The harmonisation of longitudinal data: a case study using data from cohort studies in The Netherlands and the United Kingdom

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

BACKGROUND/OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

Distress, burden, and wellbeing in siblings of people with mental illness: a mixed studies systematic review and meta-analysis

BACKGROUND

Family members of people with mental illness (MI) may experience a host of psychological adversities such as increased stress, burden, and reduced wellbeing. However, relatively little is known about siblings. This study aimed to characterise the experience of distress (viz. depressive and anxiety symptoms), burden, and wellbeing in siblings of people with MI.

METHODS

Studies reporting on quantitative measures of depression, anxiety, burden, or wellbeing in siblings; and/or qualitative findings on siblings' experience were eligible. The literature search was conducted up until 20th October 2022.

RESULTS

Sixty-two studies comprising data from 3744 siblings were included. The pooled mean percentage of depressive symptoms fell in the mild range at 15.71 (k = 28, N = 2187, 95% CI 12.99-18.43) and anxiety symptoms fell in the minimal range at 22.45 (k = 16, N = 1122, 95% CI 17.09-27.80). Moderator analyses indicate that siblings of people with a schizophrenia spectrum disorder experience greater depressive symptoms than siblings of people with other types of MI (β = -16.38, p < 0.001). Qualitative findings suggest that individuals may be particularly vulnerable during their siblings' illness onset and times of relapse. Limited communication, confusion about MI, and the need to compensate may contribute to siblings' distress and/or burden. Siblings' experience of wellbeing and caregiving were closely related.

CONCLUSION

This review highlights the complex psychological experience of siblings and the need for greater research and clinical support for this important yet often overlooked cohort.

A robust harmonization approach for cognitive data from multiple aging and dementia cohorts

INTRODUCTION

Although many cognitive measures have been developed to assess cognitive decline due to Alzheimer's disease (AD), there is little consensus on optimal measures, leading to varied assessments across research cohorts and clinical trials making it difficult to pool cognitive measures across studies.

METHODS

We used a two-stage approach to harmonize cognitive data across cohorts and derive a cross-cohort score of cognitive impairment due to AD. First, we pool and harmonize cognitive data from international cohorts of varying size and ethnic diversity. Next, we derived cognitive composites that leverage maximal data from the harmonized dataset.

RESULTS

We show that our cognitive composites are robust across cohorts and achieve greater or comparable sensitivity to AD-related cognitive decline compared to the Mini-Mental State Examination and Preclinical Alzheimer Cognitive Composite. Finally, we used an independent cohort validating both our harmonization approach and composite measures.

DISCUSSION

Our easy to implement and readily available pipeline offers an approach for researchers to harmonize their cognitive data with large publicly available cohorts, providing a simple way to pool data for the development or validation of findings related to cognitive decline due to AD.

Protocol for developing a dashboard for interactive cohort analysis of oral health-related data

INTRODUCTION

A working knowledge of data analytics is becoming increasingly important in the digital health era. Interactive dashboards are a useful, accessible format for presenting and disseminating health-related information to a wide audience. However, many oral health researchers receive minimal data visualisation and programming skills.

OBJECTIVES

The objective of this protocols paper is to demonstrate the development of an analytical, interactive dashboard, using oral health-related data from multiple national cohort surveys.

METHODS

The flexdashboard package was used within the R Studio framework to create the structure-elements of the dashboard and interactivity was added with the Shiny package. Data sources derived from the national longitudinal study of children in Ireland and the national children's food survey. Variables for input were selected based on their known associations with oral health. The data were aggregated using tidyverse packages such as dplyr and summarised using ggplot2 and kableExtra with specific functions created to generate bar-plots and tables.

RESULTS

The dashboard layout is structured by the YAML (YAML Ain't Markup Language) metadata in the R Markdown document and the syntax from Flexdashboard. Survey type, wave of survey and variable selector were set as filter options. Shiny's render functions were used to change input to automatically render code and update output. The deployed dashboard is openly accessible at https://dduh.shinyapps.io/dduh/ . Examples of how to interact with the dashboard for selected oral health variables are illustrated.

CONCLUSION

Visualisation of national child cohort data in an interactive dashboard allows viewers to dynamically explore oral health data without requiring multiple plots and tables and sharing of extensive documentation. Dashboard development requires minimal non-standard R coding and can be quickly created with open-source software.

