AndreaBozzo/ceres-open-data-index huggingface dataset:
Incidence rate of oral cavity cancer - ICB

Directly age-sex standardised rate for patients with oral cavity cancer per 100,000 population. ## Rationale Tobacco is a known risk factor for oral cancers¹. In England, 65% of hospital admissions (2014–15) for oral cancer and 64% of deaths (2014) due to oral cancer were attributed to smoking². Oral cancer registration is therefore a direct measure of smoking-related harm. Given the high proportion of these registrations that are due to smoking, a reduction in the prevalence of smoking would reduce the incidence of oral cancer. Towards a Smokefree Generation: A Tobacco Control Plan for England states that tobacco use remains one of our most significant public health challenges and that smoking is the single biggest cause of inequalities in death rates between the richest and poorest in our communities³. In January 2012 the Public Health Outcomes Framework was published, then updated in 2016. Smoking and smoking-related death play a key role in two of the four domains: Health Improvement and Preventing premature mortality⁴. ## Definition of numerator The number of diagnoses of patients with oral cavity cancer (ICD-10 code C00.3 to C00.5, C02 to C04 (excluding C02.4), C05.0, C06) National Cancer Registry Dataset from the National Disease Registration Service ## Definition of denominator ONS mid-year population estimates aggregated into quinary age bands. Office for National Statistics (ONS), Mid-year population estimates (based on 2011 census) ## Methodology Numerator data for each age sex band are divided by the respective denominator population data to give age sex specific admission rates for the area. These age sex specific rates are multiplied by the standard population for each age sex group respectively and aggregated across all the age sex groups to give the age sex adjusted count of admissions for the area. This age sex adjusted count of admissions is divided by the total standard population for the whole age sex range included in the indicator, and multiplied by 100,000 to give the age sex standardised admission rate for the area. ### Caveats Age sex standardised rates based on numbers lower than 10 are not included ### References 1. GBD 2013 Risk Factors Collaborators. Global, regional and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risk factors in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet 2015; 386:10010 2287–2323. 2. Statistics on smoking, England 2016, May 2016; [http://content.digital.nhs.uk/catalogue/PUB20781] 3. [Towards a Smokefree Generation: A Tobacco Control Plan for England], July 2017 4. [Public Health Outcomes Framework 2016 to 2019], August 2016 5. Huggett C (1995). Review of the Quality and Comparability of Data held by Regional Cancer Registries. Bristol: Bristol Cancer Epidemiology Unit incorporating the South West Cancer Registry. 6. Seddon DJ, Williams EMI (1997). Data quality in population-based cancer registration. British Journal of Cancer 76: 667–674.
Bowel cancer screening uptake - ICP Outcomes Framework - Registered Locality

This dataset presents the uptake rate of bowel cancer screening among eligible individuals aged 60 to 75 in England. It measures the percentage of those who, after receiving an invitation to participate in the Bowel Cancer Screening Programme (BCSP), responded and were adequately screened within six months. The data is sourced from NHS England and provides insight into screening engagement at the general practice level. ## Rationale Bowel cancer screening is a critical public health intervention aimed at early detection and treatment of colorectal cancer. Increasing the percentage of eligible individuals who complete screening can lead to earlier diagnoses, improved outcomes, and reduced mortality. This indicator supports efforts to monitor and enhance participation in cancer screening programmes, aligning with broader goals to improve cancer detection rates across the population. ## Numerator The numerator is defined as the number of persons registered to a general practice, aged 60 to 75, who were invited for bowel cancer screening in the previous 12 months and were adequately screened following an initial response within six months of the invitation. ## Denominator The denominator includes the total number of persons aged 60 to 74 who were invited for bowel cancer screening in the previous 12 months. ## Caveats This indicator may be based on a small number of patients for some practices, which can affect reliability. Additionally, data may be less reliable during periods of GP practice mergers or boundary changes. For data up to 2021/22, only practices with a list size of at least 1,000 were included. ## External References More information can be found on the [Fingertips Public Health Profiles] website. ## Localities Explained This 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.
IMD 2019 - Modelled Rank and Decile for Constituencies

