11

AHduampatna tHioena,l tahn: dIm Cpoa-Bctesn,eﬁts

Coordinating Lead Authors:

Kirk R. Smith (USA), Alistair Woodward (New Zealand)

Lead Authors:

Diarmid Campbell-Lendrum (WHO), Dave D. Chadee (Trinidad and Tobago), Yasushi Honda (Japan), Qiyong Liu (China), Jane M. Olwoch (South Africa), Boris Revich (Russian Federation), Rainer Sauerborn (Sweden)

Contributing Authors:

Clara Aranda (Mexico), Helen Berry (Australia), Colin Butler (Australia), Zoë Chafe (USA), Lara Cushing (USA), Kristie L. Ebi (USA), Tord Kjellstrom (New Zealand), Sari Kovats (UK), Graeme Lindsay (New Zealand), Erin Lipp (USA), Tony McMichael (Australia), Virginia Murray (UK), Osman Sankoh (Sierra Leone), Marie O’Neill (USA), Seth B. Shonkoff (USA), Joan Sutherland (Trinidad and Tobago), Shelby Yamamoto (Germany)

Review Editors:

Ulisses Confalonieri (Brazil), Andrew Haines (UK)

Volunteer Chapter Scientists:

Zoë Chafe (USA), Joacim Rocklov (Sweden)

This chapter should be cited as:

Smith, K.R., A. Woodward, D. Campbell-Lendrum, D.D. Chadee, Y. Honda, Q. Liu, J.M. Olwoch, B. Revich, and

R. Sauerborn, 2014: Human health: impacts, adaptation, and co-beneﬁts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 709-754.

Table of Contents

Executive Summary ........................................................................................................................................................... 713

11.1. Introduction ............................................................................................................................................................ 715

11.1.1. Present State of Global Health ......................................................................................................................................................... 715 11.1.2. Developments Since AR4 .................................................................................................................................................................. 715

Box 11-1. Weather, Climate, and Health: A Long-Term Observational Study in African and Asian Populations .......................... 715

11.1.3. Non-Climate Health Effects of Climate-Altering Pollutants ............................................................................................................... 716

11.2. How Climate Change Affects Health ....................................................................................................................... 716

11.3. Vulnerability to Disease and Injury Due to Climate Variability and Climate Change ............................................. 717

11.3.1. Geographic Causes of Vulnerability .................................................................................................................................................. 717 11.3.2. Current Health Status ....................................................................................................................................................................... 717 11.3.3. Age and Gender ............................................................................................................................................................................... 717 11.3.4. Socioeconomic Status ....................................................................................................................................................................... 718 11.3.5. Public Health and Other Infrastructure ............................................................................................................................................. 718 11.3.6. Projections for Vulnerability .............................................................................................................................................................. 718

11.4. Direct Impacts of Climate and Weather on Health ................................................................................................. 720

11.4.1. Heat- and Cold-Related Impacts ....................................................................................................................................................... 720 11.4.1.1. Mechanisms ...................................................................................................................................................................... 720 11.4.1.2. Near-Term Future .............................................................................................................................................................. 721 11.4.2. Floods and Storms ............................................................................................................................................................................ 721 11.4.2.1. Mechanisms ...................................................................................................................................................................... 722 11.4.2.2. Near-Term Future ............................................................................................................................................................... 722 11.4.3. Ultraviolet Radiation ......................................................................................................................................................................... 722

11.5. Ecosystem-Mediated Impacts of Climate Change on Health Outcomes ................................................................. 722

11.5.1. Vector-Borne and Other Infectious Diseases ..................................................................................................................................... 722 11.5.1.1. Malaria .............................................................................................................................................................................. 722 11.5.1.2. Dengue Fever .................................................................................................................................................................... 723

Box 11-2. Case Study: An Intervention to Control Dengue Fever .................................................................................... 724

11.5.1.3. Tick-Borne Diseases ........................................................................................................................................................... 725 11.5.1.4. Other Vector-Borne Diseases ............................................................................................................................................. 725 11.5.1.5. Near-Term Future ............................................................................................................................................................... 725 11.5.2. Food- and Water-Borne Infections .................................................................................................................................................... 726 11.5.2.1. Vibrios ............................................................................................................................................................................... 726 11.5.2.2. Other Parasites, Bacteria, and Viruses ................................................................................................................................ 726 11.5.2.3. Near-Term Future ............................................................................................................................................................... 727

11.5.3. Air Quality ........................................................................................................................................................................................ 727

Box 11-3. Health and Economic Impacts of Climate-Altering Pollutants Other than CO2 ........................................................... 728

11.5.3.1. Long-Term Outdoor Ozone Exposures ............................................................................................................................... 728 11.5.3.2. Acute Air Pollution Episodes .............................................................................................................................................. 729 11.5.3.3. Aeroallergens .................................................................................................................................................................... 729 11.5.3.4. Near-Term Future ............................................................................................................................................................... 729

11.6. Health Impacts Heavily Mediated through Human Institutions ............................................................................. 730

11.6.1. Nutrition ........................................................................................................................................................................................... 730 11.6.1.1. Mechanisms ...................................................................................................................................................................... 730 11.6.1.2. Near-Term Future ............................................................................................................................................................... 730 11.6.2. Occupational Health ......................................................................................................................................................................... 731 11.6.2.1. Heat Strain and Heat Stroke .............................................................................................................................................. 731 11.6.2.2. Heat Exhaustion and Work Capacity Loss .......................................................................................................................... 731 11.6.2.3. Other Occupational Health Concerns ................................................................................................................................ 731 11.6.2.4. Near-Term Future ............................................................................................................................................................... 732 11.6.3. Mental Health ................................................................................................................................................................................... 732 11.6.4. Violence and Conﬂict ........................................................................................................................................................................ 732

11.7. Adaptation to Protect Health ................................................................................................................................. 733

11.7.1. Improving Basic Public Health and Health Care Services .................................................................................................................. 733 11.7.2. Health Adaptation Policies and Measures ......................................................................................................................................... 733 11.7.3. Early Warning Systems ...................................................................................................................................................................... 734 11.7.4. Role of Other Sectors in Health Adaptation ...................................................................................................................................... 734

11.8. Adaptation Limits Under High Levels of Warming .................................................................................................. 735

11.8.1. Physiological Limits to Human Heat Tolerance .................................................................................................................................. 736 11.8.2. Limits to Food Production and Human Nutrition .............................................................................................................................. 736 11.8.3. Thermal Tolerance of Disease Vectors ............................................................................................................................................... 736 11.8.4. Displacement and Migration Under Extreme Warming ..................................................................................................................... 736 11.8.5. Reliance on Infrastructure ................................................................................................................................................................. 736

11.9. Co-Beneﬁts ............................................................................................................................................................. 737

11.9.1. Reduction of Co-Pollutants ............................................................................................................................................................... 737 11.9.1.1. Outdoor Sources ................................................................................................................................................................ 738 11.9.1.2. Household Sources ............................................................................................................................................................ 738 11.9.1.3. Primary Co-Pollutants ........................................................................................................................................................ 739 11.9.1.4. Secondary Co-Pollutants .................................................................................................................................................... 739 11.9.1.5. Case Studies of Co-Beneﬁts of Air Pollution Reductions .................................................................................................... 740

11.9.2. Access to Reproductive Health Services ............................................................................................................................................ 740 11.9.2.1. Birth and Pregnancy Intervals ............................................................................................................................................ 740 11.9.2.2. Maternal Age at Birth ........................................................................................................................................................ 741

11.10. Key Uncertainties and Knowledge Gaps ................................................................................................................ 741

References ......................................................................................................................................................................... 743

Frequently Asked Questions

11.1: How does climate change affect human health? .............................................................................................................................. 741 11.2: Will climate change have beneﬁts for health? .................................................................................................................................. 742 11.3: Who is most affected by climate change? ........................................................................................................................................ 742 11.4: What is the most important adaptation strategy to reduce the health impacts of climate change? ................................................. 742 11.5: What are health “co-beneﬁts” of climate change mitigation measures? ......................................................................................... 742

Executive Summary

The health of human populations is sensitive to shifts in weather patterns and other aspects of climate change (very high

confidence). These effects occur directly, due to changes in temperature and precipitation and occurrence of heat waves, floods, droughts, and fires. Indirectly, health may be damaged by ecological disruptions brought on by climate change (crop failures, shifting patterns of disease vectors), or social responses to climate change (such as displacement of populations following prolonged drought). Variability in temperatures is a risk factor in its own right, over and above the influence of average temperatures on heat-related deaths. {11.4} Biological and social adaptation is more difficult in a highly variable climate than one that is more stable. {11.7}

Until mid-century climate change will act mainly by exacerbating health problems that already exist (very high confidence). New

conditions may emerge under climate change (low confidence), and existing diseases (e.g., food-borne infections) may extend their range into areas that are presently unaffected (high confidence). But the largest risks will apply in populations that are currently most affected by climate related diseases. Thus, for example, it is expected that health losses due to climate change-induced undernutrition will occur mainly in areas that are already food-insecure. {11.3}

In recent decades, climate change has contributed to levels of ill health (likely) though the present worldwide burden of ill health from climate change is relatively small compared with other stressors on health and is not well quantified. Rising temperatures

have increased the risk of heat-related death and illness (likely). {11.4} Local changes in temperature and rainfall have altered distribution of some water-borne illnesses and disease vectors, and reduced food production for some vulnerable populations (medium confidence). {11.5-6}

If climate change continues as projected across the Representative Concentration Pathway (RCP) scenarios, the major changes in ill health compared to no climate change will occur through: • Greater risk of injury, disease, and death due to more intense heat waves and fires (very high confidence) {11.4} • Increased risk of undernutrition resulting from diminished food production in poor regions (high confidence) {11.6} • Consequences for health of lost work capacity and reduced labor productivity in vulnerable populations (high confidence) {11.6} • Increased risks of food- and water-borne diseases (very high confidence) and vector-borne diseases (medium confidence) {11.5} • Modest reductions in cold-related mortality and morbidity in some areas due to fewer cold extremes (low confidence), geographical shifts in food production, and reduced capacity of disease-carrying vectors due to exceedance of thermal thresholds (medium confidence). These positive effects will be increasingly outweighed, worldwide, by the magnitude and severity of the negative effects of climate change (high confidence). {11.4-6}

Impacts on health will be reduced, but not eliminated, in populations that benefit from rapid social and economic development (high confidence), particularly among the poorest and least healthy groups (very high confidence). {11.4, 11.6-7} Climate change is

an impediment to continued health improvements in many parts of the world. If economic growth does not benefit the poor, the health effects of climate change will be exacerbated.

In addition to their implications for climate change, essentially all the important climate-altering pollutants (CAPs) other than carbon dioxide (CO2) have near-term health implications (very high confidence). In 2010, more than 7% of the global burden of disease

was due to inhalation of these air pollutants (high confidence). {Box 11-4}

Some parts of the world already exceed the international standard for safe work activity during the hottest months of the year.

The capacity of the human body to thermoregulate may be exceeded on a regular basis, particularly during manual labor, in parts of the world during this century. In the highest Representative Concentration Pathway, RCP8.5, by 2100 some of the world’s land area will be experiencing 4°C to 7°C higher temperatures due to anthropogenic climate change (WGI AR5 Figure SPM.7). If this occurs, the combination of high temperatures and high humidity will compromise normal human activities, including growing food or working outdoors in some areas for parts of the year (high confidence). {11.8}

The most effective measures to reduce vulnerability in the near term are programs that implement and improve basic public health measures such as provision of clean water and sanitation, secure essential health care including vaccination and child health services, increase capacity for disaster preparedness and response, and alleviate poverty (very high confidence). {11.7}

In addition, there has been progress since AR4 in targeted and climate-specific measures to protect health, including enhanced surveillance and early warning systems. {11.7}

There are opportunities to achieve co-benefits from actions that reduce emissions of warming CAPs and at the same time improve health. Among others, these include: • Reducing local emissions of health-damaging and climate-altering air pollutants from energy systems, through improved energy efficiency,

and a shift to cleaner energy sources (very high confidence) {11.9}

Providing access to reproductive health services (including modern family planning) to improve child and maternal health through birth spacing and reduce population growth, energy use, and consequent CAP emissions over time (medium confidence) {11.9}

Shifting consumption away from animal products, especially from ruminant sources, in high-meat-consumption societies toward less CAP- intensive healthy diets (medium confidence) {11.9}

Designing transport systems that promote active transport and reduce use of motorized vehicles, leading to lower emissions of CAPs and better health through improved air quality and greater physical activity (high confidence). {11.9}

There are important research gaps regarding the health consequences of climate change and co-benefits actions, particularly in

low-income countries. There are now opportunities to use existing longitudinal data on population health to investigate how climate change affects the most vulnerable populations. Another gap concerns the scientific evaluation of the health implications of adaptation measures at community and national levels. A further challenge is to improve understanding of the extent to which taking health co-benefits into account can offset the costs of greenhouse gas mitigation strategies.

11.1. Introduction

infectious diseases of adults and children will remain important in some regions, particularly sub-Saharan Africa and South Asia (Hughes et al.,

This chapter examines what is known about the effects of climate 2011). change on human health and, briefly, the more direct impacts of climate altering pollutants (CAPs; see Glossary) on health. We review diseases

and other aspects of poor health that are sensitive to weather and climate. We examine the factors that influence the susceptibility of populations and individuals to ill health due to variations in weather and climate, and describe steps that may be taken to reduce the impacts of climate change on human health. The chapter also includes a section on health “co-benefits.” Co-benefits are positive effects on human health that arise from interventions to reduce emissions of those CAPs that warm the planet or vice versa.

This is a scientific assessment based on best available evidence according to the judgment of the authors. We searched the English-language literature up to August 2013, focusing primarily on publications since 2007. We drew primarily (but not exclusively) on peer-reviewed journals. Literature was identified using a published protocol (Hosking and Campbell-Lendrum, 2012) and other approaches, including extensive consultation with technical experts in the field. We examined recent substantial reviews (e.g., Gosling et al., 2009; Bassil and Cole, 2010; Hajat et al., 2010; Huang et al., 2011; McMichael, 2013b; Stanke et al., 2013) to check for any omissions of important work. In selecting citations for the chapter, we gave priority to publications that were recent (since AR4), comprehensive, added significant new findings to the literature, and included areas or population groups that have not previously been well described or were judged to be particularly policy relevant in other respects.

We begin with an outline of measures of human health, the major driving forces that act on health worldwide, recent trends in health status, and health projections for the remainder of the 21st century.

11.1.1. Present State of Global Health

The Fourth Assessment Report (AR4) pointed to dramatic improvement in life expectancy in most parts of the world in the 20th century, and this trend has continued through the first decade of the 21st century (Wang et al., 2012). Rapid progress in a few countries (especially China) has dominated global averages, but most countries have benefited from substantial reductions in mortality. There remain sizable and avoidable inequalities in life expectancy within and between nations in terms of education, income, and ethnicity (Beaglehole and Bonita, 2008) and in some countries, official statistics are so patchy in quality and coverage that it is difficult to draw firm conclusions about health trends (Byass, 2010). Years lived with disability have tended to increase in most countries (Salomon et al., 2012).

If economic development continues as forecast, it is expected that mortality rates will continue to fall in most countries; the World Health Organization (WHO) estimates the global burden of disease (measured in disability adjusted life years per capita) will decrease by 30% by 2030, compared with 2004 (WHO, 2008a). The underlying causes of global poor health are expected to change substantially, with much greater prominence of chronic diseases and injury; nevertheless, the major

11.1.2. Developments Since AR4

The relevant literature has grown considerably since publication of AR4. For instance, the annual number of MEDLINE citations on climate change and health doubled between 2007 and 2009 (Hosking and Campbell-Lendrum, 2012). In addition, there have been many reviews, reports, and international assessments that do not appear in listings such as MEDLINE but include important information nevertheless, for instance, the World Development Report 2010 (World Bank, 2010) and the 2011 UN Habitat report on cities and climate change (UN-HABITAT, 2011). Since AR4, there have been improvements in the methods applied to investigate climate change and health. These include more sophisticated modeling of possible future impacts (e.g., work linking climate change, food security, and health outcomes; Nelson et al., 2010) and new methods

Box 11-1 | Weather, Climate, and Health:

A Long-Term Observational Study in African and Asian Populations

Given the dearth of scientiﬁc evidence of the relationship between weather/climate and health in low- and middle income countries, we report on a project that spans sub Saharan Africa and Asia. The INDEPTH Network currently includes 43 surveillance sites in 20 countries. Using standardized health and demographic surveillance systems, member sites have collected up to 45 years of information on births, migrations, and deaths. Currently, there are about 3.2 million people under surveillance (Sankoh and Byass, 2012).

