Editing IPCC:AR6/WGII/Chapter-7 (section)

=== 7.3.1 Projected Future Risks for Health and Well-Being ===

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<span id="global-impacts"></span>
==== 7.3.1.1 Global Impacts ====

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''Climate change is expected to significantly increase the health risks resulting from a range of climate-sensitive diseases and conditions, with the scale of impacts depending on emissions and adaptation pathways in coming decades'' ( ''very high confidence'' ) ''.'' Sections 7.3.1.2 to 7.3.1.11 assess the available studies on future projections for risks associated with specific climate-sensitive diseases and conditions previously described in [[#7.2.1|Section 7.2.1]] . In the case of diabetes, cancer, injuries, mosquito-borne diseases other than dengue and malaria, rodent-borne diseases and most mental illnesses, insufficient literature was found to allow for assessment. Adaptation pathways and options for managing such risks are detailed in [[#7.4|Section 7.4]] .

''Even in the absence of further warming beyond current levels, the proportion of the overall global deaths caused by climate-sensitive diseases and conditions would increase marginally by mid-century'' ( ''high confidence'' ) ''.'' Two global projections of climate change health impacts have been conducted since AR5. The first focused on cause-specific mortality for eight exposures for 2030 and 2050 for a mid-range emissions scenario (A1b) and three scenarios of economic growth ( [[#WHO--2014|WHO, 2014]] ). The study estimated that the climate change projected to occur by 2050 (compared to 1961–1990) could result in an excess of approximately 250,000 deaths yr –1 , dominated by increases in deaths due to heat (94,000, mainly in Asia and high-income countries), childhood undernutrition (85,000, mainly in Africa but also in Asia), malaria (33,000, mainly in Africa) and diarrhoeal disease (33,000, mainly in Africa and Asia). Overall, more than half of this excess mortality is projected for Africa. Near-term projections (for 2030) are predominantly for childhood undernutrition (95,200 out of 241,000 total excess deaths) (Figure 7.8). The second study (Carleton et al. 2020) focused on all-cause mortality associated with warming under both a high emissions scenario (RCP8.5) and a middle emissions scenario (RCP4.5). The analyses created a metric of death equivalents that accounted for hot and cold temperature-related mortality and the costs of individual level adaptation; no acclimatization or community-level adaptation, such as early warning systems, were incorporated. Average annual temperature-mortality-income per capita relationships estimated from pooled data from 40 predominantly middle- and high-income countries (38% of the world population) were applied worldwide. Under the high emissions scenario, climate change was projected to result in approximately 85 deaths equivalents per 100,000 population.

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[[File:2e6d7dda859a6ae6050757f84509f6ba IPCC_AR6_WGII_Figure_7_008.png]]

'''Figure 7.8 |''' '''Projected additional annual deaths attributable to climate change in 2030 and 2050 compared to 1961–1990 ( [[#WHO--2014|WHO, 2014]] ).'''

''Temperature increases are projected to exceed critical risk thresholds for six key climate-sensitive health outcomes, highlighting the criticality of building adaptive capacity in health systems and in other sectors that influence health and well-being'' ( ''high confidence'' ) ''.'' Recently reported research illustrates the temperature thresholds under three adaptation scenarios describing the effectiveness of health systems to manage additional risks from climate change for heat-related morbidity and mortality; ozone-related mortality; malaria incidence rates; incidence rates of Dengue and other diseases spread by Aedes sp. mosquitos; Lyme disease; and West Nile fever ( [[#Ebi--2021a|Ebi et al., 2021a]] ). As shown in Figure 7.9, these adaptation scenarios significantly alter the warming thresholds at which risks accelerate, with the proactive adaptation scenario, a scenario that emphasises international cooperation towards achieving sustainable development, having the greatest potential to avoid significant increases in risks under all but the highest levels of warming. The incomplete adaptation scenario describes a world with moderate challenges to adaptation and mitigation. The limited adaptation scenario describes a world with high challenges to adaptation and mitigation. In the figure, transitions are based on the peer-reviewed literature projecting risks for each of the health outcomes. Projections for time intervals were changed to temperature increase above pre-industrial levels based on the climate models and scenarios used in the projections.The assessed projections were based on a range of scenarios, including SRES, CMIP5, and ISIMIP, and, in some cases, demographic trends. The black dots are levels of confidence, from very high (four dots) to low (one dot). The diagrams for the proactive and incomplete adaptation scenarios are truncated at the nearest whole °C within the range of temperature change in 2100 under three SSP scenarios used in panel (a) of SPM.3.

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[[File:42d10e12319026b12ca92b551dac98a6 IPCC_AR6_WGII_Figure_7_009.png]]

'''Figure 7.9 |''' '''Climate-sensitive human health outcomes under three adaptation scenarios.'''

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==== 7.3.1.2 Projected Changes in Heat- and Cold-Related Exposure and Related Health Outcomes ====

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This section considers the broad impacts of projected changes in heat- and cold-related exposure and related outcomes including mortality and work productivity. Several of the most common heat- and cold-related specific health outcomes (e.g., CVD) are assessed individually in later sections of this chapter.

''Population heat exposure will increase under climate change'' ( ''very high confidence'' ) ''.'' Since AR5 there has been considerable progress with quantifying future human exposure to extreme heat ( [[#Schwingshackl--2021|Schwingshackl et al., 2021]] ), especially as determined by different combinations of SSPs and RCPs ( [[#Chambers--2020|Chambers, 2020]] ; [[#Cheng--2020|Cheng et al., 2020]] ; [[#Jones--2018|Jones et al., 2018]] ; [[#Liu--2017|Liu et al., 2017]] ; [[#Ma--2021|Ma and Yuan, 2021]] ; [[#Russo--2019|Russo et al., 2019]] ). For example, Table 7.1 shows projections of population exposure to heatwaves, as expressed by the number of person-days, for the 2061–2080 period aggregated by geographical region and SSP/RCP. At the global level, projected future exposure increases from approximately 15 million person-days for the current period to 535 billion person-days for high population growth under the high GHG emission SSP3-RCP8.5 scenario, while for the low population growth/high urbanisation and business as usual SSP5-RCP4.5 scenario, the exposure is substantially lower at 170 billion person-days. Spatial variations in future heatwave frequency and population growth play out in the form of significant geographical contrasts in exposure, with the largest increases projected for low latitude regions such as India and significant portions of sub-Saharan Africa, where increases in heatwave frequency and population are expected. Over East Asia and especially eastern China, exposures are projected to rise, with the effect of increases in heatwave frequency exceeding the countering effect of projected reductions in population, especially in non-urban areas. Further, for North America and Europe, where rural depopulation is projected, the predominant driver of increases in exposure is urban growth ( [[#Jones--2018|Jones et al., 2018]] ).

'''Table 7.1 |''' Projected exposure to heatwaves in millions of person-days by region under different SSP/RCP combinations.

{| class="wikitable"
|-
! rowspan="2"| '''Region'''

! colspan="5"| '''Exposure in millions of person-days'''

|-
! '''Current'''

! '''SSP3-4.5'''

! '''SSP5-4.5'''

! '''SSP3-8.5'''

! '''SSP5-8.5'''

|-
| Global

USA

North America

Europe

Latin America and Caribbean

North Africa and Middle East

Sub-Saharan Africa

Russia and Central Asia

South Asia

East Asia

Southeast Asia

Oceania

| 14,811

375

376

191

803

1,335

1,427

272

7,194

977

711

37

| 244,807

4,769

4,821

2,967

17,287

34,721

67,442

3,074

84,044

12,176

12, 452

247

| 168,488

8,671

8,778

3,775

10,856

23,160

41,339

1,951

53,655

10,855

9,146

492

| 534,848

10,802

10,990

7,326

45,612

65,072

158,290

6,554

146,709

35,381

60,909

822

| 374,269

19,646

20,153

9,969

28,435

43,648

96,054

4,360

94,288

31,918

47,141

1,158

|}

''Comparisons of heatwave exposure for 1.5°C and 2.0°C warming for different SSPs indicate strong geographical contrasts in potential heatwave risk'' ( ''high confidence'' ) ''.'' One global level assessment for a 1.5°C warming projects that low human development index countries will experience exposure levels equal to or greater than the exposure levels for very high human development index countries under a 2°C warming (Russo, 2019). The same assessment also finds that holding global warming below 1.5°C in tandem with achieving sustainable socioeconomic development (e.g., SSP1 as opposed to SSP4) yields reduced levels of heatwave exposure, especially for low human development index countries, particularly across sub-Saharan Africa. Similar findings were found in other global level assessments. Global exposure to extreme heat increases almost 30 times under a SSP3-8.5 combination, with the average exposure for Africa 118 times greater than historical levels, in stark contrast to the four-fold increase projected for Europe. Compared to a SSP3-8.5 scenario, exposure was reduced by 65% and 85% under the SSP2-4.5 and SSP1-2.6 scenarios, respectively ( [[#Liu--2017|Liu et al., 2017]] ).

