This dissertation presents three studies that evaluate different strategies for addressing transnational health threats and social inequalities that depend upon or would benefit from global collective action. Each draws upon different academic disciplines, methods and epistemological traditions.
Chapter 1 assesses the role of international law in addressing global health challenges, specifically examining when, how and why global health treaties may be helpful. Evidence from 90 quantitative impact evaluations of past treaties was synthesized to uncover what impact can be expected from global health treaties, and based on these results, an analytic framework was developed to help determine when proposals for new global health treaties have reasonable prospects for yielding net positive effects. Findings from the evidence synthesis suggest that treaties consistently succeed in shaping economic matters and consistently fail in achieving social progress. There are three differences between these domains which point to design characteristics that new global health treaties can incorporate to achieve positive impact: 1) incentives for those with power to act upon them; 2) institutions designed to bring edicts into effect; and 3) interests advocating for their negotiation, adoption, ratification and domestic implementation. The chapter concludes by presenting an analytic framework and four criteria for determining which proposals for new global health treaties should be pursued. First, there must be a significant transnational dimension to the problem being addressed. Second, the goals should justify the coercive nature of treaties. Third, proposed global health treaties should have a reasonable chance of achieving benefits. Fourth, treaties should be the best commitment mechanism among the many competing alternatives. Applying this analytic framework to nine recent calls for new global health treaties reveals that none fully meet the four criteria. This finding suggests that efforts aiming to better utilize or revise existing international instruments may be more productive than advocating for new treaties. The one exception is the additional transnational health threat of antimicrobial resistance, which probably meets all four criteria.
Chapter 2 builds on this work by evaluating a broad range of opportunities for working towards global collective action on antimicrobial resistance. Access to antimicrobials and the sustainability of their effectiveness are undermined by deep-seated failures in both global governance and global markets. These failures can be conceptualized as political economy challenges unique to each antimicrobial policy goal, including global commons dilemmas, negative externalities, unrealized positive externalities, coordination issues and free-rider problems. Many actors, instruments and initiatives that form part of the global antimicrobial regime are addressing these challenges, yet they are insufficiently coordinated, compliant, led or financed. Taking an evidence-based approach to global strategy reveals at least ten options for promoting collective action on antimicrobial access, conservation and innovation, including those that involve building institutions, crafting incentives and mobilizing interests. While no single option is individually sufficient to tackle all political economy challenges facing the global antimicrobial regime, the most promising options seem to be monitored milestones (institution), an inter-agency task force (institution), a global pooled fund (incentive) and a special representative (interest mobilizer), perhaps with an international antimicrobial treaty driving forward their implementation. Whichever are chosen, this chapter argues that their real-world impact will depend on strong accountability relationships and robust accountability mechanisms that facilitate transparency, oversight, complaint, and enforcement. Such relationships and mechanisms, if designed properly, can promote compliance and help bring about the changes that the negotiators of any new international agreement on antimicrobial resistance will likely be aspiring to achieve. Progress should be possible if only we find the right mix of options matched with the right forum and accountability mechanisms, and if we make this grand bargain politically possible by ensuring it simultaneously addresses all three imperatives for antimicrobials – namely access, conservation and innovation.
Chapter 3 takes this dissertation beyond traditional Westphalian notions of collective action by exploring whether new disruptive technologies like cheap supercomputers, open-access statistical software, and canned packages for machine learning can theoretically provide the same global regulatory effects on health matters as state-negotiated international agreements. This kind of “techno-regulation” may be especially helpful for issues and areas of activity that are hard to control or where governments cannot reach. One example is news media coverage of health issues, which is currently far from optimal – especially during crises like pandemics – and which may be difficult to regulate through traditional strategies given constitutional freedoms of expression and the press. But techno-regulating news media coverage might be possible if there was a feasible way of automatically measuring desirable attributes of news records in real-time and disseminating the results widely, thereby incentivizing news media organizations to compete for better scores and reputational advantage. As a first move, this third chapter presents a relatively simple maximum entropy machine-learning model that automatically quantifies the relevance, scientific quality and sensationalism of news media records, and validates the model on a corpus of 163,433 news records mentioning the recent SARS and H1N1 pandemics. This involved optimizing retrieval of relevant news records, using specially tailored tools for scoring these qualities on a randomly sampled training set of 500 news records, processing the training set into a document-term matrix, utilizing a maximum entropy model for inductive machine learning to identify relationships that distinguish differently scored news records, computationally applying these relationships to classify other news records, and validating the model using a test set that compares computer and human judgments. Estimates of overall scientific quality and sensationalism based on the 500 human-scored news records were 3.17 (“potentially important but not critical shortcomings”) and 1.81 (“not too much sensationalizing”) out of 5, respectively, and updated by the computer model to 3.32 and 1.73 out of 5 after including information from 10,000 records. This confirms that news media coverage of pandemic outbreaks is far from perfect, especially its scientific quality if not also its sensationalism. The accuracy of computer scoring of individual news records for relevance, quality and sensationalism was 86%, 65% and 73%, respectively. The chapter concludes by arguing that these findings demonstrate how automated methods can evaluate news records faster, cheaper and possibly better than humans – suggesting that techno-regulating health news coverage is feasible – and that the specific procedure implemented in this study can at the very least identify subsets of news records that are far more likely to have particular scientific and discursive qualities.
Prospects for achieving global collective action on transnational health threats and social inequalities would be improved if greater efforts were taken to systematically take stock of the full-range of strategies available and to scientifically evaluate their potential effectiveness. This dissertation presents three studies that do so, which together showcase the diversity of approaches that can be mustered in pursuit of this goal. / Health Policy
| Identifer | oai:union.ndltd.org:harvard.edu/oai:dash.harvard.edu:1/23845489 |
| Date | 04 December 2015 |
| Creators | Hoffman, Steven Justin |
| Contributors | Frenk, Julio J., King, Gary, Muir Watt, Horatia |
| Publisher | Harvard University |
| Source Sets | Harvard University |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation, text |
| Format | application/pdf |
| Rights | open |
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