Examining variation in the measurement of multimorbidity in research: a systematic review of 566 studies

Inequalities in Disability-Free and Disabling Multimorbid Life Expectancy in Costa Rica, Mexico, and the United States

OBJECTIVES

To better understand variations in multimorbidity severity over time, we estimate disability-free and disabling multimorbid life expectancy (MMLE), comparing Costa Rica, Mexico, and the United States (US). We also assess MMLE inequalities by sex and education.

METHODS

Data come from the Costa Rican Study on Longevity and Healthy Aging (2005-2009), the Mexican Health and Aging Study (2012-2018), and the Health and Retirement Study (2004-2018). We apply an incidence-based multistate Markov approach to estimate disability-free and disabling MMLE and stratify models by sex and education to study within-country heterogeneity. Multimorbidity is defined as a count of 2 or more chronic diseases. Disability is defined using limitations in activities of daily living.

RESULTS

Costa Ricans have the lowest MMLE, followed by Mexicans, then individuals from the US. Individuals from the US spend about twice as long with disability-free multimorbidity compared with individuals from Costa Rica or Mexico. Females generally have longer MMLE than males, with particularly stark differences in disabling MMLE. In the US, higher education was associated with longer disability-free MMLE and shorter disabling MMLE. We identified evidence for cumulative disadvantage in Mexico and the US, where sex differences in MMLE were larger among the lower educated.

DISCUSSION

Substantial sex and educational inequalities in MMLE exist within and between these countries. Estimating disability-free and disabling MMLE reveals another layer of health inequality not captured when examining disability and multimorbidity separately. MMLE is a flexible population health measure that can be used to better understand the aging process across contexts.

Multimorbidity and associated informal care receiving characteristics for US older adults: a latent class analysis

BACKGROUND

Older adults with varying patterns of multimorbidity may require distinct types of care and rely on informal caregiving to meet their care needs. This study aims to identify groups of older adults with distinct, empirically-determined multimorbidity patterns and compare characteristics of informal care received among estimated classes.

METHODS

Data are from the 2011 National Health and Aging Trends Study (NHATS). Ten chronic conditions were included to estimate multimorbidity patterns among 7532 individuals using latent class analysis. Multinomial logistic regression model was estimated to examine the association between sociodemographic characteristics, health status and lifestyle variables, care-receiving characteristics and latent class membership.

RESULTS

A four-class solution identified the following multimorbidity groups: some somatic conditions with moderate cognitive impairment (30%), cardiometabolic (25%), musculoskeletal (24%), and multisystem (21%). Compared with those who reported receiving no help, care recipients who received help with household activities only (OR = 1.44, 95% CI 1.05-1.98), mobility but not self-care (OR = 1.63, 95% CI 1.05-2.53), or self-care but not mobility (OR = 2.07, 95% CI 1.29-3.31) had greater likelihood of being in the multisystem group versus the some-somatic group. Having more caregivers was associated with higher odds of being in the multisystem group compared with the some-somatic group (OR = 1.09, 95% CI 1.00-1.18), whereas receiving help from paid helpers was associated with lower odds of being in the multisystem group (OR = 0.36, 95% CI 0.19-0.77).

CONCLUSIONS

Results highlighted different care needs among persons with distinct combinations of multimorbidity, in particular the wide range of informal needs among older adults with multisystem multimorbidity. Policies and interventions should recognize the differential care needs associated with multimorbidity patterns to better provide person-centered care.

The interrelationship between multiple long-term conditions (MLTC) and delirium: a scoping review

INTRODUCTION

Delirium and multiple long-term conditions (MLTC) share numerous risk factors and have been shown individually to be associated with adverse outcomes following hospitalisation. However, the extent to which these common ageing syndromes have been studied together is unknown. This scoping review aims to summarise our knowledge to date on the interrelationship between MLTC and delirium.

METHODS

Searches including terms for delirium and MLTC in adult human participants were performed in PubMed, EMBASE, Medline, Psycinfo and CINAHL. Descriptive analysis was used to summarise findings, structured according to Synthesis Without Meta-analysis reporting guidelines.

RESULTS

After removing duplicates, 5256 abstracts were screened for eligibility, with 313 full-texts sought along with 17 additional full-texts from references in review articles. In total, 140 met inclusion criteria and were included in the final review. Much of the literature explored MLTC as a risk factor for delirium (n = 125). Fewer studies explored the impact of MLTC on delirium presentation (n = 5), duration (n = 3) or outcomes (n = 6) and no studies explored how MLTC impacts the treatment of delirium or whether having delirium increases risk of developing MLTC. The most frequently used measures of MLTC and delirium were the Charlson Comorbidity Index (n = 98/140) and Confusion Assessment Method (n = 81/140), respectively.

CONCLUSION

Existing literature largely evaluates MLTC as a risk factor for delirium. Major knowledge gaps identified include the impact of MLTC on delirium treatment and the effect of delirium on MLTC trajectories. Current research in this field is limited by significant heterogeneity in defining both MLTC and delirium.

The number of comorbidities as an important cofactor to ASA class in predicting postoperative outcome: An international multicentre cohort study

BACKGROUND

Multimorbidity is a growing burden in our ageing society and is associated with perioperative morbidity and mortality. Despite several modifications to the ASA physical status classification, multimorbidity as such is still not considered. Thus, the aim of this study was to quantify the burden of comorbidities in perioperative patients and to assess, independent of ASA class, its potential influence on perioperative outcome.

METHODS

In a subpopulation of the prospective ClassIntra® validation study from eight international centres, type and severity of anaesthesia-relevant comorbidities were additionally extracted from electronic medical records for the current study. Patients from the validation study were of all ages, undergoing any type of in-hospital surgery and were followed up until 30 days postoperatively to assess perioperative outcomes. Primary endpoint was the number of comorbidities across ASA classes. The associated postoperative length of hospital stay (pLOS) and Comprehensive Complication Index (CCI®) were secondary endpoints. On a scale from 0 (no complication) to 100 (death) the CCI® measures the severity of postoperative morbidity as a weighted sum of all postoperative complications.

RESULTS

Of 1421 enrolled patients, the mean number of comorbidities significantly increased from 1.5 in ASA I (95% CI, 1.1-1.9) to 10.5 in ASA IV (95% CI, 8.3-12.7) patients. Furthermore, independent of ASA class, postoperative complications measured by the CCI® increased per each comorbidity by 0.81 (95% CI, 0.40-1.23) and so did pLOS (geometric mean ratio, 1.03; 95% CI, 1.01-1.06).

CONCLUSIONS

These data quantify the high prevalence of multimorbidity in the surgical population and show that the number of comorbidities is predictive of negative postoperative outcomes, independent of ASA class.

Association between changes in cardiovascular health and the risk of multimorbidity: community-based cohort studies in the UK and Finland

Background

Better cardiovascular health is associated with lower risk of various chronic diseases, but its association with multimorbidity is poorly understood. We aimed to examine whether change in cardiovascular health is associated with multimorbidity risk.

Methods

The primary analysis was conducted in the Whitehall II multiwave prospective cohort study (UK) and the validation analysis in the Finnish Public Sector cohort study (Finland). Change in cardiovascular health was assessed using the American Heart Association Life's Simple 7 (LS7) and Life's Essential 8 (LE8) at baseline and re-assessments, using objective measures in Whitehall II and self-reports and pharmacy claims in the Finnish Public Sector cohort study, respectively. Multimorbidity was defined as the presence of two or more of 12 chronic diseases during follow-up. We estimated hazard ratios (HR) and 95% confidence intervals (CI) using Cox's proportional hazard models with age as time scale, adjusting for sex, education, occupation, marital status, and ethnicity.

Findings

In the primary analysis among 9715 participants, mean age was 44.8 (standard deviation 6.0) years and 67.6% participants were men at baseline. During the median follow-up of 31.4 (interquartile range 26.8-32.3) years, 2751 participants developed multimorbidity. The hazard of multimorbidity decreased by 8% (HR 0.92, 95% CI 0.88-0.96) per ideal LS7 metric increment over 5 years and by 14% (HR 0.86, 95% CI 0.80-0.93) per ten points increase in LE8 score over 10 years. These findings were replicated in the validation analysis among 75,377 participants in terms of 4-year change in cardiovascular health.

