Association between Diagnostic History and Cancer Incidence within 5 Years: A Real-world Observational Analysis

Cancer is associated with many preexisting health conditions (PHC); however, accurately quantifying these links remains challenging. Although some studies have examined these associations, large-scale analyses using diverse electronic health record (EHR) data remain limited and lack the ability to evaluate cancer risk when patients are stratified by interpersonal differences. Using a real-world EHR dataset from a large Louisiana health system comprising 8,283,236 records from 1,460,738 patients (2013-2022), we evaluated associations between PHCs and subsequent cancer diagnoses within a fixed 5-year risk window. We applied epidemiologic, statistical, and artificial intelligence methods to the full dataset and to subgroups stratified by gender, race, and area deprivation index (ADI) for overall cancer and 20 cancer types. We identified 9 chapters of International Classification of Diseases, Tenth Revision, including chapter 4 (metabolic) and chapter 14 (genitourinary), with 221 PHCs linked to increased cancer risk (relative risk >1, 95% confidence interval excluding 1, and Benjamini-Hochberg false discovery rate-adjusted P < 0.05). Key PHCs include systemic sclerosis, blood type, benign mammary dysplasia, immune mechanism disorders, disturbances of smell, lipoprotein metabolism disorders, human immunodeficiency virus disease, vitamin D deficiency, and diabetes. Chapter 12 (skin diseases) and chapter 9 (circulatory diseases) showed strong associations with 10 and 13 cancer types, respectively. Age-, gender-, race-, and ADI-specific high-risk PHCs were also identified. However, these findings should be interpreted carefully as ADI may not fully capture individual-level socioeconomic or environmental exposures, and the lack of tobacco data may introduce residual confounding. SIGNIFICANCE: This framework offers a systematic approach to linking PHCs with cancer risk, providing valuable insights for cancer prediction, management, and prevention across diverse patient populations. This approach reveals prediagnostic disease patterns and demographic heterogeneity in cancer incidence.