Measuring multimorbidity beyond counting diseases: systematic review of community and population studies and guide to index choice

The burden of diabetes-associated multiple long-term conditions on years of life spent and lost

Diabetes mellitus is a central driver of multiple long-term conditions (MLTCs), but population-based studies have not clearly characterized the burden across the life course. We estimated the age of onset, years of life spent and loss associated with diabetes-related MLTCs among 46 million English adults. We found that morbidity patterns extend beyond classic diabetes complications and accelerate the onset of severe MLTCs by 20 years earlier in life in women and 15 years earlier in men. By the age of 50 years, one-third of those with diabetes have at least three conditions, spend >20 years with them and die 11 years earlier than the general population. Each additional condition at the age of 50 years is associated with four fewer years of life. Hypertension, depression, cancer and coronary heart disease contribute heavily to MLTCs in older age and create the greatest community-level burden on years spent (813 to 3,908 years per 1,000 individuals) and lost (900 to 1,417 years per 1,000 individuals). However, in younger adulthood, depression, severe mental illness, learning disabilities, alcohol dependence and asthma have larger roles, and when they occur, all except alcohol dependence were associated with long periods of life spent (11-14 years) and all except asthma associated with many years of life lost (11-15 years). These findings provide a baseline for population monitoring and underscore the need to prioritize effective prevention and management approaches.

A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data

BACKGROUND

Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions.

OBJECTIVE

This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support.

METHODS

We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model's performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support.

RESULTS

We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4.

CONCLUSIONS

When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

Recommendations on Methods for Assessing Multimorbidity Changes Over Time: Aligning the Method to the Purpose

BACKGROUND

The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity.

METHODS

We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes.

RESULTS

We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains.

CONCLUSIONS

This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations.

Socioeconomic, Health Services, and Multimorbidity Disparities in Chinese Older Adults

INTRODUCTION

As one of the world's most populous countries, China persistently confronts a significant multimorbidity burden. This study aimed to elucidate the multimorbidity burden experienced by Chinese older adults, explore its interplay with socioeconomic disparity, and investigate potential correlations between these provincial disparities and health services availability.

METHODS

The fourth wave of China's national Urban and Rural Elderly Population study, conducted in 2015, was used to construct a multimorbidity index and elucidate the geographic differences in the multimorbidity burden. Incorporating macrolevel indicators about socioeconomic and health services availability, quantile regression and Spearman correlation analyses were employed to investigate the relationship between multimorbidity and socioeconomic disparities and examine the potential linkages between these provincial disparities and health services availability. Analyses were performed in 2023.

RESULTS

The final analysis included a total of 213,857 older adults. At the provincial level, significant geographic disparities in multimorbidity burden were identified. After adjusting for individual social determinants of health, an independent association was found between the human development index and a higher multimorbidity index (coefficient= -0.22; 95% CI= -0.24, -0.19). Furthermore, a significant positive correlation emerged between human development index and both population and geographic densities of health services availability. Notably, geographic density displayed greater inequality (Gini coefficients=0.45-0.48) than population density (Gini coefficients=0.03-0.10).

CONCLUSIONS

This study demonstrates that multimorbidity burden in China is linked to provincial socioeconomic disparities and that inequality in health services availability may account for this, which would advocate for a need to reduce disparities in health services availability.

Pain-phenotyping in osteoarthritis: Current concepts, evidence, and considerations towards a comprehensive framework for assessment and treatment

Objectives

Pain as central symptom of osteoarthritis (OA) needs to be addressed as part of successful treatment. The assessment of pain as feature of disease or outcome in clinical practice and drug development remains a challenge due to its multidimensionality and the plethora of confounders. This article aims at providing insights into our understanding of OA pain-phenotypes and suggests a framework for systematic and comprehensive assessments.

Methods

This narrative review is based on a search of current literature for various combinations of the search terms "pain-phenotype" and "knee OA" and summarizes current knowledge on OA pain-phenotypes, putting OA pain and its assessment into perspective of current research efforts.

Results

Pain is a complex phenomenon, not necessarily associated with tissue damage. Various pain-phenotypes have been described in knee OA. Among those, a phenotype with high pain levels not necessarily matching structural changes and a phenotype with low pain levels and impact are relatively consistent. Further subgroups can be differentiated based on patient reported outcome measures, assessments of comorbidities, anxiety and depression, sleep, activity and objective measures such as quantitative sensory testing.

Conclusions

The complexity of both OA as disease and pain in OA prompt the definition of a set of variables that facilitate assessments comparable across studies to maximize our understanding of pain, as central concern for the patient.

Adverse Events Associated With Disease-Modifying Drugs for Multiple Sclerosis: A Multiregional Population-Based Study

BACKGROUND AND OBJECTIVES

It is not possible to fully establish the safety of a disease-modifying drug (DMD) for multiple sclerosis (MS) from randomized controlled trials as only very common adverse events occurring over the short-term can be captured, and the quality of reporting has been variable. We examined the relationship between the DMDs for MS and potential adverse events in a multiregion population-based study.

METHODS

We identified people with MS using linked administrative health data from 4 Canadian provinces. MS cases were followed from the most recent of first MS or related demyelinating disease event on January 1, 1996, until the earliest of emigration, death, or December 31, 2017. DMD exposure primarily comprised β-interferon, glatiramer acetate, natalizumab, fingolimod, dimethyl fumarate, teriflunomide, and alemtuzumab. We examined associations between DMD exposure and infection-related hospitalizations and physician visits using recurrent events proportional means models and between DMD exposure and 15 broad categories of incident adverse events using stratified multivariate Cox proportional hazard models.

RESULTS

We identified 35,894 people with MS. While virtually all DMDs were associated with a 42%-61% lower risk of infection-related hospitalizations, there was a modest increase in infection-related physician visits by 10%-33% for select DMDs. For incident adverse events, most elevated risks involved a second-generation DMD, with alemtuzumab's hazard of thyroid disorders being 19.42 (95% CI 9.29-36.51), hypertension 4.96 (95% CI 1.78-13.84), and cardiovascular disease 3.72 (95% CI 2.12-6.53). Natalizumab's highest risk was for cardiovascular disease (adjusted hazard ratio [aHR] 1.61; 95% CI 1.24-2.10). For the oral DMDs, fingolimod was associated with higher hazards of cerebrovascular (aHR 2.04; 95% CI 1.27-3.30) and ischemic heart diseases (aHR 1.64; 95% CI 1.10-2.44) and hypertension (aHR 1.73; 95% CI 1.30-2.31); teriflunomide with higher hazards of thyroid disorders (aHR 2.30; 95% CI 1.11-4.74), chronic liver disease (aHR 1.94; 95% CI 1.19-3.18), hypertension (aHR 1.76; 95% CI 1.32-2.37), and hyperlipidemia (aHR 1.61; 95% CI 1.07-2.44); and from complementary analyses (in 1 province), dimethyl fumarate with acute liver injury (aHR 6.55; 95% CI 1.96-21.87).

DISCUSSION

Our study provides an extensive safety profile of several different DMDs used to treat MS in the real-world setting. Our findings not only complement those observed in short-term clinical trials but also provide new insights that help inform the risk-benefit profile of the DMDs used to treat MS in clinical practice. The results of this study highlight the continued need for long-term, independent safety studies of the DMDs used to treat MS.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that for patients with MS, while DMD exposure reduces the risk of infection-related hospitalizations, there are increased risks of infection-related physician visits and incident adverse events for select DMDs.

Patterns of multiple chronic conditions in pregnancy: Population-based study using latent class analysis

BACKGROUND

Adults with multiple chronic conditions (MCC) are a heterogeneous population with elevated risk of future adverse health outcomes. Yet, despite the increasing prevalence of MCC globally, data about MCC in pregnancy are scarce.

OBJECTIVES

To estimate the population prevalence of MCC in pregnancy and determine whether certain types of chronic conditions cluster together among pregnant women with MCC.

METHODS

We conducted a population-based cohort study in Ontario, Canada, of all 15-55-year-old women with a recognised pregnancy, from 2007 to 2020. MCC was assessed from a list of 22 conditions, identified using validated algorithms. We estimated the prevalence of MCC. Next, we used latent class analysis to identify classes of co-occurring chronic conditions in women with MCC, with model selection based on parsimony, clinical interpretability and statistical fit.

RESULTS

Among 2,014,508 pregnancies, 324,735 had MCC (161.2 per 1000, 95% confidence interval [CI] 160.6, 161.8). Latent class analysis resulted in a five-class solution. In four classes, mood and anxiety disorders were prominent and clustered with one additional condition, as follows: Class 1 (22.4% of women with MCC), osteoarthritis; Class 2 (23.7%), obesity; Class 3 (15.8%), substance use disorders; and Class 4 (22.1%), asthma. In Class 5 (16.1%), four physical conditions clustered together: obesity, asthma, chronic hypertension and diabetes mellitus.

CONCLUSIONS

MCC is common in pregnancy, with sub-types dominated by co-occurring mental and physical health conditions. These data show the importance of preconception and perinatal interventions, particularly integrated care strategies, to optimise treatment and stabilisation of chronic conditions in women with MCC.

Multimorbidity in African ancestry populations: a scoping review

OBJECTIVES

Multimorbidity (MM) is a growing concern linked to poor outcomes and higher healthcare costs. While most MM research targets European ancestry populations, the prevalence and patterns in African ancestry groups remain underexplored. This study aimed to identify and summarise the available literature on MM in populations with African ancestry, on the continent, and in the diaspora.

DESIGN

A scoping review was conducted in five databases (PubMed, Web of Science, Scopus, Science Direct and JSTOR) in July 2022. Studies were selected based on predefined criteria, with data extraction focusing on methodology and findings. Descriptive statistics summarised the data, and a narrative synthesis highlighted key themes.