Interpretation

Improvement in cardiovascular health was associated with lower multimorbidity risk in two community-based cohort studies. Interventions improving cardiovascular health of the community may contribute to multimorbidity prevention.

Funding

None.

Evidence-Based Therapist-Supported Digital Mental Health Intervention for Patients Experiencing Medical Multimorbidity: A Retrospective Cohort Intent-to-Treat Study

OBJECTIVE

Multimorbidity or the co-occurrence of multiple health conditions is increasing globally and is associated with significant psychological complications. It is unclear whether digital mental health (DMH) interventions for patients experiencing multimorbidity are effective, particularly given that this patient population faces more treatment resistance. The goal of the current study was to examine the impact of smartphone-delivered DMH interventions for patients presenting with elevated internalizing symptoms that have reported multiple lifetime medical conditions.

METHODS

This preregistered (see https://osf.io/vh2et/ ) retrospective cohort intent-to-treat study with 2819 patients enrolled in a therapist-supported DMH intervention examined the associations between medical multimorbidity (MMB) and mental health outcomes.

RESULTS

Results indicated that more MMB was significantly associated with greater presenting mental health symptom severity. MMB did not have a deleterious influence on depressive symptom trajectories across treatment, although having one medical condition was associated with a steeper decrease in anxiety symptoms compared to patients with no medical conditions. Finally, MMB was not associated with time to dropout, but was associated with higher dropout and was differentially associated with fewer beneficial treatment outcomes, although this is likely attributable to higher presenting symptom severity, rather than lesser symptom reductions during treatment.

CONCLUSIONS

Overall, the Meru Health Program was associated with large effect size decreases in depressive and anxiety symptoms regardless of the number of MMB. Future DMH treatments and research might investigate tailored barrier reduction and extended treatment lengths for patients experiencing MMB to allow for greater treatment dose to reduce symptoms below clinical outcome thresholds.

Computationally derived transition points across phases of clinical care

The objective of this study is to use statistical techniques for the identification of transition points along the life course, aiming to identify fundamental changes in patient multimorbidity burden across phases of clinical care. This retrospective cohort analysis utilized 5.2 million patient encounters from 2013 to 2022, collected from a large academic institution and its affiliated hospitals. Structured information was systematically gathered for each encounter and three methodologies - clustering analysis, False Nearest Neighbor, and transitivity analysis - were employed to pinpoint transitions in patients' clinical phase. Clustering analysis identified transition points at age 2, 17, 41, and 66, FNN at 4.27, 5.83, 5.85, 14.12, 20.62, 24.30, 25.10, 29.08, 33.12, 35.7, 38.69, 55.66, 70.03, and transitivity analysis at 7.27, 23.58, 29.04, 35.00, 61.29, 67.03, 77.11. Clustering analysis identified transition points that align with the current clinical gestalt of pediatric, adult, and geriatric phases of care. Notably, over half of the transition points identified by FNN and transitivity analysis were between ages 20 and 40, a population that is traditionally considered to be clinically homogeneous. Few transition points were identified between ages 3 and 17. Despite large social and developmental transition at those ages, the burden of multimorbidities may be consistent across the age range. Transition points derived through unsupervised machine learning approaches identify changes in the clinical phase that align with true differences in underlying multimorbidity burden. These transitions may be different from conventional pediatric and geriatric phases, which are often influenced by policy rather than clinical changes.

Multimorbidity and its Associated Factors in Korean Shift Workers: Population-Based Cross-Sectional Study

BACKGROUND

Multimorbidity is a crucial factor that influences premature death rates, poor health, depression, quality of life, and use of health care. Approximately one-fifth of the global workforce is involved in shift work, which is associated with increased risk for several chronic diseases and multimorbidity. About 12% to 14% of wage workers in Korea are shift workers. However, the prevalence of multimorbidity and its associated factors in Korean shift workers are rarely reported.

OBJECTIVE

This study aimed to assess multimorbidity prevalence, examine the factors associated with multimorbidity, and identify multimorbidity patterns among shift workers in Korea.

METHODS

This study is a population-based cross-sectional study using Korea National Health and Nutrition Examination Survey data from 2016 to 2020. The study included 1704 (weighted n=2,697,228) Korean shift workers aged 19 years and older. Multimorbidity was defined as participants having 2 or more chronic diseases. Demographic and job-related variables, including regular work status, average working hours per week, and shift work type, as well as health behaviors, including BMI, smoking status, alcohol use, physical activity, and sleep duration, were included in the analysis. A survey-corrected logistic regression analysis was performed to identify factors influencing multimorbidity among the workers, and multimorbidity patterns were identified with a network analysis.

RESULTS

The overall prevalence of multimorbidity was 13.7% (302/1704). Logistic regression indicated that age, income, regular work, and obesity were significant factors influencing multimorbidity. Network analysis results revealed that chronic diseases clustered into three groups: (1) cardiometabolic multimorbidity (hypertension, dyslipidemia, diabetes, coronary heart disease, and stroke), (2) musculoskeletal multimorbidity (arthritis and osteoporosis), and (3) unclassified diseases (depression, chronic liver disease, thyroid disease, asthma, cancer, and chronic kidney disease).

CONCLUSIONS

The findings revealed that several socioeconomic and behavioral factors were associated with multimorbidity among shift workers, indicating the need for policy development related to work schedule modification. Further organization-level screening and intervention programs are needed to prevent and manage multimorbidity among shift workers. We also recommend longitudinal studies to confirm the effects of job-related factors and health behaviors on multimorbidity among shift workers in the future.

Geriatric Syndromes Frequently (Co)-Occur in Geriatric Rehabilitation Inpatients: Restoring Health of Acutely Unwell Adults (RESORT) and Enhancing Muscle Power in Geriatric Rehabilitation (EMPOWER-GR)

OBJECTIVE

To determine the prevalence and co-occurrence of common geriatric syndromes in geriatric rehabilitation inpatients.

DESIGN

Restoring Health of Acutely Unwell Adults (RESORT) and Enhancing Muscle Power in Geriatric Rehabilitation (EMPOWER-GR) are observational, longitudinal cohorts.

SETTING

Geriatric rehabilitation.

PARTICIPANTS

Geriatric rehabilitation inpatients (N=1890 and N=200).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Geriatric syndromes included polypharmacy, multimorbidity (Cumulative Illness Rating Scale), cognitive impairment, depression (Hospital Anxiety and Depression Scale/Geriatric Depression Scale), malnutrition (Global Leadership Initiative on Malnutrition), functional limitation (Katz index), falls, physical frailty (Fried), and sarcopenia (European Working Group on Sarcopenia in Older People 2).

RESULTS

Inpatients in RESORT (R) (N=1890, 56% females) had a median age of 83.4 years (interquartile range [IQR], 77.6-88.4) and in EMPOWER-GR (E) (N=200, 57% females) of 79.8 years (IQR, 75.0-85.9). Polypharmacy (R, 82.2%; E, 84.0%), multimorbidity (R, 90.4%; E, 85.5%), functional limitation (R, 96.0%; E, 76.5%), and frailty (R, 91.8%; E, 92.2%) were most prevalent. Most inpatients had ≥5 geriatric syndromes at admission in both cohorts (R, 70.0%; E, 72.4%); few inpatients had only 1 (R, 0.4%; E, 1.5%) or no geriatric syndrome (R, 0.2%; E, 0.0%). Geriatric syndromes did not occur in isolation (without other syndromes), except for multimorbidity (R, 1%; E, 5%), functional limitation (R, 3%; E, 2%), falls (R, 0%; E, 4%), and frailty (R, 2%; E, 5%), which occurred in isolation in some inpatients; sarcopenia did not.

CONCLUSIONS

Geriatric syndromes are highly prevalent at admission to geriatric rehabilitation, with a median of 5 co-occurring syndromes. Implications for diagnosis and intervention potential should be further addressed.

Educational inequalities in multimorbidity at older ages: a multi-generational population-based study

BACKGROUND

Social inequalities in multimorbidity may occur due to familial and/or individual factors and may differ between men and women. Using population-based multi-generational data, this study aimed to (1) assess the roles of parental and individual education in the risk of multimorbidity and (2) examine the potential effect modification by sex.

