Background: Greater than 80% of women presenting for breast cancer treatment in Uganda have late-stage disease, which is attributable to a dysfunctional referral system and a lack of recognition of the early signs and symptoms among primary health care providers, and compounded by the poor infrastructure and inadequate human capacity. Improving the breast health care system requires a systemic approach beginning with situational analysis to identify systematic gaps that prevent sustainable improvements in outcome.
Methods: The authors performed a situational analysis of the breast health care system using methods developed by the Breast Health Global Initiative. Based on their findings, they developed a series of recommendations for strengthening the health system for the early diagnosis of breast cancer based on clinical detection, referral, tissue sampling, and diagnosis.
Results: Deficits in the recognition of breast cancer signs and symptoms, the underuse of clinical breast examination as a diagnostic and/or screening tool, the centralization of diagnostic tests (radiology and pathology), reliance on excisional biopsies rather than needle biopsies, and a lack of trained professionals and knowledge of the referral system all contribute to significant health system delays.
Conclusions: To strengthen referral networks and improve the early diagnosis of breast cancer in Uganda, national referral hospitals should provide educational programs to primary health care providers in community health centers (CHCs), at which the majority of women first present with symptoms. At secondary district-level facilities in which imaging and tissue sampling can be performed, the capacity for diagnostic testing could be increased through task shifting of basic interpretation (abnormal vs normal) from specialists to nonspecialists using networking technology to facilitate remote oversight from specialists at the national referral hospitals.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219536 | PMC |
http://dx.doi.org/10.1002/cncr.32890 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!