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Additional file 1: Figure S1. Secondary or tertiary case? Potential to mis-specify the infector when the primary case results in multiple secondary cases (left). The green circle represents the primary case, the blue circle a contact who did not become a case, and orange circles represent contacts who subsequently became cases. Incorrectly identifying two secondary cases as a secondary and tertiary case (right; that is, an intermediate step infector in a close co-contact) would result in a biased estimate of the serial interval. Figure S2. Secondary case or common source? Potential to mis-specify the infector when both the primary case and the case in a recorded contact are acquired from a common unidentified source (ie community transmission). The green circle represents the primary case, the blue circle a contact who did not become a case, and orange circles represent contacts of a case who subsequently became cases. Figure S3. Relationship between serial interval, incubation period and time of infection relative to symptom onset of the infector. Symptom onset is indicated with an ‘X’. Table S1. Impact of each data cleaning step on number of records. Table S2. Impact of restricting data according to the number of secondary cases per primary case.

Additional file 1.

BACKGROUND: The precise age distribution and calculated stroke risk of screen-detected atrial fibrillation (AF) is not known. Therefore, it is not possible to determine the number needed to screen (NNS) to identify one treatable new AF case (NNS-Rx) (i.e., Class-1 oral anticoagulation [OAC] treatment recommendation) in each age stratum. If the NNS-Rx is known for each age stratum, precise cost-effectiveness and sensitivity simulations can be performed based on the age distribution of the population/region to be screened. Such calculations are required by national authorities and organisations responsible for health system budgets to determine the best age cutoffs for screening programs and decide whether programs of screening should be funded. Therefore, we aimed to determine the exact yield and calculated stroke-risk profile of screen-detected AF and NNS-Rx in 5-year age strata. METHODS AND FINDINGS: A systematic review of Medline, Pubmed, and Embase was performed (January 2007 to February 2018), and AF-SCREEN international collaboration members were contacted to identify additional studies. Twenty-four eligible studies were identified that performed a single time point screen for AF in a general ambulant population, including people ≥65 years. Authors from eligible studies were invited to collaborate and share patient-level data. Statistical analysis was performed using random effects logistic regression for AF detection rate, and Poisson regression modelling for CHA2DS2-VASc scores. Nineteen studies (14 countries from a mix of low- to middle- and high-income countries) collaborated, with 141,220 participants screened and 1,539 new AF cases. Pooled yield of screening was greater in males across all age strata. The age/sex-adjusted detection rate for screen-detected AF in ≥65-year-olds was 1.44% (95% CI, 1.13%-1.82%) and 0.41% (95% CI, 0.31%-0.53%) for 70% have ≥1 additional stroke risk factor other than age/sex. Our data, based on the largest number of screen-detected AF collected to date, show the precise relationship between yield and estimated stroke risk profile with age, and strong dependence for NNS-RX on the age distribution of the population to be screened: essential information for precise cost-effectiveness calculations.