The University of Southampton
University of Southampton Institutional Repository

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
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.
2211-6753
161-178
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
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, 161-178. (doi:10.1016/j.spasta.2016.05.006).

Record type: Article

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.

Text
utazi_etal_2016.pdf - Version of Record
Download (1MB)

More information

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

Identifiers

Local EPrints ID: 399917
URI: http://eprints.soton.ac.uk/id/eprint/399917
ISSN: 2211-6753
PURE UUID: a68a7ddd-4542-4f17-94f4-36b30f55bb34
ORCID for Sujit K. Sahu: ORCID iD orcid.org/0000-0003-2315-3598
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for Natalia Tejedor Garavito: ORCID iD orcid.org/0000-0002-1140-6263
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 05 Sep 2016 09:34
Last modified: 15 Mar 2024 03:52

Export record

Altmetrics

Contributors

Author: C. Edson Utazi
Author: Sujit K. Sahu ORCID iD
Author: Peter M. Atkinson ORCID iD
Author: Andrew J. Tatem ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×