The Yellowstone Plateau Volcanic field consists of lavas from the last two million years. The most recent volcanic units are the Central Plateau Member and the older Upper Basin Member rhyolites (Christiansen, 2001). Investigations into the elemental and isotopic composition of these lavas can provide insight into the recent volcanic history of the different eruptive episodes and provide constraints on the hydrothermal fluid compositions that result from water-rock interactions occurring at depth within the hydrothermal system. In this Data Release, twenty-one samples of Yellowstone rhyolite samples from Upper Basin Member and Central Plateau Member lava flows were analyzed for major and trace element concentrations and strontium isotopic composition. Analyzed samples include recently collected samples along with samples from the rock collection of Robert L. Christiansen (Robinson and others, 2021). This data was collected to constrain models of fluid-rock interaction of Yellowsto ...

This repository contains data, scripts, plots, tables, and other supplementary material used in the study titled "Global and Regional Incidence of Intrahepatic Cholestasis of Pregnancy: A Systematic Review and Meta-Analysis". The study is currently under review in BMC Medicine. Details of the published version will be provided here.Data and file structureIn order to reproduce the results, readers are advised to source Results by 'R/main_script.R'. The main body of data structure is placed in Data 'Extraction/IHC of Pregnancy - Data extraction - v9.xlsx'. Other Excel workbooks and sheets provide supporting information.The final results are stored within 'Results by R/viz/' directory.Sharing/Access informationThis systematic review and meta-analysis followed the methodology described in a previously published protocol. Interested readers can visit here.The data provided on this repository will also be accessible via the published paper and its supporting material. The link to those will be provided after publication.Code/SoftwareThis work was completed using R version 4.3.3 [1] with the following R packages: camcorder v. 0.1.0 [2], flextable v. 0.9.7 [3], furrr v. 0.3.1.9000 [4], future v. 1.34.0 [5], ggforce v. 0.5.0 [6], glue v. 1.7.0 [7], here v. 1.0.1 [8], janitor v. 2.2.0 [9], magick v. 2.8.3 [10], marquee v. 0.1.0 [11], metafor v. 4.6.0 [12], officer v. 0.6.7 [13], rmarkdown v. 2.29 [14–16], rnaturalearth v. 1.0.1 [17], rnaturalearthdata v. 1.0.0 [18], rnaturalearthhires v. 1.0.0.9000 [19], rstatix v. 0.7.2 [20], scales v. 1.3.0 [21], sf v. 1.0.16 [22, 23], showtext v. 0.9.6 [24], tidyverse v. 2.0.0 [25].Package citationsR Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing;2024.Hughes E. camcorder: Record your plot history. 2022.Gohel D, Skintzos P. flextable: Functions for tabular reporting.Vaughan D, Dancho M. furrr: Apply mapping functions in parallel using futures. 2024.Bengtsson H. A unifying framework for parallel and distributed processing in r using futures. The R Journal. 2021;13:208–27.Pedersen TL. ggforce: Accelerating “ggplot2”. 2024.Hester J, Bryan J. glue: Interpreted string literals. 2024.Müller K. here: A simpler way to find your files. 2020.Firke S. janitor: Simple tools for examining and cleaning dirty data. 2023.Ooms J. magick: Advanced graphics and image-processing in r. 2024.Pedersen TL, Mitáš M. marquee: Markdown parser and renderer for r graphics. 2024.Viechtbauer W. Conducting meta-analyses in R with the metafor package. Journal of Statistical Software. 2010;36:1–48.Gohel D, Moog S, Heckmann M. officer: Manipulation of microsoft word and PowerPoint documents. 2024.Xie Y, Allaire JJ, Grolemund G. R markdown: The definitive guide. Boca Raton, Florida: Chapman; Hall/CRC;Xie Y, Dervieux C, Riederer E. R markdown cookbook. Boca Raton, Florida: Chapman; Hall/CRC; 2020.Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, et al. rmarkdown: Dynamic documents for r. 2024.Massicotte P, South A. rnaturalearth: World map data from natural earth. 2023.South A, Michael S, Massicotte P. rnaturalearthdata: World vector map data from natural earth used in “rnaturalearth”. 2024.South A, Michael S, Massicotte P. rnaturalearthhires: High resolution world vector map data from natural earth used in rnaturalearth. 2024.Kassambara A. rstatix: Pipe-friendly framework for basic statistical tests. 2023.Wickham H, Pedersen TL, Seidel D. scales: Scale functions for visualization.Pebesma E. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal. 2018;10:439–46.Pebesma E, Bivand R. Spatial Data Science: With applications in R. Chapman and Hall/CRC; 2023.Qiu Y, See file AUTHORS for details. authors/contributors of the included software. showtext: Using fonts more easily in r graphs.Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the tidyverse. Journal of Open Source Software. 2019;4:1686.

