Impaired Health-Related Quality of Life in Elderly Women is Associated With Multimorbidity: Results From the Korean National Health and Nutrition Examination Survey

BackgroundMultimorbidity is a common problem in elderly populations and is significantly associated with functional decline, disability, and mortality. However, the sex-specific characteristics of multimorbidity and its effect on patients' quality of life (QOL) have not been clearly established.MethodsWe analyzed the Korean National Health and Nutrition Examination Survey database. EuroQol 5D (a standardized health outcomes measurement instrument that includes 2 dimensions, the EuroQol 5 Dimension [EQ-5D] index score and the EuroQol visual analogue scale [EQ-VAS]) was used to evaluate QOL. Multimorbidity was evaluated using data on blood pressure measurements, blood chemistry examinations, and anthropometric assessments, as well as a survey that assessed health status.ResultsA total of 1419 patients aged ≥65 years were included in the analysis (age = 72.40 [0.19] years; 39.3% men). Multimorbidity was significantly associated with being a woman; however, it was not associated with age. The EQ-5D index score and EQ-VAS score were significantly lower in patients with multimorbidity, especially among the elderly women. The inverse association between QOL and the number of chronic diseases was maintained without a floor effect. Hypertension was the most common disease; however, QOL was significantly associated with musculoskeletal disease, stroke, and depression, all of which were more common in female patients. There was no significant difference in QOL between men and women with similar levels of comorbidity.ConclusionBoth the amount and pattern of chronic diseases have been associated with QOL in elderly populations. Elderly women have low levels of QOL due to multimorbidity and a higher prevalence of chronic disease, which is related to impaired QOL.     

Associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank: A comparison of longitudinal cohort studies

BackgroundCohorts such as UK Biobank are increasingly used to study multimorbidity; however, there are concerns that lack of representativeness may lead to biased results. This study aims to compare associations between multimorbidity and adverse health outcomes in UK Biobank and a nationally representative sample.Methods and findingsThese are observational analyses of cohorts identified from linked routine healthcare data from UK Biobank participants (n = 211,597 from England, Scotland, and Wales with linked primary care data, age 40 to 70, mean age 56.5 years, 54.6% women, baseline assessment 2006 to 2010) and from the Secure Anonymised Information Linkage (SAIL) databank (n = 852,055 from Wales, age 40 to 70, mean age 54.2, 50.0% women, baseline January 2011). Multimorbidity (n = 40 long-term conditions [LTCs]) was identified from primary care Read codes and quantified using a simple count and a weighted score. Individual LTCs and LTC combinations were also assessed. Associations with all-cause mortality, unscheduled hospitalisation, and major adverse cardiovascular events (MACEs) were assessed using Weibull or negative binomial models adjusted for age, sex, and socioeconomic status, over 7.5 years follow-up for both datasets.Multimorbidity was less common in UK Biobank than SAIL (26.9% and 33.0% with ≥2 LTCs in UK Biobank and SAIL, respectively). This difference was attenuated, but persisted, after standardising by age, sex, and socioeconomic status. The association between increasing multimorbidity count and mortality, hospitalisation, and MACE was similar between both datasets at LTC counts of ≤3; however, above this level, UK Biobank underestimated the risk associated with multimorbidity (e.g., mortality hazard ratio for 2 LTCs 1.62 (95% confidence interval 1.57 to 1.68) in SAIL and 1.51 (1.43 to 1.59) in UK Biobank, hazard ratio for 5 LTCs was 3.46 (3.31 to 3.61) in SAIL and 2.88 (2.63 to 3.15) in UK Biobank). Absolute risk of mortality, hospitalisation, and MACE, at all levels of multimorbidity, was lower in UK Biobank than SAIL (adjusting for age, sex, and socioeconomic status). Both cohorts produced similar hazard ratios for some LTCs (e.g., hypertension and coronary heart disease), but UK Biobank underestimated the risk for others (e.g., alcohol-related disorders or mental health conditions). Hazard ratios for some LTC combinations were similar between the cohorts (e.g., cardiovascular conditions); however, UK Biobank underestimated the risk for combinations including other conditions (e.g., mental health conditions). The main limitations are that SAIL databank represents only part of the UK (Wales only) and that in both cohorts we lacked data on severity of the LTCs included.ConclusionsIn this study, we observed that UK Biobank accurately estimates relative risk of mortality, unscheduled hospitalisation, and MACE associated with LTC counts ≤3. However, for counts ≥4, and for some LTC combinations, estimates of magnitude of association from UK Biobank are likely to be conservative. Researchers should be mindful of these limitations of UK Biobank when conducting and interpreting analyses of multimorbidity. Nonetheless, the richness of data available in UK Biobank does offers opportunities to better understand multimorbidity, particularly where complementary data sources less susceptible to selection bias can be used to inform and qualify analyses of UK Biobank.

