scholarly journals Determining the Cancer Diagnostic Interval Using Administrative Health Care Data in a Breast Cancer Cohort

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Patti A. Groome ◽  
Colleen Webber ◽  
Marlo Whitehead ◽  
Rahim Moineddin ◽  
Eva Grunfeld ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Health Care ◽  
Control Charts ◽  
Population Level ◽  
Population Based ◽  
Statistical Control ◽  
Health Care Data ◽  
Cancer Diagnostic ◽  
Breast Cancer Cohort ◽  
Diagnostic Interval

PURPOSE Population-based administrative health care data could be a valuable resource with which to study the cancer diagnostic interval. The objective of the current study was to determine the first encounter in the diagnostic interval and compute that interval in a cohort of patients with breast cancer using an empirical approach. METHODS This is a retrospective cohort study of patients with breast cancer diagnosed in Ontario, Canada, between 2007 and 2015. We used cancer registry, physician claims, hospital discharge, and emergency department visit data to identify and categorize cancer-related encounters that were more common in the three months before diagnosis. We used statistical control charts to define lookback periods for each encounter category. We identified the earliest cancer-related encounter that marked the start of the diagnostic interval. The end of the interval was the cancer diagnosis date. RESULTS The final cohort included 69,717 patients with breast cancer. We identified an initial encounter in 97.8% of patients. Median diagnostic interval was 36 days (interquartile range [IQR], 19 to 71 days). Median interval decreased with increasing stage at diagnosis and varied across initial encounter categories, from 9 days (IQR, 1 to 35 days) for encounters with other cancer as the diagnosis to 231 days (IQR 77 to 311 days) for encounters with cyst aspiration or drainage as the procedure. CONCLUSION Diagnostic interval research can inform early detection guidelines and assess the success of diagnostic assessment programs. Use of administrative data for this purpose is a powerful tool for improving diagnostic processes at the population level.

Determining the cancer diagnostic interval using administrative data in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13551-e13551
Author(s):  
Bailey Paterson ◽  
Shiying Kong ◽  
Alyson Mahar ◽  
Colleen Webber ◽  
Richard M. Lee-Ying ◽  
...  
Keyword(s):  
Administrative Data ◽  
Control Charts ◽  
Population Based ◽  
Administrative Databases ◽  
Interval Length ◽  
Ductal Adenocarcinoma ◽  
Statistical Control ◽  
Wide Range ◽  
Age Of Diagnosis ◽  
Diagnostic Interval

e13551 Background: PDAC is a leading cause of cancer death that is often diagnosed at an advanced stage. Population-based administrative data can be a valuable resource for studying the diagnostic interval, defined as the time from the first related healthcare encounter to cancer diagnosis. The objective of this study was to determine the diagnostic interval in a cohort of patients with PDAC using an empirical approach. Methods: This is a retrospective, cohort study of patients diagnosed with PDAC from 2007 – 2015 in Alberta, Canada. We used the Alberta Cancer Registry, physician billing claims, hospital discharge and emergency room visits to identify and categorize cancer-related healthcare encounters before diagnosis. We used statistical control charts to define the lookback period for each encounter category and used these lookback periods to 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 3,142 patients with PDAC. Median age of diagnosis was 71 (IQR 61-80). We identified an index contact date and thus a diagnostic interval in 96.5% of patients. The median diagnostic interval length 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 compared to stage 2 disease (+22.55 days, 95% CI 5.02-40.08, p=0.01) were associated with a longer diagnostic interval. Conclusions: In this cohort of patients with PDAC, there was a wide range in the diagnostic interval with 10% of patients having a diagnostic interval of approximately 9 months. Diagnostic interval research using administrative databases can understand variations in diagnosis times and can inform early detection efforts by identifying where and in whom delays may occur.

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

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 336-336
Author(s):  
Safiya Karim ◽  
Bailey Paterson ◽  
Shiying Kong ◽  
Alyson Mahar ◽  
Colleen Webber ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Administrative Data ◽  
Control Charts ◽  
Population Based ◽  
Administrative Databases ◽  
Ductal Adenocarcinoma ◽  
Statistical Control ◽  
Presenting Symptoms ◽  
Wide Range ◽  
Diagnostic Interval

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.

scholarly journals Wait times and patterns of care in the colorectal cancer diagnostic interval

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Colleen Webber ◽  
Jennifer Flemming ◽  
Richard Birtwhistle ◽  
Mark Rosenberg ◽  
Patti Groome
Keyword(s):  
Colorectal Cancer ◽  
Cluster Analysis ◽  
Control Charts ◽  
Population Level ◽  
Population Based ◽  
Health Data ◽  
Patterns Of Care ◽  
Cross Sectional ◽  
Administrative Health Data ◽  
Diagnostic Interval