''Regional level assessments of changes in population heat exposure for Africa, Europe, the USA, China and India corroborate general findings at the global level, that the impact of warming is amplified under divergent regional development pathways (e.g., SSP4 – inequality) compared to those fostering sustainable development (e.g., SSP1 – sustainability)'' ( ''high confidence'' ) ''( [[#Rohat--2019a|Rohat et al., 2019a]] ; [[#Weber--2020|Weber et al., 2020]] ; [[#Broadbent--2020|Broadbent et al., 2020]] ; [[#Dahl--2019|Dahl et al., 2019]] ; [[#Harrington--2018|Harrington and Otto, 2018]] ; [[#Rohat--2019b|Rohat et al., 2019b]] ; [[#Vahmani--2019|Vahmani et al., 2019]] ; Huang and et al., 2018; [[#Zhang--2020a|Zhang et al., 2020a]] ; [[#Liu--2017|Liu et al., 2017]] )'' . For some regions, such as Europe, changes in exposure are projected to be largely a consequence of climate change, while for others, such as Africa and to a lesser extent Asia, Oceania, North America and South America, the interactive effects of demographic and climate change are projected to be important ( [[#Jones--2018|Jones et al., 2018]] ; [[#Liu--2017|Liu et al., 2017]] ; [[#Russo--2016|Russo et al., 2016]] ; [[#Ma--2021|Ma and Yuan, 2021]] ) ( ''medium confidence'' ).

Compared to research that estimates the temperature only impacts of climate change on heat-related mortality (see below), the number of studies that explicitly model mortality responses considering various combinations of SSPs and RCPs is small and mostly restricted to the country or regional level. These studies point to increases in heat-related mortality especially amongst the elderly across a range of SSPs, with the greatest increases under SSP5 and RCP8.5 ( [[#Rail--2019|Rail et al., 2019]] ; [[#Yang--2021|Yang et al., 2021]] ).

''Estimates of heat-related mortality based solely on changes in temperature point to elevated levels of global and regional level mortality compared to the present, with the magnitude of this increasing from RCP4.5 through to RCP8.5'' ( ''high confidence'' ) ''( [[#Ahmadalipour--2018|Ahmadalipour and Moradkhani, 2018]] ; [[#Cheng--2019|Cheng et al., 2019]] ; [[#Kendrovski--2017|Kendrovski et al., 2017]] ; [[#Lee--2020|Lee et al., 2020]] ; [[#Limaye--2018|Limaye et al., 2018]] ; [[#Morefield--2018|Morefield et al., 2018]] )'' . Further support comes from the projection that heat-related health impacts for a 2°C increase in global temperatures will be greater than those for 1.5°C warming ( ''very high confidence'' ) ( [[#Dosio--2018|Dosio et al., 2018]] ; [[#Mitchell--2018|Mitchell et al., 2018]] ; [[#King--2017|King and Karoly, 2017]] ; [[#Vicedo-Cabrera--2018a|Vicedo-Cabrera et al., 2018a]] ).

''Estimates of future mortality that incorporate adaptation in addition to temperature change point to increases in heat-related mortality under global warming, albeit at lower levels than the case of no adaptation'' ( ''high confidence'' ) ''( [[#Anderson--2018|Anderson et al., 2018]] ; [[#Gosling--2017|Gosling et al., 2017]] ; [[#Guo--2018|Guo et al., 2018]] ; [[#Honda--2020|Honda and Onozuka, 2020]] ; [[#Vicedo-Cabrera--2018b|Vicedo-Cabrera et al., 2018b]] ; [[#Wang--2018b|Wang et al., 2018b]] )'' . Whether adaptation is considered or not, the consensus is Central and South America, southern Europe, southern and Southeast Asia and Africa will be the most affected by climate change in terms of heat-related mortality ( ''high confidence'' ). Similarly, projections of the impacts of future heat on occupational health, worker productivity and workability point to these regions as problematic under climate change ( ''high confidence'' ) ( [[#Andrews--2018|Andrews et al., 2018]] ; [[#de%20Lima--2021|de Lima et al., 2021]] ; [[#Dillender--2021|Dillender, 2021]] ; [[#Kjellstrom--2018|Kjellstrom et al., 2018]] ; [[#Orlov--2020|Orlov et al., 2020]] ; [[#Rao--2020|Rao et al., 2020]] ; [[#Tigchelaar--2020|Tigchelaar et al., 2020]] ), especially for occupations with high exposure to heat, such as agriculture and construction. This accords with the findings from independent projections of population heat exposure as outlined above ( ''high confidence'' ).

''The effect of climate change on productivity is projected to reduce GDP at a range of geographical scales'' ( ''high confidence'' ) ''( [[#Borg--2021|Borg et al., 2021]] ; [[#Oppermann--2021|Oppermann et al., 2021]] ; [[#Orlov--2020|Orlov et al., 2020]] )'' . For example, measuring economic costs using occupational health and safety recommendations, it was estimated that RCP8.5 would result in a 2.4% reduction in global GDP compared to a 0.5% reduction under RCP2.6 ( [[#Orlov--2020|Orlov et al., 2020]] ). For the USA, it was estimated that the total hours of labour supplied declined ∼ 0.11% (±0.004%) per degree Celsius increase in global mean surface temperature for low-risk workers and 0.53% (±0.01%) per degree Celsius increase for high-risk workers exposed to outdoor temperatures ( [[#Hsiang--2017|Hsiang et al., 2017]] ). Further, a systematic review of the literature indicates that extreme heat exacts a substantial economic burden on health systems, which bears implications for future heat-attributable healthcare costs ( [[#Wondmagegn--2019|Wondmagegn et al., 2019]] ).

''Since AR5, there has been an increase in the understanding of the extent to which a warming world is'' likely ''to affect cold- or winter-related health impacts. Future increases in heat-related deaths are expected to outweigh those related to cold'' ( ''high confidence'' ) ''( [[#Aboubakri--2020|Aboubakri et al., 2020]] ; [[#Achebak--2020|Achebak et al., 2020]] ; [[#Burkart--2021|Burkart et al., 2021]] ; [[#Huber--2020b|Huber et al., 2020b]] ; [[#Martinez--2018|Martinez et al., 2018]] ; [[#Rodrigues--2020|Rodrigues et al., 2020]] ; [[#Vardoulakis--2014|Vardoulakis et al., 2014]] ; [[#Weinberger--2017|Weinberger et al., 2017]] ; [[#Weinberger--2018a|Weinberger et al., 2018a]] ; [[#Weitensfelder--2020|Weitensfelder and Moshammer, 2020]] )'' . However, strong regional contrasts in heat- and cold-related mortality trends are ''likely'' under a RCP8.5 scenario, with countries in the Global North experiencing minimal to moderate decreases in cold-related mortality while warm climate countries in the Global South are projected to experience increases in heat-attributable deaths by the end of the century ( [[#Gasparrini--2017|Gasparrini et al., 2017]] ; [[#Burkart--2021|Burkart et al., 2021]] ). Projections of the magnitude of change in the temperature-related burden of disease do, however, demonstrate great variability, due to the application of a wide range of climate change, adaptation and demographic scenarios ( [[#Cheng--2019|Cheng et al., 2019]] ).

