Prevalence of multiple long-term conditions (multimorbidity) in England: a whole population study of over 60 million people.

OBJECTIVES: To determine the prevalence of multiple long-term conditions (MLTC) at whole English population level, stratifying by age, sex, socioeconomic status and ethnicity. DESIGN: A whole population study. SETTING: Individuals registered with a general practice in England and alive on 31 March 2020. PARTICIPANTS: 60,004,883 individuals. MAIN OUTCOME MEASURES: MLTC prevalence, defined as two or more of 35 conditions derived from a number of national patient-level datasets. Multivariable logistic regression was used to assess the independent associations of age, sex, ethnicity and deprivation decile with odds of MLTC. RESULTS: The overall prevalence of MLTC was 14.8% (8,878,231), varying from 0.9% (125,159) in those aged 0-19 years to 68.2% (1,905,979) in those aged 80 years and over. In multivariable regression analyses, compared with the 50-59 reference group, the odds ratio was 0.04 (95% confidence interval (CI): 0.04-0.04; p < 0.001) for those aged 0-19 years and 10.21 (10.18-10.24; p < 0.001) for those aged 80 years and over. Odds were higher for men compared with women, 1.02 (1.02-1.02; p < 0.001), for the most deprived decile compared with the least deprived, 2.26 (2.25-2.27; p < 0.001), and for Asian ethnicity compared with those of white ethnicity, 1.05 (1.04-1.05; p < 0.001). Odds were lower for black, mixed and other ethnicities (0.94 (0.94-0.95) p < 0.001, 0.87 (0.87-0.88) p < 0.001 and 0.57 (0.56-0.57) p < 0.001, respectively). MLTC for persons aged 0-19 years were dominated by asthma, autism and epilepsy, for persons aged 20-49 years by depression and asthma, for persons aged 50-59 years by hypertension and depression and for those aged 60 years and older, by cardiometabolic factors and osteoarthritis. There were large numbers of combinations of conditions in each age group ranging from 5936 in those aged 0-19 years to 205,534 in those aged 80 years and over. CONCLUSIONS: While this study provides useful insight into the burden across the English population to assist health service delivery planning, the heterogeneity of MLTC presents challenges for delivery optimisation.

Inequalities in developing multimorbidity over time: A population-based cohort study from an urban, multi-ethnic borough in the United Kingdom.

BACKGROUND: Social and material deprivation accelerate the development of multimorbidity, yet the mechanisms which drive multimorbidity pathways and trajectories remain unclear. We aimed to examine the association between health inequality, risk factors and accumulation or resolution of LTCs, taking disease sequences into consideration. METHODS: We conducted a retrospective cohort of adults aged 18 years and over, registered between April 2005 and May 2020 in general practices in one inner London borough (n = 826,936). Thirty-two long term conditions (LTCs) were selected using a consensus process, based on a definition adapted to the demographic characteristics of the local population. sThe development and resolution of these LTCs were examined according to sociodemographic and clinical risk factors (hypertension; moderate obesity (BMI 30·0-39·9 kg/m2), high cholesterol (total cholesterol > 5 mmol/L), smoking, high alcohol consumption (>14 units per week), and psychoactive substance use), through the application of multistate Markov chain models. FINDINGS: Participants were followed up for a median of 4.2 years (IQR = 1·8 - 8·4); 631,760 (76%) entered the study with no LTCs, 121,424 (15%) with 1 LTC, 41,720 (5%) with 2 LTCs, and 31,966 (4%) with three or more LTCs. At the end of follow-up, 194,777 (24%) gained one or more LTCs, while 45,017 (5%) had resolved LTCs and 27,021 (3%) died. In multistate models, deprivation (hazard ratio [HR] between 1·30 to 1·64), female sex (HR 1·13 to 1·20), and Black ethnicity (HR 1·20 to 1·30; vs White) were independently associated with increased risk of transition from one to two LTCs, and shorter time spent in a healthy state. Substance use was the strongest risk factor for multimorbidity with an 85% probability of gaining LTCs over the next year. First order Markov chains identified consistent disease sequences including: chronic pain or osteoarthritis followed by anxiety and depression; alcohol and substance dependency followed by HIV, viral hepatitis, and liver disease; and morbid obesity followed by diabetes, hypertension, and chronic pain. INTERPRETATION: We examined the relations among 32 LTCs, taking the order of disease occurrence into consideration. Distinctive patterns for the development and accumulation of multimorbidity have emerged, with increased risk of transitioning from no conditions to multimorbidity and mortality related to ethnicity, deprivation and gender. Musculoskeletal disorders, morbid obesity and substance abuse represent common entry points to multimorbidity trajectories.

