Is rigorous retrospective harmonization possible? Application of the DataSHaPER approach across 53 large studies

Harmonizing the CBCL and SDQ ADHD scores by using linear equating, kernel equating, item response theory and machine learning methods

Introduction

A problem that applied researchers and practitioners often face is the fact that different institutions within research consortia use different scales to evaluate the same construct which makes comparison of the results and pooling challenging. In order to meaningfully pool and compare the scores, the scales should be harmonized. The aim of this paper is to use different test equating methods to harmonize the ADHD scores from Child Behavior Checklist (CBCL) and Strengths and Difficulties Questionnaire (SDQ) and to see which method leads to the result.

Methods

Sample consists of 1551 parent reports of children aged 10-11.5 years from Raine study on both CBCL and SDQ (common persons design). We used linear equating, kernel equating, Item Response Theory (IRT), and the following machine learning methods: regression (linear and ordinal), random forest (regression and classification) and Support Vector Machine (regression and classification). Efficacy of the methods is operationalized in terms of the root-mean-square error (RMSE) of differences between predicted and observed scores in cross-validation.

Results and discussion

Results showed that with single group design, it is the best to use the methods that use item level information and that treat the outcome as interval measurement level (regression approach).

Evaluating the harmonization potential of oral health-related questionnaires in national longitudinal birth and child cohort surveys

BACKGROUND/OBJECTIVES

Effective use of longitudinal study data is challenging because of divergences in the construct definitions and measurement approaches over time, between studies and across disciplines. One approach to overcome these challenges is data harmonization. Data harmonization is a practice used to improve variable comparability and reduce heterogeneity across studies. This study describes the process used to evaluate the harmonization potential of oral health-related variables across each survey wave.

METHODS

National child cohort surveys with similar themes/objectives conducted in the last two decades were selected. The Maelstrom Research Guidelines were followed for harmonization potential evaluation.

RESULTS

Seven nationally representative child cohort surveys were included and questionnaires examined from 50 survey waves. Questionnaires were classified into three domains and fifteen constructs and summarized by age groups. A DataSchema (a list of core variables representing the suitable version of the oral health outcomes and risk factors) was compiled comprising 42 variables. For each study wave, the potential (or not) to generate each DataSchema variable was evaluated. Of the 2100 harmonization status assessments, 543 (26%) were complete. Approximately 50% of the DataSchema variables can be generated across at least four cohort surveys while only 10% (n = 4) variables can be generated across all surveys. For each survey, the DataSchema variables that can be generated ranged between 26% and 76%.

CONCLUSION

Data harmonization can improve the comparability of variables both within and across surveys. For future cohort surveys, the authors advocate more consistency and standardization in survey questionnaires within and between surveys.

Pioneering a Multi-Phase Framework to Harmonize Self-Reported Sleep Data Across Cohorts

STUDY OBJECTIVES

Harmonizing and aggregating data across studies enable pooled analyses that support external validation and enhance replicability and generalizability. However, the multidimensional nature of sleep poses challenges for data harmonization and aggregation. Here we describe and implement our process for harmonizing self-reported sleep data.

METHODS

We established a multi-phase framework to harmonize self-reported sleep data: (1) compile items; (2) group items into domains; (3) harmonize items; and (4) evaluate harmonizability. We applied this process to produce a pooled multi-cohort sample of five United States cohorts plus a separate yet fully harmonized sample from Rotterdam, Netherlands. Sleep and sociodemographic data are described and compared to demonstrate the utility of harmonization and aggregation.

RESULTS

We collected 190 unique self-reported sleep items and grouped them into 15 conceptual domains. Using these domains as guiderails, we developed 14 harmonized items measuring aspects of Satisfaction, Alertness/Sleepiness, Timing, Efficiency, Duration, Insomnia, and Sleep Apnea. External raters determined that 13 of these 14 items had moderate-to-high harmonizability. Alertness/Sleepiness items had lower harmonizability, while continuous, quantitative items (e.g., timing, total sleep time, efficiency) had higher harmonizability. Descriptive statistics identified features that are more consistent (e.g., wake-up time, duration) and more heterogeneous (e.g., time in bed, bedtime) across samples.

CONCLUSIONS

Our process can guide researchers and cohort stewards towards effective sleep harmonization and provides a foundation for further methodological development in this expanding field. Broader national and international initiatives promoting common data elements across cohorts are needed to enhance future harmonization and aggregation efforts.

Statistical assessment of reliability of anthropometric measurements in the multi-site South African National Dietary Intake Survey 2022

BACKGROUND

Anthropometric data quality in large multicentre nutrition surveys is seldom adequately assessed. In preparation for the South African National Dietary Intake Survey (NDIS-2022), this study assessed site leads' and fieldworkers' intra- and inter-rater reliability for measuring weight, length/height, mid-upper arm circumference (MUAC), waist circumference (WC) and calf circumference (CC).

METHODS

Standardised training materials and measurement protocols were developed, and new anthropometric equipment was procured. Following two training rounds (12 site lead teams, 46 fieldworker teams), measurement reliability was assessed for both groups, using repeated measurements of volunteers similar to the survey target population. Reliability was statistically assessed using the technical error of measurement (TEM), relative TEM (%TEM), intra-class correlation coefficient (ICC) and coefficient of reliability (R). Agreement was visualised with Bland-Altman analysis.

RESULTS

By %TEM, the best reliability was achieved for weight (%TEM = 0.260-0.923) and length/height (%TEM = 0.434-0.855), and the poorest for MUAC by fieldworkers (%TEM = 2.592-3.199) and WC (%TEM = 2.353-2.945). Whole-sample ICC and R were excellent ( > 0.90) for all parameters except site leads' CC inter-rater reliability (ICC = 0.896, R = 0.889) and fieldworkers' inter-rater reliability for MUAC in children under two (ICC = 0.851, R = 0.881). Bland-Altman analysis revealed no significant bias except in fieldworkers' intra-rater reliability of length/height measurement in adolescents/adults ( + 0.220 (0.042, 0.400) cm). Reliability was higher for site leads vs. fieldworkers, for intra-rater vs. inter-rater assessment, and for weight and length/height vs. circumference measurements.

CONCLUSION

NDIS-2022 site leads and fieldworkers displayed acceptable reliability in performing anthropometric measurements, highlighting the importance of intensive training and standardised measurement protocols. Ongoing reliability assessment during data collection is recommended.