METHODS

Data were analysed from 62 060 adults aged 50+ who participated in the Survey of Health, Ageing and Retirement in Europe, comprising 14 European countries. Intergenerational educational trajectories (exposure) were High-High (reference), Low-High, High-Low and Low-Low, corresponding to parental-individual educational attainments. Multimorbidity (outcome) was ascertained between 2013 and 2020 as self-reported occurrence of ≥2 diagnosed chronic conditions. Inequalities were quantified as multimorbidity-free years lost (MFYL) between the ages of 50 and 90 and estimated via differences in the area under the standardized cumulative risk curves. Effect modification by sex was assessed via stratification.

RESULTS

Low individual education was associated with higher multimorbidity risk regardless of parental education. Compared to the High-High trajectory, Low-High was associated with -0.2 MFYL (95% confidence intervals: -0.5 to 0.1), High-Low with 3.0 (2.4-3.5), and Low-Low with 2.6 (2.3-2.9) MFYL. This pattern was observed for both sexes, with a greater magnitude for women. This effect modification was not observed when only diseases diagnosed independently of healthcare-seeking behaviours were examined.

CONCLUSIONS

Individual education was the main contributor to intergenerational inequalities in multimorbidity risk among older European adults. These findings support the importance of achieving a high education to mitigate multimorbidity risk.

Trajectories of chronic multimorbidity patterns in older patients: MTOP study

BACKGROUND

Multimorbidity is associated with negative results and poses difficulties in clinical management. New methodological approaches are emerging based on the hypothesis that chronic conditions are non-randomly associated forming multimorbidity patterns. However, there are few longitudinal studies of these patterns, which could allow for better preventive strategies and healthcare planning. The objective of the MTOP (Multimorbidity Trajectories in Older Patients) study is to identify patterns of chronic multimorbidity in a cohort of older patients and their progression and trajectories in the previous 10 years.

METHODS

A retrospective, observational study with a cohort of 3988 patients aged > 65 was conducted, including suspected and confirmed COVID-19 patients in the reference area of Parc Taulí University Hospital. Real-world data on socio-demographic and diagnostic variables were retrieved. Multimorbidity patterns of chronic conditions were identified with fuzzy c-means cluster analysis. Trajectories of each patient were established along three time points (baseline, 5 years before, 10 years before). Descriptive statistics were performed together with a stratification by sex and age group.

RESULTS

3988 patients aged over 65 were included (58.9% females). Patients with ≥ 2 chronic conditions changed from 73.6 to 98.3% in the 10-year range of the study. Six clusters of chronic multimorbidity were identified 10 years before baseline, whereas five clusters were identified at both 5 years before and at baseline. Three clusters were consistently identified in all time points (Metabolic and vascular disease, Musculoskeletal and chronic pain syndrome, Unspecific); three clusters were only present at the earliest time point (Male-predominant diseases, Minor conditions and sensory impairment, Lipid metabolism disorders) and two clusters emerged 5 years before baseline and remained (Heart diseases and Neurocognitive). Sex and age stratification showed different distribution in cluster prevalence and trajectories.

CONCLUSIONS

In a cohort of older patients, we were able to identify multimorbidity patterns of chronic conditions and describe their individual trajectories in the previous 10 years. Our results suggest that taking these trajectories into consideration might improve decisions in clinical management and healthcare planning.

TRIAL REGISTRATION NUMBER

NCT05717309.

Multimorbid life expectancy across race, socio-economic status, and sex in South Africa

Multimorbidity is increasing globally as populations age. However, it is unclear how long individuals live with multimorbidity and how it varies by social and economic factors. We investigate this in South Africa, whose apartheid history further complicates race, socio-economic, and sex inequalities. We introduce the term 'multimorbid life expectancy' (MMLE) to describe the years lived with multimorbidity. Using data from the South African National Income Dynamics Study (2008-17) and incidence-based multistate Markov modelling, we find that females experience higher MMLE than males (17.3 vs 9.8 years), and this disparity is consistent across all race and education groups. MMLE is highest among Asian/Indian people and the post-secondary educated relative to other groups and lowest among African people. These findings suggest there are associations between structural inequalities and MMLE, highlighting the need for health-system and educational policies to be implemented in a way proportional to each group's level of need.

Multimorbidity prevalence and health outcome prediction: assessing the impact of lookback periods, disease count, and definition criteria in health administrative data at the population-based level

BACKGROUND

Health administrative databases play a crucial role in population-level multimorbidity surveillance. Determining the appropriate retrospective or lookback period (LP) for observing prevalent and newly diagnosed diseases in administrative data presents challenge in estimating multimorbidity prevalence and predicting health outcome. The aim of this population-based study was to assess the impact of LP on multimorbidity prevalence and health outcomes prediction across three multimorbidity definitions, three lists of diseases used for multimorbidity assessment, and six health outcomes.

METHODS

We conducted a population-based study including all individuals ages > 65 years on April 1st, 2019, in Québec, Canada. We considered three lists of diseases labeled according to the number of chronic conditions it considered: (1) L60 included 60 chronic conditions from the International Classification of Diseases (ICD); (2) L20 included a core of 20 chronic conditions; and (3) L31 included 31 chronic conditions from the Charlson and Elixhauser indices. For each list, we: (1) measured multimorbidity prevalence for three multimorbidity definitions (at least two [MM2+], three [MM3+] or four (MM4+) chronic conditions); and (2) evaluated capacity (c-statistic) to predict 1-year outcomes (mortality, hospitalisation, polypharmacy, and general practitioner, specialist, or emergency department visits) using LPs ranging from 1 to 20 years.

RESULTS

Increase in multimorbidity prevalence decelerated after 5-10 years (e.g., MM2+, L31: LP = 1y: 14%, LP = 10y: 58%, LP = 20y: 69%). Within the 5-10 years LP range, predictive performance was better for L20 than L60 (e.g., LP = 7y, mortality, MM3+: L20 [0.798;95%CI:0.797-0.800] vs. L60 [0.779; 95%CI:0.777-0.781]) and typically better for MM3 + and MM4 + definitions (e.g., LP = 7y, mortality, L60: MM4+ [0.788;95%CI:0.786-0.790] vs. MM2+ [0.768;95%CI:0.766-0.770]).

CONCLUSIONS

In our databases, ten years of data was required for stable estimation of multimorbidity prevalence. Within that range, the L20 and multimorbidity definitions MM3 + or MM4 + reached maximal predictive performance.

Economic inactivity and mental-physical multimorbidity

Economic inactivity and multimorbidity, including mental–physical multimorbidity, have increased in recent years, adversely impacting individuals and the economy, and widening health inequalities. There is an under-recognition of their relationship although they share important risk factors. The substantial challenges of each cannot be addressed without understanding the other. This requires access to better health and work data, and greater cooperation between clinicians, researchers and policy-makers. The central role of occupational health expertise is highlighted.

Implementation and external validation of the Cambridge Multimorbidity Score in the UK Biobank cohort

BACKGROUND

Patients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort.

METHODS

The CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions.

RESULTS

For all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66-0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68-0.70) and 0.89 (0.88-0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63-0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis.

CONCLUSIONS

In the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.

How to incorporate chronic health conditions in oncologic decision-making and care for older patients with cancer? A survey among healthcare professionals

PURPOSE

A substantial proportion of patients with cancer are older and experience multimorbidity. As the population is ageing, the management of older patients with multimorbidity including cancer will represent a significant challenge to current clinical practice.

METHODS

This study aimed to (1) identify which chronic health conditions may cause change in oncologic decision-making and care in older patients and (2) provide guidance on how to incorporate these in decision-making and care provision of older patients with cancer. Based on a scoping literature review, an initial list of prevalent morbidities was developed. A subsequent survey among healthcare providers involved in the care for older patients with cancer assessed which chronic health conditions were relevant and why.

RESULTS

A list of 53 chronic health conditions was developed, of which 34 were considered likely or very likely to influence decision-making or care according to the 39 healthcare professionals who responded. These conditions were further categorized into five patient profiles. From these conditions, five patient profiles were developed, namely, (1) a somatic profile consisting of cardiovascular, metabolic, and pulmonary disease, (2) a functional profile, including conditions that cause disability, dependency or a high caregiver burden, (3) a psychosocial profile, including cognitive impairment, (4) a nutritional profile also including digestive system diseases, and finally, (5) a concurrent cancer profile. All profiles were considered likely to impact decision-making with differences between treatment modalities. The impact on the care trajectory was generally considered less significant, except for patients with care dependency and psychosocial health problems.

