Summary Background Long-term disorders are the main challenge facing health-care systems worldwide, but health systems are largely configured for individual diseases rather than multimorbidity. We ...examined the distribution of multimorbidity, and of comorbidity of physical and mental health disorders, in relation to age and socioeconomic deprivation. Methods In a cross-sectional study we extracted data on 40 morbidities from a database of 1 751 841 people registered with 314 medical practices in Scotland as of March, 2007. We analysed the data according to the number of morbidities, disorder type (physical or mental), sex, age, and socioeconomic status. We defined multimorbidity as the presence of two or more disorders. Findings 42·2% (95% CI 42·1–42·3) of all patients had one or more morbidities, and 23·2% (23·08–23·21) were multimorbid. Although the prevalence of multimorbidity increased substantially with age and was present in most people aged 65 years and older, the absolute number of people with multimorbidity was higher in those younger than 65 years (210 500 vs 194 996). Onset of multimorbidity occurred 10–15 years earlier in people living in the most deprived areas compared with the most affluent, with socioeconomic deprivation particularly associated with multimorbidity that included mental health disorders (prevalence of both physical and mental health disorder 11·0%, 95% CI 10·9–11·2% in most deprived area vs 5·9%, 5·8%–6·0% in least deprived). The presence of a mental health disorder increased as the number of physical morbidities increased (adjusted odds ratio 6·74, 95% CI 6·59–6·90 for five or more disorders vs 1·95, 1·93–1·98 for one disorder), and was much greater in more deprived than in less deprived people (2·28, 2·21–2·32 vs 1·08, 1·05–1·11). Interpretation Our findings challenge the single-disease framework by which most health care, medical research, and medical education is configured. A complementary strategy is needed, supporting generalist clinicians to provide personalised, comprehensive continuity of care, especially in socioeconomically deprived areas. Funding Scottish Government Chief Scientist Office.
Abstract
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.
Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to ...include.
We conducted a cross-sectional study using English primary care data for 1,168,260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as ≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 commonest conditions were considered was 4.6% (95% CI 4.6, 4.6 p < 0.001), rising to 29.5% (95% CI 29.5, 29.6 p < 0.001) considering the 10 commonest, 35.2% (95% CI 35.1, 35.3 p < 0.001) considering the 20 commonest, and 40.5% (95% CI 40.4, 40.6 p < 0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0- to 9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of "comorbidity." Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition-lists, but this highlights further variability in prevalence estimates across studies.
In this study, we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence.
Multimorbidity, the presence of two or more mental or physical chronic non-communicable diseases, is a major challenge for the health system in China, which faces unprecedented ageing of its ...population. Here we examined the distribution of physical multimorbidity in relation to socioeconomic status; the association between physical multimorbidity, health-care service use, and catastrophic health expenditures; and whether these associations varied by socioeconomic group and social health insurance schemes.
In this population-based, panel data analysis, we used data from three waves of the nationally representative China Health and Retirement Longitudinal Study (CHARLS) for 2011, 2013, and 2015. We included participants aged 50 years and older in 2015, who had complete follow-up for the three waves. We used 11 physical non-communicable diseases to measure physical multimorbidity and annual per-capita household consumption spending as a proxy for socioeconomic status.
Of 17 708 participants in CHARLS, 11 817 were eligible for inclusion in our analysis. The median age of participants was 62 years (IQR 56–69) in 2015, and 5766 (48·8%) participants were male. 7320 (61·9%) eligible participants had physical multimorbidity in China in 2015. The prevalence of physical multimorbidity was increased with older age (odds ratio 2·93, 95% CI 2·71–3·15), among women (2·70, 2·04–3·57), within a higher socioeconomic group (for quartile 4 highest group 1·50, 1·24–1·82), and higher educational level (5·17, 3·02–8·83); however, physical multimorbidity was more common in poorer regions than in the more affluent regions. An additional chronic non-communicable disease was associated with an increase in the number of outpatient visits (incidence rate ratio 1·29, 95% CI 1·27–1·31), and number of days spent in hospital as an inpatient (1·38, 1·35–1·41). We saw similar effects in health service use of an additional chronic non-communicable disease in different socioeconomic groups and among those covered by different social health insurance programmes. Overall, physical multimorbidity was associated with a significantly increased likelihood of catastrophic health expenditure (for the overall population: odds ratio 1·29, 95% CI 1·26–1·32, adjusted for sociodemographic variables). The effect of physical multimorbidity on catastrophic health expenditures persisted even among the higher socioeconomic groups and across all health insurance programmes.
