• GudmundsdottirKatrin KempKKKarolinska Institute, Department of Clinical Sciences, Danderyd University Hospital, Stockholm, Sweden. Electronic address: Katrin.kemp.gudmundsdottir@ki.se., Carl Bonander, Tove Hygrell, Emma Svennberg, Viveka Frykman, Ulf Strömberg, and Johan Engdahl.
• Karolinska Institute, Department of Clinical Sciences, Danderyd University Hospital, Stockholm, Sweden. Electronic address: Katrin.kemp.gudmundsdottir@ki.se.
• Prev Med. 2022 Nov 1; 164: 107284107284.

BackgroundThe success of any screening program is dependent on participation. The characteristics of participants vs. non-participants have been studied and non-participants usually have a higher risk of disease. The potential yield of screening-detected disease in non-participants could be of interest to several screening programs.AimsThis is a sub-study to STROKESTOP II, a Swedish atrial fibrillation screening study. The aim was to study factors predicting participation and to estimate the potential yield of screening-detected disease in non-participants.MethodsIndividual, anonymized data for participants and non-participants with respect to socioeconomic factors, medical history and drugs dispensed were obtained from Swedish registries. A random forest model was trained to predict propensity scores for participation. The propensity scores were used to estimate potential screening-detected disease among non-participants.ResultsNon-participants (n = 7086) had lower income, were more likely to have been hospitalized and had higher CHA2DS2-VASc scores compared to participants (n = 6868). The strongest factor predicting non-attendance was low income. The weighted estimates suggested that the yield of new atrial fibrillation was 2.4% in non-participants compared to 2.3% in the participants, which was not significant.ConclusionsNon-participants had higher CHA2DS2-VASc scores, indicating a higher stroke-risk and presumable benefit from attending screening, although estimated new atrial fibrillation detected was not significantly more common when compared to participants. Low income was the strongest factor for predicting non-attendance and should be a focus area when planning future screening scenarios.Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

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