv Digital AD AB external ID)
• image_path: Relative path to the image (images/)

Use of the Materials

The users of the SHIBR Data Set must agree that: - The use of the data set is restricted to research purpose only - No redistribution of the dataset is allowed - In any resultant publications of research that uses the dataset, due credits will be provided to:

Abbas Cheddad, Hüseyin Kusetogullari, Agrin Hilmkil, Lena Sundin, Amir Yavariabdi, Mustapha Aouache, Johan Hall; "SHIBR-The Swedish Historical Birth Records: A Semi-Annotated Dataset," Neural Computing & Applications, Springer, 2021.

Stats NZ receive a monthly electronic file of registered marriages and civil unions from Births, Deaths, and Marriages. Stats NZ are responsible for processing and publishing statistics derived from the marriage and civil union registrations.

Background: Prioritization of acutely ill patients in the Emergency Department remains a challenge. We aimed to evaluate whether routine blood tests can predict mortality in unselected patients in an emergency department and to compare risk prediction with a formalized triage algorithm.

Methods: A prospective observational cohort study of 12,661 consecutive admissions to the Emergency Department of Nordsjælland University Hospital during two separate periods in 2010 (primary cohort, n = 6279) and 2013 (validation cohort, n = 6383). Patients were triaged in five categories by a formalized triage algorithm. All patients with a full routine biochemical screening (albumin, creatinine, c-reactive protein, haemoglobin, lactate dehydrogenase, leukocyte count, potassium, and sodium) taken at triage were included. Information about vital status was collected from the Danish Central Office of Civil registration.

Multiple logistic regressions were used to predict 30-day mortality. Validation...

CVD-COVID-UK/COVID-IMPACT, co-ordinated by the British Heart Foundation (BHF) Data Science Centre (https://bhfdatasciencecentre.org/), is one of the NIHR-BHF Cardiovascular Partnership’s National Flagship Projects.

CVD-COVID-UK aims to understand the relationship between COVID-19 and cardiovascular diseases through analyses of de-identified, pseudonymised, linked, nationally collated health datasets across the four nations of the UK. COVID-IMPACT is an expansion of this approach in England to address research questions looking at the impact of COVID-19 on other health conditions and their related risk factors. The consortium has over 400 members across more than 50 institutions including data custodians, data scientists and clinicians, all of whom have signed up to an agreed set of principles with an inclusive, open and transparent ethos.

Approved researchers access data within secure Trusted Research Environments or Secure Data Environments (TREs/SDEs) provided by NHS England (England), the National Safe Haven (Scotland), the SAIL Databank (Wales) and the Honest Broker Service (Northern Ireland). A dashboard of datasets available in each nation’s TRE/SDE can be found here: https://bhfdatasciencecentre.org/areas/cvd-covid-uk-covid-impact/

This dataset represents the linked datasets for CVD-COVID-UK/COVID-IMPACT in NHS England’s SDE for England and contains the following datasets: • GPES Data for Pandemic Planning and Research (GDPPR)(COVID-19) • Hospital Episode Statistics Admitted Patient Care (HES APC) • Hospital Episode Statistics Critical Care (HES CC) • Hospital Episode Statistics Outpatients (HES OP) • Hospital Episode Statistics Accident and Emergency (HES A&E) • Secondary Uses Services Payment By Results (SUS) • Uncurated Low Latency Hospital Data (Admitted Patient Care, Outpatients, Critical Care) • Emergency Care Data Set (ECDS) • Covid-19 Second Generation Surveillance System • Covid-19 UK Non-hospital Antigen Testing Results • Covid-19 UK Non-hospital Antibody Testing Results • COVID-19 Vaccination Status • COVID-19 Vaccination Adverse Reaction • Civil Registration of Death • Intensive Care National Audit and Research Centre (ICNARC) • COVID-19 SARI-Watch (formerly CHESS) • Medicines dispensed in Primary Care (NHSBSA data) • Secondary Care Prescribed Medicines (EPMA) • NICOR Myocardial Ischaemia National Audit Project (MINAP) • NICOR Percutaneous Coronary Interventions (PCI) • NICOR National Heart Failure Audit (NHFA) • NICOR National Adult Cardiac Surgery Audit (NACSA) • NICOR National Audit of Cardiac Rhythm Management (NACRM) • NICOR National Congenital Heart Disease Audit (NCHDA) • NICOR Transcatheter Aortic Valve Implantation (TAVI) • Sentinel Stroke National Audit Programme Clinical Dataset (SSNAP) • Improving Access to Psychological Therapies Data Set (ISAPT) • Maternity Services Data Set (MSDS • Mental Health Services Data Set (MHSDS)

This report presents the latest results and trends from the women's smoking status at time of delivery (SATOD) data collection in England. A new interactive tool has been published which allows users to select and view information for individual Clinical Commissioning Groups. This is available at the link below. These provisional results provide a measure of the prevalence of smoking among pregnant women at Commissioning Region, Region, Sustainability and Transformation Partnership and Clinical Commissioning Group level. Finalised results will be published in July 2018. Smoking during pregnancy can cause serious pregnancy-related health problems. These include complications during labour and an increased risk of miscarriage, premature birth, low birth-weight and sudden unexpected death in infancy. Reports in the series prior to 2011-12 quarter 3 are available from the Department of Health website (see below). Error Notification On 10/05/2018, NHS Digital identified an error in a small number of confidence intervals for the percentage of women smoking at the time of delivery in this report. The confidence intervals were incorrect for: The year to date national figure in table 1. The England total and the four regional totals in table 2b. The England total and the four regional totals in table 3. As the report contains provisional data, the errors will be corrected in the Q4 report which will be published on 3 July 2018 and will contain final data for 2017/18 Q1, Q2, Q3 and Q4. NHS Digital apologise for any inconvenience caused. Accessibility of the power BI dashboard This tool is in Microsoft PowerBI which does not fully support all accessibility needs. If you need further assistance, please contact us for help.

Multivariable logistic regression analysis of factors associated with cause-specific mortality against all other cause of infant death in Eastern Ethiopia, 2016–18.