Introduction:

Industrial byproducts and environmental pollutants (IBP/EP), such as polycyclic aromatic hydrocarbons(PAH), aromatic amines (AA), and heavy metals (HM), are associated with the development of urothelial carcinoma (UC). While the association with tobacco exposure (TE) as the major risk factor for UC is well-documented, the interaction between sources of IBP/EP and incidence of UC in surrounding communities has been infrequently explored. The disparities in healthcare can be attributed to both socioeconomic and environmental factors and it is necessary to understand the potential interactions of these factors on a granular level so that specific at-risk populations can be identified and, ultimately, focused interventions pursued. The purpose of this research is to identify both high-density microregions of UC prevalence and spatially-related industrial and environmental risk factors.

Methods:

After obtaining IRB approval, we retrospectively queried a multi-institutional database for patients residing in Southeastern Pennsylvania diagnosed with UC between 2008-2018. Addresses at the time of diagnosis were geocoded and mapped. Using ArcGIS software to calculate the Getis-Ord-Gi*  statistic, hotspot analysis was performed on the census-block level accounting for age-restricted population (40+) to identify hotspots of UC prevalence. Age, race, comorbidities, occupational and social risk factors, clinicopathologic characteristics, census data and proximity to sources of IBP/EP were compared between patients who did and did not reside in a UC hotspot. Demographic, clinicopathologic and environmental data were compared using Pearson’s chi-square and Student’s T-test as appropriate. Univariate analyses and multivariable multilevel logistic random-intercept regression models were fitted to test the association between patient and census block-level factors and living in a UC hotspot. Odds ratios (OR) and 95% confidence intervals (95% CI) are reported. Significance was assessed at the 0.05 level.

Results:

5,080 patients met inclusion/exclusion criteria. 148 patients (2.9%) were associated with one of three UC hotspots (Figure 1). In univariate analyses, white patients (OR: 0.10, p=<0.001) and tobacco users (OR: 0.24, p=0.004) were less likely to live in a hot spot, while lower income was associated with greater likelihood of residing in a hotspot (OR 0.73, p=0.005). Patients with PAH exposure (OR: >999, p=<0.001), and those who lived near high-traffic density (OR: >999, p=<0.001), airports (OR: >999, p=<0.001), or industrial sites (OR: 8.78, p=0.001) were more likely to live in a hotspot (Table1). In the multivariable model, white patients (OR: 0.02, 95% CI: 0.00,0.29, p=0.004) and tobacco users (OR: 0.11, 95% CI: 0.01, 0.96, p=0.045) remained less likely to live in a hotspot, while PAH exposed (OR: 106.9, 95% CI: 3.2, >999) and  those near high-traffic density (OR and CIs: >999, p=<0.001) were more likely to reside in a hotspot.

Conclusion:

Patients residing in geospatial hotspots of UC prevalence are associated with proximity to sources of IBP/EPs, such as industrial sites, airports and areas of high traffic density, while tobacco exposure was less likely to be found in these patients. Additionally, patients in hotspots were less likely to be white and more likely to have lower incomes than their non-hotspot counterparts. These findings suggest that tobacco exposure may be a less significant factor contributing to UC prevalence in patients residing in these communities compared to their proximity to IBP/EPs, and that these patients are disproportionately non-white and lower-earning compared to non-hotspot patients. Furthering research that investigates the interplay between socioeconomic status, race and environmental risk factors has the potential to better identify at-risk populations and, in doing so, improve screening, referral, diagnosis and timely intervention.

Funding: Sharpe Strumia Foundation