Longitudinal natural history studies based on real-world data in rare diseases: Opportunity and a novel approach

Growing interest in therapeutic development for rare diseases necessitate a systematic approach to the collection and curation of natural history data that can be applied consistently across this group of heterogenous rare diseases. In this study, we discuss the challenges facing natural history studies for leukodystrophies and detail a novel standardized approach to creating a longitudinal natural history study using existing medical records. Prospective studies are uniquely challenging for rare diseases. Delays in diagnosis and overall rarity limit the timely collection of natural history data. When feasible, prospective studies are often cross-sectional rather than longitudinal and are unlikely to capture pre- or early- symptomatic disease trajectories, limiting their utility in characterizing the full natural history of the disease. Therapeutic development in leukodystrophies is subject to these same obstacles. The Global Leukodystrophy Initiative Clinical Trials Network (GLIA-CTN) comprises of a network of research institutions across the United States, supported by a multi-center biorepository protocol, to map the longitudinal clinical course of disease across leukodystrophies. As part of GLIA-CTN, we developed Standard Operating Procedures (SOPs) that delineated all study processes related to staff training, source documentation, and data sharing. Additionally, the SOP detailed the standardized approach to data extraction including diagnosis, clinical presentation, and medical events, such as age at gastrostomy tube placement. The key variables for extraction were selected through face validity, and common electronic case report forms (eCRF) across leukodystrophies were created to collect analyzable data. To enhance the depth of the data, clinical notes are extracted into "original" and "imputed" encounters, with imputed encounter referring to a historic event (e.g., loss of ambulation 3 months prior). Retrospective Functional Assessments were assigned by child neurologists, using a blinded dual-rater approach and score discrepancies were adjudicated by a third rater. Upon completion of extraction, data source verification is performed. Data missingness was evaluated using statistics. The proposed methodology will enable us to leverage existing medical records to address the persistent gap in natural history data within this unique disease group, allow for assessment of clinical trajectory both pre- and post-formal diagnosis, and promote recruitment of larger cohorts.

Facilitating Harmonization of Variables in Framingham, MESA, ARIC, and REGARDS Studies Through a Metadata Repository

BACKGROUND

High-quality research in cardiovascular prevention, as in other fields, requires inclusion of a broad range of data sets from different sources. Integrating and harmonizing different data sources are essential to increase generalizability, sample size, and representation of understudied populations-strengthening the evidence for the scientific questions being addressed.

METHODS

Here, we describe an effort to build an open-access repository and interactive online portal for researchers to access the metadata and code harmonizing data from 4 well-known cohort studies-the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study, FHS (Framingham Heart Study), MESA (Multi-Ethnic Study of Atherosclerosis), and ARIC (Atherosclerosis Risk in Communities) study. We introduce a methodology and a framework used for preprocessing and harmonizing variables from multiple studies.

RESULTS

We provide a real-case study and step-by-step guidance to demonstrate the practical utility of our repository and interactive web page. In addition to our successful development of such an open-access repository and interactive web page, this exercise in harmonizing data from multiple cohort studies has revealed several key themes. These themes include the importance of careful preprocessing and harmonization of variables, the value of creating an open-access repository to facilitate collaboration and reproducibility, and the potential for using harmonized data to address important scientific questions and disparities in cardiovascular disease research.

CONCLUSIONS

By integrating and harmonizing these large-scale cohort studies, such a repository may improve the statistical power and representation of understudied cohorts, enabling development and validation of risk prediction models, identification and investigation of risk factors, and creating a platform for racial disparities research.

REGISTRATION

URL: https://precision.heart.org/duke-ninds.

Methodological Strategies for Prospective Harmonization of Studies: Application to 10 Distinct Outcomes Studies of Preventive Interventions Targeting Opioid Misuse

The Helping to End Addiction Long-Term (HEAL) Prevention Cooperative (HPC) is rapidly developing 10 distinct evidence-based interventions for implementation in a variety of settings to prevent opioid misuse and opioid use disorder. One HPC objective is to compare intervention impacts on opioid misuse initiation, escalation, severity, and disorder and identify whether any HPC interventions are more effective than others for types of individuals. It provides a rare opportunity to prospectively harmonize measures across distinct outcomes studies. This paper describes the needs, opportunities, strategies, and processes that were used to harmonize HPC data. They are illustrated with a strategy to measure opioid use that spans the spectrum of opioid use experiences (termed involvement) and is composed of common "anchor items" ranging from initiation to symptoms of opioid use disorder. The limitations and opportunities anticipated from this approach to data harmonization are reviewed. Lastly, implications for future research cooperatives and the broader HEAL data ecosystem are discussed.

Constructing the concept of healthy ageing and examining its association with loneliness in older adults

BACKGROUND

World Health Organization (WHO) has defined healthy ageing by highlighting five functional ability domains to (meet basic needs, make decisions, be mobile, build and maintain relationships, and contribute to society), which also emphasized the importance of addressing loneliness as priorities within United Nations Decade of Healthy Ageing initiative. However, the level and determinants of healthy ageing and its association with loneliness are rarely examined. This study aimed to construct a healthy ageing index to verify the WHO healthy ageing framework, measure five domains of functional ability of older adults and examine the relationship between functional ability domains and loneliness.

METHODS

A total of 10,746 older adults from the 2018 China Health and Retirement Longitudinal Study (CHARLS) were included. A healthy ageing index ranging from 0 to 17 was constructed using 17 components related to functional ability domains. Univariate and multivariate logistic regression analyses were utilized to determine the association between loneliness and healthy ageing. The STROBE guidelines with the RECORD statement for observational studies using routinely collected health data were observed.

RESULTS

The factor analysis verified the five functional ability domains for healthy ageing. After adjusting for confounders, being mobile, building and maintaining relationships, and learning, growing and making decisions were significantly associated with lesser loneliness among participants.

CONCLUSIONS

The healthy ageing index of this study can be utilized and further modified with respect to large-scale research with relevant healthy ageing topics. Our findings will support healthcare professionals to provide patient-centered care when identifying their comprehensive abilities and needs.

Substance Use Behaviors Among LGBTQ+ Youth of Color: Identification of the Populations Bearing the Greatest Burden in Three Large Samples

PURPOSE

Research has identified persistent disparities in alcohol, e-cigarette, and marijuana use, by sexual orientation, gender identity, and race/ethnicity. Using an intersectionality framework, the present study analyzes three large datasets to identify intersecting social positions bearing the highest burden of substance use.

METHODS

Data from adolescents in grades 9-12 in three samples (2019 Minnesota Student Survey, 2017-2019 California Healthy Kids Survey, and 2017 National Teen Survey) were harmonized for an analysis (N = 602,470). A Chi-squared Automatic Interaction Detection analysis compared the prevalence of four types of substance use across all combinations of four social positions (six racial/ethnic identities, five sexual orientations, three gender identities, and two sexes assigned at birth). For each substance, 10 intersectional groups with the highest prevalence of use were examined.

RESULTS

In the full sample, 12%-14% of participants reported past 30-day alcohol, e-cigarette, or marijuana use and 7% reported past 30-day binge drinking. Several intersecting marginalized social positions were consistently found to bear a high burden of substance use. For example, transgender and gender diverse (TGD) Latina/x/o young people, particularly those assigned male at birth, were in the high prevalence groups for alcohol use, binge drinking, and marijuana use. Black TGD or gender-questioning youth were commonly in the high prevalence groups.

DISCUSSION

Findings suggest that support, resources, and structural changes specifically tailored to youth with multiple marginalized identities (especially TGD) may be needed. The results argue for intersectional efforts that explicitly address racial/ethnic and cultural differences, while also integrating awareness and understanding of sexual and gender diversity.

Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine

Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication bias, and inaccessibility to the data underlying a publication ('dark data'). Co-analysis of pooled data at the level of individual research participants can offer higher levels of evidence, but this requires that researchers share raw individual participant data (IPD). FAIR (findable, accessible, interoperable, and reusable) data governance principles aim to guide data lifecycle management by providing a framework for actionable data sharing. Here we discuss the implications of FAIR for data harmonization, an essential step for pooling data for IPD analysis. We describe the harmonization-information trade-off, which states that the level of granularity in harmonizing data determines the amount of information lost. Finally, we discuss a framework for managing the trade-off and the levels of harmonization. In the coming era of funder mandates for data sharing, research communities that effectively manage data harmonization will be empowered to harness big data and advanced analytics such as machine learning and artificial intelligence tools, leading to stunning new discoveries that augment our understanding of diseases and their treatments. By elevating scientific data to the status of a first-class citizen of the scientific enterprise, there is strong potential for biomedicine to transition from a narrative publication product orientation to a modern data-driven enterprise where data itself is viewed as a primary work product of biomedical research.

dsSynthetic: synthetic data generation for the DataSHIELD federated analysis system

OBJECTIVE

Platforms such as DataSHIELD allow users to analyse sensitive data remotely, without having full access to the detailed data items (federated analysis). While this feature helps to overcome difficulties with data sharing, it can make it challenging to write code without full visibility of the data. One solution is to generate realistic, non-disclosive synthetic data that can be transferred to the analyst so they can perfect their code without the access limitation. When this process is complete, they can run the code on the real data.

RESULTS

We have created a package in DataSHIELD (dsSynthetic) which allows generation of realistic synthetic data, building on existing packages. In our paper and accompanying tutorial we demonstrate how the use of synthetic data generated with our package can help DataSHIELD users with tasks such as writing analysis scripts and harmonising data to common scales and measures.

New horizons in evidence-based care for older people: individual participant data meta-analysis

Evidence-based decisions on clinical and cost-effectiveness of interventions are ideally informed by meta-analyses of intervention trial data. However, when undertaken, such meta-analyses in ageing research have typically been conducted using standard methods whereby summary (aggregate) data are extracted from published trial reports. Although meta-analysis of aggregate data can provide useful insights into the average effect of interventions within a selected trial population, it has limitations regarding robust conclusions on which subgroups of people stand to gain the greatest benefit from an intervention or are at risk of experiencing harm. Future evidence synthesis using individual participant data from ageing research trials for meta-analysis could transform understanding of the effectiveness of interventions for older people, supporting evidence-based and sustainable commissioning. A major advantage of individual participant data meta-analysis (IPDMA) is that it enables examination of characteristics that predict treatment effects, such as frailty, disability, cognitive impairment, ethnicity, gender and other wider determinants of health. Key challenges of IPDMA relate to the complexity and resources needed for obtaining, managing and preparing datasets, requiring a meticulous approach involving experienced researchers, frequently with expertise in designing and analysing clinical trials. In anticipation of future IPDMA work in ageing research, we are establishing an international Ageing Research Trialists collective, to bring together trialists with a common focus on transforming care for older people as a shared ambition across nations.

Harmonized Phenotypes for Anxiety, Depression, and Attention-Deficit Hyperactivity Disorder (ADHD)

In multi-cohort consortia, the problem often arises that a phenotype is measured using different questionnaires. This study aimed to harmonize scores based on the Child Behaviour Check List (CBCL) and the Strength and Difficulties Questionnaire (SDQ) for anxiety/depression and ADHD. To link the scales, we used parent reports on 1330 children aged 10–11.5 years from the Raine study on both SDQ and CBCL. Harmonization was done based on Item Response Theory. We started from existing CBCL and SDQ scales related to anxiety/depression and ADHD (theoretical approach). Next, we conducted a data-driven approach using factor analysis to validate the theoretical approach. Both approaches yielded similar scales, validating the combination of existing scales. In addition, we studied the impact of harmonized (IRT-based) scores on the statistical power of the results in meta-analytic gene-finding studies. The results showed that the IRT-based harmonized scores increased the statistical power of the results compared to sum scores, even with an equal sample size. These findings can help future researchers to harmonize data from different samples and/or different questionnaires that measure anxiety, depression, and ADHD, in order to obtain the larger sample sizes, to compare research results across subpopulations or to increase generalizability, the validity or statistical power of research results. We recommend using our item parameters to estimate harmonized scores that represent commensurate phenotypes across cohorts, and we explained in detail how other researchers can use our results to harmonize data in their studies.

Development of a data classification system for preterm birth cohort studies: the RECAP Preterm project

Background

The small sample sizes available within many very preterm (VPT) longitudinal birth cohort studies mean that it is often necessary to combine and harmonise data from individual studies to increase statistical power, especially for studying rare outcomes. Curating and mapping data is a vital first step in the process of data harmonisation. To facilitate data mapping and harmonisation across VPT birth cohort studies, we developed a custom classification system as part of the Research on European Children and Adults born Preterm (RECAP Preterm) project in order to increase the scope and generalisability of research and the evaluation of outcomes across the lifespan for individuals born VPT.

Methods

The multidisciplinary consortium of expert clinicians and researchers who made up the RECAP Preterm project participated in a four-phase consultation process via email questionnaire to develop a topic-specific classification system. Descriptive analyses were calculated after each questionnaire round to provide pre- and post- ratings to assess levels of agreement with the classification system as it developed. Amendments and refinements were made to the classification system after each round.

Results

Expert input from 23 clinicians and researchers from the RECAP Preterm project aided development of the classification system’s topic content, refining it from 10 modules, 48 themes and 197 domains to 14 modules, 93 themes and 345 domains. Supplementary classifications for target, source, mode and instrument were also developed to capture additional variable-level information. Over 22,000 individual data variables relating to VPT birth outcomes have been mapped to the classification system to date to facilitate data harmonisation. This will continue to increase as retrospective data items are mapped and harmonised variables are created.

Conclusions

This bespoke preterm birth classification system is a fundamental component of the RECAP Preterm project’s web-based interactive platform. It is freely available for use worldwide by those interested in research into the long term impact of VPT birth. It can also be used to inform the development of future cohort studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01494-5.

Common Data Elements to Facilitate Sharing and Re-use of Participant-Level Data: Assessment of Psychiatric Comorbidity Across Brain Disorders

The Ontario Brain Institute's "Brain-CODE" is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple brain disorders and a wide array of data, including clinical, neuroimaging, and molecular. To help achieve these goals, consensus methodology was used to identify a set of core demographic and clinical variables that should be routinely collected across all participating programs. Establishment of Common Data Elements within Brain-CODE is critical to enable a high degree of consistency in data collection across studies and thus optimize the ability of investigators to analyze pooled participant-level data within and across brain disorders. Results are also presented using selected common data elements pooled across three studies to better understand psychiatric comorbidity in neurological disease (Alzheimer's disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia, and Parkinson's disease).

Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

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Data sharing can address open issues and clinical unmet needs in rare diseases.

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Data curation enhanced the cohort data quality in primary Sjögrens Syndrome (pSS).

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Semantic analysis yielded 7,156 harmonized patients across 21 cohorts in pSS.

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Federated tree ensembles yield explainable AI models for lymphoma development.

