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Background and Objectives Reducing the maximum red blood cell (RBC) shelf‐life is under consideration due to potential negative effects of older blood. An assessment of the impacts of this change on blood supply chain management is evaluated. Materials and Methods We performed a simulation study using data from 2017 to 2018 to estimate the outdate rate (ODR), STAT order and non‐group‐specific RBC transfusion at two Canadian health authorities (HAs). Results Shortening shelf‐life from 42 to 35 and 28 days led to the following: ODRs (in percentage) in both HAs increased from 0.52% (95% confidence interval [CI] 0.50–0.54) to 1.32% (95% CI 1.26–1.38) and 5.47% (95% CI 5.34–5.60), respectively (p < 0.05). The estimated yearly median of outdated RBCs increased from 220 (interquartile range [IQR] 199–242) to 549 (IQR 530–576) and 2422 (IQR 2308–2470), respectively (p < 0.05). The median number of outdated redistributed units increased from 152 (IQR 136–168) to 356 (IQR 331–369) and 1644 (IQR 1591–1741), respectively (p < 0.05). The majority of outdated RBC units were from redistributed units rather than units ordered from the blood supplier. The estimated weekly mean STAT orders increased from 11.4 (95% CI 11.2–11.5) to 14.1 (95% CI 13.1–14.3) and 20.9 (95% CI 20.6–21.1), respectively (p < 0.001). The non‐group‐specific RBC transfusion rate increased from 4.7% (95% CI 4.6–4.8) to 8.1% (95% CI 7.9–8.3) and 15.6% (95% CI 15.3–16.4), respectively (p < 0.001). Changes in ordering schedules, decreased inventory levels and fresher blood received simulated minimally mitigated these impacts. Conclusion Decreasing RBC shelf‐life negatively impacted RBC inventory management, including increasing RBC outdating and STAT orders, which supply modifications minimally mitigate.

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The estimation of the potential impact fraction (including the population attributable fraction) with continuous exposure data frequently relies on strong distributional assumptions. However, these assumptions are often violated if the underlying exposure distribution is unknown or if the same distribution is assumed across time or space.Nonparametric methods to estimate the potential impact fraction are available for cohort data, but no alternatives exist for cross-sectional data. In this article, we discuss the impact of distributional assumptions in the estimation of the population impact fraction, showing that under an infinite set of possibilities, distributional violations lead to biased estimates. We propose nonparametric methods to estimate the potential impact fraction for aggregated (mean and standard deviation) or individual data (e.g. observations from a cross-sectional population survey), and develop simulation scenarios to compare their performance against standard parametric procedures. We illustrate our methodology on an application of sugar-sweetened beverage consumption on incidence of type 2 diabetes. We also present an R package pifpaf to implement these methods.