Cost-Effectiveness of Routine Monitoring of Long-Term Conditions in Primary Care: Informing Decision Modelling with a Systematic Review in Hypertension, Type 2 Diabetes and Chronic Kidney Disease.

BACKGROUND: Long-term conditions (LTCs) are major public health problems with a considerable health-related and economic burden. Modelling is key in assessing costs and benefits of different disease management strategies, including routine monitoring, in the conditions of hypertension, type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) in primary care. OBJECTIVE: This review aimed to identify published model-based cost-effectiveness studies of routine laboratory testing strategies in these LTCs to inform a model evaluating the cost effectiveness of testing strategies in the UK. METHODS: We searched the Medline and Embase databases from inception to July 2023; the National Institute for Health and Care Institute (NICE) website was also searched. Studies were included if they were model-based economic evaluations, evaluated testing strategies, assessed regular testing, and considered adults aged >16 years. Studies identified were summarised by testing strategies, model type, structure, inputs, assessment of uncertainty, and conclusions drawn. RESULTS: Five studies were included in the review, i.e. Markov (n = 3) and microsimulation (n = 2) models. Models were applied within T2DM (n = 2), hypertension (n = 1), T2DM/hypertension (n = 1) and CKD (n = 1). Comorbidity between all three LTCs was modelled to varying extents. All studies used a lifetime horizon, except for a 10-year horizon T2DM model, and all used quality-adjusted life-years as the effectiveness outcome, except a TD2M model that used glycaemic control. No studies explicitly provided a rationale for their selected modelling approach. UK models were available for diabetes and CKD, but these compared only a limited set of routine monitoring tests and frequencies. CONCLUSIONS: There were few studies comparing routine testing strategies in the UK, indicating a need to develop a novel model in all three LTCs. Justification for the modelling technique of the identified studies was lacking. Markov and microsimulation models, with and without comorbidities, were used; however, the findings of this review can provide data sources and inform modelling approaches for evaluating the cost effectiveness of testing strategies in all three LTCs.

Identifying the Optimum Strategy for Identifying Adults and Children With Celiac Disease: A Cost-Effectiveness and Value of Information Analysis.

OBJECTIVES: Celiac disease (CD) is thought to affect around 1% of people in the United Kingdom, but only approximately 30% are diagnosed. The aim of this work was to assess the cost-effectiveness of strategies for identifying adults and children with CD in terms of who to test and which tests to use. METHODS: A decision tree and Markov model were used to describe testing strategies and model long-term consequences of CD. The analysis compared a selection of pre-test probabilities of CD above which patients should be screened, as well as the use of different serological tests, with or without genetic testing. Value of information analysis was used to prioritize parameters for future research. RESULTS: Using serological testing alone in adults, immunoglobulin A (IgA) tissue transglutaminase (tTG) at a 1% pre-test probability (equivalent to population screening) was most cost-effective. If combining serological testing with genetic testing, human leukocyte antigen combined with IgA tTG at a 5% pre-test probability was most cost-effective. In children, the most cost-effective strategy was a 10% pre-test probability with human leukocyte antigen plus IgA tTG. Value of information analysis highlighted the probability of late diagnosis of CD and the accuracy of serological tests as important parameters. The analysis also suggested prioritizing research in adult women over adult men or children. CONCLUSIONS: For adults, these cost-effectiveness results suggest UK National Screening Committee Criteria for population-based screening for CD should be explored. Substantial uncertainty in the results indicate a high value in conducting further research.

Variation in laboratory testing for patients with long-term conditions: a longitudinal cohort study in UK primary care.

