piecing the puzzle together: data quality...cricos no. 00213j piecing the puzzle together: data...

CRICOS No. 00213J

Piecing the puzzle together: data quality and data linkage in road safety

Angela Watson QH Data Linkage Symposium 25th November 2014

The data problem • Police reported crash data often the primary

source of crash information • National Road Safety Strategy emphasising

serious injury • Definition of serious injury within police-

reported data is not consistent or operationalised well

• Not all crashes reported to police

• Misleading estimates of impact and cost of crashes

Increasing interest in data linkage as a

possible solution to enable a more complete picture

Gaps in knowledge • No linkage on road crashes in Queensland • The following have not been thoroughly

examined: – Barriers and limitations of linkage process – The quality of data sources relating to road crash

injuries

• It has not been established whether: – data linkage is feasible in Queensland – linked data provide advantage over non-linked

data

Study 1a

Document and legislation review

Data Quality

Completeness

Consistency

Validity

Accessibility

Timeliness

Data Linkage

Potential bias

Scope of

collections

Study 3

Analysis of linked

data

Study 2

Analysis of non-linked data

Study 1b

Interviews with data custodians, users, and linkage experts

Data Quality

Completeness

Consistency

Validity

Data Quality

Completeness

Consistency

Validity

Quantification

Development of

data linkage

methodology and

assessment

framework

Data Linkage

Potential bias

Scope of collections

Data Linkage

Added cases

Added

information

Bias

Data Quality

Accessibility

Consistency

Data Linkage

Perceived barriers

Perceived benefits

Study 3

Study 3

Analysis of linked

data

Data Quality

Completeness

Consistency

Validity

Quantification

Data Linkage

Added cases

Added

information

Bias

Data collections

• QRCD; QHAPDC, QISU, EDIS, eARF • 12 months data (2009) • All recorded transport injuries/injuries • Key variables

– Age, gender – Road user type – Remoteness – Serious injury

Data linkage method

QISU/EDIS Data

Linkage Unit

QRCD

QHAPDC eARF

Researcher

Personal information Linkage key & Person ID Linkage key, Person ID, & content

Creating linked data sets • Merged using linkage key (person ID) • Police data with each other data collection • Links between non-police (health) data

collections also needed to be considered • Linkage rates based on:

– No. of police-reported injuries that link with other data

• Discordance rates based on: – No. of road crash injuries don’t link to police data

Results

Combined hospital data set links

Hospital data set

Transport injury

presentations at QISU hospital

Injury presentations at

EDIS hospital

Transport injuries

admitted to hospital

Injuries attending hospital

Police and hospital data

Injuries attending hospital

(n = 308,738)

‘Hospitalised’ police-

reported injuries

(n = 6,674)

Police-reported injuries

(n = 19,041)

Road crash injuries

attending hospital

(n = 28,220)

83% Linked (n = 5,539)

69% Discordance (n = 19,471)

As many as

2 in 3 road crash

injuries may not be reported to

police

Over

50% of police-reported road crash

injuries link to data with injury information

Over

80% of ‘hospitalised’ police-reported road crash injuries link to data

with injury information

Under-reporting bias

Road user type

92%

79%

39% 52% 47%

Age

0%10%20%30%40%50%60%70%80%90%

100%

Dis

cro

dan

ce r

ate

Age group

0-14 years 89%

15-79 years 60%

Limitations

• ‘Black box’ of linkage • Quality of coding • Selection and classification methods • Multiple counting of injuries • Other data collections? (e.g., Insurance)

Data linkage process issues

• Took an incredibly long time – Although now that agreements are in place

and method identified could be quicker – Will still take time (multiple ethics, PHA,

linkage itself) • Linked data is complex to work with and

analyse

What can linked data provide? • Confirm actual hospitalised police-reported

injuries (to fit with international definition) • Produce serious injury indicators for up to

80% of police-reported injuries • Produce estimates of under-reporting of

road crash injuries and minimise bias • Improve reporting, monitoring, cost of crash

calculations, and resource allocation • Validate variables and selections

Will it work and is it worth it?

It worked!!!

It was worth it

Will it be worth doing regularly?

Thank you • Barry Watson and Kirsten Vallmuur • Data custodians

– TMR, QH (QHAPDC,EDIS, QISU), QAS

• Ben Wilkinson, Jean Sloan, Dr. Ruth Barker, Dr. Emma Bosley, Jamie Quinn, Dr. Trisha Johnston, Catherine Taylor, and Dr. Nerida Leal

• NHMRC

Questions?