Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the NHAIS (National Health Application and Infrastructure Services) system. This release is an accurate snapshot as at 1 April 2024. This publication also includes monthly data outputs from the Personal Demographic Service, which will become the data source for this publication from May 2024. More information about the data source change can be found in the Data Quality Statement. 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.

Practice demographic data are extracted as a quarterly snapshot in time from the GP Payments system maintained by the Health and Social Care Information Centre (HSCIC). Data for GP Practices with 100 or fewer registered patients has been suppressed due to possible identification of individuals when data are linked to other data sets. These releases are an accurate snapshot as at each date. From April 2017, following a consultation, the frequency of this release has changed to monthly, and file structure has changed - there are now three files per release: Males by practice, Females by practice and all persons by commissioning region/region/CCG.

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 April 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.

Leeds GP registered patients living inside Leeds. Counts per 5 year ageband for MSOAs, Wards, Community Committees, and Leeds.

How the number of patients per doctor and nurse at GP practices in England has changed over time, and how it differs across age, region and deprivation.

Counts of GP surgeries across England and Wales. Geographies include local authority districts (LADs), built up areas (BUAs) and combined authorities.

Directly Age Standardised Rates (DASR) per 100,000. Age standardised rates compensate for differing age structures by weighting them to meet the European Standard Population (2013). Rates can then be compared for different areas, or even across area types. Attention should be given to upper and lower 95% confidence intervals as a quick method of determining whether rates could overlap or are significantly different. Wide confidence intervals are indicative of small numbers in the numerator or of very skewed age structures. Rates of course cannot be summed, and because they are age standardised cannot be reverse engineered back to counts without knowing the age of every patient in the data when their condition was recorded. Source is Leeds GP data collection programme.

This dataset presents the crude rate of GP-prescribed long acting reversible contraception (LARC), excluding injections, per 1,000 GP-registered females aged 15 to 44 years. It provides insight into the uptake of effective, long-term contraceptive methods prescribed in general practice, supporting efforts to improve reproductive health and reduce unintended pregnancies.

Rationale

The rationale for this indicator is to increase the uptake of GP-prescribed long acting reversible contraception. LARC methods are among the most effective forms of contraception and are recommended for reducing unplanned pregnancies, particularly among younger women.

Numerator

The numerator is the number of LARC items (excluding injections) prescribed by GPs, based on data from Birmingham City Council’s LARC contract.

Denominator

The denominator is the GP-registered female population aged 15 to 44 years, using data from Birmingham and Solihull GP registration records.

Caveats

Solihull data is currently unavailable. GP activity is assigned to the host local authority of the GP practice’s main base, which may not reflect the patient’s residence. Additionally, some women—particularly younger women—may choose to access LARC through Sexual and Reproductive Health Services rather than GP practices, which may lead to underrepresentation in this dataset.

External references

OHID Fingertips: Sexual Health Profile Further analysis of GP and Sexual and Reproductive Health services

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.

The aim of the publication is to inform users about activity and usage of GP appointments historically and how primary care is impacted by seasonal pressures, such as winter. The publication includes important information, however it does not show the totality of GP activity/workload. The data presented only contains information which was captured on the GP practice systems. This limits the activity reported on and does not represent all work happening within a primary care setting or assess the complexity of activity. No patient identifiable information has been collected or is included in this release. NHS England publishes this information to support winter preparedness and provide information about some activity within primary care. The publication covers historic appointments, marked as attended or did not attend, from national to sub ICB location level. As of November 2022 the data is also available at practice level in the form of an Annex which can be found in the resources section. The aim is to inform users, who range from a healthcare professional to an inquiring citizen, about appointments within primary care. The publication includes data from participating practices using EMIS, TPP, Eva Health formerly known as Microtest (up until February 2021), Informatica, Cegedim (previously Vision) and Babylon (GP at Hand) GP systems. NHS England produce this information monthly, containing information about the most recent month and previous months. Between December 2020 and present the data contained in this publication will no longer contain covid-19 vaccination activity collected from GP System Suppliers as part of the General Practice Appointments Data. These appointments have been removed using the methodology outlined in the supporting information. In order to gain a more complete picture of general practice activity we will publish covid-19 vaccination activity carried out by PCN’s or GP Practice’s from the NIMS (National Immunisation Management Service) vaccination dataset. This publication now includes statistics on the duration of appointments, SDS role and the recorded national category, service setting and context type of the appointment. Both HCP Type and SDS role are currently presented for comparison purposes, but moving forward the intention is to only publish SDS Role Groups and remove HCP Type. Further information can be found in the supporting guidance below.

