How ownership rights over microorganisms affect infectious disease control and innovation: A root-cause analysis of barriers to data sharing as experienced by key stakeholders

Abstract

Background: Genetic information of pathogens is an essential input for infectious disease control, public health and for research. Efficiency in preventing and responding to global outbreaks relies on timely access to such information. Still, ownership barriers stand in the way of timely sharing of genetic data from pathogens, frustrating efficient public health responses and ultimately the potential use of such resources in innovations. Under a One Health approach, stakeholders, their interests and ownership issues are manifold and need to be investigated. We interviewed key actors from governmental and non-governmental bodies to identify overlapping and conflicting interests, and the overall challenges for sharing pathogen data, to provide essential inputs to the further development of political and practical strategies for improved data sharing practices.

Methods & findings: To identify and prioritize barriers, 52 Key Opinion Leaders were interviewed. A root-cause analysis was performed to identify causal relations between barriers. Finally, barriers were mapped to the innovation cycle reflecting how they affect the range of surveillance, innovation, and sharing activities. Four main barrier categories were found: compliance to regulations, negative consequences, self-interest, and insufficient incentives for compliance. When grouped in sectors (research institutes, public health organizations, supra-national organizations and industry) stakeholders appear to have similar interests, more than when grouped in domains (human, veterinary and food). Considering the innovation process, most of barriers could be mapped to the initial stages of the innovation cycle as sampling and sequencing phases. These are stages of primary importance to outbreak control and public health response. A minority of barriers applied to later stages in the innovation cycle, which are of more importance to product development.

Conclusion: Overall, barriers are complex and entangled, due to the diversity of causal factors and their crosscutting features. Therefore, barriers must be addressed in a comprehensive and integrated manner. Stakeholders have different interests highlighting the diversity in motivations for sharing pathogen data: prioritization of public health, basic research, economic welfare and/or innovative capacity. Broad inter-sectorial discussions should start with the alignment of these interests within sectors. The improved sharing of pathogen data, especially in upstream phases of the innovation process, will generate substantial public health benefits through increased availability of data to inform surveillance systems, as well as to allow the (re-)use of data for the development of medical countermeasures to control infectious diseases.

Fig 1. The valorization & technology transfer cycle depicts the steps in the innovation process for infectious diseases control: From disease emergence to development and marketing of medical countermeasures [16].

The upstream phases (steps 1–7) in the cycle overlap with infectious disease surveillance and initial outbreak response, while downstream phases (steps 8–14) overlap with product development as medical countermeasures to control infectious diseases.

Fig 2. Saturation of ownership barriers for MGR sharing was reached after 14 interviews (a). Representation of KOLs across stakeholder sectors and domains (b).

The choice in stakeholders’ sectors and domains reflects their main area of expertise, since the KOLs can be active across two or more domains and sectors.

Fig 3. Root-cause analysis of the seven barriers classified under the category “compliance to regulations” (A).

Columns represent barriers, causes and root causes, and rows represent the causal argumentation of the KOLs. The barriers are shaded and numbered following the thematic classification in barriers types as the following: compliance to regulations (A, #1–7 are light grey), negative consequences (B, #8–10 are dark grey), self-interest (C, #11–14 are white) and insufficient incentives for compliance (D, #15 is black).

Fig 4. Root-cause analysis of the three barriers classified under the category “negative consequences” (B).

Columns represent barriers, causes and root causes and rows represent the causal argumentation of the KOLs. The barriers are color-coded and numbered following the thematic classification in barriers types as the following: compliance to regulations (A, #1–7 are light grey), negative consequences (B, #8–10 are dark grey), self-interest (C, #11–14 are white) and insufficient incentives for compliance (D, #15 is black).

Fig 5. Root-cause analysis of the four barriers classified under the category “self-interest” (C).

Columns represent barriers, causes and root causes and rows represent the causal argumentation of the KOLs. The barriers are color-coded and numbered following the thematic classification in barriers types as the following: compliance to regulations (A, #1–7 are light grey), negative consequences (B, #8–10 are dark grey), self-interest (C, #11–14 are white) and insufficient incentives for compliance (D, #15 is black).

Fig 6. Root-cause analysis of the barrier classified under the category “insufficient incentives for compliance” (D).

Columns represent barriers, causes and root causes and rows representing the causal argumentation of the KOLs. The barriers are color-coded and numbered following the thematic classification in barriers types as the following: compliance to regulations (A, #1–7 are light grey), negative consequences (B, #8–10 are dark grey), self-interest (C, #11–14 are white) and insufficient incentives for compliance (D, #15 is black).

Fig 7. The barriers predominantly influence the scientific domain and the transition between the market & society to the scientific domain.

Color codes and numbering correspond to the main barriers and underlying causes in the root-cause analysis trees, in Figs 3–6: A) compliance to regulations (light grey), B) negative consequences (dark grey), C) self-interest (white), and D) insufficient incentives for compliance (black).

Fig 8. Ownership barriers for timely sharing and open-access to MGR predominantly hamper the innovation process for infectious diseases in the phases from sampling through proof of principle.

Color codes and numbering correspond to the main barriers and underlying causes in the root-cause analysis trees, in Figs 3–6: A) compliance to regulations (light grey), B) negative consequences (dark grey), C) self-interest (white), and D) insufficient incentives for compliance (black).

Fig 9. Publication priority was the most frequently mentioned barrier.

Barriers are ranked according to their frequency of occurrence in absolute numbers (n KOLs) and in decreasing order. Color codes and numbering correspond to the main barriers and underlying causes in the root-cause analysis trees, in Figs 3–6: compliance to regulations (A#x light grey), negative consequences (B#x dark grey), self-interest (C#x white), and insufficient incentives for compliance (D#x black). The asterisk identifies barriers that persist in the downstream phases of the innovation cycle (Fig 7).

Fig 10. The commercial sector (CS) differentiated substantially from the other stakeholder groups in the quantification of the mentioned barriers.

The order generated by the frequency of occurrence as mentioned by the national surveillance centers (NSC) is taken as a reference, to which the behavior of the curves representing the commercial sector (CS), research institutes (RI), and supranational organizations (SO) is compared. Barriers are numbered according to the root-cause analysis (Figs 3–6) and the asterisk identifies barriers that persist in the downstream phases of the innovation cycle (Fig 7).