Peter Hanlon and colleagues compare the associations between multimorbidity and adverse health outcomes in UK Biobank and the SAIL Databank.     

Socioeconomic inequalities in prevalence and development of multimorbidity across adulthood: A longitudinal analysis of the MRC 1946 National Survey of Health and Development in the UK

BackgroundWe aimed to estimate multimorbidity trajectories and quantify socioeconomic inequalities based on childhood and adulthood socioeconomic position (SEP) in the risks and rates of multimorbidity accumulation across adulthood.Methods and findingsParticipants from the UK 1946 National Survey of Health and Development (NSHD) birth cohort study who attended the age 36 years assessment in 1982 and any one of the follow-up assessments at ages 43, 53, 63, and 69 years (N = 3,723, 51% males). Information on 18 health conditions was based on a combination of self-report, biomarkers, health records, and prescribed medications. We estimated multimorbidity trajectories and delineated socioeconomic inequalities (based on childhood and adulthood social class and highest education) in multimorbidity at each age and in longitudinal trajectories.Multimorbidity increased with age (0.7 conditions at 36 years to 3.7 at 69 years). Multimorbidity accumulation was nonlinear, accelerating with age at the rate of 0.08 conditions/year (95% CI 0.07 to 0.09, p < 0.001) at 36 to 43 years to 0.19 conditions/year (95% CI 0.18 to 0.20, p < 0.001) at 63 to 69 years. At all ages, the most socioeconomically disadvantaged had 1.2 to 1.4 times greater number of conditions on average compared to the most advantaged. The most disadvantaged by each socioeconomic indicator experienced an additional 0.39 conditions (childhood social class), 0.83 (adult social class), and 1.08 conditions (adult education) at age 69 years, independent of all other socioeconomic indicators. Adverse adulthood SEP was associated with more rapid accumulation of multimorbidity, resulting in 0.49 excess conditions in partly/unskilled compared to professional/intermediate individuals between 63 and 69 years. Disadvantaged childhood social class, independently of adulthood SEP, was associated with accelerated multimorbidity trajectories from age 53 years onwards.Study limitations include that the NSHD cohort is composed of individuals of white European heritage only, and findings may not be generalizable to the non-white British population of the same generation and did not account for other important dimensions of SEP such as income and wealth.ConclusionsIn this study, we found that socioeconomically disadvantaged individuals have earlier onset and more rapid accumulation of multimorbidity resulting in widening inequalities into old age, with independent contributions from both childhood and adulthood SEP.

Amal Khanolkar and co-workers study associations between multimorbidity and socioeconomic position in the UK.     

Socioeconomic Determinants of Multimorbidity: A Population-Based Household Survey of Hong Kong Chinese

Introduction

Multimorbidity has been well researched in terms of consequences and healthcare implications. Nevertheless, its risk factors and determinants, especially in the Asian context, remain understudied. We tested the hypothesis of a negative relationship between socioeconomic status and multimorbidity, with contextually different patterns from those observed in the West.

Methods

We conducted our study in the general Hong Kong (HK) population. Data on current health conditions, health behaviours, socio-demographic and socioeconomic characteristics was obtained from HK Government’s Thematic Household Survey. 25,780 individuals aged 15 or above were sampled. Binary logistic and negative binomial regression analyses were conducted to identify risk factors for presence of multimorbidity and number of chronic conditions, respectively. Sub-analysis of possible mediation effect through financial burden borne by private housing residents on multimorbidity was also conducted.

Results

Unadjusted and adjusted models showed that being female, being 25 years or above, having an education level of primary schooling or below, having less than HK$15,000 monthly household income, being jobless or retired, and being past daily smoker were significant risk factors for the presence of multimorbidity and increased number of chronic diseases. Living in private housing was significantly associated with higher chance of multimorbidity and increased number of chronic diseases only after adjustments.

