ReviewJournal of epidemiology and community health2021
Overview of retrospective data harmonisation in the MINDMAP project: process and results.
Review in Journal of epidemiology and community health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methodological quality of network meta-analyses of Chinese herbal medicine could be improved: an evidence mapping.Naunyn-Schmiedeberg's archives of pharmacology · 2026Pooled it
- Harmonising Depression and Anxiety Measures Across International Cohorts: A Framework Validated Using Measurement Invariance.International journal of methods in psychiatric research · 2026Article
- Statistical methods for retrospective harmonization of longitudinal epidemiological data: a scoping review.European journal of epidemiology · 2026Article
- Beyond Screen Time: A Measurement Framework for Behavioral Exposures in Childhood Myopia.Life (Basel, Switzerland) · 2026Review
- Unlocking the Power of Data Harmonization in Environmental Health Sciences: A Comprehensive Exploration of Significance, Use Cases, and Recommendations for Standardization Efforts.Environmental health perspectives · 2026Review
- Air pollution in relation to brain health indicators and global cognitive functioning in people with cardiovascular disorders along the heart-brain axis.Cerebral circulation - cognition and behavior · 2026Article
- Co-RESPOND: a federated network of cohorts on mental health and adversity during the COVID-19 pandemic. Challenges, solutions and recommendations for retrospective data harmonization.European journal of psychotraumatology · 2025Observational
- Association Between Trunk Fat Mass Index and Diabetes in a Multinational Population.Mayo Clinic proceedings. Innovations, quality & outcomes · 2025Article
- CDEMapper: enhancing National Institutes of Health common data element use with large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- The Longitudinal Aging Study Amsterdam: design and cohort update 2025.European journal of epidemiology · 2025Article
- Article
- A natural language processing approach to support biomedical data harmonization: Leveraging large language models.PloS one · 2025Article
- psHarmonize: Facilitating reproducible large-scale pre-statistical data harmonization and documentation in R.Patterns (New York, N.Y.) · 2024Article
- A General Primer for Data Harmonization.Scientific data · 2024Article
- Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration.BMC medical informatics and decision making · 2023Article
- HUNT for better public health.Scandinavian journal of public health · 2022Article
- Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
backgroundThe MINDMAP project implemented a multinational data infrastructure to investigate the direct and interactive effects of urban environments and individual determinants of mental well-being and cognitive function in ageing populations. Using a rigorous process involving multiple teams of experts, longitudinal data from six cohort studies were harmonised to serve MINDMAP objectives. This article documents the retrospective data harmonisation process achieved based on the Maelstrom Research approach and provides a descriptive analysis of the harmonised data generated.
methodsA list of core variables (the DataSchema) to be generated across cohorts was first defined, and the potential for cohort-specific data sets to generate the DataSchema variables was assessed. Where relevant, algorithms were developed to process cohort-specific data into DataSchema format, and information to be provided to data users was documented. Procedures and harmonisation decisions were thoroughly documented.
resultsThe MINDMAP DataSchema (v2.0, April 2020) comprised a total of 2841 variables (993 on individual determinants and outcomes, 1848 on environmental exposures) distributed across up to seven data collection events. The harmonised data set included 220 621 participants from six cohorts (10 subpopulations). Harmonisation potential, participant distributions and missing values varied across data sets and variable domains.
conclusionThe MINDMAP project implemented a collaborative and transparent process to generate a rich integrated data set for research in ageing, mental well-being and the urban environment. The harmonised data set supports a range of research activities and will continue to be updated to serve ongoing and future MINDMAP research needs.
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