''A particular focus since AR5 has been the impact of climate change on cities (see AR6 Chapter 6). Heat risks are expected to be greater in urban areas due to changes in regional heat exacerbated by ‘heat island’ effects'' ( ''high confidence'' ) ''( [[#Doan--2018|Doan and Kusaka, 2018]] ; [[#Heaviside--2016|Heaviside et al., 2016]] ; [[#Li--2021|Li et al., 2021]] ; [[#Rohat--2019a|Rohat et al., 2019a]] ; [[#Rohat--2019c|Rohat et al., 2019c]] ; [[#Varquez--2020|Varquez et al., 2020]] ; [[#Wouters--2017|Wouters et al., 2017]] ; [[#Zhao--2021|Zhao et al., 2021]] ), with intra-urban scale variations in heat exposure attributable to land cover contrasts and urban form and function ( [[#Avashia--2021|Avashia et al., 2021]] ; [[#Jang--2020|Jang et al., 2020]] ; [[#Macintyre--2018|Macintyre et al., 2018]] ; [[#Schinasi--2018|Schinasi et al., 2018]] ).'' However, further research is required to establish the health implications of increasing chronic slow-onset extreme heat ( [[#Oppermann--2021|Oppermann et al., 2021]] ) in addition to the acute health outcomes of UHI–heatwave synergies under climate change. The latter is particularly important as studies that address UHI–heatwave interactions have mainly focused on changes in UHI intensity (e.g., [[#Ramamurthy--2017|Ramamurthy and Bou-Zeid (2017)]] ; Scott et al. (2018)). Whether significant urban mortality anomalies arise from the interplay of heatwaves and UHIs largely remains an open question although at least one study demonstrated higher urban compared to rural mortality rates during heatwaves ( [[#Ruuhela--2021|Ruuhela et al., 2021]] ). The benefits of the winter UHI effect for cold-related mortality remain largely unexplored, but one study for Birmingham, UK, indicates the winter UHI will continue to have a protective effect in future climate ( [[#Macintyre--2021|Macintyre et al., 2021]] ).

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==== 7.3.1.3 Projected Impacts on Vector-Borne Diseases ====

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The distribution and abundance of disease vectors, and the transmission of the infections that they carry, are influenced both by changes in climate and by trends such as human population growth and migration, urbanisation, land use change, biodiversity loss and public health measures. Each of these may increase or decrease risk, interact with climate effects and may contribute to the emergence of infectious disease, although there are few studies assessing future risk of emergence ( [[#Gibb--2020|Gibb et al., 2020]] ). Unless stated otherwise, the assessments below are specifically for the effects of climate change on individual diseases, assuming other determinants remain constant.

''There is a high likelihood that climate change will contribute to increased distributional range and vectorial capacity of malaria vectors in parts of sub-Saharan Africa, Asia and South America'' ( ''high confidence'' ). In Nigeria, the range and abundance of ''Anopheles'' mosquitoes are projected to increase under both lower (RCP2.6) and especially under higher emissions scenarios (RCP8.5) due to increasing and fluctuating temperature, longer tropical rainfall seasons and rapid land use changes ( [[#Akpan--2018|Akpan et al., 2018]] ). Similarly, vegetation acclimation due to elevated atmospheric CO 2 under climate change will ''likely'' increase the abundance of ''Anopheles'' vectors in Kenya ( [[#Le--2019|Le et al., 2019]] ). Distribution of ''Anopheles'' may decrease in parts of India and Southeast Asia, but there is an expected increase in vectorial capacity in China ( [[#Khormi--2016|Khormi and Kumar, 2016]] ). In South America, climate change is projected to expand the distributions of malaria vectors to 35–46% of the continent by 2070, particularly species of the ''Albitarsis'' complex ( [[#Laporta--2015|Laporta et al., 2015]] ).

''Malaria infections have significant potential to increase in parts of sub-Saharan Africa and Asia, with risk varying according to the warming scenario'' ( ''medium confidence'' ). In Africa, where most malaria is due to the more deadly ''Plasmodium falciparum'' parasite, climate change is ''likely'' to increase the overall transmission risk due to the ''likely'' expansion of vector distribution and increase in biting rates ( [[#Bouma--2016|Bouma et al., 2016]] ; [[#M’Bra--2018|M’Bra et al., 2018]] ; [[#Nkumama--2017|Nkumama et al., 2017]] ; [[#Ryan--2015b|Ryan et al., 2015b]] ; [[#Tompkins--2016a|Tompkins and Caporaso, 2016a]] ). The projected effect of climate change varies markedly by region, with projections for west Africa tending to indicate a shortening of transmission seasons and neutral or small net reductions in overall risk, whereas studies consistently project increases in southern and eastern Africa, with potentially an additional 76 million people at risk of endemic exposure (10–12 months yr –1 ) by the 2080s ( [[#Nkumama--2017|Nkumama et al., 2017]] ; [[#Ryan--2015b|Ryan et al., 2015b]] ; [[#Semakula--2017|Semakula et al., 2017]] ; [[#Zaitchik--2019|Zaitchik, 2019]] ; [[#Leedale--2016|Leedale et al., 2016]] ; [[#Murdock--2016|Murdock et al., 2016]] ; [[#Yamana--2016|Yamana et al., 2016]] ; [[#Ryan--2020|Ryan et al., 2020]] ). In sub-Saharan Africa, malaria case incidence associated with dams in malaria-endemic regions will ''likely'' be exacerbated by climate change, with significantly higher rates projected under RCP8.5 in comparison to lower-emission scenarios ( [[#Kibret--2016|Kibret et al., 2016]] ). Incidence of malaria in Madagascar is projected to increase under RCP4.5 through RCP8.5 ( [[#Rakotoarison--2018|Rakotoarison et al., 2018]] ). Distribution of ''P. vivax'' and ''P. falciparum'' malaria in China is ''likely'' to increase under RCPs higher than 2.6, especially RCP8.5 ( [[#Hundessa--2018|Hundessa et al., 2018]] ). In India, projected scenarios for the 2030s under RCP4.5 indicate changes in the spatial distribution of malaria, with new foci and potential outbreaks in the Himalayan region, southern and eastern states, and an overall increase in months suitable for transmission overall, with some other areas experiencing a reduction in transmission months ( [[#Sarkar--2019|Sarkar et al., 2019]] ).

''Rising temperatures are'' likely ''to cause poleward shifts and overall expansion in the distribution of mosquitoes'' Aedes aegypti ''and'' Aedes albopictus '', the principal vectors of dengue, yellow fever, chikungunya and Zika'' ( ''high confidence'' ) ''.'' Globally, the population exposed to disease transmission by one of these vectors is expected to increase significantly due to the combination of climate change and non-climatic processes including urbanisation and socioeconomic inter-connectivity, with exposure rates rising under higher warming scenarios ( [[#Kamal--2018|Kamal et al., 2018]] ; [[#Kraemer--2019|Kraemer et al., 2019]] ). For example, approximately 50% of the global population is projected to be exposed to these vectors by 2050 under RCP6.0 ( [[#Kraemer--2019|Kraemer et al., 2019]] ). The effect of climate change alone is projected to increase the population exposed to ''Aedes aegypti'' by 8–12% by 2061–2080 ( [[#Monaghan--2018|Monaghan et al., 2018]] ), and its abundance is projected to increase by 20% under RCP2.6 and 30% under RCP8.5 by the end of the century ( [[#Liu-Helmersson--2019|Liu-Helmersson et al., 2019]] ; Figure 7.10). Exposure to transmission by ''Aedes albopictus'' specifically would be highest at intermediate climate change scenarios and would decrease in the warmest scenarios ( [[#Ryan--2019|Ryan et al., 2019]] ). Under scenarios other than RCP2.6, most of Europe would experience significant increases in exposure to viruses transmitted by both vectors ( [[#Liu-Helmersson--2019|Liu-Helmersson et al., 2019]] ).

