Editing IPCC:AR6/WGII/Chapter-12 (section)

=== 12.5.6 Health and Well-being ===

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The most common adaptation strategies include the development of climate services such as epidemic forecast tools, integrated climate-health surveillance and observatories and forecasting climate-related disasters (floods, heat waves). Geographic information system (GIS) technologies are being used to identify locations where vulnerable populations are exposed to climate hazards and associated health risks.

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==== 12.5.6.1 Climate Services for Health ====

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The measures most directly linked to diminishing risk are those related to climate services for health ( ''high confidence'' ). Climate services provide tailored, sector-specific information from climate forecasts to support decision-making ( [[#WHO%20and%20WMO--2016|WHO and WMO, 2016]] ); they allow decision makers and practitioners to plan interventions in anticipation of a weather/climate event ( [[#Mahon--2019|Mahon et al., 2019]] ). More recently, climate services, such as EWSs and forecast models, have been promoted for the health sector ( [[#WHO%20and%20WMO--2012|WHO and WMO, 2012]] , 2016; [[#WMO--2014|WMO, 2014]] ; [[#Thomson--2018|Thomson and Mason, 2018]] ) and are an important adaptation measure to reduce the impacts of climate on health ( ''high confidence'' ). To guide this process, the Global Framework for Climate Services (GFCS) issued a Health Exemplar ( [[#Lowe--2014|Lowe et al., 2014]] ; [[#WMO--2014|WMO, 2014]] ), which aims to foster stakeholder engagement between health and climate actors at all levels to promote the effective use of climate information within health research, policy and practice.

There exist at least 24 EWS in SA to avoid deaths and injuries from floods in the countries such as Argentina, Colombia, Ecuador, Bolivia, Brazil, Peru, Uruguay and Venezuela ( [[#Bravo--2010|Bravo et al., 2010]] ; [[#Bidegain--2014|Bidegain, 2014]] ; [[#Moreno--2014|Moreno et al., 2014]] ; [[#Dávila--2016|Dávila, 2016]] ; [[#del%20Granado--2016|del Granado et al., 2016]] ; [[#López-García--2017|López-García et al., 2017]] ; [[#Carrizo%20Sineiro--2018|Carrizo Sineiro et al., 2018]] ). A total of 149 emergency prevention and response systems are reported in CA ( [[#UNESCO--2012|UNESCO, 2012]] ). In addition, some countries implement programmes for the relocation of families who are in risk condition, like in Bogota and Medellin, Colombia ( [[#World%20Bank--2014|World Bank, 2014]] ; [[#Watanabe--2015|Watanabe, 2015]] ).

Epidemic forecast tools are an example of an adaptation measure being developed and/or implemented in this region ( ''high confidence'' ). Climate-driven forecast models have been developed for dengue in Ecuador, Puerto Rico, Peru, Brazil, Mexico, Dominican Republic, and Colombia ( [[#Lowe--2013|Lowe et al., 2013]] ; [[#Eastin--2014|Eastin et al., 2014]] ; [[#Johansson--2016|Johansson et al., 2016]] ; [[#Lowe--2017|Lowe et al., 2017]] ; [[#Johansson--2019|Johansson et al., 2019]] ); for Zika virus infections across the Americas ( [[#Muñoz--2017|Muñoz et al., 2017]] ); for cutaneous leishmaniasis in Costa Rica and Brazil ( [[#Chaves--2006|Chaves and Pascual, 2006]] ; [[#Lewnard--2014|Lewnard et al., 2014]] ); for Aedes-borne diseases across the Americas ( [[#Muñoz--2020b|Muñoz et al., 2020b]] ); and a nowcast model for chikungunya virus infections across the Americas ( [[#Johansson--2014|Johansson et al., 2014]] ). In Ecuador, a prototype system utilised forecasts of seasonal climate and ENSO forecasts of to predict dengue transmission, providing the health sector with warnings of increased transmission several months ahead of time ( [[#Stewart-Ibarra--2013|Stewart-Ibarra and Lowe, 2013]] ; [[#Lowe--2017|Lowe et al., 2017]] ). Despite these advances, few tools have become operational and mainstreamed in decision making processes. However, Brazil and Panama have been able to operationalise an EWS for the surveillance of dengue fever transmission ( [[#Codeço--2016|Codeço et al., 2016]] ; [[#McDonald--2016|McDonald et al., 2016]] ).

