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Analysis of Admission Proportions Following Injury: Setting a Frame for Variation

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sage-gibbs
11 diapositives · 232 Views · Nov 18, 2014

This study examines the variation in admission proportions of patients following injury by age, sex, and period. The research analyzes data from Denmark to compare ICISS severity grading with Australia, investigating patterns of diagnosis and admission. Key phases include assessing mortality proportions, positional stability of diagnoses, and potential underreporting issues.

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Analysis of Admission Proportions Following Injury: Setting a Frame for Variation
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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.