Conclusions

Less advantaged people tend to have higher risks of multimorbidity and utilize healthcare from the public sector with poorer primary healthcare experience. Moreover, middle-class people who are not eligible for government subsidized public housing may be of higher risk of multimorbidity due to psychosocial stress from paying for the severely unaffordable private housing.     

Multimorbidity Patterns in a National Representative Sample of the Spanish Adult Population

Background

In the context of population aging, multimorbidity has emerged as a growing concern in public health. However, little is known about multimorbidity patterns and other issues surrounding chronic diseases. The aim of our study was to examine multimorbidity patterns, the relationship between physical and mental conditions and the distribution of multimorbidity in the Spanish adult population.

Methods

Data from this cross-sectional study was collected from the COURAGE study. A total of 4,583 participants from Spain were included, 3,625 aged over 50. An exploratory factor analysis was conducted to detect multimorbidity patterns in the population over 50 years of age. Crude and adjusted binary logistic regressions were performed to identify individual associations between physical and mental conditions.

Results

Three multimorbidity patterns rose: ‘cardio-respiratory’ (angina, asthma, chronic lung disease), ‘mental-arthritis’ (arthritis, depression, anxiety) and the ‘aggregated pattern’ (angina, hypertension, stroke, diabetes, cataracts, edentulism, arthritis). After adjusting for covariates, asthma, chronic lung disease, arthritis and the number of physical conditions were associated with depression. Angina and the number of physical conditions were associated with a higher risk of anxiety. With regard to multimorbidity distribution, women over 65 years suffered from the highest rate of multimorbidity (67.3%).

Conclusion

Multimorbidity prevalence occurs in a high percentage of the Spanish population, especially in the elderly. There are specific multimorbidity patterns and individual associations between physical and mental conditions, which bring new insights into the complexity of chronic patients. There is need to implement patient-centered care which involves these interactions rather than merely paying attention to individual diseases.     

Trends in prevalence of chronic disease and multimorbidity in Ontario,Canada

BACKGROUND:New case-mix tools from the Canadian Institute for Health Information offer a novel way of exploring the prevalence of chronic disease and multimorbidity using diagnostic data. We took a comprehensive approach to determine whether the prevalence of chronic disease and multimorbidity has been rising in Ontario, Canada.METHODS:In this observational study, we applied case-mix methodology to a population-based cohort. We used 10 years of patient-level data (fiscal years 2008/09 to 2017/18) from multiple care settings to compute the rolling 5-year prevalence of 85 chronic diseases and multimorbidity (i.e., the co-occurrence of 2 or more diagnoses). Diseases were further classified based on type and severity. We report both crude and age- and sex-standardized trends.RESULTS:The number of patients with chronic disease increased by 11.0% over the 10-year study period to 9.8 million in 2017/18, and the number with multimorbidity increased 12.2% to 6.5 million. Overall increases from 2008/09 to 2017/18 in the crude prevalence of chronic conditions and multimorbidity were driven by population aging. After adjustments for age and sex, the prevalence of patients with ≥ 1 chronic conditions decreased from 70.2% to 69.1%, and the prevalence of multimorbidity decreased from 47.1% to 45.6%. This downward trend was concentrated in minor and moderate diseases, whereas the prevalence of many major chronic diseases rose, along with instances of extreme multimorbidity (≥ 8 conditions). Age- and sex-standardized resource intensity weights, which reflect relative expected costs associated with patient diagnostic profiles, increased 4.6%.INTERPRETATION:Evidence of an upward trend in the prevalence of chronic disease was mixed. However, the change in case mix toward more serious conditions, along with increasing patient resource intensity weights overall, may portend a future need for population health management and increased health system spending above that predicted by population aging.

