BMI and Lung Cancer Screening Utilization: A Cross-sectional Analysis of 2017 BRFSS
The National Lung Screening Trial found that there was a 20% reduction in lung cancer mortality when subjects received lung cancer screening.1 Studies have demonstrated that body mass index (BMI), income, gender, and other demographic variables might be associated lung cancer screening utilization in men or women. Also, there have been studies that show BMI might be a barrier to breast or cervical cancer screening.2,3 However, evidence showing association between BMI and lung cancer screening is limited, suggesting we need to disentangle how BMI may impact its utilization.
In 2013, the US Preventative Services Task Force (USPSTF) suggested that lung cancer screening for ever-smokers aged 55 to 80 years who have smoked at least 30 pack-years with no more than 15 years since quitting..4 In 2015, l lung cancer screening utilization rates was about 4% of all eligible for lung cancer screening.5 The low lung cancer screening rate has been deemed a failure.
Although lung cancer screening is beneficial, there is little understanding of what drives the low lung cancer screening utilization after the USPSTF released their guidelines in 2013. Several explanations have been proposed for the cause of low cancer screening utilization in obese and non-obese individuals.6,7
The aim of this project was to investigate if lung cancer screening utilization rate differed by BMI levels. The association between BMI and lung cancer screening utilization was examined by sex.
BRFSS is a health survey that is administered annually via telephone across states in the United States. The 50 state health departments and territories implement this survey. The BRFSS collects data from non-institutionalized adults 18 years old and older. Information is collected on their health-related risk behaviors and preventive services. 8 I only included subjects who met USPTF guidelines for lung cancer screening and were not underweight (BMI<18.5 kg/m2). This included subjects who were ever-smokers aged 55 to 80 years who had smoked at least 30 pack-years with no more than 15 years since quitting. This study used data from 2017. The question that was used for the outcome variable was lung cancer utilization in the past 12 months which was measured by the question ‘In the last 12 months, did you have a CT or CAT scan?’.
A Chi-square test was conducted to compare the distributions of categorical variables across lung cancer screening utilization. A 2-sample proportion test was done to compare lung cancer screening utilization by BMI (18.5-<25, 25-≤30, 30≤35, 35≤40, and 40 and higher). Logistic regression was used to investigate an association between BMI and lung cancer screening utilization and the model adjusted for sex. It was further investigated if there was an interaction between BMI and sex in relation to utilization by subgroup analysis, and interaction was examined by the Wald Test. A complete-case analysis was used and no statistical methods were used for imputation. Two-sided p-values <0.05 were statistically significant. All analysis was done using SAS version 9.4.
A total of 1,552 people were included in the study. Our study reported that 15% of screening eligible subjects were screened for lung cancer in 2017. After setting up a model, there appeared to be no association between BMI and lung cancer screening utilization.
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