setting

Department of Orthopaedics Accident/Injury Analysis Group

Funen County a WHO Safe Community

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• 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 are

included in analysis:n= 410139 patients nd = 481778 diagnosis codingsndc= 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


Funen County a WHO Safe Community 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)


Funen County a WHO Safe Community Phase 3

Variation in proportion of ”dead on arrival” and “all mortality”• Died as inpatient: N=736• Dead on arrival: N=382 total: 1118

Diagnostic 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.



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



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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