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Validation of a geospatial aggregation method for congressional districts and other US administrative geographies

Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estim...

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Autores principales: Spoer, Ben R., Chen, Alexander S., Lampe, Taylor M., Nelson, Isabel S., Vierse, Anne, Zazanis, Noah V., Kim, Byoungjun, Thorpe, Lorna E., Subramanian, Subu V., Gourevitch, Marc N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498302/
https://www.ncbi.nlm.nih.gov/pubmed/37711359
http://dx.doi.org/10.1016/j.ssmph.2023.101511
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author Spoer, Ben R.
Chen, Alexander S.
Lampe, Taylor M.
Nelson, Isabel S.
Vierse, Anne
Zazanis, Noah V.
Kim, Byoungjun
Thorpe, Lorna E.
Subramanian, Subu V.
Gourevitch, Marc N.
author_facet Spoer, Ben R.
Chen, Alexander S.
Lampe, Taylor M.
Nelson, Isabel S.
Vierse, Anne
Zazanis, Noah V.
Kim, Byoungjun
Thorpe, Lorna E.
Subramanian, Subu V.
Gourevitch, Marc N.
author_sort Spoer, Ben R.
collection PubMed
description Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap.
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spelling pubmed-104983022023-09-14 Validation of a geospatial aggregation method for congressional districts and other US administrative geographies Spoer, Ben R. Chen, Alexander S. Lampe, Taylor M. Nelson, Isabel S. Vierse, Anne Zazanis, Noah V. Kim, Byoungjun Thorpe, Lorna E. Subramanian, Subu V. Gourevitch, Marc N. SSM Popul Health Regular Article Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap. Elsevier 2023-09-04 /pmc/articles/PMC10498302/ /pubmed/37711359 http://dx.doi.org/10.1016/j.ssmph.2023.101511 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Regular Article
Spoer, Ben R.
Chen, Alexander S.
Lampe, Taylor M.
Nelson, Isabel S.
Vierse, Anne
Zazanis, Noah V.
Kim, Byoungjun
Thorpe, Lorna E.
Subramanian, Subu V.
Gourevitch, Marc N.
Validation of a geospatial aggregation method for congressional districts and other US administrative geographies
title Validation of a geospatial aggregation method for congressional districts and other US administrative geographies
title_full Validation of a geospatial aggregation method for congressional districts and other US administrative geographies
title_fullStr Validation of a geospatial aggregation method for congressional districts and other US administrative geographies
title_full_unstemmed Validation of a geospatial aggregation method for congressional districts and other US administrative geographies
title_short Validation of a geospatial aggregation method for congressional districts and other US administrative geographies
title_sort validation of a geospatial aggregation method for congressional districts and other us administrative geographies
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498302/
https://www.ncbi.nlm.nih.gov/pubmed/37711359
http://dx.doi.org/10.1016/j.ssmph.2023.101511
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