Data has been collected annually since 2004/05. A new GMS Contract was introduced in April 2004 and a fundamental funding stream called the Quality and Outcomes Framework (QOF) was introduced at that time. QOF is a reward framework of indicators designed to remunerate general practices for providing good quality care to their patients. An important feature of QOF is the maintenance of registers which allow prevalence of a number of long-term conditions to be calculated. Register counts and prevalence per 1,000 GP registered population are published. Where registers are age-specific, prevalence per 1,000 age-specific population are also published.

This metric is derived by the LGA (Local Government Association) from the CQC (Care Quality Commission's) Care Directory file. The file contains a complete list of the places in England where care is regulated by CQC. Using the National Statistics Postcode Lookup, we have counted the number of doctor/GP practices located in an area and then created a crude rate per 1,000 resident population.

These services involve doctors working in premises, or a room, designated for medical consultation or minor medical treatments. Often the doctor will complete medical consultations, including physical examination and simple physiological measurement (such as blood pressure tests). They will discuss diagnosis and treatment options and may prescribe medicines for the person to take at home.

They may also undertake minor invasive investigations or procedures, such as conscious endoscopy, in a treatment room designed for this purpose. There may be other healthcare professionals, for example practice nurses, supporting the work of the doctor.

Examples of services that fit under this category:

Independent doctors consulting rooms NHS GP practices Slimming clinics Early medical abortion clinics Travel vaccination services Polyclinics

Data is extracted once a quarter and provides a snapshot in time. It should be noted that due to changes to postcodes, a small proportion cannot be matched to the latest National Statistics Postcode Lookup file and are therefore excluded from these figures.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

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.

Chronic Health Condition Prevalence in Stockport, UK, 2016

Counts of Hypertension, Anxiety, Diabetes and Other Chronic Health Conditions

By data.world's Admin [source]

About this dataset

This dataset contains crucial information regarding the prevalence of various health conditions affecting Stockport, UK, in June 2016. This dataset will help you better understand the prevalence rates of Hypertension, Anxiety, Depression, Asthma, Obesity, Diabetes, Coronary Heart Disease (CHD), Falls (both accidental and medical-related), Cancer (various forms listed), Chronic Kidney Disease (CKD), Chronic Obstructive Pulmonary Disease (COPD) , Stroke/Trans-Ischaemic Attack and Atrial Fibrillation amongst individuals living in Lower Layer Super Output Areas across Stockport which are grouped by codes. The count of individuals affected by each condition cited is provided along with the GP Registered Population for each LSOA which typically ranges from 1000 to 2000 people per LSOA. This data could be utilized to identify areas most impacted by healthcare related issues from a geographical perspective as well as help provide insight into chronic illnesses that may require further attention throughout Stockport's communities

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How to use the dataset

The data contained in this dataset consists of information on chronic health conditions gathered from Lower Layer Super Output Areas (LSOA) located in Stockport, UK for June 2016. The count information provided pertains to Hypertension, Anxiety, Depression, Asthma, Obesity, Diabetes, Coronary Heart Disease (CHD), Falls, Cancer and Chronic Kidney Disease (CKD), Chronic Obstructive Pulmonary Disease (COPD), Stroke/Trans-Ischaemic Attack and Atrial Fibrillation.

• To get a better understanding of what this dataset looks like we will start by reviewing the columns it contains. The columns contain information about: Lower Layer Super Output Area Code(lsoa11cd), Lower Layer Super Output Area Name(lsoa11nm & lsoa11nmw for Welsh language version) , GP Registered Population(GPRegPop), Hypertension (Hypertens), Anxiety(Anxiety), Depression(Depression) ect .

• To get an overview of what this dataset is about use a summary statistic tool such as mean(), median(), mode() etc to aggregate your data. This can be done by computing each column’s summary statistics separately or by combining them into one table for every condition listed here. This way you can obtain an overview which accurately reflects the overall population distribution pertaining to particular chronic health condition across multiple LSOA's at one time frame only.

• For deeper analysis refine your finding further or delve down into cause and effect make use graphs & charts such as scatter plots or line charts etc,. as well correlational analysis such Joint Analysis/Common Factor Analysis & Multiple Regression Analysis which will give you an insight into co-occurrence frequency or other related variables whcih could play a role in any particular health condition cause and affect outcomes over a period of time allowing further investigation if needed be pertaining suspected underlying causes regarding chronic medical conditions observed .

