Int. J. Environ. Res. Public Health 2013, 10(10), 5011-5025; https://doi.org/10.3390/ijerph10105011
Spatially Interpolated Disease Prevalence Estimation Using Collateral Indicators of Morbidity and Ecological Risk
School of Geography, Queen Mary University of London, London E1 4NS, UK
Received: 25 August 2013 / Revised: 1 October 2013 / Accepted: 8 October 2013 / Published: 14 October 2013
(This article belongs to the Special Issue Spatial Epidemiology)
Abstract
This paper considers estimation of disease prevalence for small areas (neighbourhoods) when the available observations on prevalence are for an alternative partition of a region, such as service areas. Interpolation to neighbourhoods uses a kernel method extended to take account of two types of collateral information. The first is morbidity and service use data, such as hospital admissions, observed for neighbourhoods. Variations in morbidity and service use are expected to reflect prevalence. The second type of collateral information is ecological risk factors (e.g., pollution indices) that are expected to explain variability in prevalence in service areas, but are typically observed only for neighbourhoods. An application involves estimating neighbourhood asthma prevalence in a London health region involving 562 neighbourhoods and 189 service (primary care) areas. View Full-TextKeywords:
disease prevalence; spatial interpolation; neighbourhoods; asthma; kernel
▼
Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
Share & Cite This Article
MDPI and ACS Style
Congdon, P. Spatially Interpolated Disease Prevalence Estimation Using Collateral Indicators of Morbidity and Ecological Risk. Int. J. Environ. Res. Public Health 2013, 10, 5011-5025.
Related Articles
Article Metrics
Comments
[Return to top]
Int. J. Environ. Res. Public Health
EISSN 1660-4601
Published by MDPI AG, Basel, Switzerland
RSS
E-Mail Table of Contents Alert