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Salivary gland swelling & cryoglobulinemia increase the risk for lymphomagenesis.

For many decades, the clinical unmet needs of primary Sjögren’s Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs.

Data harmonization and data pooling from cohort studies: a practical approach for data management

Data pooling from pre-existing datasets can be useful to increase study sample size and statistical power in order to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies: All Our Families; and the Alberta Pregnancy Outcomes and Nutrition. Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were harmonized or processed under a common format across the datasets considering the frequency of measurement, the timing of measurement, the measurement scale used, and response options. Variables that were completely unmatching could not be harmonized into a single variable. The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies, which permit researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.

Development of a common scale for measuring healthy ageing across the world: results from the ATHLOS consortium

BACKGROUND

Research efforts to measure the concept of healthy ageing have been diverse and limited to specific populations. This diversity limits the potential to compare healthy ageing across countries and/or populations. In this study, we developed a novel measurement scale of healthy ageing using worldwide cohorts.

METHODS

In the Ageing Trajectories of Health-Longitudinal Opportunities and Synergies (ATHLOS) project, data from 16 international cohorts were harmonized. Using ATHLOS data, an item response theory (IRT) model was used to develop a scale with 41 items related to health and functioning. Measurement heterogeneity due to intra-dataset specificities was detected, applying differential item functioning via a logistic regression framework. The model accounted for specificities in model parameters by introducing cohort-specific parameters that rescaled scores to the main scale, using an equating procedure. Final scores were estimated for all individuals and converted to T-scores with a mean of 50 and a standard deviation of 10.

RESULTS

A common scale was created for 343 915 individuals above 18 years of age from 16 studies. The scale showed solid evidence of concurrent validity regarding various sociodemographic, life and health factors, and convergent validity with healthy life expectancy (r = 0.81) and gross domestic product (r = 0.58). Survival curves showed that the scale could also be predictive of mortality.

CONCLUSIONS

The ATHLOS scale, due to its reliability and global representativeness, has the potential to contribute to worldwide research on healthy ageing.

Establishing the value of genomics in medicine: the IGNITE Pragmatic Trials Network

PURPOSE

A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions.

METHODS

The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation.

RESULTS

Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression.

CONCLUSION

IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.

Sickle cell disease is a global prototype for integrative research and healthcare

Differences in health outcomes and treatment responses within and between global populations have been well documented. There is growing recognition of the need to move beyond simple inventories and descriptions of these differences and our linear explanations for them, and gain a better understanding of the multifaceted systems and networks underlying them in order to develop more precise and effective remedies. Typical targets for such integrative research have been common multifactorial diseases. We propose sickle cell disease, one of the most common monogenic diseases, as an ideal candidate for elucidating the complexity of the influences of endogenous and exogenous factors on disease pathophysiology, phenotypic diversity, and variations in responses to treatments at both the individual and population levels. We provide data-informed representations of diverse contributors to sickle cell disease complications that could guide innovative efforts to advance scientific knowledge, clinical practice, and policy formulation related to the disease; help improve outcomes for people worldwide with sickle cell disease; and inform approaches to studying and addressing other diseases.

Gestational Exposure to Phthalates and Social Responsiveness Scores in Children Using Quantile Regression: The EARLI and HOME Studies

Linear regression is often used to estimate associations between chemical exposures and neurodevelopment at the mean of the outcome. However, the potential effect of chemicals may be greater among individuals at the 'tails' of outcome distributions. Here, we investigated distributional effects on the associations between gestational phthalate exposure and child Autism Spectrum Disorder (ASD)-related behaviors using quantile regression. We harmonized data from the Early Autism Risk Longitudinal Investigation (EARLI) (n = 140) Study, an enriched-risk cohort of mothers who had a child with ASD, and the Health Outcomes and Measures of the Environment (HOME) Study (n = 276), a general population cohort. We measured concentrations of 9 phthalate metabolites in urine samples collected twice during pregnancy. Caregivers reported children's ASD-related behaviors using the Social Responsiveness Scale (SRS) at age 3-8 years; higher scores indicate more ASD-related behaviors. In EARLI, associations between phthalate concentrations and SRS scores were predominately inverse or null across SRS score quantiles. In HOME, positive associations of mono-n-butyl phthalate, monobenzyl phthalate, mono-isobutyl phthalate, and di-2-ethylhexyl phthalate concentrations with SRS scores increased in strength from the median to 95th percentile of SRS scores. These results suggest associations between phthalate concentrations and SRS scores may be stronger in individuals with higher SRS scores.

Centralizing environmental datasets to support (inter)national chronic disease research: Values, challenges, and recommendations

Whereas environmental data are increasingly available, it is often not clear how or if datasets are available for health research. Exposure metrics are typically developed for specific research initiatives using disparate exposure assessment methods and no mechanisms are put in place for centralizing, archiving, or distributing environmental datasets. In parallel, potentially vast amounts of environmental data are emerging due to new technologies such as high resolution imagery and machine learning.

Association between self-reported caffeine intake during pregnancy and social responsiveness scores in childhood: The EARLI and HOME studies

Maternal nutrition during gestation has been investigated for its role in child neurodevelopment. However, little is known about the potential impact of gestational caffeine exposure on child autistic behaviors. Here, we assess the relation between maternal caffeine intake during pregnancy and children's behavioral traits related to Autism Spectrum Disorder (ASD). We harmonized data from two pregnancy cohorts, Early Autism Risk Longitudinal Investigation (EARLI) (n = 120), an enriched-risk cohort of mothers who previously had a child with ASD, from Pennsylvania, Maryland, and Northern California (2009-2012), and the Health Outcomes and Measures of the Environment (HOME) Study (n = 269), a general population cohort from Cincinnati, Ohio (2003-2006). Mothers self-reported caffeine intake twice during pregnancy. Caregivers reported child behavioral traits related to ASD using the Social Responsiveness Scale (SRS) when children were aged 3-8 years. Higher scores indicate more ASD-related behaviors. We estimated covariate-adjusted differences in continuous SRS T-scores per interquartile range increase in caffeine intake. Self-reported caffeine intake during pregnancy was positively associated with SRS T-scores among children in EARLI (β: 2.0; 95% CI -0.1, 4.0), but to a lesser extent in HOME (β: 0.6; 95% CI -0.5, 1.6). In HOME, pre-pregnancy body mass index (BMI) modified the association between caffeine intake and SRS T-scores, where more positive associations were observed among women with higher BMIs. Our findings suggest gestational caffeine intake may represent a marker of vulnerability to childhood ASD-related behaviors. Additional studies are warranted to extend these findings.

Associations of self-reported physical activity and anxiety symptoms and status among 7,874 Irish adults across harmonised datasets: a DEDIPAC-study

BACKGROUND

Anxiety is an adaptive response to an objective or perceived threat; however, when symptoms become severe and chronic it that can become a maladaptive anxiety disorder. Limited evidence suggests that physical activity may be associated with prevention against anxiety. This study uses data from The Irish Longitudinal Study on Ageing (TILDA) and The Mitchelstown Cohort Study to investigate cross-sectional associations between physical activity and anxiety symptoms and status among Irish adults.

