Abstract
Low- and middle-income countries (LMICs) contribute relatively little to global carbon emissions but are recognised to be among the most vulnerable parts of the world to health-related consequences of climate change. To help inform resilient health systems and health policy strategies, we sought to systematically analyse published projections of the impact of rising global temperatures and other weather-related events on human health in LMICs. A systematic search involving multiple databases was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify studies with modelled projections of the future impact of climate change on human health. Qualitative studies, reviews and meta-analyses were excluded. The search yielded more than 2500 articles, of which 70 studies involving 37 countries met criteria for inclusion. China, Brazil and India were the most studied countries while the sub-Saharan African region was represented in only 9% of studies. Forty specific health outcomes were grouped into eight categories. Non-disease-specific temperature-related mortality was the most studied health outcome, followed by neglected tropical infections (predominantly dengue), malaria and cardiovascular diseases. Nearly all health outcomes studied were projected to increase in burden and/or experience a geographic shift in prevalence over the next century due to climate change. Progressively severe climate change scenarios were associated with worse health outcomes. Knowledge gaps identified in this analysis included insufficient studies of various high burden diseases, asymmetric distribution of studies across LMICs and limited use of some climate parameters as independent variables. Findings from this review could be the basis for future research to help inform climate mitigation and adaptation programmes aimed at safeguarding population health in LMICs.
Keywords: Environmental health; Systematic review; Infections, diseases, disorders, injuries; Global Health; Epidemiology
WHAT IS ALREADY KNOWN ON THIS TOPIC
Populations in low- and middle-income countries (LMICs) are particularly vulnerable to the health effects of climate change due to their geographical location, low socioeconomic status and weak healthcare infrastructure.
Evidence from previous systematic reviews associate climatic events including temporal climatic variations with negative human health outcomes.
No systematic review has specifically focused on synthesising evidence on the projected long-term impact of climate change on human health in LMICs.
WHAT THIS STUDY ADDS
A focused assessment of the projected impact of climate change on health of populations in LMICs specifically.
We assemble evidence from peer-reviewed literature demonstrating that climate change is projected to materially increase the burden of both communicable and non-communicable diseases in LMICs.
Currently, available studies on the future impact of climate change on human health in LMICs are unevenly distributed in terms of geography and disease scope with a main focus on relatively few countries and a small number of diseases known to be climate sensitive.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Our findings indicate that concerted efforts are needed to ensure equitable distribution of research on the projected impact of climate change across LMICs and disease areas.
Evidence from this systematic review can be used to help drive advocacy efforts and inform policy and programmatic interventions on climate adaptation and mitigation in LMICs.
Introduction
Climate scientists increasingly warn that rising global temperatures and extreme weather-related events pose an existential threat to human health. The United Nations’ Intergovernmental Panel on Climate Change (IPCC), which describes climate change as the long-term shift in average or characteristic weather patterns, recently reported that risks associated with climate effects are rapidly evolving and will become more severe on a timeframe that is shorter than previously assumed.1 2 While the IPCC report highlighted insufficient global progress to address climate concerns, it made clear that concerted human action can still significantly mitigate negative climate effects in ways that confer long-lasting benefits, findings that are consistent with analyses of other major climate monitoring groups.3
The potential impacts of climate change are varied and pervasive. For example, changes in temperature, rainfall and humidity may affect the transmission and distribution of infectious and vector-borne illnesses, leading to the emergence or re-emergence of diseases in regions and populations previously unaffected.3 4 Climate change may exacerbate food insecurity and rates of malnutrition by interfering with food and water supplies.5 Furthermore, forced migration due to environmental degradation and natural disasters could promote civil conflicts and affect mental health.6
Populations that live in lower income parts of the world may be particularly vulnerable to the effects of climate change. Most of the estimated 3.6 billion people currently living in areas designated as highly susceptible to climate change are in low- and middle-income countries (LMICs), and the Notre Dame Adaptation Initiative ranks many LMICs as having the highest climatic vulnerability based on their exposure and sensitivity to the impact of climate change in domains of ecosystem service, food, health, human habitat, infrastructure and water.7 Unfortunately, many LMICs also have the lowest climate readiness scores, an assessment of the capability to assemble and translate available resources into adaptation actions.7 Put another way, populations in LMICs, already confronted with profound health inequities, and the least responsible for global carbon emissions, are at risk of suffering the greatest burden of adverse health outcomes due to climate change.8
Given the potentially severe implications of climate change on human health in LMICs, urgent attention is needed to address research gaps that will help to inform eventual mitigation and adaptation strategies. Advancing the knowledge base concerning the pathophysiology of climate-related illness in LMICs and identifying future needs for novel therapeutics to address emergent or re-emergent diseases are examples of outstanding needs. Detailed mapping of the causal relationships between ecological change and disease epidemiology will also be crucial to help guide the planning and implementation of population health policies and programmes.
To support current and future efforts focused on population health in global low-resource settings, we sought to help establish a common understanding of the projected health impact of climate change in LMICs. Specifically, we aimed to identify diseases anticipated to experience dynamic epidemiological burdens due to climate change, assess the current state of knowledge including research gaps and link findings with potential implications for health systems and health policy strategies.
Methods
Scope and search strategy
We conducted a systematic review of published literature on the projected future effects of climate change on human health in LMICs following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.9 The focus was on studies conducted in low-income, lower middle-income and upper middle-income countries, as defined by the World Bank’s country-income-level classifications.10 The search was restricted to peer-reviewed articles published between January 2012 (coinciding with the year of the publication of the IPCC special report on Exposure, and Impacts. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation1) and June 2023.
We built a search query in Embase using the Emtree thesaurus indexes, a hierarchically structured vocabulary for biomedicine and the related life sciences.11 The Emtree thesaurus is comprehensive, up-to-date and encompasses MEDLINE’s Medical Subject Headings (‘MeSH’) terms. The search terms in the query focused on human health, climate change and country names, which were connected using Boolean and proximity operators ‘AND’ and ‘OR’ by nesting and combining search sets. Search terms related to climate change were selected based on the extreme weather parameters discussed in the Fifth Assessment Report (AR5) of the IPCC12 and used to execute searches in Embase and MEDLINE. Detailed search terms are presented in online supplemental table S1. While the search terms were in English, no language restrictions were used in the search.
Article screening and selection criteria
Inclusion and exclusion criteria for evaluating articles were developed a priori. To be eligible for inclusion, studies needed to predict the future impact of climate change on human health and explicitly report an association between climate change and/or extreme climate events and specific health outcomes. Qualitative studies on perceived impacts of climate change were excluded as were meta-analyses, reviews, editorials, books and conference abstracts. Also excluded were articles that only described interventions to reduce the impacts of climate change, articles focused only on temporal trends in seasonality and health outcomes, studies that assessed the distribution of disease vectors (eg, insects) or other measures of climactic suitability for disease without determining direct impact on human health, and retrospective studies that did not include a future projection (ie, a time point beyond 2023) of the impact of climate change.
Evaluation of articles identified through the database search was a multistep process. To optimise consistency and inter-rater reliability in the application of inclusion and exclusion criteria, the research team together evaluated the abstracts and full texts of a sample of 10 articles before initiating data collection for this study. The title and abstract of each article from the search results were then independently reviewed, and a third researcher’s assessment was sought in cases where the first two reviewers disagreed. Articles with titles and abstracts that met the inclusion criteria were selected for full-text review. Similar to the title and abstract review, two researchers independently reviewed each full-text article, and a third researcher assessed cases of disagreement. Articles written in languages other than English were translated into English using Google Translate (http://translate.google.com) before review. Articles in Portuguese and Spanish were also reviewed by a native speaker.
Data extraction
Data were extracted from the final set of articles into a Microsoft Excel 365 data collection spreadsheet (Microsoft Corporation, 2020) that captured key data fields related to the study objectives and major themes that emerged from the articles. To ensure consistency and accuracy, data extracted from each article, including the quality assessment described below, was independently verified by a second researcher. Data collected from each article included the year of publication, study geographies, baseline and future climate projection timeframes, sources of data used for the study (ie, health outcome and climate data), climate parameters, health outcomes and a summary of key findings. Authors from different studies occasionally used varying definitions of similar climate variables and, to avoid misinterpretations, we maintained the terminologies used by study authors for climate parameters. For example, in most cases, ‘precipitation’ referred to rainfall, but we retained the term, ‘precipitation’ (rather than converting the term to ‘rainfall’) in accordance with the study investigators’ descriptions.
Since the 1990s, the IPCC has published different potential climate severity scenarios, which define future implications of current policies and practices that affect the planet. These scenarios are expected to guide research projections and provide a standardised framework for evaluating potential future effects of climate change.13 We extracted data on future climate change severity scenario(s) used in each study. These commonly included representative concentration pathways (RCPs) presented in the IPCC AR5 which was published in 2014,12 Shared Socioeconomic Pathways (SSP) scenarios updated in 2018 and presented in the Sixth Assessment Report (AR6) of the IPCC,2 13 and scenarios presented in IPCC’s Special Report on Emissions Scenarios (SRES) released in the 2000s.14 The RCPs consist of four potential pathways of greenhouse gas emissions designated RCP2.6, RCP4.5, RCP6 and RCP8.5, which increase in severity from the least to the greatest greenhouse gas emission scenarios.12 The SSP scenarios include socioeconomic and climate pathways labelled SSP126, SSP245, SSP370 and SSP585, which also represent projections of increasing climate severity.2 The future SRES climate scenarios include A1 (a scenario of globalisation and rapid economic and technological advances), A2 (a scenario of greater nationalistic approach with fragmented economic and technological advances), B1 (a scenario of sustainable practices involving environmental protection, use of clean technologies and social equity at the global level) and B2 (a scenario of regional-level sustainability practices).14
For studies that were conducted in a mix of LMICs and high-income countries, only data pertaining to LMICs were extracted.
Quality score
In line with PRISMA reporting guidelines, the quality of each article was assessed using a framework adapted from published tools and similar systematic reviews.15 16 Five questions were assessed: (1) Did the article have clear research questions related to the impact of climate on health? (2) Were the study methods and data analyses (including statistical methods) clearly described and relevant to the research questions? (3) Were the research questions addressed and the results clearly described in the context of study objectives? (4) Did the article clearly describe specific future climate severity scenarios? (5) Were the study limitations clearly described and did the limitations not substantially affect the results? For each of the five questions, a ‘yes’ assessment corresponded to a score of ‘1’ and a ‘no’ assessment to a score of ‘0’. Scores were aggregated to arrive at the total quality score for each article, with the highest possible score being ‘5’. To ensure inter-rater reliability, a sample of 10 articles was evaluated independently by investigators at the start of quality assessment and resulted in a high level of agreement.
Data analyses
Data were analysed using Microsoft Excel 365. To facilitate the analyses, health outcomes studied were categorised as cardiovascular diseases, enteric infections, malaria, neglected tropical diseases (NTDS; based on the WHO definition17) and respiratory diseases. The term ‘non-disease-specific temperature-related mortality’ was used to describe the health outcome in studies that reported temperature-related mortality without describing a specific pathophysiologic disease entity. The terms, ‘other communicable diseases’ and ‘other health outcomes’, were used when the health outcome of interest was a rarely studied disease or did not reasonably fit into one of the other established categories. Articles were stratified by year of publication, study countries, World Bank income group and World Bank geographic regions.10 A heatmap of study location was generated using MapChart (www.mapchart.net). The quality score of each article was categorised as low (scores of 1–2), moderate (scores of 3–4) or high (score of 5). Finally, we qualitatively summarised the evidence from the articles on the projected impact of climate change on health outcomes.
