An analysis of 11.3 million screening tests examining the association between recall and cancer detection rates in the English NHS breast cancer screening programme.
Blanks RG., Given-Wilson RM., Cohen SL., Patnick J., Alison RJ., Wallis MG.
OBJECTIVE: To develop methods to model the relationship between cancer detection and recall rates to inform professional standards. METHODS: Annual screening programme information for each of the 80 English NHSBSP units (totalling 11.3 million screening tests) for the seven screening years from 1 April 2009 to 31 March 2016 and some Dutch screening programme information were used to produce linear and non-linear models. The non-linear models estimated the modelled maximum values (MMV) for cancers detected at different grades and estimated how rapidly the MMV was reached (the modelled 'slope' (MS)). Main outcomes include the detection rate for combined invasive/micro-invasive and high-grade DCIS (IHG) detection rate and the low/intermediate grade DCIS (LIG) detection rate. RESULTS: At prevalent screens for IHG cancers, 99% of the MMV was reached at a recall rate of 7.0%. The LIG detection rate had no discernible plateau, increasing linearly at a rate of 0.12 per 1000 for every 1% increase in recall rate. At incident screens, 99% of the MMV for IHG cancer detection was 4.0%. LIG DCIS increased linearly at a rate of 0.18 per 1000 per 1% increase in recall rate. CONCLUSIONS: Our models demonstrate the diminishing returns associated with increasing recall rates. The screening programme in England could use the models to set recall rate ranges, and other countries could explore similar methodology. KEY POINTS: • Question: How can we determine optimum recall rates in breast cancer screening? • Findings: In this large observational study, we show that increases in recall rates above defined levels are almost exclusively associated with false positive recalls and a very small increase in low/intermediate grade DCIS. • Meaning: High recall rates are not associated with increases in detection of life-threatening cancers. The models developed in this paper can be used to help set recall rate ranges that maximise benefit and minimise harm.