Table 1

Potential tensions reconciling research integrity and research fairness principles in global health epidemiological practice

Study phase
Study preparationResearch integrity emphasises the establishment of study groups and constructing meaningful research questions based on a systematic review of the literature.17–20 In addition in global health:
  • Research fairness implies a need for engagement with key local stakeholders to ensure that research is driven national public health and research priorities67 (not only those of the higher income country parties).

  • Research fairness also implies a need to ensure that collaborative research furthers local research systems and competitiveness55 (not only those of the higher income country parties).

Protocol developmentResearch integrity emphasises the need for a detailed study protocol including, which should ideally be made public, and the need for successful ethical review before starting data collection.17–20 In addition in global health:
  • Transnational research implies complications can arise when multiple reviews are required,45 especially if review is not possible at one site (for lack of institutional resources or willingness) or if reviews conflict with each other.

Data collectionResearch integrity emphasises that studies should be carried out in accordance with the study protocol. Protocol deviations should be recorded, quality checks should be included and copies of the data collected should be stored in secure places. Participants should be well informed about the study and their rights.17–20 In addition in global health:
  • Transnational research implies potentially harmful effects of data collection for the community as external researchers (non-national or, eg, from different socioeconomic, religious, ethnic background) may cause health, cultural or social or economic harm through the manner in which the conduct research.

  • Equity and population-level research implies re-analysis of nationally representative surveys and routine health information systems data.68 69

  • Research fairness principles imply that conditions for use and publication should be clearly and fairly negotiated with data owners.

Data managementResearch integrity emphasises the need for reproducible and traceable procedures.17–20 However, in global health:
  • Transnational research implies complications can arise due to poor accessibility of study sites and difficult communication when team members are geographically spread out or the conditions are unique to the place where the investigation is carried out.

Data analysisResearch integrity emphasises that statistical analysis should be conducted according to the protocol.17–20 Additional unforeseen analyses should be clearly justified. However in global health:
  • Multidisciplinary research implies that it can be difficult to specify statistical analysis plans at the outset, as methods are often adaptive with quantitative analyses informing the qualitative analyses or vice versa.70

Dissemination and communicationResearch integrity emphasises scientists’ responsibility to report study results in the form of scientific publications.17–20 Public data sharing is encouraged because reuse of data makes research more useful and cost-effective.71 However, in global health:
  • Research fairness means that data sharing should not turn into an unfair one-way process providing valuable data for scientists in high-income countries who may not have contributed to study design and data collection.72

  • Research fairness also implies the use of methods to ensure effective feedback to affected communities by means of tailored messages and appropriate means of communication.42