ArticleThe annals of applied statistics2021
SPACE-TIME SMOOTHING MODELS FOR SUBNATIONAL MEASLES ROUTINE IMMUNIZATION COVERAGE ESTIMATION WITH COMPLEX SURVEY DATA.
Article in The annals of applied statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Concordance of coverage estimates from routine and survey data of measles second dose vaccine in Western Kenya.Frontiers in epidemiology · 2025Article
- Spatial aggregation with respect to a population distribution: Impact on inference.Spatial statistics · 2022Article
- SPACE-TIME SMOOTHING MODELS FOR SUBNATIONAL MEASLES ROUTINE IMMUNIZATION COVERAGE ESTIMATION WITH COMPLEX SURVEY DATA.The annals of applied statistics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
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 controlling 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 subnational 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 subnational 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).
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.