搜索结果：Journal of multimorbidity and comorbidity

PURPOSE: This study aimed to identify dominant comorbidity patterns among women cancer survivors and examine how these patterns relate to health-related quality of life (HRQL). METHODS: 1544 participants (born 1946&#x2013;1951) from the Australian Longitudinal Study on Women&#x2019;s Health diagnosed with cancer during the follow-up period from 1993 to 2019 were included. HRQL is measured with Short Form-36 included in the survey. Latent class analysis was applied to identify comorbidity patterns, and linear regression was used to assess their association with HRQL domains, adjusting for demographic factors. RESULTS: Five distinct comorbidity classes were identified: relatively healthy (n&#x2009;=&#x2009;880, 57%); hypertension and arthritis (n&#x2009;=&#x2009;278, 18%); arthritis and osteoporosis (n&#x2009;=&#x2009;139, 9%); respiratory conditions (n&#x2009;=&#x2009;170, 11%); and complex multimorbidity (n&#x2009;=&#x2009;93, 6%). Compared to the relatively healthy class, women in all other classes had significantly lower average HRQL (p&#x2009;<&#x2009;0.01). For example, the classes&#x2019; adjusted mean score for general health domain varied: relatively healthy (mean&#x2009;=&#x2009;70.8, reference), hypertension and arthritis (mean&#x2009;=&#x2009;63.1, 95% CI&#x2009;=&#x2009;59.9, 66.3), arthritis and osteoporosis (mean&#x2009;=&#x2009;60.0, 95% CI&#x2009;=&#x2009;55.8, 64.1), respiratory conditions (mean&#x2009;=&#x2009;60.9, 95% CI&#x2009;=&#x2009;57.2, 64.7), and complex multimorbidity (mean&#x2009;=&#x2009;48.6, 95% CI&#x2009;=&#x2009;43.4, 53.8). Women in the complex multimorbidity class had the lowest HRQL across all domains: physical functioning [adjusted mean difference from relatively healthy (AMD=&#x2212;&#x2009;22.2 and 95% CI &#x2212;&#x2009;27.4, &#x2212;&#x2009;17.0)], mental health (AMD=-11.4, 95% CI=&#x2212;&#x2009;15.4, -7.5). CONCLUSION: Comorbidity patterns varied substantially among women cancer survivors and were strongly associated with differences in HRQL. Survivors with complex multimorbidity experienced the greatest impairments. Incorporating comorbidity profiling into survivorship care may help identify high-risk groups and support targeted interventions to optimise quality of life. More and more people are surviving cancer, but many also live with other long-term health conditions, which can affect their daily life and wellbeing. We carried out this study to better understand how these conditions cluster together and how they impact the quality of life among cancer survivors. Our study looked at whether some health conditions tend to occur together in Australian women cancer survivors, and how these different combinations affect how people feel physically and emotionally. We found that certain patterns of long-term conditions, such as high blood pressure, arthritis, and respiratory diseases, are common among cancer survivors. Women cancer survivors with more complex health issues generally reported lower health-related quality of life across many areas, including general health, bodily pain, physical functioning, and mental wellbeing. These findings suggest that cancer survivors with multiple long-term conditions may need more support to manage their health and improve their quality of life. Health services should consider these patterns when planning care, so survivors get the right help for their unique health needs.

Multimorbidity is widespread, influencing disease progression and patient outcomes. Understanding the impact on the use of healthcare resources and healthcare associated harm is crucial for population-level interventions to reduce disease burden and improve quality of life. This retrospective study used administrative and clinical registry data from a 30% sample of the adult Estonian population. Multimorbidity was defined as &#x2265;2 chronic diseases. Prevalence of chronic disease and multimorbidity was estimated among adults on January 1, 2024 (n = 324,942). Healthcare utilization included inpatient, outpatient, emergency visits, and prescription counts. Survival was assessed over five years following entry into multimorbidity strata (0-1, 2-4, 5-9, &#x2265;10 conditions) in 2012-2024. We estimated 5-year risk of healthcare-associated harm diagnoses comparing patients with and without multimorbidity. Chronic disease affected 60.3% of adults and multimorbidity 42.0%, increasing steeply with age. Multimorbidity was strongly associated with mortality, with adjusted hazard ratios of 1.61 (95% CI 1.57-1.66) for 2-4 conditions, 2.43 (2.36-2.51) for 5-9, and 3.35 (3.22-3.48) for &#x2265;10 conditions compared with &#x2264;1 condition. Healthcare utilization increased approximately linearly with each additional chronic disease, with 0.025 more inpatient visits, 0.034 emergency visits, 1.49 outpatient visits, and 4.04 prescriptions per person-year. Multimorbidity was also associated with higher risk of healthcare-associated adverse events, with up to sevenfold higher hazards for several complications, particularly related to procedural/surgical and prosthesis-related complications, including infections. Multimorbidity is prevalent in the Estonian population, increases mortality and healthcare use, and may increase healthcare-associated harm.

