Health system delay and its effect on clinical stage of breast cancer: Multicenter study

Abstract

Background: The objective of this study was to determine the correlation between health system delay and clinical disease stage in patients with breast cancer.

Methods: This was a cross-sectional study of 886 patients who were referred to 4 of the largest public cancer hospitals in Mexico City for the evaluation of a probable breast cancer. Data on time intervals, sociodemographic factors, and clinical stage at diagnosis were retrieved. A logistic regression model was used to estimate the average marginal effects of delay on the probability of being diagnosed with advanced breast cancer (stages III and IV).

Results: The median time between problem identification and the beginning of treatment was 7 months. The subinterval with the largest delay was that between the first medical consultation and diagnosis (median, 4 months). Only 15% of the patients who had cancer were diagnosed with stage 0 and I disease, and 48% were diagnosed with stage III and IV disease. Multivariate analyses confirmed independent correlations for the means of problem identification, patient delay, health system delay, and age with a higher probability that patients would begin cancer treatment in an advanced stage.

Conclusions: In the sample studied, the majority of patients with breast cancer began treatment after a delay. Both patient delays and provider delays were associated with advanced disease. Research aimed at identifying specific access barriers to medical services is much needed to guide the design of tailored health policies that go beyond the promotion of breast care awareness and screening participation to include improvements in health services that facilitate access to timely diagnosis and treatment.

Keywords: breast cancer; clinical stage; delay; early diagnosis; time intervals.

Figure 1

Inclusion, exclusion, and elimination criteria are illustrated for the current study.

Figure 2

Estimates of total, patient, health system/provider, diagnostic, treatment, prehospital, and in‐hospital intervals for patients with breast cancer are illustrated. Reported measures indicate the median interval, with the 25th percentile to 75th percentile indicated in parenthesis. The depicted bar lengths are proportional to the time length of the intervals.

Figure 3

Kaplan‐Meier curves for the total interval are indicated according to clinical disease stage.

Figure 4

Predictions of advanced‐stage disease are illustrated as patient and provider intervals increase. These estimates were calculated based on the estimated average marginal effects of the regression provided in Table 3. In fact, only the within‐hospital and within‐interviewer variation were exploited in the preferred specification (see Table 3, column 4).