Climate change refers to a complex series of changes in earth systems triggered primarily by the combustion of fossil fuels, land use changes, and other human activities. Climate change affects human health and well-being through many pathways, some direct, others indirect, and still others mediated through complex social and ecological pathways. These impacts manifest differently in different places and in different populations; vulnerability varies considerably. This chapter discusses five pathways that are particularly salient in tropical settings: heat; rising sea levels; severe weather events; infectious diseases; and threats to the food supply. For each of these, we sketch the health threat and the needed public health and clinical responses, known as adaptation, to protect public health. We conclude by discussing mitigation – the primary prevention of climate change – with a focus on implications for tropical regions.

AbstractBackgroundEthiopia has a history of climate related malaria epidemics. An improved understanding of malaria–climate interactions is needed to inform malaria control and national adaptation plans.MethodsMalaria–climate associations in Ethiopia were assessed using (a) monthly climate data (1981–2016) from the Ethiopian National Meteorological Agency (NMA), (b) sea surface temperatures (SSTs) from the eastern Pacific, Indian Ocean and Tropical Atlantic and (c) historical malaria epidemic information obtained from the literature. Data analysed spanned 1950–2016. Individual analyses were undertaken over relevant time periods. The impact of the El Niño Southern Oscillation (ENSO) on seasonal and spatial patterns of rainfall and minimum temperature (Tmin) and maximum temperature (Tmax) was explored using NMA online Maprooms. The relationship of historic malaria epidemics (local or widespread) and concurrent ENSO phases (El Niño, Neutral, La Niña) and climate conditions (including drought) was explored in various ways. The relationships between SSTs (ENSO, Indian Ocean Dipole and Tropical Atlantic), rainfall, Tmin, Tmax and malaria epidemics in Amhara region were also explored.ResultsEl Niño events are strongly related to higher Tmax across the country, drought in north-west Ethiopia during the July–August–September (JAS) rainy season and unusually heavy rain in the semi-arid south-east during the October–November–December (OND) season. La Niña conditions approximate the reverse. At the national level malaria epidemics mostly occur following the JAS rainy season and widespread epidemics are commonly associated with El Niño events when Tmax is high, and drought is common. In the Amhara region, malaria epidemics were not associated with ENSO, but with warm Tropical Atlantic SSTs and higher rainfall.ConclusionMalaria–climate relationships in Ethiopia are complex, unravelling them requires good climate and malaria data (as well as data on potential confounders) and an understanding of the regional and local climate system. The development of climate informed early warning systems must, therefore, target a specific region and season when predictability is high and where the climate drivers of malaria are sufficiently well understood. An El Niño event is likely in the coming years. Warming temperatures, political instability in some regions, and declining investments from international donors, implies an increasing risk of climate-related malaria epidemics.

Climate-sensitive infectious disease modelling is crucial for public health planning and is underpinned by a complex network of software tools. We identified only 37 tools that incorporated both climate inputs and epidemiological information to produce an output of disease risk in one package, were transparently described and validated, were named (for future searching and versioning), and were accessible (ie, the code was published during the past 10 years or was available on a repository, web platform, or other user interface). We noted disproportionate representation of developers based at North American and European institutions. Most tools (n=30 [81%]) focused on vector-borne diseases, and more than half (n=16 [53%]) of these tools focused on malaria. Few tools (n=4 [11%]) focused on food-borne, respiratory, or water-borne diseases. The under-representation of tools for estimating outbreaks of directly transmitted diseases represents a major knowledge gap. Just over half (n=20 [54%]) of the tools assessed were described as operationalised, with many freely available online.

Climate Change and Vectorborne DiseasesWarming temperatures and changes in precipitation associated with climate change are affecting the occurrence of vectorborne diseases. Effective, equitable strategies are needed for vector control and disease management.