RESULTS

Of the 232 publications on MM in African-ancestry groups from 2010 to June 2022-113 examined continental African populations, 100 the diaspora and 19 both. Findings revealed diverse MM patterns within and beyond continental Africa. Cardiovascular and metabolic diseases are predominant in both groups (80% continental and 70% diaspora). Infectious diseases featured more in continental studies (58% continental and 16% diaspora). Although many papers did not specifically address these features, as in previous studies, older age, being women and having a lower socioeconomic status were associated with a higher prevalence of MM, with important exceptions. Research gaps identified included limited data on African-ancestry individuals, inadequate representation, under-represented disease groups, non-standardised methodologies, the need for innovative data strategies, and insufficient translational research.

CONCLUSION

The growing global MM prevalence is mirrored in African-ancestry populations. Recognising the unique contexts of African-ancestry populations is essential when addressing the burden of MM. This review emphasises the need for additional research to guide and enhance healthcare approaches for African-ancestry populations, regardless of their geographic location.

Prevalence and patterns of multimorbidity among linguistic groups of patients receiving home care in Ontario: a retrospective cohort study

BACKGROUND

Prior studies have demonstrated the negative impact of language barriers on access, quality, and safety of healthcare, which can lead to health disparities in linguistic minorities. As the population ages, those with multiple chronic diseases will require increasing levels of home care and long-term services. This study described the levels of multimorbidity among recipients of home care in Ontario, Canada by linguistic group.

METHODS

Population-based retrospective cohort of 510,685 adults receiving home care between April 1, 2010, to March 31, 2018, in Ontario, Canada. We estimated and compared prevalence and characteristics of multimorbidity (2 or more chronic diseases) across linguistic groups (Francophones, Anglophones, Allophones). The most common combinations and clustering of chronic diseases were examined. Logistic regression models were used to explore the main predictors of 'severe' multimorbidity (defined as the presence of five or more chronic diseases).

RESULTS

The proportion of home care recipients with multimorbidity and severe multimorbidity was 92% and 44%, respectively. The prevalence of multimorbidity was slightly higher among Allophones (93.6%) than among Anglophones (91.8%) and Francophones (92.4%). However, Francophones had higher rates of cardiovascular and respiratory disease (64.9%) when compared to Anglophones (60.2%) and Allophones (61.5%), while Anglophones had higher rates of cancer (34.2%) when compared to Francophones (25.2%) and Allophones (24.3%). Relative to Anglophones, Allophones were more likely to have severe multimorbidity (adjusted OR = 1.04, [95% CI: 1.02-1.06]).

CONCLUSIONS

The prevalence of multimorbidity among Ontarians receiving home care services is high; especially for whose primary language is a language other than English or French (i.e., Allophones). Understanding differences in the prevalence and characteristics of multimorbidity across linguistic groups will help tailor healthcare services to the unique needs of patients living in minority linguistic situations.

Specific features of rational pharmacotherapy in elderly patients

OBJECTIVE

It is well-known that finding an optimum medication at the correct dose for elderly patients is challenging for the practitioner. This study aimed to examine the main trends in prescribing medications for elderly patients and their compliance with the principles of rational pharmacotherapy, and to establish the main factors affecting adherence to treatment in these patients.

METHODS

956 records of outpatients over 60 years of age were examined. The groups of medications prescribed, the dosage simultaneously prescribed to one patient, the structure of nosologies among elderly patients, and the frequency of side effects were studied. The second stage of the study with 147 patients involved examining the adherence to medications by elderly patients using the Brief Medication Questionnaire.

RESULTS

A total of 147 patients (79 (53.7%) women and 68 (46.3%) men) aged over 60 years who were taking ≥4 medications for primary and concomitant diseases were surveyed. The phenomenon of polypragmasy is clearly seen when prescribing pharmacotherapy to elderly patients. Thus, 39% of patients were prescribed 2-4 drugs simultaneously, 55.4% were prescribed ≥5 drugs, and only 5.6% were prescribed one type of medication. Consequently, 90.5% of patients did not comply with the prescribed regimen of drugs. The main reasons for low adherence to treatment were: the complexity of the drug regimen (72.1% of cases); the high cost of drugs (63.9%); lack of appropriate knowledge about disease (67.3%); and no understanding of the necessity for drug intake and the pharmacotherapeutic effect in a particular situation (61.9%).

CONCLUSION

Optimisation of pharmacotherapy for elderly and senile patients requires consideration of functional changes in the body, the peculiarities of the pharmacodynamics and pharmacokinetics of drugs prescribed, the presence of polymorbidity, the prevalence of polypragmasy, and the low adherence to treatment.

What is the impact of one's chronic illness on his or her spouse's future chronic illness: a community-based prospective cohort study

BACKGROUND

Integrating a joint approach to chronic disease management within the context of a couple has immense potential as a valuable strategy for both prevention and treatment. Although spousal concordance has been reported in specific chronic illnesses, the impact they cumulatively exert on a spouse in a longitudinal setting has not been investigated. We aimed to determine whether one's cumulative illness burden has a longitudinal impact on that of their spouse.

METHODS

Data was acquired from a community-based prospective cohort that included Koreans aged 60 years and over, randomly sampled from 13 districts nationwide. Data from the baseline assessment (conducted from November 2010 to October 2012) up to the 8-year follow-up assessment was analyzed from October 2021 to November 2022. At the last assessment, partners of the index participants were invited, and we included 814 couples in the analysis after excluding 51 with incomplete variables. Chronic illness burden of the participants was measured by the Cumulative Illness Rating Scale (CIRS). Multivariable linear regression and causal mediation analysis were used to examine the longitudinal effects of index chronic illness burden at baseline and its change during follow-up on future index and spouse CIRS scores.

RESULTS

Index participants were divided based on baseline CIRS scores (CIRS < 6 points, n = 555, mean [SD] age 66.3 [4.79] years, 43% women; CIRS ≥ 6 points, n = 259, mean [SD] age 67.7 [4.76] years, 36% women). The baseline index CIRS scores and change in index CIRS scores during follow-up were associated with the spouse CIRS scores (β = 0.154 [SE: 0.039], p < 0.001 for baseline index CIRS; β = 0.126 [SE: 0.041], p = 0.002 for change in index CIRS) at the 8-year follow-up assessment. Subgroup analysis found similar results only in the high CIRS group. The baseline index CIRS scores and change in index CIRS scores during follow-up had both direct and indirect effects on the spouse CIRS scores at the 8-year follow-up assessment.

CONCLUSIONS

The severity and course of one's chronic illnesses had a significant effect on their spouse's future chronic illness particularly when it was severe. Management strategies for chronic diseases that are centered on couples may be more effective.

Multimorbidity and Depressive Symptoms in Older Adults: A Contextual Approach

BACKGROUND AND OBJECTIVES

Among older adults, depressive symptoms increase with each chronic illness; however, specific disease-related stressors (e.g., pain) and contextual moderators (interpersonal, sociocultural, temporal) of this relationship remain understudied. We explored disease-related stressors associated with depressive symptoms and moderating effects of contextual factors on this relationship, guided by a social ecological framework.

RESEARCH DESIGN AND METHODS

Adults ≥62 years with multimorbidity (n = 366) completed validated scales assessing diagnoses, disease-related stressors (pain intensity, subjective cognitive function, physical function, somatic symptoms), and depressive symptoms. Moderators included age, expectations regarding aging, perceived social support, and difficulty affording medications. Data were analyzed using structural equation modeling.

RESULTS

Participants were 62-88 years old, with several comorbidities (M = 3.5; range: 2-9). As hypothesized, disease-related stressors were associated with depressive symptoms (b = 0.64, SE = 0.04, p < .001). The effect of disease-related stressors on depressive symptoms was greater among those reporting low social support (B = 0.70, SE = 0.06, p < .001) than for those reporting high social support (B = 0.46, SE = 0.06, p < .001). The negative effect of disease-related stressors on depressive symptoms was stronger for those with poorer expectations of aging (B = 0.68, SE = 0.07, p < .001), compared to those with more positive expectations (B = 0.47, SE = 0.06, p < .001). Age and difficulties affording medications were not significant moderators.

DISCUSSION AND IMPLICATIONS

Garnering social support and addressing low expectations for aging may prevent the detrimental effect of multimorbidity on mental health.

Improved prediction of 5-year mortality by updating the chronic related score for risk profiling in the general population: lessons from the italian region of Lombardy

Objective

The aim of this study was to improve the performance of the Chronic Related Score (CReSc) in predicting mortality and healthcare needs in the general population.

Methods

A population-based study was conducted, including all beneficiaries of the Regional Health Service of Lombardy, Italy, aged 18 years or older in January 2015. Each individual was classified as exposed or unexposed to 69 candidate predictors measured before baseline, updated to include four mental health disorders. Conditions independently associated with 5-year mortality were selected using the Cox regression model on a random sample including 5.4 million citizens. The predictive performance of the obtained CReSc-2.0 was assessed on the remaining 2.7 million citizens through discrimination and calibration.

Results

A total of 35 conditions significantly contributed to the CReSc-2.0, among which Alzheimer's and Parkinson's diseases, dementia, heart failure, active neoplasm, and kidney dialysis contributed the most to the score. Approximately 36% of citizens suffered from at least one condition. CReSc-2.0 discrimination performance was remarkable, with an area under the receiver operating characteristic curve of 0.83. Trends toward increasing short-term (1-year) and long-term (5-year) rates of mortality, hospital admission, hospital stay, and healthcare costs were observed as CReSc-2.0 increased.

Conclusion

CReSC-2.0 represents an improved tool for stratifying populations according to healthcare needs.

