  Despite substantial advances in global measles vaccination, measles disease
burden remains high in many low- and middle-income countries. A key public
health strategy for controling measles in such high-burden settings is to
conduct supplementary immunization activities (SIAs) in the form of mass
vaccination campaigns, in addition to delivering scheduled vaccination through
routine immunization (RI) programs. To achieve balanced implementations of RI
and SIAs, robust measurement of sub-national RI-specific coverage is crucial.
In this paper, we develop a space-time smoothing model for estimating
RI-specific coverage of the first dose of measles-containing-vaccines (MCV1) at
sub-national level using complex survey data. The application that motivated
this work is estimation of the RI-specific MCV1 coverage in Nigeria's 36 states
and the Federal Capital Territory. Data come from four Demographic and Health
Surveys, three Multiple Indicator Cluster Surveys, and two National Nutrition
and Health Surveys conducted in Nigeria between 2003 and 2018. Our method
incorporates information from the SIA calendar published by the World Health
Organization and accounts for the impact of SIAs on the overall MCV1 coverage,
as measured by cross-sectional surveys. The model can be used to analyze data
from multiple surveys with different data collection schemes and construct
coverage estimates with uncertainty that reflects the various sampling designs.
Implementation of our method can be done efficiently using integrated nested
Laplace approximation (INLA).
