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Research Article
Odile Sauzet
Bielefeld University
Corresponding Author
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Background: Neighbourhood is a complex structure but of high relevance for health. Its operationalisation remains however a challenge. The aim of this work is to present an approach for the evaluation of spatial effects on health. For this, we propose to estimate two parameters providing a measure of an average neighbourhood or spatial effect at city level without having to predefine any notion of physical neighbourhood.
Methods: We present the statistical method to estimate the parameters of this correlation neighbourhood by fitting an exponential model to the empirical semi-variogram at short distances. With a simulation study, we show for which sample size and sampling density the method performs well and illustrate how to use the method with data from a birth cohort using the outcome birthweight.
Results: For small sample sizes (500) the method provides reliable estimates if the density of observations is high. For larger sample sizes other parameters influencing the quality of estimates are the maximal distance at which the semi-variograms are estimated.
Conclusions: Given the complexity of spatial scales relative to neighbourhood spatial processes, our approach offers the possibility to incorporate existing approaches to the operationalisation of neighbourhood in quantitative analyses while providing a measure of the part of health inequalities which could be possibly due to unmeasured spatial exposure as well as a measure of their spatial scale.
Keywords
Spatial effects on health, spatial correlation structure of residuals, semi-variogram
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 28 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 30 Mar, 2021
Reviews received
Received 26 Mar, 2021
Reviewers agreed
On 17 Mar, 2021
Reviews received
Received 30 Jan, 2021
Reviewers agreed
On 21 Jan, 2021
Reviewers invited
Invitations sent on 02 Jan, 2021
Editor assigned
On 02 Jan, 2021
Editor invited
On 24 Dec, 2020
Submission checks complete
On 24 Dec, 2020
First submitted
On 10 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
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A new preprint design is coming!
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This preprint is under consideration at BMC Medical Research Methodology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Odile Sauzet
Bielefeld University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 28 Dec, 2020
No community comments so far
Editorial decision: Major revision
On 30 Mar, 2021
Reviews received
Received 26 Mar, 2021
Reviewers agreed
On 17 Mar, 2021
Reviews received
Received 30 Jan, 2021
Reviewers agreed
On 21 Jan, 2021
Reviewers invited
Invitations sent on 02 Jan, 2021
Editor assigned
On 02 Jan, 2021
Editor invited
On 24 Dec, 2020
Submission checks complete
On 24 Dec, 2020
First submitted
On 10 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Neighbourhood is a complex structure but of high relevance for health. Its operationalisation remains however a challenge. The aim of this work is to present an approach for the evaluation of spatial effects on health. For this, we propose to estimate two parameters providing a measure of an average neighbourhood or spatial effect at city level without having to predefine any notion of physical neighbourhood.
Methods: We present the statistical method to estimate the parameters of this correlation neighbourhood by fitting an exponential model to the empirical semi-variogram at short distances. With a simulation study, we show for which sample size and sampling density the method performs well and illustrate how to use the method with data from a birth cohort using the outcome birthweight.
Results: For small sample sizes (500) the method provides reliable estimates if the density of observations is high. For larger sample sizes other parameters influencing the quality of estimates are the maximal distance at which the semi-variograms are estimated.
Conclusions: Given the complexity of spatial scales relative to neighbourhood spatial processes, our approach offers the possibility to incorporate existing approaches to the operationalisation of neighbourhood in quantitative analyses while providing a measure of the part of health inequalities which could be possibly due to unmeasured spatial exposure as well as a measure of their spatial scale.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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