To study relationships between weather and health, the authors obtained daily meteorological data for 12 INDEPTH populations between 2000 and 2009, and projected future climate changes to 2100 under the SRES A1B, A3, and B1 scenarios (Hondula et al., 2012). The authors concluded the health of all the populations would be challenged by the new climatic conditions, especially later in the century. In another study from the Network, Diboulo et al. (2012) examined the relation between weather and all-cause mortality data in Burkina Faso. Relations between daily temperature and mortality were similar to those reported in many high-income settings, and susceptibility to heat varied by age and gender.

to model the effects of heat on work capacity and labor productivity (Kjellstrom et al., 2009b). Other developments include coupling of high quality, longitudinal mortality data sets with down-scaled meteorological data, in low-income settings (e.g., through the INDEPTH Network; see Box 11-1).

Since AR4, studies of the ways in which policies to reduce greenhouse gas (GHG) emissions may affect health, or vice versa, leading to so called “co-benefits” in the case of positive outcomes for either climate or health, have multiplied (Haines et al., 2009).

Much has been written on links between climate, socioeconomic conditions, and health—for example, related to occupational heat exposure (Kjellstrom et al., 2009b) and malaria (e.g., Gething et al., 2010; Béguin et al., 2011). There is also growing appreciation of the social upheaval and damage to population health that may arise from the interaction of large-scale food insecurity, population dislocation, and conflict (see Chapter 12).

11.1.3. Non-Climate Health Effects

of Climate-Altering Pollutants

CAPs affect health in other ways than through climate change, just as carbon dioxide (CO2) creates non-climate effects such as ocean acidification. The effects of rising CO2 levels on calcifying marine species are well documented and the risks for coral reefs are now more closely defined than they were at the time of the AR4 (see Chapter 30). There are potential implications for human health, such as undernutrition in coastal populations that depend on local fish stocks, but, so far, links between health and ocean acidification have not been closely studied (Kite-Powell et al., 2008). CAPs such as black carbon and tropospheric ozone have substantial, direct, negative effects on human health (Wang et al., 2013; see Section 11.5.3 and Box 11-3). Although CO2 is not considered a health-damaging air pollutant at levels experienced outside particular occupational and health-care settings, one study has reported a reduction in mental performance at 1000 ppm and above, within the range that all of humanity would experience in some extreme climate scenarios by 2100 (Satish et al., 2012).

11.2. How Climate Change Affects Health

There are three basic pathways by which climate change affects health (Figure 11-1), and these provide the organization for the chapter: • Direct impacts, which relate primarily to changes in the frequency of extreme weather including heat, drought, and heavy rain (Section 11.4) • Effects mediated through natural systems, for example, disease vectors, water-borne diseases, and air pollution (Section 11.5) • Effects heavily mediated by human systems, for example, occupational

impacts, undernutrition, and mental stress (Section 11.6).

Via economic and social disruption Mediated through natural systems: • Allergens • Disease vectors • Increased water/air pollution • Food production/distribution • Mental stress Indirect exposures • Geography • Baseline weather • Soil/dust • Vegetation • Baseline air/water quality • Flood damage • Storm vulnerability • Heat stress Direct exposures Environmental conditions CLIMATE CHANGE Social infrastructure • Precipitation • Heat • Floods • Storms Mediating factors • Warning systems • Socioeconomic status • Health and nutrition status • Primary health care Public health capability and adaptation HEALTH IMPACTS • Undernutrition • Drowning • Heart disease • Malaria

Figure 11-1

The negative effects of climate change on health may be reduced by improved health services, better disaster management, and poverty alleviation, although the cost and effort may be considerable (Section 11.7). The consequences of large magnitude climate change beyond 2050, however, would be much more difficult to deal with (Section 11.8). Although there are exceptions, to a first approximation climate change acts to exacerbate existing patterns of ill health, by acting on the underlying vulnerabilities that lead to ill health even without climate change. Thus, before pursuing the three pathways in Figure 11-1, we summarize what is known about vulnerability to climate-induced illness and injury.

11.3. Vulnerability to Disease and Injury Due to

Climate Variability and Climate Change

In the IPCC assessments, vulnerability is defined as the propensity or predisposition to be adversely affected (see Chapter 19 and Glossary). In this section, we consider causes of vulnerability to ill health associated with climate change and climate variability, including individual and population characteristics and factors in the physical environment.

We have outlined the causes of vulnerability separately, but in practice causes combine, often in a complex and place-specific manner. There are some factors (such as education, income, health status, and responsiveness of government) that act as generic causes of vulnerability. For example, the quality of governance—how decisions are made and put into practice—affects a community’s response to threats of all kinds (Bowen et al., 2012; see Chapter 12). The background climate-related disease rate of a population is often the best single indicator of vulnerability to climate change—doubling of risk of disease in a low disease population has much less absolute impact than doubling of the disease when the background rate is high. (Note that here, and elsewhere in the chapter, we treat “risk” in the epidemiological sense: the probability that an event will occur.) But the precise causes of vulnerability, and therefore the most relevant adaptation capacities, vary greatly from one setting to another. For example, severe drought in Australia has been linked to psychological distress—but only for those residing in rural and remote areas (Berry et al., 2010). The link between high ambient temperatures and increased incidence of salmonella food poisoning has been demonstrated in many places (e.g., Zhang et al., 2010), but the lag varies from one country to another, suggesting that the mechanisms differ. Deficiencies in food storage may be the critical link in some places; food handling problems may be most important elsewhere (Kovats et al., 2004).

The 2010 World Development Report concluded that all developing regions are vulnerable to economic and social damage resulting from climate change—but for different reasons (World Bank, 2010). The critical factors for sub-Saharan Africa, for example, are the current climate stresses (in particular, droughts and floods) that may be amplified in parts of the region under climate change, sparse infrastructure, and high dependence on natural resources (see Chapter 22). Asia and the Pacific, on the other hand, are distinguished by the very large number of people living in low-lying areas prone to flooding (see Chapters 24 and 29).

11.3.1. Geographic Causes of Vulnerability

Location has an important influence on the potential for health losses caused by climate change (Samson et al., 2011). Those working outdoors in countries where temperatures in the hottest time of the year are already at the limits of thermal tolerance for part of the year will be more severely affected by further warming than workers in cooler countries (Kjellstrom et al., 2013). The inhabitants of low-lying coral atolls are very sensitive to flooding, contamination of freshwater reservoirs due to sea level rise, and salination of soil, all of which may have important effects on health (Nunn, 2009). Rural populations that rely on subsistence farming in low rainfall areas are at high risk of undernutrition and water-related diseases if drought occurs, although this vulnerability may be modified strongly by local factors, such as access to markets and irrigation facilities (Acosta-Michlik et al., 2008). Living in rural and remote areas may confer increased risk of ill health because of limited access to services and generally higher levels of social and economic disadvantage (Smith, 2008). Populations that are close to the present limits of transmission of vector-borne diseases are most vulnerable to changes in the range of transmission as a result of rising temperatures and altered patterns of rainfall, especially when disease control systems are weak (Zhou et al., 2008; Lozano-Fuentes et al., 2012.). In cities, those who live on urban heat islands are at greater risk of ill health due to extreme heat events (Stone et al., 2010; Uejio et al., 2011).

11.3.2. Current Health Status

Climate extremes may promote the transmission of certain infectious diseases, and the vulnerability of populations to these diseases will depend on the baseline levels of pathogens and their vectors. In the USA, as one example, arboviral diseases such as dengue are rarely seen after flooding, compared with the experience in other parts of the Americas. The explanation lies in the scarcity of dengue (and other pathogenic viruses) circulating in the population, before the flooding (Keim, 2008). On the other hand, the high prevalence of HIV infection in many populations in sub-Saharan Africa will tend to multiply the health risks of climate change, due to the interactions between chronic ill health, poverty, extreme weather events, and undernutrition (Ramin and McMichael, 2009). Chronic diseases such as diabetes and ischemic heart disease magnify the risk of death or severe illness associated with high ambient temperatures (Basu and Ostro, 2008; Sokolnicki et al., 2009).

11.3.3. Age and Gender

Children, young people, and the elderly are at increased risk of climate related injury and illness (Perera, 2008). For example, adverse effects of malaria, diarrhea, and undernutrition are presently concentrated among children, for reasons of physiological susceptibility (Michon et al., 2007). In principle, children are thought to be more vulnerable to heat-related illnesses, owing to their small body mass to surface area ratio, but evidence of excess heat-related mortality in this age group is mixed (Basu and Ostro, 2008; Kovats and Hajat, 2008). Maternal antibodies acquired in utero provide some protection against dengue fever in the first year of life, but if infection does occur in infants it is more likely to provoke the severe hemorrhagic form of illness (Ranjit and Kissoon, 2011). Children are generally at greater risk when food supplies are restricted: households with children tend to have lower than average incomes, and food insecurity is associated with a range of adverse health outcomes among young children (Cook and Frank, 2008).

Older people are at greater risk from storms, floods, heat waves, and other extreme events (Brunkard et al., 2008), in part because they tend to be less mobile than younger adults and so find it more difficult to avoid hazardous situations and also because they are more likely to live alone in some cultures. Older people are also more likely to suffer from health conditions that limit the body’s ability to respond to stressors such as heat and air pollution (Gamble et al., 2013).

The relationship between gender and vulnerability is complex. Worldwide, mortality due to natural disasters, including droughts, floods, and storms, is higher among women than men (WHO, 2011). However, there is variation regionally. In the USA, males are at greater risk of death following flooding (Jonkman and Kelman, 2005). A study of the health effects of flooding in Hunan province, China, also found an excess of flood deaths among males, often related to rural farming (Abuaku et al., 2009). In Canada’s Inuit population males are exposed to dangers associated with insecure sea ice, while females may be more vulnerable to the effects of diminished food supplies (Pearce et al., 2011). In the Paris 2003 heat wave, excess mortality was greater among females overall, but there were more excess deaths among men in the working age span (25 to 64) possibly due to differential exposures to heat in occupational settings (Fouillet et al., 2006). In Bangladesh, females are more affected than males by a range of climate hazards, due to differences in prevalence of poverty, undernutrition, and exposure to water-logged environments (Neelormi et al., 2009). The effect of food insecurity on growth and development in childhood may be more damaging for girls than boys (Cook and Frank, 2008).

Pregnancy is a period of increased vulnerability to a wide range of environmental hazards, including extreme heat (Strand et al., 2012) and infectious diseases such as malaria, foodborne infections, and influenza (Van Kerkhove et al., 2011).

11.3.4. Socioeconomic Status

The poorest countries and regions are generally most susceptible to damage caused by climate extremes and climate variability (Malik et al., 2012), but wealthy countries are not immune, as shown by the deaths resulting from bushfires in Australia in 2009 (Teague et al., 2010). Also, rapid economic development may increase the risks of climate related health issues. For instance, changes in Tibet Autonomous Region, China, including new roads and substantial in-migration may explain (along with above-average warming) the appearance and establishment in Lhasa of Culex pipiens, a mosquito capable of transmitting the West Nile virus (Liu et al., 2013b).

A review of global trends in tropical cyclones 1970–2009 found that mortality risk at country-level depended most strongly on three factors: storm intensity, quality of governance, and levels of poverty (Peduzzi et al., 2012). Individuals and households most vulnerable to climate hazards tend to be those with relatively low socioeconomic status (Friel et al., 2008). A study of the impacts of flooding in Bangladesh found that household risk reduced with increases in both average income and number of income sources. Poorer households were not only more severely affected by flooding, but they also took preventive action less often and received assistance after flooding less frequently than did more affluent households (Brouwer et al., 2007).

In many countries, race and ethnicity are powerful markers of health status and social disadvantage. Black Americans have been reported to be more vulnerable to heat-related deaths than other racial groups in the USA (Basu and Ostro, 2008). This may be due to a higher prevalence of chronic conditions such as overweight and diabetes (Lutsey et al., 2010), financial circumstances (e.g., lower incomes may restrict access to air conditioning during heat-waves; Ostro et al., 2010), or community level characteristics such as higher local crime rates or disrupted social networks (Browning et al., 2006). Indigenous peoples who depend heavily on local resources, and live in parts of the world where the climate is changing quickly, are generally at greater risk of economic losses and poor health. Studies of the Inuit people, for example, show that rapid warming of the Canadian Arctic is jeopardizing hunting and many other day-to-day activities, with implications for livelihoods and well-being (Ford, 2009).

11.3.5. Public Health and Other Infrastructure

Populations that do not have access to good quality health care and essential public health services are more likely to be adversely affected by climate variability and climate change (Frumkin and McMichael, 2008). Harsh economic conditions in Europe since 2008 led to cutbacks in health services in some countries, followed by a resurgence of climate-sensitive infectious diseases including malaria (Karanikolos et al., 2013). The condition of the physical infrastructure that supports human settlements also influences health risks (this includes supply of power, provision of water for drinking and washing, waste management, and sanitation; see Chapter 8). In Cuba, a country with a well-developed public health system, dengue fever has been a persistent problem in the larger cities, due in part to the lack of a constant supply of drinking water in many neighborhoods (leading to people storing water in containers that are suitable breeding sites for the disease vector Aedes aegypti; Bulto et al., 2006). In New York, daily mortality spiked after a city-wide power failure in August 2003, due in part to increased exposure to heat (Anderson and Bell, 2012).

11.3.6. Projections for Vulnerability

Population growth is linked to climate change vulnerability. If nothing else changes, increasing numbers of people in locations that are already resource poor and are affected by climate risks will magnify harmful impacts. Virtually all the projected growth in populations will occur in urban agglomerations, mostly in large, low latitude hot countries in which a high proportion of the workforce is deployed outdoors with little protection from heat. About 150 million people currently live in cities affected by chronic water shortages and by 2050, unless there are rapid improvements in urban environments, the number will rise to almost a billion (McDonald et al., 2011). Under a “business as usual” scenario

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with mid-range population growth, the Organisation for Economic Co-operation and Development (OECD) projects that about 1.4 billion people will be without access to basic sanitation in 2050 (OECD, 2012). The age structure of the population also has implications for vulnerability (see Figure 11-2). The proportion aged over 60, worldwide, is projected to increase from about 10% presently to about 32% by the end of the century (Lutz et al., 2008). The prevalence of overweight and obesity, which is associated with relatively poor heat tolerance, has increased almost everywhere in the last 20 years, and in many countries the trend continues upwards (Finucane et al., 2011). It has been pointed out that the Sahel region of Africa may be particularly vulnerable to climate change because it already suffers so much stress from population pressure, chronic drought, and governmental instability (Diffenbaugh and Giorgi, 2012; Potts and Henderson, 2012).

Future trends in social and economic development are critically important to vulnerability. For instance, countries with a higher Human Development Index (HDI)—a composite of life expectancy, education, literacy, and gross domestic product (GDP) per capita—are less affected by the floods, droughts, and cyclones that take place (Patt et al., 2010). Therefore policies that boost health, education, and economic development should reduce future vulnerability. Overall, there have been substantial improvements in HDI in the last 30 years, but this has been accompanied by increasing inequalities between and within countries, and has come at the cost of high consumption of environmental resources (UNDP, 2011).

11.4. Direct Impacts of Climate

and Weather on Health

11.4.1. Heat- and Cold-Related Impacts

Although there is ample evidence of the effects of weather and climate on health, there are few studies of the impacts of climate change itself. (An example: Bennett et al. (2013) reported that the ratio of summer to winter deaths in Australia increased between 1968 and 2010, in association with rising annual average temperatures.) The issue is scale, as climate change is defined in decades. Robust studies require not only extremely long-term data series on climate and disease rates, but also information on other established or potential causative factors, coupled with statistical analysis to apportion changes in health states to the various contributing factors. Wherever risks are identified, health agencies are mandated to intervene immediately, biasing long-term analyses.

Nevertheless, the connection between weather and health impacts is often sufficiently direct to permit strong inferences about cause and effect (Sauerborn and Ebi, 2012). Most notably, the association between hot days (commonly defined in terms of the percentiles of daily maximum temperature for a specified location) and increases in mortality is very robust (Honda et al., 2013). The IPCC Special Report on Extreme Events (SREX) concludes that it is very likely that there has been an overall decrease in the number of cold days and nights, and an overall increase in the number of warm days and nights, at the global scale. If there has been an increase in daily maximum temperatures, then it follows, in our view, that the number of heat-related deaths is likely to have also increased. For example, Christidis et al. (2012) concluded that it is “extremely likely (probability greater than 95%)” that anthropogenic climate change at least quadrupled the risk of extreme summer heat events in Europe in the decade 1999–2008. The 2003 heat wave was one such record event; therefore, the probability that particular heat wave can be attributed to climate change is 75% or more, and on this basis it is likely the excess mortality attributed to the heat wave (about 15,000 deaths in France alone (Fouillet et al., 2008)) was caused by anthropogenic climate change.

The rise in minimum temperatures may have contributed to a decline in deaths associated with cold spells; however, the influence of seasonal factors other than temperature on winter mortality suggests that the impacts on health of more frequent heat extremes greatly outweigh benefits of fewer cold days (Kinney et al., 2012; Ebi and Mills, 2013). Quantification, globally, remains highly uncertain, as there are few studies of the large developing country populations in the tropics, and these point to effects of heat, but not cold, on mortality (Hajat et al., 2010). There is also significant uncertainty over the degree of physiological, social, or technological adaptation to increasing heat over long time periods.

