The implications of defining obesity as a disease: a report from the Association for the Study of Obesity 2021 annual conference.

Unlike various countries and organisations, including the World Health Organisation and the European Parliament, the United Kingdom does not formally recognise obesity as a disease. This report presents the discussion on the potential impact of defining obesity as a disease on the patient, the healthcare system, the economy, and the wider society. A group of speakers from a wide range of disciplines came together to debate the topic bringing their knowledge and expertise from backgrounds in medicine, psychology, economics, and politics as well as the experience of people living with obesity. The aim of their debate was not to decide whether obesity should be classified as a disease but rather to explore what the implications of doing so would be, what the gaps in the available data are, as well as to provide up-to-date information on the topic from experts in the field. There were four topics where speakers presented their viewpoints, each one including a question-and-answer section for debate. The first one focused on the impact that the recognition of obesity could have on people living with obesity regarding the change in their behaviour, either positive and empowering or more stigmatising. During the second one, the impact of defining obesity as a disease on the National Health Service and the wider economy was discussed. The primary outcome was the need for more robust data as the one available does not represent the actual cost of obesity. The third topic was related to the policy implications regarding treatment provision, focusing on the public's power to influence policy. Finally, the last issue discussed, included the implications of public health actions, highlighting the importance of the government's actions and private stakeholders. The speakers agreed that no matter where they stand on this debate, the goal is common: to provide a healthcare system that supports and protects the patients, strategies that protect the economy and broader society, and policies that reduce stigma and promote health equity. Many questions are left to be answered regarding how these goals can be achieved. However, this discussion has set a good foundation providing evidence that can be used by the public, clinicians, and policymakers to make that happen.

Where Is the Value of Laboratory Medicine and How Do You Unlock It?

BACKGROUND: The innovation pathway by which a newly discovered biomarker is developed into a medical test and used in routine clinical practice comprises a number of different processes split between 2 phases. The first follows on from biomarker discovery and involves the development of a robust analytical method, the accumulation of evidence to show its clinical and cost-effectiveness, and then adoption into clinical pathways. The second phase is one of implementation and sustainability, with active performance management to ensure that the test continues to deliver the benefits promised at the time of its adoption. CONTENT: To date there has been much more emphasis on the first phase of discovery and accumulation of evidence to demonstrate effectiveness. Insufficient attention has been paid to the second phase of translating that evidence into routine practice, with little real-world evidence to demonstrate the benefits to all of the stakeholders involved in delivering and receiving care. Changes in healthcare that include a move away from activity-based costing to a more value-based approach require more attention be paid to what happens after a test is adopted, including an understanding of the clinical pathway, the stakeholders within that pathway, and the benefits and "disbenefits" that accrue to these stakeholders. SUMMARY: The value proposition provides a guide for successful implementation of a test. Although it can address both adoption and implementation, it highlights that the requirements for test implementation are quite different to those of adoption, with an emphasis on real-world evidence and outcomes.

The cost burden of falls in people with glaucoma in National Health Service Hospital Trusts in the UK.

Aims: Falls have devastating consequences in older people with a considerable cost burden. Glaucoma is a risk factor for falls, and patients with glaucoma who fall are at high risk of hospital admission. The aim was to quantify the cost burden of falls to NHS Trusts in people with glaucoma in the UK.Methods: Financial data were used to identify non-elective episodes and associated costs from 2012 to 2018, for all admissions where glaucoma was recorded as a secondary diagnosis and admissions for falls (all, with and without a glaucoma secondary diagnosis). A secondary diagnosis is only recorded by the admitting clinician if it is clinically relevant; therefore, a secondary diagnosis of glaucoma was used as a proxy for glaucoma as a contributory factor to falls.Limitations: Use of financial records means that data on other falls risk factors was unavailable and we cannot be certain that glaucoma was the only relevant factor in all falls. Although this methodology is imperfect, case capture was biased towards cases with clinically significant glaucoma, and financial data is robust. Potential coding errors mean that we may have excluded patients in whom glaucoma was a factor in their fall.Results: At Maidstone and Tunbridge Wells (MTW) NHS Trust, 11.7% (95% confidence intervals [CI] = 10.7-12.8) of admissions for falls were in patients with a secondary diagnosis of glaucoma. This extrapolates to an estimated annual 10,056 admissions at a cost of £28.6 million across the UK. This is an under-estimate of cost, as A&E attendance without admission and outpatient appointments are excluded.Conclusions: At MTW, glaucoma potentially plays a part in around one in eight falls resulting in hospital admission, at considerable personal and financial cost. It is suggested that further work should explore early diagnosis of glaucoma, treatment, and mitigation of falls risk.