Association between Internet Usage and Quality of Life of Elderly People in England: Evidence from the English Longitudinal Study of Ageing (ELSA)

The WHO has stated that the number of senior citizens above age 65 across the world will double by the year 2050: in the UK, the whole population is projected to grow by about 2.5% over a decade, from mid-2018. Although people are living longer, they are not healthier in old age, and there is an increasing number of illnesses and disabilities in the ageing population, which have an impact on their overall well-being and quality of life (QoL). Alongside these trends, Internet technologies have improved and provide a wide range of information, including on medical and health issues. This study aimed to examine the association between the utilisation of the internet among older people in England and their QoL. This study utilised the English Longitudinal Study of Aging (ELSA), a longitudinal study of a representative sample of people aged 50 and over in England. The data from Wave 9 were analysed using bivariate analysis and logistic regression. The results show a strong association between QoL and utilisation of the Internet in older people, even when adjusting for demographic variables and health. Higher use of the internet was associated with older people being less likely to have higher QoL. The excessive use of the internet for communication and gathering information also contributed to lower QoL. From the findings, poorer QoL was also found in people in older age groups, in those who are married, and those who never suffer from chronic diseases. Our findings suggest that the quality of life in older people might not only be associated with the frequency of usage but also the purpose for which the internet is used; however, this relationship is complex and further research should explore this in greater depth. Further research should also investigate how older people's use of the Internet changed during the COVID-19 pandemic and the effects of this on the QoL in older age.

Methodological challenges in harmonisation of the variables used as indicators of social capital in epidemiological studies of ageing – results of the ATHLOS project

The present article aims to highlight methodological aspects related to understanding and conceptualising social capital for the purposes of population research as well as describing the key challenges in the harmonisation process of indicators of social capital. The study was conducted in the frame of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. After a review of social capital theories developed in social science and a subsequent review of the documentation of 18 international cohorts, decision trees of the harmonisation of social variables were developed. The known-group validity was verified. The results focused on generalised trust, civic engagement and social participation are presented. The summary of the availability of any indicators of these concepts is classified in seven domains (generalised trust, political participation, religious participation, senior-specific participation, participation in sport groups, participation in volunteer/charity group activities, any participation) across surveys. The results of the analysis for known-group validity support the construct validity of the harmonised variables.

Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI

To improve understanding of Alzheimer’s disease, large observational studies are needed to increase power for more nuanced analyses. Combining data across existing observational studies represents one solution. However, the disparity of such datasets makes this a non-trivial task. Here, a machine learning approach was applied to impute longitudinal neuropsychological test scores across two observational studies, namely the Australian Imaging, Biomarkers and Lifestyle Study (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) providing an overall harmonised dataset. MissForest, a machine learning algorithm, capitalises on the underlying structure and relationships of data to impute test scores not measured in one study aligning it to the other study. Results demonstrated that simulated missing values from one dataset could be accurately imputed, and that imputation of actual missing data in one dataset showed comparable discrimination (p < 0.001) for clinical classification to measured data in the other dataset. Further, the increased power of the overall harmonised dataset was demonstrated by observing a significant association between CVLT-II test scores (imputed for ADNI) with PET Amyloid-β in MCI APOE-ε4 homozygotes in the imputed data (N = 65) but not for the original AIBL dataset (N = 11). These results suggest that MissForest can provide a practical solution for data harmonization using imputation across studies to improve power for more nuanced analyses.

Shaping a data-driven era in dementia care pathway through computational neurology approaches

BACKGROUND

Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making.

MAIN BODY

Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations.

CONCLUSION

The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Mental health, quality of life, self-efficacy, and social support of individuals living with spinal cord injury in Switzerland compared to that of the general population

STUDY DESIGN

Comparative study of survey self-report data.

OBJECTIVES

To compare individuals living with spinal cord injury (SCI) in Switzerland to the general population in terms of mental health, quality of life, self-efficacy, and social support.

SETTING

Community, Switzerland.

METHODS

Data from the 2017 community survey of the Swiss Spinal Cord Injury Cohort Study were compared to data from two matched (1:3 nearest neighbor propensity score) general population surveys collected in the same year. Measures of mental health (mental health index, psychological distress item, vitality scale, and energy item), quality of life (WHOQOL-BREF item), self-efficacy (General Self-Efficacy Scale item), and social support (items of relationship satisfaction, living alone, and marital status) were compared across datasets using regression adjusted for non-response correction weights. The analyses were then replicated in subgroups defined by sociodemographic, lesion-related, and secondary health issues factors.