Please check Version 2 for the updated dataset: 10.5281/zenodo.4593408

This dataset includes EPMA and LA-ICP-MS raw data supporting the study published in the Journal of Petrology, entitled "Textural and geochemical evidence for magnetite production upon antigorite breakdown during subduction" from Vieira Duarte et al. (2021).

Article: https://doi.org/10.1093/petrology/egab053

This dataset presents the mortality rate from cancer among individuals under the age of 75 within the Birmingham and Solihull area. It captures the number of deaths attributed to all cancers (classified under ICD-10 codes C00 to C97) and expresses this as a directly age-standardised rate per 100,000 population. The data is structured in quinary age bands and is available for both single-year and three-year rolling averages, providing a comprehensive view of premature cancer mortality trends in the region.

Rationale Reducing premature mortality from cancer is a key public health priority. This indicator helps track progress in lowering the number of cancer-related deaths among people under 75, supporting efforts to improve early diagnosis, treatment, and prevention strategies.

Numerator The numerator is the number of deaths from all cancers (ICD-10 codes C00 to C97) registered in the respective calendar years, for individuals aged under 75. These figures are aggregated into quinary age bands and sourced from the Death Register.

Denominator The denominator is the population of individuals under 75 years of age, also aggregated into quinary age bands. For single-year rates, the population for that year is used. For three-year rolling averages, the population-years are aggregated across the three years. The source of this data is the 2021 Census.

Caveats Data may not align exactly with published Office for National Statistics (ONS) figures due to differences in postcode lookup versions and the application of comparability ratios in Office for Health Improvement and Disparities (OHID) data. Users should be cautious when comparing this dataset with other national statistics.

