Objective <p>SARS-CoV-2 serological surveillance used blood donors, research cohorts, and residual patient samples. Differences in socio-demographic characteristics across these sources may bias seroprevalence estimates, necessitating statistical adjustment.</p> Methods <p>We re-analyzed data from six serosurveillance sources, comparing the estimated percent of the population positive for SARS-CoV-2 anti-nucleocapsid antibodies for six regions during periods when the sources’ sample collection overlapped. We assessed the concordance between sources with and without using multilevel regression and poststratification (MRP) to adjust for differences in representation by age, sex, and race.</p> Results <p>Across regions and timepoints, unadjusted seroprevalence differed between sources by up to 20%. MRP did not consistently improve comparability of seroprevalence across sources. In 2022, seroprevalence was consistently highest among blood donors, and MRP increased regional seroprevalence across all sources (except in Manitoba during January–April 2022 in ABC Study). In a secondary regression analysis, immunoassay kit and sample type (dried blood spot or venous blood draw) strongly influenced the odds that a sample was classified as seropositive.</p> Conclusion <p>Adjusting for representativeness using common socio-demographic variables did not systematically improve concordance in seropositivity estimates between serosurveillance sources. While discrepancies between sources might be influenced by studies’ representativeness of characteristics we did not assess, methods for measuring seropositivity appear to explain much of the differences between sources. Serosurveillance findings are influenced by many aspects of study design beyond representativeness, such as sample type (venous blood draw or dried blood spots), choice of immunoassay, and laboratory procedures such as dilution or immunoassay calibration.</p>