BACKGROUND: Use of laboratory testing has increased in the UK over the past few decades, with considerable geographical variation. AIM: To evaluate what laboratory tests are used to monitor people with hypertension, type 2 (T2) diabetes, or chronic kidney disease (CKD) and assess variation in test use in UK primary care. DESIGN & SETTING: Longitudinal cohort study of people registered with UK general practices between June 2013 and May 2018 and previously diagnosed with hypertension, T2 diabetes, or CKD. METHOD: Clinical Practice Research Datalink (CPRD) primary care data linked to ethnic group and deprivation was used to examine testing rates over time, by GP practice, age, sex, ethnic group, and socioeconomic deprivation, with age-sex standardisation. RESULTS: Nearly 1 million patients were included, and more than 27 million tests. The most ordered tests were for renal function (1463 per 1000 person-years), liver function (1063 per 1000 person-years), and full blood count (FBC; 996 per 1000 person-years). There was evidence of undertesting (compared with current guidelines) for HbA1c and albumin:creatinine ratio (ACR) or microalbumin, and potential overtesting of lipids, FBC, liver function, and thyroid function. Some GP practices had up to 27 times higher testing rates than others (HbA1c testing among patients with CKD). CONCLUSION: Testing rates are no longer increasing, but they are not always within the guidelines for monitoring long-term conditions (LTCs). There was considerable variation by GP practice, indicating uncertainty over the most appropriate testing frequencies for different conditions. Standardising the monitoring of LTCs based on the latest evidence would provide greater consistency of access to monitoring tests.

Defining the optimum strategy for identifying adults and children with coeliac disease: systematic review and economic modelling.

BACKGROUND: Coeliac disease is an autoimmune disorder triggered by ingesting gluten. It affects approximately 1% of the UK population, but only one in three people is thought to have a diagnosis. Untreated coeliac disease may lead to malnutrition, anaemia, osteoporosis and lymphoma. OBJECTIVES: The objectives were to define at-risk groups and determine the cost-effectiveness of active case-finding strategies in primary care. DESIGN: (1) Systematic review of the accuracy of potential diagnostic indicators for coeliac disease. (2) Routine data analysis to develop prediction models for identification of people who may benefit from testing for coeliac disease. (3) Systematic review of the accuracy of diagnostic tests for coeliac disease. (4) Systematic review of the accuracy of genetic tests for coeliac disease (literature search conducted in April 2021). (5) Online survey to identify diagnostic thresholds for testing, starting treatment and referral for biopsy. (6) Economic modelling to identify the cost-effectiveness of different active case-finding strategies, informed by the findings from previous objectives. DATA SOURCES: For the first systematic review, the following databases were searched from 1997 to April 2021: MEDLINE® (National Library of Medicine, Bethesda, MD, USA), Embase® (Elsevier, Amsterdam, the Netherlands), Cochrane Library, Web of Science™ (Clarivate™, Philadelphia, PA, USA), the World Health Organization International Clinical Trials Registry Platform ( WHO ICTRP ) and the National Institutes of Health Clinical Trials database. For the second systematic review, the following databases were searched from January 1990 to August 2020: MEDLINE, Embase, Cochrane Library, Web of Science, Kleijnen Systematic Reviews ( KSR ) Evidence, WHO ICTRP and the National Institutes of Health Clinical Trials database. For prediction model development, Clinical Practice Research Datalink GOLD, Clinical Practice Research Datalink Aurum and a subcohort of the Avon Longitudinal Study of Parents and Children were used; for estimates for the economic models, Clinical Practice Research Datalink Aurum was used. REVIEW METHODS: For review 1, cohort and case-control studies reporting on a diagnostic indicator in a population with and a population without coeliac disease were eligible. For review 2, diagnostic cohort studies including patients presenting with coeliac disease symptoms who were tested with serological tests for coeliac disease and underwent a duodenal biopsy as reference standard were eligible. In both reviews, risk of bias was assessed using the quality assessment of diagnostic accuracy studies 2 tool. Bivariate random-effects meta-analyses were fitted, in which binomial likelihoods for the numbers of true positives and true negatives were assumed. RESULTS: People with dermatitis herpetiformis, a family history of coeliac disease, migraine, anaemia, type 1 diabetes, osteoporosis or chronic liver disease are 1.5-2 times more likely than the general population to have coeliac disease; individual gastrointestinal symptoms were not useful for identifying coeliac disease. For children, women and men, prediction models included 24, 24 and 21 indicators of coeliac disease, respectively. The models showed good discrimination between patients with and patients without coeliac disease, but performed less well when externally validated. Serological tests were found to have good diagnostic accuracy for coeliac disease. Immunoglobulin A tissue transglutaminase had the highest sensitivity and endomysial antibody the highest specificity. There was little improvement when tests were used in combination. Survey respondents (n = 472) wanted to be 66% certain of the diagnosis from a blood test before starting a gluten-free diet if symptomatic, and 90% certain if asymptomatic. Cost-effectiveness analyses found that, among adults, and using serological testing alone, immunoglobulin A tissue transglutaminase was most cost-effective at a 1% pre-test probability (equivalent to population screening). Strategies using immunoglobulin A endomysial antibody plus human leucocyte antigen or human leucocyte antigen plus immunoglobulin A tissue transglutaminase with any pre-test probability had similar cost-effectiveness results, which were also similar to the cost-effectiveness results of immunoglobulin A tissue transglutaminase at a 1% pre-test probability. The most practical alternative for implementation within the NHS is likely to be a combination of human leucocyte antigen and immunoglobulin A tissue transglutaminase testing among those with a pre-test probability above 1.5%. Among children, the most cost-effective strategy was a 10% pre-test probability with human leucocyte antigen plus immunoglobulin A tissue transglutaminase, but there was uncertainty around the most cost-effective pre-test probability. There was substantial uncertainty in economic model results, which means that there would be great value in conducting further research. LIMITATIONS: The interpretation of meta-analyses was limited by the substantial heterogeneity between the included studies, and most included studies were judged to be at high risk of bias. The main limitations of the prediction models were that we were restricted to diagnostic indicators that were recorded by general practitioners and that, because coeliac disease is underdiagnosed, it is also under-reported in health-care data. The cost-effectiveness model is a simplification of coeliac disease and modelled an average cohort rather than individuals. Evidence was weak on the probability of routine coeliac disease diagnosis, the accuracy of serological and genetic tests and the utility of a gluten-free diet. CONCLUSIONS: Population screening with immunoglobulin A tissue transglutaminase (1% pre-test probability) and of immunoglobulin A endomysial antibody followed by human leucocyte antigen testing or human leucocyte antigen testing followed by immunoglobulin A tissue transglutaminase with any pre-test probability appear to have similar cost-effectiveness results. As decisions to implement population screening cannot be made based on our economic analysis alone, and given the practical challenges of identifying patients with higher pre-test probabilities, we recommend that human leucocyte antigen combined with immunoglobulin A tissue transglutaminase testing should be considered for adults with at least a 1.5% pre-test probability of coeliac disease, equivalent to having at least one predictor. A more targeted strategy of 10% pre-test probability is recommended for children (e.g. children with anaemia). FUTURE WORK: Future work should consider whether or not population-based screening for coeliac disease could meet the UK National Screening Committee criteria and whether or not it necessitates a long-term randomised controlled trial of screening strategies. Large prospective cohort studies in which all participants receive accurate tests for coeliac disease are needed. STUDY REGISTRATION: This study is registered as PROSPERO CRD42019115506 and CRD42020170766. FUNDING: This project was funded by the National Institute for Health and Care Research ( NIHR ) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 44. See the NIHR Journals Library website for further project information.