External references Further information and related indicators can be found on the OHID Fingertips platform.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Meta-analysis of Intrahepatic Cholestasis of Pregnancy (ICP) IncidenceAccess this dataset on DryadThis repository contains data, scripts, plots, tables, and other supplementary material used in the study titled "Global and Regional Incidence of Intrahepatic Cholestasis of Pregnancy: A Systematic Review and Meta-Analysis". The study is currently under review in BMC Medicine. Details of the published version will be provided here.Data and file structureIn order to reproduce the results, readers are advised to source Results by 'R/main_script.R'. The main body of data structure is placed in Data 'Extraction/IHC of Pregnancy - Data extraction - v9.xlsx'. Other Excel workbooks and sheets provide supporting information.The final results are stored within 'Results by R/viz/' directory.Sharing/Access informationThis systematic review and meta-analysis followed the methodology described in a previously published protocol. Interested readers can visit here.The data provided on this repository will also be accessible via the published paper and its supporting material. The link to those will be provided after publication.Code/SoftwareThis work was completed using R version 4.3.3 [1] with the following R packages: camcorder v. 0.1.0 [2], flextable v. 0.9.7 [3], furrr v. 0.3.1.9000 [4], future v. 1.34.0 [5], ggforce v. 0.5.0 [6], glue v. 1.7.0 [7], here v. 1.0.1 [8], janitor v. 2.2.0 [9], magick v. 2.8.3 [10], marquee v. 0.1.0 [11], metafor v. 4.6.0 [12], officer v. 0.6.7 [13], rmarkdown v. 2.29 [14–16], rnaturalearth v. 1.0.1 [17], rnaturalearthdata v. 1.0.0 [18], rnaturalearthhires v. 1.0.0.9000 [19], rstatix v. 0.7.2 [20], scales v. 1.3.0 [21], sf v. 1.0.16 [22, 23], showtext v. 0.9.6 [24], tidyverse v. 2.0.0 [25].Package citationsR Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing;2024.Hughes E. camcorder: Record your plot history. 2022.Gohel D, Skintzos P. flextable: Functions for tabular reporting.Vaughan D, Dancho M. furrr: Apply mapping functions in parallel using futures. 2024.Bengtsson H. A unifying framework for parallel and distributed processing in r using futures. The R Journal. 2021;13:208–27.Pedersen TL. ggforce: Accelerating “ggplot2”. 2024.Hester J, Bryan J. glue: Interpreted string literals. 2024.Müller K. here: A simpler way to find your files. 2020.Firke S. janitor: Simple tools for examining and cleaning dirty data. 2023.Ooms J. magick: Advanced graphics and image-processing in r. 2024.Pedersen TL, Mitáš M. marquee: Markdown parser and renderer for r graphics. 2024.Viechtbauer W. Conducting meta-analyses in R with the metafor package. Journal of Statistical Software. 2010;36:1–48.Gohel D, Moog S, Heckmann M. officer: Manipulation of microsoft word and PowerPoint documents. 2024.Xie Y, Allaire JJ, Grolemund G. R markdown: The definitive guide. Boca Raton, Florida: Chapman; Hall/CRC;Xie Y, Dervieux C, Riederer E. R markdown cookbook. Boca Raton, Florida: Chapman; Hall/CRC; 2020.Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, et al. rmarkdown: Dynamic documents for r. 2024.Massicotte P, South A. rnaturalearth: World map data from natural earth. 2023.South A, Michael S, Massicotte P. rnaturalearthdata: World vector map data from natural earth used in “rnaturalearth”. 2024.South A, Michael S, Massicotte P. rnaturalearthhires: High resolution world vector map data from natural earth used in rnaturalearth. 2024.Kassambara A. rstatix: Pipe-friendly framework for basic statistical tests. 2023.Wickham H, Pedersen TL, Seidel D. scales: Scale functions for visualization.Pebesma E. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal. 2018;10:439–46.Pebesma E, Bivand R. Spatial Data Science: With applications in R. Chapman and Hall/CRC; 2023.Qiu Y, See file AUTHORS for details. authors/contributors of the included software. showtext: Using fonts more easily in r graphs.Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the tidyverse. Journal of Open Source Software. 2019;4:1686.

Total minor and trace element concentrations were determined by ICP-MS on profiles of size-fractionated (0.8-51um, >51um) particles collected by battery-operated in-situ filtration during the 2021 North Atlantic EXPORTS cruise (RRS Discovery cruise DY131). cdm_data_type=Other Conventions=COARDS, CF-1.6, ACDD-1.3 defaultDataQuery=&time<now doi=10.26008/1912/bco-dmo.946504.1 Easternmost_Easting=-14.7392 geospatial_lat_max=49.07669 geospatial_lat_min=48.78923 geospatial_lat_units=degrees_north geospatial_lon_max=-14.7392 geospatial_lon_min=-15.10891 geospatial_lon_units=degrees_east geospatial_vertical_max=500.0 geospatial_vertical_min=20.0 geospatial_vertical_positive=down geospatial_vertical_units=m infoUrl=https://www.bco-dmo.org/dataset/946504 institution=BCO-DMO Northernmost_Northing=49.07669 sourceUrl=(local files) Southernmost_Northing=48.78923 time_coverage_end=2021-05-27T21:15:00Z time_coverage_start=2021-05-06T20:07:00Z Westernmost_Easting=-15.10891

This dataset presents the under-75 mortality rate from stroke, a key indicator within the cardiovascular health domain. It captures the rate of deaths attributed to stroke among individuals aged under 75, using data classified under ICD-10 codes I60 to I69. The dataset is structured to support public health monitoring and policy development by providing age-standardised mortality rates per 100,000 population.

Rationale Reducing premature mortality from stroke is a public health priority. Monitoring this indicator helps assess the effectiveness of prevention strategies, healthcare interventions, and broader determinants of health. It supports efforts to reduce health inequalities and improve outcomes for cardiovascular conditions.

