Investigation of multimorbidity and prevalent disease combinations in older Irish adults using network analysis and association rules

The diabetes mellitus multimorbidity network in hospitalized patients over 50 years of age in China: data mining of medical records

OBJECTIVE

Many diabetes mellitus (DM) patients suffer from multimorbidity. Understanding the DM multimorbidity network should be given priority. The purpose of this study is characterize the DM multimorbidity network in people over 50 years.

METHODS

Data on 75 non-communicable diseases (NCDs) were extracted from electronic medical records of 309,843 hospitalized patients older than 50 years who had at least one NCD. The association rules analysis was used as a novel classification method and combined with the Chi-square tests to identify associations between NCDs and DM.

RESULT

A total of 12 NCDs were closely related to DM, {cholelithiasis, DM} was an unexpected combination. {dyslipidemia, DM} and {gout, DM} had the largest lift in the male and female groups, respectively. The negative related group included 7 NCDs. There were 9 NCDs included in the strong association rules. Most combinations were different by age and sex. In males, the strongest rule was {peripheral vascular disease (PVD), dyslipidemia, DM}, while {hypertension, dyslipidemia, chronic liver disease (CLD), DM} was the strongest in females. In patients younger than 70 years, hypertension, CLD, and dyslipidemia were the most dominant NCDs in the DM multimorbidity network. In patients 70 years or older, chronic kidney disease (CKD), CVD, CHD, and heart disease (HD) frequently co-occurred with DM.

CONCLUSION

Future primary healthcare policies for DM should be formulated based on age and sex. In patients younger than 70 years, more attention to hypertension, CLD, and dyslipidemia is required, while attention to CKD, CVD, CHD and HD is needed in patients older than 70 years.

Genetic effects and causal association analyses of 14 common conditions/diseases in multimorbidity patterns

BACKGROUND

Multimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown.

METHODS

Summary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases.

RESULTS

LDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (P<0.05/91 = 5.49E-04). Significant pleiotropic SNPs were detected for 11 pairs of correlated conditions/diseases. The corresponding pleiotropic genes were differentially expressed in the brain, nerves, heart, and blood vessels and enriched in gluconeogenesis and drug metabolism, biotransformation, and neurons. Comprehensive causal analyses showed strong causality between hypertension, stroke, and high cholesterol, which drive the development of multiple diseases.

CONCLUSIONS

This study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.

Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups

BACKGROUND

Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data.

METHODS

We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC.

FINDINGS

Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations.

INTERPRETATION

Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.

FUNDING

National Institute for Health and Care Research.

Exploring key components and factors that influence the use of clinical decision- support tools for prescribing to older patients with kidney disease: the perspective of healthcare providers

BACKGROUND

Clinical decision-support (CDS) tools are systems that provide healthcare providers (HCPs) with recommendations based on knowledge and patient-specific factors to facilitate informed decisions.

OBJECTIVES

To identify the key components of a CDS tool that are most important to HCPs in caring for older adults with kidney disease, and to understand the facilitators and barriers toward using CDS tools in daily clinical practice.

METHODS

Design: A cross-sectional survey of Canadian HCPs was undertaken.

DATA COLLECTION

Participants affiliated with a provincial college, nephrology organization, or advocacy body were contacted. The survey was conducted between August and October 2021.

INSTRUMENT

A 59-item questionnaire was developed and divided into five main domains/themes. Analysis was done descriptively.

RESULTS

Sixty-three participants completed the questionnaire. Physicians (60%) and pharmacists (22%) comprised the majority of the participants. Most of the participants were specialized in nephrology (65%). The most important components in a CDS tool for prescribing to older patients with kidney disease were the safety and efficacy of the medication (89%), the goal of therapy (89%), and patient's quality of life (87%). 90% were willing to use CDS tools and 57% were already using some CDS tools for prescribing. The majority of the participants selected the validation of CDS tools (95%), accompanying the recommendations by the supporting evidence (84%), and the affiliation of the tools with known organizations (84%), as factors that facilitate the use of CDS tools.

CONCLUSION

CDS tools are being used and are accepted by HCPs and have value in their assistance in engaging patients in making well-informed decisions.

Exploring attitudes of people with chronic health conditions towards the use of group-based telerehabilitation: A qualitative study

OBJECTIVE

The study explores the attitudes of people with chronic health conditions towards the use of group-based telerehabilitation.

DESIGN

A qualitative research study.

SETTING

The setting involved semi-structured focus groups via videoconferencing software.

PARTICIPANTS

A purposive sample of 18 people with chronic health conditions including cardiorespiratory, neurological and musculoskeletal conditions was recruited via national patient advocacy and support groups in Ireland and clinical contacts. The sample included both those who had, and had not, previously engaged in telerehabilitation programmes.

PROCEDURES

An online questionnaire collected demographic information and data regarding previous telerehabilitation participation and telerehabilitation preferences. Focus groups were conducted using videoconferencing software, in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) Checklist, and analysed using thematic analysis following Braun and Clarke's methodology. Findings were triangulated with quantitative questionnaire data.

RESULTS

Four focus groups were conducted including participants with chronic cardiorespiratory (n = 8), neurological (n = 6) and musculoskeletal (n = 4) conditions. Three themes were identified regarding telerehabilitation: (a) benefits and facilitators (including convenience, increased service accessibility, social connection and technological support), (b) challenges and barriers (including technological access and literacy, limited 'hands-on' therapy, safety concerns and social limitations), and (c) preferences (regarding mode of delivery, content, duration and generic programmes for mixed-condition groups).

CONCLUSIONS

Telerehabilitation is convenient for people with chronic conditions; however, concerns exist regarding the use of technology and the limitations of this healthcare delivery method. The role of telerehabilitation is valued, and future programmes should acknowledge patient preferences including a hybrid model of care, exercise and educational content, social interaction and synchronous components.

Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study

Rates of Multimorbidity (also called Multiple Long Term Conditions, MLTC) are increasing in many developed nations. People with multimorbidity experience poorer outcomes and require more healthcare intervention. Grouping of conditions by health service utilisation is poorly researched. The study population consisted of a cohort of people living in Wales, UK aged 20 years or older in 2000 who were followed up until the end of 2017. Multimorbidity clusters by prevalence and healthcare resource use (HRU) were modelled using hypergraphs, mathematical objects relating diseases via links which can connect any number of diseases, thus capturing information about sets of diseases of any size. The cohort included 2,178,938 people. The most prevalent diseases were hypertension (13.3%), diabetes (6.9%), depression (6.7%) and chronic obstructive pulmonary disease (5.9%). The most important sets of diseases when considering prevalence generally contained a small number of diseases, while the most important sets of diseases when considering HRU were sets containing many diseases. The most important set of diseases taking prevalence and HRU into account was diabetes & hypertension and this combined measure of importance featured hypertension most often in the most important sets of diseases. We have used a single approach to find the most important sets of diseases based on co-occurrence and HRU measures, demonstrating the flexibility of the hypergraph approach. Hypertension, the most important single disease, is silent, underdiagnosed and increases the risk of life threatening co-morbidities. Co-occurrence of endocrine and cardiovascular diseases was common in the most important sets. Combining measures of prevalence with HRU provides insights which would be helpful for those planning and delivering services.