WHAT IS THE PROBLEM?: Around 1 in 100 people in the UK has coeliac disease. It develops when the immune system attacks the lining of the gut after eating gluten. It is thought that only one in three people with coeliac disease is currently diagnosed. Without treatment, people with coeliac disease are at an increased risk of anaemia, osteoporosis and cancer. Treatment is a lifelong gluten-free diet. Diagnosing coeliac disease is difficult. Some people have minimal or non-specific symptoms, such as pain, indigestion or bloating, so knowing who to test is tricky. WHAT DID WE DO?: We wanted to establish who should be tested for coeliac disease, what tests should be used and whether or not invasive testing (a gut biopsy) is necessary for everyone. We looked at existing studies and data from general practices, and conducted an online survey, and brought everything together in an economic (cost) analysis. WHAT DID WE FIND?: Using individual symptoms is not helpful to identify people who may have coeliac disease. People with coeliac disease are more likely to have a combination of symptoms. People with anaemia, type 1 diabetes, osteoporosis, thyroid disorders, immunoglobulin A deficiency, Down syndrome, Turner syndrome or a family history of coeliac disease are more likely to have coeliac disease and should be offered tests. Common blood tests for coeliac disease are very accurate, particularly when used in combination with genetic testing. Blood tests alone can be used for diagnosis for some people. Others will need a biopsy to confirm the diagnosis. Whether or not this is needed depends on their risk of coeliac disease: whether or not they have symptoms and whether or not they have a condition that puts them at higher risk. Shared decision-making is important for individuals considering an invasive test, depending on how certain they want to be about their diagnosis before starting a gluten-free diet.

