Identifying multimorbidity clusters in an unselected population of hospitalised patients.

Multimorbidity (multiple coexisting chronic health conditions) is common and increasing worldwide, and makes care challenging for both patients and healthcare systems. To ensure care is patient-centred rather than specialty-centred, it is important to know which conditions commonly occur together and identify the corresponding patient profile. To date, no studies have described multimorbidity clusters within an unselected hospital population. Our aim was to identify and characterise multimorbidity clusters, in a large, unselected hospitalised patient population. Linked inpatient hospital episode data were used to identify adults admitted to hospital in Grampian, Scotland in 2014 who had ≥ 2 of 30 chronic conditions diagnosed in the 5 years prior. Cluster analysis (Gower distance and Partitioning around Medoids) was used to identify groups of patients with similar conditions. Clusters of conditions were defined based on clinical review and assessment of prevalence within patient groups and labelled according to the most prevalent condition. Patient profiles for each group were described by age, sex, admission type, deprivation and urban-rural area of residence. 11,389 of 41,545 hospitalised patients (27%) had ≥ 2 conditions. Ten clusters of conditions were identified: hypertension; asthma; alcohol misuse; chronic kidney disease and diabetes; chronic kidney disease; chronic pain; cancer; chronic heart failure; diabetes; hypothyroidism. Age ranged from 51 (alcohol misuse) to 79 (chronic heart failure). Women were a higher proportion in the chronic pain and hypothyroidism clusters. The proportion of patients from the most deprived quintile of the population ranged from 6% (hypertension) to 14% (alcohol misuse). Identifying clusters of conditions in hospital patients is a first step towards identifying opportunities to target patient-centred care towards people with unmet needs, leading to improved outcomes and increased efficiency. Here we have demonstrated the face validity of cluster analysis as an exploratory method for identifying clusters of conditions in hospitalised patients with multimorbidity.

Prevalence of secondary care multimorbidity in mid-life and its association with premature mortality in a large longitudinal cohort study.

OBJECTIVES: Multimorbidity is the coexistence of two or more health conditions in an individual. Multimorbidity in younger adults is increasingly recognised as an important challenge. We assessed the prevalence of secondary care multimorbidity in mid-life and its association with premature mortality over 15 years of follow-up, in the Aberdeen Children of the 1950s (ACONF) cohort. METHOD: A prospective cohort study using linked electronic health and mortality records. Scottish ACONF participants were linked to their Scottish Morbidity Record hospital episode data and mortality records. Multimorbidity was defined as two or more conditions and was assessed using healthcare records in 2001 when the participants were aged between 45 and 51 years. The association between multimorbidity and mortality over 15 years of follow-up (to ages 60-66 years) was assessed using Cox proportional hazards regression. There was also adjustment for key covariates: age, gender, social class at birth, intelligence at age 7, secondary school type, educational attainment, alcohol, smoking, body mass index and adult social class. RESULTS: Of 9625 participants (51% males), 3% had multimorbidity. The death rate per 1000 person-years was 28.4 (95% CI 23.2 to 34.8) in those with multimorbidity and 5.7 (95% CI 5.3 to 6.1) in those without. In relation to the reference group of those with no multimorbidity, those with multimorbidity had a mortality HR of 4.5 (95% CI 3.4 to 6.0) over 15 years and this association remained when fully adjusted for the covariates (HR 2.5 (95% CI 1.5 to 4.0)). CONCLUSION: Multimorbidity prevalence was 3% in mid-life when measured using secondary care administrative data. Multimorbidity in mid-life was associated with premature mortality.

Urban-rural and socioeconomic status: Impact on multimorbidity prevalence in hospitalized patients.

OBJECTIVE: The aim of this study was to describe multimorbidity prevalence in hospitalized adults, by urban-rural area of residence and socioeconomic status (SES). METHODS: Linked hospital episode data were used. Adults (≥18 years) admitted to hospital as an inpatient during 2014 in Grampian, Scotland, were included. Conditions were identified from admissions during the 5 years prior to the first admission in 2014. Multimorbidity was defined as ≥2 conditions and measured using Tonelli et al. based on International Classification of Diseases-10 coding (preselected list of 30 conditions). We used proportions and 95% confidence intervals (CIs) to summarize the prevalence of multimorbidity by age group, sex, urban-rural category and deprivation. The association between multimorbidity and patient characteristics was assessed using the χ 2 test. RESULTS: Forty one thousand five hundred and forty-five patients were included (median age 62, 52.6% female). Overall, 27.4% (95% CI 27.0, 27.8) of patients were multimorbid. Multimorbidity prevalence was 28.8% (95% CI 28.1, 29.5) in large urban versus 22.0% (95% CI 20.9, 23.3) in remote rural areas and 28.7% (95% CI 27.2, 30.3) in the most deprived versus 26.0% (95% CI 25.2, 26.9) in the least deprived areas. This effect was consistent in all age groups, but not statistically significant in the age group 18-29 years. Multimorbidity increased with age but was similar for males and females. CONCLUSION: Given the scarcity of research into the effect of urban-rural area and SES on multimorbidity prevalence among hospitalized patients, these findings should inform future research into new models of care, including the consideration of urban-rural area and SES.