Numerator The numerator is the number of deaths from stroke (ICD-10 codes I60 to I69) registered in the respective calendar years.

Denominator For single-year rates, the denominator is the population of individuals aged under 75, aggregated into quinary age bands. For three-year rolling averages, it is the population-years (combined populations over three years) for the same age range and structure. Population estimates are based on the 2021 Census.

Caveats Data may not align precisely with figures published by the Office for National Statistics (ONS) due to differences in postcode lookup versions and the application of comparability ratios in the Office for Health Improvement and Disparities (OHID) data. Users should consider these factors when interpreting the results.

External references Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This dataset reports the crude rate of emergency hospital admissions for asthma among children and young people aged under 19. It provides a measure of the burden of acute asthma episodes requiring urgent medical care and serves as an important indicator of respiratory health and healthcare access for this age group.

Rationale Reducing hospital admissions caused by asthma in children and young people is a key public health objective. High admission rates may reflect poor asthma control, environmental triggers, or gaps in primary care and early intervention. Monitoring this indicator supports efforts to improve asthma management and reduce preventable hospitalisations.

Numerator The numerator is the number of emergency hospital admissions for individuals aged under 19 with a primary diagnosis of asthma, identified using ICD-10 codes J45 (Asthma) and J46 (Status asthmaticus). Data are sourced from the Secondary Uses Service (SUS).

Denominator The denominator is the total population of children and young people aged under 19, based on 2021 Census data.

Caveats The data reflect episodes of admission rather than individual patients, meaning multiple admissions by the same person are counted separately. Hospital admission rates may also be influenced by local variations in referral and admission practices, as well as differences in asthma prevalence. NHS England has identified a data quality issue, though further detail is not specified in this summary.

External References Fingertips Public Health Profiles – Asthma Admissions (Under 19)

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

DOI retrieved: 2021

This data publication contains analytical data from direct analysis in real time time-of-flight mass spectrometry (DART TOFMS), inductively coupled plasma mass spectrometry (ICP-MS), and handheld laser-induced breakdown spectroscopy (LIBS) collected from five populations of Pinus ponderosa located 14 to 72 kilometers apart in Oregon between October 2021 and February 2025. These data were generated to assess the effectiveness of these techniques paired with machine learning models in determining the geographical provenance of timber. The data support research into forensic wood identification and environmental forensics by providing mass spectral and elemental composition data for comparative analysis. These data can be used to explore classification models, develop geographic reference databases, and evaluate alternative approaches for timber provenance determination. This data publication includes tabular digital data including: 1) the geographic coordinates and final ash mass for 107 Pinus ponderosa core samples, 2) ICP-MS data for 107 Pinus ponderosa wood core samples (normalized), 3) recorded DART TOFMS spectra data including the mass-to-charge ratio and corresponding relative intensity, and 4) recorded LIBS spectra data including wavelength and corresponding intensity.These data were collected to evaluate how data collected via DART TOFMS, LIBS, and ICP-MS could be used to classify different populations of Pinus ponderosa timber.For more information about this study and these data, see Price et al. (2025).

Data and code for: Vrana, Til Kristian, & Svendsen, Harald G. (2021). Quantifying the Market Value of Wave Power compared to Wind&Solar - a case study. The 9th Renewable Power Generation Conference - RPG Dublin Online 2021 (RPG 2021), (https://doi.org/10.1049/icp.2021.1383)

These files contain data for microscopy and mineral analyses on Fe-Ni-Cu sulfide minerals in the lower crust below arc systems using the example of the Ivrea Zone in Italy. Samples are lower crustal cumulates with variable concentrations of Fe-Ni-Cu sulfides collected by Dave Holwell from the Ivrea Zone. The sample details will be logged in a separate data entry and more information can be found in the open access paper by Holwell et al (2022) in Nature Geoscience, https://doi.org/10.1038/s41467-022-28275-y. Data were acquired during 2019, 2020 and 2021. Folders include: metadata (time-resolved analysis spectral data) for laser ablation ICP-MS analysis of sulfide minerals. Laser-ablation ICPMS analyses were performed using a ESI UP213 laser system coupled to a Thermo iCAPRQ ICP-MS system at the School of Earth and Environmental Sciences, Cardiff University. The data were gathered to understand the concentrations of precious and semi-metal trace elements and their likely mineral forms in the various Fe-Ni-Cu sulfide minerals. Collected under the From Arc Magma to Ore System (FAMOS) Project.