Development and external validation of a clinical prediction model to aid coeliac disease diagnosis in primary care: An observational study.

Background: Coeliac disease (CD) affects approximately 1% of the population, although only a fraction of patients are diagnosed. Our objective was to develop diagnostic prediction models to help decide who should be offered testing for CD in primary care. Methods: Logistic regression models were developed in Clinical Practice Research Datalink (CPRD) GOLD (between Sep 9, 1987 and Apr 4, 2021, n=107,075) and externally validated in CPRD Aurum (between Jan 1, 1995 and Jan 15, 2021, n=227,915), two UK primary care databases, using (and controlling for) 1:4 nested case-control designs. Candidate predictors included symptoms and chronic conditions identified in current guidelines and using a systematic review of the literature. We used elastic-net regression to further refine the models. Findings: The prediction model included 24, 24, and 21 predictors for children, women, and men, respectively. For children, the strongest predictors were type 1 diabetes, Turner syndrome, IgA deficiency, or first-degree relatives with CD. For women and men, these were anaemia and first-degree relatives. In the development dataset, the models showed good discrimination with a c-statistic of 0·84 (95% CI 0·83-0·84) in children, 0·77 (0·77-0·78) in women, and 0·81 (0·81-0·82) in men. External validation discrimination was lower, potentially because 'first-degree relative' was not recorded in the dataset used for validation. Model calibration was poor, tending to overestimate CD risk in all three groups in both datasets. Interpretation: These prediction models could help identify individuals with an increased risk of CD in relatively low prevalence populations such as primary care. Offering a serological test to these patients could increase case finding for CD. However, this involves offering tests to more people than is currently done. Further work is needed in prospective cohorts to refine and confirm the models and assess clinical and cost effectiveness. Funding: National Institute for Health Research Health Technology Assessment Programme (grant number NIHR129020).

The accuracy of diagnostic indicators for coeliac disease: A systematic review and meta-analysis.

BACKGROUND: The prevalence of coeliac disease (CD) is around 1%, but diagnosis is challenged by varied presentation and non-specific symptoms and signs. This study aimed to identify diagnostic indicators that may help identify patients at a higher risk of CD in whom further testing is warranted. METHODS: International guidance for systematic review methods were followed and the review was registered at PROSPERO (CRD42020170766). Six databases were searched until April 2021. Studies investigating diagnostic indicators, such as symptoms or risk conditions, in people with and without CD were eligible for inclusion. Risk of bias was assessed using the QUADAS-2 tool. Summary sensitivity, specificity, and positive predictive values were estimated for each diagnostic indicator by fitting bivariate random effects meta-analyses. FINDINGS: 191 studies reporting on 26 diagnostic indicators were included in the meta-analyses. We found large variation in diagnostic accuracy estimates between studies and most studies were at high risk of bias. We found strong evidence that people with dermatitis herpetiformis, migraine, family history of CD, HLA DQ2/8 risk genotype, anaemia, type 1 diabetes, osteoporosis, or chronic liver disease are more likely than the general population to have CD. Symptoms, psoriasis, epilepsy, inflammatory bowel disease, systemic lupus erythematosus, fractures, type 2 diabetes, and multiple sclerosis showed poor diagnostic ability. A sensitivity analysis revealed a 3-fold higher risk of CD in first-degree relatives of CD patients. CONCLUSIONS: Targeted testing of individuals with dermatitis herpetiformis, migraine, family history of CD, HLA DQ2/8 risk genotype, anaemia, type 1 diabetes, osteoporosis, or chronic liver disease could improve case-finding for CD, therefore expediting appropriate treatment and reducing adverse consequences. Migraine and chronic liver disease are not yet included as a risk factor in all CD guidelines, but it may be appropriate for these to be added. Future research should establish the diagnostic value of combining indicators.