ABSTRACTObjectiveThere is concern that patients are waiting too long to be diagnosed with colorectal cancer (CRC) after presenting to the healthcare system. A prolonged time from first presentation to diagnosis, also known as the diagnostic interval, may be harmful to patients and indicate problems with the delivery of healthcare. The purpose of this study is to measure the length of the CRC diagnostic interval and describe variations in care that patients receive within the interval. ApproachThis is a population-based, cross-sectional study of CRC patients diagnosed in Ontario, Canada between 2009 and 2012 using data from the Institute for Clinical Evaluative Sciences (ICES). The diagnostic interval will be measured using physician billing, hospital discharge, emergency room and registry data. Patients’ healthcare encounters in the 18 months before diagnosis will be analyzed using control charts to identify the earliest cancer-related encounter. The diagnostic interval will be defined as the date of this first relevant healthcare encounter to the CRC diagnosis date. Cluster analysis will be used to identify and characterize groups of patients with similar diagnostic intervals, based on the care received within the interval. Analyses will examine factors associated with the length of the diagnostic interval and care received within the diagnostic interval. Results Analyses for this project are ongoing and will be complete by August 2016. Results from this study will describe the length of the CRC diagnostic interval and relevant sub-intervals, and variations in these intervals according to patient and clinical characteristics. Results will describe the care that patients received within the interval, including the number and types of tests received and physicians involved in the interval, and whether the care received in the interval was associated with how long patients wait for diagnosis. ConclusionThe findings from this study will advance our understanding of the CRC diagnostic interval. The control chart methodology used to identify CRC-related healthcare encounters from administrative health data is an improvement on previous research that has used arbitrary time periods and encounters which likely underestimate the length of the diagnostic interval. The cluster analysis method is a novel approach to characterizing the diagnostic interval that will identify common patterns of care and diagnostic pathways using administrative health data. This study will provide population-level estimates of how long patients are waiting to be diagnosed with CRC and provide an understanding of how patterns of care influence the length of the diagnostic interval.

scholarly journals Population Based Utilization of Radiation Therapy by a Canadian Breast Cancer Cohort

2013 ◽  
Vol 16 (7) ◽  
pp. A430
Author(s):  
N. Mittmann ◽  
S.J. Seung ◽  
N. Liu ◽  
J. Porter ◽  
R. Saskin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Radiation Therapy ◽  
Population Based ◽  
Breast Cancer Cohort

scholarly journals High Estrogen Receptor β Expression Is Prognostic among Adjuvant Chemotherapy–Treated Patients—Results from a Population-Based Breast Cancer Cohort

2016 ◽  
Vol 23 (3) ◽  
pp. 766-777 ◽  
Author(s):  
Karin Elebro ◽  
Signe Borgquist ◽  
Ann H. Rosendahl ◽  
Andrea Markkula ◽  
Maria Simonsson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Adjuvant Chemotherapy ◽  
Population Based ◽  
Estrogen Receptor Β ◽  
Breast Cancer Cohort

A Comparative Analysis of Robust Dispersion Control Charts with Application Related to Health Care Data

2019 ◽  
Vol 48 (1) ◽  
pp. 20180572
Author(s):  
Muhammad Abid ◽  
Hafiz Zafar Nazir ◽  
Muhammad Tahir ◽  
Muhammad Riaz ◽  
Tahir Abbas
Keyword(s):  
Health Care ◽  
Comparative Analysis ◽  
Control Charts ◽  
Health Care Data

Family History, and Impact on Clinical Presentation and Prognosis, in a Population-based Breast Cancer Cohort from the Stockholm County

2006 ◽  
Vol 5 (4) ◽  
pp. 309-321 ◽  
Author(s):  
Sara Margolin ◽  
Hemming Johansson ◽  
Lars Erik Rutqvist ◽  
Annika Lindblom ◽  
Tommy Fornander
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Clinical Presentation ◽  
Population Based ◽  
Stockholm County ◽  
Breast Cancer Cohort

scholarly journals Approaches to population health care: The emerging context!

2005 ◽  
Vol 11 (2) ◽  
pp. 45 ◽  
Author(s):  
Peter Harvey
Keyword(s):  
Health Care ◽  
Community Health ◽  
Population Health ◽  
Service Management ◽  
Health Management ◽  
Population Level ◽  
Population Based ◽  
Social Contexts ◽  
Illness Management ◽  
The Impact

This paper provides a review of recent developments in population-based approaches to community health and explores the origins of the population health concept and its implications for the operation of health service management. There is a growing perception among health professionals that the key to improving health outcomes will be the implementation of integrated and preventive population-based resource management rather than investment in systems that respond to crises and health problems at the acute end of the service provision spectrum only. That is, we will need increasingly to skew our community health and welfare investments towards preventive care, education, lifestyle change, self-management and environmental improvement if we are to reduce the rate of growth in the incidence of chronic disease and mitigate the impact of these diseases upon the acute health care system. While resources will still need to be devoted to the treatment and management of physical trauma, infectious diseases, inherited illness and chronic conditions, it is suggested we could reduce the rate at which demand for these services is increasing at present by managing our environment and communities better, and through the implementation of more effective early intervention programs across particular population groups. Such approaches are known generally as population health management, as opposed to individual or illness - based health management' or even public health - and suggest that health systems might productively focus in the future on population level causation and not just upon disease-specific problems or illness management after the fact. Population health approaches attempt to broaden our understanding of causation and manage health through an emphasis on the health of whole populations and by building healthy communities rather than seeing "health care" as predominantly about illness management or responses to health crises. The concept also presupposes the existence of cleaner and healthier environments, clean water and food, and the existence of vibrant social contexts in which individuals are able to work for the overall good of communities and, ultimately, of each other.

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