Using Geographical Convergence of Obesity, Cardiovascular Disease, and Type 2 Diabetes at the Neighborhood Level to Inform Policy and Practice
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Using Geographical Convergence of Obesity, Cardiovascular Disease, and Type 2 Diabetes at the Neighborhood Level to Inform Policy and Practice

Filetype[PDF-441.82 KB]


  • English

  • Details:

    • Alternative Title:
      Prev Chronic Dis
    • Description:
      Introduction Chronic diseases are increasing across the world. Examination of local geographic variation in chronic disease patterns can enable policy makers to identify inequalities in health outcomes and tailor effective interventions to communities at higher risk. Our study aimed to determine the geographic variation of obesity, cardiovascular disease (CVD), and type 2 diabetes, using general practice clinical data. Further objectives included identifying regions of significantly high and low clusters of these conditions and assessing their association with sociodemographic characteristics. Methods A cross-sectional approach was used to determine the prevalence of obesity, CVD, and type 2 diabetes in western Adelaide, Australia. The Getis-Ord Gi* method was used to identify significant hot spots of the conditions. Additionally, we used the Pearson correlation test to determine the association between disease clusters and risk factors, including socioeconomic status (SES), smoking history, and alcohol consumption. Results The spatial distribution of obesity, CVD, and type 2 diabetes varied across communities. Hot spots of these conditions converged in 3 locations across western Adelaide. An inverse relationship was observed between area-level prevalence of CVD, obesity, and type 2 diabetes with SES. Conclusion Identification of significant disease clusters can help policy makers to target prevention strategies at the right people, in the right location. The approach taken in our study can be applied to identify clusters of other chronic diseases across the world, wherever researchers have access to clinical data.
    • Pubmed ID:
      29023234
    • Pubmed Central ID:
      PMC5645193
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