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Highlights
- While global efforts focus on limiting temperature rise to 1.5 degrees Celsius, the escalating health impacts of climate change require urgent attention.
- Tools such as AI-powered climate health data platforms can address the intertwined challenges of climate change and public health. Generative AI can enhance this by visualizing future health risks, driving proactive climate action.
- Understanding the true cost of climate change on public health can drive governments toward aggressive climate action. Digital technologies provide an opportunity to intersect climate science, health, and economics, promoting both sustainable policy and citizen engagement.
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A global, all-encompassing concern
Global warming is one of the most pressing challenges that governments, businesses, and the society at large face today.
Issues such as extreme weather events, rising temperatures, air pollution, and depletion of freshwater sources are central to the sustainability agenda worldwide. While nations prioritize climate action to limit the global temperature rise to 1.5 degrees Celsius by the end of the century, the cumulative adverse impact of climate change on public health demands immediate attention.
The World Health Organization (WHO) estimates that climate change will cause approximately 250,000 additional deaths annually between 2030 and 2050 due to malnutrition, malaria, diarrhea, and heat stress. Individuals with chronic conditions such as heart disease, chronic obstructive pulmonary disease, and diabetes are increasingly vulnerable. The World Economic Forum (WEF) prediction is even more somber. By 2050, the climate crisis could cause an additional 14.5 million deaths, $12.5 trillion in economic losses, and $1.1 trillion in extra costs to healthcare systems around the globe.
Healthcare systems worldwide are already grappling with the growing burden of aging populations, alarming rates of non-communicable diseases, and increasing cost of care. The added strain from climate change-induced health issues will further accelerate existing health inequalities across populations, making it vital for governments to invest in targeted interventions toward mitigation and adaptation. Technology can play a crucial role here.
We discuss how emerging technologies based on analytics, AI, and GenAI can empower healthcare leaders, government health departments, and research and innovation bodies to identify the long-term health implications of climate change and design effective measures to combat it.
Emerging threats, vulnerable populations
The complex intersection of climate change and public health needs careful examination.
While it may appear obvious to link environmental factors to health issues–such as air pollution triggering respiratory illnesses or excessive heat causing heatstroke–the relationship between the environment and health is far more complex and nuanced. Researchers around the world are uncovering intricate ways in which climate change exacerbates health problems.
Consider these cases which could potentially be attributed to climate change:
- Alarming rise in cancer rates across age groups: The depletion of the ozone layer, aggravated by climate change, leads to increased exposure to harmful ultraviolet radiation. This heightened exposure is directly linked to a rise in skin cancers, including melanoma, which is among the most dangerous forms of cancer.
- Mental health issues stemming from the stress of disrupted livelihoods and habitat loss: In Australia, climate-related disasters such as bushfires and floods have triggered a rise in mental health issues. Communities affected by these events report increased levels of anxiety, depression, and post-traumatic stress disorder.
- Developmental issues among children due to agrarian crisis: The prolonged drought in the Horn of Africa, driven by climate change, has led to severe food insecurity, particularly in Somalia, Ethiopia, and Kenya. Millions of people, especially children, are facing acute malnutrition due to crop failures and livestock losses. This is impeding biological development in children, the compounding effect of which – over several years – will be disastrous.
- Increased incidence and spread of diseases: There is enough evidence to connect the rampant increase in vector-borne diseases to the rising temperatures. The rise in human-animal interfaces on one hand and warmer climates on the other, are fueling the spread of newer, more dangerous diseases. In 2023, Bangladesh and other South Asian countries experienced one of the worst dengue outbreaks in recent history.
- Rise of mortality from cardiovascular and respiratory diseases: Europe experienced unprecedented heatwaves this year, with temperatures soaring above 45 degrees Celsius in some regions. Countries like Spain, Italy, and Greece reported significant increases in hospital admissions for heatstroke and cardiovascular issues.
What is particularly alarming is the uncertainty surrounding how these disease patterns will evolve as the environment continues to deteriorate. The possible emergence of new health threats poses a significant risk to our already strained healthcare infrastructure (the COVID-19 pandemic is a grim reminder of the global unpreparedness in effectively containing and controlling a contagion).
In addition, specific segments of the population globally are disproportionately vulnerable to these climate-induced health threats. Outdoor workers, farm laborers, children, pregnant women, patients with chronic diseases, and populations in island countries, coastal areas, and slum dwellers face heightened risks. They may not have the financial means or options to protect themselves or adapt to these rapidly changing conditions adequately.