Multimorbidity exists when a patient is diagnosed with 2 or more chronic diseases. Patients with multimorbidity present challenges for physicians managing their care and, as the proportion of these patients in the population increases, for health care system planning. The prevalence of multimorbidity and chronic disease has been strongly associated with primary care use, specialist consultations, number and intensity of inpatient hospital admissions and other types of care.^1–7 Among beneficiaries of fee-for-service Medicare in the United States, expenditures for those with 4 or more chronic diseases were reported to be 66 times higher than for those with none.⁸ One study found that most health spending growth (77.6%) in the US between 1987 and 2011 could be attributed to patients with 4 or more diseases.⁹Several recent studies have estimated the prevalence of chronic disease and multimorbidity in Canada.^3,10–13 Rates of multimorbidity ranged from 10% to 25%, owing to differences in classification systems used to identify chronic disease, including the choice of conditions, and variations in study population. Lack of standardization in measures of chronic disease prevalence and multimorbidity has hampered the evaluation of trends over time and across settings.Ontario provides an ideal setting to evaluate trends in the prevalence of chronic disease because patients have access to a comprehensive set of publicly funded services. The Canadian Institute for Health Information (CIHI) has created a system that maps patient diagnosis data from all health care settings to a set of 226 clinically meaningful health conditions, covering the full spectrum of acute and chronic morbidity (Jeffrey Hatcher, Canadian Institute for Health Information, Ottawa: personal communication, 2017). CIHI’s system has been independently compared with the Johns Hopkins ACG System; CIHI’s system was deemed to be more specific and less sensitive in classifying diagnoses, making it more conservative in identifying health conditions (S. Cheng, ICES, unpublished data, 2016). The purpose of this study was to evaluate trends in the prevalence of chronic disease and multimorbidity in Ontario using CIHI’s comprehensive disease classification system.     

Prevalence,Determinants and Patterns of Multimorbidity in Primary Care: A Systematic Review of Observational Studies

Introduction

Multimorbidity is a major concern in primary care. Nevertheless, evidence of prevalence and patterns of multimorbidity, and their determinants, are scarce. The aim of this study is to systematically review studies of the prevalence, patterns and determinants of multimorbidity in primary care.

Methods

Systematic review of literature published between 1961 and 2013 and indexed in Ovid (CINAHL, PsychINFO, Medline and Embase) and Web of Knowledge. Studies were selected according to eligibility criteria of addressing prevalence, determinants, and patterns of multimorbidity and using a pretested proforma in primary care. The quality and risk of bias were assessed using STROBE criteria. Two researchers assessed the eligibility of studies for inclusion (Kappa  = 0.86).

Results

We identified 39 eligible publications describing studies that included a total of 70,057,611 patients in 12 countries. The number of health conditions analysed per study ranged from 5 to 335, with multimorbidity prevalence ranging from 12.9% to 95.1%. All studies observed a significant positive association between multimorbidity and age (odds ratio [OR], 1.26 to 227.46), and lower socioeconomic status (OR, 1.20 to 1.91). Positive associations with female gender and mental disorders were also observed. The most frequent patterns of multimorbidity included osteoarthritis together with cardiovascular and/or metabolic conditions.

Conclusions

Well-established determinants of multimorbidity include age, lower socioeconomic status and gender. The most prevalent conditions shape the patterns of multimorbidity. However, the limitations of the current evidence base means that further and better designed studies are needed to inform policy, research and clinical practice, with the goal of improving health-related quality of life for patients with multimorbidity. Standardization of the definition and assessment of multimorbidity is essential in order to better understand this phenomenon, and is a necessary immediate step.     

Examining methodology to identify patterns of consulting in primary care for different groups of patients before a diagnosis of cancer: An exemplar applied to oesophagogastric cancer

BackgroundCurrent methods for estimating the timeliness of cancer diagnosis are not robust because dates of key defining milestones, for example first presentation, are uncertain. This is exacerbated when patients have other conditions (multimorbidity), particularly those that share symptoms with cancer. Methods independent of this uncertainty are needed for accurate estimates of the timeliness of cancer diagnosis, and to understand how multimorbidity impacts the diagnostic process.MethodsParticipants were diagnosed with oesophagogastric cancer between 2010 and 2019. Controls were matched on year of birth, sex, general practice and multimorbidity burden calculated using the Cambridge Multimorbidity Score. Primary care data (Clinical Practice Research Datalink) was used to explore population-level consultation rates for up to two years before diagnosis across different multimorbidity burdens. Five approaches were compared on the timing of the consultation frequency increase, the inflection point for different multimorbidity burdens, different aggregated time-periods and sample sizes.ResultsWe included 15,410 participants, of which 13,328 (86.5 %) had a measurable multimorbidity burden. Our new maximum likelihood estimation method found evidence that the inflection point in consultation frequency varied with multimorbidity burden, from 154 days (95 %CI 131.8–176.2) before diagnosis for patients with no multimorbidity, to 126 days (108.5–143.5) for patients with the greatest multimorbidity burden. Inflection points identified using alternative methods were closer to diagnosis for up to three burden groups. Sample size reduction and changing the aggregation period resulted in inflection points closer to diagnosis, with the smallest change for the maximum likelihood method.DiscussionExisting methods to identify changes in consultation rates can introduce substantial bias which depends on sample size and aggregation period. The direct maximum likelihood method was less prone to this bias than other methods and offers a robust, population-level alternative for estimating the timeliness of cancer diagnosis.     

