Using Serosurvey Data Triangulation for More Accurate Estimates of Vaccine Coverage: Measured and Modeled Coverage From Pakistan Household Surveys

Abstract

AbstractHousehold surveys remain an essential method for estimating vaccine coverage in developing countries. However, the resulting estimates have inevitable and currently unmeasurable information biases due to inaccuracies in recall, low retention of home-based records (HBRs; i.e., vaccination cards), and inaccurate recording of vaccination on HBRs. We developed an innovative method with which to overcome these biases, enhance the validity of survey results, and estimate true vaccine coverage using nested serological assessments of immune markers. We enrolled children aged 12–23 months in vaccine coverage surveys in Karachi, Pakistan, from January to December 2016. Vaccination history was collected through verbal recall by the caregiver and, when available, by HBR. One-third of survey participants were randomly enrolled for serological testing for anti–measles virus immunoglobulin G antibody. We applied Bayesian latent class models to evaluate the misalignment among measles vaccination histories derived by recall, HBRs, and measles serology and estimated true measles vaccine coverage. The model-based estimate of true measles vaccine coverage was 61.1% (95% credible interval: 53.5, 69.4) among all survey participants. The standard estimate of 73.2% (95% confidence interval: 71.3, 75.1) defined by positive recall or HBR documentation substantially overestimated the vaccine coverage. Researchers can correct for information biases using serological assessments in a subsample of survey participants and latent class analytical approaches.