Open Data—such as that found in the WHO Global Health Observatory Data Repository and from the World Bank—is especially important in the field of global health. Public health on a global scale requires the statistical power of pools of data rather than individual datasets. We need a bird’s eye view to detect trends and combat global epidemics.
Examples of the benefits of sharing data to global health research exist beyond the community resources mentioned from the WHO and World Bank. The Consortium of Health-Orientated Research in Transitioning Societies (COHORTS) study is one such example [
6]. Funded by the Wellcome Trust and led by Cesar Victora, one group of researchers pooled data from 5 major cohort studies on maternal and infant factors in low and middle income countries. This larger pool of data provided relevant information about the relevance of the first 1000 days of life on further quality of life and educational attainment and earnings. Pooling their data gave the researchers enhanced statistical power and enabled them to generate more information around the impact on the first 1000 days of life. This example highlights how greater data sharing through collaboration can greatly benefit the individual researcher, leading to more prestigious and more impactful publications.
But reuse of, and the potential for, open data is only one of the driving factors. Also key to this conversation around data access is transparency. Health interventions must be driven by evidence, and that evidence must be verifiable. Well established organisations in the global health community have overstated the case for certain health interventions, polarising views on the use of certain interventions. A good example is the secondary analyses of the WHO Antenatal Care trial performed by Vogel et al. [
7]. The large, cluster randomized WHO Antenatal Care Trial concluded that a goal-orientated package of antenatal care with reduced visits seemed not to affect maternal and perinatal outcomes [
8]. This secondary analysis of the WHO Antenatal Care Trial data indicates that there is an appreciable increased risk of fetal death at 32 to 36 weeks gestation for women receiving the goal-oriented, reduced frequency antenatal care package. In a Commentary regarding the publication of this secondary analysis, Hofmeyr and Hodnett [
9] concluded that “this re-analysis was robust after adjustment for potential confounding factors, and that the increase in perinatal mortality (was) consistent with trends in the two other cluster randomized trials conducted in Zimbabwe [
10], we find the evidence, that a reduced number of antenatal visits is associated with increased perinatal mortality, compelling” [
9]. Derivatives of these conclusions are of great magnitude since the WHO Antenatal Care Trial paper and derived publications such as the WHO manual for the implementation of the new model (
http://www.who.int/reproductivehealth/publications/maternal_perinatal_health/RHR_01_30/en/index.html) have impacted antenatal care practice in many low-income countries. What is needed in health interventions is an objective, depoliticized view of the evidence. That kind of clarity comes with data transparency. Increased data access will also help to catch mistakes in reported results, which inevitably happen.