Multi-morbidity in hospitalised older patients: who are the complex elderly?

BACKGROUND:No formal definition for the "complex elderly" exists; moreover, these older patients with high levels of multi-morbidity are not readily identified as such at point of hospitalisation, thus missing a valuable opportunity to manage the older patient appropriately within the hospital setting. OBJECTIVES:To empirically identify the complex elderly patient based on degree of multi-morbidity. DESIGN:Retrospective observational study using administrative data. SETTING:English hospitals during the financial year 2012-13. SUBJECTS:All admitted patients aged 65 years and over. METHODS:By using exploratory analysis (correspondence analysis) we identify multi-morbidity groups based on 20 target conditions whose hospital prevalence was ≥ 1%. RESULTS:We examined a total of 2788900 hospital admissions. Multi-morbidity was highly prevalent, 62.8% had 2 or more of the targeted conditions while 4.7% had six or more. Multi-morbidity increased with age from 56% (65-69yr age-groups) up to 67% (80-84yr age-group). The average multi-morbidity was 3.2±1.2 (SD). Correspondence analysis revealed 3 distinct groups of older patients. Group 1 (multi-morbidity ≤2), associated with cancer and/or metastasis; Group 2 (multi-morbidity of 3, 4 or 5), associated with chronic pulmonary disease, lung disease, rheumatism and osteoporosis; finally Group 3 with the highest level of multi-morbidity (≥6) and associated with heart failure, cerebrovascular accident, diabetes, hypertension and myocardial infarction. CONCLUSIONS:By using widely available hospital administrative data, we propose patients in Groups 2 and 3 to be identified as the complex elderly. Identification of multi-morbidity patterns can help to predict the needs of the older patient and improve resource provision

Facilitating coordinated care for multi-morbidity patients through integrated preventive Clinical Decision Support (C3-Cloud)

Introduction: A growing share of the population in OECD countries is of age 65 and over, expected to reach 22% by 2030 (compared to 15% in 2010). Life expectancy has also significantly increased. People at age of 65 are expected to live for an average of 21 and 17 years for women and men; an almost 40% increase since 1960. The profound success in improving life expectancy has resulted in a new set of challenges. Challenge: Shift of resources was necessary, redirected to address the complex needs of multi-morbidity patients. Furthermore, patients’ needs are not effectively met by current care models, which tend to operate in isolation. This results in static services that patients need to wander. It is common for patients to revisit all levels of care discussing their needs, and reconciling potentially conflicting objectives amongst their conditions (e.g., incompatible lifestyle goals, adverse drug effects and side-effects, undetected conditions). Optimal collaboration and coordination between professionals in the delivery of integrated care have become essential requirements for the provision of high-quality care. Coordinated care aims for the orderly arrangement of individual and group efforts providing unity of action in pursuit of a common goal. Method: C3-Cloud is an e-health based ICT system, offering integrated, patient-centred care, considering all aspects of multi-morbidity and creating a collaborative environment, for all involved stakeholders. The navel of the system consists of the patient care plan, a digital shared picture of the patients’ needs and care regime. The care plan allows all professionals to review and understand the implications of one condition in the presence of others; this by its nature is complex, containing a considerable amount of diverse information. Navigating, understanding, and interpreting all the information can be confounding. The C3-Cloud Clinical Decision Support Service (CDS) offers an automated means of interpreting the available data. CDSS connects to the care plan repository, and continuously searches records for relevant data. The algorithms and integration of recommendations to the service were reviewed and validated by clinicians. Human computer interaction methods were employed to ensure optimal interaction between C3-Cloud and its users. Results: C3-Cloud offers CDSS for diabetes, renal failure, depression and congenital heart failure, with over 300 rules and checks that deliver four best practice guidelines in parallel; whilst reconciling their objectives, and monitoring their outcomes. It creates warnings or recommendations for the patient as well as for formal and informal carers. Discussion and Conclusions: C3-Cloud offers a powerful way to ensure that subtle, as well as critical, information about the patient, is presented to healthcare professionals, along with guideline based recommendations. The rules reconcile potential conflicts amongst conditions. Combined with a single patient and professionals interface, it provides a seamless experience throughout the health and care service. The C3-Cloud CDS service provides support to three pilot sites throughout Europe, currently undergoing evaluation. Acknowledgements: C3-Cloud is funded from the EU Horizon 2020 research and innovation project C3-Cloud, under grant agreement No 6891810. This abstract is based on the work and material of the entire C3-Cloud consortium

