Publications by authors named "Dany Doiron"

29 Publications

  • Page 1 of 1

Ambient air pollution exposure and chronic bronchitis in the Lifelines cohort.

Thorax 2021 Jan 28. Epub 2021 Jan 28.

Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK.

Background: Few large studies have assessed the relationship of long-term ambient air pollution exposure with the prevalence and incidence of symptoms of chronic bronchitis and cough.

Methods: We leveraged Lifelines cohort data on 132 595 (baseline) and 65 009 (second assessment) participants linked to ambient air pollution estimates. Logistic regression models adjusted for sex, age, educational attainment, body mass index, smoking status, pack-years smoking and environmental tobacco smoke at home were used to assess associations of air pollution with prevalence and incidence of chronic bronchitis (winter cough and sputum almost daily for ≥3 months/year), chronic cough (winter cough almost daily for ≥3 months/year) and prevalence of cough and sputum symptoms, irrespective of duration.

Results: Associations were seen for all pollutants for prevalent cough or sputum symptoms. However, for prevalent and incident chronic bronchitis, statistically significant associations were seen for nitrogen dioxide (NO) and black carbon (BC) but not for fine particulate matter (PM). For prevalent chronic bronchitis, associations with NO showed OR: 1.05 (95% CI: 1.02 to 1.08) and with BC OR: 1.06 (95% CI: 1.03 to 1.09) expressed per IQR; corresponding results for incident chronic bronchitis were NO OR: 1.07 (95% CI: 1.02 to 1.13) and BC OR: 1.07 (95% CI: 1.02 to 1.13). In subgroup analyses, slightly stronger associations were observed among women, never smokers and younger individuals.

Conclusion: This is the largest analysis to date to examine cross-sectional and longitudinal associations between ambient air pollution and chronic bronchitis. NO and BC air pollution was associated with increased odds of prevalent and incident chronic bronchitis.
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http://dx.doi.org/10.1136/thoraxjnl-2020-216142DOI Listing
January 2021

Green spaces, subjective health and depressed affect in middle-aged and older adults: a cross-country comparison of four European cohorts.

J Epidemiol Community Health 2021 Jan 26. Epub 2021 Jan 26.

Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.

Background: Studies on associations between urban green space and mental health have yielded mixed results. This study examines associations of green space exposures with subjective health and depressed affect of middle-aged and older adults in four European cohorts.

Methods: Data came from four Western-European and Central-European ageing cohorts harmonised as part of the Mindmap project, comprising 16 189 adults with an average age of 50-71 years. Green space exposure was based on the distance to the nearest green space and the amount of green space within 800 m buffers around residential addresses. Cohort-specific and one-step individual participant data (IPD) meta-analyses were used to examine associations of green space exposures with subjective health and depressed affect.

Results: The amount of green spaces within 800 m buffers was lowest for Residential Environment and CORonary heart Disease (Paris, 15.0 hectares) and highest for Health, Alcohol and Psychosocial factors In Eastern Europe (Czech Republic, 35.9 hectares). IPD analyses indicated no evidence of an association between the distance to the nearest green space and depressed affect (OR 0.98, 95% CI 0.96 to 1.00) or good self-rated health (OR 1.01, 95% CI 0.99 to 1.02). Likewise, the amount of green space within 800 m buffers did not predict depressed affect (OR 0.98, 95% CI 0.96 to 1.00) or good self-rated health (OR 1.01, 95% CI 0.99 to 1.02). Findings were consistent across all cohorts.

Conclusions: Data from four European ageing cohorts provide no support for the hypothesis that green space exposure is associated with subjective health or depressed affect. While longitudinal evidence is required, these findings suggest that green space may be less important for older urban residents.
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http://dx.doi.org/10.1136/jech-2020-214257DOI Listing
January 2021

Overview of retrospective data harmonisation in the MINDMAP project: process and results.

J Epidemiol Community Health 2020 Nov 12. Epub 2020 Nov 12.

Maelstrom Research, Research Institute of the McGill University Health Centre, Montreal, Canada.

Background: The 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.

Methods: A 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.

Results: The 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.

Conclusion: The 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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http://dx.doi.org/10.1136/jech-2020-214259DOI Listing
November 2020

Social and physical neighbourhood characteristics and loneliness among older adults: results from the MINDMAP project.

J Epidemiol Community Health 2020 Nov 5. Epub 2020 Nov 5.

Department of Epidemiology and Biostatistics, Amsterdam UMC - Locatie VUMC, Amsterdam, The Netherlands.

Background: Loneliness is associated with several adverse mental and physical health outcomes in older adults. Previous studies have shown that a variety of individual-level and perceived area-level characteristics are associated with loneliness. This study examined the associations of objectively measured social and physical neighbourhood characteristics with loneliness.

