Background: Allergic conditions may prevent some cancers by promoting immune surveillance. We examined associations of allergic rhinitis, asthma, and eczema with cancer risk among elderly Americans. Methods: We used Surveillance Epidemiology and End Results (SEER)-Medicare linked data to perform a case-control study. Cases were individuals with first cancer diagnosed in SEER registries (1992-2013, ages 66-99; N ¼ 1,744,575). Cancer-free controls (N ¼ 100,000) were randomly selected from Medicare and matched on sex, age, and selection year. Allergic conditions were identified using Medicare claims, and logistic regression was used to estimate adjusted ORs (aOR) with significance gauged with a Bonferroni P cutoff (P < 0.00034). Results: Allergic rhinitis, asthma, and eczema were present in 8.40%, 3.45%, and 0.78% of controls, respectively. For allergic rhinitis, strong inverse associations (aORs, 0.66-0.79) were observed for cancers of the hypopharynx, esophagus (squamous cell), cervix, tonsil/oropharynx, and vagina/vulva. More modest but significant inverse associations were noted for cancers of the esophagus (adenocarcinoma), stomach, colon, rectosigmoid/rectum, liver, gallbladder, lung, uterus, bladder, and miscellaneous sites. Associations were stronger in analyses requiring a dispensed medication to confirm the presence of allergic rhinitis. Asthma was associated with reduced risk of liver cancer [aOR 0.82; 95% confidence interval (CI), 0.75-0.91], whereas eczema was associated with elevated risk of T-cell lymphoma (aOR, 4.12; 95% CI, 3.43-4.95). Conclusions: Inverse associations with allergic rhinitis are present for multiple cancers and require etiologic investigation. Impact: Understanding of mechanisms by which allergic conditions reduce cancer risk may advance cancer prevention and treatment.

PURPOSE The implementation and utilization of electronic health records is generating a large volume and variety of data, which are difficult to process using traditional techniques. However, these data could help answer important questions in cancer surveillance and epidemiology research. Artificial intelligence (AI) data processing methods are capable of evaluating large volumes of data, yet current literature on their use in this context of pharmacy informatics is not well characterized. METHODS A systematic literature review was conducted to evaluate relevant publications within four domains (cancer, pharmacy, AI methods, population science) across PubMed, EMBASE, Scopus, and the Cochrane Library and included all publications indexed between July 17, 2008, and December 31, 2018. The search returned 3,271 publications, which were evaluated for inclusion. RESULTS There were 36 studies that met criteria for full-text abstraction. Of those, only 45% specifically identified the pharmacy data source, and 55% specified drug agents or drug classes. Multiple AI methods were used; 25% used machine learning (ML), 67% used natural language processing (NLP), and 8% combined ML and NLP. CONCLUSION This review demonstrates that the application of AI data methods for pharmacy informatics and cancer epidemiology research is expanding. However, the data sources and representations are often missing, challenging study replicability. In addition, there is no consistent format for reporting results, and one of the preferred metrics, F-score, is often missing. There is a resultant need for greater transparency of original data sources and performance of AI methods with pharmacy data to improve the translation of these results into meaningful outcomes.

Cancer Medications Enquiry Database (CanMED) is comprised of two interactive, nomenclature-specific databases within the Observational Research in Oncology Toolbox: CanMED-Healthcare Common Procedure Coding System (HCPCS) and CanMED-National Drug Code (NDC), described through this study. CanMED includes medications with a) a US Food and Drug Administration-approved cancer treatment or treatment-related symptom management indication, b) inclusion in treatment guidelines, or c) an orphan drug designation. To demonstrate the joint utility of CanMED, medication codes associated with female breast cancer treatment were identified and utilization patterns were assessed within Surveillance Epidemiology and End Results-Medicare (SEER) data. CanMED-NDC (11_2018 v.1.2.4) includes 6860 NDC codes: chemotherapy (1870), immunotherapy (164), hormone therapy (3074), and ancillary therapy (1752). Treatment patterns among stage I–IIIA (20 701) and stage IIIB–IV (2381) breast cancer patients were accordant with guideline-recommended treatment by stage and molecular subtype. CanMED facilitates identification of medications from observational data (eg, claims and electronic health records), promoting more standardized and efficient treatment-related cancer research.