The long-term health effects of attending a selective school: a natural experiment.

BACKGROUND: Education is widely associated with better physical and mental health, but isolating its causal effect is difficult because education is linked with many socioeconomic advantages. One way to isolate education's effect is to consider environments where similar students are assigned to different educational experiences based on objective criteria. Here we measure the health effects of assignment to selective schooling based on test score, a widely debated educational policy. METHODS: In 1960s Britain, children were assigned to secondary schools via a test taken at age 11. We used regression discontinuity analysis to measure health differences in 5039 people who were separated into selective and non-selective schools this way. We measured selective schooling's effect on six outcomes: mid-life self-reports of health, mental health, and life limitation due to health, as well as chronic disease burden derived from hospital records in mid-life and later life, and the likelihood of dying prematurely. The analysis plan was accepted as a registered report while we were blind to the health outcome data. RESULTS: Effect estimates for selective schooling were as follows: self-reported health, 0.1 worse on a 4-point scale (95%CI - 0.2 to 0); mental health, 0.2 worse on a 16-point scale (- 0.5 to 0.1); likelihood of life limitation due to health, 5 percentage points higher (- 1 to 10); mid-life chronic disease diagnoses, 3 fewer/100 people (- 9 to + 4); late-life chronic disease diagnoses, 9 more/100 people (- 3 to + 20); and risk of dying before age 60, no difference (- 2 to 3 percentage points). Extensive sensitivity analyses gave estimates consistent with these results. In summary, effects ranged from 0.10-0.15 standard deviations worse for self-reported health, and from 0.02 standard deviations better to 0.07 worse for records-derived health. However, they were too imprecise to allow the conclusion that selective schooling was detrimental. CONCLUSIONS: We found that people who attended selective secondary school had more advantaged economic backgrounds, higher IQs, higher likelihood of getting a university degree, and better health. However, we did not find that selective schooling itself improved health. This lack of a positive influence of selective secondary schooling on health was consistent despite varying a wide range of model assumptions.

Cohort profile: The prospective study on Chinese elderly with multimorbidity in primary care in Hong Kong.

PURPOSE: This is an ongoing prospective cohort aiming to examine the biopsychosocial health profiles and predictors of health outcomes of older patients with multimorbidity in primary care in Hong Kong. PARTICIPANTS: From April 2016 to October 2017, 1077 patients aged 60+ years with at least two chronic diseases were recruited in four public primary care clinics in the New Territories East Region of Hong Kong. FINDINGS TO DATE: After weighting, the patients had 4.1 (1.8) chronic conditions and 2.5 (1.9) medications on average; 37% forgot taking medication sometimes; 71% rated their health as fair or poor; 17% were frail; 73% reported one (21%) or two or more (52%) body pain areas; 62% were overweight/obese; 23% reported chewing difficulty, 18% reported incontinence; 36% had current stage 1/2 hypertension; 38% had handgrip strength below the cut-off; 10% screened positive in sarcopenia; 17% had mild or severer cognitive impairment; 17% had mild to severe depression; 16% had mild to severe anxiety; 50% had subthreshold to severe insomnia; 28% indicated being lonely; 12% needed help in at least one out of the five daily functions and the EuroQoL-5-Dimensions-5-Level index score was 0.81 (0.20) and its Visual Analogue Scale (VAS) score was 67.6 (14.6). In the past 12 months, 17% were hospitalised, 92% attended general outpatient clinics, 70% attended specialist outpatient clinics and 10% used elderly daycare centre services, the median out-of-pocket health cost was HK$1000 (US$150). Female and male patients showed significant differences in many biopsychosocial health aspects. FUTURE PLANS: With assessments and clinical data, the cohort can be used for understanding longitudinal trajectories of biopsychosocial health profiles of Chinese older patients with multimorbidity in primary care. We are also initially planning cohort studies on factors associated with various health outcomes, as well as quality of life and healthcare use. COHORT REGISTRATION NUMBER: ChiCTR-OIC-16008477.