Patient and public involvement
This study is a systematic review involving the use of publicly available data. Patients and the public were not directly involved in this study.
Results
Literature search
The search yielded a total of 2601 unique peer-reviewed articles. After screening titles and abstracts, 2353 articles were excluded. Of the remaining 248 articles, 178 articles were excluded following full-text review. Seventy articles met inclusion criteria for data extraction and analysis. Details of the review process are shown in figure 1.
Figure 1. PRISMA diagram for literature search. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Table 1 summarises each study that contributed to the final dataset, including publication year, data sources, projection timeframes, study countries, key findings and quality assessment. The annual number of studies increased gradually from two studies in 2012 to seven studies in 2021, followed by a sharp increase to 17 studies in 2022 and 10 studies published in the first half of 2023 alone (online supplemental figure S1). Forty-seven (nearly 70%) of the eligible studies were published in the latter half of the review period (2019–2023). Sixty-five studies (93%) were deemed to be of moderate or high quality while five articles were determined to be of low quality. The main weaknesses of low-quality articles were insufficient clarity on data sources, poor description of study results or inadequate discussion of study limitations. Quality assessment details for each article are shown in online supplemental table S2.
Table 1. Studies published between 2012 and 2023 that project the impact of climate change on human health in low- and middle-income countries.
| Authors (year) | Article title | Study countries | Climate parameter(s) | Climate parameter data sources | Health outcome(s) | Health outcome data source(s) | Time period (baseline/ projected) | Climate severity projection scenarios | Key findings |
|---|---|---|---|---|---|---|---|---|---|
| Cao et al40 (2023) | Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China | China | Temperature, precipitation | Beijing Climate Center (China Meteorological Administration), WorldClim | Anthrax, brucellosis, dengue, encephalitis B, haemorrhagic fever, human avian influenza, leptospirosis, malaria, plague, rabies, schistosomiasis | Scientific Data Center for Public Health of China CDC | 2004–2017/2050, 2070 | RCP2.6, RCP4.5, RCP6.0, RCP8.5 | Transmission risks expected to increase for plague, rabies, human avian influenza, leptospirosis and encephalitis B; risks expected to decrease for anthrax, haemorrhagic fever, schistosomiasis and dengue. The risks for anthrax and malaria were also expected to shift geographically. |
| Chen et al30 (2023) | Association between temperature variability and global meningitis incidence | Global | Temperature | TerraClimate, CMIP6 | Meningitis | IHME Global Burden of Disease | 2000–2020/2021–2040, 2041–2060, 2061–2080, 2081–2100 | SSP126, SSP245, SSP370, SSP585 | Projected increase in incidence of more than 180% by 2100 in the severe climate scenario. |
| Hajat et al41 (2023) | Current and future trends in heat-related mortality in the MENA region: A health impact assessment with bias-adjusted statistically downscaled CMIP6 (SSP-based) data and Bayesian inference | Algeria, Morocco, Tunisia, Egypt, Libya, Turkey, Syria, Iraq, Iran, Jordan, Lebanon, Palestine | Temperature, humidity | CMIP6a | Heat-related mortality | World Population Prospects (2019 revision) | 2001–2020/2021–2100 | SSP126, SSP245, SSP370, SSP585 | Annual heat-related mortality predicted to increase from 2.1 at baseline to 123.4 per 100 000 people by 2081–2100 under the highest emissions scenario. |
| Liao et al42 (2023) | Extreme temperatures, mortality, and adaptation: Evidence from the county level in China | China | Temperature | China Meteorological Data Service Center, National Data Sharing Infrastructure of Earth System Science | Temperature-related mortality | China’s aggregate population census statistics | 2020/2060 | SSP245, SSP370, SSP585 | More than 5% projected increased in annual mortality rates in central and southern China in 2060 compared with 2020. |
| Liu et al43 (2023) | The impact of non-optimum temperatures, heatwaves, and cold spells on out-of-hospital cardiac arrest onset in a changing climate in China: A multi-centre, time-stratified, case-crossover study | China | Temperature | China Meteorological Data Service Center, CMIP6 | Out-of-hospital cardiac arrest | Baseline Investigation of Out-of-hospital Cardiac Arrest study | 2020/2100 | SSP245, SSP585 | Attributable fraction of out-of-hospital cardiac arrest expected to increase by up to 7%. |
| Nguyen et al44 (2023) | The impact of cold waves and heat waves on mortality: Evidence from a lower middle-income country | Vietnam | Temperature | Vietnam Institute of Meteorology, Hydrology and Climate Change | Heat-related mortality | General Statistics Office of Vietnam | 2000–2018/2020–2070 | RCP2.6, RCP4.5, RCP6,0 RCP8.5 | Deaths from heat waves projected to increase; deaths from cold waves projected to decrease. |
| Saeed et al45 (2023) | Modelling the impact of climate change on dengue outbreaks and future spatiotemporal shift in Pakistan | Pakistan | Temperature, rainfall | CMIP5a | Dengue fever | Not reported | 1976–2005/2006–2035, 2041–2070, 2071–2099 | RCP4.5, RCP8.5 | Dengue transmission suitable days (DTSD) projected to shift and expand geographically; some current hotspot areas projected to decline in DTSD. |
| Wang et al46 (2023) | Impact of climate change on dengue fever epidemics in South and Southeast Asian settings: A modelling study | Malaysia, Sri Lanka, Thailand | Temperature, rainfall | National Centers for Environmental Information, WorldClim | Dengue | Ministry of health and government data sources | 2020/2030s, 2050s, 2070s, 2090s | SSP126, SSP245, SSP585 | Dengue incidence predicted to increase with climate change, up to over 10 times in the 2090s compared with 2030s in the severe climate scenario. |
| Zhang et al47 (2023) | Increasing probability of record-population exposure to high temperature and related health-risks in China | China | Temperature, precipitation | China Meteorological Data Sharing Service System, National Meteorological Center | Temperature-related mortality | National Bureau of Statistics | 1985–2014/2021–2050, 2071–2100 | SSP245, SSP585 | Temperature-related health risks forecasted to increase over time, with excess mortality expected to increase by 1.85% in 2071–2100 in the severe climate scenario. |
| Zhou et al48 (2023) | Assessing the burden of suicide death associated with non-optimum temperature in a changing climate | China | Temperature | European Centre for Medium-Range Weather Forecasts Reanalysis Fifth Generation, National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections data set | Suicide deaths | China Cause of Death Reporting System | 1980–2009/2050 s, 2090s | SSP126, SSP245, SSP585 | Excess suicide deaths projected to increase by 8% to approximately 11% in the 2050s and 9% to 22% in the 2090s under the different climate severity scenarios. |
| Amro et al49 (2022) | Spatiotemporal analysis of cutaneous leishmaniasis in Palestine and foresight study by projections modelling until 2060 based on climate change prediction | Palestine | Temperature, precipitation | WorldClim | Cutaneous Leishmaniasis | Palestinian Ministry of Health | 1990–2020/2041–2060 | RCP4.5 | Cutaneous leishmaniasis incidence predicted to rise in several governorates of Palestine, with the diseases emerging for the first time in Gaza Strip; incidence projected to decline in other regions. |
| Bahrami et al50 (2022) | Climate change and respiratory diseases: Relationship between SARS and climatic parameters and impact of climate change on the geographical distribution of SARS in Iran | Iran | Temperature; precipitation | WorldClim | SARS | Iranian Ministry of Health and Medical Education | 2020/2050 | RCP4.5 | SARS prevalence and hospital admissions for climate-related respiratory diseases projected to increase by more than 30% in some cities. |
| Bhattarai et al31 (2022) | Malaria transmission in Nepal under climate change: anticipated shifts in extent and season, and comparison with risk definitions for intervention | Nepal | Temperature | Research program on Climate Change, Agriculture, and Food Security of the Consultative Group for International Agricultural Research, WorldClim; | Malaria | Published literature | 1960–1990/2030, 2050 | RCP4.5, RCP8.5 | Climate change will cause a geographical shift in malaria transmission suitability risk. Transmission suitability risk, and the duration of transmission season will expand in some areas and contract in other areas. |
| Chen et al22 (2022) | Projections of heatwave-attributable mortality under climate change and future population scenarios in China | China | Temperature | RegCM4.4 (regional climate model) | Heatwave-attributable deaths | China CDC | 1986–2005/2030, 2060, 2090 | RCP2.6, RCP4.5, RCP8.5, 1.5°C warming scenario | Trend in increase of heatwave-related deaths under different climate scenarios, reaching more than 72 000 deaths per annum by the 2090s under RCP8.5 compared with~10 000 at baseline. |
| Ding et al32 (2022) | Climate drives the spatiotemporal dynamics of scrub typhus in China | China | Temperature, precipitation, relative humidity | China Meteorological Data Service Center, CMIP5 | Scrub typhus | China CDC | 2010–2019/2030 s, 2050s, 2080s | RCP4.5, RCP6.0, RCP8.5 | Scrub typhus cases projected to increase, reaching up to 2 22 000 cases by the 2080s under RCP4.5 compared with 160 000 at baseline. |
| Douchet et al51 (2022) | Unravelling the invisible leptospirosis in mainland Southeast Asia and its fate under climate change | Cambodia, Lao PDR, Myanmar, Thailand, Vietnam | Temperature, precipitation, rainfall | WorldClim | Leptospirosis | Bureau of Epidemiology of the Ministry of Public Health of Thailand | 2003–2019/2041–2060, 2081–2100 | SSP126, SSP245, SSP370, SSP585 | Leptospirosis incidence predicted to decrease in all climate scenarios. |
| He et al52 (2022) | The effects of night-time warming on mortality burden under future climate change scenarios: a modelling study | China | Temperature | European Centre for Medium Range Weather Forecasts; CMIP6 | Mortality due to hot nights | China CDC, Hong Kong Census and Statistics Department, Japan Ministry of Health, Labor, and Welfare, Korea National Statistics Office | 1981–2010/2070–2099 | RCP2.6 & SSP126, RCP4.5 & SSP245, SSP585 | Attributable fraction of mortality due to hot nights projected to increase with climate change, with an increase of nearly 6% predicted under in the medium-severity climate change scenario. |
| Ingole et al53 (2022) | Local mortality impacts due to future air pollution under climate change scenarios | India, Mozambique | Climate change driven air pollution | Not provided | Mortality due to air pollution | Health and Demographic Surveillance System | 2010/2050 | RCP2.6, RCP8.5 | PM2.5-attributable deaths projected to increase considerably in several climate change severity scenarios. |
| Luo et al24 (2022) | Future injury mortality burden attributable to compound hot extremes will significantly increase in China | China | Temperature, relative humidity | China Meteorological Data Sharing Service System, National Urban Air Quality Real-time Publishing Platform, Inter-Sectoral Impact Model Intercomparison Project, CMIP | Injury mortality burden attributable to compound hot extremes (CHEs) | Disease Surveillance Points System | 2010s/2030s, 2060s, 2090s | RCP2.6, RCP4.5, RCP8.5 | Injury mortality burden of CHEs generally increased with climate change, with an 8-fold increase observed under RCP8.5 scenario by the 2090s. |
| Nili et al54 (2022) | The effect of climate change on malaria transmission in the southeast of Iran | Iran | Temperature, precipitation, relative humidity | Iranian Meteorological Organization, | Malaria | Deputy of Health of Sistan-Baluchistan University of Medical Science | 1910–2018/2021–2060 | RCP2.6, RCP8.5 | Malaria cases projected to decrease, and transmission season forecasted to shift from May, June, September, and October to year-round. |
