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  • Harmonizing Data for Collaborative Research on Aging:Why Should We Foster Such an Agenda?
  • Isabel Fortier, Dany Doiron, Christina Wolfson, and Parminder Raina

Introduction

Despite longstanding awareness that the aging process is inextricably linked to the multifaceted changes occurring throughout an individual's lifetime (from the level of the cell, to individual psychological and behavioral factors, and to broad social contexts), a clear picture of all relevant interactions and their combined effects has not yet emerged. Furthermore, impacts of the complex interactions between biological, psychological, environmental, and social factors can take years to manifest, bringing additional challenges to the development of studies investigating this multifaceted picture. Despite these challenges, recent advances in science and technology (e.g., capacity to generate genotypes at very low cost, and the development of specialist measures adapted to elderly populations) now let us look forward to promising new avenues for research. However, to advance our understanding of the causal pathways leading to both adverse events and favorable outcomes for today's and tomorrow's seniors, it is essential to invest in infrastructures that enable the ongoing collection of a wide range of information and are constructed to support the next generation of research potentials and requirements. The Canadian Longitudinal Study on Aging (CLSA) (Raina et al., 2009) is an example of a study that will provide such infrastructure in Canada.

The CLSA cohort will include 50,000 participants (45-85 years of age) to be followed over 20 years. In addition, the CLSA is planning to collaborate with a wide variety of national and international data collection efforts such as the Canadian Multicenter Osteoporosis Study (www.camos.org), CARTaGENE (www.cartagene.qc.ca), the EPIC Elderly Study (http://epic.iarc.fr/research/elder.php), and the Health and Retirement Study (http://hrsonline.isr.umich.edu) to conduct collaborative research addressing etiological and comparative policy analyses relevant to the aging population. The adoption of different designs and scientific targets in these cohorts offers unique opportunities to enable investigators representing different research infrastructures to learn from each other's experiences. However, [End Page 95] important scientific and policy advances will be achieved only if valid comparison or integration of study-specific data is feasible across cohorts and databases. Fortunately, the scientific promises of comparative and harmonized research are well recognized (National Research Council, 2001). An increasing number of countries are developing initiatives that support the creation of harmonized or compatible datasets to capture the multifaceted lives of older individuals and their families (Lee, 2007). Through various initiatives, Canada is also increasing efforts to foster harmonization of the rich existing and emerging national and international infrastructures that will help us to advance the science of aging.

Building Cohort Infrastructures to Support Research

Cohort studies continue to be invaluable resources for the scientific community in a range of research fields. The information gathered in cohorts is critical for us to leverage Canadian health and social science, support training of the next generation of researchers, and ensure advancement of our understanding of the causal pathways of a broad variety of health and social outcomes. The impact of these studies, however, depends on the quality and breadth of information collected and generated. Cohort investigators must then face important financial, scientific, and technical challenges to ensure collection of comprehensive information on a range of diverse health outcomes as well as on risk and prognostic factors. In addition, the investigators must support regular follow-up of participant health and exposure profiles over extended periods.

Although substantial resources are needed for the development of such infrastructures, individual studies will often not have the statistical power or specific data items required to explore interactions and combined effects of the numerous factors affecting healthy aging. Data linkage and data harmonization are two complementary, but distinct, processes that may enhance the value of a given cohort or database. Data linkage can be described as "the bringing together from two or more different sources, data that relate to the same individual, family, place or event" (Holman et al., 2008, p. 767). In order to enrich databases with information not originally collected, many cohorts will link individual-level data to other data sources on the participant's health (e...

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