Concerted efforts are needed to reduce health inequalities that are due to physical multimorbidity, and its adverse economic effect in population groups in China. Social health insurance reforms must place emphasis on reducing out-of-pocket spending for patients with multimorbidity to provide greater financial risk protection.
None.
The care of older people with dementia is often complicated by physical comorbidity and polypharmacy, but the extent and patterns of these have not been well described. This paper reports analysis of ...these factors within a large, cross-sectional primary care data set.
Data were extracted for 291,169 people aged 65 years or older registered with 314 general practices in the UK, of whom 10,258 had an electronically recorded dementia diagnosis. Differences in the number and type of 32 physical conditions and the number of repeat prescriptions in those with and without dementia were examined. Age–gender standardised rates were used to calculate odds ratios (ORs) of physical comorbidity and polypharmacy.
People with dementia, after controlling for age and sex, had on average more physical conditions than controls (mean number of conditions 2.9 versus 2.4; P < 0.001) and were on more repeat medication (mean number of repeats 5.4 versus 4.2; P < 0.001). Those with dementia were more likely to have 5 or more physical conditions (age–sex standardised OR sOR 1.42, 95% confidence interval (CI) 1.35–1.50; P < 0.001) and were also more likely to be on 5 or more (sOR 1.46; 95% CI 1.40–1.52; P < 0.001) or 10 or more repeat prescriptions (sOR 2.01; 95% CI 1.90–2.12; P < 0.001).
People with dementia have a higher burden of comorbid physical disease and polypharmacy than those without dementia, even after accounting for age and sex differences. Such complex needs require an integrated response from general health professionals and multidisciplinary dementia specialists.
Multimorbidity is a major challenge to health systems globally and disproportionately affects socioeconomically disadvantaged populations. We examined socioeconomic inequalities in developing ...multimorbidity across the lifecourse and investigated the contribution of five behaviour-related risk factors.
The Twenty-07 study recruited participants aged approximately 15, 35, and 55 years in 1987 and followed them up over 20 years. The primary outcome was development of multimorbidity (2+ health conditions). The relationship between five different risk factors (smoking, alcohol consumption, diet, body mass index (BMI), physical activity) and the development of multimorbidity was assessed. Social patterning in the development of multimorbidity based on two measures of socioeconomic status (area-based deprivation and household income) was then determined, followed by investigation of potential mediation by the five risk factors. Multilevel logistic regression models and predictive margins were used for statistical analyses. Socioeconomic inequalities in multimorbidity were quantified using relative indices of inequality and attenuation assessed through addition of risk factors.
Multimorbidity prevalence increased markedly in all cohorts over the 20 years. Socioeconomic disadvantage was associated with increased risk of developing multimorbidity (most vs least deprived areas: odds ratio (OR) 1.46, 95% confidence interval (CI) 1.26-1.68), and the risk was at least as great when assessed by income (OR 1.53, 95% CI 1.25-1.87) or when defining multimorbidity as 3+ conditions. Smoking (current vs never OR 1.56, 1.36-1.78), diet (no fruit/vegetable consumption in previous week vs consumption every day OR 1.57, 95% CI 1.33-1.84), and BMI (morbidly obese vs healthy weight OR 1.88, 95% CI 1.42-2.49) were strong independent predictors of developing multimorbidity. A dose-response relationship was observed with number of risk factors and subsequent multimorbidity (3+ risk factors vs none OR 1.91, 95% CI 1.57-2.33). However, the five risk factors combined explained only 40.8% of socioeconomic inequalities in multimorbidity development.
Preventive measures addressing known risk factors, particularly obesity and smoking, could reduce the future multimorbidity burden. However, major socioeconomic inequalities in the development of multimorbidity exist even after taking account of known risk factors. Tackling social determinants of health, including holistic health and social care, is necessary if the rising burden of multimorbidity in disadvantaged populations is to be redressed.
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.
Chronic kidney disease (CKD) is commonly comorbid with hypertension, diabetes, and cardiovascular disease (CVD). However, the extent of comorbidity in CKD across a range of concordant (shared ...pathophysiology and/or treatment) conditions and discordant (unrelated pathophysiology and/or different or contradictory treatment) conditions is not well documented.
