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Nursing, Midwifery and Allied Health Professions Research Unit

Health and social risk factors of Scottish suicides: a 30 year record linkage study. Nadine Dougall. Nursing, Midwifery and Allied Health Professions Research Unit. Scotland a good laboratory for eHealth research

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Nursing, Midwifery and Allied Health Professions Research Unit

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  1. Health and social risk factors of Scottish suicides: a 30 year record linkage study Nadine Dougall Nursing, Midwifery and Allied Health Professions Research Unit

  2. Scotland a good laboratory for eHealth research National Health Service (NHS), ‘free’ care at point of access, wealth does not determine access to healthcare Community Health Index (CHI) number a unique patient identifier introduced in 1970’s (~10M records) CHI full coverage since 1988, in widespread use secondary and primary care 1989: creation of permanently linked national datasets. Scotland and electronic patient records (EPRs)

  3. Scotland a good laboratory for eHealth research one of a handful of countries with indexed electronic health records spanning decades Scottish record linkage system (SHIP; SHIS-R) Oxford record linkage system Rochester epidemiology project Manitoba Population Health Information System Sweden, Denmark & Norway Wales (SAIL) British Columbia linked health database WA data linkage system Taiwan Scotland and electronic patient records (EPRs)

  4. Definition of e-Health Combined use of electronic communication and information technology in the health sector? Data linkage systems as research infrastructure “Each person in the world creates a Book of Life. This Book starts with birth and ends with death. Its pages are made of the records of the principal events in life. Record linkage is the name given to the process of assembling the pages of this Book into a volume” (Dr Halbert Dunn, 1946) Scotland and electronic patient records (EPRs)

  5. Electronic Patient Records Secondary care (hospitals) All indexed records held nationally in datasets called Scottish Morbidity Records (SMRs) Some of the best health service data in the world for quality, coverage and consistency Data controller is the National Health Service Information Services Division (NHS ISD) Access via governance and ethics committees Scotland and eHealth patient records

  6. Electronic Patient Records Primary care (general practice) NHS ISD hold summary data for general practices but not individual patient level data General practice surgeries use 2-3 different systems to hold data, but all use CHI number Inter-related research from primary to secondary care not straight-forwards but CHI numbering facilitates linkage Resources e.g. Primary Care Clinical Informatics Unit (PCCIU - Scotland), data >200 practices and ~1M patients; RCGP data (UK) Scotland and eHealth patient records

  7. National context - SHIP Scottish Health Informatics Programme • 3 year funding (WT, MRC & ESRC) to deliver national solution for Scotland-wide research platform for e-Health data linkage • Consortia of Uni’s of Dundee, Edinburgh, Glasgow, St.Andrews with NHS ISD • Led by Andrew Morris, HIC, Dundee • Leads to new HIC/ EHIRCs bid “SHIP2”

  8. National context - SHIS-R Scottish Health Information Service for Research (NHS ISD) • SHIS-R provisions all national NHSScotland routine datasets for research • SMR, community prescriptions, SHeSetc • Pilot trial: A&E, NHS24, SAS • SHIS-R will provide single interface for linkage with 3rd party research datasets • Propose a national safe haven (Gyle) • plus local safe havens supported by SAHSC nodes (N3 gateway), mostly local data

  9. Scottish Government & Census • Census costs ~£60M, Census 2021 likely scrapped • Census alternative – derive equivalent information from linked SG datasets • 2 alternative models: • ‘big’ national safehaven for Gov data (preferred by GRO/NRS) • Federated SHIP approach of lots of dummy terminals with Citrix software (N3 connection still an issue)

  10. National context SHIS-R, SHIP/ HIC Evolved solutions as model of proportionate governance • Level of access to data determined by the sort of data requested • Citrix software on researcher pc, acts as remote secure enclave and is remote monitored. Similar model to SDS, ADLS • Researcher remains on Campus, accesses data held on server behind firewall (held on NHS server if clinical data) • Timescale for solution ~April 2012

  11. HIC strategy for Forth Valley FVHB data capture • ‘East of Scotland’ region of collaboration with Tayside/ Dundee & St.Andrews/ Fife • For data management and safe havens • Discussed regional data warehouse with Medical Director Iain Wallace • Similar to Tayside & Fife but for Forth Valley data • Prescribing, SMR, deaths, lab tests, SCI-DC • Gives researchers access to local data

  12. DAta Management through E-social Science (DAMES) Universities of Stirling, Glasgow, Manchester Inter-disciplinary e-Science research spanning sociology, economics, public health and computer science 8 programme themes, 3 utilising grid technology deals with data on occupations, education, ethnicity/migration, social care and e-Health ESRC funded £1,280,000, CI: Dr Paul Lambert, UoS www.dames.org.uk DAMES eHealth research

  13. Remit: explore new ways to link e-Health data and/or other data in novel linkages Negotiated access with NHS ISD for third party data linkage provider (NeSC) to link & anonymise SMR datasets and deaths data (GRO/NRS refused) Access approved to CHI-Census lookup table held at GRO (via safe haven) Separate study using SHeS and BHPS imputed values together in one analysis DAMES eHealth research

  14. eHealth topic area - suicide 781 suicides in Scotland in 2010, age-standardised rate of suicide at ~15/ 100,000 of population Leading cause of mortality in young people, with rates of suicide 3x higher for men (~24/100,000 in 2008) Scotland has a higher overall suicide rate than England & Wales (male rate for England ~12/100,000 in 2008, double that of Scotland DAMES eHealth research

