Trajectories in chronic disease accrual and mortality across the lifespan in Wales, UK (2005-2019), by area deprivation profile: linked electronic health records cohort study on 965,905 individuals

Living arrangements and chronic disease accumulation among native-born and immigrant older adults in Europe

BACKGROUND

Who we live with in later life significantly influences the daily care support we receive, potentially moderating chronic disease trajectories. For immigrants, this relationship is further complicated by cultural preferences for certain living arrangements. This study examines the differential role of living arrangements in chronic disease accumulation among native-born and immigrant older adults in Europe.

METHODS

Using data from the Survey of Health, Ageing and Retirement in Europe (2006-2022), we analyze how living arrangements moderate the relationship between age, migration background, and the number of chronic diseases. We also conduct stratified analyses based on immigrants' countries of origin. All models employ inverse probability weights for panel attrition and panel-robust standard errors for longitudinal data. Analyses are conducted separately for men and women.

RESULTS

Immigrants generally have a higher chronic disease burden than native-born individuals, although the size of this disparity varies by living arrangements. Among native-born men and women, living with a partner/spouse or family is related to fewer chronic diseases than living alone. Conversely, among immigrants, these benefits of shared living arrangements are absent for men and weaker for women. We also find that the role of living arrangements in chronic disease accumulation among immigrants varies by their country of origin, as supported by, for instance, a considerable chronic disease burden among immigrant women from low HDI countries who live as a family.

CONCLUSION

Older immigrants, particularly men from highly developed countries and women from less developed countries, may experience fewer health benefits from living with a partner or with family. Our findings indicate that these immigrant households may have excessive caregiving burdens, which could contribute to unhealthy aging among immigrants in later life.

Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review

BACKGROUND

Chronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) are advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however, robust evidence is lacking.

OBJECTIVE

This review aims to synthesize evidence on the feasibility, opportunities, and challenges of LLMs across the disease management spectrum, from prevention to screening, diagnosis, treatment, and long-term care.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, 11 databases (Cochrane Central Register of Controlled Trials, CINAHL, Embase, IEEE Xplore, MEDLINE via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web of Science Core Collection, China National Knowledge Internet, and SinoMed) were searched on April 17, 2024. Intervention and simulation studies that examined LLMs in the management of chronic diseases were included. The methodological quality of the included studies was evaluated using a rating rubric designed for simulation-based research and the risk of bias in nonrandomized studies of interventions tool for quasi-experimental studies. Narrative analysis with descriptive figures was used to synthesize the study findings. Random-effects meta-analyses were conducted to assess the pooled effect estimates of the feasibility of LLMs in chronic disease management.

RESULTS

A total of 20 studies examined general-purpose (n=17) and retrieval-augmented generation-enhanced LLMs (n=3) for the management of chronic diseases, including cancer, cardiovascular diseases, and metabolic disorders. LLMs demonstrated feasibility across the chronic disease management spectrum by generating relevant, comprehensible, and accurate health recommendations (pooled accurate rate 71%, 95% CI 0.59-0.83; I2=88.32%) with retrieval-augmented generation-enhanced LLMs having higher accuracy rates compared to general-purpose LLMs (odds ratio 2.89, 95% CI 1.83-4.58; I2=54.45%). LLMs facilitated equitable information access; increased patient awareness regarding ailments, preventive measures, and treatment options; and promoted self-management behaviors in lifestyle modification and symptom coping. Additionally, LLMs facilitate compassionate emotional support, social connections, and health care resources to improve the health outcomes of chronic diseases. However, LLMs face challenges in addressing privacy, language, and cultural issues; undertaking advanced tasks, including diagnosis, medication, and comorbidity management; and generating personalized regimens with real-time adjustments and multiple modalities.

CONCLUSIONS

LLMs have demonstrated the potential to transform chronic disease management at the individual, social, and health care levels; however, their direct application in clinical settings is still in its infancy. A multifaceted approach that incorporates robust data security, domain-specific model fine-tuning, multimodal data integration, and wearables is crucial for the evolution of LLMs into invaluable adjuncts for health care professionals to transform chronic disease management.

TRIAL REGISTRATION

PROSPERO CRD42024545412; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024545412.

Cohort profile: creation of the SAIL MELD-B e-cohort (SMC) and SAIL MELD-B children and young adult e-cohort (SMYC) to investigate the lived experience of the 'burdensomeness' of multimorbidity

PURPOSE

We have established the SAIL MELD-B electronic cohort (e-cohort SMC) and the SAIL MELD-B children and Young adults e-cohort (SMYC) as a part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) project. Each cohort has been created to investigate and develop a deeper understanding of the lived experience of the 'burdensomeness' of multimorbidity by identifying new clusters of burdensomeness concepts, exploring early life risk factors of multimorbidity and modelling hypothetical prevention scenarios.

PARTICIPANTS

The SMC and SMYC are longitudinal e-cohorts created from routinely collected individual-level population-scale anonymised data sources available within the Secure Anonymised Information Linkage (SAIL) Databank. They include individuals with available records from linked health and demographic data sources in SAIL at any time between 1 January 2000 and 31 December 2022. The SMYC e-cohort is a subset of the SMC, including only individuals born on or after the cohort start date.

