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Indicators of Injury Incidence: Probability of Admission to Hospital

Indicators of Injury Incidence: Probability of Admission to Hospital

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Indicators of Injury Incidence: Probability of Admission to Hospital

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  1. Indicators of Injury Incidence:Probability of Admission to Hospital Colin Cryer Injury Prevention Research Unit, Univ. Of Otago NZ Presented at the ICE on Injury Statistics Meeting, 7-8 September 2006, Washington DC

  2. Background • Non-fatal injury indicators • Data sources include: • Hospital admission / discharges / separations • Should draw attention to ‘important’ injury as judged by their resulting in: • Threat-to-life • Disability / threat-of disability • Reduced quality of life • Significant cost Colin Cryer, IPRU, Univ of Otago, NZ

  3. Colin Cryer, IPRU, Univ of Otago, NZ

  4. Serious Non-fatal Injury Definition • Hospitalised cases with ICISS*<0.941 • Set so that capture injury that are judged to have a high probability of admission • Represents about 15% of all discharges from hospital for injury. • Includes the majority of the following injuries: • Fractured neck of femur • Intracranial injury (excluding concussion only) • Injuries to nerves and spinal cord at neck level • Multiple fracture of the ribs • Asphyxia etc. *International Classification of Diseases-based Injury Severity Score (based on ICD-10-AM) Colin Cryer, IPRU, Univ of Otago, NZ

  5. Serious injury definition – validity • Our experience is that cannot use hospital discharges to produce valid indicators without some pre-processing • (need to control the effect of extraneous factors on admissions to hospital). • If you do not, indicators can show potentially misleading trends • Biases can be minimised by using a severity threshold – assumed to capture only injury with a high probability of admission • high face validity • To have full confidence we need to test this assumption Colin Cryer, IPRU, Univ of Otago, NZ

  6. Aims • Primary • To empirically investigate whether diagnoses captured using ICISS<0.941 have a high probability of admission • Secondary • To identify sentinel ICD diagnoses that have a high probability of admission Colin Cryer, IPRU, Univ of Otago, NZ

  7. Possible outcomes • Confirmation of the validity of the NZ indicators. • A change to the severity threshold that define the NZIPS serious injury indicators - to ensure validity of the indicators. • The identification of valid indicators that include lower severity injuries than the current NZ indicators. • Abandonment of the NZIPS / ICISS-based serious injury indicators, and substitution with indicators based on a basket of sentinel injuries approach. • A combination of these. Colin Cryer, IPRU, Univ of Otago, NZ

  8. Proposed approach • Sources of data: ED • Operational definition of injury – to be agreed • Minimum data required • Diagnosis (ICD) • Disposal (whether or not admitted) • Estimate • Diagnosis-specific admission fractions with 95% CIs Colin Cryer, IPRU, Univ of Otago, NZ

  9. Issues • Operational definition of injury • Diagnosis • Coding frames (ICD-10-AM, ICD-10, ICD-9-CM, etc.) • Restrictive or inclusive • Accuracy of coding • Who codes • Correspondence between ED and inpatient diagnoses • Admission fractions • Stability • Dependency on • Demography (eg. older people), comorbidity, circmstances • What approach to estimation when: • Multiple diagnoses • Multiple attendances for same injuries • Cases died before arrival or in ED (before admission) Colin Cryer, IPRU, Univ of Otago, NZ

  10. Sources of data • Potential sources identified to date: • Australia • Canada • Denmark • Greece • Italy • US • Others? Colin Cryer, IPRU, Univ of Otago, NZ

  11. What now? • Description of potential sources of ED data • Coding frame used • Who codes the data • Any information on accuracy • Whether linked to inpatient data. • Presentation of the issues • Open discussion of the proposal, data sources, issues. Colin Cryer, IPRU, Univ of Otago, NZ