Associations of hospitalisation - admission, readmission and length to stay - with multimorbidity patterns by age and sex in adults and older adults: the ELSI-Brazil study

BACKGROUND

Although the association between multimorbidity (MM) and hospitalisation is known, the different effects of MM patterns by age and sex in this outcome needs to be elucidated. Our study aimed to analyse the association of hospitalisations' variables (occurrence, readmission, length of stay) and patterns of multimorbidity (MM) according to sex and age.

METHODS

Data from 8.807 participants aged ≥ 50 years sourced from the baseline of the Brazilian Longitudinal Study of Ageing (ELSI-Brazil) were analysed. Multimorbidity was defined as ≥ 2 (MM2) and ≥ 3 (MM3) chronic conditions. Poisson regression was used to verify the association between the independent variables and hospitalisation according to sex and age group. Multiple linear regression models were constructed for the outcomes of readmission and length of stay. Ising models were used to estimate the networks of diseases and MM patterns.

RESULTS

Regarding the risk of hospitalisation among those with MM2, we observed a positive association with male sex, age ≥ 75 years and women aged ≥ 75 years. For MM3, there was a positive association with hospitalisation among males. For the outcomes hospital readmission and length of stay, we observed a positive association with male sex and women aged ≥ 75 years. Network analysis identified two groups that are more strongly associated with occurrence of hospitalisation: the cardiovascular-cancer-glaucoma-cataract group stratified by sex and the neurodegenerative diseases-renal failure-haemorrhagic stroke group stratified by age group.

CONCLUSION

We conclude that the association between hospitalisation, readmission, length of stay, and MM changes when sex and age group are considered. Differences were identified in the MM patterns associated with hospitalisation according to sex and age group.

Identification of status quo and association rules for chronic comorbidity among Chinese middle-aged and older adults rural residents

Background

Chronic comorbidity has become a major challenge in chronic disease prevention and control. This issue is particularly pronounced in rural areas of developing countries, where the prevalence of chronic disease comorbidity is high, especially among middle-aged and older adults populations. However, the health status of middle-aged and older adults individuals in rural areas of China has received inadequate attention. Therefore, it is crucial to investigate the correlation among chronic diseases to establish a reference basis for adjusting health policies aimed at promoting the prevention and management of chronic diseases among middle-aged and older adults individuals.

Methods

This study selected 2,262 middle-aged and older adults residents aged 50 years or older in Shangang Village, Jiangsu Province, China, as the study population. To analyze the chronic comorbidity of middle-aged and older adults residents with different characteristics, we used the χ² test with SPSS statistical software. Data analysis was conducted using the Apriori algorithm of Python software, set to mine the strong association rules of positive correlation between chronic disease comorbidities of middle-aged and older adults residents.

Results

The prevalence of chronic comorbidity was 56.6%. The chronic disease comorbidity group with the highest prevalence rate was the lumbar osteopenia + hypertension group. There were significant differences in the prevalence of chronic disease comorbidity among middle-aged and older adults residents in terms of gender, BMI, and chronic disease management. The Apriori algorithm was used to screen 15 association rules for the whole population, 11 for genders, and 15 for age groups. According to the order of support, the most common association rules of comorbidity of three chronic diseases were: {lumbar osteopenia} → {hypertension} (support: 29.22%, confidence: 58.44%), {dyslipidemia} → {hypertension} (support: 19.14%, confidence: 65.91%) and {fatty liver} → {hypertension} (support: 17.82%, confidence: 64.17%).

Conclusion

The prevalence of chronic comorbidity among middle-aged and older adults rural residents in China is relatively high. We identified many association rules among chronic diseases, dyslipidemia is mostly the antecedent, and hypertension is primarily the result. In particular, the majority of comorbidity aggregation patterns consisted of hypertension and dyslipidemia. By implementing scientifically-proven prevention and control strategies, the development of healthy aging can be promoted.

Social determinants of multimorbidity patterns: A systematic review

Social determinants of multimorbidity are poorly understood in clinical practice. This review aims to characterize the different multimorbidity patterns described in the literature while identifying the social and behavioral determinants that may affect their emergence and subsequent evolution. We searched PubMed, Embase, Scopus, Web of Science, Ovid MEDLINE, CINAHL Complete, PsycINFO and Google Scholar. In total, 97 studies were chosen from the 48,044 identified. Cardiometabolic, musculoskeletal, mental, and respiratory patterns were the most prevalent. Cardiometabolic multimorbidity profiles were common among men with low socioeconomic status, while musculoskeletal, mental and complex patterns were found to be more prevalent among women. Alcohol consumption and smoking increased the risk of multimorbidity, especially in men. While the association of multimorbidity with lower socioeconomic status is evident, patterns of mild multimorbidity, mental and respiratory related to middle and high socioeconomic status are also observed. The findings of the present review point to the need for further studies addressing the impact of multimorbidity and its social determinants in population groups where this problem remains invisible (e.g., women, children, adolescents and young adults, ethnic groups, disabled population, older people living alone and/or with few social relations), as well as further work with more heterogeneous samples (i.e., not only focusing on older people) and using more robust methodologies for better classification and subsequent understanding of multimorbidity patterns. Besides, more studies focusing on the social determinants of multimorbidity and its inequalities are urgently needed in low- and middle-income countries, where this problem is currently understudied.

Aortic Stiffness: A Major Risk Factor for Multimorbidity in the Elderly

Multimorbidity, the coexistence of multiple health conditions in an individual, has emerged as one of the greatest challenges facing health services, and this crisis is partly driven by the aging population. Aging is associated with increased aortic stiffness (AoStiff), which in turn is linked with several morbidities frequently affecting and having disastrous consequences for the elderly. These include hypertension, ischemic heart disease, heart failure, atrial fibrillation, chronic kidney disease, anemia, ischemic stroke, and dementia. Two or more of these disorders (multimorbidity) often coexist in the same elderly patient and the specific multimorbidity pattern depends on several factors including sex, ethnicity, common morbidity routes, morbidity interactions, and genomics. Regular exercise, salt restriction, statins in patients at high atherosclerotic risk, and stringent blood pressure control are interventions that delay progression of AoStiff and most likely decrease multimorbidity in the elderly.

Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis

BACKGROUND

Knowing the prevalence of multimorbidity among adults across continents is a crucial piece of information for achieving Sustainable Development Goal 3.4, which calls for reducing premature death due to non-communicable diseases. A high prevalence of multimorbidity indicates high mortality and increased healthcare utilization. We aimed to understand the prevalence of multimorbidity across WHO geographic regions among adults.

METHODS

We performed a systematic review and meta-analysis of surveys designed to estimate the prevalence of multimorbidity among adults in community settings. We searched PubMed, ScienceDirect, Embase and Google Scholar databases for studies published between January 1, 2000, and December 31, 2021. The random-effects model estimated the pooled proportion of multimorbidity in adults. Heterogeneity was quantified using I² statistics. We performed subgroup analyses and sensitivity analyses based on continents, age, gender, multimorbidity definition, study periods and sample size. The study protocol was registered with PROSPERO (CRD42020150945).

