Determining the cancer diagnostic interval using administrative data in a cohort of patients with pancreatic cancer.

Abstract

336 Background: Pancreatic cancer is a leading cause of cancer death, largely due to vague presenting symptoms and late stage at diagnosis. Population-based administrative data can be a valuable resource for studying the diagnostic interval. The objective of this study was to determine the first encounter in the diagnostic interval and to calculate that interval in a cohort of patients with pancreatic cancer using an empirical approach. Methods: This is a retrospective, cohort study of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) from 2007 – 2015 in Alberta, Canada. We used the Alberta Cancer Registry (ACR), physician billing claims, hospital discharge and emergency room visits to identify health encounters that occurred more frequently in the 3 months prior to diagnosis compared to those in the 3-24 months prior to diagnosis. We used statistical control charts to define the lookback period for each encounter category and identify the earliest encounter that represented the start of the diagnostic interval (index contact date). The end of the interval was the diagnosis date. Quantile regression was used to determine factors associated with the diagnostic interval. Results: We identified 3142 patients with PDAC. Median age of diagnosis was 71 (IQR 61-80). We identified an index contact date in 96.5% of the patients. The median length of the diagnostic interval was 76 days (IQR 21-191; 90th percentile 276 days). A higher Elixhauser comorbidity score (+18.57 days/ 1 point increase, 95% CI 16.07-21.07, p < 0.001) and stage 3 disease (+22.55 days, 95% CI 5.02-40.08, p = 0.01) was associated with a longer diagnostic interval. Conclusions: In this cohort of patients with pancreatic cancer, there was a wide range in the diagnostic interval with 10% of patients having a diagnostic interval approaching one year. Diagnostic interval research using administrative databases can understand variations in diagnosis times, can inform early detection efforts and can improve quality of care.

Analysis of Time to Treatment and Survival Among Adults Younger Than 50 Years of Age With Colorectal Cancer in Canada

“…We identified the date of first presentation using a complex algorithm based on administrative and billing codes specifically developed for Ontario databases . While it has been used in CRC, breast cancer, oral cancer, pancreatic cancer, and we have previously described its application to young patients with CRC, it has only been directly compared against patient charts for oral cancer . Our study does not capture the interval between symptom onset and presentation, and it is possible young adults have delays to presentation that contribute to worse outcomes.…”

“…20 While it has been used in CRC, breast cancer, oral cancer, pancreatic cancer, and we have previously described its application to young patients with CRC, it has only been directly compared against patient charts for oral cancer. 9,20,21,23 Our study does not capture the interval between symptom onset and presentation, 37 and it is possible young adults have delays to presentation that contribute to worse outcomes. It has been recognized that delays in cancer care can also be related to health behavior and psychological factors such as patient knowledge, reluctance to seek help, fear or denial, and financial concerns.…”

“…Our exposure of interest was the number of days from first presentation to treatment initiation (the overall interval). To identify the date of first presentation, we used an algorithm based on administrative and billing codes, adapted from prior work by Groome et al [20][21][22][23] for CRC, breast cancer, and others. We have described our application of these methods to measure delay intervals among 6853 Ontario adults younger than age 50 years with CRC, and we were able to assign a date of presentation to more than 97% of the cohort.…”

Measurement of clinical delay intervals among younger adults with colorectal cancer using health administrative data: a population-based analysis

“…Codes were organised into categories, and category-specific lookback periods were established based on when the frequency of codes was similar to the background rate (online supplemental material 4). This method has been used for oral cancer, breast cancer and CRC15 23 24 and is being tested for use in pancreatic cancer 25. The methodology avoids arbitrary lookback periods, leverages existing health administrative datasets and allows for a more complete assessment of the diagnostic journey 23…”

“…This method has been used for oral cancer, breast cancer and CRC 15 23 24 and is being tested for use in pancreatic cancer. 25 The methodology avoids arbitrary lookback periods, leverages existing health administrative datasets and allows for a more complete assessment of the diagnostic journey. 23 Based on this algorithm, we assigned the date of first presentation for each patient using the following steps: 1.…”