Bridging climate and health through data-based technologies
AI-powered, data-driven solutions can be used to address the interconnected challenges of climate change and public health.
Multiple international and national bodies and government departments are generating, processing, and analyzing a significant volume of data across various sectors, including environment and health. On the environmental front, there are vast data sets encompassing emissions, atmospheric CO2 levels, temperature records, rainfall patterns, satellite images of green cover, sea water levels, glacier melting, and seismic activity.
Similarly, health organizations collect and analyze data on mortality rates, disease prevalence, healthcare infrastructure, and public health trends. This data spans national, state, and local jurisdictions, providing a comprehensive view of global and regional health landscapes.
Integrating the domains of environment and health offers profound opportunities to understand and mitigate the health risks emerging from rapid climate change.
The figure below represents the environment and health systems and the benefits of integrating the data sets from both domains into a climate-health data platform.
Figure 1: A climate-health data platform connecting the environment and health domains
A climate-health data platform
The convergence of environmental and health data is a powerful yet underleveraged opportunity.
The interconnected, data-centric approach can position governments and businesses to proactively address the evolving landscape of climate and health risks. A climate-health data platform integrating the domains of environment and health at various jurisdiction levels can be beneficial.
- National and state health departments:
Monitoring and mitigating health threats: As part of population health management, health departments routinely monitor the incidence of infectious and communicable diseases. Considering environmental data and correlating it with abnormal disease patterns will help them devise appropriate short- and medium-term interventions. For instance, the spread of vectors such as mosquitoes in higher altitudes and latitudes, where populations historically had minimal exposure, poses new health risks. The data can help them prepare for higher hospitalization rates for patient cohorts based on temperature records, vector population surveys, hospital visits, and infectious disease incidences.
Formulating policies: Healthcare departments could leverage climate data to develop effective countermeasures. A case in point is the impact of heat stress on pregnant women, which has been linked to higher infant mortality, especially for those living in houses with heat-absorbing roofs. Primary and maternal care departments can utilize data on demographics, projected peak temperatures, and population health records to plan and budget for interventions as part of prenatal care, thereby improving infant mortality outcomes.
Further, targeted screening programs for detecting lung cancer in populations exposed to high pollution levels can improve survival rates and enhance longevity.
- State and local authorities:
Adapting for extreme weather events: Urban bodies must prepare for the growing challenges posed by rising temperatures, increasing heat waves, and frequent and more destructive floods, hurricanes, and rainfall. Making data available in a usable form at the local level will enable authorities, organizations, and individuals to develop robust adaptation strategies, thereby preventing avoidable loss of lives.
Planning infrastructure: The concretization of urban spaces, loss of green cover in the cities, and rapid migration of populations to the cities have accelerated environmental health deterioration, which in turn is impacting the health of urban populations. With the availability of data on communicable and non-communicable diseases mapped to areas, precise disease hotspots can be identified, which can help design targeted healthcare programs by geographic regions. Urban planning authorities should consult and discuss with public health departments the health profile of the neighborhood and the likely impact future development might have on the health of populations of that region.
Regulating industry: With precise evidence on the health impacts of pollution and contamination, statutory bodies can better formulate rules and regulations and set quality standards for industries. Considering the risk of heat stress among outdoor workers, insurance companies could consider providing unemployment cover for income loss during extreme weather events. Currently, outdoor workers are forced to work even in hazardous conditions, which undermines their health and productivity.
Research and innovation:
Advancing healthcare and life sciences: COVID-19 has revealed deeper insights into the correlation between air pollution and respiratory diseases. For example, a study by the Harvard School of Public Health has found that a marginal increase in long-term exposure to PM2.5 significantly raises the COVID-19 mortality rate. Understanding the complex interplay between climate and health requires advanced data modeling tools and algorithms to generate actionable insights. Such insights can aid researchers in drug discovery while using AI-based models to test drug efficacy among specific vulnerable populations and patient cohorts.
Benefitting health economics: Health economists play a vital role in quantifying the impact of climate change on health. The social cost of carbon(SCC) estimates the damage done by each additional ton of carbon emissions. The metric is instrumental in shaping policy and regulatory decisions regarding carbon emissions across several countries. Armed with robust data from climate and health domains, health economists can strengthen the business case for climate-resilient initiatives by highlighting health-related costs embedded in SCC. Linking climate policies with improvements in population health outcomes can drive greater investment in sustainable programs.
Building climate-resilient agriculture: Climate change has a profound and multifaceted impact on agriculture and farming communities. Issues such as overexposure to pesticides, extreme weather conditions, water shortages, and mental stress due to crop yield uncertainties are adversely affecting food supplies and nutritional intake, especially for poorer populations.