| Ototo et al55 (2022) | Forecasting the potential effects of climate change on malaria in the Lake Victoria Basin using regionalized climate projections | Kenya, Tanzania, Uganda | Temperature, rainfall | Climate Hazards Group, Max Planck Institute for Meteorology, Coordinated Regional Downscaling Experiment; Swedish Meteorological and Hydrologic Institute, Koninklijk Nederlands Meteorologisch Instituut | Malaria | Not reported | 1995–2010/2030s, 2050s, 2070s | RCP2.6, RCP4.5, RCP8.5 | Climate change projected to increase malaria incidence, especially in extreme climate change scenarios; no trends observed in some low emission scenarios. |
| Parihar et al56 (2022) | Potential future malaria transmission in Odisha due to climate change | India | Temperature, rainfall | India Meteorological Department, World Climate Research Program | Malaria | Not reported | 1975–2005/2020s, 2050s, 2080s | RCP8.5 | Overall malaria transmission projected to decrease 20 to 40% during the monsoon season. |
| Van Wyk et al57 (2022) | Long-term projections of the impacts of warming temperatures on Zika and dengue risk in four Brazilian cities using a temperature-dependent basic reproduction number | Brazil | Temperature | Inter-Sectoral Impact Model Intercomparison Project | Dengue, Zika | Not reported | 2015–2019/2045–2049 | SSP126, SSP245, SSP370, SSP585 | Epidemic potential of dengue and Zika projected to increase with variations in the magnitude of increase across study cities. |
| Vassari-Pereira et al.58 (2022) | Impact of climate change and air quality on hospitalizations for respiratory diseases in municipalities of the Metropolitan Region of São Paulo (MRSP), Brazil | Brazil | Temperature, precipitation, relative humidity, atmospheric pressure | Municipal Environmental Sanitation Service of Santo André, Environmental Company of the State of São Paulo, Flood Alert System of the State of São Paulo, World Climate Research Program | Respiratory diseases | Unified Health System | 1998–2018/2070–2099 | RCP4.5, RCP8.5 | Incidence of hospitalizations due to respiratory diseases predicted to experience an increase by 10%, and a decrease by 26% in São Caetano do Sul and Santo André, respectively. |
| Xing et al59 (2022) | Projections of future temperature-related cardiovascular mortality under climate change, urbanization and population aging in Beijing, China | China | Temperature | 14 meteorological monitoring stations in Beijing; CMIP6 | Cardiovascular disease | Chinese National Center for Disease Control and Prevention, | 2006–2011/2020–2039, 2050–2069, 2080–2099 | SSP126, SSP245, SSP370, SSP585 | Temperature-related cardiovascular mortality projected to increase by 44–257% between 2039 and 2099, taking into account certain urbanisation and ageing assumptions. |
| Zhao et al60 (2022) | Mechanism of temperature on dengue fever transmission and impact of future temperature change on its transmission risk | China | Temperature | China Meteorological Scientific Data Co., Ltd. | Dengue | Guangdong Provincial CDC | 2015–2019/2030 s, 2060s, 2090s | RCP2.6, RCP4.5, RCP8.5 | The risk of dengue incidence predicted to increase under most of future climate scenarios, and the duration of transmission season is also predicted to increase. |
| Zhou et al61 (2022) | The burden of heat-related stroke mortality under climate change scenarios in 22 East Asian cities | China | Temperature | CMIP6, local meteorological departments | Stroke | China CDC, Hong Kong Census and Statistics Department, Japan Ministry of Health, Labor, and Welfare, Korea National Statistics Office | 1972–2015/2015–2099 | SSP126 & RCP1.9, SSP126 & RCP2.6, SSP245 & RCP4.5, SSP585 & RCP8.5 | Heat-related attributable fraction (AF) of stroke mortality projected to increase within the range of~1 to 8% according to climate scenarios. |
| Sadeghieh et al62 (2021) | Yellow fever virus outbreaks in Brazil under current and future climate | Brazil | Temperature | Coordinated Regional Downscaling Experiment | Yellow fever | Brazil Ministry of Health | 2017–2018/2011–2040, 2041–2070, 2071–2100 | RCP4.5, RCP8.5 | The number of cases and the duration of outbreaks were projected to decrease. |
| Sadeghieh et al63 (2021) | Zika virus outbreak in Brazil under current and future climate | Brazil | Temperature | Co-Ordinated Regional Downscaling Experiment | Zika virus | Health Information Platform for the Americas | 2016/2011–2040, 2041–2070, 2071–2100 | RCP4.5, RCP8.5 | Peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and duration of outbreak of Zika all projected to increase due to climate change. |
| Silveira et al64 (2021) | Projections of excess cardiovascular mortality related to temperature under different climate change scenarios and regionalized climate model simulations in Brazilian cities | Brazil | Temperature | Brazilian Institute of Meteorology, National Institute for Space Research, Eta-HadGEM2-ES, Eta-MIROC5. | Cardiovascular disease | Brazilian Mortality Information System of the Information Technology Department of the Public Health Care System | 2010–2019/2050–2059, 2090–2099 | RCP4.5, RCP8.5 | Temperature-related cardiovascular disease mortality projected to increase by more than 8% in 2090–99 compared with baseline. |
| Wu et al65 (2021) | Increasingly expanded future risk of dengue fever in the pearl river delta, China | China | Temperature, precipitation | WorldClim | Dengue | National Notifiable Infectious Disease Reporting Information System | 2015/2050s, 2070s | RCP2.6, RCP4.5, RCP8.5 | Population at risk of dengue fever projected to increase up to over 70 million under various climate scenarios compared with approximately 45 million at baseline. |
| Xu et al33 (2021) | Estimation of ambient PM2.5-related mortality burden in China by33 2030 under climate and population change scenarios: A modeling study | China | Air particulate pollution | Weather Research and Forecasting, US National Centers for Environmental Prediction Final Operational Model Global Tropospheric Analyses dataset, Emission Database for Global Atmospheric Research version 4.3.1 | Ambient PM2.5-related mortality | China Statistic 2016, China Health and Family Planning Statistical Yearbook 2016, 2016 City and provincial Statistical Yearbooks and Health Statistical Yearbooks, National Economic and Social Development of 2015, Public Health Science Data Center, 2016 Global Burden of Disease Study | 2015/2030 | RCP4.5, RCP8.5 | When population growth and age structure are accounted for, premature mortality projected to increase by up to 50% by 2030. |
| Yang et al66 (2021) | Projecting heat-related excess mortality under climate change scenarios in China | China | Temperature | China Meteorological Data Service Center, CMIP5 | Heat-related excess mortality | China Disease Surveillance Points system | 2010/2030s, 2050s, 2090s | RCP4.5, RCP8.5 | Excess heat-related mortality due to climate change is projected to increase to 2.4% in the 2030s and 5.5% in the 2090 compared with baseline. |
| Zhu et al21 (2021) | Non-optimum temperature-related mortality burden in China: Addressing the dual influences of climate change and urban heat islands | China | Temperature | EaSM Project Dataset | Temperature related mortality | China Statistical Yearbooks, IHME Global Burden of Disease study | 2000–2010/2050 | RCP4.5, RCP8.5 | Temperature-related deaths predicted to decrease from 1.19 million to 1.08–1.17 million, except in the most populous scenarios where deaths were projected to increase. |
| Aboubakri et al67 (2020) | Projection of mortality attributed to heat and cold; the impact of climate change in a dry region of Iran, Kerman | Iran | Temperature | National Meteorology Organization, Canadian Earth System Model | Temperature-related mortality | Health Deputy of Kerman University of Medical Science | 2010–2019/2020 s, 2030s, 2040s | RCP2.6, RCP4.5, RCP8.5 | Temperature-related mortality projected to increase, with more than 3700 temperature-attributable deaths occurring each decade from the 2020s through the 2040s. |
| Chaturvedi and Dwivedi68 (2020) | Estimating the malaria transmission over the Indian subcontinent in a warming environment using a dynamical malaria mode | India | Temperature, rainfall | National Centers for Environmental Prediction, Global Precipitation Climatology Project, CMIP5 | Malaria | Directorate of National Vector Borne Disease Control Programme, Ministry of Health and Family Welfare | 1961–2005/2006–2050 | RCP8.5 | Risk of transmission projected to increase during August to October and the transmission season projected to expand to November in southern regions; decrease in transmission projected in some northwestern and southeastern regions. |
| Henry and Mendonça69 (2020) | Past, present, and future vulnerability to dengue in Jamaica: A spatial Analysis of monthly variations | Jamaica | Temperature, precipitation | WorldClim, Climate Hazards Group Infrared Precipitation with Stations, NASA; Caribbean Community Climate Change Centre | Dengue | Ministry of Health | 1970–2000, 2002–2016/2030 | RCP 8.5 | Dengue vulnerability index (a function of exposure, susceptibility, and adaptive capacity) projected to increase during January to June and October to December. |
| Kakarla et al70 (2020) | Dengue situation in India: Suitability and transmission potential model for present and projected climate change scenario | India | Temperature | Global Historical Climatology Network, Version 2, Climate Anomaly Monitoring System (GHCN-CAMS), NOAA/OAR/ESRL PSD, Boulder, Colorado, USA | Dengue | National Vector Borne Disease Control Program, Ministry of Health and Family Welfare | 1980–2017/2018–2030, 2031–2050, 2051–2080 | RCP4.5, RCP8.5 | Dengue cases projected to increase in some regions and duration of dengue transmission season projected to extend to December (from June to October) in the southern part of the country. |
| Ryan et al71 (2020) | Shifting transmission risk for malaria in Africa with climate change: a framework for planning and intervention | Africa | Temperature | WorldClim, CMIP | Malaria | Mapping Malaria Risk in Africa | 1960–1990/2030, 2050, 2080 | RCP4.5, RCP8.5 | Transmission season projected to shorten but an overall net increase in populations at risk for malaria was projected with an additional 73 million people at risk by 2080 in Eastern Africa under extreme climate change scenarios. |
| Wang et al72 (2020) | Assessment of the impact of geogenic and climatic factors on global risk of urinary stone disease | Global | Temperature | U.S. Geological Survey, Program for Climate Model Diagnosis and Intercomparison | Urinary stone disease | Published literature | 2017/2100 | RCP8.5 | The size of the Earth’s surface associated with increased probabilities for urinary stone disease are projected to increase. |
| Wagner et al73 (2020) | Climatological, virological, and sociological drivers of current and projected dengue fever outbreak dynamics in Sri Lanka | Sri Lanka | Temperature, precipitation | ERA-5 global atmospheric reanalysis dataset, Climate Hazards Group InfraRed Precipitation with Station, CMIP5 | Dengue | Epidemiology Unit of the Sri Lankan Ministry of Health | 2010–2016/2040–2059, 2080–2099 | RCP8.6 | Average annual dengue transmission rate and number of cases projected to increase. |
| Asadgol et al74 (2019) | The effect of climate change on cholera disease: The road ahead using artificial neural network | Iran | Temperature, precipitation | Iran Meteorological Organization, IPCC data distribution centre | Cholera | Centers for Disease Control and Prevention at Qom University of Medical Sciences | 1976 to 2005/2020–2050 | RCP2.6, RCP8.5 | Incidence of cholera projected to increase and peak cholera transmission season also projected to expand. |
| Huang et al75 (2019) | Projections of the effects of global warming on the disease burden of ischemic heart disease in the elderly in Tianjin, China | China | Temperature | Tianjin Meteorological Bureau, WorldClim, Tianjin Environmental Monitoring Centre, World Climate Research Programme, CMIP5 | Ischaemic heart disease | Death Registration and Reporting System of the Chinese CDC | 2006 to 2011/2050s, 2070s | RCP2.6, RCP4.5, RCP8.5 | Annual temperature-related YLL projected to increase up to 15% in 2050s and up to 38% in 2070s; accounting for population growth, IHD is projected to increase sharply even with adaptation. |
| Le et al76 (2019) | Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya | Kenya | Atmospheric carbon dioxide, temperature | European Centre for Medium-range Weather Forecasts | Malaria | Malaria Atlas Project | 2006 to 2014/2050 | Study-derived carbon dioxide and air temperature scenarios | Malaria transmission projected to increase with number of exposed and infected cases expected to increase by 2–11% and 3–8%, respectively, compared with baseline. |
