Ian McRae Peter Del Fante Nasser Bagheri Justin Reeves Paul Konings

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Presentation transcript:

Ian McRae Peter Del Fante Nasser Bagheri Justin Reeves Paul Konings Exploring unidentified diabetes cases in the LeFevre Peninsula, Adelaide Ian McRae Peter Del Fante Nasser Bagheri Justin Reeves Paul Konings

The Task Follows Nacul et al (2010) in the UK who used: Practice level data to measure the number of people diagnosed with diabetes Data from a health survey which collects clinical measures to construct a model of total diabetes load

Applying this model of diabetes load to the clinical practice information to estimate how many patients at a practice level maybe undiagnosed diabetes patients Applying the model to small geographic areas and comparing with numbers of identified patients from the practices Then exploring patterns of individuals and areas with undiagnosed diabetes.

Model for total diabetes as function of demographic and clinical measures North West Adelaide Health Survey About to start Report back to practices Have data Have 70% of data geo-attributed Mapping of locations of undiagnosed – consideration of clustering For practices/areas Predict total diabetes subtract already diagnosed Giving undiagnosed diabetes patients Data extracted from practices in LeFevre Peninsula ABS data on SEIFA, age, gender, ethnicity Modelling of undiagnosed by area. Geocode to mesh blocks Data on distance to GP, distance to hospital, GP per capita

This is a beginning Assuming all goes well, this will be the basis for other studies In other areas and bigger areas In relation to other chronic conditions