Small-area deprivation index does not improve the capability of multisource comorbidity score in mortality prediction

Background

The stratification of the general population according to health needs allows to provide better-tailored services. A simple score called Multisource Comorbidity Score (MCS) has been developed and validated for predicting several outcomes. The aim of this study was to evaluate whether the ability of MCS in predicting 1-year mortality improves by incorporating socioeconomic data (as measured by a deprivation index).

Methods

Beneficiaries of the Italian National Health Service who in the index year (2018) were aged 50-85 years and were resident in the Sicily region for at least 2 years were identified. For each individual, the MCS was calculated according to his/her clinical profile, and the deprivation index of the census unit level of the individual's residence was collected. Frailty models were fitted to assess the relationship between the indexes (MCS and deprivation index) and 1-year mortality. Akaike information criterion and Bayesian information criterion statistics were used to compare the goodness of fit of the model that included only MCS and the model that also contained the deprivation index. The models were further compared by means of the area under the receiver operating characteristic curve (AUC).

Results

The final cohort included 1,062,221 individuals, with a mortality rate of 15.6 deaths per 1,000 person-years. Both MCS and deprivation index were positively associated with mortality.The goodness of fit statistics of the two models were very similar. For MCS only and MCS plus deprivation index models, Akaike information criterion were 17,013 and 17,038, respectively, whereas Bayesian information criterion were 16,997 and 17,000, respectively. The AUC values were 0.78 for both models.

Conclusion

The present study shows that socioeconomic features as measured by the deprivation index did not improve the capability of MCS in predicting 1-year risk of death. Future studies are needed to investigate other sources of data to enhance the risk stratification of populations.

Impact of multimorbidity on mortality in heart failure with reduced ejection fraction: which comorbidities matter most? An analysis of PARADIGM-HF and ATMOSPHERE

AIMS

Multimorbidity, the coexistence of two or more chronic conditions, is synonymous with heart failure (HF). How risk related to comorbidities compares at individual and population levels is unknown. The aim of this study is to examine the risk related to comorbidities, alone and in combination, both at individual and population levels.

METHODS AND RESULTS

Using two clinical trials in HF - the Prospective comparison of ARNI (Angiotensin Receptor-Neprilysin Inhibitor) with ACEI (Angiotensin-Converting Enzyme Inhibitor) to Determine Impact on Global Mortality and morbidity in HF trial (PARADIGM-HF) and the Aliskiren Trial to Minimize Outcomes in Patients with Heart Failure trials (ATMOSPHERE) - we identified the 10 most common comorbidities and examined 45 possible pairs. We calculated population attributable fractions (PAF) for all-cause death and relative excess risk due to interaction with Cox proportional hazard models. Of 15 066 patients in the study, 14 133 (93.7%) had at least one and 11 867 (78.8%) had at least two of the 10 most prevalent comorbidities. The greatest individual risk among pairs was associated with peripheral artery disease (PAD) in combination with stroke (hazard ratio [HR] 1.73; 95% confidence interval [CI] 1.28-2.33) and anaemia (HR 1.71; 95% CI 1.39-2.11). The combination of chronic kidney disease (CKD) and hypertension had the highest PAF (5.65%; 95% CI 3.66-7.61). Two pairs demonstrated significant synergistic interaction (atrial fibrillation with CKD and coronary artery disease, respectively) and one an antagonistic interaction (anaemia and obesity).

CONCLUSIONS

In HF, the impact of multimorbidity differed at the individual patient and population level, depending on the prevalence of and the risk related to each comorbidity, and the interaction between individual comorbidities. Patients with coexistent PAD and stroke were at greatest individual risk whereas, from a population perspective, coexistent CKD and hypertension mattered most.

Multimorbidity pattern and risk of dementia in later life: an 11-year follow-up study using a large community cohort and linked electronic health records

BACKGROUND

Several long-term chronic illnesses are known to be associated with an increased risk of dementia independently, but little is known how combinations or clusters of potentially interacting chronic conditions may influence the risk of developing dementia.

METHODS

447 888 dementia-free participants of the UK Biobank cohort at baseline (2006-2010) were followed-up until 31 May 2020 with a median follow-up duration of 11.3 years to identify incident cases of dementia. Latent class analysis (LCA) was used to identify multimorbidity patterns at baseline and covariate adjusted Cox regression was used to investigate their predictive effects on the risk of developing dementia. Potential effect moderations by C reactive protein (CRP) and Apolipoprotein E (APOE) genotype were assessed via statistical interaction.

RESULTS

LCA identified four multimorbidity clusters representing Mental health, Cardiometabolic, Inflammatory/autoimmune and Cancer-related pathophysiology, respectively. Estimated HRs suggest that multimorbidity clusters dominated by Mental health (HR=2.12, p<0.001, 95% CI 1.88 to 2.39) and Cardiometabolic conditions (2.02, p<0.001, 1.87 to 2.19) have the highest risk of developing dementia. Risk level for the Inflammatory/autoimmune cluster was intermediate (1.56, p<0.001, 1.37 to 1.78) and that for the Cancer cluster was least pronounced (1.36, p<0.001, 1.17 to 1.57). Contrary to expectation, neither CRP nor APOE genotype was found to moderate the effects of multimorbidity clusters on the risk of dementia.

CONCLUSIONS

Early identification of older adults at higher risk of accumulating multimorbidity of specific pathophysiology and tailored interventions to prevent or delay the onset of such multimorbidity may help prevention of dementia.

Development of a UK core dataset for geriatric medicine research: a position statement and results from a Delphi consensus process

BACKGROUND

There is lack of standardisation in assessment tools used in geriatric medicine research, which makes pooling of data and cross-study comparisons difficult.

METHODS

We conducted a modified Delphi process to establish measures to be included within core and extended datasets for geriatric medicine research in the United Kingdom (UK). This included three complete questionnaire rounds, and one consensus meeting. Participants were selected from attendance at the NIHR Newcastle Biomedical Research Centre meeting, May 2019, and academic geriatric medicine e-mailing lists. Literature review was used to develop the initial questionnaire, with all responses then included in the second questionnaire. The third questionnaire used refined options from the second questionnaire with response ranking.

RESULTS

Ninety-eight responses were obtained across all questionnaire rounds (Initial: 19, Second: 21, Third: 58) from experienced and early career researchers in geriatric medicine. The initial questionnaire included 18 questions with short text responses, including one question for responders to suggest additional items. Twenty-six questions were included in the second questionnaire, with 108 within category options. The third questionnaire included three ranking, seven final agreement, and four binary option questions. Results were discussed at the consensus meeting. In our position statement, the final consensus dataset includes six core domains: demographics (age, gender, ethnicity, socioeconomic status), specified morbidities, functional ability (Barthel and/or Nottingham Extended Activities of Daily Living), Clinical Frailty Scale (CFS), cognition, and patient-reported outcome measures (dependent on research question). We also propose how additional variables should be measured within an extended dataset.

CONCLUSIONS

Our core and extended datasets represent current consensus opinion of academic geriatric medicine clinicians across the UK. We consider the development and further use of these datasets will strengthen collaboration between researchers and academic institutions.

Association between maternal multimorbidity and preterm birth, low birth weight and small for gestational age: a prospective birth cohort study from the Japan Environment and Children's Study

OBJECTIVES

Multimorbidity is defined as the coexistence of two or more chronic physical or psychological conditions within an individual. The association between maternal multimorbidity and adverse perinatal outcomes such as preterm delivery and low birth weight has not been well studied. Therefore, this study aimed to investigate this association.

METHODS

We conducted a prospective cohort study using data from the Japan Environment and Children's Study of pregnant women between 2011 and 2014. Those with data on chronic maternal conditions were included in the study and categorised as having no chronic condition, one chronic condition or multimorbidities. The primary outcomes were the incidence of preterm birth (PTB), low birth weight (LBW) and small for gestational age (SGA). Adjusted logistic regression was performed to estimate ORs (aORs) and 95% CIs.

RESULTS

Of the 104 062 fetal records, 86 885 singleton pregnant women were analysed. The median maternal age and body mass index were 31 years and 20.5 kg/m², respectively. The prevalence of pregnant women with one or more chronic conditions was 40.2%. The prevalence of maternal multimorbidity was 6.3%, and that of PTB, LBW, and SGA were 4.6%, 8.1%, and 7.5%, respectively. Pre-pregnancy underweight women were the most common, observed in 15.6% of multimorbidity cases, followed by domestic violence from intimate partner in 13.0%. Maternal multimorbidity was significantly associated with PTB (aOR 1.50; 95% CI 1.33-1.69), LBW (aOR 1.49; 95% CI 1.35-1.63) and SGA (aOR 1.33; 95% CI 1.20-1.46).

CONCLUSION

Maternal multimorbidity was associated with adverse perinatal outcomes, including PTB, LBW and SGA. The risk of adverse perinatal outcomes tends to increase with a rise in the number of chronic maternal conditions. Multimorbidity becomes more prevalent among pregnant women, making our findings important for preconception counselling.

Socioeconomic gradient in mortality of working age and older adults with multiple long-term conditions in England and Ontario, Canada

BACKGROUND

There is currently mixed evidence on the influence of long-term conditions and deprivation on mortality. We aimed to explore whether number of long-term conditions contribute to socioeconomic inequalities in mortality, whether the influence of number of conditions on mortality is consistent across socioeconomic groups and whether these associations vary by working age (18-64 years) and older adults (65 + years). We provide a cross-jurisdiction comparison between England and Ontario, by replicating the analysis using comparable representative datasets.

METHODS

Participants were randomly selected from Clinical Practice Research Datalink in England and health administrative data in Ontario. They were followed from 1 January 2015 to 31 December 2019 or death or deregistration. Number of conditions was counted at baseline. Deprivation was measured according to the participant's area of residence. Cox regression models were used to estimate hazards of mortality by number of conditions, deprivation and their interaction, with adjustment for age and sex and stratified between working age and older adults in England (N = 599,487) and Ontario (N = 594,546).