Adapting the definition of multimorbidity - development of a locality-based consensus for selecting included Long Term Conditions.

BACKGROUND: Defining multimorbidity has proved elusive in spite of attempts to standardise definitions. For national studies, a broad definition is required to capture national diversity. For locally based studies, the definition may need to reflect demographic and morbidity patterns. We aimed to define multimorbidity for an inner city, multi-ethnic, deprived, young age community typical of many large cities. METHODS: We used a scoping literature review to identify the international literature, standards and guidelines on Long Term Condition (LTC) definitions for inclusion in our multimorbidity definition. Consensus was categorised into high, medium or low consensus, depending on the number of literature sources citing each LTC. Findings were presented to a workshop consisting of local health service stakeholders who were asked to select LTCs for inclusion in a second stage review. In the second stage, each LTC was tested against seven evaluation domains: prevalence, impact, preventability, treatment burden, progression to multiple LTCs, impact on younger people, data quality. These domains were used to create 12 target criteria. LTC rankings according to consensus group and target criteria scores were presented to a second workshop for a final decision about LTC inclusion. RESULTS: The literature review identified 18 literature sources citing 86 LTCs: 11 were excluded because they were LTC clusters. The remainder were allocated into consensus groupings: 13 LTCs were 'high consensus' (cited by ≥ 11 sources); 15 were 'medium consensus' (cited by 5-10 sources); 47 were 'low consensus' (cited by < 5 sources). The first workshop excluded 31 LTCs. The remaining 44 LTCs consisted of: 13 high consensus LTCs, all with high target score (score 6-12); 15 medium consensus LTCs, 11 with high target scores; 16 low consensus LTCs, 6 with high target scores. The final workshop selected the 12 high consensus conditions, 12 medium consensus LTCs (10 with high target scores) and 8 low consensus LTCs (3 with high target scores), producing a final selection of 32 LTCs. CONCLUSIONS: Redefining multimorbidity for an urban context ensures local relevance but may diminish national generalisability. We describe a detailed LTC selection process which should be generalisable to other contexts, both local and national.

Outcome After Clipping and Coiling for Aneurysmal Subarachnoid Hemorrhage in Clinical Practice in Europe, USA, and Australia.

BACKGROUND: Within randomized clinical trials (RCTs), coiling of the ruptured aneurysm to prevent rebleeding results in better outcomes than clipping in patients with aneurysmal subarachnoid hemorrhage (aSAH). OBJECTIVE: To study the association of coiling and clipping with outcome after aSAH in daily clinical practice. METHODS: In this controlled, nonrandomized study, we compared outcomes after endovascular coiling and neurosurgical clipping of ruptured intracranial aneurysms in an administrative dataset of 7658 aSAH patients (22 tertiary care hospitals from Europe, USA, Australia; 2007-2013). Because the results contradicted those of the randomized trials, findings were further explored in a large clinical dataset from 2 European centers (2006-2016) of 1501 patients. RESULTS: In the administrative dataset, the crude 14-d case-fatality rate was 6.4% (95% confidence interval [CI] 5.6%-7.2%) after clipping and 8.2% (95% CI 7.4%-9.1%) after coiling. After adjustment for age, sex, and comorbidity/severity, the odds ratio (OR) for 14-d case-fatality after coiling compared to clipping was 1.32 (95% CI 1.10-1.58). In the clinical dataset crude 14-d case fatality rate was 5.7% (95% CI 4.2%-7.8%) for clipping and 9.0% (95% CI 7.3%-11.2%) for coiling. In multivariable logistic regression analysis, the OR for 14-d case-fatality after coiling compared to clipping was 1.7 (95% CI 1.1-2.7), for 90-d case-fatality 1.28 (95% CI 0.91-1.82) and for 90-d poor functional outcome 0.78 (95% CI 0.6-1.01). CONCLUSION: In clinical practice, coiling after aSAH is associated with higher 14-d case-fatality than clipping and nonsuperior outcomes at 90 d. Both options need to be considered in aSAH patients. Further studies should address the reasons for the discrepancy between current data and those from the RCTs.