Why you should share your data during a pandemic

Smith, Emily R.; Flaherman, Valerie J.

doi:10.1136/bmjgh-2021-004940

Cited by 8 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2,[36][37][38] Ongoing and developing outbreaks, such as Ebola, Zika, and COVID-19, underscore the importance and challenges of quick, open, and effective sharing of high-quality samples and related clinepi data and human genetic data from various populations for an informed response. [39][40][41] Best and worst data and sample-sharing experiences In this study, the opportunity to form partnerships with experts and researchers worldwide was the most frequently cited best aspect of data and sample sharing, which is similar to ndings from other studies. In a qualitative study with public health and biomedical researchers in Thailand, senior researchers highlighted the importance of sharing health research data to foster cross-national collaborations and improve their research portfolio.…”

Section: Discussionsupporting

confidence: 71%

Understanding the motivators and barriers to sharing participant-level data and samples: A cross-sectional study with acute febrile illness cohort teams

Shreedhar,

Jaenisch,

Naccache

et al. 2024

Preprint

View full text Add to dashboard Cite

Background Sharing de-identified, participant-level clinical-epidemiological data, human biological samples, and human genetic data facilitates understanding diseases and the development of prevention strategies, diagnostics, and treatments. While there are increasing calls to share participant-level data and samples both during and outside the public health response to epidemics, several barriers remain. Methods We administered a cross-sectional, online survey to research teams that manage acute febrile illness (AFI) cohorts. We included questions on the researchers’ best and worst experiences, motivators, benefits, and barriers to sharing de-identified participant-level clin-epi data, human biological samples, and human genetic data during and outside epidemics. Using the political, ethical, administrative, regulatory, and legal (PEARL) framework, we classified the best and worst sharing experiences and employed the Wilcoxon signed-rank test to compare barriers between epidemic and non-epidemic settings. Results We received 78 responses to the survey from cohort study teams in 23 countries. Most respondents were cohort PIs, over 45, and advanced in their careers. Most cohorts were based in South America or Central America, focused on multiple pathogens, and collected and shared multiple data types and samples. Scientific collaborations with researchers outside their country were the most commonly reported best data or sample-sharing experience. Lack of benefit sharing was the most commonly reported worst sharing experience. Benefits and barriers to sharing did not vary significantly by data type or whether sharing happened during or outside of pandemics, except for regulatory barriers to sharing human biological samples which were significantly more important in epidemic than in non-epidemic settings. Conclusions The study highlights the need for stakeholders to improve data and sample-sharing practices for AFI researchers in LMICs, emphasising ethical considerations, benefit sharing, and streamlined administrative processes in both epidemic and non-epidemic settings.

show abstract

Section: Discussionsupporting

confidence: 71%

Understanding the motivators and barriers to sharing participant-level data and samples: A cross-sectional study with acute febrile illness cohort teams

Shreedhar,

Jaenisch,

Naccache

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Ongoing pooling has also highlighted where data elements are missing or sparse across studies, thus providing justification for additional effort from study investigators to retrospectively extract additional data or modify their data collection forms. Further, this collaborative approach of pooling data highlights the possibility of generating robust answers to questions of collective importance at a global scale [ 34 , 35 ]. We are also more likely to elucidate risks related to rare outcomes.…”

Section: Discussionmentioning

confidence: 99%

Protocol for a sequential, prospective meta-analysis to describe coronavirus disease 2019 (COVID-19) in the pregnancy and postpartum periods

Smith

Oakley

et al. 2022

PLoS ONE

Self Cite

View full text Add to dashboard Cite

We urgently need answers to basic epidemiological questions regarding SARS-CoV-2 infection in pregnant and postpartum women and its effect on their newborns. While many national registries, health facilities, and research groups are collecting relevant data, we need a collaborative and methodologically rigorous approach to better combine these data and address knowledge gaps, especially those related to rare outcomes. We propose that using a sequential, prospective meta-analysis (PMA) is the best approach to generate data for policy- and practice-oriented guidelines. As the pandemic evolves, additional studies identified retrospectively by the steering committee or through living systematic reviews will be invited to participate in this PMA. Investigators can contribute to the PMA by either submitting individual patient data or running standardized code to generate aggregate data estimates. For the primary analysis, we will pool data using two-stage meta-analysis methods. The meta-analyses will be updated as additional data accrue in each contributing study and as additional studies meet study-specific time or data accrual thresholds for sharing. At the time of publication, investigators of 25 studies, including more than 76,000 pregnancies, in 41 countries had agreed to share data for this analysis. Among the included studies, 12 have a contemporaneous comparison group of pregnancies without COVID-19, and four studies include a comparison group of non-pregnant women of reproductive age with COVID-19. Protocols and updates will be maintained publicly. Results will be shared with key stakeholders, including the World Health Organization (WHO) Maternal, Newborn, Child, and Adolescent Health (MNCAH) Research Working Group. Data contributors will share results with local stakeholders. Scientific publications will be published in open-access journals on an ongoing basis.

show abstract