Time's up. Descriptive epidemiology of multi-morbidity and time spent on health related activity by older Australians: a time use survey

Most Western health systems remain single illness orientated despite the growing prevalence of multi-morbidity. Identifying how much time people with multiple chronic conditions spend managing their health will help policy makers and health service providers make decisions about areas of patient need for support. This article presents findings from an Australian study concerning the time spent on health related activity by older adults (aged 50 years and over), most of whom had multiple chronic conditions. A recall questionnaire was developed, piloted, and adjusted. Sampling was undertaken through three bodies; the Lung Foundation Australia (COPD sub-sample), National Diabetes Services Scheme (Diabetes sub-sample) and National Seniors Australia (Seniors sub-sample). Questionnaires were mailed out during 2011 to 10,600 older adults living in Australia. 2540 survey responses were received and analysed. Descriptive analyses were completed to obtain median values for the hours spent on each activity per month. The mean number of chronic conditions was 3.7 in the COPD sub-sample, 3.4 in the Diabetes sub-sample and 2.0 in the NSA sub-sample. The study identified a clear trend of increased time use associated with increased number of chronic conditions. Median monthly time use was 5-16 hours per month overall for our three sub-samples. For respondents in the top decile with five or more chronic conditions the median time use was equivalent to two to three hours per day, and if exercise is included in the calculations, respondents spent from between five and eight hours per day: an amount similar to full-time work. Multi-morbidity imposes considerable time burdens on patients. Ageing is associated with increasing rates of multi-morbidity. Many older adults are facing high demands on their time to manage their health in the face of decreasing energy and mobility. Their time use must be considered in health service delivery and health system reform.This work was funded by the National Health and Medical Research Council ID (402793, 2006)

Projections of multi-morbidity in the older population in England to 2035: estimates from the Population Ageing and Care Simulation (PACSim) model

Background models projecting future disease burden have focussed on one or two diseases. Little is known on how risk factors of younger cohorts will play out in the future burden of multi-morbidity (two or more concurrent long-term conditions). Design a dynamic microsimulation model, the Population Ageing and Care Simulation (PACSim) model, simulates the characteristics (sociodemographic factors, health behaviours, chronic diseases and geriatric conditions) of individuals over the period 2014–2040. Population about 303,589 individuals aged 35 years and over (a 1% random sample of the 2014 England population) created from Understanding Society, the English Longitudinal Study of Ageing, and the Cognitive Function and Ageing Study II. Main outcome measures the prevalence of, numbers with, and years lived with, chronic diseases, geriatric conditions and multi-morbidity. Results between 2015 and 2035, multi-morbidity prevalence is estimated to increase, the proportion with 4+ diseases almost doubling (2015:9.8%; 2035:17.0%) and two-thirds of those with 4+ diseases will have mental ill-health (dementia, depression, cognitive impairment no dementia). Multi-morbidity prevalence in incoming cohorts aged 65–74 years will rise (2015:45.7%; 2035:52.8%). Life expectancy gains (men 3.6 years, women: 2.9 years) will be spent mostly with 4+ diseases (men: 2.4 years, 65.9%; women: 2.5 years, 85.2%), resulting from increased prevalence of rather than longer survival with multi-morbidity. Conclusions our findings indicate that over the next 20 years there will be an expansion of morbidity, particularly complex multi-morbidity (4+ diseases). We advocate for a new focus on prevention of, and appropriate and efficient service provision for those with, complex multi-morbidity

Chronic respiratory abnormalities in the multi-morbid frail elderly

Two-thirds of people aged 65 65 years have multi-morbidity, with people living in the most deprived areas developing multi-morbidity 10-15 years even earlier. Multi-morbidity is associated with higher mortality, polypharmacy and high treatment burden, higher rates of adverse drug events, and much greater health services use including emergency hospital admissions. Multi-morbidity includes both physical and mental health conditions, as anxiety and depression, that almost invariably affect patients with multiple symptomatic chronic diseases. The main message of the present paper is that the management of a patient with any of the chronic diseases that are part of multi-morbidity is not just the management of that single index disease, but must include the active search and proper treatment of concomitant chronic diseases. The presence of concomitant chronic diseases should not alter the management of the index disease (eg COPD), and concomitant chronic disease should be treated according to single diseases guidelines regardless of the presence of the index disease, obviously with careful consideration that this choice implies complex management, polypharmacy and potential adverse effects. Ongoing multidisciplinary hospital and home base management programmes suggest that an olistic integrated approach might improve quality of life and reduce hospital admissions and death in these multimorbid patients

Spatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain)

Introduction: The global burden of multi-morbidity has become a major public health challenge due to the multi stakeholder action required to its prevention and control. The Social Determinants of Health approach is the basis for the establishment of health as a cross-cutting element of public policies toward enhanced and more efficient decision making for prevention and management. Objective: To identify spatially varying relationships between the multi-morbidity of hypertension and diabetes and the sociodemographic settings (2015–2019) in Aragon (a mediterranean region of Northeastern Spain) from an ecological perspective. Materials and methods: First, we compiled data on the prevalence of hypertension, diabetes, and sociodemographic variables to build a spatial geodatabase. Then, a Principal Component Analysis (PCA) was performed to derive regression variables, i.e., aggregating prevalence rates into a multi-morbidity component (stratified by sex) and sociodemographic covariate into a reduced but meaningful number of factors. Finally, we applied Geographically Weighted Regression (GWR) and cartographic design techniques to investigate the spatial variability of the relationships between multi-morbidity and sociodemographic variables. Results: The GWR models revealed spatial explicit relationships with large heterogeneity. The sociodemographic environment participates in the explanation of the spatial behavior of multi-morbidity, reaching maximum local explained variance (R2) of 0.76 in men and 0.91 in women. The spatial gradient in the strength of the observed relationships was sharper in models addressing men’s prevalence, while women’s models attained more consistent and higher explanatory performance. Conclusion: Modeling the prevalence of chronic diseases using GWR enables to identify specific areas in which the sociodemographic environment is explicitly manifested as a driving factor of multi-morbidity. This is step forward in supporting decision making as it highlights multi-scale contexts of vulnerability, hence allowing specific action suitable to the setting to be taken

Long-term disease interactions amongst surgical patients: a population cohort study.

BACKGROUND: The average age of the surgical population continues to increase, as does prevalence of long-term diseases. However, outcomes amongst multi-morbid surgical patients are not well described. METHODS: We included adults undergoing non-obstetric surgical procedures in the English National Health Service between January 2010 and December 2015. Patients could be included multiple times in sequential 90-day procedure spells. Multi-morbidity was defined as presence of two or more long-term diseases identified using a modified Charlson comorbidity index. The primary outcome was 90-day postoperative death. Secondary outcomes included emergency hospital readmission within 90 days. We calculated age- and sex-adjusted odds ratios (OR) with 95% confidence intervals (CI) using logistic regression. We compared the outcomes associated with different disease combinations. RESULTS: We identified 20 193 659 procedure spells among 13 062 715 individuals aged 57 (standard deviation 19) yr. Multi-morbidity was present among 2 577 049 (12.8%) spells with 195 965 deaths (7.6%), compared with 17 616 610 (88.2%) spells without multi-morbidity with 163 529 deaths (0.9%). Multi-morbidity was present in 1 902 859/16 946 808 (11.2%) elective spells, with 57 663 deaths (2.7%, OR 4.9 [95% CI: 4.9-4.9]), and 674 190/3 246 851 (20.7%) non-elective spells, with 138 302 deaths (20.5%, OR 3.0 [95% CI: 3.0-3.1]). Emergency readmission followed 547 399 (22.0%) spells with multi-morbidity compared with 1 255 526 (7.2%) without. Multi-morbid patients accounted for 57 663/114 783 (50.2%) deaths after elective spells, and 138 302/244 711 (56.5%) after non-elective spells. The rate of death varied five-fold from lowest to highest risk disease pairs. CONCLUSION: One in eight patients undergoing surgery have multi-morbidity, accounting for more than half of all postoperative deaths. Disease interactions amongst multi-morbid patients is an important determinant of patient outcome

Multi-morbidity and its association with common cancer diagnoses: a UK Biobank prospective study

