Patterns of multimorbidity in primary care patients at high risk of future hospitalization

Validating and Improving Adjusted Clinical Group's Future Hospitalization and High-Cost Prediction Models for Dutch Primary Care

The rise in health care costs, caused by older and more complex patient populations, requires Population Health Management approaches including risk stratification. With risk stratification, patients are assigned individual risk scores based on medical records. These patient stratifications focus on future high costs and expensive care utilization such as hospitalization, for which different models exist. With this study, the research team validated the accuracy of risk prediction scores for future hospitalization and high health care costs, calculated by the Adjusted Clinical Group (ACG)'s risk stratification models, using Dutch primary health care data registries. In addition, they aimed to adjust the US-based predictive models for Dutch primary care. The statistical validity of the existing models was assessed. In addition, the underlying prediction models were trained on 95,262 patients' data from de Zoetermeer region and externally validated on data of 48,780 patients from Zeist, Nijkerk, and Urk. Information on age, sex, number of general practitioner visits, International Classification of Primary Care coded information on the diagnosis and Anatomical Therapeutic Chemical Classification coded information on the prescribed medications, were incorporated in the model. C-statistics were used to validate the discriminatory ability of the models. Calibrating ability was assessed by visual inspection of calibration plots. Adjustment of the hospitalization model based on Dutch data improved C-statistics from 0.69 to 0.75, whereas adjustment of the high-cost model improved C-statistics from 0.78 to 0.85, indicating good discrimination of the models. The models also showed good calibration. In conclusion, the local adjustments of the ACG prediction models show great potential for use in Dutch primary care.

Multimorbidity resilience and health behaviors among older adults: A longitudinal study using the Canadian Longitudinal Study on Aging

Objective

There has been a growing interest in examining why some individuals adapt and bounce back from multimorbidity (resilience) better than others. This paper investigates the positive role of protective health behaviors on multimorbidity resilience (MR) among older adults focusing on older persons with two or more concurrent chronic conditions, and separately for three multimorbidity chronic illness clusters.

Methods

Using Baseline and Follow-up One data from the Comprehensive Cohort of the Canadian Longitudinal Study on Aging, we studied 10,628 participants aged 65 years and older who reported two or more of 27 chronic conditions, and three multimorbidity clusters: Cardiovascular/metabolic, Musculoskeletal, and Mental health. Associations between health behaviors and MR were evaluated using Linear Mixed Models, adjusting for socio-demographic, social/environmental, and illness context social determinants of health.

Results

Among older adults with two or more illnesses, smoking, satisfaction with sleep, appetite, and skipping meals were associated with MR in the expected direction. Also, obesity (compared to normal weight) and skipping meals showed longitudinal interaction effects with survey wave. Most of the results were replicated for the physical multimorbidity clusters (Cardiovascular/metabolic and Musculoskeletal) compared to the full 2+ multimorbidity analyses; however, for the Mental health cluster, only satisfaction with sleep was supported as a lifestyle predictor of MR.

Discussion

Several modifiable health behaviors identified in the broader health and aging literature are important in affecting levels of multimorbidity resilience in older age. These factors are important strength-based areas to target. Additionally, several social determinants of health are also supported and parallel research on multimorbidity risk. The effects of lifestyle factors for resilience among older adults is dependent on the type of multimorbidity measured. We conclude that the results have significant public health, program intervention, and clinical implications for healthy aging among persons coping with multimorbidity.

Association between pre-pregnancy multimorbidity and adverse maternal outcomes: A systematic review

Objective

We reviewed the literature on the association between pre-pregnancy multimorbidity (co-occurrence of two or more chronic conditions) and adverse maternal outcomes in pregnancy and postpartum.

Data sources

Medline, EMBASE, and CINAHL were searched from inception to September, 2021.

Study selection

Observational studies were eligible if they reported on the association between ≥ 2 co-occurring chronic conditions diagnosed before conception and any adverse maternal outcome in pregnancy or within 365 days of childbirth, had a comparison group, were peer-reviewed, and were written in English.

Data extraction and synthesis

Two reviewers used standardized instruments to extract data and rate study quality and the certainty of evidence. A narrative synthesis was performed.

Results

Of 6,381 studies retrieved, seven met our criteria. There were two prospective cohort studies, two retrospective cohort studies, and 3 cross-sectional studies, conducted in the United States (n=6) and Canada (n=1), and ranging in size from n=3,110 to n=57,326,681. Studies showed a dose-response relation between the number of co-occurring chronic conditions and risk of adverse maternal outcomes, including severe maternal morbidity or mortality, hypertensive disorders of pregnancy, and acute health care use in the perinatal period. Study quality was rated as strong (n=1), moderate (n=4), or weak (n=2), and the certainty of evidence was very low to moderate.

Conclusion

Given the increasing prevalence of chronic disease risk factors such as advanced maternal age and obesity, more research is needed to understand the impact of pre-pregnancy multimorbidity on maternal health so that appropriate preconception and perinatal supports can be developed.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Caminho na rede formal de cuidado em saúde de pacientes pós-alta hospitalar segundo multimorbidade

Introdução: A gestão de informações associadas à multimorbidade na atenção hospitalar é relevante para o planejamento de estratégias de prevenção de agravos à saúde em pacientes de maior risco, a fim de oportunizar a organização de sistemas de saúde de modo eficiente. Objetivo: O presente trabalho objetivoudelinear o caminho formal percorrido por pacientes com e sem multimorbidade, considerando o uso da rede pública de saúde após a internação hospitalar. Materiais e métodos: Realizou-se um estudo quantitativo, transversal, descritivo, utilizando dados primários de 445 pacientes internados em um hospital universitário, no ano de 2018. Os dados foram coletados através de análise do prontuário médico e entrevista telefônica. Os resultados foram analisados por meio de frequência absoluta e relativa. Resultados: Desenvolveu-se um fluxograma, representando os pontos da rede de saúde utilizados pelo paciente após a alta hospitalar, segundo multimorbidade. Verificou-se uma alta prevalência de encaminhamento (com multimorbidade (CM) 93,52%; sem multimorbidade (SM) 97,71%) e comparecimento na atenção secundária à saúde (CM 86,15%; SM 89,63%), um baixo encaminhamento (CM 42,45%; SM 36,27%) e comparecimento na atenção primária à saúde (CM 61,29%; SM 64,81%), e considerando os 3 níveis de atenção juntos, houve um baixo comparecimento (CM 17,98%; SM 21,89%) para ambos os grupos investigados. Discussão: Entende-se que a semelhança de comparecimento em todos os pontos da rede por ambos os grupos é um problema, por tratar de forma igual populações desiguais e consequentemente com necessidades diversas. Conclusões: Sinaliza-se a importância de maior incentivo ao acompanhamento de pacientes com multimorbidade na rede primária de saúde, especialmente no período de pós-alta hospitalar, e a necessidade de fortalecimento da Rede de Atenção à Saúde.

Social Determinants and Health Behaviours among Older Adults Experiencing Multimorbidity Using the Canadian Longitudinal Study on Aging

This study examines associations between lifestyle behavioural factors and appraisals of “healthy aging” among older adults experiencing multimorbidity. A Social Determinants and Health Behaviour Model (SDHBM) is used to frame the analyses. Using baseline data from the Canadian Longitudinal Study on Aging (CLSA), we studied 12,272 Canadians 65 years of age or older who reported 2 or more of 27 chronic conditions. Additional analyses were conducted using three multimorbidity clusters: cardiovascular/metabolic, musculoskeletal, and mental health. Using hierarchical logistic regression, it was found that, for multmorbidity and the three illness clusters, healthy aging is consistently associated with not smoking (except for the mental health cluster), an absence of obesity (except for the cardiovascular and metabolic cluster), better sleep, and a better appetite. It is not associated with inactivity. Several socio-demographic, environmental, and illness covariates were also supported. The findings are examined using the SDHBM coupled with a resilience lens in order to elucidate how modifiable health behaviours can act as resources to mitigate multimorbidity adversities. This has implications for healthy aging for persons with multimorbidity, especially during the COVID-19 pandemic.

