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Associations of environmental variables with physical activity and sedentary time using data from the Canadian Longitudinal Study on Aging, and the Canadian Urban Environmental Health Research Consortium (Canadian Active Living Environments (Can-ALE) dataset, and Normalized Difference Vegetation Index (NDVI, greenness) dataset) were assessed. The main outcome variables were physical activity and sedentary time as measured by a modified version of the Physical Activity for Elderly Scale. The sample consisted of adults aged 45 and older (n = 36 580, mean age 62.6 ± 10.2, 51% female). Adjusted ordinal regression models consistently demonstrated that those residing in neighbourhoods in the highest Can-ALE category (most well-connected built environment) reported more physical activity and sedentary time. For example, males aged 75+ in the highest Can-ALE category had 2 times higher odds of reporting more physical activity (OR = 2.0, 95% CI = 1.1–3.5) and 1.8 times higher odds of reporting more sedentary time (OR = 1.8, 95% CI = 1.0–3.4). Neighbourhoods with higher greenness scores were also associated with higher odds of reporting more physical activity and sedentary time. It appears that an environment characterized by higher Can-ALE and higher greenness may facilitate physical activity, but it also facilitates more leisure sedentary time in older adults; research using device measured total sedentary time, and consideration of the types of sedentary activities being performed is needed. Novelty: Middle-aged and older adults living in neighbourhoods with higher Can-ALE scores and more greenness report more physical activity and leisure sedentary time Greenness is important for physical activity and sedentary time in middle-aged adults

Motivation Increasingly complex omics datasets are being generated, along with associated diverse categories of metadata (environmental, clinical, etc.). Looking at the correlation between these variables can be critical to identify potential confounding factors and novel relationships. To date, some correlation globe software has been developed to aid investigations; however, they lack secure, dynamic visualization capability. Results GlobeCorr.ca is a web-based application designed to provide user-friendly, interactive visualization and analysis of correlation datasets. Users load tabular data listing pairwise variables and their correlation values, and GlobeCorr creates a dynamic visualization using ribbons to represent positive and negative correlations, optionally grouped by domain/category (such as microbiome taxa against other metadata). GlobeCorr runs securely (locally on a user’s computer) and provides a simple method for users to visualize and summarize complex datasets. This tool is applicable to a wide range of disciplines and domains of interest, including the bioinformatics/microbiome and metadata examples provided within. Availability and Implementation See https://GlobeCorr.ca; Code provided under an open source MIT license: https://github.com/brinkmanlab/globecorr.

Background: The relationship between symptom burden and physical activity (PA) in chronic obstructive pulmonary disease (COPD) remains poorly understood with limited data on undiagnosed individuals and those with mild to moderate disease. Objective: The primary objective was to evaluate the relationship between symptom burden and moderate-to-vigorous intensity PA (MVPA) in individuals from a random population-based sampling mirroring the population at large. Methods: Baseline participants of the Canadian Cohort Obstructive Lung Disease (n=1558) were selected for this cross-sectional sub-study. Participants with mild COPD (n=406) and moderate COPD (n=331), healthy individuals (n=347), and those at risk of developing COPD (n=474) were included. The Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire was used to estimate MVPA in terms of energy expenditure. High symptom burden was classified using the COPD Assessment Test ([CAT] ≥10). Results: Significant associations were demonstrated between high symptom burden and lower MVPA levels in the overall COPD sample (β=-717.09; 95% confidence interval [CI]=-1079.78, -354.40; p

BackgroundInfections are considered as leading causes of acute exacerbations of chronic obstructive pulmonary disease (COPD). Non-infectious risk factors such as short-term air pollution exposure may play a clinically important role. We sought to estimate the relationship between short-term air pollutant exposure and exacerbations in Canadian adults living with mild to moderate COPD.MethodsIn this case-crossover study, exacerbations (‘symptom based’: ≥48 hours of dyspnoea/sputum volume/purulence; ‘event based’: ‘symptom based’ plus requiring antibiotics/corticosteroids or healthcare use) were collected prospectively from 449 participants with spirometry-confirmed COPD within the Canadian Cohort Obstructive Lung Disease. Daily nitrogen dioxide (NO2), fine particulate matter (PM2.5), ground-level ozone (O3), composite of NO2and O3(Ox), mean temperature and relative humidity estimates were obtained from national databases. Time-stratified sampling of hazard and control periods on day ‘0’ (day-of-event) and Lags (‘−1’ to ‘−6’) were compared by fitting generalised estimating equation models. All data were dichotomised into ‘warm’ (May–October) and ‘cool’ (November–April) seasons. ORs and 95% CIs were estimated per IQR increase in pollutant concentrations.ResultsIncreased warm season ambient concentration of NO2was associated with symptom-based exacerbations on Lag−3 (1.14 (1.01 to 1.29), per IQR), and increased cool season ambient PM2.5was associated with symptom-based exacerbations on Lag−1 (1.11 (1.03 to 1.20), per IQR). There was a negative association between warm season ambient O3and symptom-based events on Lag−3 (0.73 (0.52 to 1.00), per IQR).ConclusionsShort-term ambient NO2and PM2.5exposure were associated with increased odds of exacerbations in Canadians with mild to moderate COPD, further heightening the awareness of non-infectious triggers of COPD exacerbations.

