This paper contributes to research on projecting and valuing the impacts of climate change on human health by proposing and implementing a methodology that allows for rapid integrated assessment of climate change-induced disease burdens to be used in environments characterized by cumulating uncertainty relating to data gaps and the accuracy of downscaled projections. The approach is important because the countries most vulnerable to the early effects of climate change need to start laying the foundations for their adaptation policies now, regardless of the quality of their national health and environmental data sets. The methodology consists of a series of specifically delineated, iterative steps that helps to identify hierarchy of variables driving the quantitative results. The method also helps to identify key data gaps, thereby providing an important focus for subsequent research, monitoring, and data collection efforts. The paper demonstrates this methodology by applying it to the projection and valuation of the excess disease burden in Montserrat and Saint Lucia for two climate change scenarios. We illustrate their utility in the context of adaptation planning. This paper also highlights that investment in data collection and information systems is a “no regrets” action that should be considered integral to national and regional adaptation efforts, particularly in instances where current data do not facilitate the implementation of best practice health impact assessment methods.