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The imaginary of precision public health
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  1. Martha Kenney1,
  2. Laura Mamo2
  1. 1 Women and Gender Studies, San Francisco State University, San Francisco, California, USA
  2. 2 Health Equity Institute, San Francisco State University, San Francisco, California, USA
  1. Correspondence to Dr Martha Kenney, Women and Gender Studies, San Francisco State University, San Francisco, CA 94132, USA; mkenney{at}sfsu.edu

Abstract

In recent years, precision medicine has emerged as a charismatic name for a growing movement to revolutionise biomedicine by bringing genomic knowledge and sequencing to clinical care. Increasingly, the precision revolution has also included a new paradigm called precision public health—part genomics, part informatics, part public health and part biomedicine. Advocates of precision public health, such as Sue Desmond-Hellmann, argue that adopting cutting-edge big data approaches will allow public health actors to precisely target populations who experience the highest burden of disease and mortality, creating more equitable health futures. In this article we analyse precision public health as a sociotechnical imaginary, examining how calls for precision shape which public health efforts are seen as necessary and desirable. By comparing the rhetoric of precision public health to precision warfare, we find that precision prescribes technical solutions to complex problems and promises data-driven futures free of uncertainty, unnecessary suffering and inefficient use of resources. We look at how these imagined futures shape the present as they animate public health initiatives in the Global South funded by powerful philanthropic organisations, such as the Bill & Melinda Gates Foundation, as well as local efforts to address cancer disparities in San Francisco. Through our analysis of the imaginary of precision public health, we identify an emerging tension between health equity goals and precision’s technical solutions. Using large datasets to target interventions with greater precision, we argue, fails to address the upstream social determinants of health that give rise to health disparities worldwide. Therefore, we urge caution around investing in precision without a complementary commitment to addressing the social and economic conditions that are the root cause of health inequality.

  • public health
  • sociotechnical imaginaries
  • precision medicine
  • military metaphors

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Introduction

Since the completion of the Human Genome Project well over a decade ago, researchers in the biomedical sciences have sought to expand genomic knowledge and sequencing into clinical care. These efforts are driving what the biomedical sciences refer to as a revolution in healthcare, first framed as 'personalised medicine' and now as 'precision medicine'. Precision medicine offers a vision for how genomic and clinical data can be leveraged by doctors to tailor treatment and prevention strategies to a patient’s genome, environment and lifestyle, especially—but not only—through advances in pharmacogenomics. In recent years, calls for a precision revolution have expanded to the realm of public health, with key advocates arguing for the benefits of applying the 'big data' approaches of precision medicine to targeting public health problems in populations.1 While some public health experts argue that precision medicine constitutes a 'distraction from low cost and effective population-wide interventions and policies',2 others advocate for a new paradigm of 'precision public health' (PPH). Building on existing data-driven approaches in epidemiology and population health, PPH seeks to mobilise state-of-the-art information technology and data science to assess and improve health at the population level.3 Whereas precision medicine emphasises the use of genomic data in clinical care, PPH seeks to modernise public health surveillance by integrating multiple datasets in order to respond more efficiently to contain outbreaks, improve health and prevent disease.

While epidemiological data (eg, surveillance data, registries, infectious disease rates, surveys and other data collection tools) has long been a crucial aspect of public health research and practice, PPH emphasises collecting and analysing real-time data with increased granularity and harnessing this knowledge in rapid response efforts. As Weeramanthri and colleagues state:4

It is the combination of data-related skills and technologies (eg, in epidemiology, data linkage, informatics and communications) and the ability to aggregate, analyze, visualize and make available high quality data, larger or linked, in closer to real time, that is at the heart of PPH, much like epidemiology is at the heart of traditional public health.

This big data approach to public health goes beyond clinical and genomic data to include social, environmental and behavioural factors that contribute to differential rates of morbidity and mortality in populations. Following recent trends in public health such as 'digital epidemiology',5 'infodemiology'6 and 'digital pharmacovigilance',7 PPH seeks to utilise unconventional datasets such as internet search trends and cell phone GPS data to identify and understand public health risk. This is a far-reaching vision for what increased investment in data and data infrastructure can offer to public health efforts. Kirsten Bibbins-Domingo (Vice Dean for Population Health and Health Equity at the University of California San Francisco (UCSF) School of Medicine) argues that PPH, when fully realised, will 'telescope down' into the genome, microbiome and epigenetic profiles of individuals and then 'telescope back out' to look at family, community and larger social/environmental contexts.8 For example, in the future, epigenetic data could be brought together with environmental data on air quality and epidemiological data on asthma rates to address environmental justice issues that disproportionately affect people of colour.9

The emphasis on addressing health disparities more efficiently and effectively has been central to proposals for PPH from the beginning. An important milestone in the development of this new public health paradigm was the Precision Public Health Summit held in San Francisco in June 2016, co-sponsored by UCSF, the Obama White House and the Bill & Melinda Gates Foundation. Central to the framing of the Summit was the question of how precision approaches might be leveraged to improve health equity. Summit organisers argued that public health actors will be able to capitalise on new precision approaches by "creat(ing) new tools for capturing the data needed to measure the environmental and social aspects of health at the population level",10 tools that would allow them to more precisely target health disparities in populations. Here, data collection, integration and real-time analysis are framed as the means by which we can achieve more equitable public health outcomes: "The more comprehensive information we have, the better we will be able to serve everyone and target meaningful intervention and prevention".11 This vision for a more equitable future requires large-scale investment in data infrastructure and analytic capacities, so that "large swaths of seemingly disparate data sets"12 can be integrated and shared between diverse stakeholders.

While many actors in the USA and around the world are investing in these infrastructures and taking up the banner of PPH, much of the momentum continues to come from the San Francisco Bay Area and Silicon Valley. Large public research institutions such as UCSF are partnering with philanthropic organisations, tech businesses, pharmaceutical companies and public health departments to become leaders in the PPH revolution. Yet, with the highest cost of living in the USA and increasing income inequality, the San Francisco Bay Area is simultaneously a paragon of American wealth and innovation and a city where people earning less than six figures can barely afford to live, let alone those who are unemployed, underemployed, food and housing insecure or homeless. In other words, San Francisco embodies both the problem that PPH aims to address (ie, health and wealth disparities) and its proposed high-tech solutions (ie, big data).

As researchers at the Health Equity Institute at San Francisco State University, we appreciate how advocates of PPH have centred health equity in their discussions of data-driven approaches to public health. Yet, we also seek to problematise the notion that increased investment in data collection and infrastructure will ultimately be the best approach for addressing health disparities because it leaves the social determinants of health, which many in critical public health understand to be the root cause of inequalities in morbidity and mortality,13 largely untouched. Braveman et al 14 define health disparities as "a specific subset of health differences of particular relevance to social justice [because they] arise from intentional or unintentional discrimination or marginalisation and … are likely to reinforce social disadvantage and vulnerability". Because health disparities are caused by and contribute to discrimination, marginalisation, social disadvantage and vulnerability, big data approaches may be able to target interventions, but are unlikely to address the complex causes of health inequity, causes that are systemic and socially determined. Furthermore, the solutions of PPH—increased surveillance and data gathering, biomedical interventions and targeted risk-reduction strategies—may exacerbate the inequalities they seek to address by further stigmatising already marginalised populations without changing their social and economic circumstances. Finally, this bold vision for the future of public health may shift funding and attention away from strategies for addressing health disparities that are not framed explicitly in the terms of biomedicine or bioinformatics.

While challenges to the promise and premise of precision medicine are well documented,15 challenges to the big data approach of PPH have only recently emerged in the public health literature.16 As Dolley17 argues, while big data has intuitive appeal, there is little evidence that our ability to predict diseases has improved much in the past decade or that our current use of big data will improve health.18 High-profile projects that make use of unconventional datasets, such as using Google search data to predict influenza outbreaks in the USA19 or cellphone data to track the spread of Ebola in Sierra Leone, have failed to live up to their promise.20 Popularisers of PPH, of course, recognise the limitations of data-driven approaches to health and health equity. For example, Khoury et al 21 remind us that "a healthy dose of scepticism may be needed to guard against the overpromise of big data". While we share their scepticism about the technical capabilities of big data approaches,22 we also urge consideration of the larger social, political and economic context in which increased 'precision' becomes an attractive solution for health disparities in the first place. Drawing on the analytic tools of Science and Technology Studies (STS) and the Medical Humanities, we ask what social, material and rhetorical work 'precision' is doing in these recent calls for PPH and whether precision’s data-driven solutions are ultimately compatible with health equity and social justice aims.

