Pathways and Levers for prevention of Multi-morbidity from Young and Middle Adulthood
Lead Research Organisation:
Imperial College London
Department Name: School of Public Health
Abstract
Multimorbidity is the presence of two or even several more major health condition at the same time within a single person. It is a growing problem in the United Kingdom in part of the combination of increasing lifespans with unhealthy lifestyles. Although multimorbidity is most common in older age, poor diet, physical inactivity, and obesity in young adulthood and middle-age are likely important causes of the tendency to multimorbidity. We suspect that 3 conditions in particular - diabetes, hypertension, and depression - cause a large portion of multimorbidity because of the way that they work together to affect so many systems of the body. If this is the case, then programmes for focused lifestyle interventions could make a big difference in preventing multimorbidity. Unfortunately, it is difficult to know how these conditions in young and middle-age affect multimorbidity and what works to prevent it because no studies track population across many stage of life, while measuring the impact of interventions. This research progamme will tackle these questions with 3 parts. First, it will use long-term data from the UK and London populations to uncover the most common combinations of diseases occurring and whether there are particular steps and pathways in their formation. After finding those combinations and estimating how rapidly they develop at different times in life, we will construct a new computer-based model, called a "life-course simulation model" that can identify the optimal times in life, combinations of behaviors, risk factors, and diseases that cause the greatest illness over life. The computer model will also examine the effect of different ways of preventing the accumulation of multimorbidity, such as using focused support to change lifestyle in people at risk for hypertension, diabetes, and depression. The third part of the research programme will use these data and the computer model to measure the effect of two National Health Service initiatives that support people at risk of diabetes or with diabetes to change lifestyle diabetes. Since lifestyle behaviors are also crucial to hypertension, depression, and other conditions, these "natural experiments" may have a big effect on multimorbidity as well. This work will require a team with expertise in diverse areas - including medical care, epidemiology, behaviour change, mathematics, and computer modeling. This study will answer important questions about what types of conditions are causing the most multimorbidity and what are the best ways to act to prevent them. The computer model and research that results will give doctors, health planners, and the public new way to improve health in communities for the years to come. The work will be first-of-its-kind in the way that it assesses chronic conditions as they form in combination from young adulthood to older adulthood. It will also be new in the way it uses computer models and natural experiments in combination to find out what works best to reduce multimorbidity.
Technical Summary
Although multimorbidity (MM) has its greatest burden in older adulthood, its genesis lies in risk factors and conditions of middle-age and early adulthood. We hypothesize that 3 conditions in particular - hypertension, type 2 diabetes, and depression - are key leverage points in the progression to MM because they often emerge in young or middle-adulthood, have disproportionate effects on other conditions, yet can be altered by multi-component lifestyle interventions. We will build a multi-disciplinary research programme to characterize and quantify the progression of MM from its inception, prioritise sub-populations and risk levels for action, and determine the impact of major public health interventions.
We will accomplish this goal through 3 integrated work streams: First, an epidemiological work stream will characterize the phenotypes, progression and sequence of MM across life stages using a combination of a priori and agnostic data-driven analyses. Second, we will develop an individual-based, life-course simulation model (LCSM) that quantifies MM progression and identifies the 'leverage points', subgroups, and interventions for action. Third, we will conduct natural experimental studies of ongoing NHS lifestyle-based initiatives and integrate these findings with the LCSM to estimate long-term intervention impact on MM.
This work will be innovative and unprecedented for MM in its focus on young and middle-adulthood, its use of individual-based modelling to capture the multiple interactions between conditions, its study of impact of lifestyle interventions, and in its use of natural experiments to study public health initiatives. We expect our research findings and infrastructure to guide MM prevention and management policies while building an infrastructure for long-term MM surveillance. A consolidator phase is necessary because of the complexity of each of the 3 work streams and the need for pilot analyses to organize and refine study protocols
We will accomplish this goal through 3 integrated work streams: First, an epidemiological work stream will characterize the phenotypes, progression and sequence of MM across life stages using a combination of a priori and agnostic data-driven analyses. Second, we will develop an individual-based, life-course simulation model (LCSM) that quantifies MM progression and identifies the 'leverage points', subgroups, and interventions for action. Third, we will conduct natural experimental studies of ongoing NHS lifestyle-based initiatives and integrate these findings with the LCSM to estimate long-term intervention impact on MM.
This work will be innovative and unprecedented for MM in its focus on young and middle-adulthood, its use of individual-based modelling to capture the multiple interactions between conditions, its study of impact of lifestyle interventions, and in its use of natural experiments to study public health initiatives. We expect our research findings and infrastructure to guide MM prevention and management policies while building an infrastructure for long-term MM surveillance. A consolidator phase is necessary because of the complexity of each of the 3 work streams and the need for pilot analyses to organize and refine study protocols
Publications
Cicek M
(2021)
Characterizing Multimorbidity from Type 2 Diabetes: Insights from Clustering Approaches.
in Endocrinology and metabolism clinics of North America
Pearson-Stuttard J
(2021)
The Changing Nature of Mortality and Morbidity in Patients with Diabetes.
