Resources For Health Research Research Articles

Creating a health informatics data resource for hearing health research

BackgroundThe National Institute of Health and Social Care Research (NIHR) Health Informatics Collaborative (HIC) for Hearing Health has been established in the UK to curate routinely collected hearing health data to address research questions. This study defines priority research areas, outlines its aims, governance structure and demonstrates how hearing health data have been integrated into a common data model using pure tone audiometry (PTA) as a case study.MethodsAfter identifying key research aims in hearing health, the governance structure for the NIHR HIC for Hearing Health is described. The Observational Medical Outcomes Partnership (OMOP) was chosen as our common data model to provide a case study example.ResultsThe NIHR HIC Hearing Health theme have developed a data architecture outlying the flow of data from all of the various siloed electronic patient record systems to allow the effective linkage of data from electronic patient record systems to research systems. Using PTAs as an example, OMOPification of hearing health data successfully collated a rich breadth of datapoints across multiple centres.ConclusionThis study identified priority research areas where routinely collected hearing health data could be useful. It demonstrates integration and standardisation of such data into a common data model from multiple centres. By describing the process of data sharing across the HIC, we hope to invite more centres to contribute and utilise data to address research questions in hearing health. This national initiative has the power to transform UK hearing research and hearing care using routinely collected clinical data.

Preconception indicators and associations with health outcomes reported in UK routine primary care data: a systematic review.

Routine primary care data may be a valuable resource for preconception health research and informing provision of preconception care. To review how primary care data could provide information on the prevalence of preconception indicators and examine associations with maternal and offspring health outcomes. Systematic review of observational studies using UK routine primary care data. Literature searches were conducted in five databases (March 2023) to identify observational studies that used national primary care data from individuals aged 15-49 years. Preconception indicators were defined as medical, behavioural and social factors that may impact future pregnancies. Health outcomes included those that may occur during and after pregnancy. Screening, data extraction and quality assessment were conducted by two reviewers. From 5,259 records screened, 42 articles were included. The prevalence of 30 preconception indicators was described for female patients, ranging from 0.01% for sickle cell disease to >20% for each of advanced maternal age, previous caesarean section (among those with a recorded pregnancy), overweight, obesity, smoking, depression and anxiety (irrespective of pregnancy). Few studies reported indicators for male patients (n=3) or associations with outcomes (n=5). Most studies had low risk of bias, but missing data may limit generalisability. Findings demonstrate that routinely collected UK primary care data can be used to identify patients preconception care needs. Linking primary care data with health outcomes collected in other datasets is underutilised but could help quantify how optimising preconception health and care can reduce adverse outcomes for mothers and children.

A Harmonised Approach to Curating Research-Ready Datasets for Asthma, Chronic Obstructive Pulmonary Disease (COPD) and Interstitial Lung Disease (ILD) in England, Wales and Scotland Using Clinical Practice Research Datalink (CPRD), Secure Anonymised Information Linkage (SAIL) Databank and DataLoch.

Electronic healthcare records (EHRs) are an important resource for health research that can be used to improve patient outcomes in chronic respiratory diseases. However, consistent approaches in the analysis of these datasets are needed for coherent messaging, and when undertaking comparative studies across different populations. We developed a harmonised curation approach to generate comparable patient cohorts for asthma, chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD) using datasets from within Clinical Practice Research Datalink (CPRD; for England), Secure Anonymised Information Linkage (SAIL; for Wales) and DataLoch (for Scotland) by defining commonly derived variables consistently between the datasets. By working in parallel on the curation methodology used for CPRD, SAIL and DataLoch for asthma, COPD and ILD, we were able to highlight key differences in coding and recording between the databases and identify solutions to enable valid comparisons. Codelists and metadata generated have been made available to help re-create the asthma, COPD and ILD cohorts in CPRD, SAIL and DataLoch for different time periods, and provide a starting point for the curation of respiratory datasets in other EHR databases, expediting further comparable respiratory research.

