Systemic Threats, Scarce Resources

Climate change poses largely systemic health hazards. These systemic threats gradually and globally make the world's many environments more dangerous to live in and increase social and psychological stress on its inhabitants. Haines and Ebi identify four primary drivers of climate-related health hazards: air pollutants, rising temperature, rising sea levels, and increasing extreme weather events.^{Reference Haines and Ebi3}

These mechanisms create various public health risks.⁴ Exposure to extreme temperatures and air pollution as a result of climate change can adversely affect cardiovascular, respiratory, and mortality outcomes.^{Reference Liu5}

A warming climate will expand the habitats of disease-bearing vectors such as mosquitos and promote growing conditions for food- and water-borne pathogens.⁶ Changing weather patterns will gradually inflame the severity of droughts, food insecurity, sea level rise, floods, and landslides, displacing entire communities and destroying resources.^{Reference Dangerdorf and Xu7} Extreme weather events such as fires and hurricanes also become more likely, causing acute damage and psychosocial stressors and potentially leading to social unrest that could undermine human security by driving political instability, forced migration, or violence.

The climate crisis will impose these systemic health burdens unevenly, often falling hardest on the most vulnerable populations in low- and middle-income countries (LMICs). In identifying vulnerable populations, the WHO lists children and elderly populations; people with pre-existing health issues; those living in coastal, mountainous, or polar environments or dense urban areas; and those living in regions with “weak health infrastructure.”⁸ Migrants represent another vulnerable group and face compounding hardships including insufficient access to services and relegation to living in higher-risk areas when they reach a destination.⁹ Within populations, climate-related threats will likely cause the greatest harm to marginalized groups, including racial and ethnic minorities and low socioeconomic status individuals.

These systemic, insidious health threats demand robust responses from public health agencies and non-state actors. Wiley argues the diffuse and incremental health hazards of climate change require a shift from preparedness towards resilience in public health law and policy.^{Reference Wiley10} Ultimately, building effective resilience to complex climate threats will involve multiple public health instruments deployed in concert by state and nonstate actors in furtherance of the ten essential public health services, such as surveillance, preparedness, and adaptation of the built environment.^{Reference Frumkin11}

Successfully deploying many of these instruments to build health resilience will rely heavily on data and require substantial public and private resources. The WHO points to multiple roles and types of modeling, assessment, and surveillance required for climate responses.¹² These include climate modeling for early warning of extreme weather events, monitoring dynamic environmental determinants of health such as air and water quality, surveillance of climate-related infectious disease, tracking morbidity and mortality, and determining the vulnerability of and risks for individual communities. Such data-intensive tools will also require appropriate health communication to ensure that policymakers and affected communities can make informed decisions about preparing and responding to climate-related hazards.

Data informing decision-making about where to distribute resources and support health services will also play critical roles in responding to climate change in the most effective way, with the fewest errors, and at the lowest cost.^{Reference Ebi, Ebi, Smith and Burton13} Emergency preparedness will remain critical for responding to climate-related extreme weather events and disease outbreaks, and can call on traditional public health tools such as social distancing measures and epidemiology. Beyond traditional emergency preparedness tools, adaptation of infrastructure and the built environment to the needs of specific communities will also be necessary to manage risk.¹⁴ Information on the health impacts of climate change in real-time may provide a foundation for considering policy options, coordinating responses, assessing the intersections of environmental and social determinants of health, or evaluating interventions deployed.^{Reference Pascal15} These data can then enable public health interventions to be refined for greater effectiveness and efficiency.

