Background
The GACD member agencies are National public funding agencies that primarily fund health research in their own countries. These agencies have come together as a global alliance to contribute to and support infrastructure and research programmes under the auspices of the GACD through finance and management. |
GACD alliance members agree on joint research priorities and fund world-class research, fostering collaboration of research programmes between low-and middle income countries and high income countries to fight chronic diseases. Alliance members issue joint requests for applications (RFAs) on a regular basis on topics in strategic focus areas. |
Responses to RFAs undergo rigorous peer review through Alliance member’s existing funding processes, although alliance members are moving towards joint peer review by all member agencies. To date, this model has been piloted on a small scale on two of the previous funding calls, and rollout to all agencies is expected for 2017. While this peer-review panel makes recommendations for funding, funding decisions are ultimately made by each of the GACD member agencies, and they are the bodies who award and administer all research funds. |
The research teams that receive funding as part of a GACD research programme form a community of researchers and funding agency representatives under the banner of the GACD Research Network (GRN). Through the network, members have the opportunity to participate in joint activities in order to share information and develop common approaches to their research. The Research Network meets annually at the GACD Annual Scientific Meeting, with additional conference calls throughout the year. The joint activities take the form of a number of Working Groups, which are formed and chaired by researchers who choose to work together on common themes across their projects. The collaborative efforts of the GACD Research network and its Working groups are supported by the GACD Secretariat, which is based in London, UK. |
Current member agencies of GACD (as of December 2016): |
• Argentinian Ministry of Science and Technology (MINCYT), Argentina |
• National Health and Medical Research Council (NHMRC), Australia |
• São Paulo Research Foundation (FAPESP), Brazil |
• Canadian Institutes of Health Research (CIHR), Canada |
• Chinese Academy of Medical Sciences (CAMS), China |
• Research & Innovation DG, European Commission, EU |
• Indian Council of Medical Research (ICMR), India |
• Agency for Medical Research and Development (AMED), Japan |
• National Institute of Medical Sciences and Nutrition Salvador Zubirán, Mexico - funding available through Conacyt |
• South African Medical Research Council (SA MRC), South Africa |
• Health Systems Research Institute, Thailand |
• Medical Research Council (MRC), United Kingdom |
• National Institutes of Health (NIH), United States |
Methods
Date of activity | Activity undertaken |
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March–August, 2012 | Successful Hypertension GACD Programme awardees announced. |
December,2012–February, 2013 | Discussion group formed at GACD ASM. |
Data Standardisation Working Group proposed and agreed upon. | |
March, 2013 | Data Standardisation Working Group formally constituted. |
March–August, 2013 | Scoping exercise to identify potential consensus variables and summarise data across eight domains for all Hypertension Programme projects. |
August–November, 2013 | Data dictionary drafted as a recommended set of consensus measures based on previous scoping exercise and summary steps |
November, 2013 | Data Standardisation Working group presents recommendations for common measures to be adopted at 2013 GACD ASM. |
December, 2013–February, 2014 | Further refinement of draft data dictionary based on feedback received at 2013 GACD ASM. |
February, 2014 | Final version of consensus data dictionary released |
February, 2015 | Follow-up survey conducted to assess level of adoption of recommended measures. |
April–November, 2015 | Analysis of implementation of data dictionary by teams |
Preliminary discussion of data harmonisation and sharing
Data sharing and harmonization process
Results
Domain | Description |
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Demographic | Participant age, gender and information relating to household size and income. |
Diet | Variables collecting information on salt intake, and meat/vegetable consumption. |
Clinical/Anthropometry | WHO STEPS [30] blood pressure protocol, and basic anthropometric measurements. |
Personal Medical History | Participant’s history of CVD and diabetes. |
Knowledge of HTN | Participant’s knowledge and awareness of hypertension. |
Physical activity | Details concerning patient’s level of regular physical exercise. |
Behavioural | |
Smoking | Level of tobacco use |
Alcohol | Level of alcohol consumption. |
Biochemical | 24 h Urine and blood glucose measurement from WHO STEPS biochemical core [30]. |
Discussion
Why develop consensus measures?
Weighing up accuracy and precision against feasibility of consensus measures
Challenges to developing/implementing consensus measures
Consistency of methods
Ethical issues
Benefits of collecting consensus measures
Identifying gatekeepers and managers of consensus data
Who should guide the development of consensus measures and when should they be announced?
Health research funding agency protocols and timelines
Conclusion
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➢ Researchers and funders need to share a common vision for joint programme activities
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➢ Clear and specific language on data standardisation and sharing should be included in the RFA.
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➢ Funded teams should be brought together as soon as possible after funding announcements making use of communication technology to facilitate group contact.
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➢ Consensus measures (both the outcome and context variables and the measurement approach) need to be developed as early as possible, preferably prior to ethics approval and participant recruitment.
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➢ Adopt a pragmatic approach to balancing precision and direct comparability of common measures with the aims of implementation science including scalability.
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➢ Measures of intervention context should be included in the consensus measures.
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➢ Data sharing intentions should be included in informed consent documents.
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➢ Make the consensus measures easily accessible so that others can utilise similar methods and approaches, and contribute to a data repository.