| Liu et al34 (2019) | Modification effects of population expansion, ageing, and adaptation on heat-related mortality risks under different climate change scenarios in Guangzhou, China | China | Temperature | Guangdong Provincial Meteorological Bureau, CMIP5 | Heat-related Years of Life Lost (YLLs) | Guangdong Provincial Center for Disease Control and Prevention | 1980s/2030s, 2060s, 2090s | RCP2.6, RCP4.5, RCP8.5 | Annual heat-related YLLs projected to increase by approximately 2,000, 7,000, and 11 000 in 2030s, 2060s, and 2090s, respectively under RCP8.5 and fixed population scenarios; YLLs will be exacerbated by population expansion and ageing. |
| Sarkar et al77 (2019) | Shift in potential malaria transmission areas in India, using the Fuzzy-based Climate Suitability Malaria Transmission (FCSMT) Model under changing climatic conditions | India | Temperature, humidity | Indian Meteorological Department, CORDEX South Asia, CMIP5 | Malaria | National Vector Borne Disease Control Programme | 1976–2005/2030s | RCP4.5 | Spatial distribution of Plasmodium falciparum and P. vivax malaria projected to increase with increases in annual transmission window and shifting months of greatest transmission. |
| Wang et al78 (2019) | Tens of thousands additional deaths annually in cities of China between 1.5°C and 2.0°C warming | China | Temperature | National Climate Center of China Meteorological Administration | Heat-related mortality | Chinese National Center for Chronic and Non-communicable Disease Control and Prevention | 1986–2005/2060–2099 | RCP2.6, RCP4.5 | Annual heat-related mortality is projected to increase from 32.1 to 59.2–81.3 per million inhabitants for 2.0°C warming with excess mortality relatively higher in females and non-working age populations. |
| Huang et al79 (2018) | Projections for temperature-related years of life lost from cardiovascular diseases in the elderly in a Chinese city with typical subtropical climate | China | Temperature | Ningbo Meteorological Bureau, CMIP5 | Cardiovascular disease | Ningbo Municipal Center for Disease Control and Prevention | 2008–2015/2050 s, 2070s | RCP2.6, RCP4.5, RCP8.5 | Projected increase in heat-related YLL due to cardiovascular disease offset by decrease in cold-related YLL leading to net decrease in annual temperature-related YLL. |
| Li et al80 (2018) | Long-term projections of temperature-related mortality risks for ischemic stroke, hemorrhagic stroke, and acute ischemic heart disease under changing climate in Beijing, China | China | Temperature | China Meteorological Data Sharing Service System for Beijing, IPCC GCMs | Ischaemic stroke, haemorrhagic stroke, acute ischaemic heart disease | Chinese CDC | 1980s/ 2020s, 2050s, 2080s | RCP4.5, RCP8.5 | Mortality associated with ischaemic stroke projected to increase by greater than 130%; mortality due to haemorrhagic stroke and acute ischaemic heart disease projected to remain relatively stable. |
| Li et al81 (2018) | Projected temperature-related Years of Life Lost from stroke due to global warming in a temperate climate city, Asia | China | Temperature | Local Meteorological Bureau, World Climate Research Programme, CMIP5 | Stroke | China CDC | 2006–2011/2050 s, 2070s | RCP2.6, RCP4.5, RCP8.5 | Slight decrease in YLL due to stroke projected in the high emission scenario. |
| Zhang et al82 (2018) | Projection of temperature-related mortality due to cardiovascular disease in Beijing under different climate change, population, and adaptation scenarios | China | Temperature | China Meteorological Administration, Beijing Municipal Environmental Monitoring Center, IPCC | Cardiovascular disease | Chinese Center for Disease Control and Prevention | 2007–2009/2050 s, 2070s | RCP2.6, RCP4.5, RCP8.5 | Temperature-related cardiovascular deaths projected to increase 3.5%–10.2% under different RCP scenarios. |
| Chen et al83 (2017) | Impact of climate change on heat-related mortality in Jiangsu Province, China | China | Temperature | NASA Earth Exchange Global Daily Downscaled Projections dataset including GCMs from CMIP5 | Cardiovascular, respiratory, stroke, ischaemic heart disease, and chronic obstructive pulmonary disease | Jiangsu Provincial Center for Disease Prevention and Control | 1980–2005/2016–2040, 2041–2065 | RCP4.5, RCP8.5 | Heat-related mortality for total non-accidental, cardiovascular, respiratory, stroke, ischaemic heart disease, and chronic obstructive pulmonary disease projected to increase (greatest number of deaths contributed by cardiovascular, stroke, and ischaemic heart disease). |
| Gasparrin et al84 (2017) | Projections of temperature-related excess mortality under climate change scenarios | Brazil, China, Moldova, Mexico, Philippines, Thailand, Vietnam | Temperature | Inter-Sectoral Impact Model Intercomparison Project | Temperature-related mortality | Multi-Country Multi-City Collaborative Research Network | 2010–2019/2090–2099 | RCP2.6, RCP4.5, RCP6.0, RCP8.5 | Mortality in countries with traditionally warmer climates expected to rise by 3–13%; mortality in countries with temperate climates projected to be stable or mildly decrease. |
| Liu et al85 (2017) | Projected burden of disease for bacillary dysentery due to flood events in Guangxi, China | China | Flood events, temperature | Yearbooks of Meteorological Disasters, China Meteorological Data Sharing Service System | Bacillary dysentery | National Notifiable Disease Surveillance System | 2004–2010/2030, 2050, 2100 | RCP4.5 | Years lost due to disabilities from bacillary dysentery projected to increase 20% by 2050, accounting for potential changes floods, temperature, and population size. |
| Prist et al86 (2017) | Climate change and sugarcane expansion increase Hantavirus infection risk | Brazil | Temperature, precipitation | International Research Institute for Climate and Society, Colombia University Lamont-Doherty Ocean and Climate Physics Library GCMs | Hantavirus | Center for Epidemiological Surveillance of the State of São Paulo | 1993–2012/2050 | RCP4.5, RCP8.5 | Risk of Hantavirus Cardiopulmonary Syndrome projected to increase by one-third. |
| Kibret et al87 (2016) | Malaria and large dams in sub-Saharan Africa: future impacts in a changing climate | sub-Saharan Africa | Temperature, rainfall | CMIP5 | Malaria | Published literature, Malaria Atlas Project | 2000–2010/2050 s, 2080s | RCP2.6; RCP8.5 | Number of malaria cases associated with dams projected to increase by 1–2 million in 2050s and 1.3–2 million in 2080s. |
| Li et al29 (2016) | Aging will amplify the heat-related mortality risk under a changing climate: projection for the elderly in Beijing, China | China | Temperature | China Meteorological Data Sharing Service System for Beijing, Beijing Meteorological Bureau | Temperature-related mortality | China CDC | 1980s/ 2020s, 2050s, 2080s | RCP4.5, RCP8.5 | Heat-related mortality in elderly projected to increase by>250% in 2080s in a scenario of medium population growth and RCP8.5. |
| Martinez et al88 (2016) | Projected heat-related mortality under climate change in the metropolitan area of Skopje | North Macedonia | Temperature | European Centre for Medium-Range Weather Forecasts; US National Climactic Data Center | Heat-related mortality | North Macedonia Institute of Public Health | 1986–2005/2026–2045, 2081–2100 | RCP8.5 | Heat-related mortality projected to more than double in 2026–2045 and more than quadruple in 2081–2100. |
| Mellor et al23 (2016) | Systems approach to climate, water, and diarrhea in Hubli Dharwad, India | India | Temperature, rainfall | Indian Metrological Department, LARS-WG incorporating | Diarrheal disease | Published literature | 2010–2012/2011–2030, 2046–2065, 2080–2099 | A1B, A2, SRES B1 | All-cause diarrhoea disease prevalence predicted to increase by 18% in 2080–2099. |
| Ngarakana et al89 (2016) | Assessing the role of climate change in malaria transmission in Africa | Africa | Temperature, precipitation | WorldClim, HadCM3, CSIRO Mk3 GCM | Malaria | Not reported | 1950–2000/2040 | A2A | Malaria transmission projected to increase in some regions and decrease in other regions in Africa. |
| Semenza et al90 (2016) | Climate change projections of West Nile virus infections in Europe: implications for blood safety practices | Europe | Temperature | US National Centre for Atmospheric Research | West Nile Virus | European Center for Disease Prevention and Control | 2014/2025, 2050 | A1B | WNV predicted to increase, particularly at the edges of the current transmission areas (eg, parts of Turkey). |
| Shajari and Sanjereheiet91 (2015) | Modeling the distribution of urolithiasis prevalence under projected climate change in Iran | Iran | Temperature; precipitation | WorldClim; HadGEM2-CC, GFDL-ESM2G, CCSM4 | Urolithiasis | Iran national urolithiasis prevalence survey | 2005/2050 | RCP2.6, RCP4.5 | Urolithiasis prevalence predicted to increase up to 9% in 2050. |
| Banu et al92 (2014) | Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh | Bangladesh | Temperature, humidity | Bangladesh Meteorological Department | Dengue | Bangladesh Directorate General of Health Services | 2000–2010/2100 | IPCC regional climate change projection for South Asia | Dengue cases projected to rise by~16 000 cases attributable to temperature variation. |
| Cordovez et al93 (2014) | Using the basic reproduction number to assess the effects of climate change in the risk of Chagas disease transmission in Colombia | Colombia | Temperature | WorldClim | Chagas disease | Published literature | 2014/2034 | IPCC 4th Assessment Report scenarios | Chagas disease incidence projected to change in Colombia, with some higher elevation regions experiencing exacerbation and other regions experiencing less disease. |
| Colón-González et al.94 (2013) | The effects of weather and climate change on dengue | Mexico | Temperature, precipitation | Mexican National Meteorological Service | Dengue | Mexican National System of Epidemiologic Surveillance | 1970–1999/2030, 2050, 2080 | A1B, A2, B1 | Dengue incidence projected to increase up to~40% by 2080. |
| Moors et al95 (2013) | Climate change and waterborne diarrhoea in northern India: Impacts and adaptation strategies | India | Temperature, precipitation (including drought), humidity | HadCM3, ECHAM5, REMO, HadRM3 | Diarrhoea | National Integrated Disease Surveillance Project | 1980s–2000s/ 2030s–2050s | A1B | Diarrhoea incidence projected to increase to varying extents ranging from none to 21% (average 13%). |
| Sheffield et al96 (2013) | Current and future heat stress in Nicaraguan workplaces under a changing climate | Nicaragua | Wet bulb globe temperature | US National Climatic Data Center’s Global Surface Summary of the Day | Occupational heat stress | Nicaraguan Ministry of Labor | 2000/2050 | Bergen Climate Model version 2 | Projected 14% increase in workplace settings affected by heat stress exposure that exceeds the threshold for continuous work at moderate level. |
| Thomassen et al97 (2013) | Pathogen-host associations and predicted range shifts of human monkeypox in response to climate change in Central Africa | Pan-sub-Saharan Africa | Temperature, precipitation | WorldClim, MODIS, QuikScat | Mpox | National Institute of Biomedical Research and Ministry of Health; Centers for Disease Control | Not clear/2050, 2080 | 16 scenarios in IPCC-SRES | Mpox transmission dynamics projected to change, with some regions experiencing exacerbation and other regions experiencing less disease. |
| El-Fadel et al98 (2012) | Climate change and temperature rise: Implications on food- and water-borne diseases | Lebanon | Temperature | Department of Meteorology | Food- and water-borne diseases (brucellosis, cholera, dysentery, food poisoning, hydatic cyst, parasitic worms, trichinosis, typhoid fever and viral hepatitis A) | Ministry of Public Health | 2001–2009/Up to 2100 | A1B, A2; B1, A1F1 | Morbidity from food-and water-borne diarrheal disease projected to increase by~10% by year 2050 and up to 46% by 2100. |
| Zhang et al99 (2012) | Projected Years Lost due to Disabilities (YLDs) for bacillary dysentery related to increased temperature in temperate and subtropical cities of China | China | Temperature | Published literature | Bacillary (Shigella) dysentery | Local Centers for Disease Control and Prevention | 2000/2020, 2050 | IPCC 4th Assessment Report scenarios - Regional climate projections | Years lost due to disabilities from bacillary dysentery projected to rise by more than 140% by 2050. |
CDC, Centers for Disease Control and Prevention; CMIP, Coupled Model Intercomparison Project.
