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Análisis geoespacial como herramienta para la identificación de regiones potenciales y tratables para intervenciones
Fatima G Wilder1, Miles McAllister1, Anupama Singh1
1Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Background:
Racial and socioeconomic disparities in lung cancer are well documented. Given the benefits of surgery and targeted therapies for early-stage disease, there is a need to increase screening participation of high-risk groups to improve survival. Yet, best practices for ascertaining which groups would benefit are lacking. The aims of this study were to demonstrate an association between zip code and stage at diagnosis of lung cancer as well as an association between zip code and age at diagnosis of lung cancer in Massachusetts patients.
Methods:
To identify such populations in Massachusetts, patients diagnosed with lung carcinoma from 2004 to 2020 were identified utilizing the Massachusetts Cancer Registry (n=72,559). Zip code-level demographic information, median household income, and percentage of residents without a high school diploma were obtained from census data. Geospatial analysis using ArcGISPro identified spatial trends in lung cancer incidence and patient characteristics.
Results:
Average age at diagnosis was 69.4 (±10.2) years old and the majority were White (95.9%) and female (53.8%). Many patients had distant (47.6%) spread at diagnosis, compared to regional (16.3%) or local disease (36.1%). Median household income and educational attainment were correlated with late-to-early-stage prevalence ratio for lung cancer (P<0.05). Higher proportions of Black residents in a zip code correlated with an increased rate of late-stage lung cancer cases in young patients (≤55 years; P<0.05) and further demonstrated geographic trends toward higher incidence of late-stage disease, lower educational attainment, and lower income.
Conclusions:
These results demonstrate the utility of geospatial analysis to detect trends in the incidence of late-stage lung cancer relating to race, age, education, and socioeconomic factors. Importantly, these results can direct location preferences for early intervention efforts including patient/provider education and mobile lung cancer screening clinics, particularly in neighborhoods with lower income and education levels (248/250).
