Specific Disease Combinations Research Articles

Blood biomarkers of Alzheimer's disease in the community: Variation by chronic diseases and inflammatory status.

We explored the variations of blood biomarkers of Alzheimer's disease (AD) by chronic diseases and systemic inflammation. We explored the association of AD blood biomarkers with chronic diseases and systemic inflammation (interleukin-6 [IL-6]), in 2366 dementia-free participants of the Swedish National Study on Aging and Care-in Kungsholmen, using quantile regression models. A greater number of co-occurring chronic diseases was associated with higher concentrations of phosphorylated-tau 181 (p-tau181), total-tau (t-tau), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) (p<0.01). Anemia, kidney, cerebrovascular, and heart diseases were associated with variations in the levels of AD blood biomarkers. Participants in the highest (vs. lowest) interleukin-6 (IL-6) tertile had higher NfL concentration. Systemic inflammation amplified the associations between several chronic diseases and p-tau181, t-tau, NfL, and GFAP. In the community, the concentration of AD blood biomarkers varies in relation to medical conditions and systemic inflammation. Recognizing these influences is crucial for the accurate interpretation and clinical implementation of blood biomarkers. Participants with a complex clinical profile (i.e., multiple co-occurring diseases or specific disease combinations) display elevated levels of AD blood-biomarkers. Anemia, heart, cerebrovascular, and kidney diseases are associated with variations is the levels of AD blood biomarkers in cognitively intact older adults. Systemic inflammation amplifies the association between several chronic diseases and AD blood biomarkers.

Factors associated with multimorbidity in England: an analysis of the English Longitudinal Study of Ageing

BackgroundMultimorbidity, defined as the presence of two or more long-term conditions, is a growing public health challenge, especially in terms of prevention and accumulation of long-term conditions among particular population cohorts. To date, efforts to understand multimorbidity has focused mainly on specific disease combinations, with little known about the sociodemographic factors associated with it. The study aimed to assess the factors associated with multimorbidity in England. MethodsA cross-sectional study was conducted using the English Longitudinal Study of Ageing (ELSA), a dataset of people aged 50 years and older. The study identified ten long-term conditions from waves 2 to 9. Wave 2 to 9 were conducted between June 2004 to July 2005, May 2006 to August 2007, May 2008 to July 2009, June 2010 to July 2011, May 2012 to June 2013, June 2014 to May 2015, May 2016 to June 2017 and June 2018 to July 2019, respectively. The study included people with two or more long-term conditions. We identified the number of long-term conditions and multimorbidity, and we examined their association with age, gender, ethnicity, marital status, employment status, education, weekly contact with relative, and feeling lonely, sad or depressed using multinomial logistic regression. FindingsOf 16 731 people recruited from wave 2 to wave 9, we identified 10 026 people with multimorbidity aged 50 years and older. The majority had two conditions (39%) and were female (55%), aged 50–69 years (32%), of white ethnicity (96%), married (69%) and unemployed (65·3%). The adjusted odds ratio (aOR) of having more than two long-term conditions increased with age, after adjusting for sex and ethnicity (≥5 conditions: aOR 12·89, 95% CI 2·23–3·76). Being female was associated with an increased risk of having more than two long-term conditions (≥5 conditions: aOR 1·21, 1·04–1·42). Similarly, being separated, divorced, or widowed were associated with having more than two long-term conditions (≥5 conditions: aOR 1·45, 1·21–1·74). Not owning a home was independently associated with more than two long-term conditions (≥5 conditions: aOR 1·59, 1·35–1·88). InterpretationThe current analysis used only ten long-term conditions that were available in the ELSA data, so a different association might have arisen if other conditions had been considered. Our findings provide insights into which particular groups of the multimorbid population could be the target of preventive public health strategies and wider clinical and social care interventions in England to reduce the burden of multimorbidity. FundingNational Institute for Health and Care Research (NIHR).

Multimorbidity patterns and health-related quality of life in Jamaican adults: a cross sectional study exploring potential pathways.

