User interventions such as nudges, prebunking, and contextualization have been widely studied as countermeasures against misinformation, and shown to suppress individual users' sharing behavior. However, it remains unclear whether and to what extent such individual-level effects translate into reductions in collective misinformation prevalence. In this study, we incorporate user interventions as reductions in users' susceptibility within an empirically calibrated network-based misinformation diffusion model. We then systematically evaluate how intervention strength, scale, timing, target selection, and combinations of interventions affect overall misinformation prevalence through numerical simulations. The simulation results reveal that current user-level interventions may not necessarily produce sufficient collective effects. Specifically, each intervention alone only modestly suppresses misinformation prevalence, and even when design adjustments such as expanding intervention scale, implementing interventions earlier, or strategically selecting target users are introduced, the resulting gains in suppression remain limited. Although combining multiple interventions improves the suppression effect compared to using each intervention alone, achieving substantial reductions in misinformation prevalence remains difficult within realistically attainable intervention levels. This study quantitatively clarifies the gap between micro-level user interventions and macro-level misinformation spread, and demonstrates the limitations of evaluating misinformation countermeasures based solely on individual-level effectiveness.