Uptake of screening mammogram in West Central Illinois during the COVID-19 pandemic: lessons learned
Bibliographic record
Abstract
Objectives. The study aims to reveal the trend of mammogram uptake in seventeen rural counties in Illinois to understand how the COVID-19 pandemic influenced breast cancer screening in the area. Material and methods. Aggregated data on mammography screening for West Central Illinois was provided by the Illinois Hospital Association. Data for 2018 and 2019 was used to determine the typical monthly and annual screenings for the two years before the onset of COVID-19. Then, the two years’ data was compared to the 2020 data. The monthly mean values for the aggregated 2018 and 2019 data were generated as the base “year” to compare with the monthly value for 2020. Paired t-test analysis was used to find if there were any statistically significant differences between the years and between the base year and 2020. Results. January 2020 revealed an uptick to 2921, which is more than the uptake for January 2018 (2700) and January 2019 (2488), and 13% greater than the mean value of 2594 for the previous two years. This was followed by a gradual decrease in uptake in February 2020 by 4% compared to previous years at a mean of 2518 and a further decline in March (44%), with a drastic fall (98%) by April 2020 at 56 screening mammograms in all 17 counties. The lowest uptake in any three months occurred from March through May 2020. Compared to previous years, an increase in uptake was noted across the region in 2020 June (8%) and July (4%) after the pandemic restrictions were relaxed. Overall, the total uptake in 2020 was 15% less than the average annual uptake for 2018–2019 with a deficit of 5537. There was no statistically significant difference in mammogram uptake across the three years. Conclusions. The findings reveal that there was a significant reduction in uptake during the pandemic restriction period. However, increased uptake during the rest of the year effectively mitigated this reduction to such an extent that there was no statistically significant downturn compared to each of the previous two years. A rising trend in total annual uptake noted in preceding years could have continued without the COVID-19 event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".