A systematic review of risk stratification tools internationally used in primary care settings

BACKGROUND AND AIMS

In our current healthcare situation, burden on healthcare services is increasing, with higher costs and increased utilization. Structured population health management has been developed as an approach to balance quality with increasing costs. This approach identifies sub-populations with comparable health risks, to tailor interventions for those that will benefit the most. Worldwide, the use of routine healthcare data extracted from electronic health registries for risk stratification approaches is increasing. Different risk stratification tools are used on different levels of the healthcare continuum. In this systematic literature review, we aimed to explore which tools are used in primary healthcare settings and assess their performance.

METHODS

We performed a systematic literature review of studies applying risk stratification tools with health outcomes in primary care populations. Studies in Organisation for Economic Co-operation and Development countries published in English-language journals were included. Search engines were utilized with keywords, for example, "primary care," "risk stratification," and "model." Risk stratification tools were compared based on different measures: area under the curve (AUC) and C-statistics for dichotomous outcomes and R 2 for continuous outcomes.

RESULTS

The search provided 4718 articles. Specific election criteria such as primary care populations, generic health utilization outcomes, and routinely collected data sources identified 61 articles, reporting on 31 different models. The three most frequently applied models were the Adjusted Clinical Groups (ACG, n = 23), the Charlson Comorbidity Index (CCI, n = 19), and the Hierarchical Condition Categories (HCC, n = 7). Most AUC and C-statistic values were above 0.7, with ACG showing slightly improved scores compared with the CCI and HCC (typically between 0.6 and 0.7).

CONCLUSION

Based on statistical performance, the validity of the ACG was the highest, followed by the CCI and the HCC. The ACG also appeared to be the most flexible, with the use of different international coding systems and measuring a wider variety of health outcomes.

Multimorbidity patterns in people with HIV

PURPOSE OF REVIEW

With the progressive aging of populations of people with HIV (PWH), multimorbidity is increasing. Multimorbidity patterns, that is groups of comorbidities that are likely to co-occur, may suggest shared causes or common risk factors. We review the literature regarding multimorbidity patterns identified with data-driven approaches and discuss the methodology and potential implications of the findings.

RECENT FINDINGS

Despite the substantial heterogeneity in the methods used to identify multimorbidity patterns, patterns of mental health problems, cardiovascular diseases, metabolic disorders and musculoskeletal problems are consistently reported in the general population, with patterns of mental health problems, cardiovascular diseases or metabolic disorders commonly reported in PWH. In addition to these, patterns of lifestyle-related comorbidities, such as sexually transmitted diseases, substance use (alcohol, recreational drugs and tobacco smoking) or their complications, seem to occur among PWH.

SUMMARY

Multimorbidity patterns could inform the development of appropriate guidelines for the prevention, monitoring and management of multiple comorbidities in PWH. They can also help to generate new hypotheses on the causes underlying previously known and unknown associations between comorbidities and facilitate the identification of risk factors and biomarkers for specific patterns.

Multimorbidity and leisure-time physical activity over the life course: a population-based birth cohort study

BACKGROUND

We aimed to test which life course model best described the association between leisure-time physical activity (LTPA) and multimorbidity at age 55. We analyzed data from birth to age 55 using the database from the 1958 National Child Development Survey.

METHODS

Multimorbidity was considered as the presence of more than one chronic condition. LTPA was measured through questionnaires from 1965 (age 7) to 2013 (age 55), which were applied in eight different occasions. We compared the fit of a series of nested adjusted logistic regression models (representing either the critical, accumulation or sensitive period models) with a fully saturated model. Data were reported as odds ratio (OR) and 95% confidence interval (CI).

RESULTS

From an eligible sample of 15,613 cohort members, 9137 were interviewed in the latest sweep (58.5%). Men were more physically active than women at ages 11, 16, and 23 (p < 0.001). LTPA every day in the week was more frequent in women than men in ages 33, 42, and 50 (p < 0.001). The prevalence of multimorbidity at age 55 was 33.0% (n = 2778). The sensitive analysis revealed that LTPA during adolescence (OR: 0.83; 95% CI: 0.70, 0.98) and mid adult life (age 50 and 55; OR: 0.82; 95%CI: 0.69, 0.98) have a stronger effect on the risk for multimorbidity at age 55 considering all other life stages in the model. Also, adolescence showed a critical independent effect on the risk for multimorbidity (OR: 0.82; 95%CI: 0.70, 0.97). No difference was found between those models.

CONCLUSIONS

These data support the notion of a protective physical activity "legacy" at early ages of childhood against multimorbidity at older ages. We highlight the need for LTPA promotion through intervention tailored especially on schooling and older ages in order to reduce the burden of multimorbidity.

Description of multimorbidity clusters of admitted patients in medical departments of a general hospital

OBJECTIVE

We aim to identify patterns of disease clusters among inpatients of a general hospital and to describe the characteristics and evolution of each group.

METHODS

We used two data sets from the CMBD (Conjunto mínimo básico de datos - Minimum Basic Hospital Data Set (MBDS)) of the Lucus Augusti Hospital (Spain), hospitalisations and patients, realising a retrospective cohort study among the 74 220 patients discharged from the Medic Area between 01 January 2000 and 31 December 2015. We created multimorbidity clusters using multiple correspondence analysis.

RESULTS

We identified five clusters for both gender and age. Cluster 1: alcoholic liver disease, alcoholic dependency syndrome, lung and digestive tract malignant neoplasms (age under 50 years). Cluster 2: large intestine, prostate, breast and other malignant neoplasms, lymphoma and myeloma (age over 70, mostly males). Cluster 3: malnutrition, Parkinson disease and other mobility disorders, dementia and other mental health conditions (age over 80 years and mostly women). Cluster 4: atrial fibrillation/flutter, cardiac failure, chronic kidney failure and heart valve disease (age between 70-80 and mostly women). Cluster 5: hypertension/hypertensive heart disease, type 2 diabetes mellitus, ischaemic cardiomyopathy, dyslipidaemia, obesity and sleep apnea, including mostly men (age range 60-80). We assessed significant differences among the clusters when gender, age, number of chronic pathologies, number of rehospitalisations and mortality during the hospitalisation were assessed (p<0001 in all cases).

CONCLUSIONS

We identify for the first time in a hospital environment five clusters of disease combinations among the inpatients. These clusters contain several high-incidence diseases related to both age and gender that express their own evolution and clinical characteristics over time.

Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis

Electronic Medical Records (EMRs) can be used to create alerts for clinicians to identify patients at risk and to provide useful information for clinical decision-making support. In this study, we proposed a novel approach for predicting Amyotrophic Lateral Sclerosis (ALS) based on comorbidities and associated indicators using EMRs. The medical histories of ALS patients were analyzed and compared with those of subjects without ALS, and the associated comorbidities were selected as features for constructing the machine learning and prediction model. We proposed a novel weighted Jaccard index (WJI) that incorporates four different machine learning techniques to construct prediction systems. Alternative prediction models were constructed based on two different levels of comorbidity: single disease codes and clustered disease codes. With an accuracy of 83.7%, sensitivity of 78.8%, specificity of 85.7%, and area under the receiver operating characteristic curve (AUC) value of 0.907 for the single disease code level, the proposed WJI outperformed the traditional Jaccard index (JI) and scoring methods. Incorporating the proposed WJI into EMRs enabled the construction of a prediction system for analyzing the risk of suffering a specific disease based on comorbidity combinatorial patterns, which could provide a fast, low-cost, and noninvasive evaluation approach for early diagnosis of a specific disease.

