{
  "abstract": "Introduction We urgently need innovative strategies to combat a growing epidemic of chronic liver disease (CLD). Integrated Diagnostics for Early Detection of Liver Disease (ID-LIVER) was a collaborative project aiming to improve detection of reversible-stage CLD in a region with high prevalence of critical risk factors. This study assesses the cost-effectiveness of different ways to identify people with significant CLD (defined as METAVIR stage F2 or higher, using liver stiffness of ≥8 kPa on transient elastography as a proxy measure). Strategies of interest include proactive case-finding in the community (supplementing a reactive pathway where hepatology referrals are passively received from primary care) and/or risk-stratification (using Fibrosis-4 (FIB-4) or ID-LIVER-Machine Learning (ML)—a novel machine-learning risk-stratification tool).Methods We developed a state-transition decision-analytic model estimating lifetime healthcare costs (2023/2024 GBP) and quality-adjusted life-years (QALYs) associated with six alternative strategies for case-finding and risk-stratification. We simulated cohorts of people with alcohol-related liver disease and metabolic dysfunction-associated steatotic liver disease. We populated the model with data collected in ID-LIVER, supplemented by parameters from the literature and routine data sources. We estimated incremental cost-effectiveness and performed deterministic and probabilistic sensitivity analyses.Results Any case-identification strategy costing ≤£3300 per person with significant CLD identified would meet English cost-effectiveness thresholds (£20 000/QALY). In our decision set, the cheapest strategy is to use FIB-4 in the reactive-only population; however, this misses 43.6% of people with significant CLD. ID-LIVER-ML (using a cut-off of 0.4) generates more population health at a reasonable cost (£10 498/QALY gained). Introducing proactive case-finding generates further health benefits, costing £12 952/QALY gained. Using ID-LIVER-ML in the proactive-and-reactive population has the highest probability of maximising cost-effectiveness, when valuing QALYs at £20 000.Conclusions Smart methods of case-finding and risk-stratification identify people with significant CLD in the community and are likely to represent good value for money in England.",
  "authors": [
    {
      "affiliations": [
        "Manchester Centre for Health Economics, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK"
      ],
      "name": "Gabriel Rogers"
    },
    {
      "affiliations": [
        "The University of Manchester Faculty of Biology Medicine and Health, Manchester, England, UK",
        "Manchester University NHS Foundation Trust, Manchester, UK"
      ],
      "name": "Stephanie Landi"
    },
    {
      "affiliations": [
        "The University of Manchester Faculty of Biology Medicine and Health, Manchester, England, UK",
        "Manchester University NHS Foundation Trust, Manchester, UK"
      ],
      "name": "Huw Purssell"
    },
    {
      "affiliations": [
        "Manchester Centre for Health Economics, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK"
      ],
      "name": "Tonia Momoh"
    },
    {
      "affiliations": [
        "Manchester Centre for Health Economics, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK"
      ],
      "name": "Sol Yates"
    },
    {
      "affiliations": [
        "The University of Manchester Faculty of Biology Medicine and Health, Manchester, England, UK"
      ],
      "name": "Oliver Street"
    },
    {
      "affiliations": [
        "The University of Manchester Faculty of Biology Medicine and Health, Manchester, England, UK"
      ],
      "name": "Karen Piper Hanley"
    },
    {
      "affiliations": [
        "The University of Manchester Faculty of Biology Medicine and Health, Manchester, England, UK",
        "College of Medicine and Health, University of Birmingham, Birmingham, UK",
        "University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK"
      ],
      "name": "Neil Hanley"
    },
    {
      "affiliations": [
        "The University of Manchester Faculty of Biology Medicine and Health, Manchester, England, UK",
        "Manchester University NHS Foundation Trust, Manchester, UK"
      ],
      "name": "Varinder Athwal"
    },
    {
      "affiliations": [
        "Manchester Centre for Health Economics, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK"
      ],
      "name": "Katherine Payne"
    }
  ],
  "title": "Proactive case-finding and risk-stratification in people at risk of chronic liver disease in Greater Manchester: a cost-effectiveness analysis",
  "uid": "58a4c15a-760c-5397-8acc-7ed00146d8ec"
}