FINDINGS

We analyzed data from 126 peer-reviewed studies that included nearly 15.4 million people (32.1% were male) with a weighted mean age of 56.94 years (standard deviation of 10.84 years) from 54 countries around the world. The overall global prevalence of multimorbidity was 37.2% (95% CI = 34.9-39.4%). South America (45.7%, 95% CI = 39.0-52.5) had the highest prevalence of multimorbidity, followed by North America (43.1%, 95% CI = 32.3-53.8%), Europe (39.2%, 95% CI = 33.2-45.2%), and Asia (35%, 95% CI = 31.4-38.5%). The subgroup study highlights that multimorbidity is more prevalent in females (39.4%, 95% CI = 36.4-42.4%) than males (32.8%, 95% CI = 30.0-35.6%). More than half of the adult population worldwide above 60 years of age had multimorbid conditions (51.0%, 95% CI = 44.1-58.0%). Multimorbidity has become increasingly prevalent in the last two decades, while the prevalence appears to have stayed stable in the recent decade among adults globally.

INTERPRETATION

The multimorbidity patterns by geographic regions, time, age, and gender suggest noticeable demographic and regional differences in the burden of multimorbidity. According to insights about prevalence among adults, priority is required for effective and integrative interventions for older adults from South America, Europe, and North America. A high prevalence of multimorbidity among adults from South America suggests immediate interventions are needed to reduce the burden of morbidity. Furthermore, the high prevalence trend in the last two decades indicates that the global burden of multimorbidity continues at the same pace. The low prevalence in Africa suggests that there may be many undiagnosed chronic illness patients in Africa.

FUNDING

None.

Social inequalities in all-cause mortality among adults with multimorbidity: a 10-year prospective study of 0.5 million Chinese adults

BACKGROUND

Chinese individuals face an increase in multimorbidity, but little is known about the mortality gradients of multimorbid people in different socio-economic groups. This study measures relative and absolute socio-economic inequality in mortality among multimorbid Chinese.

METHODS

For this study, the prospective China Kadoorie Biobank (CKB) enrolled 512 712 participants ages 30-79 y from 10 areas of China between 25 June 2004 and 15 July 2008. All-cause mortality was accessed with a mean follow-up period of 10 y (to 31 December 2016). Associations between multimorbidity and mortality were assessed using Cox proportional hazards models, with the relative index of inequality (RII) and slope index of inequality (SII) in mortality calculated to measure disparities.

RESULTS

Mortality risk was highest for those who had not attended formal school and with four or more long-term conditions (LTCs) (hazard ratio 3.11 [95% confidence interval {CI} 2.75 to 3.51]). Relative educational inequality was lower in participants with four or more LTCs (RII 1.92 [95% CI 1.60 to 2.30]), especially in rural areas. Absolute disparities were greater in adults with more LTCs (SII 0.18 [95% CI 0.14 to 0.21] for rural participants with three LTCs).

CONCLUSIONS

Whereas the relative inequality in all-cause mortality was lower among multimorbid people, absolute inequality was greater among multimorbid men, especially in rural areas.

Development and validation of sustainable employability index among older employees

BACKGROUND

Sustainable employability (SE) has become an important factor for keeping people in the labour market and enabling the extension of working life.

AIMS

We developed and validated an SE index to predict assured workability in 2 years. Additionally, we developed a scoring tool to use in practice.

METHODS

A questionnaire survey of postal employees aged ≥50 years was conducted in 2016 and followed up in 2018 (n = 1102). The data were divided into training and validation sets. The outcome was defined as whether the employees had an assured workability after 2 years or not. Multivariable log-binomial regression was used to calculate the SE index. The area under the curve (AUC) was calculated to assess the discriminative power of the index.

RESULTS

The probability of assured workability increased with increasing quintiles of the SE index. The highest quintiles of the SE index showed the highest observed and expected assured workability in 2 years. The predictive ability, area under the curve (AUC) for training was 0.79 (95% CI 0.75-0.83) and for validation data was 0.76 (95% CI 0.73-0.80). In the scoring tool, the self-rated health, workability, job satisfaction and perceived employment had the highest contribution to the index.

CONCLUSIONS

The SE index was able to distinguish the employees based on whether they had assured workability after 2 years. The scoring method could be used to calculate the potentiality of future employability among late midlife postal employees.

"Older People Want to Be in Their Own Homes": A Service Evaluation of Patient and Carer Feedback after Pathfinder Responded to Their Emergency Calls

OBJECTIVE

Older people experience high rates of adverse outcomes following emergency department (ED) presentation. There is growing evidence to support alternative care pathways for certain types of emergency medical services (EMS) calls. Pathfinder is one such service and targets patients aged 65 years and over, whose presenting issues can be safely managed at home by immediate paramedic, occupational therapy, and/or physiotherapy interventions. The aim of this service evaluation was to understand how older people feel about being treated at home as a result of EMS calls and to understand their experiences of the Pathfinder service.

METHODS

This was a thematic analysis of open-ended responses recorded from telephone interviews during routine service evaluation with service users (patients or their next-of-kin).

RESULTS

Of 573 service users, telephone interviews were conducted with 429 (75%). Five primary themes were identified: (1) professionalism of the multidisciplinary clinical team; (2) "the right service, in the right place, at the right time"; (3) role of Pathfinder in "getting the ball rolling"; (4) lasting effects of the experience on the patient and his or her next-of-kin; (5) value of skilled communication with the older person.

CONCLUSION

Older people and their next-of-kin voiced a clear preference for hospital avoidance, and strongly valued the opportunity to be treated in their homes at the time of an EMS call rather than default conveyance to the ED. They appreciated the importance of a skilled multidisciplinary team with a follow-up service that effectively positions itself between the acute hospital and community services.

Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System

Background

People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear.

Objective

The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder.

Methods

A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships.

Results

The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality.

Conclusion

Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.

Patterns and Predictors of Multimorbidity in the Azar Cohort

BACKGROUND

The co-existence of chronic diseases (CDs), a condition defined as multimorbidity (MM), is becoming a major public health issue. Therefore, we aimed to determine the patterns and predictors of MM in the Azar Cohort.

METHODS

We evaluated the prevalence of MM in 15,006 (35-70-year old) subjects of the Azar Cohort Study. MM was defined as the co-existence of two or more CDs. Data on the subjects' socioeconomic status, demographics, sleeping habits, and physical activity were collected using questionnaires.

RESULTS

The overall prevalence of MM was 28.1%. The most prevalent CDs, in decreasing order, were obesity, hypertension, depression, and diabetes. Obesity, depression, and diabetes were the most co-occurring CDs. The MM risk increased significantly with age, illiteracy, and in females. Also, the subjects within the lowest tertile of physical activity level (OR=1.89; 95% CI: 1.75-2.05) showed higher MM risk than those with the highest level of physical activity. Findings regarding current smoking status indicated that being an ex-smoker or smoker of other types of tobacco significantly increased the risk of MM.

CONCLUSION

The reduction of MM is possible by promoting public health from an early age among people of various socioeconomic conditions. It is vital to offer the necessary health support to the aging population of Iran.

Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures

Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multi-chemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer's disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.

Association rules analysis on patterns of multimorbidity in adults: based on the National Health and Nutrition Examination Surveys database

OBJECTIVE

To explore the prevalence and patterns of multimorbidity in population with different genders and age ranges.

DESIGN

A cross-sectional study.

SETTING

National Health and Nutrition Examination Surveys database.

PARTICIPANTS

12 576 patients.

PRIMARY AND SECONDARY OUTCOME MEASURES

The prevalence and patterns of multimorbidity.