A variety of data sources were used to project the impact of climate on health outcomes. Government institutions such as Ministries of Health, national disease surveillance programmes, national statistics offices and disease control programmes were the most common sources of baseline health outcomes data. Historical and future climate data were obtained from sources including national and regional weather stations, the WorldClim database,18 the European Centre for Medium Range Weather Forecasts and the Coupled Model Intercomparison Project.19 The studies used various analytical models to determine baseline relationships between climate parameters and health outcomes that formed the basis for forward-looking projections. Examples of models utilised include generalised linear models, non-linear models, standard time-series regression, Poisson regression models, Spearman’s correlation and multivariate meta-regression. Most studies incorporated population growth or other measures relating to population dynamics into their projections; however, 26 studies either did not consider population trends or did not report the population trends data source used.
Most studies applied projection timeframes of up to 2080s, 2090s or 2100s. In several cases, these were divided into short-term (eg, 2030s) and medium-term (eg, 2050s) projections. No projection was made beyond the year 2100. Fifteen studies (21%) used a nearer term projection timeframe of 2030 to 2040s.
Future climate projections based on RCPs were used in 48 (69%) of the studies, with two of these studies combining RCP and SSP climate severity scenarios. Ten studies (14%) relied on SSP scenarios alone while 7 studies (10%) used SRES scenarios. Three studies used specific regional climate change projection scenarios from IPCC’s Fourth Assessment report20 and two studies used Bergen Climate Model V.2 and ‘study-derived carbon dioxide and air temperature scenarios’ (table 1). All studies using SRES scenarios were published before 2017, the first studies using RCPs were published in 2015 and SSP-based studies were used beginning in 2022 (which is generally consistent with the dates of original publication of various climate scenarios).
Of the 70 studies in scope for this review, a total of six (9%) focused on sub-Saharan Africa, compared with 31 (44%) in East Asia and Pacific, 12 (17%) in South Asia, 10 (14%) each in the Latin America and the Caribbean region and the Middle East and North Africa region, four (6%) in Europe and two global studies (table 1).
Figure 2 shows the geographic distribution of studies by country. Sixty-three studies took place in 36 specific countries. Seven additional studies had a global or regional focus that did not define specific countries nor stratify results by country (eg, studies with a broad focus on Africa or Europe). China had the largest number of studies (28 studies; 44%), followed by Brazil and India with seven studies each (11%). Three studies focused on Vietnam, and the following countries were the focus of two studies: Bangladesh, Kenya, Lebanon, Mexico and Palestine. The remaining countries had one study each. Eleven of the 36 countries were in the Middle East and North Africa region (31%), eight in the East Asia and Pacific (22%) and five each in Latin America and the Caribbean, and South Asia regions (14%). Four of these study countries were in sub-Saharan Africa (SSA) and three in Europe. Stratifying these countries by income group, 20 were lower middle-income (56%), 13 were upper middle-income (36%) and three were low-income countries (8%).
Figure 2. Geographic distribution of 63 studies that focused on specific countries. Data pertaining to seven additional studies (one global study and six studies that aggregated data across Africa, Europe and Asia) were excluded from this diagram. LMICs, low- and middle-income countries.
Projected impact of climate change on health outcomes
Data from 70 studies involving 40 health outcomes were categorised into eight major groups of health outcomes as summarised in figure 3.
Figure 3. Categorisation of studies by health outcomes. Data were drawn from 70 studies comprising 75 health outcomes (several studies focused on multiple health outcomes).
The key independent climate variables included temperature in 68 studies (97%), and temperature was the sole climate variable in 36 studies (51%). Other studies evaluated the impact of temperature together with additional climate variables: temperature and precipitation in 13 studies (19%); temperature and rainfall events in eight studies; temperature and humidity in four studies; temperature, precipitation and humidity in four studies; temperature, precipitation, relative humidity, atmospheric pressure in one study; wet bulb globe temperature (a consolidated measure of ambient temperature, humidity, radiative heat) in one study; and temperature and atmospheric carbon dioxide in one study. Climate change-related air pollution was the primary independent variable in two studies.
Most studies focused on a single health outcome with four studies evaluating the impact of climate on multiple health outcomes. Non-disease-specific temperature-related mortality was the most frequently studied health outcome and the focus of 14 studies (20%), nine of which were conducted in China. Thirteen studies (19%) focused on NTDs including dengue (10 studies), Chagas disease (1 study), cutaneous leishmaniasis (1 study) and schistosomiasis (1 study). Studies focused on NTDs were conducted in 13 countries in Latin America and the Caribbean, East Asia and Pacific, Middle East and North Africa, and South Asia; none of the NTD-focused studies was conducted in SSA. Malaria was the focus of 11 studies (16%) conducted in Africa, East Asia and Pacific and South Asia. Ten studies (nine of which took place in China and one in Brazil) focused on cardiovascular diseases, including ischaemic heart disease, stroke and temperature-related cardiovascular disease mortality and cardiac arrest. Enteric infections were the focus of six studies (9%), which took place in China, India, Iran and Lebanon; the specific study outcomes were diarrheal diseases, bacillary dysentery, cholera, food poisoning, parasitic worms and typhoid fever. Four studies (6%) conducted in China, Brazil and Iran focused on respiratory diseases including chronic obstructive pulmonary disease, human avian influenza and SARS. Other communicable diseases, including anthrax, brucellosis, encephalitis B, Hantavirus, haemorrhagic fever, leptospirosis, meningitis, mpox (formerly known as monkeypox), plague, rabies scrub typhus, West Nile virus, yellow fever and Zika virus, were evaluated in 10 studies (14%). Finally, seven studies (10%) focused on other health outcomes including air pollution-related mortality, occupational heat stress, suicide deaths, urinary stone diseases and heat-related years of life lost (YLL).
Table 2 summarises the evidence relating to projected impacts of climate change on specific health outcomes. Nearly all health outcomes studied were projected to increase in burden or experience a geographical shift in prevalence due to climate change.
Table 2. Summary of the impacts of climate change on health outcomes in low- and middle-income countries.
| Health outcome | Total number of studies | Geography (number of studies) | Key findings |
|---|---|---|---|
| Non-disease-specific temperature-related mortality | 14 | China (9); Iran (1); North Macedonia (1); Vietnam (1); Brazil, China, Moldova, Mexico, Philippines, Thailand and Vietnam (1)*; Algeria, Egypt, Iran, Iraq, Jordan, Lebanon, Libya, Morocco, Palestine, Syria, Tunisia and Turkey (1)* |
|
| Neglected tropical diseases | 13 | China (3); Bangladesh (1); Brazil (1); Colombia (1); India (1); Jamaica (1); Mexico (1); Pakistan (1); Palestine (1); Sri-Lanka (1); Malaysia and Sri Lanka (1)*. |
|
| Malaria | 11 | Africa (3); India (3); Iran (1); China (1); Kenya (1); Nepal (1); Kenya, Tanzania and Uganda (1)* |
|
| Cardiovascular diseases (CVD) | 10 | China (9 studies); Brazil (1 study) |
|
| Enteric infections | 6 | China (2); India (2); Iran (1); Lebanon (1) |
|
| Respiratory diseases | 4 | Brazil (1); China (1); Iran (1) |
|
| Other communicable diseases | 10 | Africa (1); Europe (1); Global (1); Brazil (4); China (2); Cambodia, Myanmar, Lao PDR, Thailand and Vietnam (1)* |
|
| Other health outcomes | 7 | Global (1); China (3); Iran (1); India and Mozambique (1)*; Nicaragua (1) |
|
One study conducted in multiple countries as specified.
Non-disease-specific temperature-related mortality was widely projected to increase in 13 out of 14 studies. One study that projected a decrease under certain conditions also projected an increase in mortality in the event of a high population growth scenario.21 Twelve of 13 studies focused on NTDs predicted an increase in NTD disease burdens due to dengue, rabies and cutaneous leishmaniasis, with the latter disease projected to occur for the first time in the Gaza strip region. Nine of ten studies focused on other communicable diseases projected increases in disease burdens for brucellosis, encephalitis B, Hantavirus, haemorrhagic fever, meningitis, plague, scrub typhus, West Nile virus and Zika virus. Nine of 10 studies focused on cardiovascular diseases projected an increase in disease burden, with one study predicting a decrease. Six studies on enteric infections (out of six) and three studies on respiratory diseases (out of four) predicted the burden of these diseases will increase with climate change. Other health outcomes including heat stress, urinary stone disease, ambient PM2.5-related mortality, annual heat-related YLLs due to climate change and suicide deaths were all projected to increase in disease burden.
Future projections regarding the burden of malaria due to climate change presented a mixed picture. Out of 11 studies, five studies predicted an increase while two predicted a decrease in risk of transmission and/or incidence. The transmission season was estimated to expand in four studies and to decrease in one study. Five studies estimated a geographical shift in the burden of disease across or within countries. All five studies in Africa projected an overall increase in the population at risk or a geographical shift in the burden of malaria.
The burden of leptospirosis was projected to increase in one study and to decrease in another study, while the risk for mpox infection was projected to shift geographically according to one study. Chagas disease was projected to experience a geographical shift in one study that took place in Colombia, South America.
The transmission risks of anthrax and haemorrhagic fever and yellow fever were estimated to decrease, with anthrax also projected to experience a geographical shift in burden. Among the NTDs studied, schistosomiasis was the only disease projected to decrease in burden (based on one study in China).
Twenty-seven (39%) of the studies specifically focused on cities. Only one of the remaining 43 studies (which were multicountry, national or regional in scope) stratified results by non-urban and urban areas. This study reported a higher impact of climate change on excess heat-related mortality in non-urban counties compared with urban counties in China.
