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E-health records research : optimising congenital anomaly data. Dr. Shantini Paranjothy Cochrane Institute of Primary Care and Public Health, College of Biomedical and Life Sciences - Cardiff University Centre for Improvement in Population Health through E-records Research (CIPHER). - PowerPoint PPT Presentation
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E-health records research: optimising congenital anomaly data
Dr. Shantini Paranjothy
Cochrane Institute of Primary Care and Public Health, College of Biomedical and Life Sciences - Cardiff University
Centre for Improvement in Population Health through E-records Research (CIPHER)
• E-health record linkage studies focussed on congenital anomalies– Literature review
• Wales Electronic Cohort for Children– Exemplar analyses: Outcomes for children
with Down’s syndrome
• Conclusion / reflections
Overview
E-health record linkage studies focussed on congenital anomalies
Search strategy:"data linkage" OR "record linkage" OR "database studies" AND "congenital anomalies" - 26 results (OvidSP)
USA (n=6), Canada (n=4), England (n=3), Scotland (n=1), Australia (n=2), Denmark (n=1)
Literature review
17 distinct studies
Types of studies• Trends and inequalities in birth prevalence (n=4)
• Aetiology of congenital anomalies (n=7)– Risk factors:
• maternal characteristics (age, parity, cigarette smoking, socio-economic status),
• occupational exposures• parental cancer treatment • prenatal alcohol exposure
– Limited by poor characterisation of exposure measures
E-health record linkage studies focussed on congenital anomalies
Refs: BMJ 1993;307:164-8, BDR Part A97(7): 497 – 504, BDR Part(A) 91(12): 1011-1018, Int J Environ Res Public Health 10(4):1312-1323, Epidemiology 13(2):197-204, Prenat Diagn 29():613-619, Occup Environ Med 54(9):629-635, Scand J Public Health 37(3):246-251, Dev Med Child Neurol 52(4):345-351, Arch Dis Child: Fetal and Neonatal Edition 94(1):F23-F27, BDR A Clin Mol Teratol 73(10):663-668
Types of studies• Follow-up studies
– Survival at 1 year, 6 years, 10 years (n=2)– Childhood cancers (n=2)– Hospital admissions (n=1)
Limited data from total population studies– Healthcare utilisation – GP consultations, hospital
admissions– Social care, education– Inequalities in health and social outcomes
E-health record linkage studies focussed on congenital anomalies
Refs: BDR A Clin Mol Teratol 67(9):656-661, BDR A Clin Mol Teratol 79(11):792-797, Am J Public Health 89(6):887-892, Am J Epi 175(12): 1210-1224, Pediatric Blood and Cancer 51(5):608-612, PLOS One 2013:8(8)e70401
Population ~3M, ~35,000 births per year
1. Welsh Demographic Service2. Office for National Statistics (birth and mortality
files)
3. National Community Child Health Database4. Patient Episode Database for Wales (PEDW)
5. General Practice consultations6. Congenital Anomaly Registry and Information Service
(CARIS)
7. National Pupil Dataset
Routinely collected data in Wales
• Platform for translating routinely collected data into an anonymised population based e-cohort of children to
– Investigate the widest possible range of social and environmental determinants of child health and social outcomes
– Inform the development of interventions to reduce health inequalities of children in Wales
• E-cohort development
• Exemplar analysis: Down syndrome
Wales Electronic Cohort for Children (WECC)
• Inclusion criteria– Children born or resident in Wales– Phase 1: Date of birth between 1st Jan 1990 – 31st Dec 2008– Phase 2: extended to include births until 7th October 2012
• Core databases– Welsh Demographic Service (WDS)– National Community Child Health Database (NCCHD)
• Linking field– NHS number --- encrypted anonymised linking field (ALF_E)
WECC development
Birth records
(ONS births)
Mortality records
(ONS deaths)
Wales Electronic Cohort for Children
N=981,404
WECC eligibility criteria applied
Data cleaning: rules for removal of duplicates and errors
WDSChild
Health(NCCHD)
ALF_E
WDS: Welsh Demographic Service, NCCHD: National Community Child Health, ONS: Office for National Statistics
WECC development
• Links with health and education data via ALF_E• Links with maternal health data via mALF_E• Links with SAIL eGIS data via ALF_E/RALF_E
WECC coren = 981,404
♂: 500,181 (51.0%)♀ : 481,205 (49.0%)
Inpatient
GP consultation
s
Perinatal and Child
health
Environment
House Moves
Non-Welsh births
n=215,095♂: 107,222 (49.8%)♀ : 107,872 (50.2%)
Born in Walesn= 766,309
♂: 392,959 (51.3%)♀ : 373,333 (49.0%)
WECC derived tables
National dataset
Education
Gestational Age, Birth Weight, and Risk of Respiratory Hospital Admission in Childhood (Paranjothy S. et al (2013) Pediatrics 132:6 e1562-e1569)
Association between hospitalisation for childhood head injury and academic performance (Gabbe B.J. et al (2014)Journal of Epidemiology and Community Health, J Epidemiol Community Health.68:5 466-470 )
Frequent house moves and educational outcomes (Hutchings H. et al (2013) PLoS One. 8(8) e70601)
Examples of analyses
How do survival and hospital admission rates compare between the following groups of children?