CONCLUSIONS

Chronic health conditions have various ways of influencing oncologic decision-making and the care trajectory in older adults with cancer. Understanding why specific chronic health conditions may impact the oncologic care trajectory can aid clinicians in the management of older patients with multimorbidity, including cancer.

Rising to the challenge of defining and operationalising multimorbidity in a UK hospital setting: the ADMISSION research collaborative

PURPOSE

Greater transparency and consistency when defining multimorbidity in different settings is needed. We aimed to: (1) adapt published principles that can guide the selection of long-term conditions for inclusion in research studies of multimorbidity in hospitals; (2) apply these principles and identify a list of long-term conditions; (3) operationalise this list by mapping it to International Classification of Diseases 10th revision (ICD-10) codes.

METHODS

Review by independent assessors and ratification by an interdisciplinary programme management group.

RESULTS

Agreement was reached that when defining multimorbidity in hospitals for research purposes all conditions must meet the following four criteria: (1) medical diagnosis; (2) typically present for ≥ 12 months; (3) at least one of currently active; permanent in effect; requiring current treatment, care or therapy; requiring surveillance; remitting-relapsing and requiring ongoing treatment or care, and; (4) lead to at least one of: significantly increased risk of death; significantly reduced quality of life; frailty or physical disability; significantly worsened mental health; significantly increased treatment burden (indicated by an increased risk of hospital admission or increased length of hospital stay). Application of these principles to two existing lists of conditions led to the selection of 60 conditions that can be used when defining multimorbidity for research focused on hospitalised patients. ICD-10 codes were identified for each of these conditions to ensure consistency in their operationalisation.

CONCLUSIONS

This work contributes to achieving the goal of greater transparency and consistency in the approach to the study of multimorbidity, with a specific focus on the UK hospital setting.

Association of systemic anticholinergic medication use and accelerated decrease in lung function in older adults

Older adults are frequently exposed to medicines with systemic anticholinergic properties, which are linked to increased risk of negative health outcomes. The association between systemic anticholinergics and lung function has not been reported. The aim of this study was to investigate if exposure to systemic anticholinergics influences lung function in older adults. Participants of the southernmost centres of the Swedish National study on Aging and Care (SNAC) were followed from 2001 to 2021. In total, 2936 subjects (2253 from Good Aging in Skåne and 683 from SNAC-B) were included. An extensive medical examination including spirometry assessments was performed during the study visits. The systemic anticholinergic burden was described using the anticholinergic cognitive burden scale. The effect of new use of systemic anticholinergics on the annual change in forced expiratory volume (FEV1s) was estimated using mixed models. During follow-up, 802 (27.3%) participants were exposed to at least one systemic anticholinergic medicine. On average, the FEV1s of participants without systemic anticholinergic exposure decreased 37.2 ml/year (95% CI [33.8; 40.6]) while participants with low and high exposure lose 47.2 ml/year (95% CI [42.4; 52.0]) and 43.7 ml/year (95% CI [25.4; 62.0]). A novel association between new use of medicines with systemic anticholinergic properties and accelerated decrease in lung function in older adults was found. The accelerated decrease is comparable to that observed in smokers. Studies are needed to further explore this potential side effect of systemic anticholinergics.

A model-based approach to estimating the prevalence of disease combinations in South Africa

BACKGROUND

The development of strategies to better detect and manage patients with multiple long-term conditions requires estimates of the most prevalent condition combinations. However, standard meta-analysis tools are not well suited to synthesising heterogeneous multimorbidity data.

METHODS

We developed a statistical model to synthesise data on associations between diseases and nationally representative prevalence estimates and applied the model to South Africa. Published and unpublished data were reviewed, and meta-regression analysis was conducted to assess pairwise associations between 10 conditions: arthritis, asthma, chronic obstructive pulmonary disease (COPD), depression, diabetes, HIV, hypertension, ischaemic heart disease (IHD), stroke and tuberculosis. The national prevalence of each condition in individuals aged 15 and older was then independently estimated, and these estimates were integrated with the ORs from the meta-regressions in a statistical model, to estimate the national prevalence of each condition combination.

RESULTS

The strongest disease associations in South Africa are between COPD and asthma (OR 14.6, 95% CI 10.3 to 19.9), COPD and IHD (OR 9.2, 95% CI 8.3 to 10.2) and IHD and stroke (OR 7.2, 95% CI 5.9 to 8.4). The most prevalent condition combinations in individuals aged 15+ are hypertension and arthritis (7.6%, 95% CI 5.8% to 9.5%), hypertension and diabetes (7.5%, 95% CI 6.4% to 8.6%) and hypertension and HIV (4.8%, 95% CI 3.3% to 6.6%). The average numbers of comorbidities are greatest in the case of COPD (2.3, 95% CI 2.1 to 2.6), stroke (2.1, 95% CI 1.8 to 2.4) and IHD (1.9, 95% CI 1.6 to 2.2).

CONCLUSION

South Africa has high levels of HIV, hypertension, diabetes and arthritis, by international standards, and these are reflected in the most prevalent condition combinations. However, less prevalent conditions such as COPD, stroke and IHD contribute disproportionately to the multimorbidity burden, with high rates of comorbidity. This modelling approach can be used in other settings to characterise the most important disease combinations and levels of comorbidity.

The Prevalence of Physical and Mental Health Multimorbidity Among People Held in U.S. Jails

American jails process millions of bookings each year, and prior research has documented high rates of mental and physical ailments among people held in jails. The existing literature, however, provides only minimal insight into the occurrence of multiple health conditions. This study sought to estimate the prevalence of physical and mental health multimorbidity among people held in jails in the United States. Using a nationally representative sample of responses to the National Inmate Survey, 2011-2012 (N = 5,494), we analyzed reports of physical health conditions, mental health conditions, and disabilities among people in local jails. Prevalence of two or more conditions was 28.5% (95% confidence interval [CI] = 27.3%, 29.7%) for mental health, 55.5% (95% CI = 54.2%, 56.8%) for physical health, and 15.5% (95% CI = 14.6%, 16.5%) for disabilities. At least one condition across all three health domains was estimated at 29.4% (95% CI = 28.2%, 30.6%). Prevalence of two or more co-occurring conditions without regard for domain was 76.9% (95% CI = 75.8%, 78.0%). Rates were consistently higher among women than among men. Jailed people show a high rate of co-occurring mental and physical health conditions.

Utilization of Primary Healthcare Services in Patients with Multimorbidity According to Their Risk Level by Adjusted Morbidity Groups: A Cross-Sectional Study in Chamartín District (Madrid)

Patients with multimorbidity have increased and more complex healthcare needs, posing their management a challenge for healthcare systems. This study aimed to describe their primary healthcare utilization and associated factors. A population-based cross-sectional study was conducted in a Spanish basic healthcare area including all patients with chronic conditions, differentiating between having multimorbidity or not. Sociodemographic, functional, clinical and service utilization variables were analyzed, stratifying the multimorbid population by the Adjusted Morbidity Groups (AMG) risk level, sex and age. A total of 6036 patients had multimorbidity, 64.2% being low risk, 28.5% medium risk and 7.3% high risk. Their mean age was 64.1 years and 63.5% were women, having on average 3.5 chronic diseases, and 25.3% were polymedicated. Their mean primary care contacts/year was 14.9 (7.8 with family doctors and 4.4 with nurses). Factors associated with primary care utilization were age (B-coefficient [BC] = 1.15;95% Confidence Interval [CI] = 0.30-2.01), female sex (BC = 1.04; CI = 0.30-1.78), having a caregiver (BC = 8.70; CI = 6.72-10.69), complexity (B-coefficient = 0.46; CI = 0.38-0.55), high-risk (B-coefficient = 2.29; CI = 1.26-3.32), numerous chronic diseases (B-coefficient = 1.20; CI = 0.37-2.04) and polypharmacy (B-coefficient = 5.05; CI = 4.00-6.10). This study provides valuable data on the application of AMG in multimorbid patients, revealing their healthcare utilization and the need for a patient-centered approach by primary care professionals. These results could guide in improving coordination among professionals, optimizing multimorbidity management and reducing costs derived from their extensive healthcare utilization.