Multimorbidity and health-related quality of life (HRQoL) are intimately linked. Multiple chronic conditions may adversely affect physical and mental functioning, while poorer HRQoL may contribute to the worsening course of diseases. Understanding mechanisms through which specific combinations of diseases affect HRQoL outcomes can facilitate identification of factors which are amenable to intervention. Jamaica, a middle-income country with high multimorbidity prevalence, has a health service delivery system dominated by public sector provision via a broad healthcare network. This study aims to examine whether multimorbidity classes differentially impact physical and mental dimensions of HRQoL in Jamaicans and quantify indirect effects on the multimorbidity-HRQoL relationship that are mediated by health system factors pertaining to financial healthcare access and service use. Latent class analysis (LCA) was used to estimate associations between multimorbidity classes and HRQoL outcomes, using latest available data from the nationally representative Jamaica Health and Lifestyle Survey 2007/2008 (N = 2,551). Multimorbidity measurement was based on self-reported presence/absence of 11 non-communicable diseases (NCDs). HRQoL was measured using the 12-item short-form (SF-12) Health Survey. Mediation analyses guided by the counterfactual approach explored indirect effects of insurance coverage and service use on the multimorbidity-HRQoL relationship. LCA revealed four profiles, including a Relatively Healthy class (52.7%) characterized by little to no morbidity and three multimorbidity classes characterized by specific patterns of NCDs and labelled Metabolic (30.9%), Vascular-Inflammatory (12.2%), and Respiratory (4.2%). Compared to the Relatively Healthy class, Vascular-Inflammatory class membership was associated with lower physical functioning (β = -5.5; p < 0.001); membership in Vascular-Inflammatory (β = -1.7; p < 0.05), and Respiratory (β = -2.5; p < 0.05) classes was associated with lower mental functioning. Significant mediated effects of health service use, on mental functioning, were observed for Vascular-Inflammatory (p < 0.05) and Respiratory (p < 0.05) classes. Specific combinations of diseases differentially impacted HRQoL outcomes in Jamaicans, demonstrating the clinical and epidemiological value of multimorbidity classes for this population, and providing insights that may also be relevant to other settings. To better tailor interventions to support multimorbidity management, additional research is needed to elaborate personal experiences with healthcare and examine how health system factors reinforce or mitigate positive health-seeking behaviours, including timely use of services.

Multimorbidity patterns and mortality in older adults: Results from the KORA-Age study.

The coexistence of several chronic diseases is very common in older adults, making it crucial to understand multimorbidity (MM) patterns and associated mortality. We aimed to determine the prevalence of MM and common chronic disease combinations, as well as their impact on mortality in men and women aged 65 years and older using the population-based KORA-Age study, based in South of Germany. The chronic disease status of the participants was determined in 2008/9, and mortality status was followed up until 2016. MM was defined as having at least two chronic diseases. We used Cox proportional hazard models to calculate the hazard ratios (HRs) and the 95% confidence intervals (CIs) for associations between MM and all-cause mortality. During the study period 495 men (24.6%) and 368 women (17.4%) died. Although the MM prevalence was almost the same in men (57.7%) and women (60.0%), the overall effect of MM on mortality was higher in men (HR: 1.81, 95% CI: 1.47-2.24) than in women (HR: 1.28, 95% CI: 1.01-1.64; p-value for interaction <0.001). The type of disease included in the MM patterns had a significant impact on mortality risk. For example, when both heart disease and diabetes were included in the combinations of two and three diseases, the mortality risk was highest. The risk of premature death does not only depend on the number of diseases but also on the specific disease combinations. In this study, life expectancy depended strongly on a few diseases, such as diabetes, hypertension, and heart disease.

