Analysis of Delays in Breast Cancer Treatment and Late-Stage Diagnosis in Kazakhstan

Abstract

Objective: Although Kazakhstan has made significant investments to improve health and life expectancy of its population, high cancer rates persist, with breast cancer being the most prevalent type. Factors contributing to delays in treatment and late staging for breast cancer patients were assessed. Methods: A retrospective follow-up study with registry data identified 4,248 breast cancer patients in sixteen regions of Kazakhstan in 2014. We used logistic regressions to estimate (i) associations of treatment delays with patient demographics and cancer center regions; and (ii) associations of late-stage (III and IV) cancer diagnosis with patient demographics and cancer center regions, with and without controlling for treatment delays. Results: Breast cancer patients treated in regions located further away from Almaty City had higher risks of treatment delays. However, the risks of late-stage cancer diagnosis were greater for patients treated in Almaty City and those with treatment delays. Conclusion: The main driver of delayed treatment is cancer center region. Residents of Almaty City, a major urban area of Kazakhstan, may have a better access to a tertiary cancer center, resulting in less treatment delays. Referrals of sicker patients from neighboring regions to Almaty City for cancer treatment is likely to increase risks of late-stage diagnosis. New or upgraded cancer centers may reduce treatment delays, but their case-mix is likely to increase.

Keywords: Breast cancer; delays in treatment; late-stage diagnosis; Kazakhstan.

Figure 1

Map of Delays in Treatment by Region, Kazakhstan 2014. Note: Estimates shown are the regression-adjusted probabilities of delays in breast cancer treatment in each region. Models adjust for patient demographics, including occupation.

Figure 2

Map of Late-Stage Diagnosis by Region, Kazakhstan 2014. Note: Estimates shown are the regression-adjusted probabilities of late-stage (stages III and IV) breast cancer diagnosis in each region. Models adjust for patient demographics, including occupation.