Methods: We used cross-sectional data from 1959 older adults (63-98 years) who participated in the Longitudinal Ageing Study Amsterdam (LASA; wave 2011/12) and the Health and Living Conditions of the Population of Eindhoven and Surroundings study (GLOBE; wave 2014) in the Netherlands. Study-specific loneliness scores were harmonised across both cohort studies and divided into tertiles denoting low, medium and high levels of loneliness. Objectively measured neighbourhood characteristics, including area-level percentages of low educated residents, social security beneficiaries and unoccupied dwellings, average income, crime levels and land use mix, were linked to individual-level data. Multinomial logistic regression analyses were conducted to examine the associations of interest.

Results: There was no statistical evidence for an association of the included neighbourhood characteristics with loneliness. Although not statistically significant, the observed associations suggested that participants living in neighbourhoods with more heterogeneous land use mix were less likely to have a medium and high level of loneliness than those living in more homogeneous neighbourhoods in terms of land use mix (OR=0.54, 95% CI=0.18-1.67; OR=0.67, 95% CI=0.21-2.11).

Conclusion: The results indicate that the included objectively measured social and physical neighbourhood characteristics are not associated with loneliness in old age.
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http://dx.doi.org/10.1136/jech-2020-214217DOI Listing
November 2020

Gender, marital and educational inequalities in mid- to late-life depressive symptoms: cross-cohort variation and moderation by urbanicity degree.

J Epidemiol Community Health 2020 Nov 5. Epub 2020 Nov 5.

Research Department of Epidemiology and Public Health, University College London, London, UK.

Background: Although ageing populations are increasingly residing in cities, it is unknown whether depression inequalities are moderated by urbanicity degree. We estimated gender, marital and educational inequalities in depressive symptoms among older European and Canadian adults, and examined whether higher levels of urbanicity, captured by population density, heightened these inequalities.

Methods: Harmonised cross-sectional data on 97 826 adults aged ≥50 years from eight cohorts were used. Prevalence ratios (PRs) were calculated for probable depression, depressed affect and depressive symptom severity by gender, marital status and education within each cohort, and combined using random-effects meta-analysis. Using a subsample of 73 123 adults from six cohorts with available data on population density, we tested moderating effects measured by the number of residents per square kilometre.

Results: The pooled PRs for probable depression by female gender, unmarried or non-cohabitating status and low education were 1.48 (95% CI 1.28 to 1.72), 1.44 (95% CI 1.29 to 1.61) and 1.29 (95% CI 1.18 to 1.41), respectively. PRs for depressed affect and high symptom severity were broadly similar. Except for one Dutch cohort with findings in an unexpected direction, there was no evidence that population density modified depressive symptom inequalities.

Conclusions: Despite cross-cohort variation in gender, marital status and educational inequalities in depressive symptoms, there was weak evidence that these inequalities differed by levels of population density.
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http://dx.doi.org/10.1136/jech-2020-214241DOI Listing
November 2020

Impact of road traffic noise on obesity measures: Observational study of three European cohorts.

Environ Res 2020 12 15;191:110013. Epub 2020 Aug 15.

Department of Public Health and Nursing, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway; Department of Research and Development, Levanger Hospital, Nord-Trøndelag Hospital Trust, Norway.

Background: Environmental stressors such as transport noise may contribute to development of obesity through increased levels of stress hormones, sleep deprivation and endocrine disruption. Epidemiological evidence supporting an association of road traffic noise with obesity markers is still relatively scant and confined to certain geographical regions. We aimed to examine the cross-sectional associations between road traffic noise and obesity markers in three large European cohorts involving nearly 500,000 individuals.

Methods: Three population-based cohorts (UK Biobank, Lifelines, HUNT3) were established between 2006 and 2013 in the UK, the Netherlands and Norway respectively. For all three cohorts, residential 24-h road traffic noise (Lden) for 2009 was modelled from a standardised European noise assessment framework. Residential exposures to NO2 for 2007 and PM2.5 for 2010 were estimated from Europe-wide land use regression models. Obesity markers including body mass index and waist circumference were measured at recruitment. Obesity and central obesity status were subsequently derived. Regression models were fitted in each cohort, adjusting for a harmonised set of demographic and lifestyle covariates, with further adjustments for air pollution in the main model.

Results: The main analyses included 412,934 participants of UK Biobank, 61,032 of Lifelines and 30,305 of HUNT3, with a mean age of 43-56 years and Lden ranging 42-89 dB(A) across cohorts. In UK Biobank, per 10 dB(A) higher of Lden: BMI was higher by 0.14kg/m2 (95%CI: 0.11-0.18), waist circumference higher by 0.27 cm (95%CI: 0.19-0.35), odds of obesity was 1.06 (95%CI: 1.04-1.08) and of central obesity was 1.05 (95%CI: 1.04-1.07). These associations were robust to most other sensitivity analyses but attenuated by further adjustment of PM2.5 or area-level socioeconomic status. Associations were more pronounced among women, those with low physical activity, higher household income or hearing impairment. In HUNT3, associations were observed for obesity or central obesity status among those exposed to Lden greater than 55 dB(A). In contrast, no or negative associations were observed in the Lifelines cohort.