PICO question Does occurrence of ventricular arrhythmia reduce the survival rate in dogs with gastric dilatation and volvulus (GDV)? Clinical bottom line Category of research question Prognosis The number and type of study designs reviewed The number and type of study designs that were critically appraised were three retrospective observational case-control studies (Brourman et al., 1996; Green et al., 2012; and Mackenzie et al., 2010) and one prospective, observational study (Aona et al., 2017) Strength of evidence Critical evaluation and appraisal of the papers that met the inclusion criteria provided only weak evidence to support the clinical question. This is due to the lack of recent (within the last 5 years) and specific (do the presence of cardiac arrythmias affect mortality of dogs with GDV) studies conducted on the subject. Additionally, more in-depth statistical analysis (e.g. P values and confidence intervals (CI)) may also help to determine the strength of association between the presence of ventricular arrythmia and survival rates. However, there is room for further research to continue investigating the proposed hypothesis. Several of the evaluated studies were carried out more than 10 years before this Knowledge Summary was written, meaning that the knowledge and technology at the time may not be relevant to clinical practice today Outcomes reported Green et al. (2012) concluded that ‘cardiac arrhythmia was not a prognostic indicator’ for GDV. Of the two papers (Mackenzie et al., 2010; and Brourman et al., 1996) that found a significant association between the development of cardiac arrhythmias (specifically, those of ventricular origin) and an increase in the mortality rates of dogs with GDV, one (Brourman et al., 1996) noted that a greater number of dogs that died prior to discharge were diagnosed with preoperative ventricular tachycardia, while the other (Mackenzie et al., 2010) found that the greatest mortality rate was among those dogs that developed postoperative ventricular tachycardia. The final study, Aona et al. (2017), was the only paper to categorise and grade the ventricular arrhythmias using previously published scales. It was discovered that increased levels of cTn1 (cardiac troponin 1) made a dog more likely to develop a higher grade of arrhythmia, however, no association was found between the type or grade of arrhythmia and patient mortality Conclusion Taking into account the strength of evidence and the outcomes presented by the appraised studies the following conclusion has been drawn; although there is some evidence to suggest that ventricular tachycardia may be associated with an increase in mortality rates in patients with GDV, further research is required in order to make any further conclusions that may definitively answer the clinical question How to apply this evidence in practice The application of evidence into practice should take into account multiple factors, not limited to: individual clinical expertise, patient’s circumstances and owners’ values, country, location or clinic where you work, the individual case in front of you, the availability of therapies and resources. Knowledge Summaries are a resource to help reinforce or inform decision making. They do not override the responsibility or judgement of the practitioner to do what is best for the animal in their care.

Two recent initiatives, the World Health Organization (WHO) Strategic Advisory Group on Malaria Eradication and the Lancet Commission on Malaria Eradication, have assessed the feasibility of achieving global malaria eradication and proposed strategies to achieve it. Both reports rely on a climate-driven model of malaria transmission to conclude that long-term trends in climate will assist eradication efforts overall and, consequently, neither prioritize strategies to manage the effects of climate variability and change on malaria programming. This review discusses the pathways via which climate affects malaria and reviews the suitability of climate-driven models of malaria transmission to inform long-term strategies such as an eradication programme. Climate can influence malaria directly, through transmission dynamics, or indirectly, through myriad pathways including the many socioeconomic factors that underpin malaria risk. These indirect effects are largely unpredictable and so are not included in climate-driven disease models. Such models have been effective at predicting transmission from weeks to months ahead. However, due to several well-documented limitations, climate projections cannot accurately predict the medium- or long-term effects of climate change on malaria, especially on local scales. Long-term climate trends are shifting disease patterns, but climate shocks (extreme weather and climate events) and variability from sub-seasonal to decadal timeframes have a much greater influence than trends and are also more easily integrated into control programmes. In light of these conclusions, a pragmatic approach is proposed to assessing and managing the effects of climate variability and change on long-term malaria risk and on programmes to control, eliminate and ultimately eradicate the disease. A range of practical measures are proposed to climate-proof a malaria eradication strategy, which can be implemented today and will ensure that climate variability and change do not derail progress towards eradication.

edes -borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. In the United States of America, the vast majority of Aedes -borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Aedes -borne d iseases’ e nvironmental s uitability ( Ae DES) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of Ae DES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies .

BackgroundMalaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions.MethodsRoutine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps.ResultsBetween 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi.ConclusionThe modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

The potential to use sub-seasonal to seasonal (S2S) prediction systems for outcomes in health is presented, using four case studies of malaria, dengue, heat waves, and meningococcal meningitis. While promising, many such applications are currently in the demonstration phase, and examples of operationalizing S2S-based early warning systems, fully integrated with decision support, have yet to emerge. Potential reasons for this operationalization bottleneck are discussed, which include restrictions on open access to health and climate data, the unfulfilled requirement for training in the use of such systems, and the mismatch between the prediction paradigm and the decision entry points in health-planning systems. The S2S project sponsored by the World Meteorological Organization may help to demonstrate the potential application of climate information, but the lack of real-time access inhibits the operationalization of evaluated systems. It is recommended that partnership platforms, established through the Global Framework for Climate Services and related mechanisms, enable the climate and health academic and operational communities to work together on real-time provision and assessment of health early warning systems. This is particularly important in developing countries where climate-driven health outcomes can be severe.

Climate resilience is increasingly prioritized by international development agencies and national governments. However, current approaches to informing communities of future climate risk are problematic. The predominant focus on end‐of‐century projections neglects more pressing development concerns, which relate to the management of shorter‐term risks and climate variability, and constitutes a substantial opportunity cost for the limited financial and human resources available to tackle development challenges. When a long‐term view genuinely is relevant to decision‐making, much of the information available is not fit for purpose. Climate model projections are able to capture many aspects of the climate system and so can be relied upon to guide mitigation plans and broad adaptation strategies, but the use of these models to guide local, practical adaptation actions is unwarranted. Climate models are unable to represent future conditions at the degree of spatial, temporal, and probabilistic precision with which projections are often provided, which gives a false impression of confidence to users of climate change information. In this article, we outline these issues, review their history, and provide a set of practical steps for both the development and climate scientist communities to consider. Solutions to mobilize the best available science include a focus on decision‐relevant timescales, an increased role for model evaluation and expert judgment and the integration of climate variability into climate change services.This article is categorized under: Climate and Development > Knowledge and Action in Development

The Zika virus epidemic that emerged in northeast Brazil in 2015 occurred during an unusually warm and dry year. Both natural climate variability as well as longterm trends were responsible for the extreme temperatures observed and these climate conditions are likely to have contributed to the timing and scale of this devastating epidemic. Knowledge of this climate context is derived from analyses of large-scale global climate datasets and models, which provide policy-makers with broad insights into changes in hydro-meteorological extremes. However, societal response to epidemics works at multiple levels. For instance, policies and resource commitments may be developed at international and national levels, while targeted prevention and control efforts are managed at local levels by district health teams and community leaders. Adaptation to climate change also needs to be developed at multiple levels. National level information may be needed for planning, but an understanding of the local weather and climate that individuals and communities experience is also required. Once specific climate-sensitive health risks are identified, information on the past, present or future climate can be used to help mitigate risks and identify new opportunities for improved health outcomes. This information needs to be provided as a routine service if it is to support operational decision-making. 2

Temperature is one of the most important of all climate variables for health because of its direct impact on the human body as well as many indirect impacts, such as its effects on disease transmission. Most people have an approximate sense of how hot or cold a given temperature is, but it can be helpful to have a more precise indication of what the temperature might mean in terms of its impacts, or in terms of how hot or cold the temperature feels in the context of the weather conditions. Apparent temperatures measure how hot or cold the air feels: a hot day feels even hotter if it is muggy, and a cold day even colder if it is windy. Heat indices and wind chill factors are examples of apparent temperatures that account for these different perceptions. Various heat indices have been devised to combine temperature and relative humidity, the details of which vary from country to country.

are described here; some of these options are more suitable for weather forecasting, others for climate forecasting,