Precision as a sociotechnical imaginary

In this article, we analyse PPH as a sociotechnical imaginary.23 STS scholar Sheila Jasanoff defines sociotechnical imaginaries as "collectively held and performed visions of desirable futures … animated by shared understandings of forms of social life and social order attainable through, and supportive of, advances in science and technology".24 Conceptualising precision as a sociotechnical imaginary allows us to consider how these imagined futures shape agendas, research trajectories and investment in technology, infrastructure and expertise in the present.25 Sociotechnical imaginaries are comprised of specific practices of storytelling and visualisation, habits of organising space and time, and 'structures of pleasure and anxiety'.26 They inform what kinds of futures are possible and desirable, how problems and solutions are framed,27 what techniques and technologies become 'theright tool for thejob',”28 how we respond to threats and contain uncertainty29 and, ultimately, what we hope for, expect and fear from science, technology and medicine.30 The precision imaginary, which has recently been taken up across disparate technoscientific fields including medicine, agriculture and military strategy, offers an appealing vision of the future where data collection and analysis can revolutionise the application of science, technology and medicine.

In the case of precision medicine, medical sociologists and STS scholars have emphasised how precision has been framed as the next revolution in biomedicine, moving away from 'one-size-fits-all' medicine to tailored treatment and prevention. However, despite the ongoing investment in bioinformatics and data infrastructure, "the exuberance that characterised the emergence of venture sciences such as pharmacogenomics and personal genomics has not been fully met".31 The precision imaginary, then, is best understood as belonging to a particular 'anticipatory regime' where new technologies like clinical exome sequencing promise to lead to biomedical breakthroughs, always figured as 'just around the corner—coming soon'.32 Precision medicine is an "aspirational label for the goal of translational genomics33" rather than descriptive of current practices. However, as Adams et al remind us, “such anticipations are not only diagnostic; they are productive".34 If these projected futures never fully materialise, it becomes important to pay attention to what this precision imaginary produces in the present. Sandra Soo-Jin Lee, for example, argues that while researchers often speak about an 'imminent era of individualised medicine',35 what happens in practice is that, in the absence of new biomedically significant groupings based on drug response and disease rates, race instead becomes 'a shorthand for variation'.36 When race stands in for human variation, racial categories are reified as biological, health disparities can more easily be framed as genetic rather than sociopolitical and racial categories continue to be used in clinical trials. So, although precision medicine has not been fully realised, the promise of precision medicine shapes biomedical research in ways that often go unnoticed. As precision makes its way from biomedicine to public health, it is important to track how precision as a sociotechnical imaginary makes particular claims on the future and, in doing so, shapes the present of public health.

Structure of the article

In what follows, we take stock of the precision imaginary as it takes shape around us in conferences, TED talks and public health initiatives, both local and global. While the futures that PPH promise seem bright and, at times, necessary, we encourage reflection on its form and potential consequences. Specifically, we ask: How does precision operate as a sociotechnical imaginary that structures public health strategies to reduce health inequities? What actors, partnerships and forms of expertise emerge as the architects and drivers of these anticipated futures? What is hiding in "the shadow of imprecision trailing behind those luminous technologies of precision?"37 And, finally, while we are waiting on the promise of precision,38 what other public health futures are foreclosed? Here we join those in the fields of health equity and critical public health who urge caution in adopting the new paradigm of PPH and other data-centric solutions for public health.39 Beyond the practical questions of whether the outcomes will live up to the promise or will eventually be revealed as empty hype,40 we are interested in interrogating why precision has such cultural charisma at this historical moment, enrolling powerful actors from Mark Zuckerberg to Barak Obama. What are the consequences of investing in this paradigm over other proposals for achieving health equity goals?

To address these questions, we begin with a medical humanities approach to mapping the contours of the precision imaginary. Following Susan Sontag, Donna Haraway, Emily Martin and others who have analysed the military metaphors that populate our understandings of health and illness, we consider how precision emerged in health and medicine alongside the Obama-era investment in new data-driven forms of precision warfare. Although those who work in medicine and public health might bristle at the comparison between actions intended to heal and actions intended to kill, comparing the rhetoric of precision across these different domains helps us highlight the ways precision emphasises technical solutions to complex problems and promises data-driven futures free of uncertainty, unnecessary suffering and inefficient use of resources. Building on this analysis, we draw attention to how military metaphors are mobilised by Sue Desmond-Hellmann, CEO of the Bill & Melinda Gates Foundation, in her 2016 TED talk, 'A Smarter More Precise Way to Think about Public Health'.41 We consider who/what is targeted for intervention in the Gates Foundation's vision for global public health and why targeted solutions are attractive for public health philanthropy. Finally, we turn to a local PPH initiative that seeks to address cancer disparities in San Francisco, the San Francisco Cancer Initiative (SF CAN). We consider how SF CAN’s framing as a 'precision population health' project shapes the data-driven approaches offered as well as the prescription for how health equity should be realised in San Francisco. We conclude by urging caution in adopting this military metaphor to circumscribe a data-driven future for public health to the exclusion of other visions for achieving health equity.

As feminist science studies scholars located in the San Francisco Bay Area, we are invested in conjugating public health with social justice in the face of persistent health disparities worldwide. It is well documented that members of disadvantaged groups (by race/ethnicity, gender, sexuality, socioeconomic status and other social categories) experience shorter lives and higher rates of disease, injury and disability.42 While public health on the population level—from epidemiology to population health—has long relied on large datasets, we argue that calls for an intensification of investment in data infrastructure can often come at the expense of addressing these upstream social determinants of health. Only when social justice questions are framed as intractable does rapid data-driven targeting become the 'right tool for the job'. As Meagher et al argue:43

If social policies or institutions distribute health risks in ways that unfairly render some populations more vulnerable, the just solution is social reform, not merely remediation of downstream consequences. As precision public health research advances, it will be increasingly important to resist the eclipse of these broader social dynamics.

Throughout this article we consider what happens to the goals of public health in general, and specifically the aspiration of achieving health equity, when the precision imaginary enters public health; what possibilities are generated, and which are foreclosed? By analysing the precision imaginary and how it increasingly animates public health initiatives locally and globally, we hope to open up possibilities for imagining different public health futures—futures that are vibrant and livable with benefits that reach beyond those who are already invested in the precision revolution.

Precision as a military metaphor: the affective economies of the War on Terror

In 2016—'the year of precision public health',44—the US Assistant to the President for Homeland Security and Counterterrorism stated: “In Iraq and Syria, coalition forces have conducted almost 11 000 precision airstrikes”. We begin our analysis by considering why, in the USA in 2016, we see precision as a sociotechnical imaginary animating discourses of medicine, public health and military airstrikes? Although bringing these domains together might seem unsavoury, examining the appeal of precision in US military discourse can help us to understand the possibilities and limitations of 'targeted' approaches to public health. There is, in fact, a long history of cross-traffic between military and medical metaphors, especially in the context of cancer treatment45 and understandings of the immune system.46 Medical metaphors can lend an air of efficiency and humanitarianism to weapons that kill and maim (eg, surgical strikes), thereby 'sanitiz(ing) the battlefield';47 military metaphors bring urgency and agency to treating illnesses that are often experienced as disempowering and socially stigmatising (eg, a battle with cancer). Susan Sontag, who was one of the first to point to the pervasive military metaphors in oncology in 1978, predicted that the metaphors of cancer treatment would shift over time: “It is, of course, likely”, she wrote, “that the language about cancer will evolve in the coming years".48 However, what she did not predict is that, rather than evolving away from military metaphors, the language of medicine has evolved alongside the US military. Calls for precision in medicine in many ways belong to this moment in US military strategy, when unmanned aerial vehicles (drones) carry out airstrikes on official battlegrounds in Iraq, Afghanistan and Syria as well as perform 'targeted killings' in Pakistan, Yemen, Somalia and Libya.49 Although the 'precision' of precision medicine did not originate from the War on Terror, its rhetorical and material operation in this domain offers important insight into its deployment into health, in particular the affective allure of precision’s promises. In both the military and medical context, we argue, precision projects are 'predicated on a faith in technological solutions'50 that works affectively to contain risk and uncertainty.