Impact of Multimorbidity on Disability and Quality of Life in the Spanish Older Population

Background

Population aging is closely related to high prevalence of chronic conditions in developed countries. In this context, health care policies aim to increase life span cost-effectively while maintaining quality of life and functional ability. There is still, however, a need for further understanding of how chronic conditions affect these health aspects. The aim of this paper is to assess the individual and combined impact of chronic physical and mental conditions on quality of life and disability in Spain, and secondly to show gender trends.

Methods

Cross-sectional data were collected from the COURAGE study. A total of 3,625 participants over 50 years old from Spain were included. Crude and adjusted multiple linear regressions were conducted to detect associations between individual chronic conditions and disability, and between chronic conditions and quality of life. Separate models were used to assess the influence of the number of diseases on the same variables. Additional analogous regressions were performed for males and females.

Results

All chronic conditions except hypertension were statistically associated with poor results in quality of life and disability. Depression, anxiety and stroke were found to have the greatest impact on outcomes. The number of chronic conditions was associated with substantially lower quality of life [β for 4+ diseases: −18.10 (−20.95,−15.25)] and greater disability [β for 4+ diseases: 27.64 (24.99,30.29]. In general, women suffered from higher rates of multimorbidity and poorer results in quality of life and disability.

Conclusions

Chronic conditions impact greatly on quality of life and disability in the older Spanish population, especially when co-occurring diseases are added. Multimorbidity considerations should be a priority in the development of future health policies focused on quality of life and disability. Further studies would benefit from an expanded selection of diseases. Policies should also deal with gender idiosyncrasy in certain cases.     

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

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

In an observational analysis of primary care data from the UK, Naomi Launders and colleagues study the prevalence and clustering of physical health conditions and multimorbidity in individuals with severe mental illnesses.     

Multimorbidity and Its Patterns according to Immigrant Origin. A Nationwide Register-Based Study in Norway

Introduction

As the flows of immigrant populations increase worldwide, their heterogeneity becomes apparent with respect to the differences in the prevalence of chronic physical and mental disease. Multimorbidity provides a new framework in understanding chronic diseases holistically as the consequence of environmental, social, and personal risks that contribute to increased vulnerability to a wide variety of illnesses. There is a lack of studies on multimorbidity among immigrants compared to native-born populations.

Methodology

This nationwide multi-register study in Norway enabled us i) to study the associations between multimorbidity and immigrant origin, accounting for other known risk factors for multimorbidity such as gender, age and socioeconomic levels using logistic regression analyses, and ii) to identify patterns of multimorbidity in Norway for immigrants and Norwegian-born by means of exploratory factor analysis technique.

Results

Multimorbidity rates were lower for immigrants compared to Norwegian-born individuals, with unadjusted odds ratios (OR) and 95% confidence intervals 0.38 (0.37–0.39) for Eastern Europe, 0.58 (0.57–0.59) for Asia, Africa and Latin America, and 0.67 (0.66–0.68) for Western Europe and North America. Results remained significant after adjusting for socioeconomic factors. Similar multimorbidity disease patterns were observed among Norwegian-born and immigrants, in particular between Norwegian-born and those from Western European and North American countries. However, the complexity of patterns that emerged for the other immigrant groups was greater. Despite differences observed in the development of patterns with age, such as ischemic heart disease among immigrant women, we were unable to detect the systematic development of the multimorbidity patterns among immigrants at younger ages.

Conclusions

Our study confirms that migrants have lower multimorbidity levels compared to Norwegian-born. The greater complexity of multimorbidity patterns for some immigrant groups requires further investigation. Health care policies and practice will require a holistic approach for specific population groups in order to meet their health needs and to curb and prevent diseases.     