This dataset presents the crude rate of emergency hospital admissions for epilepsy among children and young people aged under 19. It provides insight into the burden of acute epileptic episodes requiring urgent care and serves as a key indicator of neurological health and service provision for this age group.

Rationale Reducing hospital admissions for epilepsy in children and young people is a public health priority. High admission rates may reflect challenges in managing epilepsy in community settings, medication adherence, or access to specialist care. Monitoring this indicator supports efforts to improve epilepsy management and reduce preventable admissions.

Numerator The numerator is the number of emergency hospital admissions for individuals aged under 19 with a primary diagnosis of epilepsy, identified using ICD-10 codes G40 (Epilepsy) and G41 (Status epilepticus). Data are sourced from NHS England’s Secondary Uses Service (SUS).

Denominator The denominator is the total resident population aged under 19, based on 2021 Census data.

Caveats No specific caveats were noted for this dataset. However, as with all hospital admission indicators, local variations in clinical coding, referral practices, and healthcare access may influence the results.

External References Fingertips Public Health Profiles – Epilepsy Admissions (Under 19)

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

This dataset is about: LA-ICP-MS quality control data from tephra layer in IODP 374 Expedition Site U1524. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.933481 for more information. All values given in mg/kg are determined in ppm.

This dataset is about: LA-ICP-MS trace-element data from tephra layer in IODP 374 Expedition Site U1524. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.933481 for more information. All values given in mg/kg are determined in ppm.

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Non-Invasive Intracranial Pressure Monitoring Devices Market Size 2025-2029

The non-invasive intracranial pressure monitoring devices market size is forecast to increase by USD 126.1 million, at a CAGR of 5.8% between 2024 and 2029.

The Non-Invasive Intracranial Pressure (ICP) Monitoring Devices market is experiencing significant growth due to increasing awareness of these devices' benefits. This trend is driven by the growing number of clinical studies and research activities focused on non-invasive ICP monitoring techniques. However, the market faces challenges from the widespread availability and use of invasive ICP monitoring devices. Despite this competition, non-invasive ICP monitoring devices offer advantages such as improved patient comfort and reduced risk of complications associated with invasive procedures. Companies seeking to capitalize on market opportunities should focus on enhancing the accuracy and reliability of non-invasive ICP monitoring technologies to differentiate themselves from invasive alternatives. Additionally, collaborations with healthcare institutions and research organizations can help drive innovation and expand market reach. Overall, the Non-Invasive ICP Monitoring Devices market holds potential for significant growth, with opportunities for companies to address unmet clinical needs and improve patient care.

What will be the Size of the Non-Invasive Intracranial Pressure Monitoring Devices Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by advancements in technology and the growing demand for improved patient safety and clinical outcomes in critical care medicine. These devices, which utilize various techniques such as data acquisition, clinical trials, early warning systems, data visualization, and predictive analytics, are essential tools in neurocritical care. CE marking and compliance with medical device regulations are crucial aspects of this market, ensuring the quality and safety of these devices. The integration of wireless technology, signal processing, and wireless communication enables remote monitoring and real-time data access, enhancing the efficiency and effectiveness of healthcare delivery. Power consumption and data security are significant concerns in this market, with ongoing efforts to develop longer battery life and robust data protection measures. The use of optical sensors and external sensors expands the application of these devices, offering flexibility and versatility in various healthcare settings. The supply chain and distribution channels play a vital role in ensuring timely access to these devices, with technical support and service contracts essential for maintaining optimal performance. FDA approval and regulatory approvals are necessary steps in product development, ensuring the devices meet the highest standards of safety and efficacy. The healthcare economics of non-invasive intracranial pressure monitoring devices continue to evolve, with ongoing discussions on reimbursement models and cost-effectiveness. As the market unfolds, we can expect continued innovation and development in this field, driven by the need for improved patient care and clinical outcomes.