RESULTS

Individuals with SCI had significantly higher psychological distress and poorer mental health, vitality, energy, and quality of life than the general population, with medium to large effect sizes (Cohen's d: 0.35-1.08). They also had lower self-efficacy and relationship satisfaction, lived more frequently alone, and were more frequently single. Individuals with less severe secondary health issues reported mental health and quality of life more similar to the general population than those reporting more severe issues.

CONCLUSIONS

This study highlights a significant long-term impact of SCI on well-being and psychosocial resources, underlining the need for ongoing biopsychosocial care beyond inpatient rehabilitation.

Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.

Income inequality and social gradients in children's height: a comparison of cohort studies from five high-income countries

BACKGROUND

Health and well-being are better, on average, in countries that are more equal, but less is known about how this benefit is distributed across society. Height is a widely used, objective indicator of child health and predictor of lifelong well-being. We compared the level and slope of social gradients in children's height in high-income countries with different levels of income inequality, in order to investigate whether children growing up in all socioeconomic circumstances are healthier in more equal countries.

METHODS

We conducted a coordinated analysis of data from five cohort studies from countries selected to represent different levels of income inequality (the USA, UK, Australia, the Netherlands and Sweden). We used standardised methods to compare social gradients in children's height at age 4-6 years, by parent education status and household income. We used linear regression models and predicted height for children with the same age, sex and socioeconomic circumstances in each cohort.

RESULTS

The total analytic sample was 37 063 children aged 4-6 years. Gradients by parent education and household income varied between cohorts and outcomes. After adjusting for differences in age and sex, children in more equal countries (Sweden, the Netherlands) were taller at all levels of parent education and household income than children in less equal countries (USA, UK and Australia), with the greatest between-country differences among children with less educated parents and lowest household incomes.

CONCLUSIONS

The study provides preliminary evidence that children across society do better in more equal countries, with greatest benefit among children from the most disadvantaged socioeconomic groups.

Depressive psychopathology in first-episode schizophrenia spectrum disorders: a systematic review, meta-analysis and meta-regression

BACKGROUND

Despite knowing for many decades that depressive psychopathology is common in first-episode schizophrenia spectrum disorders (FES), there is limited knowledge regarding the extent and nature of such psychopathology (degree of comorbidity, caseness, severity) and its demographic, clinical, functional and treatment correlates. This study aimed to determine the pooled prevalence of depressive disorder and caseness, and the pooled mean severity of depressive symptoms, as well as the demographic, illness, functional and treatment correlates of depressive psychopathology in FES.

METHODS

This systematic review, meta-analysis and meta-regression was prospectively registered (CRD42018084856) and conducted in accordance with PRISMA and MOOSE guidelines.

RESULTS

Forty studies comprising 4041 participants were included. The pooled prevalence of depressive disorder and caseness was 26.0% (seven samples, N = 855, 95% CI 22.1-30.3) and 43.9% (11 samples, N = 1312, 95% CI 30.3-58.4), respectively. The pooled mean percentage of maximum depressive symptom severity was 25.1 (38 samples, N = 3180, 95% CI 21.49-28.68). Correlates of depressive psychopathology were also found.

CONCLUSIONS

At least one-quarter of individuals with FES will experience, and therefore require treatment for, a full-threshold depressive disorder. Nearly half will experience levels of depressive symptoms that are severe enough to warrant diagnostic investigation and therefore clinical intervention - regardless of whether they actually fulfil diagnostic criteria for a depressive disorder. Depressive psychopathology is prominent in FES, manifesting not only as superimposed comorbidity, but also as an inextricable symptom domain.

The Use of Data Mining Methods for the Prediction of Dementia: Evidence From the English Longitudinal Study of Aging

Dementia in older age is a major health concern with the increase in the aging population. Preventive measures to prevent or delay dementia symptoms are of utmost importance. In this study, a large and wide variety of factors from multiple domains were investigated using a large nationally representative sample of older people from the English Longitudinal Study of Ageing. Seven machine learning algorithms were implemented to build predictive models for performance comparison. A simple model ensemble approach was used to combine the prediction results of individual base models to further improve predictive power. A series of important factors in each domain area were identified. The findings from this study provide new evidence on factors that are associated with the dementia in later life. This information will help our understanding of potential risk factors for dementia and identify warning signs of the early stages of dementia. Longitudinal research is required to establish which factors may be causative and which factors may be a consequence of dementia.

