By Jad Bahhur

In 2012, the incidence rate of lung cancer deaths as a percentage of total cancer deaths in the United States was 27%, the median age of diagnosis was 70 years old, and only 15% of lung cancer cases at the time were diagnosed at an early stage.

All of this points to a deficiency in the amount of people being screened for lung cancer with consistent timeliness. As with any form of cancer, early detection is optimal to guarantee the most effective treatment, but not enough is currently being done to amplify participation in lung cancer screening programs.

In 2011, a study called the National Lung Cancer Screening Trial (NLST) demonstrated the association between low-dose computed tomography (LDCT) lung cancer screening and lower lung cancer mortality rates. It was found that those who received LDCT screenings had a 15 to 20 percent lower risk of dying from lung cancer than participants who received standard chest X-rays.

Since the results of the NLST were published, national organizations such as the Centers for Medicare and Medicaid Services (CMS) and the United States Preventative Services Task Force (USPSTF) have developed lung cancer screening guidelines and eligibility criteria that, if met, allow patients to receive covered LDCT screenings from participating medical centers.

As cancer screening programs become more ubiquitous, our ability to assess effectiveness and reach remains extremely important. Organizations must be aware of social, economic, and lifestyle barriers that may influence patient participation. Rush University Medical Center (RUMC) maintains its own efforts to address health inequities caused by a life expectancy gap of up to sixteen years between those who live in neighborhoods on Chicago’s west side and those closer to the downtown area. RUMC also began its own lung cancer screening program in 2015 based on the CMS and USPSTF guidelines.

The study that my team and I conducted at RUMC attempted to define the extent to which neighborhood disadvantage affects a patient’s ability to complete a lung cancer screening. To do this, we used data from the RUMC lung cancer screening program and a Neighborhood Disadvantage Index (NDI) that factors in the prevalence of four components in a patient’s zip code: Poverty, mother-only households, college education, and home ownership.

The prevalence of poverty is at the core of every observation that attempts to determine the extent to which neighborhood disadvantage affects an area.

Home ownership indicates some level of economic advantage and commitment to a neighborhood.

Female-headed households are indicative of socioeconomic disadvantage not only because single parents have less control over their children and expenses, but also because single-parent neighbors are less likely to watch each other’s children. Additionally, homes that contain only a mother and children are the poorest of any family type and areas where mother-only households are common tend to be poorer as well.

Lastly, a higher prevalence of college educated adults means that there is a higher level of human capital and role models for other children in the neighborhood. Moreover, college educated adults often have connections outside of the neighborhood that lead to new skills and professional development, which lets others in the neighborhood know that opportunities exist for those who stay in school and avoid criminal charges.

What we found was a significant association between two of the four NDI components — college education and home ownership — and LDCT screening completion. Those who live in neighborhoods with a higher prevalence of college educated adults are more likely to complete a lung cancer screening. Contrarily, those who live in neighborhoods where home ownership is more common are less likely to complete screenings.

College education and home ownership are both typically considered protective factors in this context, so why does home ownership lead to lower screening participation?

The answer may lie in the smoking status for those patients living in high home ownership areas. Our study also found that former smokers are less likely to complete screenings than current smokers. If there are more former smokers living in areas where home ownership is more common, it may account for the lack of screening participation.

In any case, the results of our study point to socioeconomic disparities as a probable cause for poor screening outcomes. The impact of living in a disadvantaged neighborhood on physical well-being both directly and indirectly warrants more research in future studies.

The model used to measure neighborhood disadvantage in this study can be replicated to focus on other geographic locations or social determinants of health. There are, of course, other ways to measure how disadvantaged a neighborhood is (and to therefore alter the NDI we used) including various observable signs that the neighborhood is disorderly: dirty streets, dangerous activity, abandoned or run-down buildings, vandalism, and drug and alcohol use leading to a sense of danger.

The significance of these studies hinges on the ability of medical centers and legislators to view neighborhood disadvantage as a serious threat to the physical health of individuals.