Degree Sheet: 61 SE, Sheet Number (Directorate of Overseas Survey): 186, Report: Not applicable

Degree Sheet: 66 SE, Sheet Number (Directorate of Overseas Survey): Not applicable, Report: Not applicable

Degree Sheet: 43 NE, Sheet Number (Directorate of Overseas Survey): Not applicable, Report: Not applicable

This Zenodo entry details the methodology for extracting and reconciling ethnicity data from the Clinical Practice Research Datalink (CPRD), incorporating both General Practitioner (GP) and Hospital Episode Statistics (HES) sources. The approach aims to resolve discrepancies between these sources and provide a standardized single ethnicity value per patient, categorized into 6 and 12 levels according to NHS coding guidelines. Materials and Methods: Ethnicity data from the CPRD are recorded in multiple formats. This study harmonizes these data to achieve consistent ethnicity classification across patient records, following a hierarchal reconciliation protocol prioritizing hospital data over GP records. Ethnicity Levels: Ethnicity data are processed to conform to two levels of granularity: Six high-level categories: White, Black, Asian, Mixed, Other, Unknown Twelve detailed categories: Bangladeshi, Black African, Black Caribbean, Black Other, Chinese, Indian, Mixed, Other Asian, Other, Pakistani, Unknown, White Source Data Mapping: CPRD Medcodes: Directly mapped to 490 SNOMED codes SNOMED to NHS Codes: SNOMED codes are linked to 26 NHS ethnicity codes NHS to HES Codes: These NHS codes further map into 12 HES hospital ethnicities, which then consolidate into the 6 broad categories mentioned above Algorithm (AIM-CISC): Hospital Data Priority: Ethnicity records from hospital sources override those from GP records unless the hospital data is classified as "Unknown", null, or empty. Conflict Resolution Within GP Data: The frequency of recorded ethnicities determines the selection. The most frequently recorded ethnicity prevails. If frequencies are tied, the most recent record is used. In cases where records are equally recent, the first alphabetically listed ethnicity is selected. Unique Patient Identifiers: Each patient is represented once in hospital data, ensuring a single source of truth for hospital-based ethnicities. This simplifies reconciliation with GP data when discrepancies arise. Source Documentation and References: Reference for Code Lists: Digital ethnicity data in population-wide electronic health records in England: a description of completeness, coverage, and granularity of diversity (Pineda-Moncusí et al., 2022): https://doi.org/10.1101/2022.11.11.22282217 GitHub Repository for Code Lists: https://github.com/BHFDSC/CCU037_01/blob/main/england/phenotypes/snomed_meaning_and_map_to_primary_code.csv NHS Ethnicity Codes Documentation: https://www.datadictionary.nhs.uk/attributes/ethnic_category_code_2001.html open_in_new Notes on mapping: Instances were noted where multiple Medcodes map back to a single SNOMED code, highlighting the importance of careful data cross-referencing. For example, two different Medcodes represent the New Zealand European ethnicity, which both map back to the identical SNOMED code.

Degree Sheet: 51 SW, Sheet Number (Directorate of Overseas Survey): 160, Report: Not applicable

Degree Sheet: 69, Sheet Number (Directorate of Overseas Survey): Not applicable, Report: 24

There were 20,944 incidences of racist hate crime reported by the London Metropolitan Police in the 2023/24 reporting year, by far the most of any police force area in England and Wales. In the same time period, there were 7,466 racist incidents reported in the West Midlands.

Degree Sheet: 33 SE, Sheet Number (Directorate of Overseas Survey): Not applicable, Report: Not applicable

Degree Sheet: 41 SE, Sheet Number (Directorate of Overseas Survey): Not applicable, Report: Not applicable

Degree Sheet: 52 SW, Sheet Number (Directorate of Overseas Survey): Not applicable, Report: Not applicable

Degree Sheet: 59 NW, Sheet Number (Directorate of Overseas Survey): Not applicable, Report: Not applicable