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) -> 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

6. 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?

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

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 dataApparent 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)