1. No major life-threatening congenital anomalies2. Major life-threatening congenital anomalies (excl DS)3. Down’s syndrome without major life-threatening congenital
anomalies4. Down’s syndrome and major life-threatening congenital
anomalies
Follow-up of children with Down’s syndrome in WECC
Welsh births 1st Jan 1998 – 7th Oct 2012N = 491,036
No Down’s syndrome
N = 488,850
No LTCAN = 486,468
1,941,801 pyrs
LTCAN = 2,3828,575 pyrs
Down’s syndromeN = 502
No LTCAN = 432
1588 pyrs
LTCAN = 70
215 pyrs
Excluded stillbirthsN = 1,684
% survival(95%CI)
No LTCA LTCA DS - LTCA DS + LTCA
6 months 99.7(99.7, 99.7)
90.0(88.0, 91.0)
97.0(95.0, 98.0)
81.0(70.0, 89.0)
1 year 99.7(99.7, 99.7)
89.0(87.0, 90.0)
96.0(94.0, 98.0)
78.0(66.0, 86.0)
3 years 99.7(99.7, 99.7)
88.0(86.0, 89.0)
94.0(91.0, 96.0)
73.0(60.0, 82.0)
5 years 99.6(99.6, 99.6)
87.0(86.0, 88.0)
92.0(89.0, 95.0)
73.0(60.0, 82.0)
Survival up to age 5 years
No LTCAN =
486,468
LTCA N = 2,382
DS – LTCAN = 432
DS + LTCAN = 70
IncidenceNo. of
admissions per 100 person
years (95%CI)
11.6(11.5, 11.7)
21.3(20.4, 22.3)
21.9(19.4, 24.0)
28.4(22.1, 36.5)
Number of children
admitted
225,299 1,828 343 61
Median age at first admission
9 months 2 months 4 months 2 months
Emergency hospital admissions
HR (95% CI) No LTCA LTCA (excl DS)
DS - LTCA DS + LTCA
6 months 1.0 2.8 (2.6 – 2.9) 4.1 (3.6 – 4.7)
5.7 (4.2 – 7.8)
1 year 1.0 2.4 (2.2 – 2.6) 4.2 (3.7 – 4.8)
5.5 (3.8 – 7.8)
3 years 1.0 2.0 (1.8 – 2.2) 4.3 (3.5 – 5.3)
5.2 (3.1 – 8.8)
5 years 1.0 1.8 (1.6 – 2.0) 4.4 (3.4 – 5.7)
5.1 (2.6 – 9.8)
Risk of emergency respiratory hospital admission up to age 5 years
HR for maternal age 25 – 34 years and middle quintile of social deprivation
Welsh births (1998 – 2004)
Entered for KS1
Yes No
No LTCA 186,354 (85.2%) 32,295 (14.8%)
LTCA (excl DS) 789 (76.2%) 247 (23.8%)
DS 142 (70%) 59 (29.4%)
Children in LEA maintained schools
Welsh births (1998 – 2004)
No LTCAN = 186,354
LTCA (excl DS)N = 789
DSN = 142
School action 16.0% 18.9% <5%
School action plus
7.5% 17.4% 7.0%
Statemented 1.8% 11.8% 89.4%
Provision for children with special educational needs (SEN)
• Feasible to use anonymised record linkage of routinely collected datasets across disciplines to create a population based e-cohort of children
• Cost-effective resource for research to support policy
• System facilitates:– Interdisciplinary, observational and interventional research at any
geographical level– appropriate hierarchical analyses– augmentation of traditional survey cohorts
Conclusion/reflections
• Platforms for congenital anomaly research– WECC– Euromedicat (Safety of medicines in pregnancy)– MEPREP (Medical exposure in Pregnancy Risk Evaluation
Programme)
• Potential for defining exposure variables– Alcohol exposure, stressful life events
• Future:– Potential for web-based assessment of exposures and
behaviours, integration of biological data (e.g. newborn bloodspots)
Conclusion/reflections
Cardiff University• Annette Evans• David Fone• Frank Dunstan
Public Health Wales• Sion Lingard• David Tucker• Ciaran Humphreys
Swansea University• Ronan Lyons• Sinead Brophy• Joanne Demmler• Amrita Banyopadhyay
Acknowledgements
This study makes use of the anonymized data held in the SAIL system which is part of the national e-health records research infrastructure for Wales.We acknowledge all the data providers who make anonymized data available for research.WECC was funded by NISCHR Translational Health Research Platform Award (2009 – 12)D-WECC was funded by NISCHR (2012 – 15)
Thank you
Any questions?