FINDINGS

There is a deprivation gradient in mortality between those living in the most deprived areas compared to the least deprived areas in England and Ontario. Number of conditions at baseline was associated with increasing mortality. The association was stronger in working age compared with older adults respectively in England (HR = 1.60, 95% CI 1.56,1.64 and HR = 1.26, 95% CI 1.25,1.27) and Ontario (HR = 1.69, 95% CI 1.66,1.72 and HR = 1.39, 95% CI 1.38,1.40). Number of conditions moderated the socioeconomic gradient in mortality: a shallower gradient was seen for persons with more long-term conditions.

CONCLUSIONS

Number of conditions contributes to higher mortality rate and socioeconomic inequalities in mortality in England and Ontario. Current health care systems are fragmented and do not compensate for socioeconomic disadvantages, contributing to poor outcomes particularly for those managing multiple long-term conditions. Further work should identify how health systems can better support patients and clinicians who are working to prevent the development and improve the management of multiple long-term conditions, especially for individuals living in socioeconomically deprived areas.

Competences of internal medicine specialists for the management of patients with multimorbidity. EFIM multimorbidity working group position paper

Patients with multimorbidity increasingly impact healthcare systems, both in primary care and in hospitals. This is particularly true in Internal Medicine. This population associates with higher mortality rates, polypharmacy, hospital readmissions, post-discharge syndrome, anxiety, depression, accelerated age-related functional decline, and development of geriatric syndromes, amongst others. Internists and Hospitalists, in one of their roles as Generalists, are increasingly asked to attend to these patients, both in their own Departments as well as in surgical areas. The management of polypathology and multimorbidity, however, is often complex, and requires specific clinical skills and corresponding experience. In addition, patients' needs, health-care environment, and routines have changed, so emerging and re-emerging specific competences and approaches are required to offer the best coordinated, continuous, and comprehensive integrated care to these populations, to achieve optimal health outcomes and satisfaction of patients, their relatives, and staff. This position paper proposes a set of emerging and re-emerging competences for internal medicine specialists, which are needed to optimally address multimorbidity now and in the future.

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.

Primary and secondary data in emergency medicine health services research - a comparative analysis in a regional research network on multimorbid patients

BACKGROUND

This analysis addresses the characteristics of two emergency department (ED) patient populations defined by three model diseases (hip fractures, respiratory, and cardiac symptoms) making use of survey (primary) and routine (secondary) data from hospital information systems (HIS). Our aims were to identify potential systematic inconsistencies between both data samples and implications of their use for future ED-based health services research.

METHODS

The research network EMANET prospectively collected primary data (n=1442) from 2017-2019 and routine data from 2016 (n=9329) of eight EDs in a major German city. Patient populations were characterized using socio-structural (age, gender) and health- and care-related variables (triage, transport to ED, case and discharge type, multi-morbidity). Statistical comparisons between descriptive results of primary and secondary data samples for each variable were conducted using binomial test, chi-square goodness-of-fit test, or one-sample t-test according to scale level.

RESULTS

Differences in distributions of patient characteristics were found in nearly all variables in all three disease populations, especially with regard to transport to ED, discharge type and prevalence of multi-morbidity. Recruitment conditions (e.g., patient non-response), project-specific inclusion criteria (e.g., age and case type restrictions) as well as documentation routines and practices of data production (e.g., coding of diagnoses) affected the composition of primary patient samples. Time restrictions of recruitment procedures did not generate meaningful differences regarding the distribution of characteristics in primary and secondary data samples.

CONCLUSIONS

Primary and secondary data types maintain their advantages and shortcomings in the context of emergency medicine health services research. However, differences in the distribution of selected variables are rather small. The identification and classification of these effects for data interpretation as well as the establishment of monitoring systems in the data collection process are pivotal.

TRIAL REGISTRATION

DRKS00011930 (EMACROSS), DRKS00014273 (EMAAGE), NCT03188861 (EMASPOT).

Polypharmacy during pregnancy and associated risk factors: a retrospective analysis of 577 medication exposures among 1.5 million pregnancies in the UK, 2000-2019

BACKGROUND

The number of medications prescribed during pregnancy has increased over the past few decades. Few studies have described the prevalence of multiple medication use among pregnant women. This study aims to describe the overall prevalence over the last two decades among all pregnant women and those with multimorbidity and to identify risk factors for polypharmacy in pregnancy.

METHODS

A retrospective cohort study was conducted between 2000 and 2019 using the Clinical Practice Research Datalink (CPRD) pregnancy register. Prescription records for 577 medication categories were obtained. Prevalence estimates for polypharmacy (ranging from 2+ to 11+ medications) were presented along with the medications commonly prescribed individually and in pairs during the first trimester and the entire pregnancy period. Logistic regression models were performed to identify risk factors for polypharmacy.

RESULTS

During the first trimester (812,354 pregnancies), the prevalence of polypharmacy ranged from 24.6% (2+ medications) to 0.1% (11+ medications). During the entire pregnancy period (774,247 pregnancies), the prevalence ranged from 58.7 to 1.4%. Broad-spectrum penicillin (6.6%), compound analgesics (4.5%) and treatment of candidiasis (4.3%) were commonly prescribed. Pairs of medication prescribed to manage different long-term conditions commonly included selective beta 2 agonists or selective serotonin re-uptake inhibitors (SSRIs). Risk factors for being prescribed 2+ medications during the first trimester of pregnancy include being overweight or obese [aOR: 1.16 (1.14-1.18) and 1.55 (1.53-1.57)], belonging to an ethnic minority group [aOR: 2.40 (2.33-2.47), 1.71 (1.65-1.76), 1.41 (1.35-1.47) and 1.39 (1.30-1.49) among women from South Asian, Black, other and mixed ethnicities compared to white women] and smoking or previously smoking [aOR: 1.19 (1.18-1.20) and 1.05 (1.03-1.06)]. Higher and lower age, higher gravidity, increasing number of comorbidities and increasing level of deprivation were also associated with increased odds of polypharmacy.

CONCLUSIONS

The prevalence of polypharmacy during pregnancy has increased over the past two decades and is particularly high in younger and older women; women with high BMI, smokers and ex-smokers; and women with multimorbidity, higher gravidity and higher levels of deprivation. Well-conducted pharmaco-epidemiological research is needed to understand the effects of multiple medication use on the developing foetus.

Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review

The inclusion of machine-learning-derived models in systematic reviews of risk prediction models for colorectal cancer is rare. Whilst such reviews have highlighted methodological issues and limited performance of the models included, it is unclear why machine-learning-derived models are absent and whether such models suffer similar methodological problems. This scoping review aims to identify machine-learning models, assess their methodology, and compare their performance with that found in previous reviews. A literature search of four databases was performed for colorectal cancer prediction and prognosis model publications that included at least one machine-learning model. A total of 14 publications were identified for inclusion in the scoping review. Data was extracted using an adapted CHARM checklist against which the models were benchmarked. The review found similar methodological problems with machine-learning models to that observed in systematic reviews for non-machine-learning models, although model performance was better. The inclusion of machine-learning models in systematic reviews is required, as they offer improved performance despite similar methodological omissions; however, to achieve this the methodological issues that affect many prediction models need to be addressed.

Estimation of the demand for palliative care in non-oncologic patients in Chile

BACKGROUND

Access to palliative care is an emerging global public health challenge. In Chile, a palliative care law was recently enacted to extend palliative care coverage to the non-oncologic population. Thus, a reliable and legitimate estimate of the demand for palliative care is needed for proper health policy planning.

OBJECTIVE

To estimate the demand for Palliative Care in Chile.

METHODOLOGY

Diseases likely to require palliative care were identified according to literature and expert judgement. Annual deaths of diseases identified were estimated for the periods 2018-2020. Demand estimation corresponds to the identification of the proportion of deceased patients requiring palliative care based on the burden of severe health-related suffering. Finally, patient-years were estimated based on the expected survival adjustment.

RESULTS

The estimated demand for palliative care varies between 25,650 and 21,679 patients depending on the approximation used. In terms of annual demand, this varies between 1,442 and 10,964 patient-years. The estimated need has a minor variation between 2018 and 2019 of 0.85% on average, while 2020 shows a slightly higher decrease (7.26%).

CONCLUSION

This is a replicable method for estimating the demand of palliative care in other jurisdictions. Future studies could approach the demand based on the decedent population and living one for a more precise estimation and better-informed health planning. It is hoped that our methodological approach will serve as an input for implementing the palliative care law in Chile, and as an example of estimating the demand for palliative care in other jurisdictions.

Simple approaches to characterising multiple long-term conditions (multimorbidity) and rates of emergency hospital admission: Findings from 495,465 UK Biobank participants

BACKGROUND

Numerous approaches are used to characterise multiple long-term conditions (MLTC), including counts and indices. Few studies have compared approaches within the same dataset. We aimed to characterise MLTC using simple approaches, and compare their prevalence estimates of MLTC and associations with emergency hospital admission in the UK Biobank.

METHODS

We used baseline data from 495,465 participants (age 38-73 years) to characterise MLTC using four approaches: Charlson index (CI), Byles index (BI), count of 43 conditions (CC) and count of body systems affected (BC). We defined MLTC as more than two conditions using CI, BI and CC, and more than two body systems using BC. We categorised scores (incorporating weightings for the indices) from each approach as 0, 1, 2 and 3+. We used linked hospital episode statistics and performed survival analyses to test associations with an endpoint of emergency hospital admission or death over 5 years.