Digital farming solutions are helping build the necessary agricultural resilience by providing critical inputs on soil health, weather patterns, crop yield, and pest risks.
Leveraging AI: Health departments must constantly track and monitor emerging health threats, making the climate-health data platform a critical tool in addressing these challenges. AI-driven modeling can further enhance health research by uncovering known unknowns and unknown unknowns, which traditional data analysis often overlooks. For example, determining the long-term mental development effects on children born to mothers with high air pollution exposure or the possible resurgence of eradicated diseases due to species migration triggered by climate change.
A significant obstacle to climate action is the present bias, where individuals and policymakers prioritize immediate concerns over long-term consequences.
GenAI has the potential to mitigate this by visualizing future scenarios and creating compelling narratives that encourage proactive behavior. By generating images that reflect the health risks of climate change, GenAI can serve as a powerful tool to influence public opinion and drive the adoption of adaptive policies by governments, aligning with broader efforts to safeguard health and well-being.
Risks and guardrails
Integrating AI and GenAI presents significant opportunities in the evolving landscape of public health.
However, it also necessitates a cautious and measured approach. It is critical to consider the risks and implement clear guardrails to ensure the responsible adoption of these technologies. We strongly recommend that the following be given due consideration:
- Distinguishing correlation from causation: Public health policymakers must exercise caution when attributing causation for correlation in data, which might imply false positives. During the COVID-19 pandemic, numerous treatment protocols were initially lauded based on correlation but were later proven ineffective and, in some cases, harmful. To avoid such missteps, robust experimental design and the intelligent use of historical data are paramount in drawing accurate inferences, especially when public health outcomes are at stake.
- Balancing data privacy and security: In population health data management, there is a delicate balance between the privacy and security of personal health data and the benefits of data sharing at a population level for disease monitoring, drug discovery, and development. Governments and health departments must prioritize building public trust by ensuring that collected data remains anonymous, safe, and confined for its intended purpose.
- Mitigating GenAI hallucinations: As GenAI-based technologies become integrated into public health systems, it is crucial to be cognizant of the hallucinations or false narratives generated by large language models. As in automation scenarios, human oversight–in this case, experienced clinicians, public health experts, and scientists–is essential to validate AI-driven insights. The COVID-19 pandemic demonstrated the rampant damage caused by misinformation and disinformation, which was responsible for the loss of several lives. Proper safeguards must be in place to prevent similar risks with AI. When using AI technologies for health-related research, planning, and decision-making, ethical considerations must be accounted for so that human safety, equity, and inclusiveness are not compromised. We recommend the WHO guidance on the ethics and governance of AI for health when considering employing AI in the health sector.
Next steps
The integration of environmental and health data is complex, given the variety and volume of datasets involved.
Hence, we recommend initiating pilot projects on key use cases. These pilots can help organizations make incremental progress while refining approaches for broader deployment.
Additionally, we recommend using the International Classification of Diseases (ICD-11), a global standard developed by the WHO for cataloging health information. Healthcare researchers can leverage sample datasets from the list of 17,000 disease codes available in ICD-11 to examine how climate change impacts specific populations and patient cohorts. This analysis could reveal trends that inform the prioritization of countermeasures, such as public health interventions or policy shifts, especially for climate-sensitive health outcomes like respiratory diseases or vector-borne infections. For example, the UK has successfully piloted the integration of health and environmental data to assess the impact of air pollution on public health, guiding targeted interventions in high-risk urban areas.
Despite progress toward Sustainable Development Goal 3 (ensuring healthy lives and promoting well-being for all at all ages), significant challenges—particularly those driven by climate change—pose significant obstacles to meeting the targets by 2030.
Understanding the true cost of climate-induced health conditions can catalyze governments to take bold actions toward achieving net-zero emissions. For example, Denmark has linked climate data to public health policy, allowing the government to address environmental and health challenges concurrently.
Further, public awareness campaigns driven by data insights can encourage citizens to make sustainable lifestyle and consumption choices. Singapore’s AI-driven strategy, for instance, includes AI-driven public health and sustainability programs, urging its citizens to adopt eco-friendly behaviors while considering the long-term impact of climate change on health.
Digital technologies enable the fusion of climate science, population health data, and economic analysis, providing governments with a roadmap to fast-track their climate adaptation efforts. By embracing tools such as a climate-health data platform, nations can better assess climate change's health and economic impacts and take decisive steps toward a sustainable future.