How is this Non-Invasive Intracranial Pressure Monitoring Devices Industry segmented?

The non-invasive intracranial pressure monitoring devices industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ApplicationTraumatic brain injuryIntracerebral hemorrhageSubarachnoid hemorrhageMeningitisOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Application Insights

The traumatic brain injury segment is estimated to witness significant growth during the forecast period.Traumatic brain injury (TBI), a significant cause of disability and death in adults, results from violent head blows or jolts. In the US, according to the Centers for Disease Control and Prevention (CDC), there were approximately 214,110 TBI-related hospitalizations and 69,473 TBI-related deaths in 2020 and 2021, respectively - equating to over 580 hospitalizations and nearly 200 deaths daily. The medical community's focus on improving patient safety and clinical outcomes has led to advancements in intracranial pressure monitoring technology. Non-invasive intracranial pressure monitoring devices have gained traction due to their ability to provide real-time data acquisition, enabling early warning systems and predictive analytics. These devices employ various technologies, including wireless communication, signal processing, and

This dataset presents the under-75 mortality rate from all cardiovascular diseases in England. It captures the rate of deaths attributed to circulatory diseases (ICD-10 codes I00–I99) among individuals aged under 75, using directly age-standardised rates per 100,000 population. The data is aggregated into quinary age bands and is available for both single years and three-year rolling averages, providing a comprehensive view of premature cardiovascular mortality trends.

Rationale Cardiovascular diseases remain a leading cause of premature mortality in England. Monitoring under-75 mortality rates helps identify health inequalities, assess the effectiveness of public health interventions, and guide resource allocation. This indicator supports efforts to reduce preventable deaths and improve cardiovascular health outcomes.

Numerator The numerator is the number of deaths from all circulatory diseases (ICD-10 codes I00 to I99) registered in the respective calendar years, among individuals aged under 75. These figures are aggregated into quinary age bands (e.g., 0–4, 5–9, ..., 70–74) and sourced from the national Death Register.

Denominator The denominator is the population of individuals aged under 75, also aggregated into quinary age bands. For single-year rates, the population estimate for that year is used. For three-year rolling averages, the denominator is the sum of the populations over the three years. Population data is sourced from the 2021 Census.

Caveats Data may not align exactly with published Office for National Statistics (ONS) figures due to differences in postcode lookup versions and the application of comparability ratios in the Office for Health Improvement and Disparities (OHID) data. Users should consider these factors when comparing with other sources.

External references Further information and related indicators can be found on the OHID Fingertips platform.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Šajās datnēs ir dati mikroskopijai un minerālu analīzei par Fe-Ni-Cu sulfīda minerāliem apakšējā garozā zem loka sistēmām, izmantojot Ivrea zonas piemēru Itālijā. Paraugi ir zemāki garozas kumulējumi ar dažādām Fe-Ni-Cu sulfīdu koncentrācijām, ko Dave Holwell savācis no Ivrea zonas. Sīkāka informācija par izlasi tiks reģistrēta atsevišķā datu ierakstā, un plašāka informācija ir pieejama Holwell et al (2022) atvērtās piekļuves dokumentā Nature Geoscience, https://doi.org/10.1038/s41467-022-28275-y. Dati tika iegūti 2019., 2020. un 2021. gadā. Mapes ietver: metadati (laikā iegūti analīzes spektrālie dati) sulfīdu minerālu lāzera ablācijas ICP-MS analīzei. Lāzera ablācijas ICPMS analīzes tika veiktas, izmantojot ESI UP213 lāzera sistēmu kopā ar Thermo iCAPRQ ICP-MS sistēmu Zemes un vides zinātņu skolā, Kārdifas Universitātē. Dati tika vākti, lai izprastu dārgmetālu un pusmetālu mikroelementu koncentrāciju un to iespējamās minerālu formas dažādajos Fe-Ni-Cu sulfīda minerālos. Savākts projekta "No loka Magma līdz rūdai" (FAMOS) ietvaros. Šajās datnēs ir dati mikroskopijai un minerālu analīzei par Fe-Ni-Cu sulfīda minerāliem apakšējā garozā zem loka sistēmām, izmantojot Ivrea zonas piemēru Itālijā. Paraugi ir zemāki garozas kumulējumi ar dažādām Fe-Ni-Cu sulfīdu koncentrācijām, ko Dave Holwell savācis no Ivrea zonas. Sīkāka informācija par izlasi tiks reģistrēta atsevišķā datu ierakstā, un plašāka informācija ir pieejama Holwell et al (2022) atvērtās piekļuves dokumentā Nature Geoscience, https://doi.org/10.1038/s41467-022-28275-y. Dati tika iegūti 2019., 2020. un 2021. gadā. Mapes ietver: metadati (laikā iegūti analīzes spektrālie dati) sulfīdu minerālu lāzera ablācijas ICP-MS analīzei. Lāzera ablācijas ICPMS analīzes tika veiktas, izmantojot ESI UP213 lāzera sistēmu kopā ar Thermo iCAPRQ ICP-MS sistēmu Zemes un vides zinātņu skolā, Kārdifas Universitātē. Dati tika vākti, lai izprastu dārgmetālu un pusmetālu mikroelementu koncentrāciju un to iespējamās minerālu formas dažādajos Fe-Ni-Cu sulfīda minerālos. Savākts projekta "No loka Magma līdz rūdai" (FAMOS) ietvaros.