Incidence and Predictors of Multimorbidity in the Elderly: A Population-Based Longitudinal Study

Background

We aimed to calculate 3-year incidence of multimorbidity, defined as the development of two or more chronic diseases in a population of older people free from multimorbidity at baseline. Secondly, we aimed to identify predictors of incident multimorbidity amongst life-style related indicators, medical conditions and biomarkers.

Methods

Data were gathered from 418 participants in the first follow up of the Kungsholmen Project (Stockholm, Sweden, 1991–1993, 78+ years old) who were not affected by multimorbidity (149 had none disease and 269 one disease), including a social interview, a neuropsychological battery and a medical examination.

Results

After 3 years, 33.6% of participants who were without disease and 66.4% of those with one disease at baseline, developed multimorbidity: the incidence rate was 12.6 per 100 person-years (95% CI: 9.2–16.7) and 32.9 per 100 person-years (95% CI: 28.1–38.3), respectively. After adjustments, worse cognitive function (OR, 95% CI, for 1 point lower Mini-Mental State Examination: 1.22, 1.00–1.48) was associated with increased risk of multimorbidity among subjects with no disease at baseline. Higher age was the only predictor of multimorbidity in persons with one disease at baseline.

Conclusions

Multimorbidity has a high incidence at old age. Mental health-related symptoms are likely predictors of multimorbidity, suggesting a strong impact of mental disorders on the health of older people.     

Development of a Multimorbidity Illness Perceptions Scale (MULTIPleS)

Background

Illness perceptions are beliefs about the cause, nature and management of illness, which enable patients to make sense of their conditions. These perceptions can predict adjustment and quality of life in patients with single conditions. However, multimorbidity (i.e. patients with multiple long-term conditions) is increasingly prevalent and a key challenge for future health care delivery. The objective of this research was to develop a valid and reliable measure of illness perceptions for multimorbid patients.

Methods

Candidate items were derived from previous qualitative research with multimorbid patients. Questionnaires were posted to 1500 patients with two or more exemplar long-term conditions (depression, diabetes, osteoarthritis, coronary heart disease and chronic obstructive pulmonary disease). Data were analysed using factor analysis and Rasch analysis. Rasch analysis is a modern psychometric technique for deriving unidimensional and intervally-scaled questionnaires.

Results

Questionnaires from 490 eligible patients (32.6% response) were returned. Exploratory factor analysis revealed five potential subscales ‘Emotional representations’, ‘Treatment burden’, ‘Prioritising conditions’, ‘Causal links’ and ‘Activity limitations’. Rasch analysis led to further item reduction and the generation of a summary scale comprising of items from all scales. All scales were unidimensional and free from differential item functioning or local independence of items. All scales were reliable, but for each subscale there were a number of patients who scored at the floor of the scale.

Conclusions

The MULTIPleS measure consists of five individual subscales and a 22-item summary scale that measures the perceived impact of multimorbidity. All scales showed good fit to the Rasch model and preliminary evidence of reliability and validity. A number of patients scored at floor of each subscale, which may reflect variation in the perception of multimorbidity. The MULTIPleS measure will facilitate research into the impact of illness perceptions on adjustment, clinical outcomes, quality of life, and costs in patients with multimorbidity.     

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

BackgroundPatients 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.Methods and findingsWe 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.ConclusionsOur 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.

Jonathan Stokes and co-workers explore patterns of multimorbidity and implications for the organization and costs of care.     

Multimorbidity patterns in the elderly: a new approach of disease clustering identifies complex interrelations between chronic conditions

Objective

Multimorbidity is a common problem in the elderly that is significantly associated with higher mortality, increased disability and functional decline. Information about interactions of chronic diseases can help to facilitate diagnosis, amend prevention and enhance the patients'' quality of life. The aim of this study was to increase the knowledge of specific processes of multimorbidity in an unselected elderly population by identifying patterns of statistically significantly associated comorbidity.

Methods

Multimorbidity patterns were identified by exploratory tetrachoric factor analysis based on claims data of 63,104 males and 86,176 females in the age group 65+. Analyses were based on 46 diagnosis groups incorporating all ICD-10 diagnoses of chronic diseases with a prevalence ≥ 1%. Both genders were analyzed separately. Persons were assigned to multimorbidity patterns if they had at least three diagnosis groups with a factor loading of 0.25 on the corresponding pattern.