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[[File:78378ba3c9c7cdbb4869928af561b408 IPCC_AR6_WGII_Figure_7_010.png]]

'''Figure 7.10 |''' '''Projected change in the potential abundance of''' '''Aedes aegypti''' '''over the 21st century (2090–2099 relative to 1987–2016) (Liu-Helmersson et al''' '''.''' ''', 2019).'''

''Climate change is expected to increase dengue risk and facilitate its global spread, with the risk being greatest under high emissions scenarios'' ( ''high confidence'' ) ''.'' Future exposure to risk will be influenced by the combined effects of climate change and non-climatic factors such as population density and economic development ( [[#Akter--2017|Akter et al., 2017]] ). Overall, risk levels are expected to rise on all continents ( [[#Akter--2017|Akter et al., 2017]] ; [[#Messina--2015|Messina et al., 2015]] ; [[#Rogers--2015|Rogers, 2015]] ; [[#Liu-Helmersson--2016|Liu-Helmersson et al., 2016]] ; [[#Messina--2019|Messina et al., 2019]] ). Compared to 2015, an additional 1 billion people are projected to be at risk of dengue exposure by 2080 under an SSP1-4.5 scenario, 2.25 billion under SSP2-6.0, and 5 billion under SSP3-8.5 ( [[#Messina--2019|Messina et al., 2019]] ). In North America, risk is projected to expand in north-central Mexico, with annual dengue incidence in Mexico increasing by up to 40% by 2080, and expand from US southern states to mid-western regions ( [[#Proestos--2015|Proestos et al., 2015]] ; [[#Colon-Gonzalez--2013|Colon-Gonzalez et al., 2013]] ). In China, under RCP8.5, dengue exposure would increase from 168 million people in 142 counties to 490 million people in 456 counties by the late 2100s ( [[#Fan--2019|Fan and Liu, 2019]] ). In Nepal, dengue fever is expected to expand throughout the 2050s and 2070s under all RCPs ( [[#Acharya--2018|Acharya et al., 2018]] ). In Tanzania, there is a projected shift in distribution towards central and northeastern areas and risk intensification in nearly all parts of the country by 2050 ( [[#Mweya--2016|Mweya et al., 2016]] ). Dengue vectorial capacity is projected to increase in Korea under higher RCP scenarios ( [[#Lee--2018a|Lee et al., 2018a]] ).

''There are insufficient studies for assessment of projected effects of climate change on other arboviral diseases, such as chikungunya and Zika.'' Zika virus transmits under different temperature optimums than does dengue, suggesting environmental suitability for Zika transmission could expand with future warming ( ''low confidence'' ) ( [[#Tesla--2018|Tesla et al., 2018]] ).

''Climate change can be expected to continue to contribute to the geographical spread of the Lyme disease vector'' Ixodes scapularis ( ''high confidence'' ) ''and the spread of tick-borne encephalitis and Lyme disease vector'' Ixodes ricinus ''in Europe'' ( ''medium confidence'' ) ''.'' In Canada, vector surveillance of the black-legged tick ''I. scapularis'' identified strong temperature effects on the limits of their occurrence, on recent geographic spread, temporal coincidence in emergence of tick populations and acceleration of the speed of spread ( [[#Clow--2017|Clow et al., 2017]] ; [[#Cheng--2017|Cheng et al., 2017]] ). In Europe, increasing temperatures over the 1950–2018 period significantly accelerated the life cycle of ''Ixodes ricinus'' and contributed to its spread ( [[#Estrada-Peña--2020|Estrada-Peña and Fernández-Ruiz, 2020]] ). Under RCP4.5 and RCP8.5 scenarios, projections indicate a northward and eastward shift of the distribution of ''I. persulcatus and I. ricinus,'' vectors of Lyme disease and tick-borne encephalitis in northern Europe and Russia, with an overall large increase in distribution in the second half of the current century ( [[#Popov--2014|Popov and Yasyukevich, 2014]] ; [[#Yasjukevich--2018|Yasjukevich et al., 2018]] ) and increases in intensity of tick-borne encephalitis transmission in central Europe ( [[#Nah--2020|Nah et al., 2020]] ).

''Climate change is projected to increase the incidence of Lyme disease and tick-borne encephalitis in the Northern Hemisphere'' ( ''high confidence'' ) (Figure 7.9). The basic reproduction number (R0) of ''I. scapularis'' in at least some regions of Canada is projected to increase under all RCP scenarios ( [[#McPherson--2017|McPherson et al., 2017]] ). In the USA, a 2°C warming could increase the number of Lyme disease cases by over 20% over the coming decades and lead to an earlier onset and longer length of the annual Lyme disease season ( [[#Dumic--2018|Dumic and Severnini, 2018]] ; [[#Monaghan--2015|Monaghan et al., 2015]] ).

''Climate change is projected to change the distribution of schistosomiasis in Africa and Asia'' ( ''high confidence'' ) '', with a possible increase in global land area suitable for transmission'' ( ''medium confidence'' ). A global increase in land area with temperatures suitable for transmission by the three main species of ''Schistosoma'' ( ''S. japonicum'' , ''S. mansoni'' and ''S. haematobium)'' is projected under the RCP4.5 scenario for the 2021–2050 and 2071–2100 periods ( [[#Yang--2018|Yang and Bergquist, 2018]] ), but regional outcomes are expected to vary. In Africa, shifting temperature regimes associated with climate change are expected to lead to reduced snail populations in areas with already high temperatures and higher populations in areas with currently low winter temperatures ( [[#Kalinda--2017|Kalinda et al., 2017]] ; [[#McCreesh--2014|McCreesh and Booth, 2014]] ). Infection risk with ''Schistosoma mansoni'' may increase by up to 20% over most of eastern Africa over the next 20–50 years but decrease by more than 50% in parts of north and east Kenya, southern South Sudan and eastern People’s Democratic Republic of Congo (PDRC) ( [[#McCreesh--2015|McCreesh et al., 2015]] ), with a possible overall net contraction ( [[#Stensgaard--2013|Stensgaard et al., 2013]] ). In China, currently endemic areas in Sichuan Province may become unsuitable for snail habitats, but currently non-endemic areas in Sichuan and Hunan/Hubei provinces may see a new emergence ( [[#Yang--2018|Yang and Bergquist, 2018]] ). In addition to the projected effects of temperature described above, distribution and transmission of schistosomiasis will also be affected positively or negatively by changes in the availability of freshwater bodies, which were not included in these models.

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<span id="projected-impacts-on-waterborne-diseases"></span>
==== 7.3.1.4 Projected Impacts on Waterborne Diseases ====

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''Climate change will contribute to additional deaths and mortality due to diarrhoeal diseases in the absence of adaptation'' ( ''medium confidence'' ) ''(see Figure 7.8).'' Risk factors for future excess deaths due to diarrhoeal diseases are highly mediated by future levels of socioeconomic development and adaptation. An additional 1°C increase in mean average temperature is expected to result in a 7% (95% CI, 3–10%) increase in all-cause diarrhoea ( [[#Carlton--2016|Carlton et al., 2016]] ), an 8% (95% CI, 5–11%) increase in the incidence of diarrheic ''E. coli'' ( [[#Philipsborn--2016|Philipsborn et al., 2016]] ) and a 3–11% increase in deaths attributable to diarrhoea ( [[#WHO--2014|WHO, 2014]] ) ''.'' WHO Quantitative Risk Assessments for the effects of climate change on selected causes of death for the 2030s and 2050s project that overall deaths from diarrhoea should fall due to socioeconomic development but that the effect of climate change under higher emission scenarios could cause an additional 48,000 deaths in children aged under 15 years in 2030 and 33,000 deaths for 2050, particularly in Africa and parts of Asia. In Ecuador, projected increases in rainfall variability and heavy rainfall events may increase diarrhoea burden in urban regions ( [[#Deshpande--2020|Deshpande et al., 2020]] ). A limit in the assessable literature is a lack of studies in the highest risk areas ( [[#Liang--2017|Liang and Gong, 2017]] ; [[#UNEP--2018|UNEP, 2018]] ).

