A causal inference framework for cancer cluster investigations using publicly available data

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients

Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data are available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-world healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields.

Changes in spatial clusters of cancer incidence and mortality over 15 years in South Korea: Implication to cancer control

BACKGROUND

The temporal investigation of high-risk areas of cancer incidence and mortality can provide practical implications in cancer control. We aimed to investigate the changes in spatial clusters of incidence and mortality from 1999 through 2013 by major cancer types in South Korea.

METHODS

We applied flexible scan statistics to identify spatial clusters of cancer incidence and mortality by three 5-year periods and seven major cancer types using the counts of new cases and deaths and population in 244 districts during 1999-2013. Then, we compared the changes across three periods in the locations of primary clusters of incidence and mortality by cancer types. To explore the determinants that possibly affect cancer cluster areas, we compared geographic characteristics between clustered and non-clustered areas.

RESULTS

While incidence clusters for lung, stomach, and liver cancer remained in the same areas over 15 years, mortality clusters were relocated to the areas similar to those of incidence clusters. In contrast, colorectal, breast, cervical, and prostate cancer displayed consistently different locations of clusters over time, indicating the disappearance of existing clusters and the appearance of new clusters. Cluster areas tended to show higher portions of older population, unemployment, smoking, and cancer screening compared to non-cluster areas particularly for mortality.

CONCLUSIONS

Our findings of diverse patterns of changes in cancer incidence and mortality clusters over 15 years can indicate the degree of effectiveness in cancer prevention and treatment depending on the area and suggest the need for area-specific applications of different cancer control programs.

Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review

The increasing pattern of colorectal cancer (CRC) in specific geographic region, compounded by interaction of multifactorial determinants, showed the tendency to cluster. The review aimed to identify and synthesize available evidence on clustering patterns of CRC incidence, specifically related to the associated determinants. Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and EBSCOHost. The approach for identification of the final articles follows PRISMA guidelines. Selected full-text articles were published between 2016 and 2021 of English language and spatial studies focusing on CRC cluster identification. Articles of systematic reviews, conference proceedings, book chapters, and reports were excluded. Of the final 12 articles, data on the spatial statistics used and associated factors were extracted. Identified factors linked with CRC cluster were further classified into ecology (health care accessibility, urbanicity, dirty streets, tree coverage), biology (age, sex, ethnicity, overweight and obesity, daily consumption of milk and fruit), and social determinants (median income level, smoking status, health cost, employment status, housing violations, and domestic violence). Future spatial studies that incorporate physical environment related to CRC cluster and the potential interaction between the ecology, biology and social determinants are warranted to provide more insights to the complex mechanism of CRC cluster pattern.