Experiences of hospital care for people with multiple long-term conditions: a scoping review of qualitative research

BACKGROUND

Multiple long-term conditions-the co-existence of two or more chronic health conditions in an individual-present an increasing challenge to populations and healthcare systems worldwide. This challenge is keenly felt in hospital settings where care is oriented around specialist provision for single conditions. The aim of this scoping review was to identify and summarise published qualitative research on the experiences of hospital care for people living with multiple long-term conditions, their informal caregivers and healthcare professionals.

METHODS

We undertook a scoping review, following established guidelines, of primary qualitative research on experiences of hospital care for people living with multiple long-term conditions published in peer-reviewed journals between Jan 2010 and June 2022. We conducted systematic electronic searches of MEDLINE, CINAHL, PsycInfo, Proquest Social Science Premium, Web of Science, Scopus and Embase, supplemented by citation tracking. Studies were selected for inclusion by two reviewers using an independent screening process. Data extraction included study populations, study design, findings and author conclusions. We took a narrative approach to reporting the findings.

RESULTS

Of 8002 titles and abstracts screened, 54 papers reporting findings from 41 studies conducted in 14 countries were identified as eligible for inclusion. The perspectives of people living with multiple long-term conditions (21 studies), informal caregivers (n = 13) and healthcare professionals (n = 27) were represented, with 15 studies reporting experiences of more than one group. Findings included poor service integration and lack of person-centred care, limited confidence of healthcare professionals to treat conditions outside of their specialty, and time pressures leading to hurried care transitions. Few studies explored inequities in experiences of hospital care.

CONCLUSIONS

Qualitative research evidence on the experiences of hospital care for multiple long-term conditions illuminates a tension between the desire to provide and receive person-centred care and time pressures inherent within a target-driven system focussed on increasing specialisation, reduced inpatient provision and accelerated journeys through the care system. A move towards more integrated models of care may enable the needs of people living with multiple long-term conditions to be better met. Future research should address how social circumstances shape experiences of care.

Report on the Joint Workshop on the Relations between Health Inequalities, Ageing and Multimorbidity, Iceland, May 3-4, 2023

This paper is a summary of key presentations from a workshop in Iceland on May 3-4, 2023 arranged by Aarhus University and with participation of the below-mentioned scientists. Below you will find the key messages from the presentations made by: Professor Jan Vandenbroucke, Department of Clinical Epidemiology, Aarhus University, Emeritus Professor, Leiden University; Honorary Professor, London School of Hygiene & Tropical Medicine, UKProfessor, Chair Henrik Toft Sørensen, Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, DenmarkProfessor David H. Rehkopf, Director, the Stanford Center for Population Health Sciences, Stanford University, CA., USProfessor Jaimie Gradus, Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Johan Mackenbach, Emeritus Professor, Department of Public Health, Erasmus University Rotterdam, HollandProfessor, Chair M Maria Glymour, Department of Epidemiology, Boston University School of Public Health, Boston University, Boston, Massachusetts, USProfessor, Dean Sandro Galea, School of Public Health, Boston University, Boston, Massachusetts, USProfessor Victor W. Henderson, Departments of Epidemiology & Population Health and of Neurology & Neurological Sciences, Stanford University, Stanford, CA, US; Department of Clinical Epidemiology, Aarhus University, Aarhus, DK.

Environmental inequalities and multimorbidity: Insights from the Southwest China Multi-Ethnic Cohort Study

Multimorbidity is an increasingly significant public health challenge worldwide. Although the association between environmental factors and the morbidity and mortality of individual chronic diseases is well-established, the relationship between environmental inequalities and multimorbidity, as well as the patterns of multimorbidity across different areas and ethnic groups, remains unclear. We first focus on analyzing the differences in environmental exposures and patterns of multimorbidity across diverse areas and ethnic groups. The results show that individuals of Han ethnicity residing in Chongqing and Sichuan are exposure to higher levels of air pollutants such as PM2.5, PM10, and NO2. Conversely, Tibetans in Tibet and Yi people in Yunnan face elevated concentrations of O3. Furthermore, the Dong, Miao, Buyi ethnicities in Guizhou and Bai in Yunnan have greater access to green spaces. The key multimorbidity patterns observed in Southwest China are related to metabolic abnormalities combined with digestive system diseases. However, significant differences in multimorbidity patterns exist among different regions and ethnic groups. Further utilizing the logistic regression model, the analysis demonstrates that increased exposure to environmental pollutants (PM2.5, PM10, NO2, O3) is significantly associated with higher odds ratios of multimorbidity. Conversely, a greater presence of green spaces (NDVI 250, NDVI 500, NDVI 1000) significantly reduces the risk of multimorbidity. This large-scale epidemiological study provides some evidence of a significant association between environmental inequalities and multimorbidity. By addressing these environmental inequalities and promoting healthy environments for all, we can work towards reducing the prevalence of multimorbidity and improving overall population health.

Impact of multimorbidity and complex multimorbidity on healthcare utilisation in older Australian adults aged 45 years or more: a large population-based cross-sectional data linkage study

OBJECTIVES

As life expectancy increases, older people are living longer with multimorbidity (MM, co-occurrence of ≥2 chronic health conditions) and complex multimorbidity (CMM, ≥3 chronic conditions affecting ≥3 different body systems). We assessed the impacts of MM and CMM on healthcare service use in Australia, as little was known about this.

DESIGN

Population-based cross-sectional data linkage study.

SETTING

New South Wales, Australia.

PARTICIPANTS

248 496 people aged ≥45 years who completed the Sax Institute's 45 and Up Study baseline questionnaire.

PRIMARY OUTCOME

High average annual healthcare service use (≥2 hospital admissions, ≥11 general practice visits and ≥2 emergency department (ED) visits) during the 3-year baseline period (year before, year of and year after recruitment).

METHODS

Baseline questionnaire data were linked with hospital, Medicare claims and ED datasets. Poisson regression models were used to estimate adjusted and unadjusted prevalence ratios for high service use with 95% CIs. Using a count of chronic conditions (disease count) as an alternative morbidity metric was requested during peer review.

RESULTS

Prevalence of MM and CMM was 43.8% and 15.5%, respectively, and prevalence increased with age. Across three healthcare settings, MM was associated with a 2.02-fold to 2.26-fold, and CMM was associated with a 1.83-fold to 2.08-fold, increased risk of high service use. The association was higher in the youngest group (45-59 years) versus the oldest group (≥75 years), which was confirmed when disease count was used as the morbidity metric in sensitivity analysis.When comparing impact using three categories with no overlap (no MM/CMM, MM with no CMM, and CMM), CMM had greater impact than MM across all settings.

CONCLUSION

Increased healthcare service use among older adults with MM and CMM impacts on the demand for primary care and hospital services. Which of MM or CMM has greater impact on risk of high healthcare service use depends on the analytic method used. Ageing populations living longer with increasing burdens of MM and CMM will require increased Medicare funding and provision of integrated care across the healthcare system to meet their complex needs.

Multimorbidity measured with Charlson Comorbidity Index is not associated with clinically relevant risk of revision after primary total hip arthroplasty: a population-based cohort study on 98,647 patients from the Danish Hip Arthroplasty Register

BACKGROUND AND PURPOSE

Evidence for guiding healthcare professionals on the risks of total hip arthroplasty (THA) in multimorbid patients is sparse. We aimed to examine the association between multimorbidity and the risk of revision due to any cause and specific causes after primary THA due to osteoarthritis.

PATIENTS AND METHODS

We identified 98,647 THA patients and subsequent revisions in the Danish Hip Arthroplasty Register from 1995 to 2018. Multimorbidity was measured with the Charlson Comorbidity Index (CCI). Using the CCI (low, medium, high), we calculated the cumulative incidence function (CIF) of first revision up to 10 years after THA. Adjusted cause-specific hazard ratios (aHRs) were estimated using Cox regressions. All estimates are presented with 95% confidence intervals (CI).