''Climate change is expected to increase future health risks associated with a range of other WBDs and parasites, with effects varying by region'' ( ''medium confidence'' ) ''.'' WBDs attributable to protozoan parasites including ''Cryptosporidium'' spp. and ''Giardia duodenalis (intestinalis)'' are expected to increase in Africa due to increasing temperatures and drought ( [[#Ahmed--2018|Ahmed et al., 2018]] ; [[#Efstratiou--2017|Efstratiou et al., 2017]] ). Recent data suggest a poleward expansion of ''Vibrios'' to areas with no previous incidence, particularly in mid- to high-latitude regions in areas where rapid warming is taking place ( [[#Baker-Austin--2017|Baker-Austin et al., 2017]] ). The number of ''Vibrio'' -induced diarrhoea cases yr –1 increased in past decades in the Baltic Sea region, and the projected risk of vibriosis will increase in northern areas, where waters are expected to become warmer and more saline due to reduced precipitation and have higher chlorophyll concentrations ( [[#Escobar--2015|Escobar et al., 2015]] ; [[#Semenza--2017|Semenza et al., 2017]] ).

''The risk of Campylobacteriosis and other enteric pathogens could rise in regions where heavy precipitation events or flooding are projected to increase'' ( ''medium confidence'' ) ''.'' In Europe, the risk of Campylobacteriosis and diseases caused by other enteric pathogens could rise in regions where precipitation or extreme flooding are projected to increase (European Environment Agency, 2017), although incidence rates may be further mediated by seasonal social activities ( [[#Rushton--2019|Rushton et al., 2019]] ; [[#Williams--2015b|Williams et al., 2015b]] ). Accelerated releases of dissolved organic matter to inland and coastal waters through increases in precipitation are expected to reduce the potential for solar ultraviolet inactivation of pathogens and increase risks for associated WBDs ( [[#Williamson--2017|Williamson et al., 2017]] ). The combined relative risk for waterborne campylobacteriosis, salmonellosis and diseases due to Verotoxin-producing ''Escherichia coli'' was estimated to be 1.1 (i.e., a 10% increase) for every 1°C in mean annual temperature, while by the 2080s, under RCP8.5, annual rates of cryptosporidiosis and giardiasis could rise by approximately 16% due to more severe precipitation events ( [[#Brubacher--2020|Brubacher et al., 2020]] ; [[#Chhetri--2019|Chhetri et al., 2019]] ).

<div id="7.3.1.5" class="h3-container"></div>

<span id="projected-impacts-on-food-borne-diseases"></span>
==== 7.3.1.5 Projected Impacts on Food-Borne Diseases ====

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''The prevalence of'' Salmonella ''infections is expected to rise as higher temperatures enable more rapid replication'' ( ''medium confidence'' ). Research from Canada finds a very strong association of salmonellosis and other FBDs with higher temperatures, suggesting that climate change could increase food safety risks ranging from increased public health burden to emergent risks not currently seen in the food chain ( [[#Smith--2019|Smith and Fazil, 2019]] ). In Europe, the average annual number of temperature-related cases of salmonellosis under high emissions scenarios could increase by up to 50% more than would be expected on the basis of on population change alone by 2100 ( [[#Lake--2017|Lake, 2017]] ; European Environment Agency, 2017). Warming trends in the southern USA may lead to increased rates of Salmonella infections ( [[#Akil--2014|Akil et al., 2014]] ).

<div id="7.3.1.6" class="h3-container"></div>

<span id="projected-impacts-on-pollution--and-aeroallergens-related-health-outcomes"></span>
==== 7.3.1.6 Projected Impacts on Pollution- and Aeroallergens-Related Health Outcomes ====

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''Global air pollution-related mortality attributable directly to climate change—the human health climate penalty associated with climate-induced changes in air quality—is'' likely ''to increase and partially counteract any decreases in air pollution-related mortality achieved through ambitious emission reduction scenarios or stabilisation of global temperature change at 2°C'' ( ''medium confidence'' ) ''.'' Demographic trends in aging and more vulnerable population are ''likely'' to be important determinants of future air quality—a human health climate penalty ''(high confidence)'' .

Poor air quality contributes to a range of NCDs, including cardiovascular, respiratory and neurological, commonly resulting in hospitalisation or death. This section considers the possible risks for health of future climate-related changes in ozone and PM. The climate penalty, the degree to which global warming could affect future air quality, is better understood for ozone than for PM ( [[#von%20Schneidemesser--2020|von Schneidemesser et al., 2020]] ). This is because increases in air temperature enhance ozone formation via associated photochemical processes ( [[#Archibald--2020|Archibald et al., 2020]] ; [[#Fu--2019|Fu and Tian, 2019]] ). The association between climate and PM is complex and moderated by a diverse range of PM components as well as formation and removal mechanisms ( [[#von%20Schneidemesser--2020|von Schneidemesser et al., 2020]] ), added to which is uncertainty about future climate-related PM sources such as wildfires ( [[#Ford--2018|Ford et al., 2018]] ) and changes in aridity ( [[#Achakulwisut--2019|Achakulwisut et al., 2019]] ). As noted in AR6 WGI [[IPCC:Wg2:Chapter:Chapter-6|Chapter 6]] (Naik et al 2021), future air quality will largely depend on precursor emissions, with climate change projected to have mixed effects. Because of the uncertainty in how natural processes will respond, there is ''low confidence'' in the projections of surface ozone and PM under climate change ( [[#Naik--2021|Naik et al., 2021]] ). This has implications on the levels of confidence in the projections of the health climate penalty associated with climate-induced changes in air quality ( [[#Orru--2017|Orru et al., 2017]] ; [[#Orru--2019|Orru et al., 2019]] ; [[#Silva--2017|Silva et al., 2017]] ).

There is a rich literature on global and regional level projections of air quality-related health effects arising from changes in emissions. Comparatively few studies assess how changes in air pollution directly attributable to climate change are ''likely'' to affect future mortality levels. Projections indicate that emission reduction scenarios consistent with stabilisation of global temperature change at 2°C or below would yield substantial co-benefits for air quality-related health outcomes ( [[#Chowdhury--2018b|Chowdhury et al., 2018b]] ; [[#von%20Schneidemesser--2020|von Schneidemesser et al., 2020]] ; [[#Silva--2016c|Silva et al., 2016c]] ; [[#Markandya--2018|Markandya et al., 2018]] ; [[#Orru--2019|Orru et al., 2019]] ; [[#Shindell--2018|Shindell et al., 2018]] ) ( ''high confidence'' ). For example, by 2030, compared to 2000, it was estimated that globally and annually 289,000 PM2.5-related premature deaths could have been avoided under RCP4.5 compared to 17,200 PM2.5-related excess premature deaths under RCP8.5 ( [[#Silva--2016c|Silva et al., 2016c]] ). Further, and notwithstanding estimated reductions in global PM2.5 levels and an associated increase in the number of avoidable deaths, the benefits of following a low emissions pathway are expected to be apparent by 2100, with avoidable deaths estimated at 2.39 million deaths yr –1 under RCP4.5. This contrasts with the 1.31 million deaths estimated under RCP8.5. A few projections of the health-related climate penalty indicate a possible increase in ozone and PM2.5-associated mortality under RCP8.5 ( [[#Doherty--2017|Doherty et al., 2017]] ; [[#Orru--2019|Orru et al., 2019]] ; [[#Silva--2017|Silva et al., 2017]] ).