RESULTS

High cholesterol had the highest prevalence in all population (33.4 (95% CI: 32.0 to 34.9)) and males. In females <65 years, the most prevalent disease was sleep disorder (32.1 (95% CI: 29.6 to 34.5)) while in females ≥65 years, hypertension was the most prevalent disease (63.9 (95% CI: 59.9 to 67.9)). Hypertension and high cholesterol were associated with the highest support (occur together most frequently) in all population regardless of genders. Hypertension displayed the highest betweenness centrality (mediating role in the network) followed by high cholesterol and arthritis in all population. For males aged <65 years, hypertension and high cholesterol presented the highest betweenness centrality. In males ≥65 years, hypertension, high cholesterol and arthritis were the top three diseases of degree centrality (direct association with other conditions). As for females ≥65 years, hypertension showed the highest betweenness centrality followed by high cholesterol and arthritis. The associations of hypertension, arthritis and one other item with high cholesterol presented the highest support in all population. In males, the associations of depression, hypertension with sleep disorders had the highest lift (the chance of co-occurrence of the conditions and significant association). Among females, the associations of depression, arthritis with sleep disorders had the highest lift.

CONCLUSION

Hypertension and high cholesterol were prevalent in all population, regardless of females and males. Hypertension and high cholesterol, arthritis and hypertension, and diabetes and hypertension were more likely to coexist. The findings of this study might help make plans for the management and primary care of people with one or more diseases.

Male infertility and somatic health - insights into lipid damage as a mechanistic link

Over the past decade, mounting evidence has shown an alarming association between male subfertility and poor somatic health, with substantial evidence supporting the increased incidence of oncological disease, cardiovascular disease, metabolic disorders and autoimmune diseases in men who have previously received a subfertility diagnosis. This paradigm is concerning, but might also provide a novel window for a crucial health reform in which the infertile phenotype could serve as an indication of potential pathological conditions. One of the major limiting factors in this association is the poor understanding of the molecular features that link infertility with comorbidities across the life course. Enzymes involved in the lipid oxidation process might provide novel clues to reconcile the mechanistic basis of infertility with incident pathological conditions. Building research capacity in this area is essential to enhance the early detection of disease states and provide crucial information about the disease risk of offspring conceived through assisted reproduction.

Overprescribing among older people near end of life in Ireland: Evidence of prevalence and determinants from The Irish Longitudinal Study on Ageing (TILDA)

International evidence shows that people approaching end of life (EOL) have high prevalence of polypharmacy, including overprescribing. Overprescribing may have adverse side effects for mental and physical health and represents wasteful spending. Little is known about prescribing near EOL in Ireland. We aimed to describe the prevalence of two undesirable outcomes, and to identify factors associated with these outcomes: potentially questionable prescribing, and potentially inadequate prescribing, in the last year of life (LYOL). We used The Irish Longitudinal Study on Ageing, a biennial nationally representative dataset on people aged 50+ in Ireland. We analysed a sub-sample of participants with high mortality risk and categorised their self-reported medication use as potentially questionable or potentially inadequate based on previous research. We identified mortality through the national death registry (died in <365 days versus not). We used descriptive statistics to quantify prevalence of our outcomes, and we used multivariable logistic regression to identify factors associated with these outcomes. Of 525 observations, 401 (76%) had potentially inadequate and 294 (56%) potentially questionable medications. Of the 401 participants with potentially inadequate medications, 42 were in their LYOL. OF the 294 participants with potentially questionable medications, 26 were in their LYOL. One factor was significantly associated with potentially inadequate medications in LYOL: male (odds ratio (OR) 4.40, p = .004) Three factors were associated with potentially questionable medications in LYOL: male (OR 3.37, p = .002); three or more activities of daily living (ADLs) (OR 3.97, p = .003); and outpatient hospital visits (OR 1.03, p = .02). Thousands of older people die annually in Ireland with potentially inappropriate or questionable prescribing patterns. Gender differences for these outcomes are very large. Further work is needed to identify and reduce overprescribing near EOL in Ireland, particularly among men.

Associations Among Multimorbid Conditions in Hospitalized Middle-aged and Older Adults in China: Statistical Analysis of Medical Records

BACKGROUND

Multimorbidity has become a new challenge for medical systems and public health policy. Understanding the patterns of and associations among multimorbid conditions should be given priority. It may assist with the early detection of multimorbidity and thus improve quality of life in older adults.

OBJECTIVE

This study aims to comprehensively analyze and compare associations among multimorbid conditions by age and sex in a large number of middle-aged and older Chinese adults.

METHODS

Data from the home pages of inpatient medical records in the Shenzhen National Health Information Platform were evaluated. From January 1, 2017, to December 31, 2018, inpatients aged 50 years and older who had been diagnosed with at least one of 40 conditions were included in this study. Their demographic characteristics (age and sex) and inpatient diagnoses were extracted. Association rule mining, Chi-square tests, and decision tree analyses were combined to identify associations between multiple chronic conditions.

RESULTS

In total, 306,264 hospitalized cases with available information on related chronic conditions were included in this study. The prevalence of multimorbidity in the overall population was 76.46%. The combined results of the 3 analyses showed that, in patients aged 50 years to 64 years, lipoprotein metabolism disorder tended to be comorbid with multiple chronic conditions. Gout and lipoprotein metabolism disorder had the strongest association. Among patients aged 65 years or older, there were strong associations between cerebrovascular disease, heart disease, lipoprotein metabolism disorder, and peripheral vascular disease. The strongest associations were observed between senile cataract and glaucoma in men and women. In particular, the association between osteoporosis and malignant tumor was only observed in middle-aged and older men, while the association between anemia and chronic kidney disease was only observed in older women.

CONCLUSIONS

Multimorbidity was prevalent among middle-aged and older Chinese individuals. The results of this comprehensive analysis of 4 age-sex subgroups suggested that associations between particular conditions within the sex and age groups occurred more frequently than expected by random chance. This provides evidence for further research on disease clusters and for health care providers to develop different strategies based on age and sex to improve the early identification and treatment of multimorbidity.

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.

CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.

Psychometric properties of performance-based measures of physical function administered via telehealth among people with chronic conditions: A systematic review

Background

Telehealth could enhance rehabilitation for people with chronic health conditions. This review examined the psychometric properties of performance-based measures of physical function administered via telehealth among people with chronic health conditions using the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) approach.

Methods

This systematic review was registered with Prospero (Registration number: CRD42021262547). Four electronic databases were searched up to June 2022. Study quality was evaluated by two independent reviewers using the COSMIN risk of bias checklist. Measurement properties were rated by two independent reviewers in accordance with COSMIN guidance. Results were summarised according to the COSMIN approach and the modified GRADE approach was used to grade quality of the summarised evidence.

Results

Five articles met the eligibility criteria. These included patients with Parkinson’s Disease (n = 2), stroke (n = 1), cystic fibrosis (n = 1) and chronic heart failure (n = 1). Fifteen performance-based measures of physical function administered via videoconferencing were investigated, spanning measures of functional balance (n = 7), other measures of general functional capacity (n = 4), exercise capacity (n = 2), and functional strength (n = 2). Studies were conducted in Australia (n = 4) and the United States (n = 1). Reliability was reported for twelve measures, with all twelve demonstrating sufficient inter-rater and intra-rater reliability. Criterion validity for all fifteen measures was reported, with eight demonstrating sufficient validity and the remaining seven demonstrating indeterminate validity. No studies reported data on measurement error or responsiveness.