Results

Three multimorbidity patterns were found: 1) cardiovascular/metabolic disorders [prevalence female: 30%; male: 39%], 2) anxiety/depression/somatoform disorders and pain [34%; 22%], and 3) neuropsychiatric disorders [6%; 0.8%]. The sampling adequacy was meritorious (Kaiser-Meyer-Olkin measure: 0.85 and 0.84, respectively) and the factors explained a large part of the variance (cumulative percent: 78% and 75%, respectively). The patterns were largely age-dependent and overlapped in a sizeable part of the population. Altogether 50% of female and 48% of male persons were assigned to at least one of the three multimorbidity patterns.

Conclusion

This study shows that statistically significant co-occurrence of chronic diseases can be subsumed in three prevalent multimorbidity patterns if accounting for the fact that different multimorbidity patterns share some diagnosis groups, influence each other and overlap in a large part of the population. In recognizing the full complexity of multimorbidity we might improve our ability to predict needs and achieve possible benefits for elderly patients who suffer from multimorbidity.     

Multimorbidity in Adult Asylum Seekers: A First Overview

Principals

Over the last two decades, the total annual number of applications for asylum in the countries of the European Union has increased from 15,000 to more than 300,000 people. The aim of this study was to give a first overview on multimorbidity of adult asylum seekers.

Methods

Our retrospective Swiss single center data analysis examined multimorbidity of adult asylums seekers admitted to our ED between 1 January 2000 and 31 December 2012.

Results

A total of 3170 patients were eligible for the study; they were predominantly male (2392 male, 75.5% versus 778 female, 24.5). The median age of the patients was 28 years (range 28–82). The most common region of origin was Africa (1544, 48.7%), followed by the Middle East (736, 23.6%). 2144 (67.6%) of all patients were not multimorbid. A total of 1183 (37.7%) of our patients were multimorbid. The mean Charlson comorbidity index was 0.25 (SD 1.1, range 0–12). 634 (20%) of all patients sufferem from psychiatric diseases, followed by chronic medical conditions (12.6%, 399) and infectious diseases (4.7%, 150). Overall, 11% (349) of our patients presented as a direct consequence of prior violence. Patients from Sri Lanka/India most often suffered from addictions problems (50/240, 20.8%, p<0.0001). Infectious diseases were most frequent in patients from Africa (6.6%), followed by the Balkans and Eastern Europe/Russia (each 3.8%).

Conclusion

The health care problems of asylum seekers are manifold. More than 60% of the study population assessed in our study did not suffer from more than one disease. Nevertheless a significant percentage of asylum seekers is multimorbid and exhibits underlying psychiatric, infectious or chronic medical conditions despite their young age.     

Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis

Objectives

The primary objective of this study was to identify the existence of chronic disease multimorbidity patterns in the primary care population, describing their clinical components and analysing how these patterns change and evolve over time both in women and men. The secondary objective of this study was to generate evidence regarding the pathophysiological processes underlying multimorbidity and to understand the interactions and synergies among the various diseases.

Methods

This observational, retrospective, multicentre study utilised information from the electronic medical records of 19 primary care centres from 2008. To identify multimorbidity patterns, an exploratory factor analysis was carried out based on the tetra-choric correlations between the diagnostic information of 275,682 patients who were over 14 years of age. The analysis was stratified by age group and sex.

Results

Multimorbidity was found in all age groups, and its prevalence ranged from 13% in the 15 to 44 year age group to 67% in those 65 years of age or older. Goodness-of-fit indicators revealed sample values between 0.50 and 0.71. We identified five patterns of multimorbidity: cardio-metabolic, psychiatric-substance abuse, mechanical-obesity-thyroidal, psychogeriatric and depressive. Some of these patterns were found to evolve with age, and there were differences between men and women.

Conclusions

Non-random associations between chronic diseases result in clinically consistent multimorbidity patterns affecting a significant proportion of the population. Underlying pathophysiological phenomena were observed upon which action can be taken both from a clinical, individual-level perspective and from a public health or population-level perspective.     