RESULTS

Overall, the prevalence of patients with low, medium, and high CCI was 70%, 24%, and 6%. The CIF of any revision within 10 years was 6.5% (CI 6.2-6.7) in low and 6.5% (CI 5.8-7.3) in high CCI, with an aHR of 1.4 (CI 1.2-1.6) for patients with high compared with low CCI. The corresponding aHRs for cause-specific revision were 1.3 (CI 1.0-1.6) for aseptic loosening within 10 years, 1.2 (CI 0.9-1.6) for infection, and 1.7 (CI 1.3-2.2) for dislocation, both within 2 years.

CONCLUSION

Multimorbidity is associated with a minor but not clinically relevant increased risk of revision up to 10 years after primary THA.

Epidemiology and impact of chronic disease multimorbidity in India: a systematic review and meta-analysis

Objectives

This is the first systematic review and meta-analysis of the prevalence of multimorbidity, its risk factors including socioeconomic factors, and the consequences of multimorbidity on health systems and broader society in India.

Methods

A systematic review of both published and grey literature from five databases (Medline, Embase, EBSCO, Scopus, and ProQuest) was conducted including original studies documenting prevalence or patient outcomes associated with multimorbidity among adults in India. We excluded studies that did not explicitly mention multimorbidity. Three independent reviewers did primary screening based on titles and abstracts followed by full-text review for potential eligibility. The risk of bias was independently assessed by two reviewers following the Appraisal Tool for Cross-Sectional Studies. We presented both qualitative and quantitative (through meta-analysis) summaries of the evidence. The protocol for this study was prospectively registered with PROSPERO (CRD42021257281).

Results

The review identified 5442 articles out of which 35 articles were finally included in this study. Twenty-three studies were based on the primary data while 12 used secondary data. Eleven studies were conducted in hospital/primary care setting while 24 were community-based. The pooled prevalence of multimorbidity based on (n=19) studies included for meta-analysis was 20% (95% CI: 19% to 20%). The most frequent outcomes were increased healthcare utilization, reduced health-related quality of life, physical and mental functioning.

Conclusion

We identified a wide variance in the magnitude of multimorbidity across age groups and regions with most of the studies from eastern India. Nation-wide studies, studies on vulnerable populations and interventions are warranted.

Assigning disease clusters to people: A cohort study of the implications for understanding health outcomes in people with multiple long-term conditions

Background

Identifying clusters of co-occurring diseases may help characterise distinct phenotypes of Multiple Long-Term Conditions (MLTC). Understanding the associations of disease clusters with health-related outcomes requires a strategy to assign clusters to people, but it is unclear how the performance of strategies compare.

Aims

First, to compare the performance of methods of assigning disease clusters to people at explaining mortality, emergency department attendances and hospital admissions over one year. Second, to identify the extent of variation in the associations with each outcome between and within clusters.

Methods

We conducted a cohort study of primary care electronic health records in England, including adults with MLTC. Seven strategies were tested to assign patients to fifteen disease clusters representing 212 LTCs, identified from our previous work. We tested the performance of each strategy at explaining associations with the three outcomes over 1 year using logistic regression and compared to a strategy using the individual LTCs.

Results

6,286,233 patients with MLTC were included. Of the seven strategies tested, a strategy assigning the count of conditions within each cluster performed best at explaining all three outcomes but was inferior to using information on the individual LTCs. There was a larger range of effect sizes for the individual LTCs within the same cluster than there was between the clusters.

Conclusion

Strategies of assigning clusters of co-occurring diseases to people were less effective at explaining health-related outcomes than a person's individual diseases. Furthermore, clusters did not represent consistent relationships of the LTCs within them, which might limit their application in clinical research.

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

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

Systematic review and meta-analysis of disease clustering in multimorbidity: a study protocol

INTRODUCTION

Multimorbidity is defined as the presence of two or more chronic diseases. Co-occurring diseases can have synergistic negative effects, and are associated with significant impacts on individual health outcomes and healthcare systems. However, the specific effects of diseases in combination will vary between different diseases. Identifying which diseases are most likely to co-occur in multimorbidity is an important step towards population health assessment and development of policies to prevent and manage multimorbidity more effectively and efficiently. The goal of this project is to conduct a systematic review and meta-analysis of studies of disease clustering in multimorbidity, in order to identify multimorbid disease clusters and test their stability.

METHODS AND ANALYSIS

We will review data from studies of multimorbidity that have used data clustering methodologies to reveal patterns of disease co-occurrence. We propose a network-based meta-analytic approach to perform meta-clustering on a select list of chronic diseases that are identified as priorities for multimorbidity research. We will assess the stability of obtained disease clusters across the research literature to date, in order to evaluate the strength of evidence for specific disease patterns in multimorbidity.

ETHICS AND DISSEMINATION

This study does not require ethics approval as the work is based on published research studies. The study findings will be published in a peer-reviewed journal and disseminated through conference presentations and meetings with knowledge users in health systems and public health spheres.

PROSPERO REGISTRATION NUMBER

CRD42023411249.

Diabetes and Multiple Long-term Conditions: A Review of Our Current Global Health Challenge

Use of effective treatments and management programs is leading to longer survival of people with diabetes. This, in combination with obesity, is thus contributing to a rise in people living with more than one condition, known as multiple long-term conditions (MLTC or multimorbidity). MLTC is defined as the presence of two or more long-term conditions, with possible combinations of physical, infectious, or mental health conditions, where no one condition is considered as the index. These include a range of conditions such as cardiovascular diseases, cancer, chronic kidney disease, arthritis, depression, dementia, and severe mental health illnesses. MLTC has major implications for the individual such as poor quality of life, worse health outcomes, fragmented care, polypharmacy, poor treatment adherence, mortality, and a significant impact on health care services. MLTC is a challenge, where interventions for prevention and management are lacking a robust evidence base. The key research directions for diabetes and MLTC from a global perspective include system delivery and care coordination, lifestyle interventions and therapeutic interventions.

Multimorbidity associated with urinary incontinence among older women and men with complex needs in Aotearoa | New Zealand

AIMS

To investigate the association between multimorbidity and urinary incontinence (UI) among community living older adults with complex needs in sex-specific crude and adjusted analyses.

METHODS

Since 2012 in Aotearoa | New Zealand (NZ) all community-living older people with complex needs who require publicly funded assistance undergo a comprehensive standardized geriatric needs assessment using the interRAI-HC instrument. Consenting adults aged ≥65 years who undertook this assessment between July 5, 2012 and December 31, 2020 were investigated. Multimorbidity was defined as having ≥2 chronic conditions. Recent bladder incontinence episodes were elicited and UI dichotomized into continent and incontinent groups.

RESULTS

The study included 140 401 participants with an average age of 82.0 years (range: 65-107 years), of whom 85 746 (61.1%) were female. Overall, 36 185 (42.2%) females and 17 988 (32.9%) males reported UI. Participants had a median of 3 (range: 0-12) chronic conditions, with 109 135 (77.9%) classified as having multimorbidity. In adjusted modified Poisson regression analyses, the prevalence ratio for UI was 1.21 (95% confidence interval [CI]: 1.19, 1.24) times higher in females and 1.18 (95% CI: 1.14, 1.22) times higher for males with multimorbidity compared to those without multimorbidity.

CONCLUSIONS

Although significant, the estimated sex-specific effect sizes were modest for the association between multimorbidity and UI in this population. However, despite using the comprehensive interRAI-HC instrument, several potentially core chronic conditions were not adequately captured. Although increasingly recognized as an important and growing public health issue, capturing all relevant chronic conditions challenges many epidemiological investigations into multimorbidity.

Multimorbidity in rheumatoid arthritis: common mechanistic links and impact and challenges in routine clinical practice

Early identification and management of multimorbidity in patients with rheumatic and musculoskeletal diseases (RMDs), such as RA, is an integral, but often neglected, aspect of care. The prevalence and incidence of conditions such as osteoporosis, cardiovascular disease, pulmonary disease and malignancies, often co-existing with RA, continues to have significant implications for the management of this patient group. Multimorbidity in RMDs can be associated with inflammatory disease activity and target organ damage. Lifestyle factors, such as smoking and inactivity, further contribute to the burden of disease. Inflammation is the underlying factor, not just in RA but also many comorbidities. The current framework of a treat-to-target approach focuses on achieving early remission and inflammatory activity suppression. We describe how the comorbidity burden in people with RMDs impacts on disease outcome and treatment response. The importance of addressing comorbidity at an early stage and adopting a patient centred approach is critical in modern practice.