The incidence of multimorbidity and patterns in accumulation of chronic conditions: A systematic review

Multimorbidity, the presence of 1+ chronic condition in an individual, remains one of the greatest challenges to health on a global scale. Although the prevalence of multimorbidity has been well-established, its incidence is not fully understood. This systematic review determined the incidence of multimorbidity across the lifespan; the order in which chronic conditions accumulate to result in multimorbidity; and cataloged methods used to determine and report accumulation of chronic conditions resulting in multimorbidity. Studies were identified by searching MEDLINE, Embase, CINAHL, and Cochrane electronic databases. Two independent reviewers evaluated studies for inclusion and performed quality assessments. Of 36 included studies, there was high heterogeneity in study design and operational definitions of multimorbidity. Studies reporting incidence (n = 32) reported a median incidence rate of 30.7 per 1,000 person-years (IQR 39.5 per 1,000 person-years) and a median cumulative incidence of 2.8% (IQR 28.7%). Incidence was notably higher for persons with older age and 1+ chronic conditions at baseline. Studies reporting patterns in accumulation of chronic conditions (n = 5) reported hypertensive and heart diseases, and diabetes, as among the common starting conditions resulting in later multimorbidity. Methods used to discern patterns were highly heterogenous, ranging from the use of latent growth trajectories to divisive cluster analyses, and presentation using alluvial plots to cluster trajectories. Studies reporting the incidence of multimorbidity and patterns in accumulation of chronic conditions vary greatly in study designs and definitions used. To allow for more accurate estimations and comparison, studies must be transparent and consistent in operational definitions of multimorbidity applied.

Patterns of multimorbidity in medical inpatients: a multinational retrospective cohort study

Multimorbidity is frequent and represents a significant burden for patients and healthcare systems. However, there are limited data on the most common combinations of comorbidities in multimorbid patients. We aimed to describe and quantify the most common combinations of comorbidities in multimorbid medical inpatients. We used a large retrospective cohort of adults discharged from the medical department of 11 hospitals across 3 countries (USA, Switzerland, and Israel) between 2010 and 2011. Diseases were classified into acute versus chronic. Chronic diseases were grouped into clinically meaningful categories of comorbidities. We identified the most prevalent combinations of comorbidities and compared the observed and expected prevalence of the combinations. We assessed the distribution of acute and chronic diseases and the median number of body systems in relationship to the total number of diseases. Eighty-six percent (n = 126,828/147,806) of the patients were multimorbid (≥ 2 chronic diseases), with a median of five chronic diseases; 13% of the patients had ≥ 10 chronic diseases. Among the most frequent combinations of comorbidities, the most prevalent comorbidity was chronic heart disease. Other high prevalent comorbidities included mood disorders, arthropathy and arthritis, and esophageal disorders. The ratio of chronic versus acute diseases was approximately 2:1. Multimorbidity affected almost 90% of patients, with a median of five chronic diseases. Over 10% had ≥ 10 chronic diseases. This identification and quantification of frequent combinations of comorbidities among multimorbid medical inpatients may increase awareness of what should be taken into account when treating such patients, a growth in the need for special care considerations.

Identifying Populations with Complex Needs: Variation in Approaches Used to Select Complex Patient Populations

Interventions to support patients with complex needs are proliferating. However, little attention has been paid to methods for identifying complex patients. This study aims to summarize approaches used to define populations with complex needs in practice, by cataloging specific population criteria and organizing them into a taxonomy. The authors conducted a pragmatic review of literature published January 2000-December 2018 using PubMed. Search results were limited to English-language studies of adults that specified a set of objective criteria to identify a population with complex needs. The authors abstracted data from each article on population parameters, and conducted thematic analysis guided by deductive coding. The review identified 70 studies reflecting 90 unique complex population definitions. Complex populations criteria reflected 3 approaches: stratification, segmentation, and targeting. Six domains of population criteria were found within, including age-based criteria (59 populations); income (12); health care costs (45); health care utilization (39); health conditions (35); and subjective criteria (15). Criteria from multiple domains were frequently used in combination, and exact specifications were highly variable within each domain. Overall, 83% of the 90 population definitions included at least 1 cost- or utilization-based criterion. Nearly every study in the review presented a unique approach to identifying patients with complex needs but a limited number of "schools of thought" were found. Variability in definitions and inconsistent terminology are potential sources of ambiguity between stakeholders. Greater specificity and transparency in complex population definition would be a substantial contribution to the emerging field of complex care.

Role of Interprofessional primary care teams in preventing avoidable hospitalizations and hospital readmissions in Ontario, Canada: a retrospective cohort study

BACKGROUND

Improving health system value and efficiency are considered major policy priorities internationally. Ontario has undergone a primary care reform that included introduction of interprofessional teams. The purpose of this study was to investigate the relationship between receiving care from interprofessional versus non-interprofessional primary care teams and ambulatory care sensitive condition (ACSC) hospitalizations and hospital readmissions.

METHODS

Population-based administrative databases were linked to form data extractions of interest between the years of 2003-2005 and 2015-2017 in Ontario, Canada. The data sources were available through ICES. The study design was a retrospective longitudinal cohort. We used a "difference-in-differences" approach for evaluating changes in ACSC hospitalizations and hospital readmissions before and after the introduction of interprofessional team-based primary care while adjusting for physician group, physician and patient characteristics.

RESULTS

As of March 31st, 2017, there were a total of 778 physician groups, of which 465 were blended capitation Family Health Organization (FHOs); 177 FHOs (22.8%) were also interprofessional teams and 288 (37%) were more conventional group practices ("non-interprofessional teams"). In this period, there were a total of 13,480 primary care physicians in Ontario of whom 4848 (36%) were affiliated with FHOs-2311 (17.1%) practicing in interprofessional teams and 2537 (18.8%) practicing in non-interprofessional teams. During that same period, there were 475,611 and 618,363 multi-morbid patients in interprofessional teams and non-interprofessional teams respectively out of a total of 2,920,990 multi-morbid adult patients in Ontario. There was no difference in change over time in ACSC admissions between interprofessional and non-interprofessional teams between the pre- and post intervention periods. There were no statistically significant changes in all cause hospital readmission s between the post- and pre-intervention periods for interprofessional and non-interprofessional teams.

CONCLUSIONS

Our study findings indicate that the introduction of interprofessional team-based primary care was not associated with changes in ACSC hospitalization or hospital readmissions. The findings point for the need to couple interprofessional team-based care with other enablers of a strong primary care system to improve health services utilization efficiency.

Comorbidities in Osteoarthritis: A Systematic Review and Meta-Analysis of Observational Studies

OBJECTIVE

Osteoarthritis (OA) is a common chronic condition in older individuals, but its association with other chronic conditions is largely unknown. This study aimed to systematically review the literature on comorbidities in individuals with OA compared to those without.

METHODS

We searched 4 databases for observational studies on comorbidities in individuals with OA. Studies of OA only or in comparison with non-OA controls were included. The risk of bias and study quality were assessed using the Newcastle-Ottawa Scale. The prevalence of comorbidities in the OA group and the prevalence ratio (PR) and 95% confidence interval (95% CI) between OA and non-OA groups were calculated.