Costs of multimorbidity: a systematic review and meta-analyses

BackgroundMultimorbidity is a rising global phenomenon, placing strains on countries’ population health and finances. This systematic review provides insight into the costs of multimorbidity through addressing the following primary and secondary research questions: What evidence exists on the costs of multimorbidity? How do costs of specific disease combinations vary across countries? How do multimorbidity costs vary across disease combinations? What “cost ingredients” are most commonly included in these multimorbidity studies?MethodsWe conducted a systematic review (PROSPERO: CRD42020204871) of studies published from January 2010 to January 2022, which reported on costs associated with combinations of at least two specified conditions. Systematic string-based searches were conducted in MEDLINE, The Cochrane Library, SCOPUS, Global Health, Web of Science, and Business Source Complete. We explored the association between costs of multimorbidity and country Gross Domestic Product (GDP) per capita using a linear mixed model with random intercept. Annual mean direct medical costs per capita were pooled in fixed-effects meta-analyses for each of the frequently reported dyads. Costs are reported in 2021 International Dollars (I$).ResultsFifty-nine studies were included in the review, the majority of which were from high-income countries, particularly the United States. (1) Reported annual costs of multimorbidity per person ranged from I$800 to I$150,000, depending on disease combination, country, cost ingredients, and other study characteristics. (2) Our results further demonstrated that increased country GDP per capita was associated with higher costs of multimorbidity. (3) Meta-analyses of 15 studies showed that on average, dyads which featured Hypertension were among the least expensive to manage, with the most expensive dyads being Respiratory and Mental Health condition (I$36,840), Diabetes and Heart/vascular condition (I$37,090), and Cancer and Mental Health condition in the first year after cancer diagnosis (I$85,820). (4) Most studies reported only direct medical costs, such as costs of hospitalization, outpatient care, emergency care, and drugs.ConclusionsMultimorbidity imposes a large economic burden on both the health system and society, most notably for patients with cancer and mental health condition in the first year after cancer diagnosis. Whether the cost of a disease combination is more or less than the additive costs of the component diseases needs to be further explored. Multimorbidity costing studies typically consider only a limited number of disease combinations, and few have been conducted in low- and middle-income countries and Europe. Rigorous and standardized methods of data collection and costing for multimorbidity should be developed to provide more comprehensive and comparable evidence for the costs of multimorbidity.

Comparisons of disease cluster patterns, prevalence and health factors in the USA, Canada, England and Ireland

BackgroundIdentification of those who are most at risk of developing specific patterns of disease across different populations is required for directing public health policy. Here, we contrast prevalence and patterns of cross-national disease incidence, co-occurrence and related risk factors across population samples from the U.S., Canada, England and Ireland.MethodsParticipants (n = 62,111) were drawn from the US Health and Retirement Study (n = 10,858); the Canadian Longitudinal Study on Ageing (n = 36,647); the English Longitudinal Study of Ageing (n = 7938) and The Irish Longitudinal Study on Ageing (n = 6668). Self-reported lifetime prevalence of 10 medical conditions, predominant clusters of multimorbidity and their specific risk factors were compared across countries using latent class analysis.ResultsThe U.S. had significantly higher prevalence of multimorbid disease patterns and nearly all diseases when compared to the three other countries, even after adjusting for age, sex, BMI, income, employment status, education, alcohol consumption and smoking history. For the U.S. the most at-risk group were younger on average compared to Canada, England and Ireland. Socioeconomic gradients for specific disease combinations were more pronounced for the U.S., Canada and England than they were for Ireland. The rates of obesity trends over the last 50 years align with the prevalence of eight of the 10 diseases examined. While patterns of disease clusters and the risk factors related to each of the disease clusters were similar, the probabilities of the diseases within each cluster differed across countries.ConclusionsThis information can be used to better understand the complex nature of multimorbidity and identify appropriate prevention and management strategies for treating multimorbidity across countries.

