Dataset Information

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity.

ABSTRACT:

Introduction

Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbidity, characterise clusters that lead to highest burden on individuals and healthcare services, and evaluate and provide new multimorbidity phenotypes and algorithms to the National Health Service and research communities to support prevention, healthcare planning and the management of individuals with multimorbidity.

Methods and analysis

The WMC has been created and derived from multisourced demographic, administrative and electronic health record data relating to the Welsh population in the Secure Anonymised Information Linkage (SAIL) Databank. The WMC consists of 2.9 million people alive and living in Wales on the 1 January 2000 with follow-up until 31 December 2019, Welsh residency break or death. Published comorbidity indices and phenotype code lists will be used to measure and conceptualise multimorbidity.Study outcomes will include: (1) a description of multimorbidity using published data phenotype algorithms/ontologies, (2) investigation of the associations between baseline demographic factors and multimorbidity, (3) identification of temporal trajectories of clusters of conditions and multimorbidity and (4) investigation of multimorbidity clusters with poor outcomes such as mortality and high healthcare service utilisation.

Ethics and dissemination

The SAIL Databank independent Information Governance Review Panel has approved this study (SAIL Project: 0911). Study findings will be presented to policy groups, public meetings, national and international conferences, and published in peer-reviewed journals.

SUBMITTER: Lyons J

PROVIDER: S-EPMC7817800 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity.

Lyons Jane J Akbari Ashley A Agrawal Utkarsh U Harper Gill G Azcoaga-Lorenzo Amaya A Bailey Rowena R Rafferty James J Watkins Alan A Fry Richard R McCowan Colin C Dezateux Carol C Robson John P JP Peek Niels N Holmes Chris C Denaxas Spiros S Owen Rhiannon R Abrams Keith R KR John Ann A O'Reilly Dermot D Richardson Sylvia S Hall Marlous M Gale Chris P CP Davies Jan J Davies Chris C Cross Lynsey L Gallacher John J Chess James J Brookes Anthony J AJ Lyons Ronan A RA

BMJ open 20210119 1

<h4>Introduction</h4>Multimorbidity is widely recognised as the presence of two or more concurrent long-term conditions, yet remains a poorly understood global issue despite increasing in prevalence.We have created the Wales Multimorbidity e-Cohort (WMC) to provide an accessible research ready data asset to further the understanding of multimorbidity. Our objectives are to create a platform to support research which would help to understand prevalence, trajectories and determinants in multimorbi ...[more]

PMID: 33468531

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Project description:PurposeRecruitment and follow-up in epidemiological studies are time-consuming and expensive. Combining online data collection with a register of individuals who agree to be contacted about research opportunities provides an efficient, cost-effective platform for population-based research. HealthWise Wales (HWW) aims to facilitate research by recruiting a cohort of individuals who have consented to be informed about research projects, advertising studies to participants, supporting data collection on specific topics and providing access to linked healthcare data for secondary analyses. In this paper, we describe the design of the project, ongoing data collection, methods of data linkage to routine healthcare records, baseline characteristics of participants, the strengths and limitations of the register, and the ways in which the project can support researchers.ParticipantsAdults (aged 16 years and above) living or receiving their healthcare in Wales are eligible for inclusion. Participants consent to be contacted for follow-up data collection and for their details to be used to access their routinely collected National Health Service records for research purposes. Data are collected using a web-based application, with new questionnaires added every 6?months. Data collection on sociodemographic and lifestyle factors is repeated at intervals of 2-3 years. Recruitment is ongoing, with 21?779 participants alive and currently registered.Findings to date99% of participants have complete information on age and sex, and 64% have completed questionnaires on sociodemographic and lifestyle factors. These data can be linked with national health databases within the Secure Anonymised Information Linkage (SAIL) databank, with 93% of participants matching a record in SAIL. HWW has facilitated the recruitment of 43?826 participants to 15 different studies.Future plansThe medium-term goal for the project is to enrol at least 50?000 adults. Recruitment strategies are being devised to achieve a study sample that closely models the population of Wales. Potential biosampling methods are also currently being explored.