Conclusions: This largest study to date providing mixed findings on impacts of long-term exposure to road traffic noise on obesity, which necessitates future analyses using longitudinal data to further investigate this potentially important epidemiological link.
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http://dx.doi.org/10.1016/j.envres.2020.110013DOI Listing
December 2020

Healthy built environment: Spatial patterns and relationships of multiple exposures and deprivation in Toronto, Montreal and Vancouver.

Environ Int 2020 10 30;143:106003. Epub 2020 Jul 30.

Southern Ontario Centre for Atmospheric Aerosol Research, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.

Background: Various aspects of the urban environment and neighbourhood socio-economic status interact with each other to affect health. Few studies to date have quantitatively assessed intersections of multiple urban environmental factors and their distribution across levels of deprivation.

Objectives: To explore the spatial patterns of urban environmental exposures within three large Canadian cities, assess how exposures are distributed across socio-economic deprivation gradients, and identify clusters of favourable or unfavourable environmental characteristics.

Methods: We indexed nationally standardized estimates of active living friendliness (i.e. "walkability"), NO air pollution, and greenness to 6-digit postal codes within the cities of Toronto, Montreal and Vancouver. We compared the distribution of within-city exposure tertiles across quintiles of material deprivation. Tertiles of each exposure were then overlaid with each other in order to identify potentially favorable (high walkability, low NO, high greenness) and unfavorable (low walkability, high NO, and low greenness) environments.

Results: In all three cities, high walkability was more common in least deprived areas and less prevalent in highly deprived areas. We also generally saw a greater prevalence of postal codes with high vegetation indices and low NO in areas with low deprivation, and a lower greenness prevalence and higher NO concentrations in highly deprived areas, suggesting environmental inequity is occurring. Our study showed that relatively few postal codes were simultaneously characterized by desirable or undesirable walkability, NOand greenness tertiles.

Discussion: Spatial analyses of multiple standardized urban environmental factors such as the ones presented in this manuscript can help refine municipal investments and policy priorities. This study illustrates a methodology to prioritize areas for interventions that increase active living and exposure to urban vegetation, as well as lower air pollution. Our results also highlight the importance of considering the intersections between the built environment and socio-economic status in city planning and urban public health decision-making.
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http://dx.doi.org/10.1016/j.envint.2020.106003DOI Listing
October 2020

Air pollution, lung function and COPD: results from the population-based UK Biobank study.

Eur Respir J 2019 07 25;54(1). Epub 2019 Jul 25.

Dept of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Ambient air pollution increases the risk of respiratory mortality, but evidence for impacts on lung function and chronic obstructive pulmonary disease (COPD) is less well established. The aim was to evaluate whether ambient air pollution is associated with lung function and COPD, and explore potential vulnerability factors.We used UK Biobank data on 303 887 individuals aged 40-69 years, with complete covariate data and valid lung function measures. Cross-sectional analyses examined associations of land use regression-based estimates of particulate matter (particles with a 50% cut-off aerodynamic diameter of 2.5 and 10 µm: PM and PM, respectively; and coarse particles with diameter between 2.5 μm and 10 μm: PM) and nitrogen dioxide (NO) concentrations with forced expiratory volume in 1 s (FEV), forced vital capacity (FVC), the FEV/FVC ratio and COPD (FEV/FVC
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http://dx.doi.org/10.1183/13993003.02140-2018DOI Listing
July 2019

Fostering population-based cohort data discovery: The Maelstrom Research cataloguing toolkit.

PLoS One 2018 24;13(7):e0200926. Epub 2018 Jul 24.

Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada.

Background: The lack of accessible and structured documentation creates major barriers for investigators interested in understanding, properly interpreting and analyzing cohort data and biological samples. Providing the scientific community with open information is essential to optimize usage of these resources. A cataloguing toolkit is proposed by Maelstrom Research to answer these needs and support the creation of comprehensive and user-friendly study- and network-specific web-based metadata catalogues.

Methods: Development of the Maelstrom Research cataloguing toolkit was initiated in 2004. It was supported by the exploration of existing catalogues and standards, and guided by input from partner initiatives having used or pilot tested incremental versions of the toolkit.

