W. Scott Clark, Eli Lilly and Company; Michelle Shardell, University of Maryland School of Medicine; on behalf of the ASA SPAIG Committee

The Statistical Partnerships Among Academe, Industry, and Government (SPAIG) Award annually recognizes outstanding partnerships among academe, industry, and government organizations and aims to promote new cross-sector collaborations. This distinct ASA award emphasizes recognition of outstanding collaborations between organizations, while also recognizing key individual contributors and important advances.

The SPAIG award winner was announced at the 2022 Joint Statistical Meetings in Washington, DC. This year, the SPAIG award honors the collaboration between Harvard Medical School and Partners In Health as they formed the COVID-19 Multicountry Research Group to address critical data and clinical needs across eight countries.

We had the opportunity to communicate with the following individual contributors and leaders to learn about the winning collaboration, the challenges they faced, and their keys to success:

• Moses Banda Aron, Monitoring and Evaluation Manager, Partners In Health/Malawi
• Isabel (Izzie) Fulcher, Biostatistician, Harvard Data Science Initiative Fellow, Harvard Medical School
• Bethany Hedt-Gauthier, Biostatistician, Associate Professor, Harvard Medical and Chan Schools
• Jean Claude (JC) Mugunga, Deputy Chief Medical Officer, Partners In Health

Can you briefly describe how the collaboration started?

Mugunga: Partners In Health and scholars from Harvard Medical School’s Global Health Research Core have partnered for many years with the goal of advancing research and research equity in Partners In Health–supported countries. Up until the start of the pandemic, the joint work was mostly focused on specific research projects in a single country. With the onset of Partners In Health’s COVID-19 response in March 2020, this group formed to provide real-time information for data-driven clinical responses and coordinate our research efforts and diverse skills across our organizations and country teams.

Hedt-Gauthier: As JC mentioned, many of the individuals in the CovMRG were already connected, but certainly not at this scale. JC and the Global Health Research Core director, Megan Murray, convened biweekly meetings to outline the common research questions across the sites. Our team then developed a set of methods to address these questions and worked closely with the site leads to make sure the methods integrated well into existing systems. Because of the enormity of the work, we engaged other Harvard faculty and trainees to respond to the countries’ data needs more quickly.

What are the major benefits coming from the collaboration that would not have otherwise happened?

Aron: This was my first time working with a diverse team of statisticians, epidemiologists, clinicians, and public health care workers toward a single goal. Our team included individuals from nine high-income countries and lower- and middle-income countries, with the goal of monitoring for pandemic surges and the use of essential health services during COVID-19. Most importantly, the collaboration shaped program design and improvements to avert the disruption of essential health services in lower- and middle-income countries. In addition, the group published multiple research articles in reputable journals such as BMJ Global Health, WHO Bulletin, and the International Journal of Epidemiology and received research grants to support this work. Finally, the CovMRG team led several capacity-building courses, including a time series data analysis workshop and manuscript writing course, that have been beneficial to the group members and our site teams.

Fulcher: As statisticians, we often develop tools for broad and general use cases, but the CovMRG collaboration enabled the statisticians on our team to develop statistical methods that were suited for a specific purpose. One of our primary projects was to leverage routine data sources in resource-constrained settings to identify potential COVID-19 outbreaks. As Moses mentioned, we taught an in-depth statistical training course for our in-country collaborators to accompany the methods development. Because we developed methods for this specific purpose and provided targeted training, we were able to build the foundation for continual adaptation and integration of our methods within health data systems.

What have been the most rewarding and most challenging aspects of the collaboration?

Mugunga: There were a lot of unknowns surrounding the COVID-19 pandemic at the onset, coupled with a lack of proper testing in our supported countries. The most rewarding aspect of this collaboration has been the opportunity for all Partners In Health–supported countries and Harvard researchers to sit together regularly and continue to identify and respond to the most pressing research questions affecting our sites’ day-to-day care of communities.

As for challenges, our teams spanned multiple countries with varying internet connectivity for a proper virtual engagement. Izzie was often on calls at 5 a.m., and our colleagues in Lesotho as late as 7 p.m. Limited resources and few research funding grants were other challenges to overcome, and these challenges persist as we aim to continue this collaboration even beyond our COVID-19 studies.

Fulcher: From the statistics side, the cross-country collaboration was an exercise in building an analytic platform both tailored to the needs of each site and general enough to not reinvent the wheel for each use case. This was a challenging task, as it required a deep understanding of each site’s data systems, goals, and target audience, which was followed by a translation of these needs into an automated data-processing pipeline. Despite the challenge, it was extremely rewarding to be able to work closely with and across country sites to enable a data-driven response to the COVID-19 pandemic.

What advice would you give to individuals and organizations looking to be more collaborative?

Hedt-Gauthier: My biggest lesson learned is that sometimes you have to slow down to go fast. We spent almost the entire first two months (April and May of 2020) discussing our common goals and desired approaches. This was difficult because the issues felt incredibly pressing. However, I believe our work is stronger by investing time in developing our relationships and ensuring we truly were on the same page.

Aron: I think statisticians and epidemiologists are critical in translating the work implemented by clinical and community health teams to inform evidence-based decisions. The collaboration between these groups remains essential to improving health care, especially in lower- and middle-income countries, and especially in the COVID-19 pandemic era. Our experience has shown that multidisciplinary teams solving problems is more effective than individual scientific work. I would urge organizations, especially those implementing programs in lower- and middle-income countries, to consider engaging top-tier institutions to collaborate on research and generate the evidence that would inform policy change.