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