Purpose: A multicenter, observational, cross-sectional study was conducted to assess factors delaying presentation of breast cancer cases. Materials and Methods: Data were collected from a pair of highly specialized referral centers, both located in the center of the Sudanese capital, Khartoum. For a total of 153 eligible respondents, durations of delay, clinicodemographic factors and reasons of referral were collected from our respondents through self-administered questionnaires. Logistic regression analysis and ANOVA were used to test the relation between periods of delay and different factors. Odd ratios (OR's) and their correspondent Confidence intervals (95% CI's). Delay periods were studied with Andersen's model. Results: The average duration of delay in our study was 11.9 (±11.2) months. Only a quarter of our patients presented early within the first 3 months after onset of their symptoms. About 47.7% arrived later during the course of the first year, while it took beyond that for the last 27% to come. A prior diagnosis of BC was the only predictor of early presentation (for 3-12 months OR=9.6 (p<0.00), 95% CI 9.55-9.75; for >12 months OR=9.3 (p<0.00), 95% CI 9.33-9.33). Out of the 12 different reasons for delay given by our respondents, none showed a significant difference between patients presenting early or late. Financial incapacity (17.5%), ignorance about BC (14.3), and misinterpreting symptoms (12.7%) were the top three whys of delay. Conclusions: Our findings support existence of a non-uniform pattern of delay among Sudanese BC patients. Changing currently adopted awareness elevating strategies into much more inclusive approaches is strongly recommended.

BackgroundBreast cancer risk prediction models are widely used in clinical settings. Although most of the well-known models were designed based on data collected from western population, yet they have been utilized for surveillance purposes in many limited-resource countries. Given the genetic variations in risk factors that exist between different races, we therefore aimed to develop and validate a tool for breast cancer risk assessment among Sudanese women.MethodsUsing cross-sectional design, 153 subjects were eligible to participate in our study. Data were collected from the only couple of tertiary centers in Sudan. They underwent multiple logistic regression using purposeful selection method to build the model. Various adjustments were made to determine significant predictors. Overall performance, calibration and discrimination were assessed by R 2, O/E ratio and c-statistic, respectively.ResultsSUDAN predictors of breast cancer were: age, menarche, family history, vegetables and fruits weekly servings, and type of cereals that traditional cuisine is made of. Both Nagelkerke R 2 (0.495) and O/E ratio (0.78) were good. c-statistic expressed the excellent discriminatory power of the model (0.864, p < 0.001, 95% CI 0.81–0.92).ConclusionsOur findings suggest that SUDAN provides a simple, efficient and well-calibrated tool to predict and classify women’s lifetime risks of developing breast cancer. Input from our model could be deployed to guide utilization of the more advanced screening modalities in resource-limited settings to maximize cost effectiveness. Consequently, this might improve the stage at which the diagnosis is usually made.