Although the drone is the most recent face of precision warfare, discourses of accuracy and precision have long been central to the logic of US airpower. The Oxford English Dictionary finds references to precision bombing as early as 1934. In WWII, bombardiers were supposedly able to “drop a bomb into a pickle barrel";51 despite the doctrine of mutually assured destruction, the accuracy of nuclear missile guidance remained a top priority for the US military throughout the Cold War;52 in the Vietnam War so-called 'smart bombs' “made it possible to strike closer than ever to civilian areas";53 precision-guided munitions were central to framing US actions in the first Gulf War as constituting a technologically and morally superior form of modern warfare.54

In the post-9/11 USA these discourses of precision have been newly articulated with regimes of (in)security that keep us in a state of permanent war. Against nebulous and barbarous enemies, figured not as troops or soldiers but as unlawful enemy combatants, as terrorists and insurgents, as hiding in caves, as possessing WMDs, as Islamic fundamentalists, as living in 'tribal regions', as capable of chemical and biological warfare, the discourse of precision warfare renders the USA as a technologically superior and humanitarian global actor.55 As is evidenced by these ubiquitous tropes of the War on Terror, the understanding of ‘us’ versus ‘them’ that circulates widely in popular media and US military policy is mediated by the modern/traditional binary. Placing the enemy always on the traditional side of the binary simultaneously renders them killable, and rhetorically guarantees 'our' modern military advantage against 'their' premodern threat.56 Claiming 'precision' as a central logic of US military strategy pits technological prowess against the ambient insecurity of terror and insurgency, providing comfort via the promise of technoscientific containment.57

To say that a practice is 'precise' obscures as much as it illuminates. For example, the humanitarianism and efficiency that precision gives to bombing conceals the ways terror (not accuracy) is one of the main effects of airpower.58 And although the laser and GPS systems guiding these bombs are technically impressive, precision discourses chronically overestimate technological control and obscure the unruliness and uncertainty of real-world conditions. As Caren Kaplan points out, throughout the history of US airpower, “evidence runs so counter to the discourse on precision and technological mastery".59 Precision, then, is as much an animating fantasy as it is a technological achievement.

What else do discourses of precision obscure? In the case of US drone strikes, precision obscures the fact that resources that are put into executing these airstrikes are not the same as those that go into measuring the outcomes of those strikes. In 2016 the Obama administration released an estimate of civilian causalities in 'counterterrorism' drone strikes in Pakistan, Yemen, Somalia and Libya; in this statement, which many believe to underestimate the number of drone killings,60 the White House begins by attesting to the difficulty in counting causalities: “First … there are inherent limitations on determining the precise number of combatant and non-combatant deaths, particularly when operating in non-permissive environments” (emphasis ours). This qualification absolves the White House of any real accountability for 'civilian' casualties and obscures the (de)valuation of life enacted in these practices by framing it as a practical and economic question.61 However, even if more resources were allocated to counting those who are immediately killed in the airstrikes, it still wouldn’t account for the slow violence that unfolds in the wake of 'modern' warfare (eg, unexploded munitions and environmental contamination)62 and the 'damage to the social fabric' that results from targeted killings.63 The efficiency and humanitarianism that 'precision' projects belies the death and destruction that all warfare brings. As Caren Kaplan argues, “discourses of precision” have long “obscured information about civilian deaths or rendered them inconsequential".64 This takes on a new form in the War on Terror where the individual becomes the target of the strike and “all other people affected by it are removed from view".65

Precision, then, is a sociotechnical fiction that conceals what falls outside of its official targets. When precision is applied in health and medicine, it too capitalises on affective economies of fear and insecurity, providing a technoscientific reassurance that diseases—and the risk of illness itself—can be targeted by modern biomedicine with greater certainty and minimal suffering. Although it performs different work in the medical domain, we argue that precision in medicine and in public health relies on the affective economies of the War on Terror,66 where it works to contain insecurities and manage risk. For Sara Ahmed, affective economies refer to the ways that emotions circulate between objects and individuals, 'bind subjects together'67and attach to particular objects, futures and visions of the good life. Post 9/11, Ahmed argues, affective economies of fear and insecurity have played an important role in justifying US securitisation and militarism: “It must be presumed that things are not secure, in and of themselves, in order to justify the imperative to make things secure".68 War metaphors and discourses of securitisation in medicine and public health69 encourage us to imagine both individual bodies and populations as fundamentally insecure; investing in precision solutions, therefore, provides comfort in its promise to contain emerging and enduring bioinsecurities.70

Introducing precision public health: targeted solutions for global public health

Precision has gradually taken root in health, first in medicine and now in global and local public health efforts, bringing with it the military logic of data-driven targeting and the affective economies of securitisation. A significant and powerful exemplar of the ways these rhetorical strategies operate in health can be found in Sue Desmond-Hellmann’s 2016 TED talk where she introduces her vision for bringing precision to global public health.71 Building on the analysis of the previous section, we consider how this proposal mobilises the rhetoric of precision warfare, articulating the ways it joins together with the economic logics of global public health philanthropy.

Desmond-Hellmann is an important figure in bringing the promise of precision medicine into public health. First trained as a clinical oncologist, she worked for the biotechnology company Genentech from 1995 to 2009 where she held several leadership roles including Chief of Product Development. After Genentech was bought by Roche Pharmaceuticals, she was hired as Chancellor of UCSF (2009–2014), where she advocated strongly for building partnerships between UCSF and private industry. In 2014 she shifted to an even more visible global platform when she became CEO of the Bill & Melinda Gates Foundation managing their $50.7 billion dollar endowment. As a leader in pharmacogenomics, public/private partnerships and global public health philanthropy, it is not surprising that Desmond-Hellmann uses examples from her own career to lay out her vision for PPH in her 2016 TED talk. Paying attention to how Desmond-Hellmann’s biography informs her proposal for PPH on a global scale reminds us that this is a situated vision rather than a universally-held blueprint for the future of public health.

At the beginning of her talk, Desmond-Hellmann describes her early days as an oncologist on the 'frontlines on the War on Cancer' when the side effects of cancer treatment were making her patients sick. The chemotherapy she was prescribing “couldn’t differentiate from the cancer cells we wanted to hit hard and those healthy cells we wanted to preserve”. Later in her career, as Chief of Product Development at Genentech, she helped bring Herceptin to market, an oft-cited success story for pharmacogenomics and precision medicine. In the TED talk she characterises Herceptin’s precision in two ways: (1) oncogene biomarker: Herceptin can “precisely target HER2+ breast cancer”; and (2) cell type: Herceptin “hit[s] hard on the cancer cells, while sparing and being more gentle on the normal cells”. In this brief narrative about precision medicine, Desmond-Hellmann uses military metaphorsthat are specific to precision warfare and targeted killings. Herceptin is a “guided missile targeted to …HER2”. Treating cancer becomes a war, where the cancer cells need to be mercilessly killed and the normal cells must be benevolently spared. Compare Desmond-Hellmann’s narrative to this statement from senior counterterrorism advisor John Brennan (2009–2013): “For the past year there hasn’t been a single collateral death because of the exceptional proficiency, precision of the capabilities that we’ve been able to develop".72 Brennan’s statement is comforting, both in terms of his promise that no innocent people have been killed by US drone attacks and that it reinforces the exceptional technological superiority of the US military.73 The Herceptin story belongs to the same affective register: it offers the moral comfort and technological satisfaction of targeting bad actors while sparing the healthy, normal and innocent. These are fantasies of war and medicine with no collateral damage.

The imaginary of precision medicine relies heavily on a narrative of technoscientific progress in which smart R&D can bring humanitarian efficiency to something previously barbaric and undiscriminating. Traditional chemotherapy which indiscriminately kills quickly dividing cells in the body74 is relegated to the biomedical past; Herceptin becomes the future. However, the progress narrative obscures the reality that untargeted chemotherapies are still very much in use and targeted therapies like Herceptin are more messy in clinical practice than the rhetoric suggests. Phillips et al,75 for example, found that many patients treated with Herceptin did not receive genetic testing, received testing but were HER2 negative, or received incorrect test results. Furthermore, tumour profiling is not formally covered by most US health insurance.76 The progress narrative turns attention away from ongoing uncertainty in clinical oncology while rendering expensive drug R&D a moral imperative.77 Here, the precision imaginary offers a Star Trek future free of ineffective and debilitating treatments, where algorithms can predict and pre-empt adverse outcomes. This fantasy comforts because it promises to contain risk and guarantee the most optimal clinical results. However, the comfort it provides belongs to a familiar biopolitical regime where the 'securitisation of human life'78—or rather the securitisation of some human life—becomes an imperative at the expense of other ways of imagining health/illness. The military metaphor structures how we feel about our bodies and what kinds of treatment we feel we deserve.