One of the most promising climate services for the health sector are heat and cold early-warning and alert systems ( ''medium confidence'' ). These have been developed by the national meteorological institutes in Peru, Argentina, and Uruguay ( [[#Bidegain--2014|Bidegain, 2014]] ). A heat alert system was implemented in Argentina in 2017 and daily alerts are issued for 57 localities across the country. A stoplight colour scheme is used to issue alerts, identifying specific groups at risk and actions to be taken to reduce the risk ( [[#Herrera--2018b|Herrera et al., 2018b]] ).

The public dissemination of climate–health warnings via bulletins, websites and other outlets can be an adaptation measure to address climate change and weather variability to reduce health risks ( ''high confidence'' ). The information produced is systematised to be communicated to authorities and the general public. The Caribbean Health-Climatic Bulletin has been issued quarterly since 2018 to health ministries across the region, including CA and NSA. Regional climate and health authorities meet to review 3-month climate forecasts and issue statements about the probable impacts on health ( [[#Trotman--2018|Trotman et al., 2018]] ). In Panama, information on dengue is distributed in a monthly bulletin that is used by health authorities to inform vector control activities ( [[#McDonald--2016|McDonald et al., 2016]] ). Another example is the climate-driven forecast of dengue risk that was produced prior to Brazil’s 2014 FIFA World Cup to inform disease prevention interventions ( [[#Lowe--2014|Lowe et al., 2014]] , 2016). In Colombia, the Intersectoral National Technical Commission for Environmental Health publishes a monthly bulletin with regional weather forecasts and potential effects on health ( [[#CONASA--2019|CONASA, 2019]] ). Paraguay improves epidemiological surveillance and trains first-level health staff via information campaigns on the prevention of climate-sensitive diseases and promotes health networks with the participation of civil society ( [[#Environmental%20Secretariat%20of%20Paraguay--2011|Environmental Secretariat of Paraguay, 2011]] ).

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==== 12.5.6.2 Integrated Climate–Health Surveillance and Observatories ====

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Integrated climate–health surveillance systems are another key adaptation strategy ( ''medium confidence'' ). This information can be used by the health sector to inform decision-making about when and where to deploy a public health intervention. It can also feed into an EWS, particularly if the data are compatible in format and spatiotemporal scales. An integrated climate–health surveillance system for vector-borne disease control was developed in southern coastal Ecuador through a partnership among the climate and health sectors and academia ( [[#Borbor-Cordova--2016|Borbor-Cordova et al., 2016]] ; [[#Lowe--2017|Lowe et al., 2017]] ). Additionally, an interdisciplinary multi-national team working at the border of Ecuador and Peru created a cooperation network for climate-informed dengue surveillance ( [[#Quichi--2016|Quichi et al., 2016]] ), and their successful binational collaboration resulted in the local elimination of malaria ( [[#Krisher--2016|Krisher et al., 2016]] ). A similar tool is innovative community-based data collection to understand and find solutions to rainfall-related diarrheal diseases in Ecuador ( [[#Palacios--2016|Palacios et al., 2016]] ).

