12-year evolution of multimorbidity patterns among older adults based on Hidden Markov Models

Background: The evolution of multimorbidity patterns during aging is still an under-researched area. We lack evidence concerning the time spent by older adults within one same multimorbidity pattern, and their transitional probability across different patterns when further chronic diseases arise. The aim of this study is to fill this gap by exploring multimorbidity patterns across decades of age in older adults, and longitudinal dynamics among these patterns. Methods: Longitudinal study based on the Swedish National study on Aging and Care in Kungsholmen (SNAC-K) on adults ≥60 years (N=3,363). Hidden Markov Models were applied to model the temporal evolution of both multimorbidity patterns and individuals' transitions over a 12-year follow-up. Findings: Within the study population (mean age 76.1 years, 66.6% female), 87.2% had ≥2 chronic conditions at baseline. Four longitudinal multimorbidity patterns were identified for each decade. Individuals in all decades showed the shortest permanence time in an Unspecific pattern lacking any overrepresented diseases (range: 4.6-10.9 years), but the pattern with the longest permanence time varied by age. Sexagenarians remained longest in the Psychiatric-endocrine and sensorial pattern (15.4 years); septuagenarians in the Neuro-vascular and skin-sensorial pattern (11.0 years); and octogenarians and beyond in the Neuro-sensorial pattern (8.9 years). Transition probabilities varied across decades, sexagenarians showing the highest levels of stability. Interpretation: Our findings highlight the dynamism and heterogeneity underlying multimorbidity by quantifying the varying permanence times and transition probabilities across patterns in different decades. With increasing age, older adults experience decreasing stability and progressively shorter permanence time within one same multimorbidity pattern

Five-year trajectories of multimorbidity patterns in an elderly Mediterranean population using Hidden Markov Models

This is the final version. Available on open access from Nature Research via the DOI in this recordThis study aimed to analyse the trajectories and mortality of multimorbidity patterns in patients aged 65 to 99 years in Catalonia (Spain). Five year (2012–2016) data of 916,619 participants from a primary care, population-based electronic health record database (Information System for Research in Primary Care, SIDIAP) were included in this retrospective cohort study. Individual longitudinal trajectories were modelled with a Hidden Markov Model across multimorbidity patterns. We computed the mortality hazard using Cox regression models to estimate survival in multimorbidity patterns. Ten multimorbidity patterns were originally identified and two more states (death and drop-outs) were subsequently added. At baseline, the most frequent cluster was the Non-Specific Pattern (42%), and the least frequent the Multisystem Pattern (1.6%). Most participants stayed in the same cluster over the 5 year follow-up period, from 92.1% in the Nervous, Musculoskeletal pattern to 59.2% in the Cardio-Circulatory and Renal pattern. The highest mortality rates were observed for patterns that included cardio-circulatory diseases: Cardio-Circulatory and Renal (37.1%); Nervous, Digestive and Circulatory (31.8%); and Cardio-Circulatory, Mental, Respiratory and Genitourinary (28.8%). This study demonstrates the feasibility of characterizing multimorbidity patterns along time. Multimorbidity trajectories were generally stable, although changes in specific multimorbidity patterns were observed. The Hidden Markov Model is useful for modelling transitions across multimorbidity patterns and mortality risk. Our findings suggest that health interventions targeting specific multimorbidity patterns may reduce mortality in patients with multimorbidity.Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain)European Regional Development FundDepartment of Health of the Catalan GovernmentCatalan Governmen

Depression and chronic diseases in old age : understanding their interplay for better health

