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Analysis of Admissions Following Injury: Patterns and Mortality Trends in Denmark

Explore the variation in admission rates by age, sex, and diagnosis position, and the trends in mortality composition in trauma patients at Odense University Hospital. Data recorded from 1994 to 2006 suggests possible global comparability in survival probability calculations.

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Analysis of Admissions Following Injury: Patterns and Mortality Trends in Denmark

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  1. Proportion of patients admitted following injury – setting a “frame” for analysis of variation with diagnosis, calendar year, age and sex?JM.LauritsenAccident/Injury AnalysisGroup. Dept. of Orthopaedics Odense University Hospital, Odense Denmark. Contributions by Thomas Foged.

  2. Setting • Denmark – Europe • Odense University Hospital A&E dept. • Public financing (tax) no fee for contact • Data Recording: Patient system • Population: 225000 • Trauma center level 1 (one fifth of Denmark about 1 mio) • Previous study showed that ICISS figures are comparable btw. Denmark and Australia

  3. ICISS severity grading of emergency room contacts – Are Danish values comparable to the original AUS/NZ values ? • Overall comparability between Denmark and Australia. • Although there are differences in diagnosis specific survival proportions btw. Danish and Australia – the good story is that when calculating overall survival probability (ICISS) differences are minor. • This indicates that possibly ICISS can be calculated based on SRRs derived from data from other parts of the world. • The question is then about patterns of diagnosis and admission

  4. Material for study • Period: Jan 1st 1994 to June 30th 2006 • Contacts: Injury general: (n=421283) Violence: (n=12104) • Excluding: Medical cause and attempted suicide • Data completeness:No diagnosis coded: n=1 (whole period)No admission status: n=5 (whole period)No cause of contact registration: < 50 records per year

  5. Material in analysis • Period: Jan 1st 1994 to June 30th 2006 • From 1 – 5 S/T diagnoses per patient • Only contacts with at least one S/T diagnosis areincluded in analysis: n= 410139 patients nd = 481778 diagnosis codings ndc= 1024 different S/T codes (3 digit) 108 (2 digit)

  6. Analysis phases • Phase 1: Variation in admission by age, sex and period (patient level) • Phase 2: Investigation of positional stability of diagnosis • Phase 3: Proportion of mortality known • Phase 4: Analysis of variation by diagnosis

  7. Phase 1: Percentage of admission by age, sex and period (patient level): Age Percent 95% CI< 20 5.4 5.3- 5.5 20-40: 6.8 6.7- 6.9 41-64: 10.9 10.7-11.2 65+Males: 25.0 24.2-25.8 Females: 30.0 29.5-31.0 No Period effect

  8. Phase 2: Position of diagnosis • From 1- 6 diagnoses coded. • For 1% of patients S/T was not the first. • These patients had 15.8 % of all S/T diagnosis • Admission percentage: • S/T was not first: 22.3% (CI 22.0-22.6) • S/T was first: 9.0% (CI 8.9-9.1) • No period effect, but large “position effect” - OR 2.6 (CI 2.5-2.7)

  9. Phase 3Variation in proportion of ”dead on arrival” and “all mortality” • Died as inpatient: N=736 • Dead on arrival: N=382 total: 1118Diagnostic problem: Only 1/3 autopsy • Time pattern: percentage ”dead on arrival” 40% in 1994 26% at end of period (Highly significant trend) • Consequence: Registerfollow-up to determine e.g. 7 day, 30 day and 1 year mortality, plus alternate sources.

  10. The problem is what we do not see (unreported cases)

  11. Conclusion • Phase1: Variation in admission levels with - sex and age, but not with period. • Phase 2: Variation with position of first S/T diagnosis, but no period effect. • Phase 3: Highly significant variation (trend) in composition of mortality known at hospital (Underreporting and insufficient diagnostics). • Phase 4:Before starting we need clear definitions of who to include, how to handle mortality, age and sex issues.

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