GP's perspectives on laboratory test use for monitoring long-term conditions: an audit of current testing practice.

BACKGROUND: We have shown previously that current recommendations in UK guidelines for monitoring long-term conditions are largely based on expert opinion. Due to a lack of robust evidence on optimal monitoring strategies and testing intervals, the guidelines are unclear and incomplete. This uncertainty may underly variation in testing that has been observed across the UK between GP practices and regions. METHODS: Our objective was to audit current testing practices of GPs in the UK; in particular, perspectives on laboratory tests for monitoring long-term conditions, the workload, and how confident GPs are in ordering and interpreting these tests. We designed an online survey consisting of multiple-choice and open-ended questions that was promoted on social media and in newsletters targeting GPs practicing in UK. The survey was live between October-November 2019. The results were analysed using a mixed-methods approach. RESULTS: The survey was completed by 550 GPs, of whom 69% had more than 10 years of experience. The majority spent more than 30 min per day on testing (78%), but only half of the respondents felt confident in dealing with abnormal results (53%). There was a high level of disagreement for whether liver function tests and full blood counts should be done 'routinely', 'sometimes', or 'never' in patients with a certain long-term condition. The free text comments revealed three common themes: (1) pressures that promote over-testing, i.e. guidelines or protocols, workload from secondary care, fear of missing something, patient expectations; (2) negative consequences of over-testing, i.e. increased workload and patient harm; and (3) uncertainties due to lack of evidence and unclear guidelines. CONCLUSION: These results confirm the variation that has been observed in test ordering data. The results also show that most GPs spent a significant part of their day ordering and interpreting monitoring tests. The lack of confidence in knowing how to act on abnormal test results underlines the urgent need for robust evidence on optimal testing and the development of clear and unambiguous testing recommendations. Uncertainties surrounding optimal testing has resulted in an over-use of tests, which leads to a waste of resources, increased GP workload and potential patient harm.

What methods are being used to create an evidence base on the use of laboratory tests to monitor long-term conditions in primary care? A scoping review.

BACKGROUND: Studies have shown unwarranted variation in test ordering among GP practices and regions, which may lead to patient harm and increased health care costs. There is currently no robust evidence base to inform guidelines on monitoring long-term conditions. OBJECTIVES: To map the extent and nature of research that provides evidence on the use of laboratory tests to monitor long-term conditions in primary care, and to identify gaps in existing research. METHODS: We performed a scoping review-a relatively new approach for mapping research evidence across broad topics-using data abstraction forms and charting data according to a scoping framework. We searched CINAHL, EMBASE and MEDLINE to April 2019. We included studies that aimed to optimize the use of laboratory tests and determine costs, patient harm or variation related to testing in a primary care population with long-term conditions. RESULTS: Ninety-four studies were included. Forty percent aimed to describe variation in test ordering and 36% to investigate test performance. Renal function tests (35%), HbA1c (23%) and lipids (17%) were the most studied laboratory tests. Most studies applied a cohort design using routinely collected health care data (49%). We found gaps in research on strategies to optimize test use to improve patient outcomes, optimal testing intervals and patient harms caused by over-testing. CONCLUSIONS: Future research needs to address these gaps in evidence. High-level evidence is missing, i.e. randomized controlled trials comparing one monitoring strategy to another or quasi-experimental designs such as interrupted time series analysis if trials are not feasible.

Alcohol badly affects eye movements linked to steering, providing for automatic in-car detection of drink driving.

Driving is a classic example of visually guided behavior in which the eyes move before some other action. When approaching a bend in the road, a driver looks across to the inside of the curve before turning the steering wheel. Eye and steering movements are tightly linked, with the eyes leading, which allows the parts of the brain that move the eyes to assist the parts of the brain that control the hands on the wheel. We show here that this optimal relationship deteriorates with levels of breath alcohol well within the current UK legal limit for driving. The eyes move later, and coordination reduces. These changes lead to bad performance and can be detected by an automated in-car system, which warns the driver is no longer fit to drive.