Desmond-Hellmann’s proposal for PPH relies on the same affective economies of the Herceptin story, this time promising modern technology and humanitarian solutions to the world’s most pressing public health problems. Whereas precision medicine is defined as the 'ability to target individuals with the right remedies at the right times', Desmond-Hellmann describes PPH as a smarter, more precise way to 'tackle public health problems in populations'. She offers the example of the UNAIDS Global Plan launched in 2011 that targets geographical regions in sub-Saharan Africa where HIV rates are the highest and treats HIV+ pregnant women with antiretroviral therapy to avoid perinatal transmission.79

When moving scales from the individual to the population, the meaning of precision markedly shifts. Whereas, in the Herceptin example, the area not targeted is spared an attack; in the public health example, the area not targeted is spared treatment. Desmond-Hellmann explains that a blanketed approach to HIV treatment in sub-Saharan Africa is 'just not practical' (read: too expensive). Precision, here, is framed as an economic necessity for public health projects funded by private foundations.80 While this kind of risk prioritisation may 'pay off' according to the calculus of philanthropic organisations and NGOs, it also leaves many outside the field of intervention. What is saved by targeted interventions is not lives but dollars; economic efficiency is the defining logic, not health equity. Achieving health equity in sub-Saharan Africa would require universal public health measures and investment into health and other infrastructures rather than targeted solutions.

In shifting the discourse from precision medicine to PPH, Desmond-Hellmann notes the discrepancy between the two contexts (ie, some Americans who have insurance coverage for ~$100 000 of cancer treatment; sub-Saharan Africans in areas with the highest HIV rates who have limited access to antiretroviral therapy or basic healthcare). She clarifies that the Herceptin story that began her talk was meant only as an analogy: “Don’t misunderstand me; I’m not talking about bringing expensive medicines like Herceptin to the developing world … [awkwardly] although I’d actually kind of like that”. This statement evinces a two-tiered approach to precision: individualised data-driven therapies for the wealthy in the Global North; biomedical surveillance and data modelling to identify the areas of most in need of public health interventions in the Global South and the poorest zip codes in the USA; and benign neglect for those who fall outside of these provisional zones of protection. Here, the discourse of precision in public health obscures the ways philanthropy acts as Neoliberal triage, providing funding for specific initiatives without changing the political and economic systems that create and maintain inequality.

This vision of PPH relies on the authority of science, data and market-based logics to authorise wealthy philanthropic organisations, such as the Gates Foundation, to define and contain health problems in the Global South. As countries in economic crisis have been forced into structural adjustments in the last decades by the World Bank and IMF, many have reduced public investment in health infrastructure. As NGOs and philanthropic organisations take the place of governments in providing public health services, targeted data-driven solutions have become attractive ways to imagine public health response. As Susan L Erikson argues, “The rise of big data solution-making [in global public health has been coincidental with the global ceding of meaningful commitment to the slow hard slog of building healthcare infrastructure in the places that need it most".81 For powerful organisations such as the Gates Foundation, PPH promises innovative and cost-effective interventions that can target problems without investing in long-term infrastructure. However, as Anne-Emanuelle Birn points out, these kinds of interventions are premised on “a narrowly conceived understanding of health as a product of technical interventions divorced from economic, social, and political contexts".82 For example, in Desmond-Hellmann’s story about the Global Plan to prevent perinatal HIV transmission, she focuses on targeted distribution of ARTs without discussing how the Plan addresses the social, economic and political circumstances that contribute to high HIV rates in sub-Saharan Africa. Framing global public health as a problem with 'technology-based solutions'83 takes the focus away from the uneven distribution of health and wealth (which makes philanthropy possible to begin with) and a system of drug development where publicly-traded pharmaceutical companies are the only actors wealthy enough to bring a new molecule to market.84 Here, calls for increased precision effect a kind of humanitarian misdirection that hides the inhumane truths that the lives and health of some are more valuable than others and people are dying of AIDS in a world where effective antiretroviral therapies are available to the wealthy.

However, although the scale of global health inequalities is well known, the bright biomedical future that Sue Desmond-Hellmann presents still beckons philanthropists, corporations and universities. Affective economies of individualised fear and insecurity draw our attention away from structural inequalities and towards biopolitical projects that promise us more secure tomorrows. In precision warfare, precision medicine and precision public health, the modifier precision shores up the projects that it attaches to and makes other ways of framing problems/solutions seem impossible, undesirable or backward. The charisma of precision in the USA in the 21st century lies in its ability to comfort and contain insecurities by promising technologies capable of distinguishing cancer cells from healthy cells, civilians from insurgents and the communities most in need of intervention from those where resources would be wasted. And while many of these technologies are more precise (in some ways) than the available alternatives, it leaves the social, political and economic systems that give rise to the demand for precision fundamentally unquestioned. In a world where we can target what needs targeting and spare everything else, why question our emotional and financial investments?

Precision as a solution to cancer disparities: The San Francisco Cancer Initiative, SF CAN

In this section we bring our analysis of the broad vision for global health offered by the Bill & Melinda Gates Foundation to bear on a local initiative that seeks to leverage PPH to achieve health equity in San Francisco. Our analysis of the precision imaginary in this local initiative begins on the home page of UCSF’s Helen Diller Comprehensive Cancer Centre85 (http://cancer.ucsf.edu/) and its large hyperlink box promoting the newly formed San Francisco precision public health Initiative, SF CAN (figure 1).

Figure 1

San Francisco cancer initiative (SF CAN) hyperlink (accessed 15 June 2017).

Here, a bold title—SF CAN—is placed over a bifurcated set of background images. On one side, a daytime sunny street view, a local-scape, represents the Mission—a San Francisco neighbourhood with Latinx roots indexed by the background image of a community mural. On the other side, a night-time aerial view, a cityscape, references a vibrant San Francisco downtown looking from City Hall southeast toward the Financial District skyline. We draw attention to the ways the hyperlinked box for SF CAN and the Helen Diller Comprehensive Cancer Center itself are embedded in and part of the infrastructure of UCSF. UCSF’s tagline, 'Advancing Health Worldwide', is used to convey what they refer to as their 'collective action on a global scale'.86 UCSF’s international research profile contributes significantly to the San Francisco Bay Area’s reputation as a known leader in biomedical innovation along with the broader biotech, big data and precision health industries located throughout San Francisco and Silicon Valley.

SF CAN, a partnership between UCSF and the San Francisco Department of Public Health, was launched in 2016 with the aim of 'reduc(ing) the cancer burden and particularly address inequities in the occurrence and outcome of cancer'.87 This is a clear vision of a desirable future that is capable of uniting different actors around the common goal of reducing pervasive and unjust differences in health and illness. Its branded slogan, 'SF CAN', frames this future as one that is attainable for San Francisco despite complexities not named such as the increasing income inequality in the city. The health disparities SF CAN seeks to address are well-known through the surveillance data of regional cancer registries that track rates of diagnoses and mortality maintained by medical centres such as Kaiser and by the Department of Public Health. What is new, however, is the epistemological framework deployed in the proposed efforts: “[W]e advance the concept of ‘precision population health’, which refers to having the data and the capability to tailor preventive interventions precisely for communities to more directly meet their needs".88 This vision represents not only an intensification of public health surveillance and data collection, but a consolidation of 'big data' as a privileged means by which the Department of Public Health can meet the needs of communities. Echoing Desmond-Hellmann’s TED talk, SF CAN defines cancer disparities as a biomedical problem that has data-driven solutions:

Biomedical science is also developing the means to understand and analyze very large amounts of information (referred to as 'Big Data') from genetics and biology to electronic health records to large scale population databases derived from surveys, state record systems, the census, geographic information systems and social media. These new developments can be harnessed to make cancer more easily prevented and better treated in San Francisco and beyond.89

These data-driven interventions, they argue, hold the promise to “identify, reduce, and ultimately eliminate the inequities between communities and subpopulations in the city so that all citizens benefit from effective new scientific discoveries, programs, and policies that we know to be effective”.

While we applaud the goal of addressing cancer disparities in San Francisco, we highlight how precision as a sociotechnical imaginary defines the problem and constructs its solutions to achieve a future without cancer disparities. We ask: How is the problem of cancer risk identified and on what scale? What is the target of the interventions proposed and what is left out? And, finally, in what ways is PPH different from its non-precise public health predecessors? Looking at how the imaginary of PPH informs the rhetoric and practices of SF CAN allows us to identify tensions between health equity goals and the affective economies of targeted solutions. Specifically, we highlight how targeted solutions leave the upstream social determinants of health outside of the sphere of public health intervention. For example, in the logic of PPH, the risk of lung cancer from smoking in a predominately African-American neighbourhood can be targeted by public health interventions whereas racism and income inequality cannot.