Results: The cataloguing toolkit is built upon two main components: a metadata model and a suite of open-source software applications. The model sets out specific fields to describe study profiles; characteristics of the subpopulations of participants; timing and design of data collection events; and datasets/variables collected at each data collection event. It also includes the possibility to annotate variables with different classification schemes. When combined, the model and software support implementation of study and variable catalogues and provide a powerful search engine to facilitate data discovery.

Conclusions: The Maelstrom Research cataloguing toolkit already serves several national and international initiatives and the suite of software is available to new initiatives through the Maelstrom Research website. With the support of new and existing partners, we hope to ensure regular improvements of the toolkit.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0200926PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6057635PMC
January 2019

MINDMAP: establishing an integrated database infrastructure for research in ageing, mental well-being, and the urban environment.

BMC Public Health 2018 01 19;18(1):158. Epub 2018 Jan 19.

Department of Public Health, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000, CA, Rotterdam, The Netherlands.

Background: Urbanization and ageing have important implications for public mental health and well-being. 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 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.

Methods: 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. Individual-level data is derived from baseline and follow-up measurements of ten participating cohort studies and provides information on mental well-being outcomes, sociodemographic variables, health behaviour characteristics, social factors, measures of frailty, physical function indicators, and chronic conditions, as well as blood derived clinical biochemistry-based biomarkers and genetic biomarkers. Area-level information on physical environment characteristics (e.g. green spaces, transportation), socioeconomic and sociodemographic characteristics (e.g. neighbourhood income, residential segregation, residential density), and social environment characteristics (e.g. social cohesion, criminality) and national and urban social policies is derived from publically available sources such as geoportals and administrative databases. The linkage, harmonization, and analysis of data from different sources are being carried out using piloted tools to optimize the validity of the research results and transparency of the methodology.

Discussion: MINDMAP is a novel research collaboration that 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 help to explain the differences in ageing-related mental and cognitive disorders both within as well as between cities in Europe, the US, Canada, and Russia and to 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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http://dx.doi.org/10.1186/s12889-018-5031-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5775623PMC
January 2018

The Canadian Urban Environmental Health Research Consortium - a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health.

BMC Public Health 2018 01 8;18(1):114. Epub 2018 Jan 8.

Research Institute of McGill University Health Centre, Montreal, Canada.

Background: Multiple external environmental exposures related to residential location and urban form including, air pollutants, noise, greenness, and walkability have been linked to health impacts or benefits. The Canadian Urban Environmental Health Research Consortium (CANUE) was established to facilitate the linkage of extensive geospatial exposure data to existing Canadian cohorts and administrative health data holdings. We hypothesize that this linkage will enable investigators to test a variety of their own hypotheses related to the interdependent associations of built environment features with diverse health outcomes encompassed by the cohorts and administrative data.

Methods: We developed a protocol for compiling measures of built environment features that quantify exposure; vary spatially on the urban and suburban scale; and can be modified through changes in policy or individual behaviour to benefit health. These measures fall into six domains: air quality, noise, greenness, weather/climate, and transportation and neighbourhood factors; and will be indexed to six-digit postal codes to facilitate merging with health databases. Initial efforts focus on existing data and include estimates of air pollutants, greenness, temperature extremes, and neighbourhood walkability and socioeconomic characteristics. Key gaps will be addressed for noise exposure, with a new national model being developed, and for transportation-related exposures, with detailed estimates of truck volumes and diesel emissions now underway in selected cities. Improvements to existing exposure estimates are planned, primarily by increasing temporal and/or spatial resolution given new satellite-based sensors and more detailed national air quality modelling. Novel metrics are also planned for walkability and food environments, green space access and function and life-long climate-related exposures based on local climate zones. Critical challenges exist, for example, the quantity and quality of input data to many of the models and metrics has changed over time, making it difficult to develop and validate historical exposures.

Discussion: CANUE represents a unique effort to coordinate and leverage substantial research investments and will enable a more focused effort on filling gaps in exposure information, improving the range of exposures quantified, their precision and mechanistic relevance to health. Epidemiological studies may be better able to explore the common theme of urban form and health in an integrated manner, ultimately contributing new knowledge informing policies that enhance healthy urban living.
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http://dx.doi.org/10.1186/s12889-017-5001-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5759244PMC
January 2018

Software Application Profile: Opal and Mica: open-source software solutions for epidemiological data management, harmonization and dissemination.

Int J Epidemiol 2017 10;46(5):1372-1378

Ontario Institute for Cancer Research, Toronto, ON, Canada.

Motivation: Improving the dissemination of information on existing epidemiological studies and facilitating the interoperability of study databases are essential to maximizing the use of resources and accelerating improvements in health. To address this, Maelstrom Research proposes Opal and Mica, two inter-operable open-source software packages providing out-of-the-box solutions for epidemiological data management, harmonization and dissemination.

