Health losses attributed to anthropogenic climate change

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Carlson , Dann Mitchell , View ORCID Profile Rory Gibb , View ORCID Profile Rupert F. Stuart-Smith , View ORCID Profile Tamma Carleton , View ORCID Profile Torre E. Lavelle , View ORCID Profile Catherine A. Lippi , View ORCID Profile Megan Lukas-Sithole , View ORCID Profile Michelle A. North , View ORCID Profile Sadie J. Ryan , Dorcas Stella Shumba , Matthew Chersich , View ORCID Profile Mark New , View ORCID Profile Christopher H. Trisos doi: https://doi.org/10.1101/2024.08.07.24311640 Colin J. Carlson 1 Yale University School of Public Health , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Colin J. Carlson For correspondence: colin.carlson{at}yale.edu Dann Mitchell 2 University of Bristol , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rory Gibb 3 University College London , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rory Gibb Rupert F. Stuart-Smith 4 University of Oxford , UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rupert F. Stuart-Smith Tamma Carleton 5 University of California , Berkeley, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tamma Carleton Torre E. Lavelle 1 Yale University School of Public Health , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Torre E. Lavelle Catherine A. Lippi 6 University of Florida , USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catherine A. Lippi Megan Lukas-Sithole 7 Cape Peninsula University of Technology , South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Megan Lukas-Sithole Michelle A. North 8 University of KwaZulu-Natal , South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michelle A. North Sadie J. Ryan 6 University of Florida , USA 8 University of KwaZulu-Natal , South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sadie J. Ryan Dorcas Stella Shumba 9 African Climate and Development Initiative, University of Cape Town , South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Matthew Chersich 10 University of the Witwatersrand , South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mark New 9 African Climate and Development Initiative, University of Cape Town , South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mark New Christopher H. Trisos 9 African Climate and Development Initiative, University of Cape Town , South Africa 11 African Synthesis Centre for Environment Climate Change and Development (ASCEND), University of Cape Town , South Africa Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christopher H. Trisos Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Over the last decade, health impact attribution studies have shown that climate change is a present-day public health emergency, with substantial impacts felt through death, disability, and illness, equivalent to financial losses on the order of US$ trillions. However, these studies have so far been biased towards the direct effects of heat and extreme weather in high-income countries, and so capture a small fraction of the total global burden of climate change. Expanding the use of attribution science in public health could help put pressure on policymakers to take action for human health. It is unequivocal that recent climate change is outside the realm of normal natural variability, that natural factors in the earth system cannot explain the observed changes, and that anthropogenic influences are responsible for – at the time of writing – roughly +1.3°C of global warming from pre-industrial levels 1 . Climate scientists have developed this consensus through a set of quantitative methods that are grouped under the joint umbrella of climate change detection (showing that the climate has changed) and attribution (distinguishing the relative contributions of both anthropogenic and natural influences on the global climate system). In the last decade, researchers have also started using the same methods to isolate the effects of anthropogenic forcings on the social and ecological consequences of climate change. These end-to-end impact attribution studies 2 account for a small fraction of total research on climate change impacts, but represent some of the strongest evidence in terms of both methodological rigor and ability to articulate clear, quantitative estimates of historical and present-day impacts. Human health – especially loss of life, but also illness, disability, and poor well-being – is one of the most visible categories of climate change impacts. However, most work on the health impacts of climate change has stopped a step short of end-to-end attribution, focusing on long-term trends in health outcomes and their relationship to temperature and precipitation, or on the health outcomes of specific extreme weather events 3 – 5 . The first end-to-end health impact attribution study, which estimated the contribution of human-caused climate change to heat-related mortality during the 2003 European heat wave, was conducted in 2016 6 . Over the last decade, at least 20 studies have estimated the present-day health impacts