Intersectionality in cigarette smoking cessation: A latent class analysis to predict 12-month cessation in a...

Area of Science:

• Public Health
• Behavioral Science
• Digital Health

Background:

• Smoking cessation research often overlooks the intersectionality of social identities and lived experiences.
• Understanding these intersecting factors is crucial for developing effective interventions for marginalized populations.

Purpose of the Study:

• To examine how multiple theory-driven baseline factors (social identities, lived experiences) interact and predict smoking cessation.
• To identify distinct subgroups within a diverse population undergoing digital smoking cessation intervention.

Main Methods:

• Latent class analysis (LCA) was applied to data from 2415 adult participants in a randomized controlled trial of digital smoking cessation apps.
• Six theory-based factors were analyzed: gender, race/ethnicity, marital status, sexual and gender minority (SGM) identity, socioeconomic status (SES), and depression symptoms.
• The primary outcome was 30-day point prevalence abstinence at 12 months.

Main Results:

• Four distinct classes emerged, characterized by combinations of social identities and depression symptoms.
• Class 2 (men, SGM, socioeconomically advantaged) had the highest cessation rate (32.8%).
• Class 3 (socioeconomically disadvantaged, depression symptoms) had the lowest cessation rate (15.4%) and significantly lower odds of quitting compared to Class 2.

Conclusions:

• Socioeconomic disadvantage and depression symptoms significantly hinder smoking cessation.
• Digital interventions should consider tailoring support based on identified risk classes to improve quit rates.
• Future research should explore intersectionality in smoking cessation for marginalized groups.