Translational health economics: The key to accountable adoption of in vitro diagnostic technologies.

Adoption of new technologies, including diagnostic tests, is often considered not to deliver the expected return on investment. The reasons for this poor link between expectation and outcome include lack of evidence, variation in use of the technology, and an inability of the health system to manage the balance between investment and disinvestment associated with the change in care pathway. The challenges lie in the complex nature of healthcare provision where the investment is likely to be made in the jurisdiction of one stakeholder while the benefits (as well as dis-benefits) accrue to the other stakeholders. A prime example is found in the field of laboratory medicine and the use of diagnostic tests. The current economic tools employed in healthcare are primarily used to make policy and strategic decisions, particularly across health systems, and in purchaser and provider domains. These tools primarily involve cost effectiveness and budget impact analyses, both of which have been applied in health technology assessment of diagnostic technologies. However, they lack the granularity to translate findings down to the financial management and operational decision making at the provider department level. We propose an approach to translational health economics based on information derived from service line management and time-driven activity-based costing, identifying the resource utilisation for each of the units involved in the delivery of a care pathway, before and after adoption of new technology. This will inform investment and disinvestment decisions, along with identifying where the benefits, and dis-benefits, can be achieved for all stakeholders.

Process mining routinely collected electronic health records to define real-life clinical pathways during chemotherapy.

BACKGROUND: There is growing interest in the use of routinely collected electronic health records to enhance service delivery and facilitate clinical research. It should be possible to detect and measure patterns of care and use the data to monitor improvements but there are methodological and data quality challenges. Driven by the desire to model the impact of a patient self-test blood count monitoring service in patients on chemotherapy, we aimed to (i) establish reproducible methods of process-mining electronic health records, (ii) use the outputs derived to define and quantify patient pathways during chemotherapy, and (iii) to gather robust data which is structured to be able to inform a cost-effectiveness decision model of home monitoring of neutropenic status during chemotherapy. METHODS: Electronic Health Records at a UK oncology centre were included if they had (i) a diagnosis of metastatic breast cancer and received adjuvant epirubicin and cyclosphosphamide chemotherapy or (ii) colorectal cancer and received palliative oxaliplatin and infusional 5-fluorouracil chemotherapy, and (iii) were first diagnosed with cancer between January 2004 and February 2013. Software and a Markov model were developed, producing a schematic of patient pathways during chemotherapy. RESULTS: Significant variance from the assumed care pathway was evident from the data. Of the 535 patients with breast cancer and 420 with colorectal cancer there were 474 and 329 pathway variants respectively. Only 27 (5%) and 26 (6%) completed the planned six cycles of chemotherapy without having unplanned hospital contact. Over the six cycles, 169 (31.6%) patients with breast cancer and 190 (45.2%) patients with colorectal cancer were admitted to hospital. CONCLUSION: The pathways of patients on chemotherapy are complex. An iterative approach to addressing semantic and data quality issues enabled the effective use of routinely collected patient records to produce accurate models of the real-life experiences of chemotherapy patients and generate clinically useful information. Very few patients experience the idealised patient pathway that is used to plan their care. A better understanding of real-life clinical pathways through process mining can contribute to care and data quality assurance, identifying unmet needs, facilitating quantification of innovation impact, communicating with stakeholders, and ultimately improving patient care and outcomes.