Project Summary
Cigarette smoking accounts for 1 in 5 preventable deaths in the United States. Sexual and gender minorities
(SGM) are at higher risk of cigarette smoking and tobacco-related deaths. Preliminary evidence indicates that
sexual minorities have higher eligibility rates for low dose computed tomography (LDCT), a reliable screening
test that reduces the risk of lung cancer mortality caused by tobacco smoking. Recently, the age and smoking
pack years were reduced in the eligibility guidelines, which determine who qualifies for LDCT screening. This
led to an increase in the number of people who were eligible, but no work on differences by sexuality or gender
identity comparing the new and old criteria has been conducted. One possible cause of SGM individuals
having higher eligibility rates is the number of risk factors SGM individuals experience at the individual,
interpersonal, community, and policy levels based on the social ecological model. However, individual national
data sets do not contain enough individuals that identify as SGM to make inferences about possible risk factors
and the corresponding high LDCT eligibility for SGM individuals. To improve estimates and identity potential
disparities and risk factors among SGM individuals for LDCT eligibility, one promising option is to merge data
from multiple national data sources. Additionally, there is a need to determine whether SGM individuals who
are eligible for LDCT are smoking at a higher rate compared to heterosexual and cisgender individuals,
especially over time. Overall, I have three main aims: 1) Merge four nationally representative data sets
together, and compare these methods of merging to generate better estimates of cigarette smoking and LDCT
eligibility for SGM individuals.; 2) Compare trends in the new and old LDCT screening eligibility criteria over
time to understand how many people are eligible and how SGM individuals compare to their cisgender and
heterosexual counterparts; and 3) Guided by the social ecological model, identify the risk factors associated
with LDCT eligibility for SGM individuals. To address these aims, state-of-the-art techniques for merging data,
complex survey analysis using longitudinal and multi-year cross-sectional data, and structural equation
modeling will be used under the mentorship of Drs. West and McCabe to understand the effects multiple levels
of the social ecological model have on LDCT eligibility. Upon completion of the aims, the papers,
presentations, and reports will enhance my methodological and substantive training and advance multiple
fields of research. In the survey methodology field, I will compare methods of data merging for small
subpopulations, understand measurement differences, and improve estimates for SGM individuals. In the
public health realm, I will expand our understanding of possible differences between SGM, heterosexual, and
cis-gender individuals in LDCT eligibility, lung cancer, and lung conditions, and I will aim to understand the
effects of multiple levels of the social ecological model on SGM individuals and their LDCT eligibility.
