Addressing methodological challenges in multiple long-term conditions research: a stakeholder workshop using a nominal group technique method

Abstract

Background: multiple long-term conditions (MLTC) - which refer to the coexistence in an individual of two or more long-term conditions - are a growing global concern, causing significant strain on healthcare systems and increasing care costs. Research into MLTC is a strategic priority for healthcare services, policymakers and research funders.

Methods: to address these complexities, the UK's National Institute for Health and Care Research (NIHR) established the MLTC Cross-NIHR Collaboration (MLTC CNC) programme, to foster interdisciplinary collaboration and address key gaps in MLTC research. As part of this initiative, the Methodologies Workstream organised a two-day stakeholder workshop in March 2024 aimed at identifying current methodological challenges in MLTC research, prioritising key areas for improvement, and developing strategies to enhance research methodologies. The workshop employed a participatory and iterative approach, using structured presentations, facilitated group work, and the Nominal Group Technique (NGT) to promote cross-disciplinary collaboration and achieve consensus on key research priorities for MLTC.

Results: twenty-three delegates attended the workshop from a range of institutions and sectors, including representatives from data science, epidemiology, clinical trials, quality improvement, social sciences, healthcare management, clinical practice, industry, patient advocacy groups, policymakers, patients, carers, and public representatives. The workshop identified critical knowledge gaps in MLTC research methodologies, including challenges with disease classification, data integration, analytical approaches, and the inclusion of diverse population subgroups.

Conclusions: by addressing these methodological gaps and fostering collaboration across disciplines, the MLTC research community can generate more rigorous, inclusive, and impactful evidence, ultimately improving healthcare delivery and patient outcomes.

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