Patterns of multimorbidity among low-income adults who smoke with implications for tailored interventions: a cluster analysis using a Mixture of Bernoulli model.
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.
Individuals who smoke and experience economic hardship (N = 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.
Cluster analysis identified three groups: Physical Multimorbidity, Mental-Physical Multimorbidity, and Lower Health Burden. Gender was significantly associated with cluster membership: males were more likely to be in the Physical Multimorbidity cluster, and females were more likely to be in the Mental-Physical Multimorbidity cluster (p < 0.01). Findings should be interpreted cautiously given the small sample size and exploratory nature of the cluster analysis.
Individuals who smoke and experience economic hardship exhibited three multimorbidity clusters-Physical Multimorbidity, Mental-Physical Multimorbidity, and Lower Health Burden-indicating both overlapping and distinct patterns of chronic health conditions. Chronic pain was common across the more complex clusters, whereas depression and anxiety characterized the Mental-Physical Multimorbidity 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.
Individuals who smoke and experience economic hardship (N = 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.
Cluster analysis identified three groups: Physical Multimorbidity, Mental-Physical Multimorbidity, and Lower Health Burden. Gender was significantly associated with cluster membership: males were more likely to be in the Physical Multimorbidity cluster, and females were more likely to be in the Mental-Physical Multimorbidity cluster (p < 0.01). Findings should be interpreted cautiously given the small sample size and exploratory nature of the cluster analysis.
Individuals who smoke and experience economic hardship exhibited three multimorbidity clusters-Physical Multimorbidity, Mental-Physical Multimorbidity, and Lower Health Burden-indicating both overlapping and distinct patterns of chronic health conditions. Chronic pain was common across the more complex clusters, whereas depression and anxiety characterized the Mental-Physical Multimorbidity 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.