Abstract P3-04-02: Population level access to diagnostic mammography and ultrasound guided breast biopsy in Nigeria: a geospatial analysis
Bibliographic record
Abstract
Abstract Introduction: Breast cancer is the most common cause of cancer-related mortality among women in Nigeria. Physical proximity to a diagnostic center is an important component of access, which has direct implication on the cost and timeliness of diagnosis and treatment. Beyond diagnostic mammography, ultrasound (US)-guided biopsy is an essential tool in the diagnostic pathway for suspicious lesions of the breast and is recommended by the Whole Health Organization Breast Health Global Initiative for LMICs. Unfortunately, there is a dearth of data on the population level access to mammography and US-guided breast biopsy in Nigeria and little is known about the geospatial inequities in access that targeted programming or investment could potentially address. This study undertook a comprehensive evaluation of breast imaging and diagnostic services in Nigeria and using a previously validated geographic information system (GIS) model, evaluated geospatial access to diagnostic mammography and US-guided breast biopsy in Nigeria. Methods: A comprehensive list of public and private facilities offering diagnostic mammography and/or US-guided breast biopsy was compiled using publicly available facility data from the Nigerian Ministry of Health, a survey administered to members of the Breast Imaging Society of Nigeria (BISON) as well as key stakeholder interviews from each of the countries six geopolitical zones. A novel survey was delivered to BISON members to identify additional/new facilities not captured by the latest Nigerian Ministry of Health data. Facility location, administration (public vs. private) and duration of service delivery were elicited from respondents and paired with the Ministry of Health facility dataset. Data on provider training and volume were also captured in the survey. All facilities were geolocated using Google Earth™ (Google, Mountain View, CA). A previously described cost-distance model, that uses open-source population density data to 100m2 (GeoData Institute) and road network data (OpenStreetMap) was used to estimate population level travel time to the nearest diagnostic center. Any portion of a route that included travel over terrain without roads was assigned a walking speed of 5 kmh-1. Geospatial access was calculated for mammography and US-guided biopsy separately and as well as by geopolitical zone. This study was approved by the research ethics board at Obafemi Awolowo University. Results: In addition to publicly available data from the Ministry of Health, facility and practice data was obtained from 63 Nigerian Radiologists from across the country. In total, 124 centers were identified that offer diagnostic mammography, of which 78 (63%) are privately administered. Nine of the countries 36 states did not have a center offering this service. Across the country, 33 centers offer US-guided breast biopsy, of which the majority (72.7%) are public. At a population level, 83.1% of the population has access within 120 minutes of continuous one-way travel to a center with diagnostic mammogram or US. At 240 minutes of continuous one-way travel, which corresponds to a full day of travel round-trip, 80.8% of the population has access to US-guided breast biopsy. However, there are differences in access between geopolitical zones. Just 68.7% of the population in the North East geopolitical zone has access to US-guided biopsy within a day’s travel (i.e. 240 minutes one-way). The remaining five geopolitical zones have population level access to this service of ≥80%. Conclusions: This is the first comprehensive evaluation of breast cancer imaging and diagnostic services in Nigeria. Our results, demonstrate that the majority of the population in Nigeria has reasonable geospatial access to basic breast cancer imaging services. However, there are inequalities in access between states and geopolitical zones in the north and south of the country, which may have an impact on timely diagnosis and care. Citation Format: Adeleye Omisore, Elizabeth J. Sutton, Gavin Tansley, Rachael Adeyanju AKINOLA, Gregory Knapp. Population level access to diagnostic mammography and ultrasound guided breast biopsy in Nigeria: a geospatial analysis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".