A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping.

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 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

Fine resolution mapping of population age-structures for health and development applications.

The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.

Fever prevalence and management among three rural communities in the North West Zone, Somalia.

Between March and August 2008 we undertook 2 cross-sectional surveys among 1375 residents of 3 randomly selected villages in the district of Gebiley in the North-West Zone, Somalia. We investigated for the presence of malaria infection and the period prevalence of self-reported fever 14 days prior to both surveys. All blood samples examined were negative for both species of Plasmodium. The period prevalence of 14-day fevers was 4.8% in March and 0.6% in August; the majority of fevers (84.4%) were associated with other symptoms including cough, running nose and sore throat; 48/64 cases had resolved by the day of interview (mean duration 5.4 days). Only 18 (37.5%) fever cases were managed at a formal health care facility: 7 within 24 hours and 10 within 24-72 hours of onset. None of the fevers were investigated for malaria; they were treated with antibiotics, antipyretics and vitamins.

Fever prevalence and management among three rural communities in the North West Zone, Somalia

Between March and August 2008 we undertook 2 cross-sectional surveys among 1375 residents of 3 randomly selected villages in the district of Gebiley in the North-West Zone, Somalia. We investigated for the presence of malaria infection and the period prevalence of self-reported fever 14 days prior to both surveys. All blood samples examined were negative for both species of Plasmodium. The period prevalence of 14-day fevers was 4.8% in March and 0.6% in August; the majority of fevers [84.4%] were associated with other symptoms including cough, running nose and sore throat; 48/64 cases had resolved by the day of interview [mean duration 5.4 days]. Only 18 [37.5%] fever cases were managed at a formal health care facility: 7 within 24 hours and 10 within 24-72 hours of onset. None of the fevers were investigated for malaria; they were treated with antibiotics, antipyretics and vitamins