11.4.1.1. Mechanisms

The basic processes of human thermoregulation are well understood. If the body temperature rises above 38°C (“heat exhaustion”), physical and cognitive functions are impaired; above 40.6°C (“heat stroke”), risks of organ damage, loss of consciousness, and death increase sharply. Detailed exposure-response relationships were described long ago (Wyndham, 1969), but the relationships in different community settings and for different age/sex groups are not yet well established. The early studies are supported by more recent experimental and field studies (Ramsey and Bernard, 2000; Parsons, 2003) and meta-analysis (Bouchama et al., 2007) that show significant effects of heat stress as body temperatures exceed 40°C, and heightened vulnerability in individuals with preexisting disease.

At high temperatures, displacement of blood to the surface of the body may lead to circulatory collapse. Indoor thermal conditions, including ventilation, humidity, radiation from walls or ceiling, and the presence or absence of air conditioning, are important in determining whether adverse events occur, but these variables are seldom well-measured in epidemiological studies (Anderson et al., 2012). Biological mechanisms are less evident for other causes of death, such as suicide, that are sometimes related to high temperature (Page et al., 2007; Kim et al., 2011; Likhvar et al., 2011).

Heat waves refer to a run of hot days; precisely how many days, and how high the temperatures must rise, are defined variously (Kinney et al., 2008). Some investigators have reported that mortality increases more during heat waves than would be anticipated solely on the basis of the short-term temperature mortality relationship (D’Ippoliti et al., 2010; Anderson and Bell, 2011), although the added effect is relatively small in some series, and most evident with prolonged heat waves (Gasparrini and Armstrong, 2011). Because heat waves are relatively infrequent compared with the total number of days with temperatures greater than the optimum for that location, the effects of heat waves are only a fraction of the total impact of heat on health. Some studies have shown larger effects of heat and heat waves earlier in the hot season (Anderson and Bell, 2011; Rocklov et al., 2011). This may be testament to the importance of acclimatization and adaptive measures, or may result from a large group in the population that is more susceptible to heat early in the season (Rocklov et al., 2009, 2011).

The extreme heat wave in Europe in 2003 led to numerous epidemiological studies. Reports from France (Fouillet et al., 2008) concluded that most of the extra deaths occurred in elderly people (80% of those who died were older than 75 years). Questions were raised at the time as to why this event had such a devastating effect (Kosatsky, 2005). It is still not clear, but one contributing factor may have been the relatively mild influenza season the year before. Recent studies have found that when the previous year’s winter mortality is low, the effect of summer heat is increased (Rocklov and Forsberg, 2009; Ha et al., 2011) because mild winters may leave a higher proportion of vulnerable people (Stafoggia et al., 2009). Most studies of heat have been in high-income countries, but there has been work recently in low- and middle-income countries, suggesting heterogeneity in vulnerability by age groups and socioeconomic factors similar to that seen in higher-income settings (Bell et al., 2008b; McMichael et al., 2008; Pudpong and Hajat, 2011).

Numerous studies of temperature-related morbidity, based on hospital admissions or emergency presentations, have reported increases in events due to cardiovascular, respiratory, and kidney diseases (Hansen et al., 2008; Knowlton et al., 2009; Lin and Chan, 2009) and the impact has been related to the duration and intensity of heat (Nitschke et al., 2011).

There is evidence now that both average levels and variability in temperature are important influences on human health. The standard deviation of summer temperatures was associated with survival time in a U.S. cohort study of persons aged older than 65 years with chronic disease who were tracked from 1985 to 2006 (Zanobetti et al., 2012). Greater variability was associated with reduced survival. A study that modeled separately projected increases in temperature variability and average temperatures for six cities for 2070–2099 found that, with one exception, variability had an effect (increased deaths) over and above what was estimated from the rise in average temperatures (Gosling et al., 2009). Relevant to Section 11.5, rapid changes in temperature may also alter the balance between humans and parasites, increasing opportunities for new and resurgent diseases. The speed with which organisms adapt to changes in temperatures is, broadly speaking, a function of mass, and laboratory studies have shown that microbes respond more quickly to a highly variable climate than do their multi cellular hosts (Raffel et al., 2012).

Health risks during heat extremes are greater in people who are physically active (e.g., manual laborers). This has importance for recreational activity outdoors and it is relevant especially to the impacts of climate change on occupational health (Kjellstrom et al., 2009a; Ebi and Mills, 2013; see also Section 11.6.2).

Heat also acts on human health through its effects, in conjunction with low rainfall, on fire risk. In Australia in 2009, record high temperatures, combined with long-term drought, caused fires of unprecedented intensity and 173 deaths from burns and injury (Teague et al., 2010).

Smoke from forest fires has been linked elsewhere with increased mortality and morbidity (Analitis et al., 2012; see Section 11.5.3.2).

11.4.1.2. Near-Term Future

The climate change scenarios modeled by WGI AR5 project rising temperatures and an increase in frequency and intensity of heat waves (Section 2.6.1; Chapter 1) in the near-term future, defined as roughly midway through the 21st century, or the era of climate responsibility (see SPM). It is uncertain how much acclimatization may mitigate the effects on human health (Wilkinson et al., 2007a; Bi and Parton, 2008; Baccini et al., 2011; Hanna et al., 2011; Maloney and Forbes, 2011; Peng et al., 2011; Honda et al., 2013). In New York, it was estimated that acclimatization may reduce the impact of added summer heat in the 2050s by roughly a quarter (Knowlton et al., 2007). In Australia, the number of “dangerously hot” days, when core body temperatures may increase by ≥2°C and outdoor activity is hazardous, is projected to rise from the current 4 to 6 days per year to 33 to 45 days per year by 2070 (with SRES A1FI) for non-acclimatized people. Among acclimatized people, an increase from 1 to 5 days per year to 5 to 14 days per year is expected (Hanna et al., 2011).

For reasons given above, it is not clear whether winter mortality will decrease in a warmer, but more variable, climate (Kinney et al., 2012; Ebi and Mills, 2013). Overall, we conclude that the increase in heat related mortality by mid-century will outweigh gains due to fewer cold periods, especially in tropical developing countries with limited adaptive capacities and large exposed populations (Wilkinson et al., 2007b). A similar pattern has been projected for temperate zones. A study of three Quebec cities, based on SRES A2 and B2, extended to 2099, showed an increase in summer mortality that clearly outweighed a small reduction in autumn deaths, and only slight variations in winter and spring (Doyon et al., 2008). Another study in Brisbane, Australia, using years of life lost as the outcome, found the gains associated with fewer cold days were less than the losses caused by more hot days, when warming exceeded 2°C (Huang et al., 2012).

11.4.2. Floods and Storms

Floods are the most frequently occurring type of natural disaster (Guha Sapir et al., 2011). In 2011, 6 of the 10 biggest natural disasters were flood events, when considered in terms of both number affected (112 million people) and number of deaths (3140 people) (Guha-Sapir et al., 2011). Globally, the frequency of river flood events has been increasing, as well as economic losses, due to the expansion of population and property in flood plains (Chapter 18). There is little information on health trends attributable to flooding, except for mortality and there are large differences in mortality risk between countries (UNISDR, 2011). Mortality from flooding and storm events is generally declining, but there is good evidence that mortality risks first increase with economic development before declining (De Haen and Hemrich, 2007; Kellenberg and Mobarak, 2008; Patt et al., 2010). For instance, migration to slums in coastal cities may increase population exposure at a greater pace than can be compensated for by mitigation measures (see Chapter 10 on urban risks). Severe damaging floods in Australia in 2010–2011 and in the northeastern USA in 2012 indicate that high-income countries may still be affected (Guha-Sapir et al., 2011).

11.4.2.1. Mechanisms

Flooding and windstorms adversely affect health through drowning, injuries, hypothermia, and infectious diseases (e.g., diarrheal disease, leptospirosis, vector-borne disease, cholera; Schnitzler et al., 2007; Jakubicka et al., 2010). Since AR4, more evidence has emerged on the long-term (months to years) implications of flooding for health. Flooding and storms may have profound effects on peoples’ mental health (Neria, 2012). The prevalence of mental health symptoms (psychological distress, anxiety, and depression) was two to five times higher among individuals who reported flood water in the home compared to non-flooded individuals (2007 flood in England and Wales; Paranjothy et al., 2011). In the USA, signs of hurricane-related mental illness were observed in a follow-up of New Orleans’ residents almost 2 years after Hurricane Katrina (Kessler et al., 2008). The attribution of deaths to flood events is complex; most reports of flood deaths include only immediate traumatic deaths, which means that the total mortality burden is under reported (Health Protection Agency, 2012). There is some uncertainty as to whether flood events are associated with a longer-term (6 to 12 months) effect on mortality in the flooded population. No persisting effects were observed in a study in England and Wales (Milojevic et al., 2011), but longer-term increases in mortality were found in a rural population in Bangladesh (Milojevic et al., 2012).

11.4.2.2. Near-Term Future

Under most climate change scenarios, it is expected that more frequent intense rainfall events will occur in most parts of the world in the future (IPCC, 2012). If this happens, floods in small catchments will be more frequent, but the consequence is uncertain in larger catchments (see Chapter 3). In terms of exposure, it is expected that more people will be exposed to floods in Asia, Africa, and Central and South America (Chapter 3). Also, increases in intense tropical cyclones are likely in the late 21st century (WGI AR5 Table SPM.1). It has been estimated conservatively that around 2.8 billion people were affected by floods between 1980 and 2009, with more than 500,000 deaths (Doocy et al., 2013). On this basis we conclude it is very likely that health losses caused by storms and floods will increase this century if no adaptation measures are taken. What is not clear is how much of this projected increase can be attributed to climate change. Dasgupta et al. (2009) developed a spatially explicit mortality model for 84 developing countries and 577 coastal cities. They modeled 1-in-100 year storm-surge events, and assessed future impacts under climate change, accounting for sea level rise and a 10% increase in event intensity. In the 84 developing countries, an additional 52 million people and 30,000 km2 of land were projected to be affected by 2100.

11.4.3. Ultraviolet Radiation

Ambient ultraviolet (UV) levels and maximum summertime day temperatures are related to the prevalence of non-melanoma skin

cancers and cataracts in the eye. In one study in the USA, the number of cases of squamous cell carcinoma was 5.5% higher for every 1°C increment in average temperatures, and basal cell carcinoma was 2.9% more common with every 1°C increase. These values correspond to an increase in the effective UV dose of 2% for each 1°C (van der Leun et al., 2008). However, exposure to the sun has beneficial effects on synthesis of vitamin D, with important consequences for health. Accordingly the balance of gains and losses due to increased UV exposures vary with location, intensity of exposure, and other factors (such as diet) that influence vitamin D levels (Lucas et al., 2013). Studies of stratospheric ozone recovery and climate change project that ultraviolet radiation levels at the Earth’s surface will generally return to pre-1980 levels by mid-century, and may diminish further by 2100, although there is high uncertainty around the projections (Correa et al., 2013). On the other hand, higher temperatures in countries with temperate climates may result in an increase in the time which people spend outdoors (Bélanger et al., 2009) and lead to additional UV-induced adverse effects.

11.5. Ecosystem-Mediated Impacts

of Climate Change on Health Outcomes

11.5.1. Vector-Borne and Other Infectious Diseases

Vector-borne diseases (VBDs) refer most commonly to infections transmitted by the bite of blood-sucking arthropods such as mosquitoes or ticks. These are some of the best-studied diseases associated with climate change, due to their widespread occurrence and sensitivity to climatic factors (Bangs et al., 2006; Bi et al., 2007; Halide and Ridd, 2008; Wu et al., 2009). Table 11-1 summarizes what is known about the influence of weather and climate on selected VBDs.

11.5.1.1. Malaria

Malaria is mainly caused by five distinct species of plasmodium parasite (Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, Plasmodium ovale, Plasmodium knowlesi), transmitted by Anopheline mosquitoes between individuals. In 2010 there were an estimated 216 million episodes of malaria worldwide, mostly among children younger than 5 years in the African Region (WHO, 2010). The number of global malaria deaths was estimated to be 1,238,000 in 2010 (Murray et al., 2012). Worldwide, there have been significant advances made in malaria control in the last 20 years (Feachem et al., 2010).

The influence of temperature on malaria development appears to be nonlinear, and is vector specific (Alonso et al., 2011). Increased variations in temperature, when the maximum is close to the upper limit for vector and pathogen, tend to reduce transmission, while increased variations of mean daily temperature near the minimum boundary increase transmission (Paaijmans et al., 2010). Analysis of environmental factors associated with the malaria vectors Anopheles gambiae and A. funestus in Kenya found that abundance, distribution, and disease transmission are affected in different ways by precipitation and temperature (Kelly Hope et al., 2009). There are lag times according to the lifecycle of the vector and the parasite: a study in central China reported that malaria incidence was related to the average monthly temperature, the average

Table 11-1

1 About 50 million About 220 million About 10,000 1 Climate sensitivity and conﬁdence in climate effect Cases per year About 20,000 in USA % Mainly Africa, SE Asia Europe, Russian Fed., Mongolia, China 100 countries, esp. Asia Paciﬁc 0.15–0.2 million Temperate areas of Europe, Asia, North Area % % About 40,000 Mosquito-borne diseases America Other vector-borne diseases Tick-borne diseases % Omumbo et al. (2011) Beebe (2009); Pham et al. (2011); Astrom et al. (2012); Earnest et al. (2012); Descloux (2012) WHO (2008); Kelly-Hope et al. (2009); Alonso et al. (2011); Endemic in many locations worldwide Tokarevich et al. (2011) Climate driver variables Global Bennet (2006); Ogden et al. (2008) decrease Increase or >Increased < Decreased # of cases Tick-borne encephalitis Disease Fang et al. (2010) Hemorrhagic fever with renal Dengue Key references Malaria syndrome (HFRS) More Fewer Stenseth et al. (2006); Ari et al. (2010); Xu et al. (2011) Climate drivers % Lyme Footnote 1 Effects are speciﬁc to Anopheles spp Humidity Plague Conﬁdence levels High conﬁdence in global effect High conﬁdence in local effect Low conﬁdence in effect Temperature Precipitation

temperature of the previous 2 months, and the average rainfall of the current month (Zhou et al., 2010).

More work has been done since AR4 to elucidate the role of local warming on malaria transmission in the East African highlands, but this is hampered by the lack of time series data on levels of drug resistance and intensity of vector control programs. Earlier research had failed to find a clear increase in temperatures accompanying increases in malaria transmission, but new studies with aggregated meteorological data over longer periods have confirmed increasing temperatures since 1979 (Omumbo et al., 2011; Stern et al., 2011). The strongly nonlinear response to temperature means that even modest warming may drive large increases in transmission of malaria, if conditions are otherwise suitable (Pascual et al., 2006; Alonso et al., 2011). On the other hand, at relatively high temperatures modest warming may reduce the potential of malaria transmission (Lunde et al., 2013). One review (Chaves and Koenraadt, 2010) concluded that decadal temperature changes have played a role in changing malaria incidence in East Africa. But malaria is very sensitive also to socioeconomic factors and health interventions, and the generally more conducive climate conditions have been offset by more effective disease control activities. The incidence of malaria has reduced over much of East Africa (Stern et al., 2011), although increased variability in disease rates has been observed in some high-altitude areas (Chaves et al., 2012).

At the global level, economic development and control interventions have dominated changes in the extent and endemicity of malaria over the last 100 years (Gething et al., 2010). Although modest warming has facilitated malaria transmission (Pascual et al., 2006; Alonso et al., 2011), the proportion of the world’s population affected by the disease has been reduced, largely due to control of P. vivaxmalaria in moderate climates with low transmission intensity. However, the burden of disease is still high and may actually be on the increase again, in some locations (WHO, 2012). For instance, locally transmitted malaria has re-emerged in Greece in association with economic hardship and cutbacks in government spending (Danis et al., 2011; Andriopoulos et al., 2013).

11.5.1.2. Dengue Fever

Dengue is the most rapidly spreading mosquito-borne viral disease, showing a 30-fold increase in global incidence over the past 50 years (WHO, 2013). Each year there occur about 390 million dengue infections worldwide, of which roughly 96 million manifest with symptoms (Bhatt et al., 2013). Three quarters of the people exposed to dengue are in the Asia-Pacific region, but many other regions are affected also. The first sustained transmission of dengue in Europe since the 1920s was reported in 2012 in Madeira, Portugal (Sousa et al., 2012). The disease is associated with climate on spatial (Beebe et al., 2009; Russell et al.,

Box 11-2 | Case Study: An Intervention to Control Dengue Fever

Seasonality in dengue transmission is well established in many parts of the world, and transmission occurs mostly during the wettest months of the year (Gubler and Kuno, 1997; Chadee et al., 2007). Figure 11-3 shows that about 80% of dengue fever cases in Trinidad were recorded during the wet season, a period when the Ae. aegypti mosquito population density was four to nine times higher than the dengue transmission threshold (Macdonald, 1956). This led to a control program that concentrated on reducing the mosquito population before the onset of the rains, by application of insecticides (temephos) into the water drums that serve as primary breeding sites of Ae. aegypti in the Caribbean. The one-off treatment effectively controlled the mosquito populations for almost 12 weeks after which the numbers reverted to levels observed in the untreated control areas.