Late-life depression is intricately linked with somatic diseases. This thesis aimed to systematically explore this complex interplay. Specifically, we investigated: 1) the symptom-level interconnectedness between depression and somatic diseases, 2) the association of depression with somatic multimorbidity accumulation, 3) the role of somatic disease burden in depression development, and 4) the association of somatic burden with transitions across depressive states in older adults. Data were gathered from the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), a populationbased study comprising 3,363 individuals aged 60+ years who underwent clinical assessments over a 15-year follow-up. Study I. Using a network approach, we aimed to describe the interconnectedness between depressive symptoms and somatic disease burden in older people. We found that sadness, pessimism, anxiety, and suicidal thoughts were central to the network, whereas somatic symptoms of depression appeared peripherally with fewer interconnections. When examining the association between depressive symptoms and measures of somatic disease burden, we found that suicidal thoughts, reduced appetite, and cognitive difficulties were bridge symptoms, linking late-life depression with somatic health. Study II. We investigated the impact of depression severity and phenotypes (i.e., affective, anxiety, cognitive, and psychomotor) on the progression of somatic multimorbidity over 15 years. Compared to those without depression, individuals with major (β*year: 0.33, 95%CI: 0.06-0.61) and subsyndromal depression (β*year: 0.21, 95%CI: 0.12-0.30) presented an accelerated accumulation of somatic multimorbidity. An increase in the cognitive phenotype burden (and not in the other three) was associated with faster accumulation of somatic diseases in old age (β*year: 0.07, 95%CI: 0.03-0.10). Study III. We aimed to examine the association between quantitative and qualitative measures of somatic disease burden and the incidence of depression in older adults. Each additional somatic disease was associated with an increased hazard of depression over a 15-year follow-up (HR 1.16, 95%CI: 1.08-1.24). Individuals presenting with disease patterns of sensory/anemia (HR 1.91, 95%CI: 1.03-3.53), thyroid/musculoskeletal (HR 1.90, 95%CI: 1.06-3.39), and cardiometabolic patterns (HR 2.77, 95%CI: 1.40-5.46) had higher depression hazards compared to those without multimorbidity. In the subsample of multimorbid participants, the cardiometabolic pattern remained associated with a higher depression risk (HR 1.71, 95%CI: 1.02-2.84) compared to the unspecific pattern. Study IV. We examined the course of old-age depression by investigating 15-year transitions along the depressive continuum and exploring time-varying factors associated with specific transition patterns. Over the follow-up, 19.1% had ≥1 transitions across depressive states (no depression, subsyndromal depression [SSD], depression), while 6.5% had ≥2 transitions. A higher number of somatic diseases was associated with progression from no depression to both SSD (HR 1.09, 95%CI: 1.07-1.10) and depression (HR 1.06, 95%CI: 1.04-1.08), and with lower recovery rates from SSD (HR 0.95, 95%CI: 0.93- 0.97) and depression (HR 0.96, 95%CI: 0.93-0.99). A richer social network was linked to lower transition rates to depressive states (HRNoDep-SSD 0.81, 95%CI: 0.70-0.94; HRNoDep-Dep 0.58, 95%CI: 0.46-0.73; HRSSD-Dep 0.66, 95%CI: 0.44-0.98), and higher recovery rates (HRSSD-NoDep 1.44, 95%CI: 1.26-1.66; HRDep-NoDep 1.51, 95%CI: 1.34-1.71). Being physically active was associated with higher recovery rates (HRSSD-NoDep 1.49, 95%CI: 1.28-1.73; HRDep-NoDep 1.20, 95%CI: 1.00-1.44). Conclusions. Our findings suggest that several dimensions of complexity characterize the interconnection of depression and somatic disease burden in old age. A symptomlevel characterisation of depression, along with a consideration of subsyndromal severity, may help clarify the comorbidity of depression and somatic diseases, as well as predict health decline in people with depressive symptoms. Similarly, recognizing disease patterns may help improve risk stratification for depression development in clinically complex older adults. Last, the natural course of depression in late life is dynamic and involves complex patterns of transitions through symptom severities, which can be influenced by the time-varying burden of somatic diseases. Developing person-centered care that integrates these complexities could enhance resilience and contribute to better health in old age

Multimorbidity of cardiometabolic diseases and effectiveness of integrated healthcare system response in sub-Saharan Africa

