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

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

7 September Colin Cryer, IPRU, Univ of Otago, NZ

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

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

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

7 September Colin Cryer, IPRU, Univ of Otago, NZ Possible outcomes Confirmation of the validity of the NZ indicators. 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. 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. 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. 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. A combination of these.

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

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

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

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