Notably, however, even though data-driven decisions may reduce costs in the long term, LMICs may lack the initial resources necessary to adopt these tools and techniques that could benefit the highly vulnerable groups there. Nor will using big data offer a panacea. The COVID-19 pandemic has illustrated how a confluence of factors such as structural racism, disagreement over whether and how to use digital contact tracing, and misalignments between the interests of for-profit technology firms and the public health can complicate and problematize the implementation of digital technologies to serve the public health.^{Reference Ram, Gray and Ross16}

Precision Public Health

Amid the systemic challenges to public health incited by climate change and the need for more and higher quality data, scholars and policymakers have begun discussing new and bold strategies for more “precise” public and global health.¹⁷ The growing interest in precision public health (PPH) interventions may soon capture the attention of climate-driven public health decision-makers seeking improved use of data.

The concept of PPH is new and remains contested, in both its definition and its application.^{Reference Chowkwanyun, Taylor-Robinson and Kee18} Broadly construed, PPH seeks to improve current public health practices and capacities by infusing them with emerging science and technologies. The term itself has begun to collect hype with substantial PPH projects recently announced by the Bill and Melinda Gates Foundation and the Rockefeller Foundation. However, PPH can mean different policies and measures to different audiences. Despite heightened interest in the last four to five years, plans for how PPH should achieve its broader goals and its underlying rationale remain imprecise. The PPH discourse has given rise to two similar but distinct ideologies backing the concept, and Figure I aims to distinguish the two.

Figure 1. Two Models of Precision Public/Population Health

The first model focuses on applying ideas from precision medicine to population health. In precision medicine, physicians integrate insights from an individual's genomics with their symptoms, behaviors, and environment to generate tailored treatment plans. The discipline of “public health genomics” aims to convert this precision medicine approach into population-level strategies, and gave rise to the earlier depictions of PPH.^{Reference Molster19} Officials at the Western Australian Department of Health and US Centers for Disease Control and Prevention both pushed the expansion of public health genomics into a broader concept of PPH.^{Reference Weeramanthri20} As an outcrop of precision medicine, this first model would describe PPH as providing “the right intervention at the right time, every time to the right population.”^{Reference Khoury21}

Encoded in this definition and its roots in precision medicine is the promise of extrapolating insights about prevention and population health from new information about individuals' health and interventions on individuals.^{Reference Topol22} The US All of Us program and UK 100,000 Genomes Project, and several translational medical programs which proceeded them,^{Reference Ginsburg and Phillips23} embody this vision of PPH by asserting that population wellbeing can be increased by learning more about the health of individuals through genomic, clinical, and behavioral data. While this first vision of PPH does seek to draw on human genomic information, its advocates describe a broader focus on big data and an emphasis on prevention by methods such as surveillance using pathogen genomics or electronic health records (EHR).^{Reference Khoury24} The interest in using EHR during the COVID-19 pandemic illustrates this first model through its assumption that data on individuals can scale up to provide information on which groups have the greatest vulnerabilities and require the most assistance.^{Reference Stoeger and Schmidhuber25}

Critics of this first concept of PPH have expressed concern that scaling up insights about individuals' treatment and disease vulnerability will inevitably miss the social structures in which individuals exist.^{Reference Bayer and Galea26} Part of the problem arises from the reality that most genomic data available for analysis comes from populations of European descent, potentially exacerbating health inequities by providing low quality analysis for communities of color which may lead to under- or over-diagnosis or treatment. Proponents of precision medicine based PPH call for data collection on social and environmental determinants of health, while retaining a strong interest in genomics.^{Reference Khoury27} However, genomic myopia in methods could potentially result in overlooking non-genomic determinants of health and downgrade ongoing, successful public health efforts to address these roots of health.^{Reference Cooper and Paneth28}

The second model specifically declines to use precision medicine as a starting point for defining PPH. Indeed, the most recognized definition of this second variant of PPH came from an article responding to critics of the first model, describing PPH as using “the best available data to target more effectively and efficiently interventions … to those most in need.”^{Reference Horton29}