Participatory soil citizen science: An unexploited resource for European soil research

AbstractSoils are key components of our ecosystems and provide 95%–99% of our food. This importance is reflected by an increase in participatory citizen science projects on soils. Citizen science is a participatory research method that actively involves and engages the public in scientific enquiry to generate new knowledge or understanding. Here, we review past and current citizen science projects on agricultural soils across Europe. We conducted a web‐based survey and described 24 reviewed European citizen science projects in the light of the 10 principles of citizen science and identified success factors for citizen science. Over 66% of the projects generated soil biodiversity data; 54% and 42% of the projects generated data on vegetation cover and soil organic carbon, respectively. Our findings show that soil citizen science projects aligned with the 10 principles of citizen science offer an unexploited resource for European soil health research. We conclude that promoting co‐creation, fostering knowledge‐sharing networks and enabling long‐term communication and commitment with citizens are success factors for further development of citizen science on soils.

From Posts to Knowledge: Annotating a Pandemic-Era Reddit Dataset to Navigate Mental Health Narratives

Mental illness is increasingly recognized as a substantial public health challenge worldwide. With the advent of social media, these platforms have become pivotal for individuals to express their emotions, thoughts, and experiences, thereby serving as a rich resource for mental health research. This paper is devoted to the creation of a comprehensive dataset and an innovative data annotation methodology to explore the underlying causes of these mental health issues. Our approach included the extraction of over one million Reddit posts from five different subreddits, spanning the pre-pandemic, during-pandemic, and post-pandemic periods. These posts were methodically annotated using a set of specific criteria, aimed at identifying various root causes. This rigorous process produced a richly categorized dataset, invaluable for detailed analysis. The complete unlabelled dataset, along with a subset that has been expertly annotated, is prepared for public release, as outlined in the data availability section. This dataset is a critical resource for training and fine-tuning machine learning models to identify the foundational triggers of individual mental health issues, offering valuable insights for practical interventions and future research in this domain.

Method validation for (ultra)-trace element concentrations in urine for small sample volumes in large epidemiological studies: application to the population-based epidemiological multi-ethnic study of atherosclerosis (MESA).

Analysis of essential and non-essential trace elements in urine has emerged as a valuable tool for assessing occupational and environmental exposures, diagnosing nutritional status and guiding public health and health care intervention. Our study focused on the analysis of trace elements in urine samples from the Multi-Ethnic Study of Atherosclerosis (MESA), a precious resource for health research with limited sample volumes. Here we provide a comprehensive and sensitive method for the analysis of 18 elements using only 100 μL of urine. Method sensitivity, accuracy, and precision were assessed. The analysis by inductively coupled plasma mass spectrometry (ICP-MS) included the measurement of antimony (Sb), arsenic (As), barium (Ba), cadmium (Cd), cesium (Cs), cobalt (Co), copper (Cu), gadolinium (Gd), lead (Pb), manganese (Mn), molybdenum (Mo), nickel (Ni), selenium (Se), strontium (Sr), thallium (Tl), tungsten (W), uranium (U), and zinc (Zn). Further, we reported urinary trace element concentrations by covariates including gender, ethnicity/race, smoking and location. The results showed good accuracy and sensitivity of the ICP-MS method with the limit of detections rangings between 0.001 μg L-1 for U to 6.2 μg L-1 for Zn. Intra-day precision for MESA urine analysis varied between 1.4% for Mo and 26% for Mn (average 6.4% for all elements). The average inter-day precision for most elements was <8.5% except for Gd (20%), U (16%) and Mn (19%) due to very low urinary concentrations. Urinary mean concentrations of non-essential elements followed the order of Sr > As > Cs > Ni > Ba > Pb > Cd > Gd > Tl > W > U. The order of urinary mean concentrations for essential trace elements was Zn > Se > Mo > Cu > Co > Mn. Non-adjusted mean concentration of non-essential trace elements in urine from MESA participants follow the order Sr > As > Cs > Ni > Ba > Pb > Cd > Gd > Tl > W > U. The unadjusted urinary mean concentrations of essential trace elements decrease from Zn > Se > Mo > Cu > Co > Mn.

Health research prioritization in Somalia: setting the agenda for context specific knowledge to advance universal health coverage.

