Taking HEED

Exploratory Anonymisation of Home Energy Efficiency Data to explore intervention outcomes

Contact details: M.L.Heaven@swansea.ac.uk

Demonstration project reports: http://gov.wales/statistics-and-research/data-linking-demonstration-

projects/?lang=en

A brief outline of this presentation …

• How the Fuel Poverty demonstration project came about: – Welsh Government’s Data Maximisation Programme

– The availability of / gaining access to rich routinely collected datasets

– The power of data linking

• The project: – What, how, and findings

– Lessons learnt

– Where this is now leading…

Acronyms

• HEED: Home energy efficiency data

• EST: Energy Saving Trust

• SAIL: Secure Anonymised Information Linkage

• KT: Knowledge Team (The study team)

• LSOA: Lower Super Output Area – a census derived small area building block (~ 1,600 people)

Welsh Governments data maximisation programme

• Credit to Welsh Government for:

– Recognising the potential of data linkage

– Supporting the development of SAIL

– Investing in the Data Maximisation Programme

• Four fellowships commencing in 2011

• Demonstrating the power of linked data

• Outlined a series of potential data linkage projects to inform policy decision making

• Narrowing that down – practical availability of data

SAIL

Home energy efficiency database

Gov. funded home energy efficiency improvement programmes, including the Home Energy Efficiency Scheme

Address level data, with intervention and date of intervention detail

Anonymised into SAIL at address level

The anonymisation uses a trusted third party, the NHS Information Service in Wales.

They hold the patient registration database for Wales, a history of who everyone is and where they have lived

Both the person and their address history have been anonymised

An address is just a number, but we know which LSOA it is in.

HEED

SAIL secure env.

Data flow

Anonymisation by NWIS

HEED @ EST Anonymised Addresses

Linked SAIL

technical team

Primary Care

Hospital

Emergency Room

Population

Data sharing agreement

SAIL Information Governance

Review Application

Mortality data

The power of Data linkage

• Building up a picture

– From addresses to study population

– Longitudinal history enables migration to be accounted for

• No survey expense (or bias) incurred

• Instant, safe access to desensitised data

– Data rich environment

– Over ten years of longitudinal data

What KT did…

Issues

• Controlling for – Underlying changes

• Weather

• Economic situation

• Health service delivery changes

– Migration (exposure)

• Establishing a control group – No idea what similar

interventions were happening outside of the study population

Study design

• Compared the study population split into two groups – 2000 to 2006 (early

intervention

– 2007 to 2012 ( later intervention)

• Only those people who did not move house were included

• Only insulation upgrades and heating upgrades were included

What KT did next…

Compared

• Health service provision over time, mainly as European Age Standardised Rates in the years following the intervention dates

• Observed the pattern of changes over the whole time period

Issues occurring

• Non matching of addresses in SAIL

• Choosing suitable health indicators

• Observing an effect

– Data Quality and Completeness

– Not likely to be instantaneous

– Participants aging throughout the study

– Limited resource of demonstration project

What KT showed

• Relative changes between the early and later intervention groups over time, if you allow for a lag period before a measureable difference is observed.

• The later group catching up over time as would be expected because of interventions.

What KT showed

What is happening to this gap over time?

What KT showed

Lessons KT learned

• It takes longer than you might estimate – Getting data sharing agreements in place – Working with ‘stretched’ organisations – Getting Information Governance approval

• Its not all “plain SAILing” following the data linkage – Data were not collected for the research

• Address accuracy for linkage • Contents • ‘Ghostly’ presence in GP registration • Completeness of potential indicators in the health data • Duplicate records in the health data

• Potential for disclosure – Ground source heat pumps

• But its worth doing – Will get easier as systems develop over time – With new data sources added

Where is this leading..

Fuel poverty analyst and PHD funding

Potentially linkage richer datasets

Other projects on housing improvements