Anchoring Convenience Survey Samples to a Baseline Census for Vaccine Coverage Monitoring in Global Health
While conducting probabilistic surveys is the gold standard for assessing vaccine coverage, implementing these surveys poses challenges for global health. There is a need for more convenient option that is more affordable and practical. Motivated by childhood vaccine monitoring programs in rural areas of Chad and Niger, we conducted a simulation study to evaluate calibration-weighted design-based and logistic regression-based imputation estimators of the finite-population proportion of MCV1 coverage. These estimators use a hybrid approach that anchors non-probabilistic follow-up survey to probabilistic baseline census to account for selection bias. We explored varying degrees of non-ignorable selection bias (odds ratios from 1.0-1.5), percentage of villages sampled (25-75%), and village-level survey response rate to the follow-up survey (50-80%). Our performance metrics included bias, coverage, and proportion of simulated 95% confidence intervals falling within equivalence margins of 5% and 7.5% (equivalence tolerance). For both adjustment methods, the performance worsened with higher selection bias and lower response rate and generally improved as a larger proportion of villages was sampled. Under the worst scenario with 1.5 OR, 25% village sampled, and 50% survey response rate, both methods showed empirical biases of 2% or less, below 95% coverage, and low equivalence tolerances. In more realistic scenarios, the performance of our estimators showed lower biases and close to 95% coverage. For example, at OR$\leq$1.2, both methods showed high performance, except at the lowest village sampling and participation rates. Our simulations show that a hybrid anchoring survey approach is a feasible survey option for vaccine monitoring.