ArticleStatistical methods in medical research
A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping.
Article in Statistical methods in medical research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys.International journal of health geographics · 2020Pooled it
- A harmonized dataset of education- and age-specific fertility rates for DHS countries.Scientific data · 2026Article
- An age-structured spatially varying coefficient model for high-resolution mapping of vaccination coverage.PLoS computational biology · 2026Article
- Improved effectiveness of vaccination campaigns against rabies by reducing spatial heterogeneity in coverage.PLoS biology · 2025Article
- Inferring the regional distribution of Visceral Leishmaniasis incidence from data at different spatial scales.Communications medicine · 2024Article
- High-Resolution Geospatial Mapping of Zero-Dose and Underimmunized Children Following Nigeria's 2021 Multiple Indicator Cluster Survey/National Immunization Coverage Survey.The Journal of infectious diseases · 2024Article
- High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach.BMJ open · 2024Article
- Bayesian transformation model for spatial partly interval-censored data.Journal of applied statistics · 2024Article
- Area-based determinants of outreach vaccination for reaching vulnerable populations: A cross-sectional study in Pakistan.PLOS global public health · 2023Article
- Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework.Bayesian analysis · 2023Article
- Bayesian disease mapping: Past, present, and future.Spatial statistics · 2022Article
- Unmet need for COVID-19 vaccination coverage in Kenya.Vaccine · 2022Article
- Estimates of the number and distribution of zero-dose and under-immunised children across remote-rural, urban, and conflict-affected settings in low and middle-income countries.PLOS global public health · 2022Article
- Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease.Journal of geographical systems · 2022Article
- Assessing the characteristics of un- and under-vaccinated children in low- and middle-income countries: A multi-level cross-sectional study.PLOS global public health · 2022Article
- Estimating and predicting snakebite risk in the Terai region of Nepal through a high-resolution geospatial and One Health approach.Scientific reports · 2021Article
- SPACE-TIME SMOOTHING MODELS FOR SUBNATIONAL MEASLES ROUTINE IMMUNIZATION COVERAGE ESTIMATION WITH COMPLEX SURVEY DATA.The annals of applied statistics · 2021Article
- Harmonizing child mortality data at disparate geographic levels.Statistical methods in medical research · 2021Article
- Modeling and presentation of vaccination coverage estimates using data from household surveys.Vaccine · 2021Article
- Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia.eLife · 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
9 authors.
Funding
Abstract
The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of 'leaving no one behind' has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and 'coldspots' of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km
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.