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Injury surveillance in Australia: aims and issues

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Title: Injury surveillance in Australia: aims and issues


1
Injury surveillance in Australia aims and issues
  • James Harrison
  • Research Centre for Injury Studies
  • Adelaide, South Australia
  • September 2006

2
Acknowledgements
  • Colleagues at NISU/Research Centre for Injury
    Studies, Flinders University
  • Other colleagues in Australia, especially Andrew
    Hayen and Soufianne Boufous at the Injury Risk
    Management Research Centre, University of NSW

3
Purpose
  • National injury surveillance
  • especially for primary prevention of injury in
    the general community
  • also interest in outcomes, costs, injury other
    complications of care
  • How much (serious) injury occurs in Australia?
  • What are the injuries?
  • Diagnoses, body parts affected
  • How does injury occur?
  • External causes, etc
  • How is it distributed?
  • By person
  • age, sex, Indigenous status, etc.
  • By place
  • State/territory, remoteness, etc.
  • Over time
  • trends
  • What are its consequences?
  • Survival, hospital utilisation, rehabilitation,
    quality of life, economic costs, etc

4
Current sources
  • Cases
  • Deaths (ABS/registrations NCIS/coroner notified)
  • Universal unit record (case) ICD-10 diagnosis
    external cause other data text
  • Hospital admissions (public private via States
    under agreed NMDS)
  • Universal unit record (episode) ICD-10-AM
    diagnosis external cause other data
  • Ambulatory health service data
  • Patchy National ED collection in development for
    service utilisation (Dx? Ext??) regional ED
    injury surveillance (ICD-based NDSIS) GP/Primary
    Care Physician rolling sample (ICPC).
  • Self-reported occurrences
  • Population surveys nb National Health Survey
    (currently each 3 yrs) ICD-based codes
  • Special-purpose collections
  • Eg. registers (SCI trauma) ambulance service
    data OHS inspectorates workers compensation
    compulsory third party road traffic insurance
    air safety inspectors
  • Denominators/exposure
  • Population estimates (census annual estimated
    resident population)
  • Various estimates relevant to aspects of injury
    surveillance eg. workforce (persons hours)
    road transport (registered vehicles distance)
    sport (participants) etc.

5
Can do (more or less)
  • Injury mortality
  • Rates, trends, description
  • Issues (i) mismatch b/w sources (ii) late
    deaths (iii) uncertain reliability limited
    detail
  • Responses (i) (iii) link ABS NCIS deaths
    data (ii) link death hospital data (iii) link
    deaths to other sources (e.g. OHS fatality
    reports)
  • Injury hospitalisation
  • Rates, trends, description
  • Issues (i) cases vs episodes (ii) uncertain
    reliability limited case detail
  • Responses (i) internal linkage (special study
    (WA) -gt routine) (ii) quality assurance /
    enhancement study
  • High threat-to-life injury
  • Rates, trends, description
  • Based on deaths non-fatal hospital cases with
    ICISS gt threshold
  • Issues (i) linking hospital and deaths data
    (ii) ICISS technical issues (method for deriving
    weights method for applying weights
    comparability of weights over time and between
    settings)
  • Responses (i) Special study (WA) -gt broader
    application (ii) further use development of
    ICISS

6
Cant do (except as special studies)
  • Surveillance of serious injury
  • i.e. injury presenting threat to well-being,
    quality of life
  • Issues
  • Definition of serious (i.e. Which cases to
    include? Specify in terms of diagnosis? Outcome?
  • How to find those not in the deaths or admitted
    patient collections?
  • Outcome of hospitalised injury
  • Current data tell us vital status type of
    destination at the end of an episode in hospital
  • Issues
  • How useful is information currently in hospital
    records for assessing QoL, cost, or other
    dimensions of outcome? In aggregate? At case
    level?
  • What additional data would enable importantly
    better outcome assessment? Is it feasible to
    obtain this information?

7
Special study counting cases
  • The problem
  • Records in national hospital data collection
    refer to episodes (separations), not cases or
    persons.
  • We want to analyse in terms of cases persons.
  • Initial solution
  • Use mode of admission and/or mode of
    separation to omit classes of records likely to
    be counted more than once / case.
  • This approach might deal with transfers type
    change within a hospital but not with
    readmissions.
  • Preliminary study
  • Used person-linked data for one state (Western
    Australia c 10 of national total)
  • Findings mode of admission method is better than
    mode of separation method, but not very reliable.
  • Work in progress
  • Seeking collaboration of all states and
    territories in a project to
  • document hospital data person-specific internal
    record linkage activities done/in progress
  • compare methods variables used and assess
    likely effect of differences
  • identify technically feasible opportunities for
    linkage where not yet done
  • specify a preferred method (technically feasible
    in all states), seek agreement and apply it.
  • Issues
  • No national person ID and great sensitivity
    concerning use of proxies for one
  • Status
  • Proposal with jurisdictions for consideration

8
Special study external cause coding of hospital
data
  • The problem
  • Australian hospital admission records for injury
    include external cause codes, but there is little
    published evidence on their quality.
  • Work in progress
  • ARC-funded project Developing and enhancing the
    quality of national injury-related hospital
    morbidity data (2005-2007).
  • Lead investigator Kirsten McKenzie, Queensland
    University of Technology / NCCH. Other
    Investigators S Walker G Waller (NCCH), J
    Harrison G Henley (Flinders), R McClure
    (Griffith). Four health departments are partners.
  • Aims (i) better understanding of data (ii) guide
    to QA (iii) guide to ICD-10-AM
  • Stages
  • Analysis of unit record administrative data
    Apparent completeness and specificity of external
    cause coding. (Done)
  • Surveys of clinical coders and end-users of data
    Knowledge, attitudes and perceptions concerning
    the external cause classification in ICD-10-AM,
    the quality of its use, barriers to use,
    potential for improvement. (In field)
  • Examination re-coding of a sample of records
    Probability sample of records in four states.
    (Planning)
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