FINDINGS TO DATE

The SMC and SMYC cohorts include 5 180 602 (50.3% female and 49.7% male) and 896 155 (48.7% female and 51.3% male) individuals, respectively. Considering both primary and secondary care health data, the five most common long-term conditions for individuals in SMC are 'Depression', affecting 21.6% of the cohort, 'Anxiety' (21.1%), 'Asthma' (17.5%), 'Hypertension' (16.2%) and 'Atopic Eczema' (14.1%) and the five most common conditions for individuals in SMYC are 'Atopic Eczema' (21.2%), 'Asthma' (11.6%), 'Anxiety' (6.0%), 'Deafness' (4.6%) and 'Depression' (4.3%).

FUTURE PLANS

The SMC and SMYC e-cohorts have been developed using a reproducible, maintainable concept curation pipeline, which allows for the cohorts to be updated dynamically over time and manages for the request and processing of further approved long-term conditions and burdensomeness concepts extraction. Best practices from the MELD-B project can be utilised across other projects, accessing similar data with population-scale data sources and trusted research environments.

Spinal pain prevalence and associated determinants: A population-based study using the National Survey for Wales

Spinal pain (SP) remains the leading cause of disability worldwide. The present study aimed to establish a current prevalence of SP and associated determinants in Wales by retrospectively analyzing data from the National Survey for Wales Dataset (NSWD). The NSWD is a large-scale cross-sectional, representative sample of adults across Wales, UK. A univariable and multivariable regression analysis was carried out on self-reported answers to health and well-being questions contained within the NSWD (2016-2020) to determine the strength of association of various determinants and comorbidities related to spinal pain. A total population of 38,954 of adults were included in the analysis. The study population included interview responses of 21,735 females and 17,219 males. The prevalence of SP in Wales was 4.95% (95% CI: 4.74%-5.15%) with a total of 847 males (4.92%, CI: 4.60%-5.24%) and 1082 females (4.98%, CI: 4.69%-5.27%) reporting spinal pain. The age group with the highest prevalence of SP was in the 70+ years age group for both males (5.44%, CI: 4.82%-6.07%) and females (5.95%, CI: 5.37%-6.54%). The strength of association between age and SP reaches its peak at 50-59 years with an adjusted Odds Ratio (aOR) of 3.74 (p = <0.001), that decreases slightly at 60-69 years and 70+ years. For various comorbidities included in the NSWD, significant associations with SP were confirmed for: mental illness (aOR = 1.42, p = <0.001), migraine (aOR = 2.73, p = <0.001), nervous system issues (aOR = 1.61, p = <0.001), arthritis (aOR = 1.30, p = <0.001) and issues with bones/joints/muscles (aOR = 1.93, p = <0.001). For lifestyle factors, associations were confirmed for current smokers (aOR = 1.41, p = <0.001) and ex-smokers (aOR = 1.23, p = 0.003). This study demonstrates a low prevalence of SP in Wales when compared to global estimates and strong associations to a variety of determinants. This still represents a significant societal burden and these findings may help inform public health initiatives to encourage prevention and evidence-based interventional strategies and ultimately, improve the quality of life for those suffering with SP in Wales.

Illness Trajectories After Revascularization in Patients With Peripheral Artery Disease: A Unified Approach to Understanding the Risk of Major Amputation and Death

BACKGROUND

The aim of this study was to investigate the illness trajectories of patients with peripheral artery disease (PAD) after revascularization and estimate the independent risks of major amputation and death (from any cause) and their interaction.

METHODS

Data from Hospital Episode Statistics Admitted Patient Care were used to identify patients (≥50 years of age) who underwent lower limb revascularization for PAD in England from April 2013 to March 2020. A Markov illness-death model was developed to describe patterns of survival after the initial lower limb revascularization, if and when patients experienced major amputation, and survival after amputation. The model was also used to investigate the association between patient characteristics and these illness trajectories. We also analyzed the relative contribution of deaths after amputation to overall mortality and how the risk of mortality after amputation was related to the time from the index revascularization to amputation.

RESULTS

The study analyzed 94 690 patients undergoing lower limb revascularization for PAD from 2013 to 2020. The majority were men (65.6%), and the median age was 72 years (interquartile range, 64-79). One-third (34.8%) of patients had nonelective revascularization, whereas others had elective procedures. For nonelective patients, the amputation rate was 15.2% (95% CI, 14.4-16.0) and 19.9% (19.0-20.8) at 1 and 5 years after revascularization, respectively. For elective patients, the corresponding amputation rate was 2.7% (95% CI, 2.4-3.1) and 5.3% (4.9-5.8). Overall, the risk of major amputation was higher among patients who were younger, had tissue loss, diabetes, greater frailty, nonelective revascularization, and more distal procedures. The mortality rate at 5 years after revascularization was 64.3% (95% CI, 63.2-65.5) for nonelective patients and 33.0% (32.0-34.1) for elective patients. After major amputation, patients were at an increased risk of mortality if they underwent major amputation within 6 months after the index revascularization.

CONCLUSIONS

The illness-death model provides an integrated framework to understand patient outcomes after lower limb revascularization for PAD. Although mortality increased with age, the study highlights patients <60 years of age were at increased risk of major amputation, particularly after nonelective revascularization.