Accuracy of patient race and ethnicity data in a central cancer registry.

PURPOSE: Race and Hispanic ethnicity data can be challenging for central cancer registries to collect. We evaluated the accuracy of the race and Hispanic ethnicity variables collected by the Utah Cancer Registry compared to self-report. METHODS: Participants were 3,162 cancer survivors who completed questionnaires administered in 2015-2022 by the Utah Cancer Registry. Each survey included separate questions collecting race and Hispanic ethnicity, respectively. Registry-collected race and Hispanic ethnicity were compared to self-reported values for the same individuals. We calculated sensitivity and specificity for each race category and Hispanic ethnicity separately. RESULTS: Survey participants included 323 (10.2%) survivors identifying as Hispanic, a lower proportion Hispanic than the 12.1% in the registry Hispanic variable (sensitivity 88.2%, specificity 96.5%). For race, 43 participants (1.4%) self-identified as American Indian or Alaska Native (AIAN), 32 (1.0%) as Asian, 23 (0.7%) as Black or African American, 16 (0.5%) Pacific Islander (PI), and 2994 (94.7%) as White. The registry race variable classified a smaller proportion of survivors as members of each of these race groups except White. Sensitivity for classification of race as AIAN was 9.3%, Asian 40.6%, Black 60.9%, PI 25.0%, and specificity for each of these groups was > 99%. Sensitivity and specificity for White were 98.8% and 47.4%. CONCLUSION: Cancer registry race and Hispanic ethnicity data often did not match the individual's self-identification. Of particular concern is the high proportion of AIAN individuals whose race is misclassified. Continued attention should be directed to the accurate capture of race and ethnicity data by hospitals.

Pathways to ovarian cancer diagnosis: a qualitative study.

BACKGROUND: Ovarian cancer is often diagnosed at a late stage, when survival is poor. Qualitative narratives of patients' pathways to ovarian cancer diagnoses may identify opportunities for earlier cancer detection and, consequently, earlier stage at diagnosis. METHODS: We conducted semi-structured interviews of ovarian cancer patients and survivors (n = 14) and healthcare providers (n = 11) between 10/2019 and 10/2021. Interviews focused on the time leading up to an ovarian cancer diagnosis. Thematic analysis was conducted by two independent reviewers using a two-phase deductive and inductive coding approach. Deductive coding used a priori time intervals from the validated Model of Pathways to Treatment (MPT), including self-appraisal and management of symptoms, medical help-seeking, diagnosis, and pre-treatment. Inductive coding identified common themes within each stage of the MPT across patient and provider interviews. RESULTS: The median age at ovarian cancer diagnosis was 61.5 years (range, 29-78 years), and the majority of participants (11/14) were diagnosed with advanced-stage disease. The median time from first symptom to initiation of treatment was 2.8 months (range, 19 days to 4.7 years). The appraisal and help-seeking intervals contributed the greatest delays in time-to-diagnosis for ovarian cancer. Nonspecific symptoms, perceptions of health and aging, avoidant coping strategies, symptom embarrassment, and concerns about potential judgment from providers prolonged the appraisal and help-seeking intervals. Patients and providers also emphasized access to care, including financial access, as critical to a timely diagnosis. CONCLUSION: Interventions are urgently needed to reduce ovarian cancer morbidity and mortality. Population-level screening remains unlikely to improve ovarian cancer survival, but findings from our study suggest that developing interventions to improve self-appraisal of symptoms and reduce barriers to help-seeking could reduce time-to-diagnosis for ovarian cancer. Affordability of care and insurance may be particularly important for ovarian cancer patients diagnosed in the United States.

Pathways to lung cancer diagnosis among individuals who did not receive lung cancer screening: a qualitative study.