RESULTS

The prevalence of MLTC was 44% (BC), 33% (CC), 6% (BI) and 2% (CI). Higher scores using all approaches were associated with greater outcome rates independent of sex and age group. For example, using CC, compared with score 0, score 2 had 1.95 (95% CI: 1.91, 1.99) and a score of 3+ had 3.12 (95% CI: 3.06, 3.18) times greater outcome rates. The discriminant value of all approaches was modest (C-statistics 0.60-0.63).

CONCLUSIONS

The counts classified a greater proportion as having MLTC than the indices, highlighting that prevalence estimates of MLTC vary depending on the approach. All approaches had strong statistical associations with emergency hospital admission but a modest ability to identify individuals at risk.

Multimorbidity transitions and the associated healthcare cost among the Finnish adult population during a two-year follow-up

Background

Ageing of the population increases the prevalence and coexistence of many chronic diseases; a condition called multimorbidity. In Finland, information on the significance of multimorbidity and its relation to the sustainability of healthcare is scarce.

Aim

To assess the prevalence of multimorbidity, the transitions between patient groups with and without multiple diseases and the associated healthcare cost in Finland in 2017-2019.

Methods

A register-based cohort study covering all adults (n = 3,326,467) who used Finnish primary or specialised healthcare services in 2017. At baseline, patients were classified as 'non-multimorbid', 'multimorbid' or 'multimorbid at risk' based on the recordings of a diagnosis of interest. The costs were calculated using the care-related patient grouping and national standard rates. Transition plots were drawn to observe the transition of patients and costs between groups during the two-year follow-up.

Results

At baseline, 62% of patients were non-multimorbid, 23% multimorbid and 15% multimorbid at risk. In two years, the proportion of multimorbid patients increased, especially those at risk. Within the multimorbid at-risk group, total healthcare costs were greatest (€5,027 million), accounting for 62% of the total healthcare cost of the overall patient cohort in 2019. Musculoskeletal diseases, cardiometabolic diseases and tumours were the most common and expensive chronic diseases contributing to the onset of multimorbidity.

Conclusion

Multimorbidity is causing a heavy burden on Finnish healthcare. The estimates of its effect on healthcare usage and costs should be used to guide healthcare planning.

Development and validation of novel multimorbidity indices for older adults

BACKGROUND

Measuring multimorbidity in claims data is used for risk adjustment and identifying populations at high risk for adverse events. Multimorbidity indices such as Charlson and Elixhauser scores have important limitations. We sought to create a better method of measuring multimorbidity using claims data by incorporating geriatric conditions, markers of disease severity, and disease-disease interactions, and by tailoring measures to different outcomes.

METHODS

Health conditions were assessed using Medicare inpatient and outpatient claims from subjects age 67 and older in the Health and Retirement Study. Separate indices were developed for ADL decline, IADL decline, hospitalization, and death, each over 2 years of follow-up. We validated these indices using data from Medicare claims linked to the National Health and Aging Trends Study.

RESULTS

The development cohort included 5012 subjects with median age 76 years; 58% were female. Claims-based markers of disease severity and disease-disease interactions yielded minimal gains in predictive power and were not included in the final indices. In the validation cohort, after adjusting for age and sex, c-statistics for the new multimorbidity indices were 0.72 for ADL decline, 0.69 for IADL decline, 0.72 for hospitalization, and 0.77 for death. These c-statistics were 0.02-0.03 higher than c-statistics from Charlson and Elixhauser indices for predicting ADL decline, IADL decline, and hospitalization, and <0.01 higher for death (p < 0.05 for each outcome except death), and were similar to those from the CMS-HCC model. On decision curve analysis, the new indices provided minimal benefit compared with legacy approaches. C-statistics for both new and legacy indices varied substantially across derivation and validation cohorts.

CONCLUSIONS

A new series of claims-based multimorbidity measures were modestly better at predicting hospitalization and functional decline than several legacy indices, and no better at predicting death. There may be limited opportunity in claims data to measure multimorbidity better than older methods.

Clinical Multiteam System Composition and Complexity Among Newly Diagnosed Early-Stage Breast, Colorectal, and Lung Cancer Patients With Multiple Chronic Conditions: A SEER-Medicare Analysis

PURPOSE

Sixty percent of adults have multiple chronic conditions at cancer diagnosis. These patients may require a multidisciplinary clinical team-of-teams, or a multiteam system (MTS), of high-complexity involving multiple specialists and primary care, who, ideally, coordinate clinical responsibilities, share information, and align clinical decisions to ensure comprehensive care needs are managed. However, insights examining MTS composition and complexity among individuals with cancer and comorbidities at diagnosis using US population-level data are limited.

METHODS

Using SEER-Medicare data (2006-2016), we identified newly diagnosed patients with breast, colorectal, or lung cancer who had a codiagnosis of cardiopulmonary disease and/or diabetes (n = 75,201). Zaccaro's theory-based classification of MTSs was used to categorize clinical MTS complexity in the 4 months following cancer diagnosis: high-complexity (≥ 4 clinicians from ≥ 2 specialties) and low-complexity (1-3 clinicians from 1-2 specialties). We describe the proportions of patients with different MTS compositions and quantify the incidence of high-complexity MTS care by patient groups.

RESULTS

The most common MTS composition was oncology with primary care (37%). Half (50.3%) received high-complexity MTS care. The incidence of high-complexity MTS care for non-Hispanic Black and Hispanic patients with cancer was 6.7% (95% CI, -8.0 to -5.3) and 4.7% (95% CI, -6.3 to -3.0) lower than non-Hispanic White patients with cancer; 13.1% (95% CI, -14.1 to -12.2) lower for rural residents compared with urban; 10.4% (95% CI, -11.2 to -9.5) lower for dual Medicaid-Medicare beneficiaries compared with Medicare-only; and 16.6% (95% CI, -17.5 to -15.8) lower for colorectal compared with breast cancer.

CONCLUSION

Incidence differences of high-complexity MTS care were observed among cancer patients with multiple chronic conditions from underserved populations. The results highlight the need to further understand the effects of and mechanisms through which care team composition, complexity, and functioning affect care quality and outcomes.

Prevalence of and factors associated with multimorbidity among 18 101 adults in the South East Asia Community Observatory Health and Demographic Surveillance System in Malaysia: a population-based, cross-sectional study of the MUTUAL consortium

OBJECTIVES

To assess the prevalence and factors associated with multimorbidity in a community-dwelling general adult population on a large Health and Demographic Surveillance System (HDSS) scale.

DESIGN

Population-based cross-sectional study.

SETTING

South East Asia Community Observatory HDSS site in Malaysia.

PARTICIPANTS

Of 45 246 participants recruited from 13 431 households, 18 101 eligible adults aged 18-97 years (mean age 47 years, 55.6% female) were included.

MAIN OUTCOME MEASURES

The main outcome was prevalence of multimorbidity. Multimorbidity was defined as the coexistence of two or more chronic conditions per individual. A total of 13 chronic diseases were selected and were further classified into 11 medical conditions to account for multimorbidity. The conditions were heart disease, stroke, diabetes mellitus, hypertension, chronic kidney disease, musculoskeletal disorder, obesity, asthma, vision problem, hearing problem and physical mobility problem. Risk factors for multimorbidity were also analysed.

RESULTS

Of the study cohort, 28.5% people lived with multimorbidity. The individual prevalence of the chronic conditions ranged from 1.0% to 24.7%, with musculoskeletal disorder (24.7%), obesity (20.7%) and hypertension (18.4%) as the most prevalent chronic conditions. The number of chronic conditions increased linearly with age (p<0.001). In the logistic regression model, multimorbidity is associated with female sex (adjusted OR 1.28, 95% CI 1.17 to 1.40, p<0.001), education levels (primary education compared with no education: adjusted OR 0.63, 95% CI 0.53 to 0.74; secondary education: adjusted OR 0.60, 95% CI 0.51 to 0.70; tertiary education: adjusted OR 0.65, 95% CI 0.54 to 0.80; p<0.001) and employment status (working adults compared with retirees: adjusted OR 0.70, 95% CI 0.60 to 0.82, p<0.001), in addition to age (adjusted OR 1.05, 95% CI 1.05 to 1.05, p<0.001).

CONCLUSIONS

The current single-disease services in primary and secondary care should be accompanied by strategies to address complexities associated with multimorbidity, taking into account the factors associated with multimorbidity identified. Future research is needed to identify the most commonly occurring clusters of chronic diseases and their risk factors to develop more efficient and effective multimorbidity prevention and treatment strategies.

Prediction of hospital readmission of multimorbid patients using machine learning models

OBJECTIVE

The objective of this study is twofold. First, we seek to understand the characteristics of the multimorbid population that needs hospital care by using all diagnoses information (ICD-10 codes) and two aggregated multimorbidity and frailty scores. Second, we use machine learning prediction models on these multimorbid patients characteristics to predict rehospitalization within 30 and 365 days and their length of stay.

METHODS

This study was conducted on 8 882 anonymized patients hospitalized at the University Hospital of Saint-Étienne. A descriptive statistical analysis was performed to better understand the characteristics of the patient population. Multimorbidity was measured using raw diagnoses information and two specific scores based on clusters of diagnoses: the Hospital Frailty Risk Score and the Calderon-Larrañaga index. Based on these variables different machine learning models (Decision Tree, Random forest and k-nearest Neighbors) were used to predict near future rehospitalization and length of stay (LoS).

RESULTS

The use of random forest algorithms yielded better performance to predict both 365 and 30 days rehospitalization and using the diagnoses ICD-10 codes directly was significantly more efficient. However, using the Calderon-Larrañaga's clusters of diagnoses can be used as an efficient substitute for diagnoses information for predicting readmission. The predictive power of the algorithms is quite low on length of stay indicator.