Prevalence and demographic variation of cardiovascular, renal, metabolic, and mental health conditions in 12 million english primary care records

BACKGROUND

Primary care electronic health records (EHR) are widely used to study long-term conditions in epidemiological and health services research. Therefore, it is important to understand how well the recorded prevalence of these conditions in EHRs, compares to other reliable sources overall, and varies by socio-demographic characteristics. We aimed to describe the prevalence and socio-demographic variation of cardiovascular, renal, and metabolic (CRM) and mental health (MH) conditions in a large, nationally representative, English primary care database and compare with prevalence estimates from other population-based studies.

METHODS

This was a cross-sectional study using the Clinical Practice Research Datalink (CPRD) Aurum primary care database. We calculated prevalence of 18 conditions and used logistic regression to assess how this varied by age, sex, ethnicity, and socio-economic status. We searched the literature for population prevalence estimates from other sources for comparison with the prevalences in CPRD Aurum.

RESULTS

Depression (16.0%, 95%CI 16.0-16.0%) and hypertension (15.3%, 95%CI 15.2-15.3%) were the most prevalent conditions among 12.4 million patients. Prevalence of most conditions increased with socio-economic deprivation and age. CRM conditions, schizophrenia and substance misuse were higher in men, whilst anxiety, depression, bipolar and eating disorders were more common in women. Cardiovascular risk factors (hypertension and diabetes) were more prevalent in black and Asian patients compared with white, but the trends in prevalence of cardiovascular diseases by ethnicity were more variable. The recorded prevalences of mental health conditions were typically twice as high in white patients compared with other ethnic groups. However, PTSD and schizophrenia were more prevalent in black patients. The prevalence of most conditions was similar or higher in the primary care database than diagnosed disease prevalence reported in national health surveys. However, screening studies typically reported higher prevalence estimates than primary care data, especially for PTSD, bipolar disorder and eating disorders.

CONCLUSIONS

The prevalence of many clinically diagnosed conditions in primary care records closely matched that of other sources. However, we found important variations by sex and ethnicity, which may reflect true variation in prevalence or systematic differences in clinical presentation and practice. Primary care data may underrepresent the prevalence of undiagnosed conditions, particularly in mental health.

Impact of individual, household, and area characteristics on health and social care outcomes for people with multimorbidity: Protocol for a multilevel analysis

BACKGROUND

Multimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes.

METHODS

The study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period.

DISCUSSION

Household and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources.

Multimorbidity in persons with non-traumatic spinal cord injury and its impact on healthcare utilization and health outcomes

STUDY DESIGN

Cross-sectional survey in Canada.

OBJECTIVES

To explore multimorbidity (the coexistence of two/more health conditions) in persons with non-traumatic spinal cord injury (NTSCI) and evaluate its impact on healthcare utilization (HCU) and health outcomes.

SETTING

Community-dwelling persons.

METHODS

Data from the Spinal Cord Injury Community Survey (SCICS) was used. A multimorbidity index (MMI) consisting of 30 secondary health conditions (SHCs), the 7-item HCU questionnaire, the Short Form-12 (SF-12), Life Satisfaction-11 first question, and single-item Quality of Life (QoL) measure were administered. Additionally, participants were grouped as "felt needed healthcare was received" (Group 1, n = 322) or "felt needed healthcare was not received" (Group 2, n = 89) using the HCU question. Associations among these variables were assessed using multivariable analysis.

RESULTS

408 of 412 (99%) participants with NTSCI reported multimorbidity. Constipation, spasticity, and fatigue were the most prevalent self-reported SHCs. Group 1 had a higher MMI score compared to Group 2 (p < 0.001). A higher MMI score correlated with the feeling of not receiving needed care (OR 1.4, 95% CI 1.08-1.21), lower SF-12 (physical/mental component summary scores), being unsatisfied with life, and lower QoL (all p < 0.001). Additionally, Group 1 had more females (p < 0.001), non-Caucasians (p = 0.034), and lower personal annual income (p = 0.025).

CONCLUSIONS

Persons with NTSCI have multimorbidity, and the MMI score was associated with increased HCU and worse health outcomes. This work emphasizes the critical need for improved healthcare and monitoring. Future work determining specific thresholds for the MMI could be helpful for triage screening to identify persons at higher risk of poor outcomes.

The development of a core outcome set for studies of pregnant women with multimorbidity

BACKGROUND

Heterogeneity in reported outcomes can limit the synthesis of research evidence. A core outcome set informs what outcomes are important and should be measured as a minimum in all future studies. We report the development of a core outcome set applicable to observational and interventional studies of pregnant women with multimorbidity.

METHODS

We developed the core outcome set in four stages: (i) a systematic literature search, (ii) three focus groups with UK stakeholders, (iii) two rounds of Delphi surveys with international stakeholders and (iv) two international virtual consensus meetings. Stakeholders included women with multimorbidity and experience of pregnancy in the last 5 years, or are planning a pregnancy, their partners, health or social care professionals and researchers. Study adverts were shared through stakeholder charities and organisations.

RESULTS

Twenty-six studies were included in the systematic literature search (2017 to 2021) reporting 185 outcomes. Thematic analysis of the focus groups added a further 28 outcomes. Two hundred and nine stakeholders completed the first Delphi survey. One hundred and sixteen stakeholders completed the second Delphi survey where 45 outcomes reached Consensus In (≥70% of all participants rating an outcome as Critically Important). Thirteen stakeholders reviewed 15 Borderline outcomes in the first consensus meeting and included seven additional outcomes. Seventeen stakeholders reviewed these 52 outcomes in a second consensus meeting, the threshold was ≥80% of all participants voting for inclusion. The final core outcome set included 11 outcomes. The five maternal outcomes were as follows: maternal death, severe maternal morbidity, change in existing long-term conditions (physical and mental), quality and experience of care and development of new mental health conditions. The six child outcomes were as follows: survival of baby, gestational age at birth, neurodevelopmental conditions/impairment, quality of life, birth weight and separation of baby from mother for health care needs.

CONCLUSIONS

Multimorbidity in pregnancy is a new and complex clinical research area. Following a rigorous process, this complexity was meaningfully reduced to a core outcome set that balances the views of a diverse stakeholder group.

Cardiometabolic multimorbidity and associated patterns of healthcare utilization and quality of life: Results from the Study on Global AGEing and Adult Health (SAGE) Wave 2 in Ghana

Understanding the patterns of multimorbidity, defined as the co-occurrence of more than one chronic condition, is important for planning health system capacity and response. This study assessed the association of different cardiometabolic multimorbidity combinations with healthcare utilization and quality of life (QoL). Data were from the World Health Organization (WHO) study on global AGEing and adult health Wave 2 (2015) conducted in Ghana. We analysed the clustering of cardiometabolic diseases including angina, stroke, type 2 diabetes, and hypertension with unrelated conditions such as asthma, chronic lung disease, arthritis, cataract and depression. The clusters of adults with cardiometabolic multimorbidity were identified using latent class analysis and agglomerative hierarchical clustering algorithms. We used negative binomial regression to determine the association of multimorbidity combinations with outpatient visits. The association of multimorbidity clusters with hospitalization and QoL were assessed using multivariable logistic and linear regressions. Data from 3,128 adults aged over 50 years were analysed. We identified four distinct classes of multimorbidity: relatively "healthy class" with no multimorbidity (47.9%): abdominal obesity only (40.7%): cardiometabolic and arthritis class comprising participants with hypertension, type 2 diabetes, stroke, abdominal and general obesity, arthritis and cataract (5.7%); and cardiopulmonary and depression class including participants with angina, chronic lung disease, asthma, and depression (5.7%). Relative to the class with no multimorbidity, the cardiopulmonary and depression class was associated with a higher frequency of outpatient visits [β = 0.3; 95% CI 0.1 to 0.6] and higher odds of hospitalization [aOR = 1.9; 95% CI 1.0 to 3.7]. However, cardiometabolic and arthritis class was associated with a higher frequency of outpatient visits [β = 0.8; 95% CI 0.3 to 1.2] and not hospitalization [aOR = 1.1; 95% CI 0.5 to 2.9]. The mean QoL scores was lowest among participants in the cardiopulmonary and depression class [β = -4.8; 95% CI -7.3 to -2.3] followed by the cardiometabolic and arthritis class [β = -3.9; 95% CI -6.4 to -1.4]. Our findings show that cardiometabolic multimorbidity among older persons in Ghana cluster together in distinct patterns that differ in healthcare utilization. This evidence may be used in healthcare planning to optimize treatment and care.

Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service

BACKGROUND

Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources.

METHODS

A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality.

RESULTS

Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%).

CONCLUSIONS

The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.

Multimorbidity and Serological Response to SARS-CoV-2 Nine Months after 1st Vaccine Dose: European Cohort of Healthcare Workers-Orchestra Project

Understanding antibody persistence concerning multimorbidity is crucial for vaccination policies. Our goal is to assess the link between multimorbidity and serological response to SARS-CoV-2 nine months post-first vaccine. We analyzed Healthcare Workers (HCWs) from three cohorts from Italy, and one each from Germany, Romania, Slovakia, and Spain. Seven groups of chronic diseases were analyzed. We included 2941 HCWs (78.5% female, 73.4% ≥ 40 years old). Multimorbidity was present in 6.9% of HCWs. The prevalence of each chronic condition ranged between 1.9% (cancer) to 10.3% (allergies). Two regression models were fitted, one considering the chronic conditions groups and the other considering whether HCWs had diseases from ≥2 groups. Multimorbidity was present in 6.9% of HCWs, and higher 9-months post-vaccine anti-S levels were significantly associated with having received three doses of the vaccine (RR = 2.45, CI = 1.92-3.13) and with having a prior COVID-19 infection (RR = 2.30, CI = 2.15-2.46). Conversely, lower levels were associated with higher age (RR = 0.94, CI = 0.91-0.96), more time since the last vaccine dose (RR = 0.95, CI = 0.94-0.96), and multimorbidity (RR = 0.89, CI = 0.80-1.00). Hypertension is significantly associated with lower anti-S levels (RR = 0.87, CI = 0.80-0.95). The serological response to vaccines is more inadequate in individuals with multimorbidity.

Lifestyle factors related to prevalent chronic disease multimorbidity: A population-based cross-sectional study

BACKGROUND

Multimorbidity is associated with poor quality of life, polypharmacy, health care costs and mortality, with those affected potentially benefitting from a healthy lifestyle. We assessed a comprehensive set of lifestyle factors in relation to multimorbidity with major chronic diseases.

METHODS

This cross-sectional study utilised baseline data for adults from the prospective Lifelines Cohort in the north of the Netherlands (N = 79,345). We defined multimorbidity as the co-existence of two or more chronic diseases (i.e. cardiovascular disease, cancer, respiratory disease, type 2 diabetes) and evaluated factors in six lifestyle domains (nutrition, physical (in)activity, substance abuse, sleep, stress, relationships) among groups by the number of chronic diseases (≥2, 1, 0). Multinomial logistic regression models were created, adjusted for appropriate confounders, and odds ratios (OR) with 95% confidence intervals (95%CI) were reported.

RESULTS

3,712 participants had multimorbidity (4.7%, age 53.5 ± 12.5 years), and this group tended to have less healthy lifestyles. Compared to those without chronic diseases, those with multimorbidity reported physical inactivity more often (OR, 1.15; 95%CI, 1.06-1.25; not significant for one condition), chronic stress (OR, 2.14; 95%CI, 1.92-2.38) and inadequate sleep (OR, 1.70; 95%CI, 1.41-2.06); as expected, they more often watched television (OR, 1.70; 95%CI, 1.42-2.04) and currently smoked (OR, 1.91; 95%CI, 1.73-2.11), but they also had lower alcohol intakes (OR, 0.66; 95%CI, 0.59-0.74).

CONCLUSIONS

Chronic stress and poor sleep, in addition to physical inactivity and smoking, are lifestyle factors of great concern in patients with multimorbidity.

Sociodemographic and lifestyle determinants of multimorbidity among community-dwelling older adults: findings from 346,760 SHARE participants

BACKGROUND

This study aimed to investigate the prevalence of multimorbidity and its associated factors among the older population in China to propose policy recommendations for the management of chronic diseases in older adults.

METHODS

This study was conducted based on the 2021 Shenzhen Healthy Ageing Research (SHARE), and involved analysis of 346,760 participants aged 65 or older. Multimorbidity is defined as the presence of two or more clinically diagnosed or non self-reported chronic diseases among the eight chronic diseases surveyed in an individual. The Logistic analysis was adopted to explore the potential associated factors of multimorbidity.

RESULTS

The prevalences of obesity, hypertension, diabetes, anemia, chronic kidney disease, hyperuricemia, dyslipidemia and fatty liver disease were 10.41%, 62.09%, 24.21%, 12.78%, 6.14%, 20.52%, 44.32%, and 33.25%, respectively. The prevalence of multimorbidity was 63.46%. The mean count of chronic diseases per participant was 2.14. Logistic regression indicated that gender, age, marriage status, lifestyle (smoking status, drinking status, and physical activity), and socioeconomic status (household registration, education level, payment method of medical expenses) were the common predictors of multimorbidity for older adults, among which, being women, married, or engaged in physical activity was found to be a relative determinant as a protective factor for multimorbidity after the other covariates were controlled.

CONCLUSION

Multimorbidity is prevalent among older adults in Chinese. Guideline development, clinical management,and public intervention should target a group of diseases instead of a single condition.

Biological and functional multimorbidity-from mechanisms to management

Globally, the number of people with multiple co-occurring diseases will increase substantially over the coming decades, with important consequences for patients, carers, healthcare systems and society. Addressing this challenge requires a shift in the prevailing clinical, educational and scientific thinking and organization-with a strong emphasis on the maintenance of generalist skills to balance the specialization trends of medical education and research. Multimorbidity is not a single entity but differs quantitively and qualitatively across life stages, ethnicities, sexes, socioeconomic groups and geographies. Data-driven science that quantifies the impact of disease co-occurrence-beyond the small number of currently well-studied long-term conditions (such as cardiometabolic diseases)-can help illuminate the pathological diversity of multimorbidity and identify common, mechanistically related, and prognostically relevant clusters. Broader access to data opportunities across modalities and disciplines will catalyze vertical and horizontal integration of multimorbidity research, to enable reconfiguring of medical services, clinical trials, guidelines and research in a way that accounts for the complexity of multimorbidity-and provides efficient, joined-up services for patients.

Contribution of Frailty to Multimorbidity Patterns and Trajectories: Longitudinal Dynamic Cohort Study of Aging People

BACKGROUND

Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people.

OBJECTIVE

This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older.

METHODS

Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d'Informació pel Desenvolupament de la Investigació a l'Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period.

RESULTS

The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern.

CONCLUSIONS

Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.

What can death records tell us about multimorbidity?

BACKGROUND

Multimorbidity has been measured from many data sources which show that prevalence increases with age and is usually greater among women than men and in more recent periods. Analyses of multiple cause of death data have shown different patterns of multimorbidity associated with demographic and other characteristics.

METHODS

Deaths in Australia among over 1.7 million decedents aged 55+ were stratified into three types: medically certified deaths, coroner-referred deaths with natural underlying causes and coroner-referred deaths with external underlying causes. Multimorbidity was measured by prevalence of ≥2 causes and analysed over three periods based on administrative changes: 2006-2012, 2013-2016 and 2017-2018. Poisson regression was used to examine the influence of gender, age and period.

RESULTS

The prevalence of deaths with multimorbidity was 81.0% for medically certified deaths, 61.1% for coroner-referred deaths with natural underlying causes and 82.4% for coroner-referred deaths with external underlying causes. For medically certified deaths, multimorbidity increased with age: incidence rate ratio (IRR 1.070, 95% CI 1.068, 1.072) was lower for women than men (0.954, 95% CI 0.952, 0.956) and changed little over time. For coroner-referred deaths with natural underlying causes, multimorbidity showed the expected pattern increasing with age (1.066, 95% CI 1.062, 1.070) and being higher for women than men (1.025, 95% CI 1.015, 1.035) and in more recent periods. For coroner-referred deaths with external underlying causes, there were marked increases over time that differed by age group due to changes in coding processes.

CONCLUSION

Death records can be used to examine multimorbidity in national populations but, like other data sources, how the data were collected and coded impacts the conclusions.