This second vision of PPH emphasizes using big data beyond genomics, data science, and related technological tools to analyze health outcomes for the smallest subpopulations possible under those methodologies. Proponents argue that rapidly collecting more precise data on populations and using powerful data analysis tools will enable public health officials to allocate scare resources more efficiently, thereby resolving problems more effectively.^{Reference Dowell30} The promise of methods such as geospatial modeling and artificial intelligence (AI), in combination with data collection from novel sources such as smartphones, internet of things (IoT), and smart infrastructure, buttress this vision. Prioritizing big data and data science also creates an emphasis on data sharing and interoperability within the vision.^{Reference Dolley31} For this second model, genomic data can play a role but is deemphasized relative to proponents of the first vision of PPH. Recent studies have deployed this version of PPH by creating high resolution maps of child mortality in LMICs.^{Reference Burstein32} By combining and processing vast amounts of health and location data with geo-statistical modeling tools, the researchers could map mortality rates to 5 km x 5 km zones, instead of at the country-level.

Criticisms of this second model of PPH have focused more on the reality that these new methods merely bolster traditional public health functions, suggesting that the moniker “precision” is redundant in public health practice and risks overhyping tools with inevitable limitations.³³ This criticism manifested during the COVID-19 pandemic with debates over whether to use digital contact tracing, which epitomizes this second model of PPH in its top-down approach of parsing larger populations for relevant subpopulations, or to proceed with more traditional methods of contact tracing which do not directly rely on private technology firms and geolocation data from mobile phones. Commentators noted public health agencies have established methods of providing contact tracing, which still act on the individual level without raising data privacy concerns and using rapidly developed tools which may have technical errors or issues.³⁴ While empirical data will be needed to assess whether digital contact tracing may have increased the effectiveness of pandemic responses, this example demonstrates both that precision tools often merely augment existing methods rather than creating new ones and that using digital technologies because of their “precision” can be contested when other public health methods exist which still operate on subpopulations or individuals. Yet, lessons from other emerging technologies suggests that appealing catch-all terms, such as “nanotechnology,” help aggregate policy conversations and additional funding for multidisciplinary spaces.^{Reference Hodge35} The term PPH may therefore come to act as more of an organizational principle to capture existing policy tools and prove durable in the coming years, despite the contestability of viewing digital technologies as inherently precise or more precise than other tools.

Overall, both narratives of PPH may support similar projects and espouse the use of new technologies in public health, though they carry different underlying assumptions and values. These can be divided into three core differences: (1) the rationale behind the approach, (2) how subpopulations are conceived, and (3) how to use data.

First, the two models adopt different approaches to PPH, where the first vision aims to begin with individuals and build up to insights about subpopulations while the second strives to begin with populations and narrow down progressively smaller subpopulations within the larger group. Second, the first vision of PPH defines populations as a group with the same disease or disease vulnerability, whereas the second model views populations as geographic entities distinct from larger political jurisdictions.

Third, both visions rely on emerging technologies for data collection and analysis but with slightly different scopes. The first model of PPH places greater value on research and identifying new routes for prevention through genomic and other types of investigation, whereas the second model instead places less emphasis on knowledge creation and stresses optimizing the use of available tools and best practices to address widespread public health issues. Additionally, while both concepts originate in Western countries, the precision medicine based PPH model grew out of use cases in industrialized countries while the second PPH model has been more grounded in applications in LMICs and global health.

Opportunities for PPH in Climate Change

The concept of PPH has multiple potential benefits and opportunities for use in augmenting the public health response to climate change. Various emerging technologies and tools have begun to create opportunities to use big datasets in public health practice, which may be necessary for responding to the systemic, insidious threats of climate change. Already, the idea of infusing “precision” into climate-related public health approaches has entered the academic literature.^{Reference Hansen and Bi36}

PPH in climate responses could draw on new data sources, collection and aggregation methods, and analytical tools to bolster improved health resilience through preparedness and adaptation. First, new data sources such as social media posts, electronic medical records, and mobile health (mHealth) devices may prompt insights into location-specific morbidity from climate-driven outbreaks, heat waves, food scarcity, or social unrest. Falling prices of genomic sequencing techniques could also boost efforts to determine the origin or epicenter of an outbreak, especially as a warming climate permits greater vector travel and pathogen proliferation.