CONCLUSION

Using machine learning techniques using patients' diagnoses information and Calderon-Larrañaga's score yielded efficient results to predict hospital readmission of multimorbid patients. These methods could help improve the management of care of multimorbid patients in hospitals.

Immunosenescence profiles of lymphocyte compartments and multiple long-term conditions (multimorbidity) in very old adults: The Newcastle 85+ Study

Immunosenescence, a decline in immune system function, has been linked to several age-related diseases and ageing syndromes. Very old adults (aged ≥ 85 years) live with multiple long-term conditions (MLTC, also known as multimorbidity)-a complex phenomenon of poor health defined by either counts, indices, or patterns, but little is known about the relationship between an ageing immune system and MLTC in this age group. We utilised baseline data from the Newcastle 85+ Study to investigate the associations between previously defined immunosenescence profiles of lymphocyte compartments and MLTC counts and patterns (from 16 chronic diseases/ageing syndromes). Seven hundred and three participants had MLTC and complete data for all 16 conditions, a median and mean of 5 (range 2-11) and 62.2% had ≥ 5 conditions. Three distinct MLTC patterns emerged by clustering: Cluster 1 ('Low frequency cardiometabolic-cerebrovascular diseases', n = 209), Cluster 2 ('High ageing syndromes-arthritis', n = 240), and Cluster 3 ('Hypertensive-renal impairment', n = 254). Although having a more senescent phenotype, characterised by higher frequency of CD4 and CD8 senescence-like effector memory cells and lower CD4/CD8 ratio, was not associated with MLTC compared with less senescent phenotype, the results warrant further investigation, including whether immunosenescence drives change in MLTC and influences MLTC severity in late adulthood.

Prevalence of multimorbidity combinations and their association with medical costs and poor health: A population-based study of U.S. adults

Background

Multimorbidity is common, but the prevalence and burden of the specific combinations of coexisting disease has not been systematically examined in the general U.S. adult population.

Objective

To identify and estimate the burden of highly prevalent combinations of chronic conditions that are treated among one million or more adults in the United States.

Methods

Cross-sectional analysis of U.S. households in the Medical Expenditure Panel Survey (MEPS), 2016-2019, a large nationally-representative sample of the community-dwelling population. Association rule mining was used to identify the most common combinations of 20 chronic conditions that have high relevance, impact, and prevalence in primary care. The main measures and outcomes were annual treated prevalence, total medical expenditures, and perceived poor health. Logistic regression models with poor health as the outcome and each multimorbidity combination as the exposure were used to calculate adjusted odds ratios and 95% confidence intervals.

Results

Frequent pattern mining yielded 223 unique combinations of chronic disease, including 74 two-way (dyad), 115 three-way (triad), and 34 four-way combinations that are treated in one million or more U.S. adults. Hypertension-hyperlipidemia was the most common two-way combination occurring in 30.8 million adults. The combination of diabetes-arthritis-cardiovascular disease was associated with the highest median annual medical expenditures ($23,850, interquartile range: $11,593-$44,616), and the combination of diabetes-arthritis-asthma/COPD had the highest age-race-sex adjusted odds ratio of poor self-rated health (adjusted odd ratio: 6.9, 95%CI: 5.4-8.8).

Conclusion

This study demonstrates that many multimorbidity combinations are highly prevalent among U.S. adults, yet most research and practice-guidelines remain single disease focused. Highly prevalent and burdensome multimorbidity combinations could be prioritized for evidence-based research on optimal prevention and treatment strategies.

Multimorbidity, Frailty and Diabetes in Older People-Identifying Interrelationships and Outcomes

Multimorbidity and frailty are highly prevalent in older people with diabetes. This high prevalence is likely due to a combination of ageing and diabetes-related complications and other diabetes-associated comorbidities. Both multimorbidity and frailty are associated with a wide range of adverse outcomes in older people with diabetes, which are proportionally related to the number of morbidities and to the severity of frailty. Although, the multimorbidity pattern or cluster of morbidities that have the most adverse effect are not yet well defined, it appears that mental health disorders enhance the multimorbidity-related adverse outcomes. Therefore, comprehensive diabetes guidelines that incorporate a holistic approach that includes screening and management of mental health disorders such as depression is required. The adverse outcomes predicted by multimorbidity and frailty appear to be similar and include an increased risk of health care utilisation, disability and mortality. The differential effect of one condition on outcomes, independent of the other, still needs future exploration. In addition, prospective clinical trials are required to investigate whether interventions to reduce multimorbidity and frailty both separately and in combination would improve clinical outcomes.

Frailty, multimorbidity and sarcopaenia in haemodialysis patients

PURPOSE OF REVIEW

It is well recognised that haemodialysis patients have higher levels of multimorbidity, frailty and sarcopaenia. This review examines the current understanding of the three concepts in relation to the general population and haemodialysis patients, and the methods used to quantify them. It also looks at the interaction between multimorbidity, frailty and sarcopaenia in this patient group and proposes a new model that utilises muscle mass index and fat mass index as a surrogate representation of the three concepts.

RECENT FINDINGS

Multimorbidity in on the rise in the general population and this is one of the contributing factors to higher rates of chronic kidney disease, progression to end-stage renal disease and multimorbidity in haemodialysis patients. Malnutrition and haemodialysis induced end organ damage further contributes to muscle loss and frailty in this patient group. There is a significant overlap and interaction between multimorbidity, frailty and sarcopaenia in haemodialysis and their presence carries a significant impact on quality of life and survival. There are multiple scores for measuring multimorbidity, frailty and sarcopenia and there is no consensus on their utilisation in haemodialysis patients. We propose the use of fat mass index and muscle mass index model as a surrogate method for clinically quantifying multimorbidity, frailty and sarcopaenia.

SUMMARY

Effective public health policies are likely to have an impact on reducing the prevalence of multimorbidity and the development of end stage renal disease. Future research is required to develop interventions that are targeted at maintaining muscle mass and function in haemodialysis patients.

Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity

Multimorbidity generally refers to concurrent occurrence of multiple chronic conditions. These patients are inherently at high risk and often lead a poor quality of life due to delayed treatments. With the emergence of personalized medicine and stratified healthcare, there is a need to stratify patients right at the primary care setting. Here we developed multimorbidity analysis pipeline (MulMorPip), which can stratify patients into multimorbid subgroups or endotypes based on their lifetime disease diagnosis and characterize them based on demographic features and underlying disease-disease interaction networks. By implementing MulMorPip on UK Biobank cohort, we report five distinct molecular subclasses or endotypes of multimorbidity. For each patient, we calculated the existence of broad disease classes defined by Charlson's comorbidity classification using the International Classification of Diseases-10 encoding. We then applied multiple correspondence analysis in 77 524 patients from UK Biobank, who had multimorbidity of more than one disease, which resulted in five multimorbid clusters. We further validated these clusters using machine learning and were able to classify 20% model-blind test set patients with an accuracy of 97% and an average Jaccard similarity of 84%. This was followed by demographic characterization and development of interlinking disease network for each cluster to understand disease-disease interactions. Our identified five endotypes of multimorbidity draw attention to dementia, stroke and paralysis as important drivers of multimorbidity stratification. Inclusion of such patient stratification at the primary care setting can help general practitioners to better observe patients' multiple chronic conditions, their risk stratification and personalization of treatment strategies.

Comparing machine learning algorithms for multimorbidity prediction: An example from the Elsa-Brasil study

BACKGROUND

Multimorbidity is a worldwide concern related to greater disability, worse quality of life, and mortality. The early prediction is crucial for preventive strategies design and integrative medical practice. However, knowledge about how to predict multimorbidity is limited, possibly due to the complexity involved in predicting multiple chronic diseases.

METHODS

In this study, we present the use of a machine learning approach to build cost-effective multimorbidity prediction models. Based on predictors easily obtainable in clinical practice (sociodemographic, clinical, family disease history and lifestyle), we build and compared the performance of seven multilabel classifiers (multivariate random forest, and classifier chain, binary relevance and binary dependence, with random forest and support vector machine as base classifiers), using a sample of 15105 participants from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). We developed a web application for the building and use of prediction models.

RESULTS

Classifier chain with random forest as base classifier performed better (accuracy = 0.34, subset accuracy = 0.15, and Hamming Loss = 0.16). For different feature sets, random forest based classifiers outperformed those based on support vector machine. BMI, blood pressure, sex, and age were the features most relevant to multimorbidity prediction.

CONCLUSIONS

Our results support the choice of random forest based classifiers for multimorbidity prediction.

The emergence of multimorbidity as a matter of concern: a critical review

Multimorbidity is considered one of the greatest emerging challenges for contemporary health care systems. However, the meaning of the term ‘multimorbidity’ is not straightforward. Despite many attempts to clarify the definition and its measurement, the concept remains elusive. Still, academic interest in the study of multimorbidity has grown exponentially in the past ten years. In this paper, we trace the emergence of multimorbidity as a ‘matter of concern’ within health care research, exploring what has been called ‘the multimorbidity epidemic’ in the context of changing disease categories. We analyse how multimorbidity as a concept lays bare some major unresolved challenges within contemporary care services and summons up traditional primary care ideals of holistic, person-centred care. However, we argue that the current focus on the measurement and the identification of disease clusters falls short in contributing to better care for people who live with multiple long-term conditions now. Instead, we propose a novel understanding of ‘multimorbidity’ as an experience that manifests through people’s navigations of care infrastructures. To study this experience of multimorbidity, we discuss the potential of social science approaches that focus on ‘living well’ with illness.