METHODS

Both datasets were harmonized (n = 7874). The short form International Physical Activity Questionnaire measured physical activity. Participants were classified as meeting World Health Organization physical activity guidelines (≥150 min weekly of moderate intensity physical activity, ≥75 min weekly of vigorous intensity physical activity, or ≥ 600 MET-minutes) or not. They were also divided into three groups based on weekly MET-minutes of moderate-to-vigorous physical activity (Low: 0-599; Moderate: 600-1199; High: ≥1200), and three groups based on weekly minutes of walking (Low: 0-209; Moderate: 210-419; High: 420+). Anxiety symptoms were measured by the Hospital Anxiety and Depression Scale with a score of ≥8 indicating anxiety. Binomial logistic regression, adjusted for relevant confounders examined physical activity-anxiety associations.

RESULTS

Females had higher rates of anxiety than males (28.0% vs 20.0%; p < 0.001). Following adjustment for relevant covariates, meeting physical activity guidelines was associated with 13.5% (95% CI: 2.0-23.7; p = 0.023) lower odds of anxiety. Moderate and High physical activity were associated with 13.5% (- 11.0-32.6; p = 0.254) and 13.6% (1.4-4.2; p = 0.030) lower odds of anxiety compared to Low physical activity, respectively. Moderate and High walking were associated with 2.1% (- 14.5-16.3; p = 0.789) and 5.1% (- 9.3-17.6; p = 0.467) lower odds of anxiety compared to Low walking, respectively.

CONCLUSION

Meeting physical activity guidelines is associated with lower odds of anxiety, but the strength of associations did not increase considerably with increased physical activity levels.

Immediate Breast Reconstruction in The Netherlands and the United States: A Proof-of-Concept to Internationally Compare Quality of Care Using Cancer Registry Data

BACKGROUND

Studies based on large-volume databases have made significant contributions to research on breast cancer surgery. To date, no comparison between large-volume databases has been made internationally. This is the first proof-of-concept study exploring the feasibility of combining two existing operational databases of The Netherlands and the United States, focusing on breast cancer care and immediate breast reconstruction specifically.313/291 METHODS:: The National Breast Cancer Organization The Netherlands Breast Cancer Audit (NBCA) (2011 to 2015) and the U.S. Surveillance, Epidemiology, and End Results (SEER) database (2010 to 2013) were compared on structure and content. Data variables were grouped into general, treatment-specific, cancer-specific, and follow-up variables and were matched. As proof-of-concept, mastectomy and immediate breast reconstruction rates in patients diagnosed with invasive breast cancer or ductal carcinoma in situ were analyzed.

RESULTS

The NBCA included 115 variables and SEER included 112. The NBCA included significantly more treatment-specific variables (n = 46 versus 6), whereas the SEER database included more cancer-specific variables (n = 74 versus 26). In patients diagnosed with breast cancer or ductal carcinoma in situ, immediate breast reconstruction was performed in 19.3 percent and 24.0 percent of the breast cancer cohort and 44.0 percent and 35.3 percent of the ductal carcinoma in situ cohort in the NBCA and SEER, respectively. Immediate breast reconstruction rates increased significantly over time in both data sets.

CONCLUSIONS

This study provides a first overview of available registry data on breast cancer care in The Netherlands and the United States, and revealed limited data on treatment in the United States. Comparison of treatment patterns of immediate breast reconstruction showed interesting differences. The authors advocate the urgency for an international database with alignment of (treatment) variables to improve quality of breast cancer care for patients across the globe.

Harmonization of the Health and Risk Factor Questionnaire data of the Canadian Partnership for Tomorrow Project: a descriptive analysis

BACKGROUND

The Canadian Partnership for Tomorrow Project is a multistudy platform integrating the British Columbia Generations Project, Alberta's Tomorrow Project, the Ontario Health Study, CARTaGENE (Quebec) and the Atlantic Partnership for Tomorrow's Health. This paper describes the process used to harmonize the Health and Risk Factor Questionnaire data and provides an overview of the key information required to properly use the core data set generated.

METHODS

This is a descriptive analysis of the harmonization process that was developed on the basis of the Maelstrom Research guidelines for retrospective harmonization. Core variables (DataSchema) to be generated across cohorts were defined and the potential for cohort-specific data sets to generate the DataSchema variables was assessed. Where relevant, algorithms were developed and applied to process cohort-specific data into the DataSchema format, and information to be provided to data users was documented.

RESULTS

The Health and Risk Factor Questionnaire DataSchema (version 2.0, October 2017) comprised 694 variables. The assessment of harmonization potential for the variables over 12 cohort-specific data sets resulted in 6799 (81.6%) of the variables being considered as harmonizable. A total of 307 017 participants were included in the harmonized data set. Through the cohort data portal, researchers can find information about the definitions of variables, harmonization potential, algorithms applied to generate harmonized variables and participant distributions.

INTERPRETATION

The harmonization process enabled the creation of a unique data set including data on health and risk factors from over 307 000 Canadians. These data, in combination with complementary data sets, can be used to investigate the impact of biological, environmental and behavioural factors on cancer and chronic diseases.

Integrating Clinical and Epidemiologic Data on Allergic Diseases Across Birth Cohorts: A Harmonization Study in the Mechanisms of the Development of Allergy Project

The numbers of international collaborations among birth cohort studies designed to better understand asthma and allergies have increased in the last several years. However, differences in definitions and methods preclude direct pooling of original data on individual participants. As part of the Mechanisms of the Development of Allergy (MeDALL) Project, we harmonized data from 14 birth cohort studies (each with 3-20 follow-up periods) carried out in 9 European countries during 1990-1998 or 2003-2009. The harmonization process followed 6 steps: 1) organization of the harmonization panel; 2) identification of variables relevant to MeDALL objectives (candidate variables); 3) proposal of a definition for each candidate variable (reference definition); 4) assessment of the compatibility of each cohort variable with its reference definition (inferential equivalence) and classification of this inferential equivalence as complete, partial, or impossible; 5) convocation of a workshop to agree on the reference definitions and classifications of inferential equivalence; and 6) preparation and delivery of data through a knowledge management portal. We agreed on 137 reference definitions. The inferential equivalence of 3,551 cohort variables to their corresponding reference definitions was classified as complete, partial, and impossible for 70%, 15%, and 15% of the variables, respectively. A harmonized database was delivered to MeDALL investigators. In asthma and allergy birth cohorts, the harmonization of data for pooled analyses is feasible, and high inferential comparability may be achieved. The MeDALL harmonization approach can be used in other collaborative projects.

Rationale for a Swedish cohort consortium

We herein outline the rationale for a Swedish cohort consortium, aiming to facilitate greater use of Swedish cohorts for world-class research. Coordination of all Swedish prospective population-based cohorts in a common infrastructure would enable more precise research findings and facilitate research on rare exposures and outcomes, leading to better utilization of study participants' data, better return of funders' investments, and higher benefit to patients and populations. We motivate the proposed infrastructure partly by lessons learned from a pilot study encompassing data from 21 cohorts. We envisage a standing Swedish cohort consortium that would drive development of epidemiological research methods and strengthen the Swedish as well as international epidemiological competence, community, and competitiveness.