In general, worsening climate change scenarios correlated with more severe health outcomes. For example, approximately 20 000 deaths due to heatwaves were projected to occur in China in 2090 under RCP2.6, compared with 35 000 and 72 000 deaths under conditions of RCP4.5 and RCP8.5, respectively.22 In several studies, adverse health outcomes due to climate change were projected to increase steadily over time. For example, a subanalysis in one study in India projected a statistically significant 5% increase in diarrhoea cases attributable to Cryptosporidium spp. by 2046−2065 compared with baseline (2011–2030), which was projected to then double by 2080–2099.23 In other studies, health outcomes were projected to worsen over time and then improve, as was observed in a study of injury mortality attributable to compound hot extremes in China in which the health outcome was projected under RCP2.6 to increase through the 2060s (by two-fold) and then decrease in the 2090s.24
Discussion
To our knowledge, this is the first systematic review aimed at synthesising evidence from published literature on the projected impacts of climate change on human health in LMICs. The main findings can be classified into three broad categories. First, there has been a significant increase in the number of studies examining the future impact of climate change on human health in LMICs in recent years, even though the geographic distribution of these studies is uneven. Second, most studies projected that climate change will significantly increase the burden of disease in LMICs, including for various communicable and non-communicable diseases. Third, there continue to be important knowledge gaps that will need to be addressed to inform evidence-based approaches that mitigate health-related threats of climate change in LMICs. We discuss these findings in detail below and suggest implications for health systems and health policy in LMICs.
Publication trends
The progressive increase in the number of studies focusing on the impact of climate change on human health in LMICs in recent years presumably reflects the growing awareness of the critical importance of this topic among population health advocates, researchers, policymakers and other stakeholders. Seventy per cent of studies included in this systematic review were published in the latter 5 years (2019–2023) of the 12-year study period, a trend that is consistent with observations from other studies.25 26 Despite this growth, our analysis revealed substantial asymmetry in the geographic distribution of studies with a disproportionately large number of investigations (40%) focused on China and relatively few studies (9%) focusing on Africa, a continent that is currently home to nearly 20% of the global population and is anticipated to contribute considerable population growth over the next decades. Moreover, while Africa is estimated to be responsible for less than 4% of global greenhouse gas emissions, 16 of the 20 countries designated as most vulnerable to climate change are located on the continent.7
Overall, only three country-specific studies (4%) in scope of our analysis were conducted in low-income countries, a trend that has been previously observed.25 According to data from the Institute of Health Metrics and Evaluation, low-income countries bear 15% of the global YLLs due to disease.27 Juxtaposing the vulnerability of low-income countries to the negative effects of climate change with the disproportionately low research investments highlights important opportunities for climate and environmental justice on the global climate change agenda.28
There appears to be a general alignment in the sequence of IPCC reports on climate severity scenarios and the emergence of these scenarios in peer-reviewed literature, an indication of how the IPCC has been influential in guiding research on climate change and health. For example, the SRES (A1, A2, B1 and B2) scenarios, introduced in the 2000s,14 were used in many studies until the release of new climate severity scenarios, the RCPs, in 2014.12 The use of RCP scenarios subsequently started appearing in studies in 2015. Additionally, SSP scenarios, which were updated by the IPCC in 2018, were used in publications from 2022.13
Impact of climate change on health outcomes
A main finding from our analyses is that climate change is largely projected to increase disease burdens in LMICs across a range of health outcomes. Considering that more than 80% of the global population currently resides in LMICs, climate change, therefore, risks impacting a massive proportion of the global population.10 In the studies assessed, disease burden was defined by a variety of epidemiologic measures including disease attributable fraction, incidence, prevalence, duration of transmission season, mortality, YLLs and YLDs—all of which were projected to increase in various studies for both communicable and non-communicable diseases in LMICs. In some cases, the projected increases were extraordinarily high and essentially indicated potential for emerging health crises in LMICs due to climate change. For example, heat-related mortality among the elderly was projected to rise by more than 250% in the 2080s under RCP8.5 and a medium population growth scenario in China,29 and the global meningitis incidence was projected to increase by more than 180% in severe climate change scenarios by 2100.30 Additionally, it is worth noting that several studies projected increases in disease burden due to climate change as early as in the 2030s for health outcomes including dengue, heat-related YLLs, malaria, PM2.5-related mortality and scrub typhus.31,34
There are numerous health systems and health policy implications of the findings in this systematic review. The expansion or shifting footprint of communicable diseases (many of which are vector-borne diseases) such as anthrax, leishmaniasis, malaria and mpox to new regions poses significant threats to populations that may have limited immunity to these diseases—in other words, shifting geographic presence is likely to increase disease incidence and prevalence.3 4 For diseases such as malaria, where substantial investments have been made over a period of decades to control and eliminate the disease, hard-won progress may be lost if the disease re-emerges in countries and regions where it had previously been eliminated.
The projected increases in non-communicable diseases including cardiovascular disease are equally concerning. Populations in LMICs already bear a substantial proportion of the global burden of non-communicable diseases; more than 85% of the 14 million premature deaths that take place each year worldwide due to non-communicable diseases occur in LMICs.35 Escalating this disparity will have even greater consequences for human health and quality of life in LMICs. Moreover, in addition to direct health impacts, increasing non-communicable diseases in LMICs would also be expected to increase healthcare costs, exacerbate loss of productivity and impose negative socioeconomic impacts.36
The apparent direct correlation between more severe climate change scenarios and worse health outcomes that was observed in many studies supports calls for the urgent adoption of climate mitigation measures to reduce carbon emissions. Studies have shown that appropriate climate change mitigation interventions could result in significant health benefits.37 The adverse effects of climate change in LMICs are compounded by the variability in healthcare systems needed to cope with climate change impacts4 7 10 as well as by the direct threat of climate change to the existing healthcare infrastructures in LMICs. Indeed, in mid-2023, the WHO called on national governments and other stakeholders to urgently take action to develop robust and sustainable health systems in response to the threat of climate change. Considering resource constraints, competing priorities and potential political challenges within countries, it is likely that concerted multistakeholder efforts will be needed to translate evidence to effective and sustained policy and programmatic interventions that address the risks of climate change and protect the health of populations in LMICs.
Research gaps
We identified an uneven geographic distribution of investigations of the impact of climate change on human health in LMICs with regions such as Africa, and low-income countries in general, being studied the least. Given this disparity, particularly in the context of the high vulnerability of low-income countries to climate change, we suspect that the true projected impact of climate change on health in these parts of the world is under-reported. More evidence is needed to fully understand the projected impact of climate change in low-income countries to ensure advocacy for targeted policy interventions. In addition to intercountry comparisons, there are needs to better understand potential regional differences that may exist within LMICs relating to projected health impacts of climate change—for example, in rural compared with urban settings.
In addition to the geographic disparity identified, the health outcomes that were evaluated in the dataset generated by this systematic review ultimately comprised a relatively small number of diseases that have been previously reported to be sensitive to climate change. For example, the 29 communicable diseases evaluated by the studies in scope represent less than 15% of the 218 human bacterial, fungal, protozoan and viral diseases reported to be at risk of exacerbation due to climatic hazards.38 Furthermore, only 4 out of the 20 WHO-listed NTDs were analysed, and neither these studies nor the studies on enteric infections took place in SSA, where disease burdens are among the highest globally.27 Non-communicable disease conditions including autoimmune diseases, diabetes, cancers, malnutrition, mental health, snake bites and others, which have been recognised to be climate-sensitive conditions, were not evaluated in any of the studies.5 6 Finally, while temperature, precipitation and humidity were the most frequently studied climate predictors of health outcomes, other predictors including air quality and drought were rarely studied. It is noteworthy that WHO has cited the lack of effective projection models as one of the factors hampering research on these and other variables.39
Study limitations
The aim of this systematic review was to synthesise evidence from across LMICs. However, the identified studies were not evenly distributed but rather heavily focused on a single country (China). Additionally, the list of health outcomes evaluated in these publications was relatively narrow. While these limitations may have affected the broad generalisation of the evidence synthesised, they represent important findings regarding the current state of research on climate change and health in LMICs.
This systematic review excluded articles that focused on environmental suitability and vector influences but that did not explicitly report impact on human health. The rationale for this methodology was to examine as precisely as possible the direct effect of climate change on human health. Had those studies been included, the dataset of eligible articles would have been much larger. Furthermore, the search terms were in English, and no local databases were included in the search. Another limitation relates to comparability of studies given that the methodologies often differed in terms of data sources, statistical models used and projection timelines. Nevertheless, we found it telling that, irrespective of these differences, the studies generally came to similar conclusions.
Evidence reported in the articles included in this study was based on projections using anticipated future greenhouse gas emission scenarios. The relationship between climatic variables such as temperature and health outcomes can be reasonably established within specific greenhouse gas emission scenarios. However, achieving the various greenhouse gas emission scenarios that were used by studies identified in this review is not guaranteed. The concomitant effects of other variables (eg, health impacts of political instability and human migration) are also challenging to accurately predict and incorporate into modelling exercises. Moreover, it is theoretically possible for there to be causal relationship among weather variables that could affect the results of studies included in this review.
While we attempted to be as comprehensive as possible in selecting search terms used for climate change variables, we acknowledge there may have been relevant search terms that were not used. We also did not include socioeconomic parameters such as land use, which can also have direct and indirect impacts on disease trends. Including these search terms may have yielded additional studies for inclusion and potentially additional insights regarding the relationship between climatic variables and human health.
Conclusion
The findings of this systematic review suggest that, given currently available projection models, climate change is anticipated to significantly worsen disease burdens in LMICs. The adverse impact on human health applies across a range of communicable and non-communicable diseases as well as across regions in the Americas, Africa and Asia. Climate change scenarios of increasing severity generally correlated with worsened health outcomes. Despite attention to this topic as evidenced by progressively increasing number of studies conducted over the past decade, there remain substantial knowledge gaps that could be the basis for future research to inform climate mitigation and adaptation policies and programmes in LMICs.
Supplementary material
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Provenance and peer review: Not commissioned; externally peer-reviewed.
Handling editor: Helen J Surana
Patient consent for publication: Not applicable.
Ethics approval: Not applicable.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Map disclaimer: The depiction of boundaries on this map does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. This map is provided without any warranty of any kind, either express or implied.
Data availability statement
Data sharing not applicable as no datasets generated and/or analysed for this study. All data relevant to the study are included in the article or uploaded as supplementary information.
References
- 1.IPCC . Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Field CB, Barros V, Stocker TF, et al. Eds. Cambridge, UK, and New York, NY, USA: Cambridge University Press; 2012. p. 582. [Google Scholar]
- 2.Lee H, Romero J. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team; Climate change 2023 synthesis report IPCC, 2023: sections; pp. 35–115. n.d. [Google Scholar]
- 3.Romanello M, Napoli C di, Green C, et al. The 2023 report of the Lancet Countdown on health and climate change: the imperative for a health-centred response in a world facing irreversible harms. Lancet. 2023;402:2346–94. doi: 10.1016/S0140-6736(23)01859-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Salas RN, Solomon CG. The Climate Crisis - Health and Care Delivery. N Engl J Med. 2019;381:e13. doi: 10.1056/NEJMp1906035. [DOI] [PubMed] [Google Scholar]
- 5.Agostoni C, Baglioni M, La Vecchia A, et al. Interlinkages between Climate Change and Food Systems: The Impact on Child Malnutrition-Narrative Review. Nutrients. 2023;15:416. doi: 10.3390/nu15020416. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Rother HA. Controlling and preventing climate-sensitive noncommunicable diseases in urban sub-Saharan Africa. Sci Total Environ. 2020;722:137772. doi: 10.1016/j.scitotenv.2020.137772. [DOI] [PubMed] [Google Scholar]
- 7.University of Notre Dame Notre Dame Global Adaptation Initiative. Country rankings. [28-Nov-2023];2023 https://gain.nd.edu/our-work/country-index/rankings/ Available. accessed.
- 8.Ritchie Hannah Global inequalities in CO2 emissions. 2023. [28-Nov-2023]. https://ourworldindata.org/inequality-co2 Available. Accessed.
