A geographic information system-based method for estimating cancer rates in non-census defined geographical areas

Commentary: Some water in the data desert: the Cancer Intervention and Surveillance Modeling Network's capacity to guide mitigation of cancer health disparities

Despite significant progress in cancer research and treatment, a persistent knowledge gap exists in understanding and addressing cancer care disparities, particularly among populations that are marginalized. This knowledge deficit has led to a "data divide," where certain groups lack adequate representation in cancer-related data, hindering their access to personalized and data-driven cancer care. This divide disproportionately affects marginalized and minoritized communities such as the U.S. Black population. We explore the concept of "data deserts," wherein entire populations, often based on race, ethnicity, gender, disability, or geography, lack comprehensive and high-quality health data. Several factors contribute to data deserts, including underrepresentation in clinical trials, poor data quality, and limited access to digital technologies, particularly in rural and lower-socioeconomic communities.The consequences of data divides and data deserts are far-reaching, impeding equitable access to precision medicine and perpetuating health disparities. To bridge this divide, we highlight the role of the Cancer Intervention and Surveillance Modeling Network (CISNET), which employs population simulation modeling to quantify cancer care disparities, particularly among the U.S. Black population. We emphasize the importance of collecting quality data from various sources to improve model accuracy. CISNET's collaborative approach, utilizing multiple independent models, offers consistent results and identifies gaps in knowledge. It demonstrates the impact of systemic racism on cancer incidence and mortality, paving the way for evidence-based policies and interventions to eliminate health disparities. We suggest the potential use of voting districts/precincts as a unit of aggregation for future CISNET modeling, enabling targeted interventions and informed policy decisions.

Modeling spatial distribution of population for environmental epidemiological studies: Comparing the exposure estimates using choropleth versus dasymetric mapping

Precise population information is critical for identifying more accurate environmental exposures for air pollution impacts analysis. Basically, there are two methods for estimating spatial distribution of population, choropleth and dasymetric mapping. While the choropleth approach accounts for linear distribution of population over area based on census tract units, the dasymetric model accounts for a more heterogeneous population density by quantifying the association between the area-class map data categories and values of the statistical surface as encoded in the census dataset. Environmental epidemiological studies have indicated the dasymetric mapping as a more accurate approach to estimate and characterize population densities in large urban areas. However, investigations that have attempted to compare the exposure estimates from choropleth versus dasymetric mapping in environmental health analysis are still missing. This paper addresses this gap and compares the impact of using choropleth and dasymetric mapping in different exposure metrics. We compare the impact of using choropleth and dasymetric mapping in three case studies, defined here as case study A (relationship between urban structure types and health), case study B (PM_2.5 emissions and human exposure), and case study C (distance-decays of mortality risk related to PM_2.5 emitted by traffic along major highways). These case studies represent previous investigations performed by our research group where spatial distribution of population was an essential input for analysis. Our findings indicate that the method used to estimate spatial distribution of population impacts significantly the exposure estimates. We observed that the choropleth mapping overestimated exposure for the case study A and B, while for the case study C the exposure was underestimated by the choropleth approach. Our findings show that the dasymetric model is a preferred method for creating spatially-explicit information about population distribution for health exposure studies. The results presented here can be useful for the environmental health community to more accurately assess the relationship between environmental factors and health risks.