The Indices of Deprivation (IOD) 2019 are the Government’s official measure of deprivation for English local authorities and neighbourhoods, released in 2019 by the Ministry for Housing Communities and Local Government (MHCLG). The IOD includes the headline Index of Multiple Deprivation (IMD) as well as indices covering income deprivation for young people and for older people. The IMD is based on indicators organised across seven sub domains of deprivation, which are combined and weighted to calculate the Index of Multiple Deprivation 2019. MHCLG did not produce deprivation data for wards and constituencies. They did provide a methodology for aggregating LSOA data into higher geographies, and this was used to create rankings for the city’s constituencies, originally published in a Birmingham City Council [Economic Policy report on deprivation]. The data shows: * the Birmingham rank from most (rank 1) to least deprived (rank 10) * the national decile, from decile 1 (nationally most deprived 10%) to decile 10 (nationally least deprived)
Travel to work on foot (Percentage of pop. aged 16-74) - WMCA Wards (2025)

This is the proportion of residents that travel to work on foot. This dataset provides estimates that classify usual residents aged 16 to 74 by the method of travel used for the longest part, by distance, of the usual journey to work. This topic is only applicable to people who were in employment in the week before the census and estimates are as at census day. Travel to work estimates from the 2001 Census, for example in UV39, are not compatible with this estimates in this dataset because of the differences in the way that people who indicated that they work from home but also have a method of travel to work have been handled. Estimates that are compatible with 2001 are available in dataset CT0015. Information on travel to work informs both national and local transport services and policies. It provides a basis for transport planning, for example, whether new bus routes or changes to existing bus routes are needed. Statistical Disclosure Control: In order to protect against disclosure of personal information from the Census, there has been swapping of records in the Census database between different geographic areas, and so some counts will be affected. In the main, the greatest effects will be at the lowest geographies, since the record swapping is targeted towards those households with unusual characteristics in small areas. Data is [Powered by LG Inform Plus] and automatically checked for new data on the 4th of each month.
Bowel cancer screening uptake - ICP Outcomes Framework - Birmingham and Solihull

This dataset presents the uptake rate of bowel cancer screening among eligible individuals aged 60 to 75 in England. It measures the percentage of those who, after receiving an invitation to participate in the Bowel Cancer Screening Programme (BCSP), responded and were adequately screened within six months. The data is sourced from NHS England and provides insight into screening engagement at the general practice level. ## Rationale Bowel cancer screening is a critical public health intervention aimed at early detection and treatment of colorectal cancer. Increasing the percentage of eligible individuals who complete screening can lead to earlier diagnoses, improved outcomes, and reduced mortality. This indicator supports efforts to monitor and enhance participation in cancer screening programmes, aligning with broader goals to improve cancer detection rates across the population. ## Numerator The numerator is defined as the number of persons registered to a general practice, aged 60 to 75, who were invited for bowel cancer screening in the previous 12 months and were adequately screened following an initial response within six months of the invitation. ## Denominator The denominator includes the total number of persons aged 60 to 74 who were invited for bowel cancer screening in the previous 12 months. ## Caveats This indicator may be based on a small number of patients for some practices, which can affect reliability. Additionally, data may be less reliable during periods of GP practice mergers or boundary changes. For data up to 2021/22, only practices with a list size of at least 1,000 were included. ## External References More information can be found on the [Fingertips Public Health Profiles] website. [Click here] to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.
Emergency admissions for self-harm - ICP Outcomes Framework - Birmingham and Solihull