Conclusions

Several performance-based measures of physical function across the domains of exercise capacity, strength, balance and general functional capacity may have sufficient reliability and criterion validity when administered via telehealth. However, the evidence is of low-very low quality, reflecting the small number of studies conducted and the small sample sizes included in the studies. Future research is needed to explore the measurement error, responsiveness, interpretability and feasibility of these measures administered via telehealth.

The development and validation of a medicines optimisation tool to protect the physical health of people with severe mental illness (OPTIMISE)

BACKGROUND

The life expectancy of people with severe mental illness (SMI) is shorter than those without SMI, with multimorbidity and poorer physical health contributing to health inequality. Screening tools could potentially assist the optimisation of medicines to protect the physical health of people with SMI. The aim of our research was to design and validate a medicines optimisation tool (OPTIMISE) to help clinicians to optimise physical health in people with SMI.

METHODS

A review of existing published guidelines, PubMed and Medline was carried out. Literature was examined for medicines optimisation recommendations and also for reference to the management of physical illness in people with mental illness. Potential indicators were grouped according to physiological system. A multidisciplinary team with expertise in mental health and the development of screening tools agreed that 83 indicators should be included in the first draft of OPTIMISE. The Delphi consensus technique was used to develop and validate the contents. A 17-member multidisciplinary panel of experts from the UK and Ireland completed 2 rounds of Delphi consensus, rating their level of agreement to 83 prescribing indicators using a 5-point Likert scale. Indicators were accepted for inclusion in the OPTIMISE tool after achieving a median score of 1 or 2, where 1 indicated strongly agree and 2 indicated agree, and 75^th centile value of ≤ 2. Interrater reliability was assessed among 4 clinicians across 20 datasets and the chance corrected level of agreement (kappa) was calculated. The kappa statistic was interpreted as poor if 0.2 or less, fair if 0.21-0.4, moderate if 0.41-0.6, substantial if 0.61-0.8, and good if 0.81-1.0.

RESULTS

Consensus was achieved after 2 rounds of Delphi for 62 prescribing indicators where 53 indicators were accepted after round 1 and a further 9 indicators were accepted after round 2. Interrater reliability of OPTIMISE between physicians and pharmacists indicated a substantial level of agreement with a kappa statistic of 0.75.

CONCLUSIONS

OPTIMISE is a 62 indicator medicines optimisation tool designed to assist decision making in those treating adults with SMI. It was developed using a Delphi consensus methodology and interrater reliability is substantial. OPTIMISE has the potential to improve medicines optimisation by ensuring preventative medicines are considered when clinically indicated. Further research involving the implementation of OPTIMISE is required to demonstrate its true benefit.

TRIAL REGISTRATION

This article does not report the results of a health care intervention on human participants.

Differentials in Health and Wellbeing in Older Adults with Obesity in England: A Cross-Sectional Analysis Using the English Longitudinal Study of Ageing

The aim of the study is to explore the association of obesity by body mass index (BMI) measurements with subjective health status (SHS), objective health status (OHS) and wellbeing status among older adults in England. The sample of 5640 participants (aged 50 years and over) are considered from the English Longitudinal Study of Ageing Wave 8 dataset. Multivariate logistic regression analysis is performed to explore the cross-sectional relationship of the study variables. The statistical analyses explored those overweight and obese older adults are progressively vulnerable to increasing odds of poor SHS, OHS and poor wellbeing in an adjusted model compared to their normal-weight counterparts. The outcome of the present study would enable policymakers and healthcare providers to have greater insight into the effects of socio-demographic and lifestyle factors and the effect of high BMI on older adults’ health and wellbeing.

Indicators of Sustainable Employability among Older Finnish Postal Service Employees: A Longitudinal Study of Age and Time Effects

We first clarify the definition of sustainable employability, and then we study how the indicators of sustainable employability among older Finnish postal service employees have changed over time. Finally, we estimate the effect of age on these indicators in a two-year follow up. A questionnaire survey among the Finnish postal service employees was conducted in 2016, and a follow-up was conducted in 2018. We analyze data from 1262 subjects who replied to both the baseline and the follow-up surveys. Sustainable employability is defined as a multidimensional construct using nine indicators and covering three domains (health, well-being and employability) based on Fleuren and colleagues’ model. Measurement time (repeated measure) is used as a within-subjects factor, and age is used as a between-subjects factor. The estimated marginal means of the indicators of sustainable employability at the baseline and the follow-up by age in years are calculated. No significant change is found in eight indicators (work ability, time and resources, recovery after work, job satisfaction, motivation, perceived employment, enough training on the job and relevance of work) of sustainable employability after the two-year follow-up. We find a statistically significant effect of time on self-rated health (F = 6.56, p = 0.011). Six out of nine indicators (self-rated health, work ability, time and resources, recovery after work, job satisfaction, and perceived employment) have a statistically significant effect of age between subjects. Partial Eta Squared (ŋ2p) shows a very small difference in the indicators of sustainable employability during the follow-up, indicating that the employability of the workers was sustained throughout. We used the Fleuren model as the basis for our definition of sustainable employability. Although they are based on single items, these indicators of sustainable employability remain stable after the two-year follow-up. Significant effects of age between subjects are found for six out of nine indicators. The results suggest that age may be an important determinant of sustainable employability.

Clustering of comorbidities and associated outcomes in people with osteoarthritis - A UK Clinical Practice Research Datalink study

OBJECTIVE

To examine the clusters of chronic conditions present in people with osteoarthritis and the associated risk factors and health outcomes.

METHODS

Clinical Practice Research Datalink (CPRD) GOLD was used to identify people diagnosed with incident osteoarthritis (n = 221,807) between 1997 and 2017 and age (±2 years), gender, and practice matched controls (no osteoarthritis, n = 221,807) from UK primary care. Clustering of people was examined for 49 conditions using latent class analysis. The associations between cluster membership and covariates were quantified by odds ratios (OR) using multinomial logistic regression. General practice (GP) consultations, hospitalisations, and all-cause mortality rates were compared across the clusters identified at the time of first diagnosis of osteoarthritis (index date).

RESULTS

In both groups, conditions largely grouped around five clusters: relatively healthy; cardiovascular (CV), musculoskeletal-mental health (MSK-MH), CV-musculoskeletal (CV-MSK) and metabolic (MB). In the osteoarthritis group, compared to the relatively healthy cluster, strong associations were seen for 1) age with all clusters; 2) women with the MB cluster (OR 5.55: 5.14-5.99); 3) obesity with the CV-MSK (OR 2.11: 2.03-2.20) and CV clusters (OR 2.03: 1.97-2.09). The CV-MSK cluster in the osteoarthritis group had the highest number of GP consultations and hospitalisations, and the mortality risk was 2.45 (2.33-2.58) times higher compared to the relatively healthy cluster.

CONCLUSIONS

Of the five identified clusters, CV-MSK, CV, and MSK-MH are more common in OA and CV-MSK cluster had higher health utilisation. Further research is warranted to better understand the mechanistic pathways and clinical implications.