Despite recognition that health research is an imperative to progress toward universal health coverage, resources for health research are limited. Yet, especially in sub-Saharan Africa, more than 85% of the resources available for health research are spent on answering less relevant research questions. This misalignment is partially due to absence of locally determined health research priorities. In this study, we identified health research priorities which, if implemented, can inform local interventions required to accelerate progress toward universal health coverage in Somalia. We adapted the child health and nutrition research initiative method for research priority setting and applied it in 4 major phases: (1) establishment of an exercise management team, (2) a web-based survey among 84 respondents to identify health research questions; (3) categorization of identified health research questions; and (4) a workshop with 42 participants to score and rank the identified health research questions. Ethical approval was received from ethics review committee of the London School of Hygiene and Tropical Medicine (Ref:26524) and the Somali Research and Development Institute (Ref: EA0143). Two hundred and thirty-one unique health research questions were identified and categorized under health systems, services and social determinants (77), communicable diseases (54), non-communicable diseases (41) and reproductive, maternal, new-born, child, adolescent health and nutrition (59). A priority score ranging from 1 to 9 was assigned to each of the questions. For each category, a list of 10 questions with the highest priority scores was developed. Across the four categories, an overall list of 10 questions with the highest priority scores was also developed. These related to bottlenecks to accessing essential health services, use of evidence in decision making, antimicrobial resistance, distribution and risk factors for non-communicable diseases, post-traumatic stress disorder and factors associated with low antenatal care attendance among others. The developed priority research questions can be used to focus health research and to inform appropriation of health research resources to questions that contribute to generation of local health system knowledge which is required to accelerate progress toward universal health coverage in Somalia. The Somalia national institute of health should set up a consortium for provision of technical and financial support for research addressing the identified priority research questions, establish a mechanism to continuously monitor the extent to which new health interventions in Somalia are informed by knowledge generated through conducting prioritized health research and prioritize interventions aimed at strengthening the broader national health research system for Somalia.

Age-related alterations in the cardiovascular responses to acute exercise in males and females: role of the exercise pressor reflex.

Autonomic adjustments of the cardiovascular system are critical for initiating and sustaining exercise by facilitating the redistribution of blood flow and oxygen delivery to meet the metabolic demands of the active skeletal muscle. Afferent feedback from active skeletal muscles evokes reflex increases in sympathetic nerve activity and blood pressure (BP) (i.e., exercise pressor reflex) and contributes importantly to these primary neurovascular adjustments to exercise. When altered, this reflex contributes significantly to the exaggerated sympathetic and BP response to exercise observed in many cardiovascular-related diseases, highlighting the importance of examining the reflex and its underlying mechanism(s). A leading risk factor for the pathogenesis of cardiovascular disease in both males and females is aging. Although regular exercise is an effective strategy for mitigating the health burden of aging, older adults face a greater risk of experiencing an exaggerated cardiovascular response to exercise. However, the role of aging in mediating the exercise pressor reflex remains highly controversial, as conflicting findings have been reported. This review aims to provide a brief overview of the current understanding of the influence of aging on cardiovascular responses to exercise, focusing on the role of the exercise pressor reflex and proposing future directions for research. We reason that this review will serve as a resource for health professionals and researchers to stimulate a renewed interest in this critical area.

The Kids’ Environment and Health Cohort: a national, linked data resource for environmental child health research

