Working Towards Eye Health Equity for Indigenous Australians with Diabetes.
Jose J EstevezNatasha J HowardJamie E CraigAlex Dh BrownPublished in: International journal of environmental research and public health (2019)
Type 2 diabetes mellitus (T2DM) poses significant challenges to individuals and broader society, much of which is borne by disadvantaged and marginalised population groups including Indigenous people. The increasing prevalence of T2DM among Indigenous people has meant that rates of diabetes-related complications such as blindness from end-stage diabetic retinopathy (DR) continue to be important health concerns. Australia, a high-income and resource-rich country, continues to struggle to adequately respond to the health needs of its Indigenous people living with T2DM. Trends among Indigenous Australians highlight that the prevalence of DR has almost doubled over two decades, and the prevalence of diabetes-related vision impairment is consistently reported to be higher among Indigenous Australians (5.2%-26.5%) compared to non-Indigenous Australians (1.7%). While Australia has collated reliable estimates of the eye health burden owing to T2DM in its Indigenous population, there is fragmentation of existing data and limited knowledge on the underlying risk factors. Taking a systems approach that investigates the social, environmental, clinical, biological and genetic risk factors, and-importantly-integrates these data, may give valuable insights into the most important determinants contributing to the development of diabetes-related blindness. This knowledge is a crucial initial step to reducing the human and societal impacts of blindness on Indigenous Australians, other priority populations and society at large.
Keyphrases
- risk factors
- glycemic control
- healthcare
- type diabetes
- mental health
- public health
- cardiovascular disease
- diabetic retinopathy
- health information
- metabolic syndrome
- physical activity
- human health
- big data
- adipose tissue
- electronic health record
- gene expression
- machine learning
- artificial intelligence
- dna methylation
- deep learning