This dataset presents the age-standardised rate of emergency hospital admissions for intentional self-harm across all age groups. It provides a critical measure of mental health-related harm requiring urgent medical intervention and supports the monitoring of trends in self-harm incidents that result in hospitalisation. ## Rationale Emergency admissions for self-harm are a significant public health concern, reflecting underlying mental health issues, social stressors, and access to support services. Monitoring this indicator helps inform mental health strategies, service planning, and targeted interventions aimed at reducing self-harm and improving wellbeing. ## Numerator The numerator is the number of first finished emergency admission episodes in which the patient was admitted with a cause code indicating intentional self-harm (ICD-10 codes X60 to X84). Admissions are included if the episode number is 1 and the admission method begins with '2'. Regular and day attenders are excluded. Data are sourced from the Secondary Uses Service (SUS) Inpatients Report. ## Denominator The denominator is the mid-year population estimate for all ages, as provided by the Office for National Statistics (ONS). ## Caveats In 2023, NHS England introduced a methodological change requiring Trusts to report Same Day Emergency Care (SDEC) to the Emergency Care Data Set (ECDS) by July 2024. Early adopter sites began reporting from 2021/22, with others following in 2022/23 and 2023/24. Some Trusts previously reported this activity under Admitted Patient Care, and the shift to ECDS may reduce the number of admissions captured by this indicator. NHSE has advised that SDEC activity cannot currently be accurately identified in existing data flows, and the impact of this change is expected to vary by diagnosis, particularly for indicators related to injuries and external causes. ## External References [Fingertips Public Health Profiles – Self-Harm Admissions] [Click here] to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.
Carbon Intensity Forecast and Power Generation Mix - National

This dataset utilizes the [Carbon Intensity Region API] to provide real-time information on the forecast carbon intensity (gCO2/kWh) of electricity consumption across various regions in Great Britain and our electricity generation mix. The carbon intensity of electricity is a measure of how much CO2 emissions are produced per kilowatt hour of electricity consumed. The generation mix shows what percentage is generated from various sources such as solar, wind, nuclear, hydro and fossil fuels such as gas and coal. Updated every hour and forecasted up to 96 hours ahead. The values are calculated using state-of-the-art machine learning and power system modelling techniques, incorporating emissions from large metered power stations, interconnector imports, transmission and distribution losses, and embedded wind and solar generation. Data is available at the [Distribution Network Operator (DNO) regions]. Please note, these regions do not align with the standard UK regions. The Intensity Index applied depends on the Intensity Forecast which is measured in gCO2/kWh and varies from year to year. See the table below for conversion; ────┬────────┬─────────┬──────────┬──────────┬───────── Year│Very low│Low │Moderate │High │Very high ────┼────────┼─────────┼──────────┼──────────┼───────── 2024│0 to 34 │35 to 109│110 to 189│190 to 270│270+ ────┼────────┼─────────┼──────────┼──────────┼───────── 2025│0 to 29 │30 to 99 │100 to 179│180 to 250│250+ ────┼────────┼─────────┼──────────┼──────────┼───────── 2026│0 to 24 │25 to 89 │90 to 169 │170 to 230│230+ ────┼────────┼─────────┼──────────┼──────────┼───────── 2027│0 to 19 │20 to 79 │80 to 159 │160 to 210│210+ ────┼────────┼─────────┼──────────┼──────────┼───────── 2028│0 to 14 │15 to 69 │70 to 149 │150 to 190│190+ ────┼────────┼─────────┼──────────┼──────────┼───────── 2029│0 to 9 │10 to 59 │60 to 139 │140 to 170│170+ ────┼────────┼─────────┼──────────┼──────────┼───────── 2030│0 to 4 │5 to 49 │50 to 129 │130 to 150│150+ ────┴────────┴─────────┴──────────┴──────────┴─────────
Emergency admissions for stroke - ICP Outcomes Framework - Birmingham and Solihull

This dataset presents the age-standardised rate of emergency hospital admissions for stroke. It provides a key measure of the burden of acute cerebrovascular events requiring urgent medical care and supports the evaluation of stroke prevention and early intervention strategies across local populations. ## Rationale Reducing emergency admissions for stroke is a critical public health objective. High admission rates may indicate gaps in the management of risk factors such as hypertension, atrial fibrillation, and diabetes, or delays in accessing preventative care. Monitoring this indicator helps inform targeted interventions and resource planning to reduce avoidable strokes and improve outcomes. ## Numerator The numerator is the number of emergency hospital admissions where the primary diagnosis is stroke, identified using ICD-10 codes I61, I62, I63, and I64. Admissions are included if they meet specific criteria: admission method codes ('21', '22', '23', '24', '25', '28', '2A', '2B', '2C', '2D') and patient classification as 'ordinary' (1 or 2). Data are sourced from the Secondary Uses Service (SUS). ## 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 the 2021 Census. Due to disclosure control, some values have not been published. ## Caveats NHS Digital has identified a data quality issue 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, some values have not been published due to disclosure control. ## External References [Fingertips Public Health Profiles – Stroke Admissions] [Click here] to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.
Emergency admissions for stroke - ICP Outcomes Framework - Birmingham and Solihull Wards