Introduction &amp; BackgroundEvidence is mounting that children’s physical environment (e.g. in and around the home, school, and neighbourhood) is critical for their long-term health and education. Early life exposure to factors such as indoor and outdoor air pollution, or a lack of access to greenspaces are associated with the development of long-term health conditions such as asthma or mental health problems. Local and central government in England are implementing numerous policies to improve air quality and housing, and mitigate climate change. Further, England has seen large scale changes to local service provision (including childcare and libraries) due to austerity policies and the COVID-19 pandemic. Currently, there is no national, linked data resource for England that allows research into how the local environment impacts children’s health and education.&#x0D; Objectives &amp; ApproachThe Kids’ Environment and Health Cohort will be a new, linked national data resource for England currently being developing by researchers from UCL, London School of Hygiene and Tropical Medicine, London School of Economics and Political Science, Brock University, and City, University of London in collaboration with the Office for National Statistics (ONS), and funded by Administrative Data Research-UK (ADR-UK). The Kids’ Environment and Health Cohort will be a de-identified and annually updated national birth cohort of all children born in England from 2006 onwards – around 10.5 million children until 2023. The cohort will be constructed using linked administrative data from vital registration (live and stillbirth, and death registration), Census (housing and socio-economic indicators), health (hospital contacts, mental health referrals, and community dispensing data), and education (key stage results, special educational needs, absenteeism). Environmental exposure data can be securely linked to the Cohort via longitudinal residential unique property reference numbers (UPRNs) and postcodes from the Personal Demographic Service, and school location from education records.&#x0D; Relevance to Digital FootprintsThe Kids’ Environment and Health Cohort will, for the first time, link health, education, Census and environmental data at national level in England. It will allow researchers to integrate data on local environments, including physical characteristics (such as temperature, building energy efficiency, or greenspace access) or the social environment (including proximity to food outlets, or services like libraries) with individual level data on health and education outcomes in children. This will be done using the ONS’s 5 safes framework, ensuring highest standards of data security and confidentiality.&#x0D; ResultsThe Kids’ Environment and Health Cohort will be constructed using administrative datasets, including national linked vital statistics, health, education and Census data from multiple data providers (ONS, NHS England and Department for Education), combined with small-area level environmental data for England. Together, these datasets allow detailed analyses of the impact of environmental exposures on health and education outcomes in children, with robust confounder adjustment. The Kids’ Environment and Health Cohort will be made available in a de-identified format in the ONS Secure Research Service (SRS).&#x0D; Conclusions &amp; ImplicationsThe Kids’ Environment and Health Cohort will provide researchers secure access to a national data resource integrating environmental and administrative health and education data, for child public health research.

The national child odontology registry (SCOR): a valuable resource for odontological and public health research

BackgroundSince 1972 The National Child Odontology Registry has collected data on the oral health of most of all Danish children and adolescents. However, comprehensive information on the registry has not previously been available, making it difficult to approach and use the registry for research purposes.MethodsBy combining historical documentation and simple descriptive statistics we provide an overview of major events in the timeline of The National Child Odontology Registry and discuss how they impact the available data. We provide a broad overview of the dental variables in the registry, and how the registration criteria for some of the core dental variables (gingivitis, periodontitis, and dental caries) have changed over time. We then provide examples of how aggregate variables for the core dental diseases, allowing for comparison across registration criteria, can be created.ResultsMost of the Danish population born during or after 1965 have a least one entry in the National Child Odontology Registry, with 68% having entries spanning their entire childhood and adolescence. The prevalence of gingivitis and periodontitis seem to increase significantly in the years immediately following changes in how registration criteria for these variables, raising questions as to whether these diseases are generally underreported, or subject to overreporting in the years following the registration changes. The mandatory ages of registration instituted in 2003, do not appear to have had a strong impact on the ages at which registrations are made. For variables not directly comparable across datasets due to changes in registration criteria aggregate variables of measurements can be computed in most cases.ConclusionsThe National Child Odontology Registry provides a unique opportunity to study the impact of childhood oral health on life trajectories, but using the registry is not without issues, and we strongly recommend consulting with experts in the field of odontology to ensure the best use of available data.

A genderful research world: rapid review, design, and pilot study of an interactive platform for curated sex and gender health research resources

BackgroundIntegration of sex and gender into health research is best practice for designing and conducting equitable, rigorous scientific research. Many evidence-based resources exist to support researchers in this endeavour, but such resources often remain underutilized as they are difficult to find, are not publicly accessible, or are specific to a particular research phase, context, or population. The development and evaluation of a repository of resources was deemed important to create an accessible platform for promoting sex- and gender-integration in health research.MethodsA rapid review was conducted of critical resources for conducting sex and gender health research. These were integrated into a prototype website design (the Genderful Research World; GRW) that provided an interactive digital landscape for researchers to access these resources. A pilot study evaluated the GRW website for applicability, desirability, and usability with an international sample of 31 health researchers from various disciplines and career stages. Quantitative data from the pilot study was summarized with descriptive statistics. Qualitative data was summarized narratively and used to identify concrete elements for improvement in a second design iteration.ResultsResults of the pilot study revealed that the GRW was considered user friendly and desirable by health researchers and helped them access relevant information. Feedback suggested that providing these resources in a playful way may enhance the experience of the user, particularly given the high ‘desirability’ scores and that users emphasized the interactive layout as being key to their intention to integrate it into their teaching endeavors. Key feedback from the pilot study (e.g., addition of resources specific to research with transgender populations, revision of website layout) was integrated into the current version of the website: www.genderfulresearchworld.com.ConclusionsThe present research suggests a utility for a repository of resources for integrating sex and gender considerations into research, and that providing a logical, intuitive means of cataloguing and navigating such resources is critical for usability. The results of this study may inform the development of other novel researcher-directed resource curation efforts to address health equity issues and encourage and support health researchers to integrate a sex and gender perspective in their work.

