Anchoring Convenience Survey Samples to a Baseline Census for Vaccine Coverage Monitoring in Global Health

2025-11-24

AI Summary

In global vaccine coverage monitoring, probability sampling remains the gold standard but faces high costs and logistical challenges—particularly in resource-constrained rural settings. To address this, we propose a hybrid anchored survey design: a low-cost probabilistic baseline census serves as an “anchor” to correct selection bias in subsequent non-probability follow-up surveys. Our method innovatively integrates calibration weighting with logistic regression imputation. Through systematic simulation studies, we demonstrate its robust performance across varying bias magnitudes, sampling fractions, and response rates: estimation bias remains ≤2.1%, confidence interval coverage approaches 95%, and the approach remains feasible and stable under realistic conditions (odds ratio ≤1.2). This work establishes a new paradigm for vaccine coverage surveillance—one that balances scientific rigor with field practicality.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Probabilistic Programming
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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 2.1% 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.

Problem

• Evaluating hybrid survey methods for vaccine coverage monitoring in global health
• Addressing selection bias in non-probabilistic surveys through calibration techniques
• Assessing MCV1 vaccine coverage estimation under varying survey conditions

Innovation

• Hybrid approach anchors non-probabilistic surveys to baseline census
• Uses calibration-weighted design and logistic regression imputation estimators
• Corrects selection bias in vaccine coverage monitoring surveys