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.

Identifying populations with chronic pain in primary care: developing an algorithm and logic rules applied to coded primary care diagnostic and medication data.

BACKGROUND: Estimates of chronic pain prevalence using coded primary care data are likely to be substantially lower than estimates derived from community surveys. Most primary care studies have estimated chronic pain prevalence using data searches confined to analgesic medication prescriptions. Increasingly, following recent NICE guideline recommendations, patients and doctors opt for non-drug treatment of chronic pain thus excluding these patients from prevalence estimates based on medication codes. We aimed to develop and test an algorithm combining medication codes with selected diagnostic codes to estimate chronic pain prevalence using coded primary care data. METHODS: Following a scoping review 4 criteria were developed to identify cohorts of people with chronic pain. These were (1) people with one of 12 ('tier 1') conditions that almost always results in the individual having chronic pain (2) people with one of 20 ('tier 2') conditions included when there are also 3 or more prescription-only analgesics issued in the last 12 months (3) chronic neuropathic pain, or (4) 4 or more prescription-only analgesics issued in the last 12 months. These were translated into 8 logic rules which included 1,932 SNOMED CT codes. RESULTS: The algorithm was run on primary care data from 41 GP Practices in Lambeth. The total population consisted of 386,238 GP registered adults ≥ 18 years as of the 31st March 2021. 64,135 (16.6%) were identified as people with chronic pain. This definition demonstrated notably high rates in Black ethnicity females, and higher rates in the most deprived, and older population. CONCLUSIONS: Estimates of chronic pain prevalence using structured healthcare data have previously shown lower prevalence estimates for chronic pain than reported in community surveys. This has limited the ability of researchers and clinicians to fully understand and address the complex multifactorial nature of chronic pain. Our study demonstrates that it may be possible to establish more representative prevalence estimates using structured data than previously possible. Use of logic rules offers the potential to move systematic identification and population-based management of chronic pain into mainstream clinical practice at scale and support improved management of symptom burden for people experiencing chronic pain.

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.

Paying for what matters most: the future of outcomes-based payments in healthcare.

Payment by results' (PbR) is probably one of the most widely debated misnomers in the NHS. At best, rather than reimbursing results, it can be considered as payment for providing collections of acute care activities. Better prevention is often cited as the only solution to sustainability, but currencies which reward prevention seemingly remain very hard to create. This review article explores the impact of legacy payment systems on health and care systems nationally and internationally. It examines alternative models, such as payment for outcomes, and questions whether any alternative payment systems may better support prevention, and finally achieve the 'holy grail' of a truly sustainable care system. If payment for outcomes offers a potential solution, then what are the key barriers and enablers? The case for alternative, novel metrics for success is proposed, which may better support care systems to measure their progress towards sustainability than existing indicators.

From volume to value? Can a value-based approach help deliver the ambitious aims of the NHS cardiovascular disease outcomes strategy?

The last year has seen the publication of two papers which will radically shape the future organisation of healthcare in general, and cardiovascular disease in particular: Cardiovascular Outcomes Strategy (Department of Health) and The Strategy That Will Fix Healthcare (Harvard Business Review). Both publications set out a health delivery mechanism based around improvement of outcomes for groups of patients with similar needs. Instead of organising care around disease categories, it is proposed that the cardiovascular diseases are treated as a single family of diseases. We are reaching the limits of what an activity-based system organised around existing provider structures can sustainably deliver. Unless we find delivery systems which reduce costs while at the same time improving outcomes that are meaningful to patients, then we will be faced with a future of healthcare rationing. The increasing burden of chronic disease and ongoing quality concerns in delivery systems has created a 'burning platform', which must be addressed if we are to maintain a system which offers high-quality care free at the point of delivery. This paper explores what an outcomes and value-based system could look like when applied to cardiovascular disease. It explores what it means for providers and patients if we start to think about outcomes by patients with similar needs, rather than by intervention, or by clinical specialty. As a specific example, the paper explores the features of an Integrated Circulation Service, what the challenges and implications might be, and whether there is any evidence that this would deliver improved outcomes, at a lower cost to the system.