Data sharing in public health emergencies: A study of current policies, practices and infrastructure supporting the sharing of data to prevent and respond to epidemic and pandemic threats

Discussions around sharing public health research data have been running for close to a decade, and yet when the Ebola epidemic hit West Africa in 2014, data sharing remained the exception, not the norm. In response, the GloPID-R consortium of research funders commissioned a study to take stock of current data sharing practices involving research on pathogens with pandemic potential. The study catalogued different data sharing practices, and investigated the governance and curation standards of the most frequently used platforms and mechanisms. In addition, it sought to identify specific areas of support or investment which could lead to more effective sharing of data to prevent or limit future epidemics

Sharing health research data in low-resource settings: Supporting necessary infrastructure and building on good practices

<p>The primary purpose of this report is to provide an overview of emerging good practices in the sharing of data related to infectious diseases in low and middle income settings, and to suggest how funders of health research can best support systems and infrastructures that make data sharing more useful, equitable, ethical and efficient.</p><p><br></p> This report synthesises reflections, conclusions and recommendations from two pieces of commissioned research, and from the consultative meeting of different constituencies

Trials differ in Europe and Africa.

<p>Classification of a random sample of 100 trials for each of five countries in Europe (France, Germany, Italy, Spain, and the United Kingdom) and Sub-Saharan Africa (Kenya, Mali, Tanzania, Uganda, and Zambia). Trials in Africa focus predominantly on paediatric populations (A) and infectious disease (B) and are non-industry sponsored (C). Data were abstracted from the ClinicalTrials.gov website in August 2009.</p

Patient screening and enrolment.

<p>*One patient did not give consent. One patient was not competent to give consent, and a suitable proxy to provide consent could not be identified within inclusion time limits. **Two patients who died within 48 h of admission were excluded from the primary outcome analysis, as specified in the protocol.</p

Box and whisker plot of vital signs in TKM-130803 recipients, before, during, and after TKM-130803 infusions.

<p>Heart rate, respiratory rate, mean arterial blood pressure, and tympanic temperature in patients administered TKM-130803 at the following time points: immediately prior to TKM-130803 infusion (PRE), during the infusion, immediately at the end of the infusion (END), and at 1, 2, 4, and 8 h after the end of the infusion. The middle line shows the median value, the box shows the interquartile range, and the whiskers spread from the lower to the upper adjacent values. Outside values, that is, observations that are larger/smaller than the upper/lower adjacent values, are shown as circles.</p

Survival plot with futility boundary for TKM-130803 recipients.

<p>The red line denotes the futility boundary. The points and dashed line denote the number of survivors at day 14 plotted against the number of day 14 reports.</p

Baseline demographic and clinical characteristics of trial population.

<p>Baseline demographic and clinical characteristics of trial population.</p

Ebola virus RT-PCR cycle threshold values and RNA copies/ml over time.

<p>Top row: TKM-130803 recipients. Bottom row: Observational patients. RT-PCR Ct upper limit of quantitation (LOQ) = 40. RT-qPCR lower limit of quantitation = 1,000 genome copies. The Ebola virus RT-qPCR quantification is expressed as the number of genome copies/millilitre of plasma. Black diamonds denote results for survivors. Grey circles denote results for non-survivors.</p