Climate scenarios that extend to 2071–2100 project changes in the intensity and frequency of rainfall events in the Caribbean (Campbell et al., 2011). In these scenarios, there is greater variability in rainfall patterns during November to January, with the northern Caribbean region receiving more rainfall than in the southern Caribbean (Campbell et al., 2011). There may be water shortages during drought periods, and ﬂooding after episodes of heavy rainfall, both of which affect the breeding habitats of Ae. aegypti and Ae. albopictus. Vector control strategies will need to be planned and managed astutely to systematically reduce mosquito populations.

(a) Rainfall, Breteau index, and Dengue cases, Trinidad (2002–2004)

1600 45 40 35 30 25 20 15 10 5 0 50 0 Jul y Sep t Nov 200 1400 Mar ch 400 1200 600 1000 Jul y Sep t Nov 800 Dengue cases Rainfall (mm) Breteau Mar ch Jul y Sep t Nov Mar ch

(b) Efﬁcacy of pre-seasonal treatment with temephos on Aedes aegypti ovitrap egg counts in Curepe (treatment) and St. Joseph (control), Trinidad (2003)

Control Treatment 15 17 19 21 7 9 11 13 Weeks 3 5 Rains start 1 1500 500 1000 2000 0

Figure 11-3 | (a) Rainfall, Breteau index (number of water containers with Ae. aegypti larvae per 100 houses), and dengue fever cases, Trinidad (2002–2004). Rainfall was found to be signiﬁcantly correlated with an increase in the Ae. aegypti population and dengue fever incidence, with a clearly deﬁned “dengue season” between June and November over two years of the study (Chadee et al., 2007). b) Efﬁcacy of pre-seasonal treatment with temephos on Ae. aegypti ovitrap egg counts in Curepe (treatment) and St. Joseph (control), Trinidad (2003). Evidence of the efﬁcacy of the pre-seasonal larval control through focal treatment of Ae. aegypti population is provided. Treatment at the onset of the rainy season can effectively prevent the rapid increase in Ae. aegypti populations and therefore suppress the onset of dengue transmission (Chadee, 2009).

2009; Li et al., 2011), temporal (Hii et al., 2009; Hsieh and Chen, 2009; Herrera-Martinez and Rodriguez-Morales, 2010; Gharbi et al., 2011; Pham et al., 2011; Descloux et al., 2012; Earnest et al., 2012) and spatiotemporal (Chowell et al., 2008, 2011; Lai, 2011) scales.

The principal vectors for dengue, Aedes aegypti and Ae. albopictus, are climate sensitive. Over the last 2 decades, climate conditions have become more suitable for albopictus in some areas (e.g., over central northwestern Europe) but less suitable elsewhere (e.g., over southern Spain) (Caminade et al., 2012). Distribution of Ae. albopictus in northwestern China is highly correlated with annual temperature and precipitation (Wu et al., 2011). Temperature, humidity, and rainfall are positively associated with dengue incidence in Guangzhou, China, and wind velocity is inversely associated with rates of the disease (Lu and Lin, 2009; Li et al., 2011). Several studies in Taiwan reported that typhoons remain an important factor affecting vector population and dengue fever (Hsieh and Chen, 2009; Lai, 2011). Typhoons result in extreme rainfall, high humidity, and water pooling, and may generate fresh mosquito breeding sites. A study in Dhaka, Bangladesh, reported increased rates of admissions to hospital due to dengue with both high and low river levels (Hashizume and Dewan, 2012). In some circumstances, it is apparent that heavy precipitation favors the spread of dengue fever, but drought can also be a cause if households store water in containers that provide suitable mosquito breeding sites (Beebe et al., 2009; Padmanabha et al., 2010).

11.5.1.3. Tick-Borne Diseases

Tick-borne encephalitis (TBE) is caused by tick-borne encephalitis virus, and is endemic in temperate regions of Europe and Asia. Lyme disease is an acute infectious disease caused by the spirochaete bacteria Borrelia burgdorferi and is reported in Europe, the USA, and Canada. Borrelia is transmitted to humans by the bite of infected ticks belonging to a few species of the genus Ixodes (“hard ticks”). Many studies have reported associations between climate and tick-borne diseases (Okuthe and Buyu, 2006; Lukan et al., 2010; Tokarevich et al., 2011; Andreassen et al., 2012; Estrada-Peña et al., 2012; Jaenson et al., 2012). In North America, there is good evidence of northward expansion of the distribution of the tick vector (Ixodes scapularis) in the period 1996–2004 based on an analysis of active and passive surveillance data (Ogden et al., 2010). However, there is no evidence so far of any associated changes in the distribution in North America of human cases of tick-borne diseases.

There was a marked rise in TBE cases from the 1970s in central and Eastern Europe. Spring-time daily maximum temperatures rose in the late 1980s, sufficient to encourage transmission of the TBE virus. For instance, in the Czech Republic, between 1970 and 2008, there were signs of lengthening transmission season and higher altitudinal range in association with warming (Kriz et al., 2012). However variations in illness rates across the region demonstrate that climate change alone cannot explain the increase. Socioeconomic changes (including changes in agriculture and recreational activities) have affected patterns of disease in Europe (Sumilo et al., 2008; Randolph, 2010). The complex ecology of tick-borne diseases such as Lyme disease and TBE make it difficult to attribute particular changes in disease frequency and distribution to specific environmental factors such as climate (Gray et al., 2009).

11.5.1.4. Other Vector-Borne Diseases

Hemorrhagic fever with renal syndrome (HFRS) is a zoonosis caused by the Hanta virus, and leads to approximately 200,000 hospitalized cases each year. The incidence of this disease has been associated with temperature, precipitation, and relative humidity (Pettersson et al., 2008; Fang et al., 2010; Liu et al., 2011). Plague, one of the oldest diseases known to humanity, persists in many parts of the world. Outbreaks have been linked to seasonal and interannual variability in climate (Stenseth et al., 2006; Nakazawa et al., 2007; Holt et al., 2009; Xu et al., 2011; MacMillan et al., 2012). Chikungunya fever is a climate-sensitive mosquito transmitted viral disease (Anyamba et al., 2012), first identified in Africa, now present also in Asia, and the disease has recently emerged in parts of Europe (Angelini et al., 2008). The incidence in China of Japanese encephalitis, another mosquito-borne viral disease, is correlated with temperature and rainfall, especially during the warmer months of the year (Bai et al., 2013). In West Africa, outbreaks of Rift Valley Fever, an acute viral disease affecting humans and domestic animals, are linked to within-season variability in rainfall (Caminade et al., 2011).

11.5.1.5. Near-Term Future

Using the A1B climate change scenario, Béguin et al. (2011) projected the population at risk of malaria to 2030 and 2050. With GDP per capita held constant at 2010 values, the model projected 5.2 billion people at risk in 2050, out of a predicted global population of 8.5 billion. Keeping climate constant, and assuming strong economic growth allied with social development (“best case”), the model projected 1.74 billion people at risk (approximately half the present number at risk) in 2050. Factoring in climate change would increase the “best case” estimate of the number of people at risk of malaria in 2050 to 1.95 billion, which is 200 million more than if disease control efforts were not opposed by higher temperatures and shifts in rainfall patterns.

There are no recent studies that project the return of established malaria to North America or Europe, where it was once prevalent. However, suitable vectors for P. vivax malaria abound in these parts of the world, and recent experience in southern Europe demonstrates how rapidly the disease may reappear if health services falter (Bonovas and Nikolopoulos, 2012).

A systematic review of research on the distribution of dengue and possible influence of climate change (Van Kleef et al., 2010) concluded that the area of the planet that was climatically suitable for dengue would increase under most scenarios, but it was not possible to project the impact on disease incidence. Åström et al. (2012) estimated the population at risk out to the year 2050. The study was based on routine disease reports, surveys, population projections, estimates of GDP growth, and the A1B scenario for climate change. Assuming high GDP growth that benefits all populations, the number exposed to dengue in 2050 falls to 4.46 billion; that is, the adverse effects of climate change are balanced by the beneficial outcomes of development. This study considered only the margins of the geographic distribution of dengue (where economic development has its strongest effect) and did not examine changes in intensity of transmission in areas where the disease is already established.

Kearney (2009) used biophysical models to examine the potential extension of vector range in Australia. He predicted that climate change would increase habitat suitability throughout much of Australia. Changes in water storage as a response to a drier climate may be an indirect pathway, through which climate change affects mosquito breeding (Beebe et al., 2009).

11.5.2. Food- and Water-Borne Infections

Human exposure to climate-sensitive pathogens occurs by ingestion of contaminated water or food; incidental ingestion during swimming; or by direct contact with eyes, ears, or open wounds. Pathogens in water may be zoonotic in origin, concentrated by bivalve shellfish (e.g., oysters), or deposited on irrigated food crops. Pathogens of concern include enteric organisms that are transmitted by the fecal oral route and also bacteria and protozoa that occur naturally in aquatic systems. Climate may act directly by influencing growth, survival, persistence, transmission, or virulence of pathogens; indirect influences include climate-related perturbations in local ecosystems or the habitat of species that act as zoonotic reservoirs.

11.5.2.1. Vibrios

Vibrio is a genus of native marine bacteria that includes a number of human pathogens, most notably V. cholerae which causes cholera. Cholera may be transmitted by drinking water or by environmental exposure in seawater and seafood; other Vibrio species are solely linked to seawater and shellfish. These include V. parahaemolyticus and V. vulnificus, with V. alginolyticus emerging in importance (Weis, 2011). Risk of infection is influenced by temperature, precipitation, and accompanying changes in salinity due to freshwater runoff, addition of organic carbon or other nutrients, or changes in pH. These factors all affect the spatial and temporal range of the organism and also influence exposure routes (e.g., direct contact or via seafood). In countries with endemic cholera, there appears to be a robust relationship between temperature and the disease (Islam, 2009; Paz, 2009; Reyburn et al., 2011). In addition, heavy rainfall promotes the transmission of pathogens when there is not secure disposal of fecal waste. An unequivocal positive relationship between Vibrio numbers and sea surface temperature in the North Sea has been established by DNA analyses of formalin-fixed samples collected over a 44-year period (Vezzulli et al., 2012). Cholera outbreaks have been linked to variations in temperature and rainfall, and other variables including sea and river levels, sea chlorophyll and cyanobacteria contents, and Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO) events (de Magny et al., 2008; Hashizume, 2008; Bompangue et al., 2011; Reyburn et al., 2011; Rinaldo et al., 2012).

11.5.2.2. Other Parasites, Bacteria, and Viruses

Rates of diarrhea have been associated with high temperatures (Kolstad and Johansson, 2011). Mostly, however, neither the specific causes of the diarrheal illness are known, nor the mechanism for the association with temperature. Exceptions include Salmonella and Campylobacter, among the most common zoonotic food- and water-borne bacterial pathogens worldwide, which both show distinct seasonality in infection and higher disease rates at warmer temperatures. The association between climate (especially temperature) and non-outbreak (“sporadic”) cases of salmonellosis may, in part, explain seasonal and latitudinal trends in diarrhea (Lake, 2009).

Among the enteric viruses, there are distinct seasonal patterns in infection that can be related indirectly to temperature. Enterovirus infections in the USA peak in summer and fall months (Khetsuriani et al., 2006). After controlling for seasonality and interannual variations, hand, foot, and mouth disease (caused by coxsackievirus A16 and enterovirus 71) shows a linear relationship with temperature in Singapore, with a rapid rise in incidence when the temperature exceeds 32°C (Hii et al., 2011). However, it is not clear what the underlying driver is and if temperature is confounded by other seasonal factors.

Temperature is directly linked with risk of enteric disease in Arctic communities, as melt of the permafrost hastens transport of sewage (which is often captured in shallow lagoons) into groundwater, drinking water sources, or other surface waters (Martin et al., 2007). In addition, thawing may damage drinking water intake systems (for those communities with such infrastructure) (Hess, 2008).

Rainfall has also been associated with enteric infections. Bacterial pathogens are more likely to grow on produce crops (e.g., lettuce) in simulations of warmer conditions (Liu et al., 2013a), and become attached to leafy crops under conditions of both flooding and drought (Ge et al., 2012). This latter pattern is reflected in patterns of illness (Bandyopadhyay et al., 2012). Higher concentrations of enteric viruses have been reported frequently in drinking water and recreational water following heavy rainfall (Delpla et al., 2009).

Worldwide, rotavirus infections caused about 450,000 deaths in children younger than 5 years old in 2008 (Tate et al., 2012). There are seasonal peaks in the number of cases in temperate and subtropical regions but less distinct patterns are seen within 10° latitude of the equator (Cook et al., 1990). Variations in the timing of peak outbreaks between countries or regions (Turcios et al., 2006; Atchison et al., 2010) and variations with time in the same country (Dey et al., 2010) have been attributed to fluctuations in the number and seasonality of births (Pitzer et al., 2009, 2011). While vaccination against rotavirus is expected to reduce the total burden of disease, it may also increase seasonal variation (Tate et al., 2009; Pitzer et al., 2011).

Harmful algal blooms can be formed by (1) dinoflagellates that cause outbreaks of paralytic shellfish poisoning, ciguatera fish poisoning, and neurotoxic shellfish poisoning; (2) cyanobacteria that produce toxins causing liver, neurological, digestive, and skin diseases; and (3) diatoms that can produce domoic acid, a potent neurotoxin that is bioaccumulated in shellfish and finfish (Erdner et al., 2008). Increasing temperatures promote bloom formation in both freshwater (Paerl et al., 2011) and marine environments (Marques et al., 2010; see Chapter 5). Increasing temperature favored growth of toxic over non-toxic strains of Microcystis in lakes in the USA (Davis et al., 2009). Projections of toxin-producing blooms in Puget Sound using an A1B scenario suggest that by the end of the century the “at risk” period may begin 2 months earlier and last up to 1 month longer than at present (Moore et al., 2011).

11.5.2.3. Near-Term Future

Kolstad and Johansson (2011) projected an increase of 8 to 11% in the risk of diarrhea in the tropics and subtropics in 2039 due to climate change, using the A1B scenario and 19 coupled atmosphere-ocean climate models from CMIP3. This study did not account for future changes in economic growth and social development. Application of down-scaled climate change models showed that overflows of sewage into Chicago’s watersheds would increase by 50 to 120% by 2100, as a result of more frequent and intense rainfall (Patz et al., 2008). In Botswana, if hot, dry conditions begin earlier in the year, and are prolonged, as projected by down-scaled climate scenarios, the present dry season peak in diarrheal disease may be amplified (Alexander et al., 2013). However, the same analysis projected that incidence of diarrheal disease in the wet season would decline. Zhou et al. (2008) studied the effect of climate on transmission of schistosomiasis due to S. japonicum in China. They concluded that an additional 784,000 km2 would become suitable for schistosomiasis transmission in China by 2050, as the mid-winter freezing line moves northward (Figure 11-4).

Mangal et al. (2008) constructed a mechanistic model of the transmission cycle of another species, S. mansoni, and reported a peak in the worm burden in humans at an ambient temperature of 30°C, falling sharply as temperature rises to 35°C. The authors attribute this to the increasing mortality of both the snails and the water-borne intermediate forms of the parasite, and noted that worm burden is not directly linked to the prevalence of schistosomiasis.

11.5.3. Air Quality

Nearly all the non-CO2 climate-altering pollutants (see WGI AR5 Chapters 7 and 8) are health damaging, either directly or by contributing to secondary pollutants in the atmosphere. Thus, like the ocean acidification

Range of schistosomiasis in China in 2000 Additional area suitable for disease transmission in 2050 N 0 250 500 1000 km

Figure 11-4

Box 11-3 | Health and Economic Impacts of Climate-Altering Pollutants Other than CO2

Although other estimates of the global health impacts of human exposures to particle and ozone pollution have been published in recent years (e.g., UNEP, 2011), the most comprehensive was the Comparative Risk Assessment carried out as part of the 2010 Global Burden of Disease Project (Lim et al., 2012). It found that the combined health impact of the household exposures to particle air pollution from poor combustion of solid cooking fuels, plus general ambient pollution, was about 6.8 million premature deaths annually, with about 5% overlapping, that is, coming from the contribution to general ambient pollution of household fuels. It also found that about 150,000 premature deaths could be attributed to ambient ozone pollution. Put into terms of disability-adjusted life years (DALYs), particle air pollution was responsible for about 190 million lost DALYs in 2010, or about 7.6% of all DALYs lost. This burden puts particle air pollution among the largest risk factors globally, far higher than any other environmental risk and rivaling or exceeding all of the ﬁve dozen risk factors examined, including malnutrition, smoking, high blood pressure, and alcohol.

