Having two or more long-term conditions (multimorbidity) is increasingly common, with two-thirds of people over 65 expected to live with multimorbidity by 2035. Preventing and managing multimorbidity is challenging for health care providers, policy makers, and patients. Individuals with multimorbidity usually have to navigate a healthcare system designed for single conditions, often resulting in an unmanageable amount of appointments and medications. This patient group often experiences worse health outcomes and higher healthcare costs than those without multimorbidity. Large differences in disease combinations and severity exist among individuals with multimorbidity. Diseases tend to accumulate over time, but most studies to date characterise multimorbidity based on a single point in time. Describing trends over time and variability across patients is important to understand who has the highest risk of poor disease progression and when the peaks of healthcare need are in the multimorbidity journey. This project aims to describe how healthcare use and costs of individuals with multimorbidity change over time as individuals accumulate further conditions and across subgroups defined by medical conditions and patient characteristics such as gender and age. This research is important to understand inequalities in healthcare use and improve care for individuals with multimorbidity.
People with multimorbidity (defined as two or more long-term conditions -LTCs) have complex healthcare needs. Costs after diagnosis of LTCs, such as cancer or diabetes, vary within and across patients over time. Understanding outcome trajectories (rather than a single data point) is important to not only describe peaks of healthcare need in the multimorbidity progression pathway, but also explore the effect of changes (such as the diagnosis of additional LTCs) along this pathway. This research aims to: (1) Describe multimorbidity trajectories in healthcare use (including primary and secondary care) and costs over time, (2) Assess how these trajectories change as new LTCs accumulate, (3) Characterise inequalities in the trajectories and their variability over time across groups defined by sociodemographic variables, such as gender and age, and (4) Describe the relationship between primary and secondary care over the multimorbidity journey to understand if services are substitutes or complements. Primary and secondary care use of individuals with multimorbidity will be identified using CPRD data (both primary care data and the Hospital Episode Statistics). The primary outcomes are healthcare use (including primary and secondary care) and costs, and the main exposure is the accumulation of LTCs and multimorbidity. A longitudinal study design will be conducted, with the use of control groups when possible to adjust for confounding. Several statistical methods will be employed and compared to identify trajectories and their changes over time, including Interrupted Time Series (ITS), Autoregressive Integrated Moving Average (ARIMA), and group-based trajectory models. Multivariate regression analysis and control groups will be applied when possible to control for confounding. This analysis will describe points in the multimorbidity journey where access to each type of service may be most crucial to reduce costs and improve health outcomes. High healthcare users will be described to inform care prioritisation efforts and clinical guidelines.
Healthcare use (primary care consultations, A&E visits, hospital inpatient stays, hospital outpatient visits); healthcare costs (computed based on healthcare use and publicly available unit cost estimates).
MARINA SOLEY-BORI - Chief Investigator - King's College London (KCL)
MARINA SOLEY-BORI - Corresponding Applicant - King's College London (KCL)
Alice McGreevy - Collaborator - King's College London (KCL)
Emma Rezel-Potts - Collaborator - King's College London (KCL)
Julia Fox-Rushby - Collaborator - King's College London (KCL)
Mark Ashworth - Collaborator - King's College London (KCL)
HES Accident and Emergency;HES Admitted Patient Care;HES Diagnostic Imaging Dataset;HES Outpatient;Patient Level Index of Multiple Deprivation