Wait times for breast cancer care

Saint-Jacques, Nathalie; Younis, Tallal; Dewar, Ron; Rayson, Daniel

doi:10.1038/sj.bjc.6603523

Cited by 34 publications

(24 citation statements)

References 5 publications

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“…In addition, all studies examined inequity in access to cancer services by either receipt of a service or wait times for the service, but never both. For example, Younis et al [25] examined whether systemic therapy was received, while Saint Jacques et al [32] examined wait times for systemic therapy services. The vast majority of studies used administrative data or a combination of administrative data and retrospective chart reviews, with the remaining few applying self-reported questionnaires [33,35], chart reviews as the primary data source [29,33,35,38,39], or population health surveys [16,28,40].…”

Section: Methods Used In the Literaturementioning

confidence: 99%

“…Study design considerations: Among the 51 studies included in our review, only 10 studies examined inequities at multiple points of services along the continuum of care [19,25,26,[32][33][34][35][36][37]. In addition, all studies examined inequity in access to cancer services by either receipt of a service or wait times for the service, but never both.…”

Section: Methods Used In the Literaturementioning

confidence: 99%

“…Research has identified that women in Nova Scotia are more likely to be enrolled in a palliative care program, to receive physician home visits, and to die at home than men [4,14,30]. Education has received limited research attention with only 7 studies; yet, available studies have demonstrated that breast cancer patients with higher education have greater access to screening and shorter wait times for physician referrals and initiation of radiotherapy, Table 1 Canadian literature on inequity in access to cancer services compared to those with lower education [16,31,32]. Some studies also examine inequity based on employment and ethnicity; however, there are too few to discern trends.…”

Section: Equity Stratifiersmentioning

confidence: 97%

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Inequity in access to cancer care: a review of the Canadian literature

Maddison

Asada

Urquhart

2011

Cancer Causes Control

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Despite the policy and research attention on ensuring equitable access--equal access for equal need--to health care, research continues to identify inequities in access to cancer services. We conducted a literature review to identify the current state of knowledge about inequity in access to cancer health services in Canada in terms of the continuum of care, disease sites, and dimensions of inequity (e.g., income). We searched MEDLINE, CINAHL, and Embase for studies published between 1990 and 2009. We retrieved 51 studies, which examine inequity in access to cancer services from screening to end-of-life care, for multiple cancer types, and a variety of socioeconomic, geographic, and demographic factors that may cause concern for inequity in Canada. This review demonstrates that income has the most consistent influence on inequity in access to screening, while age and geography are most influential for treatment services and end-of-life care, even after adjusting for patient need. Our review also reports on methods used in the literature and new techniques to explore. Equitable access to cancer care is vitally important in all health systems. Obtaining information on the current status of inequities in access to cancer care is a critical first step toward action.

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Section: Methods Used In the Literaturementioning

confidence: 99%

Section: Methods Used In the Literaturementioning

confidence: 99%

Section: Equity Stratifiersmentioning

confidence: 97%

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Inequity in access to cancer care: a review of the Canadian literature

Maddison

Asada

Urquhart

2011

Cancer Causes Control

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“…19 Administrative data using detailed records still renders some patients with a paucity of information about care points despite rigorous retrospective study. 20 Both these methods of study do not consider the effect that prioritizing a program (such as cancer care) can have on the wait times for other surgeries.…”

Section: Discussionmentioning

confidence: 99%

Measurement of surgical wait times in a universal health care system

Kawakami

Hopman

Smith-Tryon

2008

CUAJ

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Introduction: Reported increases in surgical wait times for cancer have intensified the focus on this quality of health care indicator and have created a very public, concerted effort by providers to decrease wait times for cancer surgeryin Ontario. Delays in access to health care are multifactorial and their measurement from existing administrative databases can lack pertinent detail. The purpose of our study was to use a real-time surgery-booking software program to examine surgical wait times at a single centre.Methods: The real-time wait list management system Axcess.Rx has been used exclusively by the department of urology at the Kingston General Hospital to book all nonemergency surgery for 4 years. We reviewed the length of time from the decision to perform surgery to the actual date of surgery for patients in our group urological practice. Variables thought to be potentially important in predicting wait time were also collected, including the surgeon’s assessment of urgency, the type of procedure (i.e., diagnostic, minor cancer, major cancer, minor benign, major benign), age and sex of the patient, inpatient versus outpatient status and year of surgery. Analysis was planned a priori to determine factors that affected wait time by using multivariate analysis to analyze variables that were significant in univariate analysis.Results: There were 960 operations for cancer and 1654 for benign conditions performed during the evaluation period. The overall mean wait time was 36 days for cancer and 47 days for benign conditions, respectively. The mean wait time for cancer surgery reached a nadir in 2004 at 29.9 days and subsequently increased every year, reaching 56 days in 2007. In comparison, benign surgery reached a nadir wait time of 33.7 days in 2004 and in 2007 reached 74 days at our institution. Multivariate analysis revealed that the year of surgery was still a significant predictor of wait time. Urgency score, type of procedure and inpatient versus outpatient status were also predictive of wait time.Conclusion: The application of a prospectively collected data set is an effective and important tool to measure and subsequently examine surgical wait times. This tool has been essential to the accurate assessment of the effect of resource allocation on wait times for priority and nonpriority surgical programs within a discipline. Such tools are necessary to more fully assess and follow wait times at an institution or across a region.

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“…Saint-Jacques and colleagues reported that when using a high-and low-resolution analysis of the care intervals, both detected an increase in wait times over the 2 time periods, but only the high-resolution analysis could pinpoint where the increases were occurring. 12 The complex nature of wait times is demonstrated in a high-resolution analysis of non-small-cell lung cancer cases, which showed that delays in referrals were often "made up for" by quicker consults. 13 The effective use of administrative databases for determining time from decision to operate to the actual operation has already been established, 14 but to our knowledge its accuracy for assessing other time points has not.…”

Section: Introductionmentioning

confidence: 99%

High resolution analysis of wait times and factors affecting surgical expediency

Cole¹,

Hopman²,

Kawakami³

2011

CUAJ

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Objectives: Wait times in Canada are increasingly being monitored as an indicator of quality health care delivery. We created a higher resolution picture of the wait experienced by urological surgery patients beginning with the initial referral. In doing so, we hoped to (a) identify potential bottlenecks and common delays at our centre, and (b) identify predictors of wait time. Methods: The charts of 322 patients undergoing surgery from November 2007 to March 2008 were reviewed and specific dates, patient factors and delays were recorded. The data were used to detail the patient's wait and to determine the patient factors which were predictive of wait time. Results: The mean time from decision to operate to the day of operation was 75.87 days for all patients. This number accounts for 53% of the wait time, while the time from referral to decision to operate is 47%. Predictors of a decreased wait time include cancer cases, younger age, urgency score, repeat patients and female gender in multivariate analysis. Delays were experienced by 16.8% of patients; most common delays were operating room cancellations/ time constraints, patients requiring further optimization and delays in referral (4.7%, 3.4% and 3.1%, respectively). Conclusions:The waiting process is complex; the actual waiting time that a patient must endure is much longer than the wait times traditionally recorded and reported by hospitals. As strategies are implemented to decrease wait times, it will become increasingly important to monitor the entire wait time from referral to operation and to ensure that changes are being made that truly decrease wait times and not simply shift where and when the patient waits.

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Wait times for breast cancer care

Cited by 34 publications

References 5 publications

Inequity in access to cancer care: a review of the Canadian literature

Inequity in access to cancer care: a review of the Canadian literature

Measurement of surgical wait times in a universal health care system

High resolution analysis of wait times and factors affecting surgical expediency

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