Implementation: Opal and Mica are two standalone but inter-operable web applications written in Java, JavaScript and PHP. They provide web services and modern user interfaces to access them.

General Features: Opal allows users to import, manage, annotate and harmonize study data. Mica is used to build searchable web portals disseminating study and variable metadata. When used conjointly, Mica users can securely query and retrieve summary statistics on geographically dispersed Opal servers in real-time. Integration with the DataSHIELD approach allows conducting more complex federated analyses involving statistical models.

Availability: Opal and Mica are open-source and freely available at [www.obiba.org] under a General Public License (GPL) version 3, and the metadata models and taxonomies that accompany them are available under a Creative Commons licence.
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http://dx.doi.org/10.1093/ije/dyx180DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5837212PMC
October 2017

Residential Air Pollution and Associations with Wheeze and Shortness of Breath in Adults: A Combined Analysis of Cross-Sectional Data from Two Large European Cohorts.

Environ Health Perspect 2017 09 29;125(9):097025. Epub 2017 Sep 29.

MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London , London, UK.

Background: Research examining associations between air pollution exposure and respiratory symptoms in adults has generally been inconclusive. This may be related in part to sample size issues, which also preclude analysis in potentially vulnerable subgroups.

Objectives: We estimated associations between air pollution exposures and the prevalence of wheeze and shortness of breath using harmonized baseline data from two very large European cohorts, Lifelines (2006-2013) and UK Biobank (2006-2010). Our aim was also to determine whether the relationship between air pollution and respiratory symptom prevalence differed between individuals with different characteristics.

Methods: Cross-sectional analyses explored associations between prevalence of self-reported wheeze and shortness of breath and annual mean particulate matter with aerodynamic diameter <2.5μm, 2.5-10μm, and <10μm (PM2.5, PMcoarse, and PM10, respectively) and nitrogen dioxide (NO2) concentrations at place of residence using logistic regression. Subgroup analyses and tests for interaction were performed for age, sex, smoking status, household income, obesity status, and asthma status.

Results: All PM exposures were associated with respiratory symptoms based on single-pollutant models, with the largest associations seen for PM2.5 with prevalence of wheezing {odds ratio (OR)=1.16 per 5μg/m³ [95% confidence interval (CI): 1.11, 1.21]} and shortness of breath [OR=1.61 per 5μg/m³ (95% CI: 1.45, 1.78)]. The association between shortness of breath and a 5-μg/m³ increment in PM2.5 was significantly higher for individuals from lower-[OR=1.73 (95% CI: 1.52, 1.97)] versus higher-income households [OR=1.31 (95% CI: 1.11, 1.55); p-interaction=0.005), whereas the association between PM2.5 and wheeze was limited to lower-income participants [OR=1.30 (95% CI: 1.22, 1.38) vs. OR=1.02; (95% CI: 0.96, 1.08); p-interaction<0.001]. Exposure to NO2 also showed positive associations with wheeze and shortness of breath.

Conclusion: Exposure to PM and NO2 air pollution was associated with the prevalence of wheeze and shortness of breath in this large study, with stronger associations between PM2.5 and both outcomes among lower- versus higher-income participants. https://doi.org/10.1289/EHP1353.
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http://dx.doi.org/10.1289/EHP1353DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5915193PMC
September 2017

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

Eur Heart J 2017 Aug;38(29):2290-2296

Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG, London, UK.

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.
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http://dx.doi.org/10.1093/eurheartj/ehx263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5837618PMC
August 2017

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

Eur Respir J 2017 01 11;49(1). Epub 2017 Jan 11.

MRC-PHE Centre for Environment and Health, Dept of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

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 (PM) and nitrogen dioxide (NO)) 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.PM or NO higher by 10 µg·m 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 PM 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.
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http://dx.doi.org/10.1183/13993003.02127-2015DOI Listing
January 2017

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

Am J Epidemiol 2016 11;184(10):770-778

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.
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http://dx.doi.org/10.1093/aje/kww098DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5141949PMC
November 2016

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

Environ Res 2016 Nov 28;151:804-813. Epub 2016 Sep 28.

University of Groningen, University Medical Center Groningen, Departments of Psychiatry and Internal Medicine, Groningen, The Netherlands.

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 PM or NO 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.
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http://dx.doi.org/10.1016/j.envres.2016.09.014DOI Listing
November 2016

Maelstrom Research guidelines for rigorous retrospective data harmonization.

Int J Epidemiol 2017 02;46(1):103-105

University of Bristol, D2K Research Group, School of Social and Community Medicine, Bristol, UK.

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.
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http://dx.doi.org/10.1093/ije/dyw075DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407152PMC
February 2017

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

Bioinformatics 2016 07 21;32(14):2176-83. Epub 2016 Mar 21.

