ArticleBMJ open2026
Engaging community to co-design a multilevel intervention to reduce lung cancer disparities in persistent poverty tracts in California through group model building and simulation.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesThis study examined social determinants and structural barriers to lung cancer outcomes in Kern and Fresno counties, California, and co-developed a multilevel intervention strategy informed by community perspectives.
designEngaging stakeholders through group model building (GMB) to elicit their knowledge to build a system dynamics (SD) simulation model for intervention strategy design. SETTING AND
participantsWe identified and trained four community members from two community-based organisations in Central Valley, California, to help recruit GMB participants. 14 community members representing patient advocacy organisations, cancer survivors, clinicians, caregivers, public health professionals, medical interpreters, housing, agriculture, sanitation, healthcare payer organisations and local policymaking sectors were recruited. PROCEDURES: The GMB protocol consisted of two in-person and four virtual workshops from 22 August to 11 November 2024. The SD simulation model was built with iSee System's Stella Architect SD modelling software (V.4.0).
resultsThe 181 variables suggested by the GMB workshop participants were categorised into 3 themes and 13 subthemes, which shaped the system boundary and model structure. 7 of the 16 intervention scenarios tested showed a cumulative reduction in the at-risk population and increases in screening, diagnoses, treatment and cancer-free survival. Participants selected a multilevel strategy focused on expanding public health and insurance education and advocating for air pollution-related screening within existing protocols.
conclusionCommunity engagement is essential for understanding lung cancer disparities and designing practical multilevel interventions. Scenario testing enables informed planning to improve long-term population health outcomes.
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