The impact of multimorbidity patterns on health-related quality of life in the general population: results of the Belgian Health Interview Survey

BackgroundChronic diseases and multimorbidity are a major cause of disease burden—for patients, caregivers, and society. Little is known however about potential interaction effects between specific disease combinations. Besides an additive effect, the presence of multiple conditions could also act synergistically or antagonistically regarding the impact on patients’ health-related quality of life (HRQoL). The aim was to estimate the impact of coexisting chronic diseases on HRQoL of the adult general Belgian population.MethodsThe Belgian Health Interview Survey 2018 provided data on self-reported chronic conditions and HRQoL (EQ-5D-5L) for a nationally representative sample. Linear mixed models were used to analyze two-way and three-way interactions of disease combinations on HRQoL.ResultsMultimorbidity had a prevalence of 46.7% (≥ 2 conditions) and 29.7% (≥ 3 conditions). HRQoL decreased considerably with the presence of multiple chronic diseases. 14 out of 41 dyad combinations and 5 out of 13 triad combinations showed significant interactions, with a dominant presence of negative/synergistic effects. Positive/antagonistic effects were found in more subjective chronic diseases such as depression and chronic fatigue. Conditions appearing the most frequently in significant disease pair interactions were dorsopathies, respiratory diseases, and arthropathies.ConclusionsDiverse multimorbidity patterns, both dyads and triads, were synergistically or antagonistically associated with lower HRQoL. Tackling the burden of multimorbidity is needed, especially because most disease combinations affect each other synergistically, resulting in a greater reduction in HRQoL. Further knowledge about those multimorbidity patterns with a greater impact on HRQoL is needed to better understand disease burden beyond mortality and morbidity data.

Multimorbidity combinations, costs of hospital care and potentially preventable emergency admissions in England: A cohort study.

Patients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent "clusters" in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest (potentially preventable) secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs. We used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions (we backfilled conditions from 2009/2010 to address potential undercoding). We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation (area level, deciles) subgroups and sensitivity to considering a smaller number of diseases. There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear "high cost" combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions (dyads/triads, most including hypertension) affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses. Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions. Our findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care.

Comorbidity is associated with higher risk of financial burden in Medicare beneficiaries with cancer but not heart disease or diabetes.

The aim of the study was to examine how multimorbidity influences the prevalence of financial burden among older adults with heart disease, diabetes, or cancer.The study was a cross-sectional analysis of prospective observational cohort survey study.Older adults (age 65 or older) who did not report 1/6 major chronic illnesses (n = 2773; reference group), reported 1/3 major chronic illnesses without comorbidity (heart disease n = 206; diabetes n = 460; cancer n = 417), and reported 1/3 major chronic illnesses with comorbidity (heart disease n = 232; diabetes n = 202; cancer n = 109).The measures were presence of chronic diseases (heart disease, diabetes, cancer), comorbid chronic diseases (stroke, lung disease, dementia), medical-related financial burden (credit card debt due to medical costs, paying medical bills over time), and overall financial burden (financial help from family, credit card debt, help with food, utilities, and other necessities).The proportion reporting financial burden ranged from 15% to 27% across samples. Heart disease was unrelated to medical or overall financial burden, regardless of comorbidity. Diabetes was unrelated to financial burden except diabetes without comorbidity was associated with lower odds of overall financial burden compared to healthy older adults (odds ratio [OR] = 0.655, 95% confidence interval [CI]: 0.468-0.917). Cancer with comorbidity, but not cancer without comorbidity, was associated with greater odds of medical related (OR = 1.678, 95% CI: 1.057-2.664) and overall financial burden (OR = 1.748, 95% CI: 1.064-2.872).The association of multimorbidity with financial burden likely varies based on specific diseases. Future research on financial burden should focus on specific disease combinations such as cancer with comorbidity.

Choosing Wisely – An international and multimorbid perspective

Some medical diagnostic and therapeutic interventions are non-beneficial or even harmful. The Choosing Wisely campaign has encouraged the generation of “top five” lists of unnecessary low-value services in different specialist areas. In the USA alone, where the campaign was launched, these lists include a total of 450 evidence-based recommendations. Medical scientific societies in further countries such as Canada, Australia, New Zealand, England, Switzerland and Germany have since initiated Choosing Wisely campaigns. Besides implementing top five lists, these aim to change attitudes, expectations and practices in the culture of medicine. The field of internal medicine has initiated change in Switzerland (Swiss Society of General Internal Medicine: Smarter Medicine) and Germany (German Society of Internal Medicine: Klug entscheiden). Formulating Choosing Wisely principles in managing complex patients with multiple concurrent acute or chronic diseases, i. e., multimorbidity (MM), will present a particular challenge. Research is needed to determine the primary sources of overuse in specific combinations of diseases (i. e., MM clusters) and spearhead corresponding recommendations. National Choosing Widely campaigns may serve as a forerunner to a more global initiative.

