The health and social care costs of a selection of health conditions and multi-morbidities

Background Multimorbidity (MM) is the presence of 2 or more long-term health conditions in a single individual. It impacts an individual’s quality of life, mental health and wellbeing, daily function, and often results in greater healthcare utilisation the more co-existing conditions they have (1-4). MM is a big challenge facing the NHS, especially given England’s ageing population, with an estimated two-thirds of individuals aged 65 and over having 2 or more long-term conditions (5-6). Yet, little is known about the resource use of these patients despite being the group with the largest impact on the NHS and with the worst health outcomes (7). Existing evidence focuses on specific health conditions and their interactions with other conditions using different methodologies, making comparisons across different conditions difficult. This work has empirically assessed the impact of multi-morbidity on NHS and social care costs. With the aim of answering the question: is the impact of developing a condition on health and social care costs greater for someone with no prior conditions, or for someone with an existing condition. If patients have multiple conditions, there may be some economies of scale involved with treatment, for example they may be able to discuss multiple queries during a single GP appointment, or in some cases the treatment provided will address multiple conditions. However, treating patients with multi-morbidities could theoretically also be more expensive than treating 2 conditions separately, as patients may be more likely to experience complications. Methodology This work considered the individual cost of 11 health conditions with high prevalence in the English population and their most common interactions. These were: chronic obstructive pulmonary disease (COPD), diabetes (types 1 and 2), lung cancer, breast cancer, coronary heart disease (CHD), stroke, hypertension, dementia, liver disease, depression and colorectal cancer. This project had 2 components: a literature review and an empirical estimation of the costs associated with MM. The literature review was used to inform and establish the methodology used in the empirical estimation. The empirical estimation used data on primary healthcare, secondary healthcare, and prescriptions usage from 2015 to estimate annual aggregated healthcare costs per patient. We assessed the cost impact of MM in a systematic way by applying advanced econometric methods to account for the specificities of the data distribution. Our methodology allowed us to attribute healthcare costs to specific conditions. For social care costs, we calculated the estimated costs using 2 different methodologies. For the first (preferred) methodology, we used Somerset Symphony data to calculate the 2014/15 social care costs of patients in South Somerset. This is a dataset that combines primary healthcare, secondary healthcare, and social care data. We thus applied the same methodology that was used to calculate primary and secondary healthcare costs. For the second methodology, we used the estimated health-related quality of life for patients with different conditions and combinations of conditions. We then used a regression (‘line of best fit’) to estimate their probability of requiring social care. Finally, we used unit cost estimates to arrive at estimated values for the costs of social care for individuals with different diseases. What this publication adds Average ‘cost per case’ estimates for individuals with single conditions or multimorbidities, each calculated based on the average age of patients with the condition or multi-morbidity of interest. These average ‘costs per case’ figures are always higher for individuals with multimorbidities than individuals with a single condition, as individuals with multi-morbidities tend to be older and additional conditions incur additional costs. We found that the cost of treating an individual with a multimorbidity is not statistically different than the additive cost of treating 2 individuals, each with one of the conditions, controlling for age and costs unrelated to the condition. As an illustrative example, if it costs £200 to treat a patient with depression and £200 to treat a patient with CHD, we did not find any evidence that it would cost more than £400 to treat a single patient with both depression and CHD (controlling for age and unrelated disease costs). In numerous cases, when considering healthcare costs, we have found that multimorbidity is associated with a reduction of the total individual cost compared to the sum of individual costs of patients. For example, a male patient with diabetes and CHD will cost between 77% and 78% (depending on the definition of sample prevalence) of the cost of treating 2 patients, one with diabetes and one with CHD, controlling for age and unrelated costs. Applying the same methodology for social care costs as for healthcare costs, we did not find any evidence that multi-morbidity is associated with either an increase or a reduction in total individual cost compared to the sum of individual costs of patient, for social care costs. This may be due to the relatively small sample size of the South Somerset data we used to estimate social care costs. Applying the alternative methodology for social care costs, which estimated social care need based on age and quality of life, we estimated higher social care costs than we found by analysing the South Somerset data. This implies that social care need may be greater than local authority social costs in South Somerset. This may be due to the relative affluence of South Somerset, which would limit the proportion of patients eligible for local authority-funded social care

Analysis of factors determining the survival capacity of small and medium-sized enterprises

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