Self-management support for older adults with chronic illness: implications for nursing practice

Self-management is a key skill that older adults with multiple comorbidities require. Self-management interventions include medication management, self-monitoring and self-awareness and self-management often requires the older adult to manage the emotional consequences of having multiple comorbidities. The benefits of self-management for older adults include reduced reliance on the health system, enhanced quality of life, empowerment of the individual and reduction in the burden associated with chronic illness. Many factors can influence an older adult's ability to self-manage, including health literacy, mental health difficulties and socio-economic factors. Self-management support is the provision of structures, services and programmes to support and enhance the skills of older adults in managing their own conditions. Nurses are in a pivotal position across the continuum of care, using both person-centred care and the 'Making Every Contact Count' approach, to support older adults to self-manage their conditions.

Multimorbidity and mortality: A data science perspective

Background

With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide.

Methods

Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality.

Results

The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality.

Conclusions

We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients.

Multimorbidity matters in low and middle-income countries

Multimorbidity is a complex challenge affecting individuals, families, caregivers, and health systems worldwide. The burden of multimorbidity is remarkable in low- and middle-income countries (LMICs) given the many existing challenges in these settings. Investigating multimorbidity in LMICs poses many challenges including the different conditions studied, and the restriction of data sources to relatively few countries, limiting comparability and representativeness. This has led to a paucity of evidence on multimorbidity prevalence and trends, disease clusters, and health outcomes, particularly longitudinal outcomes. In this paper, based on our experience of investigating multimorbidity in LMICs contexts, we discuss how the structure of the health system does not favor addressing multimorbidity, and how this is amplified by social and economic disparities and, more recently, by the COVID-19 pandemic. We argue that generating epidemiologic data around multimorbidity with similar methods and definition is essential to improve comparability, guide clinical decision-making and inform policies, research priorities, and local responses. We call for action on policy to refinance and prioritize primary care and integrated care as the center of multimorbidity.

The development and validation of population clusters for integrating health and social care: A protocol for a mixed-methods study in Multiple Long-Term Conditions (Cluster-AIM) (Preprint)

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.

International Registered Report Identifier (IRRID)

PRR1-10.2196/34405

Methods of analyzing patterns of multimorbidity using network analysis: a scoping review protocol

OBJECTIVE

The purpose of this review is to summarize the techniques used for network analysis of multimorbidity to inform development of a standard methodology.

INTRODUCTION

There is a growing trend of using network analysis to investigate relationships between chronic illnesses in people with multimorbidities. However, there is currently no recommended approach to calculating and displaying networks of chronic health conditions. This review intends to summarize the current literature to further the development of a standard methodology.

INCLUSION CRITERIA

Studies will be included if they investigated the relationships between multiple chronic health conditions without referring to an index condition, using network analysis techniques. Studies using both survey and administrative data will be included. Studies including biological or genomic data sets will not be included as they are out of scope.

METHODS

Databases searched will include MEDLINE, ScienceDirect, Scopus, and PsycINFO. All relevant publications will be included provided they were published before October 2020. Publications from all languages will be included where an appropriate translation in English can be obtained. Data extracted will include country of origin, type of data used, measure of association, software used, and notes on any specific points of methodological interest relevant to the review question.

Ranking sets of morbidities using hypergraph centrality

Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the centrality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and information available in EHR data.

Is Pathfinder a safe alternative to the emergency department for older patients? An observational analysis

BACKGROUND

many patients brought to emergency departments (EDs) following an emergency medical services (EMS) call have non-urgent needs that could be treated elsewhere. Older people are particularly vulnerable to adverse events while attending the ED. Alternative care pathway models can reduce ED crowding and improve outcomes. Internationally, there is no consensus on which model is recommended.

AIM

the aim of this study is to investigate the impact of the Pathfinder model on ED conveyance rates and patient safety.

METHODS

the Pathfinder service is a collaboration between the National Ambulance Service and Beaumont Hospital Occupational Therapy and Physiotherapy Departments. It is supported by the Government of Ireland's Sláintecare Integration fund. This is a retrospective cohort study of the Pathfinder service over a 5-month period.

RESULTS

one-hundred and seventy-eight patients were responded to by the Pathfinder 'Rapid Response Team'. Average age was 79.6 years (standard deviation 7.6), median clinical frailty score was 6 (interquartile range: 5-6). Sixty-four percent remained at home following initial review. None re-presented to the ED within 24 hours, and 10% re-presented within 7 days. The majority (67%) of patients required follow-up by the Pathfinder 'Follow-Up Team' and/or another community-based service. Feedback demonstrates 99% patient satisfaction with the service.

CONCLUSION

the Pathfinder service is a safe alternative to ED conveyance for older people following an EMS call. It is the first model of this kind to be evaluated in Ireland. The overwhelmingly positive feedback confirms that older people want this service. This model could expand, with local adaptation, nationally and internationally.

Functional limitations in people with multimorbidity and the association with mental health conditions: Baseline data from the Canadian Longitudinal Study on Aging (CLSA)

INTRODUCTION

Increasing multimorbidity is often associated with declining physical functioning, with some studies showing a disproportionate impact on functioning when mental health conditions are present. More research is needed because most multimorbidity studies exclude mental health conditions.

OBJECTIVES

This study aims to improve our understanding of the association between functional limitation and multimorbidity, including a comparison of those with multimorbidity that includes versus excludes mental health conditions.

METHODS

This is a population-based, cross-sectional analysis of data from The Canadian Longitudinal Study on Aging. Functional limitation was defined as the presence of any of 14 activities of daily living (ADLs) or instrumental activities of daily living (IADLs). Multimorbidity, measured by the number of chronic conditions, included mood and anxiety disorders. Logistic regression explored the association between multimorbidity (with and without mental health conditions) and functional limitation. Factor analysis identified common condition clusters to help understand clinical complexity in those with mood/anxiety disorders and the potential influences on functional limitation.

RESULTS

There were 51,338 participants, with a similar proportion of men and women (49% versus 51%) and 42% age 65 years or older. Fifteen percent (15%) had no chronic conditions and 17% had 5+. Ten percent (10%) reported at least one ADL or IADL limitation. Odds ratios (ORs) for functional limitation increased with multimorbidity and were generally higher for those with versus without mental health conditions (e.g., ORs from 1 to 5+ chronic conditions increased 1.9 to 15.8 for those with mood/anxiety disorders versus 1.8 to 10.2 for those without). Factor analysis showed that mood/anxiety conditions clustered with somatic conditions (e.g., migraines, bowel/gastrointestinal disorders).

CONCLUSION

This study found higher odds of functional limitation for those with multimorbidity that included versus excluded mental health conditions, at all levels of multimorbidity. It highlights the need for concurrent management of mental and physical comorbidities to prevent functional limitations and future decline. This approach is aligned with the NICE clinical assessment and management guidelines for people with multimorbidity.

Health status and health care utilization after discharge from geriatric in-hospital stay - description of a register-based study

Background

This study is the first part of a register-based research program with the overall aim to increase the knowledge of the health status among geriatric patients and to identify risk factors for readmission in this population. The aim of this study was two-fold: 1) to evaluate the validity of the study cohorts in terms of health care utilization in relation to regional cohorts; 2) to describe the study cohorts in terms of health status and health care utilization after discharge.

