Year

2020

Degree Name

Doctor of Philosophy

Department

School of Medicine

Abstract

Introduction: Cardiovascular disease (CVD) associated metabolic risk factors are a growing human health concern in Australia and worldwide. This thesis investigated the small-area geographic variation in the distribution of cardiometabolic risk factors (CMRFs) in the Illawarra-Shoalhaven region of Australia, their association with area-level disadvantage and access to primary care and whether area-level disadvantage and primary care access contribute to the geographic variation of CMRFs.

Materials and methods: Geographic variation in the distribution of individual CMRFs was analysed at Statistical Area Level 1 (SA1), which is the smallest unit that disaggregated census data are reported in Australia. Individual-level data used in this thesis included de-identified CMRF test data from non-pregnant adult (≥18 years) residents of the Illawarra-Shoalhaven region between 2012–2017, which was sourced from the largest pathology service provider in the study region. These data included the most recent individual-level test results for: 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. The test results were dichotomised into higher and lower cardiometabolic risk values based on the existing clinical guidelines. Area-level data included: SA1-level disadvantage, sourced from the 2011 Australian Census of Population and Housing Index of Relative Socioeconomic Disadvantage; and primary care provider data retrieved from publicly available sources current in year 2016. Choropleth maps describing the distribution of CMRFs rates were produced using an Empirical Bayes (EB) approach to smooth the rates. Spatial clustering of CMRFs was assessed using Moran’s I test and Local Indicators of Spatial Autocorrelation (LISA). A two-step floating catchment area (2SFCA) method was used to calculate the primary care access index of the SA1s within the study region. Multilevel logistic regression models were used to elucidate the association of the area-level socioeconomic disadvantage and primary care access with the geographic variation of CMRFs in the study region, after adjusting for individual- and area-level covariates.

Share

COinS
 

Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.