Multimorbidity in Australia: Comparing estimates derived using administrative data sources and survey data.

BackgroundEstimating multimorbidity (presence of two or more chronic conditions) using administrative data is becoming increasingly common. We investigated (1) the concordance of identification of chronic conditions and multimorbidity using self-report survey and administrative datasets; (2) characteristics of people with multimorbidity ascertained using different data sources; and (3) whether the same individuals are classified as multimorbid using different data sources.MethodsBaseline survey data for 90,352 participants of the 45 and Up Study—a cohort study of residents of New South Wales, Australia, aged 45 years and over—were linked to prior two-year pharmaceutical claims and hospital admission records. Concordance of eight self-report chronic conditions (reference) with claims and hospital data were examined using sensitivity (Sn), positive predictive value (PPV), and kappa (κ).The characteristics of people classified as multimorbid were compared using logistic regression modelling.ResultsAgreement was found to be highest for diabetes in both hospital and claims data (κ = 0.79, 0.78; Sn = 79%, 72%; PPV = 86%, 90%). The prevalence of multimorbidity was highest using self-report data (37.4%), followed by claims data (36.1%) and hospital data (19.3%). Combining all three datasets identified a total of 46 683 (52%) people with multimorbidity, with half of these identified using a single dataset only, and up to 20% identified on all three datasets. Characteristics of persons with and without multimorbidity were generally similar. However, the age gradient was more pronounced and people speaking a language other than English at home were more likely to be identified as multimorbid by administrative data.ConclusionsDifferent individuals, with different combinations of conditions, are identified as multimorbid when different data sources are used. As such, caution should be applied when ascertaining morbidity from a single data source as the agreement between self-report and administrative data is generally poor. Future multimorbidity research exploring specific disease combinations and clusters of diseases that commonly co-occur, rather than a simple disease count, is likely to provide more useful insights into the complex care needs of individuals with multiple chronic conditions.

Multimorbidity as specific disease combinations, an important predictor factor for mortality in octogenarians: the Octabaix study.

BackgroundThe population is aging and multimorbidity is becoming a common problem in the elderly.ObjectiveTo explore the effect of multimorbidity patterns on mortality for all causes at 3- and 5-year follow-up periods.Materials and methodsA prospective community-based cohort (2009–2014) embedded within a randomized clinical trial was conducted in seven primary health care centers, including 328 subjects aged 85 years at baseline. Sociodemographic variables, sensory status, cardiovascular risk factors, comorbidity, and geriatric tests were analyzed. Multimorbidity patterns were defined as combinations of two or three of 16 specific chronic conditions in the same individual.ResultsOf the total sample, the median and interquartile range value of conditions was 4 (3–5). The individual morbidities significantly associated with death were chronic obstructive pulmonary disease (COPD; hazard ratio [HR]: 2.47; 95% confidence interval [CI]: 1.3; 4.7), atrial fibrillation (AF; HR: 2.41; 95% CI: 1.3; 4.3), and malignancy (HR: 1.9; 95% CI: 1.0; 3.6) at 3-year follow-up; whereas dementia (HR: 2.04; 95% CI: 1.3; 3.2), malignancy (HR: 1.84; 95% CI: 1.2; 2.8), and COPD (HR: 1.77; 95% CI: 1.1; 2.8) were the most associated with mortality at 5-year follow-up, after adjusting using Barthel functional index (BI). The two multimorbidity patterns most associated with death were AF, chronic kidney disease (CKD), and visual impairment (HR: 4.19; 95% CI: 2.2; 8.2) at 3-year follow-up as well as hypertension, CKD, and malignancy (HR: 3.24; 95% CI: 1.8; 5.8) at 5 years, after adjusting using BI.ConclusionMultimorbidity as specific combinations of chronic conditions showed an effect on mortality, which would be higher than the risk attributable to individual morbidities. The most important predicting pattern for mortality was the combination of AF, CKD, and visual impairment after 3 years. These findings suggest that a new approach is required to target multimorbidity in octogenarians.

