Lauren Maxwell, PhD, MPH
Junior Group Leader
Lauren Maxwell is an epidemiologist, data scientist, and qualitative researcher with 20 years of experience developing and managing global health research. Her interest is in bringing together teams, disciplines, methods, and geographies to address complex challenges in global health with a focus on maximizing the utility of data and samples and on maternal and child health. She leads the HIGH research group, FAIR, ethical, and legal issues in biomedical data reuse. Her team works on addressing barriers to implementing the FAIR (findable, accessible, interoperable, reusable) principles for data resources, including adopting standards for capturing and exchanging health research and system data, best practice in data governance, and on ELSI barriers to data and sample reuse. She is an active member of the RDA and CODATA communities and leads the biostatistics module for HIGH MSc students.
Education
2017 PhD, Epidemiology, McGill University School of Medicine
2010 Graduate Certificate, Field Epidemiology, Gillings Global School of Public Health, University of North Carolina, Chapel Hill
2010 MPH, Gillings Global School of Public Health, University of North Carolina, Chapel Hill
2000 BA, University of Michigan, Ann Arbor
Contributions
Projects & Grants
Data and sample reuse in pandemic response
Zika Virus Individual Participant Data Consortium. The Zika Virus Individual Participant Data Consortium: A Global Initiative to Estimate the Effects of Exposure to Zika Virus during Pregnancy on Adverse Fetal, Infant, and Child Health Outcomes. Trop. Med. Infect. Dis. 2020. 5, no. 4: 152. Corresponding Author. doi.org/10.3390/tropicalmed5040152
Carabali M, Maxwell L, Levis B The ZIKV IPD-MA Consortium, et al. Heterogeneity of Zika virus exposure and outcome ascertainment across cohorts of pregnant women, their infants and their children: a metadata surveyBMJ Open 2022;12:e064362. doi: 10.1136/bmjopen-2022-064362
Wilder-Smith A, Wei Y, Barreto de Araujo TV, VanKerkhove M, Turchi Martelli CM, Turchi Martelli, MD, Teixeira M, Tami A, Souza J, Sousa P, Soriano-Arandes A, Soria-Segarra C, Sanchez-Clemente N, Rosenberger KD, Reveiz L, Prata-Barbosa A, Pomar L, Pelá Rosado LE, Perez F, Passos SD, Nogueira M, Noel TP, Moura da Silva A, Moreira MA, Morales I, Miranda Montoya MC, Miranda-Filho DB, Maxwell L, et. al. Understanding the relation between Zika virus infection during pregnancy and adverse fetal, infant, and child outcomes: A systematic review and individual participant data meta-analysis of Zika virus-related cohorts of pregnant women and their infants and children (IPD-MA Protocol). BMJ Open. 2019. 9, e026092. Corresponding Author. dx.doi.org/10.1136/bmjopen-2018-026092
ELSI barriers to data and sample reuse
Maxwell L, Chamorro JB, Leegstra LM, Laguna HS, Miranda Montoya MC (2023) "How about me giving blood for the COVID vaccine and not being able to get vaccinated?" A cognitive interview study on understanding of and agreement with broad consent for future use of data and samples in Colombia and Nicaragua. PLOS Global Public Health 3(5): e0001253. https://doi.org/10.1371/journal.pgph.0001253
Miranda Montoya MC, Bravo Chamorro J, Leegstra LM, Duque Ortiz D, Maxwell L (2022) A blank check or a global public good? A qualitative study of how ethics review committee members in Colombia weigh the risks and benefits of broad consent for data and sample sharing during a pandemic. PLOS Global Public Health 2(6): e0000364. doi.org/10.1371/journal.pgph.0000364
Enguita‐Fernàndez C, Marban-Castro E, Mander O, Maxwell L, Correa Matta G. "The COVID‐19 epidemic through a gender lens: what if a gender approach had been applied to inform public health measures to fight the COVID‐19 pandemic?." Social Anthropology (2020). 10.1111/1469-8676.12803
Methodological innovations in data reuse de Jong V M T, Rousset R Z, Antonio-Villa N E, Buenen A G, Van Calster B, Bello-Chavolla O Y…Maxwell L…, et al. Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis BMJ 2022; 378 :e069881 doi:10.1136/bmj-2021-069881
Campbell H, de Jong VMT, Maxwell L, Jaenisch T, Debray TPA, Gustafson P. Measurement error in meta-analysis (MEMA)—A Bayesian framework for continuous outcome data subject to non-differential measurement error. Res Syn Meth. 2021; 1- 20. doi.org/10.1002/jrsm.1515
Publications
https://scholar.google.com/citations?user=vto-tNYAAAAJ&hl=en