Impact of educational attainment on the association between social class at birth and multimorbidity in middle age in the Aberdeen Children of the 1950s cohort study.

OBJECTIVE: Multimorbidity (the coexistence of two or more health conditions) is increasingly prevalent. No long-term cohort study has examined the impact of contemporaneously measured birth social class along with educational attainment on adult self-reported multimorbidity. We investigated the impact of educational attainment on the relationship between social class at birth and adult self-reported multimorbidity in the Aberdeen Children of the 1950s (ACONF) cohort. METHODS: A prospective cohort study using the ACONF cohort. ACONF included 12 150 individuals born in Aberdeen, Scotland 1950-1956. In 2001, 7184 (64%) responded to a questionnaire providing information including self-reported morbidity and educational attainment. The exposure was father's social class at birth from birth records and the outcome was self-reported multimorbidity.Logistic regression assessed the association between social class and multimorbidity with adjustment for gender, then by educational attainment and finally by childhood cognition and secondary school type. ORs and 95% CIs were presented. RESULTS: Of 7184 individuals (mean age 48, 52% female), 5.4% reported multimorbidity. Birth social class was associated with adult multimorbidity. For example, the OR of multimorbidity adjusted by gender was 0.62 (95% CI 0.39 to 1.00) in the highest social class group (I/II) in relation to the reference group (III (manual)) and was 1.85 (95% CI 1.19 to 2.88) in the lowest social class group. This was partially attenuated in all social class categories by educational attainment, for example, the OR was 0.74 (95% CI 0.45 to 1.21) in group I/II following adjustment. CONCLUSION: Lower social class at birth was associated with developing multimorbidity in middle age. This was partially mediated by educational attainment and future research should consider identifying the other explanatory variables. The results are relevant to researchers and to those aiming to reduce the impact of multimorbidity.

Defining and measuring multimorbidity: a systematic review of systematic reviews.

Background: Multimorbidity, the coexistence of multiple health conditions, is a growing public health challenge. Research and intervention development are hampered by the lack of consensus regarding defining and measuring multimorbidity. The aim of this systematic review was to pool the findings of systematic reviews examining definitions and measures of multimorbidity. Methods: Medline, Embase, PubMed and Cochrane were searched from database inception to February 2017. Two authors independently screened titles, abstracts and full texts and extracted data from the included papers. Disagreements were resolved with a third author. Reviews were quality assessed. Results: Of six reviews, two focussed on definitions and four on measures. Multimorbidity was commonly defined as the presence of multiple diseases or conditions, often with a cut-off of two or more. One review developed a holistic definition including biopsychosocial and somatic factors as well as disease. Reviews recommended using measures validated for the outcome of interest. Disease counts are an alternative if no validated measure exists. Conclusions: To enable comparison between studies and settings, researchers and practitioners should be explicit about their choice of definition and measure. Using a cut-off of two or more conditions as part of the definition is widely adopted. Measure selection should be based on tools validated for the outcome being considered. Where there is no validated measure, or where multiple outcomes or populations are being considered, disease counts are appropriate.

Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures.

