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