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.

Exposure to air pollution is a major contributor to the pathogenesis of COPD worldwide. Indeed, most recent estimates suggest that 50% of the total attributable risk of COPD may be related to air pollution. In response, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) Scientific Committee performed a comprehensive review on this topic, qualitatively synthesised the evidence to date and proffered recommendations to mitigate the risk. The review found that both gaseous and particulate components of air pollution are likely contributors to COPD. There are no absolutely safe levels of ambient air pollution and the relationship between air pollution levels and respiratory events is supra-linear. Wildfires and extreme weather events such as heat waves, which are becoming more common owing to climate change, are major threats to COPD patients and acutely increase their risk of morbidity and mortality. Exposure to air pollution also impairs lung growth in children and as such may lead to developmental COPD. GOLD recommends strong public health policies around the world to reduce ambient air pollution and for implementation of public warning systems and advisories, including where possible the use of personalised apps, to alert patients when ambient air pollution levels exceed acceptable minimal thresholds. When household particulate content exceeds acceptable thresholds, patients should consider using air cleaners and filters where feasible. Air pollution is a major health threat to patients living with COPD and actions are urgently required to reduce the morbidity and mortality related to poor air quality around the world.

This mini-tutorial describes how combining aggregate-level data about the physical, built and social environment can facilitate our understanding of factors shaping the human brain and, in turn, brain health. It provides entry-level information about methods and approaches one can use to uncover how inequalities in the local environment lead to health inequalities in general, and those in brain health in particular. This background knowledge should be helpful to those who are interested in using neuroimaging to investigate how environmental factors shape inter-individual variations in the human brain.

Background: Chronic obstructive pulmonary disease (COPD) is a common and chronic lung condition characterized by sudden flare-ups known as acute exacerbations (AECOPD). Relative increases in air pollution concentration and ambient temperature may play a clinically relevant role in precipitating AECOPDs. Objectives: To estimate the association between short-term exposures to air pollution and exacerbations in patients with mild-moderate COPD, as well as the influence of temperature on this relationship. Methods: In this case-crossover study, AECOPD events were collected prospectively from COPD participants within the Canadian Cohort Obstructive Lung Disease (CanCOLD). Daily particulate matter Results: Consistent positive associations (P Measurements and Main Results: Exposure to ambient PM2.5 and NO2 was associated with an increased odds of AECOPD (particularly on lag Day -1 and during the Cool period), challenging the conventional understanding of the precipitants for AECOPD.

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.

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.

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 (ORmedium=0.54, 95% CI=0.18–1.67; ORhigh=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.

Background: 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. Objectives: The Canadian Urban Environmental Health Research Consortium (CANUE) and the Geoscience and Health Cohort Consortium (GECCO) provide a proof of concept that centralizing and disseminating environmental data for health research is valuable and can accelerate discovery. In this essay, we argue that more efficient use of exposure data for environmental epidemiological research over the next decade requires progress in four key areas: metadata and data access portals, linkage with health databases, harmonization of exposure measures and models over large areas, and leveraging "big data" streams for exposure characterization and evaluation of temporal changes. Discussion: Optimizing the use of existing environmental data and exploiting emerging data streams can provide unprecedented research opportunities in environmental epidemiology through a better characterization of individuals' exposures and the ability to study the intersecting impacts of multiple environmental features or urban attributes across different populations around the world. Proper documentation, linkage, and dissemination of new and emerging exposure data leads to a better awareness of data availability, a reduction of duplication of effort and increases research output.

BackgroundFew 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.MethodsWe 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.ResultsAssociations 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 (NO2) and black carbon (BC) but not for fine particulate matter (PM2.5). For prevalent chronic bronchitis, associations with NO2 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 NO2 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.ConclusionThis is the largest analysis to date to examine cross-sectional and longitudinal associations between ambient air pollution and chronic bronchitis. NO2 and BC air pollution was associated with increased odds of prevalent and incident chronic bronchitis.

