An increasing number of people worldwide are living with two or more long-term conditions (LTCs), commonly referred to as multimorbidity. Most healthcare services and research focus on the investigation and treatment of single diseases which may be less relevant to people with multiple conditions, where the management of different diseases may interact and overlap. For people with multimorbidity, the way services are structured can lead to a people needing a greater number of healthcare visits across different organisations and specialties.
Much of the existing research in multimorbidity treats it simply as a count of the number of LTCs, which may miss patterns in how some people with certain types of conditions interact with services. This research proposal extends our earlier application exploring different groups (or ‘clusters’) of people with similar LTCs. We now aim to look at whether people belonging to a particular cluster interact differently with healthcare services and whether their care is fragmented across different organisations and specialist services. This may provide insight into how healthcare services could be better designed to support people with multimorbidity.
In addition, the COVID-19 pandemic has had a major impact on how people interact with healthcare services, including increased use of digital technologies and remote care but it is unclear how this has impacted on people with multimorbidity. We therefore aim to compare clinical outcomes and use of healthcare in different clusters both before and during the pandemic.
Multimorbidity is defined as the presence of multiple long-term conditions in one person and is growing in prevalence worldwide. Multimorbidity is associated with reduced quality of life, poorer clinical outcomes, increased use of healthcare services, and higher healthcare costs. Most existing research defines multimorbidity based on a count of the number of conditions, which may miss concordant or discordant patterns in the aetiology or management between diseases. Previous research has found that people with multimorbidity face increased challenges with navigating often complex and fragmented care across specialties and organisations. Identification of clusters of multimorbidity defined by similar LTCs may help with the design of more holistic and integrated services.
Our ongoing work (protocol reference 22_001818) has identified clusters of patients with similar patterns of disease co-occurrence using statistical and machine learning methods, including models based on the sequence of disease acquisition. In this protocol, using additional linked data from HES and ONS, we aim to refine these models and explore whether membership of a cluster derived in our earlier work is associated with i) clinical outcomes and ii) healthcare utilisation. We will include all patients registered in CPRD between 2015 and 2020 and compare to outcomes in patients with a single LTC or no LTCs. We will use survival analysis and regression models adjusted for sociodemographic, health and lifestyle related confounders. The COVID-19 pandemic has had a major impact on how people interact with services, and we aim to explore patterns before and during the pandemic, accounting for both COVID-19 related and unrelated attendances. The intended public health benefit is to identify whether particular patient clusters have differences in outcomes and service use and whether this can lead to insights into the design of services.
Primary outcomes: all-cause mortality; all-cause GP consultations; all-cause A&E attendances; hospital outpatient appointments (stratified by treatment specialty); emergency hospital admissions; elective hospital admissions; intensive care admissions. For the COVID-19 pandemic cohort, these outcomes will be stratified by those associated with/without COVID-19 infection.
Secondary outcomes: number of primary care prescriptions; total number of interactions with different healthcare organisations over the follow-up period
Thomas Beaney - Chief Investigator - Imperial College London
Thomas Beaney - Corresponding Applicant - Imperial College London
Azeem Majeed - Collaborator - Imperial College London
Jonathan Clarke - Collaborator - Imperial College London
Mark Cunningham - Collaborator - Imperial College London
Mauricio Barahona - Collaborator - Imperial College London
Paul Aylin - Collaborator - Imperial College London
Thomas Woodcock - Collaborator - Imperial College London
HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;SGSS (Second Generation Surveillance System);COVID-19 Linkages