This dataset presents the age-standardised rate of emergency hospital admissions for stroke. It provides a key measure of the burden of acute cerebrovascular events requiring urgent medical care and supports the evaluation of stroke prevention and early intervention strategies across local populations. ## Rationale Reducing emergency admissions for stroke is a critical public health objective. High admission rates may indicate gaps in the management of risk factors such as hypertension, atrial fibrillation, and diabetes, or delays in accessing preventative care. Monitoring this indicator helps inform targeted interventions and resource planning to reduce avoidable strokes and improve outcomes. ## Numerator The numerator is the number of emergency hospital admissions where the primary diagnosis is stroke, identified using ICD-10 codes I61, I62, I63, and I64. Admissions are included if they meet specific criteria: admission method codes ('21', '22', '23', '24', '25', '28', '2A', '2B', '2C', '2D') and patient classification as 'ordinary' (1 or 2). Data are sourced from the Secondary Uses Service (SUS). ## 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 the 2021 Census. Due to disclosure control, some values have not been published. ## Caveats NHS Digital has identified a data quality issue 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, some values have not been published due to disclosure control. ## External References [Fingertips Public Health Profiles – Stroke Admissions] [Click here] to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.
Percentage of households with 2 cars or vans - WMCA Wards (2025)

This dataset provides Census estimates that classify households by the number of cars or vans owned or available for use by household members. Vehicles included: * pick-ups, camper vans and motor homes * vehicles that are temporarily not working * vehicles that have failed their MOT * vehicles owned or used by a lodger * company cars or vans if they're available for private use. Vehicles not included: * motorbikes, trikes, quad bikes or mobility scooters * vehicles that have a Statutory Off Road Notification (SORN) * vehicles owned or used only by a visitor * vehicles that are kept at another address or not easily accessed. Statistical disclose control would have been applied to the data if it would have been possible to identify a household. Data is [Powered by LG Inform Plus] and automatically checked for new data on the 4th of each month.
Boundaries - Regions (2024) - England

This file contains the digital vector boundaries for Regions in England as at December 2024. The boundaries available are: Generalised Clipped (BGC) - Generalised to 20m and clipped to the coastline (Mean High Water mark) and more generalised than the BFE boundaries. Contains both Ordnance Survey and ONS Intellectual Property Rights. ## TopoJSON Shapefile for Power BI On the Export tab you will find a file under the Alternative exports. This file is in TopoJSON format and is ready for use in compatible visualisation tools such as Power BI or Mapbox.
Census 2021 Ethnicity - Constituencies

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance. ## Coverage This dataset is focused on the data for Birmingham at 2021 constituency level. ## About the 2021 Census The Census takes place every 10 years and gives us a picture of all the people and households in England and Wales. ## Protecting personal data The ONS sometimes need to make changes to data if it is possible to identify individuals. This is known as statistical disclosure control. In Census 2021, they: * Swapped records (targeted record swapping), for example, if a household was likely to be identified in datasets because it has unusual characteristics, they swapped the record with a similar one from a nearby small area. Very unusual households could be swapped with one in a nearby local authority. * Added small changes to some counts (cell key perturbation), for example, we might change a count of four to a three or a five. This might make small differences between tables depending on how the data are broken down when they applied perturbation. For more geographies, aggregations or topics see the link in the Reference below. Or, to create a custom dataset with multiple variables use the ONS [Create a custom dataset tool]. ## Population value The value column represents All usual residents.