1. Addressing address quality in public health surveillance data
Kate Zinszer

2. Infectious disease surveillance in Canada
Reportable diseases
List of diseases that are of significant public health importance
Laboratories submit reports to the regional public health office (fax, electronically)
Information entered into a database & extracted for monitoring and potential intervention

3. Public health surveillance
Orient data in terms of person, place, and time
Detect high-risk groups, spatial and temporal clusters
Residential location of case
Assumption of being true residential location
This assumption has never been investigated in public health surveillance data
Source: Jones et al. Nature 2009

4. Study objectives
To determine the prevalence of address errors for all reportable diseases in Montreal
To determine if these errors have the potential to impact surveillance findings

5. Methods
Extracted all reportable disease records from the Montreal public health department from 1 January to 31 December 2008
No demographic information
Temporal and spatial information
Location information: street name, street number, and postal code

6. Applied an address correcting algorithm
Exact match: No editing needed (valid match)
Recoverable address error: does not match the Canada Post file
‘De Lorimmier’ would be corrected to ‘De Lorimier’
Unprocessable error: missing critical pieces of information

7. Methods
Determined the prevalence of address errors for reportable diseases
Examined the impact of address errors for cases of campylobacter:
Obtained (x,y) coordinates for postal codes
Calculated straight line (Euclidian) distance moved for those with ‘recoverable’ address errors
(x,y)
(x,y)

8. Methods Calculated the cumulative incidence rates of campylobacter
By subregion (FSA)
Using the original address and ‘corrected’ address
Calculated absolute rate differences
Simulated increased address error prevalence

9. Results & Discussion

20. Conclusions
Address errors are a prevalent problem in surveillance data
This results in significant positional errors, which impacts surveillance findings
Next step is to determine impact on cluster detection using SaTScan and Bayesian methods
Limitations
Address corrections have not been not validated
Simulation is based upon address error distribution for cases of campylobacter in Montreal

21. Conclusions
Space is an important component in surveillance and findings need to be communicated to public health practitioners and researchers
Need to examine the extent of address errors in public health data
Changes in practice can reduce these errors

22. Thank you David Buckeridge Katia Charland Christian Jauvin Aman Verma
Luc De Montigny
Kevin Schwartzman
Robert Allard
Lucie Bedard
Geoconnections