Climate and health observatories represent a promising strategy that is being developed at sub-national, national (e.g., Brazil, Argentina) and regional levels ( ''high confidence'' ) ( [[#Muñoz--2016|Muñoz et al., 2016]] ; [[#Rusticucci--2020|Rusticucci et al., 2020]] ). The Brazilian Observatory of Climate and Health brings together climate and health information for the Amazon region of Manaus ( [[#Barcellos--2016|Barcellos et al., 2016]] ). At the national level, Brazil has created a climate and health observatory, where information and data visualisations are available for various climate-sensitive health indicators ( [[#Ministério%20da%20Saúde%20and%20FIOCRUZ--2021|Ministério da Saúde and FIOCRUZ, 2021]] ).

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==== 12.5.6.3 Vulnerability and Risk Maps ====

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Vulnerability and risk maps have been widely used as an adaptation strategy to understand the potential impacts of climate on health outcomes both directly (e.g., maps of disease risk) and indirectly (e.g., maps of populations vulnerable to climate disasters) ( ''high confidence'' ). There are many examples of where climate services have been used to construct vulnerability maps for health outcomes, including maps in the aforementioned climate–health observatories. Dengue, malaria and Zika vulnerability maps using climate, social and environmental information have been developed in Brazil and Colombia ( [[#Cunha--2016b|Cunha et al., 2016b]] ; [[#López-Álvarez--2016|López-Álvarez, 2016]] ; [[#Pereda--2016|Pereda, 2016]] ; [[#IDEAM--2017|IDEAM, 2017]] ). Argentina focuses on improving its health system using a climate change risk map system as a tool that identifies the risks and allows assessing their management ( [[#OPS%20and%20WHO--2018|OPS and WHO, 2018]] ).

Vulnerability and risk maps for climate disasters have been developed at the city level, for example in Bogota, Cartagena de Indias and Mocoa in Colombia ( [[#Yamin--2013|Yamin et al., 2013]] ; [[#Guzman%20Torres--2014|Guzman Torres and Barrera Arciniegas, 2014]] ; [[#Tehelen--2017|Tehelen and Pacha, 2017]] ; [[#Zamora--2018|Zamora, 2018]] ), and for the metropolitan district of Quito in Ecuador ( [[#Tehelen--2017|Tehelen and Pacha, 2017]] ). In addition, vulnerability maps were created for the primary road network of Colombia ( [[#Tehelen--2017|Tehelen and Pacha, 2017]] ). At the regional level, vulnerability maps using climate-change probability, disaster risk and food insecurity variables have been produced for the Andean region ( [[#WFP--2014|WFP, 2014]] ). In Brazil, vulnerability maps that consider exposure, sensitivity and adaptive capacity, coupled with climate scenarios, were designed to support the NAP on a municipal scale ( [[#Chang--2018|Chang and Garcia, 2018]] ; Duval et al., 2018; [[#Marinho--2018|Marinho and Silva, 2018]] ; [[#Menezes--2018|Menezes, 2018]] ; Santos and Marinho, 2018; Silva et al., 2018). A Climate Change Vulnerability Index was used to generate vulnerability maps for countries of the Latin American and Caribbean region ( [[#Vörösmarty--2013|Vörösmarty et al., 2013]] ; [[#CAF--2014|CAF, 2014]] ).

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==== 12.5.6.4 Other Adaptation Actions ====

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Diverse adaptation measures are being implemented through public policies, private household responses and communal management that directly or indirectly reduce the impacts of climate change on human health ( ''high confidence'' ) (Table 12.9) ''.'' Private and communal management measures could be considered indirect measures because they might be adopted even in the absence of climate change.

'''Table 12.9 |''' Hazards from climate change that impact human health and examples of adaptation strategies proposed or implemented in CSA. Based on McMichael et al. (2006), Miller et al. (2013a, b, c, d), [[#Hardoy--2014|Hardoy et al. (2014)]] , [[#IPCC--2014|IPCC (2014)]] , Janches et al. (2014), [[#Lee--2014|Lee et al. (2014)]] , [[#Mejia--2014|Mejia (2014)]] , [[#Sosa-Rodriguez--2014|Sosa-Rodriguez (2014)]] , [[#Vergara--2014|Vergara et al. (2014)]] , [[#Lemos--2016|Lemos et al. (2016)]] , [[#Villamizar--2017|Villamizar et al. (2017)]] , [[#Magoni--2018|Magoni and Munoz (2018)]] and [[#Zhao--2019|Zhao et al. (2019)]] .