Interaction effects of multimorbidity and frailty on adverse health outcomes in elderly hospitalised patients

We quantified the interaction of multimorbidity and frailty and their impact on adverse health outcomes in the hospital setting. Using aretrospective cohort study of persons aged ≥ 75 years, admitted to hospital during 2010–2012 in New South Wales, Australia, and linked with mortality data, we constructed multimorbidity, frailty risk and outcomes: prolonged length of stay (LOS), 30-day mortality and 30-day unplanned readmissions. Relative risks (RR) of outcomes were obtained using Poisson models with random intercept for hospital. Among 257,535 elderly inpatients, 33.6% had multimorbidity and elevated frailty risk, 14.7% had multimorbidity only, 19.9% had elevated frailty risk only and 31.8% had neither. Additive interactions were present for all outcomes, with a further multiplicative interaction for mortality and LOS. Mortality risk was 4.2 (95% CI 4.1–4.4), prolonged LOS 3.3 (95% CI 3.3–3.4) and readmission 1.8 (95% CI 1.7–1.9) times higher in patients with both factors present compared with patients with neither. In conclusion, multimorbidity and frailty coexist in older hospitalized patients and interact to increase the risk of adverse outcomes beyond the sum of their individual effects. Their joint effect should be considered in health outcomes research and when administering hospital resources.

Multimorbidity

Multimorbidity (two or more coexisting conditions in an individual) is a growing global challenge with substantial effects on individuals, carers and society. Multimorbidity occurs a decade earlier in socioeconomically deprived communities and is associated with premature death, poorer function and quality of life and increased health-care utilization. Mechanisms underlying the development of multimorbidity are complex, interrelated and multilevel, but are related to ageing and underlying biological mechanisms and broader determinants of health such as socioeconomic deprivation. Little is known about prevention of multimorbidity, but focusing on psychosocial and behavioural factors, particularly population level interventions and structural changes, is likely to be beneficial. Most clinical practice guidelines and health-care training and delivery focus on single diseases, leading to care that is sometimes inadequate and potentially harmful. Multimorbidity requires person-centred care, prioritizing what matters most to the individual and the individual's carers, ensuring care that is effectively coordinated and minimally disruptive, and aligns with the patient's values. Interventions are likely to be complex and multifaceted. Although an increasing number of studies have examined multimorbidity interventions, there is still limited evidence to support any approach. Greater investment in multimorbidity research and training along with reconfiguration of health care supporting the management of multimorbidity is urgently needed.

Over- and under-prescribing, and their association with functional disability in older patients at risk of further decline in Germany - a cross-sectional survey conducted as part of a randomised comparative effectiveness trial

Background

Older patients at risk of functional decline are frequently affected by polypharmacy. This is associated with a further loss of independence. However, a relationship between functional disability and medications, such as ‘Potentially Inappropriate Medications’ (PIMs) and ‘Potential Prescribing Omissions’ (PPOs), as itemised for (de) prescribing in practice-orientated medication lists, has yet to be established.

Methods

As part of a randomised comparative effectiveness trial, LoChro, we conducted a cross-sectional analysis of the association between PIMs and PPOs measured using the ‘Screening Tool of Older Persons’ Prescription Criteria / Screening Tool To Alert to Right Treatment’ (STOPP/START) Version 2, with functional disability assessed using the ‘World Health Organization Disability Assessment Schedule 2.0’ (WHODAS). Individuals aged 65 and older at risk of loss of independence were recruited from the inpatient and outpatient departments of the local university hospital. Multiple linear regression analysis was used to model the potential prediction of functional disability using the numbers of PIMs and PPOs, adjusted for confounders including multimorbidity.

Results

Out of 461 patients, both the number of PIMs and the number of PPOs were significantly associated with an increase in WHODAS-score (Regression coefficients B 2.7 [95% confidence interval: 1.5-3.8] and 1.5 [95% confidence interval: 0.2-2.7], respectively). In WHODAS-score prediction modelling the contribution of the number of PIMs exceeded the one of multimorbidity (standardised coefficients beta: PIM 0.20; multimorbidity 0.13; PPO 0.10), whereas no significant association between the WHODAS-score and the number of medications was seen. 73.5 % (339) of the participants presented with at least one PIM, and 95.2% (439) with at least one PPO. The most common PIMs were proton pump inhibitors and analgesic medication, with frequent PPOs being pneumococcal and influenza vaccinations, as well as osteoporosis prophylaxis.

Conclusions

The results indicate a relationship between inappropriate prescribing, both PIMs and PPOs, and functional disability, in older patients at risk of further decline. Long-term analysis may help clarify whether these patients benefit from interventions to reduce PIMs and PPOs.

Study protocol for an epidemiological study 'Multimorbidity - identifying the most burdensome patterns, risk factors and potentials to reduce future burden (MOLTO)' based on the Finnish health examination surveys and the ongoing register-based follow-up

INTRODUCTION

Multimorbidity, defined as the co-occurrence of two or more long-term medical conditions, is an increasing public health concern worldwide causing enormous burden to individuals, healthcare systems and societies. The most effective way of decreasing the burden caused by multimorbidity is to find tools for its successful prevention but gaps in research evidence limit capacities to develop prevention strategies. The aim of the MOLTO study (Multimorbidity - identifying the most burdensome patterns, risk factors and potentials to reduce future burden) is to provide novel evidence required for cost-effective prevention of multimorbidity by defining the multimorbidity patterns causing the greatest burden at the population level, by examining their risk and protective factors and by estimating the potentials to reduce the future burden.

METHODS AND ANALYSIS

The MOLTO study is based on the data from the Finnish population-based cross-sectional (FINRISK 2002-2012, FinHealth 2017 the Migrant Health and Well-being Study 2010-2012) and longitudinal (Health 2000/2011) health examination surveys with individual-level link to administrative health registers, allowing register-based follow-up for the study participants. Both cross-sectional and longitudinal study designs will be used. Multimorbidity patterns will be defined using latent class analysis. The burden caused by multimorbidity as well as risk and protective factors for multimorbidity will be analysed by survival analysis methods such as Cox proportional hazards and Poisson regression models.

ETHICS AND DISSEMINATION

The survey data have been collected following the legislation at the time of the survey. The ethics committee of the Hospital District of Helsinki and Uusimaa has approved the data collection and register linkages for each survey. The results will be published as peer-reviewed scientific publications.

Handgrip strength and all-cause dementia incidence and mortality: findings from the UK Biobank prospective cohort study

BACKGROUND

This study aimed to investigate the associations of grip strength with incidence and mortality from dementia and whether these associations differ by sociodemographic and lifestyle factors.

METHODS

A total of 466 788 participants of the UK Biobank (median age 56.5 years, 54.5% women). The outcome was all-cause dementia incidence and mortality and the exposure was grip strength. Grip strength was assessed using a Jamar J00105 hydraulic hand dynamometer.

RESULTS

Excluding the first 2 years of follow-up (landmark analysis), mean follow-up was 9.1 years (inter-quartile range: 8.3; 9.7) for incidence and 9.3 (inter-quartile range: 8.7; 10.0) for mortality. During this time, 4087 participants developed dementia, and 1309 died from it. Lower grip strength was associated with a higher risk of dementia incidence and mortality independent of major confounding factors (P < 0.001). Individuals in the lowest quintile of grip strength had 72% [95% confidence interval (CI): 1.55; 1.92] higher incident dementia risk and 87% [95% CI: 1.55; 2.26] higher risk of dementia mortality compared with those in the highest quintile. Our PAF analyses indicate that 30.1% of dementia cases and 32.3% of dementia deaths are attributable to having low grip strength. The association between grip strength and dementia outcomes did not differ by lifestyle or sociodemographic factors.

CONCLUSIONS

Lower grip strength was associated with a higher risk of all-cause dementia incidence and mortality, independently of important confounding factors.

Construction and validation of a morbidity index based on the International Classification of Primary Care

OBJECTIVES

In epidemiological studies it is often necessary to describe morbidity. The aim of the present study is to construct and validate a morbidity index based on the International Classification of Primary Care (ICPC-2).

DESIGN AND SETTING

This is a cohort study based on linked data from national registries. An ICPC morbidity index was constructed based on a list of longstanding health problems in earlier published Scottish data from general practice and adapted to diagnostic ICPC-2 codes recorded in Norwegian general practice 2015 - 2017.

SUBJECTS

The index was constructed among Norwegian born people only (N = 4 509 382) and validated in a different population, foreign-born people living in Norway (N = 959 496).

MAIN OUTCOME MEASURES

Predictive ability for death in 2018 in these populations was compared with the Charlson index. Multiple logistic regression was used to identify morbidities with the highest odds ratios (OR) for death and predictive ability for different combinations of morbidities was estimated by the area under receiver operating characteristic curves (AUC).

RESULTS

An index based on 18 morbidities was found to be optimal, predicting mortality with an AUC of 0.78, slightly better than the Charlson index (AUC 0.77). External validation in a foreign-born population yielded an AUC of 0.76 for the ICPC morbidity index and 0.77 for the Charlson index.

CONCLUSIONS

The ICPC morbidity index performs equal to the Charlson index and can be recommended for use in data materials collected in primary health care.Key pointsThis is the first morbidity index based on the International Classification of Primary Care, 2^nd edition (ICPC-2)It predicted mortality equal to the Charlson index and validated acceptably in a different populationThe ICPC morbidity index can be used as an adjustment variable in epidemiological research in primary care databases.