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 2006-2013 in the UK, the Netherlands and Norway respectively. For all three cohorts, residential 24-hour 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 0.27cm(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 55dB(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.

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 mu g/m(3)) or NO2 (7.4 mu g/m(3)) 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 mu g/m(3)) 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.

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.

Air pollution has been found to be associated with reduced lung function; also 279 genetic signals of association with lung function have been identified that implicate multiple genes and pathways. In this study, we examined if any of these signals interact with air pollution measures NO2, PM2.5 and PM10.In 259,389 unrelated European individuals from UK Biobank, we tested for a gene-environment interaction effect on lung function with NO2, PM2.5 and PM10 for each of the 279 signals. Lung function measures (FEV1, FEV1/FVC and FVC) were adjusted for sex, age, age2, height, and smoking, income and educational status, with adjustment for 15 principal components for fine-scale population structure. A significance threshold of P

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: PM2.5 and PM10, respectively; and coarse particles with diameter between 2.5 μm and 10 μm: PMcoarse) and nitrogen dioxide (NO2) concentrations with forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), the FEV1/FVC ratio and COPD (FEV1/FVC Higher exposures to each pollutant were significantly associated with lower lung function. A 5 µg·m−3 increase in PM2.5 concentration was associated with lower FEV1 (−83.13 mL, 95% CI −92.50– −73.75 mL) and FVC (−62.62 mL, 95% CI −73.91– −51.32 mL). COPD prevalence was associated with higher concentrations of PM2.5 (OR 1.52, 95% CI 1.42–1.62, per 5 µg·m−3), PM10 (OR 1.08, 95% CI 1.00–1.16, per 5 µg·m−3) and NO2 (OR 1.12, 95% CI 1.10–1.14, per 10 µg·m−3), but not with PMcoarse. Stronger lung function associations were seen for males, individuals from lower income households, and “at-risk” occupations, and higher COPD associations were seen for obese, lower income, and non-asthmatic participants.Ambient air pollution was associated with lower lung function and increased COPD prevalence in this large study.

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 crosssectional 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.

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.

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”), NO2 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 NO2, high greenness) and unfavorable (low walkability, high NO2, 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 NO2 in areas with low deprivation, and a lower greenness prevalence and higher NO2 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, NO2 and 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.

IntroductionAmbient air pollution increases the risk of respiratory mortality but evidence for impacts on lung function and obstructive lung disease is less well established.MethodsModeled ambient ...

IntroductionHealth and environmental exposure databases are generally siloed in different research institutions across Canada and integrating them for environmental health research is a considerable challenge. Facilitating the linkage of these databases is essential to provide new analytical opportunities and help create efficiencies for research on environmental determinants of health. Objectives and ApproachCANUE is a Canadian Institutes of Health Research-funded platform for supporting environmental health research. CANUE collates and generates standardized environmental data on air and noise pollution, land use, green/natural spaces, climate change/extreme weather, and socioeconomic conditions for every postal code in Canada and makes them freely available to researchers. Systems and procedures are being developed by CANUE to facilitate the sharing and integration of these extensive geospatial exposures with existing observational cohorts and administrative health databases across Canada. This linkage will enable investigators to test hypotheses on the interdependent associations of environmental features with health impacts or benefits. ResultsCANUE now hosts a dozen national exposure databases and related metadata files, and actively adds new regional and national datasets. Streamlined processes for data sharing have been developed to facilitate easy merging with health data. Substantial consultation has also taken place with a wide range of health data holders to establish appropriate processes for receiving and managing environmental data, with particular focus on addressing challenges presented by differing ethics, consent and confidentiality requirements. These processes help accelerate the research process by making analysis-ready data available to investigators, create opportunities to study how multiple environmental factors are linked to a wide range of health outcomes, and generally increase the use of health and population databases for environmental health research. Conclusion/ImplicationsThe CANUE collaborative model illustrates how the production of policy-relevant evidence can be advanced through better coordination among environmental health researchers and linkage with health databases. CANUE is improving the scientific potential and cost-effectiveness of research in environmental epidemiology through streamlining linkage and access to standardized exposure datasets.

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. © The Author(s). 2018 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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. Keywords: Harmonization; Meta-analysis; Cognition; Individual participant data; Data pooling; Transformation; Combination

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

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.

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 10 dB(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 >60 dB(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.

Introduction Research examining associations between air pollution exposure and respiratory symptoms such as wheezing and shortness of breath in adults has generally been inconclusive. This may in ...