At the global level for PM2.5, annual premature deaths due to climate change were projected to be 55,600 (−34,300 to 164,000) and 215,000 (−76,100 to 595,000) in 2030 and 2100, respectively, countering by 16% the projected decline in PM2.5-related mortality between 2000 and 2100 without climate change ( [[#Silva--2017|Silva et al., 2017]] ). Similarly for ozone, the number of annual premature ozone-related deaths due to climate change was projected to be 3,340 in 2030 and 43,600 in 2050, with climate change accounting for 1.2% (14%) of the annual premature deaths in 2030 (2100) ( [[#Silva--2017|Silva et al., 2017]] ). These global level projections average over considerable geographical variations ( [[#Silva--2017|Silva et al., 2017]] ). Projections of the climate change effect on ozone mortality in 2100 were greatest for East Asia (41 deaths yr –1 per million people), India (8 deaths yr –1 per million people) and North America (13 deaths yr –1 per million people). For PM2.5, mortality was projected to increase across all regions except Africa (−25,200 deaths yr –1 per million people) by 2100, with estimated increases greatest for India (40 deaths yr –1 per million people), the Middle East (45 deaths yr –1 per million people), East Asia (43 deaths yr –1 per million people) and the Former Soviet Union (57 deaths yr –1 per million people). Overall, higher ozone-related health burdens were projected to occur in highly populated regions, and greater PM2.5 health burdens were projected in high PM emission regions ( [[#Doherty--2017|Doherty et al., 2017]] ).

For central and southern Europe, climate change alone could result in an 11% increase in ozone-associated mortality by 2050. However, projected declines in ozone precursor emissions could reduce the EU-wide climate change effect on ozone-related mortality by up to 30%; the reduction was projected to be approximately 24% if aging and an increasingly susceptible population were accounted for in projections to 2050 ( [[#Orru--2019|Orru et al., 2019]] ). For the USA in 2069, the impact of climate change alone on annual PM2.5- and ozone-related deaths was estimated to be 13,000 and 3,000 deaths, respectively, with heat-driven adaptation of air conditioning accounting for 645 and 315 of the PM2.5- and ozone-related annual excess deaths, respectively ( [[#Abel--2018|Abel et al., 2018]] ). An aging population is a determinant of future air quality-related mortality levels. An aging population along with an increase in the number of vulnerable people may work to offset the decrease in deaths associated with a low emission pathway (RCP4.5) and possibly dominate the net increase in deaths under a business as usual pathway (RCP8.5) ( [[#Chen--2020|Chen et al., 2020]] ; [[#Doherty--2017|Doherty et al., 2017]] ; [[#Hong--2019|Hong et al., 2019]] ; [[#Schucht--2015|Schucht et al., 2015]] ).

Complementing the longer-term changes in air quality arising from climate change are those associated with air pollution sensitive short-term meteorological events, such as heatwaves. Studies of individual heat events ( [[#Garrido-Perez--2019|Garrido-Perez et al., 2019]] ; [[#Johansson--2020|Johansson et al., 2020]] ; [[#Kalisa--2018|Kalisa et al., 2018]] ; [[#Pu--2017|Pu et al., 2017]] ; [[#Pyrgou--2018|Pyrgou et al., 2018]] ; [[#Schnell--2017|Schnell and Prather, 2017]] ; [[#Varotsos--2019|Varotsos et al., 2019]] ) and systematic reviews ( [[#Anenberg--2020|Anenberg et al., 2020]] ) provide evidence for synergistic effects of heat and air pollution. However, the health consequences of a possible additive effect of air pollutants during heatwave events were heterogeneous, varying by location and moderated by socioeconomic factors at the intra-urban scale ( [[#Analitis--2014|Analitis et al., 2014]] ; [[#Fenech--2019|Fenech et al., 2019]] ; [[#Krug--2020|Krug et al., 2020]] ; [[#Pascal--2021|Pascal et al., 2021]] ; [[#Schwarz--2021|Schwarz et al., 2021]] ; [[#Scortichini--2018|Scortichini et al., 2018]] ). This, combined with the challenges associated with projecting future concentrations of health-relevant pollutants during heatwave events ( [[#Jahn--2021|Jahn and Hertig, 2021]] ; [[#Meehl--2018|Meehl et al., 2018]] ), makes it difficult to say with any certainty that synergistic effects of heat and poor air quality will result in a heatwave–air pollution health penalty under climate change.

''The burden of disease associated with aeroallergens is anticipated to grow due to climate change'' ( ''high confidence'' ) ''.'' The incidence of pollen allergy and associated allergic disease increases with pollen exposure, and the timing of the pollen season and pollen concentrations are expected to change under climate change ( [[#Beggs--2021|Beggs, 2021]] ; [[#Ziska--2019|Ziska et al., 2019]] ; [[#Ziska--2020|Ziska, 2020]] ). The overall length of the pollen season and total seasonal pollen counts/concentrations for allergenic species such as birch ( ''Betula'' ) and ragweed ( ''Ambrosia'' ) are expected to increase as a result of CO 2 fertilisation and warming, leading to greater sensitisation ( [[#Hamaoui-Laguel--2015|Hamaoui-Laguel et al., 2015]] ; [[#Lake--2017|Lake et al., 2017]] ; [[#Zhang--2013|Zhang et al., 2013]] ). Changes in pollen levels for several species of trees and grasses are projected to increase annual emergency department visits in the USA by between 8% for RCP4.5 and 14% for RCP8.5 by the year 2090 ( [[#Neumann--2019|Neumann et al., 2019]] ) with the exposure to some pollen types estimated to double beyond present levels in Europe by 2041–2060 ( [[#Lake--2017|Lake et al., 2017]] ). The prospect of increases in summer thunderstorm events under climate change ( [[#Brooks--2013|Brooks, 2013]] ) may hold implications for changes in the occurrence of epidemic thunderstorm asthma ( [[#Bannister--2021|Bannister et al., 2021]] ; [[#Emmerson--2021|Emmerson et al., 2021]] ; [[#Price--2021|Price et al., 2021]] ). Similarly, projected alterations in hydroclimate under climate change may bear implications for increased exposure to mould allergens in some climates ( [[#D’Amato--2020|D’Amato et al., 2020]] ; [[#Paudel--2021|Paudel et al., 2021]] ).

<div id="7.3.1.7" class="h3-container"></div>

<span id="future-risks-related-to-cardiovascular-diseases"></span>
==== 7.3.1.7 Future Risks Related to Cardiovascular Diseases ====

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''Climate change is expected to increase heat-related CVD mortality by the end of the 21st century, particularly under higher emission scenarios'' ( ''high confidence'' ) ''.'' Most modelling studies conducted since AR5 project higher rates of heat-related CVD mortality throughout the remainder of this century (Huang and et al., 2018; [[#Li--2015|Li et al., 2015]] ; [[#Li--2018|Li et al., 2018]] ; [[#Limaye--2018|Limaye et al., 2018]] ; [[#Zhang--2018a|Zhang et al., 2018a]] ; [[#Silveira--2021a|Silveira et al., 2021a]] ; [[#Yang--2021|Yang et al., 2021]] ). CVD mortality in Beijing, China, could increase by an average of 18.4%, 47.8% and 69.0% in the 2020s, 2050s and 2080s, respectively, under RCP4.5 and by 16.6%, 73.8% and 134%, respectively, under RCP8.5 relative to a 1980s baseline ( [[#Li--2015|Li et al., 2015]] ). Projections of temperature-related mortality from CVD for Beijing in the 2080s vary depending on RCP and population assumptions ( [[#Zhang--2018a|Zhang et al., 2018a]] ). Projections for Ningo, China, suggest heat-related years of life lost (YLL) could increase significantly in the month of August by between 3 and 11.5 times over current baselines by the 2070s, even with adaptation (Huang and et al., 2018). Yang and colleagues project that heat-related excess CVD mortality in China could increase to approximately 6% (from a 2010 baseline of under 2%) by the end of the century under RCP8.5 and to over 3% under RCP4.5 ( [[#Yang--2021|Yang et al., 2021]] ). The future burden of temperature-related myocardial infarctions in Germany is projected to rise under high emissions scenarios ( [[#Chen--2019|Chen et al., 2019]] ), while in the eastern USA, [[#Limaye--2018|Limaye et al. (2018)]] projected an additional 11,562 annual deaths (95% CI: 2,641–20,095) by mid-century due to cardiovascular stress in the population 65 years of age and above. CVD mortality in Brazil is projected to increase up to 8.6% by the end of the century under RCP8.5, compared with an increase of 0.7% for RCP4.5 ( [[#Silveira--2021a|Silveira et al., 2021a]] ).

