Abstract
BackgroundThe imperative to improve global health has prompted transnational research partnerships to investigate common health issues on a larger scale. The Global Alliance for Chronic Diseases (GACD) is an alliance of national research funding agencies. To enhance research funded by GACD members, this study aimed to standardise data collection methods across the 15 GACD hypertension research teams and evaluate the uptake of these standardised measurements. Furthermore we describe concerns and difficulties associated with the data harmonisation process highlighted and debated during annual meetings of the GACD funded investigators.With these concerns and issues in mind, a working group comprising representatives from the 15 studies iteratively identified and proposed a set of common measures for inclusion in each of the teams’ data collection plans. One year later all teams were asked which consensus measures had been implemented.ResultsImportant issues were identified during the data harmonisation process relating to data ownership, sharing methodologies and ethical concerns. Measures were assessed across eight domains; demographic; dietary; clinical and anthropometric; medical history; hypertension knowledge; physical activity; behavioural (smoking and alcohol); and biochemical domains. Identifying validated measures relevant across a variety of settings presented some difficulties. The resulting GACD hypertension data dictionary comprises 67 consensus measures. Of the 14 responding teams, only two teams were including more than 50 consensus variables, five teams were including between 25 and 50 consensus variables and four teams were including between 6 and 24 consensus variables, one team did not provide details of the variables collected and two teams did not include any of the consensus variables as the project had already commenced or the measures were not relevant to their study.ConclusionsDeriving consensus measures across diverse research projects and contexts was challenging. The major barrier to their implementation was related to the time taken to develop and present these measures. Inclusion of consensus measures into future funding announcements would facilitate researchers integrating these measures within application protocols. We suggest that adoption of consensus measures developed here, across the field of hypertension, would help advance the science in this area, allowing for more comparable data sets and generalizable inferences.
Highlights
The imperative to improve global health has prompted transnational research partnerships to investigate common health issues on a larger scale
We suggest that adoption of consensus measures developed here, across the field of hypertension, would help advance the science in this area, allowing for more comparable data sets and generalizable inferences
Preliminary discussion of data harmonisation and sharing Discussions concerning the potential for both data sharing and data harmonisation and standardisation were initiated at the 2012 Global Alliance for Chronic Diseases (GACD) Joint Technical Steering Committee (JTSC), ( known as the GACD Research Network (GRN)), Annual Scientific Meeting (ASM) in Ottawa (8–11 December 2012) (Table 2 and Fig. 2)
Summary
The imperative to improve global health has prompted transnational research partnerships to investigate common health issues on a larger scale. We describe concerns and difficulties associated with the data harmonisation process highlighted and debated during annual meetings of the GACD funded investigators With these concerns and issues in mind, a working group comprising representatives from the 15 studies iteratively identified and proposed a set of common measures for inclusion in each of the teams’ data collection plans. The need to enhance global health research that can inform action on pressing health issues such as chronic diseases, infectious diseases and maternal and child health has prompted transnational partnerships among researchers and research funders. Data may be collected either contemporaneously or in sequence, but there is an underlying aim to facilitate robust comparisons by using pooled data to compare effect sizes
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