- 9.Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.World Bank World development indicators. [03-Oct-2023]. https://datatopics.worldbank.org/world-development-indicators/ Available. Accessed.
- 11.Embase ® indexing guide 2018 a comprehensive guide to embase’s indexing policy 2. [10-Nov-2023]. www.embase.com Available. Accessed.
- 12.IPCC . Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, Pachauri RK, Meyer LA, Eds. Geneva, Switzerland: IPCC; 2014. pp. 116–51. [Google Scholar]
- 13.Riahi K, van Vuuren DP, Kriegler E, et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob Environ Change. 2017;42:153–68. doi: 10.1016/j.gloenvcha.2016.05.009. [DOI] [Google Scholar]
- 14.IPCC . Geneva, Switzerland: IPCC; 2000. Special report. Emissions scenarios. A special report of IPCC working group III. Summary for policymakers. [Google Scholar]
- 15.Brugueras S, Fernández-Martínez B, Martínez-de la Puente J, et al. Environmental drivers, climate change and emergent diseases transmitted by mosquitoes and their vectors in southern Europe: A systematic review. Environ Res. 2020;191:110038. doi: 10.1016/j.envres.2020.110038. [DOI] [PubMed] [Google Scholar]
- 16.Nha Hong Q, Pluye P, Fàbregues S, et al. Mixed methods appraisal tool (MMAT). Version 2018 user guide. [05-Oct-2023]; http://mixedmethodsappraisaltoolpublic.pbworks.com/ Available. accessed.
- 17.World Health Organization Ending the neglect to attain the sustainable development goals: a road map for neglected tropical diseases 2021–2030. Geneva license: CC BY-NC-SA 3.0 IGO. [17-Nov-2023];2020 https://www.who.int/publications/i/item/9789240010352 Available. accessed.
- 18.WorldClim database. [11-Jan-2024]. https://worldclim.org/ Available. Accessed.
- 19.Lawrence Livermore National Laboratory Program for climate model diagnosis & intercomparison. 2024. https://pcmdi.llnl.gov/index.html Available.
- 20.IPCC . Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Core Writing Team, Pachauri RK, Reisinger A, Eds. Geneva, Switzerland: IPCC; 2007. pp. 103–4.https://www.ipcc.ch/site/assets/uploads/2018/02/ar4_syr_full_report.pdf Available. [Google Scholar]
- 21.Zhu D, Zhou Q, Liu M, et al. Non-optimum temperature-related mortality burden in China: Addressing the dual influences of climate change and urban heat islands. Sci Total Environ. 2021;782:146760. doi: 10.1016/j.scitotenv.2021.146760. [DOI] [PubMed] [Google Scholar]
- 22.Chen H, Zhao L, Cheng L, et al. Projections of heatwave-attributable mortality under climate change and future population scenarios in China. Lancet Reg Health West Pac. 2022;28:100582. doi: 10.1016/j.lanwpc.2022.100582. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Mellor J, Kumpel E, Ercumen A, et al. Systems Approach to Climate, Water, and Diarrhea in Hubli-Dharwad, India. Environ Sci Technol. 2016;50:13042–51. doi: 10.1021/acs.est.6b02092. [DOI] [PubMed] [Google Scholar]
- 24.Luo L, Zeng F, Bai G, et al. Future injury mortality burden attributable to compound hot extremes will significantly increase in China. Sci Total Environ. 2022;845:157019. doi: 10.1016/j.scitotenv.2022.157019. [DOI] [PubMed] [Google Scholar]
- 25.Berrang-Ford L, Sietsma AJ, Callaghan M, et al. Systematic mapping of global research on climate and health: a machine learning review. Lancet Planet Health. 2021;5:e514–25. doi: 10.1016/S2542-5196(21)00179-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Rocque RJ, Beaudoin C, Ndjaboue R, et al. Health effects of climate change: an overview of systematic reviews. BMJ Open. 2021;11:e046333. doi: 10.1136/bmjopen-2020-046333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Institute for Health Metrics and Evaluation (IHME) WA: IHME, University of Washington; 2019. [03-Dec-2023]. GBD seattle.https://www.healthdata.org/research-analysis/gbd Available. accessed. [Google Scholar]
- 28.Resnik DB. Environmental justice and climate change policies. Bioethics . 2022;36:735–41. doi: 10.1111/bioe.13042. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Li T, Horton RM, Bader DA, et al. Aging Will Amplify the Heat-related Mortality Risk under a Changing Climate: Projection for the Elderly in Beijing, China. Sci Rep. 2016;6 doi: 10.1038/srep28161. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Chen J, Jiao Z, Liang Z, et al. Association between temperature variability and global meningitis incidence. Environ Int. 2023;171:107649. doi: 10.1016/j.envint.2022.107649. [DOI] [PubMed] [Google Scholar]
- 31.Bhattarai S, Blackburn JK, Ryan SJ. Malaria transmission in Nepal under climate change: anticipated shifts in extent and season, and comparison with risk definitions for intervention. Malar J. 2022;21:390. doi: 10.1186/s12936-022-04417-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ding F, Wang Q, Hao M, et al. Climate drives the spatiotemporal dynamics of scrub typhus in China. Glob Chang Biol. 2022;28:6618–28. doi: 10.1111/gcb.16395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Xu J, Yao M, Wu W, et al. Estimation of ambient PM2.5-related mortality burden in China by 2030 under climate and population change scenarios: A modeling study. Environ Int. 2021;156:106733. doi: 10.1016/j.envint.2021.106733. [DOI] [PubMed] [Google Scholar]
- 34.Liu T, Ren Z, Zhang Y, et al. Modification Effects of Population Expansion, Ageing, and Adaptation on Heat-Related Mortality Risks Under Different Climate Change Scenarios in Guangzhou, China. Int J Environ Res Public Health. 2019;16:376. doi: 10.3390/ijerph16030376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.World Health Organization Global health observatory. 2022. [11-Jun-2024]. https://www.who.int/data/gho Available. Accessed.
- 36.Demirci H, Yuksek B, Training I, et al. Incorporating neglected non-communicable diseases into the national health program—a review. doi: 10.3389/fpubh.2022.1093170. [DOI] [PMC free article] [PubMed]
- 37.Gao J, Kovats S, Vardoulakis S, et al. Public health co-benefits of greenhouse gas emissions reduction: A systematic review. Sci Total Environ. 2018;627:388–402. doi: 10.1016/j.scitotenv.2018.01.193. [DOI] [PubMed] [Google Scholar]
- 38.Mora C, McKenzie T, Gaw IM, et al. Over half of known human pathogenic diseases can be aggravated by climate change. Nat Clim Chang. 2022;12:869–75. doi: 10.1038/s41558-022-01426-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.WHO Climate change. 2023. [27-Nov-2023]. https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health#:~:text=The%20WHO%20conservatively%20projects%20250,like%20malaria%20and%20coastal%20flooding Available. Accessed.
- 40.Cao B, Bai C, Wu K, et al. Tracing the future of epidemics: Coincident niche distribution of host animals and disease incidence revealed climate-correlated risk shifts of main zoonotic diseases in China. Glob Chang Biol. 2023;29:3723–46. doi: 10.1111/gcb.16708. [DOI] [PubMed] [Google Scholar]
- 41.Hajat S, Proestos Y, Araya-Lopez JL, et al. Current and future trends in heat-related mortality in the MENA region: a health impact assessment with bias-adjusted statistically downscaled CMIP6 (SSP-based) data and Bayesian inference. Lancet Planet Health. 2023;7:e282–90. doi: 10.1016/S2542-5196(23)00045-1. [DOI] [PubMed] [Google Scholar]
- 42.Liao H, Zhang C, Burke PJ, et al. Extreme temperatures, mortality, and adaptation: Evidence from the county level in China. Health Econ. 2023;32:953–69. doi: 10.1002/hec.4649. [DOI] [PubMed] [Google Scholar]
- 43.Liu J, Lv C, Zheng J, et al. The impact of non-optimum temperatures, heatwaves and cold spells on out-of-hospital cardiac arrest onset in a changing climate in China: a multi-center, time-stratified, case-crossover study. Lancet Reg Health West Pac. 2023;36:100778. doi: 10.1016/j.lanwpc.2023.100778. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Nguyen CV, Nguyen MH, Nguyen TT. The impact of cold waves and heat waves on mortality: Evidence from a lower middle‐income country. Health Econ. 2023;32:1220–43. doi: 10.1002/hec.4663. [DOI] [PubMed] [Google Scholar]
- 45.Saeed A, Ali S, Khan F, et al. Modelling the impact of climate change on dengue outbreaks and future spatiotemporal shift in Pakistan. Environ Geochem Health. 2023;45:3489–505. doi: 10.1007/s10653-022-01429-z. [DOI] [PubMed] [Google Scholar]
- 46.Wang Y, Zhao S, Wei Y, et al. Impact of climate change on dengue fever epidemics in South and Southeast Asian settings: A modelling study. Infect Dis Model. 2023;8:645–55. doi: 10.1016/j.idm.2023.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Zhang H, Sun M, Yao X, et al. Increasing probability of record-population exposure to high temperature and related health-risks in China. Environ Res. 2023;231:116176. doi: 10.1016/j.envres.2023.116176. [DOI] [PubMed] [Google Scholar]
- 48.Zhou Y, Gao Y, Yin P, et al. Assessing the Burden of Suicide Death Associated With Nonoptimum Temperature in a Changing Climate. JAMA Psychiatry. 2023;80:488–97. doi: 10.1001/jamapsychiatry.2023.0301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Amro A, Moskalenko O, Hamarsheh O, et al. Spatiotemporal analysis of cutaneous leishmaniasis in Palestine and foresight study by projections modelling until 2060 based on climate change prediction. PLoS ONE. 2022;17:e0268264. doi: 10.1371/journal.pone.0268264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Bahrami G, Rafiey H, Shakiba A, et al. Climate Change and Respiratory Diseases: Relationship between SARS and Climatic Parameters and Impact of Climate Change on the Geographical Distribution of SARS in Iran. Adv Respir Med. 2022;90:378–90. doi: 10.3390/arm90050048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Douchet L, Goarant C, Mangeas M, et al. Unraveling the invisible leptospirosis in mainland Southeast Asia and its fate under climate change. Sci Total Environ. 2022;832:155018. doi: 10.1016/j.scitotenv.2022.155018. [DOI] [PubMed] [Google Scholar]
- 52.He C, Kim H, Hashizume M, et al. The effects of night-time warming on mortality burden under future climate change scenarios: a modelling study. Lancet Planet Health. 2022;6:e648–57. doi: 10.1016/S2542-5196(22)00139-5. [DOI] [PubMed] [Google Scholar]
- 53.Ingole V, Dimitrova A, Sampedro J, et al. Local mortality impacts due to future air pollution under climate change scenarios. Sci Total Environ. 2022;823:153832. doi: 10.1016/j.scitotenv.2022.153832. [DOI] [PubMed] [Google Scholar]
- 54.Nili S, Asadgol Z, Dalaei H, et al. The effect of climate change on malaria transmission in the southeast of Iran. Int J Biometeorol. 2022;66:1613–26. doi: 10.1007/s00484-022-02305-2. [DOI] [PubMed] [Google Scholar]
