Background
From 1990 to 2010, the burden of mental ill-health increased by 38%, an increase mostly attributable to population ageing [
1]. Mental disorders in old age lead to impairments in the ability to function socially, decreased quality of life, and increased risk of health problems and comorbidities. Poor mental well-being in later life carries significant social and economic impacts on families and societies, imposing a substantial burden on health and social care services [
1]. Mental disorders associated with ageing, therefore, have become a key priority for public health policy and prevention.
Today, over 70% of Europeans and over 80% of North Americans reside in cities [
2]. While urbanization is expected to increase in these regions over the coming decades, there is limited understanding of the critical contribution of the urban environment to mental well-being in ageing societies. Cities pose major challenges for older citizens, but also offer opportunities to develop, test, and implement policies, services, infrastructure, and interventions that promote mental well-being. The MINDMAP project, building on a novel database infrastructure, aims to identify the opportunities and challenges posed by urban environmental characteristics for the promotion and management of mental well-being and cognitive function of older individuals.
Funded from 2016 to 2020 by the Horizon2020 programme of the European Commission, MINDMAP aims to achieve its research objectives by bringing together ten longitudinal studies from eight European countries, the United States (US), Canada and Russia (in total over 35 cities of different sizes) linked to databases of area-level environmental exposures and social and urban policy indicators. Linking micro- (i.e. individual), meso- (i.e. neighbourhood), and macro- (i.e. city or national) level data enables MINDMAP to investigate the causal pathways and multi-level interactions between characteristics of the urban environment and the behavioural, social, and biological determinants of mental well-being and cognitive function in older adults.
Compared to studies based on a single country or city, integrating data from cohort studies in multiple cities offers many advantages for research exploring the impact of the urban environment on mental well-being. Harmonizing information across international cohort studies and combining them with data from different sources (physical, social and socioeconomic environmental characteristics, policy indicators) allows examining contextual determinants of variation in mental well-being across different populations and exploring the impact of neighbourhood, urban, and national policies for the prevention of mental disorders in older people. Furthermore, integrating data increases sample sizes and statistical power necessary to identify high-risk population subgroups, study relatively rare health conditions, unravel causal pathways and explore interactions between risk factors. Finally, and potentially most relevant for studies investigating environmental influences on health, integrating data from different geographical locations increases the variation in environmental characteristics and policies that influence mental well-being and cognitive function both within as well as between cities.
The MINDMAP database infrastructure will support these research objectives by integrating data from multiple sources and providing investigators with a platform to analyse it. The infrastructure will allow multiple MINDMAP investigators to safely and remotely co-analyse data from multiple sources and across different populations. Integration of different data sources will facilitate analyses exploring the importance of individual- and area-level determinants of mental well-being and cognitive function.
Discussion
The MINDMAP project aims to identify the opportunities and challenges posed by the urban environment for the promotion of mental well-being and cognitive function in later life. MINDMAP aims to achieve its research objectives by bringing together longitudinal studies from 11 countries covering over 35 cities linked to databases of area-level environmental exposures and social and urban policy indicators. The infrastructure supporting integration of this data will allow multiple MINDMAP investigators to safely and remotely co-analyse individual-level and area-level data through a single platform.
The MINDMAP project has several important strengths. Integrating data from cohort studies in multiple cities and across various exposure or policy databases allows examining the role of contextual determinants on variations in mental well-being across different populations. It also increases variations across these contextual determinants and it raises sample sizes and statistical power and, because the data is pooled from different regions and jurisdictions, allows exploring the effect of policy on mental well-being. The harmonization approach and tools that are employed by the project have been methodically developed by Maelstrom Research [
19,
20] and put to use in similar research collaborations [
21‐
23]. These tools and approaches have been adapted to accommodate the specific needs of the MINDMAP project and ensure that all aspects of the harmonization project are carried out in a uniform, open, and methodical way to optimize the validity of the research results and transparency of the methodology. Moreover, the research teams contributing to the project bring a wide range of experiences and expertise that complement each other.
The integration of different data sources from different countries also present several challenges. Firstly, different questions and scales have been used within the participating cohort studies to measure similar underlying concepts. For some measures, harmonizing across the cohort studies is relatively straightforward (e.g. simple algorithmic transformations or calibrations). However, for measures such as mental well-being outcomes, this process is more complex, requiring the application of statistical modelling (e.g. standardization, latent variable or multiple imputation)
[11]. Further, in many instances not all variables can be harmonized and constructed for all participating studies, because this might compromise the quality of the constructed variables. Secondly, all environmental data needs to be methodically checked for accuracy, completeness (e.g. missing roads), and geocoding or projection errors (e.g. a road is projected next to the real location of the road) to ensure the validity of the data. Furthermore, there is often a lack of historical data due to rapid changes in geographical information system (GIS) techniques and the tendency to only publish the most recent data by many of the sources publishing geospatial data. Extensive efforts are therefore needed to obtain high quality historical measures of environmental exposures. Thirdly, linking environmental data to cohort data can lead to privacy concerns when not dealt with properly. To prevent this, we developed a process to link the environmental data to cohort data that protects participant privacy by isolating residential addresses from privacy sensitive health data. Finally, integrating data from 10 longitudinal studies requires extensive coordination. Streamlining this process while respecting each study’s guidelines and regulations necessitates considerable time investments and meticulous planning.
MINDMAP is a novel research collaboration which is combining population-based cohort data with publicly available datasets not typically used for ageing and mental well-being research. Integration of various data sources and observational units into a single platform will facilitate multilevel analyses exploring the influence of individual- and area-level determinants of mental well-being. In the end, this infrastructure will help to explain the differences in ageing-related mental and cognitive disorders both within as well as between cities around the world and assess the causal pathways and interactions between the urban environment and the individual determinants of mental well-being and cognitive ageing in older adults.
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