Second, emerging methods of collecting data, from social media scraping to IoT sensor networks, offer methods of collecting data and aggregating multiple types of data for novel insights. Collecting mortality and morbidity data with geolocation-enabled mobile phones could offer more accurate and swift information on where climate-related harms occur and where interventions could be targeted.³⁷ Already, some localities such as Chicago have installed IoT sensor networks to measure spatial differences in air pollution.^{Reference Clark38} Officials at the US National Institute of standards and Technology have suggested embedding a diffuse sensor network to measure sea level rise and flooding at tight intervals along coastal regions.^{Reference Loftis39} Distributed sensor networks deployed in coastal regions and small island states could prompt improved models, identify time-frames for decision-making, and determine the most vulnerable communities for intervention. Distributed ledger technologies including blockchain could boost the security and reliability of climate-related public health data collected and allow for related data to be more readily linked and traced.^{Reference Ting40}

Third, using new data sources and collection methods, analytical tools such as geospatial mapping and AI can assist in processing data, modeling, and decision-making around the health impacts of climate change and assessing interventions deployed. For instance, AI systems played a role in early detection and monitoring of the COVID-19 pandemic by incorporating various data sources, including scouring social media posts for depictions of illness and symptomatology clustered in geographic proximity.^{Reference Ross41} Similar tools could potentially monitor for outbreaks of vector-, food-, or water-borne disease. AI may also enable modeling, predicting, and preparing for extreme weather events and their likely impacts on specific localities.^{Reference McGovern42}

Given the systemic scope of the public and global health challenges posed by climate change, the first vision of PPH grounded in precision medicine will not likely offer an appropriate strategy in the near future. The precision medicine based approach of extrapolating insights from individuals will likely break down if taken to the global level, as the sheer scale of variations between individuals, in their environmental and social settings, will likely render this approach prohibitively costly and time consuming. Further, too little genomic data on populations outside of developed countries appears to be available to make appropriate use of genomic insights at the global level. The prospective value of human genomic insights for adapting to climate change in the near-to mid-term term is similarly limited. These “individual-up” approaches to climate PPH may instead be most effective when combined with other individual-centric public health initiatives such community-based participatory research or citizen science, though difficulties may remain in generalizing these data to the national or global levels.

Instead, the second, more data science driven model of PPH fits better with climate applications. Using any type of data, rather than looking first to human genomics, should enable PPH interventions to be deployed globally, not solely in high-income nations. The view of populations on which to intervene as geographic communities also suits climate change well, as hazards from heat waves, rising sea levels, and viral vectors will depend strongly on location and geography (though higher resolution climate models may also be needed^{Reference Palmer43}). The even newer idea of “precision” global health (PGH) is more consistent with the second vision of PPH and climate-related applications more generally, as PGH emphasizes characterizing and intervening on health determinants and disparities in precisely drawn locations across the globe.⁴⁴

Multiple technical barriers remain before these tools can be fully appreciated, such as improving the capacities of AI and better understanding how digital data can model human behavior and behavioral change.⁴⁵ Further, data sources from laypersons such as social media posts and search terms will need to be carefully assessed and processed, as these data may report sign, symptom, and disease terms incorrectly or interchangeably. Learning lessons from missteps with resources such as Google Flu Trends will require carefully interrogating datasets and the structure and outputs of data processing tools to avoid over- or under-estimations based on faulty assumptions or meaningless patterns.^{Reference Lazer46} Yet, overall, adopting PPH and PGH strategies for addressing climate change offers the possibility of matching systemic hazards with broad-based, evidence-based responses. Whether by better prediction and preparedness for greater effectiveness, coordinating services for increased efficiency, targeting interventions to reduce liberty infringements, or automating some public health tasks to free up scare resources, PPH strategies could have promising benefits in the climate context.