- 55.Ototo EN, Ogutu JO, Githeko A, et al. Forecasting the Potential Effects of Climate Change on Malaria in the Lake Victoria Basin Using Regionalized Climate Projections. Acta Parasit. 2022;67:1535–63. doi: 10.1007/s11686-022-00588-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Parihar RS, Bal PK, Saini A, et al. Potential future malaria transmission in Odisha due to climate change. Sci Rep. 2022;12:9048. doi: 10.1038/s41598-022-13166-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Van Wyk H, Eisenberg JNS, Brouwer AF. Long-term projections of the impacts of warming temperatures on Zika and dengue risk in four Brazilian cities using a temperature-dependent basic reproduction number. PLoS Negl Trop Dis. 2023;17:e0010839. doi: 10.1371/journal.pntd.0010839. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Vassari-Pereira D, Valverde MC, Asmus GF. Impact of climate change and air quality on hospitalizations for respiratory diseases in municipalities in the Metropolitan Region of São Paulo (MRSP), Brazil. Cien Saude Colet. 2022;27:2023–34. doi: 10.1590/1413-81232022275.08632021. [DOI] [PubMed] [Google Scholar]
- 59.Xing Q, Sun Z, Tao Y, et al. Projections of future temperature-related cardiovascular mortality under climate change, urbanization and population aging in Beijing, China. Environ Int. 2022;163:107231. doi: 10.1016/j.envint.2022.107231. [DOI] [PubMed] [Google Scholar]
- 60.Zhao J, He G, Xiao J, et al. Mechanism of temperature on dengue fever transmission and impact of future temperature change on its transmission risk. J Environ Occup Med. 2022;39:309–14. doi: 10.11836/JEOM21457. [DOI] [Google Scholar]
- 61.Zhou L, He C, Kim H, et al. The burden of heat-related stroke mortality under climate change scenarios in 22 East Asian cities. Environ Int. 2022;170:107602. doi: 10.1016/j.envint.2022.107602. [DOI] [PubMed] [Google Scholar]
- 62.Sadeghieh T, Sargeant JM, Greer AL, et al. Yellow fever virus outbreak in Brazil under current and future climate. Infect Dis Model. 2021;6:664–77. doi: 10.1016/j.idm.2021.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Sadeghieh T, Sargeant JM, Greer AL, et al. Zika virus outbreak in Brazil under current and future climate. Epidemics. 2021;37 doi: 10.1016/j.epidem.2021.100491. [DOI] [PubMed] [Google Scholar]
- 64.Silveira IH, Cortes TR, de Oliveira BFA, et al. Projections of excess cardiovascular mortality related to temperature under different climate change scenarios and regionalized climate model simulations in Brazilian cities. Environ Res. 2021;197:110995. doi: 10.1016/j.envres.2021.110995. [DOI] [PubMed] [Google Scholar]
- 65.Wu W, Ren H, Lu L. Increasingly expanded future risk of dengue fever in the Pearl River Delta, China. PLoS Negl Trop Dis. 2021;15:e0009745. doi: 10.1371/journal.pntd.0009745. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Yang J, Zhou M, Ren Z, et al. Projecting heat-related excess mortality under climate change scenarios in China. Nat Commun. 2021;12 doi: 10.1038/s41467-021-21305-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Aboubakri O, Khanjani N, Jahani Y, et al. Projection of mortality attributed to heat and cold; the impact of climate change in a dry region of Iran, Kerman. Sci Total Environ. 2020;728:138700. doi: 10.1016/j.scitotenv.2020.138700. [DOI] [PubMed] [Google Scholar]
- 68.Chaturvedi S, Dwivedi S. Estimating the malaria transmission over the Indian subcontinent in a warming environment using a dynamical malaria model. J Water Health. 2020;18:358–74. doi: 10.2166/wh.2020.148. [DOI] [PubMed] [Google Scholar]
- 69.Henry S, Mendonça AF de A. Past, Present, and Future Vulnerability to Dengue in Jamaica: A Spatial Analysis of Monthly Variations. Int J Environ Res Public Health. 2020;17:3156. doi: 10.3390/ijerph17093156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Kakarla SG, Bhimala KR, Kadiri MR, et al. Dengue situation in India: Suitability and transmission potential model for present and projected climate change scenarios. Sci Total Environ. 2020;739:140336. doi: 10.1016/j.scitotenv.2020.140336. [DOI] [PubMed] [Google Scholar]
- 71.Ryan SJ, Lippi CA, Zermoglio F. Shifting transmission risk for malaria in Africa with climate change: a framework for planning and intervention. Malar J. 2020;19:170. doi: 10.1186/s12936-020-03224-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Wang Y, Wang Q, Deng Y, et al. Assessment of the impact of geogenic and climatic factors on global risk of urinary stone disease. Sci Total Environ. 2020;721:137769. doi: 10.1016/j.scitotenv.2020.137769. [DOI] [PubMed] [Google Scholar]
- 73.Wagner CE, Hooshyar M, Baker RE, et al. Climatological, virological and sociological drivers of current and projected dengue fever outbreak dynamics in Sri Lanka. J R Soc Interface. 2020;17:20200075. doi: 10.1098/rsif.2020.0075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Asadgol Z, Mohammadi H, Kermani M, et al. The effect of climate change on cholera disease: The road ahead using artificial neural network. PLoS ONE. 2019;14:e0224813. doi: 10.1371/journal.pone.0224813. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Huang J, Zeng Q, Pan X, et al. Projections of the effects of global warming on the disease burden of ischemic heart disease in the elderly in Tianjin, China. BMC Public Health. 2019;19:1465. doi: 10.1186/s12889-019-7678-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Le PVV, Kumar P, Ruiz MO, et al. Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya. PLoS ONE. 2019;14:e0211258. doi: 10.1371/journal.pone.0211258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Sarkar S, Gangare V, Singh P, et al. Shift in Potential Malaria Transmission Areas in India, Using the Fuzzy-Based Climate Suitability Malaria Transmission (FCSMT) Model under Changing Climatic Conditions. Int J Environ Res Public Health. 2019;16:3474. doi: 10.3390/ijerph16183474. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Wang Y, Wang A, Zhai J, et al. Tens of thousands additional deaths annually in cities of China between 1.5 °C and 2.0 °C warming. Nat Commun. 2019;10 doi: 10.1038/s41467-019-11283-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Huang J, Li G, Liu Y, et al. Projections for temperature-related years of life lost from cardiovascular diseases in the elderly in a Chinese city with typical subtropical climate. Environ Res. 2018;167:614–21. doi: 10.1016/j.envres.2018.08.024. [DOI] [PubMed] [Google Scholar]
- 80.Li T, Horton RM, Bader DA, et al. Long-term projections of temperature-related mortality risks for ischemic stroke, hemorrhagic stroke, and acute ischemic heart disease under changing climate in Beijing, China. Environ Int. 2018;112:1–9. doi: 10.1016/j.envint.2017.12.006. [DOI] [PubMed] [Google Scholar]
- 81.Li G, Guo Q, Liu Y, et al. Projected Temperature-Related Years of Life Lost From Stroke Due To Global Warming in a Temperate Climate City, Asia: Disease Burden Caused by Future Climate Change. Stroke. 2018;49:828–34. doi: 10.1161/STROKEAHA.117.020042. [DOI] [PubMed] [Google Scholar]
- 82.Zhang B, Li G, Ma Y, et al. Projection of temperature-related mortality due to cardiovascular disease in beijing under different climate change, population, and adaptation scenarios. Environ Res. 2018;162:152–9. doi: 10.1016/j.envres.2017.12.027. [DOI] [PubMed] [Google Scholar]
- 83.Chen K, Horton RM, Bader DA, et al. Impact of climate change on heat-related mortality in Jiangsu Province, China. Environ Pollut. 2017;224:317–25. doi: 10.1016/j.envpol.2017.02.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Gasparrini A, Guo Y, Sera F, et al. Projections of temperature-related excess mortality under climate change scenarios. Lancet Planet Health. 2017;1:e360–7. doi: 10.1016/S2542-5196(17)30156-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Liu X, Liu Z, Ding G, et al. Projected burden of disease for bacillary dysentery due to flood events in Guangxi, China. Sci Total Environ. 2017;601–602:1298–305. doi: 10.1016/j.scitotenv.2017.05.020. [DOI] [PubMed] [Google Scholar]
- 86.Prist PR, Uriarte M, Fernandes K, et al. Climate change and sugarcane expansion increase Hantavirus infection risk. PLoS Negl Trop Dis. 2017;11:e0005705. doi: 10.1371/journal.pntd.0005705. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Kibret S, Lautze J, McCartney M, et al. Malaria and large dams in sub-Saharan Africa: future impacts in a changing climate. Malar J. 2016;15:448. doi: 10.1186/s12936-016-1498-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Martinez GS, Baccini M, De Ridder K, et al. Projected heat-related mortality under climate change in the metropolitan area of Skopje. BMC Public Health. 2016;16:407. doi: 10.1186/s12889-016-3077-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Ngarakana-Gwasira ET, Bhunu CP, Masocha M, et al. Assessing the Role of Climate Change in Malaria Transmission in Africa. Malar Res Treat. 2016;2016:7104291. doi: 10.1155/2016/7104291. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Semenza JC, Tran A, Espinosa L, et al. Climate change projections of West Nile virus infections in Europe: implications for blood safety practices. Env Health. 2016;15 Suppl 1:28. doi: 10.1186/s12940-016-0105-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Shajari A, Sanjerehei MM. Modeling the distribution of urolithiasis prevalence under projected climate change in Iran. Urolithiasis. 2015;43:339–47. doi: 10.1007/s00240-015-0784-2. [DOI] [PubMed] [Google Scholar]
- 92.Banu S, Hu W, Guo Y, et al. Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh. Environ Int. 2014;63:137–42. doi: 10.1016/j.envint.2013.11.002. [DOI] [PubMed] [Google Scholar]
- 93.Cordovez JM, Rendon LM, Gonzalez C, et al. Using the basic reproduction number to assess the effects of climate change in the risk of Chagas disease transmission in Colombia. Acta Trop . 2014;129:74–82. doi: 10.1016/j.actatropica.2013.10.003. [DOI] [PubMed] [Google Scholar]
- 94.Colón-González FJ, Fezzi C, Lake IR, et al. The effects of weather and climate change on dengue. PLoS Negl Trop Dis. 2013;7:e2503. doi: 10.1371/journal.pntd.0002503. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Moors E, Singh T, Siderius C, et al. Climate change and waterborne diarrhoea in northern India: Impacts and adaptation strategies. Sci Total Environ. 2013;468–469:S139–51. doi: 10.1016/j.scitotenv.2013.07.021. [DOI] [PubMed] [Google Scholar]
- 96.Sheffield PE, Herrera JGR, Lemke B, et al. Current and future heat stress in Nicaraguan work places under a changing climate. Ind Health. 2013;51:123–7. doi: 10.2486/indhealth.2012-0156. [DOI] [PubMed] [Google Scholar]
- 97.Thomassen HA, Fuller T, Asefi-Najafabady S, et al. Pathogen-Host Associations and Predicted Range Shifts of Human Monkeypox in Response to Climate Change in Central Africa. PLoS ONE . 2013;8:e66071. doi: 10.1371/journal.pone.0066071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.El-Fadel M, Ghanimeh S, Maroun R, et al. Climate change and temperature rise: Implications on food- and water-borne diseases. Sci Total Environ. 2012;437:15–21. doi: 10.1016/j.scitotenv.2012.07.041. [DOI] [PubMed] [Google Scholar]
- 99.Zhang Y, Bi P, Sun Y, et al. Projected Years Lost due to Disabilities (YLDs) for bacillary dysentery related to increased temperature in temperate and subtropical cities of China. J Environ Monit. 2012;14:510–6. doi: 10.1039/C1EM10391A. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data sharing not applicable as no datasets generated and/or analysed for this study. All data relevant to the study are included in the article or uploaded as supplementary information.