Background Whilst multi-morbidity is known to be a concern in people with cancer, very little is known about the risk of cancer in multi-morbid patients. This study aims to investigate the risk of being diagnosed with lung, colorectal, breast and prostate cancer associated with multi-morbidity. Methods We investigated the association between multi-morbidity and subsequent risk of cancer diagnosis in UK Biobank. Cox models were used to estimate the relative risks of each cancer of interest in multi-morbid participants, using the Cambridge Multimorbidity Score. The extent to which reverse causation, residual confounding and ascertainment bias may have impacted on the findings was robustly investigated. Results Of the 436,990 participants included in the study who were cancer-free at baseline, 21.6% (99,965) were multi-morbid (≥ 2 diseases). Over a median follow-up time of 10.9 [IQR 10.0–11.7] years, 9,019 prostate, 7,994 breast, 5,241 colorectal, and 3,591 lung cancers were diagnosed. After exclusion of the first year of follow-up, there was no clear association between multi-morbidity and risk of colorectal, prostate or breast cancer diagnosis. Those with ≥ 4 diseases at recruitment had double the risk of a subsequent lung cancer diagnosis compared to those with no diseases (HR 2.00 [95% CI 1.70–2.35] p for trend  Conclusions Individuals with multi-morbidity are at an increased risk of lung cancer diagnosis. While this association did not appear to be due to common sources of bias in observational studies, further research is needed to understand what underlies this association

Chronic diseases and multi-morbidity in persons experiencing homelessness: results from a cross-sectional study conducted at three humanitarian clinics in Germany in 2020

Background Persons experiencing homelessness (PEH) suffer a high burden of chronic diseases and multi-morbidity, yet face significant barriers in accessing healthcare services. These health inequalities were further aggravated during the COVID-19 pandemic. While there is a lack of comprehensive health data on PEH, even less is known about populations experiencing housing exclusion, a hidden form of homelessness. This study examines and compares chronic diseases and multi-morbidity in PEH, persons experiencing housing exclusion, and persons with secure housing who lacked access to regular healthcare services in the wake of the COVID-19 pandemic in Germany. Methods Study participants were adults who sought medical care at clinics of the humanitarian organisation “Ärzte der Welt” in Munich, Hamburg and Berlin in 2020. The patients were categorised into three housing groups according to the ETHOS classification of homelessness and housing exclusion. Socio-demographic characteristics, self-rated health, chronic diseases and multi-morbidity were described in each group. Logistic regression analysis was used to identify socio-demographic factors associated with higher odds of chronic diseases and multi-morbidity in each housing group. Results Of the 695 study participants, 333 experienced homelessness, 292 experienced housing exclusion and 70 had secure housing. 92.3% of all patients had either no or limited health coverage, and 96.7% were below the poverty line. Males and EU/EEA citizens were highly represented among PEH (74.2% and 56.8% respectively). PEH had lower self-rated health (47.8%, p = 0.04), and a higher prevalence of psychiatric illness (20.9%, p = 0.04). In adjusted analyses, belonging to the age group 35–49 and ≥ 50 years were associated with greater odds of chronic disease (AOR = 2.33, 95% CI = 1.68–3.24; AOR = 3.57, 95% CI = 2.55–5.01, respectively) while being ≥ 50 years old was associated with multi-morbidity (AOR = 2.01, 95% CI = 1.21, 3.33). Of the 18 participants tested for SARS-COV-2, 15 were PEH, 1 of whom tested positive. Conclusions Housing status was not an independent risk factor for chronic disease and multi-morbidity in our study population. However, PEH reported poorer self-rated and psychiatric health. Strategies to improve access to healthcare services amongst persons experiencing homelessness and housing exclusion are needed in Germany

Quantitative Neuropathological Assessment to Investigate Cerebral Multi-Morbidity

The aging brain is characterized by the simultaneous presence of multiple pathologies, and the prevalence of cerebral multi-morbidity increases with age. To understand the impact of each subtype of pathology and the combined effects of cerebral multi-morbidity on clinical signs and symptoms, large clinico-pathological correlative studies have been performed. However, such studies are often based on semi-quantitative assessment of neuropathological hallmark lesions. Here, we discuss some of the new methods for high-throughput quantitative neuropathological assessment. These methods combine increased quantitative rigor with the added technical capacity of computers and networked analyses. There are abundant new opportunities - with specific techniques that include slide scanners, automated microscopes, and tissue microarrays - and also potential pitfalls. We conclude that quantitative and digital neuropathologic approaches will be key resources to further elucidate cerebral multi-morbidity in the aged brain and also hold the potential for changing routine neuropathologic diagnoses