PURPOSE: People with chronic diseases are known to have lower EQ-5D-5L utility scores, but data are not readily available in an Australian context. Using linked administrative hospital and population survey data, we aimed to calculate utility scores for adults with different disease profiles. METHODS: We conducted a retrospective cohort study using a cross-sectional population-level health survey (2022&#x2013;2023) linked to administrative hospital data for adults 18&#xa0;years and older in Queensland, Australia to assess: (1) chronic disease and comorbidity prevalence, (2) Health-related quality of life (HRQoL) differences among adults with pre-existing chronic conditions, and (3) to model differences in disutility days by chronic diseases. RESULTS: The mean EQ-5D-5L utility score for the cohort was 0.917, but was lower among those with chronic diseases, for example chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD) and diabetes had corresponding mean values of 0.780, 0.850 and 0.832, respectively. After adjustment, on average the disutility days that could be averted by preventing chronic diseases equalled approximately a month annually for some conditions, ranging from 14.8&#xa0;days for CHD to 36.5&#xa0;days for COPD. Prevalence estimates using linked administrative hospital data were comparable to results from the National Health Survey, which used self-report, although comorbidity was found to be substantially higher in the current study. CONCLUSION: People living with chronic diseases have substantially higher number of disutility days annually. Preventing or delaying onset of chronic conditions would likely improve HRQoL and positively impact individuals, society and the economy. Why is this study needed?Health-related quality of life (HRQoL) is lower among those with chronic diseases than those without. While there are methods to evaluate HRQoL numerically, estimates for people living with chronic diseases are not available in an Australian context.What is the key problem/issue/question this manuscript addresses?The key questions this manuscript addresses are to: (1) assess chronic disease and comorbidity prevalence using population-level survey data linked to administrative health records, (2) quantify HRQoL differences among adults with pre-existing chronic conditions, and (3) model differences in disutility days by listed chronic diseases.What is the main point of your study?Using a population health survey linked to hospital admission and emergency presentation data, this study estimated the prevalence of selected chronic diseases in Queensland, Australia, and evaluated differences in HRQoL scores and the number of days not in optimal health (disutility day) among people with different disease profiles.What are your main results and what do they mean?Survey data linked to administrative hospital data provided chronic disease prevalence estimates comparable to a national health survey, although multimorbidity estimates were higher. Multimorbidity is commonly associated with higher health care costs and utilisation, and is a growing challenge to healthcare sector sustainability. Robust multimorbidity prevalence better informs healthcare system planning. Population-level HRQoL scores were also lower among Queensland adults with chronic diseases. A high number of disutility days could be avoided if chronic disease onset could be averted or delayed.

Identify subgroups of oncology patients with distinct joint chemotherapy-induced nausea (CIN) AND morning fatigue profiles and distinct joint CIN AND evening fatigue profiles, as well as modifiable and non-modifiable risk factors. Oncology patients receiving chemotherapy completed self-report questionnaires that provided information on demographic and clinical characteristics, as well as on CIN and morning and evening fatigue. The three symptoms were assessed six times over two cycles of chemotherapy. Joint latent class profile analyses (LCPA) were performed to identify subgroups of patients with distinct joint symptom profiles. Parametric and non-parametric tests were used to evaluate for differences in modifiable and non-modifiable risk factors among the profiles. Five and four subgroups were identified with distinct joint CIN and morning fatigue and distinct joint CIN and evening fatigue profiles, respectively. Risk factors associated with membership in the worse profiles included younger age, lower annual household income, high comorbidity burden, lower functional status, self-reported diagnosis of depression, and higher levels of neuropsychological and gastrointestinal symptoms. Across both LCPAs, 60% of the sample reported CIN with occurrence rates that ranged from approximately 30% to 90%. In addition, wide variations were found in both morning and evening fatigue severity scores depending on the distinct profile. These initial findings suggest that CIN co-occurs with both morning and evening fatigue. The co-occurrence of CIN and fatigue may be related to shared biological mechanisms that warrant evaluation in future studies.

