Spatial characterization of hypertension clusters using a rural Australian clinical database

Research output: Book chapter/Published conference paperConference paperpeer-review

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
Title of host publicationProceedings of Computing in Cardiology 2017
EditorsChristine Pickett, Cristiana Corsi, Pablo Laguna, Rob MacLeod
Place of PublicationRennes, France
PublisherComputing in Cardiology
Pages1-4
Number of pages4
Volume44
Publication statusPublished - 2017
Event44th Computing in Cardiology Conference, CinC 2017 - University of Rennes, Rennes, France
Duration: 24 Sep 201727 Sep 2017
https://www.cinc2017.org/

Conference

Conference44th Computing in Cardiology Conference, CinC 2017
Country/TerritoryFrance
CityRennes
Period24/09/1727/09/17
Internet address

Fingerprint

Dive into the research topics of 'Spatial characterization of hypertension clusters using a rural Australian clinical database'. Together they form a unique fingerprint.

Cite this