• Finally it is important that comprehensive datasets are created using wide range factors relevant local determinants before drawing conclusions so allow public bodies with decision making power make informed decisions accordingly when devising strategies for tackling causes associated with specific chronic medical coniditons target population groups required provide assistance towards public welfare goal become more efficient targeting

Research Ideas

• Analyzing the geographic variation of health conditions in Stockport in order to inform public health policy decisions. For example, to identify areas where specific interventions are needed to improve healthcare outcomes, or target resources at particular (at-risk) populations.
• Examining the correlations between different health conditions and identifying potential links or risk factors for developing one condition when another is present.
• Utilizing the GP registered population for each LSOA as a metric for predicting which areas of Stockport are likely to require additional funds or resources in order ensure adequate access to healthcare services for their residents

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more informat...

This dataset presents the rate of Accident & Emergency (A&E) attendances among young children aged 0–4 years across England. It provides a crude rate per 1,000 population, offering insight into the frequency with which this age group accesses emergency care services. The data is derived from the Emergency Care Data Set (ECDS) maintained by NHS England and is linked to the child’s local authority of residence at the time of attendance.

Rationale Monitoring A&E attendances for children aged 0–4 years is crucial for understanding patterns of urgent healthcare use in early childhood. High rates may indicate issues such as limited access to primary care, parental health-seeking behaviour, or broader public health concerns. Reducing unnecessary A&E attendances in this age group is a key public health objective, aiming to ensure children receive appropriate care in the most suitable settings.

Numerator The numerator includes all A&E attendances for children aged 0–4 years at the time of attendance, with a valid gender recorded, and who are residents of England. Each child is assigned to their local authority of residence based on the location at the time of the A&E visit.

Denominator The denominator is the resident population of children aged 0–4 years, based on data from the 2021 Census.

Caveats There are no specific caveats noted for this dataset. However, users should consider potential limitations such as data completeness, accuracy of residency assignment, and changes in healthcare-seeking behaviour over time.

External References Public Health England – Fingertips Indicator

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.

GP Practices - are UK wide and we cover the Practice Manager, the Senior GP and Senior Nurses. In every practice one of these is nominated as the main contact (our 'Chief Officer' category), to allow you to reach one person per practice if required. This will normally be the Practice Manager and is the contact for which we list an email address.

The National Health Service is the largest employer in the UK but is not a single homogenous organisation. Following devolution and major re-organisations in the past few years, the ways in which it is organised in England, Scotland, Wales and Northern Ireland are continuing to diverge.

Our database covers senior and mid-level posts across all functions and areas of the NHS. This includes both the Management and Medical/Clinical sides.

England - the NHS has undergone considerable re-organisation since 2011 with Strategic Health Authorities and Primary Care Trusts being replaced by a new structure of healthcare provision. The vast majority of services are now provided or commissioned at a local level via groups of GP Surgeries, known as Clinical Commissioning Groups (CCG's), or at a secondary care level via Hospital Trusts. Public Health services are now provided by Local Authorities who also work with CCG's via Health and Wellbeing Boards to commission services jointly. There are also a number of new 'Community Healthcare' providers, in the form of Health and Care Trusts (NHS organisations) and Community Interest Companies (Social Enterprises). These organisations provide a range of community, mental health, primary care and nursing functions and sit alongside Local Authorities, CCG's and Secondary Care providers in many areas. These, along with some Secondary Care Acute Trusts which inherited them following the dissolution of PCT's run Community Hospitals, Clinics, Walk in Centres and some Dental services.

Scotland - has a simplified structure with Scottish Health Boards having control of all operational responsibilities within their geographical area. The Community Health Partnerships provide a range of community health services and they work closely with primary health care professionals as well as hospitals and local councils.

Wales - has established Local Health Boards and with the exception of one remaining NHS Trust, they deal with all Primary and Secondary Healthcare services.