RESULTS

In all, 42 studies from 16 countries (27 case-only and 15 comparative studies) met the inclusion criteria. The mean age of participants varied from 51 to 76 years. The pooled prevalence of any comorbidity was 67% (95% CI 57-74) in individuals with OA versus 56% (95% CI 44-68) in individuals without OA. The pooled PR for any comorbidity was 1.21 (95% CI 1.02-1.45). The PR increased from 0.73 (95% CI 0.43-1.25) for 1 comorbidity to 1.58 (95% CI 1.03-2.42) for 2, and to 1.94 (95% CI 1.45-2.59) for ≥3 comorbidities. The key comorbidities associated with OA were stroke (PR 2.61 [95% CI 2.13-3.21]), peptic ulcer (PR 2.36 [95% CI 1.71-3.27]), and metabolic syndrome (PR 1.94 [95% CI 1.21-3.12]).

CONCLUSION

Individuals with OA are more likely to have other chronic conditions. The association is dose-dependent in terms of the number of comorbidities, suggesting multimorbidities. Further studies on the causality of this association and clinical implications are needed.

Evidence-based quality indicators for primary healthcare in association with the risk of hospitalisation: a population-based cohort study in Switzerland

OBJECTIVES

The quality of ambulatory care in Switzerland is widely unknown. Therefore, this study aimed to evaluate the recently proposed quality indicators (QIs) based on a nationwide healthcare claims database and determine their association with the risk of subsequent hospitalisation at patient-level.

DESIGN

Retrospective cohort study.

SETTING

Inpatient and outpatient claims data of a large health insurance in Switzerland covering all regions and population strata.

PARTICIPANTS

520 693 patients continuously insured during 2015 and 2016.

MEASURES

A total of 24 QIs were obtained by adapting the existing instruments to the Swiss national context and measuring at patient-level. The association between each QI and hospitalisation in the subsequent year was assessed using multiple logistic regression models.

RESULTS

The proportion of patients with good adherence to QIs was high for the secondary prevention of diabetes and myocardial infarction (glycated haemoglobin (HbA1c) control, 89%; aspirin use, 94%) but relatively low for polypharmacy (53%) or using potentially inappropriate medications (PIMs) in the elderly (PIM, 33%). Diabetes-related indicators such as the HbA1c control were significantly associated with a lower risk of hospitalisation (OR, 0.87; 95% CI, 0.80 to 0.95), whereas the occurrence of polypharmacy and PIM increased the risk of hospitalisation in the following year (OR, 1.57/1.08; 95% CI, 1.51 to 1.64/1.05 to 1.12).

CONCLUSIONS

This is the first study to evaluate the recently presented QIs in Switzerland using nationwide real-life data. Our study suggests that the quality of healthcare, as measured by these QIs, varied. The majority of QIs, in particular QIs reflecting chronic care and medication use, are considered beneficial markers of healthcare quality as they were associated with reduced risk of hospitalisation in the subsequent year. Results from this large practical test on real-life data show the feasibility of these QIs and are beneficial in selecting the appropriate QIs for healthcare implementation in general practice.

Evaluating quality of overall care among older adults with diabetes with comorbidities in Ontario, Canada: a retrospective cohort study

OBJECTIVES

This study aimed to: (1) explore whether the quality of overall care for older people with diabetes is differentially affected by types and number of comorbid conditions and (2) examine the association between process of care measures and the likelihood of all-cause hospitalisations.

DESIGN

A population-based, retrospective cohort study.

SETTING

The province of Ontario, Canada.

PARTICIPANTS

We identified 673 197 Ontarians aged 65 years and older who had diabetes comorbid with hypertension, chronic ischaemic heart disease, osteoarthritis or depression on 1 April 2010.

MAIN OUTCOME MEASURES

The study outcome was the likelihood of having at least one hospital admission in each year, during the study period, from 1 April 2010 to 3 March 2014. Process of care measures specific to older adults with diabetes and these comorbidities, developed by means of a Delphi panel, were used to assess the quality of care. A generalised estimating equations approach was used to examine associations between the process of care measures and the likelihood of hospitalisations.

RESULTS

The study findings suggest that patients are at risk of suboptimal care with each additional comorbid condition, while the incidence of hospitalisations and number of prescribed drugs markedly increased in patients with 2 versus 1 selected comorbid condition, especially in those with discordant comorbidities. The median continuity of care score was higher among patients with diabetes-concordant conditions compared with those with diabetes-discordant conditions, and it declined with additional comorbid conditions in both groups. Greater continuity of care was associated with lower hospital utilisation for older diabetes patients with both concordant and discordant conditions.

CONCLUSIONS

There is a need for focusing on improving continuity of care and prioritising treatment in older adults with diabetes with any multiple conditions but especially in those with diabetes-discordant conditions (eg, depression).

Health behaviors and multimorbidity resilience among older adults using the Canadian Longitudinal Study on Aging

OBJECTIVE

Recently, there has been a growing interest in examining forms of illness-related resilience. This study examines associations between lifestyle behavioral factors and multimorbidity resilience (MR) among older adults.

METHODS

Using baseline data from the Comprehensive Cohort of the Canadian Longitudinal Study on Aging, we studied 6,771 Canadian adults aged 65 or older who reported two or more of 27 chronic conditions, and three multimorbidity clusters: cardiovascular/metabolic, osteo-related, and mental health. Associations were explored using hierarchical linear regression modeling, controlling for sociodemographic, social/environmental, and illness context covariates.

RESULTS

Among older adults with two or more illnesses, as well as the cardiovascular/metabolic and osteo-related illness clusters, having a non-obese body mass, being a non-smoker, satisfaction with quality of sleep, having a good appetite, and not skipping meals are associated with MR. However, the mental-health cluster resulted in different behavioral lifestyle associations, where MR was not associated with obesity, smoking, or appetite, but inactivity demonstrated moderate positive associations with MR.

DISCUSSION

While there are similar patterns of lifestyle behaviors across multimorbidity and multimorbidity clusters involving physiological chronic illnesses, those associated with mental health are distinct. The results have implications for healthy aging among persons coping with multimorbidity.

Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older

Introduction

Multimorbidity associated with significant disease and economic burdens is common among the aged. We identified chronic disease multimorbidity patterns in Koreans 50 years of age or older, and explored whether such patterns were associated with particular sociodemographic factors and health-related quality-of-life.

Methods

The multimorbidity patterns of 10 chronic diseases (hypertension, dyslipidemia, stroke, osteoarthritis, tuberculosis, asthma, allergic rhinitis, depression, diabetes mellitus, and thyroid disease) were identified via latent class analysis of data on 8,370 Korean adults aged 50+ years who participated in the sixth Korean National Health and Nutrition Examination Survey (2013–2015). The associations between multimorbidity patterns, and sociodemographic factors and health-related quality of life, were subjected to regression analysis.

Results

Three patterns of multimorbidity were identified: 1) a relatively healthy group (60.4% of the population); 2) a ‘cardiometabolic conditions’ group (27.8%); and, 3) an ‘arthritis, asthma, allergic rhinitis, depression, and thyroid disease’ group (11.8%). The female (compared to male) gender was associated with an increased likelihood of membership of the cardiometabolic conditions group (odds ratio [OR] = 1.32, 95% confidence interval [CI] = 1.15–1.51) and (to a much greater extent) the arthritis, asthma, allergy, depression, and thyroid disease group (OR = 4.32, 95% CI = 3.30–5.66). Low socioeconomic status was associated with membership of the two multimorbidity classes. Membership of the arthritis, asthma, allergy, depression, and thyroid disease group was associated with a significantly poorer health-related quality-of-life than was membership of the other two groups.

Conclusion

The co-occurrence of chronic diseases was not attributable to chance. Multimorbidity patterns were associated with sociodemographic factors and quality-of-life. Our results suggest that targeted, integrated public health and clinical strategies dealing with chronic diseases should be based on an understanding of multimorbidity patterns; this would improve the quality-of-life of vulnerable multimorbid adults.

