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Longitudinal data for ….
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- De-identified/ anonymised
- Includes people not
receiving services
- Define groups for targeted
services
- Individuals identifiable
- Data limited to those
Longitudinal data for population-based evaluation of childrens - - PowerPoint PPT Presentation
Longitudinal data for population-based evaluation of childrens - - PowerPoint PPT Presentation
Longitudinal data for population-based evaluation of childrens social care Ruth Gilbert professor of clinical epidemiology University College London Great Ormond Street Institute of Child Health, UK r.gilbert@ucl.ac.uk Longitudinal =
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What does population-based, longitudinal data tell us about services? A child life course perspective
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CiN is a common childhood experience
Yearly prevalence 2.7% of children <5y
By 18 years children EVER CiN likely to exceed 30%
Longitudinal data – EVER CiN
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What is driving rise in out of home care?
Driven by:
- Duration of stay
- Number ever
entering
- Repeat entries
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1.6% 0.5% 0.9% 2.0% 3.3% Cross-sectional prevalence of 0.6%
Longitudinal data – number EVER in care increasing
0.9% 2.0%
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3.4% 2.7% 6.7% 9.5%
Percentage of children born 1992-94 placed in care, by ethnic group
Ever in care very high for Black and Mixed ethnic groups – but rise driven by white ethnic group
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Proportion of children ever in care varies between local authorities
7% <1%
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Re-entry to care within 5 years decreasing
Re-entries
- Decreasing over time
Increased for:
- Adolescents
- Voluntary placement
- Short placement
- Repeated placements
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School entry Reception year Age 4-5y
Birth
Year 6 Age 11y Special education needs 2011 Any CiN Any period looked after No social care
Children in care have very high levels of SEND
n= 544,879 children Year 11 Age 16y
Jay, 2018, unpublished
Results not shown as work in progress Contact Matthew Jay with any queries matthew.jay.15@ucl.ac.uk
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Linking longitudinal datasets
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12 Pregnancy Hospital admissions Self harm Pregnancy Childhood Adolescence Young adulthood Birth School exclusion Special Educational needs Birth Care proceedings (family courts) Placed in care Birth Admissions
Longitudinal datasets from related services
HEALTH SERVICE SCHOOLS SOCIAL SERVICES
CiN CiN referral
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13 Pregnancy Hospital admissions Self harm Pregnancy Childhood Adolescence Young adulthood Birth School exclusion Special Educational needs Birth Care proceedings (family courts) Placed in care Birth Admissions
Linking longitudinal events
HEALTH SERVICE SCHOOLS SOCIAL SERVICES
CiN CiN referral CiN referral
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14 Pregnancy Hospital admissions Self harm Pregnancy Childhood Adolescence Young adulthood Birth School exclusion Special Educational needs Birth Care proceedings (family courts) CiN Placed in care Birth Admissions
Linking longitudinal datasets
HEALTH SERVICE SCHOOLS SOCIAL SERVICES
CiN referral
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15 Pregnancy Care proceedings (family courts) Hospital Self harm Referred to children’s social care Pregnancy Childhood Adolescence Young adulthood Placed in care School exclusion Special Educational needs Admissions
HEALTH SERVICE SCHOOLS SOCIAL SERVICES
Linked longitudinal data
Birth CiN referral
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…and maintain confidentiality and trust
‘Identifiability spectrum’ by Understanding Patient Data is licensed under CC BY.
Use ANONYMISED linked longitudinal data…
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Match status
Pair from same subject (matches) Pair from different subjects (non-matches)
Linkage status
Links
Identified match
Non- links
Identified non-match False match Missed match
Linkage error matters
Underestimate events in groups with poor quality data Weaken associations
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Linkage error:
- is not random
- depends on data quality
- is worse for certain groups eg:
- Marginalised (homeless, insecure accommodation)
- Uncertain dates of birth (runaways, asylum seekers)
- Unconventional surnames
- Misleading / missing information (drug user, parent
withholding details, foster and adoptive parents)
- Address issues - communal establishments, traveller,
changing care placements
- Multiple births and siblings
Linkage error:
- is not random
- depends on data quality
- is worse for certain groups eg:
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Linkage error can be reduced or taken into account by:
- Using probabilistic linkage (account for uncertain linkages)
- Avoiding encrypted linkages – increases error, obscures causes
- Validating methods against a gold standard (if possible)
- Conducting sensitivity analyses
- Comparing linked and unlinked and take disparities into account
- Comparing against expected results from external evidence
- Transparent reporting of linkage methods
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Summary
Linked longitudinal, population-based data provides evidence for policy and service decisions at local and national level. Anonymity and confidentiality is critical for public support for unconsented use of population data. Linkage error addressed through methods and transparency.
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References:
- Mc Grath-Lone, L. Using longitudinal administrative data to characterise the use of out-
- f-home care among children in England. UCL PhD thesis
http://discovery.ucl.ac.uk/10038396/
- Mc Grath-Lone, L, et al. Factors associate with re-entry to out-of-home care among
children in England. Child Abuse and Neglect 2017;63:73-83.
- Mc Grath-Lone, L, et al. Changes in first entry to out-of-home care from 1992 to 2012
among children in England. Child Abuse and Neglect 2016;51:163-171.
- Matthew A Jay, et al. Who cares for children? Population data for family justice research.
http://wp.lancs.ac.uk/observatory-scoping-study/files/2017/10/FJO-NATIONAL-DATA- SCOPING-FINAL.pdf
- Bohensky et al 2010. Data Linkage: A powerful research tool with potential problems.
BMC Health Services Research
- Harron KL, et al. A guide to evaluating linkage quality for the analysis of linked data. Int J
- Epidemiol. 2017 Oct 1;46(5):1699-1710.
- Gilbert R, et al. GUILD: GUidance for Information about Linking Data sets. J Public
Health (Oxf). 2018 Mar 1;40(1):191-198.
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