Geographic variation in cardiometabolic risk distribution: A cross-sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia


Autoři: Renin Toms aff001;  Darren J. Mayne aff001;  Xiaoqi Feng aff002;  Andrew Bonney aff001
Působiště autorů: School of Medicine, University of Wollongong, Wollongong, NSW, Australia aff001;  Illawarra Health and Medical Research Institute, Wollongong, NSW, Australia aff002;  Public Health Unit, Illawarra Shoalhaven Local Health District, Warrawong, NSW, Australia aff003;  School of Public Health, The University of Sydney, Sydney, NSW, Australia aff004;  School of Public Health and Community Medicine, University of New South Wales, Sydney, NSW, Australia aff005;  Population Wellbeing and Environment Research Lab (PowerLab), School of Health and Society, University of Wollongong, Wollongong, NSW, Australia aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223179

Souhrn

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 was used to undertake descriptive and spatial analyses. CMRF test results were dichotomised into ‘higher risk’ and ‘lower risk’ values based on existing risk definitions. Australian Census Statistical Area Level 1 (SA1) were used as the geographic units of analysis, and an Empirical Bayes (EB) approach was used to smooth rates at SA1 level. Choropleth maps demonstrating the distribution of CMRFs rates at SA1 level were produced. Spatial clustering of CMRFs was assessed using Global Moran’s I test and Local Indicators of Spatial Autocorrelation (LISA).

Results

A total of 1,132,016 test data derived from 256,525 individuals revealed significant geographic variation in the distribution of ‘higher risk' CMRF findings. The populated eastern seaboard of the study region demonstrated the highest rates of CMRFs. Global Moran’s I values were significant and positive at SA1 level for all CMRFs. The highest spatial autocorrelation strength was found among obesity rates (0.328), and the lowest for albuminuria (0.028). LISA tests identified significant High-High (HH) and Low-Low (LL) spatial clusters of CMRFs, with LL predominantly in the less populated northern, central and southern regions of the study area.

Conclusion

The study describes a range of CMRFs with different distributions in the study region. The results allow generation of hypotheses to test in future research concerning location specific population health approaches.

Klíčová slova:

Australia – Cardiovascular diseases – Diabetes mellitus – Geographic distribution – HbA1c – Health services research – Medical risk factors – Spatial autocorrelation


Zdroje

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