Medical diagnoses showed low relatedness in an explorative mutual information analysis of 190,837 inpatient cases

OBJECTIVES

We aimed to quantify the shared information between medical diagnoses of an adult inpatient population to explore both multimorbidity patterns and vice versa the unrelatedness of medical diagnoses.

STUDY DESIGN AND SETTING

This was a cross-sectional study, performed at a tertiary care center in Switzerland. Diagnoses were routinely coded using the International Classification of Diseases, 10th revision.

RESULTS

Among 190,837 inpatient cases, 7,994 unique diagnoses were coded. There were 31.9 million possible diagnosis pairs; the respective mutual information scores in diagnosis pairs were low (range, 10^-7 to 0.237). There were 148 pairs of diagnoses with a mutual information score higher than 0.01, which formed several clinically plausible disease clusters; 27.2% of cases did not have a diagnosis that belonged to one of the morbidity clusters.

CONCLUSION

In an explorative analysis, we observed a high unrelatedness of diagnoses in a tertiary-care inpatient population. This finding indicates that although multimorbidity patterns can be observed, inpatient cases frequently have further, unrelated diagnoses, which share little information with specific other diagnoses. Therefore, management of multimorbid patients should be individualized and may not be generalized based on a few multimorbidity patterns or clusters.

The contribution of musculoskeletal disorders in multimorbidity: Implications for practice and policy

People frequently live for many years with multiple chronic conditions (multimorbidity) that impair health outcomes and are expensive to manage. Multimorbidity has been shown to reduce quality of life and increase mortality. People with multimorbidity also rely more heavily on health and care services and have poorer work outcomes. Musculoskeletal disorders (MSDs) are ubiquitous in multimorbidity because of their high prevalence, shared risk factors, and shared pathogenic processes amongst other long-term conditions. Additionally, these conditions significantly contribute to the total impact of multimorbidity, having been shown to reduce quality of life, increase work disability, and increase treatment burden and healthcare costs. For people living with multimorbidity, MSDs could impair the ability to cope and maintain health and independence, leading to precipitous physical and social decline. Recognition, by health professionals, policymakers, non-profit organisations, and research funders, of the impact of musculoskeletal health in multimorbidity is essential when planning support for people living with multimorbidity.

Addressing the Evidence Gap in Stroke Rehabilitation for Complex Patients: A Preliminary Research Agenda

Evidence suggests that a stroke occurs in isolation (no comorbid conditions) in less than 6% of patients. Multimorbidity, compounded by psychosocial issues, makes treatment and recovery for stroke increasingly complex. Recent research and health policy documents called for a better understanding of the needs of this patient population, and for the development and testing of models of care that meet their needs. A research agenda specific to complexity is required. The primary objective of the think tank was to identify and prioritize research questions that meet the information needs of stakeholders, and to develop a research agenda specific to stroke rehabilitation and patient complexity. A modified Delphi and World Café approach underpinned the think tank meeting, approaches well recognized to foster interaction, dialogue, and collaboration between stakeholders. Forty-three researchers, clinicians, and policymakers attended a 2-day meeting. Initial question-generating activities resulted in 120 potential research questions. Sixteen high-priority research questions were identified, focusing on predetermined complexity characteristics-multimorbidity, social determinants, patient characteristics, social supports, and system factors. The final questions are presented as a prioritized research framework. An emergent result of this activity is the development of a complexity and stroke rehabilitation research network. The research agenda reflects topics of importance to stakeholders working with stroke patients with increasingly complex care needs. This robust process resulted in a preliminary research agenda that could provide policymakers with the evidence needed to make improvements toward better-organized services, better coordination between settings, improved patient outcomes, and lower system costs.

Prevalence and overlap of Disease Management Program diseases in older hospitalized patients

Many countries, like Denmark, have tailored Disease Management Programs (DMPs) based on patients having single chronic diseases [defined institutionally as "program diseases" (PDs)], which can complicate treatment for those with multiple chronic diseases. The aims of this study were (a) to assess the prevalence and overlap among acutely hospitalized older medical patients of PDs defined by the DMPs, and (b) to examine transitions between different departments during hospitalization and mortality and readmission within two time intervals among patients with the different PDs. We conducted a registry study of 4649 acutely hospitalized medical patients ≥65 years admitted to Copenhagen University Hospital, Hvidovre, Denmark, in 2012, and divided patients into six PD groups (type 2 diabetes, chronic obstructive pulmonary disease, cardiovascular disease, musculoskeletal disease, dementia and cancer), each defined by several ICD-10 codes predefined in the DMPs. Of these patients, 904 (19.4%) had 2 + PDs, and there were 47 different combinations of the six different PDs. The most prevalent pair of PDs was type 2 diabetes with cardiovascular disease in 203 (22.5%) patients, of whom 40.4% had an additional PD. The range of the cumulative incidence of being readmitted within 90 days was between 28.8% for patients without a PD and 46.6% for patients with more than one PD. PDs overlapped in many combinations, and all patients had a high probability of being readmitted. Hence, developing strategies to create a new generation of DMPs applicable to older patients with comorbidities could help clinicians organize treatment across DMPs.

Identifying High-Cost, High-Risk Patients Using Administrative Databases in Tuscany, Italy

Objective

(1) Assessing the performance of the algorithm in terms of sensitivity and positive predictive value, considering General Practitioners' (GPs) judgement as benchmark, and (2) describing adverse events (hospitalisation, death, and health services' consumption) of complex patients compared to the general population.

Data Sources

(i) Tuscany administrative database containing health data (2013-5); (ii) lists of complex patients indicated by GPs; and (iii) annual health registry of Tuscany.

Study Design

The present study is a validation study. It compares a list of complex patients extracted through an administrative algorithm (criteria of high health consumption) to a gold standard list of patients indicated by GPs. GPs' decision was subjective but fairly well reasoned. The study compares also adverse outcomes (Emergency Room visits, hospitalisation, and death) between identified complex patients and general population.

Principal Findings

Considering GPs' judgement, the algorithm showed a sensitivity of 72.8% and a positive predictive value of 64.4%. The complex cases presented here have higher incidence rates/100,000 (death 46.8; ER visits 223.2, hospitalisations 110.87, laboratory tests 1284.01, and specialist examinations 870.37) compared to the general population.

Conclusions

The final validated algorithm showed acceptable sensitivity and positive predictive value.

Patterns and Consequences of Multimorbidity in the General Population: There is No Chronic Disease Management Without Rheumatic Disease Management

OBJECTIVE

To identify empirical model-based patterns of multimorbidity from chronic noncommunicable diseases in the general population, with a focus on the contribution of rheumatic and musculoskeletal diseases (RMDs), and to quantify their association with adverse health outcomes.

METHODS

Cross-sectional data from the Portuguese Fourth National Health Survey were analyzed (n = 23,754). Latent class analysis was used to identify patterns of coexistence of 11 chronic noncommunicable diseases (RMDs, diabetes mellitus, hypertension, chronic obstructive pulmonary disease, stroke, depression, myocardial infarction, cancer, osteoporosis, asthma, and renal failure). Based on the Outcome Measures in Rheumatology, filter 2.0, health outcomes included life impact, pathophysiologic manifestations, and resource use. We assessed the association between patterns and adverse health outcomes, through sex-, age-, and body mass index-adjusted prevalence ratios with 95% confidence intervals, obtained using Poisson regression.

RESULTS

Four patterns of chronic noncommunicable diseases co-occurrence were identified and labeled as low disease probability, cardiometabolic conditions, respiratory conditions, and RMDs and depression. RMDs were highly prevalent in patients with chronic diseases (from 38.6% in cardiometabolic conditions to 66.7% in RMDs and depression). While negative self-rated health, short-term disability, and chronic pain were more strongly associated with cardiometabolic conditions and respiratory conditions, all multimorbidity patterns were similarly associated with long-term disability, frequent health care utilization, and out-of-pocket health care expenses.