The economic impact of this burden is difﬁcult to assess as evaluation methods vary dramatically in the literature. Most in the health ﬁeld prefer to consider some version of a lost healthy life year as the best metric although the economics literature often uses willingness to pay for avoiding a lost life (Jamison et al., 2006). Another difﬁculty is that any valuation technique that weights the economic loss according to local incomes per capita will value health effects in rich countries more than in poor countries, which would seem to violate some of the premises of a global assessment; see WGIII AR5 Chapter 3 for more discussion. Here, however, we will use the mean global income per capita (approximately US$10,000 in 2010) to scope out the scale of the impact globally without attempting to be speciﬁc by country or region.

The WHO CHOICE approach for evaluating what should be spent on health interventions indicates that one annual per capita income per DALY is a reasonable upper bound (WHO, 2009a). This would imply that the total lost economic value from global climate-altering pollutants in the form of particles is roughly US$1.9 trillion, in the sense that the world ought to be willing to pay this much to reduce it. This is about 2.7% of the global economy (approximately US$70 trillion in 2010).

On the one hand, this shows that global atmospheric pollution already has a major impact on the health and economic well-being of humanity today, due mainly to the direct effects rather than those mediated through climate. If CO2 is not controlled and climate change continues to intensify while air pollutant controls become more stringent, the climate impacts will become more prominent. The quite different time scales for the two types of impacts make comparisons difﬁcult, however.

Air pollution reductions do not always promote the twin goals of protecting health and climate but can pose trade-offs. All particles are dangerous for health, for example, but some are cooling, such as sulfates, and some warming, such as black carbon (Smith et al., 2009). Indeed elimination of all anthropogenic particles in the atmosphere, a major success for health, would have only a minor net impact on climate (WGI AR5 Figure TS-6). As discussed in Section 11.9, there are nevertheless speciﬁc actions that will work toward both goals.

and ecosystem/agriculture fertilization impacts of CO2, the other CAPs have non-climate-mediated impacts, particularly on health. Although not reviewed in detail in this assessment, the health impacts of non CO2 CAPs are substantial globally. See Box 11-3.

Although there is a large body of literature on the health effects of particulate air pollution (see Box 11-3), WGI indicates that there is little evidence that climate change, per se, will affect long-term particle levels in a consistent way (WGI AR5 Section 11.3.5 and Annex II). Thus, we focus here on chronic ozone exposures, which are found by WGI to be enhanced in some, but not all, scenarios of future climate change (WGI AR5 TS.5.4.8).

11.5.3.1. Long-Term Outdoor Ozone Exposures

Tropospheric ozone is formed through photochemical reactions that involve nitrogen oxides (NOx), carbon monoxide (CO), methane (CH4), and volatile organic compounds (VOCs) in the presence of sunlight and elevated temperatures (WGI AR5 Chapter 8). Therefore, if temperatures rise, many air pollution models (Ebi and McGregor, 2008; Tsai et al., 2008; Chang et al., 2010; Polvani et al., 2011) project increased ozone production especially within and surrounding urban areas (Hesterberg et al., 2009). Enhanced temperature also accelerates destruction of ozone, and the net direct impact of climate change on ozone concentrations worldwide is thought to be a reduction (WGI AR5 TS.5.4.8). Some WGI (TS.5.4.8) scenarios, however, indicate tropospheric ozone may rise from additional CH4 emissions stimulated by climate change. Models also show that local variations can have a different sign to the global one (Selin et al., 2009).

Even small increases in atmospheric concentrations of ground-level ozone may affect health (Bell et al., 2006; Ebi and McGregor, 2008; Jerrett et al., 2009). For instance, Bell et al. (2006) found that levels that meet the USEPA 8-hour regulation (0.08 ppm over 8 hours) were associated with increased risk of premature mortality. There is a lack of association between ozone and premature mortality only at very low concentrations (from 0 to ~10 ppb) but the association becomes positive and approximately linear at higher concentrations (Bell et al., 2006; Ebi and McGregor, 2008; Jerrett et al., 2009). In an analysis of 66 U.S. cities with 18 years of follow-up (1982–2000), tropospheric ozone levels were found to be significantly associated with cardiopulmonary mortality (Smith et al., 2009). See also the global review by WHO, which includes data from developing countries (WHO, 2006).

11.5.3.2. Acute Air Pollution Episodes

Wildfires, which occur more commonly following heat waves and drought, release particulate matter and other toxic substances that may affect large numbers of people for days to months (Finlay et al., 2012; Handmer et al., 2012). During a fire near Denver (USA) in June 2009, 1-hour concentrations of particulate matter with aerodynamic diameter <10 μm (PM10) and particulate matter with aerodynamic diameter <2.5 μm (PM2.5) reached 370 µg m–3 and 200 µg m–3, and 24-hour average concentrations reached 91 µg m–3 and 44 µg m–3 (Vedal and Dutton, 2006), compared to the 24-hour WHO Air Quality Guidelines (AQGs) for these pollutants of 50 µg m–3 and 25 µg m–3, respectively. One study of worldwide premature mortality attributable to air pollution from forest fires estimated there were 339,000 deaths per year (range 260,000 to 600,000) (Johnston et al., 2012). The regions most affected are sub Saharan Africa and Southeast Asia (Johnston et al., 2012). Extremely high levels of PM10 were observed in Moscow due to forest fires caused by a heat wave in 2010. Daily mean temperatures in Moscow exceeded the respective long-term averages by 5°C or more for 45 days. Ten new temperature records were established in July and nine in August, based on measurements since 1885, and an anti-cyclone in the Moscow region prevented dispersion of air pollutants. The highest 24-hour pollution levels recorded in Moscow during these conditions were between 430 and 900 µg m–3 PM10 most days, but occasionally reached 1500 µg m–3. The highest 24-hour CO concentration was 30 mg m–3 compared to the WHO AQGs of 7 mg m–3, and the levels of formaldehyde, ethyl benzene, benzene, toluene, and styrene were also increased (State Environmental Institution “Mosecomonitoring,” 2010).

There may be an interaction of tropospheric ozone and heat waves. Dear et al. (2005) modeled the daily mortality due to heat and exposure to ozone during the European summer heat wave of 2003 and found that possibly 50% of the deaths could have been associated with ozone exposure rather than the heat itself.

11.5.3.3. Aeroallergens

Allergic diseases are common and some are climate sensitive. Warmer conditions generally favor the production and release of airborne allergens (such as fungal spores and plant pollen) and, consequently, there may be an effect on asthma and other allergic respiratory diseases such as allergic rhinitis, as well as effects on conjunctivitis and dermatitis (Beggs, 2010). Children are particularly susceptible to most allergic diseases (Schmier and Ebi, 2009). Increased release of allergens may be amplified if higher CO2 levels stimulate plant growth. Visual monitoring and experiments have shown that increases in air temperature cause earlier flowering of prairie tallgrass (Sherry et al., 2007). Droughts and high winds may produce windborne dust and other atmospheric materials, which contain pollen and spores, and transport these allergens to new regions.

Studies have shown that increasing concentrations of grass pollen lead to more frequent ambulance calls due to asthma symptoms, with a time lag of 3 to 5 days (Heguy et al., 2008). Pollen levels have also been linked to hospital visits with rhinitis symptoms (Breton et al., 2006). A cross-sectional study in the three climatic regions of Spain documented a positive correlation between the rate of child eczema and humidity, and negative correlation between child eczema and air temperature or the number of sunshine hours (Suarez-Varela et al., 2008).

11.5.3.4. Near-Term Future

It is projected by WGI that climate change could affect future air quality, including levels of photochemical oxidants and, with much less certainty, fine particles (PM2.5). If this occurs, there will be consequences for human health (Bell et al., 2007; Dong et al., 2011; Chang et al., 2012; Lepeule et al., 2012; Meister et al., 2012; West et al., 2013). High temperatures may also magnify the effects of ozone (Ren et al., 2008; Jackson et al., 2010). Increasing urbanization, use of solid biomass fuels, and industrial development in the absence of emission controls could also lead to increases in ozone chemical precursors (Selin et al., 2009; Wilkinson et al., 2009).

Most post-2006 studies on the projected impacts of future climate change on air pollution-related morbidity and mortality have focused on ozone in Europe, the USA, and Canada (Bell et al., 2007; Selin et al., 2009; Tagaris et al., 2009). Projections are rare for other areas of the world, notably the developing countries where air pollution is presently a serious problem and is expected to worsen unless controls are strengthened.

Higher temperatures may magnify the effects of air pollutants like ozone, although estimates of the size of this effect vary (Ren et al., 2008; Jackson et al., 2010). In general, all-cause mortality related to ozone is expected to increase in the USA and Canada (Bell et al., 2007; Tagaris et al., 2009; Jackson et al., 2010; Cheng et al., 2011). Under a scenario in which present air quality legislation is rolled out everywhere, premature deaths due to ozone would be wound back in Africa, South Asia, and East Asia. Under a maximum feasible CO2 reduction scenario related to A2, it is projected that 460,000 premature ozone-related deaths could be avoided in 2030, mostly in South Asia (West et al., 2007a). All-cause mortality, however, is not the best metric for comparing air pollution health impacts across regions, given that background disease conditions vary so widely. HIV deaths and malaria deaths, which are prominent in sub-Saharan Africa, for example, are not expected to increase from air pollution exposures in the same way as deaths from cardiovascular disease that dominate other regions.

A study that investigated regional air quality in the USA in 2050, using a down-scaled climate model (Goddard Institute for Space Studies, Global Climate Model), concluded there would be about 4000 additional annual premature deaths due to increased exposures to PM2.5 (Tagaris et al., 2009). Air pollutant-related mortality increases are also projected for Canada, but in this case they are largely driven by the effects of ozone (Cheng et al., 2011). On the basis of the relation of asthma to air quality in the last decade (1999–2010), Thompson et al. (2012) anticipate that the prevalence of asthma in South Africa will increase substantially by 2050. Sheffield et al. (2011), applying the SRES A2 scenario, projected a median 7.3% increase in summer ozone-related asthma emergency department visits for children (0 to 17 years) across New York City by the 2020s compared to the 1990s.

11.6. Health Impacts Heavily Mediated

through Human Institutions

11.6.1. Nutrition

Nutrition is a function of agricultural production (net of post-harvest wastes and storage losses), socioeconomic factors, such as food prices and access, and human diseases, especially those that affect appetite, nutrient absorption, and catabolism (Black et al., 2008; Lloyd et al., 2011). All three may be influenced by climate but only agricultural production has been modeled in a climate impacts framework. Here we use the terms undernutrition, which is a health outcome, and undernourishment, which reflects national (post-trade) calories available for human consumption, and is expressed as estimated percent of the population receiving “insufficient” calories. We do not use the term “malnutrition,” as it includes overnutrition, which is not considered here (except under co-benefits in Section 11.9). Undernutrition can be chronic, leading to stunting (low height for age), or acute, leading to wasting (low weight for height); underweight (low weight for age) is a combination of chronic and acute undernutrition.

11.6.1.1. Mechanisms

The processes through which climate change can affect human nutrition are complex (see Section 7.2.2). Higher temperatures and changes in precipitation may reduce both the quantity and quality of food harvested (e.g., Battisti and Naylor, 2009). Lobell et al. (2011b) showed for African maize that for each degree above 30°C, yields decreased by 1% under optimal rainfall conditions and by 1.7 % under drought conditions. From their systematic review of more than a thousand studies, Knox et al. (2012) drew the conclusion that “climate change is a threat to crop productivity in areas that are already food insecure.” Rising temperatures may also affect food security through the impact of heat on productivity of farmers (see Section 11.6.2).

The magnitude of detected and predicted decline in land-based agricultural production due to increasing temperatures and changes in rainfall must be put in perspective to other changes, such as increase in harvests due to improved farming knowledge and technology, the amount of food fed to livestock, used for biofuels, consumed beyond baseline needs by the overnourished, or wasted in other ways (Foley et al., 2011). There is good evidence that local food price increases have negative effects on food consumption, and therefore on health (Green et al., 2013). Against this background, the global food price fluctuates, though with a recently rising trend. While the main driver is higher energy costs, amplified by speculation (Piesse and Thirtle, 2009), there is growing evidence (Auffhammer, 2011) that extreme weather events, especially floods, droughts (Williams and Funk, 2011), and heat waves, may have contributed to higher prices. All else being equal, higher prices increase the number of malnourished people. See Chapter 7 for a more detailed discussion of the impact of climate change on food production.

11.6.1.2. Near-Term Future

Since AR4 at least four studies have been published which project the effect of climate change on undernourishment and undernutrition.

Nelson et al. (2009, 2010) conducted two studies using a crop simulation model (DSSAT) and a global agricultural trade model (IMPACT 2009) to estimate crop production (with and without CO2 enrichment), calorie availability, child underweight, and adaptation costs. The first study (Nelson et al., 2009) was carried out under the A2 emission scenario, using two General Circulation Models (GCMs): National Center for Atmospheric Research (NCAR) and Commonwealth Scientific and Industrial Research Organisation (CSIRO) and relative to a “no climate change” future. The authors found that yields of most important crops would decline in developing countries by 2050, that per capita calorie

Table 11-2

Scenario |  | South Asia | East | Asia / Paciﬁ c | CEeunrotrpael Aansida | Latin America and Caribbean | Middle East / North Africa | Sub-Saharan Africa | All developing countries
2000 |  | 75.6 |  | 23.8 | 4.1 | 7.7 | 3.5 | 32.7 | 147.9
| No climate change | 52.3 |  | 10.1 | 2.7 | 5.0 | 1.1 | 41.7 | 113.3
2050 |  |  |  |  |  |  |  |  |
| Climate change | 59.1 |  | 14.5 | 3.7 | 6.4 | 2.1 | 52.2 | 138.5
730 |  |  |  |  |  |  |  |  |

availability would drop below levels that applied in the year 2000, and that child underweight would be approximately 20% higher (in the absence of carbon enrichment effects). That is, about 25 million children would be affected (see Table 11-2). Of note, the underweight estimates do not account for possible improvements in socioeconomic conditions between 2000 and 2050. However, it was estimated that substantial improvements would be necessary to counteract the effects of climate change. These included a 60% increase in yield growth (all crops) over baseline, 30% faster growth in animal numbers, and a 25% increase in the rate of expansion of irrigated areas. The second study by Nelson et al. used a wider range of socioeconomic and climate scenarios but health impacts were similar to the first study. Estimates of improved socioeconomic conditions were insufficient to fully offset the potential impacts of climate change: child underweight was estimated to be approximately 10% higher with climate change compared to a future without climate change.

Lloyd et al. (2011) built a model for estimating future stunting driven by two principal inputs: estimates of undernourishment (i.e., “food related” causes of stunting) and socioeconomic conditions (i.e., “non food-related” causes of stunting). The former were based on calorie availability estimates from Nelson et al. (2009), and the latter on GDP per capita projections and estimates of the Gini index for income distribution. They estimated that by 2050, under A2 emissions with moderate to high economic growth and compared to a future without climate change, there may be a relative increase of severe stunting of 31 to 55% across regions of sub-Saharan Africa and 61% in South Asia. It should be noted here that severe stunting carries three to four times the mortality risk of moderate stunting. In a future without climate change, undernutrition was projected to decline, leading the authors to conclude that climate change would hold back efforts to reduce child undernutrition in the most severely affected parts of the world, even after accounting for the potential benefits of economic growth.

In addition to global studies, regional projections of the impacts of climate change on undernutrition have also been carried out since AR4. Grace et al. (2012) modeled the relationship between climate variables (temperature and precipitation), food production and availability, as well as child stunting in Kenya. The authors concluded that climate change will increase the proportion of stunted children in countries such as Kenya that are dependent on rain-fed agriculture, unless there are substantial adaptation efforts, such as investment in education and agricultural technology.

Similarly, Jankowska et al. (2012) included climate, livelihood, and health variables (stunting and underweight). The authors identified a link between type of livelihood and risk of undernutrition, and climate and stunting. Applying the model to Mali, the authors projected impacts to 2025 and estimated that nearly 6 million people may experience undernutrition due to changes in climate, livelihood, and demography; three-quarter to one million of this number will be children younger than five.

In summary, we conclude that climate change will have a substantial negative impact on (1) per capita calorie availability; (2) childhood undernutrition, particularly stunting; and (3) on undernutrition-related child deaths and DALYs lost in developing countries (high confidence).

11.6.2. Occupational Health

Since AR4, much has been written on the effects of heat on working people (Kjellstrom et al., 2009a; Dunne et al., 2013) and on other climate related occupational health risks (Bennett and McMichael, 2010; Schulte and Chun, 2009).

