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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. Background. Non-fatal injury indicators Data sources include:

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Indicators of Injury Incidence: Probability of Admission to Hospital

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Indicators of injury incidence probability of admission to hospital l.jpg

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


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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


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Colin Cryer, IPRU, Univ of Otago, NZ


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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


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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


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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


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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


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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


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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


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Sources of data

  • Potential sources identified to date:

    • Australia

    • Canada

    • Denmark

    • Greece

    • Italy

    • US

    • Others?

Colin Cryer, IPRU, Univ of Otago, NZ


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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


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