# Presentation: 523714

## 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 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

**Notes:**
Logistic regression -  LR=9.9 (df=1) p=0.002
Odds Ratio= 1.005 (CI 1.001-1.008)

## 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)

**Notes:**
Non first S/T: (n=4230) of all patients did not have the S/T diagnosis as the first one (1 %)

Odds Ratio of Not first: 2.6 (CI 2.5-2-6) in a logistic regression including age, sex with no period variation (LR < 2) at diagnosis level and only minute indication at person level (OR 1.05 CI 1.01-1.08)

## Phase 3Variation 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.

## 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.**