11.6.2.1. Heat Strain and Heat Stroke

Worldwide, more than half of all non-household labor-hours occur outdoors, mainly in agriculture and construction (IFAD, 2010; ILO, 2013). Individuals who are obliged to work outside in hot conditions, without access to shade, or sufficient water, are at heightened risk of heat strain (ICD code T.67, “heat exhaustion”) and heat stroke. Health risks increase with the level of physical exertion. Agricultural and construction workers in tropical developing countries are therefore among the most exposed, but heat stress is also an issue for those working indoors in environments that are not temperature-controlled, and even for some workers in high income countries such as the USA (Luginbuhl et al., 2008; see Figure 11-5). Moreover, at higher temperatures there is potential conflict between health protection and economic productivity (Kjellstrom et al., 2011): as workers take longer rests to prevent heat stress, hourly productivity goes down (Sahu et al., 2013).

11.6.2.2. Heat Exhaustion and Work Capacity Loss

There are international standards of maximum recommended workplace heat exposure and hourly rest time (e.g., ISO, 1989; Parsons, 2003) for both acclimatized and non-acclimatized people. In hot countries during the hot season, large proportions of the workforce are affected by heat, and the economic impacts of reduced work capacity may be sufficient to jeopardize livelihoods (Lecocq and Shalizi, 2007; Kjellstrom et al., 2009a, 2011; Kjellstrom and Crowe, 2011). Kjellstrom and Crow (2011) and Dunne et al. (2013) report that loss of work productivity during the hottest and wettest seasons has already occurred, at least in Asia and Africa.

11.6.2.3. Other Occupational Health Concerns

In areas where vector-borne diseases, such as malaria and dengue fever, are common, people working in fields without effective protection may experience a higher incidence of these diseases when climatic conditions favor mosquito breeding and biting (Bennett and McMichael, 2010). Increasing heat exposure in farm fields during the middle of the day may lead to more work during dawn and dusk when some of the vectors are biting humans more actively. Exposure to heat affects psychomotor, perceptual, and cognitive performance (Hancock et al., 2007) and increases risk of injuries (Ramsey, 1995). Extreme weather events and climate-sensitive infectious diseases also pose occupational risks to health workers, which may in turn undermine health protection for the wider population (WHO, 2009b). Other mechanisms include elevated occupational exposures to toxic chemical solvents that evaporate faster at higher temperatures (Bennett and McMichael, 2010) and rising temperatures reducing sea ice and increasing risk of drowning in those engaged in traditional hunting and fishing in the Arctic (Ford et al., 2008).

Job exertion required (Watts) e.g. approximately halves 2˚C work rise output 100 500 400 300 200 80 60 40˚C 35˚C 40 Wet bulb globe temperature (WBGT) Low risk 30˚C <20 21 20 0 25˚C 22 23 24 25 26 27 28 29 30 31 Heat exposure WBGT °C Moderate risk High risk 32 33 34 >35

Figure 11-5

11.6.2.4. Near-Term Future

Projections have been made of the future effects of heat on work capacity (Kjellstrom et al., 2009b; Dunne et al., 2013). Temperature and humidity were both included, and the modeling took into account the changes in the workforce distribution relating to the need for physical activity. In Southeast Asia, in 2050, the model indicates that more than half the afternoon work hours will be lost due to the need for rest breaks (Kjellstrom et al., 2013). By 2100, under RCP4.5, Dunne et al. (2013) project up to a 20% loss of productivity globally. There is an unfortunate trade-off between health impact and productivity, which creates risks for poor and disenfranchised laborers working under difficult working conditions and inflexible rules (Kjellstrom et al., 2009a, 2011; Sahu et al., 2013).

11.6.3. Mental Health

Harsher weather conditions such as floods, droughts, and heat waves tend to increase the stress on all those who are already mentally ill, and may create sufficient stress for some who are not yet ill to become so (Berry et al., 2010). Manifestations of disaster-related psychiatric trauma include severe anxiety reactions (such as post-traumatic stress) and longer-term impacts such as generalized anxiety, depression, aggression, and complex psychopathology (Ahern et al., 2005; Ronan et al., 2008).

For slow-developing events such as prolonged droughts, impacts include chronic psychological distress and increased incidence of suicide (Alston and Kent, 2008; Hanigan et al., 2012). Extreme weather conditions may have indirect effects on those with mental illness, through the impacts on agricultural productivity, fishing, forestry, and other economic activities. Disasters such as cyclones, heat waves, and major floods may also have destructive effects in cities. Here again, the mentally ill may be at risk: cities often feature zones of concentrated disadvantage where mental disorders are more common (Berry, 2007) and there is also higher risk of natural disasters (such as flooding).

In addition to effects of extreme weather events on mental health via the risk/disadvantage cycle, there may be a distressing sense of loss, known as “solastalgia,” that people experience when their land is damaged (Albrecht et al., 2007) and they lose amenity and opportunity.

11.6.4. Violence and Conflict

Soil degradation, freshwater scarcity, population pressures, and other forces that are related to climate are all potential causes of conflict. The relationships are not straightforward, however, as many factors influence conflict and violence. The topic is reviewed closely in Chapter 12, which concludes that factors associated with risk of violent conflict, such as poverty and impaired state institutions, are sensitive to climate variability, but evidence of an effect of climate change on violence is contested. Also, it is noted that populations affected by violence are particularly vulnerable to the impacts of climate change on health and social well being.

11.7. Adaptation to Protect Health

Climate change may threaten the progress that has been made in reducing the burden of climate-related disease and injury. The degree to which programs and measures will need modification to address additional pressures from climate change will depend on the current burden of ill health; the effectiveness of current interventions; projections of where, when, and how the health burden could change with climate change; the feasibility of implementing additional programs; other stressors that could increase or decrease resilience; and the social, economic, and political context for intervention (Ebi et al., 2006).

The scientific literature on adaptation to climate change has expanded since AR4, and there are many more national adaptation plans that include health, but investment in specific health protection activities is growing less rapidly. A review by the World Health Organization in 2012 estimated that commitments to health adaptation internationally amount to less than 1% of the annual health costs attributable to climate change in 2030 (WHO Regional Office for Europe, 2013).

The value of adaptation is demonstrated by the health impacts of recent disasters associated with extreme weather and climate events, although not necessarily attributed with confidence to climate change itself. For example, approximately 500,000 people died when Cyclone Bhola (category 3 in severity) hit East Pakistan (present day Bangladesh) in 1970 (Khan, 2008). In 1991, a cyclone of similar severity caused about 140,000 deaths. In November 2007, Cyclone Sidr (category 4) resulted in approximately 3400 deaths. The population had grown by more than 30 million in the intervening period (Mallick et al., 2005). Bangladesh achieved this remarkable reduction in mortality through effective collaborations between governmental and non-governmental organizations and local communities (Khan, 2008).

Alongside improving general disaster education (greatly assisted by rising literacy rates, especially among women), the country deployed early warning systems and built a network of cyclone shelters. Early warning systems included high-technology information systems and relatively simple measures such as training volunteers to distribute warning messages by bicycle.

Efforts to adapt to the health impacts of climate change can be categorized as incremental, transitional, and transformational actions (O’Brien et al., 2012). Incremental adaptation includes improving public health and health care services for climate-related health outcomes, without necessarily considering the possible impacts of climate change. Transitional adaptation means shifts in attitudes and perceptions, leading to initiatives such as vulnerability mapping and improved surveillance systems that specifically integrate environmental factors. Transformational adaptation (see Chapter 16), which requires fundamental changes in systems, has yet to be implemented in the health sector.

11.7.1. Improving Basic Public Health

and Health Care Services

Although the short time period since health adaptation options have been implemented means evidence of effectiveness in specifically reducing climate change-related impacts is currently lacking, there is abundant evidence of steps that may be taken to improve relevant public health functions (Woodward et al., 2011). This is important because the present health status of a population may be the single most important predictor of both the future health impacts of climate change and the costs of adaptation (Pandey, 2010). Most health adaptation focuses on improvements in public health functions to reduce the current adaptation deficit, such as enhancing disease surveillance, monitoring environmental exposures, improving disaster risk management, and facilitating coordination between health and other sectors to deal with shifts in the incidence and geographic range of diseases (Woodward et al., 2011).

Examples of incremental health care interventions include introduction of vaccination programs in the USA, after which seasonal outbreaks of rotavirus, a common climate-sensitive pathogen, were delayed and diminished in magnitude (Tate et al., 2009). Post-disaster initiatives also are important. For example, an assessment of actions to improve the resilience of vulnerable populations to heat waves recommended staff planning over the summer period, cooling of health care facilities, training of staff to recognize and treat heat strain, and monitoring of those in the highest risk population groups (WHO Regional Office for Europe, 2009). Ensuring essential medical supplies for care of individuals with chronic conditions, including effective post-disaster distribution, would increase the ability of communities to manage large-scale floods and storms. In Benin, one measure proposed as part of the national response to sea level rise and flooding is expanded health insurance arrangements, so that diseases such as malaria and enteric infections can be treated promptly and effectively (Dossou and Glehouenou-Dossou, 2007).

11.7.2. Health Adaptation Policies and Measures

Transitional adaptation moves beyond focusing on reducing the current adaptation deficit to considerations of how a changing climate could alter health burdens and the effectiveness of interventions (Frumkin et al., 2008). For example, maintaining and improving food safety in the face of rising temperatures and rainfall extremes depends on effective interactions between human health and veterinary authorities, integrated monitoring of food-borne and animal diseases, and improved methods to detect pathogens and contaminants in food (Tirado et al., 2010). Indicators of community functioning and connectedness also are relevant because communities with high levels of social capital tend to be more successful in disseminating health and related messages, providing support to those in need (Frumkin et al., 2008).

11.7.2.1. Vulnerability Mapping

Vulnerability mapping is being increasingly used to better understand current and possible future risks related to climate change. For example, Reid et al. (2009) mapped community determinants of heat vulnerability in the USA. The four factors explaining most of the variance were a combination of social and environmental factors, social isolation, prevalence of air conditioning, and the proportion of the population who were elderly or diabetic. Remote-sensing technologies are now sufficiently fine-grained to map local vulnerability. For example, these technologies can be used to map surface temperatures and urban heat island effects at the neighborhood scale, indicating where city greening and other urban cooling measures could be most effective, and alerting public health authorities to populations that may be at greatest risk of heat waves (Luber and McGeehin, 2008). In another example, spatial modeling of geo-referenced climate and environmental information was used to identify characteristics of domestic malaria transmission in 2009–2012 in Greece, to guide malaria control efforts (Sudre et al., 2013). Mapping at regional and larger scales may be useful to guide adaptation actions. In Portugal, modeling of Lyme disease indicates that future conditions will be less favorable for disease transmission in the south, but more favorable in the center and northern parts of the country (Casimiro et al., 2006). This information can be used to modify surveillance programs before disease outbreaks occur. To capture a more complete picture of vulnerability, mapping exercises also could consider climate sensitivity and adaptation capacity, such as was done in an assessment of climate change and risk of poverty in Africa (Thornton et al., 2008).

11.7.3. Early Warning Systems

Early warning systems have been developed in many areas to prevent negative health impacts through alerting public health authorities and the general public about climate-related health risks. Effective early warning systems take into consideration the range of factors that can drive risk and are developed in collaboration with end users.

Early warning systems have been developed also for vector-borne and food-borne infections, although evidence of their effectiveness in reducing disease burdens is limited. In Botswana, an early warning system forecasts malaria incidence up to 4 months in advance based on observed rainfall; interannual and seasonal variations in climate are associated with outbreaks of malaria in this part of Africa. Model outputs include probability distributions of disease risk and measures of the uncertainty associated with the forecasts (Thomson et al., 2006). A weather-based forecasting model for dengue, developed in Singapore, predicted epidemics 13 months ahead of the peak in new cases, which gave the national control program time to increase control measures (Hii et al., 2012). A study of campylobacteriosis in the USA developed models of monthly disease risk with a very good fit in validation data sets (R2 up to 80%) (Weisent et al., 2010).

11.7.4. Role of Other Sectors in Health Adaptation

Other sectors—including ecosystems, water supply and sanitation, agriculture, infrastructure, energy and transportation, land use management, and others—play an important part in determining the risks of disease and injury resulting from climate change.

Components of effective early warning systems include forecasting weather conditions associated with increased morbidity or mortality, predicting possible health outcomes, identifying triggers of effective and timely response plans that target vulnerable populations, communicating risks and prevention responses, and evaluating and revising the system to increase effectiveness in a changing climate (Lowe et al., 2011). Heat wave early warning systems are being increasingly implemented, 2012). primarily in high-income countries. Of seven studies of the effectiveness

of heat wave early warning systems or heat prevention activities to reduce heat-related mortality, six reported fewer deaths during heat waves after implementation of the system (Palecki et al., 2001; Weisskopf et al., 2002; Ebi et al., 2004; Tan et al., 2007; Fouillet et al., 2008; Chau et al., 2009); only Morabito et al. (2012) was inconclusive. For example, in the summer of 2006, France experienced high temperatures with about 2000 excess deaths. This was more than 4000 fewer deaths than was anticipated on the basis of what occurred in the 2003 heat wave. A national assessment attributed the lower than expected death toll to greater public awareness of the health risks of heat, improved health care facilities, and the introduction in 2004 of a heat wave early warning system (Fouillet et al., 2008). A review of the heat wave early warning systems in the 12 European countries with such plans concluded that evaluations of the effectiveness of these systems is urgently needed to inform good practices, particularly understanding which actions increase resilience (Lowe et al., 2011).

Within the context of the EuroHEAT project, a review of public health responses to extreme heat in Europe identified transport policies, building design, and urban land use as important elements of national and municipal heat wave and health action plans (WHO Regional Office for Europe, 2009). A study examining well-established interventions to reduce the urban heat island effect (replacing bitumen and concrete with more heat-reflective surfaces, and introducing more green spaces to the city) estimated these would reduce heat-related emergency calls for medical assistance by almost 50% (Silva et al., 2010). Urban green spaces lower ambient temperatures, improve air quality, provide shade, and may be good for mental health (van den Berg et al., 2010). However, the extent to which changes in these factors reduce heat wave-related morbidity and mortality depend on location. A study in London, UK, found that built form and other dwelling characteristics more strongly influenced indoor temperatures during heat waves than did the urban health island effect (Oikonomou and Wilkinson,

A review of food aid programs indicates that a rapid response to the risk of child undernutrition, targeted to those in greatest need, with flexible financing and the capacity to rapidly scale up depending on need, may reduce damaging health consequences (Alderman, 2010). Community-based programs designed for other purposes can facilitate adaptation, including disaster risk management. In the Philippines, for example, interventions in low-income urban settings with the potential to reduce the harmful effects of climate extremes on health include savings schemes, small-scale loans, hygiene education, local control and maintenance of water supplies, and neighborhood level solid waste management strategies (Dodman et al., 2010). It is important to note that climate change adaptation in other sectors may influence health in a positive manner (e.g., re-vegetation of watersheds to improve water quality), or on occasion, exacerbate health risks (e.g., urban wetlands designed primarily for flood control may promote mosquito breeding) (Medlock and Vaux, 2011).

Present

Food- and water-borne infections Heat Vector-borne diseases Air quality Risk and potential for adaptation Extreme weather events Occupational health Risk level with current adaptation Vector-borne diseases Mental health and violence Occupational health Mental health and violence Risk level with high adaptation Potential for adaptation to reduce risk Food- and water-borne infections Extreme weather events Heat Air quality Extreme weather events Food- and water-borne infections Heat Air quality Vector-borne diseases Occupational health Mental health and violence

Figure 11-6

11.8. Adaptation Limits

Under High Levels of Warming

Most attempts to quantify health burdens associated with future climate change consider modest increases in global average temperature, typically less than 2°C. However, research published since AR4 raises doubt over whether it will be possible to limit global warming to 2°C above preindustrial temperatures (Rogelj et al., 2009; Anderson and Bows, 2011; PriceWaterhouseCoopers, 2012). It is therefore increasingly important to examine the likely health consequences of warming beyond 2°C, including extreme warming of 4°C to 6°C or higher. Predictions of this nature are limited by uncertainty about climatic as well as key, non climatic determinants of health including the nature and degree of adaptation. Here, we instead focus primarily on physiological or ecological limits that constrain our ability to adapt and protect human health and wellbeing (Section 16.4.1).

It can be assumed that the increase in many important climate-related health impacts at increasingly higher levels of warming will be greater than simple linear increments; that is, that the health consequences of a 4°C temperature increase will be more than twice those of a +2°C world (see Figure 11-6). Nonlinear and threshold effects have been

bserved in the mortality response to extreme heat (Anderson and Bell,

McMichael, 2013a), agricultural crop yields, as key determinants

f childhood nutrition and development (Schlenker and Roberts, 2009; Lobell et al., 2011a), and infectious diseases (Altizer et al., 2006), for example. These are also briefly elaborated here.

