Patterns of multimorbidity among low-income adults who smoke with implications for tailored interventions: a cluster analysis using a Mixture of Bernoulli model

Introduction: Smoking cigarettes remains a leading modifiable risk factor for preventable health conditions. In the United States, the health burden of smoking disproportionately impacts low-income individuals. Multimorbidity is common in this group, complicating treatment and worsening outcomes. Identifying multimorbidity clusters can support targeted, individualized interventions. This study aimed to identify multimorbidity clusters among individuals who smoke and experience economic hardship and provide clinical recommendations to enhance health outcomes. Method: = 60) were recruited from the San Francisco Health Network (SFHN) and were assessed for physical and mental conditions. Cluster analysis was conducted using a Mixture of Bernoulli (MoB) model to identify subgroups of participants based on co-occurring physical and mental health conditions. Relative risks (RRs) were calculated to compare the likelihood of each condition across clusters, and 95% confidence intervals were used to assess statistical significance. Results: < 0.01). Findings should be interpreted cautiously given the small sample size and exploratory nature of the cluster analysis. Conclusion: cluster. These findings highlight the need for tailored and person-centered smoking cessation strategies that address both shared and unique physical and mental health challenges in this high-risk population.