1. 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)

2. 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

3. 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

4. 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

5. 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

6. Population ~3M, ~35,000 births per year 1.Welsh Demographic Service 2. Office for National Statistics (birth and mortality files) 3. National Community Child Health Database 4.Patient Episode Database for Wales (PEDW) 5.General Practice consultations 6.Congenital Anomaly Registry and Information Service (CARIS) 7.National Pupil Dataset Routinely collected data in Wales

7. 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)

8. Inclusion criteria – Children born or resident in Wales – Phase 1: Date of birth between 1 st Jan 1990 – 31 st Dec 2008 – Phase 2: extended to include births until 7 th 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

9. Birth records (ONS births) Mortality records (ONS deaths) Wales Electronic Cohort for Children N=981,404 Wales Electronic Cohort for Children N=981,404 WECC eligibility criteria applied Data cleaning: rules for removal of duplicates and errors WDS Child Health (NCCHD) Child Health (NCCHD) ALF_E WDS: Welsh Demographic Service, NCCHD: National Community Child Health, ONS: Office for National Statistics WECC development

10. 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 core n = 981,404 ♂: 500,181 (51.0%) ♀ : 481,205 (49.0%) WECC core n = 981,404 ♂: 500,181 (51.0%) ♀ : 481,205 (49.0%) Inpatient GP consultations Perinatal and Child health Environment House Moves Non-Welsh births n=215,095 ♂: 107,222 (49.8%) ♀ : 107,872 (50.2%) Non-Welsh births n=215,095 ♂: 107,222 (49.8%) ♀ : 107,872 (50.2%) Born in Wales n= 766,309 ♂: 392,959 (51.3%) ♀ : 373,333 (49.0%) Born in Wales n= 766,309 ♂: 392,959 (51.3%) ♀ : 373,333 (49.0%) WECC derived tables National dataset Education

11.  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

12. How do survival and hospital admission rates compare between the following groups of children? 1.No major life-threatening congenital anomalies 2.Major life-threatening congenital anomalies (excl DS) 3.Down’s syndrome without major life-threatening congenital anomalies 4.Down’s syndrome and major life-threatening congenital anomalies Follow-up of children with Down’s syndrome in WECC

13. Welsh births 1 st Jan 1998 – 7 th Oct 2012 N = 491,036 No Down’s syndrome N = 488,850 No LTCA N = 486,468 1,941,801 pyrs LTCA N = 2,382 8,575 pyrs Down’s syndrome N = 502 No LTCA N = 432 1588 pyrs LTCA N = 70 215 pyrs Excluded stillbirths N = 1,684

16. % survival (95%CI) No LTCALTCADS - LTCADS + 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

17. No LTCA N = 486,468 LTCA N = 2,382 DS – LTCA N = 432 DS + LTCA N = 70 Incidence No. 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 225,2991,82834361 Median age at first admission 9 months2 months4 months2 months Emergency hospital admissions

19. HR (95% CI)No LTCALTCA (excl DS)DS - LTCADS + LTCA 6 months1.0 2.8 (2.6 – 2.9)4.1 (3.6 – 4.7)5.7 (4.2 – 7.8) 1 year1.0 2.4 (2.2 – 2.6)4.2 (3.7 – 4.8)5.5 (3.8 – 7.8) 3 years1.0 2.0 (1.8 – 2.2)4.3 (3.5 – 5.3)5.2 (3.1 – 8.8) 5 years1.0 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

20. Welsh births (1998 – 2004)Entered for KS1 YesNo No LTCA186,354 (85.2%)32,295 (14.8%) LTCA (excl DS)789 (76.2%)247 (23.8%) DS142 (70%)59 (29.4%) Children in LEA maintained schools

21. Welsh births (1998 – 2004) No LTCA N = 186,354 LTCA (excl DS) N = 789 DS N = 142 School action16.0%18.9%<5% School action plus 7.5%17.4%7.0% Statemented1.8%11.8%89.4% Provision for children with special educational needs (SEN)

22. 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

23. 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

24. 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)

25. Thank you Any questions?