BACKGROUND: Although early detection of lung cancer through screening is associated with better prognosis, most lung cancers are diagnosed among unscreened individuals. We therefore sought to characterize pathways to lung cancer diagnosis among unscreened individuals. METHODS: Participants were individuals with lung cancer who did not undergo asymptomatic lung cancer screening (n = 13) and healthcare providers who may be involved in the pathway to lung cancer diagnosis (n = 13). We conducted semi-structured interviews to identify themes in lung cancer patients' narratives of their cancer diagnoses and providers' personal and/or professional experiences of various pathways to lung cancer diagnoses, to identify delays in diagnosis. We audio-recorded, transcribed, and coded interviews in two stages. First, we conducted deductive coding using three time-period intervals from the Models of Pathways to Treatment framework: appraisal, help-seeking, and diagnostic (i.e., excluding pre-treatment). Second, we conducted inductive coding to identify themes within each time-period interval, and classified these themes as either barriers or facilitators to diagnosis. Coding and thematic summarization were completed independently by two separate analysts who discussed for consensus. RESULTS: Eight of the patient participants had formerly smoked, and five had never smoked. We identified eight barrier/facilitator themes within the three time-period intervals. Within the appraisal interval, the barrier theme was (1) minimization or misattribution of symptoms, and the facilitator theme was (2) acknowledgment of symptoms. Within the help-seeking interval, the barrier theme was (3) hesitancy to seek care, and the facilitator theme was (4) routine care. Within the diagnosis interval, barrier themes were (5) health system challenges, and (6) social determinants of health; and facilitator themes were (7) severe symptoms and known risk factors, and (8) self-advocacy. Many themes were interrelated, including minimization or misattribution of symptoms and hesitancy to seek care, which may collectively contribute to care and imaging delays. CONCLUSIONS: Interventions to reduce hesitancy to seek care may facilitate timely lung cancer diagnoses. More prompt referral to imaging-especially computed tomography (CT)-among symptomatic patients, along with patient self-advocacy for imaging, may reduce delays in diagnosis.

Improving Precision of Do Not Contact Codes: Results of a Manual Review to Inform Coding and Case Contact Procedures.

Introduction: Central cancer registries are responsible for managing appropriate research contacts and record releases. Do not contact (DNC) flags are used by some registries to indicate patients who should not be contacted or included in research. Longitudinal changes in DNC coding practices and definitions may result in a lack of code standardization and inaccurately include or exclude individuals from research. Purpose: We performed a comprehensive manual review of DNC cases in the Utah Cancer Registry to inform updates to standardization of DNC code definitions, and use of DNC codes for exclusion/inclusion in research. Methods: We identified 858 cases with a current or prior DNC flag in the SEER Data Management System (SEER*DMS) or a research database, with cancers diagnosed from 1957-2021. We reviewed scanned images of correspondence with cases and physicians, incident forms, and comments in SEER*DMS and research databases. We evaluated whether there was evidence to support the current DNC code, a different DNC code, or insufficient evidence for any code. Results: Of the 755 cases that had a current DNC flag and reason code in SEER*DMS, the distribution was as follows: 58%, Patient requested no contact; 20%, Physician denied; 13%, Patient is not aware they have cancer; 4%, Patient is mentally disabled [sic]; 4%, Other; and 1%, Unknown. In 5% of these cases, we found evidence supporting a different DNC reason code. Among cases included because of a prior DNC flag in SEER*DMS (n = 10) or a DNC flag in a research database (ie, cases with no current DNC flag or reason code in SEER*DMS, n = 93), we found evidence supporting the addition of a SEER*DMS DNC flag and reason code in 50% and 40% of cases, respectively. We identified DNC reason codes with outdated terminology (Patient is mentally disabled) and codes that may not accurately reflect patient research preferences (Physician denied without asking the patient). To address this, we identified new reason codes, retired old reason codes, and updated current reason code definitions and research handlings. Conclusion: The time and resource investment in manual review allowed us to identify and, in most cases, resolve discordance in DNC flags and reason codes, adding reason codes when they were missing. This process was valuable because it informed recommended changes to DNC code definitions and research handlings that will ensure more appropriate inclusion and exclusion of cancer cases in research.