Generating baseline evidence on climate change impacts on human health in Fiji

The purpose of this Request for Proposals (RFP) is to enter into a contractual agreement with a successful bidder and select a suitable contractor to generate scientific evidence on the impacts of climate change on human health in Fiji through a project funded by the Korean International Cooperation Agency (KOICA), Strengthening Health Adaptation Project: Responding to Climate Change in Fiji (KOICA SHAPE).

BACKGROUND

 A new WHO global strategy on health, environment and climate change was approved by member states in 2019 at the 72nd Annual World Health Assembly for 2019–2023, outlining the transformation needed to improve lives and well-being sustainably through healthy environments. With Small Island Developing States (SIDS) being extremely vulnerable to climate change and its potential health impacts, WHO Member States approved a WHO global plan of action on climate change and health in SIDS in 2019 for 2019–2023.

Fiji is prone to the impacts of climate change, e.g., sea-level rise, cyclones, and floods, and prediction models suggest that there would be an increase in both the dry and wet season rainfall with an increased likelihood of extreme rainfall days. Given that scientific evidence indicates SIDS, including Fiji, are likely to experience more extreme weather events such as floods and droughts consequent from climate change, these changes are expected to influence disease patterns, community vulnerability, and resilience, including health systems in general.

The KOICA SHAPE project in Fiji is being implemented by WHO with the government of Fiji, primarily the Ministry of Health and medical Services (MOHMS). The aims of this project, among others, is to develop an early warning system for climate-sensitive diseases based on baseline evidence of the linkages between climate change and health, as well as to understand the capacities and needs of communities in adapting to climate change.

REQUIREMENTS/WORK TO BE PERFORMED

Output 1: Review the data quality, collection and management of existing data, and identify potential data errors and their effect.