CONCLUSION

Our study emphasizes RMDs as a major presence in multimorbidity in the general population. All multimorbidity patterns were associated with a wide set of adverse health outcomes. Management strategies for the patient with chronic cardiometabolic, respiratory, or depressive conditions should also target RMDs.

The association between multimorbidity and hospitalization is modified by individual demographics and physician continuity of care: a retrospective cohort study

Background

Multimorbidity poses a significant clinical challenge and has been linked to greater health services use, including hospitalization; however, we have little knowledge about the influence of contextual factors on outcomes in this population. Objectives: To describe the extent to which the association between multimorbidity and hospitalization is modified by age, gender, primary care practice model, or continuity of care (COC) among adults with at least one chronic condition.

Methods

A retrospective cohort study with linked population-based administrative data.

Setting: Ontario, Canada. Cohort: All individuals 18 and older with at least one of 16 priority chronic conditions as of April 1, 2009 (baseline). Main Outcome Measures: Any hospitalization, 3 or more hospitalizations, non-medical discharge delay, and 30-day readmission within the 1 year following baseline.

Results

Of 5,958,514 individuals, 484,872 (8.1 %) experienced 646,347 hospitalizations. There was a monotonic increase in the likelihood of hospitalization and related outcomes with increasing multimorbidity which was modified by age, gender, and COC but not primary care practice model. The effect of increasing multimorbidity was greater in younger adults than older adults and in those with lower COC than with higher COC. The effect of increasing multimorbidity on hospitalization was greater in men than women but reversed for the other outcomes.

Conclusions

The effect of multimorbidity on hospitalization is influenced by age and gender, important considerations in the development of person-centred care models. Greater continuity of physician care lessened the effect of multimorbidity on hospitalization, further demonstrating the need for care continuity across providers for people with chronic conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-016-1415-5) contains supplementary material, which is available to authorized users.

Depression-related treatment and costs in Germany: Do they change with comorbidity? A claims data analysis

BACKGROUND

Existing diverse bottom-up estimations of direct costs associated with depression in Germany motivated a detailed patient-level analysis of depression-related treatment (DRT), -costs (DRC) and Comorbidity.

METHODS

A large sickness fund's claims data was used to retrospectively identify patients aged 18-65 years with new-onset depression treatment between January 1st and February 15th 2010, and follow them until December 31st 2010, describe DRT, estimate associated DRC, and predict DRC with a generalised linear model.

RESULTS

A total of 18,139 patients were analysed. Mean direct DRC were €783. Predictors of DRC regarding psychiatric comorbidities were: "Delusion, psychotic disorders and personality disorders" (DRC-ratio 1.72), "Alcohol/drug addiction" (1.82), "abuse of alcohol/drugs" (1.57). Predictors of DRC regarding medical comorbidities were: "Rheumatoid arthritis" (0.77), "atherosclerosis" (0.65), "pregnancy" (0.66), and "Osteoarthritis" (1.87). Of all patients, 60.8% received their most intense/specialised DRT from a general practitioner, a medical specialist (23.7%), a psychotherapist (8.0%), a medical specialist and psychotherapist (2.9%), or in hospital (4.6%). Serious psychiatric comorbidity nearly tripled depression-related hospitalisation rates.

LIMITATIONS

Seasonal affective disorder and missing psychiatric outpatient clinic data must be considered.

CONCLUSIONS

Estimated DRC are significantly below the assessment of the German national guideline. Differing definitions of observation period and cost attribution might explain differing German DRC results. Signs of hospital psychiatric comorbidity bias indicate overestimation of hospital DRC. Identified associations of DRC with certain medical diseases in older adults warrant further research. Up to one quarter of patients with severe depression diagnosis might lack specialist treatment.

Multimorbidity Patterns in Elderly Primary Health Care Patients in a South Mediterranean European Region: A Cluster Analysis

OBJECTIVE

The purpose of this study was to identify clusters of diagnoses in elderly patients with multimorbidity, attended in primary care.

DESIGN

Cross-sectional study.

SETTING

251 primary care centres in Catalonia, Spain.

PARTICIPANTS

Individuals older than 64 years registered with participating practices.

MAIN OUTCOME MEASURES

Multimorbidity, defined as the coexistence of 2 or more ICD-10 disease categories in the electronic health record. Using hierarchical cluster analysis, multimorbidity clusters were identified by sex and age group (65-79 and ≥80 years).

RESULTS

322,328 patients with multimorbidity were included in the analysis (mean age, 75.4 years [Standard deviation, SD: 7.4], 57.4% women; mean of 7.9 diagnoses [SD: 3.9]). For both men and women, the first cluster in both age groups included the same two diagnoses: Hypertensive diseases and Metabolic disorders. The second cluster contained three diagnoses of the musculoskeletal system in the 65- to 79-year-old group, and five diseases coincided in the ≥80 age group: varicose veins of the lower limbs, senile cataract, dorsalgia, functional intestinal disorders and shoulder lesions. The greatest overlap (54.5%) between the three most common diagnoses was observed in women aged 65-79 years.

CONCLUSION

This cluster analysis of elderly primary care patients with multimorbidity, revealed a single cluster of circulatory-metabolic diseases that were the most prevalent in both age groups and sex, and a cluster of second-most prevalent diagnoses that included musculoskeletal diseases. Clusters unknown to date have been identified. The clusters identified should be considered when developing clinical guidance for this population.

Global Multimorbidity Patterns: A Cross-Sectional, Population-Based, Multi-Country Study

BACKGROUND

Population ageing challenges health care systems due to the high prevalence and impact of multimorbidity in older adults. However, little is known about how chronic conditions present in certain multimorbidity patterns, which could have great impact on public health at several levels. The aim of our study was to identify and describe multimorbidity patterns in low-, middle-, and high-income countries.

METHODS

We analyzed data from the Collaborative Research on Ageing in Europe project (Finland, Poland, and Spain) and the World Health Organization's Study on Global Ageing and Adult Health (China, Ghana, India, Mexico, Russia, and South Africa). These cross-sectional studies obtained data from 41,909 noninstitutionalized adults older than 50 years. Exploratory factor analysis was performed to detect multimorbidity patterns. Additional adjusted binary logistic regressions were performed to identify associations between sociodemographic factors and multimorbidity.

RESULTS

Overall multimorbidity prevalence was high across countries. Hypertension, cataract, and arthritis were the most prevalent comorbid conditions. Two or three multimorbidity patterns were found per country. Several patterns were identified across several countries: "cardio-respiratory" (angina, asthma, and chronic obstructive pulmonary disease), "metabolic" (diabetes, obesity, and hypertension), and "mental-articular" (arthritis and depression).

CONCLUSIONS

A high prevalence of multimorbidity occurs in older adults across countries, with low- and middle-income countries gradually approaching the figures of richer countries. Certain multimorbidity patterns are present in several countries, which suggest that common underlying etiopathogenic factors may play a role. Deeper understanding of these patterns may lead to the development of preventive actions to diminish their prevalence and also give rise to new, comprehensive approaches for the management of these co-occurring conditions.

National cohort study of opioid analgesic dose and risk of future hospitalization

BACKGROUND

High daily and total doses of opioid analgesics (OAs) increase the risk for drug overdose and may be risks for all-cause hospitalization.

OBJECTIVE

To examine the association of OA dose measures with future all-cause hospitalization.

DESIGN/PATIENTS

Cohort study of 87,688 national health maintenance organization enrollees aged 45 to 64 years with noncancer pain who filled ≥2 OA prescriptions from January 2009 to July 2012.