When precise targeting is framed as the solution to health disparities, it is important to pay attention to how the military metaphor of 'targeting' incites specific spatialisation practices. By bringing together biomedical and public health knowledges, SF CAN promises to direct medico-scientific attention ‘inward’ to the molecular level of the agents causally linked to cancers (eg, genes and proteins) and ‘outward’ to the worlds of risky behaviours, environments and social structures that may be causally linked to the risk of disease.90 This outward risk is primarily defined in geographical terms. Zip codes with the highest cancer burdens are targeted for population-based prevention and screening efforts; those who reside within its bounds are understood as at-risk based on various data points and predictive modelling. The biomedical knowledge represented by the cityscape is mobilised to intervene into the at-risk neighbourhood, represented on the website by images of neighbourhoods associated with LGBTQ communities (ie, the Castro) and people of colour (ie, the Mission, Chinatown and Bayview-Hunter’s Point)—images that always include visible markers of difference: community murals, paper lanterns and rainbow flags.

Here, the biomarkers of biomedicine become the geomarkers of PPH. In a recent article in Health Affairs, 91Andrew Beck and co-authors illustrate this geographical approach to reducing health disparities and its relation to the concept of the biomarker:

Biomedical research is increasingly creating opportunities for personalized medical care. Biomarkers, a key area of focus, ‘can be measured in the body or its products and influence or predict the incidence of outcome or disease’. We define geomarkers similarly, as ‘any objective, contextual, or geographic measure’ that influences or predicts the incidence of outcome or disease. By complementing biology with geography, we are able to tap into health-relevant data that generally exist in isolation from clinical care.

Although SF CAN does not explicitly use the language of geomarkers, we argue that they rely on similar spatialisation practices, mapping cancer data onto the landscape of San Francisco (figure 2) and determining which zip codes and, thereby, which populations to target with precisely tailored interventions. Meeting the health needs of communities requires identifying problem zip codes, mapping their locations and targeting interventions to these sites. Although traditional public health campaigns have long worked with specific neighbourhoods—for example, the Castro neighbourhood has, for decades, been disproportionately targeted for HIV campaigns—PPH represents an intensification of the logics of place-based interventions in public health. Here, big data (eg, clinical, surveillance, behavioural, environmental and any other relevant datasets) promises to provide economically efficient evidence-based interventions, which can be precisely targeted geographically in the same way Herceptin targets HER2+ cancer cells in the body.92

Figure 2

San Francisco Cancer Initiative map (accessed 15 June 2017).

For SF CAN, the precision imaginary authorises a particular vision of the public health problem that resonates with the Bill and Melinda Gates vision for Global Public Health: a problem of geographically localised at-risk populations that can be targeted with economically efficient interventions instead of—or alongside— identifying the social, political and economic conditions that produce certain neighbourhoods as 'at-risk' in the first place. For example, beginning in 2006 'revitalisation' efforts in the predominantly African-American neighbourhood of Bayview-Hunter’s Point began to send carcinogenic serpentine asbestos dust into the air, part of a long history of environmental injustice in this neighbourhood which is home to a notorious Superfund site—the Hunter’s Point Shipyard. To say the residents of Bayview-Hunter’s Point are more 'at-risk' for cancer fails to account for the 'slow violence' of breathing in the asbestos dust from a construction site that seeks to reclaim an ongoing 'sacrifice zone' for financial speculation.93 To address health equity here would require investments in biomedicine and a cancer screening programme as well as in land use, public housing, environmental regulation and remediation, and economic redistribution. Instead, using the framework of PPH, SF CAN geographically maps disease rates and citywide disparities, identifies cancer burdens and predictively models future cancer rates by zip code. The hope, they assert in their founding documents, is to move methodically in three steps: (1) describe the cancer burden; (2) identify the options to address this burden; and (3) develop action plans. Although SF CAN imagines a future where 'a new, updated synthesis of existing data' will replace traditional epidemiological approaches, they currently describe the cancer burden using long-held public health surveillance data (morbidity and mortality rates). Their biomedical and public health approaches to reducing cancer rates and outcomes are also familiar: cancer screening surveillance, better communication strategies for primary care clinicians, public health ad campaigns and behavioural approaches (diet, exercise and tobacco control). Similarly, their goal of bringing these strategies to our most impoverished neighbourhoods such as Bayview-Hunter’s Point has long been a priority of the Department of Public Health.

By describing how PPH is different from and similar to its non-precise public health predecessors, we are able to consider how this sociotechnical imaginary shapes the present by predicting the future. SF CAN envisions a future with fewer cancers and reduced inequities, but also a future of increased data infrastructures, shareable data systems and advanced analytics able to provide the predictive modelling necessary to reach these health equity goals. Before SF CAN, it was well known that health disparities—including those found in San Francisco—fall along lines of race, socioeconomics and the situated inequalities of geographical exposures;94 critical public health scholars have ample evidence that these differences are, in part, driven by the historical and structural violence of economic inequality, discrimination and neglect. Now, PPH reframes the unruly, intractable and complex multifactorial causation of health and illness as a data problem with precise, contained, objects of biomedical knowledge and intervention. Unlike the clunky survey-based surveillance systems of its predecessor—traditional epidemiology—the big data of PPH will be realised through a military-style response: “robust primary surveillance data, rapid application of sophisticated analytics to track the geographical distribution of disease, and the capacity to act on such information".95 Here, as in the War on Terror, the language of securitisation works to contain uncertainty, promises to reduce suffering with economic efficiency and obscures the slow violence of environmental harm. The future imagined here is a new public health that delivers interventions with 'speed, accuracy, equity'96 that can prevent and alleviate health disparities and contain the bioinsecurities of human cancers.

However, in the present, SF CAN’s interventions are no more fast, accurate or precise than its predecessors. In fact, the first 'tangible success'97 for SF CAN has been to introduce legislation banning menthol and other flavoured tobacco, which have long been known to target African-American communities.98 These legislative efforts have been paired with an annual slam poetry competition and other YouTube ad campaigns aimed at African-American audiences. As zip codes with high rates of lung cancer endure an undue burden of suffering are over-surveilled and become the targeted objects of lifestyle interventions such as smoking cessation programmes, we ask: What is new and in what ways is this more precise?

If SF CAN is indeed a more traditional public health initiative than its label as 'precision public health' suggests, is 'precision' being strategically mobilised to bring funding to much-needed public health initiatives or deployed to further bolster one of the most well-resourced biomedical systems in the USA along with its private partners? Can this 'innovative' nomenclature be leveraged for health equity to reduce uneven cancer burdens across racial/ethnic, gender and sexual, geographic, class and other lines or will it ensure the best healthcare for those with access to and resources for the innovations that will follow? While PPH might offer a charismatic name for the work that public health has been doing all along, it comes at a cost. The targeted solutions provided by PPH are not the only solutions possible; one could easily imagine that a shift upstream to affordable equitable healthcare or even further upstream to addressing income inequality in San Francisco would better address the problem of cancer disparities.

For example, the leader of SF CAN’s Colorectal Cancer Task Force was recently awarded a modest grant from the California Colorectal Cancer Coalition which they will use to create multilingual culturally-sensitive informational brochures designed to increase the rate of colonoscopy completion among 'safety-net' patients who have received an abnormal stool test result. Since 45% of San Franciscans speak a language other than English at home, this is an important initiative. However, there are barriers to colonoscopy completion beyond appropriate communication of medical information. Due to the need for bowel prep the day before and sedation for the procedure, a patient requires paid sick leave, childcare, housing, privacy and another healthy adult with sick leave to accompany the patient to the procedure. Without access to fair working and housing conditions, healthcare support and affordable childcare, completing a colonoscopy is simply not possible. In this case, partnering with and even investing in organisations that advocate for workers’ rights, free health assistance or affordable public housing in San Francisco might be more advantageous than pursuing the public-private partnerships of the new precision economy.

The problem of disparities in cancer morbidity and mortality is an important public health concern in San Francisco and elsewhere. PPH’s solution of biomarkers and geomarkers that produce at-risk communities ready for targeted interventions is only one set of possible approaches. The future remains to be written. Although much of the precision rhetoric makes a future of big data, algorithms and geomarkers seem like a foregone conclusion, the most recent publications from SF CAN no longer make any reference to 'precision' or to data collection and analysis beyond standard epidemiological data. Their targeting remains low-tech: 'targeted advertisements' and 'targeted screening and navigation programme for disadvantaged populations in the city'.99 The far-reaching spatialisation of precision medicine with its telescoping inward to the genome and telescoping back out to social, environmental and behavioural factors is missing from these accounts. In its place is a sense that cancer disparities in San Francisco is a complex problem, an emphasis on the importance of investigating the upstream social determinants of health and an acknowledgement that their goals will take time to accomplish. What other visions will emerge for public health in this city where technological promises have, in many ways, created the income inequality we see today remain to be seen.