{| class="wikitable"
|-
! rowspan="2"| '''Hazard and'''

'''impacts on human health'''

! colspan="3"| '''Examples of adaptation strategies'''

|-
! '''Public'''

! '''Private'''

! '''Communal'''

|-
| Extreme heat and cold: deaths/illness by thermal stress

| * Creation of urban green spaces
* Health promotion campaigns
* Shelters during heatwaves
* Technology transfer for home heating

| * Cooling by swamp coolers, air conditioning, open windows, wet floors, shade trees
* Bioclimatic building design

| * Training of community health volunteers to recognise and treat heat strain

|-
| Extreme rainfall, wildfire, wind speed: injuries/deaths from floods, storms, cyclones, bushfires and landslides (Key risk 2, Table 12.6)

| * EWSs for extreme climate events
* Safe housing programmes and relocation
* GGI (e.g., channels, drainage systems)

| * GGI to prevent landslides
* Insurance mechanisms and financing for long-term recovery

| * Communal efforts to clear debris from canals to reduce flood risk
* Cooperative efforts to rebuild following flood events

|-
| Drought and dryness: poor nutrition due to reduced food yields and dehydration due to limited or inadequate management of freshwater (Key risk 1, Table 12.6)

| * Formalising land ownership for small farmers and Indigenous people
* Address emerging water conflicts

| * Water infrastructure and irrigation
* Soil moisture retention techniques
* Insurance mechanisms
* Selection of drought-resistant crops

| * Incorporation of local stakeholders in formulating adaptation responses
* Recognition of Indigenous and local wisdom and knowledge

|-
| Changes in climate that promote microbial proliferation: food poisoning and unsafe drinking water (Key risk 3, Table 12.6).

| * Restoration of watersheds
* Integrated health-climate surveillance
* Improve access to drinking water, drainage, sanitation and waste removal

| * Water disinfection: boiling, chlorination
* Purchasing water or water filters

| * Participatory water management strategies, including protection of drinking water sources

|-
| Changes in climate that affect vector–pathogen host relations and infectious disease geography/seasonality (Key risk 4, Table 12.6)

| * Vector control
* EWS for epidemics
* NbS (e.g., forest conservation)

| * Use of bed nets and screens
* Use of repellent and insecticides
* Elimination of standing water

| * Community volunteers to collect blood smears for malaria diagnosis
* Community-led elimination of vector habitat

|-
| SLR and storm surges: impaired crop, livestock and fisheries yields; unsafe drinking water, leading to impaired nutrition (Key risk 8, Table 12.6)

| * Improve governance of water utilities
* Address emerging water conflicts
* Protection, restoration and soil conservation to recharge aquifers

| * Improve water efficiency in agriculture

| * Incorporation of local stakeholders in formulating adaptation responses
* Recognition of Indigenous and local wisdom and knowledge

|-
| Environmental degradation: loss of livelihoods and displacement leading to poverty and adverse health outcomes (related to Key risk 6, Table 12.6)

| * Long-term risk management planning for cities
* Sustainable forestry programmes
* Protection and restoration of lacustrine areas

| * Identification of alternative livelihoods

| * Community-led efforts to reforest and restore/protect watersheds

|}

Participatory management can be relevant in the case of mosquito-borne disease prevention (e.g., dengue fever or malaria), where the reduction in mosquito habitat in one area or ‘hotspot’ can reduce the risk for all surrounding households. This approach is also relevant when considering new places where vector-borne diseases can emerge because of changes in climate ( [[#Andersson--2015|Andersson et al., 2015]] ).