This thesis aims to strengthen the responsiveness of healthcare systems to the management of cardiometabolic multimorbidity in sub-Saharan Africa (SSA). More specifically, four main issues on cardiometabolic multimorbidity in SSA were investigated: the burden of cardiometabolic multimorbidity, chronic care models, the readiness of healthcare facilities to provide integrated care, and the effect of multimorbidity on self-care interventions. A latent class analysis and hierarchical agglomerative cluster analysis in part one show that cardiometabolic diseases occur in distinct clusters of concordant and discordant multimorbidity. These clusters are significant predictors of outpatient visits, hospitalisation, functional disability and quality of life. Multimorbidity is disproportionately highest among persons of high socioeconomic status, women, the middle and old-aged, and those with sedentary lifestyles and obesity. A systematic review and meta-analysis in part two shows that integrated care versus standard care improved systolic blood pressure control in people with multimorbidity. In part three, a national facility assessment survey in Kenya shows that only one in every four healthcare facilities (at all levels) was ready to provide integrated care for cardiovascular diseases and type 2 diabetes. The clinical integration barriers included vertical and unresponsive healthcare services. In part four, a quasi-experimental study of patients with hypertension undergoing a home-based self-care program in Kenya shows that multimorbidity attenuated the effectiveness of patient support groups for hypertension. Overall, the findings of this thesis provide crucial evidence for multimorbidity risk stratification and underscore the importance of tailoring patient-centered care interventions to match the needs of people with cardiometabolic multimorbidity in SSA

Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions)

Background: Multiple long-term health conditions (multimorbidity) (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality, and health care expenditure. Strategies to address this have primarily focused on the biological aspects of disease, but MLTC-M also result from and are associated with additional psychosocial, economic, and environmental barriers. A shift toward more personalized, holistic, and integrated care could be effective. This could be made more efficient by identifying groups of populations based on their health and social needs. In turn, these will contribute to evidence-based solutions supporting delivery of interventions tailored to address the needs pertinent to each cluster. Evidence is needed on how to generate clusters based on health and social needs and quantify the impact of clusters on long-term health and costs. Objective: We intend to develop and validate population clusters that consider determinants of health and social care needs for people with MLTC-M using data-driven machine learning (ML) methods compared to expert-driven approaches within primary care national databases, followed by evaluation of cluster trajectories and their association with health outcomes and costs. Methods: The mixed methods program of work with parallel work streams include the following: (1) qualitative semistructured interview studies exploring patient, caregiver, and professional views on clinical and socioeconomic factors influencing experiences of living with or seeking care in MLTC-M; (2) modified Delphi with relevant stakeholders to generate variables on health and social (wider) determinants and to examine the feasibility of including these variables within existing primary care databases; and (3) cohort study with expert-driven segmentation, alongside data-driven algorithms. Outputs will be compared, clusters characterized, and trajectories over time examined to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity, and 10-year health and social care costs. Results: The study will commence in October 2021 and is expected to be completed by October 2023. Conclusions: By studying MLTC-M clusters, we will assess how more personalized care can be developed, how accurate costs can be provided, and how to better understand the personal and medical profiles and environment of individuals within each cluster. Integrated care that considers “whole persons” and their environment is essential in addressing the complex, diverse, and individual needs of people living with MLTC-M

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

Social inequalities in multimorbidity patterns in Europe: A multilevel latent class analysis using the European Social Survey (ESS)

Multimorbidity is associated with lower quality of life, greater disability and higher use of health services and is one of the main challenges facing governments in Europe. There is a need to identify and characterize patterns of chronic conditions and analyse their association with social determinants not only from an individual point of view but also from a collective point of view. This paper aims to respond to this knowledge gap by detecting patterns of chronic conditions and their social determinants in 19 European countries from a multilevel perspective. We used data from the ESS round 7. The final sample consisted of 18,933 individuals over 18 years of age, and patterns of multimorbidity from 14 chronic conditions were detected through Multilevel Latent Class Analysis, which also allows detecting similarities between countries. Gender, Age, Housing Location, Income Level and Educational Level were used as individual covariates to determine possible associations with social inequalities. The goodness-of-fit indices derived in a model with six multimorbidity patterns and five countries clusters. The six patterns were "Back, Digestive and Headaches", "Allergies and Respiratory", "Complex Multi -morbidity", "Cancer and Cardiovascular", "Musculoskeletal" and "Cardiovascular"; the five clusters could be associated with some geographical areas or welfare states. Patterns showed significant differences in the cova-riates of interest, with differences in education and income being of particular interest. Some significant dif-ferences were found among patterns and the country groupings. Our findings show that chronic diseases tend to appear in a combined and interactive way, and socioeconomic differences in the occurrence of patterns are not only of the individual but also of group importance, emphasising how the welfare states in each country can influence in the health of their inhabitants