INTRODUCTION: Multimorbidity is a complex and growing health challenge. There is no accepted "gold standard" multimorbidity measure for hospital resource planning, and few studies have compared measures in hospitalised patients. AIM: To evaluate operationalisation of two multimorbidity measures in routine hospital episode data in NHS Grampian, Scotland. METHODS: Linked hospital episode data (Scottish Morbidity Record (SMR)) for the years 2009-2016 were used. Adults admitted to hospital as a general/acute inpatient during 2014 were included. Conditions (ICD-10) were identified from general/acute (SMR01) and psychiatric (SMR04) admissions during the five years prior to first admission in 2014. Two count-based multimorbidity measures were used (Charlson Comorbidity Index and Tonelli et al.), and multimorbidity was defined as ≥2 conditions. Kappa statistics assessed agreement. The association between multimorbidity and length of stay, readmission and mortality was assessed using logistic and negative binomial regression as appropriate. RESULTS: In 41,545 adults (median age 62 years, 52.6% female), multimorbidity prevalence was 15.1% (95% CI 14.8%, 15.5%) using Charlson and 27.4% (27.0%, 27.8%) using Tonelli - agreement 85.1% (Kappa 0.57). Multimorbidity prevalence, using both measures, increased with age. Multimorbidity was higher in males (16.5%) than females (13.9%) using the Charlson measure, but similar across genders when measured with Tonelli. After adjusting for covariates, multimorbidity remained associated with longer length of stay (Charlson IRR 1.1 (1.0, 1.2); Tonelli IRR 1.1 (1.0, 1.2)) and readmission (Charlson OR 2.1 (1.9, 2.2); Tonelli OR 2.1 (2.0, 2.2)). Multimorbidity had a stronger association with mortality when measured using Charlson (OR 2.7 (2.5, 2.9)), than using Tonelli (OR 1.8 (1.7, 2.0)). CONCLUSIONS: Multimorbidity measures operationalised in hospital episode data identified those at risk of poor outcomes and such operationalised tools will be useful for future multimorbidity research and use in secondary care data systems. Multimorbidity measures are not interchangeable, and the choice of measure should depend on the purpose. HIGHLIGHTS: Operationalisation of two count-based multimorbidity measures using linked electronic hospital episode data was evaluated (Charlson and Tonelli).First study to compare the Tonelli measure with another measure for investigating multimorbidity in hospitalised patients.Multimorbidity prevalence differed depending on measure used, but both multimorbidity measures identified those at risk of poor outcomes.Operationalised multimorbidity tools have uses for future multimorbidity research and use in secondary care data systems.Multimorbidity measures are not interchangeable, and choice of measure should depend on purpose.

Physical disease and resilient outcomes: a systematic review of resilience definitions and study methods.

BACKGROUND: Findings from physical disease resilience research may be used to develop approaches to reduce the burden of disease. However, there is no consensus on the definition and measurement of resilience in the context of physical disease. OBJECTIVE: The aim was to summarize the range of definitions of physical disease resilience and the approaches taken to study it in studies examining physical disease and its relationship to resilient outcomes. METHODS: Electronic databases were searched from inception to March 2013 for studies in which physical disease was assessed for its association with resilient outcomes. Article screening, data extraction, and quality assessment were carried out independently by 2 reviewers, with disagreements being resolved by a third reviewer. The results were combined using a narrative technique. RESULTS: Of 2280 articles, 12 met the inclusion criteria. Of these studies, 1 was of high quality, 9 were of moderate quality, and 2 were low quality. The common findings were that resilience involves maintaining healthy levels of functioning following adversity and that it is a dynamic process not a personality trait. Studies either assessed resilience based on observed outcomes or via resilience measurement scales. They either considered physical disease as an adversity leading to resilience or as a variable modifying the relationship between adversity and resilience. CONCLUSION: This work begins building consensus as to the approach to take when defining and measuring physical disease resilience. Resilience should be considered as a dynamic process that varies across the life-course and across different domains, therefore the choice of a resilience measure should reflect this.

Charlson index scores from administrative data and case-note review compared favourably in a renal disease cohort.

BACKGROUND: The Charlson index is a widely used measure of comorbidity. The objective was to compare Charlson index scores calculated using administrative data to those calculated using case-note review (CNR) in relation to all-cause mortality and initiation of renal replacement therapy (RRT) in the Grampian Laboratory Outcomes Mortality and Morbidity Study (GLOMMS-1) chronic kidney disease cohort. METHODS: Modified Charlson index scores were calculated using both data sources in the GLOMMS-1 cohort. Agreement between scores was assessed using the weighted Kappa. The association with outcomes was assessed using Poisson regression, and the performance of each was compared using net reclassification improvement. RESULTS: Of 3382 individuals, median age 78.5 years, 56% female, there was moderate agreement between scores derived from the two data sources (weighted kappa 0.41). Both scores were associated with mortality independent of a number of confounding factors. Administrative data Charlson scores were more strongly associated with death than CNR scores using net reclassification improvement. Neither score was associated with commencing RRT. CONCLUSION: Despite only moderate agreement, modified Charlson index scores from both data sources were associated with mortality. Neither was associated with commencing RRT. Administrative data compared favourably and may be superior to CNR when used in the Charlson index to predict mortality.

Approaches to ascertaining comorbidity information: validation of routine hospital episode data with clinician-based case note review.

