OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

This dataset is grouped by service provider specialty, and provides information about the number of recipients, number of claims, and dollar amount for given diagnosis claims. Restricted to claims with service date between 01/2012 to 12/2017. Restricted to claims with a primary diagnosis only. Restricted to top 100 most frequent diagnosis codes that are marked as primary diagnosis of a claim. Provider is the rendering provider marked in the claim. Provider specialty is the primary specialty of the rendering provider. This data is for research purposes and is not intended to be used for reporting. Due to differences in geographic aggregation, time period considerations, and units of analysis, these numbers may differ from those reported by FSSA. Archived as of 7/10/2025: The datasets will no longer receive updates but the historical data will continue to be available for download.

Patient Demographics and Injury Characteristics.

PMI: post-mortem interval, h: hour, C: cortex, H: hippocampus.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data. The complete Data Set of annual utilization data reported by hospitals contains basic licensing information including bed classifications; patient demographics including occupancy rates, the number of discharges and patient days by bed classification, and the number of live births; as well as information on the type of services provided including the number of surgical operating rooms, number of surgeries performed (both inpatient and outpatient), the number of cardiovascular procedures performed, and licensed emergency medical services provided.

Medicare provides access to medical and hospital services for all Australian residents and certain categories of visitors to Australia. The Medicare Benefits Schedule (MBS) lists services that are subsidised by the Australian Government under Medicare. These reports provide patient age range and gender, number of services and total benefit amount per State/ Territory on Items in the MBS Schedule. An Item is a number that references a Medicare service. Item numbers are subject to change. Data is provided in the following formats: Excel/ xlxs: the human readable data for the current year is provided in individual excel files according to the relevant quarter. Historical data (1993-2015) may be found in the excel zipped file. CSV: the machine readable data for the current year is provided in individual csv files according to the relevant quarter. Historical data (1993-2015) may be found in the csv zipped file. Additional Medicare statistics may be found on the Department of Human Services website. Disclaimer: The information and data contained in the reports and tables have been provided by Medicare Australia for general information purposes only. While Medicare Australia takes care in the compilation and provision of the information and data, it does not assume or accept liability for the accuracy, quality, suitability and currency of the information or data, or for any reliance on the information and data. Medicare Australia recommends that users exercise their own care, skill and diligence with respect to the use and interpretation of the information and data.

This dataset aggregates detailed, standardized patient satisfaction and outcome survey responses from hospitals and healthcare facilities, including ratings on staff, communication, cleanliness, and outcomes, along with patient demographics and visit details. It enables robust benchmarking, quality improvement, and predictive analytics for healthcare providers and researchers.

Demographics, characteristics and comorbidities of patients hospitalized with a SARS-CoV-2 infection or COVID-19 diagnosis, total and stratified by rural/urban zip codes.

Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the PDS (Personal Demographics Service) system. This release is an accurate snapshot as at 1 May 2025. GP Practice; Primary Care Network (PCN); Sub Integrated Care Board Locations (SICBL); Integrated Care Board (ICB) and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive PCN; SICBL; ICB and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations.

PIONEER geography The West Midlands (WM) has a population of 5.9million & includes a diverse ethnic, socio-economic mix. There is a higher than average % of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. WM has a high prevalence of COPD, reflecting the high rates of smoking and industrial exposure. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. This is the SAMBA dataset from 4 NHS hospitals. EHR University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. Scope: All patients from 2015 onwards, curated to focus on Stroke. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (admissions, wards and discharge outcomes), presenting complaints, therapies, all physiology readings (pulse, temperature, blood pressure, screening for dysphagia, all sample analysis results (urine specimens, blood specimens), all prescribed & administered treatments and all outcomes. Available supplementary data:
More extensive data including granular serial physiology, bloods, conditions, interventions, treatments. Ambulance, 111, 999 data, synthetic data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services

Note. Groups: SA =  subacute, CH =  chronic, CG =  control group. Pt =  patient; M/F =  male/female. NIHSS: National Institutes of Health Stroke Scale. Stroke etiology: i =  ischemic, h =  hemorrhagic stroke. V&TDS: visual and tactile double stimulation. CAV screen: CAV visual field screening. CAV-ET: CAV extinction test. NET Score: for subtests 1 to 8 and for the whole test battery. Mean (M) and standard deviation (SD) given for patients and healthy controls.

