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

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          Abstract

          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.

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          Most cited references20

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          Continuous Inspection Schemes

          E. S. PAGE (1954)
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            Influence of delay on survival in patients with breast cancer: a systematic review.

            Most patients with breast cancer are detected after symptoms occur rather than through screening. The impact on survival of delays between the onset of symptoms and the start of treatment is controversial and cannot be studied in randomised controlled trials. We did a systematic review of observational studies (worldwide) of duration of symptoms and survival. We identified 87 studies (101,954 patients) with direct data linking delay (including delay by patients) and survival. We classified studies for analysis by type of data in the original reports: category I studies had actual 5-year survival data (38 studies, 53,912 patients); category II used actuarial or multivariate analyses (21 studies, 25,102 patients); and category III was all other types of data (28 studies, 22,940 patients). We tested the main hypothesis that longer delays would be associated with lower survival, and a secondary hypothesis that longer delays were associated with more advanced stage, which would account for lower survival. In category I studies, patients with delays of 3 months or more had 12% lower 5-year survival than those with shorter delays (odds ratio for death 1.47 [95% CI 1.42-1.53]) and those with delays of 3-6 months had 7% lower survival than those with shorter delays (1.24 [1.17-1.30]). In category II, 13 of 14 studies with unrestricted samples showed a significant adverse relation between longer delays and survival, whereas four of five studies of only patients with operable disease showed no significant relation. In category III, all three studies with unrestricted samples supported the primary hypothesis. The 13 informative studies showed that longer delays were associated with more advanced stage. In studies that controlled for stage, longer delay was not associated with shorter survival when the effect of stage on survival was taken into account. Delays of 3-6 months are associated with lower survival. These effects cannot be accounted for by lead-time bias. Efforts should be made to keep delays by patients and providers to a minimum.
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              Evidence of increasing mortality with longer diagnostic intervals for five common cancers: a cohort study in primary care.

              Early diagnosis is considered a key factor in improving the outcomes in cancer therapy; it remains unclear, however, whether long pre-diagnostic patient pathways influence clinical outcomes negatively. The aim of this study was to assess the association between the length of the diagnostic interval and the five-year mortality for the five most common cancers in Denmark while addressing known biases. A total of 1128 patients with colorectal, lung, melanoma skin, breast or prostate cancer were included in a prospective, population-based study in a Danish county. The diagnostic interval was defined as the time from the first presentation of symptoms in primary care till the date of diagnosis. Each type of cancer was analysed separately and combined, and all analyses were stratified according to the general practitioner's (GP's) interpretation of the presenting symptoms. We used conditional logistic regression to estimate five-year mortality odds ratios as a function of the diagnostic interval using restricted cubic splines and adjusting for comorbidity, age, sex and type of cancer. We found increasing mortality with longer diagnostic intervals among the approximately 40% of the patients who presented in primary care with symptoms suggestive of cancer or any other serious illness. In the same group, very short diagnostic intervals were also associated with increased mortality. Patients presenting with vague symptoms not directly related to cancer or any other serious illness had longer diagnostic intervals and the same survival probability as those who presented with cancer suspicious/serious symptoms. For the former, we found no statistically significant association between the length of the diagnostic interval and mortality. In full coherence with clinical logic, the healthcare system instigates prompt investigation of seriously ill patients. This likely explains the counter-intuitive findings of high mortality with short diagnostic intervals; but it does not explain the increasing mortality with longer diagnostic intervals. Thus, the study provides further evidence for the hypothesis that the length of the diagnostic interval affects mortality negatively. Copyright © 2013 Elsevier Ltd. All rights reserved.
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                Author and article information

                Journal
                JCO Clin Cancer Inform
                JCO Clin Cancer Inform
                cci
                cci
                CCI
                JCO Clinical Cancer Informatics
                American Society of Clinical Oncology
                2473-4276
                2019
                21 May 2019
                : 3
                : CCI.18.00131
                Affiliations
                [ 1 ]Queen’s University, Kingston, Ontario, Canada
                [ 2 ]ICES Queen’s, Kingston, Ontario, Canada
                [ 3 ]Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
                [ 4 ]University of Toronto, Toronto, Ontario, Canada
                [ 5 ]Ontario Institute for Cancer Research, Toronto, Ontario, Canada
                [ 6 ]Cancer Care Ontario, Toronto, Ontario, Canada
                [ 7 ]Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
                [ 8 ]South East Regional Cancer Program, Kingston, Ontario, Canada
                Author notes
                Patti A. Groome, PhD, Division of Cancer Care and Epidemiology, Queen’s Cancer Research Institute, 10 Stuart St, Level 2, Kingston, ON K7L 3N6, Canada; e-mail: groomep@ 123456queensu.ca .
                Article
                1800131
                10.1200/CCI.18.00131
                6874005
                31112418
                c9bc2989-6a2b-4b9d-959f-77a9581866ed
                © 2019 by American Society of Clinical Oncology

                Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: https://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 01 March 2019
                Page count
                Figures: 2, Tables: 4, Equations: 0, References: 39, Pages: 10
                Categories
                , Biostatistics: Data Use and Curation
                , Breast Cancer
                , Diagnosis and Staging
                , Epidemiology
                , Health Services Research: Quality of Care
                Original Report
                Custom metadata
                v1

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