BACKGROUND: In clinical practice, research, and increasingly health surveillance, planning and costing, there is a need for high quality information to determine comorbidity information about patients. Electronic, routinely collected healthcare data is capturing increasing amounts of clinical information as part of routine care. The aim of this study was to assess the validity of routine hospital administrative data to determine comorbidity, as compared with clinician-based case note review, in a large cohort of patients with chronic kidney disease. METHODS: A validation study using record linkage. Routine hospital administrative data were compared with clinician-based case note review comorbidity data in a cohort of 3219 patients with chronic kidney disease. To assess agreement, we calculated prevalence, kappa statistic, sensitivity, specificity, positive predictive value and negative predictive value. Subgroup analyses were also performed. RESULTS: Median age at index date was 76.3 years, 44% were male, 67% had stage 3 chronic kidney disease and 31% had at least three comorbidities. For most comorbidities, we found a higher prevalence recorded from case notes compared with administrative data. The best agreement was found for cerebrovascular disease (κ = 0.80) ischaemic heart disease (κ = 0.63) and diabetes (κ = 0.65). Hypertension, peripheral vascular disease and dementia showed only fair agreement (κ = 0.28, 0.39, 0.38 respectively) and smoking status was found to be poorly recorded in administrative data. The patterns of prevalence across subgroups were as expected and for most comorbidities, agreement between case note and administrative data was similar. Agreement was less, however, in older ages and for those with three or more comorbidities for some conditions. CONCLUSIONS: This study demonstrates that hospital administrative comorbidity data compared moderately well with case note review data for cerebrovascular disease, ischaemic heart disease and diabetes, however there was significant under-recording of some other comorbid conditions, and particularly common risk factors.

Relationship of EQ-5D quality of life with the presence of co-morbidity and extra-articular features in patients with rheumatoid arthritis.

PURPOSE: Rheumatoid arthritis (RA) is associated with extra-articular features (ExRA) and other co-morbidities. The aim of this study is to quantify their relative contribution to quality of life (QOL) in patients with RA. METHODS: A consecutive series of 114 ambulatory RA patients aged between 40 and 65 years were assessed by a research nurse on a single occasion. Assessment included a patient questionnaire (including EQ-5D), medication review and fasting venous blood sample. Medical records were reviewed by a rheumatologist for co-existing conditions. Multiple linear regression was used to adjust mean differences in EQ-5D in the presence/absence of co-existing conditions for age, sex, university education, arthritis duration, rheumatoid factor, erythrocyte sedimentation rate, current disease-modifying drug therapy, previous hand joint erosions and joint surgery. RESULTS: Mean age was 54 years (82% female) and median arthritis duration 10 years. Unadjusted EQ-5D was -0.09 (95% CI -0.18 to -0.01) lower in patients with any co-existing condition. EQ-5D scores were inversely correlated with the overall number of co-existing conditions (Spearman's ρ -0.31, p = 0.001), number of co-morbidities (ρ -0.22, p = 0.02) and number of ExRA features (ρ -0.22, p = 0.02). There was a linear trend of lower EQ-5D with increasing number of co-existing conditions (p = 0.003). EQ-5D was -0.18 (95% CI -0.33 to -0.02) lower in the presence of more than two co-existing conditions compared to none. Co-morbidity and ExRA features were associated with comparable adjusted reductions (-0.05 vs. -0.06) in EQ-5D scores. CONCLUSION: A wide range of co-existing conditions are associated with poorer QOL in patients with RA.

Inequalities in the distribution of the costs of alcohol misuse in Scotland: a cost of illness study.

AIMS: To examine the distribution of the costs of alcohol misuse across Scotland in 2009/2010, in relation to deprivation. METHODS: A cost of illness approach was used. Alcohol-related harmful effects were assessed for inclusion using a literature review. This was based upon the following categories: direct healthcare costs, intangible health costs, social care costs, crime costs and labour and productivity costs. An analysis of secondary data supplemented by a literature review was carried out to quantify each harmful effect, determine its value and provide an estimate of the distribution by deprivation. The deprivation distributions used were area measures (primarily the Scottish Index of Multiple Deprivation). RESULTS: The overall cost was £7457 million. Two alcohol harmful effects were not included in the overall cost by deprivation due to a lack of data. These were 'children's social work and hearing system' and the criminal justice system costs from 'alcohol-specific offences'. The included alcohol harmful effects demonstrated that 40.41% of the total cost arose from the 20% most deprived areas. The intangible cost category was the largest category (78.65%). CONCLUSION: The study found that the burden of alcohol harmful effects is greater in deprived groups and these burdens do not simply arise from deprived groups but are also experienced more by these groups. The study was limited by a lack of data availability in certain areas, leading to less-precise cost estimates.