in Endocrinology and metabolism clinics of North America
Title | Life State Simulation Model to Characterise the development of multimorbidity and the impact of interventions. |
Description | We have developed the structure for and conducted preliminary programming for an agent-based model that simulates the UK population, tracking individuals through life as they adopt risk factors, developing specific conditions, and undergo treatments or are exposed to preventive interventions. The model will serve several purposes: it will permit aggregation and integration of data across life stages and conditions in ways neither a single cohort or trial can. Second, it will combine risk level, incidence, and intervention effectiveness to identify and quantify leverage points. Third, it can estimate effects of the interventions on multimorbidity beyond the period of intervention and potentially for conditions beyond those directly measured. Using the agent-based modelling framework we have already developed, we will program the model in Python using modern programming practices, including test-led development and modularization to maximize ease of use and reproducibility. The model will include 5 modules: a demographic module, which determines the birth and date of death to each individual; a lifestyle module, which determines if/when individuals adopt health behaviours; an epidemiological engine, which determines the risk of incident disease for each person at any moment, as a function of their current disease status and risk factors; a healthcare module, which represents care being provided to individuals following a diagnosis; and an intervention module, which represents how all elements of the other modules may be subjected to intervention effort. The model will permit analyses in 3 areas: 1) Quantifying and comparing the burden of multimorbidity. 2) Comparative effectiveness of public health interventions on long-term MLTCs impact. 3) Identifying everage points by incorporating alternative combinations of risk levels, demographic factors, and intervention effects to estimate health impact. |
Type Of Material | Computer model/algorithm |
Year Produced | 2020 |
Provided To Others? | No |
Impact | As a preliminary stage for WS2, we constructed a harmonized dataset of prevalence, transition probabilities, and mortality for 7 risk factors and 5 conditions (T2DM, HTN, CKD, depression, CHD). Using a previously developed agent-based simulation framework, we programmed a new module flexible to accommodate an arbitrary number of conditions and their mutual interactions. As portrayed in the figures below, we tested the feasibility and face validity, with projections of: a) age-specific prevalence (example of T2DM); b) number of conditions; and c) predicted prevalence of combinations amongst 5 conditions by age. |
Description | MM Consolidator Diabetes Centre Leicester |
Organisation | University of Leicester |
Department | Leicester Diabetes Centre |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | something goes here |
Collaborator Contribution | something else goes here |
Impact | some outputs go here |
Start Year | 2020 |
Description | MM Consolidator Health Sciences Leicester (2020 - Still Active) |
Organisation | University of Leicester |
Department | Department of Health Sciences |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | some content here |
Collaborator Contribution | some other content here |
Impact | some outputs here |
Start Year | 2020 |
Description | MM Consolidator NW London CCG (2020 - Still Active) |
Organisation | NHS West London CCG |
Country | United Kingdom |
Sector | Public |
PI Contribution | description |
Collaborator Contribution | description |
Impact | outputs |
Start Year | 2020 |
Description | NHS England Multimorbidity Workgroup |
Organisation | NHS England |
Country | United Kingdom |
Sector | Public |
PI Contribution | I joined an advisory group convened by NHS England to guide analyses to inform national programme related to monitoring, preventing, and caring for multimorbidity. |
Collaborator Contribution | I have advised on and helped coordinate a new series of novel analyses examining the years of life spent and lost due to different types of multimorbidity in England. This has led to the development of new metrics that will be used to inform the national programme. |
Impact | - Manuscripts in development - Presentation of data at national meeting. |
Start Year | 2021 |
Description | Diabetes lifestyle intervention advocacy meeting |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Public/other audiences |
Results and Impact | We hosted an online advocacy group with 38 individuals living with diabetes from the North West London area. This was a collaboration with the 'Partners in Diabetes' group of patient representatives with who we have worked closely within other PPI activities. We promoted health literacy and provided information on how to adapt to a low-carbohydrate diet in an informal, non-judgemental manner. We also encouraged individuals to ask questions about diabetes and the lifestyle programme (REWIND), encouraging sign-ups and GP-visits. We also pointed participants to the KnowDiabetes website where they could find more information on programmes in their area. Participants indicated they felt more confident speaking to their GP about REWIND, had a better understanding of the relationship between starchy foods and raised glucose levels, and more positive about taking control of their own health (improved self-efficacy). This work strengthened our overall project dissemination plan and allowed us to build a community of individuals to recruit for later PPI meetings. |
Year(s) Of Engagement Activity | 2021 |
URL | http://knowdiabetes.org.uk/ |
Description | Meetings with NHS clinical directors |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Professional Practitioners |
Results and Impact | We met with five clinical directors in our wider NHS network to discuss the state of diabetes research nationally and in the North West London area. We discussed the evaluation of the REWIND and NDPP lifestyle programmes and access to large population-based datasets. These meetings also allowed us to ascertain if new interventions were being brought into the health system. This ensured our model incorporated the relevant key health metrics, remained focused on the emerging health system challenges, and promoted sustained engagement with policymakers/clinical directors. |
Year(s) Of Engagement Activity | 2020,2021 |
Description | Patient representative group meetings |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Patients, carers and/or patient groups |
Results and Impact | We formed a group of four patients with multiple long term conditions. The group met virtually monthly over the consolidator grant phase. We discussed the concepts of multimorbidity, epidemiology, what microsimulation models do, and the value proposition of our approach to the wider system. We also received actionable feedback on the budget for PPI involvement and our schematics of multimorbidity. Subsequent discussions mapped stakeholders and co-designed plain-language summary documents for the recruitment of additional patient collaborators. Group members indicated that they appreciated the visualisation of health trajectories and developed an improved understanding of the spectrum of risk factors that influence multimorbidity. We also provided training on how to use Microsoft Teams and this will be extended into the full programme. This enables all participants to contribute effectively to the virtual conversation and raise any relevant questions they may have. We also piloted training participants to use the mind-mapping software 'MIRO'. The feedback garnered was largely positive, with users indicating they were surprised how user-friendly the interface was and that the sessions felt more interactive as contributions could be made in real-time compared to a PowerPoint. |
Year(s) Of Engagement Activity | 2020,2021 |