Publications
Selected Publications
“Trump’s Second Presidency Begins: Evaluating Effects on the US Health System.” The Lancet Regional Health-Americas, 2025 (with Scott Greer, Holly Jarman, and Miranda Yaver).
“The Second Trump Administration: A Policy Analysis of Challenges and Opportunities for European Health Policymakers.” Health Policy, 2025 (with Scott Greer, Holly Jarman, Dimitra Panteli, Ewout van Ginneken, and Matthias Wismar).
“The Future of Global Health, without the United States.” Bulletin of the Atomic Scientsts, 2025 (with Scott Greer).
“Identifying COVID-19 Outbreaks From Contact-Tracing Interview Forms for Public Health Departments: Development of a Natural Language Processing Pipeline” JIMR Public Heatlh and Surveillance, 2022 (with John Caskey, Iain L McConnell et al).
“Maternal Mortality Ratios in 2852 Chinese Counties, 1996–2015, and Achievement of Millennium Development Goal 5 in China: A Subnational Analysis of the Global Burden of Disease Study 2016.” The Lancet, 2019 (with Juan Liang, Xiaohong Li, et al).
“Population and Fertility by Age and Sex for 195 Countries and Territories, 1950–2017: A Systematic Analysis for the Global Burden of Disease Study 2017.” The Lancet, 2018 (with Christopher Murray, Charlton Callender, et al).
“Global, Regional, and National under-5 Mortality, Adult Mortality, Age-Specific Mortality, and Life Expectancy, 1970–2016: A Systematic Analysis for the Global Burden of Disease Study 2016.” The Lancet, 2017 (with GBD 2016 Mortality Collaborators).
“Health Metrics Priorities: A Perspective from Young Researchers.” The Lancet, 2016 (with Julia Morris, Grant Nguyen, et al).
“Global, Regional, National, and Selected Subnational Levels of Stillbirths, Neonatal, Infant, and under-5 Mortality, 1980–2015: A Systematic Analysis for the Global Burden of Disease Study 2015” The Lancet, 2016 (with Haidong Wang, Matthew Coates, et al).
Working Papers
“The Use of Health Data in Political Science: Mechanisms, Comparability, and Endogeneity” (Under review)
Political scientists are increasingly using quantitative data outside of our core disciplinary expertise. This paper asks: what are the major pitfalls inherent in this cross-disciplinary research? How should we best work with data that is unfamiliar to us to make empirically accurate and theoretically sound political science? This paper discusses three challenges in using unfamiliar data: specifying the full causal mechanism by choosing the appropriate variable, understanding the comparability of raw and modeled data across time and over different geographies, and accounting for the ways in which data is endogenous to politics. It argues that it is important to understand the broader data landscape in domains unfamiliar to political science because it has a potential impact on political science inference and theory. This paper uses quantitative health data, including infant mortality rates and life expectancy, as an example to illustrate some of the ways in which these choices of variables and research designs influence conclusions, and makes recommendations for best practices when making use of unfamiliar data in political science research.
“Interpreting and Visualizing Complex Data: The Case of COVID-19 Data Dashboards” (Book Chapter in Palgrave Handbook for the History of Epidemiology), currently under review
The COVID-19 pandemic led to an explosion of data dashboards that communicated, visualized, and interpreted underlying epidemiological data. These dashboards were produced by academic institutions, governments, and the news media. They shaped the way that that the general public understood the course of the pandemic. In this chapter, I seek to understand the construction of these data dashboards: the complexity of the underlying COVID-19 data sources, the interpretative choices made by dashboard creators in presenting the data, and the ways in which the creation and maintenance of dashboards shaped and was shaped by the professional norms of their creators. I argue that while COVID-19 dashboards and the visual interpretations inherent in their creation do have antecedents in the history of data visualizations, these dashboards are also historically unique. Contemporary COVID-19 data dashboards were created for a large, general audience. They presented global data across a broad range of geographies, necessitating technical sophistication to manage the fragmented underlying data and engendering assumptions that this data is commensurable. Crucially, COVID-19 data dashboards are spawning more dashboards, shaping the future of public health data communication and visualization.
“Can We Trust Politicized Public Health Data? The Case of COVID-19 in the United States”
The second Trump administration has caused concerns about the integrity and availability of public health surveillance and data. The COVID-19 pandemic, starting during his first administration, can offer historical answers to questions about data trustworthiness. COVID-19 was both enormously politically salient, and also underwent a quick politicization across party lines in the United States. This paper attempts evaluate the trustworthiness of politicized government data by asking: is there evidence of differential undercounting of COVID-19 deaths by state-level partisanship in the US? It finds that while heterogeneity exists in state-level COVID-19 death undercounting, there is only weak evidence of partisanship driving these differences, and these results are not methodologically or empirically robust. That is, while both Democratic and Republican states undercounted COVID-19 deaths, there is little evidence that states with Republican governors systematically manipulated their COVID-19 data to downplay the pandemic, or that Democratic governors overplayed their success in mitigating the effects of the pandemic. In the polarized political environment of the COVID-19 pandemic, it is unlikely that partisanship influenced the quality of COVID-19 data collected and disseminated by state government.