DECOVID, a multi-centre research consortium, was founded in March 2020 by two United Kingdom (UK) National Health Service (NHS) Foundation Trusts (comprising three acute care hospitals) and three research institutes/universities: University Hospitals Birmingham (UHB), University College London Hospitals (UCLH), University of Birmingham, University College London and The Alan Turing Institute. The original aim of DECOVID was to share harmonised electronic health record (EHR) data from UCLH and UHB to enable researchers affiliated with the DECOVID consortium to answer clinical questions to support the COVID-19 response.   ​​   ​​The DECOVID database has now been placed within the infrastructure of PIONEER, a Health Data Research (HDR) UK funded data hub that contains data from acute care providers, to make the DECOVID database accessible to external researchers not affiliated with the DECOVID consortium.  

This highly granular dataset contains 256,804 spells and 165,414 hospitalised patients. The data includes demographics, serial physiological measurements, laboratory test results, medications, procedures, drugs, mortality and readmission.

Geography: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UCLH provides first-class acute and specialist services in six hospitals in central London, seeing more than 1 million outpatient and 100,000 admissions per year. Both UHB and UCLH have fully electronic health records. Data has been harmonised using the OMOP data model. Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

This dataset provides detailed records of emergency department triage decisions, including patient demographics, structured symptoms, vital signs, and triage outcomes. It enables urgent care optimization, patient flow modeling, and clinical research into triage patterns and outcomes. The comprehensive structure supports both operational analytics and advanced predictive modeling.

Users are able to access data related discharge information on all emergency department visits. Data is focused on but not limited to emergency room diagnoses, procedures, demographics, and payment source. Background The State Emergency Department Databases (SEDD) is focused on capturing discharge information on all emergency department visits that do not result in an admission, (Information on patients initially seen in the emergency room and then admitted to the hospital is included in the State Inpatient Databases (SID)). The SEDD contains emergency department information from 27 states. The SEDD contain more than 100 clinical and non-clinical variables included in a hospital dis charge abstract, such as: diagnoses, procedures, patient demographics, expected payment source and total charges. User functionality Users must pay to access the SEDD database. SEDD files from 1999-2009 are available through the HCUP Central Distributor. The SEDD data set can be run on desktop computers with a CD-ROM reader, and comes in ASCII format. The data on the CD set require a statistical software package such as SAS or SPSS to use for analytic purposes. The data set comes with full documentation. SAS and SPSS users are provided programs for converting ASCII files. Data Notes Data is available from 1999-2009. The website does not indicate when new data will be updated. Twenty-seven States now currently participate in the SEDD including Arizona, California, Connecticut, Florida, Georgia, Hawaii, Indiana, Iowa, Kansas, Maine, Maryland, Massachusetts, Minnesota, Missouri, Nebraska, New Hampshire, New Jersey, New York, North Carolina, Ohio, Rhode Island, South Carolina, South Dakota, Tennessee, Utah, Vermont, and Wisconsin.

Background Acute compartment syndrome (ACS) is an emergency orthopaedic condition wherein a rapid rise in compartmental pressure compromises blood perfusion to the tissues leading to ischaemia and muscle necrosis. This serious condition is often misdiagnosed or associated with significant diagnostic delay, and can lead to limb amputations and death.