Adaptation strategies implemented by the public sector include a diverse suite of strategies ranging from the creation of green spaces in urban areas, relocation of families located in disaster-prone areas, ecosystem restoration and improved access to clean water, among many others ( ''high confidence'' ) (Table 12.9). Building GGI has been a popular public adaptation measure to reduce deaths and injuries because of floods ( [[#12.5.5.3.2|Section 12.5.5.3.2]] ). Infrastructure has been improved at schools, public buildings and drainage systems in cities such as Bogota, Colombia ( [[#World%20Bank--2014|World Bank, 2014]] ) and La Paz, Bolivia ( [[#Fernández--2016|Fernández and Buss, 2016]] ). In Brazil, channel works were implemented to reduce the flooding of the Tiete River, which crosses the metropolitan area of São Paulo; these projects were designed based on simulated flood scenarios ( [[#Hori--2017|Hori et al., 2017]] ).

Another example of a public adaptation measure is the protection and restoration of natural areas, which have the potential to decrease the transmission of water- and vector-borne infectious diseases ( ''medium confidence: robust evidence, low agreement'' ). Studies have shown that these measures can diminish the cases of malaria and diarrhoea in Brazil and cases of diarrhoea in children in Colombia ( [[#Bauch--2015|Bauch et al., 2015]] ; [[#Herrera--2017|Herrera et al., 2017]] ; [[#Chaves--2018|Chaves et al., 2018]] ). However, deforestation and malaria have a complex relationship that relies on local context interactions, where land use and land cover changes play an important role due to vector ecology alterations and social conditions of human settlements ( [[#Rubio-Palis--2013|Rubio-Palis et al., 2013]] ). Forest conservation can improve hydrological cycle control and soil erosion that can help to improve water quality and reduce the burden of water-borne diseases. In addition, forest cover can help to diminish the habitat for larval mosquitoes that transmit malaria. These measures can help to design policies at sites where these problems do not currently exist but can emerge as a consequence of climate change and the increase in the frequency of weather extreme events.

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==== 12.5.6.5 Challenges and Opportunities ====

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Despite the proliferation of disaster EWSs in the region, only 37 can be considered operational, because many of these systems do not operate or function properly or do not meet the requirements that would allow them to be considered EWSs ( [[#UNESCO--2012|UNESCO, 2012]] ). Sustainable financing and political support are needed to ensure the functioning of disaster EWSs ( ''high confidence'' ) (Table 12.11). Several studies identified difficulties in implementing disaster EWSs due to a lack of community engagement and response to the alerts that are issued ( [[#del%20Granado--2016|del Granado et al., 2016]] ; [[#López-García--2017|López-García et al., 2017]] ). To address these challenges, the document ‘Developing Early Warning Systems: A Checklist’ provides guidance for the implementation of a ''people-centred approach to early warning systems'' , as proposed in the Hyogo Framework for Action 2005–2015 ( [[#Wiltshire--2006|Wiltshire, 2006]] ).

With respect to the development of climate-driven epidemic forecasts, efforts are needed to improve the utility of such forecasts for the health sector. Few such forecasts have been operationalised to inform health-sector decision-making. A review of 73 studies that predicted and forecasted Zika virus infections (42% from the Americas) identified a high degree of variation in access, reproducibility, timeliness and incorporation of uncertainty ( [[#Kobres--2019|Kobres et al., 2019]] ). A recent systematic review of epidemic forecasting and prediction studies found that no reporting guidelines exist; the development of guidance to improve the transparency, quality and implementation of forecast models in the public health sector was recommended ( [[#Pollett--2020|Pollett et al., 2020]] ). An earlier review of dengue early-warning models found that few models incorporated both spatial and temporal aspects of disease risk ( [[#Racloz--2012|Racloz et al., 2012]] ), limiting their potential application as an adaptation strategy by the health sector. Advances have been made in the last decade with respect to modelling and computing tools, increasing access to digital climate information and health records and the use of Earth observations to forecast climate-sensitive diseases ( [[#Fletcher--2021|Fletcher et al., 2021]] ; [[#Wimberly--2021|Wimberly et al., 2021]] ).