Conclusion

Precision public health promises a data-driven future that can deliver the 'speed, accuracy and equity' to address the world’s more pressing public health problems. Like the rhetoric of precision warfare, PPH seeks to leverage data to map targets onto geographical locations and intervene pre-emptively and precisely into the people and places most at risk. PPH approaches are attractive because they promise the most efficient use of resources without wasted time, money, uncertainty and suffering; but in our pursuit of this precision future, we suggest caution. As Susan L Erikson points out, big data approaches to public health do not always lead to rapid and effective responses, but nonetheless commandeer more media and policy attention than tried-and-true public health strategies.100 The futures they promise may be little more than repackaged stratifications of the present. With PPH, some bodies and lives will continue to be over-medicalised,101 targeted as repositories of bio and behavioural data, and required to adhere to the latest health app/wellness promotion. Those with the highest cancer burden, for example, could easily be tasked with “the responsibility for reducing their own risks through regular medical surveillance and behaviour modification strategies".102 We therefore urge attention to the social and spatial stratification of the precision imaginary and, more so, the ways this framework reduces complex pathology into biomedical solutions and avoids framing the social and environmental determinants of health such as carcinogen exposure, poverty, racism and homophobia as actionable and instead looks to biomedicine and informatics to provide technical solutions.

By considering how precision operates as a sociotechnical imaginary, we are able to ask what kinds of futures PPH promises, how these promises inevitably shape present public health initiatives and what kinds of infrastructures are built and maintained. The purpose of our analysis is not to demonise current PPH efforts but to ask what happens to health equity when we employ military metaphors to contain the social, economic and medical complexities of health disparities. As Richard Tutton argues at the end of Genomics and the Reimaging of Personalised Medicine, it is only when we recognise the sociotechnical imaginaries that underwrite our visions for the future of healthcare that we are able collectively to reimagine the relationship between health, medicine and society: “Together, with scientists, clinicians, and citizens, new imaginaries can be forged".103 Although there are no easy answers for the complex and persistent problem of achieving healthier lives for all, we argue for the importance of understanding this problem in terms of inequality rather than insecurity. When our sociotechnical imaginaries are limited by the logics of warfare and even the logic of biomedicine, we seek to locate easy targets instead of reckoning with the larger social and political context that contribute to the uneven distribution of health and healthcare. Without foregrounding and investing in social, economic and environmental justice, PPH cannot provide long-term solutions to the persistent problem of health disparities. Targeted solutions may comfort investors, but will likely go unnoticed by those whose lives are attenuated by the slow violence of ongoing transgenerational inequality in San Francisco and beyond.

Acknowledgments

The authors wish to thank the participants of "Just Data? Justice, Knowledge and Care in an Age of Precision Medicine” at UC Santa Cruz (May 2016) for the conversations that led to this article and members of the STS Hub at San Francisco State University for feedback on our preliminary ideas. We owe special gratitude to Ugo Edu, former Postdoctoral Fellow at the SFSU Health Equity Institute and STS Hub co-organiser, for co-envisioning this project with us and sharing an anthropological perspective on the precision revolution. We also thank our colleagues Kate Darling, M V Eitzel Solera, Jesse Goldstein, and Ruth Müller for their helpful comments on earlier versions of this manuscript, as well as the anonymous reviewers of Medical Humanities for their insightful reviews. Finally, we are grateful to Ashley Pérez for providing research support in the last stages of submission.

Notes

1. Elaine Grant (2012), “The Promise of Big Data,” Harvard Public Health (Spring/Summer 2012).

2. John H. Coote and Michael J. Joyner (2015), “Is Precision Medicine the Route to a Healthy World?“, The Lancet 385: 1617; Ronald Bayer and Sandro Galea (2015), “Public Health in the Precision-Medicine Era,” New England Journal of Medicine 373, no. 6: 499–501.

3. Muin J. Khoury, Michael F. Iademarco, and William T. Riley (2016), “Precision Public Health for the Era of Precision Medicine,” American Journal of Preventative Medicine 50, no. 3: 398–401; Scott F. Dowell, David Blazes, and Susan Desmond-Hellmann (2016), “Four Steps to Precision Public Health,” Nature 540: 89–91; Tarun Stephen Weeramanthri et al. (2018), “Editorial: Precision Public Health,” Frontiers in Public Health 6, no. 121, doi: 10.3389/fpubh.2018.00121 [published Online First: April 30 2018].

4. Weeramanthri et al., 3.

5. Marcel Salathé et al. (2012), “Digital Epidemiology,” PLoS Computational Biology 8, no. 7: e1002616, doi: 10.1371/journal.pcbi.1002616 [published Online First: 2012/07/31].

6. Gunther Eysenbach (2009), "Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet,” Journal of Medical Internet Research 11, no. 1: e11, doi: 10.2196/jmir.1157 [published Online First: 2009/03/31].

7. Marcel Salathé (2016), “Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health,” The Journal of Infectious Diseases 214, no. suppl_4: S399-S403, doi: 10.1093/infdis/jiw281 [published Online First: 2017/08/24].

8. Precision Public Health Summit (2016), Summit Report: June 6–7, 2016 (San Francisco: UCSF), 6.

9. Khoury, Iademarco, and Riley, “Precision Public Health,” 398–401.

10. Precision Public Health Summit, Summit Report, 16.

11. Precision Public Health Summit, Summit Report, 12.

12. Precision Public Health Summit, Summit Report, 14.

13. Paula A. Braveman, Susan A. Egerter, and Robin E. Mockenhaupt (2011), “Broadening the Focus: The Need to Address the Social Determinants of Health,” American Journal of Preventive Medicine 40: S4-S18; US Department of Health and Human Services (2010), Healthy People 2020 (Washington, DC: Office of Disease Prevention and Health Promotion); Michael Marmot and Richard G. Wilkinson (2006), Social Determinants of Health (Oxford University Press).

14. Paula A. Braveman et al. (2011), “Health Disparities and Health Equity: The Issue Is Justice,” American Journal of Public Health 101, no. S1: S150.

15. Ramya Ramaswami, Ronald Bayer, and Sandeo Galea (2018), “Precision Medicine from a Public Health Perspective,” Annual Review of Public Health 39, no. 1: 153–68, doi: 10.1146/annurev-publhealth-0 40 617–0 14 158.

16. Coote and Joyner, “Is Precision Medicine the Route to a Healthy World?“, 1617; Bayer and Galea, “Public Health in the Precision-Medicine Era,” 499–501; Ramaswami, Bayer, and Galea, “Precision Medicine,” 153–68.

17. Shawn Dolley (2018), “Big Data’s Role in Precision Public Health,” Frontiers in Public Health 6: 68 [published Online First: March 2018].

18. Howard Hu et al. (2017), “Big Data and Population Health,” Epidemiology 28, no. 6: 759–62, doi: 10.1097/ede.0000000000000711.

19. David Lazer et al. (2014), “The Parable of Google Flu: Traps in Big Data Analysis,” Science 343, no. 6176: 1203–05, doi: 10.1126/science.1248506.

20. Susan L. Erikson (2018), “Cell Phones ≠ Self and Other Problems with Big Data Detection and Containment during Epidemics,” Medical Anthropology Quarterly 32, no. 3: 315–39, doi: 10.1111/maq.12440 [published Online First: 2018/03/10].

21. Khoury, Iademarco, and Riley, “Precision Public Health,” 400.

22. Julie Livingston (2012), Improvising Medicine: An African Oncology Ward in an Emerging Cancer Epidemic (Durham, North Carolina: Duke University Press); James C Scott (1998), Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed (New Haven: Yale University Press).

23. Sheila Jasanoff and Sang-Hyun Kim (2009), “Containing the Atom: Sociotechnical Imaginaries and Nuclear Power in the United States and South Korea,” Minerva 47, no. 2: 119–46; Sheila Jasanoff (2015), “Future Imperfect: Science, Technology, and the Imaginations of Modernity,” in Dreamscapes of Modernity: Sociotechnical Imaginaries and the Fabrication of Power, eds. Sheila Jasanoff and Sang-Hyun Kim (Chicago, IL: University of Chicago Press); Sheila Jasanoff and Sang-Hyun Kim (2015), eds., Dreamscapes of Modernity: Sociotechnical Imaginaries and the Fabrication of Power (Chicgo, IL: University of Chicago Press); Maureen McNeil et al. (2017), “Conceptualizing Imaginaries of Science, Technology, and Society,” in The Handbook of Science and Technology Studies, fourth ed., eds. Ulrike Felt et al. (Cambridge, MA/ London, UK: MIT Press), 435–64.