of human-caused climate change ( Figure 1 ; see Online Methods for search criteria). Download figure Open in new tab Figure 1. The rise of health impact attribution. (A) Distribution of research effort by health impact and geography of the affected population. The map is divided by World Health Organization global regions. Circle size is proportional to the number of applicable studies focused on each region, ranging from 7 in the Americas to 14 in Europe. (B-E) Distribution of studies across years (B) and study focus (C-E). Some studies are in multiple categories. With a single exception 7 , every health impact attribution study to date has reported a substantial negative health impact of climate change – most often, a significant loss of life due to rising temperatures or extreme weather. Estimates of community-level mortality have ranged from 10 deaths (on a single day of the 2006 heat wave in London 8 ) to 1,683 deaths (associated with heat in Zürich between 1969 and 2018 9 ); at the national level and above, mortality estimates have ranged from 370 deaths (associated with the summer 2022 heat wave in Switzerland 10 ) to 271,656 deaths (associated with heat across 43 countries between 1991 and 2018 11 ) ( Table S1 ). So far, research effort has been heavily biased towards temperature-related risks (n = 17 of 20), mortality (n = 11 of 20), and extreme weather events in Europe (n = 4 of 6). However, these studies have diversified over time, with recent research addressing the growing burden of mosquito-borne viral diseases 12 , 13 , mortality due to air pollution from wildfires 14 , population displacement by floods 15 , and several unique health risks experienced by children, including neonatal deaths 16 , 17 , preterm births (and life-long associations with asthma, type 1 and 2 diabetes, and cognitive disabilities) 17 , low birth weight 18 , and childhood malaria 19 . In some cases, the estimated economic impact or value of these losses has been substantial. For example, one study estimated that medical costs related to heat wave-related preterm births in China could exceed US $300m per year, and the loss of lifetime earnings associated with cognitive disabilities could exceed US $1B per year 17 . Another study estimated that life-years lost due to Hurricane Harvey could be worth roughly US $17B 20 , while a global study of 185 extreme weather events estimated an average attributable loss of life valued at US $22.7B per year 21 . Applying the same standard estimates of the value of statistical life 22 , even with adjustment for national differences in GDP, suggests that annual losses due to some other attributable health impacts are also on the order of at least US $10B (see Online Methods ). For example, we estimate attributable annual adjusted losses equivalent to US $17.1B (unadjusted: $106B) due to temperature-related neonatal deaths in 29 low- and middle-income countries 16 ; $17.9B (unadjusted: $111.6B) due to heat-related deaths across 43 countries 11 ; and US $23.2B (unadjusted: US $144.5B) due to global PM2.5 air pollution from wildfires[Citation error]. In some cases, directly quantified losses could be approaching the trillions: for example, Vicedo-Cabrera et al .’s estimate of 271,656 climate change-attributable heat-related deaths across 43 countries between 1991 and 2018 would be equivalent to a loss of US $502.6B (unadjusted: US $3.1T) 11 . These kinds of estimates are likely to be increasingly important as countries seek financing for loss and damage resulting from climate change 23 , particularly given that human health impacts dominate estimates of aggregate economic damages from future climate change 24 . Another important frontier for health impact attribution is source attribution , a set of methods that quantify the contributions of specific major greenhouse gas emitters to extreme events and long-term warming trends, and in some cases, downstream impacts. For example, a recent preprint 9 estimated that dozens of heat-related deaths in Switzerland between 1969 and 2018 could be attributed to specific fossil fuel companies, led by Chevron (59 deaths), ExxonMobil (54 deaths), and Saudi Aramco (53 deaths). Using the same value of statistical life approach as above, these estimates would be equivalent to losses of US $109.2m (unadjusted: $678.5m), US $99.9m (unadjusted: US $621m), and US $98.1m (unadjusted: US $609.5m) attributable to each emitter, respectively. Source attribution studies are likely to have a unique relevance to legal actions against emitters and governments, which can be supported by evidence linking anthropogenic greenhouse gas emissions – and potentially, the specific emissions of the defendant company – to the claimants’ losses 25 , 26 ( Box 1 ). Increasingly, these studies represent the strongest available line of evidence regarding the present-day health impacts of climate change. In many cases, they are already substantially more up-to-date than modeling-based estimates: the only comprehensive estimate of present-day (circa 2000) global mortality and morbidity due to climate change