ABSTRACT
INTRODUCTION: measures of vaccine timing require data on vaccination dates, which may be unavailable. This study compares estimates of vaccine coverage and timing; and compares regression techniques that model these measures in the presence of incomplete data. METHODS: this cross-sectional study used the 2016 Ethiopian Demographic and Health Survey (DHS), and a 2016 survey from Worabe, Ethiopia. Three measures of vaccine uptake were calculated: coverage (regardless of timing), timeliness (within 1 week of recommended administration), and delay (the number of days between the recommended and actual date of vaccination). Vaccine coverage and timeliness were modeled with logistic regressions. After excluding those without dates, vaccine delay was estimated using linear regression or survival analysis. Vaccine delay was also estimated using accelerated failure time (AFT) models. RESULTS: the DHS survey included 3819 children aged 12-60 months and the Worabe survey included 484 children aged 12-23 months. In the Worabe survey, vaccine coverage for pentavalent vaccine dose 3 was 87.4%, with 8.6% receiving it within 1 week, and 71.7% within 4 weeks; the median delay was 19 days. Predictors of outcomes were similar in both the Worabe survey and Ethiopian DHS, with the largest numbers of significant associations seen in models with vaccine coverage or delays (with AFT models) as the outcomes. CONCLUSION: estimates of coverage may miss a substantial proportion of infants who have delayed vaccination. Accelerated failure time (AFT) models are useful to estimate vaccine delay because they include information from all respondents (those with full and partial data on vaccination dates) and are agnostic about an age limit for timely vaccination.