METHODS

Outcomes were all-cause hospitalization and hospital days in 6-month intervals after the first OA was filled. In generalized linear mixed models, we examined interactions of 5 daily OA dose categories and 5 total dose categories in each 6-month interval adjusted for demographics, clinical conditions, psychotropic drugs, and current hospitalization. For high total OA doses, percentage of days covered by OA prescriptions in 6 months was examined.

RESULTS

Over 3 years, an average of 12% of subjects were hospitalized yearly for a mean 6.5 (standard deviation = 8.5) days. Compared with no OAs, adjusted odds of future hospitalization for high total opioid dose (>1830 mg) were 35% to 44% greater depending on daily dose category (all P < 0.05), but total OA dose ≤1830 mg had weak or no association with future hospitalization regardless of daily OA dose. For high total OA doses, odds of hospitalization were 41% to 51% greater for categories of percentage of time on OAs above >50% (>3 months) versus no OAs (all P < 0.05). Similar effects were observed for hospital days.

CONCLUSIONS

Higher total OA doses for >3 months within a 6-month period significantly increased the risk for all-cause hospitalization and longer inpatient stays in the next 6 months.

Aging and Multimorbidity: New Tasks, Priorities, and Frontiers for Integrated Gerontological and Clinical Research

Aging is characterized by rising susceptibility to development of multiple chronic diseases and, therefore, represents the major risk factor for multimorbidity. From a gerontological perspective, the progressive accumulation of multiple diseases, which significantly accelerates at older ages, is a milestone for progressive loss of resilience and age-related multisystem homeostatic dysregulation. Because it is most likely that the same mechanisms that drive aging also drive multiple age-related chronic diseases, addressing those mechanisms may reduce the development of multimorbidity. According to this vision, studying multimorbidity may help to understand the biology of aging and, at the same time, understanding the underpinnings of aging may help to develop strategies to prevent or delay the burden of multimorbidity. As a consequence, we believe that it is time to build connections and dialogue between the clinical experience of general practitioners and geriatricians and the scientists who study aging, so as to stimulate innovative research projects to improve the management and the treatment of older patients with multiple morbidities.

A public health approach to musculoskeletal health

An ageing, physically inactive and increasingly obese population, coupled with finite health and social care resources, requires a shift from treating musculoskeletal disease when it arises to a preventive approach promoting lifelong musculoskeletal health. A public health approach to musculoskeletal health ensures that people are able to live not only long, but also well. Supporting self-management, addressing common misconceptions about the inevitability of musculoskeletal conditions, and offering brief interventions to support necessary lifestyle changes are basic public health functions that all health professionals can deliver. More specialist public health skills including needs assessment, data interpretation and service planning are also needed to deliver high quality services. These will require improvements in the data collected about musculoskeletal health nationally. A public health approach would benefit individuals through reduced pain and improved function due to musculoskeletal conditions, and wider society by minimising lost economic productivity and lowering health and social care costs.

Impact of medical and psychiatric multi-morbidity on mortality in diabetes: emerging evidence

BACKGROUND

Multi-morbidity, or the presence of multiple chronic diseases, is a major problem in clinical care and is associated with worse outcomes. Additionally, the presence of mental health conditions, such as depression, anxiety, etc., has further negative impact on clinical outcomes. However, most health systems are generally configured for management of individual diseases instead of multi-morbidity. The study examined the prevalence and differential impact of medical and psychiatric multi-morbidity on risk of death in adults with diabetes.

METHODS

A national cohort of 625,903 veterans with type 2 diabetes was created by linking multiple patient and administrative files from 2002 through 2006. The main outcome was time to death. Primary independent variables were numbers of medical and psychiatric comorbidities over the study period. Covariates included age, gender, race/ethnicity, marital status, area of residence, service connection, and geographic region. Cox regression was used to model the association between time to death and multi-morbidity adjusting for relevant covariates.

RESULTS

Hypertension (78%) and depression (13%) were the most prevalent medical and psychiatric comorbidities, respectively; 23% had 3+ medical comorbidities, 3% had 2+ psychiatric comorbidities and 22% died. Among medical comorbidities, mortality risk was highest in those with congestive heart failure (hazard ratio, HR = 1.92; 95% CI 1.89-1.95), Lung disease (HR = 1.42; 95% CI 1.40-1.44) and cerebrovascular disease (HR = 1.39; 95% CI 1.37-1.40). Among psychiatric comorbidities, mortality risk was highest in those with substance abuse (HR = 1.50; 95% CI 1.46-1.54), psychoses (HR = 1.16; 95% CI 1.14-1.19) and depression (HR = 1.05; 95% CI 1.03-1.07). There was an interaction between medical and psychiatric comorbidity (p = 0.003) so stratified analyses were performed. HRs for effect of 3+ medical comorbidity (2.63, 2.66, 2.15) remained high across levels of psychiatric comorbidities (0, 1, 2+), respectively. HRs for effect of 2+ psychiatric comorbidity (1.69, 1.63, 1.42, 1.38) declined across levels of medical comorbidity (0, 1, 2, 3+), respectively.

CONCLUSIONS

Medical and psychiatric multi-morbidity are significant predictors of mortality among older adults (veterans) with type 2 diabetes with a graded response as multimorbidity increases.

The association of comorbid conditions with patient-reported outcomes in Veterans with hip and knee osteoarthritis

There is limited understanding of how comorbid health conditions affect osteoarthritis (OA)-related outcomes. This study examined associations of different comorbidity measures with baseline OA-related patient-reported outcomes (PROs) among patients with hip and knee OA. Data were from patients (N = 300, 9 % female, mean age = 61.1; SD = 9.2) enrolled in a randomized control trial at the Durham Veterans Affairs Medical Center. Separate multivariable regression models, adjusted for demographic and clinical characteristics, examined the association of each comorbidity measure with baseline PROs: pain, physical function, depressive symptoms, fatigue, and insomnia. Comorbidity measures included the Self-Administered Comorbidity Questionnaire (SACQ), conditions reported as activity-limiting (SACQ-AL), and indicators of depression, diabetes, hypertension, and back pain. Mean (SD) numbers of comorbid conditions and activity-limiting conditions were 3.4 (1.8) and 1.6 (1.4), respectively. Comorbidity scores (SACQ overall and SACQ-AL) and individual comorbidity conditions were each associated with worse OA-related PROs adjusting for demographic and clinical factors. Worse SACQ overall and SACQ-AL scores were associated with worse mean scores for pain, depressive symptoms, fatigue, and insomnia (p values <0.01). Additionally, increasing SACQ-AL scores were associated with worse mean scores for function (p < 0.01). Depression was associated with worse pain (p = 0.03), fatigue, and insomnia (p values <0.01). Diabetes was associated with worse fatigue (p = 0.01), depressive symptoms (p = 0.02), and insomnia (p = 0.03). Back pain was associated with worse pain scores (p = 0.02). Results provide evidence that comorbidity burden, particularly activity-limiting conditions, is associated with worse OA-related PROs. Interventions for patients with OA need to address comorbid conditions and their impact on key outcomes.

Impact of multimorbidity: acute morbidity, area of residency and use of health services across the life span in a region of south Europe

Background

Concurrent diseases, multiple pathologies and multimorbidity patterns are topics of increased interest as the world’s population ages. To explore the impact of multimorbidity on affected patients and the consequences for health services, we designed a study to describe multimorbidity by sex and life-stage in a large population sample and to assess the association with acute morbidity, area of residency and use of health services.

Methods

A cross-sectional study was conducted in Catalonia (Spain). Participants were 1,749,710 patients aged 19+ years (251 primary care teams). Primary outcome: Multimorbidity (≥2 chronic diseases). Secondary outcome: Number of new events of each acute disease. Other variables: number of acute diseases per patient, sex, age group (19–24, 25–44, 45–64, 65–79, and 80+ years), urban/rural residence, and number of visits during 2010.