Department of Genetics, University Medical Center Groningen, Genomics Coordination Center, University of Groningen, Groningen, The Netherlands Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

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.
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http://dx.doi.org/10.1093/bioinformatics/btw155DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937195PMC
July 2016

Statistical approaches to harmonize data on cognitive measures in systematic reviews are rarely reported.

J Clin Epidemiol 2015 Feb 8;68(2):154-62. Epub 2014 Dec 8.

Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. Electronic address:

Objectives: To identify statistical methods for harmonization, the procedures aimed at achieving the comparability of previously collected data, which could be used in the context of summary data and individual participant data meta-analysis of cognitive measures.

Study Design And Setting: Environmental scan methods were used to conduct two reviews to identify (1) studies that quantitatively combined data on cognition and (2) general literature on statistical methods for data harmonization. Search results were rapidly screened to identify articles of relevance.

Results: All 33 meta-analyses combining cognition measures either restricted their analyses to a subset of studies using a common measure or combined standardized effect sizes across studies; none reported their harmonization steps before producing summary effects. In the second scan, three general classes of statistical harmonization models were identified (1) standardization methods, (2) latent variable models, and (3) multiple imputation models; few publications compared methods.

Conclusion: Although it is an implicit part of conducting a meta-analysis or pooled analysis, the methods used to assess inferential equivalence of complex constructs are rarely reported or discussed. Progress in this area will be supported by guidelines for the conduct and reporting of the data harmonization and integration and by evaluating and developing statistical approaches to harmonization.
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http://dx.doi.org/10.1016/j.jclinepi.2014.09.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4685455PMC
February 2015

DataSHIELD: taking the analysis to the data, not the data to the analysis.

Int J Epidemiol 2014 Dec 26;43(6):1929-44. Epub 2014 Sep 26.

School of Social and Community Medicine, University of Bristol, Bristol, UK, Maelstrom Research Group, Research Institute of the McGill University Health Centre, McGill University, Montreal, Canada, Norwegian Institute of Public Health, Oslo, Norway, Department Statistical Science, University College London, London, UK, Department of Infection, Immunity and Inflammation, Health Sciences, University of Leicester, Leicester, UK, Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland, Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland, Department of Health Sciences, University of Leicester, Leicester, UK, Department of Sociology, University of Leicester, Leicester, UK, Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands, Institut für Community Medicine, University Medicine of Greifswald, Greifswald, Germany, International Prevention Research Institute, Lyon, France, Telethon Kids Institute, University of Western Australia, Perth, WA, Australia, School of Geosciences, University of Edinburgh, Edinburgh, UK, Norwegian University of Science and Technology, Levanger, Norway, HRB Centre for Diet and Health Research, Department of Epidemiology and Public Health, University College Cork, Cork, Ireland, Research Unit of Molecular Epidemiology, Research Center for Environmental Health, Neuherberg, Germany, MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK, University of Tartu, Estonian Genome Center, Tartu, Estonia, University Medical Center Groningen, Medical Statistics, Groningen, The Netherlands, Centre of Genomics and Policy, McGill University, Montreal, Canada, University Medical Center Groningen, LifeLines Cohort Study, Groningen, The Netherlands, Department of Endocrinology, University Medical Center Groningen, Groningen, The Netherlands, School of Social and Community Medicine, University of Bristol, Bristol, UK and Onta

Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK's proposed 'care.data' initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data.

Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC.

Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach.

Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property-the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.
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http://dx.doi.org/10.1093/ije/dyu188DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4276062PMC
December 2014

The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies.

BMC Endocr Disord 2014 Feb 1;14. Epub 2014 Feb 1.

Department of Endocrinology, University of Groningen, University Medical Center Groningen, HPC AA31, P,O, Box 30001, Groningen RB 9700, The Netherlands.

Background: Not all obese subjects have an adverse metabolic profile predisposing them to developing type 2 diabetes or cardiovascular disease. The BioSHaRE-EU Healthy Obese Project aims to gain insights into the consequences of (healthy) obesity using data on risk factors and phenotypes across several large-scale cohort studies. Aim of this study was to describe the prevalence of obesity, metabolic syndrome (MetS) and metabolically healthy obesity (MHO) in ten participating studies.

Methods: Ten different cohorts in seven countries were combined, using data transformed into a harmonized format. All participants were of European origin, with age 18-80 years. They had participated in a clinical examination for anthropometric and blood pressure measurements. Blood samples had been drawn for analysis of lipids and glucose. Presence of MetS was assessed in those with obesity (BMI ≥ 30 kg/m2) based on the 2001 NCEP ATP III criteria, as well as an adapted set of less strict criteria. MHO was defined as obesity, having none of the MetS components, and no previous diagnosis of cardiovascular disease.