Project description:BackgroundMultimorbidity is defined as the presence of two or more chronic diseases and associated comorbidities. There is a need to improve best practices around the provision of well-coordinated, person-centered care for persons with multimorbidities. Present health systems across the European Union (EU) focus on supporting a single-disease framework of care; the primary challenge is to create a patient-centric, integrated care ecosystem to understand and manage multimorbidity. ProACT is a large-scale project funded by the European Commission under the Horizon 2020 programme, that involved the design, development, and evaluation of a digital health platform to improve and advance home-based integrated care, and supported self-management, for older adults (aged ≥65 years) living with multimorbidity.ObjectiveThis paper describes the trial implementation protocol of a proof-of-concept digital health platform (ProACT) in 2 EU member states (Ireland and Belgium) to support older persons with multimorbidities self-managing at home, supported by their care network (CN).MethodsResearch was conducted across 2 EU member states, Ireland and Belgium. A 12-month action research trial design, divided into 3 evaluation cycles and lasting 3 months each, with a reflective redesign and development phase of 1 month after cycles 1 and 2 was conducted. Participants were 120 (60/120, 50% in Ireland and 60/120, 50% in Belgium) older persons with multimorbidities diagnosed with two or more of the following chronic conditions: diabetes, chronic obstructive pulmonary disease, chronic heart failure, and cardiovascular diseases. With permission from persons with multimorbidities, members of their CN were invited to participate in the study. Persons with multimorbidities were provided with ProACT technologies (tablet, devices, or sensors) to support them in self-managing their conditions. CN members also received access to an app to remotely support their persons with multimorbidity. Qualitative and quantitative feedback and evaluation data from persons with multimorbidity and CN participants were collected across four time points: baseline (T1), at the end of each 3-month action research cycle (T2 and T3), and in a final posttrial interview (T4). Thematic analysis was used to analyze the qualitative interview data. Quantitative data were analyzed via platform use statistics (to assess engagement) and standardized questionnaires (using descriptive and inferential statistics). This study is approved by the ethics committees of Ireland and Belgium.ResultsThe trial implementation phase for this 44-month (2016-2019) funded study was April 2018 to June 2019. The trial outcomes are at various stages of publication since 2021.ConclusionsProACT aims to co-design and develop a digital intervention with persons with multimorbidities and their CN, incorporating clinical guidelines with the state of the art in human-computer interaction, behavioral science, health psychology, and data analytic methods to deliver a digital health platform to advance self-management of multimorbidity at home, as part of a proactive, integrated model of supported person-centered care.International registered report identifier (irrid)RR1-10.2196/22125.

Project description:INTRODUCTION:Long-term and late effects of cancer treatments can cause functional limitations and reduce quality of life. Cancer rehabilitation services, which can comprise physical exercise, psychological support and educational interventions depending on the individual's needs, have been found to have a positive effect on health-related quality of life worldwide. However, accessibility or the lack of awareness on available help can act as barriers and influence the uptake of services, resulting in people having unmet rehabilitation needs. In Wales, UK, 41% of people, who have had health and social care needs resulting from cancer and its treatments, reported that they did not receive care when needed. The reason for this lack of support has not yet been fully investigated. The aim of this study is to investigate the conditions in which cancer rehabilitation services work and their underpinning mechanisms in South Wales, UK, specifically addressing barriers, facilitators and costs. METHODS AND ANALYSIS:Realist evaluation, which explains for whom a service works in what circumstances and how through context-mechanism-outcome pattern conjunctions, will be used in three phases to investigate the conditions in which cancer rehabilitation services work and their underpinning mechanisms. Phase 1 will be secondary analysis of a cancer rehabilitation database from a local Health Board to give context to who are accessing rehabilitation. Phase 2 will be thematic analysis of face-to-face, semistructured rehabilitation participant (n=20) and healthcare professional (n=20) interviews to explore the mechanisms of how cancer rehabilitation works. Phase 3 will be two case studies and cost-consequences analysis of cancer rehabilitation services. ETHICS AND DISSEMINATION:This study received favourable ethical opinion from London South-East Research Ethics Committee (17/LO/2123) in December 2017. This project is part of the author's PhD thesis and it is expected that the findings will be disseminated in academic journals and at local and international conferences.

Project description:BACKGROUND:Multimorbidity is a global health issue, particularly for older adults in the primary care setting. An adequate portrayal of its epidemiology is essential to properly identify and understand the health care needs of this population. This study aimed to compare the differences in the prevalence of selected chronic conditions and multimorbidity, including its associated characteristics, using health survey/self-reported (SR) information only, administrative (Adm) data only and the combined (either) sources. METHODS:This was a secondary analysis of survey data from the first cycle of the Longitudinal Survey on Senior's Health and Health Services Use linked to health-Adm data. The analytical sample consisted of 1625 community-dwelling older adults (≥65 years) recruited in the waiting rooms of primary health clinics in a selected administrative region of the province of Quebec. Seventeen chronic conditions were assessed according to two different data sources. We examined the differences in the observed prevalence of chronic conditions and multimorbidity and the agreement between data sources. RESULTS:The prevalence of each of the 17 chronic conditions ranged from 1.2 to 68.7% depending on the data source. The agreement between different data sources was highly variable, with kappa coefficients (κ) ranging from 0.05 to 0.73. Multimorbidity was very high in this population, with an estimated prevalence of up to 95.9%. In addition, we found that the association between sociodemographic and behavioural factors and the presence of multimorbidity varied according to the different data sources and thresholds. CONCLUSIONS:This is the first study to simultaneously investigate chronic conditions and multimorbidity prevalence among primary care older adults using combined SR and health-Adm data. Our results call attention to (1) the possibility of underestimating cases when using a single data source and (2) the potential benefits of integrating information from different data sources to increase case identification. This is an important aspect of characterizing the health care needs of this fast-growing population.

Project description:Background: There is limited knowledge of the scale and impact of multimorbidity for patients who have had an acute myocardial infarction (AMI). Therefore, this study aimed to determine the extent to which multimorbidity is associated with long-term survival following AMI.Methods and findings: This national observational study included 693,388 patients (median age 70.7 years, 452,896 [65.5%] male) from the Myocardial Ischaemia National Audit Project (England and Wales) who were admitted with AMI between 1 January 2003 and 30 June 2013. There were 412,809 (59.5%) patients with multimorbidity at the time of admission with AMI, i.e., having at least 1 of the following long-term health conditions: diabetes, chronic obstructive pulmonary disease or asthma, heart failure, renal failure, cerebrovascular disease, peripheral vascular disease, or hypertension. Those with heart failure, renal failure, or cerebrovascular disease had the worst outcomes (39.5 [95% CI 39.0-40.0], 38.2 [27.7-26.8], and 26.6 [25.2-26.4] deaths per 100 person-years, respectively). Latent class analysis revealed 3 multimorbidity phenotype clusters: (1) a high multimorbidity class, with concomitant heart failure, peripheral vascular disease, and hypertension, (2) a medium multimorbidity class, with peripheral vascular disease and hypertension, and (3) a low multimorbidity class. Patients in class 1 were less likely to receive pharmacological therapies compared with class 2 and 3 patients (including aspirin, 83.8% versus 87.3% and 87.2%, respectively; ?-blockers, 74.0% versus 80.9% and 81.4%; and statins, 80.6% versus 85.9% and 85.2%). Flexible parametric survival modelling indicated that patients in class 1 and class 2 had a 2.4-fold (95% CI 2.3-2.5) and 1.5-fold (95% CI 1.4-1.5) increased risk of death and a loss in life expectancy of 2.89 and 1.52 years, respectively, compared with those in class 3 over the 8.4-year follow-up period. The study was limited to all-cause mortality due to the lack of available cause-specific mortality data. However, we isolated the disease-specific association with mortality by providing the loss in life expectancy following AMI according to multimorbidity phenotype cluster compared with the general age-, sex-, and year-matched population.Conclusions: Multimorbidity among patients with AMI was common, and conferred an accumulative increased risk of death. Three multimorbidity phenotype clusters that were significantly associated with loss in life expectancy were identified and should be a concomitant treatment target to improve cardiovascular outcomes.Trial registration: ClinicalTrials.gov NCT03037255.

Project description:BackgroundHealth is a result of influences operating at multiple levels. For example, inadequate housing, poor educational attainment, and reduced access to health care are clustered together, and are all associated with reduced health. Policies which try to change individual people's behaviour have limited effect when people have little control over their environment. However, structural environmental change and an understanding of the way that influences interact with each other, has the potential to facilitate healthy choices irrespective of personal resources. The aim of Environments for Healthy Living (EHL) is to investigate the impact of gestational and postnatal environments on health, and to examine where structural change can be brought about to optimise health outcomes. The baseline assessment will focus on birth outcomes and maternal and infant health.Methods/designEHL is a longitudinal birth cohort study. We aim to recruit 1000 pregnant women in the period April 2010 to March 2013. We will examine the impact of the gestational environment (maternal health) and the postnatal environment (housing and neighbourhood conditions) on subsequent health outcomes for the infants born to these women. Data collection will commence during the participants' pregnancy, from approximately 20 weeks gestation. Participants will complete a questionnaire, undergo anthropometric measurements, wear an accelerometer, compile a food diary, and have environmental measures taken within their home. They will also be asked to consent to having a sample of umbilical cord blood taken following delivery of their baby. These data will be complemented by routinely collected electronic data such as health records from GP surgeries, hospital admissions, and child health and development records. Thereafter, participants will be visited annually for follow-up of subsequent exposures and child health outcomes.DiscussionThe baseline assessment of EHL will provide information concerning the impact of gestational and postnatal environments on birth outcomes and maternal and infant health. The findings can be used to inform the development of complex interventions targeted at structural, environmental factors, intended to reduce ill-health. Long-term follow-up of the cohort will focus on relationships between environmental exposures and the later development of adverse health outcomes, including obesity and diabetes.

Project description:There is increasing interest in using systematic review to synthesize evidence on the social and environmental effects of and adaptations to climate change. Use of systematic review for evidence in this field is complicated by the heterogeneity of methods used and by uneven reporting. In order to facilitate synthesis of results and design of subsequent research a method, construct-centered methods aggregation, was designed to 1) provide a transparent, valid and reliable description of research methods, 2) support comparability of primary studies and 3) contribute to a shared empirical basis for improving research practice. Rather than taking research reports at face value, research designs are reviewed through inductive analysis. This involves bottom-up identification of constructs, definitions and operationalizations; assessment of concepts' commensurability through comparison of definitions; identification of theoretical frameworks through patterns of construct use; and integration of transparently reported and valid operationalizations into ideal-type research frameworks. Through the integration of reliable bottom-up inductive coding from operationalizations and top-down coding driven from stated theory with expert interpretation, construct-centered methods aggregation enabled both resolution of heterogeneity within identically named constructs and merging of differently labeled but identical constructs. These two processes allowed transparent, rigorous and contextually sensitive synthesis of the research presented in an uneven set of reports undertaken in a heterogenous field. If adopted more broadly, construct-centered methods aggregation may contribute to the emergence of a valid, empirically-grounded description of methods used in primary research. These descriptions may function as a set of expectations that improves the transparency of reporting and as an evolving comprehensive framework that supports both interpretation of existing and design of future research.

Dataset Information

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity.

Introduction

Methods and analysis

Ethics and dissemination

Publications

Protocol for the development of the Wales Multimorbidity e-Cohort (WMC): data sources and methods to construct a population-based research platform to investigate multimorbidity.

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