Atrial fibrillation (AF) is the most common arrhythmia in older adults and often coexists with other chronic conditions, exacerbating physical and cognitive decline and contributing to frailty. The interplay between frailty and comorbidity in AF remains underexplored, particularly regarding quality of life (QoL), health management, and outcome prioritization. Within the AFFIRMO project, this study investigated the experiences of older adults with AF and at least one chronic condition via an online survey. Frailty was assessed using the FRAIL questionnaire, and participants were grouped by frailty status and number of comorbidities. Health-related quality of life (HRQoL) was measured using the EQ-5D-3L and Visual Analogue Scale (VAS). Challenges in health management and prioritized outcomes were also explored. We included 659 participants (median age 72&#xa0;years, 52.8% female). Those with pre-frailty or frailty and &#x2265;&#x2009;3 comorbidities reported the poorest HRQoL. Comorbidity, particularly combined with frailty, was associated with health management difficulties, including healthcare visits, polypharmacy, and mobility limitations. Across all groups, maintaining independence and improving QoL were prioritized outcomes. Pain relief was especially important for those with higher comorbidities. In older adults with AF, comorbidity and frailty significantly affect QoL and health burden. Tailored, patient-centred care strategies and routine assessment of frailty and comorbidity are essential to improve care coordination and outcomes.

Multimorbidity, having two or more chronic conditions, is a growing public health concern associated with substantial health and societal burdens. However, evidence on its impact on health-related quality of life (HRQoL) among U.S. adults remains limited. This study fills a major research gap by examining the association between multimorbidity and HRQoL among U.S. adults, a population often overlooked in prior research and provides evidence to inform policies aligned with national and global health goals for reducing the chronic disease burden. A cross-sectional study was conducted among adults aged 18 to 64 years. Data from the Medical Expenditure Panel Survey was used in this study for years 2019 to 2021. The primary study outcome was the HRQoL; it was evaluated using the 12-item Veterans RAND 12. Descriptive analysis was used to describe the characteristics of the study sample. The adjusted relationship between Multimorbidity and HRQoL was assessed using the multivariable linear regression after other factors were adjusted in the regression analysis. The study sample consists of 30,827 adults. Multimorbidity was prevalent among 23.4% of adults. It was higher among women, unemployed, poor, and physically inactive adults. Adults with multimorbidity had a lower mean HRQoL score than those without multimorbidity (Physical health = 46.06 vs. 53.29, Mental health = 47.62 vs. 52.37). Results from the adjusted linear regression model found that adults with multimorbidity have a significantly lower HRQoL in both the physical domain (&#x3b2; = -2.658, p-value<0.0001), and the mental domain (&#x3b2; = -3.119, p-value<0.0001). Multimorbidity has a substantial negative impact on both physical and mental aspects of HRQoL in U.S. adults. These findings highlight the need for targeted public health strategies and clinical interventions, such as promoting integrated chronic disease management to address the burden of multimorbidity. Future research should explore specific condition clusters most strongly associated with reduced HRQoL to better inform policy and care models.

