Spatial characterization of hypertension clusters using a rural Australian clinical database

Rachel Whitsed, Ana Horta, Herbert F. Jelinek*, Faezeh Marzbanrad

*Corresponding author for this work

Research output: Contribution to journalConference paperpeer-review

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Abstract

This study aimed to characterize the spatial distribution of hypertension (HT) clusters in a rural Australian city using self-reported HT data collected at a local health-screening clinic. HT status was recorded for 515 self-selected participants in a free health-screening program in Albury, New South Wales, Australia. We compared predictions of HT clusters computed using spatial scan statistic and Generalised Additive Model (GAM). We then implemented a new approach incorporating sensitivity analysis in GAM to combine cluster predictions at multiple span sizes. A statistically significant cluster for HT was identified in Albury centered to the north of the main urban center, with relative risk up to 2.29. The sensitivity analysis confirmed the cluster location and highlighted other potential HT clusters. Our approach allows detection of irregularly-shaped disease clusters and highlights potential clusters that may be overlooked using traditional methods. This is important in cases using local, small datasets where regularly-shaped or overly smoothed disease clusters may not provide enough detail to be suitable for targeting place-based interventions.

Original languageEnglish
Pages (from-to)1-4
Number of pages4
JournalComputing in Cardiology
Volume44
DOIs
Publication statusPublished - 1 Jan 2017
Event44th Computing in Cardiology Conference, CinC 2017 - Rennes, France
Duration: 24 Sep 201727 Sep 2017

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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