24. Jasanoff, “Future Imperfect,” 19.

25. McNeil et al., “Conceptualizing Imaginaries,” 450.

26. Donna J. Haraway (1997), Modest_Witness@Second_Millennium. Femaleman©_Meets_OncoMouse: Feminism and Technoscience (New York: Routledge), 15.

27. McNeil et al., “Conceptualizing Imaginaries,” 435–60; Joan H. Fujimura (1988), “The Molecular Biological Bandwagon in Cancer Research: Where Social Worlds Meet,” Social Problems 35, no. 3: 261–83; Barbara L. Marshall (2009), “Sexual Medicine, Sexual Bodies and the ‘Pharmaceutical Imagination,’" Science as Culture 18, no. 2: 133–49;Adele E. Clarke and Joan H. Fujimura (1992), The Right Tools for the Job: At Work in Twentieth Century Life Sciences (Princeton: Princeton University Press); Joan H Fujimura (1996), Crafting Science: A Sociohistory of the Quest for the Genetics of Cancer (Cambridge, MA: Harvard University Press); Mamo, Laura Mamo et al. (2010), “Producing and Protecting Risky Girlhoods,” in Three Shots at Prevention: The HPV Vaccine and the Politics of Medicine’s Simple Solutions, ed. Keith Wailoo, Julie Livingston, Steven Epstein, et al. (Baltimore, Maryland: The Johns Hopkins University Press), 127.

28. Adele E. Clarke and Joan H. Fujimura (1992), The Right Tools for the Job: At Work in Twentieth Century Life Sciences (Princeton: Princeton University Press, 1992); Joan H Fujimura (1996), Crafting Science: A Sociohistory of the Quest for the Genetics of Cancer (Cambridge, MA: Harvard University Press, 1996); Mamo, Laura Mamo et al. (2010), “Producing and Protecting Risky Girlhoods,” in Three Shots at Prevention: The HPV Vaccine and the Politics of Medicine’s Simple Solutions, ed. Keith Wailoo, Julie Livingston, Steven Epstein, et al. (Baltimore, Maryland: The Johns Hopkins University Press, 2010), 127.

29. Jasanoff and Kim, “Containing the Atom,” 119–46; Andrew Lakoff (2008), “The Generic Biothreat, or, How We Became Unprepared,” Cultural Anthropology 23, no. 3: 399–428.

30. McNeil et al., “Conceptualizing Imaginaries,” 457F; Jasanoff, “Future Imperfect,” 415.

31. Eric Juengst et al. (2016), “From 'Personalized' to 'Precision' Medicine: The Ethical and Social Implications of Rhetorical Reform in Genomic Medicine,” Hastings Center Report 46, no. 5: 21–33, doi: 10.1002/hast.614; Richard Tutton (2014), Genomics and the Reimagining of Personalized Medicine (Farnham, England: Ashgate Publishing), 167.

32. Vincanne Adams, Michelle Murphy, and Adele E Clarke (2009), “Anticipation: Technoscience, Life, Affect, Temporality,” Subjectivity 28, no. 1: 258, doi: 10.1057/sub.2009.18

33. Juengst et al., “From ‘Personalised‘ to ’Precision’ Medicine,” 24.

34. Adams et al., “Anticipation,” 257.

35. Sandra Soo-Jin Lee, (2011), “Waiting on the Promise of Prescribing Precision: Race in the Era of Pharmacogenomics,” in Genetics and The Unsettled Past: The Collision of DNA, Race, and History, eds. Keith Wailoo, Alondra Nelson, and Catherine Lee (New Brunswick, NJ: Rutgers University Press), 165.

36. Lee, “Prescribing Precision,” 175.

37. Rob Nixon (2011), Slow Violence and the Environmentalism of the Poor (Cambridge, MA: Harvard University Press).

38. Lee, “Prescribing Precision,” 164–80.

39. Coote and Joyner, “Is Precision Medicine the Route to a Healthy World?“, 1617; Bayer and Galea, “Public Health in the Precision-Medicine Era,” 499–501; Dolley, “Big Data’s Role in Precision Public Health,” 68; Merlin Chowkwanyun, Ronald Bayer, and Sandro Galea (2018), "'Precision' Public Health — between Novelty and Hype,” New England Journal of Medicine 379, no. 15: 1–3, doi: 10.1056/NEJMp1806634 [published Online First: Sept 05 2018]; Nancy Krieger (2011), Epidemiology and the People's Health: Theory and Context (New York: Oxford University Press, 2011); Nancy Krieger (1994), “Epidemiology and the Web of Causation: Has Anyone Seen the Spider?," Social Science & Medicine 39, no. 7: 887–903; Cindy Patton (2002), Globalising AIDS (Minneapolis: University of Minnesota Press).

40. Chowkwanyun et al., "'Precision' Public Health — Between Novelty and Hype," 1–3.

41. Sue Desmond-Hellmann (2016), “A Smarter, More Precise Way to Think About Public Health," TED Talk.

42. Steven H. Woolf (2017), “Progress in Achieving Health Equity Requires Attention to Root Causes,” Health Affairs 36, no. 6: 984–91.

43. Karen M. Meagher et al. (2017), “Precisely Where Are We Going? Charting the New Terrain of Precision Prevention,” Annual Review of Genomics and Human Genetics 18: 379, doi: 10.1146/annurev-genom-0 91 416–0 35 222 [published Online First: 2017/04/26].

44. Muin J. Khoury (2016), "2016: The Year of Precision Public Health!” Genomics and Health Impact Blog (Atlanta, GA: Centers for Disease Control and Prevention).

45. Susan Sontag (1978), Illness as Metaphor (New York, NY: Farrar, Strauss, and Giroux), 64–67.

46. Donna J Haraway (1991), Simians, Cyborgs, and Women: The Reinvention of Nature (New York: Routledge); Emily Martin (1994), Flexible Bodies: The Role of Immunity in American Culture from the Days of Polio to the Age of AIDS (Boston: Beacon Press).

47. Derek Gregory (2014), “Drone Geographies,” Radical Philosophy 183: 11.

48. Sontag, Illness as Metaphor, 64–67.

49. Gregory, “Drone Geographies.”

50. Lauren B. Wilcox (2015), Bodies of Violence: Theorizing Embodied Subjects in International Relations (Oxford, UK: Oxford University Press), 132.

51. Caren Kaplan (2006), “Precision Targets: GPS and the Militarization of US Consumer Identity,” American Quarterly 58, no. 3: 700. Many of these claims to precision have this kind of ’folksy‘ ring to them. Modern remote drone pilots speak of putting “warheads on foreheads,” (see Anna Mulrine, 2008) which has a distinct Seussian quality. Anna Mulrine (2008), “Warheads on Foreheads,” Air Force Magazine 91, no. 10 (2008): 44–47.

52. Donald MacKenzie (1990), Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance (Cambridge, MA: MIT Press).

53. John T. Correll (2010), “The Emergence of Precision Bombing," Air Force Magazine: 63.

54. Nixon, Slow Violence and the Environmentalism of the Poor.

55. It’s not difficult to find examples of this framework from the US military. Here Ned Price, the National Security Council spokesperson, justified US drone strikes to the Guardian in 2016: “The United States goes to extraordinary lengths to avoid non-combatant casualties in lethal operations, providing protections as a matter of policy that go beyond those required by the law of armed conflict…Unlike our enemies, which [sic] deliberately and pointedly violate the law of armed conflict, the United States takes great care to adhere to the fundamental law of armed conflict principle of distinction, which requires that attacks be directed only against military objectives and that civilians and civilian objects not be the target of attack” (see Ackerman; emphasis ours). Spencer Ackerman (2016), “After Drones: The Indelible Mark of America's Remote Control Warfare,” The Guardian, April 21, 2016.

56. See Mamdani for an excellent argument against this framing of terrorism as endemic to so-called traditional cultures. He writes: “Terrorism is not born of the residue of a premodern culture in modern politics. Rather, terrorism is a modern construction. Even when it harnesses one or another aspect of tradition and culture, the result is a modern ensemble at the service of a modern project.” Mahmood Mamdani (2002), “Good Muslim, Bad Muslim: A Political Perspective on Culture and Terrorism,” American Anthropologist 104, no. 3: 767.

57. This formulation is based on Nicole Archer’s insight that “many of the comforts we experience on the home front provide the textures of violence that others are subjected to on the battlefield.” See also Cohler for a discussion the constitutive relationship between security and domesticity on the homefront with combat and violence on the frontlines of the Iraq War. Nicole Archer (2014), “Security Blankets: Uniforms, Hoods, and the Textures of Terror,” Women & Performance: a Journal of Feminist Theory 24, no. 2–3: 195; Deborah Cohler (2017), “American Sniper and American Wife: Domestic Biopolitics at Necropolitical War,” Feminist Formations 29, no. 1: 71–96.