Methods

The project consist of two cohorts with data from patient records of geriatric in-hospital stays, health care utilization data from Stockholm Regional Healthcare Data Warehouse 6 months after discharge, socioeconomic data from Statistics Sweden. The 2012 cohort include 6710 patients and the 2016 cohort, 8091 patients; 64% are women, mean age is 84 (SD 8).

Results

Mean days to first visit in primary care was 12 (23) and 10 (19) in the 2012 and 2016 cohort, respectively. Readmissions to hospital was 38% in 2012 and 39% in 2016. The validity of the study cohorts was evaluated by comparing them with regional cohorts. The study cohorts were comparable in most cases but there were some significant differences between the study cohorts and the regional cohorts, especially regarding amount and type of primary care.

Conclusion

The study cohorts seem valid in terms of health care utilization compared to the regional cohorts regarding hospital care, but less so regarding primary care. This will be considered in the analyses and when interpreting data in future studies based on these study cohorts. Future studies will explore factors associated with health status and re-admissions in a population with multi-morbidity and disability.

Multimorbidity patterns in old adults and their associated multi-layered factors: a cross-sectional study

BACKGROUND

Influenced by various factors such as socio-demographic characteristics, behavioral lifestyles and socio-cultural environment, the multimorbidity patterns in old adults remain complex. This study aims to identify their characteristics and associated multi-layered factors based on health ecological model.

METHODS

In 2019, we surveyed a total of 7480 participants aged 60+ by using a multi-stage random cluster sampling method in Shanxi province, China. Latent class analysis was used to discriminate the multimorbidity patterns in old adults, and hierarchical regression was performed to determine the multi-layered factors associated with their various multimorbidity patterns.

RESULTS

The prevalence of multimorbidity was 34.70% among the old patients with chronic disease. Over half (60.59%) of the patients with multimorbidity had two co-existing chronic diseases. "Degenerative/digestive diseases", "metabolic diseases" and "cardiovascular diseases" were three specific multimorbidity patterns. Behavioral lifestyles-layered factors had the most explanatory power for the three patterns, whose proportions of explanatory power were 54.00, 43.90 and 48.15% individually. But the contributions of other multi-layered factors were different in different patterns; balanced diet, medication adherence, the size of family and friendship network, and different types of basic medical insurance might have the opposite effect on the three multimorbidity patterns (p < 0.05).

CONCLUSIONS

In management of old patients with multimorbidity, we should prioritize both the "lifestyle change"-centered systematic management strategy and group-customized intervention programs.

Diabetes Mellitus and Its Implications in Aortic Stenosis Patients

Aortic stenosis (AS) and diabetes mellitus (DM) are both progressive diseases that if left untreated, result in significant morbidity and mortality. Several studies revealed that the prevalence of DM is substantially higher in patients with AS and, thus, the progression from mild to severe AS is greater in those patients with DM. DM and common comorbidities associated with both diseases, DM and AS, increase patient management complexity and make aortic valve replacement the only effective treatment. For that reason, a better understanding of the pathogenesis underlying both these diseases and the relationships between them is necessary to design more appropriate preventive and therapeutic approaches. In this review, we provided an overview of the main aspects of the relationship between AS and DM, including common comorbidities and risk factors. We also discuss the established treatments/therapies in patients with AS and DM.

Cardiovascular Outcomes and Mortality Associated With Discontinuing Statins in Older Patients Receiving Polypharmacy

IMPORTANCE

Polypharmacy is a major health concern among older adults. While deprescribing may reduce inappropriate medicine use, its effect on clinical end points remains uncertain.

OBJECTIVE

To assess the clinical implications of discontinuing the use of statins while maintaining other drugs in a cohort of older patients receiving polypharmacy.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, population-based cohort study included the 29 047 residents in the Italian Lombardy region aged 65 years or older who were receiving uninterrupted treatment with statins, blood pressure-lowering, antidiabetic, and antiplatelet agents from October 1, 2013, until January 31, 2015, with follow-up through June 30, 2018. Data were collected using the health care utilization database of Lombardy region in Italy. Data analysis was conducted from March to November 2020.

EXPOSURES

Cohort members were followed up to identify those who discontinued statins. Among this group, those who maintained other therapies during the first 6 months after statin discontinuation were 1:1 propensity score matched with patients who discontinued neither statins nor other drugs.

MAIN OUTCOME AND MEASURES

The pairs of patients discontinuing and maintaining statins were followed up from the initial discontinuation until June 30, 2018, to estimate the hazard ratios (HRs) and 95% CIs for fatal and nonfatal outcomes associated with statin discontinuation.

RESULTS

The full cohort inclued 29 047 patients exposed to polypharmacy (mean [SD] age, 76.5 [6.5] years; 18 257 [62.9%] men). Of them, 5819 (20.0%) discontinued statins while maintaining other medications, and 4010 (68.9%) of them were matched with a comparator. In the discontinuing group, the mean (SD) age was 76.5 (6.4) years, 2405 (60.0%) were men, and 506 (12.6%) had Multisource Comorbidity Scores of 4 or 5. In the maintaining group, the mean (SD) age was 76.1 (6.3) years, 2474 (61.7%) were men, and 482 (12.0%) had multisource comorbidity scores of 4 or 5. Compared with the maintaining group, patients in the discontinuing group had increased risk of hospital admissions for heart failure (HR, 1.24; 95% CI, 1.07-1.43) and any cardiovascular outcome (HR, 1.14; 95% CI, 1.03-1.26), deaths from any cause (HR, 1.15; 95% CI, 1.02-1.30), and emergency admissions for any cause (HR, 1.12; 95% CI, 1.05-1.19).

CONCLUSIONS AND RELEVANCE

In this study of patients receiving polypharmacy, discontinuing statins while maintaining other drug therapies was associated with an increase in the long-term risk of fatal and nonfatal cardiovascular outcomes.

Discovery of Association Rules Patterns and Prevalence of Comorbidities in Adult Patients Hospitalized with Mental and Behavioral Disorders

The objectives of this study were to identify the prevalence of comorbidities of mental and behavioral disorders and to identify the association rules related to comorbidities as a way to improve patient management efficiently. We extracted comorbidities of 20,690 patients (≥19 years old) whose principal diagnosis was a mental disorder from the Korean National Hospital Discharge In-depth Injury Survey (KNHDS) between 2006 and 2016. Association rules analysis between comorbid diseases using the Apriori algorithm was used. The prevalence of comorbidities in all patients was 61.98%. The frequent comorbidities of mental and behavioral disorders were analyzed in the order of hypertensive diseases (11.06%), mood disorders (8.34%), diabetes mellitus (7.98%), and diseases of esophagus, stomach, and duodenum (7.04%). Nine major association pathways were analyzed. Significant pathways were analyzed as diabetes mellitus and hypertensive diseases (IS scale = 0.386), hypertensive diseases, and cerebrovascular diseases (IS scale = 0.240). The association pathway of diabetes mellitus and hypertensive diseases was common in subgroups of mental and behavioral disorders, excluding mood disorders and disorders of adult personality and behavior. By monitoring related diseases based on major patterns, it can predict comorbid diseases in advance, improve the efficiency of managing patients with mental and behavioral disorders, and furthermore, it can be used to establish related health policies.