This cross-sectional study aims to explore the independent and combined effects of physical frailty and sleep quality on cognitive function in low-income older adults in the urban-rural fringe of China, and to examine whether daily activities (ADL) play a mediating role between frailty and cognitive function. A combination of convenience sampling and stratified sampling was used to recruit 198 people over 55 and above from a community in the urban-rural fringe of Keerqin District, Tongliao City, Inner Mongolia. Cognitive function was assessed using the Montreal Cognitive Assessment Beijing version (MoCA BJ), frailty was assessed using the FRAIL scale, sleep quality was measured using the Pittsburgh Sleep Quality Index (PSQI), depressive symptoms were assessed using the 15-item version of the Geriatric Depression Scale (GDS-15), and daily functioning was assessed using the ADL scale. Covariates included age, sex, years of education, smoking status, alcohol consumption, and number of chronic diseases. The statistical methods include hierarchical multiple linear regression and Bootstrap mediation analysis (resampling 2,000 times). 48.5% of participants in the sample had cognitive impairment (MoCA<18 points), and 65.2% reported poor sleep quality (PSQI &#x2265; 5 points). One-way ANOVA showed significant differences in MoCA scores among the three groups of frailty grades (F&#x202f;=&#x202f;7.26, p&#x202f;<&#x202f;0.001, &#x3b7; 2&#x202f;=&#x202f;0.069), with the frailty group scoring significantly lower than the robust group (Bonferroni adjusted p&#x202f;=&#x202f;0.001). Hierarchical regression analysis (Model 4, R2&#x202f;=&#x202f;0.254) showed that the PSQI total score (B&#x202f;=&#x202f;-0.78, p&#x202f;=&#x202f;0.016) and ADL score (B&#x202f;=&#x202f;0.85, p&#x202f;=&#x202f;0.010) were independent predictors of MoCA, while the health and lifestyle covariates (smoking, alcohol, chronic disease count) were non-significant. Bootstrap mediation analysis showed that ADL function exhibited a significant mediation effect in the frailty-cognition association (indirect effect&#x202f;=&#x202f;-0.42, 95% CI [-0.80, -0.14]), accounting for 45.4% of the total effect; however, after controlling for lifestyle and comorbidity covariates, this mediation effect was not statistically significant (indirect effect&#x202f;=&#x202f;-0.19, 95% CI [-0.40, 0.03], p&#x202f;=&#x202f;0.092). The sleep-depression-cognition pathway did not reach statistical significance. Frailty and sleep quality (especially sleep latency) are independently correlated with cognitive function. Daily functional impairment shows a significant mediation trend in the frailty-cognition association, and this effect only reaches marginal significance after controlling for chronic disease burden and lifestyle factors. This suggests that comprehensive interventions addressing multimorbidity may be more fundamental than simple daily functional rehabilitation. Therefore, on the basis of systematic management of multiple chronic diseases, combining interventions aimed at improving sleep difficulties and maintaining physical functional independence can provide practical entry points for promoting cognitive health in this vulnerable group.

Atrial fibrillation (AF) often coexists with multiple chronic conditions, worsening health-related quality of life (HRQoL) and increasing the burden on patients and caregivers. While multimorbidity is known to worsen clinical outcomes, the role of distinct comorbidity patterns in shaping patients' experience remains unclear. This cross-sectional study assessed whether the number and patterns of comorbidities differentially affect HRQoL, care needs, and priorities of AF patients and caregivers. An online survey on living with AF and multimorbidity was disseminated between May 2022 and January 2023 in the UK, Italy, Spain, Romania, and Denmark. The analysis included 633 AF patients (46.9% females, median age 73 years) and 198 caregivers (26.8% females, median age 57 years). Exposure variables were the number and patterns (derived through latent class analysis) of comorbidities. Outcomes included HRQoL (measured with the EQ-5D-3L), perceived management problems and health priorities assessed through a structured questionnaire developed ad hoc for the survey. Three patterns emerged: unspecific (65.5%), diabetes-kidney-liver (18.2%), and complex (16.4%). More comorbidities and belonging to the complex pattern were associated with worse HRQoL, mainly due to limited mobility, dependency, and pain. Main issues were managing multiple diseases, medi ions, and appointments. The diabetes-kidney-liver group prioritized improving quality of life (OR=3.08, 95%CI:1.68-6.00) and living longer (OR=1.67, 95%CI:1.05-2.64), while pain relief was a distinct priority in the complex pattern (OR=2.32, 95%CI:1.38-3.86). Both number and combinations of AF comorbidities shape patients' and caregivers' experiences. Considering comorbidity profiles can help define targeted care plans and caregiver support initiatives.

Heart failure (HF) remains a global cause of morbidity and mortality and is increasingly complex to manage due to high prevalence of multimorbidity and coexisting cardiorenal and metabolic (CaReMe) syndrome. Individualised care through integrated service models can improve quality and maximise patient outcomes. This scoping review identifies and synthesises models of integrated care for multimorbid HF and CaReMe syndrome, analysing organisation, implementation, and reported outcome measures. A systematic search was conducted in MEDLINE, PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), COCHRANE library, and grey literature. Eligible studies included primary research published between 2014 and 2025 describing integrated, multispecialty, or multidisciplinary team (MDT) models of care for adults with multimorbid HF and CaReMe disease. The search followed PRISMA-ScR reporting standards and studies reviewed using standardised tools informed by Joanna Briggs Institute (JBI) and Cochrane Collaborations Tool methodologies. Five studies were identified from upper-middle and high-income countries that incorporated MDT integrated care models. Models were mapped to the Effective Practice and Organisation of Care (EPOC) framework explaining key components of integrated care. Positive outcomes included reduced hospitalisations, improved treatment adherence, enhanced collaborative processes, and patient engagement. Limited governance structures, variable outcome measures, gaps in financial and technological evaluations were also identified. Integrated care models for multimorbid HF and CaReMe syndrome demonstrate potential to enhance care coordination and quality of life. Evidence gaps persist regarding practical implementation, economic viability, and flexibility across healthcare settings. Future research should prioritise patient co-design, standardised outcomes, and shared decision-making frameworks.

