Multimorbidity

Multimorbidity—the co-existence of two or more chronic health conditions in an individual—has emerged as a defining feature of modern healthcare. Once considered an exception, it now affects the majority of older adults and a growing proportion of middle-aged populations worldwide. The challenge lies in transcending single-disease paradigms to deliver coordinated, person-centred care that accounts for the interplay between conditions, treatments and the wider determinants of health. Key obstacles include polypharmacy and its associated risks, discordant clinical guidelines, fragmented service delivery, and the burden imposed on patients and carers. Advances in data science have begun to unravel the temporal and spatial patterns by which conditions accumulate and interact, offering routes to stratify patients by risk, predict adverse trajectories and tailor interventions. Meanwhile, population-level projections underscore the urgency of preventive measures and health-system reforms to mitigate inequalities in onset, progression and outcomes. Integrating biological insights into ageing, patient preferences and social context promises to guide the reconfiguration of care towards minimally disruptive, outcomes that matter most to individuals living with multiple chronic diseases.

Research from Nature Portfolio

Large-scale regional analysis in southern Spain characterised multimorbidity across more than 1.3 million patients, combining latent class analysis with geospatial mapping. Twenty-five distinct profiles of coexisting conditions emerged, with patterns of increasing cluster complexity most prevalent in areas of socioeconomic deprivation. These high-complexity clusters exhibited higher rates of health-service use and mortality, emphasising the need for locality-specific integrated care models that also address social inequalities.

A twelve-year longitudinal study applied fuzzy c-means clustering to older adults, identifying five stable multimorbidity clusters and an unspecific cluster that seeded transitions between patterns. Individuals shifted among clusters over time, with each trajectory carrying different mortality risks. This dynamic view supports the design of cluster-based stratification tools to personalise preventive and therapeutic strategies for ageing populations.

Foundational work using a national electronic registry of 6.2 million Danish patients distilled 1,171 significant temporal disease trajectories. Patterns centred on sentinel diagnoses such as chronic obstructive pulmonary disease and gout revealed early branching points in disease progression. Such trajectory maps lay the groundwork for predictive frameworks to anticipate future comorbid conditions and inform timely, preventive interventions.

Multimorbidity publication trend

The graph below shows the total number of articles in multimorbidity across all publications each year (not limited to Nature Index journals).

Technical terms

Multimorbidity: The simultaneous presence of two or more chronic health conditions in one individual.

Polypharmacy: The use of multiple medications by a patient, often defined as five or more concurrently, increasing the risk of adverse drug reactions.

Latent class analysis: A statistical method for grouping individuals into unobserved subpopulations based on patterns of observed variables.

Fuzzy c-means clustering: An algorithm that assigns degrees of membership to data points across clusters, allowing individuals to transition between patterns over time.

Multilayer comorbidity network: A graph model in which nodes represent diagnoses stratified by age or time layer, and links encode statistical associations within and between layers.

Microsimulation: A computational technique that simulates individual life courses through different health states to project population-level outcomes under varying scenarios.

References

1. Multimorbidity. Nature Reviews Disease Primers (2022).
2. Epidemiology, mortality, and health service use of local-level multimorbidity patterns in South Spain. Nature Communications (2023).
3. Twelve-year clinical trajectories of multimorbidity in a population of older adults. Nature Communications (2020).
4. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nature Communications (2014).
5. Socioeconomic inequalities in accumulation of multimorbidity in England from 2019 to 2049: a microsimulation projection study. The Lancet Public Health (2024).
6. Unraveling cradle-to-grave disease trajectories from multilayer comorbidity networks. npj Digital Medicine (2024).