Developing an algorithm across integrated healthcare systems to identify a history of cancer using electronic medical records.

OBJECTIVE: Tumor registries in integrated healthcare systems (IHCS) have high precision for identifying incident cancer but often miss recently diagnosed cancers or those diagnosed outside of the IHCS. We developed an algorithm using the electronic medical record (EMR) to identify people with a history of cancer not captured in the tumor registry to identify adults, aged 40-65 years, with no history of cancer. MATERIALS AND METHODS: The algorithm was developed at Kaiser Permanente Colorado, and then applied to 7 other IHCS. We included tumor registry data, diagnosis and procedure codes, chemotherapy files, oncology encounters, and revenue data to develop the algorithm. Each IHCS adapted the algorithm to their EMR data and calculated sensitivity and specificity to evaluate the algorithm's performance after iterative chart review. RESULTS: We included data from over 1.26 million eligible people across 8 IHCS; 55 601 (4.4%) were in a tumor registry, and 44848 (3.5%) had a reported cancer not captured in a registry. The common attributes of the final algorithm at each site were diagnosis and procedure codes. The sensitivity of the algorithm at each IHCS was 90.65%-100%, and the specificity was 87.91%-100%. DISCUSSION: Relying only on tumor registry data would miss nearly half of the identified cancers. Our algorithm was robust and required only minor modifications to adapt to other EMR systems. CONCLUSION: This algorithm can identify cancer cases regardless of when the diagnosis occurred and may be useful for a variety of research applications or quality improvement projects around cancer care.

Characterizing employment of colorectal cancer survivors using electronic health records.

OBJECTIVE: Although the value of collecting occupational data is well-established, these data are not systematically collected in clinical practice. We assessed the availability of electronic health record (EHR)-based occupation data within a large integrated health care system to determine the feasibility of its use in research. MATERIALS AND METHODS: We used a mixed-methods approach to extract EHR data and define employment status, employer, and employment industry of 1107 colorectal cancer survivors. This was a secondary analysis of a subset of the Patient Outcomes Research to Advance Learning (PORTAL) colorectal cancer cohort. RESULTS: We categorized the employment industry for 46% of the cohort. Employment status was available for 58% of the cohort. The employer was missing for over 95% of the cohort. CONCLUSION: By combining data from structured and free-text EHR fields, we identified employment status and industry for approximately half of our sample. Findings demonstrate limitations of EHR data and underscore the need for systematic collection of occupation data in clinical practice.

Use of antidepressants after colon cancer diagnosis and risk of recurrence.

OBJECTIVE: Prior research examining the association between use of antidepressants after colon cancer diagnosis and risk of recurrence is scant. We evaluated this association among colon cancer patients diagnosed at two integrated health care delivery systems in the United States. METHODS: We conducted a cohort study of stage I to IIIA colon cancer patients diagnosed at greater than or equal to 18 years of age at Kaiser Permanente Colorado and Kaiser Permanente Washington during 1995 to 2014. We used pharmacy records to identify dispensings for antidepressants and tumor registry records and patients' medical charts to identify cancer recurrences. Using Cox proportional hazards models, we estimated the adjusted hazard ratio (HR) of colon cancer recurrence comparing patients who used antidepressants after diagnosis to those who did not. We also evaluated the risk associated with use of selective serotonin reuptake inhibitors (SSRIs) and tricyclic antidepressants (TCAs) separately. RESULTS: Among the 1923 eligible colon cancer patients, 807 (42%) used an antidepressant after diagnosis and 139 had a colon cancer recurrence during an average 5.6 years of follow-up. Use of antidepressants after colon cancer diagnosis was not associated with risk of recurrence (HR: 1.14; 95% confidence interval [CI], 0.69-1.87). The HR for use of SSRIs was 1.22 (95% CI, 0.64-2.30), and for TCAs, it was 1.18 (95% CI, 0.68-2.07). CONCLUSIONS: Our findings suggest that use of antidepressants after colon cancer diagnosis was common and not associated with risk of recurrence. Future larger studies with greater power to examine risk associated with individual antidepressants would be valuable additions to the evidence base.

Risk of colon cancer recurrence in relation to diabetes.

PURPOSE: To describe the association between diabetes and colon cancer recurrence. METHODS: We conducted a cohort study at two integrated health care delivery systems in the United States. Using tumor registry data, we identified patients aged ≥ 18 years when diagnosed with stage I-IIIA adenocarcinomas of the colon during 1995-2014. Pre-existing diabetes was ascertained via diagnosis codes. Medical records were reviewed for eligibility and to abstract recurrence and covariate information. Recurrence was ascertained beginning 90 days after the end of colon cancer treatment (i.e., cohort entry). Recurrence of any cancer or a new primary cancer at any site was a secondary outcome. We used multivariable Cox proportional hazards models to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for the associations between diabetes at cohort entry and study outcomes. RESULTS: Among the 1,923 eligible patients, 393 (16.7%) had diabetes at cohort entry. Diabetes was not associated with recurrence (HR 0.87; 95% CI 0.56-1.33) or with any subsequent cancer (HR 1.09; 95% CI 0.85-1.40). When the definition of recurrence included second primary colorectal cancer, risk was non-significantly higher in patients with diabetes than without diabetes. CONCLUSIONS: The risk of colon cancer recurrence appears to be similar in patients with and without diabetes at diagnosis. IMPACT: Future studies should evaluate the association between diabetes and colorectal cancer outcomes, especially second primary colon cancers, in larger populations.

Food Insecurity in Older Adults in an Integrated Health Care System.

OBJECTIVES: To estimate food insecurity prevalence and develop a statistical prediction model for food insecurity. DESIGN: Retrospective cohort study. SETTING: Kaiser Permanente Colorado. PARTICIPANTS: Adult members who completed a pre-Medicare Annual Wellness Visit survey. MEASUREMENTS: Food insecurity was assessed using a single screening question. Sociodemographic and clinical characteristics from electronic health records and self-reported characteristics from the survey were used to develop the prediction model. RESULTS: Of 130,208 older adult members between January 2012 and December 2015, 50,097 (38.5%) completed food insecurity screening, 2,859 of whom (5.7% of respondents) reported food insecurity. The prevalence of food insecurity was 10.0% or greater among individuals who were black or Hispanic, had less than high school education, had Medicaid insurance, were extremely obese, had poor health status or quality of life, had depression or anxiety, had impairments in specific activities of daily living, had other nutritional risk factors, or were socially isolated (all p<.001). A multivariable model based on these and other characteristics showed moderate discrimination (c-statistic = 0.74) between individuals with food insecurity and those without and 14.3% of individuals in the highest quintile of risk reported food insecurity. CONCLUSION: Food insecurity is prevalent even in older adults with private-sector healthcare coverage. Specific individual characteristics, and a model based on those characteristics, can identify older adults at higher risk of food insecurity. System-level interventions will be necessary to connect older adults with community-based food resources.