The growing field of implementation science—defined as ‘a discipline focused on systematically examining the gap between knowledge and action’—represents another opportunity to address the challenges and barriers to using climate information for health-sector decision-making ( [[#Boyer--2020|Boyer et al., 2020]] ). Implementation science in the health sector in CSA is nascent; research in this area could help to address barriers to mainstreaming climate information in the health sector as an adaptation strategy (Table 12.11; Table SM12.7).

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==== 12.5.6.6 Governance and Financing ====

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A description of the governance and financing dimensions of the feasibility of implementing EWSs is presented in Table 12.11 and Table SM12.7.

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===== 12.5.6.6.1 National Health Plans =====

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Some countries have developed national plans on health including the role of climate. Chile has a Climate Change Adaptation Plan of the Health Sector that proposes several actions to enhance monitoring, institutions and citizen information and education ( [[#Ministry%20of%20Health%20of%20Chile%20and%20Ministry%20of%20Environment%20of%20Chile--2016|Ministry of Health of Chile and Ministry of Environment of Chile, 2016]] ). Based on the identification of vulnerability to climate change, Colombia has developed 11 regional adaptation plans to strengthen institutional capacities, climate-change education for behavioural changes and cost estimation to promote health resilience ( [[#WHO%20and%20UNFCCC--2015|WHO and UNFCCC, 2015]] ). In addition, El Salvador implemented actions to strengthen health infrastructure using high latrines for housing in flood communities, as well as other measures focused on water supply and quality based on an education and awareness programme ( [[#Ministry%20of%20Environment%20and%20Natural%20Resources%20of%20El%20Salvador--2013|Ministry of Environment and Natural Resources of El Salvador, 2013]] ). Only Brazil and Peru have implemented actions so far in the region derived from national health adaptation plans, and only Brazil completed a national assessment of impacts, vulnerability and adaptation for health ( [[#Watts--2018|Watts et al., 2018]] ). Some countries include health as a priority sector in their NAPs, as in the case of Ecuador and Costa Rica, which has a national plan addressing the prevention and care of climate-sensitive diseases coupled with a National Health Plan (2016–2020) ( [[#Ministry%20of%20Health%20Costa%20Rica--2016|Ministry of Health Costa Rica, 2016]] ; Jiménez, n. d.).

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===== 12.5.6.6.2 National Disaster Management Plans =====

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National Risk Management Plans or National Disaster Response Plans are tools for adapting to climate change that can help to diminish death and injuries from disasters ( ''high confidence'' ). These plans are generally promoted by governments as national instruments that guide the processes of estimating, preventing and reducing disaster risk. Updated National Risk Management Plans have been found for Guatemala ( [[#CONRED--2014|CONRED, 2014]] ), Honduras ( [[#COPECO--2014|COPECO, 2014]] ), El Salvador ( [[#Ministry%20of%20Health%20of%20El%20Salvador--2017|Ministry of Health of El Salvador, 2017]] ), Costa Rica ( [[#CNE--2016|CNE, 2016]] ), Ecuador ( [[#SGR--2018|SGR, 2018]] ), Peru ( [[#SGRD--2014|SGRD et al., 2014]] ), Argentina ( [[#Ministerio%20de%20Seguridad%20de%20Argentina--2018|Ministerio de Seguridad de Argentina, 2018]] ), Bolivia ( [[#VIDECI--2017|VIDECI, 2017]] ), Chile ( [[#ONEMI--2015|ONEMI, 2015]] ) and Colombia ( [[#UNGRD--2015|UNGRD, 2015]] ). It has been shown in Brazil that information on drought conditions can be used to reduced health impacts of drought using a national disaster risk reduction framework ( [[#Sena--2016|Sena et al., 2016]] ).

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