Suchergebnisse

ObjectiveThe UK Government increasingly emphasises a comprehensive, multi-agency approach to tackling crime. This pilot study explores the feasibility of integrating police data with routine health data to develop a holistic understanding of the predictors of domestic abuse (DA).
ApproachThe study encompasses three work-packages a) coding the narrative data from Domestic Abuse, Stalking and Harassment (DASH) risk assessment report from Public Protection Notifications (PPNs), which are information-sharing documents recording safeguarding concerns and shared with partner agencies b) exploring to identify what works to harmonise different software systems within police data-sharing approach, c) constructing a case study by linking PPN DASH data with routine health and administrative records. These efforts illustrate the potential of national data-sharing initiatives.
Results
a) Text mining methods successfully identified and coded (with over 95% accuracy) 17 refereed/involved agencies from the PPN DASH data.
b) Barriers to data sharing were attributed to a lack of clarity and consensus regarding appropriate information sharing, rather than technical obstacles. This barrier can be overcome by an unambiguous framework, endorsed at an elevated level, highlighting which data should be shared.
c) The data-linkage study revealed that victims of DA had prior interactions with healthcare services before their initial PPN, and younger pregnant victims had higher risk of future healthcare emergency visits.
Conclusion and ImplicationPolice and health data integration enhances evidence-based prevention and early identification of vulnerable individuals by both law enforcement and public health services.

IntroductionBirth cohorts are valuable resources for studying early life, the determinants of health, disease, and development. They are essential for studying life course. Dynamic longitudinal electronic cohorts use routinely collected data, are live, and can reduce selection bias specifically associated with direct recruitment in traditional birth cohorts. However, they are limited to health and administrative data and may lack contextual information.
The MIREDA (Mother and Infant Research Electronic Data Analysis) partnership creates a UK-wide birth cohort by aligning existing electronic birth cohorts to have the same structure, content, and vocabularies, enabling UK-wide federated analyses.
Objectives1) Create a core dynamic, live UK-wide electronic birth cohort with approximately 500,000 new births per year using a common data model (CDM).
2) Provide data linkage and automation for long-term follow up of births from the Clinical Practice Research Datalink (CPRD), MuM-PreDiCT and the `Born in' initiatives of Bradford, Wales, Scotland, and South London for comparable analyses.
MethodsWe will establish core data content and collate linkable data. A suite of extraction, transformation, and load (ETL) tools will be used to transform data for each birth cohort into the CDM. Transformed datasets will remain within each cohort's trusted research environment (TRE). Metadata will be uploaded for the public to the Health Data Research (HDRUK) Innovation Gateway. We will develop a single online data access request for researchers. A cohort profile will be developed for researchers to reference the resource.
EthicsEach cohort has approval from their TRE through compliance with their project application processes and information governance.
DisseminationWe will engage with researchers in the field to promote our resource through partnership networking, publication, research collaborations, conferences, social media, and marketing communications strategies.