Developing a synthetic psychosocial stress measure and harmonizing CVD-risk data: a way forward to GxE meta- and mega-analyses

Objectives

Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process.

Results

We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial stress from a dozen studies. Using our synthetic stress algorithm, we constructed a synthetic chronic psychosocial stress measure in nine out of twelve studies where a formal self-rated stress measure was not available. The mega-analytic partial correlation between the stress measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p < 0.0001). This evidence of the validity and the detailed account of our data harmonization approaches demonstrated that it is possible to overcome the inconsistencies in the collection and measurement of human health risk variables.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3595-z) contains supplementary material, which is available to authorized users.

Associations of self-reported physical activity and depression in 10,000 Irish adults across harmonised datasets: a DEDIPAC-study

BACKGROUND

Depression is a prevalent, debilitating, and often recurrent mood disorder for which successful first-line treatments remains limited. The purpose of this study was to investigate the cross-sectional associations between self-reported physical activity (PA) and depressive symptoms and status among Irish adults, using two existing datasets, The Irish Longitudinal Study on Ageing (TILDA) and The Mitchelstown Cohort Study.

METHODS

The two selected databases were pooled (n = 10,122), and relevant variables were harmonized. PA was measured using the short form International Physical Activity Questionnaire. Depressive symptoms were measured by the Center for Epidemiologic Studies Depression (CES-D) questionnaire. Participants were classified as meeting World Health Organization moderate-to-vigorous PA (MVPA) guidelines or not, and divided into tertiles based on weekly minutes of MVPA. A CES-D score of ≥16 indicated elevated depressive symptoms. Data collection were conducted in 2010-2011.

RESULTS

Significantly higher depressive symptoms were reported by females (7.11 ± 7.87) than males (5.74 ± 6.86; p < 0.001). Following adjustment for age, sex, BMI, and dataset, meeting the PA guidelines was associated with 44.7% (95%CI: 35.0 to 52.9; p < 0.001) lower odds of elevated depressive symptoms. Compared to the low PA tertile, the middle and high PA tertiles were associated with 25.2% (95%CI: 8.7 to 38.6; p < 0.01) and 50.8% (95%CI: 40.7 to 59.2; p < 0.001) lower odds of elevated depressive symptoms, respectively.

CONCLUSION

Meeting the PA guidelines is associated with lower odds of elevated depressive symptoms, and increased volumes of MVPA are associated with lower odds of elevated depressive symptoms.

An Algorithm for Creating Virtual Controls Using Integrated and Harmonized Longitudinal Data

We introduce a strategy for creating virtual control groups-cases generated through computer algorithms that, when aggregated, may serve as experimental comparators where live controls are difficult to recruit, such as when programs are widely disseminated and randomization is not feasible. We integrated and harmonized data from eight archived longitudinal adolescent-focused data sets spanning the decades from 1980 to 2010. Collectively, these studies examined numerous psychosocial variables and assessed past 30-day alcohol, cigarette, and marijuana use. Additional treatment and control group data from two archived randomized control trials were used to test the virtual control algorithm. Both randomized controlled trials (RCTs) assessed intentions, normative beliefs, and values as well as past 30-day alcohol, cigarette, and marijuana use. We developed an algorithm that used percentile scores from the integrated data set to create age- and gender-specific latent psychosocial scores. The algorithm matched treatment case observed psychosocial scores at pretest to create a virtual control case that figuratively "matured" based on age-related changes, holding the virtual case's percentile constant. Virtual controls matched treatment case occurrence, eliminating differential attrition as a threat to validity. Virtual case substance use was estimated from the virtual case's latent psychosocial score using logistic regression coefficients derived from analyzing the treatment group. Averaging across virtual cases created group estimates of prevalence. Two criteria were established to evaluate the adequacy of virtual control cases: (1) virtual control group pretest drug prevalence rates should match those of the treatment group and (2) virtual control group patterns of drug prevalence over time should match live controls. The algorithm successfully matched pretest prevalence for both RCTs. Increases in prevalence were observed, although there were discrepancies between live and virtual control outcomes. This study provides an initial framework for creating virtual controls using a step-by-step procedure that can now be revised and validated using other prevention trial data.

Road traffic noise, air pollution and incident cardiovascular disease: A joint analysis of the HUNT, EPIC-Oxford and UK Biobank cohorts

BACKGROUND

This study aimed to investigate the effects of long-term exposure to road traffic noise and air pollution on incident cardiovascular disease (CVD) in three large cohorts: HUNT, EPIC-Oxford and UK Biobank.

METHODS

In pooled complete-case sample of the three cohorts from Norway and the United Kingdom (N = 355,732), 21,081 incident all CVD cases including 5259 ischemic heart disease (IHD) and 2871 cerebrovascular cases were ascertained between baseline (1993-2010) and end of follow-up (2008-2013) through medical record linkage. Annual mean 24-hour weighted road traffic noise (Lden) and air pollution (particulate matter with aerodynamic diameter ≤ 10 μm [PM10], ≤2.5 μm [PM2.5] and nitrogen dioxide [NO2]) exposure at baseline address was modelled using a simplified version of the Common Noise Assessment Methods in Europe (CNOSSOS-EU) and European-wide Land Use Regression models. Individual-level covariate data were harmonised and physically pooled across the three cohorts. Analysis was via Cox proportional hazard model with mutual adjustments for both noise and air pollution and potential confounders.

RESULTS

No significant associations were found between annual mean Lden and incident CVD, IHD or cerebrovascular disease in the overall population except that the association with incident IHD was significant among current-smokers. In the fully adjusted models including adjustment for Lden, an interquartile range (IQR) higher PM10 (4.1 μg/m3) or PM2.5 (1.4 μg/m3) was associated with a 5.8% (95%CI: 2.5%-9.3%) and 3.7% (95%CI: 0.2%-7.4%) higher risk for all incident CVD respectively. No significant associations were found between NO2 and any of the CVD outcomes.

CONCLUSIONS

We found suggestive evidence of a possible association between road traffic noise and incident IHD, consistent with current literature. Long-term particulate air pollution exposure, even at concentrations below current European air quality standards, was significantly associated with incident CVD.

Long-term exposure to road traffic noise, ambient air pollution, and cardiovascular risk factors in the HUNT and lifelines cohorts

Aims

Blood biochemistry may provide information on associations between road traffic noise, air pollution, and cardiovascular disease risk. We evaluated this in two large European cohorts (HUNT3, Lifelines).