Results: Data for 163,517 individuals were available; 17% were obese (11,465 men and 16,612 women). The prevalence of obesity varied from 11.6% in the Italian CHRIS cohort to 26.3% in the German KORA cohort. The age-standardized percentage of obese subjects with MetS ranged in women from 24% in CHRIS to 65% in the Finnish Health2000 cohort, and in men from 43% in CHRIS to 78% in the Finnish DILGOM cohort, with elevated blood pressure the most frequently occurring factor contributing to the prevalence of the metabolic syndrome. The age-standardized prevalence of MHO varied in women from 7% in Health2000 to 28% in NCDS, and in men from 2% in DILGOM to 19% in CHRIS. MHO was more prevalent in women than in men, and decreased with age in both sexes.

Conclusions: Through a rigorous harmonization process, the BioSHaRE-EU consortium was able to compare key characteristics defining the metabolically healthy obese phenotype across ten cohort studies. There is considerable variability in the prevalence of healthy obesity across the different European populations studied, even when unified criteria were used to classify this phenotype.
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http://dx.doi.org/10.1186/1472-6823-14-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3923238PMC
February 2014

Data harmonization and federated analysis of population-based studies: the BioSHaRE project.

Emerg Themes Epidemiol 2013 Nov 21;10(1):12. Epub 2013 Nov 21.

Research Institute of the McGill University Health Centre, 2155 Guy, office 458, Montreal, Quebec H3H 2R9, Canada.

Background: Individual-level data pooling of large population-based studies across research centres in international research projects faces many hurdles. The BioSHaRE (Biobank Standardisation and Harmonisation for Research Excellence in the European Union) project aims to address these issues by building a collaborative group of investigators and developing tools for data harmonization, database integration and federated data analyses.

Methods: Eight population-based studies in six European countries were recruited to participate in the BioSHaRE project. Through workshops, teleconferences and electronic communications, participating investigators identified a set of 96 variables targeted for harmonization to answer research questions of interest. Using each study's questionnaires, standard operating procedures, and data dictionaries, harmonization potential was assessed. Whenever harmonization was deemed possible, processing algorithms were developed and implemented in an open-source software infrastructure to transform study-specific data into the target (i.e. harmonized) format. Harmonized datasets located on server in each research centres across Europe were interconnected through a federated database system to perform statistical analysis.

Results: Retrospective harmonization led to the generation of common format variables for 73% of matches considered (96 targeted variables across 8 studies). Authenticated investigators can now perform complex statistical analyses of harmonized datasets stored on distributed servers without actually sharing individual-level data using the DataSHIELD method.

Conclusion: New Internet-based networking technologies and database management systems are providing the means to support collaborative, multi-center research in an efficient and secure manner. The results from this pilot project show that, given a strong collaborative relationship between participating studies, it is possible to seamlessly co-analyse internationally harmonized research databases while allowing each study to retain full control over individual-level data. We encourage additional collaborative research networks in epidemiology, public health, and the social sciences to make use of the open source tools presented herein.
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http://dx.doi.org/10.1186/1742-7622-10-12DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4175511PMC
November 2013

Linking Canadian population health data: maximizing the potential of cohort and administrative data.

Can J Public Health 2013 Mar 6;104(3):e258-61. Epub 2013 Mar 6.

Public Population Project in Genomics and Society, Montreal, QC, Canada.

Linkage of data collected by large Canadian cohort studies with provincially managed administrative health databases can offer very interesting avenues for multidisciplinary and cost-effective health research in Canada. Successfully co-analyzing cohort data and administrative health data (AHD) can lead to research results capable of improving the health and well-being of Canadians and enhancing the delivery of health care services. However, such an endeavour will require strong coordination and long-term commitment between all stakeholders involved. The challenges and opportunities of a pan-Canadian cohort-to-AHD data linkage program have been considered by cohort study investigators and data custodians from each Canadian province. Stakeholders acknowledge the important public health benefits of establishing such a program and have established an action plan to move forward.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3880355PMC
http://dx.doi.org/10.17269/cjph.104.3775DOI Listing
March 2013

Harmonizing data for collaborative research on aging: why should we foster such an agenda?

Can J Aging 2012 Mar;31(1):95-9

Research Institute - McGill University Health Centre, Montreal, QC, Canada.

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http://dx.doi.org/10.1017/S0714980811000729DOI Listing
March 2012

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

Int J Epidemiol 2011 Oct 30;40(5):1314-28. Epub 2011 Jul 30.

Research Institute - McGill University Health Centre, Montreal, Quebec, Canada.