Northern Ireland - also has single organisations - Health & Social Care Trusts, which along with several other national bodies, deal with co-ordinating and providing all the regions Healthcare services.

av_patient
Patient information - demographics and death details.

av_tumour
Tumour catalogue and characterisation for all patients with registerable tumour. Table's anon_tumour_id is used to link treatment tables also available in NCRAS. One row per tumour (av* table specific anon_tumour_id), per participant at the point of registration of that cancer/tumour with NCRAS.

av_treatment
Tumour linked catalogue of treatments and sites that provided them for all patients with registerable tumour.

av_imd
The Income Deprivation Domain (IMD table) measures the proportion of the population experiencing deprivation relating to low income. The definition of low income used includes both those people that are out-of-work and those that are in work but who have low earnings.

av_rtd
Routes to Diagnosis: cancer registration data are combined with Administrative Hospital Episode Statistics data, Cancer Waiting Times data and data from the cancer screening programmes. Using these datasets cancers registered in England which were diagnosed in 2006 to 2016 are categorised into one of eight Routes to Diagnosis. The methodology is described in detail in the British Journal of Cancer article 'Routes to Diagnosis for cancer - Determining the patient journey using multiple routine datasets'.

cwt The National Cancer Waiting Times Monitoring Data Set supports the continued management and monitoring of waiting times.

sact
Systemic Anti-Cancer Therapy (chemotherapy detail) data for cancer participants from NHSE covering regimens between 04/2012 and 08/2022. One row per chemotherapy cycle, per tumour (SACT-specific anon_tumour_id), per participant.

rtds
The Radiotherapy Data Set (RTDS) standard (SCCI0111) is an existing standard that has required all NHS Acute Trust providers of radiotherapy services in England to collect and submit standardised data monthly against a nationally defined data set since 2009. The purpose of the standard is to collect consistent and comparable data across all NHS Acute Trust providers of radiotherapy services in England in order to provide intelligence for service planning, commissioning, clinical practice and research and the operational provision of radiotherapy services across England. Data is available from 01/04/2009. The data is linked at a patient level and can be linked to the latest available av_patient table.

ncras_did
The Diagnostic Imaging Dataset (DID) is a central collection of detailed information about diagnostic imaging tests carried out on NHS patients, extracted from local radiology information systems and submitted monthly. The DID captures information about referral source, details of the test (type of test and body site), demographic information such as GP registered practice, patient postcode, ethnicity, gender and date of birth, plus data items about different events (date of imaging request, date of imaging, date of reporting, which allows calculation of time intervals.

lucada_2013 The National Lung Can

PIONEER: Deeply-phenotyped hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 4.0

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 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.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – May 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes but also primary care records& clinic letters. 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. Linked images available (radiographs, CT, MRI, ultrasound).

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; 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.

This metric is derived by the LGA (Local Government Association) from the CQC (Care Quality Commission's) Care Directory file. The file contains a complete list of the places in England where care is regulated by CQC. Using the National Statistics Postcode Lookup, we have counted the number of doctor/GP practices located in an area and then created a crude rate per 1,000 resident population.

These services involve doctors working in premises, or a room, designated for medical consultation or minor medical treatments. Often the doctor will complete medical consultations, including physical examination and simple physiological measurement (such as blood pressure tests). They will discuss diagnosis and treatment options and may prescribe medicines for the person to take at home.

They may also undertake minor invasive investigations or procedures, such as conscious endoscopy, in a treatment room designed for this purpose. There may be other healthcare professionals, for example practice nurses, supporting the work of the doctor.

Examples of services that fit under this category:

Independent doctors consulting rooms NHS GP practices Slimming clinics Early medical abortion clinics Travel vaccination services Polyclinics

Data is extracted once a quarter and provides a snapshot in time. It should be noted that due to changes to postcodes, a small proportion cannot be matched to the latest National Statistics Postcode Lookup file and are therefore excluded from these figures.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This dataset presents the age-standardised mortality rate among children aged 1 to 17 years. It captures the number of deaths from all causes within this age group, registered during calendar years, and expresses the rate per 100,000 population. The data is age-standardised to allow for fair comparisons across different populations and time periods, accounting for variations in age structure.

Rationale Reducing child mortality is a key public health priority. Monitoring mortality rates among children aged 1 to 17 years provides critical insight into the overall health and wellbeing of this population group, and helps identify areas where interventions may be needed to improve outcomes and reduce preventable deaths.

Numerator The numerator is defined as the number of deaths from all causes among children aged 1 to 17 years, registered in the calendar year. This data is sourced from the Death Register.

Denominator The denominator is the mid-year population estimate for children aged 1 to 17 years, based on single year of age and sex for local authorities in England and Wales. These estimates are derived from the 2021 Census.

Caveats There are no specific caveats noted for this dataset. However, users should consider potential limitations related to registration delays or changes in population estimates over time.

External References Further information and related indicators can be found on the Fingertips Public Health Profiles website.

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.