58. Kaplan, “Precision Targets,” 693–713; Spencer Ackerman, “After Drones: The Indelible Mark of America's Remote Control Warfare,” The Guardian, April 21, 2016.

59. Kaplan, “Precision Targets,” 705.

60. Jack Serle (2016), “Obama Drone Casualty Numbers a Fraction of Those Recorded by the Bureau," The Bureau of Investigative Journalism.

61. Even the category of ‘civilian’ is tricky here, as all men 18 years of age or more are considered ‘enemy combatants,’ regardless of whether they are the person targeted.

62. Nixon, Slow Violence and the Environmentalism of the Poor.

63. Gregory, “Drone Geographies,” 13.

64. Kaplan, “Precision Targets,” 705.

65. Gregory, “Drone Geographies,” 13.

66. Sara Ahmed (2004) “Affective Economies,” Social Text 22(2):117–39.

67. Ahmed, 119.

68. Ahmed, 132.

69. Lakoff, “The Generic Biothreat, or, How We Became Unprepared,” 399–428; Ventura, Deisy de Freitas Lima (2016), "From Ebola to Zika: International Emergencies and the Securitization of Global Health,” Perspectiva/Public Health Journal 32, no. 4, doi: 10.1590/0102-311×00033316 [published Online First: 19-Apr-2016].

70. Neel Ahuja (2016), Bioinsecurities: Disease Interventions, Empire, and the Government of Species (Durham, NC: Duke University Press).

71. Dowell et al., “Four Steps to Precision Public Health,” 89–91.

72. Scott Shane (2011), “C.I.A. Is Disputed on Civilian Toll in Drone Strikes,” The New York Times, August 11.

73. As Lauren B. Wilcox (2015) points out, “The sparing of civilian lives is given as a key rationale for the development of precision weapons.” Ironically, this justifies airstrikes in residential areas that “may have been off-limits in the past.” Wilcox, Bodies of Violence, 133.

74. Sarah S. Lochlann Jain (2013), Malignant: How Cancer Becomes Us (Berkeley, California: University of California Press), 122.

75. Kathryn A. Phillips et al. (2009), “Clinical Practice Patterns and Cost Effectiveness of Human Epidermal Growth Receptor 2 Testing Strategies in Breast Cancer Patients,” Cancer 115, no. 22: 5166–74.

76. Julia R. Trosman et al. (2015), “Challenges of Coverage Policy Development for Next-Generation Tumor Sequencing Panels: Experts and Payers Weigh,” Journal of the National Comprehensive Cancer Network 13, no. 3: 311–18.

77. Herceptin can also be used to tell a story about pharmaceutical companies using claims of precision to protect profits. Because HER2 is also found in pancreatic cancer, Roche/Genentech successfully applied for Orphan Drug classification under the Orphan Drug Act, despite Herceptin benefitting more than 200 000 people. Herceptin was the sixth top selling pharmaceutical globally in 2015. Michael G. Daniel et al. (2016), “The Orphan Drug Act: Restoring the Mission to Rare Diseases,” American Journal of Clinical Oncology 39, no. 2: 210–13.

78. Melinda Cooper (2006), “Preempting Emergence: The Biological Turn in the War on Terror,” Theory, Culture & Society 23, no. 4: 118.

79. UNAIDS “Global Plan Toward the Elimination of New HIV Infections Among Children by 2015 and Keeping their Mothers Alive” is a more complex initiative than Desmond-Hellmann describes, placing emphasis on improving social, political, and economic conditions for women living with HIV/AIDS and government investment in healthcare. However, by calling this a ’precision‘ approach to public health, she reduces it to the logic of using data to target places with the greatest risk of perinatal transmission. Here we present an analysis of the rhetoric of precision rather than a critique of the goals of the Global Plan. Joint United Nations Programme on HIV/AIDS (2011), Countdown to Zero: Global Plan Towards the Elimination of New HIV Infections among Children by 2015 and Keeping Their Mothers Alive (Geneva, Switzerland: UNAIDS).

80. Another revealing example is a project funded by the Center for Research on Genomics and Global Health that uses family health history to determine which children should receive shoes to prevent parasite (podoconiosis) infection: “children at high genetic and environmental risk…were targeted for distribution of shoes” (Tekola-Ayele and Rotimi 2015). Both the value of clinical genomics and the impossibility of distributing shoes to all children are taken for granted in the study design. Fasil Tekola-Ayele and Charles N Rotimi (2015), “Translational Genomics in Low and Middle Income Countries: Opportunities and Challenges,” Public Health Genomics 18, no. 4: 246, doi: 10.1159/000433518.

81. Erikson, “Big Data Detection,” 331.

82. Anne-Emanuelle Birn (2005), “Gates's Grandest Challenge: Transcending Technology as Public Health Ideology,” The Lancet 366, no. 9484: 515 [published Online First: March 11, 2005].

83. Tachi Yamada (2009), “Global Health and the Bill & Melinda Gates Foundation,” The Lancet 373, no. 9682: P2195.

84. Joseph Dumit (2012), Drugs for Life: How Pharmaceutical Companies Define Our Health (Durham, North Carolina: Duke University Press).

85. The Helen Diller Family Comprehensive Cancer Center at UCSF is world renowned as a leading cancer center led by Dr. Alan Ashworth. A preeminent cancer researcher and key member of the team that identified the breast cancer susceptibility gene, BRCA2, Ashworth brings a vision of both biotech solutions and a more equitable public health care system into the US context.

86. UCSF (2018) [Available from: https://www.ucsf.edu/about/mission-and-values].

87. Robert A. Hiatt and Alan Ashworth (2016) The San Francisco Cancer Initiative: SF CAN (San Francisco, CA: UCSF Helen Miller Family Comprehensive Care Center), 5.

88. Hiatt and Ashworth, SF CAN, 4.

89. Hiatt and Ashworth, SF CAN, 5.

90. Sara Shostak (2003), “Locating Gene-Environment Interaction: At the Intersections of Genetics and Public Health,” Social Science & Medicine 56, no. 11: 2327–42; Sara Shostak (2010), “Marking Populations and Persons at Risk: Molecular Epidemiology and Environmental Health,” in Biomedicalization: Technoscientific Transformations of Health, Illness, and US Biomedicine, eds. Adele E. Clarke et al., (Durham, North Carolina: Duke University Press), 242–62; Laura Mamo and Steven Epstein (2014), “The Pharmaceuticalization of Sexual Risk: Vaccine Development and the New Politics of Cancer Prevention,” Social Science & Medicine 101: 155–65.

91. Andrew F. Beck et al. (2017), “Mapping Neighborhood Health Geomarkers to Clinical Care Decisions to Promote Equity in Child Health,” Health Affairs 36, no. 6: 1000.

92. For example, much of the HIV prevention efforts in the city have moved to identify neighbourhoods (not people) with high viral loads that then become the targets for both HIV treatment and HIV prevention.

93. Lindsey Dillon (2018), “The Breathers of Bayview Hill: Redevelopment and Environmental Justice in Southeast San Francisco,” Hastings Environmental Law Journal 24 no. 2: 227–36.

94. In the mid-1990s regional public health experts convened to prioritise health disparities in the region. In 2002, their efforts resulted in the launch of the Bay Area Regional Health Inequities Initiative. Bay Area Regional Health Inequities Initiative (2015), “BARHII: Bay Area Regional Health Inequities Initiative” [Available from: http://barhii.org/].

95. Khoury et al., “Precision Public Health,” 398–401; Dowell et al., “Four Steps to Precision Public Health,” 89–91.

96. Dowell et al., “Four Steps to Precision Public Health,” 89–91.

97. Alan Ashworth (2017), “SF CAN: A City Working Together to Fight Cancer,” Association of American Cancer Institutes Commentary, Summer.

98. Sarah S. Lochlann Jain (2006), Injury: The Politics of Product Design and Safety Law in the United States (Princeton, NJ: Princeton University Press).

99. Erikson, “Big Data Detection,” 315–39.

100. Robert A. Hiatt et al., 2018. The San Francisco Cancer Initiative: A Community Effort to Reduce the Population Burden of Cancer. Health Affairs 37(1):54–61.

101. Khiara Bridges (2011), Reproducing Race: An Ethnograpny of Pregnancy as a Site of Racialization (Berkeley: CA: University of California Press).

102. Meagher et al., “Precisely Where Are We Going?", 7.

103. Tutton, Genomics, 168.

Bibliography

Footnotes

  • Contributors Both authors have made substantial contributions to drafting this manuscript and revising it critically. They have given final approval and are accountable for the content.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.