A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks
A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks
Health and demographic surveillance systems, formed into networks of sites, are increasingly being established to circumvent unreliable national civil registration systems for estimates of mortality and its determinants in low income countries. Health outcomes, as measured by morbidity and mortality, generally correlate strongly with socioeconomic and environmental characteristics. Therefore, to enable comparison between sites, understand which sites can be grouped and where additional sites would aid understanding of rates and determinants, determining the environmental and socioeconomic representativeness of networks becomes important. This paper proposes a full Bayesian methodology for assessing current representativeness and consequently, identification of future sites, focusing on the INDEPTH network in sub-Saharan Africa as an example. Using socioeconomic and environmental data from the current network of 39 sites, we develop a multi-dimensional finite Gaussian mixture model for clustering the existing sites. Using the fitted model we obtain the posterior predictive probability distribution for cluster membership of each 1×1 km grid cell in Africa. The maximum of the posterior predictive probability distribution for each grid cell is proposed as the criterion for representativeness of the network for that particular grid cell. We demonstrate the conceptual superiority and practical appeal of the proposed Bayesian probabilistic method over previously applied deterministic clustering methods. As an example of the potential utility and application of the method, we also suggest optimal site selection methods for possible additions to the network.
161-178
Utazi, C. Edson
e69ca81e-fb23-4bc1-99a5-25c9e0f4d6f9
Sahu, Sujit K.
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Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Tejedor Garavito, Natalia
26fd242c-c882-4210-a74d-af2bb6753ee3
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
August 2016
Utazi, C. Edson
e69ca81e-fb23-4bc1-99a5-25c9e0f4d6f9
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Tejedor Garavito, Natalia
26fd242c-c882-4210-a74d-af2bb6753ee3
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Utazi, C. Edson, Sahu, Sujit K., Atkinson, Peter M., Tejedor Garavito, Natalia and Tatem, Andrew J.
(2016)
A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks.
Spatial Statistics, 17, .
(doi:10.1016/j.spasta.2016.05.006).
Abstract
Health and demographic surveillance systems, formed into networks of sites, are increasingly being established to circumvent unreliable national civil registration systems for estimates of mortality and its determinants in low income countries. Health outcomes, as measured by morbidity and mortality, generally correlate strongly with socioeconomic and environmental characteristics. Therefore, to enable comparison between sites, understand which sites can be grouped and where additional sites would aid understanding of rates and determinants, determining the environmental and socioeconomic representativeness of networks becomes important. This paper proposes a full Bayesian methodology for assessing current representativeness and consequently, identification of future sites, focusing on the INDEPTH network in sub-Saharan Africa as an example. Using socioeconomic and environmental data from the current network of 39 sites, we develop a multi-dimensional finite Gaussian mixture model for clustering the existing sites. Using the fitted model we obtain the posterior predictive probability distribution for cluster membership of each 1×1 km grid cell in Africa. The maximum of the posterior predictive probability distribution for each grid cell is proposed as the criterion for representativeness of the network for that particular grid cell. We demonstrate the conceptual superiority and practical appeal of the proposed Bayesian probabilistic method over previously applied deterministic clustering methods. As an example of the potential utility and application of the method, we also suggest optimal site selection methods for possible additions to the network.
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utazi_etal_2016.pdf
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Accepted/In Press date: 13 May 2016
e-pub ahead of print date: 21 June 2016
Published date: August 2016
Organisations:
Statistical Sciences Research Institute, WorldPop
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Local EPrints ID: 399917
URI: http://eprints.soton.ac.uk/id/eprint/399917
ISSN: 2211-6753
PURE UUID: a68a7ddd-4542-4f17-94f4-36b30f55bb34
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Date deposited: 05 Sep 2016 09:34
Last modified: 15 Mar 2024 03:52
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Author:
Peter M. Atkinson
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