Assessing the magnitude of surveillance bias in prostate cancer, melanoma and lung cancer

Abstract Background Changes in cancer incidence can result from changes in screening and diagnostic practices rather than changes in the true occurrence of cancer, leading to surveillance bias. Quantitative approaches to estimate this bias are currently lacking. Objectives To develop and apply an approach to estimate the magnitude of surveillance bias in prostate cancer, melanoma, and lung cancer in Switzerland. Methods We used population-based data from Swiss cancer registries on incidence and mortality rates for prostate cancer, melanoma, and lung cancer from 1989 to 2021. Age-standardized incidence trends were analyzed using joinpoint regression to segment the time series into periods with distinct trends. The same periods were used to segment mortality trends. The magnitude of surveillance bias was assessed for each period by computing the natural logarithm of the ratio (nLR) between the mean annual changes in age-standardized incidence and mortality rates, since mortality is more likely to represent true changes in cancer burden. The higher the nLR, the greater the magnitude of surveillance bias. Analyses were also conducted by cancer stage. Results Surveillance bias was high for melanoma across the entire study period (nLR = 2.8). For prostate cancer, it varied over time: it was moderate between 1989 and 2004 (nLR = 1.6), low between 2004 and 2011 (nLR = 0.6), and high from 2011 to 2014 and 2014 to 2021 (nLR = 2.4 and 1.9, respectively). For lung cancer, surveillance bias was moderate over the entire study period (nLR = 1.1), and consistently lower than for the other two cancers. In stage-specific analyses, the bias tended to be greater for cancers diagnosed at earlier stages than at more advanced stages. Conclusions We attempted to estimate the magnitude of surveillance bias, but further studies are needed to refine these estimates. Assessing surveillance bias is essential for correctly interpreting cancer incidence trends and informing public health decisions. Key messages • Integrating surveillance bias estimates into trend interpretation is essential to understanding cancer incidence and informing public health decisions. • This study provides estimates of the magnitude of surveillance bias for three cancers, paving the way for more refined and standardized methods to account for this bias in epidemiological analyses.