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Geographic Variation In Cardiometabolic Risk Distribution: A Cross-Sectional Study Of 256,525 Adult Residents In The Illawarra-Shoalhaven Region Of The Nsw, Australia, Renin Toms, Darren J. Mayne, Xiaoqi Feng, Andrew D. Bonney
Geographic Variation In Cardiometabolic Risk Distribution: A Cross-Sectional Study Of 256,525 Adult Residents In The Illawarra-Shoalhaven Region Of The Nsw, Australia, Renin Toms, Darren J. Mayne, Xiaoqi Feng, Andrew D. Bonney
Illawarra Health and Medical Research Institute
Introduction Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches. Method A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status …
Exploring The Geography Of Serious Mental Illness And Type 2 Diabetes Comorbidity In Illawarra-Shoalhaven, Australia (2010 -2017), Ramya Walsan, Darren J. Mayne, Nagesh B. Pai, Xiaoqi Feng, Andrew D. Bonney
Exploring The Geography Of Serious Mental Illness And Type 2 Diabetes Comorbidity In Illawarra-Shoalhaven, Australia (2010 -2017), Ramya Walsan, Darren J. Mayne, Nagesh B. Pai, Xiaoqi Feng, Andrew D. Bonney
Illawarra Health and Medical Research Institute
Objectives The primary aim of this study was to describe the geography of serious mental illness (SMI)-type 2 diabetes comorbidity (T2D) in the Illawarra-Shoalhaven region of NSW, Australia. The Secondary objective was to determine the geographic concordance if any, between the comorbidity and the single diagnosis of SMI and diabetes. Methods Spatial analytical techniques were applied to clinical data to explore the above objectives. The geographic variation in comorbidity was determined by Moran's I at the global level and the local clusters of significance were determined by Local Moran's I and spatial scan statistic. Choropleth hotspot maps and spatial scan …