It is important to note that the assessed studies typically take an observed epidemiological relationship and apply future temperature projections (often derived from regional climate projections) to these relationships. Because the relationships between temperature and CVD deaths are influenced by both climatic and non-climatic factors (such as population fitness and aging), future projections are highly sensitive to assumptions about interactions between climate, population characteristics and adaptation pathways. Changes in air quality because of climate change are an additional important factor. For example, an assessment of future annual and seasonal excess mortality from short-term exposure to higher levels of ambient ozone in Chinese cities under RCP8.5 projected approximately 1,500 excess annual CVD deaths in 2050 ( [[#Chen--2018|Chen et al., 2018]] ). To the extent possible, the relationships reported above reflect changes derived from changes in heat exposure driven by climate change and not changes in population demographics or air pollution exposure.

Climate change could impact CVD through other pathways, including exposure to fine dust. For example, adult mortality attributable to fine dust exposure in the American southwest could increase by 750 deaths yr –1 (a 130% increase over baseline) by the end of the century under RCP8.5 ( [[#Achakulwisut--2018|Achakulwisut et al., 2018]] ).

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<span id="future-risks-related-to-maternal-foetal-and-neonatal-health"></span>
==== 7.3.1.8 Future Risks Related to Maternal, Foetal and Neonatal Health ====

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Additional research is needed on future impacts of climate change on maternal, foetal and neonatal health ''.'' Maternal heat exposure is a risk factor for several adverse maternal, foetal and neonatal outcomes ( [[#Kuehn--2017|Kuehn and McCormick, 2017]] ), including foetal growth ( [[#Sun--2019|Sun et al., 2019]] ) and congenital anomalies ( [[#Haghighi--2021|Haghighi et al., 2021]] ). There is very limited research on this subject, an exception being Zhang et al. (2020), which projected a 34% increase in congenital health disease risk in the USA in 2025 and 2035 based on increased maternal extreme heat exposure.

<div id="7.3.1.9" class="h3-container"></div>

<span id="future-health-risks-related-to-food-diets-and-nutrition"></span>
==== 7.3.1.9 Future Health Risks Related to Food, Diets and Nutrition ====

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<div id="7.3.1.9.1" class="h4-container"></div>

<span id="malnutrition"></span>
===== 7.3.1.9.1 Malnutrition =====

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''Climate change is projected to exacerbate malnutrition'' ( ''high confidence'' ). Moderate and severe stunting in children less than five years of age was projected for 2030 across 44 countries to be an additional 570,000 cases under a prosperity and low climate change scenario (RCP2.6) to one million cases under a poverty and high climate change scenario (RCP8.5), with the highest effects in rural areas ( [[#Lloyd--2018|Lloyd, 2018]] ). Future DALYs lost due to protein-energy undernutrition and micronutrient deficiencies without climate change have been projected to increase between 2010 and 2050 by over 30 million; with climate change (RCP8.5), DALYs were projected to increase by nearly 10%, with the largest increases in Africa and Asia ( [[#Sulser--2021|Sulser et al., 2021]] ).

The projected risks of hunger and childhood underweight vary under the five SSPs, with population growth, improvement in the equality of food distribution and income-related increases in food consumption influencing future risks ( [[#Ishida--2014|Ishida et al., 2014]] ; [[#Hasegawa--2015|Hasegawa et al., 2015]] ). A review of 57 studies projecting global food security to 2050 under the SSPs concluded that global food demand was expected to increase by 35–56% between 2010 and 2050 ( [[#van%20Dijk--2021|van Dijk et al., 2021]] ). In the same review, estimates of the change in population at risk of hunger by 2050 range between −91 to +8% if climate change is not considered and between -91 to +30% if climate change is considered, with the inclusion of climate change not leading to statistically significant differences in projections ( [[#van%20Dijk--2021|van Dijk et al., 2021]] ).

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<span id="climate-change-carbon-dioxide-diets-and-health"></span>
===== 7.3.1.9.2 Climate Change, Carbon Dioxide, Diets and Health =====

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''Climate change could further limit equitable access to affordable, culturally acceptable, and healthy diets'' ( ''high confidence'' ) ''.'' Climate impacts on agricultural production and regional food availability will affect the composition of diets, which can have major consequences for health. Variable by region and context, healthy diets are an outcome of the four inter-connected domains of sustainable food systems, namely ecosystems, society, economics and health ( [[#Drewnowski--2020|Drewnowski et al., 2020]] ; [[#Fanzo--2020|Fanzo et al., 2020]] ). Climate change limits the potential for healthy diets through adverse impacts on natural and human systems that are disproportionately experienced by low-income countries and communities ( [[#FAO--2021|FAO et al., 2021]] ). Climate-driven droughts, floods, storms, wildfires and extreme temperatures reduce food production potential by diminishing soil health, water security and biological and genetic diversity ( [[#Macdiarmid--2019|Macdiarmid and Whybrow, 2019]] ). Models project that climate-related reductions in food availability, specifically fruit and vegetables, could result in an additional 529,000 deaths a year by 2050 ( [[#Springmann--2016b|Springmann et al., 2016b]] ).

Diets reliant on marine fisheries and fish also face complex climate-driven challenges ( [[#Hollowed--2013|Hollowed et al., 2013]] ). Rapidly warming oceans ( [[#Cheng--2020|Cheng et al., 2020]] ) limit the size of many fish and hamper their ability to relocate or adapt; many commonly consumed fish, like sardines, pilchards and herring, could face extinction due to these pressures ( [[#Avaria-Llautureo--2021|Avaria-Llautureo et al., 2021]] ). Other fisheries models project end-of-century pollock and Pacific cod fisheries decreasing by &gt; 70% and &gt; 35% under RCP8.5 ( [[#Holsman--2020|Holsman et al., 2020]] ). Climate-driven increases in marine mercury concentrations ( [[#Booth--2005|Booth and Zeller, 2005]] ) and harmful algal blooms ( [[#Jardine--2020|Jardine et al., 2020]] ) could impact dietary quality and human health.

Global crop and economic models project higher cereal prices of up to 29% by 2050 under RCP6.0, resulting in an additional 183 million people in low-income households at risk of hunger ( [[#Hasegawa--2018|Hasegawa et al., 2018]] ). Climate impacts on human health disrupt agricultural labour, food supply chain workers and ultimately regional food availability and affordability. A recent meta-analysis focused on sub-Saharan Africa and Southeast Asia combined metrics of heat stress and labour to project that a 3°C increase in global mean temperature, without adaptation or mechanisation, could reduce agricultural labour capacity by 30–50%, leading to 5% higher crop prices and a global welfare loss of USD 136 billion ( [[#de%20Lima--2021|de Lima et al., 2021]] ).