A simple fruit and vegetable score is a valid tool to assess actual fruit and vegetable intake.

With the recent growing interest in improving fruit and vegetable intake for better health and limited research resources in many settings, simple-to-administer and low-priced indicators are essential tools for monitoring fruit and vegetable intake at the population level. A potential candidate indicator is the fruit and vegetable component of the Global Dietary Recommendation score (FV-GDR) based on data collected using the Diet Quality Questionnaire (DQQ). We investigated the relative validity of FV-GDR collected with the DQQ to measure fruit and vegetable intake by comparison with a 24-h recall (24hR) as a reference collected from 620 Vietnamese and 630 Nigerian adults in 2021. We found proportional differences in the prevalence of intake of vitamin A-rich vegetables, other vegetables and other fruits in Vietnam and all vegetable food groups in Nigeria. In both countries, we found a small difference in the total FV-GDR from DQQ compared with the 24hR, and the percentage of agreement between the two methods was quite high for the majority of the food groups. The FV-GDR calculated from the DQQ correlated with the actual intake, although less strongly than the FV-GDR from 24hR. The DQQ is a promising low-burden, low-cost and simple tool to calculate FV-GDR and to monitor fruit and vegetable consumption at the population level. This provides the possibility of evaluating an important aspect of diet quality in low-resource settings.

Using electronic health record data to link families: an illustrative example using intergenerational patterns of obesity.

Electronic health record (EHR) data are a valuable resource for population health research but lack critical information such as relationships between individuals. Emergency contacts in EHRs can be used to link family members, creating a population that is more representative of a community than traditional family cohorts. We revised a published algorithm: relationship inference from the electronic health record (RIFTEHR). Our version, Pythonic RIFTEHR (P-RIFTEHR), identifies a patient's emergency contacts, matches them to existing patients (when available) using network graphs, checks for conflicts, and infers new relationships. P-RIFTEHR was run on December 15, 2021 in the Northwestern Medicine Electronic Data Warehouse (NMEDW) on approximately 2.95 million individuals and was validated using the existing link between children born at NM hospitals and their mothers. As proof-of-concept, we modeled the association between parent and child obesity using logistic regression. The P-RIFTEHR algorithm matched 1 157 454 individuals in 448 278 families. The median family size was 2, the largest was 32 persons, and 247 families spanned 4 generations or more. Validation of the mother-child pairs resulted in 95.1% sensitivity. Children were 2 times more likely to be obese if a parent is obese (OR: 2.30; 95% CI, 2.23-2.37). P-RIFTEHR can identify familiar relationships in a large, diverse population in an integrated health system. Estimates of parent-child inheritability of obesity using family structures identified by the algorithm were consistent with previously published estimates from traditional cohort studies.

An evaluation of existing text de-identification tools for use with patient progress notes from Australian general practice

IntroductionDigitized patient progress notes from general practice represent a significant resource for clinical and public health research but cannot feasibly and ethically be used for these purposes without automated de-identification. Internationally, several open-source natural language processing tools have been developed, however, given wide variations in clinical documentation practices, these cannot be utilized without appropriate review. We evaluated the performance of four de-identification tools and assessed their suitability for customization to Australian general practice progress notes. MethodsFour tools were selected: three rule-based (HMS Scrubber, MIT De-id, Philter) and one machine learning (MIST). 300 patient progress notes from three general practice clinics were manually annotated with personally identifying information. We conducted a pairwise comparison between the manual annotations and patient identifiers automatically detected by each tool, measuring recall (sensitivity), precision (positive predictive value), f1-score (harmonic mean of precision and recall), and f2-score (weighs recall 2x higher than precision). Error analysis was also conducted to better understand each tool’s structure and performance. ResultsManual annotation detected 701 identifiers in seven categories. The rule-based tools detected identifiers in six categories and MIST in three. Philter achieved the highest aggregate recall (67%) and the highest recall for NAME (87%). HMS Scrubber achieved the highest recall for DATE (94%) and all tools performed poorly on LOCATION. MIST achieved the highest precision for NAME and DATE while also achieving similar recall to the rule-based tools for DATE and highest recall for LOCATION. Philter had the lowest aggregate precision (37%), however preliminary adjustments of its rules and dictionaries showed a substantial reduction in false positives. ConclusionExisting off-the-shelf solutions for automated de-identification of clinical text are not immediately suitable for our context without modification. Philter is the most promising candidate due to its high recall and flexibility however will require extensive revising of its pattern matching rules and dictionaries.

Application of artificial intelligence to the public health education.

With the global outbreak of coronavirus disease 2019 (COVID-19), public health has received unprecedented attention. The cultivation of emergency and compound professionals is the general trend through public health education. However, current public health education is limited to traditional teaching models that struggle to balance theory and practice. Fortunately, the development of artificial intelligence (AI) has entered the stage of intelligent cognition. The introduction of AI in education has opened a new era of computer-assisted education, which brought new possibilities for teaching and learning in public health education. AI-based on big data not only provides abundant resources for public health research and management but also brings convenience for students to obtain public health data and information, which is conducive to the construction of introductory professional courses for students. In this review, we elaborated on the current status and limitations of public health education, summarized the application of AI in public health practice, and further proposed a framework for how to integrate AI into public health education curriculum. With the rapid technological advancements, we believe that AI will revolutionize the education paradigm of public health and help respond to public health emergencies.

Experiences of Gender-Based Violence Among Disabled Women: A Qualitative Systematic Review and Meta-Synthesis Protocol

Background Gender-based violence (GBV) is a major public health concern and a human rights issue disproportionately affecting disabled women. Disabled women are twice as likely to experience GBV than nondisabled women, yet there has been a lack of attention to this issue. This review aims to gain a greater understanding of the experiences of GBV of disabled women through a systematic and qualitative meta-synthesis. The qualitative meta-synthesis will be conducted by a research team of academic and community members and students with varying lived and service provider experiences with disabilities and GBV. The study findings aim to promote best practices by offering solutions to increase accessible and inclusive resources and services responsive to disabled women. Methods A systematic review of qualitative studies will be performed based on searches of 12 academic databases, including MEDLINE (Ovid), APA PsycINFO (EBSCO), Sociological Abstracts (ProQuest), Social Services Abstracts (ProQuest), and SocINDEX (EBSCO). A search of the gray literature will be performed by searching the Google search engine, Google Scholar, the Community Health Online Digital Archive and Research Resource (CHODARR), and the Global Database on Violence Against Women. In addition to the database and gray literature searching, we will complete backward and forward citation tracing. Two research team members will be involved in all screening, review, data extraction, and quality assessment of studies. A third reviewer will resolve any disagreements and consult with the research team. Thematic synthesis steps will include becoming familiar with the data, developing a thematic framework, indexing the data to identify themes and codes, charting the data, and mapping and interpreting the data. The Critical Appraisal Skills Programme checklist will be used to appraise the quality of included studies. Confidence in the meta-synthesis findings will be assessed by applying the GRADECERQual approach. Review Registration This protocol is registered with the International Prospective Register of Systematic Reviews (PROSPERO): CRD42023400410.

Taiwan Biobank: A rich biomedical research database of the Taiwanese population

The Taiwan Biobank (TWB) is an ongoing prospective study of >150,000 individuals aged 20-70 in Taiwan. A comprehensive list of phenotypes was collected for each consented participant at recruitment and follow-up visits through structured interviews and physical measurements. Biomarkers and genetic data were generated from blood and urine samples. We present here an overview of TWB's genetic data quality, population structure, and familial relationship, which consists of predominantly Han Chinese ancestry, and highlight its important attributes and genetic findings thus far. A linkage to Taiwan's National Health Insurance database of >25 years and other registries is underway to enrich the phenotypic spectrum and enable deep and longitudinal genetic investigations. TWB provides one of the largest biobank resources for biomedical and public health research in East Asia that will contribute to our understanding of the genetic basis of health and disease in global populations through collaborative studies with other biobanks.