Aging underlies heterogeneity between comorbidity and multimorbidity frameworks

Studies exploring differences between comorbidity (i.e., the co-existence of additional diseases with reference to an index condition) and multimorbidity (i.e., the presence of multiple diseases in which no one holds priority) are lacking. In this single-center, observational study conducted in an academic, internal medicine ward, we aimed to evaluate the prevalence of patients with two or more multiple chronic conditions (MCC), comorbidity, or multimorbidity, correlating them with other patients' characteristics. The three categories were compared to the Cumulative Illness Rating Scale (CIRS) comorbidity index, age, gender, polytherapy, 30-day readmission, in-hospital and 30-day mortalities. Overall, 1394 consecutive patients (median age 80 years, IQR 69-86; F:M ratio 1.16:1) were included. Of these, 1341 (96.2%; median age 78 years, IQR 65-84; F:M ratio 1.17:1) had MCC. Fifty-three patients (3.8%) had no MCC, 286 (20.5%) had comorbidity, and 1055 (75.7%) had multimorbidity, showing a statistically significant (p < 0.001) increasing age trend (median age 38 years vs 71 vs 82, respectively) and increasing mean CIRS comorbidity index (1.53 ± 0.95 vs 2.97 ± 1.43 vs 4.09 ± 1.70, respectively). The CIRS comorbidity index was always higher in multimorbid patients, but only in the subgroups 75-84 years and ≥ 85 years was a significant (p < 0.001) difference (1.24 and 1.36, respectively) noticed. At multivariable analysis, age was always independently associated with in-hospital mortality (p = 0.002), 30-day mortality (p < 0.001), and 30-day readmission (p = 0.037), while comorbidity and multimorbidity were not. We conclude that age determines the most important differences between comorbid and multimorbid patients, as well as major outcomes, in a hospital setting.

The health status: the ignored risk factor in dementia incidence. NEDICES cohort

BACKGROUND

The causes of the dementia decrease in affluent countries are not well known but health amelioration could probably play a major role. Nevertheless, although many vascular and systemic disorders in adult life are well-known risk factors (RF) for dementia and Alzheimer disease (AD), health status is rarely considered as a single RF.

AIM

To analyse whether the health status and the self-perceived health (SPH) could be RF for dementia and AD and to discuss its biological basis.

METHODS

We analysed different objective health measures and SPH as RF for dementia and AD incidence in 4569 participants of the NEDICES cohort by means of Cox-regression models. The mean follow-up period was 3.2 (range: 0.03-6.6) years.

RESULTS

Ageing, low education, history of stroke, and "poor" SPH were the main RF for dementia and AD incidence, whereas physical activity was protective. "Poor" SPH had a hazard ratio = 1.66 (95% CI 1.17-2.46; p = 0.012) after controlling for different confounders.

DISCUSSION

According to data from NEDICES cohort, SPH is a better predictor of dementia and AD than other more objective health status proxies. SPH should be considered a holistic and biologically rooted indicator of health status, which can predict future development of dementia and AD in older adults.

CONCLUSIONS

Our data indicate that it is worthwhile to include the SPH status as a RF in the studies of dementia and AD incidence and to explore the effect of its improvement in the evolution of this incidence.

Comorbidities and depressive symptoms among older adults with asthma

OBJECTIVE

Depression is associated with poor outcomes among older adults with asthma, and the presence of multiple comorbidities may magnify this relationship. We sought to determine the association of comorbidities with depressive symptoms among older adults with asthma.

METHODS

Secondary analysis of data from a randomized controlled trial of older adults with poorly controlled asthma and comorbidities. Comorbidities were measured in two ways: (1) as a count of all the patient's chronic diseases, and (2) as a count of chronic illnesses with self-management intensive needs (diabetes, hypertension, congestive heart failure). Depressive symptoms were measured using the PROMIS SF8a scale. Multiple regression analyses tested the relationship between comorbidities and depressive symptoms, adjusting for sociodemographic factors.

RESULTS

Overall, 25% of participants had moderate-severe levels of depressive symptoms, 87% had ≥ two comorbidities, and 41% had ≥ one comorbidity with self-management intensive needs. The count of all comorbidities was significantly associated with depressive symptoms (F (8, 330) = 7.7, p < 0.0001, R2 = 0.158) in adjusted models, whereas the count of self-management intensive conditions was not significantly associated with depressive symptoms in adjusted analyses.

CONCLUSIONS

In older adults with asthma and multiple comorbidities, depressive symptoms increased with the overall count of comorbidities but not with the count of comorbidities with self-management intensive needs. Given the impact of depression on asthma outcomes for older adults, the mechanisms by which comorbid illness contributes to depressive symptoms in older asthmatics warrants further evaluation.

The Charlson Comorbidity Index: problems with use in epidemiological research

The Charlson Comorbidity Index (CCI) is a highly cited and well established tool for measuring comorbidity in clinical research, but there are problems with its use in practice. Like most comorbidity summary measures, the CCI was developed to adjust for prognostic comorbidities in statistical models, particularly those exploring associations between risk of death or survival time and other patient- and disease-related factors. Despite this, the CCI is often used in cancer research to measure all comorbidity, or as a multimorbidity measure, and CCI scores are often used to assess the prognostic importance of multiple health conditions. In the latter case, it is not at all surprising that researchers report a significant association between CCI scores and risk of death or survival times because CCI scores provide a summary of the presence or absence of a set of prognostic comorbidities. Advances in multimorbidity research require specific attention to the methods used to develop relevant indices. Published literature on the association between comorbidity and risk of death or survival time should be interpreted with caution, especially if the CCI was used to provide a measure of comorbidities.

Clustering of physical health multimorbidity in people with severe mental illness: An accumulated prevalence analysis of United Kingdom primary care data

BACKGROUND

People with severe mental illness (SMI) have higher rates of a range of physical health conditions, yet little is known regarding the clustering of physical health conditions in this population. We aimed to investigate the prevalence and clustering of chronic physical health conditions in people with SMI, compared to people without SMI.

METHODS AND FINDINGS

We performed a cohort-nested accumulated prevalence study, using primary care data from the Clinical Practice Research Datalink (CPRD), which holds details of 39 million patients in the United Kingdom. We identified 68,783 adults with a primary care diagnosis of SMI (schizophrenia, bipolar disorder, or other psychoses) from 2000 to 2018, matched up to 1:4 to 274,684 patients without an SMI diagnosis, on age, sex, primary care practice, and year of registration at the practice. Patients had a median of 28.85 (IQR: 19.10 to 41.37) years of primary care observations. Patients with SMI had higher prevalence of smoking (27.65% versus 46.08%), obesity (24.91% versus 38.09%), alcohol misuse (3.66% versus 13.47%), and drug misuse (2.08% versus 12.84%) than comparators. We defined 24 physical health conditions derived from the Elixhauser and Charlson comorbidity indices and used logistic regression to investigate individual conditions and multimorbidity. We controlled for age, sex, region, and ethnicity and then additionally for health risk factors: smoking status, alcohol misuse, drug misuse, and body mass index (BMI). We defined multimorbidity clusters using multiple correspondence analysis (MCA) and K-means cluster analysis and described them based on the observed/expected ratio. Patients with SMI had higher odds of 19 of 24 conditions and a higher prevalence of multimorbidity (odds ratio (OR): 1.84; 95% confidence interval [CI]: 1.80 to 1.88, p < 0.001) compared to those without SMI, particularly in younger age groups (males aged 30 to 39: OR: 2.49; 95% CI: 2.27 to 2.73; p < 0.001; females aged 18 to 30: OR: 2.69; 95% CI: 2.36 to 3.07; p < 0.001). Adjusting for health risk factors reduced the OR of all conditions. We identified 7 multimorbidity clusters in those with SMI and 7 in those without SMI. A total of 4 clusters were common to those with and without SMI; while 1, heart disease, appeared as one cluster in those with SMI and 3 distinct clusters in comparators; and 2 small clusters were unique to the SMI cohort. Limitations to this study include missing data, which may have led to residual confounding, and an inability to investigate the temporal associations between SMI and physical health conditions.

CONCLUSIONS

In this study, we observed that physical health conditions cluster similarly in people with and without SMI, although patients with SMI had higher burden of multimorbidity, particularly in younger age groups. While interventions aimed at the general population may also be appropriate for those with SMI, there is a need for interventions aimed at better management of younger-age multimorbidity, and preventative measures focusing on diseases of younger age, and reduction of health risk factors.

Multimorbidity patterns and association with mortality in 0.5 million Chinese adults

BACKGROUND

Few studies have assessed the relationship between multimorbidity patterns and mortality risk in the Chinese population. We aimed to identify multimorbidity patterns and examined the associations of multimorbidity patterns and the number of chronic diseases with the risk of mortality among Chinese middle-aged and older adults.

METHODS

We used data from the China Kadoorie Biobank and included 512,723 participants aged 30 to 79 years. Multimorbidity was defined as the presence of two or more of the 15 chronic diseases collected by self-report or physical examination at baseline. Multimorbidity patterns were identified using hierarchical cluster analysis. Cox regression was used to estimate the associations of multimorbidity patterns and the number of chronic diseases with all-cause and cause-specific mortality.

RESULTS

Overall, 15.8% of participants had multimorbidity. The prevalence of multimorbidity increased with age and was higher in urban than rural participants. Four multimorbidity patterns were identified, including cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), gastrointestinal and hepatorenal multimorbidity (gallstone disease, chronic kidney disease, cirrhosis, peptic ulcer, and cancer), and mental and arthritis multimorbidity (neurasthenia, psychiatric disorder, and rheumatoid arthritis). During a median of 10.8 years of follow-up, 49,371 deaths occurred. Compared with participants without multimorbidity, cardiometabolic multimorbidity (hazard ratios [HR] = 2.20, 95% confidence intervals [CI]: 2.14 - 2.26) and respiratory multimorbidity (HR = 2.13, 95% CI:1.97 - 2.31) demonstrated relatively higher risks of mortality, followed by gastrointestinal and hepatorenal multimorbidity (HR = 1.33, 95% CI:1.22 - 1.46). The mortality risk increased by 36% (HR = 1.36, 95% CI: 1.35 - 1.37) with every additional disease.

CONCLUSION

Cardiometabolic multimorbidity and respiratory multimorbidity posed the highest threat on mortality risk and deserved particular attention in Chinese adults.