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)

4th ESRC Research Methods FestivalSt Catherine’s College, Oxford. 5-8 July 2010

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

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)

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.

Potential sources of relevant data

SMR and Census data and known risk / preventative factors

Types of data linkage technique

Selecting a data linkage method

CHI No

CHI No. Surname Sex Year of Birth

Postcode

DATASET 1

3456 DAWSON F 1966 FK9 4LA

DATASET 2

3456 DOWSON F 1966 FK9 4LA

DATASET 3

- DAWSON F 2066 FK9 4LA

Concerns when linking SMR and Census datasets

Ethical issues: Informed consentConfidentialityData securityDisclosure

Technical difficulties:Linking datasets from different

domainsProviding infrastructure that addresses

ethical concerns

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

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)

OPERA – Key elements

Incorporating Home Care Costs into OPERA

Problem 1 - Have a dataset – collected from Welsh Local authorities

Problem 2 - Distribution of costs is highly skewed40% of clients account for 10% of costs10% of clients account for 40% of costs

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)

Determinants of LA Costs of providing Personal Care

Costs increase with disabilitydecrease with agedecrease with presence of informal carerunaffected by gender and ethnicityvary by local authority

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

IoRN Classifications (used in Welsh dataset)Activities of Daily Living (ADLs)

Eating - Transfers (bed to chair) - Toilet

Personal care tasksWash self /hair, bath, dress

Food and drink prepMain meal, snack, hot drink

Mental wellbeing and behaviour

Agitation, disturbance, verbal aggression, resistance,

relationships, risk

Bowel managementAssistance, guiding,

prompting, supervision

Mental wellbeing and

behaviour

MEDIUM HIGHLOW

LOWLOW

LOW HIGH

HIGH

HIGH

HIGH

LOW

MEDIUMMEDIUM

A B D C E G

F H

I

LEVELS OF HELP REQUIRED DETERMINEIoRN CLASSIFICATION

Recipient Sample Before MatchingObs Var X Var Y Var Z

1 x1 z1

2 x2 z2

.. .. ..

nA xNA zNA

Donor Sample Before MatchingObs Var X Var Y Var Z

1 y1 z1

2 y2 z2

.. ..

nB yNB zNB

Recipient Sample After Matching

Obs Var X Var Y Var Z

1 x1 z1

2 x2 z2

.. ..

nA xNA zNA

Data Fusion - General

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.

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)

Data Fusion – OPERA and Welsh home care costs data

OPERA Mk 1(Mainly FRS data including: )

AGEINFORMAL CARER?

Derived Disability Classification

Welsh home care costsDataset including:

AGEINFORMAL CARER?

Indicator of Relative Need (IoRN)

Hours of care per weekCost of care to LA per week

OPERA Mk 2AGE

INFORMAL CARER?DISABILITY DATA

+ imputed values for:Hours of care per week

Costs of care to LA per week

RECIPIENT DATASET DONOR DATASET

COMMON VARIABLES

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

LA Costs of providing Personal Care by Age and Gender

LA Costs of Personal Care - Averages over individuals receiving care (£ per week)

0.00

50.00

100.00

150.00

200.00

250.00

15-24 25-34 35-44 45-54 55-64 65-74 75-84 85+

£'s

per

wee

k

Male Female

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

LA Costs of Personal Care - Averages over individuals receiving care (£ per week)

0

50

100

150

200

250

A B C D E F G H I

IoRN

£'s

per

wee

k

Male Female

(Free) Personal Care in Scotland

Scottish Government estimate of cost of providing FPC at home to pensioners in 2003-04 ~ £120m

Model estimate ~ £170mConsistent 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

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