To ascertain the prevalence of comorbidity, across 39 physical and mental health comorbidities, in adults with CKD in a large, nationally representative primary care population.
Cross-sectional analysis of a primary care dataset representing 1 274 374 adults in Scotland.
This study was a secondary analysis of general practice electronic medical record data using binary logistic regression models adjusted for age, sex, and socioeconomic status. Data of adults aged ≥25 years and 40 long-term conditions were used.
A total of 98.2% of adults with CKD had at least one comorbidity, versus 51.8% in controls. After adjustment for age, sex, and deprivation, people with CKD were more likely to have 1 (adjusted odds ratio aOR 6.5, 95% confidence interval CI = 6.0 to 7.1), 2-3 (aOR 15.2, 95% CI = 14.0 to 16.5), 4-6 (odds ratio OR 26.6, 95% CI = 24.4 to 28.9), and ≥7 other conditions (OR 41.9, 95% CI = 38.3 to 45.8). Furthermore, all concordant (seven out of seven), the majority of discordant physical health conditions (17 out of 24), and mental health conditions (six out of eight) had statistically significant positive associations with CKD after adjustment.
Chronic kidney disease is associated with extreme comorbidity across a wide range of mental and physical conditions. Routine care for people with CKD should include recognition and management of comorbidities, and clinical guidelines should support clinicians to do this.
Parkinson's disease is complicated by comorbidity and polypharmacy, but the extent and patterns of these are unclear. We describe comorbidity and polypharmacy in patients with and without Parkinson's ...disease across 31 other physical, and seven mental health conditions.
We analysed primary health-care data on 510,502 adults aged 55 and over. We generated standardised prevalence rates by age-groups, gender, and neighbourhood deprivation, then calculated age, sex and deprivation adjusted odds ratios (OR) and 95% confidence intervals (95% CI) for those with PD compared to those without, for the prevalence, and number of conditions.
Two thousand six hundred forty (0.5%) had Parkinson's disease, of whom only 7.4% had no other conditions compared with 22.9% of controls (adjusted OR aOR 0.43, 95% 0.38-0.49). The Parkinson's group had more conditions, with the biggest difference found for seven or more conditions (PD 12.1% vs. controls 3.9%; aOR 2.08 95% CI 1.84-2.35). 12 of the 31 physical conditions and five of the seven mental health conditions were significantly more prevalent in the PD group. 44.5% with Parkinson's disease were on five to nine repeat prescriptions compared to 24.5% of controls (aOR 1.40; 95% CI 1.28 to 1.53) and 19.2% on ten or more compared to 6.2% of controls (aOR 1.90; 95% CI 1.68 to 2.15).
Parkinson's disease is associated with substantial physical and mental co-morbidity. Polypharmacy is also a significant issue due to the complex nature of the disease and associated treatments.
Abstract Purpose We set out to compare patients' expectations, consultation characteristics, and outcomes in areas of high and low socioeconomic deprivation, and to examine whether the same factors ...predict better outcomes in both settings. Methods Six hundred fifty-nine patients attending 47 general practitioners in high- and low-deprivation areas of Scotland participated. We assessed patients' expectations of involvement in decision making immediately before the consultation and patients' perceptions of their general practitioners' empathy immediately after. Consultations were video recorded and analyzed for verbal and nonverbal physician behaviors. Symptom severity and related well-being were measured at baseline and 1 month post-consultation. Consultation factors predicting better outcomes at 1 month were identified using backward selection methods. Results Patients in deprived areas had less desire for shared decision-making ( P <.001). They had more problems to discuss ( P = .01) within the same consultation time. Patients in deprived areas perceived their general practitioners (GPs) as less empathic ( P = .02), and the physicians displayed verbal and nonverbal behaviors that were less patient centered. Outcomes were worse at 1 month in deprived than in affluent groups (70% response rate; P <.001). Perceived physician empathy predicted better outcomes in both groups. Conclusions Patients' expectations, GPs' behaviors within the consultation, and health outcomes differ substantially between high- and low-deprivation areas. In both settings, patients' perceptions of the physicians' empathy predict health outcomes. These findings are discussed in the context of inequalities and the “inverse care law.”
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