Results

Multimorbidity was present in 46.8% (95% CI, 46.7%-46.8%) of the sample, and increased as age increased, being higher in women and in rural areas. The most prevalent pair of chronic diseases was hypertension and lipid disorders in patients older than 45 years. Infections (mainly upper respiratory infection) were the most common acute diagnoses. In women, the highest significant RR of multimorbidity vs. non-multimorbidity was found for teeth/gum disease (aged 19–24) and acute upper respiratory infection. In men, this RR was only positive and significant for teeth/gum disease (aged 65–79). The adjusted analysis showed a strongly positive association with multimorbidity for the oldest women (80+ years) with acute diseases and women aged 65–79 with 3 or more acute diseases, compared to patients with no acute diseases (OR ranged from 1.16 to 1.99, p < 0.001). Living in a rural area was significantly associated with lower probability of having multimorbidity. The odds of multimorbidity increased sharply as the number of visits increased, reaching the highest probability in those aged 65–79 years.

Conclusions

Multimorbidity is related to greater use of health care services and higher incidence of acute diseases, increasing the burden on primary care services. The differences related to sex and life-stage observed for multimorbidity and acute diseases suggest that further research on multimorbidity should be stratified according to these factors.

Multimorbidity patterns: a systematic review

OBJECTIVES

The aim of this review was to identify studies on patterns of associative multimorbidity, defined as the nonrandom association between diseases, focusing on the main methodological features of the studies and the similarities among the detected patterns.

STUDY DESIGN AND SETTING

Studies were identified through MEDLINE and EMBASE electronic database searches from their inception to June 2012 and bibliographies.

RESULTS

The final 14 articles exhibited methodological heterogeneity in terms of the sample size, age and recruitment of study participants, the data source, the number of baseline diseases considered, and the statistical procedure used. A total of 97 patterns composed of two or more diseases were identified. Among these, 63 patterns were composed of three or more diseases. Despite the methodological variability among studies, this review demonstrated relevant similarities for three groups of patterns. The first one comprised a combination of cardiovascular and metabolic diseases, the second one was related with mental health problems, and the third one with musculoskeletal disorders.

CONCLUSION

The existence of associations beyond chance among the different diseases that comprise these patterns should be considered with the aim of directing future lines of research that measure their intensity, clarify their nature, and highlight the possible causal underlying mechanisms.

Multimorbidity in primary care in Portugal (MM-PT): a cross-sectional three-phase observational study protocol

INTRODUCTION

Multimorbidity is defined as the co-occurrence of more than one chronic disease in one person without assigning an index disease. This rapidly increasing phenomenon markedly influences patients' overall health, has major implications for effective provision of healthcare services and has a significant economic toll on individuals and society. Since Portugal is a country with a growing ageing population, a better understanding of the role of multimorbidity should be assessed. The aim of this study is to further the knowledge of the epidemiological factors associated with multimorbidity in Portugal, chiefly its prevalence and the health and social implications.

METHODS AND ANALYSIS

This study protocol describes a primary care nationwide three-phase study. The first phase is drawn to access the prevalence and patterns of multimorbidity. In the second phase, individual parameters are assessed, such as patients' health-related quality of life, perceived family support and unmet health needs of patients with multimorbidity. The third and last phase of this study aims to characterise general practitioners' knowledge, awareness and practices related to multimorbidity management.

ETHICS AND DISSEMINATION

The study will be conducted in accordance with the principles expressed in the Declaration of Helsinki. It has full approval from the Ethics Committee of the Faculty of Health Sciences, University of Beira Interior, and the Ethics Committee of the Central Health Region of Portugal. Study results will be published in peer-reviewed journals and presented at national and international conferences.

Multimorbidity patterns of and use of health services by Swedish 85-year-olds: an exploratory study

BACKGROUND

As life expectancy continues to rise, more elderly are reaching advanced ages (≥80 years). The increasing prevalence of multimorbidity places additional demands on health-care resources for the elderly. Previous studies noted the impact of multimorbidity on the use of health services, but the effects of multimorbidity patterns on health-service use have not been well studied, especially for very old people. This study determines patterns of multimorbidity associated with emergency-room visits and hospitalization in an 85-year-old population.

METHODS

Health and living conditions were reported via postal questionnaire by 496 Linköping residents aged 85 years (189 men and 307 women). Diagnoses of morbidity were reviewed in patients' case reports, and the local health-care register provided information on the use of health services. Hierarchical cluster analysis was applied to evaluate patterns of multimorbidity with gender stratification. Factors associated with emergency-room visits and hospitalization were analyzed using logistic regression models.

RESULTS

Cluster analyses revealed five clusters: vascular, cardiopulmonary, cardiac (only for men), somatic-mental (only for men), mental disease (only for women), and three other clusters related to aging (one for men and two for women). Heart failure in men (OR = 2.4, 95% CI = 1-5.7) and women (OR = 3, 95% CI = 1.3-6.9) as a single morbidity explained more variance than morbidity clusters in models of emergency-room visits. Men's cardiac cluster (OR = 1.6; 95% CI = 1-2.7) and women's cardiopulmonary cluster (OR = 1.7, 95% CI = 1.2-2.4) were significantly associated with hospitalization. The combination of the cardiopulmonary cluster with the men's cardiac cluster (OR = 1.6, 95% CI = 1-2.4) and one of the women's aging clusters (OR = 0.5, 95% CI = 0.3-0.8) showed interaction effects on hospitalization.

CONCLUSION

In this 85-year-old population, patterns of cardiac and pulmonary conditions were better than a single morbidity in explaining hospitalization. Heart failure was superior to multimorbidity patterns in explaining emergency-room visits. A holistic approach to examining the patterns of multimorbidity and their relationships with the use of health services will contribute to both local health care policy and geriatric practice.

Primary care practice-based care management for chronically ill patients (PraCMan): study protocol for a cluster randomized controlled trial [ISRCTN56104508]

BACKGROUND

Care management programmes are an effective approach to care for high risk patients with complex care needs resulting from multiple co-occurring medical and non-medical conditions. These patients are likely to be hospitalized for a potentially "avoidable" cause. Nurse-led care management programmes for high risk elderly patients showed promising results. Care management programmes based on health care assistants (HCAs) targeting adult patients with a high risk of hospitalisation may be an innovative approach to deliver cost-efficient intensified care to patients most in need.

METHODS/DESIGN

PraCMan is a cluster randomized controlled trial with primary care practices as unit of randomisation. The study evaluates a complex primary care practice-based care management of patients at high risk for future hospitalizations. Eligible patients either suffer from type 2 diabetes mellitus, chronic obstructive pulmonary disease, chronic heart failure or any combination. Patients with a high likelihood of hospitalization within the following 12 months (based on insurance data) will be included in the trial. During 12 months of intervention patients of the care management group receive comprehensive assessment of medical and non-medical needs and resources as well as regular structured monitoring of symptoms. Assessment and monitoring will be performed by trained HCAs from the participating practices. Additionally, patients will receive written information, symptom diaries, action plans and a medication plan to improve self-management capabilities. This intervention is addition to usual care. Patients from the control group receive usual care. Primary outcome is the number of all-cause hospitalizations at 12 months follow-up, assessed by insurance claims data. Secondary outcomes are health-related quality of life (SF12, EQ5D), quality of chronic illness care (PACIC), health care utilisation and costs, medication adherence (MARS), depression status and severity (PHQ-9), self-management capabilities and clinical parameters. Data collection will be performed at baseline, 12 and 24 months (12 months post-intervention).

DISCUSSION

Practice-based care management for high risk individuals involving trained HCAs appears to be a promising approach to face the needs of an aging population with increasing care demands.