''The nutritional density, including protein content, micronutrients and B-vitamins, of wheat, rice, barley and other important food crops is negatively affected by higher CO'' 2 ''concentrations'' ( ''very high confidence'' ) ''(Mbow, 2019 ; [[#Smith--2018|Smith and Myers, 2018]] )'' . Projections indicate negative impacts on human nutrition by rising CO 2 concentrations by mid- to late-century ( [[#Medek--2017|Medek et al., 2017]] ; [[#Smith--2018|Smith and Myers, 2018]] ; [[#Weyant--2018|Weyant et al., 2018]] ; [[#Zhu--2018|Zhu et al., 2018]] ; [[#Beach--2019|Beach et al., 2019]] ). Staple crops are projected to have protein and mineral concentrations decreased by 5–15% and B vitamins up to 30% when the concentrations of CO 2 double above pre-industrial levels ( [[#Ebi--2019|Ebi and Loladze, 2019]] ; [[#Beach--2019|Beach et al., 2019]] ; [[#Smith--2018|Smith and Myers, 2018]] ). Without changes in diets and accounting for nutrient declines in staple crops, a projected additional 175 million people could be zinc deficient and an additional 122 million people could become protein deficient ( [[#Smith--2018|Smith and Myers, 2018]] ). [[#Weyant--2018|Weyant et al. (2018)]] projected that CO 2 -related reductions in crop zinc and iron levels could result in 125.8 million DALYs lost globally, with Southeast Asian and sub-Saharan African countries most affected. [[#Zhu--2018|Zhu et al. (2018)]] estimated 600 million people at risk from reductions in the protein, micronutrient and B-vitamin content of widely grown rice cultivars in Southeast Asia.

The combined effect of CO 2 and rising temperatures because of climate change could result in a 2.4–4.3% penalty on expected gains by mid-century in nutritional content because of technology change, market responses and the fertilisation effects of CO 2 on yield ( [[#Beach--2019|Beach et al., 2019]] ). These penalties are expected to slow progress in achieving reductions in global nutrient deficiencies, disproportionately affecting countries with high levels of such deficiencies.

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<span id="projected-impacts-on-harmful-algal-blooms-mycotoxins-aflatoxins-and-chemical-contaminants"></span>
==== 7.3.1.10 Projected Impacts on Harmful Algal Blooms, Mycotoxins, Aflatoxins and Chemical Contaminants ====

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''Harmful algal blooms are projected to increase globally, thus increasing the risk of seafood contamination with marine toxins'' ( ''high confidence'' ) ''(European Food Safety Authority et al., 2020; [[#Gobler--2017|Gobler et al., 2017]] ; [[#Barange--2018|Barange et al., 2018]] ; [[#IPCC--2019b|IPCC, 2019b]] ; [[#Wells--2020|Wells et al., 2020]] )'' . Climate change impacts on oceans could generate increased risks of ciguatera poisoning in some regions ''(medium confidence).'' Studies suggest that rising sea surface temperatures could increase rates of ciguatera poisoning in Spain ( [[#Botana--2016|Botana, 2016]] ) and other parts of Europe (European Food Safety Authority et al., 2020).

''Mycotoxins and aflatoxins may become more prevalent due to climate change'' ( ''medium agreement, low evidence'' ) ''.'' Models of aflatoxin occurrence in maize under climate change scenarios of +2°C and +5°C in Europe over the next 100 years project that aflatoxin B1 may become a major food safety issue in maize, especially in Eastern Europe, the Balkan Peninsula and the Mediterranean regions (Battilani, 2016). The occurrence of toxin-producing fungal phytopathogens has the potential to increase and expand from tropical and subtropical regions into regions where such contamination does not currently occur (Battilani, 2016).

''Climate change may alter regional and local exposures to anthropogenic chemical contaminants'' ( ''medium agreement, low evidence'' ) ''.'' Changes in future occurrences of wildfires could lead to a 14% increase in global emissions of mercury by 2050, depending on the scenarios used ( [[#Kumar--2018a|Kumar et al., 2018a]] ). Mercury exposure via consumption of fish may be affected by warming waters. Warming trends in the Gulf of Maine could increase the methyl mercury levels in resident tuna by 30% between 2015 and 2030 (Schartup et al., 2019). An observed annual 3.5% increase in mercury levels was attributed to fish having higher metabolism in warmer waters, leading them to consume more prey. The combined impacts of climate change and the presence of arsenic in paddy fields are projected to potentially double the toxic heavy metal content of rice in some regions, potentially leading to a 39% reduction in overall production by 2100 under some models ( [[#Muehe--2019|Muehe et al., 2019]] ).

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<span id="future-risks-related-to-mental-health-and-well-being"></span>
==== 7.3.1.11 Future Risks Related to Mental Health and Well-Being ====

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''Climate change is expected to have adverse impacts on well-being, some of which will become serious enough to threaten mental health'' ( ''very high confidence'' ) ''.'' However, changes ( [[#Hayes--2018|Hayes and Poland, 2018]] ) in extreme events due to climate change, including floods (Baryshnikova, 2019), droughts ( [[#Carleton--2017|Carleton, 2017]] ) and hurricanes ( [[#Kessler--2008|Kessler et al., 2008]] ; [[#Boscarino--2013|Boscarino et al., 2013]] , [[#Boscarino--2017|Boscarino et al., 2017]] ; [[#Obradovich--2018|Obradovich et al., 2018]] ), which are projected to increase due to climate change, directly worsen mental health and well-being and increase anxiety ( ''high confidence)'' . Projections suggest that sub-Saharan African children and adolescents, particularly girls, are extremely vulnerable to negative direct and indirect impacts on their mental health and well-being ( [[#Atkinson--2015|Atkinson and Bruce, 2015]] ; [[#Owen--2016|Owen et al., 2016]] ). The direct risks are greatest for people with existing mental disorders, physical injuries, and compromised respiratory, cardiovascular and reproductive systems, with indirect impacts potentially arising from displacement, migration, famine and malnutrition, degradation or destruction of health and social care systems, conflict, and climate-related economic and social losses ( ''high to very high confidence'' ) ( [[#Burke--2018|Burke et al., 2018]] ; [[#Curtis--2017|Curtis et al., 2017]] ; [[#Hayes--2018|Hayes et al., 2018]] ; [[#Serdeczny--2017|Serdeczny et al., 2017]] ; [[#Watts--2019|Watts et al., 2019]] ). Demographic factors increasing vulnerability include age, gender and low socioeconomic status, though the effect of these will vary depending on the specific manifestation of climate change; overall, climate change is predicted to increase inequality in mental health across the globe ( [[#Cianconi--2020|Cianconi et al., 2020]] ). Based on evidence assessed in Section 7.2, future direct impacts of increased heat risks and associated illnesses can be expected to have negative implications for mental health and well-being, with outcomes being highly mediated by adaptation, but there are no assessable studies that quantify such risks. There may be some benefits to mental health and well-being associated with fewer very cold days in the winter; however, research is inconsistent. Any positive effect associated with reduced low-temperature days is projected to be outweighed by the negative effects of increased high temperatures ( [[#Cianconi--2020|Cianconi et al., 2020]] ).

''Human behaviours and systems will be disrupted by climate change in a myriad of ways, and the potential consequences for mental health and well-being are correspondingly large in number and complex in mechanism'' ( ''high confidence'' ) ''.'' For example, climate change may alter human physical activity and mobility patterns, in turn producing alterations in the mental health statuses promoted by regular physical activity ( [[#Obradovich--2017|Obradovich and Fowler, 2017]] ; [[#Obradovich--2019|Obradovich and Rahwan, 2019]] ). Climate change may affect labour capacity, because heat can compromise the ability to engage in manual labour as well as cognitive functioning, with impacts on the economic status of individual households as well as societies ( [[#Kjellstrom--2016|Kjellstrom et al., 2016]] ; [[#Liu--2020|Liu, 2020]] ). Migrations and displacement caused by climate change may worsen the well-being of those affected ( [[#Vins--2015|Vins et al., 2015]] ; [[#Missirian--2017|Missirian and Schlenker, 2017]] ). Climate change is expected to increase aggression through both direct and indirect mechanisms, with one study predicting a 6% increase in homicides globally for a 1°C temperature increase, although noting significant variability across countries ( [[#Mares--2016|Mares and Moffett, 2016]] ). Broad societal outcomes such as economic unrest, political conflict or governmental dysfunction assessed in [[#7.3|Section 7.3.5]] may undermine the mental health of populations in the future ( ''medium confidence'' ). Food insecurity presents its own severe risks for mental health and cognitive function ( [[#Jones--2017|Jones, 2017]] ).

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