Dataset Information

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

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?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.

SUBMITTER: Utazi CE

PROVIDER: S-EPMC6745613 | biostudies-literature | 2019 Oct-Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Utazi C E CE Thorley J J Alegana V A VA Ferrari M J MJ Nilsen K K Takahashi S S Metcalf Cje C Lessler J J Tatem A J AJ

Statistical methods in medical research 20180919 10-11

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 mappin ...[more]

PMID: 30229698

Similar Datasets

Project description:BackgroundThe expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and 'coldspots' of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized.MethodsUsing measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1?km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods.ResultsMeasles vaccination coverage was found to be strongly predicted by just 4-5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1?×?1?km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets.ConclusionThe elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.

Project description:IntroductionThe Bacille-Calmette-Guerin (BCG) vaccination remains the primary strategy to prevent severe disseminated TB in young children, particularly in high TB-burden countries such as Ethiopia. Accurate knowledge of vaccination coverage in small geographical areas is critically important to developing targeted immunization campaigns. Thus, this study aimed to investigate the spatiotemporal distributions and ecological level determinants of BCG vaccination coverage in Ethiopia.MethodBacille-Calmette-Guerin immunization coverage and geographical information data were obtained from five different Demographic and Health Surveys, conducted in Ethiopia between 2000 and 2019. Data for independent variables were obtained from publicly available sources. Bayesian geostatistical models were used to predict the spatial distribution of BCG vaccination coverage in Ethiopia.ResultThe overall national BCG vaccination coverage between 2000 and 2019 was 65.5%. The BCG vaccine coverage was 53.5% in 2000, 56.9% in 2005, 64.4% in 2011, 79.6% in 2016, and 79.0% in 2019. BCG vaccination coverage increased by 47.6% in Ethiopia from 2000 to 2019, but substantial geographical inequalities in BCG coverage remained at sub-national and local levels. High vaccination coverage was observed in northern, western, and central parts of Ethiopia. Climatic and demographic factors such as temperature, altitude, and population density were positively associated with BCG vaccination coverage. Whereas, healthcare access factors such as distance to health facilities and travel time to the nearest cities were negatively associated with BCG vaccine coverage in Ethiopia.ConclusionDespite substantial progress in national BCG vaccination coverage, marked spatial variation in BCG coverage persists throughout the country at sub-national and local levels. Healthcare access and climatic and demographic factors determined the spatial distribution of BCG vaccination coverage. Maintaining a high level of vaccination coverage across geographical areas is important to prevent TB in Ethiopia.

Project description:Structural properties of articular cartilage such as proteoglycan content, collagen content and collagen alignment are known to vary over length scales as small as a few microns (Bullough and Goodfellow, 1968; Bi et al., 2006). Characterizing the resulting variation in mechanical properties is critical for understanding how the inhomogeneous architecture of this tissue gives rise to its function. Previous studies have measured the depth-dependent shear modulus of articular cartilage using methods such as particle image velocimetry (PIV) that rely on cells and cell nuclei as fiducial markers to track tissue deformation (Buckley et al., 2008; Wong et al., 2008a). However, such techniques are limited by the density of trackable markers, which may be too low to take full advantage of optical microscopy. This limitation leads to noise in the acquired data, which is often exacerbated when the data is manipulated. In this study, we report on two techniques for increasing the accuracy of tissue deformation measurements. In the first technique, deformations were tracked in a grid that was photobleached on each tissue sample (Bruehlmann et al., 2004). In the second, a numerical technique was implemented that allowed for accurate differentiation of optical displacement measurements by minimizing the propagated experimental error while ensuring that truncation error associated with local averaging of the data remained small. To test their efficacy, we employed these techniques to compare the depth-dependent shear moduli of neonatal bovine and adult human articular cartilage. Using a photobleached grid and numerical optimization to gather and analyze data led to results consistent with those reported previously (Buckley et al., 2008; Wong et al., 2008a), but with increased spatial resolution and characteristic coefficients of variation that were reduced up to a factor of 3. This increased resolution allowed us to determine that the shear modulus of neonatal bovine and adult human tissue both exhibit a global minimum at a depth z of around 100 microm and plateau at large depths. The consistency of the depth dependence of |G*|(Z) for adult human and neonatal bovine tissue suggests a functional advantage resulting from this behavior.

Project description:Objectives:Decades of empirical research demonstrate that crime is concentrated at a range of spatial scales, including street segments. Further, the degree of clustering at particular geographic units remains noticeably stable and consistent; a finding that Weisburd (Criminology 53:133-157, 2015) has recently termed the 'law of crime concentration at places'. Such findings suggest that the future locations of crime should-to some extent at least-be predictable. To date, methods of forecasting where crime is most likely to next occur have focused either on area-level or grid-based predictions. No studies of which we are aware have developed and tested the accuracy of methods for predicting the future risk of crime at the street segment level. This is surprising given that it is at this level of place that many crimes are committed and policing resources are deployed. Methods:Using data for property crimes for a large UK metropolitan police force area, we introduce and calibrate a network-based version of prospective crime mapping [e.g. Bowers et al. (Br J Criminol 44:641-658, 2004)], and compare its performance against grid-based alternatives. We also examine how measures of predictive accuracy can be translated to the network context, and show how differences in performance between the two cases can be quantified and tested. Results:Findings demonstrate that the calibrated network-based model substantially outperforms a grid-based alternative in terms of predictive accuracy, with, for example, approximately 20 % more crime identified at a coverage level of 5 %. The improvement in accuracy is highly statistically significant at all coverage levels tested (from 1 to 10 %). Conclusions:This study suggests that, for property crime at least, network-based methods of crime forecasting are likely to outperform grid-based alternatives, and hence should be used in operational policing. More sophisticated variations of the model tested are possible and should be developed and tested in future research.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets