Understanding Multiple Long-Term Condition Multimorbidity
Examining the rise of MLTC-M and its impact on health.
― 5 min read
Table of Contents
Multiple Long-Term Condition Multimorbidity (MLTC-M) refers to the situation when a person has two or more ongoing health issues at the same time. This condition is becoming more common worldwide due to various reasons, such as lifestyle changes, an older population, and more frequent diagnosis of chronic diseases. In the UK, it's estimated that more than half of people aged 65 and older live with multiple long-term conditions, and by 2035, this number is expected to rise to two-thirds of that age group.
Living with MLTC-M can be difficult for individuals, their caregivers, and Healthcare services. It is often linked with a poorer quality of life, complicated and expensive medical care, the need for multiple medications, increased stress, longer hospital stays, and higher death rates. It also adds to inefficiencies and costs in healthcare systems.
Despite its significance, different aspects of MLTC-M are not fully understood. Much of the research has mainly looked at older adults in wealthy countries and has often concentrated on a few health conditions. There is not enough research on how factors like social status and long-term trends relate to MLTC-M. Additionally, there's limited information on how various social and behavioral factors contribute to rare patterns of MLTC-M and how early exposure to risk factors affects the development of long-term conditions.
Moreover, there hasn’t been enough focus on ways to stop the development of MLTC-M.
What is the MELD-B Project?
The Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) project seeks to fill these gaps by looking closely at what it means to live with burdensome multimorbidity. The project aims to find new combinations of health issues and identify important risk factors from early life. It also plans to map out how people in their younger years develop burdensome clusters and model prevention strategies to guide health policy.
To conduct this research, the project uses Data from various birth Cohorts and electronic health records. It combines Artificial Intelligence with traditional research methods to analyze this data.
Challenges with Using Electronic Health Records
Using electronic health records (EHR) for this research comes with challenges. The data collected in EHRs is mainly for clinical and administrative needs rather than research. This means the records can sometimes be incomplete or incorrect, and they may not all follow the same standards.
Despite these challenges, MELD-B recognizes the value of using large EHR databases, such as those in Wales. These databases offer large samples, long follow-up times, and a variety of information that can be applied to a broader population.
Creating the E-Cohorts
To support the MELD-B project, two longitudinal population-based e-cohorts have been created: the SAIL MELD-B e-cohort (SMC) and the SAIL MELD-B Children and Young Adult e-cohort (SMYC). These e-cohorts represent the wider Welsh population regarding age, sex, and socio-economic status. They will be used to address multiple research questions within the MELD-B project.
The SMC includes all individuals registered with a Welsh GP from January 1, 2000, to December 31, 2022. It allows for a general view of the population over time. SMYC, on the other hand, focuses on individuals born after January 1, 2000, who have demographic data before the age of 18.
Characteristics of the Cohorts
The SMC is based on a variety of demographic and health data collected from the Welsh population. This data helps in assessing the burden of MLTC-M and understanding its broader factors. The inclusion of young individuals in SMYC helps researchers learn how early life environments affect the risk of developing these conditions later.
Both cohorts will provide insights into healthcare interactions and patient experiences related to MLTC-M.
Data Sources Used
The SAIL Databank contains anonymized data for all Welsh residents receiving National Health Service services. It links individual records using a unique identifier, which protects privacy. To create the SMC and SMYC, links were made to various demographic and health data sources, such as birth and death records, child health databases, and maternity records.
This data will be analyzed to explore various health conditions and uncover their connections to broader social and economic factors.
Concept Curation Pipeline
The MELD-B project has developed a structured process called a concept curation pipeline to better understand burdens associated with MLTC-M. This pipeline helps in identifying and organizing various health conditions and other relevant factors from the collected data.
The first step is for clinicians to propose concepts that reflect different burdens. Approved concepts are processed and linked with routine health data to ensure that relevant information can be analyzed.
This process is crucial for creating a clear picture of how living with multiple long-term conditions affects individuals.
Early Findings
The SMC and SMYC cohorts are designed to be representative of the Welsh population. From the available data, over 5 million individuals are part of SMC, while nearly 900,000 are included in SMYC. These cohorts will allow researchers to track health changes and social Demographics over time.
Initial data shows that considerable portions of both cohorts engage with healthcare services, highlighting the importance of these interactions in understanding and managing MLTC-M.
Strengths and Limitations of the Study
The principal strength of the SMC and SMYC e-cohorts lies in their comprehensive coverage of the population, which makes them valid for examining a wide range of research questions. Being able to link various demographic and health data enhances the scope of the research.
However, there are limitations to using EHR data, such as potential gaps and inaccuracies. Additionally, EHRs may lack insights into personal experiences that are vital for understanding the full impact of living with multiple chronic conditions.
Conclusion
MLTC-M is a growing concern, particularly within older populations. The MELD-B project aims to shed light on this issue by studying its burden and associated factors over a person's lifetime. The created e-cohorts will provide valuable data to help improve understanding and inform future healthcare strategies.
As researchers continue to investigate the complexities of MLTC-M, the findings will assist in developing effective interventions and improving the quality of life for those affected.
Title: Cohort profile for the creation of the SAIL MELD-B e-cohort (SMC) and SAIL MELD-B children and Young adult e-cohort (SMYC)
Abstract: PurposeWe have established the SAIL MELD-B electronic cohort (e-cohort SMC) and the SAIL MELD-B children and Young adults e-cohort (SMYC) as a part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) project. Each cohort has been created to investigate and develop a deeper understanding of the lived experience of the burdensomeness of multimorbidity by identifying new clusters of burdensomeness indicators, exploring early life risk factors of multimorbidity and modelling hypothetical prevention scenarios. ParticipantsThe SMC and SMYC are longitudinal e-cohorts created from routinely-collected individual-level population-scale anonymised data sources available within the Secure Anonymised Information Linkage (SAIL) Databank. They include individuals with available records from linked health and demographic data sources in SAIL at any time between 1st January 2000 and 31st December 2022. The SMYC e-cohort is a subset of the SMC, including only individuals born on or after the cohort start date. Findings to dateThe SMC and SMYC cohorts include 5,180,602 (50.3% female and 49.7% male) and 896,155 (48.7% female and 51.3% male) individuals respectively. Considering both primary and secondary care health data, the five most common long-term conditions for individuals in SMC are Depression, affecting 21.6% of the cohort, Anxiety (21.1%), Asthma (17.5%), Hypertension (16.2%) and Atopic Eczema (14.1%), and the five most common conditions for individuals in SMYC are Atopic Eczema (21.2%), Asthma (11.6%), Anxiety (6.0%), Deafness (4.6%) and Depression (4.3%). Future plansThe SMC and SMYC e-cohorts have been developed using a reproducible, maintainable concept curation pipeline, which allows for the cohorts to be updated dynamically over time and manages for the request and processing of further approved long-term conditions and burdensomeness indicators extraction. Best practices from the MELD-B project can be utilised across other projects, accessing similar data with population-scale data sources and trusted research environments. STRENGTHS AND LIMITATIONS OF THIS STUDY- SMC and SMYC are representative of the Welsh population. - Anonymised cohorts serve as an effective strategy for overcoming consent-related barriers, enabling seamless data aggregation and analysis. - The creation of a reproducible concept curation pipeline to manage and process data extraction for the e-cohorts enables efficient delivery of datasets in support of multiple research questions and outcomes. - Routine data does not capture data on important aspects such as quality of life. - Routine data can be subject to missing data or errors. - Lack of coverage of burdensomeness indicators in routine data.
Authors: Roberta Chiovoloni, J. Dylag, N. A. Alwan, A. Berrington, M. Boniface, N. Fair, E. Holland, R. B. Hoyle, M. Shiranirad, S. Stannard, Z. Zlatev, R. K. Owen, S. S. Fraser, A. Akbari
Last Update: 2024-04-22 00:00:00
Language: English
Source URL: https://www.medrxiv.org/content/10.1101/2024.04.22.24306168
Source PDF: https://www.medrxiv.org/content/10.1101/2024.04.22.24306168.full.pdf
Licence: https://creativecommons.org/licenses/by/4.0/
Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.
Thank you to medrxiv for use of its open access interoperability.