# Presentation: 015742

## Injury surveillance in Australia: aims and issues

- James Harrison
- Research Centre for Injury Studies
- Adelaide, South Australia
- September 2006

## 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

## 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

## 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.

## 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) -> routine); (ii) quality assurance / enhancement study
- High threat-to-life injury
- Rates, trends, description
- Based on deaths + non-fatal hospital cases with ICISS > 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) -> broader application; (ii) further use & development of ICISS

## Can’t 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?

## 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
- Positive responses so far from three jurisdictions covering >50% population. No refusals so far.

## 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)