Towards the Exposome Project for Health and Occupational Research (EPHOR) Mega Cohort

BACKGROUND AND AIM: Existing cohort studies in Europe capturing occupational information enrol millions of persons. They are invaluable resources for understanding relationships between occupation and health. Within the Exposome Project for Health and Occupational Research (EPHOR) we aim to systematically construct a mega cohort combining data from multiple individual cohort studies to study the working life exposome and health. METHODS: Participating cohorts are registered in an open online inventory containing cohort meta-data. A set of harmonised variables is being prepared, including occupational as well as non-occupational factors (personal, lifestyle, sociodemographic, other), and a framework for de-centralized large-scale pooling of data for epidemiological analyses is being implemented. Lifetime occupational histories are being coded and harmonised across coding systems and will be linked with a European Job-Exposure Matrix (JEM) being developed. A range of harmonised outcome data including cancer, as well as respiratory, metabolic and cardiovascular, neurodegenerative, and musculoskeletal diseases, mental disorders, and work participation will be defined based on registry, record, or other follow-up methods. RESULTS: The EPHOR mega cohort currently includes participation of 28 cohorts from eight European countries, as well as multi-country studies, containing data on over 20 million participants. Initial cohorts were selected from the inventory with cancer diagnoses, respiratory outcomes, and/or information on working time prioritised in this first phase. Additional cohorts will be invited to participate here. The mega cohort will be used for a range of exposome analyses including 1) systematic and agnostic exploration of multiple risk factors in relation to health outcomes, 2) to identify vulnerable life stages and population subgroups, 3) to study rare exposures/outcomes. CONCLUSIONS: We expect collaboration to be useful to explore relationships between occupations, multiple work-related exposures and health. The mega cohort is intended to be a long-term resource for occupational health researchers. KEYWORDS: cohorts, data pooling, exposome, harmonisation, occupation

Closing the global pain divide: balancing access and excess

Closing the global pain divide: balancing access and excess

Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research.

BackgroundAdvances in automated data processing and machine learning (ML) models, together with the unprecedented growth in the number of social media users who publicly share and discuss health-related information, have made public health surveillance (PHS) one of the long-lasting social media applications. However, the existing PHS systems feeding on social media data have not been widely deployed in national surveillance systems, which appears to stem from the lack of practitioners and the public’s trust in social media data. More robust and reliable data sets over which supervised ML models can be trained and tested reliably is a significant step toward overcoming this hurdle. The health implications of daily behaviors (physical activity, sedentary behavior, and sleep [PASS]), as an evergreen topic in PHS, are widely studied through traditional data sources such as surveillance surveys and administrative databases, which are often several months out-of-date by the time they are used, costly to collect, and thus limited in quantity and coverage.ObjectiveThe main objective of this study is to present a large-scale, multicountry, longitudinal, and fully labeled data set to enable and support digital PASS surveillance research in PHS. To support high-quality surveillance research using our data set, we have conducted further analysis on the data set to supplement it with additional PHS-related metadata.MethodsWe collected the data of this study from Twitter using the Twitter livestream application programming interface between November 28, 2018, and June 19, 2020. To obtain PASS-related tweets for manual annotation, we iteratively used regular expressions, unsupervised natural language processing, domain-specific ontologies, and linguistic analysis. We used Amazon Mechanical Turk to label the collected data to self-reported PASS categories and implemented a quality control pipeline to monitor and manage the validity of crowd-generated labels. Moreover, we used ML, latent semantic analysis, linguistic analysis, and label inference analysis to validate the different components of the data set.ResultsLPHEADA (Labelled Digital Public Health Dataset) contains 366,405 crowd-generated labels (3 labels per tweet) for 122,135 PASS-related tweets that originated in Australia, Canada, the United Kingdom, or the United States, labeled by 708 unique annotators on Amazon Mechanical Turk. In addition to crowd-generated labels, LPHEADA provides details about the three critical components of any PHS system: place, time, and demographics (ie, gender and age range) associated with each tweet.ConclusionsPublicly available data sets for digital PASS surveillance are usually isolated and only provide labels for small subsets of the data. We believe that the novelty and comprehensiveness of the data set provided in this study will help develop, evaluate, and deploy digital PASS surveillance systems. LPHEADA will be an invaluable resource for both public health researchers and practitioners.