Multimorbidity is a growing global challenge, associated with premature death, impaired activities of daily living, reduced capacity for independent living, poor functional outcomes, and lower quality of life. However, there is limited evidence on multimorbidity and their determining factors among stroke survivors in the Ethiopian context. This study aimed to assess the prevalence of multimorbidity and its associated factors among stroke survivors in public hospitals of Amhara Regional State Northwest Ethiopia. A multi-center, institution-based cross-sectional study was conducted from June 26 to August 30, 2024. Systematic random sampling was used to select 292 study participants. Data were collected using a structured, interviewer-administered questionnaire and chart review. Bivariable and multivariable logistic regression analyses were performed to identify factors associated with multimorbidity. Variables with a p-value < 0.05 in multivariable analysis were considered statistically significant. The prevalence of multimorbidity was 72.9%. Hypertension was the most frequently reported comorbidity. Significant factors associated with multimorbidity included age 50 and above (AOR: 2.48, 95% CI: 1.29, 4.74), having no formal education (AOR: 3.72, 95% CI: 1.49, 9.26), secondary education (AOR: 3.76, 95% CI: 1.46, 9.73), use of assistive technology (AOR: 2.60, 95% CI: 1.32, 5.09), duration of hospitalization (AOR:3.08,95%CI:1.37,6.95), and post-stroke disability (AOR: 4.47, 95% CI: 2.23,8.93). Multimorbidity is highly prevalent. Targeted interventions particular focus on aged population, educational status, assistive technology provision, and post-stroke disability are essential to improve health outcomes.

Patient and public involvement (PPI) in clinical trials for adults with multimorbidity (multiple long-term conditions) in primary care is essential to ensure research is person-centred. However, PPI is often underreported, limiting understanding of its application and impact. This protocol describes a systematic review examining the uptake, impact and reporting quality of PPI in clinical trials of interventions to improve mental health, clinical or quality-of-life outcomes for adults with multimorbidity in primary care. The review will be guided by the Cochrane Handbook and reported according to PRISMA-P guidelines. Eligible studies include completed and ongoing randomised and non-randomised controlled trials. Multimorbidity is defined as the co-existence of two or more long-term conditions. Electronic databases (MEDLINE, CINAHL, Embase, Cochrane) will be searched from 2019 to update Smith et al. (2021) without language restrictions. Trial registries and grey literature will identify protocols and supplementary data. Inclusion criteria - Population: adults with multimorbidity; Interventions: targeted at this population; Comparison: usual care; Outcomes: mental health, clinical or quality-of-life; Setting: primary or community care. Studies will be included irrespective of whether PPI was reported. Data extraction will capture PPI presence, characteristics, activities, training and acknowledgement. A narrative synthesis will describe reported PPI in clinical trials. Two PPI partners will contribute throughout the review. The protocol is registered with PROSPERO (CRD420251090082). This review will enhance understanding of PPI in trials aiming to improve outcomes for adults with multimorbidity in primary and community care, identify gaps in reporting, and inform future trials to support person-centred research.

In people with multimorbidity, traditional, disease-oriented approaches may overlook the impact of symptoms on daily functioning. To explore the assumption that symptoms and signs provide information on functional limitations beyond that of diseases in older adults, specifically those with multimorbidity. 4025 participants in the Longitudinal Aging Study Amsterdam (1995-2022). Analyses included six symptoms, five signs and eight diseases as exposures and a sum score of six functional limitations as the outcome. Partial Information Decomposition was used to partition the total variability in functional limitations into unique, redundant and synergistic information provided by the exposures in the total sample and in the multimorbidity subgroup. Random forest prediction models were run to examine the added predictive value of symptoms, signs and diseases. In the total sample, 59% had multimorbidity. Symptoms, signs and diseases together explained 13.3% of variability in functional limitations. None of the three domains contributed unique information. Synergy accounted for most of the explained variability (signs&#x2009;=&#x2009;9.2%, symptoms&#x2009;=&#x2009;34.3%, diseases&#x2009;=&#x2009;34.3%). In the multimorbidity subgroup, symptoms, signs and diseases together explained 8.7% of variability in functional limitations. Symptoms uniquely contributed 35.5% of their information, while signs and diseases were redundant. Prediction models showed that symptoms provided substantial predictive value beyond diseases alone, with a 110% increase in predictive agreement when symptoms were added to diseases in the multimorbidity subgroup, compared to 58% in the total sample. In people with multimorbidity, symptoms and signs explain more variability in functional limitations than diseases alone, supporting the need for a symptom-oriented approach in clinical care and research.