Background: Proper understanding of the roles of, and interactions between genetic, lifestyle, environmental and psycho-social factors in determining the risk of development and/or progression of chronic diseases requires access to very large high-quality databases. Because of the financial, technical and time burdens related to developing and maintaining very large studies, the scientific community is increasingly synthesizing data from multiple studies to construct large databases. However, the data items collected by individual studies must be inferentially equivalent to be meaningfully synthesized. The DataSchema and Harmonization Platform for Epidemiological Research (DataSHaPER; http://www.datashaper.org) was developed to enable the rigorous assessment of the inferential equivalence, i.e. the potential for harmonization, of selected information from individual studies.

Methods: This article examines the value of using the DataSHaPER for retrospective harmonization of established studies. Using the DataSHaPER approach, the potential to generate 148 harmonized variables from the questionnaires and physical measures collected in 53 large population-based studies (6.9 million participants) was assessed. Variable and study characteristics that might influence the potential for data synthesis were also explored.

Results: Out of all assessment items evaluated (148 variables for each of the 53 studies), 38% could be harmonized. Certain characteristics of variables (i.e. relative importance, individual targeted, reference period) and of studies (i.e. observational units, data collection start date and mode of questionnaire administration) were associated with the potential for harmonization. For example, for variables deemed to be essential, 62% of assessment items paired could be harmonized.

Conclusion: The current article shows that the DataSHaPER provides an effective and flexible approach for the retrospective harmonization of information across studies. To implement data synthesis, some additional scientific, ethico-legal and technical considerations must be addressed. The success of the DataSHaPER as a harmonization approach will depend on its continuing development and on the rigour and extent of its use. The DataSHaPER has the potential to take us closer to a truly collaborative epidemiology and offers the promise of enhanced research potential generated through synthesized databases.
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http://dx.doi.org/10.1093/ije/dyr106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3204208PMC
October 2011

Invited commentary: consolidating data harmonization--how to obtain quality and applicability?

Am J Epidemiol 2011 Aug 11;174(3):261-4; author reply 265-6. Epub 2011 Jul 11.

Public Population Project in Genomics, Montreal, Quebec, Canada.

It is recognized that very large sample sizes capable of providing adequate statistical power are required to properly investigate and understand the role and interaction of genetic, lifestyle, and environmental factors in modulating the risk and progression of chronic diseases. However, very few one-off studies provide access to very large numbers of participants, and the collection of high-quality data necessitates a major investment of resources. The scientific community is thus increasingly engaged in collaborative efforts to facilitate harmonization and synthesis of data across studies. Complementary harmonization approaches may be adopted to support these efforts. In the current issue of the American Journal of Epidemiology, Hamilton et al. (Am J Epidemiol. 2011;174(3):253-260) present the consensus measures for Phenotypes and eXposures (PhenX) Toolkit, which promotes the use of identical data collection tools and procedures to support harmonization across emerging studies. Data synthesis is greatly facilitated by the use of common measures and procedures. However, the "stringent" criteria required by PhenX can limit its utilization. The opportunity to make use of rigorous but more "flexible" harmonization approaches should also be considered.
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http://dx.doi.org/10.1093/aje/kwr194DOI Listing
August 2011

Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies.

Int J Epidemiol 2010 Oct 2;39(5):1383-93. Epub 2010 Sep 2.

Public Population Project in Genomics (P³G), Montreal, QC, Canada.

Background: Vast sample sizes are often essential in the quest to disentangle the complex interplay of the genetic, lifestyle, environmental and social factors that determine the aetiology and progression of chronic diseases. The pooling of information between studies is therefore of central importance to contemporary bioscience. However, there are many technical, ethico-legal and scientific challenges to be overcome if an effective, valid, pooled analysis is to be achieved. Perhaps most critically, any data that are to be analysed in this way must be adequately 'harmonized'. This implies that the collection and recording of information and data must be done in a manner that is sufficiently similar in the different studies to allow valid synthesis to take place.

Methods: This conceptual article describes the origins, purpose and scientific foundations of the DataSHaPER (DataSchema and Harmonization Platform for Epidemiological Research; http://www.datashaper.org), which has been created by a multidisciplinary consortium of experts that was pulled together and coordinated by three international organizations: P³G (Public Population Project in Genomics), PHOEBE (Promoting Harmonization of Epidemiological Biobanks in Europe) and CPT (Canadian Partnership for Tomorrow Project).

Results: The DataSHaPER provides a flexible, structured approach to the harmonization and pooling of information between studies. Its two primary components, the 'DataSchema' and 'Harmonization Platforms', together support the preparation of effective data-collection protocols and provide a central reference to facilitate harmonization. The DataSHaPER supports both 'prospective' and 'retrospective' harmonization.

Conclusion: It is hoped that this article will encourage readers to investigate the project further: the more the research groups and studies are actively involved, the more effective the DataSHaPER programme will ultimately be.
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http://dx.doi.org/10.1093/ije/dyq139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2972444PMC
October 2010