  15. Aim To explore as many individual level health and socio-economic risk factors together in an analysis assessing hospital utilisation patterns Study design Retrospective cohort study using NHS hospital episode data (SMRs) and NRS deaths data DAMES eHealth research

  16. Research questions What differences exist in hospital utilisation prior to death? By gender and other SE factors? By decade? By duration of stay in hospital? By physical ill-health or mental health admissions? DAMES eHealth research

  17. Methods Permissions NHS PAC, CG, REC Eligibility criteria all deaths recorded as suicide* since records were available 1981-2010 Age 15y+ and no upper limit NHS ISD provided pseudonymised data Death records, SMR01(general hospital), SMR02 (maternity) & SMR04 (mental health) Stored at UoS, linked datasets using deterministic match-merge Stata v10 *as a result of intentional self-harm DAMES eHealth research

  18. Variable operationalisation - consistent categories over time Occupation NRS coded to SOC80 & SOC2000 1990-99 coded in non-standard way Obtained NRS coding framework and recoded 1990-99 to SOC90 Stata syntax available online www.dames.org.uk Harmonised SOC80, SOC90 & SOC2000 to CAMSIS scale scores, an indicator of social advantage based upon occupations DAMES eHealth research

  19. Variable operationalisation Hospital episode SMR data coded by ICD 9 & 10 ICD-9  ICD-10 cross-mapping diagnostic codes ICD-9 ~13,5K numeric codes (1980-98) ICD-10 expansion ~68K alphanumeric codes (‘99 to date) Solution: Clinical classification software to aggregate & harmonise ICD catalogues CCS from the US Agency for Healthcare Research and Quality. Used for statistical analysis of data for financial and research purposes CCS syntax implemented in Stata, classified ICD-9 & ICD-10 codes into 260 aggregated & clinically homogenous CCS categories DAMES eHealth research

  20. Variables operationalised Carstairs deprivation index (postcode at death) RGSC (coded by RG, death certificate); NS-SEC Health board of residence Employment status Severity of disease burden by hospital admission e.g. type of admission: I/P, O/P proxy: length of stay in hospital Under consideration: Comorbidity weightings (e.g. Charlson, Elixhauser, MACSS) Postcode sector - ‘GeoConvert’, service will match on postcode e.g. urban/rural indicators Not possible: Ethnicity & Migration DAMES eHealth research

  21. DAMES eHealth research Summary data for the linked suicide cohort 1981-2010 All deaths recorded 16,475* All deaths with CHI number 14,325 (87%) All deaths no CHI number 2,150 (13%) M:F with CHI no. 10,607 (74%): 3,718 (26%) Individuals with CHI No. & 11,231 SMR hospital episodes No. of SMR hospital episodes 85,278 records for 11,231 with CHI No. 85,278 records with multiple diagnostic codings *does not include undetermined deaths

  22. Summary data – hospital episodes by gender

  23. Summary data – SMR common ICD10 codes

  24. Summary data – ICD-10 main condition codes

  25. DAMES eHealth research

  26. Socio-economic aspects of suicide completers, Scotland, 1981-2010 Women were significantly older at death than men, age gap narrowed with each decade (female mean age decreased by each decade; women sig.higher CAMSIS for all decades i.e. in socially advantaged positions

  27. Figure 1: Proportionof recorded cases with previous hospital admissions, by type of admission (for deaths occurring in 1991-1999 and 2000-2009)

  28. DAMES eHealth research Median duration of stay in hospital for all prior hospital episodes (geometric mean in days) All prior episodes, both genders 36 days All M 30 M 25-49y 32 M 50-65+y 30 All F 55 F 25-49y 51 F 50-65+y 70 M:F advantaged job 46:77 days M:F deprived area 33:58 days M:F cohabiting 21:46 People spent less time in hospital if they were older men, personally less affluent, cohabiting, living in more deprived areas. 10537 suicides, 1991-2009

  29. Scaling of episode type by total previous duration of stay(s) in hospital (used to derive a scaling of episode type as ‘Visibility of MH problems to health service’) Median length of stay: Physical health CCS 9 & 11 days for M & F respectively. Mental health CCS codes 68 & 97 days for M&F Other external causes of harm 14 & 20 days for M&F 10473 individuals with 11,531 prior hospital records.

  30. DAMES eHealth research • Regression models predicting time in hospital due to mental health problems • Outcome variable: broad diagnostic categories of physical health, mental health, ‘other’ external causes or no prior episode • The most parsimonious model fit (R-square 0.066) with significant predictor variables* • Being female, single, in employment, having relatively poor occupational attainment, living deprived area • *t-statistic >or= 2.0 at 95% confidence limits) • 10537 suicides, 1991-2009

  31. Time between last previous discharge and death 2395 suicides happened within one month of discharge. (4756 & 9281 within one year & at any time since 1981, respectively) 2395 suicides - many more for physical health than mental health episodes

  32. THANK YOUUoS: Paul Lambert, Margaret Maxwell, Alison DawsonNeSC: Richard Sinnott, Susan McCafferty, John WattNHS ISD: Anthea Springbett, Carole Morris, David Clarke NRS: Frank Dixon Nadine DougallSenior Research FellowNMAHP Research UnitIris Murdoch BuildingUniversity of Stirlingnadine.dougall@stir.ac.uk01786 466285 Delivering, supporting and promoting high quality research to improve health Nursing, Midwifery and Allied Health Professions Research Unit

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