Factors of Resilience in Informal Caregivers of People with Dementia from Integrative International Data Analysis

BACKGROUND/AIMS

Although caring for a person with dementia can be stressful, some caregivers appear to experience few negative consequences to their well-being. This study aimed to examine what proportion of caregivers demonstrates resilience under different challenging circumstances and to identify factors related to their resilience.

METHODS

Baseline data from 4 studies from the Netherlands and UK among informal caregivers of people with dementia were harmonized and integrated. Caregiver resilience was defined as high levels of psychological well-being despite different types of high caregiving demands. Multivariate regression analyses identified factors significantly related to caregiver resilience.

RESULTS

The integrated data set included 15 harmonized variables with data from 1,048 caregivers facing a high care demand. The prevalence of resilience varied between 35 and 43%, depending on the demand for high care. Being a male caregiver, caring for a female, living apart from your relative, and low caregiver burden were positively related to caregiver resilience.

CONCLUSION

Caregivers have the capacity to demonstrate resilience despite significant challenges. This study demonstrates how harmonization of data from multiple existing studies can be used to increase power and explore the consistency of findings. This contributes to a better understanding of which factors are likely to facilitate caregiver resilience and offers insights for developing services.

Maelstrom Research guidelines for rigorous retrospective data harmonization

Background

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

Methods

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

Results

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

Conclusions

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

Data Harmonization in Aging Research: Not so Fast

BACKGROUND/STUDY CONTEXT

Harmonizing measures in order to conduct pooled data analyses has become a scientific priority in aging research. Retrospective harmonization where different studies lack common measures of comparable constructs presents a major challenge. This study compared different approaches to harmonization with a crosswalk sample who completed multiple versions of the measures to be harmonized.

METHODS

Through online recruitment, 1061 participants aged 30 to 98 answered two different depression scales, and 1065 participants answered multiple measures of subjective health. Rational and configural methods of harmonization were applied, using the crosswalk sample to determine their success; and empirical item response theory (IRT) methods were applied in order empirically to compare items from different measures as answered by the same person.

RESULTS

For depression, IRT worked well to provide a conversion table between different measures. The rational method of extracting semantically matched items from each of the two scales proved an acceptable alternative to IRT. For subjective health, only configural harmonization was supported. The subjective health items used in most studies form a single robust factor.

CONCLUSION

Caution is required in aging research when pooling data across studies using different measures of the same construct. Of special concern are response scales that vary widely in the number of response options, especially if the anchors are asymmetrical. A crosswalk sample that has completed items from each of the measures being harmonized allows the investigator to use empirical approaches to identify flawed assumptions in rational or configural approaches to harmonizing.

[Harmonisation of databases for the study of frailty in older people: INTAFRADE study]

OBJECTIVES

The main objective of the present work is to evaluate the feasibility of harmonising the available information from different independent databases, in order to build an integrated database to study frailty.

MATERIAL AND METHODS

This work is based on the European project, Integral Approach to the Transition between Frailty and Dependence on older adults: Patterns of occurrence, identification tools and model of care (INTAFRADE), developed by 4 groups, 3 in Spain and one in France. Each partner provided their databases related to the study of frailty. As a previous step to the creation of an integrated database the characteristics and variables included in each study were mapped, specifying whether their harmonisation was possible or not.

RESULTS

A total of 30 different variables that corresponded to 8 dimensions were identified: Sociodemographic and social characteristics, health status, lifestyle habits, anthropometric measures, other physical measurements, use of health services, and adverse health results. Of them all, 28 (93%) variables were harmonisable, although only 20% were present in all databases, with 47% in 3 of them. In relation to the frailty instruments, all of them were lacking at least 50% of the items. The harmonisation process will allow us to jointly analyse information available on 2,361 people.

CONCLUSIONS

The European INTAFRADE study will allow a deeper understanding of the frailty process in older people by harmonising information from heterogeneous databases.