This dataset presents the average number of years a man aged 65 can expect to live in good health, known as healthy life expectancy (HLE). It is a key measure of quality of life in later years and reflects both longevity and the prevalence of good health among older men.

Rationale Increasing healthy life expectancy at age 65 for males is a major public health objective. It highlights the importance of not only living longer but also maintaining good health and independence in later life. This indicator supports the planning of health and social care services and helps assess the impact of health inequalities and lifestyle factors on aging populations.

Numerator The numerator is derived from the number of deaths registered in the respective calendar years and the weighted prevalence of individuals reporting good or very good health, as captured by the Annual Population Survey (APS). Data are provided by the Office for National Statistics (ONS).

Denominator The denominator is based on population estimates from the 2021 Census and the APS sample, weighted to reflect local authority population totals. These data are also provided by the ONS.

Caveats Healthy life expectancy figures exclude residents of communal establishments, except for NHS housing and students in halls of residence who are included based on their parents' address. This may affect comparability in areas with large institutional populations.

External References Fingertips Public Health Profiles – Healthy Life Expectancy (Male)

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

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This dataset presents the age-standardised rate of emergency hospital admissions for coronary heart disease (CHD) across England. It provides insight into the burden of acute cardiovascular events requiring urgent care, helping to inform public health strategies and healthcare planning. The indicator focuses on unplanned hospital admissions where CHD is the primary diagnosis, offering a measure of both disease prevalence and healthcare system responsiveness.

Rationale Reducing emergency admissions for coronary heart disease is a key public health objective. High rates of emergency admissions may indicate poor disease management in the community or delayed access to preventative care. Monitoring this indicator supports efforts to improve cardiovascular health outcomes and reduce strain on emergency services.

Numerator The numerator is the count of emergency hospital admissions where the primary diagnosis is coronary heart disease, identified using ICD-10 codes I20 to I25. Admissions are included if they meet specific criteria: admission method codes ('21', '22', '23', '24', '25', '28', '2A', '2B', '2C', '2D'), patient classification as 'ordinary' (1 or 2), episode status equal to 3, and episode order equal to 1. These values are derived from Secondary Uses Service (SUS) data.

Denominator The denominator is the expected number of admissions, calculated by applying England's age-specific rates to the local registered population. Population estimates are based on Census 2021 data.

Caveats There are known data quality issues affecting Hospital Episode Statistics (HES) data for Nottingham University Hospitals Trust during the 2016–2017 financial year. Over 30% of records lacked valid geographic information, and as a result, Public Health England (PHE) has not published a value for this trust. Additionally, due to disclosure control, some values have not been published.

External References Fingertips Public Health Profiles – Coronary Heart Disease Indicator

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