Advances in mapping population and demographic characteristics at small-area levels
Advances in mapping population and demographic characteristics at small-area levels
Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health.
In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys.
We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.
Population, administrative data, big data, census, spatio-temporal analysis
i15-i25
Fecht, Daniela
27ee4a08-18e3-4227-8856-4463b465243d
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Hodgson, Susan
8872a814-51e1-4661-970c-dcf465bba576
Piel, Frédéric
84d5dace-0a5d-4a70-8abc-d59d63855ecd
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Waller, Lance
36ddd514-27f0-485f-87c1-6a4514d7cd11
April 2020
Fecht, Daniela
27ee4a08-18e3-4227-8856-4463b465243d
Cockings, Samantha
53df26c2-454e-4e90-b45a-48eb8585e800
Hodgson, Susan
8872a814-51e1-4661-970c-dcf465bba576
Piel, Frédéric
84d5dace-0a5d-4a70-8abc-d59d63855ecd
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Waller, Lance
36ddd514-27f0-485f-87c1-6a4514d7cd11
Fecht, Daniela, Cockings, Samantha, Hodgson, Susan, Piel, Frédéric, Martin, David and Waller, Lance
(2020)
Advances in mapping population and demographic characteristics at small-area levels.
International Journal of Epidemiology, 49 (Issue Supplemen), .
(doi:10.1093/ije/dyz179).
Abstract
Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health.
In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys.
We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.
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More information
Accepted/In Press date: 9 August 2019
e-pub ahead of print date: 15 April 2020
Published date: April 2020
Additional Information:
© The Author(s) 2020. Published by Oxford University Press on behalf of the International Epidemiological Association.
Keywords:
Population, administrative data, big data, census, spatio-temporal analysis
Identifiers
Local EPrints ID: 445506
URI: http://eprints.soton.ac.uk/id/eprint/445506
ISSN: 0300-5771
PURE UUID: 686bb7e0-a7a6-4da5-aa9a-2de670dcf92b
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Date deposited: 14 Dec 2020 17:30
Last modified: 17 Mar 2024 02:53
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Contributors
Author:
Daniela Fecht
Author:
Susan Hodgson
Author:
Frédéric Piel
Author:
Lance Waller
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