The most common causes of ACS are high impact trauma, especially fractures of the lower limbs which account for 40% of ACS cases. ACS is a challenge to diagnose and treat effectively, with differing clinical thresholds being utilised which can result in unnecessary osteotomy. The highly granular synthetic data for over 900 patients with ACS provide the following key parameters to support critical research into this condition:

1. Patient data (injury type, location, age, sex, pain levels, pre-injury status and comorbidities)
2. Physiological parameters (intracompartmental pressure, pH, tissue oxygenation, compartment hardness)
3. Muscle biomarkers (creatine kinase, myoglobin, lactate dehydrogenase)
4. Blood vessel damage biomarkers (glycocalyx shedding markers, endothelial permeability markers)

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: Enabling data-driven research and machine learning models towards improving the diagnosis of Acute compartment syndrome. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, physiological parameters, muscle biomarkers, blood biomarkers and co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, lab analysis results (eGFR, troponin, CRP, INR, ABG glucose), systolic and diastolic blood pressures, procedures and surgery details.

Available supplementary data: ACS cohort, Matched controls; ambulance, OMOP data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

A continuous, record level dataset of patients admitted to hospital in England and Wales with COPD since February 2017, with Scotland from late 2018. Includes patient demographics, acute observations, admission and review, comorbidities and discharge.

M, male; F, female; TBI, traumatic brain injury; EDH, epidural hematoma; SDH, subdural hematoma; ICH, intracerebral hematoma; BG, basal ganglion; F, frontal; T, temporal; P, parietal; DC, decompressive craniectomy; Uni+HR, unilateral craniectomy+removal of hematoma; Bil+HR, bilateral craniectomy+removal of hematoma; CIS, cranial index of symmetry; CAD, computer-assisted design.

About this dataset

Context

The dataset tabulates the Heath population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Heath. The dataset can be utilized to understand the population distribution of Heath by age. For example, using this dataset, we can identify the largest age group in Heath.

Key observations

The largest age group in Heath, AL was for the group of age 70 to 74 years years with a population of 83 (34.87%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Heath, AL was the 85 years and over years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Heath is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Heath total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Heath Population by Age. You can refer the same here

Demographic characteristics of sampled patients in each department.

The VA National Clozapine Registry tracks the health and demographics of patients who have been prescribed clozapine by the VA. Clozapine, or the brand name Clozaril, is a drug used to treat the most serious cases of schizophrenia. Unfortunately, clozapine may also affect portions of the blood, lowering the body's resistance to infection and sometimes creating life-threatening circumstances. Realizing the severity of the problem, the Food and Drug Administration (FDA) established guidelines for analysis of White Blood Cells and Neutrophils and set strict minimum limits. The FDA also mandated that any manufacturer of clozapine must maintain a Clozapine Registry. These registries are to track the location and the health of clozapine patients and to ensure 'weekly White Blood Cell testing prior to delivery of the next week's supply of medication'. To date, the clozapine manufacturer registries have been unable to develop sufficient controls to meet these requirements, especially the ability to prevent dispensing clozapine when blood results are abnormal. However, because of the unique structure of Veterans Health Information Systems and Technology Architecture, the Veterans Health Administration obtained permission from the FDA and clozapine manufacturers to use its in-place computer network to gather and evaluate weekly patient information, then export this data to manufacturer clozapine registries. The VA assigned functional administration of this effort to the National Clozapine Coordinating Center (NCCC) located in Dallas, Texas. Weekly data on each VA clozapine patient is processed at two locations. Facility Level --When a clozapine prescription is written, a computer program in each facility's internal computer system retrieves white blood cell count, neutrophil count, and clozapine dose and evaluates the information according to FDA guidelines. If an adverse blood condition is found, the computer may warn to trigger a physician reevaluation, or lock out entirely to prevent dispensing, depending on the severity. Weekly, this information, along with certain patient demographic information, is gathered locally and transmitted to Hines Office of Information & Technology Field Office for centralized storage. This data can only be accessed by the NCCC. Raw data is downloaded from the Hines OI Field Office database on a weekly basis. An ancillary computer program reformats the data and evaluates the information for inconsistencies and data gathering errors. The computer-corrected data is manually compared with hand-written facsimile information sent to the NCCC by each site. This manually corrected data is again reformatted for data storage in MS Access format at the NCCC. The corrected data is also reformatted into American Standard Code for Information Interchange fixed-length fields and transmitted via modem to the manufacturers' Clozapine Registry and, in turn, to the FDA.