Limited evidence exists on how multimorbidity combinations influence the risk of secondary bacterial infections. This study identified multimorbidity clusters among hospitalised COVID-19 patients in Victoria (2020-2023) and examined their association with secondary bacterial infections and admission outcomes, including ICU admission, hospital and ICU length of stay, and mortality. We used population-wide linked hospital data and applied cluster analysis to ICD-10 coded chronic conditions to identify multimorbidity clusters. Risks of secondary bacterial infection across clusters were compared to patients with one or no chronic conditions. We used multivariate logistic regression, negative binomial regression, Kaplan-Meier curves, and Cox proportional hazards models to analyse associations with secondary bacterial infection and admission outcomes. Among 179,688 COVID-19 hospital admissions, three multimorbidity clusters were identified: neuropsychiatric, cardiometabolic-multisystem, and neoplastic. Compared to no multimorbidity, each cluster was associated with significantly higher odds of secondary bacterial infection: neuropsychiatric (Odds Ratio (OR) 2.74, 95% CI 2.59-2.89), cardiometabolic-multisystem (OR 3.87, 95% CI 3.63-4.13), and neoplastic (OR 1.98, 95% CI 1.84-2.14; all p<0.001). For patients with secondary bacterial infection, cardiometabolic-multisystem multimorbidity had the highest increase in hospital length of stay (Incident Rate Ratio (IRR) 1.94, 95% CI 1.82-2.05) and ICU length of stay (IRR 2.75, 95% CI 2.36-3.20). Mortality was significantly elevated across all clusters and was highest in the cardiometabolic-multisystem group. Multimorbidity was associated with increased risk of secondary bacterial infection and poorer clinical outcomes in hospitalised COVID-19 patients. Integrating multimorbidity profiles into clinical decision-making may enhance antimicrobial stewardship by identifying patients most likely to benefit from antibiotic therapy.

People living with multimorbidity often experience unmet social care needs, which can negatively affect wellbeing and increase pressure on health and social care systems. Artificial intelligence (AI)-enabled tools may support more timely and tailored responses to these needs. Large language models (LLMs) are emerging as tools to support qualitative research, although research detailing their integration into qualitative analytic workflows remains limited. We conducted a secondary thematic analysis of 75 qualitative interview transcripts involving people with multimorbidity and their carers. The dataset was coded according to an analytic framework of exploratory, interpretive, and integrative layers of meaning. The dataset was analysed according to two parallel analytic streams: human reflexive thematic analysis, and qualitative analysis using Claude Sonnet 4. Model outputs were iteratively reviewed and compared against manual thematic analysis for convergence and divergence. Across the analytic workflow, twelve themes from the original human-led analysis were used as a reference framework for examining areas of alignment, extension, or divergence in LLM-generated interpretations. The LLM-assisted analysis highlighted shifts in analytic emphasis and candidate interpretive nuances, including emotive tone and latent cross-cutting concerns, while requiring human oversight to determine evidential grounding. We present a structured methodological illustration for integrating LLM-assisted outputs within qualitative analysis. Using convergence-divergence mapping, we examine how LLM-generated interpretations may function as an additional analytic lens that can support reflexivity, transparency, and analytic auditability in qualitative research applied within the context of multimorbidity.

