Kent Integrated Dataset Produced by Emily Weitzel, Senior Analyst Rachel Kennard, Senior Analyst Version: 1 Last updated: 13th July 2018

Contents The KID Analyses Limitations of the KID Developments Conception & datasets Analyses Self harm Limitations of the KID Developments Hisbi Optum

The KID Person level dataset linking routinely collected administrative activity and cost data National pilot in 2012 Initially NHS England funded Co-funded by Health & Social Care KCC oversight with Hisbi data warehouse

The KID: datasets April 2014 1.8 million ~900 million rows of data vs ~1000 columns, spread across 40 data tables Pseudonymised dataset

The KID: datasets GP 210 practices flowing as of June 2018 Community health Mental health Out of hours Acute hospital Public health Adult social care Hospice HISBI data warehouse (Trusted Third Party Data Processor) KID minimum dataset: data on activity, cost, service/treatment received, staffing, commissioning and providing organisation, patient diagnosis, demographics and location. Datasets linked on a common patient identifier (NHS number) and pseudonymised derived from Patient Master Index (Household level data is linked via pseudonymised UPRN) Pseudo at source KENT INTEGRATED DATASET Accessed securely by Kent County Council Public Health

Data quality Provider responsibility DQ matrix Data receipt Data quantity Data accuracy and completeness All participating organisations have provided data for each of their service areas Data received is stable over time (i.e. within expected stochastic tolerances for month-to-month changes) Data volumes are comparable with external reference data, such as published numbers of hospital admissions All data items are complete Coding of events (such as Read Codes) appears consistent across data providers Data includes costs for all contacts Data is free of duplicates (to acceptable levels)

Questions?

Self harm KID analysis Background Kent Public Health Observatory http://www.kpho.org.uk/ http://www.kpho.org.uk/__data/assets/pdf_file/0019/82054/CYP-self-harm.pdf

Admission rates have remained stable over time In 2016/17 alone, there were 1,018 admissions to hospital for self-harm amongst 10-24 year olds resident in Kent, involving 823 individuals Admission rates have remained stable over time From HSCM. Shows large number of admissions, although a stable trend Analysis of hospital admissions for self-harm

Gender Girls account for almost three-quarters of the admissions for self-harm amongst 10-24 year olds in Kent Relates to all admissions (not individuals) Analysis of hospital admissions for self-harm

Age distribution Around half of these admissions involve young people aged between 14 and 18 48% of total admissions involve a young person aged between 14 and 18 Analysis of hospital admissions for self-harm

Deprivation A third of the admissions for self-harm involve children & young people living in the 20% most deprived areas Alternatively you could say that children living in deprived areas are twice as likely to end up in hospital for self-harm than those living in the most affluent areas. Analysis of hospital admissions for self-harm

The KID: Self harm This analysis focusses on 10-24 year olds admitted for self-harm in 2015/16 or 2016/17 This then allows before-and-after analysis of service usage for a cohort of children & young people admitted to hospital for self-harm Usage of acute, out of hours, community and (adult) secondary mental health services have been explored using data contained within the KID http://www.kpho.org.uk/ http://www.kpho.org.uk/__data/assets/pdf_file/0019/82054/CYP-self-harm.pdf Kent Public Health Observatory

Service usage Since their registration with a Maidstone GP… DEVELOPMENTAL STATISTICS Since their registration with a Maidstone GP… IN THE YEAR PRIOR TO THE FIRST SELF-HARM ADMISSION IN THE YEAR AFTER THE FIRST SELF-HARM ADMISSION Including the admission day Community contacts are a variety of needs – but top two: 25% are HV, 15% LAC. Source: MBC, KID, prepared by KPHO (RK) ADULT ONLY Images from Noun Project Analysis of hospital admissions for self-harm *includes telephone consultations Analysis restricted to individuals registered with a GP flowing data to the KID

Service usage Since their registration with a Maidstone GP… DEVELOPMENTAL STATISTICS Since their registration with a Maidstone GP… IN THE YEAR PRIOR TO THE FIRST SELF-HARM ADMISSION IN THE YEAR AFTER THE FIRST SELF-HARM ADMISSION IN THE 3-12 MONTHS AFTER THE FIRST SELF-HARM ADMISSION Including the admission day Community contacts are a variety of needs – but top two: 25% are HV, 15% LAC. Source: MBC, KID, prepared by KPHO (RK) ADULT ONLY Images from Noun Project Analysis of hospital admissions for self-harm *includes telephone consultations Analysis restricted to individuals registered with a GP flowing data to the KID

Service usage: Prior usage of A&E DEVELOPMENTAL STATISTICS FREQUENCY OF USAGE A&E in the 12 months prior to first self-harm admission TIMING OF USAGE Timing of latest A&E in relation to first self-harm admission 6+ months before 1 day before 2-7 days before 4-6 months before 1 week – 1 month before 1-3 months before Were discharged with no follow-up required 44% Were discharged to GP follow-up Left before being seen or refused treatment 18% 9% Analysis of hospital admissions for self-harm Source: MBC, KID, prepared by KPHO (RK) Analysis restricted to individuals registered with a GP flowing data to the KID

Service Usage: findings Around 25% of persons were already known to Adult MH services prior to admission for self harm. This increases to around 60% in the 12 months following admission (including day of admission) However, in the 3-12 months following admission contact with MH services ‘normalises’ to around 30% Around 30% of persons had been seen in A&E one month before admission for self harm

Questions?

KID: Finnamore review review of the KID 2016 “The achievement in overcoming Information Governance hurdles to establish this dataset and the permissions to use it is considerable. This is an invaluable resource” “The KID has known issues with data flow and data quality and is undergoing ongoing improvement and development to address these” “No children's Mental Health data” “No children's Social Care activity”

Linkage of routinely collected data from public services has the potential to improve how local health, education and social care are delivered to children and young people1. 1https://www.researchgate.net/publication/313015618_Linking_health_and_education_data_to_plan_and_evaluate_services_for_children

Context Care dot data

KID: Challenges CYP MH data: SPFT / NELFT Education & Specialist Children’s services – NHS number KCC restructure Data quality? Local providers Public consultation Data warehouse: Hisbi / Optum

KID: Optum SUS MHMDS MHLDDS MHSDS IAPT CCG managed MSDS CYPHS DIDS CCG managed Contract management rather than research? NHS number linkage MHSDS IAPT

KID: Developments Optum Hisbi NHS D datasets inc. MHSDS NHS number linkage Broader role-based access DARS application Hisbi Local datasets NHS number linkage Limited access Deterministic matching possibility

KID: DARS application Optum KID MHSDS in the Optum KID (Autumn?) Data batching service to add NHS number to KCC SCS & education Pseudonymisation of NHS number Secondary research

KID: Deterministic matching Hisbi KID Use deterministic matching to link CSC & education data to the CYP MH data First name, surname, DOB, postcode CYP MH has NHS number in Link matched CYP MH / education / CSC data to the KID Would only be possible to link CYP with a MH record (NHS organisation) into the KID

Conclusions KID Optum / Hisbi Children’s data Great resource, limitations, potential Optum / Hisbi Building on existing KID Children’s data MHSDS Education & Specialist Children’s Service

Questions? Type 2 – if person objects to sharing they can tell their GP and come out of the PMI for sharing To what extent would analysis of linked data be useful within your organisation? Which additional data would you want to see linked? How could linked data inform practice within your organisation? Examples of linked datasets and outcomes that you’ve heard of?