Despite the fact that public health surveillance data is gathered extensively and routinely in many countries, there is currently no global systematic framework for exchanging public health data, and no global guidelines for the systematic sharing of public health data. Of course, the U.S. has its Privacy Act and Europe has itsGeneral Data Protection Regulation (GDPR). What about other continents? Even if we have GDPR, every institution/hospital has its own additional set of regulations, so how do we navigate this situation? How do we make sure we reap the benefits of sharing public health data and not the downsides?
Public health data is collected and shared via national and regional networks. These existing networks have often arisen as a consequence of a specific local public health crisis and could be integrated into any global framework. Nevertheless, a global data-sharing legal framework is unlikely to be successful. Data sharing is more likely to succeed if global data standards or ethical frameworks are reinforced with local agreements that take into consideration the local environment.
Various organisations have encouraged the sharing of data and building of knowledge, such as the World HealthOrganisation, The Bill & Melinda Gates Foundation and The European Centre for Disease Prevention and Control (ECDC). Since 2008, the ECDC has been running The European Surveillance System (TESSy), which collects national surveillance data from all European Union (EU) and European Economic Area (EEA)countries using standardized data sets; it then disseminates the data and produces outputs for public health action.
At times, data is not shared or there is simply a reluctance that lengthens the process of communication, this can be seen in West Africa where the limited sharing of viral sequences during theEbola outbreak has made it harder to evaluate the virus's potential for mutations. Global outbreaks have shown that inadequate surveillance and response in a single country can endanger national populations and the public health security of the entire world.
The interaction between barriers to data sharing in public health is complex, and single solutions to single barriers are unlikely to be successful.
Public health data is extremely important in the study and response to infectious outbreaks and chronic diseases such as cancer and obesity. In the next years, European health systems must respond more efficiently to the exponential increase of chronic patients by identifying the most efficient interventions.
Public health-related data sharing is a force for good when it comes to various outbreaks. In 2003 and 2009, the GlobalInfluenza Surveillance Network created by the World Health Organization was able to prevent the spread of two different acute respiratory viruses.Moreover, in the ongoing COVID-19 pandemic, platforms were developed to facilitate the sharing of patient data to help other patients who have contracted the infection.
According to the European Commission data sharing can:
· Advance early signal detection, increase treatment effectiveness and reduced the probability of adverse reactions
· Advance disease prevention by better analyzing risk factors
· Advance pharmacovigilance and therefore safety of patients
· Optimise outcome and overall survival rate prediction
The most challenging barriers to data sharing to overcome are political, economic and legal barriers. However, data sharing in public health is successful when a perceived need is addressed, and the social, political and cultural context is taken into account.
Policy changes and developments have a large impact on the way institutions operate, for instance, the EU Decision No. 1082/2013/EU has pushed member states to comply with better public health standards. Legal barriers also come in the shape of "grey areas" where institutions do not know exactly what to share, when, how and with whom which impacts what will and will not be done with the data. Nevertheless, there are effective tools for sharing data such as data standardisation and system development.
Data standardisation is achieved by:
· Implementing data set requirements
· Providing quality data
· Creating a platform for the uniform collection and data-sharing
System and human resources development are necessary because when competent systems of data collection are in place data-quality is ensured. This includes trained medical staff and a platform that is user friendly and efficient to operate.
Examples of technical barriers are:
· Inadequate data collection
· A lack of standardisation
· Varied data quality
· Inconsistent protocols across organisations and monitoring sites
· Mismatches between surveillance systems and language barriers
When organizations like the WHO or the ECDC decide on data formats, minimum data set requirements, and validation systems, these may be incompatible with locally-produced data. At a national level, this complicates the data sharing process for public health institutions by delaying or hindering data-sharing. This can be prevented if data-sharing methods are designed beforehand.
The lack of incentives to share data creates motivational barriers. On a personal level, providing data typically entails more time and effort, with no feedback on how the data was used and no credit for the labour.In a 'publish or perish' academic environment, data sharing could be discouraged because it can be perceived as a missed opportunity for publication. As a result, data collectors limit access to the information until all of the analyses they intend to conduct have been published.
This is understandable but can be mitigated with the right incentives. The key is to reward researchers that share data using professional bonuses, grants or promotions, which can help disrupt the current conventions that impact data sharing in academia.
For hospitals and institutions, there must be clear academic or financial benefits of data-sharing for them to invest precious time and medical staff into such a project. Nationally, a lack of benefits can impact the decisions of policymakers and thus restrict the data that leaves a country. This desire to limit data access is exacerbated by alack of understanding of how the information will be used.
A global ethical framework could be a practical way to address data sharing disparities. The value of data may be perceived differently by various stakeholders, and it is critical to recognize and acknowledge the values and interests that people have in the data sharing process. The guidelines should cover the dangers and rewards of reciprocal sharing while also clearly communicating mutual expectations to all parties.Reciprocity also contributes to the development of trust.
Shared data should be robustly anonymised, accessible, updated in real-time, multidisciplinary, reusable and appropriately recorded when shared. It is not enough to just hide a person's name and publish everything else as public health data; privacy and anonymization must be prioritized.
Global data sharing guidelines that are responsive to stakeholders' concerns and demands, as well as adaptive to particular circumstances, are essential to overcoming ethical challenges. It is important to ensure that data is only used for the previously agreed-upon objectives and that any further use would require consultation with data providers.