Innovative practices to care for people with multimorbidity in Europe

Background With this first presentation, the challenge of care for people with multimorbidity will be discussed and characteristics of 101 integrated care practices targeting people with multimorbidity will be presented. Based on these characteristics, a typology of integrated care practices addressing multimorbidity will be presented. Methods Integrated care practices addressing multimorbidity were identified by country experts in 24 European countries. Contact persons provided data on their characteristics, implementation and outcomes. Besides descriptive analysis, we conducted cluster analysis to explore whether some prototypes of integrated practices addressing multimorbidity could be distinguished based on their characteristics (e.g. target group, main objectives, organizations and care providers involved, organizational structures). Results Of the 101 integrated care practices, 58% focused on multimorbidity in general (irrespective of the type of chronic diseases of the patients) and 42% on specific combinations of diseases. Primary care practices were involved in 69% of the practices. Primary care practices, nursing homes, social care and community services were significantly more often involved in practices focusing on multimorbidity in general than in practices focusing on specific diseases. Medical specialists were involved in 83% of the practices focusing on specific combinations of diseases, but only in 54% of the practices focusing on multimorbidity in general. Some prototypes of practices could be distinguished, mainly based on variation in the involvement of primary care. Conclusion European practices addressing multimorbidity often have similar objectives, but differ in the organizations or care providers involved. The next step in the ICARE4EU project is to reveal whether the occurrence of practices of certain types relate to specific characteristics of a country or region such as its population or the structure of its healthcare system.

Multimorbidity of chronic diseases and health care utilization in general practice

BackgroundMultimorbidity is common among ageing populations and it affects the demand for health services. The objective of this study was to examine the relationship between multimorbidity (i.e. the number of diseases and specific combinations of diseases) and the use of general practice services in the Dutch population of 55 years and older.MethodsData on diagnosed chronic diseases, contacts (including face-to-face consultations, phone contacts, and home visits), drug prescription rates, and referral rates to specialised care were derived from the Netherlands Information Network of General Practice (LINH), limited to patients whose data were available from 2006 to 2008 (N = 32,583). Multimorbidity was defined as having two or more out of 28 chronic diseases. Multilevel analyses adjusted for age, gender, and clustering of patients in general practices were used to assess the association between multimorbidity and service utilization in 2008.ResultsPatients diagnosed with multiple chronic diseases had on average 18.3 contacts (95% CI 16.8 19.9) per year. This was significantly higher than patients with one chronic disease (11.7 contacts (10.8 12.6)) or without any (6.1 contacts (5.6 6.6)). A higher number of chronic diseases was associated with more contacts, more prescriptions, and more referrals to specialized care. However, the number of contacts per disease decreased with an increasing number of diseases; patients with a single disease had between 9 to 17 contacts a year and patients with five or more diseases had 5 or 6 contacts per disease per year. Contact rates for specific combinations of diseases were lower than what would be expected on the basis of contact rates of the separate diseases.ConclusionMultimorbidity is associated with increased health care utilization in general practice, yet the increase declines per additional disease. Still, with the expected rise in multimorbidity in the coming decades more extensive health resources are required.

Implications of comorbidity for primary care costs in the UK: a retrospective observational study

Comorbidity is increasingly common in primary care. The cost implications for patient care and budgetary management are unclear. To investigate whether caring for patients with specific disease combinations increases or decreases primary care costs compared with treating separate patients with one condition each. Retrospective observational study using data on 86 100 patients in the General Practice Research Database. Annual primary care cost was estimated for each patient including consultations, medication, and investigations. Patients with comorbidity were defined as those with a current diagnosis of more than one chronic condition in the Quality and Outcomes Framework. Multiple regression modelling was used to identify, for three age groups, disease combinations that increase (cost-increasing) or decrease (cost-limiting) cost compared with treating each condition separately. Twenty per cent of patients had at least two chronic conditions. All conditions were found to be both cost-increasing and cost-limiting when co-occurring with other conditions except dementia, which is only cost-limiting. Depression is the most important cost-increasing condition when co-occurring with a range of conditions. Hypertension is cost-limiting, particularly when co-occurring with other cardiovascular conditions. Three categories of comorbidity emerge, those that are: cost-increasing, mainly due to a combination of depression with physical comorbidity; cost-limiting because treatment for the conditions overlap; and cost-limiting for no apparent reason but possibly because of inadequate care. These results can contribute to efficient and effective management of chronic conditions in primary care.