Methods and results

Road traffic noise exposure was modelled for 2009 using a simplified version of the Common Noise Assessment Methods in Europe (CNOSSOS-EU). Annual ambient air pollution (PM10, NO2) at residence was estimated for 2007 using a Land Use Regression model. The statistical platform DataSHIELD was used to pool data from 144 082 participants aged ≥20 years to enable individual-level analysis. Generalized linear models were fitted to assess cross-sectional associations between pollutants and high-sensitivity C-reactive protein (hsCRP), blood lipids and for (Lifelines only) fasting blood glucose, for samples taken during recruitment in 2006-2013. Pooling both cohorts, an inter-quartile range (IQR) higher day-time noise (5.1 dB(A)) was associated with 1.1% [95% confidence interval (95% CI: 0.02-2.2%)] higher hsCRP, 0.7% (95% CI: 0.3-1.1%) higher triglycerides, and 0.5% (95% CI: 0.3-0.7%) higher high-density lipoprotein (HDL); only the association with HDL was robust to adjustment for air pollution. An IQR higher PM10 (2.0 µg/m3) or NO2 (7.4 µg/m3) was associated with higher triglycerides (1.9%, 95% CI: 1.5-2.4% and 2.2%, 95% CI: 1.6-2.7%), independent of adjustment for noise. Additionally for NO2, a significant association with hsCRP (1.9%, 95% CI: 0.5-3.3%) was seen. In Lifelines, an IQR higher noise (4.2 dB(A)) and PM10 (2.4 µg/m3) was associated with 0.2% (95% CI: 0.1-0.3%) and 0.6% (95% CI: 0.4-0.7%) higher fasting glucose respectively, with both remaining robust to adjustment for air/noise pollution.

Conclusion

Long-term exposures to road traffic noise and ambient air pollution were associated with blood biochemistry, providing a possible link between road traffic noise/air pollution and cardio-metabolic disease risk.

Risk of acute myocardial infarction with NSAIDs in real world use: bayesian meta-analysis of individual patient data

Objective To characterise the determinants, time course, and risks of acute myocardial infarction associated with use of oral non-steroidal anti-inflammatory drugs (NSAIDs).Design Systematic review followed by a one stage bayesian individual patient data meta-analysis.Data sources Studies from Canadian and European healthcare databases.Review methods Eligible studies were sourced from computerised drug prescription or medical databases, conducted in the general or an elderly population, documented acute myocardial infarction as specific outcome, studied selective cyclo-oxygenase-2 inhibitors (including rofecoxib) and traditional NSAIDs, compared risk of acute myocardial infarction in NSAID users with non-users, allowed for time dependent analyses, and minimised effects of confounding and misclassification bias. Exposure and outcomes Drug exposure was modelled as an indicator variable incorporating the specific NSAID, its recency, duration of use, and dose. The outcome measures were the summary adjusted odds ratios of first acute myocardial infarction after study entry for each category of NSAID use at index date (date of acute myocardial infarction for cases, matched date for controls) versus non-use in the preceding year and the posterior probability of acute myocardial infarction.Results A cohort of 446 763 individuals including 61 460 with acute myocardial infarction was acquired. Taking any dose of NSAIDs for one week, one month, or more than a month was associated with an increased risk of myocardial infarction. With use for one to seven days the probability of increased myocardial infarction risk (posterior probability of odds ratio >1.0) was 92% for celecoxib, 97% for ibuprofen, and 99% for diclofenac, naproxen, and rofecoxib. The corresponding odds ratios (95% credible intervals) were 1.24 (0.91 to 1.82) for celecoxib, 1.48 (1.00 to 2.26) for ibuprofen, 1.50 (1.06 to 2.04) for diclofenac, 1.53 (1.07 to 2.33) for naproxen, and 1.58 (1.07 to 2.17) for rofecoxib. Greater risk of myocardial infarction was documented for higher dose of NSAIDs. With use for longer than one month, risks did not appear to exceed those associated with shorter durations.Conclusions All NSAIDs, including naproxen, were found to be associated with an increased risk of acute myocardial infarction. Risk of myocardial infarction with celecoxib was comparable to that of traditional NSAIDS and was lower than for rofecoxib. Risk was greatest during the first month of NSAID use and with higher doses.

Automatic identification of variables in epidemiological datasets using logic regression

Background

For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable.

Methods

For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated.

Results

In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables.

Conclusions

We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0429-1) contains supplementary material, which is available to authorized users.

Maelstrom Research guidelines for rigorous retrospective data harmonization

Background

It is widely accepted and acknowledged that data harmonization is crucial: in its absence, the co-analysis of major tranches of high quality extant data is liable to inefficiency or error. However, despite its widespread practice, no formalized/systematic guidelines exist to ensure high quality retrospective data harmonization.

Methods

To better understand real-world harmonization practices and facilitate development of formal guidelines, three interrelated initiatives were undertaken between 2006 and 2015. They included a phone survey with 34 major international research initiatives, a series of workshops with experts, and case studies applying the proposed guidelines.

Results

A wide range of projects use retrospective harmonization to support their research activities but even when appropriate approaches are used, the terminologies, procedures, technologies and methods adopted vary markedly. The generic guidelines outlined in this article delineate the essentials required and describe an interdependent step-by-step approach to harmonization: 0) define the research question, objectives and protocol; 1) assemble pre-existing knowledge and select studies; 2) define targeted variables and evaluate harmonization potential; 3) process data; 4) estimate quality of the harmonized dataset(s) generated; and 5) disseminate and preserve final harmonization products.

Conclusions

This manuscript provides guidelines aiming to encourage rigorous and effective approaches to harmonization which are comprehensively and transparently documented and straightforward to interpret and implement. This can be seen as a key step towards implementing guiding principles analogous to those that are well recognised as being essential in securing the foundational underpinning of systematic reviews and the meta-analysis of clinical trials.

Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach

We investigated the effects of both ambient air pollution and traffic noise on adult asthma prevalence, using harmonised data from three European cohort studies established in 2006-2013 (HUNT3, Lifelines and UK Biobank).Residential exposures to ambient air pollution (particulate matter with aerodynamic diameter ≤10 µm (PM10) and nitrogen dioxide (NO2)) were estimated by a pan-European Land Use Regression model for 2007. Traffic noise for 2009 was modelled at home addresses by adapting a standardised noise assessment framework (CNOSSOS-EU). A cross-sectional analysis of 646 731 participants aged ≥20 years was undertaken using DataSHIELD to pool data for individual-level analysis via a "compute to the data" approach. Multivariate logistic regression models were fitted to assess the effects of each exposure on lifetime and current asthma prevalence.PM10 or NO2 higher by 10 µg·m-3 was associated with 12.8% (95% CI 9.5-16.3%) and 1.9% (95% CI 1.1-2.8%) higher lifetime asthma prevalence, respectively, independent of confounders. Effects were larger in those aged ≥50 years, ever-smokers and less educated. Noise exposure was not significantly associated with asthma prevalence.This study suggests that long-term ambient PM10 exposure is associated with asthma prevalence in western European adults. Traffic noise is not associated with asthma prevalence, but its potential to impact on asthma exacerbations needs further investigation.

Predictive Performance of a Fall Risk Assessment Tool for Community-Dwelling Older People (FRAT-up) in 4 European Cohorts

BACKGROUND AND OBJECTIVE

The fall risk assessment tool (FRAT-up) is a tool for predicting falls in community-dwelling older people based on a meta-analysis of fall risk factors. Based on the fall risk factor profile, this tool calculates the individual risk of falling over the next year. The objective of this study is to evaluate the performance of FRAT-up in predicting future falls in multiple cohorts.