Trajectories of healthy ageing among older adults with multimorbidity: A growth mixture model using harmonised data from eight ATHLOS cohorts

Objectives

In this study we aimed to 1) describe healthy ageing trajectory patterns, 2) examine the association between multimorbidity and patterns of healthy ageing trajectories, and 3) evaluate how different groups of diseases might affect the projection of healthy ageing trajectories over time.

Setting and participants

Our study was based on 130880 individuals from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) harmonised dataset, as well as 9171 individuals from Waves 2–7 of the English Longitudinal Study of Ageing (ELSA).

Methods

Using a healthy ageing index score, which comprised 41 items, covering various domains of health and ageing, as outcome, we employed the growth mixture model approach to identify the latent classes of individuals with different healthy ageing trajectories. A multinomial logistic regression was conducted to assess if and how multimorbidity status and multimorbidity patterns were associated with changes in healthy ageing, controlled for sociodemographic and lifestyle risk factors.

Results

Three similar patterns of healthy ageing trajectories were identified in the ATHLOS and ELSA datasets: 1) a ‘high stable’ group (76% in ATHLOS, 61% in ELSA), 2) a ‘low stable’ group (22% in ATHLOS, 36% in ELSA) and 3) a ‘rapid decline’ group (2% in ATHLOS, 3% in ELSA). Those with multimorbidity were 1.7 times (OR = 1.7, 95% CI: 1.4–2.1) more likely to be in the ‘rapid decline’ group and 11.7 times (OR = 11.7 95% CI: 10.9–12.6) more likely to be in the ‘low stable’ group, compared with people without multimorbidity. The cardiorespiratory/arthritis/cataracts group was associated with both the ‘rapid decline’ and the ‘low stable’ groups (OR = 2.1, 95% CI: 1.2–3.8 and OR = 9.8, 95% CI: 7.5–12.7 respectively).

Conclusion

Healthy ageing is heterogeneous. While multimorbidity was associated with higher odds of having poorer healthy ageing trajectories, the extent to which healthy ageing trajectories were projected to decline depended on the specific patterns of multimorbidity.

Self-rated health in individuals with and without disease is associated with multiple biomarkers representing multiple biological domains

Self-rated health (SRH) is one of the most frequently used indicators in health and social research. Its robust association with mortality in very different populations implies that it is a comprehensive measure of health status and may even reflect the condition of the human organism beyond clinical diagnoses. Yet the biological basis of SRH is poorly understood. We used data from three independent European population samples (N approx. 15,000) to investigate the associations of SRH with 150 biomolecules in blood or urine (biomarkers). Altogether 57 biomarkers representing different organ systems were associated with SRH. In almost half of the cases the association was independent of disease and physical functioning. Biomarkers weakened but did not remove the association between SRH and mortality. We propose three potential pathways through which biomarkers may be incorporated into an individual’s subjective health assessment, including (1) their role in clinical diseases; (2) their association with health-related lifestyles; and (3) their potential to stimulate physical sensations through interoceptive mechanisms. Our findings indicate that SRH has a solid biological basis and it is a valid but non-specific indicator of the biological condition of the human organism.

AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity

Multimorbidity refers to the coexistence of two or more chronic diseases in one person. Therefore, patients with multimorbidity have multiple and special care needs. However, in practice it is difficult to meet these needs because the organizational processes of current healthcare systems tend to be tailored to a single disease. To improve clinical decision making and patient care in multimorbidity, a radical change in the problem-solving approach to medical research and treatment is needed. In addition to the traditional reductionist approach, we propose interactive research supported by artificial intelligence (AI) and advanced big data analytics. Such research approach, when applied to data routinely collected in healthcare settings, provides an integrated platform for research tasks related to multimorbidity. This may include, for example, prediction, correlation, and classification problems based on multiple interaction factors. However, to realize the idea of this paradigm shift in multimorbidity research, the optimization, standardization, and most importantly, the integration of electronic health data into a common national and international research infrastructure is needed. Ultimately, there is a need for the integration and implementation of efficient AI approaches, particularly deep learning, into clinical routine directly within the workflows of the medical professionals.

Untangling the complexity of multimorbidity with machine learning

The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.

Healthy life-expectancy and multimorbidity among older adults: Do inequality and poverty matter?

Multimorbidity among older adults increases with age. There are large socioeconomic differences across states in Brazil. We believe that estimates of healthy life expectancy differ according to poverty and income inequality status. The objective of the study is to describe patterns of life expectancy with multimorbidity with distinct levels of poverty and inequality in Brazil. We constructed life tables for Brazilian states and estimated the prevalence of multimorbidity for populations aged 60 and over, and divided the states into three groups according to poverty and inequality status and compare them. The group with high poverty and inequality lives fewer years with multimorbidity than the group with lower poverty and inequality. We believe this approach can be used to compare estimates between populations and to identify health inequalities within the country that require attention, optimizing resources, and planning interventions to improve population health, mainly through primary health care.

Organ System Crosstalk in Cardiometabolic Disease in the Age of Multimorbidity

The close association among cardiovascular, metabolic, and kidney diseases suggests a common pathological basis and significant interaction among these diseases. Metabolic syndrome and cardiorenal syndrome are two examples that exemplify the interlinked development of disease or dysfunction in two or more organs. Recent studies have been sorting out the mechanisms responsible for the crosstalk among the organs comprising the cardiovascular, metabolic, and renal systems, including heart-kidney and adipose-liver signaling, among many others. However, it is also becoming clear that this crosstalk is not limited to just pairs of organs, and in addition to organ-organ crosstalk, there are also organ-system and organ-body interactions. For instance, heart failure broadly impacts various organs and systems, including the kidney, liver, lung, and nervous system. Conversely, systemic dysregulation of metabolism, immunity, and nervous system activity greatly affects heart failure development and prognosis. This is particularly noteworthy, as more and more patients present with two or more coexisting chronic diseases or conditions (multimorbidity) due in part to the aging of society. Advances in treatment also contribute to the increase in multimorbidity, as exemplified by cardiovascular disease in cancer survivors. To understand the mechanisms underlying the increasing burden of multimorbidity, it is vital to elucidate the multilevel crosstalk and communication within the body at the levels of organ systems, tissues, and cells. In this article, we focus on chronic inflammation as a key common pathological basis of cardiovascular and metabolic diseases, and discuss emerging mechanisms that drive chronic inflammation in the context of multimorbidity.

Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel

This study aimed to identify the prevalence and patterns of multimorbidity among Korean adults. A descriptive study design was used. Of 11,232 adults aged 18 and older extracted from the 2014 Korean Health Panel Survey, 7118 had one or more chronic conditions. The chronic conditions code uses the Korean Standard Classification of Diseases. Association rule analysis and network analysis were conducted to identify patterns of multimorbidity among 4922 participants with multimorbidity. The prevalence of multimorbidity in the overall population was 34.8%, with a higher prevalence among women (40.8%) than men (28.6%). Hypertension had the highest prevalence in both men and women. In men, diabetes mellitus and hypertension yielded the highest probability of comorbidity (10.04%). In women, polyarthrosis and hypertension yielded the highest probability of comorbidity (12.51%). The results of the network analysis in four groups divided according to gender and age showed different characteristics for each group. Public health practitioners should adopt an integrated approach to manage multimorbidity rather than an individual disease-specific approach, along with different strategies according to age and gender groups.