was published twenty years ago 27 , 28 ; in 2014, these estimates were extended from 2030 through 2050 29 . For some health impacts, such as heat- and extreme weather-related mortality, attribution studies have been broadly concordant with these projections ( Table 2 ). In other cases, the divergence has been notable: for example, climate change-attributable mortality related to dengue fever has been an order of magnitude greater than expected, while malaria mortality is estimated to be an order of magnitude lower (see Online Methods ). Some major expected sources of mortality have still not been re-assessed. For example, one attribution study estimated that every 1°C of anthropogenic warming has resulted in a 1-2% increase in food insecurity 30 , but no estimate exists of attributable mortality from malnutrition. Similarly, there have been no impact attribution studies focused on diarrheal diseases, despite several closely-related observational studies 31 – 33 . New health impact attribution research continues to be published 34 , 35 . As the field of health impact attribution continues to grow, these studies can provide a more comprehensive view of the impacts of climate change on mortality, morbidity, life expectancy, and well-being 5 , 36 . In particular, future studies could assess the impact of climate change on dozens of infectious diseases; non-communicable diseases, including asthma, cancer, diabetes, heart disease, and kidney disease 37 ; health impacts of food insecurity, including malnutrition, stunting, and direct mortality 30 ; and mental health, including anxiety, depression, and suicides 38 . Future studies should also explore the uneven impacts of climate change within populations. These analyses are currently rare in the attribution literature, although one study found that women and the elderly accounted for 60% and 90% of attributable heat-related deaths, respectively, with older women experiencing mortality at 1.8 times the rate of the general population 10 . Finally, future studies should aim to provide a more geographically representative view of the health impacts of climate change. For example, almost all studies at the subnational level have so far focused on communities in high-income countries (n = 6 of 7). These biases represent the research community behind these efforts, which are almost entirely led out of Global North institutions — even when focused on health impacts in the Global South ( Figure S2 ). The authorship dynamics in this subfield are not atypical in global health, which suffers from a deeply inequitable and colonial system for exchanging data, knowledge, scientific credit, and international aid 39 . Some researchers have suggested that surfacing more open access health datasets from governments in the Global South will help close this gap 5 , but this is at best a partial solution, and at worst will reinforce existing dynamics by making it easier for Global North researchers to bypass collaboration altogether. The best way to increase knowledge about the health impacts of climate change is to increase investment in research led by climate scientists and public health researchers living at the frontlines of climate injustice: as one expert recently observed, “knowledge from the global South is in the global South” 40 . Online Methods Search strategy In this study, we focus on studies that conduct end-to-end health impact attribution, which we define as a statistical analysis that quantifies present-day or historical health impacts or risks resulting from anthropogenic (human-caused) forcings on the climate through the comparison of factual and counterfactual scenarios (where the counterfactual scenario usually excludes all anthropogenic forcings). These studies constitute some of the strongest evidence for the health impacts of climate change. However, this is a narrow definition: for example, most health outcomes that are attributable to climate change, based on the broader definition used by the Intergovernmental Panel on Climate Change 41 , have not been identified through the use of an end-to-end impact attribution study. We used the following keyword set to search for relevant literature on the detection and attribution of human health outcomes to human-caused climate change: (“climate chang*” OR “climatic change” OR “changing climate” OR “global warming” OR “drought” OR “flood*” OR “storm*” OR “cyclone” OR “extreme weather” OR “monsoon” OR “sea level rise” OR “sea-level rise” OR “heat stress” OR “global heating”) AND (health OR mortality OR morbidity OR “infectious disease” OR “non-communicable disease” OR suicide OR stunting OR miscarriage OR diarrhea OR diarrhoea OR injuries OR cancer OR diabetes OR cardiovascular disease OR stroke OR malnutrition OR malnourish OR anxiety OR depression) AND (attribut* OR counterfactual OR “excess mortality” OR “excess cases” OR DAMIP). We screened PubMed for studies containing these keywords anywhere in the title, abstract, or full text (search conducted July 21, 2023), and to ensure completeness, ran a second search of Web of Science