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

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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.

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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

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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?

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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)