UNLABELLED

Measuring waiting times is a good indicator of quality of cancer care and could reveal inequalities in cancer care access.

AIMS

To determine the most representative waiting times in breast, lung, colon and prostate cancer care in several regions of France. To analyze the influence of individual, medical or health care system factors on those waiting times.

METHODS

This study was piloted by the French Cancer Institute in partnership with the National Federation of the Regional Health Observatories and was driven by the Regional Oncology Networks and the Regional Health Observatories. In 2011, 2,530 women with breast cancer and 1,945 patient with lung cancer were included in eight regions, and in 2012, 3,248 patients with colon cancer and 4,207 men with prostate cancer were included in 13 regions, two of which were overseas departments. Data were analyzed from multidisciplinary discussion reports and from medical records.

RESULTS

The mean time intervals (± standard deviation) for the various components of access to care were as follows in breast cancer: mammography to pathologist diagnosis, 17,7 days (±15,9); diagnosis (or treatment proposal) to surgery, 22,9 days (±13,9). In lung cancer: first suspect medical image to pathologist diagnosis, 21,5 days (±17,6); diagnosis to treatment proposal, 13,5 days (±10,7). In colon cancer: coloscopy to pathologist diagnosis, 4,5 days (±4,1); diagnosis to surgery, 18,9 days (±14,9). In prostate cancer: pathologist diagnosis to treatment proposal, 36,5 days (±26,5); treatment proposal to surgery, 45,2 days (±30,1). Data collection was particularly difficult because of very heterogeneous way in medical records filling by care centers, so the data collection method used in the study could not be used in routine procedures. Waiting times measured in the four cancers had an important variability. In fact, age, circumstance of diagnosis, tumor stage and category of care center had an influence. After considering those different factors, differences between regions remained from range 2 to 4. Those regional differences could be explained by organizational factors but were not explored in our study. In the same way, data on individual factors (social vulnerability, category of employment) were not available to measure their effects on this study. Besides, our results were comparable to those in international publications or national recommendations in other countries.

CONCLUSION

These results suggest that waiting times could be good indicators and could reveal inequalities in cancer care access. Measuring them would lead to characterize those inequalities and to propose actions to improve access to cancer care whose impact could be measured.