11.8.1. Physiological Limits to Human Heat Tolerance

In standard (or typical) conditions, core body temperatures will reach lethal levels under sustained periods of wet-bulb temperatures above about 35°C (Sherwood and Huber, 2010). Sherwood and Huber (2010) conclude that a global mean warming of roughly 7°C above current temperatures would create small land areas where metabolic heat dissipation would become impossible. An increase of 11°C to 12°C would enlarge these zones to encompass most of the areas occupied by today’s human population. This analysis is likely a conservative estimate of an absolute limit to human heat tolerance because working conditions are hazardous at lower thresholds. The U.S. military, for example, suspends all physical training and strenuous exercise when the wet-bulb globe temperature (WBGT) exceeds 32°C (Willett and Sherwood, 2012) while international labor standards suggest the time acclimatized individuals spend doing low intensity labor such as office work be halved under such conditions (Kjellstrom et al., 2009a).1 One estimate suggests global labor productivity will be reduced during the hottest months to 60% in 2100 and less than 40% in 2200 under the RCP8.5 scenario in which global mean temperatures rise 3.4°C by 2100 and 6.2°C by 2200 relative to 1861–1960 (Dunne et al., 2013). It is projected that tropical and mid-latitude regions including India, Northern Australia, and the southeastern USA will be particularly badly affected (Willett and Sherwood, 2012; Dunne et al., 2013).

11.8.2. Limits to Food Production and Human Nutrition

Agricultural crops and livestock similarly have physiological limitations in terms of thermal and water stress. For example, production of the staple crops maize, rice, wheat, and soybean is generally assumed to face an absolute temperature limit in the range of 40°C to 45°C (Teixeira et al., 2011), while key phenological stages such as sowing to emergence, grain-filling, and seed set have maximum temperature thresholds near or below 35°C (Yoshida et al., 1981; Porter and Gawith, 1999; Porter and Semenov, 2005 ). The existence of critical climatic thresholds and evidence of nonlinear responses of staple crop yields to temperature and rainfall (Brázdil et al., 2009; Schlenker and Roberts, 2009; Lobell et al., 2011b) thus suggest that there may be a threshold of global warming beyond which current agricultural practices can no longer support large human civilizations, and the impacts on malnourishment and undernutrition described in Section 11.6.1 will become much more severe. However, current models to estimate the human health consequences of climate impaired food yields at higher global temperatures generally incorporate neither critical thresholds nor nonlinear response functions (Lloyd et al., 2011; Lake et al., 2012), reflecting uncertainties about exposure-response relations, future extreme events, the scale and feasibility of adaptation, and climatic thresholds for other influences such as infestations and plant diseases. Extrapolation from current models nevertheless suggests that the global risk to food security becomes very severe under an increase of 4°C to 6°C or higher in global mean temperature (medium evidence, high agreement) (Chapter 7, Executive Summary).

11.8.3. Thermal Tolerance of Disease Vectors

Substantial warming in higher-latitude regions will open up new terrain for some infectious diseases that are limited at present by low temperature boundaries, as already evidenced by the northward extensions in Canada and Scandinavia of tick populations, the vectors for Lyme disease and tick-borne encephalitis (Lindgren and Gustafson, 2001; Ogden et al., 2006). On the other hand, the emergence of new temperature regimes that exceed optimal conditions for vector and host species will reduce the potential for infectious disease transmission and, with high enough temperature rise, may eventually eliminate some infectious diseases that exist at present close to their upper tolerable temperature limits. For example, adults of two malaria-transmitting mosquito species are unable to survive temperatures much above 40°C in laboratory experiments (Lyons et al., 2012), although in the external world they may seek out tolerable microclimates. Reproduction of the malaria parasite within the mosquito is impaired at lesser raised temperatures (Paaijmans et al., 2009). Larval development of Aedes albopictus, an Asian mosquito vector of dengue and chikungunya, also does not occur at or above 40°C (Delatte et al., 2009).

11.8.4. Displacement and Migration

Under Extreme Warming

Weather extremes and longer term environmental change including sea level rise lead to both more people displaced and increase in populations that are effectively trapped (Section 12.4.1.2). This trend is expected to be more pronounced under extreme levels of warming (Section 16.5). Gemenne (2011) argues that the most significant difference between the nature of human migration in response to 4°C of warming relative to 2°C would be to remove many people’s ability to choose whether to stay or leave when confronted with environmental changes. Health studies of refugees, migrants, and people in resettlement schemes suggest that forced displacement, in turn, is likely to lead to more adverse health impacts than voluntary migration or planned resettlement (McMichael et al., 2012). The health risks associated with forced displacement include undernutrition; food- and water-borne illnesses; diseases related to overcrowding such as measles, meningitis, and acute respiratory infections; sexually transmitted diseases; increased maternal mortality; and mental health disorders (McMichael et al., 2012).

11.8.5. Reliance on Infrastructure

Under severe climate regimes, societies may be able to protect themselves by enclosing places for living and working, first for their most vulnerable

1 WBGT is a heat index closely related to the wet-bulb temperature that also incorporates measures of radiant heat from the sun and evaporative cooling due to wind.

members: the young, old, ill, and manual laborers. This strategy will mean increased vulnerability to infrastructure failure and unreliable energy and water supplies. Electrical power outages have been linked to both accidental and disease-related deaths in temperate climates (Anderson and Bell, 2012), and failures in power supplies are more likely to occur during extreme weather events (Section 19.6.2.1). Large-scale reliance on air conditioning under a significantly hotter climate regime would therefore pose a serious health risk.

11.9. Co-Benefits

Essentially every human activity affects (and is affected by) climate and health status in some way, but not all are strongly linked to either and even fewer strongly to both. Here we focus on measures to mitigate the atmospheric concentration of warming climate-altering pollutants that also hold the potential to significantly benefit human health. These so-called co-benefits include health gains from strategies that are directed primarily at climate change, and mitigation of climate change from well chosen policies for health advancement (Haines et al., 2007; Apsimon et al., 2009; Smith and Balakrishnan, 2009; UNEP, 2011; Shindell et al., 2012). The literature on health co-benefits associated with climate change mitigation strategies falls into several categories (Smith and Balakrishnan, 2009; Smith et al., 2009). These include: • Reduce emissions of health-damaging pollutants, either primary or precursors to other pollutants in association with changes in energy production, energy efficiency, or control of landfills • Increase access to reproductive health services • Decrease meat consumption (especially from ruminants) and

substitute low-carbon healthy alternatives

Increase active transport particularly in urban areas

Increase urban green space.

In addition, although not discussed here, there are potential health side effects of mitigation measures, such as geoengineering, biofuel expansion, and carbon taxes that are potentially deleterious for human health (Tilman et al., 2009; see Chapter 19). In Table 11-3, we summarize what is known about the main categories of co-benefits, but because of space limitation, we only provide additional detail for two of them below.

11.9.1. Reduction of Co-Pollutants

Most of the publications related to CAPs and health-damaging pollutants refer to fuel combustion and fall into three major categories: (1) improvement in energy efficiency will reduce emissions of CO2 and health-damaging pollutants, providing these gains are not outpaced by increases in energy demand, and the energy is derived from combustion of fossil fuels or non-renewable biomass fuels, either directly or through the electric power system; (2) increases of combustion efficiency (decreasing emission of incomplete combustion products) will have both climate and health benefits, even if there is no change in energy efficiency and/or fuel itself is renewable, because a number of the products of incomplete combustion are climate altering and nearly all are damaging to health (Smith and Balakrishnan, 2009); and (3) increased use of non-combustion sources, such as wind, solar, tidal, wave, and geothermal energy, would reduce emissions of warming CAPs and health damaging air pollutants, providing benefits for climate and health (Jacobson et al., 2013).

Studies of the health co-benefits of reduction in air pollutants include sources that produce outdoor air pollution (Bell et al., 2008a) and household sources (Po et al., 2011). In many parts of the world, household fuels (poorly combusted biomass and coal) are responsible for a substantial percent of primary outdoor fine particle pollution as well, perhaps a quarter in India for example (Lim et al., 2012). In many parts of the world, household fuel (poorly combusted biomass and coal) is responsible for much fine particle outdoor air pollution and may contribute to long-range transport of hazardous air pollutants (Anenberg et al., 2013). This indicates that reductions in emissions from household sources will yield co-benefits through the outdoor pollution pathway.

300

100

10

1

China: wind household coal to propane/LPG stoves nuclear hybrid vehicles United States: United States: wind United States: China: nuclear China: India: improved biomass stoves China: household coal to biomass gasiﬁer stoves tCO2-eq offset DALYs avoided solar PV United States: solar PV China:

100

10, 00

10, 000

100 ,00 0

1,0 00, 000

10, 000 ,00 0

Health cost effectiveness (Int$/DALY)

100 ,00 0,0 00

Figure 11-7

Table 11-3

Co-beneﬁ t category |  |  | Beneﬁ ts | for health | Beneﬁ ts for | climate |  |  | References |
Reduction of co-pollutants solid fuel combustion | from household (see also WGIII AR5, | with | Potentially reduce disease, chronic and | exposures that are associated acute respiratory | Reduces CAP emissions with household solid | associated fuel use | Bell et al. Wilkinson | (2008); et al. | Smith et al. (2009); Lefohn | (2008); et al. (2010);
Chapters 7 to 10) |  | illnesses, | lung cancer, low stillbirths, and possibly | birth weight and tuberculosis | including CO2, CO, black and CH4 | carbon, | Venkataraman Organization (2010); | et al. Regional Po et al. | (2010); World Ofﬁ ce for (2011); Anenberg | Health Europe et al. (2012)
Reduction of greenhouse associated co-pollutants | gases and from industrial |  | Reductions in health-damaging emissions would decrease | co-pollutant exposures to outdoor air | Reductions in emissions carbon, CO, CH4, and | of CO2, black other CAPs | Bell et al. Jacobson | (2008); (2009); | Apsimon et al. Puppim de | (2009); Oliveira et al.
sources, such as power more efﬁ cient generation | plants and landﬁ or substitution | pollution lls, by of disease, | and could chronic and | reduce risks of cardiovascular acute respiratory illnesses, |  |  | (2009); (2009); | Smith et al. Dennekamp | (2009); Tollefsen et al. (2010); | et al. Jacobson
low carbon alternatives | (Section 27.3.7.2) | lung | cancer, and preterm | birth. |  |  | (2010); (2010); | Nemet et al. Shonkoff et | (2010); Rive al. (2011); | and Aunan Shindell et al.
|  |  |  |  |  |  | (2012); | West et al. | (2012); West | et al. (2013)
Energy efﬁ ciency. Actual may sometimes be less | energy reduction than anticipated |  | Reductions in fuel emissions of CAPs | demand potentially can reduce associated with fuel combustion | Reductions in emission to decreases in fuel | of CAPs due consumption | Markandya | et al. | (2009); Wilkinson | et al. (2009)
because part of the efﬁ | ciency beneﬁ t is | taken and | subsequent exposures | to pollutants that are |  |  |  |  |  |
as more service. |  | known | to be health | damaging. |  |  |  |  |  |
Increases in active travel pollution due to modiﬁ | and reductions cations to the built | in reduced | Increased physical non-communicable | activity; reduced obesity; disease burden, health | Reductions of CAP associated with vehicle | emissions transport; | Babey et (2007); | al. (2007); Kaczynski | Reed and and Henderson | Ainsworth (2008);
environment, including public transport and | better access to higher density of | service urban reduced | costs averted; exposure to air | improved mental health; pollution; increased local | replacing existing vehicles emission vehicles could | with lower reduce air | Casagrande Rundle | et al. et al. (2009); | (2009); Jarrett Woodcock | et al. (2009); et al. (2009);
settlements (see also 24.6, 24.7, 26.8) | Sections 24.4, 24.5, | access | to essential enhanced safety | services, including food stores; | pollution. |  | Durand McCormack | et al. (2011); and | Grabow et Shiell (2011); | al. (2011); Jensen et al.
|  |  |  |  |  |  | (2013); | Woodcock et | al. (2013) |
Healthy low greenhouse | gas emission | diets, Reduced | dietary saturated | fat in some populations | Reductions in CO2 and | CH4 emissions | McMichael | et al. | (2007); Friel et | al. (2009);
which can have beneﬁ of health outcomes (see | cial effects on a also Table 11.3) | range plant | (particularly from sources associated | ruminants) and replacement by with decreased risk of | from energy-intensive systems | livestock | Sinha et (2009); | al. (2009); Jakszyn et al. | Smith and (2011); | Balakrishnan Hooper et al.
|  | and | (ischemic) heart disease, (processed meat consumption). vegetable consumption | stroke, colorectal cancer Increased fruit can reduce risk of |  |  | (2012); | Pan et al. | (2012); Xu et | al. (2012)
|  | chronic | diseases. Reduced | CH4 emissions due to |  |  |  |  |  |
|  | a would | decreased demand for reduce tropospheric | ruminant meat products ozone. |  |  |  |  |  |
Greater access to | reproductive health | services Lower | child and maternal | mortality from increased | Potentially slower growth | of energy | Tsui et al. | (2007); | Gribble et al. | (2009); Prata
|  | birth | intervals and shifts | in maternal age | consumption and related emissions; less impact | CAP on land use | (2009); and Potts | O’Neill et al. (2011); | (2010); Potts and | Diamond-Smith Henderson (2012);
|  |  |  |  | change, etc. |  | Kozuki et | al. (2013) |  |
Increases in urban green | space (Table | 25-5) Reduced reduced | temperatures noise; enhanced | and heat island effects; safety; psychological | Reduces atmospheric sequestration in plant | CO2 via carbon tissue and soil | Mitchell (2008); | and Popham Maas et al. | (2007); (2009); van | Babey et al. den Berg et al.
|  | beneﬁ | ts; better self-perceived | health status |  |  | (2010); | van Dillen et | al. (2011) |
Carbon sequestration | in forest plantations, | Poverty | alleviation and | livelihood / job generation | Reduces emissions of | CAPs and | Holmes | (2010); | Ezzine-de-Blas | et al. (2011)
reducing emissions from degradation, and carbon | deforestation offset sales (see | and through and | sale of Clean voluntary market | Development Mechanism credits. Ameliorate | promotes carbon through reducing | sequestration emissions from |  |  |  |
Chapter 13 and Section Sections 20.4.1 and | 15.3.4; see also 26.8.4.3) | declines | in production or communities | competitiveness in rural | deforestation and | degradation |  |  |  |

If interventions result in reductions in coal combustion, there are a range of other potential health benefits beyond reduction of particulate air pollution emissions, including reducing other types of health-damaging emissions and the human impacts from coal mining (Lockwood, 2012; Smith et al., 2013).

Another category of air pollution co-benefits comes from controls on methane emissions that both reduce radiative forcing and potentially reduce human exposures to ambient ozone, for which methane is a precursor.

11.9.1.1. Outdoor Sources

Primary co-pollutants, such as particulate matter (PM) and carbon monoxide (CO) are those released at the point of combustion, while secondary co-pollutants, such as tropospheric ozone and sulfate particles, are formed downwind from the combustion source via atmospheric chemical interactions (Jerrett et al., 2009) and can be transported long distances.

The burden of disease from outdoor exposures in a country may often be greater in populations with low socioeconomic status, both because of living in areas with higher exposures and because these populations often have worse health and are subjected to multiple additional negative environmental and social exposures (Morello-Frosch et al., 2011).

11.9.1.2. Household Sources

Globally, the largest exposures from the pollutants from poor fuel combustion occur in the poorest populations. This is because household use of biomass for cooking is distributed nearly inversely with income. Essentially, no poor family can afford gas or electricity for cooking and very few families who can afford to do so, do not. Thus, the approximate 41% of all world households using solid fuels for cooking are all among the poor in developing countries (Bonjour et al., 2013). Although biomass makes up the bulk of this fuel and creates substantial health impacts from products of incomplete combustion when burned in simple stoves (Lim et al., 2012), probably the greatest health and largest climate impacts per household result from use of coal, which can also be contaminated with sulfur and a range of toxic elements as well (Edwards et al., 2004; Zhang and Smith, 2007). Successfully accelerating the reduction of impacts from these fuels, however, has not been found to be easily accomplished with biomass/coal stove programs implemented to date and may require moving to clean fuels (Bruce et al., 2013). The climate benefits from improving household biomass fuel combustion come in part from potential reduction of net warming by reducing emissions of aerosols (including black carbon), but more confidently from reduction of CH4 and other CAPs that are produced by incomplete combustion, as well as reductions in net CO2 emissions if interventions are applied in areas relying on non-renewably harvested wood fuel (WGI AR5 Section 8.5.3).

11.9.1.3. Primary Co-Pollutants

Outdoor exposure to PM, especially to particles with diameters less than 2.5 µm (PM2.5), contributes significantly to ill health including cardio and cerebrovascular disease, adult chronic and child acute respiratory illnesses, lung cancer, and possibly other diseases. The Comparative Risk Assessment (CRA) for outdoor air pollution done as part of the Global Burden of Disease (GBD) 2010 Project found approximately 3.2 million premature deaths globally from ambient particle pollution or about 3% of the global burden of disease (Lim et al., 2012). Importantly, reductions in ambient PM concentrations have also been shown to decrease morbidity and premature mortality (Boldo et al., 2010). A significant portion of ambient particle pollution derives from fuel combustion, perhaps 80% globally (GEA, 2012).