METHODS

Information about fall risk factors in 4 European cohorts of older people [Activity and Function in the Elderly (ActiFE), Germany; English Longitudinal Study of Aging (ELSA), England; Invecchiare nel Chianti (InCHIANTI), Italy; Irish Longitudinal Study on Aging (TILDA), Ireland] was used to calculate the FRAT-up risk score in individual participants. Information about falls that occurred after the assessment of the risk factors was collected from subsequent longitudinal follow-ups. We compared the performance of FRAT-up against those of other prediction models specifically fitted in each cohort by calculation of the area under the receiver operating characteristic curve (AUC).

RESULTS

The AUC attained by FRAT-up is 0.562 [95% confidence interval (CI) 0.530-0.594] for ActiFE, 0.699 (95% CI 0.680-0.718) for ELSA, 0.636 (95% CI 0.594-0.681) for InCHIANTI, and 0.685 (95% CI 0.660-0.709) for TILDA. Mean FRAT-up AUC as estimated from meta-analysis is 0.646 (95% CI 0.584-0.708), with substantial heterogeneity between studies. In each cohort, FRAT-up discriminant ability is surpassed, at most, by the cohort-specific risk model fitted on that same cohort.

CONCLUSIONS

We conclude that FRAT-up is a valid approach to estimate risk of falls in populations of community-dwelling older people. However, further studies should be performed to better understand the reasons for the observed heterogeneity across studies and to refine a tool that performs homogeneously with higher accuracy measures across different populations.

Comparison of Standardization Methods for the Harmonization of Phenotype Data: An Application to Cognitive Measures

Standardization procedures are commonly used to combine phenotype data that were measured using different instruments, but there is little information on how the choice of standardization method influences pooled estimates and heterogeneity. Heterogeneity is of key importance in meta-analyses of observational studies because it affects the statistical models used and the decision of whether or not it is appropriate to calculate a pooled estimate of effect. Using 2-stage individual participant data analyses, we compared 2 common methods of standardization, T-scores and category-centered scores, to create combinable memory scores using cross-sectional data from 3 Canadian population-based studies (the Canadian Study on Health and Aging (1991-1992), the Canadian Community Health Survey on Healthy Aging (2008-2009), and the Quebec Longitudinal Study on Nutrition and Aging (2004-2005)). A simulation was then conducted to assess the influence of varying the following items across population-based studies: 1) effect size, 2) distribution of confounders, and 3) the relationship between confounders and the outcome. We found that pooled estimates based on the unadjusted category-centered scores tended to be larger than those based on the T-scores, although the differences were negligible when adjusted scores were used, and that most individual participant data meta-analyses identified significant heterogeneity. The results of the simulation suggested that in terms of heterogeneity, the method of standardization played a smaller role than did different effect sizes across populations and differential confounding of the outcome measure across studies. Although there was general consistency between the 2 types of standardization methods, the simulations identified a number of sources of heterogeneity, some of which are not the usual sources considered by researchers.

Road traffic noise, blood pressure and heart rate: Pooled analyses of harmonized data from 88,336 participants

INTRODUCTION

Exposure to road traffic noise may increase blood pressure and heart rate. It is unclear to what extent exposure to air pollution may influence this relationship. We investigated associations between noise, blood pressure and heart rate, with harmonized data from three European cohorts, while taking into account exposure to air pollution.

METHODS

Road traffic noise exposure was assessed using a European noise model based on the Common Noise Assessment Methods in Europe framework (CNOSSOS-EU). Exposure to air pollution was estimated using a European-wide land use regression model. Blood pressure and heart rate were obtained by trained clinical professionals. Pooled cross-sectional analyses of harmonized data were conducted at the individual level and with random-effects meta-analyses.

RESULTS

We analyzed data from 88,336 participants, across the three participating cohorts (mean age 47.0 (±13.9) years). Each 10dB(A) increase in noise was associated with a 0.93 (95% CI 0.76;1.11) bpm increase in heart rate, but with a decrease in blood pressure of 0.01 (95% CI -0.24;0.23) mmHg for systolic and 0.38 (95% CI -0.53; -0.24) mmHg for diastolic blood pressure. Adjustments for PM10 or NO2 attenuated the associations, but remained significant for DBP and HR. Results for BP differed by cohort, with negative associations with noise in LifeLines, no significant associations in EPIC-Oxford, and positive associations with noise >60dB(A) in HUNT3.

CONCLUSIONS

Our study suggests that road traffic noise may be related to increased heart rate. No consistent evidence for a relation between noise and blood pressure was found.

International Data Sharing in Practice: New Technologies Meet Old Governance

The social structures that govern data/sample release aim to safeguard the confidentiality and privacy of cohort research participants (without whom there would be no data or samples) and enable the realization of societal benefit through optimizing the scientific use of those cohorts. Within collaborations involving multiple cohorts and biobanks, however, the local, national, and supranational institutional and legal guidelines for research (which produce a multiplicity of data access governance structures and guidelines) risk impeding the very science that is the raison d'etre of these consortia. We present an ethnographic study, which examined the epistemic and nonepistemic values driving decisions about data access and their consequences in the context of the pilot of an integrated approach to co-analysis of data. We demonstrate how the potential analytic flexibility offered by this approach was lost under contemporary data access governance. We identify three dominant values: protecting the research participant, protecting the study, and protecting the researcher. These values were both supported by and juxtaposed against a "public good" argument, and each was used as a rationale to both promote and inhibit sharing of data. While protection of the research participants was central to access permissions, decisions were also attentive to the desire of researchers to see their efforts in building population biobanks and cohorts realized in the form of scientific outputs. We conclude that systems for governing and enabling data access in large consortia need to (1) protect disclosure of research participant information or identity, (2) ensure the specific expectations of research participants are met, (3) embody systems of review that are transparent and not compromised by the specific interests of one particular group of stakeholders, and (4) facilitate data access procedures that are timely and efficient. Practical solutions are urgently needed. New approaches to data access governance should be trialed (and formally evaluated) with input from and discussion with stakeholders.

MOLGENIS/connect: a system for semi-automatic integration of heterogeneous phenotype data with applications in biobanks

Motivation: While the size and number of biobanks, patient registries and other data collections are increasing, biomedical researchers still often need to pool data for statistical power, a task that requires time-intensive retrospective integration.

Results: To address this challenge, we developed MOLGENIS/connect, a semi-automatic system to find, match and pool data from different sources. The system shortlists relevant source attributes from thousands of candidates using ontology-based query expansion to overcome variations in terminology. Then it generates algorithms that transform source attributes to a common target DataSchema. These include unit conversion, categorical value matching and complex conversion patterns (e.g. calculation of BMI). In comparison to human-experts, MOLGENIS/connect was able to auto-generate 27% of the algorithms perfectly, with an additional 46% needing only minor editing, representing a reduction in the human effort and expertise needed to pool data.

Availability and Implementation: Source code, binaries and documentation are available as open-source under LGPLv3 from http://github.com/molgenis/molgenis and www.molgenis.org/connect.

Contact: m.a.swertz@rug.nl

Supplementary information:Supplementary data are available at Bioinformatics online.