Total: 140
A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping
Statistical Methods in Medical Research 0(0) 1–16.
Author(s): CE Utazi, J Thorley, VA Alegana, MJ Ferrari, K Nilsen, S Takahashi, CJE Metcalf, J Lessler and AJ Tatem.
Type: method. Year: 2018
DOI: 10.1177/0962280218797362.

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.
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Access to emergency hospital care provided by the public sector in sub-Saharan Africa in 2015: a geocoded inventory and spatial analysis.
The Lancet Global Health, 26 January 2018.
Author(s): Ouma Paul O, Maina Joseph, Thuranira Pamela N, Macharia Peter M, Alegana Victor A, English Mike, Okiro Emelda A, Snow Robert W.
Type: application. Year: 2018
DOI: 10.1016/S2214-109X(17)30488-6.

Abstract: Timely access to emergency care can substantially reduce mortality. International benchmarks for access to emergency hospital care have been established to guide ambitions for universal health care by 2030. However, no Pan-African database of where hospitals are located exists; therefore, we aimed to complete a geocoded inventory of hospital services in Africa in relation to how populations might access these services in 2015, with focus on women of child bearing age.
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Geographic coverage of demographic surveillance systems for characterising the drivers of childhood mortality in sub-Saharan Africa
BMJ Global Health - 2018.
Author(s): Utazi C Edson, Sahu Sujit K, Atkinson Peter M, Tejedor-Garavito Natalia, Lloyd Christopher T, Tatem, Andrew J.
Type: application. Year: 2018
DOI: 10.1136/bmjgh-2017-000611.

Abstract: A major focus of international health and development goals is the reduction of mortality rates in children under 5 years of age. Achieving this requires understanding the drivers of mortality and how they vary geographically to facilitate the targeting and prioritisation of appropriate interventions. Much of our knowledge on the causes of, and trends in, childhood mortality come from longitudinal demographic surveillance sites, with a renewed focus recently on the establishment and growth of networks of sites from which standardised outputs can facilitate broader understanding of processes. To ensure that the collective outputs from surveillance sites can be used to derive a comprehensive understanding and monitoring system for driving policy on tackling childhood mortality, confidence is needed that existing and planned networks of sites are providing a reliable and representative picture of the geographical variation in factors associated with mortality. Here, we assembled subnational data on childhood mortality as well as key factors known to be associated with it from household surveys in 27 sub-Saharan African countries. We then mapped the locations of existing longitudinal demographic surveillance sites to assess the extent of current coverage of the range of factors, identifying where gaps exist. The results highlight regions with unique combinations of factors associated with childhood mortality that are poorly represented by the current distribution of sites, such as southern Mali, central Nigeria and southern Zambia. Finally, we determined where the establishment of new surveillance systems could improve coverage.
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Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia.
Scientific Data volume 5, Article number: 180073 (2018).
Author(s): Amir S. Siraj, Isabel Rodriguez-Barraquer, Christopher M. Barker, Natalia Tejedor-Garavito, Dennis Harding, Christopher Lorton, Dejan Lukacevic, Gene Oates, Guido Espana, Moritz U.G. Kraemer, Carrie Manore, Michael A. Johansson, Andrew J. Tatem, Robert C. Reiner & T. Alex Perkins
Type: application. Year: 2018
DOI: 10.1038/sdata.2018.73.

Abstract: Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015–2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events.
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