Data management case studies: Enhancing the analysis of e-Health data and data on social care

1. 4th ESRC Research Methods Festival St Catherine’s College, Oxford. 5-8 July 2010 Data management case studies: Enhancing the analysis of e-Health data and data on social care Alison Dawson University of Stirling (DAMES research Node, www.dames.org.uk)

2. The case studies • 1) e-Health – linking eHealth and social science datasets to enhance understanding of risk of suicide in Scotland • 2) Social care – ‘fusing’ datasets to enhance analyses of the costs of funding care for older people in Scotland and the UK

3. Suicides in Scotland • In 2008 there were 843 deaths by suicide in Scotland (defined as deaths from intentional self harm and events of undetermined intent), an age-standardised rate of 16.1 per 100,000 population per year • Suicide rates are around three times as high for men as for women • Rates of suicide in the most deprived areas of Scotland are significantly higher than the Scottish average (ISD Scotland August 2009)

4. Previous focus on single factors Previous studies have tended to focus on single risk / preventative factors associated with suicide: • Health related (e.g. history of mental illness) • Psychological factors (e.g. coping behaviours, religious beliefs) • Social and economic factors (e.g. unemployment, deprivation) Few attempts to statistically model the interplay between different risk and protective factors in populations completing suicides.

5. Potential sources of relevant data

6. SMR and Census data and known risk / preventative factors

7. Types of data linkage technique

8. Selecting a data linkage method

9. Concerns when linking SMR and Census datasets Ethical issues: • Informed consent • Confidentiality • Data security • Disclosure Technical difficulties: • Linking datasets from different domains • Providing infrastructure that addresses ethical concerns

10. OPERA - Older PEople’s Resource Allocation model • Dynamic microsimulation model - takes micro level units (individuals) as the basic unit of analysis and uses simulation techniques to project the sample forward in time in order to investigate the effects of future social and economic policies • Focuses on events towards end-of-life, particularly the impacts of long-term limiting illness and increasing levels of dependency

11. More on OPERA • Software – Stata / Mata • Main datasets used • Family Resources Survey (FRS) – boosted sample in Scotland • HMRC Survey of Personal Incomes • Outputs • Statistics (distributions, panel, survival) • Graphics (graphs, plots, choropleth maps)

12. OPERA – Key elements

13. Incorporating Home Care Costs into OPERA • Problem 1 - Have a dataset – collected from Welsh Local authorities • Problem 2 - Distribution of costs is highly skewed • 40% of clients account for 10% of costs • 10% of clients account for 40% of costs

14. Model calibration (1) • Estimate determinants of costs of care using Welsh dataset • Estimate determinants of needing care and of being in receipt of local authority care using FRS data • Match FRS disability classification with that used in Welsh survey (IoRN)

15. Determinants of LA Costs of providing Personal Care • Costs • increase with disability • decrease with age • decrease with presence of informal carer • unaffected by gender and ethnicity • vary by local authority

16. Detailed disability question in FRS • Does this/Do these health problem(s) or disability(ies) mean that you have substantial difficulties with any of these areas of your life? Please read out the numbers from the card next to the ones which apply to you. • PROBE: Which others? 1: Mobility (moving about) 2: Lifting, carrying or moving objects 3: Manual dexterity (using your hands to carry out everyday tasks) 4: Continence (bladder and bowel control) 5: Communication (speech, hearing or eyesight) 6: Memory or ability to concentrate, learn or understand 7: Recognising when you are in physical danger 8: Your physical co-ordination (eg: balance) 9: Other health problem or disability 10: None of these

17. IoRN Classifications (used in Welsh dataset) Activities of Daily Living (ADLs) Eating - Transfers (bed to chair) - Toilet LOW MEDIUM HIGH Personal care tasks Wash self /hair, bath, dress Food and drink prep Main meal, snack, hot drink Mental wellbeing and behaviour Agitation, disturbance, verbal aggression, resistance, relationships, risk Bowel management Assistance, guiding, prompting, supervision LOW HIGH LOW MEDIUM HIGH LOW MEDIUM HIGH Mental wellbeing and behaviour I A B D C E G LOW HIGH LEVELS OF HELP REQUIRED DETERMINE IoRN CLASSIFICATION F H

18. Data Fusion - General Common variable(s) Values of unobserved variable(s) are imputed for recipient sample Before data fusion: No values for Var Y After data fusion: Imputed values for Var Y (not true values) • Fusion proceeds by a number of (mostly numerically intensive) procedures. • The objective is to define new variables whose properties differ as little as possible from those of the (unobserved) underlying data.

19. Data Fusion – OPERA and Welsh home care costs data DONOR DATASET RECIPIENT DATASET Welsh home care costs Dataset including: AGE INFORMAL CARER? Indicator of Relative Need (IoRN) Hours of care per week Cost of care to LA per week OPERA Mk 1 (Mainly FRS data including: ) AGE INFORMAL CARER? Derived Disability Classification COMMON VARIABLES OPERA Mk 2 AGE INFORMAL CARER? DISABILITY DATA + imputed values for: Hours of care per week Costs of care to LA per week

20. Model calibration (2) • Select most disabled of those receiving LA care in FRS sample to receive personal care – match with proportions receiving LA personal care in Scotland (thus model mimics Scottish policy setting) • Stochastic simulation of model to maintain distribution of costs rather than focus on point estimate • Results weighted using FRS weights to represent UK/Scottish population

21. LA Costs of providing Personal Care by Age and Gender

22. LA costs of providing Personal Care by Index of Disability (IoRN)

23. (Free) Personal Care in Scotland • Scottish Government estimate of cost of providing FPC at home to pensioners in 2003-04 ~ £120m • Model estimate ~ £170m • Consistent with LAs spending approx £50m prior to introduction of policy • What about the personal care costs of those aged under 65 requiring PC? • Model estimate ~ £130m • Fewer clients, higher cost per client

24. Summary • Two examples of where data management techniques have been used to enhance analysis • The techniques discussed (linkage, fusion) can be technically difficult • The DAMES project is working to create resources that will assist with these kinds of processes