Because of higher exposures, an additional set of diseases has also been associated with combustion products in households burning biomass and/or coal for cooking and heating. Thus, in addition to the diseases noted above, cataracts, low birth weight, and stillbirth have been associated strongly with exposures to incomplete combustion products, such as PM and CO. CO has impacts on unborn children in utero through exposures to their pregnant mothers (WHO Regional Office for Europe, 2010). There is also growing evidence of exacerbation of tuberculosis (Pokhrel et al., 2010) in adults and cognitive effects in children (Dix-Cooper et al., 2012). The CRA of the GBD-2010 found 3.5 million premature deaths annually from household air pollution derived from cooking fuels or 4.4% of the global burden of disease (Lim et al., 2012). Importantly, there are also studies showing health benefits of household interventions, for child pneumonia (Smith et al., 2011), blood pressure (McCracken et al., 2007; Baumgartner et al., 2011) , lung cancer (Lan et al., 2002), and chronic obstructive pulmonary disease (Chapman et al., 2005). Another half a million premature deaths are attributed to household cookfuel’s contribution to outdoor air pollution, making a total of about 4 million in 2010 or 4.9% of the global burden of disease (Lim et al., 2012).

Black carbon (BC), a primary product of incomplete combustion, is both a strong CAP and health-damaging (IPCC, 2007; Ramanathan and Carmichael, 2008; Bond et al., 2013). A systematic review, meta-analysis, and the largest cohort study to date of the health effects of BC found that there were probably stronger effects on mortality from exposure to BC than for undifferentiated fine particles (PM2.5) (Smith et al., 2009). Reviews have concluded that abatement of particle emissions including BC represents an opportunity to achieve both climate mitigation and health benefits (UNEP, 2011; Shindell et al., 2012). WGI AR5 (Box TS-6), however, concluded that the net impact of BC emissions reductions overall is not certain as to sign, i.e., whether net warming or cooling. Nevertheless, there would be co-benefits in circumstances where BC is emitted without many other cooling aerosols, as with diesel and kerosene combustion (Lam et al., 2012).

Other examples of climate forcing, health-damaging co-pollutants of CO2 from fuel use are carbon monoxide, non-methane hydrocarbons, and sulfur and nitrogen oxides. Each co-pollutant poses risks as well as being climate altering in different ways. See WGI for more on climate potential and WHO reviews of health impacts (WHO, 2006; WHO Regional Office for Europe, 2010).

11.9.1.4. Secondary Co-Pollutants

In addition to being a strong GHG, methane is also a significant precursor to regional anthropogenic tropospheric ozone production, which itself is both a GHG and damaging to health, crops, and ecosystems (WGI AR5 TS.5.4.8). Thus, reductions in CH4 could lead to reductions in ambient tropospheric ozone concentrations, which in turn could result in reductions in population morbidity and premature mortality and climate forcing.

One study found that a reduction of global anthropogenic CH4 emissions by 20% beginning in 2010 could decrease the average daily maximum 8-hour surface ozone by 1 ppb by volume, globally—sufficient to prevent 30,000 premature all-cause mortalities globally in 2030, and 370,000 between 2010 and 2030 (West et al., 2012). CH4 emissions are generally accepted as the primary anthropogenic source of tropospheric ozone concentrations above other human-caused emissions of ozone precursors (West et al., 2007b), and thus the indirect health co-benefits of CH4 reductions are epidemiologically significant. On the other hand, work done for the GBD-2010 estimated 150,000 premature deaths from all ozone exposures globally in 2010, indicating a more conservative interpretation of the evidence for mortality from ozone (Lim et al., 2012).

In an analysis of ozone trends from 1998–2008 in the USA, Lefohn et al. (2010) found that 1-hour and 8-hour ambient ozone averages have either decreased or failed to increase due to successful regulations of ozone precursors, predominantly NOx and CH4. This is consistent with the EPA (2013) conclusion that in the USA, for the period 1980–2012, emissions of nitrogen oxides and volatile organic compounds fell by 59% and 57%, respectively (Lefohn et al., 2010; EPA, 2013). These results point to the effectiveness of reducing ambient ozone concentrations through regulatory tools that reduce the emissions of ozone precursors, some of which, like CH4, are GHGs.

Not every CAP emitted from fuel combustion is warming. The most prominent example is sulfur dioxide emitted from fossil fuel combustion, which changes to particle sulfate in the atmosphere. Although health damaging, sulfate particles have a cooling effect on global radiative forcing. Thus, reduction of sulfur emissions, which is important for health protection, does not qualify as a co-benefit activity because it actually acts to unmask more of the warming effect of other CAP emissions (Smith et al., 2009).

11.9.1.5. Case Studies of Co-Benefits of Air Pollution Reductions

A recent United Nations Environment Programme (UNEP)- and World Meteorological Organization (WMO)-led study of BC and tropospheric ozone found that, if all of 400 proposed BC and CH4 mitigation measures were implemented on a global scale, the estimated benefits to health would come predominately from reducing PM2.5 (0.7 to 4.6 million avoided premature deaths; 5.3 to 37.4 million avoided years of life lost) compared to tropospheric ozone (0.04 to 0.52 million avoided premature deaths; 0.35 to 4.7 million avoided years of life lost) based on 2030 population figures (UNEP, 2011). About 98% of the avoided deaths would come from reducing PM2.5, with 80% of the estimated health benefits occurring in Asia (Anenberg et al., 2012). Another study of the reduction of PM and ozone exposures due to CAPs emissions controls and including climate change feedback showed potential reductions of 1.3 million premature deaths by 2050 with avoided costs of premature mortality many times those of the estimated cost of abatement (West et al., 2013).

A study of the benefits of a hypothetical 10-year program to introduce advanced combustion cookstoves in India found that in addition to reducing premature mortality by about 2 million and DALYs by 55 million over that period, there would be a reduction of 0.5 to 1.0 billion tons CO2-eq (Wilkinson et al., 2009). Another study of India found a potential to reduce 570,000 premature deaths a year, one-third of national BC emissions, and 4% of all national GHG emissions by hypothetical substitution of clean household fuel technologies (Venkataraman et al., 2010).

In their estimation of effects of hypothetical physical and behavioral modifications in UK housing, Wilkinson and colleagues (Wilkinson et al., 2009) found that the magnitude and direction of implications for health depended heavily on the details of the intervention. However, the interventions were found to be generally positive for health. In a strategy of housing modification that included insulation, ventilation control, and fuel switching, along with behavioral changes, it was estimated that 850 fewer DALYs, and a savings of 0.6 megatonnes of CO2 per million population in 1 year, could be achieved. These calculations were made by comparing the health of the 2010 population with and without the specified physical and behavioral modifications (Wilkinson et al., 2009).

Markandya et al. (2009) assessed the changes in emissions of PM2.5 and subsequent effects on population health that could result from climate change mitigation measures aimed to reduce GHG emissions by 50% by 2050 (compared with 1990 emissions) from the electricity generation sector in the EU, China, and India. In all three regions, changes in modes of production of electricity to reduce CO2 emissions were found to reduce PM2.5 and associated mortality. The greatest effect was found in India and the smallest in the EU. The analysis also found that if the health benefits were valued similarly to the approach used by the EU for air pollution, they offset the cost of GHG emission reductions, especially in the Indian context where emissions are high but costs of implementing the measures are low (Markandya et al., 2009).

11.9.2. Access to Reproductive Health Services

Population growth influences the consumption of resources and emissions of CAPs (Cohen, 2010). Although population growth rates and total population size do not alone determine emissions, population size is an important factor. One study showed that CO2 emissions could be lower by 30% by 2100 if access to contraception was provided to those women expressing a need for it (O’Neill et al., 2012). Providing the unmet need for these services in areas such as the Sahel region of Africa that has both high fertility and high vulnerability to climate change can potentially significantly reduce human suffering as climate change proceeds (Potts and Henderson, 2012).

This is important not only in poor countries, however, but also some rich ones like the USA, where there is unmet need for reproductive health services as well as high CO2 emissions per capita (Cohen, 2010). Also, because of income rise in developing countries and concurrent reduction of greenhouse emissions in developed countries, a convergence in emissions per capita is expected in most scenarios by 2100 (WGI AR5 TS.5.2). Slowing population growth through lowering fertility, as might be achieved by increasing access to family planning, has been associated with improved maternal and child health—the co-benefit— in two main ways: increased birth spacing and reducing births by very young and old mothers.

11.9.2.1. Birth and Pregnancy Intervals

Current evidence supports, with medium confidence, that short birth intervals (defined as birth intervals ≤24 months and inter-pregnancy intervals <6 months) are associated with increased risks of uterine rupture and bleeding (placental abruption and placenta previa) (Bujold et al., 2002; Conde-Agudelo et al., 2007).

There is also a correlation between short birth interval and elevated risk of low birth weight (Zhu, 2005). Zhu (2005) found, in a review of three studies performed in the USA, that the smallest risk of low birth weight was found with inter-pregnancy spacing between 18 and 23 months. Another review of five cohort studies found that a birth interval shorter than 18 months was significantly associated with increased low birth weight, preterm birth, and infant mortality after controlling for confounding factors (Kozuki et al., 2013).

Although an ecological analysis, a review across 17 countries shows a strikingly coherent picture of the relationship between birth spacing and reductions in child, infant, and neonatal mortality, with risk of child undernutrition and mortality both increasing with shorter birth intervals (Rutstein, 2005). One study estimated that shifting birth spacing from current patterns in the world to a minimum of 24 months would reduce by 20% (approximately 2 million) the current excess child mortality in the world (Rutstein, 2005; Gribble et al., 2009).

11.9.2.2. Maternal Age at Birth

Risk of death during delivery is highest in very young and very old mothers, and these are also the age groups that most often want to control their fertility (Engelman, 2010). Women who begin child bearing under the age of 20 years are at an increased risk of developing pregnancy complications such as cephalopelvic disproportion, obstructed labor, preterm delivery, toxemia, bleeding, and maternal death (Tsui et al., 2007). In addition, children born to women younger than the age of 20 are at increased risk of fetal growth retardation and low birth weight, both of which can lead to long-term physical and mental developmental problems (Tsui et al., 2007). Childbearing at later ages (>35 years) is associated with increased risk of miscarriage and other adverse health outcomes (Cleary-Goldman et al., 2005; Ujah et al., 2005).

Providing access to family planning saves women’s lives by reducing the total number of births and, in particular, through the reduction of births in high-risk groups (Prata, 2009) while simultaneously reducing total fertility and subsequent CAP emissions. Studies have found that when women have access to family planning, it is the highest risk age groups (youngest and oldest women) who reduce their fertility the most. In other words, family planning has a differential impact on maternal mortality reduction through reducing births in the highest risk groups (Diamond-Smith and Potts, 2011).

11.10. Key Uncertainties and Knowledge Gaps

There is evidence that poverty alleviation, public health interventions such as provision of water and sanitation, and early warning and response systems for disasters and epidemics will help to protect health from climate risks. The key uncertainty is the extent to which society will strengthen these services, including taking into account the risks posed by climate change. With a strong response, climate change health effects are expected to be relatively small in the next few decades, but otherwise climate-attributable cases of disease and injury will steadily increase.

Since AR4, national governments, through the World Health Assembly, have specifically called for increased research on (1) the scale and nature of health risks from climate change; (2) effectiveness of interventions to protect health; (3) health implications of adaptation and mitigation decisions taken in other sectors, (4) improvement in decision support systems and surveillance, and (5) estimation of resource requirements. A recent scoping review identified quantitative peer-reviewed studies across all of these areas, with the exception of studies on the effectiveness or cost-effectiveness of targeted adaptation measures (Hosking and Campbell-Lendrum, 2012). There are also comparatively few studies of vulnerability in low- and middle-income populations, or of more complex disease pathways, such as the effect of more extreme weather on water and sanitation provision and diarrhea rates, on zoonotic diseases, or mental health. Studies of health co-benefits of climate change mitigation policies also remain rare compared to the size of the potential health gains. Potential negative side effects also need to be addressed, for example those arising from biofuel policies that compete with food production.

Relevant research for health protection in the near term is therefore likely to come from cross-disciplinary studies, including public health decision makers, in the following areas: improved vulnerability and adaptation assessments that focus on particularly vulnerable populations and encompass complex causal pathways; quantitative estimation of the effectiveness of health adaptation measures; surveillance, monitoring, and observational systems that link climate, health, and economic impact data and provide a basis for early warning systems as well as development of future scenarios; and assessment of the health co benefits of alternative climate mitigation policies.

In the longer term, research will need to make the best use of traditional epidemiologic methods, while also taking into account the specific characteristics of climate change. These include the long-term and uncertain nature of the exposure and effects on multiple physical and biotic systems, with the potential for diverse and widespread effects, including high-impact events. There are low-probability, but plausible, scenarios for extreme climate regimes before the end of the century. Although difficult, it is important to develop robust methods to investigate the health implications of conditions that may apply in 2100, as decisions today about mitigation will determine their likelihood. Given the increase globally in life expectancies, many babies born this decade will be alive at the end of the century, and will be personally affected by the climate that is in place in 2100.

Frequently Asked Questions

FAQ 11.1 | How does climate change affect human health?

Climate change affects health in three ways: (1) directly, such as the mortality and morbidity (including “heat exhaustion”) due to extreme heat events, ﬂoods, and other extreme weather events in which climate change may play a role; (2) indirect impacts from environmental and ecosystem changes, such as shifts in patterns of disease carrying mosquitoes and ticks, or increases in waterborne diseases due to warmer conditions and increased precipitation and runoff; and (3) indirect impacts mediated through societal systems, such as undernutrition and mental illness from altered agricultural production and food insecurity, stress, and violent conﬂict caused by population displacement; economic losses due to widespread “heat exhaustion” impacts on the workforce; or other environmental stressors, and damage to health care systems by extreme weather events.

Frequently Asked Questions

FAQ 11.2 | Will climate change have beneﬁts for health?

Yes. For example some populations in temperate areas may be at less risk from extreme cold, and may beneﬁt from greater agricultural productivity, at least for moderate degrees of climate change. Some areas currently prone to ﬂooding may become less so. However, the overall impact for nearly all populations and for the world as a whole is expected to be more negative than positive, increasingly so as climate change progresses. In addition, the latitude range in the world that may beneﬁt from less cold (e.g., the far north of the Northern Hemisphere) has fewer inhabitants compared with the equatorial latitudes where the burden will be greatest.

Frequently Asked Questions

FAQ 11.3 | Who is most affected by climate change?

While the direct health effects of extreme weather events receive great attention, climate change mainly harms human health by exacerbating existing disease burdens and negative impacts on daily life among those with the weakest health protection systems, and with the least capacity to adapt. Thus, most assessments indicate that poor and disenfranchised groups will bear the most risk and, globally, the greatest burden will fall on poor countries, particularly on poor children, who are most affected today by such climate-related diseases as malaria, undernutrition, and diarrhea. However, the diverse and global effects of climate change mean that higher income populations may also be affected by extreme events, emerging risks, and the spread of impacts from more vulnerable populations.

Frequently Asked Questions

FAQ 11.4 | What is the most important adaptation strategy to reduce the health impacts of climate change?

In the immediate future, accelerating public health and medical interventions to reduce the present burden of disease, particularly diseases in poor countries related to climatic conditions, is the single most important step that can be taken to reduce the health impacts of climate change. Priority interventions include improved management of the environmental determinants of health (such as provision of water and sanitation), infectious disease surveillance, and strengthening the resilience of health systems to extreme weather events. Alleviation of poverty is also a necessary condition for successful adaptation.

There are limits to health adaptation, however. For example, the higher-end projections of warming indicate that before the end of the 21st century, parts of the world may experience temperatures that exceed physiological limits during periods of the year, making it impossible to work or carry out other physical activity outside.

Frequently Asked Questions

FAQ 11.5 | What are health “co-beneﬁts” of climate change mitigation measures?

Many mitigation measures that reduce emissions of those climate-altering pollutants (CAPs) that warm the planet have important direct health beneﬁts in addition to reducing the risk of climate change. This relationship is called “co-beneﬁts.” For example, increasing combustion efﬁciency in households cooking with biomass or coal could have climate beneﬁts by reducing CAPs and at the same time bring major health beneﬁts among poor populations. Energy efﬁciency and reducing reliance on coal for electricity generation not only reduces emissions of greenhouse gases, but also reduces emissions of ﬁne particles that cause many premature deaths worldwide as well as reducing other health impacts from the coal fuel cycle. Programs that encourage “active transport” (walking and cycling) in place of travel by motor vehicle reduce both CAP emissions and offer direct health beneﬁts. A major share of greenhouse gas emissions from the food and agriculture sector arises from cows, goats, and sheep—ruminants that create the greenhouse gas methane as part of their digestive process. Reducing consumption of meat and dairy products from these animals may reduce ischemic heart disease (assuming replacement with plant-based polyunsaturates) and some types of cancer. Programs to provide access to reproductive health services for all women will not only lead to slower population growth and its associated energy demands, but also will reduce the numbers of child and maternal deaths.

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