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Population-based injury data in Ontario Presentation for ICE meeting Washington, September 7, 2006 Alison K. Macpherson, PhD Assistant Professor School.

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Presentation on theme: "Population-based injury data in Ontario Presentation for ICE meeting Washington, September 7, 2006 Alison K. Macpherson, PhD Assistant Professor School."— Presentation transcript:

1 Population-based injury data in Ontario Presentation for ICE meeting Washington, September 7, 2006 Alison K. Macpherson, PhD Assistant Professor School of Kinesiology and Health Science York University

2 Sources of data 1.National ambulatory care reporting system (NACRS) database Includes all patients reporting to ED in Ontario Reporting required by government in the context of a one-party payment system (universal healthcare) Coded by nosologists using a standardized process Uses international classification system (ICD- 10-CA) Includes unique identifier (scrambled OHIP number) 1.National ambulatory care reporting system (NACRS) database Includes all patients reporting to ED in Ontario Reporting required by government in the context of a one-party payment system (universal healthcare) Coded by nosologists using a standardized process Uses international classification system (ICD- 10-CA) Includes unique identifier (scrambled OHIP number)

3 Sources of data (2) 2.Discharge Abstract Database (DAD) Includes all patients hospitalized in Ontario Can be linked to NACRS by unique identifier Uses international classification system (ICD- 10) Both datasets include: –mechanism of injury –geographic indicators –Diagnoses (ICD-10) –sociodemographic information 2.Discharge Abstract Database (DAD) Includes all patients hospitalized in Ontario Can be linked to NACRS by unique identifier Uses international classification system (ICD- 10) Both datasets include: –mechanism of injury –geographic indicators –Diagnoses (ICD-10) –sociodemographic information

4 Injuries in Ontario: An ICES Research Atlas Objective: To describe the injury problem in Ontario, paying special attention to variation by: Age Gender SES Geographic location Mechanism of injury

5 Methods National ambulatory care reporting system (NACRS) database linked with Discharge Abstract Database (DAD) One year ( ) All patients reporting to ED in Ontario Grouped by –county (49 in Ontario) –SES based on average family income in the residential census tract National ambulatory care reporting system (NACRS) database linked with Discharge Abstract Database (DAD) One year ( ) All patients reporting to ED in Ontario Grouped by –county (49 in Ontario) –SES based on average family income in the residential census tract

6 Variable definition Injury variable - Diagnosis (ICD-10 codes) - Visits with an e-code and a trauma diagnosis included Grouped according to ICE categories for cause: –Falls –Motor vehicle crashes –Bicycle-related injuries –Pedestrian injuries –Overexertion –Drowning –-etc…. Injury variable - Diagnosis (ICD-10 codes) - Visits with an e-code and a trauma diagnosis included Grouped according to ICE categories for cause: –Falls –Motor vehicle crashes –Bicycle-related injuries –Pedestrian injuries –Overexertion –Drowning –-etc….

7 12,068,300 4,921,085 1,211,550 Population of Ontario Number of ED visits ED visits for injury (25% of ED visits)

8 Results 1.2 million ED visits for an injury in one year 13,678/100,000 injury rate 62,377 (2.6%) admitted to hospital 2700 (0.02%) died in hospital 1.2 million ED visits for an injury in one year 13,678/100,000 injury rate 62,377 (2.6%) admitted to hospital 2700 (0.02%) died in hospital

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13 How do NACRS and DAD compare for all injury admissions ? Agree perfectly (same code for ED and hospitalization) N (%) Do not agree perfectly N (%) Primary diagnosis (40%)37161 (60%) Cause of injury32947 (53%)29430 (47%) Intent (unintentional/unkno wn, self inflicted, assault) (82%)11271 (18%)

14 How do NACRS and DAD compare for injury admissions > 3 days ? Agree perfectly (same code for ED and hospitalization) N (%) Do not agree perfectly N (%) Primary diagnosis (37%)23385 (63%) Cause of injury18636 (51%)18227 (49%) Intent (unintentional/unkno wn, self inflicted, assault) (81%)7181 (19%)

15 Strengths and Limitations of Ontario injury data Strengths Population-based study Linked data Coded using standardized practicesLimitations Administrative data Possibility of coding errors Variation in injury rates may partially reflect variation in ED visitsStrengths Population-based study Linked data Coded using standardized practicesLimitations Administrative data Possibility of coding errors Variation in injury rates may partially reflect variation in ED visits

16 Conclusions Ontario has rich sources of injury dataOntario has rich sources of injury data Can be used for local planning and international comparisonsCan be used for local planning and international comparisons Linkable data can help with validation for ICE injury projectsLinkable data can help with validation for ICE injury projects Atlas exhibits available at under publicationsAtlas exhibits available at under publicationswww.ices.on.ca


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