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Impacts of Climate Change on Health and Health Services in Northern New South Wales, Australia: A Rapid Review.

Grace W LeeKristina VineAmba-Rose AtkinsonMichael TongJo M LongmanAlexandra BarrattRoss Stewart BailieSotiris VardoulakisVeronica Matthews QuandamookaKazi Mizanur Rahman
Published in: International journal of environmental research and public health (2023)
Climate change is exposing populations to increasing temperatures and extreme weather events in many parts of Australia. To prepare for climate challenges, there is a growing need for Local Health Districts (LHDs) to identify potential health impacts in their region and strengthen the capacity of the health system to respond accordingly. This rapid review summarised existing evidence and research gaps on the impact of climate change on health and health services in Northern New South Wales (NSW)-a 'hotspot' for climate disaster declarations. We systematically searched online databases and selected 11 peer-reviewed studies published between 2012-2022 for the Northern NSW region. The most explored health outcome was mental health in the aftermath of floods and droughts, followed by increased healthcare utilisation due to respiratory, cardiovascular and mortality outcomes associated with bushfire smoke or heat waves. Future research directions were recommended to understand: the compounding impacts of extreme events on health and the health system, local data needs that can better inform models that predict future health risks and healthcare utilisation for the region, and the needs of vulnerable populations that require a whole-of-system response during the different phases of disasters. In conclusion, the review provided climate change and health research directions the LHD may undertake to inform future adaptation and mitigation policies and strategies relevant to their region.
Keyphrases
  • climate change
  • healthcare
  • mental health
  • public health
  • human health
  • health information
  • randomized controlled trial
  • systematic review
  • machine learning
  • deep learning
  • current status
  • mental illness
  • genetic diversity