Loneliness and Health in Older Adults: A Mini-Review and Synthesis

Increasing evidence suggests that perceived social isolation or loneliness is a major risk factor for physical and mental illness in later life. This review assesses the status of research on loneliness and health in older adults. Key concepts and definitions of loneliness are identified, and the prevalence, correlates, and health effects of loneliness in older individuals are reviewed. Theoretical mechanisms that underlie the association between loneliness and health are also described, and illustrative studies examining these mechanisms are summarized. Intervention approaches to reduce loneliness in old age are highlighted, and priority recommendations for future research are presented.

A Nonparametric, Multiple Imputation-Based Method for the Retrospective Integration of Data Sets

Complex research questions often cannot be addressed adequately with a single data set. One sensible alternative to the high cost and effort associated with the creation of large new data sets is to combine existing data sets containing variables related to the constructs of interest. The goal of the present research was to develop a flexible, broadly applicable approach to the integration of disparate data sets that is based on nonparametric multiple imputation and the collection of data from a convenient, de novo calibration sample. We demonstrate proof of concept for the approach by integrating three existing data sets containing items related to the extent of problematic alcohol use and associations with deviant peers. We discuss both necessary conditions for the approach to work well and potential strengths and weaknesses of the method compared to other data set integration approaches.

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

Abstracts

European ageing research in the social, behavioural and health areas: a multidimensional account

At the 10th anniversary of the European Journal of Ageing in 2013, this article makes an attempt towards a comprehensive overview of European ageing research (EAR) from the three perspectives of this Journal: the social, behavioural and health related domains. First, we provide some general observations on the state of EAR, noting the difficulties of defining this field. Second, we provide a description of EAR and its current trends and characteristics in terms of research themes and theoretical directions. Towards this end, we use the 10 volumes of the European Journal of Ageing as well as other available information. Third, we address the infrastructure of EAR as reflected in major studies, looking at data availability, language, education and training. Given this background, we derive a set of six recommendations to further consolidate and expand EAR.

European project on osteoarthritis: design of a six-cohort study on the personal and societal burden of osteoarthritis in an older European population

Background

Osteoarthritis (OA), the most common form of arthritis, is a major contributor to functional impairment and loss of independence in older persons. The European Project on OSteoArthritis (EPOSA) is a collaborative study involving six European cohort studies on ageing. This project focuses on the personal and societal burden and its determinants of osteoarthritis. This paper describes the design of the project, and presents some descriptive analyses on selected variables across countries.

Methods/design

EPOSA is an observational study including pre-harmonized data from European cohort studies (Germany, Italy, the Netherlands, Spain, Sweden and the United Kingdom) on older community-dwelling persons aged 65 to 85 years. In total, 2942 persons were included in the baseline study with a mean age of 74.2 years (SD 5.1), just over half were women (51,9%). The baseline assessment was conducted by a face-to-face interview followed by a clinical examination. Measures included physical, cognitive, psychological and social functioning, lifestyle behaviour, physical environment, wellbeing and care utilisation. The clinical examination included anthropometry, muscle strength, physical performance and OA exam. A follow-up assessment was performed 12–18 months after baseline.

Discussion

The EPOSA study is the first population-based study including a clinical examination of OA, using pre-harmonized data across European countries. The EPOSA study provides a unique opportunity to study the determinants and consequences of OA in general populations of older persons, including both care-seeking and non care-seeking persons.

Genes, environments, and developmental research: methods for a multi-site study of early substance abuse

The importance of including developmental and environmental measures in genetic studies of human pathology is widely acknowledged, but few empirical studies have been published. Barriers include the need for longitudinal studies that cover relevant developmental stages and for samples large enough to deal with the challenge of testing gene-environment-development interaction. A solution to some of these problems is to bring together existing data sets that have the necessary characteristics. As part of the National Institute on Drug Abuse-funded Gene-Environment-Development Initiative, our goal is to identify exactly which genes, which environments, and which developmental transitions together predict the development of drug use and misuse. Four data sets were used of which common characteristics include (1) general population samples, including males and females; (2) repeated measures across adolescence and young adulthood; (3) assessment of nicotine, alcohol, and cannabis use and addiction; (4) measures of family and environmental risk; and (5) consent for genotyping DNA from blood or saliva. After quality controls, 2,962 individuals provided over 15,000 total observations. In the first gene-environment analyses, of alcohol misuse and stressful life events, some significant gene-environment and gene-development effects were identified. We conclude that in some circumstances, already collected data sets can be combined for gene-environment and gene-development analyses. This greatly reduces the cost and time needed for this type of research. However, care must be taken to ensure careful matching across studies and variables.