for additional studies containing these keywords in the title or abstract (search conducted September 11, 2023). In total, we screened 3,677 study abstracts for broad relevance to the health impacts of climate change, and evaluated the full text of the 552 studies that passed the first round of screening. This led to the identification of only five eligible studies ( Figure S1 ). However, given the lack of standardized language across studies, which we found limited the completeness of the systematic search, we also searched Google Scholar using ad hoc combinations of keywords related to climate change, health, and attribution. We also searched five preprint servers (arXiv, medRxiv, bioRxiv, ResearchSquare, and SSRN) using the same ad hoc approach. In total, this approach led to the identification of thirteen eligible studies in our original systematic search 5 , including one that was originally excluded in a prior version of the search 15 , based on a narrower definition of health impacts. Since the conclusion of our original systematic literature search, we have continued to monitor Google Scholar and preprint servers for newly published or preprinted studies, and have periodically added new studies to an open-access database of health impact attribution literature called the Health Attribution Library (HAL; healthattribution.org ). Here, we include all seven additional publications and preprints that were posted by December 31, 2024. Inclusion criteria We identified a total of 20 peer-reviewed publications and preprints that have conducted end-to-end attribution of human health outcomes to human-caused climate change. These studies all include (1) a statistical model that relates health outcomes to climate variables, and (2) an estimate of the health impact of human-caused climate change, isolated through the use of a counterfactual scenario that captures natural climate variability without anthropogenic forcings. We excluded studies that quantified health effects of observed climate change by comparing health outcomes at different points in time (e.g., 42 , 43 ), rather than comparing present-day impacts to a present-day counterfactual that omitted anthropogenic influence on the climate. (However, we included one study that compared present-day temperature anomalies, baselined on 1880-1910, to a “pre-industrial” climate defined as an anomaly of +0°C. 44 ) We also excluded a small number of studies that used present-day counterfactual climate scenarios that did not sufficiently distinguish natural and anthropogenic sources of variability 45 , 46 . Finally, we excluded studies that included a health-related analysis alongside a climate change attribution component, but stopped short of end-to-end attribution of health impacts 47 . Value of statistical life analyses Following Newman and Noy 21 , we conducted a secondary analysis of economic losses based on the studies in Table S1 . Like Newman and Noy, we use the value of statistical life to assess the economic value of deaths attributable to human-caused climate change. Such an approach monetizes lives lost by leveraging estimates of how individuals make their own tradeoffs between income (e.g., a higher-paying job) and mortality risk (e.g., a riskier job). Resulting calculations reflect the willingness of individuals to pay to avoid the elevated risk of death, and are widely used for policy evaluation in the U.S. and internationally 48 . However, such a valuation approach does not account for any broader societal economic losses, such as costs to a public healthcare system induced by elevated morbidity or mortality. To implement this, we follow Newman and Noy in using the U.S. Environmental Protection Agency’s value for the VSL, and we treat VSL as equivalent across countries on equity-related grounds. However, we adjust the EPA figure for inflation (US $7.4m in 2006 = US $11.5m in 2024). We also present two options for how to adjust VSL estimates derived by the EPA for the U.S. population to global populations. In option 1, we adjust for the difference between U.S. and global GDP per capita (World Bank estimates: US $81,695 versus US $13,138) using an income elasticity of the VSL of 1, reflecting recent economic consensus 48 . This approach reduces our VSL estimate to US $1.85m. In option 2 (which we present in parentheticals throughout), we use the full U.S.