Multimorbidity in older women: The negative impact of specific combinations of chronic conditions on self-rated health

Background: Chronic diseases are considered major threats to self-rated health (SRH). In many elderly people multimorbidity is present, in elderly women more than in elderly men. This study aims at establishing the impact of multimorbidity and specific disease combinations on SRH in elderly women.Objectives: To study the relationship between the number of chronic diseases and SRH and explore possible effects of combinations of chronic conditions on SRH in elderly women.Methods: Health interview data used for this study originated from the second Dutch National Survey of General Practice, a study with a response rate of 64.5%. From the 12 699 respondents, 315 were females between 70 and 74 years old.Results: Of the women, 87% reported one or more chronic condition. Women without any chronic condition rated their health significantly better than those with one or more chronic conditions. Either severe back pain or severe headache was included in return the most prevalent combinations of two chronic conditions with a significantly higher negative impact on SRH than expected.Conclusion: All combinations including severe headache and some combinations including severe back pain and another chronic condition had a significantly more negative impact on SRH than expected in women aged 70–74 years. General practitioners should be alert on severe headache and severe back pain in elderly women to improve proactive the quality of care and thus add to the quality of later years of life.

Multimorbidity and comorbidity in the Dutch population – data from general practices

BackgroundMultimorbidity is increasingly recognized as a major public health challenge of modern societies. However, knowledge about the size of the population suffering from multimorbidity and the type of multimorbidity is scarce. The objective of this study was to present an overview of the prevalence of multimorbidity and comorbidity of chronic diseases in the Dutch population and to explore disease clustering and common comorbidities.MethodsWe used 7 years data (2002–2008) of a large Dutch representative network of general practices (212,902 patients). Multimorbidity was defined as having two or more out of 29 chronic diseases. The prevalence of multimorbidity was calculated for the total population and by sex and age group. For 10 prevalent diseases among patients of 55 years and older (N = 52,014) logistic regressions analyses were used to study disease clustering and descriptive analyses to explore common comorbid diseases.ResultsMultimorbidity of chronic diseases was found among 13% of the Dutch population and in 37% of those older than 55 years. Among patients over 55 years with a specific chronic disease more than two-thirds also had one or more other chronic diseases. Most disease pairs occurred more frequently than would be expected if diseases had been independent. Comorbidity was not limited to specific combinations of diseases; about 70% of those with a disease had one or more extra chronic diseases recorded which were not included in the top five of most common diseases.ConclusionMultimorbidity is common at all ages though increasing with age, with over two-thirds of those with chronic diseases and aged 55 years and older being recorded with multimorbidity. Comorbidity encompassed many different combinations of chronic diseases. Given the ageing population, multimorbidity and its consequences should be taken into account in the organization of care in order to avoid fragmented care, in medical research and healthcare policy.

Prevalence and patterns of morbidity among adults in Germany

To describe the prevalence and patterns of morbidity among adults in Germany, we collected self-reported information on 22chronic health conditions in a nationally representative health survey among 21,262participants (51.5% women, aged 18-100years). Age- and sex-specific prevalences were calculated for single health conditions, disease categories, the most prevalent disease dyads and triads, and for multimorbidity defined by condition count. In both sexes, hypertension, hyperlipidemia, chronic back pain, obesity, and osteoarthritis were the most prevalent single health conditions and significantly increased with age. Cardiometabolic and musculoskeletal conditions were the two most prevalent disease categories in all age and sex groups. The most prevalent disease category dyads and triads included combinations between cardiometabolic conditions, cardiovascular and musculoskeletal disease, depression, sensory limitations, and cancer. The prevalence and magnitude of multimorbidity strongly increased with age in both sexes. Among German adults, the prevalence of chronic health conditions varies greatly by age and sex. In contrast, patterns of morbidity and comorbidity differ according to age, but are highly consistent between men and women. The predominant role of cardiometabolic conditions in all groups underlines the need for preventive efforts. The co-occurrence of chronic health conditions among older is highly prevalent and this calls for tailoring health care towards specific disease combinations.