Multiple long-term conditions co-occur in people with type 1 diabetes. We aim to investigate the association between comorbidities and physical function, fall risk and hospitalization cost. A cross-sectional study was conducted at the First Affiliated Hospital of Sun Yat-sen University. Adult patients with type 1 diabetes admitted between 2021 and 2025 were included. Prevalence of each morbidity was compared in people with different diabetes duration. ADL was assessed by the Barthel Index. Fall risk was evaluated by the Johns Hopkins Fall Risk Assessment Tool. Logistic regression models were used to analyze the association between multimorbidity and physical function and fall risk. Linear regression was used between eight variables and hospitalization costs. Variables associated with increasing risk of multimorbidity were identified using multivariate logistic regression model. The mean number of morbidities per patient was 3.6 &#xb1; 1.8, with 31.4% (n=118), 41.5% (n=156), and 27.1% (n=102) had 1-2, 3-4, and &#x2265;5 morbidities. Cardiovascular, kidney, metabolic conditions were the most prevalent comorbidities. Cataract, anemia, cancer, autoimmune thyroid disorders, chronic obstructive pulmonary disease, and mental health disorders were also notable. Older age and longer diabetes duration were strongly associated with higher multimorbidity burden. Increased multimorbidity was associated with higher fall risk (odds ratio (OR): 1.23, 95% confidence interval (CI): 1.01-1.52) (P<0.05), greater dependence in Activities of daily living (OR: 1.58, 95% CI: 1.08-2.32) (P&#xa0;=&#xa0;0.02) and elevated hospitalization costs (&#x3b2;: 66.12, 95% CI: 14.65-117.58) (P&#xa0;=&#xa0;0.012). Our findings demonstrate that multimorbidity is highly prevalent among Chinese adults with T1D. A higher burden of multimorbidity is significantly associated with adverse functional outcomes, including increased fall risk and greater dependence in ADL, as well as higher hospitalization costs. These findings highlight the critical need to integrate assessments of functional status, fall risk, and multimorbidity into routine clinical care for adults with T1D.

Previous reports of transcatheter aortic valve implantation (TAVI) in adults &#x2265;90 years emphasise procedural success and survival. Whether nonagenarians experience clinically meaningful, sustained improvements in patient-reported outcomes, and how frailty and multimorbidity may modify these benefits, remains uncertain. We performed an exploratory single-centre retrospective cohort study of consecutive nonagenarians undergoing TAVI (January 2019 - 31 October 2024). EuroQol 5-Dimensions 3-Level (EQ-5D-3L) (including Visual Analogue Scale, VAS) and the Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) Summary Score were collected at baseline, 30 days and 12 months. Responders were defined using a &#x2265;10-point minimally important difference. Deterioration was any negative pre- to 12-month change. Frailty was stratified into three prespecified Clinical Frailty Scale (CFS) categories: 1-3 (fit to managing well), 4-5 (vulnerable to mildly frail), and 6-9 (moderately to severely frail/terminally ill). Of 26 nonagenarians, 19 met inclusion criteria. Most were mildly-moderately frail (CFS 3-5: n=18; CFS 6: n=1). Median 30-day changes were +25 (IQR 10-38) for EQ-5D VAS (exact sign test p=0.001) and +39 (30-50) for KCCQ-12 (p<0.001), with gains largely sustained at 12-months (EQ-5D VAS p=0.096; KCCQ-12 p<0.001). At 12-months, 13/18 (72%) were EQ-5D VAS responders and 17/18 (94%) KCCQ-12 responders. Responder proportions were highest in less frail strata, noting CFS 6-9 contained a single patient. EQ-5D-3L problem burden improved across all domains, especially Mobility (p=0.002) and Usual activities (p=0.012). Twelve-month survival was 94% (18/19). In this single-centre nonagenarian cohort, we observed 30-day improvements in symptoms and function following TAVI that were maintained to 12 months. Observed changes appeared smaller with greater frailty and multimorbidity, and higher comorbidity burden coincided with a higher probability of deterioration. Findings should be interpreted as exploratory and hypothesis-generating.

Projections suggest that the number of adults living with multimorbidity will continue growing in the coming decades. Little is known, however, about the potential impact of prevention policies on multimorbidity. We applied a validated microsimulation model of multimorbidity accumulation to simulate theoretical scenarios of health improvement and inequality reduction in England over 30 years (2019-2049), compared to a baseline scenario of continuing patterns in accumulation. Four theoretical scenarios were based on Benach et al.'s typology of health policies: 1) targeted intervention on the worst-off; 2) universal policy + additional focus on the gap; 3) redistributive policy; 4) proportionate universalism; plus an idealistic fifth scenario completely removing socioeconomic inequality in transition times between states. We selected a target of 3% reduction in mortality for scenarios 1-4, based on reductions seen from tobacco control policies. Outputs compared were: difference in 2049 projected prevalence and numbers compared to baseline, total cases prevented/postponed compared to baseline, and expected years lived without multimorbidity at age 30. Our results suggest that gains in levelling socioeconomic inequalities in health would prevent/postpone multimorbidity cases and reduce relative health inequalities among those aged <65. However, this would also likely lead to increased absolute numbers living with multimorbidity overall. Our theoretical modelling suggests effective and equitable policies have potential to reduce the population-level burden of multimorbidity, postponing a substantial number of multimorbidity cases, particularly before age 65. This is, however, likely to lead to greater absolute numbers of multimorbidity cases as individuals live for longer.