-based value everywhere, as Newman and Noy did. Estimating climate change-attributable mortality from vector-borne diseases To supplement the three studies that generated global, cause-specific estimates of climate change-attributable mortality 11 , 21 , we also extrapolated mortality resulting from two vector-borne diseases, based on preliminary results from two preprints 12 , 19 . For dengue fever, we used Childs et al .’s estimate that 18% (95% CI: 11% – 27%) of present-day (1995-2014) cases are due to climate change. We multiplied this proportion by an estimated 21,803 (95% CI: 17,408 – 26,030) deaths per year due to dengue fever over the same time frame, derived from the Global Burden of Disease study database 49 . For malaria, we used Carlson et al .’s estimate that climate change was responsible for 0.09 (95% CI: −0.30 – 0.51) percentage points of the prevalence of malaria in children (ages 2 to 10) in sub-Saharan Africa between 2010 and 2014, compared to an estimated continent-wide prevalence of 24% in 2015 50 . In the absence of an epidemiological model relating changes in childhood malaria prevalence to age-structured incidence dynamics (and ideally, accounting for interventions 51 ), we assumed changes in prevalence reflect proportional changes in mortality, and used an estimate of 631,000 total deaths (95% CI: 394,000 – 914,000) across ages and across the continent in 2015 52 . Display Items View this table: View inline View popup Download powerpoint Table 1. Major estimates of global cause-specific mortality from climate change. See Online Methods for methods used in derivation of vector-borne disease mortality estimates. Box 1. Health impact attribution and litigation. Courts are increasingly hearing cases that aim to hold major emitters liable for their contribution to the impacts of climate change. Health impacts are often at the heart of this litigation – and health impact attribution studies are becoming an important source of potential evidence. Many of these suits seek financial compensation for high-emitting companies’ contributions to costs incurred by climate change impacts on human health (see e.g., Luciano Lliuya v. RWE , where a Peruvian Ministry of Health assessment of flood risk experienced by the plaintiff was cited; or County of Multnomah v. Exxon Mobil Corp ., where plaintiffs sought damages for “impacts on public health and infrastructure”). In these lawsuits, claimants may need to demonstrate a causal link between firms’ greenhouse gas emissions and the impacts on plaintiffs, a legal need that may be met through reference to the findings of attribution studies 25 . For example, in May 2024, three NGOs brought a criminal complaint grounded in attribution science that argued that Total Energies’ directors and main shareholders should be held criminally liable for endangering lives on the basis of the company’s contribution to climate change. Cases like these could deliver justice for victims of climate change, and discourage continued harmful conduct if firms are held liable for the costs of their emissions or directors are held personally responsible 53 . Other cases seek accelerated mitigation action. In the 2024 ruling of the European Court of Human Rights in Verein Klimaseniorinnen Schweiz and others v. Switzerland , the Court referred to attribution findings in finding evidence of a causal relationship between greenhouse gas emissions and the risk of heat wave-related deaths; this assessment was necessary for the claimants to have standing to bring the case. The Court then ruled that the Swiss government’s climate policy was insufficient to protect the claimant’s rights under the European Convention. Switzerland is now forced to reassess the ambition of their climate policy in light of this ruling — or else, be found to continue to be in breach of the European Convention on Human Rights. Supplemental Figures and Tables Download figure Open in new tab Figure S1. Results of the systematic literature review. The PRISMA template was generated by the PRISMA2020 R Shiny app 54 . Download figure Open in new tab Figure S2. Geography of study scope and author affiliations. Gray values indicate no studies were applicable. (Middle and bottom panel indicate the number of studies with at least one author, or lead author, with an affiliation listed in that country.) View this table: View inline View popup Table S1. Estimates of deaths attributed to anthropogenic climate change. Estimates that are italicized are preliminary findings from preprints, and so may be subject to change. Only estimates of attributable mortality are shown, and not estimates of deaths averted 7 , 16 , 21 . Author Contributions CJC and DM designed the study. CJC, RJG, TEL, CAL, MLS, MAN, SJR, and DSS generated data. CJC, RJG, and TAC contributed to data analysis and data visualization. CJC, RFSS, and TAC drafted the manuscript, and all authors edited and approved the final manuscript. Funding Statement This project was supported by the Wellcome Trust. Data Availability Statement All data and code are publicly available at github.com/carlsonlab/AttributableLosses . Acknowledgments We thank Felipe J. Colon-Gonzalez and Christopher Callahan for thoughtful conversations about health impact attribution. The authors declare no conflict of interest. Footnotes Major revision including change to Brief Communication format and addition of 8 studies. References 1. ↵ Forster , P. M. et al. Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence . Earth Syst. Sci. Data 16 , 2625 – 2658 ( 2024 ). OpenUrl 2. ↵ Carlson , C. J. et al. Designing and describing climate change impact attribution studies: A guide to common approaches . earthRxiv ( 2024 ) doi : 10.31223/X5CD7M . OpenUrl CrossRef 3. ↵ Ebi , K. 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