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

Martha M C Elwenspoek, Howard H Z Thom, Athena L Sheppard, Edna Keeney, Rachel O'Donnell, Joni Jackson, Cristina Roadevin, Sarah Dawson, Deborah Lane, Jo Stubbs, Hazel Everitt, Jessica C Watson, Alastair D Hay, Peter Gillett, Gerry Robins, Hayley E Jones, Sue Mallett, Penny F Whiting

Research output: Book/ReportCommissioned report

6 Citations (Scopus)

Abstract

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 (WHOICTRP) 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, WHOICTRP 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.

Original languageEnglish
Place of PublicationHealth Technology Assessment
PublisherNIHR Journals Library
Number of pages350
Volume26
Edition44
DOIs
Publication statusPublished - 1 Nov 2022

Publication series

NameHealth Technology Assessment
PublisherNIHR Journals Library
ISSN (Print)1366-5278

Bibliographical note

Publisher Copyright:
© 2022 Elwenspoek et al.

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