Veterans using Department of Veterans Affairs (VA) healthcare have a high burden of pre-pregnancy chronic disease that likely contributes to the observed high rate of pregnancy-related morbidity. Many common diseases frequently co-occur; understanding patterns of multimorbidity may inform the design and delivery of pre-pregnancy interventions to lower pregnancy morbidity risk. The current study sought to identify patterns of co-occurrence of pre-pregnancy chronic disease among Veterans. We conducted a retrospective cohort study using VA administrative data. Our population included Veterans ages 18-45 with &#x2a7e;1 pregnancy outcome (ectopic, spontaneous abortion, stillbirth, and/or live birth) during fiscal years 2010-2019. Presence of common chronic diseases with implications for pregnancy was detected using encounter International Classification of Diseases, 9th and 10th Revision (ICD-9 and ICD-10) codes in the 2&#x2009;years prior to pregnancy. Patients were grouped based on latent class models of diagnosis patterns; two to seven latent groups were examined for model fit and clinical interpretability. We identified 56,853 pregnancies from 41,034 Veterans. More than half of pregnancies were complicated by an array of pre-pregnancy medical and mental health conditions that may negatively impact pregnancy health and contribute to adverse pregnancy outcomes. The most frequently occurring conditions included chronic pain (51.2% of pregnancies), depression (31.4%), anxiety (25.9%), and post-traumatic stress disorder (22.8%). A five-group model demonstrated the best balance between model fit and clinical interpretability. Groups included: "Pain and Mental Health" (28%), with high prevalence of chronic pain, depression, and anxiety; "Pain and Metabolic" (17%), high prevalence of chronic pain, obesity, and migraines; "Substance Use and Mental Health" (7%), high prevalence of alcohol use disorder, depression, and post-traumatic stress disorder; "Low Diagnosis" (43%), lower than average prevalence of diagnoses; and "High Complexity" (5%), high prevalence of conditions across multiple physiologic systems. We identified five distinct, clinically meaningful groups of Veterans based on co-occurring pre-pregnancy diseases. Tailoring interventions to these groups may address Veterans' complex pre-pregnancy health risks effectively and efficiently.

Childhood socioeconomic disadvantage is linked to individual chronic diseases in adulthood, but its relationship with multimorbidity remains unclear. Understanding this is crucial for informing prevention strategies and the economic case for investment. This review and meta-analysis evaluated the association between childhood disadvantage and adult multimorbidity. Following pre-registration (PROSPERO: CRD42024588657), we searched MEDLINE, SocIndex, ASSIA, and ProQuest Public Health to March 2025 for studies assessing childhood socioeconomic circumstances (SECs) and adult multimorbidity (&#x2265;2 chronic conditions). Risk of bias was assessed using ROBINS-E and evidence certainty using GRADE. Random-effects meta-analysis and synthesis without meta-analysis (SWiM) were conducted. Subgroup analyses explored heterogeneity by region, design, and exposure type. From 5,617 records, 10 studies met inclusion criteria. Most were cross-sectional, using retrospective reports of exposure and self-reported outcomes. Exposures included perceived childhood economic adversity (n=6), parental education (n=4), parental occupation (n=1), and composite measures (n=3). Meta-analyses found no clear associations for perceived adversity (OR 1.08, 95% CI 0.87-1.23; I2 = 94.4%) or parental education (father's (Odds ratio (OR) 0.95, 95% CI 0.66-1.37; I2 = 66.8%); mother's (OR 1.07, 95% CI 0.70-1.61; I2 = 36.9%)). Relative Index of Inequality estimates generally indicated higher mortality risk with greater childhood disadvantage, though effect sizes varied widely and some studies suggested the reverse.. All studies were high/very high risk of bias with very low certainty. Evidence for an association between childhood socioeconomic disadvantage and adult multimorbidity is limited and uncertain. Findings suggest possible harmful effects but remain constrained by methodological weaknesses and heterogeneity. High-quality longitudinal studies with standardised multimorbidity definitions are needed.