Ethical and Legal Challenges for PPH in Climate Responses

Advocates of the models of PPH (and PGH) presented above appear to assume that infusing public health with “precision” would inherently lead to better interventions in any public health arena, including climate change. Within this narrative, the opportunities of PPH may soon appeal to practitioners and policymakers seeking robust answers to the systemic challenges imposed by the changing climate. However, whether adopting PPH strategies in the climate context will improve public health outcomes is uncertain and presents both empirical and normative questions. The empirical question cannot be answered at this time, but the normative dimension requires asking whether precision will likely further the core goals of public health for addressing climate-related threats and, if so, for whom?

By emphasizing data and efficiency, PPH strategies may create opportunities but could also pose ethical and legal challenges or corrode the core purposes of public health policy. Pragmatic issues of implementation also arise, especially in LMICs where climate vulnerabilities are highest while digital resources and infrastructure with which to collect data are sparser. These potential pitfalls are discussed below in the emerging data sources, collection methods, and processing tools PPH strategies would promote in climate responses, reflected in Figure II.

Figure 2. Opportunities and Challenges for PPH in Climate Change

Health Justice as a Guide for PPH Climate Responses

As an emerging strategy, PPH presents both opportunities and challenges. If PPH can deliver significant communal benefits, then these challenges alone may not provide sufficient reason to categorically prohibit precision climate responses.^{Reference Hodge74} However, social justice and good governance principles demand attention to health inequities in any current or proposed public health policy. Avoiding challenges identified above may also prevent potential legal challenges to PPH interventions, enabling swifter implementation. Justifying the use of PPH tools in the climate response will require anticipating disparities before they occur and modifying the design of proposed PPH interventions to mitigate such disparities.

Health justice offers a promising framework for guiding more equitable uses of PPH for responding to climate-driven health hazards. The concept of health justice embodies both a theoretical and pragmatic orientation towards equitably promoting health for all through understanding health as a product of myriad factors outside individuals' control.^{Reference Venkatapuram75} Wiley describes health justice as a lens for critically evaluating (1) how various social determinants of health will mediate public health interventions, (2) how systemic biases and structural barriers necessitate collective responsibility for public health, and (3) how to encourage meaningful community participation in designing and deploying interventions.^{Reference Wiley76} In previous work, health justice has been used to diagnose health inequities in existing systems, primarily in the US, and prescribe solutions.^{Reference Benfer and Benfer77}

The recent advent of health justice builds on lessons learned from environmental justice and complements other social justice frameworks applicable to the health hazards of a changing climate, most notably climate justice. Various conceptualizations of climate justice stress a rights-based approach to ensuring multigenerational wellbeing, development rights for LMICs, accountability for historic contributors, and equity in efforts to transition towards a more sustainable global economy.^{Reference Schlosberg and Collins78} This article emphasizes a health justice approach to center the health consequences of climate change and identify potential solutions trained on health, rather than climate justice's broader focus on development and sustainable economies. However, the climate justice tenants of multigenerational well-being and equitable treatment of LMICs must augment any health justice approach applied to climate change, especially as development and economic stability are intimately linked with health. Gostin calls for a more global health justice to animate global governance, one which declares that “human health is a globally shared responsibility, reflecting common risks and vulnerabilities — an obligation of health justice that demands a fair contribution from everyone.”^{Reference Gostin79} Health justice for climate change and its systemic risks will require characterizing social determinants of health, collective responsibility, and participatory action as concepts which extend beyond any one generation or jurisdiction.

Here, Wiley's three elements of health justice can help anticipate how PPH could most likely create or worsen health inequities in the future and illuminate opportunities for mitigating inequitable outcomes. This method is used below to identify two examples of how health justice can guide more responsible PPH in climate responses.