*Purpose: Analysis of the impact of policies or natural occurrences on transplant system outcomes requires comparison of a treatment (post-policy) period to a control (pre-policy) period. Choosing a proper control period requires an understanding of seasonality of outcomes of interest. This study describes seasonality of death on the solid organ transplant waiting lists and compares it to that of the US adult population.

*Methods: Using SRTR standard analysis files, mortality rates were calculated for each month from January 1999 to December 2018 for candidates prevalent on each solid organ transplant waitlist at any point in the month. Monthly rates were then standardized as a ratio to the 13-month simple moving average centered on the month to give an interpretation of more deaths (ratio > 1) than the yearly average or fewer deaths (ratio < 1). Linear regression with month as a categorical predictor modeled seasonality of standardized mortality for each organ.

*Results: Mortality rates on the kidney, liver, lung, and pancreas waitlists showed statistically significant variation by month. For kidney, liver, and lung, mortality rates were higher in the winter, particularly January and February, and lower in the summer. Pancreas waitlist mortality rates showed broader confidence intervals and a less clear seasonal trend. In comparison, heart waitlist mortality rates were fairly consistent through the year. Seasonal trends in mortality rates for kidney, pancreas, liver, and heart differed significantly from seasonal trends in the US population. In the case of kidney, liver, and heart, seasonal trends were less pronounced than in the general population (Figure 1).

*Conclusions: Mortality rates on many solid organ transplant waitlists followed seasonal trends. Analysis of mortality outcomes before and after policy changes or natural experiments should use cohorts matched on time of year. For example, if data after a policy change are available only for January to April of a given year, an appropriate control cohort would most likely be January to April of the previous year.

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