Association between clusters of diseases and polypharmacy in hospitalized elderly patients: Results from the REPOSI study

Background Although the association between multimorbidity and polypharmacy has been clearly documented, no study has analyzed whether or not specific combinations of diseases influence the prescription of polypharmacy in older persons. We assessed which clusters of diseases are associated with polypharmacy in acute-care elderly in-patients. Methods This cross-sectional study was held in 38 Italian internal medicine and geriatric wards participating in the Registro Politerapie SIMI (REPOSI) study during 2008. The study sample included 1155 in-patients aged 65 years or older. Clusters of diseases, defined as two or more co-occurring specific chronic diseases, were identified using the odds ratio (OR) for the associations between pairs of diseases followed by cluster analysis. Polypharmacy was defined as the prescription of five or more different medications at hospital discharge. Logistic regression models were run to analyze the association between clusters of diseases and polypharmacy. Results Among clusters of diseases, the highest mean number of drugs (> 8) was found in patients affected by heart failure (HF) plus chronic obstructive pulmonary disease (COPD), HF plus chronic renal failure (CRF), COPD plus coronary heart disease (CHD), diabetes mellitus plus CRF, and diabetes mellitus plus CHD plus cerebrovascular disease (CVD). The strongest association between clusters of diseases and polypharmacy was found for diabetes mellitus plus CHD plus CVD, diabetes plus CHD, and HF plus atrial fibrillation (AF). Conclusions The observed knowledge of the relationship among co-occurring diseases and polypharmacy should help to identify and monitor older in-patients at risk of polypharmacy.

Association between inflammatory-related disease burden and frailty: Results from the Women's Health and Aging Studies (WHAS) I and II

Frailty is associated with a pro-inflammatory state, which has been characterized by elevated levels of systemic inflammatory biomarkers, but has not been related to the number of co-existing chronic diseases associated with inflammation. We sought to determine the extent to which a higher number of inflammatory-related diseases is associated with frailty and to identify the most common disease patterns associated with being frail in older adults. We performed binomial regression analyses to assess whether a higher count of inflammatory-related diseases increases the probability of frailty using data from the WHAS I and II, companion cohorts composed of 70–79-year-old community-dwelling older women in Baltimore, Maryland ( n = 620). An increase of one inflammatory-related disease was associated log-linearly with frailty (Prevalence Ratio (PR) = 2.28, 95% Confidence Interval (CI) = 1.81–2.87). After adjusting for age, race, education, and smoking status, the probability of frailty remained significant (PR = 1.97, 95%CI = 1.52–2.55). In the frail population, chronic kidney disease (CKD) and depressive symptoms (Prevalence = 22.9%, 95%CI = 14.2–34.8%); CVD and depressive symptoms (21.7%, 95%CI = 13.2–33.5%); CKD and anemia (18.7%, 95%CI = 11.1–29.7%); cardiovascular disease (CVD), CKD, and pulmonary disease (10.7%, 95%CI = 5.2–21.0%); CKD, anemia, and depressive symptoms (8.7%, 95%CI = 3.9–18.2%); and CVD, anemia, pulmonary disease, and depressive symptoms (5.0%, 95%CI = 1.6–14.4%) were among the most frequent disease combinations. Their prevalence percentages were significantly higher in the frail versus non-frail women. A higher inflammatory-related disease count, perhaps reflecting a greater pro-inflammatory burden, increases the likelihood of frailty. Shared mechanisms among specific disease combinations may further contribute to this risk.