Patterns of multimorbidity in the aged population. Results from the KORA-Age study

Multimorbidity is a common problem in aged populations with a wide range of individual and societal consequences. The objective of the study was to explore patterns of comorbidity and multimorbidity in an elderly population using different analytical approaches. Data were gathered from the population-based KORA-Age project, which included 4,127 persons aged 65-94 years living in the city of Augsburg and its two surrounding counties in Southern Germany. Information on the presence of 13 chronic conditions was collected in a standardized telephone interview and a self-administered questionnaire. Patterns of comorbidity and multimorbidity were analyzed using prevalence figures, logistic regression models and exploratory tetrachoric factor analysis. The prevalence of multimorbidity (≥2 diseases) was 58.6% in the total sample. Hypertension and diabetes (Odds Ratio [OR] 2.95, 99.58% confidence interval [CI] [2.19-3.96]), as well as hypertension and stroke (OR 2.00, 99.58% CI [1.26-3.16]) most often occurred in combination. This association was independent of age, sex and the presence of other conditions. Using factor analysis, we identified four patterns of multimorbidity: the first pattern includes cardiovascular and metabolic diseases, the second includes joint, liver, lung and eye diseases, the third covers mental and neurologic diseases and the fourth pattern includes gastrointestinal diseases and cancer. 44% of the persons were assigned to at least one of the four multimorbidity patterns; 14% could be assigned to both the cardiovascular/metabolic and the joint/liver/lung/eye pattern. Further common pairs were the mental/neurologic pattern combined with the cardiovascular/metabolic pattern (7.2%) or the joint/liver/lung/eye pattern (5.3%), respectively. Our results confirmed the existence of co-occurrence of certain diseases in elderly persons, which is not caused by chance. Some of the identified patterns of multimorbidity and their overlap may indicate common underlying pathological mechanisms.     

The European General Practice Research Network Presents the Translations of Its Comprehensive Definition of Multimorbidity in Family Medicine in Ten European Languages

Background

Multimorbidity, according to the World Health Organization, exists when there are two or more chronic conditions in one patient. This definition seems inaccurate for the holistic approach to Family Medicine (FM) and long-term care. To avoid this pitfall the European General Practitioners Research Network (EGPRN) designed a comprehensive definition of multimorbidity using a systematic literature review.

Objective

To translate that English definition into European languages and to validate the semantic, conceptual and cultural homogeneity of the translations for further research.

Method

Forward translation of the EGPRN’s definition of multimorbidity followed by a Delphi consensus procedure assessment, a backward translation and a cultural check with all teams to ensure the homogeneity of the translations in their national context. Consensus was defined as 70% of the scores being higher than 6. Delphi rounds were repeated in each country until a consensus was reached

Results

229 European medical expert FPs participated in the study. Ten consensual translations of the EGPRN comprehensive definition of multimorbidity were achieved.

Conclusion

A comprehensive definition of multimorbidity is now available in English and ten European languages for further collaborative research in FM and long-term care.     

The Influence of Multimorbidity on Clinical Progression of Dementia in a Population-Based Cohort

Introduction

Co-occurrence with other chronic diseases may influence the progression of dementia, especially in case of multiple chronic diseases. We aimed to verify whether multimorbidity influenced cognitive and daily functioning during nine years after dementia diagnosis compared with the influence in persons without dementia.

Methods

In the Kungsholmen Project, a population-based cohort study, we followed 310 persons with incident dementia longitudinally. We compared their trajectories with those of 679 persons without dementia. Progression was studied for cognition and activities of daily life (ADLs), measured by MMSE and Katz Index respectively. The effect of multimorbidity and its interaction with dementia status was studied using individual growth models.

Results

The mean (SD) follow-up time was 4.7 (2.3) years. As expected, dementia related to both the decline in cognitive and daily functioning. Irrespective of dementia status, persons with more diseases had significantly worse baseline daily functioning. In dementia patients having more diseases also related to a significantly faster decline in daily functioning. Due to the combination of lower functioning in ADLs at baseline and faster decline, dementia patients with multimorbidity were about one to two years ahead of the decline of dementia patients without any co-morbidity. In persons without dementia, no significant decline in ADLs over time was present, nor was multimorbidity related to the decline rate. Cognitive decline measured with MMSE remained unrelated to the number of diseases present at baseline.

Conclusion

Multimorbidity was related to baseline daily function in both persons with and without dementia, and with accelerated decline in people with dementia but not in non-demented individuals. No relationship of multimorbidity with cognitive functioning was established. These findings imply a strong interconnection between physical and mental health, where the greatest disablement occurs when both somatic and mental disorders are present.